\n {\n internal Transformer(IntPtr handle, IntPtr boxedHandle) : base(handle, boxedHandle) { }\n\n /// <summary>\n /// Take in and process masked source/target sequences.\n /// </summary>\n /// <param name=\"src\">The sequence to the encoder (required).</param>\n /// <param name=\"tgt\">The sequence to the decoder (required).</param>\n /// <param name=\"src_mask\">The additive mask for the src sequence (optional).</param>\n /// <param name=\"tgt_mask\">The additive mask for the tgt sequence (optional).</param>\n /// <param name=\"memory_mask\">The additive mask for the encoder output (optional).</param>\n /// <param name=\"src_key_padding_mask\">The ByteTensor mask for src keys per batch (optional).</param>\n /// <param name=\"tgt_key_padding_mask\">The ByteTensor mask for tgt keys per batch (optional).</param>\n /// <param name=\"memory_key_padding_mask\">The ByteTensor mask for memory keys per batch (optional).</param>\n /// <returns></returns>\n public Tensor call(Tensor src, Tensor tgt, Tensor src_mask, Tensor? tgt_mask = null, Tensor? memory_mask = null, Tensor? src_key_padding_mask = null, Tensor? tgt_key_padding_mask = null, Tensor? memory_key_padding_mask = null)\n {\n var res = THSNN_Transformer_forward(handle,\n src.Handle,\n tgt.Handle,\n src_mask?.Handle ?? IntPtr.Zero,\n tgt_mask?.Handle ?? IntPtr.Zero,\n memory_mask?.Handle ?? IntPtr.Zero,\n src_key_padding_mask?.Handle ?? IntPtr.Zero,\n tgt_key_padding_mask?.Handle ?? IntPtr.Zero,\n memory_key_padding_mask?.Handle ?? IntPtr.Zero);\n if (res == IntPtr.Zero) { torch.CheckForErrors(); }\n return new Tensor(res);\n }\n\n /// <summary>\n /// Take in and process masked source/target sequences.\n /// </summary>\n /// <param name=\"src\">The sequence to the encoder (required).</param>\n /// <param name=\"tgt\">The sequence to the decoder (required).</param>\n public override Tensor forward(Tensor src, Tensor tgt)\n {\n var res = THSNN_Transformer_forward(handle,\n src.Handle,\n tgt.Handle,\n IntPtr.Zero,\n IntPtr.Zero,\n IntPtr.Zero,\n IntPtr.Zero,\n IntPtr.Zero,\n IntPtr.Zero);\n if (res == IntPtr.Zero) { torch.CheckForErrors(); }\n return new Tensor(res);\n }\n }\n }\n\n public static partial class torch\n {\n public static partial class nn\n {\n public enum Activations\n {\n ReLU = 0,\n GELU = 1\n }\n\n /// <summary>\n /// A transformer model. User is able to modify the attributes as needed. The architecture is based on the paper “Attention Is All You Need”.\n /// </summary>\n /// <param name=\"d_model\">The number of expected features in the encoder/decoder inputs (default=512).</param>\n /// <param name=\"nhead\">The number of heads in the multiheadattention models (default=8).</param>\n /// <param name=\"num_encoder_layers\">The number of sub-encoder-layers in the encoder (default=6).</param>\n /// <param name=\"num_decoder_layers\">The number of sub-decoder-layers in the decoder (default=6).</param>\n /// <param name=\"dim_feedforward\">The dimension of the feedforward network model (default=2048).</param>\n /// <param name=\"dropout\">The dropout value (default=0.1).</param>\n /// <param name=\"activation\">The activation function of encoder/decoder intermediate layer, relu or gelu (default=relu).</param>\n /// <returns></returns>\n public static Transformer Transformer(long d_model = 512, long nhead = 8, long num_encoder_layers = 6, long num_decoder_layers = 6, long dim_feedforward = 2048, double dropout = 0.1, Activations activation = nn.Activations.ReLU)\n {\n var res = THSNN_Transformer_ctor(d_model, nhead, num_encoder_layers, num_decoder_layers, dim_feedforward, dropout, (long)activation, out var boxedHandle);\n if (res == IntPtr.Zero) { torch.CheckForErrors(); }\n return new Transformer(res, boxedHandle);\n }\n\n public static partial class functional\n {\n /// <summary>\n /// Computes scaled dot product attention on query, key and value tensors, using an optional attention mask if passed, and applying dropout if a probability greater than 0.0 is specified.\n /// </summary>\n /// <param name=\"query\">Query tensor, shaped (N, ..., L, E)</param>\n /// <param name=\"key\">Key tensor, shaped (N, ..., S, E)</param>\n /// <param name=\"value\">Value tensor, shaped (N, ..., S, Ev)</param>\n /// <param name=\"attn_mask\">\n /// Attention mask, shaped (N, ..., L, S).\n /// Two types of masks are supported:\n /// A boolean mask where a value of True indicates that the element should take part in attention.\n /// A float mask of the same type as query, key, value that is added to the attention score.\n /// </param>\n /// <param name=\"p\">Dropout probability</param>\n /// <param name=\"is_casual\">If true, assumes causal attention masking and errors if both attn_mask and is_causal are set.</param>\n /// <returns></returns>\n public static Tensor scaled_dot_product_attention(Tensor query, Tensor key, Tensor value, Tensor? attn_mask = null, double p = 0.0, [MarshalAs(UnmanagedType.U1)] bool is_casual = false)\n {\n if (p < 0) throw new ArgumentException(\"Dropout probability must be greater than or equal to zero.\");\n if (is_casual && attn_mask is not null) throw new ArgumentException(\"Casual attention masking cannot pass a mask.\");\n var res = THSNN_scaled_dot_product_attention(query.Handle, key.Handle, value.Handle, attn_mask is null ? IntPtr.Zero : attn_mask.Handle, p, is_casual);\n if (res == IntPtr.Zero) { torch.CheckForErrors(); }\n return new Tensor(res);\n }\n }\n }\n }\n}\n"},{"alpha_fraction":0.5679244995117188,"alphanum_fraction":0.5679244995117188,"avg_line_length":27.675676345825195,"blob_id":"8b43c3b4da4d2ec476a327f8faf9a0e810793b4a","content_id":"a106064df77514dc888aecfa057f11150b1f6508","detected_licenses":["MIT"],"is_generated":false,"is_vendor":false,"language":"C#","length_bytes":1060,"license_type":"permissive","max_line_length":130,"num_lines":37,"path":"/src/TorchVision/Solarize.cs","repo_name":"NiklasGustafsson/TorchSharp","src_encoding":"UTF-8","text":"// Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information.\n\nusing static TorchSharp.torch;\n\n\nnamespace TorchSharp\n{\n public static partial class torchvision\n {\n internal class Solarize : ITransform\n {\n internal Solarize(double threshold)\n {\n this.threshold = threshold;\n }\n\n public Tensor call(Tensor input)\n {\n return transforms.functional.solarize(input, threshold);\n }\n\n private double threshold;\n }\n\n public static partial class transforms\n {\n /// <summary>\n /// Solarize the image by inverting all pixel values above a threshold.\n /// </summary>\n /// <param name=\"threshold\">All pixels equal or above this value are inverted.</param>\n static public ITransform Solarize(double threshold)\n {\n return new Solarize(threshold);\n }\n }\n }\n}"},{"alpha_fraction":0.6311787366867065,"alphanum_fraction":0.6425855755805969,"avg_line_length":25.399999618530273,"blob_id":"f9759eeff22e157cef190ed3566126bfd3dffbd0","content_id":"c6707d8f83b6dd3cd0631cdc02af30edd3da3e32","detected_licenses":["MIT"],"is_generated":false,"is_vendor":false,"language":"C#","length_bytes":265,"license_type":"permissive","max_line_length":131,"num_lines":10,"path":"/src/TorchSharp/Tensor/Enums/PrintOptionsProfile.cs","repo_name":"NiklasGustafsson/TorchSharp","src_encoding":"UTF-8","text":"// Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information.\nnamespace TorchSharp\n{\n public enum PrintOptionsProfile\n {\n @default = 0,\n @short = 1,\n full = 2\n }\n}"},{"alpha_fraction":0.4884297549724579,"alphanum_fraction":0.5034238696098328,"avg_line_length":41.349998474121094,"blob_id":"db0f258d5c48f7757d3c0a06eff29cbc5f20e171","content_id":"a01824120b72a56d733887ca19434e6b6d29031d","detected_licenses":["MIT"],"is_generated":false,"is_vendor":false,"language":"C#","length_bytes":8470,"license_type":"permissive","max_line_length":134,"num_lines":200,"path":"/src/TorchVision/Ops/Losses.cs","repo_name":"NiklasGustafsson/TorchSharp","src_encoding":"UTF-8","text":"// Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information.\nusing System;\nusing System.Drawing;\nusing System.Text;\nusing System.Threading.Tasks;\nusing static TorchSharp.torch;\nusing static TorchSharp.torch.nn.functional;\nusing static TorchSharp.torchvision;\n\nnamespace TorchSharp\n{\n public static partial class torchvision\n {\n public static partial class ops\n {\n // Ported from https://github.com/pytorch/vision/blob/main/torchvision/ops/focal_loss.py\n\n /// <summary>\n /// Loss used in RetinaNet for dense detection: https://arxiv.org/abs/1708.02002.\n /// </summary>\n /// <param name=\"inputs\">A float tensor of arbitrary shape. The predictions for each example. </param>\n /// <param name=\"targets\">A float tensor with the same shape as inputs.\n /// Stores the binary classification label for each element in inputs\n /// (0 for the negative class and 1 for the positive class).\n /// </param>\n /// <param name=\"alpha\">Weighting factor in range (0,1) to balance positive vs negative examples or -1 for ignore.</param>\n /// <param name=\"gamma\">Exponent of the modulating factor (1 - p_t) to balance easy vs hard examples.</param>\n /// <param name=\"reduction\">The kind of reduction to apply to the result.</param>\n /// <returns></returns>\n public static Tensor sigmoid_focal_loss(\n Tensor inputs,\n Tensor targets,\n double alpha = 0.25, double gamma = 2.0,\n nn.Reduction reduction = nn.Reduction.None)\n {\n var p = torch.sigmoid(inputs);\n var ce_loss = binary_cross_entropy_with_logits(inputs, targets, reduction: nn.Reduction.None);\n var p_t = p * targets + (1 - p) * (1 - targets);\n var loss = ce_loss * (1 - p_t).pow(gamma);\n\n if (alpha >= 0) {\n var alpha_t = alpha * targets + (1 - alpha) * (1 - targets);\n loss = alpha_t * loss;\n }\n\n switch (reduction) {\n case nn.Reduction.None:\n break;\n case nn.Reduction.Mean:\n loss = loss.mean();\n break;\n case nn.Reduction.Sum:\n loss = loss.sum();\n break;\n }\n\n return loss;\n }\n\n /// <summary>\n /// Gradient-friendly IoU loss with an additional penalty that is non-zero when the\n /// boxes do not overlap.This loss function considers important geometrical\n /// factors such as overlap area, normalized central point distance and aspect ratio.\n /// This loss is symmetric, so the boxes1 and boxes2 arguments are interchangeable.\n /// </summary>\n /// <param name=\"boxes1\">(Tensor[N, 4] or Tensor[4]) first set of boxes</param>\n /// <param name=\"boxes2\">(Tensor[N, 4] or Tensor[4]) second set of boxes</param>\n /// <param name=\"reduction\">The kind of reduction to apply to the result.</param>\n /// <param name=\"eps\">Small number to prevent division by zero.</param>\n public static Tensor complete_box_iou_loss(\n Tensor boxes1,\n Tensor boxes2,\n nn.Reduction reduction = nn.Reduction.None,\n double eps = 1e-7)\n {\n boxes1 = _upcast_non_float(boxes1);\n boxes2 = _upcast_non_float(boxes2);\n\n var (diou_loss, iou) = _diou_iou_loss(boxes1, boxes2);\n\n var xy = boxes1.unbind(dimension: -1);\n var xyg = boxes2.unbind(dimension: -1);\n\n // width and height of boxes\n var w_pred = xy[2] - xy[0];\n var h_pred = xy[3] - xy[1];\n var w_gt = xyg[2] - xyg[0];\n var h_gt = xyg[3] - xyg[1];\n\n var v = (4 / Math.Pow(Math.PI, 2)) * torch.pow((torch.atan(w_gt / h_gt) - torch.atan(w_pred / h_pred)), 2);\n\n Tensor alpha = null;\n\n using (var _ = torch.no_grad()) {\n alpha = v / (1 - iou + v + eps);\n }\n\n var loss = diou_loss + alpha * v;\n\n switch (reduction) {\n case nn.Reduction.None:\n break;\n case nn.Reduction.Mean:\n loss = loss.mean();\n break;\n case nn.Reduction.Sum:\n loss = loss.sum();\n break;\n }\n\n return loss;\n }\n\n /// <summary>\n /// Gradient-friendly IoU loss with an additional penalty that is non-zero when the\n /// distance between boxes' centers isn't zero.Indeed, for two exactly overlapping\n /// boxes, the distance IoU is the same as the IoU loss.\n /// This loss is symmetric, so the boxes1 and boxes2 arguments are interchangeable.\n /// </summary>\n /// <param name=\"boxes1\">(Tensor[N, 4] or Tensor[4]) first set of boxes</param>\n /// <param name=\"boxes2\">(Tensor[N, 4] or Tensor[4]) second set of boxes</param>\n /// <param name=\"reduction\">The kind of reduction to apply to the result.</param>\n /// <param name=\"eps\">Small number to prevent division by zero.</param>\n public static Tensor distance_box_iou_loss(\n Tensor boxes1,\n Tensor boxes2,\n nn.Reduction reduction = nn.Reduction.None,\n double eps = 1e-7)\n {\n boxes1 = _upcast_non_float(boxes1);\n boxes2 = _upcast_non_float(boxes2);\n\n var (loss, _) = _diou_iou_loss(boxes1, boxes2);\n\n switch (reduction) {\n case nn.Reduction.None:\n break;\n case nn.Reduction.Mean:\n loss = loss.mean();\n break;\n case nn.Reduction.Sum:\n loss = loss.sum();\n break;\n }\n\n return loss;\n }\n\n /// <summary>\n /// Gradient-friendly IoU loss with an additional penalty that is non-zero when the\n /// boxes do not overlap and scales with the size of their smallest enclosing box.\n /// This loss is symmetric, so the boxes1 and boxes2 arguments are interchangeable.\n /// </summary>\n /// <param name=\"boxes1\">(Tensor[N, 4] or Tensor[4]) first set of boxes</param>\n /// <param name=\"boxes2\">(Tensor[N, 4] or Tensor[4]) second set of boxes</param>\n /// <param name=\"reduction\">The kind of reduction to apply to the result.</param>\n /// <param name=\"eps\">Small number to prevent division by zero.</param>\n public static Tensor generalized_box_iou_loss(\n Tensor boxes1,\n Tensor boxes2,\n nn.Reduction reduction = nn.Reduction.None,\n double eps = 1e-7)\n {\n boxes1 = _upcast_non_float(boxes1);\n boxes2 = _upcast_non_float(boxes2);\n\n var (intsctk, unionk) = _loss_inter_union(boxes1, boxes2);\n var iouk = intsctk / (unionk + eps);\n\n var (x1, y1, x2, y2) = unwrap4(boxes1.unbind(dimension: -1));\n var (x1g, y1g, x2g, y2g) = unwrap4(boxes2.unbind(dimension: -1));\n\n // smallest enclosing box\n\n var xc1 = torch.min(x1, x1g);\n var yc1 = torch.min(y1, y1g);\n var xc2 = torch.max(x2, x2g);\n var yc2 = torch.max(y2, y2g);\n\n var area_c = (xc2 - xc1) * (yc2 - yc1);\n var miouk = iouk - ((area_c - unionk) / (area_c + eps));\n\n var loss = 1 - miouk;\n\n switch (reduction) {\n case nn.Reduction.None:\n break;\n case nn.Reduction.Mean:\n loss = loss.mean();\n break;\n case nn.Reduction.Sum:\n loss = loss.sum();\n break;\n }\n\n return loss;\n }\n }\n }\n}\n"},{"alpha_fraction":0.5421236753463745,"alphanum_fraction":0.543523907661438,"avg_line_length":43.404144287109375,"blob_id":"f5300b88f4ee7c621bea8996b591f75d2d1d0420","content_id":"e607e9069d624100dc6a46ec7c0d0db872aa6376","detected_licenses":["MIT"],"is_generated":false,"is_vendor":false,"language":"C#","length_bytes":8570,"license_type":"permissive","max_line_length":149,"num_lines":193,"path":"/src/TorchSharp/Distributions/OneHotCategorical.cs","repo_name":"NiklasGustafsson/TorchSharp","src_encoding":"UTF-8","text":"// Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information.\nusing System;\nusing static TorchSharp.torch;\n\nnamespace TorchSharp\n{\n using Modules;\n\n namespace Modules\n {\n /// <summary>\n /// A Bernoulli distribution parameterized by `probs` or `logits` (but not both).\n /// </summary>\n public class OneHotCategorical : torch.distributions.Distribution\n {\n /// <summary>\n /// The mean of the distribution.\n /// </summary>\n public override Tensor mean => _categorical._probs;\n\n /// <summary>\n /// The mode of the distribution.\n /// </summary>\n public override Tensor mode {\n get {\n var probs = _categorical.probs;\n var mode = probs.argmax(-1);\n return torch.nn.functional.one_hot(mode, num_classes: probs.shape[probs.ndim - 1]).to(probs);\n }\n }\n\n /// <summary>\n /// The variance of the distribution\n /// </summary>\n public override Tensor variance => probs * (1 - _categorical.probs);\n\n /// <summary>\n /// Constructor\n /// </summary>\n /// <param name=\"p\"></param>\n /// <param name=\"l\"></param>\n /// <param name=\"generator\"></param>\n public OneHotCategorical(Tensor p = null, Tensor l = null, torch.Generator generator = null) : base(generator)\n {\n _categorical = new Categorical(p, l, generator);\n\n if ((p is null && logits is null) || (p is not null && l is not null))\n throw new ArgumentException(\"One and only one of 'probs' and logits should be provided.\");\n\n this.batch_shape = p is null ? l.size() : p.size();\n this._probs = p?.alias().DetachFromDisposeScope();\n this._logits = l?.alias().DetachFromDisposeScope();\n }\n\n /// <summary>\n /// The probability of sampling 1\n /// </summary>\n public Tensor probs {\n get {\n return _probs ?? LogitsToProbs(_logits, true);\n }\n }\n\n /// <summary>\n /// The log-odds of sampling 1\n /// </summary>\n public Tensor logits {\n get {\n return _logits ?? ProbsToLogits(_probs);\n }\n }\n\n private Categorical _categorical;\n private Tensor _probs;\n private Tensor _logits;\n\n /// <summary>\n /// Generates a sample_shape shaped reparameterized sample or sample_shape shaped batch of reparameterized samples\n /// if the distribution parameters are batched.\n /// </summary>\n /// <param name=\"sample_shape\">The sample shape.</param>\n /// <returns></returns>\n public override Tensor rsample(params long[] sample_shape)\n {\n var probs = _categorical.probs;\n var num_events = _categorical.num_events;\n var indices = _categorical.sample(sample_shape);\n return torch.nn.functional.one_hot(indices, num_events).to(probs);\n }\n\n /// <summary>\n /// Returns the log of the probability density/mass function evaluated at `value`.\n /// </summary>\n /// <param name=\"value\"></param>\n /// <returns></returns>\n\n public override Tensor log_prob(Tensor value)\n {\n using var _ = torch.NewDisposeScope();\n var indices = value.max(-1).indexes;\n return _categorical.log_prob(indices).MoveToOuterDisposeScope();\n }\n\n /// <summary>\n /// Returns entropy of distribution, batched over batch_shape.\n /// </summary>\n public override Tensor entropy()\n {\n return _categorical.entropy();\n }\n\n /// <summary>\n /// Returns a new distribution instance (or populates an existing instance provided by a derived class) with batch dimensions expanded to\n /// `batch_shape`. This method calls `torch.Tensor.expand()` on the distribution's parameters. As such, this does not allocate new\n /// memory for the expanded distribution instance.\n /// </summary>\n /// <param name=\"batch_shape\">Tthe desired expanded size.</param>\n /// <param name=\"instance\">new instance provided by subclasses that need to override `.expand`.</param>\n /// <returns></returns>\n public override distributions.Distribution expand(Size batch_shape, distributions.Distribution instance = null)\n {\n if (instance != null && !(instance is Bernoulli))\n throw new ArgumentException(\"expand(): 'instance' must be a Bernoulli distribution\");\n\n var p = _probs?.expand(batch_shape);\n var l = _logits?.expand(batch_shape);\n\n var newDistribution = ((instance == null) ? new OneHotCategorical(p, l, generator) : instance) as OneHotCategorical;\n\n newDistribution.batch_shape = batch_shape;\n if (newDistribution == instance) {\n newDistribution._probs = p;\n newDistribution._logits = l;\n newDistribution._categorical = _categorical.expand(batch_shape) as Categorical;\n }\n return newDistribution;\n }\n }\n\n }\n public static partial class torch\n {\n public static partial class distributions\n {\n /// <summary>\n /// Creates a Bernoulli distribution parameterized by `probs` or `logits` (but not both).\n /// </summary>\n /// <param name=\"probs\">The probability of sampling '1'</param>\n /// <param name=\"logits\">The log-odds of sampling '1'</param>\n /// <param name=\"generator\">An optional random number generator object.</param>\n /// <returns></returns>\n public static OneHotCategorical OneHotCategorical(Tensor probs = null, Tensor logits = null, torch.Generator generator = null)\n {\n return new OneHotCategorical(probs, logits, generator);\n }\n\n /// <summary>\n /// Creates a Bernoulli distribution parameterized by `probs` or `logits` (but not both).\n /// </summary>\n /// <param name=\"probs\">The probability of sampling '1'</param>\n /// <param name=\"logits\">The log-odds of sampling '1'</param>\n /// <param name=\"generator\">An optional random number generator object.</param>\n /// <returns></returns>\n public static OneHotCategorical OneHotCategorical(float? probs, float? logits, torch.Generator generator = null)\n {\n if (probs.HasValue && !logits.HasValue)\n return new OneHotCategorical(torch.tensor(probs.Value), null, generator);\n else if (!probs.HasValue && logits.HasValue)\n return new OneHotCategorical(null, torch.tensor(logits.Value), generator);\n else\n throw new ArgumentException(\"One and only one of 'probs' and logits should be provided.\");\n }\n\n\n /// <summary>\n /// Creates a Bernoulli distribution parameterized by `probs` or `logits` (but not both).\n /// </summary>\n /// <param name=\"probs\">The probability of sampling '1'</param>\n /// <param name=\"logits\">The log-odds of sampling '1'</param>\n /// <param name=\"generator\">An optional random number generator object.</param>\n /// <returns></returns>\n public static OneHotCategorical OneHotCategorical(double? probs, double? logits, torch.Generator generator = null)\n {\n if (probs.HasValue && !logits.HasValue)\n return new OneHotCategorical(torch.tensor(probs.Value), null, generator);\n else if (!probs.HasValue && logits.HasValue)\n return new OneHotCategorical(null, torch.tensor(logits.Value), generator);\n else\n throw new ArgumentException(\"One and only one of 'probs' and 'logits' should be non-null\");\n }\n }\n }\n}\n"},{"alpha_fraction":0.7004733681678772,"alphanum_fraction":0.7033941149711609,"avg_line_length":47.19902801513672,"blob_id":"834d40b704c6e3a33bdc762e29ce1bcc4823262f","content_id":"394898ec8c659b5d1cc5dc707c5c5f06eb4c7963","detected_licenses":["MIT"],"is_generated":false,"is_vendor":false,"language":"C#","length_bytes":9929,"license_type":"permissive","max_line_length":130,"num_lines":206,"path":"/src/TorchSharp/PInvoke/LibTorchSharp.THSSpecial.cs","repo_name":"NiklasGustafsson/TorchSharp","src_encoding":"UTF-8","text":"// Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information.\n#nullable enable\nusing System;\nusing System.Runtime.InteropServices;\n\nnamespace TorchSharp.PInvoke\n{\n internal static partial class NativeMethods\n {\n [DllImport(\"LibTorchSharp\")]\n internal static extern IntPtr THSSpecial_airy_ai(IntPtr tensor);\n [DllImport(\"LibTorchSharp\")]\n internal static extern IntPtr THSSpecial_airy_ai_out(IntPtr tensor, IntPtr _out);\n\n [DllImport(\"LibTorchSharp\")]\n internal static extern IntPtr THSSpecial_bessel_j0(IntPtr tensor);\n [DllImport(\"LibTorchSharp\")]\n internal static extern IntPtr THSSpecial_bessel_j0_out(IntPtr tensor, IntPtr _out);\n\n [DllImport(\"LibTorchSharp\")]\n internal static extern IntPtr THSSpecial_bessel_j1(IntPtr tensor);\n [DllImport(\"LibTorchSharp\")]\n internal static extern IntPtr THSSpecial_bessel_j1_out(IntPtr tensor, IntPtr _out);\n\n [DllImport(\"LibTorchSharp\")]\n internal static extern IntPtr THSSpecial_bessel_y0(IntPtr tensor);\n [DllImport(\"LibTorchSharp\")]\n internal static extern IntPtr THSSpecial_bessel_y0_out(IntPtr tensor, IntPtr _out);\n\n [DllImport(\"LibTorchSharp\")]\n internal static extern IntPtr THSSpecial_bessel_y1(IntPtr tensor);\n [DllImport(\"LibTorchSharp\")]\n internal static extern IntPtr THSSpecial_bessel_y1_out(IntPtr tensor, IntPtr _out);\n\n [DllImport(\"LibTorchSharp\")]\n internal static extern IntPtr THSSpecial_modified_bessel_i0(IntPtr tensor);\n [DllImport(\"LibTorchSharp\")]\n internal static extern IntPtr THSSpecial_modified_bessel_i0_out(IntPtr tensor, IntPtr _out);\n\n [DllImport(\"LibTorchSharp\")]\n internal static extern IntPtr THSSpecial_modified_bessel_i1(IntPtr tensor);\n [DllImport(\"LibTorchSharp\")]\n internal static extern IntPtr THSSpecial_modified_bessel_i1_out(IntPtr tensor, IntPtr _out);\n\n [DllImport(\"LibTorchSharp\")]\n internal static extern IntPtr THSSpecial_modified_bessel_k0(IntPtr tensor);\n [DllImport(\"LibTorchSharp\")]\n internal static extern IntPtr THSSpecial_modified_bessel_k0_out(IntPtr tensor, IntPtr _out);\n\n [DllImport(\"LibTorchSharp\")]\n internal static extern IntPtr THSSpecial_modified_bessel_k1(IntPtr tensor);\n [DllImport(\"LibTorchSharp\")]\n internal static extern IntPtr THSSpecial_modified_bessel_k1_out(IntPtr tensor, IntPtr _out);\n\n [DllImport(\"LibTorchSharp\")]\n internal static extern IntPtr THSSpecial_scaled_modified_bessel_k0(IntPtr tensor);\n [DllImport(\"LibTorchSharp\")]\n internal static extern IntPtr THSSpecial_scaled_modified_bessel_k0_out(IntPtr tensor, IntPtr _out);\n\n [DllImport(\"LibTorchSharp\")]\n internal static extern IntPtr THSSpecial_scaled_modified_bessel_k1(IntPtr tensor);\n [DllImport(\"LibTorchSharp\")]\n internal static extern IntPtr THSSpecial_scaled_modified_bessel_k1_out(IntPtr tensor, IntPtr _out);\n\n [DllImport(\"LibTorchSharp\")]\n internal static extern IntPtr THSSpecial_spherical_bessel_j0(IntPtr tensor);\n [DllImport(\"LibTorchSharp\")]\n internal static extern IntPtr THSSpecial_spherical_bessel_j0_out(IntPtr tensor, IntPtr _out);\n\n [DllImport(\"LibTorchSharp\")]\n internal static extern IntPtr THSSpecial_chebyshev_polynomial_t(IntPtr x, IntPtr n);\n [DllImport(\"LibTorchSharp\")]\n internal static extern IntPtr THSSpecial_chebyshev_polynomial_t_out(IntPtr x, IntPtr n, IntPtr @out);\n [DllImport(\"LibTorchSharp\")]\n internal static extern IntPtr THSSpecial_chebyshev_polynomial_u(IntPtr x, IntPtr n);\n [DllImport(\"LibTorchSharp\")]\n internal static extern IntPtr THSSpecial_chebyshev_polynomial_u_out(IntPtr x, IntPtr n, IntPtr @out);\n [DllImport(\"LibTorchSharp\")]\n internal static extern IntPtr THSSpecial_chebyshev_polynomial_v(IntPtr x, IntPtr n);\n [DllImport(\"LibTorchSharp\")]\n internal static extern IntPtr THSSpecial_chebyshev_polynomial_v_out(IntPtr x, IntPtr n, IntPtr @out);\n [DllImport(\"LibTorchSharp\")]\n internal static extern IntPtr THSSpecial_chebyshev_polynomial_w(IntPtr x, IntPtr n);\n [DllImport(\"LibTorchSharp\")]\n internal static extern IntPtr THSSpecial_chebyshev_polynomial_w_out(IntPtr x, IntPtr n, IntPtr @out);\n\n [DllImport(\"LibTorchSharp\")]\n internal static extern IntPtr THSSpecial_shifted_chebyshev_polynomial_t(IntPtr x, IntPtr n);\n [DllImport(\"LibTorchSharp\")]\n internal static extern IntPtr THSSpecial_shifted_chebyshev_polynomial_t_out(IntPtr x, IntPtr n, IntPtr @out);\n [DllImport(\"LibTorchSharp\")]\n internal static extern IntPtr THSSpecial_shifted_chebyshev_polynomial_u(IntPtr x, IntPtr n);\n [DllImport(\"LibTorchSharp\")]\n internal static extern IntPtr THSSpecial_shifted_chebyshev_polynomial_u_out(IntPtr x, IntPtr n, IntPtr @out);\n [DllImport(\"LibTorchSharp\")]\n internal static extern IntPtr THSSpecial_shifted_chebyshev_polynomial_v(IntPtr x, IntPtr n);\n [DllImport(\"LibTorchSharp\")]\n internal static extern IntPtr THSSpecial_shifted_chebyshev_polynomial_v_out(IntPtr x, IntPtr n, IntPtr @out);\n [DllImport(\"LibTorchSharp\")]\n internal static extern IntPtr THSSpecial_shifted_chebyshev_polynomial_w(IntPtr x, IntPtr n);\n [DllImport(\"LibTorchSharp\")]\n internal static extern IntPtr THSSpecial_shifted_chebyshev_polynomial_w_out(IntPtr x, IntPtr n, IntPtr @out);\n\n [DllImport(\"LibTorchSharp\")]\n internal static extern IntPtr THSSpecial_hermite_polynomial_h(IntPtr x, IntPtr n);\n [DllImport(\"LibTorchSharp\")]\n internal static extern IntPtr THSSpecial_hermite_polynomial_h_out(IntPtr x, IntPtr n, IntPtr @out);\n [DllImport(\"LibTorchSharp\")]\n internal static extern IntPtr THSSpecial_hermite_polynomial_he(IntPtr x, IntPtr n);\n [DllImport(\"LibTorchSharp\")]\n internal static extern IntPtr THSSpecial_hermite_polynomial_he_out(IntPtr x, IntPtr n, IntPtr @out);\n\n [DllImport(\"LibTorchSharp\")]\n internal static extern IntPtr THSSpecial_laguerre_polynomial_l(IntPtr x, IntPtr n);\n [DllImport(\"LibTorchSharp\")]\n internal static extern IntPtr THSSpecial_laguerre_polynomial_l_out(IntPtr x, IntPtr n, IntPtr @out);\n [DllImport(\"LibTorchSharp\")]\n internal static extern IntPtr THSSpecial_legendre_polynomial_p(IntPtr x, IntPtr n);\n [DllImport(\"LibTorchSharp\")]\n internal static extern IntPtr THSSpecial_legendre_polynomial_p_out(IntPtr x, IntPtr n, IntPtr @out);\n\n [DllImport(\"LibTorchSharp\")]\n internal static extern IntPtr THSSpecial_entr(IntPtr tensor);\n\n [DllImport(\"LibTorchSharp\")]\n internal static extern IntPtr THSSpecial_erf(IntPtr tensor);\n\n [DllImport(\"LibTorchSharp\")]\n internal static extern IntPtr THSSpecial_erfc(IntPtr tensor);\n\n [DllImport(\"LibTorchSharp\")]\n internal static extern IntPtr THSSpecial_erfcx(IntPtr tensor);\n\n [DllImport(\"LibTorchSharp\")]\n internal static extern IntPtr THSSpecial_erfinv(IntPtr tensor);\n\n [DllImport(\"LibTorchSharp\")]\n internal static extern IntPtr THSSpecial_expit(IntPtr tensor);\n\n [DllImport(\"LibTorchSharp\")]\n internal static extern IntPtr THSSpecial_expm1(IntPtr tensor);\n\n [DllImport(\"LibTorchSharp\")]\n internal static extern IntPtr THSSpecial_exp2(IntPtr tensor);\n\n [DllImport(\"LibTorchSharp\")]\n internal static extern IntPtr THSSpecial_gammaln(IntPtr tensor);\n\n [DllImport(\"LibTorchSharp\")]\n internal static extern IntPtr THSSpecial_gammainc(IntPtr tensor, IntPtr other);\n\n [DllImport(\"LibTorchSharp\")]\n internal static extern IntPtr THSSpecial_gammaincc(IntPtr tensor, IntPtr other);\n\n [DllImport(\"LibTorchSharp\")]\n internal static extern IntPtr THSSpecial_polygamma(long n, IntPtr tensor);\n\n [DllImport(\"LibTorchSharp\")]\n internal static extern IntPtr THSSpecial_multigammaln(IntPtr tensor, long p);\n\n [DllImport(\"LibTorchSharp\")]\n internal static extern IntPtr THSSpecial_digamma(IntPtr tensor);\n\n [DllImport(\"LibTorchSharp\")]\n internal static extern IntPtr THSSpecial_i0(IntPtr tensor);\n\n [DllImport(\"LibTorchSharp\")]\n internal static extern IntPtr THSSpecial_i0e(IntPtr tensor);\n\n [DllImport(\"LibTorchSharp\")]\n internal static extern IntPtr THSSpecial_i1(IntPtr tensor);\n\n [DllImport(\"LibTorchSharp\")]\n internal static extern IntPtr THSSpecial_i1e(IntPtr tensor);\n\n [DllImport(\"LibTorchSharp\")]\n internal static extern IntPtr THSSpecial_logit(IntPtr tensor);\n\n [DllImport(\"LibTorchSharp\")]\n internal static extern IntPtr THSSpecial_log_softmax(IntPtr tensor, long dim, sbyte scalar_type);\n\n [DllImport(\"LibTorchSharp\")]\n internal static extern IntPtr THSSpecial_softmax(IntPtr tensor, long dim, sbyte scalar_type);\n\n [DllImport(\"LibTorchSharp\")]\n internal static extern IntPtr THSSpecial_ndtr(IntPtr tensor);\n\n [DllImport(\"LibTorchSharp\")]\n internal static extern IntPtr THSSpecial_ndtri(IntPtr tensor);\n\n [DllImport(\"LibTorchSharp\")]\n internal static extern IntPtr THSSpecial_sinc(IntPtr tensor);\n\n [DllImport(\"LibTorchSharp\")]\n internal static extern IntPtr THSSpecial_xlog1py(IntPtr tensor, IntPtr other);\n\n [DllImport(\"LibTorchSharp\")]\n internal static extern IntPtr THSSpecial_zeta(IntPtr tensor, IntPtr other);\n\n [DllImport(\"LibTorchSharp\")]\n internal static extern double THSSpecial_erf_scalar(double x);\n\n [DllImport(\"LibTorchSharp\")]\n internal static extern double THSSpecial_erfc_scalar(double x);\n }\n}\n"},{"alpha_fraction":0.5633528232574463,"alphanum_fraction":0.5698505640029907,"avg_line_length":34.79069900512695,"blob_id":"aae537606737be2746ed36854e0b1c789b26a76c","content_id":"8679c97dd3df0a40eed01855ba1314e235f0b698","detected_licenses":["MIT"],"is_generated":false,"is_vendor":false,"language":"C#","length_bytes":1539,"license_type":"permissive","max_line_length":141,"num_lines":43,"path":"/src/TorchVision/Grayscale.cs","repo_name":"NiklasGustafsson/TorchSharp","src_encoding":"UTF-8","text":"// Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information.\nusing System;\nusing static TorchSharp.torch;\n\n\nnamespace TorchSharp\n{\n public static partial class torchvision\n {\n internal class Grayscale : ITransform\n {\n internal Grayscale(int outputChannels = 1)\n {\n if (outputChannels != 1 && outputChannels != 3) throw new ArgumentException(\"The number of output channels must be 1 or 3.\");\n this.outputChannels = outputChannels;\n }\n\n public Tensor call(Tensor input)\n {\n int cDim = (int)input.Dimensions - 3;\n if (input.shape[cDim] == 1) return input.alias();\n if (input.shape[cDim] != 3)\n throw new ArgumentException(\"RGB input to 'Grayscale' transform must have 3 channels.\");\n return transforms.functional.rgb_to_grayscale(input, outputChannels);\n }\n\n protected int outputChannels;\n }\n\n public static partial class transforms\n {\n /// <summary>\n /// Convert image to grayscale. \n /// </summary>\n /// <param name=\"outputChannels\">The number of channels in the output image tensor.</param>\n /// <returns></returns>\n static public ITransform Grayscale(int outputChannels = 1)\n {\n return new Grayscale(outputChannels);\n }\n }\n }\n}\n"},{"alpha_fraction":0.5795321464538574,"alphanum_fraction":0.5826510787010193,"avg_line_length":47.40565872192383,"blob_id":"52144a5704fde98b5e62ad5f75147b9cafae5c3b","content_id":"1cfe0a2f2e3957f48604aa66bea7cf5c0506c269","detected_licenses":["MIT"],"is_generated":false,"is_vendor":false,"language":"C#","length_bytes":5130,"license_type":"permissive","max_line_length":160,"num_lines":106,"path":"/src/TorchVision/GaussianBlur.cs","repo_name":"NiklasGustafsson/TorchSharp","src_encoding":"UTF-8","text":"// Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information.\nusing System;\nusing System.Collections.Generic;\nusing System.Linq;\nusing static TorchSharp.torch;\n\n\nnamespace TorchSharp\n{\n public static partial class torchvision\n {\n internal class GaussianBlur : ITransform\n {\n internal GaussianBlur(IList<long> kernelSize, float min, float max)\n {\n if (kernelSize == null || kernelSize.Count != 2 || kernelSize.Any(x => x <= 0)) {\n throw new ArgumentException(\"Invalid kernel size argument.\");\n }\n if (min < 0 || max < 0 || min >= max) {\n throw new ArgumentException(\"Invalid GaussianBlur arguments.\");\n }\n this.sigma = (min == max) ?\n min :\n (float)(new Random().NextDouble() * (max - min) + min);\n this.kernelSize = kernelSize.ToArray();\n }\n\n public Tensor call(Tensor input)\n {\n return transforms.functional.gaussian_blur(input, kernelSize, new float[] { sigma });\n }\n\n protected long[] kernelSize;\n protected float sigma;\n }\n\n public static partial class transforms\n {\n /// <summary>\n /// Apply a Gaussian blur effect to the image.\n /// </summary>\n /// <param name=\"kernelSize\">Gaussian kernel size</param>\n /// <param name=\"sigma\">Gaussian kernel standard deviation</param>\n /// <returns></returns>\n static public ITransform GaussianBlur(IList<long> kernelSize, float sigma)\n {\n return new GaussianBlur(kernelSize, sigma, sigma);\n }\n\n /// <summary>\n /// Apply a Gaussian blur effect to the image.\n /// </summary>\n /// <param name=\"kernelSize\">Gaussian kernel size</param>\n /// <param name=\"min\">Minimum value of the range for the uniform distribution from which the Gaussian kernel standard deviation will sampled</param>\n /// <param name=\"max\">Maximum value of the range for the uniform distribution from which the Gaussian kernel standard deviation will sampled</param>\n static public ITransform GaussianBlur(IList<long> kernelSize, float min = 0.1f, float max = 2.0f)\n {\n return new GaussianBlur(kernelSize, min, max);\n }\n\n /// <summary>\n /// Apply a Gaussian blur effect to the image.\n /// </summary>\n /// <param name=\"kernelSize\">Gaussian kernel size</param>\n /// <param name=\"sigma\">Gaussian kernel standard deviation</param>\n static public ITransform GaussianBlur(long kernelSize, float sigma)\n {\n return new GaussianBlur(new long[] { kernelSize, kernelSize }, sigma, sigma);\n }\n\n /// <summary>\n /// Apply a Gaussian blur effect to the image.\n /// </summary>\n /// <param name=\"kernelSize\">Gaussian kernel size</param>\n /// <param name=\"min\">Minimum value of the range for the uniform distribution from which the Gaussian kernel standard deviation will sampled</param>\n /// <param name=\"max\">Maximum value of the range for the uniform distribution from which the Gaussian kernel standard deviation will sampled</param>\n static public ITransform GaussianBlur(long kernelSize, float min = 0.1f, float max = 2.0f)\n {\n return new GaussianBlur(new long[] { kernelSize, kernelSize }, min, max);\n }\n\n /// <summary>\n /// Apply a Gaussian blur effect to the image.\n /// </summary>\n /// <param name=\"kernelHeight\">Gaussian kernel height</param>\n /// <param name=\"kernelWidth\">Gaussian kernel width</param>\n /// <param name=\"sigma\">Gaussian kernel standard deviation</param>\n static public ITransform GaussianBlur(long kernelHeight, long kernelWidth, float sigma)\n {\n return new GaussianBlur(new long[] { kernelHeight, kernelWidth }, sigma, sigma);\n }\n\n /// <summary>\n /// Apply a Gaussian blur effect to the image.\n /// </summary>\n /// <param name=\"kernelHeight\">Gaussian kernel height</param>\n /// <param name=\"kernelWidth\">Gaussian kernel width</param>\n /// <param name=\"min\">Minimum value of the range for the uniform distribution from which the Gaussian kernel standard deviation will sampled</param>\n /// <param name=\"max\">Minimum value of the range for the uniform distribution from which the Gaussian kernel standard deviation will sampled</param>\n static public ITransform GaussianBlur(long kernelHeight, long kernelWidth, float min = 0.1f, float max = 2.0f)\n {\n return new GaussianBlur(new long[] { kernelHeight, kernelWidth }, min, max);\n }\n }\n }\n}"},{"alpha_fraction":0.45181626081466675,"alphanum_fraction":0.4575202763080597,"avg_line_length":33.340206146240234,"blob_id":"e4947a432e287f760d67e84bf80de2c4a0e373d6","content_id":"132721ee72a404b6bf8a59cbe62c86c78b83388d","detected_licenses":["MIT"],"is_generated":false,"is_vendor":false,"language":"C#","length_bytes":9993,"license_type":"permissive","max_line_length":130,"num_lines":291,"path":"/src/TorchSharp/Tensor/Size.cs","repo_name":"NiklasGustafsson/TorchSharp","src_encoding":"UTF-8","text":"// Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information.\n\nusing System.Linq;\nusing System.Collections.Generic;\nusing System.Collections;\n\n#nullable enable\nnamespace TorchSharp\n{\n public static partial class torch\n {\n /// <summary>\n /// Represents the dimensions of a tensor, that is, its shape.\n /// </summary>\n /// <remarks>\n /// The primary purpose of this type, at the moment, is to avoid having to declare\n /// too many overloads on tensor factories that take input sizes.\n /// The name was chosen to coincide with 'torch.Size' in PyTorch. It may later be\n /// used as the return value of 'Tensor.shape' and 'Tensor.size()'\n /// </remarks>\n public struct Size : IEnumerable<long>\n {\n /// <summary>\n /// Represents an empty size.\n /// </summary>\n public static Size Empty = new Size(System.Array.Empty<long>());\n\n /// <summary>\n /// Constructor\n /// </summary>\n /// <param name=\"shape\">An array of longs, the size of an N-D tensor.</param>\n public Size(long[] shape)\n {\n _shape = (long[])shape.Clone();\n }\n\n /// <summary>\n /// Constructor\n /// </summary>\n /// <param name=\"shape\">An enumerable of longs, the size of an N-D tensor.</param>\n public Size(IEnumerable<long> shape)\n {\n _shape = shape.ToArray();\n }\n\n /// <summary>\n /// Constructor\n /// </summary>\n /// <param name=\"shape\">An array of longs, the size of an N-D tensor.</param>\n public Size(int[] shape)\n {\n _shape = shape.Select(i => (long)i).ToArray();\n }\n\n /// <summary>\n /// Constructor\n /// </summary>\n /// <param name=\"size\">The size of a 1D tensor.</param>\n public Size(long size)\n {\n _shape = new long[] { size };\n }\n\n /// <summary>\n /// Constructor\n /// </summary>\n /// <param name=\"size\">The size of a 2D tensor.</param>\n public Size((long, long) size)\n {\n _shape = new long[] { size.Item1, size.Item2 };\n }\n\n /// <summary>\n /// Constructor\n /// </summary>\n /// <param name=\"size\">The size of a 3D tensor.</param>\n public Size((long, long, long) size)\n {\n _shape = new long[] { size.Item1, size.Item2, size.Item3 };\n }\n\n /// <summary>\n /// Constructor\n /// </summary>\n /// <param name=\"size\">The size of a 4D tensor.</param>\n public Size((long, long, long, long) size)\n {\n _shape = new long[] { size.Item1, size.Item2, size.Item3, size.Item4 };\n }\n\n /// <summary>\n /// Constructor\n /// </summary>\n /// <param name=\"size\">The size of a 5D tensor.</param>\n public Size((long, long, long, long, long) size)\n {\n _shape = new long[] { size.Item1, size.Item2, size.Item3, size.Item4, size.Item5 };\n }\n\n /// <summary>\n /// Constructor\n /// </summary>\n /// <param name=\"size\">The size of a 6D tensor.</param>\n public Size((long, long, long, long, long, long) size)\n {\n _shape = new long[] { size.Item1, size.Item2, size.Item3, size.Item4, size.Item5, size.Item6 };\n }\n\n\n /// <summary>\n /// Constructor\n /// </summary>\n /// <param name=\"size\">The size of a 1D tensor.</param>\n public Size(int size)\n {\n _shape = new long[] { size };\n }\n\n /// <summary>\n /// Constructor\n /// </summary>\n /// <param name=\"size\">The size of a 2D tensor.</param>\n public Size((int, int) size)\n {\n _shape = new long[] { size.Item1, size.Item2 };\n }\n\n /// <summary>\n /// Constructor\n /// </summary>\n /// <param name=\"size\">The size of a 3D tensor.</param>\n public Size((int, int, int) size)\n {\n _shape = new long[] { size.Item1, size.Item2, size.Item3 };\n }\n /// <summary>\n /// Constructor\n /// </summary>\n /// <param name=\"size\">The size of a 4D tensor.</param>\n public Size((int, int, int, int) size)\n {\n _shape = new long[] { size.Item1, size.Item2, size.Item3, size.Item4 };\n }\n\n /// <summary>\n /// Constructor\n /// </summary>\n /// <param name=\"size\">The size of a 5D tensor.</param>\n public Size((int, int, int, int, int) size)\n {\n _shape = new long[] { size.Item1, size.Item2, size.Item3, size.Item4, size.Item5 };\n }\n\n /// <summary>\n /// Constructor\n /// </summary>\n /// <param name=\"size\">The size of a 6D tensor.</param>\n public Size((int, int, int, int, int, int) size)\n {\n _shape = new long[] { size.Item1, size.Item2, size.Item3, size.Item4, size.Item5, size.Item6 };\n }\n\n /// <summary>\n /// Implicit conversion operators. Useful to avoid overloads everywhere.\n /// </summary>\n public static implicit operator Size(long size) => new Size(size);\n public static implicit operator Size((long, long) size) => new Size(size);\n public static implicit operator Size((long, long, long) size) => new Size(size);\n public static implicit operator Size((long, long, long, long) size) => new Size(size);\n public static implicit operator Size((long, long, long, long, long) size) => new Size(size);\n public static implicit operator Size((long, long, long, long, long, long) size) => new Size(size);\n\n public static implicit operator Size(long[] size) => new Size(size);\n\n public static Size operator +(Size left, Size right)\n {\n return new Size(left._shape.AsEnumerable<long>().Concat<long>(right._shape).ToArray());\n }\n\n public static Size operator +(Size left, IEnumerable<long> right)\n {\n return new Size(left._shape.AsEnumerable<long>().Concat<long>(right).ToArray());\n }\n\n //public static implicit operator Size(long[] size) => new Size(size);\n //public static implicit operator long[](Size size) => size._shape;\n\n public static bool operator ==(Size left, Size right) => left._shape == right._shape;\n\n public static bool operator !=(Size left, Size right) => left._shape != right._shape;\n\n public override bool Equals(object? obj)\n {\n if (obj is null || !(obj is Size)) return false;\n\n return _shape.Equals(((Size)obj)._shape);\n }\n\n public Size Slice(int first, int next)\n {\n if (next < 0) {\n next = _shape.Length + next;\n }\n\n if (first < 0) {\n first = _shape.Length + first;\n }\n\n if (next <= first) {\n return Size.Empty;\n }\n\n if (first == 0) {\n if (next == _shape.Length) {\n return this;\n }\n else {\n return new Size(_shape.Take(next));\n }\n }\n else {\n if (next == _shape.Length) {\n return new Size(_shape.Skip(first));\n } else {\n return new Size(_shape.Skip(first).Take(next-first));\n }\n }\n }\n\n public override int GetHashCode()\n {\n return _shape.GetHashCode();\n }\n\n /// <summary>\n /// The number of elements in a tensor, the product of its shape elements.\n /// </summary>\n /// <returns></returns>\n public long numel()\n {\n long size = 1;\n foreach (var s in _shape) {\n size *= s;\n }\n return size;\n }\n\n public bool IsEmpty => _shape.Length == 0;\n\n public bool IsScalar => IsEmpty;\n\n public int Length => _shape.Length;\n\n public IEnumerable<long> Take(int i) => _shape.Take<long>(i);\n\n public IEnumerator<long> GetEnumerator()\n {\n return _shape.AsEnumerable<long>().GetEnumerator();\n }\n\n IEnumerator IEnumerable.GetEnumerator()\n {\n return this.GetEnumerator();\n }\n\n /// <summary>\n /// Element accessor\n /// </summary>\n /// <param name=\"idx\">The element index</param>\n /// <returns></returns>\n public long this[int idx] {\n get { return _shape[idx]; }\n }\n\n /// <summary>\n /// Element accessor\n /// </summary>\n /// <param name=\"idx\">The element index</param>\n /// <returns></returns>\n public long this[long idx] {\n get { return _shape[(int)idx]; }\n }\n\n public long[] Shape { get { return _shape; } }\n\n private long[] _shape;\n }\n\n\n }\n}\n"},{"alpha_fraction":0.5404923558235168,"alphanum_fraction":0.5419193506240845,"avg_line_length":39.62318801879883,"blob_id":"f0b863ec67b2532966e2e0584f867ac097f894a7","content_id":"fe1d94bd532d41d3987fc502104cc688cf884957","detected_licenses":["MIT"],"is_generated":false,"is_vendor":false,"language":"C#","length_bytes":2803,"license_type":"permissive","max_line_length":151,"num_lines":69,"path":"/src/TorchSharp/NN/PixelShuffle.cs","repo_name":"NiklasGustafsson/TorchSharp","src_encoding":"UTF-8","text":"// Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information.\nusing System;\nusing static TorchSharp.torch;\nusing static TorchSharp.PInvoke.NativeMethods;\n\nnamespace TorchSharp\n{\n using Modules;\n\n namespace Modules\n {\n /// <summary>\n /// This class is used to represent a dropout module.\n /// </summary>\n public sealed class PixelShuffle : torch.nn.Module<Tensor, Tensor>\n {\n internal PixelShuffle(IntPtr handle, IntPtr boxedHandle) : base(handle, boxedHandle) { }\n\n /// <summary>\n /// Forward pass.\n /// </summary>\n /// <param name=\"tensor\">Input tensor</param>\n /// <returns></returns>\n public override Tensor forward(Tensor tensor)\n {\n var res = THSNN_PixelShuffle_forward(handle, tensor.Handle);\n if (res == IntPtr.Zero) { torch.CheckForErrors(); }\n return new Tensor(res);\n }\n }\n }\n\n public static partial class torch\n {\n public static partial class nn\n {\n /// <summary>\n /// Rearranges elements in a tensor of shape (*, C * r^2, H, W) to a tensor of shape(*, C, H * r, W * r), where r is an upscale factor.\n /// This is useful for implementing efficient sub-pixel convolution with a stride of 1/r.\n /// </summary>\n /// <param name=\"upscaleFactor\">Factor to increase spatial resolution by</param>\n /// <returns></returns>\n public static PixelShuffle PixelShuffle(long upscaleFactor)\n {\n var handle = THSNN_PixelShuffle_ctor(upscaleFactor, out var boxedHandle);\n if (handle == IntPtr.Zero) { torch.CheckForErrors(); }\n return new PixelShuffle(handle, boxedHandle);\n }\n\n public static partial class functional\n {\n /// <summary>\n /// Rearranges elements in a tensor of shape (*, C * r^2, H, W) to a tensor of shape(*, C, H * r, W * r), where r is an upscale factor.\n /// This is useful for implementing efficient sub-pixel convolution with a stride of 1/r.\n /// </summary>\n /// <param name=\"x\">Input tensor</param>\n /// <param name=\"upscaleFactor\">Factor to increase spatial resolution by</param>\n /// <returns></returns>\n /// <returns></returns>\n public static Tensor pixel_shuffle(Tensor x, long upscaleFactor)\n {\n using (var d = nn.PixelShuffle(upscaleFactor)) {\n return d.call(x);\n }\n }\n }\n }\n }\n}\n"},{"alpha_fraction":0.6810643672943115,"alphanum_fraction":0.7021636962890625,"avg_line_length":37.554405212402344,"blob_id":"c1b1f31364fa1153b8a3affa31ad03ba3e1400aa","content_id":"e1500d939016fb20da8539ac042dec4511f255bf","detected_licenses":["MIT"],"is_generated":false,"is_vendor":false,"language":"C++","length_bytes":37205,"license_type":"permissive","max_line_length":259,"num_lines":965,"path":"/src/Native/LibTorchSharp/THSConvolution.cpp","repo_name":"NiklasGustafsson/TorchSharp","src_encoding":"UTF-8","text":"// Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information.\n#include \"THSNN.h\"\n\n#include <torch/nn/init.h>\n\n\n\nNNModule THSNN_AvgPool1d_ctor(const int64_t* kernelSize, const int64_t* stride, const int64_t* padding,\n bool ceil_mode, bool count_include_pad, int64_t divisor_override,\n NNAnyModule* outAsAnyModule)\n{\n CATCH_RETURN_NNModule(\n auto opts = torch::nn::AvgPool1dOptions(at::ArrayRef<int64_t>(kernelSize, 1)).ceil_mode(ceil_mode).count_include_pad(count_include_pad);\n if (stride)\n opts = opts.stride(at::ArrayRef<int64_t>(stride, 1));\n if (padding)\n opts = opts.padding(at::ArrayRef<int64_t>(padding, 1));\n if (divisor_override > 0)\n opts = opts.divisor_override(divisor_override);\n res = create_module<torch::nn::AvgPool1dImpl>(opts, outAsAnyModule);\n );\n}\n\nTensor THSNN_AvgPool1d_forward(const NNModule module, const Tensor tensor)\n{\n CATCH_TENSOR((*module)->as<torch::nn::AvgPool1d>()->forward(*tensor));\n}\n\nNNModule THSNN_AvgPool2d_ctor(const int64_t* kernelSize, const int kernelSizeLength, const int64_t* stride, const int strideLength, const int64_t* padding, const int paddingLength,\n bool ceil_mode, bool count_include_pad, int64_t divisor_override,\n NNAnyModule* outAsAnyModule)\n{\n CATCH_RETURN_NNModule(\n auto opts = torch::nn::AvgPool2dOptions(at::ArrayRef<int64_t>(kernelSize, kernelSizeLength)).ceil_mode(ceil_mode).count_include_pad(count_include_pad);\n if (stride)\n opts = opts.stride(at::ArrayRef<int64_t>(stride, strideLength));\n if (padding)\n opts = opts.padding(at::ArrayRef<int64_t>(padding, paddingLength));\n if (divisor_override > 0)\n opts = opts.divisor_override(divisor_override);\n res = create_module<torch::nn::AvgPool2dImpl>(opts, outAsAnyModule);\n );\n}\n\nTensor THSNN_AvgPool2d_forward(const NNModule module, const Tensor tensor)\n{\n CATCH_TENSOR((*module)->as<torch::nn::AvgPool2d>()->forward(*tensor));\n}\n\nNNModule THSNN_AvgPool3d_ctor(const int64_t* kernelSize, const int kernelSizeLength, const int64_t* stride, const int strideLength, const int64_t* padding, const int paddingLength,\n bool ceil_mode, bool count_include_pad, int64_t divisor_override,\n NNAnyModule* outAsAnyModule)\n{\n CATCH_RETURN_NNModule(\n auto opts = torch::nn::AvgPool3dOptions(at::ArrayRef<int64_t>(kernelSize, kernelSizeLength)).ceil_mode(ceil_mode).count_include_pad(count_include_pad);\n if (stride)\n opts = opts.stride(at::ArrayRef<int64_t>(stride, strideLength));\n if (padding)\n opts = opts.padding(at::ArrayRef<int64_t>(padding, paddingLength));\n if (divisor_override > 0)\n opts = opts.divisor_override(divisor_override);\n res = create_module<torch::nn::AvgPool3dImpl>(opts, outAsAnyModule);\n );\n}\n\nTensor THSNN_AvgPool3d_forward(const NNModule module, const Tensor tensor)\n{\n CATCH_TENSOR((*module)->as<torch::nn::AvgPool3d>()->forward(*tensor));\n}\n\nNNModule THSNN_AdaptiveAvgPool1d_ctor(const int64_t* kernelSize, const int kernelSizeLength,\n NNAnyModule* outAsAnyModule)\n{\n CATCH_RETURN_NNModule(\n auto opts = torch::nn::AdaptiveAvgPool1dOptions(at::ArrayRef<int64_t>(kernelSize, kernelSizeLength));\n res = create_module<torch::nn::AdaptiveAvgPool1dImpl>(opts, outAsAnyModule);\n );\n}\n\nTensor THSNN_AdaptiveAvgPool1d_forward(const NNModule module, const Tensor tensor)\n{\n CATCH_TENSOR((*module)->as<torch::nn::AdaptiveAvgPool1d>()->forward(*tensor));\n}\n\nNNModule THSNN_AdaptiveAvgPool2d_ctor(const int64_t* kernelSize, const int kernelSizeLength,\n NNAnyModule* outAsAnyModule)\n{\n CATCH_RETURN_NNModule(\n auto opts = torch::nn::AdaptiveAvgPool2dOptions(at::ArrayRef<int64_t>(kernelSize, kernelSizeLength));\n res = create_module<torch::nn::AdaptiveAvgPool2dImpl>(opts, outAsAnyModule);\n );\n}\n\nTensor THSNN_AdaptiveAvgPool2d_forward(const NNModule module, const Tensor tensor)\n{\n CATCH_TENSOR((*module)->as<torch::nn::AdaptiveAvgPool2d>()->forward(*tensor));\n}\n\nNNModule THSNN_AdaptiveAvgPool3d_ctor(const int64_t* kernelSize, const int kernelSizeLength,\n NNAnyModule* outAsAnyModule)\n{\n CATCH_RETURN_NNModule(\n auto opts = torch::nn::AdaptiveAvgPool3dOptions(at::ArrayRef<int64_t>(kernelSize, kernelSizeLength));\n res = create_module<torch::nn::AdaptiveAvgPool3dImpl>(opts, outAsAnyModule);\n );\n}\n\nTensor THSNN_AdaptiveAvgPool3d_forward(const NNModule module, const Tensor tensor)\n{\n CATCH_TENSOR((*module)->as<torch::nn::AdaptiveAvgPool3d>()->forward(*tensor));\n}\n\nNNModule THSNN_AdaptiveMaxPool1d_ctor(const int64_t* kernelSize, const int kernelSizeLength,\n NNAnyModule* outAsAnyModule)\n{\n CATCH_RETURN_NNModule(\n auto opts = torch::nn::AdaptiveMaxPool1dOptions(at::ArrayRef<int64_t>(kernelSize, kernelSizeLength));\n res = create_module<torch::nn::AdaptiveMaxPool1dImpl>(opts, outAsAnyModule);\n );\n}\n\nTensor THSNN_AdaptiveMaxPool1d_forward(const NNModule module, const Tensor tensor)\n{\n CATCH_TENSOR((*module)->as<torch::nn::AdaptiveMaxPool1d>()->forward(*tensor));\n}\n\nNNModule THSNN_AdaptiveMaxPool2d_ctor(const int64_t* kernelSize, const int kernelSizeLength,\n NNAnyModule* outAsAnyModule)\n{\n CATCH_RETURN_NNModule(\n auto opts = torch::nn::AdaptiveMaxPool2dOptions(at::ArrayRef<int64_t>(kernelSize, kernelSizeLength));\n res = create_module<torch::nn::AdaptiveMaxPool2dImpl>(opts, outAsAnyModule);\n );\n}\n\nTensor THSNN_AdaptiveMaxPool2d_forward(const NNModule module, const Tensor tensor)\n{\n CATCH_TENSOR((*module)->as<torch::nn::AdaptiveMaxPool2d>()->forward(*tensor));\n}\n\nNNModule THSNN_AdaptiveMaxPool3d_ctor(const int64_t* kernelSize, const int kernelSizeLength,\n NNAnyModule* outAsAnyModule)\n{\n CATCH_RETURN_NNModule(\n auto opts = torch::nn::AdaptiveMaxPool3dOptions(at::ArrayRef<int64_t>(kernelSize, kernelSizeLength));\n res = create_module<torch::nn::AdaptiveMaxPool3dImpl>(opts, outAsAnyModule);\n );\n}\n\nTensor THSNN_AdaptiveMaxPool3d_forward(const NNModule module, const Tensor tensor)\n{\n CATCH_TENSOR((*module)->as<torch::nn::AdaptiveMaxPool3d>()->forward(*tensor));\n}\n\nNNModule THSNN_LPPool1d_ctor(double norm_type, const int64_t* kernelSize, const int64_t* stride, const bool ceil_mode,\n NNAnyModule* outAsAnyModule)\n{\n CATCH_RETURN_NNModule(\n auto opts = torch::nn::LPPool1dOptions(norm_type, at::ArrayRef<int64_t>(kernelSize, 1)).ceil_mode(ceil_mode);\n if (stride)\n opts = opts.stride(at::ArrayRef<int64_t>(stride, 1));\n res = create_module<torch::nn::LPPool1dImpl>(opts, outAsAnyModule);\n );\n}\n\nTensor THSNN_LPPool1d_forward(const NNModule module, const Tensor tensor)\n{\n CATCH_TENSOR((*module)->as<torch::nn::LPPool1d>()->forward(*tensor));\n}\n\nNNModule THSNN_LPPool2d_ctor(double norm_type, const int64_t* kernelSize, const int kernelSizeLength, const int64_t* stride, const int strideLength, const bool ceil_mode,\n NNAnyModule* outAsAnyModule)\n{\n CATCH_RETURN_NNModule(\n auto opts = torch::nn::LPPool2dOptions(norm_type, at::ArrayRef<int64_t>(kernelSize, kernelSizeLength)).ceil_mode(ceil_mode);\n if (stride)\n opts = opts.stride(at::ArrayRef<int64_t>(stride, strideLength));\n res = create_module<torch::nn::LPPool2dImpl>(opts, outAsAnyModule);\n );\n}\n\nTensor THSNN_LPPool2d_forward(const NNModule module, const Tensor tensor)\n{\n CATCH_TENSOR((*module)->as<torch::nn::LPPool2d>()->forward(*tensor));\n}\n\nNNModule THSNN_MaxPool1d_ctor(const int64_t* kernelSize, const int64_t* stride, const int64_t* padding, const int64_t* dilation, bool ceil_mode,\n NNAnyModule* outAsAnyModule)\n{\n CATCH_RETURN_NNModule(\n auto opts = torch::nn::MaxPool1dOptions(at::ArrayRef<int64_t>(kernelSize, 1)).ceil_mode(ceil_mode);\n if (stride)\n opts = opts.stride(at::ArrayRef<int64_t>(stride, 1));\n if (padding)\n opts = opts.padding(at::ArrayRef<int64_t>(padding, 1));\n if (dilation)\n opts = opts.dilation(at::ArrayRef<int64_t>(dilation, 1));\n\n res = create_module<torch::nn::MaxPool1dImpl>(opts, outAsAnyModule);\n );\n}\n\nTensor THSNN_MaxPool1d_forward(const NNModule module, const Tensor tensor)\n{\n CATCH_TENSOR((*module)->as<torch::nn::MaxPool1d>()->forward(*tensor));\n}\n\nTensor THSNN_MaxPool1d_forward_with_indices(const NNModule module, const Tensor tensor, Tensor* indices)\n{\n std::tuple<at::Tensor, at::Tensor> res;\n CATCH(res = (*module)->as<torch::nn::MaxPool1d>()->forward_with_indices(*tensor););\n *indices = ResultTensor(std::get<1>(res));\n return ResultTensor(std::get<0>(res));\n}\n\nNNModule THSNN_MaxPool2d_ctor(const int64_t* kernelSize, const int kernelSizeLength, const int64_t* stride, const int strideLength,\n const int64_t* padding, const int paddingLength, const int64_t* dilation, const int dilationLength, bool ceil_mode,\n NNAnyModule* outAsAnyModule)\n{\n CATCH_RETURN_NNModule(\n auto opts = torch::nn::MaxPool2dOptions(at::ArrayRef<int64_t>(kernelSize, kernelSizeLength)).ceil_mode(ceil_mode);\n if (stride)\n opts = opts.stride(at::ArrayRef<int64_t>(stride, strideLength));\n if (padding)\n opts = opts.padding(at::ArrayRef<int64_t>(padding, paddingLength));\n if (dilation)\n opts = opts.dilation(at::ArrayRef<int64_t>(dilation, dilationLength));\n\n res = create_module<torch::nn::MaxPool2dImpl>(opts, outAsAnyModule);\n );\n}\n\nTensor THSNN_MaxPool2d_forward(const NNModule module, const Tensor tensor)\n{\n CATCH_TENSOR((*module)->as<torch::nn::MaxPool2d>()->forward(*tensor));\n}\n\nTensor THSNN_MaxPool2d_forward_with_indices(const NNModule module, const Tensor tensor, Tensor* indices)\n{\n std::tuple<at::Tensor, at::Tensor> res;\n CATCH(res = (*module)->as<torch::nn::MaxPool2d>()->forward_with_indices(*tensor););\n *indices = ResultTensor(std::get<1>(res));\n return ResultTensor(std::get<0>(res));\n}\n\nNNModule THSNN_MaxPool3d_ctor(const int64_t* kernelSize, const int kernelSizeLength, const int64_t* stride, const int strideLength,\n const int64_t* padding, const int paddingLength, const int64_t* dilation, const int dilationLength, bool ceil_mode,\n NNAnyModule* outAsAnyModule)\n{\n CATCH_RETURN_NNModule(\n auto opts = torch::nn::MaxPool3dOptions(at::ArrayRef<int64_t>(kernelSize, kernelSizeLength)).ceil_mode(ceil_mode);\n if (stride)\n opts = opts.stride(at::ArrayRef<int64_t>(stride, strideLength));\n if (padding)\n opts = opts.padding(at::ArrayRef<int64_t>(padding, paddingLength));\n if (dilation)\n opts = opts.dilation(at::ArrayRef<int64_t>(dilation, dilationLength));\n\n res = create_module<torch::nn::MaxPool3dImpl>(opts, outAsAnyModule);\n );\n}\n\nTensor THSNN_MaxPool3d_forward(const NNModule module, const Tensor tensor)\n{\n CATCH_TENSOR((*module)->as<torch::nn::MaxPool3d>()->forward(*tensor));\n}\n\nTensor THSNN_MaxPool3d_forward_with_indices(const NNModule module, const Tensor tensor, Tensor* indices)\n{\n std::tuple<at::Tensor, at::Tensor> res;\n CATCH(res = (*module)->as<torch::nn::MaxPool3d>()->forward_with_indices(*tensor););\n *indices = ResultTensor(std::get<1>(res));\n return ResultTensor(std::get<0>(res));\n}\n\nNNModule THSNN_MaxUnpool1d_ctor(const int64_t* kernelSize, const int64_t* stride, const int64_t* padding, NNAnyModule* outAsAnyModule)\n{\n CATCH_RETURN_NNModule(\n auto opts = torch::nn::MaxUnpool1dOptions(at::ArrayRef<int64_t>(kernelSize, 1));\n if (stride)\n opts = opts.stride(at::ArrayRef<int64_t>(stride, 1));\n if (padding)\n opts = opts.padding(at::ArrayRef<int64_t>(padding, 1));\n\n res = create_module<torch::nn::MaxUnpool1dImpl>(opts, outAsAnyModule);\n );\n}\n\nTensor THSNN_MaxUnpool1d_forward(const NNModule module, const Tensor tensor, const Tensor indices, const int64_t* outputSize)\n{\n if (outputSize != nullptr) {\n std::vector<int64_t> outSize;\n outSize.push_back(*outputSize);\n\n CATCH_TENSOR((*module)->as<torch::nn::MaxUnpool1d>()->forward(*tensor, *indices, outSize));\n }\n else {\n CATCH_TENSOR((*module)->as<torch::nn::MaxUnpool1d>()->forward(*tensor, *indices));\n }\n}\n\nNNModule THSNN_MaxUnpool2d_ctor(const int64_t* kernelSize, const int kernelSizeLength, const int64_t* stride, const int strideLength, const int64_t* padding, const int paddingLength, NNAnyModule* outAsAnyModule)\n{\n CATCH_RETURN_NNModule(\n auto opts = torch::nn::MaxUnpool2dOptions(at::ArrayRef<int64_t>(kernelSize, kernelSizeLength));\n if (stride)\n opts = opts.stride(at::ArrayRef<int64_t>(stride, strideLength));\n if (padding)\n opts = opts.padding(at::ArrayRef<int64_t>(padding, paddingLength));\n\n res = create_module<torch::nn::MaxUnpool2dImpl>(opts, outAsAnyModule);\n );\n}\n\nTensor THSNN_MaxUnpool2d_forward(const NNModule module, const Tensor tensor, const Tensor indices, const int64_t* outputSize, const int outputSizeLength)\n{\n if (outputSize != nullptr) {\n std::vector<int64_t> outSize;\n for (auto i = 0L; i < outputSizeLength; i++) {\n outSize.push_back(outputSize[i]);\n }\n\n CATCH_TENSOR((*module)->as<torch::nn::MaxUnpool2d>()->forward(*tensor, *indices, outSize));\n }\n else {\n CATCH_TENSOR((*module)->as<torch::nn::MaxUnpool2d>()->forward(*tensor, *indices));\n }\n}\n\nNNModule THSNN_MaxUnpool3d_ctor(const int64_t* kernelSize, const int kernelSizeLength, const int64_t* stride, const int strideLength, const int64_t* padding, const int paddingLength, NNAnyModule* outAsAnyModule)\n{\n CATCH_RETURN_NNModule(\n auto opts = torch::nn::MaxUnpool3dOptions(at::ArrayRef<int64_t>(kernelSize, kernelSizeLength));\n if (stride)\n opts = opts.stride(at::ArrayRef<int64_t>(stride, strideLength));\n if (padding)\n opts = opts.padding(at::ArrayRef<int64_t>(padding, paddingLength));\n\n res = create_module<torch::nn::MaxUnpool3dImpl>(opts, outAsAnyModule);\n );\n}\n\nTensor THSNN_MaxUnpool3d_forward(const NNModule module, const Tensor tensor, const Tensor indices, const int64_t* outputSize, const int outputSizeLength)\n{\n if (outputSize != nullptr) {\n std::vector<int64_t> outSize;\n for (auto i = 0L; i < outputSizeLength; i++) {\n outSize.push_back(outputSize[i]);\n }\n\n CATCH_TENSOR((*module)->as<torch::nn::MaxUnpool3d>()->forward(*tensor, *indices, outSize));\n }\n else {\n CATCH_TENSOR((*module)->as<torch::nn::MaxUnpool3d>()->forward(*tensor, *indices));\n }\n}\n\n\nNNModule THSNN_FractionalMaxPool2d_ctor(const int64_t* kernelSize, const int kernelSizeLength, const int64_t* outputSize, const int outputSizeLength, const double* outputRatio, const int outputRatioLength, NNAnyModule* outAsAnyModule)\n{\n CATCH_RETURN_NNModule(\n auto opts = torch::nn::FractionalMaxPool2dOptions(at::ArrayRef<int64_t>(kernelSize, kernelSizeLength));\n if (outputSize)\n opts = opts.output_size(at::ArrayRef<int64_t>(outputSize, outputSizeLength));\n if (outputRatio)\n opts = opts.output_ratio(at::ArrayRef<double>(outputRatio, outputRatioLength));\n\n res = create_module<torch::nn::FractionalMaxPool2dImpl>(opts, outAsAnyModule);\n );\n}\n\nTensor THSNN_FractionalMaxPool2d_forward(const NNModule module, const Tensor tensor)\n{\n CATCH_TENSOR((*module)->as<torch::nn::FractionalMaxPool2d>()->forward(*tensor));\n}\n\nTensor THSNN_FractionalMaxPool2d_forward_with_indices(const NNModule module, const Tensor tensor, Tensor* indices)\n{\n std::tuple<at::Tensor, at::Tensor> res;\n CATCH(res = (*module)->as<torch::nn::FractionalMaxPool2d>()->forward_with_indices(*tensor););\n *indices = ResultTensor(std::get<1>(res));\n return ResultTensor(std::get<0>(res));\n}\n\nNNModule THSNN_FractionalMaxPool3d_ctor(const int64_t* kernelSize, const int kernelSizeLength, const int64_t* outputSize, const int outputSizeLength, const double* outputRatio, const int outputRatioLength, NNAnyModule* outAsAnyModule)\n{\n CATCH_RETURN_NNModule(\n auto opts = torch::nn::FractionalMaxPool3dOptions(at::ArrayRef<int64_t>(kernelSize, kernelSizeLength));\n if (outputSize)\n opts = opts.output_size(at::ArrayRef<int64_t>(outputSize, outputSizeLength));\n if (outputRatio)\n opts = opts.output_ratio(at::ArrayRef<double>(outputRatio, outputRatioLength));\n\n res = create_module<torch::nn::FractionalMaxPool3dImpl>(opts, outAsAnyModule);\n );\n}\n\nTensor THSNN_FractionalMaxPool3d_forward(const NNModule module, const Tensor tensor)\n{\n CATCH_TENSOR((*module)->as<torch::nn::FractionalMaxPool3d>()->forward(*tensor));\n}\n\nTensor THSNN_FractionalMaxPool3d_forward_with_indices(const NNModule module, const Tensor tensor, Tensor* indices)\n{\n std::tuple<at::Tensor, at::Tensor> res;\n CATCH(res = (*module)->as<torch::nn::FractionalMaxPool3d>()->forward_with_indices(*tensor););\n *indices = ResultTensor(std::get<1>(res));\n return ResultTensor(std::get<0>(res));\n}\n\nNNModule THSNN_ZeroPad2d_ctor(const int64_t padding, NNAnyModule* outAsAnyModule)\n{\n CATCH_RETURN_NNModule(\n auto opts = torch::nn::ZeroPad2dOptions(padding);\n res = create_module<torch::nn::ZeroPad2dImpl>(opts, outAsAnyModule);\n );\n}\n\nNNModule THSNN_ZeroPad2d_ctor_tuple(const int64_t padding_left, const int64_t padding_right, const int64_t padding_top, const int64_t padding_bottom, NNAnyModule* outAsAnyModule)\n{\n CATCH_RETURN_NNModule(\n auto opts = torch::nn::ZeroPad2dOptions({ padding_left, padding_right, padding_top, padding_bottom });\n res = create_module<torch::nn::ZeroPad2dImpl>(opts, outAsAnyModule);\n );\n}\n\nTensor THSNN_ZeroPad2d_forward(const NNModule module, const Tensor tensor)\n{\n CATCH_TENSOR((*module)->as<torch::nn::ZeroPad2d>()->forward(*tensor));\n}\n\nNNModule THSNN_ConstantPad1d_ctor(const double value, const int64_t padding, NNAnyModule* outAsAnyModule)\n{\n CATCH_RETURN_NNModule(\n auto opts = torch::nn::ConstantPad1dOptions(padding, value);\n res = create_module<torch::nn::ConstantPad1dImpl>(opts, outAsAnyModule);\n );\n}\n\nTensor THSNN_ConstantPad1d_forward(const NNModule module, const Tensor tensor)\n{\n CATCH_TENSOR((*module)->as<torch::nn::ConstantPad1d>()->forward(*tensor));\n}\n\nNNModule THSNN_ConstantPad2d_ctor(const double value, const int64_t padding, NNAnyModule* outAsAnyModule)\n{\n CATCH_RETURN_NNModule(\n auto opts = torch::nn::ConstantPad2dOptions(padding, value);\n res = create_module<torch::nn::ConstantPad2dImpl>(opts, outAsAnyModule);\n );\n}\n\nTensor THSNN_ConstantPad2d_forward(const NNModule module, const Tensor tensor)\n{\n CATCH_TENSOR((*module)->as<torch::nn::ConstantPad2d>()->forward(*tensor));\n}\n\nNNModule THSNN_ConstantPad3d_ctor(const double value, const int64_t padding, NNAnyModule* outAsAnyModule)\n{\n CATCH_RETURN_NNModule(\n auto opts = torch::nn::ConstantPad3dOptions(padding, value);\n res = create_module<torch::nn::ConstantPad3dImpl>(opts, outAsAnyModule);\n );\n}\n\nTensor THSNN_ConstantPad3d_forward(const NNModule module, const Tensor tensor)\n{\n CATCH_TENSOR((*module)->as<torch::nn::ConstantPad3d>()->forward(*tensor));\n}\n\nNNModule THSNN_ConstantPad1d_ctor_tuple(const double value, const int64_t padding_left, const int64_t padding_right, NNAnyModule* outAsAnyModule)\n{\n CATCH_RETURN_NNModule(\n auto opts = torch::nn::ConstantPad1dOptions({ padding_left, padding_right }, value);\n res = create_module<torch::nn::ConstantPad1dImpl>(opts, outAsAnyModule);\n );\n}\n\nNNModule THSNN_ConstantPad2d_ctor_tuple(const double value, const int64_t padding_left, const int64_t padding_right, const int64_t padding_top, const int64_t padding_bottom, NNAnyModule* outAsAnyModule)\n{\n CATCH_RETURN_NNModule(\n auto opts = torch::nn::ConstantPad2dOptions({ padding_left, padding_right, padding_top, padding_bottom }, value);\n res = create_module<torch::nn::ConstantPad2dImpl>(opts, outAsAnyModule);\n );\n}\n\nNNModule THSNN_ConstantPad3d_ctor_tuple(const double value, const int64_t padding_left, const int64_t padding_right, const int64_t padding_top, const int64_t padding_bottom, const int64_t padding_front, const int64_t padding_back, NNAnyModule* outAsAnyModule)\n{\n CATCH_RETURN_NNModule(\n auto opts = torch::nn::ConstantPad3dOptions({ padding_left, padding_right, padding_top, padding_bottom, padding_front, padding_back }, value);\n res = create_module<torch::nn::ConstantPad3dImpl>(opts, outAsAnyModule);\n );\n}\n\nNNModule THSNN_ReplicationPad1d_ctor(const int64_t padding, NNAnyModule* outAsAnyModule)\n{\n CATCH_RETURN_NNModule(\n auto opts = torch::nn::ReplicationPad1dOptions(padding);\n res = create_module<torch::nn::ReplicationPad1dImpl>(opts, outAsAnyModule);\n );\n}\n\nTensor THSNN_ReplicationPad1d_forward(const NNModule module, const Tensor tensor)\n{\n CATCH_TENSOR((*module)->as<torch::nn::ReplicationPad1d>()->forward(*tensor));\n}\n\nNNModule THSNN_ReplicationPad2d_ctor(const int64_t padding, NNAnyModule* outAsAnyModule)\n{\n CATCH_RETURN_NNModule(\n auto opts = torch::nn::ReplicationPad2dOptions(padding);\n res = create_module<torch::nn::ReplicationPad2dImpl>(opts, outAsAnyModule);\n );\n}\n\nTensor THSNN_ReplicationPad2d_forward(const NNModule module, const Tensor tensor)\n{\n CATCH_TENSOR((*module)->as<torch::nn::ReplicationPad2d>()->forward(*tensor));\n}\n\nNNModule THSNN_ReplicationPad3d_ctor(const int64_t padding, NNAnyModule* outAsAnyModule)\n{\n CATCH_RETURN_NNModule(\n auto opts = torch::nn::ReplicationPad3dOptions(padding);\n res = create_module<torch::nn::ReplicationPad3dImpl>(opts, outAsAnyModule);\n );\n}\n\n\nTensor THSNN_ReplicationPad3d_forward(const NNModule module, const Tensor tensor)\n{\n CATCH_TENSOR((*module)->as<torch::nn::ReplicationPad3d>()->forward(*tensor));\n}\n\nNNModule THSNN_ReplicationPad1d_ctor_tuple(const int64_t padding_left, const int64_t padding_right, NNAnyModule* outAsAnyModule)\n{\n CATCH_RETURN_NNModule(\n auto opts = torch::nn::ReplicationPad1dOptions({ padding_left, padding_right });\n res = create_module<torch::nn::ReplicationPad1dImpl>(opts, outAsAnyModule);\n );\n}\n\nNNModule THSNN_ReplicationPad2d_ctor_tuple(const int64_t padding_left, const int64_t padding_right, const int64_t padding_top, const int64_t padding_bottom, NNAnyModule* outAsAnyModule)\n{\n CATCH_RETURN_NNModule(\n auto opts = torch::nn::ReplicationPad2dOptions({ padding_left, padding_right, padding_top, padding_bottom });\n res = create_module<torch::nn::ReplicationPad2dImpl>(opts, outAsAnyModule);\n );\n}\n\nNNModule THSNN_ReplicationPad3d_ctor_tuple(const int64_t padding_left, const int64_t padding_right, const int64_t padding_top, const int64_t padding_bottom, const int64_t padding_front, const int64_t padding_back, NNAnyModule* outAsAnyModule)\n{\n CATCH_RETURN_NNModule(\n auto opts = torch::nn::ReplicationPad3dOptions({ padding_left, padding_right, padding_top, padding_bottom, padding_front, padding_back });\n res = create_module<torch::nn::ReplicationPad3dImpl>(opts, outAsAnyModule);\n );\n}\n\nNNModule THSNN_ReflectionPad1d_ctor(const int64_t padding, NNAnyModule* outAsAnyModule)\n{\n CATCH_RETURN_NNModule(\n auto opts = torch::nn::ReflectionPad1dOptions(padding);\n res = create_module<torch::nn::ReflectionPad1dImpl>(opts, outAsAnyModule);\n );\n}\n\nTensor THSNN_ReflectionPad1d_forward(const NNModule module, const Tensor tensor)\n{\n CATCH_TENSOR((*module)->as<torch::nn::ReflectionPad1d>()->forward(*tensor));\n}\n\nNNModule THSNN_ReflectionPad2d_ctor(const int64_t padding, NNAnyModule* outAsAnyModule)\n{\n CATCH_RETURN_NNModule(\n auto opts = torch::nn::ReflectionPad2dOptions(padding);\n res = create_module<torch::nn::ReflectionPad2dImpl>(opts, outAsAnyModule);\n );\n}\n\nTensor THSNN_ReflectionPad2d_forward(const NNModule module, const Tensor tensor)\n{\n CATCH_TENSOR((*module)->as<torch::nn::ReflectionPad2d>()->forward(*tensor));\n}\n\nNNModule THSNN_ReflectionPad3d_ctor(const int64_t padding, NNAnyModule* outAsAnyModule)\n{\n CATCH_RETURN_NNModule(\n auto opts = torch::nn::ReflectionPad3dOptions(padding);\n res = create_module<torch::nn::ReflectionPad3dImpl>(opts, outAsAnyModule);\n );\n}\n\nTensor THSNN_ReflectionPad3d_forward(const NNModule module, const Tensor tensor)\n{\n CATCH_TENSOR((*module)->as<torch::nn::ReflectionPad3d>()->forward(*tensor));\n}\n\nNNModule THSNN_ReflectionPad1d_ctor_tuple(const int64_t padding_left, const int64_t padding_right, NNAnyModule* outAsAnyModule)\n{\n CATCH_RETURN_NNModule(\n auto opts = torch::nn::ReflectionPad1dOptions({ padding_left, padding_right });\n res = create_module<torch::nn::ReflectionPad1dImpl>(opts, outAsAnyModule);\n );\n}\n\nNNModule THSNN_ReflectionPad2d_ctor_tuple(const int64_t padding_left, const int64_t padding_right, const int64_t padding_top, const int64_t padding_bottom, NNAnyModule* outAsAnyModule)\n{\n CATCH_RETURN_NNModule(\n auto opts = torch::nn::ReflectionPad2dOptions({ padding_left, padding_right, padding_top, padding_bottom });\n res = create_module<torch::nn::ReflectionPad2dImpl>(opts, outAsAnyModule);\n );\n}\n\nNNModule THSNN_ReflectionPad3d_ctor_tuple(const int64_t padding_left, const int64_t padding_right, const int64_t padding_top, const int64_t padding_bottom, const int64_t padding_front, const int64_t padding_back, NNAnyModule* outAsAnyModule)\n{\n CATCH_RETURN_NNModule(\n auto opts = torch::nn::ReflectionPad3dOptions({ padding_left, padding_right, padding_top, padding_bottom, padding_front, padding_back });\n res = create_module<torch::nn::ReflectionPad3dImpl>(opts, outAsAnyModule);\n );\n}\n\n\ntemplate<typename T>\nvoid ApplyPaddingMode(T& opts, const int64_t padding)\n{\n if (padding == 0)\n opts = opts.padding_mode(torch::kZeros);\n if (padding == 1)\n opts = opts.padding_mode(torch::kReflect);\n if (padding == 2)\n opts = opts.padding_mode(torch::kReplicate);\n if (padding == 3)\n opts = opts.padding_mode(torch::kCircular);\n}\n\nNNModule THSNN_Conv1d_ctor(const int64_t inputChannel, const int64_t outputChannel,\n const int64_t kernelSize, const int64_t stride, const int64_t padding,\n const int64_t dilation, const int64_t paddingMode, const int64_t groups, const bool bias,\n NNAnyModule* outAsAnyModule)\n{\n torch::nn::Conv1dOptions::padding_t padd(padding);\n if (padding == -1)\n {\n padd = torch::kSame;\n }\n\n CATCH_RETURN_NNModule(\n auto opts = torch::nn::Conv1dOptions(inputChannel, outputChannel, kernelSize)\n .stride(stride)\n .padding(padd)\n .dilation(dilation)\n .groups(groups)\n .bias(bias);\n\n ApplyPaddingMode(opts, paddingMode);\n\n res = create_module<torch::nn::Conv1dImpl>(opts, outAsAnyModule);\n );\n}\n\nTensor THSNN_Conv1d_forward(const NNModule module, const Tensor tensor)\n{\n CATCH_TENSOR((*module)->as<torch::nn::Conv1d>()->forward(*tensor));\n}\n\nTensor THSNN_Conv1d_bias(const NNModule module)\n{\n return get_bias<torch::nn::Conv1d>(module);\n}\n\nvoid THSNN_Conv1d_set_bias(const NNModule module, const Tensor bias)\n{\n set_bias<torch::nn::Conv1d>(module, bias);\n}\n\nTensor THSNN_Conv1d_weight(const NNModule module)\n{\n return get_weight<torch::nn::Conv1d>(module);\n}\n\nvoid THSNN_Conv1d_set_weight(const NNModule module, const Tensor weight)\n{\n set_weight<torch::nn::Conv1d>(module, weight);\n}\n\nNNModule THSNN_Conv2d_ctor(const int64_t inputChannel, const int64_t outputChannel,\n const int64_t kernelSize, const int64_t stride, const int64_t padding,\n const int64_t dilation, const int64_t paddingMode, const int64_t groups, const bool bias,\n NNAnyModule* outAsAnyModule)\n{\n torch::nn::Conv2dOptions::padding_t padd =\n (padding == -1) ? torch::kSame :\n (padding == 0) ? torch::kValid :\n torch::nn::Conv2dOptions::padding_t(padding);\n\n CATCH_RETURN_NNModule(\n auto opts = torch::nn::Conv2dOptions(inputChannel, outputChannel, kernelSize)\n .stride(stride)\n .padding(padd)\n .dilation(dilation)\n .groups(groups)\n .bias(bias);\n ApplyPaddingMode(opts, paddingMode);\n\n res = create_module<torch::nn::Conv2dImpl>(opts, outAsAnyModule);\n );\n}\n\nNNModule THSNN_Conv2d_ctor_1(const int64_t inputChannel, const int64_t outputChannel,\n const int64_t kernelX, const int64_t kernelY,\n const int64_t strideX, const int64_t strideY,\n const int64_t paddingX, const int64_t paddingY,\n const int64_t dilationX, const int64_t dilationY,\n const int64_t paddingMode, const int64_t groups, const bool bias,\n NNAnyModule* outAsAnyModule)\n{\n torch::nn::Conv2dOptions::padding_t padd =\n (paddingX == -1) && (paddingY == 0) ? torch::kSame :\n (paddingX == 0) && (paddingY == 0) ? torch::kValid :\n (torch::nn::Conv2dOptions::padding_t)torch::ExpandingArray<2>({ paddingX, paddingY });\n\n CATCH_RETURN_NNModule(\n auto opts = torch::nn::Conv2dOptions(inputChannel, outputChannel, { kernelX, kernelY })\n .stride({ strideX, strideY })\n .padding(padd)\n .dilation({ dilationX, dilationY })\n .groups(groups)\n .bias(bias);\n ApplyPaddingMode(opts, paddingMode);\n\n res = create_module<torch::nn::Conv2dImpl>(opts, outAsAnyModule);\n );\n}\n\nTensor THSNN_Conv2d_forward(const NNModule module, const Tensor tensor)\n{\n CATCH_TENSOR((*module)->as<torch::nn::Conv2d>()->forward(*tensor));\n}\n\nTensor THSNN_Conv2d_bias(const NNModule module)\n{\n return get_bias<torch::nn::Conv2d>(module);\n}\n\nvoid THSNN_Conv2d_set_bias(const NNModule module, const Tensor bias)\n{\n set_bias<torch::nn::Conv2d>(module, bias);\n}\n\nTensor THSNN_Conv2d_weight(const NNModule module)\n{\n return get_weight<torch::nn::Conv2d>(module);\n}\n\nvoid THSNN_Conv2d_set_weight(const NNModule module, const Tensor weight)\n{\n set_weight<torch::nn::Conv2d>(module, weight);\n}\n\nNNModule THSNN_Conv3d_ctor(const int64_t inputChannel, const int64_t outputChannel,\n const int64_t kernelSize, const int64_t stride, const int64_t padding,\n const int64_t dilation, const int64_t paddingMode, const int64_t groups, const bool bias,\n NNAnyModule* outAsAnyModule)\n{\n torch::nn::Conv3dOptions::padding_t padd =\n (padding == -1) ? torch::kSame :\n (padding == 0) ? torch::kValid :\n torch::nn::Conv3dOptions::padding_t(padding);\n\n CATCH_RETURN_NNModule(\n auto opts = torch::nn::Conv3dOptions(inputChannel, outputChannel, kernelSize)\n .stride(stride)\n .padding(padd)\n .dilation(dilation)\n .groups(groups)\n .bias(bias);\n ApplyPaddingMode(opts, paddingMode);\n\n res = create_module<torch::nn::Conv3dImpl>(opts, outAsAnyModule);\n );\n}\n\nNNModule THSNN_Conv3d_ctor_1(const int64_t inputChannel, const int64_t outputChannel,\n const int64_t kernelX, const int64_t kernelY, const int64_t kernelZ,\n const int64_t strideX, const int64_t strideY, const int64_t strideZ,\n const int64_t paddingX, const int64_t paddingY, const int64_t paddingZ,\n const int64_t dilationX, const int64_t dilationY, const int64_t dilationZ,\n const int64_t paddingMode, const int64_t groups, const bool bias,\n NNAnyModule* outAsAnyModule)\n{\n torch::nn::Conv3dOptions::padding_t padd =\n (paddingX == -1) && (paddingY == 0) && (paddingZ == 0) ? torch::kSame :\n (paddingX == 0) && (paddingY == 0) && (paddingZ == 0) ? torch::kValid :\n (torch::nn::Conv3dOptions::padding_t)torch::ExpandingArray<3>({ paddingX, paddingY, paddingZ });\n\n CATCH_RETURN_NNModule(\n auto opts = torch::nn::Conv3dOptions(inputChannel, outputChannel, { kernelX, kernelY, kernelZ })\n .stride({ strideX, strideY, strideZ })\n .padding(padd)\n .dilation({ dilationX, dilationY, dilationZ })\n .groups(groups)\n .bias(bias);\n ApplyPaddingMode(opts, paddingMode);\n\n res = create_module<torch::nn::Conv3dImpl>(opts, outAsAnyModule);\n );\n}\n\nTensor THSNN_Conv3d_bias(const NNModule module)\n{\n return get_bias<torch::nn::Conv3d>(module);\n}\n\nvoid THSNN_Conv3d_set_bias(const NNModule module, const Tensor bias)\n{\n set_bias<torch::nn::Conv3d>(module, bias);\n}\n\nTensor THSNN_Conv3d_forward(const NNModule module, const Tensor tensor)\n{\n CATCH_TENSOR((*module)->as<torch::nn::Conv3d>()->forward(*tensor));\n}\n\nTensor THSNN_Conv3d_weight(const NNModule module)\n{\n return get_weight<torch::nn::Conv3d>(module);\n}\n\nvoid THSNN_Conv3d_set_weight(const NNModule module, const Tensor weight)\n{\n set_weight<torch::nn::Conv3d>(module, weight);\n}\n\n\nNNModule THSNN_ConvTranspose1d_ctor(const int64_t inputChannel, const int64_t outputChannel,\n const int64_t kernelSize, const int64_t stride, const int64_t padding, const int64_t output_padding,\n const int64_t dilation, const int64_t paddingMode, const int64_t groups, const bool bias,\n NNAnyModule* outAsAnyModule)\n{\n CATCH_RETURN_NNModule(\n auto opts = torch::nn::ConvTranspose1dOptions(inputChannel, outputChannel, kernelSize)\n .stride(stride)\n .padding(padding)\n .dilation(dilation)\n .groups(groups)\n .bias(bias)\n .output_padding(output_padding);\n ApplyPaddingMode(opts, paddingMode);\n\n res = create_module<torch::nn::ConvTranspose1dImpl>(opts, outAsAnyModule);\n );\n}\n\nTensor THSNN_ConvTranspose1d_forward(const NNModule module, const Tensor tensor)\n{\n CATCH_TENSOR((*module)->as<torch::nn::ConvTranspose1d>()->forward(*tensor));\n}\n\nTensor THSNN_ConvTranspose1d_bias(const NNModule module)\n{\n return get_bias<torch::nn::ConvTranspose1d>(module);\n}\n\nvoid THSNN_ConvTranspose1d_set_bias(const NNModule module, const Tensor bias)\n{\n set_bias<torch::nn::ConvTranspose1d>(module, bias);\n}\n\nTensor THSNN_ConvTranspose1d_weight(const NNModule module)\n{\n return get_weight<torch::nn::ConvTranspose1d>(module);\n}\n\nvoid THSNN_ConvTranspose1d_set_weight(const NNModule module, const Tensor weight)\n{\n set_weight<torch::nn::ConvTranspose1d>(module, weight);\n}\n\nNNModule THSNN_ConvTranspose2d_ctor(const int64_t inputChannel, const int64_t outputChannel,\n const int64_t kernelSize, const int64_t stride, const int64_t padding, const int64_t output_padding,\n const int64_t dilation, const int64_t paddingMode, const int64_t groups, const bool bias,\n NNAnyModule* outAsAnyModule)\n{\n CATCH_RETURN_NNModule(\n auto opts = torch::nn::ConvTranspose2dOptions(inputChannel, outputChannel, kernelSize)\n .stride(stride)\n .padding(padding)\n .dilation(dilation)\n .groups(groups)\n .bias(bias)\n .output_padding(output_padding);\n ApplyPaddingMode(opts, paddingMode);\n\n res = create_module<torch::nn::ConvTranspose2dImpl>(opts, outAsAnyModule);\n );\n}\n\nTensor THSNN_ConvTranspose2d_forward(const NNModule module, const Tensor tensor)\n{\n CATCH_TENSOR((*module)->as<torch::nn::ConvTranspose2d>()->forward(*tensor));\n}\n\nTensor THSNN_ConvTranspose2d_bias(const NNModule module)\n{\n return get_bias<torch::nn::ConvTranspose2d>(module);\n}\n\nvoid THSNN_ConvTranspose2d_set_bias(const NNModule module, const Tensor bias)\n{\n set_bias<torch::nn::ConvTranspose2d>(module, bias);\n}\n\nTensor THSNN_ConvTranspose2d_weight(const NNModule module)\n{\n return get_weight<torch::nn::ConvTranspose2d>(module);\n}\n\nvoid THSNN_ConvTranspose2d_set_weight(const NNModule module, const Tensor weight)\n{\n set_weight<torch::nn::ConvTranspose2d>(module, weight);\n}\n\nNNModule THSNN_ConvTranspose3d_ctor(const int64_t inputChannel, const int64_t outputChannel,\n const int64_t kernelSize, const int64_t stride, const int64_t padding, const int64_t output_padding,\n const int64_t dilation, const int64_t paddingMode, const int64_t groups, const bool bias,\n NNAnyModule* outAsAnyModule)\n{\n CATCH_RETURN_NNModule(\n auto opts = torch::nn::ConvTranspose3dOptions(inputChannel, outputChannel, kernelSize)\n .stride(stride)\n .padding(padding)\n .dilation(dilation)\n .groups(groups)\n .bias(bias)\n .output_padding(output_padding);\n ApplyPaddingMode(opts, paddingMode);\n\n res = create_module<torch::nn::ConvTranspose3dImpl>(opts, outAsAnyModule);\n );\n}\n\nTensor THSNN_ConvTranspose3d_bias(const NNModule module)\n{\n return get_bias<torch::nn::ConvTranspose3d>(module);\n}\n\nvoid THSNN_ConvTranspose3d_set_bias(const NNModule module, const Tensor bias)\n{\n set_bias<torch::nn::ConvTranspose3d>(module, bias);\n}\n\nTensor THSNN_ConvTranspose3d_forward(const NNModule module, const Tensor tensor)\n{\n CATCH_TENSOR((*module)->as<torch::nn::ConvTranspose3d>()->forward(*tensor));\n}\n\nTensor THSNN_ConvTranspose3d_weight(const NNModule module)\n{\n return get_weight<torch::nn::ConvTranspose3d>(module);\n}\n\nvoid THSNN_ConvTranspose3d_set_weight(const NNModule module, const Tensor weight)\n{\n set_weight<torch::nn::ConvTranspose3d>(module, weight);\n}\n"},{"alpha_fraction":0.7513944506645203,"alphanum_fraction":0.7641434073448181,"avg_line_length":43.29411697387695,"blob_id":"ad82228860773a71eb344eccd4a8592acc6c7816","content_id":"a2610eca553080496e1d69c83d4979668d55b100","detected_licenses":["MIT"],"is_generated":false,"is_vendor":false,"language":"C","length_bytes":3765,"license_type":"permissive","max_line_length":241,"num_lines":85,"path":"/src/Native/LibTorchSharp/THSJIT.h","repo_name":"NiklasGustafsson/TorchSharp","src_encoding":"UTF-8","text":"// Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information.\n#pragma once\n\n#include \"../Stdafx.h\"\n\n#include \"torch/script.h\"\n\n#include \"Utils.h\"\n\n// Copied from libtorch to share the type as an int8_t.\nenum TypeKind : int8_t {\n#define DEFINE_TYPE(T) T,\n C10_FORALL_TYPES(DEFINE_TYPE)\n#undef DEFINE_TYPE\n};\n\n// API.\n\nstruct TensorOrScalar\n{\n int64_t TypeCode;\n int64_t ArrayIndex;\n ptrdiff_t Handle;\n};\n\n\nEXPORT_API(JITModule) THSJIT_load(const char* filename, int64_t device, int64_t index);\nEXPORT_API(void) THSJIT_save(JITModule module, const char* filename);\nEXPORT_API(JITCompilationUnit) THSJIT_compile(const char* script);\n\nEXPORT_API(void) THSJIT_Module_dispose(const JITModule module);\nEXPORT_API(void) THSJIT_CompilationUnit_dispose(const JITCompilationUnit module);\n\nEXPORT_API(int) THSJIT_Module_num_inputs(const JITModule method);\nEXPORT_API(int) THSJIT_Module_num_outputs(const JITModule method);\n\nEXPORT_API(void) THSJIT_Module_forward(const JITModule module, const TensorOrScalar* tensorPtrs, const int length, TensorOrScalar* (*allocator)(int32_t idx, size_t length), int8_t* typeCode, int32_t idx);\nEXPORT_API(void) THSJIT_Module_invoke(const JITModule module, const char* name, const TensorOrScalar* tensorPtrs, const int length, TensorOrScalar* (*allocator)(int32_t idx, size_t length), int8_t* typeCode, int32_t idx);\n\nEXPORT_API(void) THSJIT_CompilationUnit_Invoke(const JITCompilationUnit module, const char* method, const TensorOrScalar* tensorPtrs, const int length, TensorOrScalar* (*allocator)(int32_t idx, size_t length), int8_t* typeCode, int32_t idx);\n\nEXPORT_API(int) THSJIT_Module_is_training(JITModule module);\nEXPORT_API(void) THSJIT_Module_train(JITModule module, bool on);\nEXPORT_API(void) THSJIT_Module_eval(JITModule module);\n\nEXPORT_API(void) THSJIT_Module_to_device_dtype(JITModule module, int8_t dtype, int64_t device, int64_t index);\nEXPORT_API(void) THSJIT_Module_to_device(JITModule module, int64_t device, int64_t index);\nEXPORT_API(void) THSJIT_Module_to_dtype(JITModule module, int8_t dtype);\n\nEXPORT_API(JITType) THSJIT_Module_getInputType(JITModule module, int8_t dtype);\n\nEXPORT_API(int8_t) THSJIT_Type_kind(JITType handle);\nEXPORT_API(void*) THSJIT_Type_cast(const JITType type);\n\nEXPORT_API(int8_t) THSJIT_TensorType_dtype(const JITTensorType type);\nEXPORT_API(void) THSJIT_TensorType_sizes(const JITTensorType type, int64_t* (*allocator)(int64_t length));\n\nEXPORT_API(void) THSJIT_Type_dispose(const JITType type);\nEXPORT_API(void) THSJIT_TensorType_dispose(const JITTensorType type);\n\nEXPORT_API(void) THSJIT_Module_modules(const JITModule module, JITModule* (*allocator)(size_t length));\nEXPORT_API(void) THSJIT_Module_named_modules(const JITModule module,\n JITModule* (*allocator)(size_t length),\n const char** (*allocator2)(size_t length));\n\nEXPORT_API(void) THSJIT_Module_named_children(const JITModule module,\n JITModule* (*allocator)(size_t length),\n const char** (*allocator2)(size_t length));\n\nEXPORT_API(JITMethod) THSJIT_Module_get_method(const JITModule module, const char* name);\n\nEXPORT_API(void) THSJIT_Module_parameters(const JITModule module, Tensor* (*allocator)(size_t length));\nEXPORT_API(void) THSJIT_Module_named_parameters(const JITModule module,\n Tensor* (*allocator)(size_t length),\n const char** (*allocator2)(size_t length));\n\nEXPORT_API(void) THSJIT_Module_named_buffers(const JITModule module,\n Tensor* (*allocator)(size_t length),\n const char** (*allocator2)(size_t length));\n\nEXPORT_API(int) THSJIT_Method_num_inputs(const JITMethod method);\n\nEXPORT_API(void) THSJIT_Method_dispose(const JITMethod method);\n\nEXPORT_API(const char*) THSJIT_Method_name(const JITMethod method);\n"},{"alpha_fraction":0.6331987977027893,"alphanum_fraction":0.6352169513702393,"avg_line_length":40.3125,"blob_id":"07afdd45ed80d06eac1a840ebb05273477fcea3b","content_id":"4a2b244a97522255f43d3f133d199bd10dbcce53","detected_licenses":["MIT"],"is_generated":false,"is_vendor":false,"language":"C#","length_bytes":1982,"license_type":"permissive","max_line_length":117,"num_lines":48,"path":"/src/TorchSharp/ThreadDisposeScopeStatistics.cs","repo_name":"NiklasGustafsson/TorchSharp","src_encoding":"UTF-8","text":"#nullable enable\nnamespace TorchSharp\n{\n /// <summary>\n /// Keeps track of statistics for a dispose scope. Can be queried to figure out performance/memory issues.\n /// </summary>\n public class ThreadDisposeScopeStatistics\n {\n /// <summary>\n /// The number of disposables that were created on this thread, but weren't captured by a DisposeScope.\n /// </summary>\n public long CreatedOutsideScopeCount { get; internal set; }\n\n /// <summary>\n /// The number of disposables that were created on this thread and were captured by a DisposeScope.\n /// </summary>\n public long CreatedInScopeCount { get; internal set; }\n\n /// <summary>\n /// The number of disposables that were disposed on this thread and were disposed while in a DisposeScope.\n /// </summary>\n public long DisposedInScopeCount { get; internal set; }\n\n /// <summary>\n /// Number of disposables that were once included in the scope, but were subsequently detached.\n /// </summary>\n public long DetachedFromScopeCount { get; internal set; }\n\n /// <summary>\n /// The number of disposables that are currently live on the current thread. If a It's aproximate, see\n /// Tensor.TotalCount. Disposables that weren't created within a DisposeScope, or detached from the dispose\n /// scope, will not be counted as alive.\n /// </summary>\n public long ThreadTotalLiveCount => CreatedInScopeCount - DisposedInScopeCount - DetachedFromScopeCount;\n\n /// <summary>\n /// Resets the counts for the current thread. See ThreadTotalLiveCount etc. Mainly used in tests to make sure\n /// we get a clean slate on the thread.\n /// </summary>\n public void Reset()\n {\n CreatedOutsideScopeCount = 0;\n CreatedInScopeCount = 0;\n DisposedInScopeCount = 0;\n DetachedFromScopeCount = 0;\n }\n }\n}"},{"alpha_fraction":0.5151354074478149,"alphanum_fraction":0.5264205932617188,"avg_line_length":42.2931022644043,"blob_id":"be2b0ba50ddf365ffbcc0593ba07837aef4ce7b5","content_id":"53cccf5fa827536eea4ebdcc9b6fcb196152bee0","detected_licenses":["MIT"],"is_generated":false,"is_vendor":false,"language":"C#","length_bytes":7532,"license_type":"permissive","max_line_length":140,"num_lines":174,"path":"/src/TorchAudio/Transforms/InverseMelScale.cs","repo_name":"NiklasGustafsson/TorchSharp","src_encoding":"UTF-8","text":"// Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information.\n\n// A number of implementation details in this file have been translated from the Python version of torchaudio,\n// largely located in the files found in this folder:\n//\n// https://github.com/pytorch/audio/blob/bb77cbebb620a46fdc0dc7e6dae2253eef3f37e2/torchaudio/transforms/_transforms.py\n//\n// The origin has the following copyright notice and license:\n//\n// https://github.com/pytorch/audio/blob/main/LICENSE\n//\n\nusing System;\nusing System.Collections.Generic;\nusing static TorchSharp.torch;\nusing static TorchSharp.torch.nn;\nusing static TorchSharp.torchaudio;\nusing F = TorchSharp.torchaudio.functional;\n\nnamespace TorchSharp\n{\n namespace Transforms\n {\n public sealed class InverseMelScale : Module<Tensor, Tensor>, ITransform\n {\n public readonly double f_max;\n public readonly double f_min;\n public readonly long max_iter;\n public readonly long n_mels;\n public readonly long sample_rate;\n public readonly double tolerance_change;\n public readonly double tolerance_loss;\n public readonly Tensor fb;\n\n protected override void Dispose(bool disposing)\n {\n if (disposing) {\n fb.Dispose();\n }\n base.Dispose(disposing);\n }\n\n internal InverseMelScale(\n string name,\n long n_stft,\n long n_mels = 128,\n long sample_rate = 16000,\n double f_min = 0.0,\n double? f_max = null,\n long max_iter = 100000,\n double tolerance_loss = 1e-05,\n double tolerance_change = 1e-08,\n MelNorm norm = MelNorm.none,\n torchaudio.MelScale mel_scale = torchaudio.MelScale.htk) : base(name)\n {\n this.n_mels = n_mels;\n this.sample_rate = sample_rate;\n this.f_max = f_max ?? (sample_rate / 2);\n this.f_min = f_min;\n this.max_iter = max_iter;\n this.tolerance_loss = tolerance_loss;\n this.tolerance_change = tolerance_change;\n\n if (f_min > this.f_max) {\n throw new ArgumentException($\"Require f_min: {this.f_min} <= f_max: {this.f_max}\");\n }\n\n this.fb = F.melscale_fbanks(n_stft, this.f_min, this.f_max, this.n_mels, this.sample_rate, norm, mel_scale);\n this.register_buffer(\"fb\", this.fb);\n }\n\n /// <param name=\"melspec\">A Mel frequency spectrogram of dimension (..., ``n_mels``, time)</param>\n /// <returns>Linear scale spectrogram of size (..., freq, time)</returns>\n /// <exception cref=\"ArgumentException\"></exception>\n public override Tensor forward(Tensor melspec)\n {\n // pack batch\n var shape = melspec.size();\n melspec = melspec.view(-1, shape[shape.Length - 2], shape[shape.Length - 1]);\n\n var n_mels = shape[shape.Length - 2];\n var time = shape[shape.Length - 1];\n var freq = this.fb.size(0); // (freq, n_mels)\n melspec = melspec.transpose(-1, -2);\n if (this.n_mels != n_mels) {\n throw new ArgumentException($\"Expected an input with {this.n_mels} mel bins. Found: {n_mels}\");\n }\n\n var specgram = nn.Parameter(torch.rand(\n melspec.size(0), time, freq, requires_grad: true, dtype: melspec.dtype, device: melspec.device));\n\n var optim = torch.optim.SGD(\n new List<Modules.Parameter> { specgram },\n learningRate: 0.1, momentum: 0.9);\n\n var loss = float.PositiveInfinity;\n for (long i = 0; i < this.max_iter; i++) {\n optim.zero_grad();\n var diff = melspec - specgram.matmul(this.fb);\n var new_loss = diff.pow(2).sum(dim: -1).mean();\n // take sum over mel-frequency then average over other dimensions\n // so that loss threshold is applied par unit timeframe\n new_loss.backward();\n optim.step();\n using (torch.no_grad())\n specgram.set_(specgram.clamp(min: 0));\n\n var new_loss_value = new_loss.item<float>();\n if (new_loss_value < this.tolerance_loss || Math.Abs(loss - new_loss_value) < this.tolerance_change) {\n break;\n }\n loss = new_loss_value;\n }\n\n specgram.requires_grad_(false);\n var specgram_tensor = specgram.clamp(min: 0).transpose(-1, -2);\n\n // unpack batch\n shape[shape.Length - 2] = freq;\n shape[shape.Length - 1] = time;\n specgram_tensor = specgram_tensor.view(shape);\n return specgram_tensor;\n }\n }\n }\n\n public partial class torchaudio\n {\n public partial class transforms\n {\n /// <summary>\n /// Estimate a STFT in normal frequency domain from mel frequency domain.\n /// It minimizes the euclidian norm between the input mel-spectrogram and the product between\n /// the estimated spectrogram and the filter banks using SGD.\n /// </summary>\n /// <param name=\"n_stft\">Number of bins in STFT. See ``n_fft`` in :class:`Spectrogram`.</param>\n /// <param name=\"n_mels\">Number of mel filterbanks.</param>\n /// <param name=\"sample_rate\">Sample rate of audio signal.</param>\n /// <param name=\"f_min\">Minimum frequency.</param>\n /// <param name=\"f_max\">Maximum frequency.</param>\n /// <param name=\"max_iter\">Maximum number of optimization iterations.</param>\n /// <param name=\"tolerance_loss\">Value of loss to stop optimization at.</param>\n /// <param name=\"tolerance_change\">Difference in losses to stop optimization at.</param>\n /// <param name=\"norm\">If 'slaney', divide the triangular mel weights by the width of the mel band (area normalization).</param>\n /// <param name=\"mel_scale\">Scale to use: ``htk`` or ``slaney``.</param>\n /// <returns></returns>\n public static Transforms.InverseMelScale InverseMelScale(\n long n_stft,\n long n_mels = 128,\n long sample_rate = 16000,\n double f_min = 0.0,\n double? f_max = null,\n long max_iter = 100000,\n double tolerance_loss = 1e-05,\n double tolerance_change = 1e-08,\n MelNorm norm = MelNorm.none,\n torchaudio.MelScale mel_scale = torchaudio.MelScale.htk)\n {\n return new Transforms.InverseMelScale(\n \"InverseMelScale\",\n n_stft,\n n_mels,\n sample_rate,\n f_min,\n f_max,\n max_iter,\n tolerance_loss,\n tolerance_change,\n norm,\n mel_scale);\n }\n }\n }\n}"},{"alpha_fraction":0.523976743221283,"alphanum_fraction":0.5435646176338196,"avg_line_length":48.87557601928711,"blob_id":"8fe05f4dcd57c2692e5e27bc9d344ba7b5c5fef3","content_id":"134e89a63eecc85729dff5c9d3d64a0b8eaa87f2","detected_licenses":["MIT"],"is_generated":false,"is_vendor":false,"language":"C#","length_bytes":10823,"license_type":"permissive","max_line_length":169,"num_lines":217,"path":"/src/TorchVision/Ops/Boxes.cs","repo_name":"NiklasGustafsson/TorchSharp","src_encoding":"UTF-8","text":"// Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information.\n\n// A number of implementation details in this file have been translated from the Python version of torchvision,\n// largely located in the files found in this folder:\n//\n// https://github.com/pytorch/vision/blob/a4f53308b2d0f1aa9191686e326f45c26053f686/torchvision/ops/boxes.py\n//\n// The origin has the following copyright notice and license:\n//\n// https://github.com/pytorch/vision/blob/main/LICENSE\n//\n\nusing System;\nusing System.Collections.Generic;\nusing System.Reflection;\nusing System.Runtime.CompilerServices;\nusing static TorchSharp.torch;\n\nnamespace TorchSharp\n{\n public static partial class torchvision\n {\n\n public static partial class ops\n {\n /// <summary>\n /// Converts boxes from given in_fmt to out_fmt.\n /// Supported in_fmt and out_fmt are:\n ///\n /// 'xyxy': boxes are represented via corners, x1, y1 being top left and x2, y2 being bottom right. This is the format that torchvision utilities expect.\n /// 'xywh' : boxes are represented via corner, width and height, x1, y2 being top left, w, h being width and height.\n /// 'cxcywh' : boxes are represented via centre, width and height, cx, cy being center of box, w, h being width and height.\n /// </summary>\n /// <param name=\"boxes\">Boxes which will be converted</param>\n /// <param name=\"in_fmt\">Input format of given boxes.</param>\n /// <param name=\"out_fmt\">Output format of given boxes.</param>\n /// <returns></returns>\n public static Tensor box_convert(Tensor boxes, BoxFormats in_fmt, BoxFormats out_fmt)\n {\n if (in_fmt == out_fmt) return boxes.clone();\n\n if (in_fmt != BoxFormats.xyxy && out_fmt != BoxFormats.xyxy) {\n boxes = (in_fmt == BoxFormats.xywh) ? _box_xywh_to_xyxy(boxes) : _box_cxcywh_to_xyxy(boxes);\n in_fmt = BoxFormats.xyxy;\n }\n\n if (in_fmt == BoxFormats.xyxy) {\n boxes = (out_fmt == BoxFormats.xywh) ? _box_xyxy_to_xywh(boxes) : _box_xyxy_to_cxcywh(boxes);\n } else if (out_fmt == BoxFormats.xyxy) {\n boxes = (in_fmt == BoxFormats.xywh) ? _box_xywh_to_xyxy(boxes) : _box_cxcywh_to_xyxy(boxes);\n }\n\n return boxes;\n }\n\n /// <summary>\n /// Computes the area of a set of bounding boxes, which are specified by their (x1, y1, x2, y2) coordinates.\n /// </summary>\n /// <param name=\"boxes\">Boxes for which the area will be computed. They are expected to be in (x1, y1, x2, y2) format.</param>\n /// <returns></returns>\n public static Tensor box_area(Tensor boxes)\n {\n boxes = _upcast(boxes);\n return (boxes[colon, 2] - boxes[colon, 0]) * (boxes[colon, 3] - boxes[colon, 1]);\n }\n\n /// <summary>\n /// Return intersection-over-union (Jaccard index) between two sets of boxes.\n /// </summary>\n /// <param name=\"boxes1\">First set of boxes</param>\n /// <param name=\"boxes2\">Second set of boxes</param>\n /// <returns>The NxM matrix containing the pairwise IoU values for every element in boxes1 and boxes2</returns>\n public static Tensor box_iou(Tensor boxes1, Tensor boxes2)\n {\n var inter = _box_inter_union(boxes1, boxes2, out var union);\n return inter / union;\n }\n\n /// <summary>\n /// Return generalized intersection-over-union (Jaccard index) between two sets of boxes.\n /// </summary>\n /// <param name=\"boxes1\">First set of boxes</param>\n /// <param name=\"boxes2\">Second set of boxes</param>\n /// <returns>The NxM matrix containing the pairwise generalized IoU values for every element in boxes1 and boxes2</returns>\n public static Tensor generalized_box_iou(Tensor boxes1, Tensor boxes2)\n {\n using var _ = NewDisposeScope();\n var inter = _box_inter_union(boxes1, boxes2, out var union);\n var iou = inter / union;\n\n var lti = torch.min(boxes1[colon, None, (null, 2)], boxes2[colon, (null, 2)]);\n var rbi = torch.max(boxes1[colon, None, (2, null)], boxes2[colon, (2, null)]);\n\n var whi = _upcast(rbi - lti).clamp(min: 0); // [N,M,2]\n var areai = whi[colon, colon, 0] * whi[colon, colon, 1];\n\n return (iou - (areai - union) / areai).MoveToOuterDisposeScope();\n }\n\n /// <summary>\n /// Return complete intersection-over-union (Jaccard index) between two sets of boxes.\n /// </summary>\n /// <param name=\"boxes1\">First set of boxes</param>\n /// <param name=\"boxes2\">Second set of boxes</param>\n /// <param name=\"eps\">Small number to prevent division by zero</param>\n /// <returns>The NxM matrix containing the complete distance IoU values for every element in boxes1 and boxes2</returns>\n public static Tensor complete_box_iou(Tensor boxes1, Tensor boxes2, double eps = 1e-7)\n {\n using var _ = NewDisposeScope();\n boxes1 = _upcast(boxes1);\n boxes2 = _upcast(boxes2);\n\n var diou = _box_diou_iou(boxes1, boxes2, out Tensor iou, eps);\n\n var w_pred = boxes1[colon, None, 2] - boxes1[colon, None, 0];\n var h_pred = boxes1[colon, None, 3] - boxes1[colon, None, 1];\n\n var w_gt = boxes2[colon, 2] - boxes2[colon, 0];\n var h_gt = boxes2[colon, 3] - boxes2[colon, 1];\n\n var v = (4 / (Math.Pow(Math.PI, 2))) * torch.pow(torch.atan(w_pred / h_pred) - torch.atan(w_gt / h_gt), 2);\n\n Tensor alpha;\n\n using (var ng = torch.no_grad()) {\n alpha = v / (1 - iou + v + eps);\n }\n return (diou - alpha * v).MoveToOuterDisposeScope();\n }\n\n\n /// <summary>\n /// Return generalized intersection-over-union (Jaccard index) between two sets of boxes.\n /// </summary>\n /// <param name=\"boxes1\">First set of boxes</param>\n /// <param name=\"boxes2\">Second set of boxes</param>\n /// <param name=\"eps\">Small number to prevent division by zero</param>\n /// <returns>The NxM matrix containing the pairwise distance IoU values for every element in boxes1 and boxes2</returns>\n public static Tensor distance_box_iou(Tensor boxes1, Tensor boxes2, double eps = 1e-7)\n {\n using var _ = NewDisposeScope();\n boxes1 = _upcast(boxes1);\n boxes2 = _upcast(boxes2);\n var diou = _box_diou_iou(boxes1, boxes2, out var _, eps: eps);\n return diou.MoveToOuterDisposeScope();\n }\n\n /// <summary>\n /// Compute the bounding boxes around the provided masks.\n /// </summary>\n /// <param name=\"masks\">masks to transform where N is the number of masks and (H, W) are the spatial dimensions.</param>\n /// <returns>A [N, 4] tensor containing bounding boxes. The boxes are in the (x1, y1, x2, y2) format</returns>\n /// <exception cref=\"ArgumentException\">Raised if the input is not a three-dimensional tensor.</exception>\n public static Tensor masks_to_boxes(Tensor masks)\n {\n if (masks.ndim != 3) throw new ArgumentException(\"'masks' should have three dimensions: NxHxW\");\n if (masks.numel() == 0) {\n return torch.zeros(0, 4, device: masks.device, dtype: torch.float32);\n }\n\n var n = masks.shape[0];\n var bounding_boxes = torch.zeros(n, 4, device: masks.device, dtype: torch.float32);\n\n for (int i = 0;i < n; i++) {\n var yx = torch.where(masks[i] != 0);\n bounding_boxes[i, 0] = torch.min(yx[1]);\n bounding_boxes[i, 1] = torch.min(yx[0]);\n bounding_boxes[i, 2] = torch.max(yx[1]);\n bounding_boxes[i, 3] = torch.max(yx[0]);\n }\n return bounding_boxes;\n }\n\n private static Tensor _box_inter_union(Tensor boxes1, Tensor boxes2, out Tensor union)\n {\n var area1 = box_area(boxes1);\n var area2 = box_area(boxes2);\n\n var lt = torch.max(boxes1[colon, None, (null, 2)], boxes2[colon, (null, 2)]); // [N,M,2];\n var rb = torch.min(boxes1[colon, None, (2, null)], boxes2[colon, (2, null)]); // [N,M,2];\n\n var wh = _upcast(rb - lt).clamp(min: 0); // [N,M,2];\n var inter = wh[colon, colon, 0] * wh[colon, colon, 1]; // [N,M];\n\n union = area1[colon, None] + area2 - inter;\n return inter;\n }\n\n private static Tensor _box_diou_iou(Tensor boxes1, Tensor boxes2, out Tensor iou, double eps = 1e-7)\n {\n iou = box_iou(boxes1, boxes2);\n var lti = torch.min(boxes1[colon, None, (null, 2)], boxes2[colon, (null, 2)]);\n var rbi = torch.max(boxes1[colon, None, (2, null)], boxes2[colon, (2, null)]);\n var whi = _upcast(rbi - lti).clamp(min: 0); // [N,M,2];\n var diagonal_distance_squared = whi[colon, colon, 0].pow(2) + whi[colon, colon, 1].pow(2) + eps;\n // centers of boxes\n var x_p = (boxes1[colon, 0] + boxes1[colon, 2]) / 2;\n var y_p = (boxes1[colon, 1] + boxes1[colon, 3]) / 2;\n var x_g = (boxes2[colon, 0] + boxes2[colon, 2]) / 2;\n var y_g = (boxes2[colon, 1] + boxes2[colon, 3]) / 2;\n // The distance between boxes' centers squared.\n var centers_distance_squared = _upcast((x_p[colon, None] - x_g[None, colon])).pow(2) + _upcast((y_p[colon, None] - y_g[None, colon])).pow(2);\n // The distance IoU is the IoU penalized by a normalized\n // distance between boxes' centers squared.\n return iou - (centers_distance_squared / diagonal_distance_squared);\n }\n\n public enum BoxFormats\n {\n xyxy, xywh, cxcywh\n }\n\n private static TensorIndex colon = TensorIndex.Colon;\n private static TensorIndex None = TensorIndex.None;\n }\n }\n}\n"},{"alpha_fraction":0.5940752625465393,"alphanum_fraction":0.6172938346862793,"avg_line_length":32.53020095825195,"blob_id":"2c92de7a2557dbdcc81b9be1ca2150af183fbbe4","content_id":"1bf14cab930ead617b095000c7e8e12984aee989","detected_licenses":["MIT"],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":4996,"license_type":"permissive","max_line_length":99,"num_lines":149,"path":"/test/TorchSharpTest/pyimporttest.py","repo_name":"NiklasGustafsson/TorchSharp","src_encoding":"UTF-8","text":"from collections import OrderedDict\n\nimport numpy as np\nimport torch\nimport torch.nn as nn\nimport torch.optim as optim\n\nimport importsd\n\n\nNUM_WORDS = 100\nEMBEDDING_VEC_LEN = 100\nHIDDEN_SIZE = 128\n\n\nclass LSTMModel(nn.Module):\n # The names of properties should be the same in C# and Python\n # otherwise, you have to manually change the key name in the state_dict\n def __init__(self):\n super(LSTMModel, self).__init__()\n self.embedding = nn.Embedding(NUM_WORDS, EMBEDDING_VEC_LEN)\n self.lstm = nn.LSTM(EMBEDDING_VEC_LEN, HIDDEN_SIZE, batch_first=True)\n self.dropout = nn.Dropout(0.5)\n self.dense = nn.Linear(HIDDEN_SIZE, 1)\n self.sigmoid = nn.Sigmoid()\n\n def forward(self, x):\n x_embed = self.embedding(x)\n x_lstm, _ = self.lstm(x_embed)\n x_lstm_last_seq = x_lstm[:, -1, :]\n x_lstm_last_seq = self.dropout(x_lstm_last_seq)\n logits = self.dense(x_lstm_last_seq)\n out = self.sigmoid(logits)\n return out\n\n\nclass LeNet1Model(nn.Module):\n # The names of properties should be the same in C# and Python\n # in this case, we both name the Sequential as layers\n def __init__(self):\n super(LeNet1Model, self).__init__()\n # the names of each layer should also be the same in C# and Python\n modules = OrderedDict([\n (\"conv-1\", nn.Conv2d(1, 4, 5, padding=2)),\n (\"bnrm2d-1\", nn.BatchNorm2d(4)),\n (\"relu-1\", nn.ReLU()),\n (\"maxpool-1\", nn.MaxPool2d(2, stride=2)),\n (\"conv-2\", nn.Conv2d(4, 12, 5)),\n (\"bnrm2d-2\", nn.BatchNorm2d(12)),\n (\"relu-2\", nn.ReLU()),\n (\"maxpool-2\", nn.MaxPool2d(2, stride=2)),\n (\"flatten\", nn.Flatten()),\n (\"linear\", nn.Linear(300, 10)),\n ])\n self.layers = nn.Sequential(modules)\n\n def forward(self, x):\n return self.layers.forward(x)\n\n\ndef testLSTM():\n print(\"testing LSTM\")\n mylstm = LSTMModel()\n with open(\"lstm.dat\", \"rb\") as f:\n sd = importsd.load_state_dict(f)\n # you can change the loaded key names here, for example:\n # sd = {k + \"py\": v for k, v in sd}\n # you can check the key names of state_dict in python by:\n # print(mylstm.state_dict())\n mylstm.load_state_dict(sd)\n\n # init values & functions\n torch.manual_seed(0)\n np.random.seed(0)\n labels = torch.tensor(np.random.randint(0, 1, [100, 1]), dtype=torch.float)\n inputs = torch.tensor(np.random.randint(0, 100, [100, 100]))\n opt = optim.Adam(mylstm.parameters(), lr=8e-5)\n loss_func = nn.BCELoss()\n\n # evaluation before training\n mylstm.eval()\n preds = mylstm.forward(inputs)\n preds = torch.round(preds)\n correct_num = torch.sum(preds == labels).item()\n print(f\"before training: {correct_num} corrected\")\n\n # training for 50 steps\n mylstm.train()\n for i in range(50):\n opt.zero_grad() # Reset the gradient in every iteration\n outputs = mylstm(inputs)\n loss = loss_func(outputs, labels) # Loss forward pass\n loss.backward() # Loss backaed pass\n opt.step() # Update all the parameters by the given learnig rule\n\n # evaluation after training\n mylstm.eval()\n preds = mylstm.forward(inputs)\n preds = torch.round(preds)\n correct_num = torch.sum(preds == labels).item()\n print(f\"after training: {correct_num} corrected\")\n\n\ndef testLeNet1():\n print(\"testing LeNet1\")\n mylenet = LeNet1Model()\n with open(\"lenet1.dat\", \"rb\") as f:\n sd = importsd.load_state_dict(f)\n # you can change the loaded key names here, for example:\n # sd = {k + \"py\": v for k, v in sd}\n # you can check the key names of state_dict in python by:\n # print(mylenet.state_dict())\n mylenet.load_state_dict(sd)\n\n # init values & functions\n torch.manual_seed(0)\n np.random.seed(0)\n labels = torch.tensor(np.random.randint(0, 10, [100]))\n inputs = torch.tensor(np.random.randint(0, 255, [100, 1, 28, 28]) / 255.0, dtype=torch.float32)\n opt = optim.Adam(mylenet.parameters(), lr=8e-5)\n loss_func = nn.CrossEntropyLoss()\n\n # evaluation before training\n mylenet.eval()\n output = mylenet.forward(inputs)\n _, preds = torch.max(output.data, dim=1)\n correct_num = torch.sum(preds == labels).item()\n print(f\"before training: {correct_num} corrected\")\n\n # training for 200 steps\n mylenet.train()\n for i in range(200):\n opt.zero_grad() # Reset the gradient in every iteration\n outputs = mylenet(inputs)\n loss = loss_func(outputs, labels) # Loss forward pass\n loss.backward() # Loss backaed pass\n opt.step() # Update all the parameters by the given learnig rule\n\n # evaluation after training\n mylenet.eval()\n output = mylenet.forward(inputs)\n _, preds = torch.max(output.data, dim=1)\n correct_num = torch.sum(preds == labels).item()\n print(f\"after training: {correct_num} corrected\")\n\n\nif __name__ == '__main__':\n testLSTM()\n testLeNet1()\n"},{"alpha_fraction":0.5116005539894104,"alphanum_fraction":0.5163080096244812,"avg_line_length":37.6298713684082,"blob_id":"4e6bfd60bf576dcac142404bfac714616492790b","content_id":"0e153f7e037a2eff04c00cb6e5aeed9c23bae806","detected_licenses":["MIT"],"is_generated":false,"is_vendor":false,"language":"C#","length_bytes":5948,"license_type":"permissive","max_line_length":130,"num_lines":154,"path":"/src/TorchVision/Randomizer.cs","repo_name":"NiklasGustafsson/TorchSharp","src_encoding":"UTF-8","text":"// Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information.\n\nusing static TorchSharp.torch;\n\n\nnamespace TorchSharp\n{\n public static partial class torchvision\n {\n internal class Randomizer : ITransform\n {\n internal Randomizer(ITransform transform, double p = 0.1)\n {\n this.p = p;\n this.transform = transform;\n }\n\n public Tensor call(Tensor input)\n {\n using (var chance = torch.rand(1))\n\n if (chance.item<float>() < p) {\n return transform.call(input);\n } else {\n return input;\n }\n }\n\n private ITransform transform;\n private double p;\n }\n\n public static partial class transforms\n {\n /// <summary>\n /// Randomly apply a transform given a probability.\n /// </summary>\n /// <param name=\"transform\">The transform that will be applied randomly.</param>\n /// <param name=\"p\">The probablity of applying the transform</param>\n /// <returns></returns>\n /// <remarks>This uses the default TorchSharp RNG.</remarks>\n static public ITransform Randomize(ITransform transform, double p = 0.1)\n {\n return new Randomizer(transform, p);\n }\n\n /// <summary>\n /// Posterize the image randomly with a given probability by reducing the number of bits for each color channel. \n /// </summary>\n /// <param name=\"bits\">Number of bits to keep for each channel (0-8)</param>\n /// <param name=\"p\">Probability of the transform being applied.</param>\n /// <returns></returns>\n /// <remarks>The tensor must be an integer tensor</remarks>\n static public ITransform RandomPosterize(int bits, double p = 0.5)\n {\n return new Randomizer(Posterize(bits), p);\n }\n\n /// <summary>\n /// Equalize the image randomly with a given probability. \n /// </summary>\n /// <param name=\"p\">Probability of the transform being applied.</param>\n /// <returns></returns>\n /// <remarks>The tensor must be an integer tensor</remarks>\n static public ITransform Equalize(double p = 0.5)\n {\n return new Randomizer(Equalize(), p);\n }\n\n /// <summary>\n /// Solarize the image randomly with a given probability by inverting all pixel values above a threshold.\n /// </summary>\n /// <param name=\"threshold\">All pixels equal or above this value are inverted.</param>\n /// <param name=\"p\">Probability of the transform being applied.</param>\n /// <returns></returns>\n static public ITransform RandomSolarize(double threshold, double p = 0.5)\n {\n return new Randomizer(Solarize(threshold), p);\n }\n\n /// <summary>\n /// \n /// </summary>\n /// <param name=\"p\">Probability of the transform being applied.</param>\n /// <returns></returns>\n static public ITransform RandomVerticalFlip(double p = 0.5)\n {\n return new Randomizer(VerticalFlip(), p);\n }\n\n /// <summary>\n /// \n /// </summary>\n /// <param name=\"p\">Probability of the transform being applied.</param>\n /// <returns></returns>\n static public ITransform RandomHorizontalFlip(double p = 0.5)\n {\n return new Randomizer(HorizontalFlip(), p);\n }\n\n /// <summary>\n /// \n /// </summary>\n /// <param name=\"p\">Probability of the transform being applied.</param>\n /// <returns></returns>\n static public ITransform RandomGrayscale(double p = 0.1)\n {\n return new Randomizer(Grayscale(3), p);\n }\n\n /// <summary>\n /// Adjust the sharpness of the image randomly with a given probability. \n /// </summary>\n /// <param name=\"sharpness\">The sharpness factor</param>\n /// <param name=\"p\">Probability of the transform being applied.</param>\n /// <returns></returns>\n static public ITransform RandomAdjustSharpness(double sharpness, double p = 0.5)\n {\n return new Randomizer(AdjustSharpness(sharpness), p);\n }\n\n /// <summary>\n /// \n /// </summary>\n /// <param name=\"p\">Probability of the transform being applied.</param>\n /// <returns></returns>\n static public ITransform RandomAutoContrast(double p = 0.5)\n {\n return new Randomizer(AutoContrast(), p);\n }\n\n /// <summary>\n /// \n /// </summary>\n /// <param name=\"p\">Probability of the transform being applied.</param>\n /// <returns></returns>\n static public ITransform RandomInvert(double p = 0.5)\n {\n return new Randomizer(Invert(), p);\n }\n\n /// <summary>\n /// Apply randomly a list of transformations with a given probability.\n /// </summary>\n /// <param name=\"transforms\">A list of transforms to compose serially.</param>\n /// <param name=\"p\">Probability of the transforms being applied.</param>\n /// <returns></returns>\n static public ITransform RandomApply(ITransform[] transforms, double p = 0.5)\n {\n return new Randomizer(Compose(transforms), p);\n }\n }\n }\n}"},{"alpha_fraction":0.5785340070724487,"alphanum_fraction":0.5785340070724487,"avg_line_length":24.53333282470703,"blob_id":"850f2d67e96d73c6e54789304ea3903d34539b8a","content_id":"42d5ee63e86aa67f0b007befdbe432dae9c045ed","detected_licenses":["MIT"],"is_generated":false,"is_vendor":false,"language":"C#","length_bytes":382,"license_type":"permissive","max_line_length":130,"num_lines":15,"path":"/src/TorchAudio/MelScale.cs","repo_name":"NiklasGustafsson/TorchSharp","src_encoding":"UTF-8","text":"// Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information.\nnamespace TorchSharp\n{\n public static partial class torchaudio\n {\n /// <summary>\n /// Scale type of mel filterbanks\n /// </summary>\n public enum MelScale\n {\n slaney,\n htk\n }\n }\n}"},{"alpha_fraction":0.7017045617103577,"alphanum_fraction":0.7017045617103577,"avg_line_length":24.214284896850586,"blob_id":"7f197adec0be9a871b7c046cbf103f45f993681c","content_id":"4505451f652ccebecc58ce450c5a3ba2a18ebbf9","detected_licenses":["MIT"],"is_generated":false,"is_vendor":false,"language":"C#","length_bytes":352,"license_type":"permissive","max_line_length":130,"num_lines":14,"path":"/src/TorchAudio/Transforms/ITransform.cs","repo_name":"NiklasGustafsson/TorchSharp","src_encoding":"UTF-8","text":"// Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information.\n\nusing static TorchSharp.torch;\nusing static TorchSharp.torch.nn;\n\nnamespace TorchSharp\n{\n public partial class torchaudio\n {\n public interface ITransform : IModule<Tensor, Tensor>\n {\n }\n }\n}"},{"alpha_fraction":0.6907563209533691,"alphanum_fraction":0.6941176652908325,"avg_line_length":26.045454025268555,"blob_id":"f9e25e86405b9b91a2f7adf77f3d9cfca4e24108","content_id":"740fc71d2a1e639d4cf824d1eee1675c13950f4b","detected_licenses":["MIT"],"is_generated":false,"is_vendor":false,"language":"C++","length_bytes":595,"license_type":"permissive","max_line_length":130,"num_lines":22,"path":"/src/Native/LibTorchSharp/Utils.cpp","repo_name":"NiklasGustafsson/TorchSharp","src_encoding":"UTF-8","text":"// Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information.\n#include \"Utils.h\"\n\n#include <cstring>\n#include <fstream>\n#if _WINDOWS\n#include <combaseapi.h>\n#define TP_CoTaskMemAlloc(t) CoTaskMemAlloc(t)\n#else\n#define TP_CoTaskMemAlloc(t) malloc(t)\n#endif\n\nthread_local char * torch_last_err = nullptr;\n\nconst char * make_sharable_string(const std::string str)\n{\n size_t n = str.length();\n char* result = (char *)TP_CoTaskMemAlloc(n + 1); \n strncpy(result, str.c_str(), n);\n result[n] = '\\0';\n return result;\n}\n"},{"alpha_fraction":0.5925925970077515,"alphanum_fraction":0.595678985118866,"avg_line_length":31.399999618530273,"blob_id":"3b28b40671e0eaca3666fefca066870f738848b6","content_id":"8d93f7de1dccb3b78515b0f6d06b3ca4b64b9d17","detected_licenses":["MIT"],"is_generated":false,"is_vendor":false,"language":"C#","length_bytes":648,"license_type":"permissive","max_line_length":130,"num_lines":20,"path":"/src/Examples/Program.cs","repo_name":"NiklasGustafsson/TorchSharp","src_encoding":"UTF-8","text":"// Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information.\n\nnamespace TorchSharp.Examples\n{\n public static class Program\n {\n public static void Main(string[] args)\n {\n //MNIST.Main(args);\n //AdversarialExampleGeneration.Main(args);\n CIFAR10.Main(args);\n //SequenceToSequence.Main(args);\n //TextClassification.Main(args);\n //ImageTransforms.Main(args);\n //SpeechCommands.Main(args);\n IOReadWrite.Main(args);\n Tensorboard.Main(args).Wait();\n }\n }\n}\n"},{"alpha_fraction":0.6174419522285461,"alphanum_fraction":0.621430516242981,"avg_line_length":52.49214553833008,"blob_id":"9fdcf0e95ca2bb4c20b3e73902726533fce1d061","content_id":"fb4568b5c73292578f63e34c7406b7e4ec92ddd0","detected_licenses":["MIT"],"is_generated":false,"is_vendor":false,"language":"C#","length_bytes":40897,"license_type":"permissive","max_line_length":254,"num_lines":764,"path":"/src/TorchSharp/Tensor/torch.OtherOperations.cs","repo_name":"NiklasGustafsson/TorchSharp","src_encoding":"UTF-8","text":"// Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information.\n#nullable enable\nusing System;\nusing System.Collections.Generic;\nusing System.Linq;\nusing TorchSharp.PInvoke;\nusing static TorchSharp.PInvoke.NativeMethods;\n\nnamespace TorchSharp\n{\n // https://pytorch.org/docs/stable/torch#other-operations\n public static partial class torch\n {\n // https://pytorch.org/docs/stable/generated/torch.atleast_1d\n /// <summary>\n /// Returns a 1-dimensional view of each input tensor with zero dimensions. Input tensors with one or more dimensions are returned as-is.\n /// </summary>\n public static IEnumerable<Tensor> atleast_1d(params Tensor[] input) => input.Select(t => t.atleast_1d());\n\n // https://pytorch.org/docs/stable/generated/torch.atleast_2d\n /// <summary>\n /// Returns a 2-dimensional view of each input tensor with zero or one dimensions. Input tensors with two or more dimensions are returned as-is.\n /// </summary>\n public static IEnumerable<Tensor> atleast_2d(params Tensor[] input) => input.Select(t => t.atleast_2d());\n\n // https://pytorch.org/docs/stable/generated/torch.atleast_3d\n /// <summary>\n /// Returns a 1-dimensional view of each input tensor with fewer than three dimensions. Input tensors with three or more dimensions are returned as-is.\n /// </summary>\n public static IEnumerable<Tensor> atleast_3d(params Tensor[] input) => input.Select(t => t.atleast_3d());\n\n // https://pytorch.org/docs/stable/generated/torch.bincount\n /// <summary>\n /// Count the frequency of each value in an array of non-negative ints.\n /// </summary>\n public static Tensor bincount(Tensor input, Tensor? weights = null, long minlength = 0) => input.bincount(weights, minlength);\n\n // https://pytorch.org/docs/stable/generated/torch.block_diag\n /// <summary>\n /// Create a block diagonal matrix from provided tensors.\n /// </summary>\n /// <param name=\"tensors\">One or more tensors with 0, 1, or 2 dimensions.</param>\n /// <returns></returns>\n public static Tensor block_diag(params Tensor[] tensors)\n {\n using var parray = new PinnedArray<IntPtr>();\n IntPtr tensorsRef = parray.CreateArray(tensors.Select(p => p.Handle).ToArray());\n\n var res = THSTensor_block_diag(tensorsRef, parray.Array.Length);\n if (res == IntPtr.Zero) { CheckForErrors(); }\n return new Tensor(res);\n }\n\n // https://pytorch.org/docs/stable/generated/torch.broadcast_tensors\n /// <summary>\n /// Broadcasts the given tensors according to Torch broadcasting semantics.\n /// </summary>\n /// <param name=\"tensors\">Any number of tensors of the same type</param>\n public static IList<Tensor> broadcast_tensors(params Tensor[] tensors)\n {\n if (tensors.Length == 0) {\n throw new ArgumentException(nameof(tensors));\n }\n if (tensors.Length == 1) {\n return tensors;\n }\n\n IntPtr[] ptrArray;\n\n using (var pa = new PinnedArray<IntPtr>())\n using (var parray = new PinnedArray<IntPtr>()) {\n\n IntPtr tensorsRef = parray.CreateArray(tensors.Select(p => p.Handle).ToArray());\n\n THSTensor_broadcast_tensors(tensorsRef, tensors.Length, pa.CreateArray);\n CheckForErrors();\n ptrArray = pa.Array;\n }\n\n return ptrArray.Select(x => new Tensor(x)).ToList();\n }\n\n // https://pytorch.org/docs/stable/generated/torch.broadcast_to\n public static Tensor broadcast_to(Tensor input, params long[] shape) => input.broadcast_to(shape);\n\n // https://pytorch.org/docs/stable/generated/torch.broadcast_shapes\n /// <summary>\n /// This is equivalent to <code>torch.broadcast_tensors(*map(torch.empty, shapes))[0].shape</code>\n /// but avoids the need create to intermediate tensors.\n /// This is useful for broadcasting tensors of common batch shape but different rightmost shape,\n /// e.g. to broadcast mean vectors with covariance matrices.\n /// </summary>\n /// <param name=\"shapes\">Shapes of tensors</param>\n /// <returns>A shape compatible with all input shapes</returns>\n /// <exception cref=\"ArgumentException\">If shapes are incompatible.</exception>\n public static Size broadcast_shapes(params long[][] shapes)\n {\n var max_len = 0;\n foreach (var shape in shapes) {\n var s = shape.Length;\n if (s > max_len) max_len = s;\n }\n\n var result = Enumerable.Repeat<long>(1, max_len).ToArray();\n\n foreach (var shape in shapes) {\n for (var i = shape.Length - 1; i >= 0; i--) {\n if (shape.Length == 0 || shape[i] == 1 || shape[i] == result[i])\n continue;\n if (result[i] != 1)\n throw new ArgumentException(\"Shape mismatch: objects cannot be broadcast to a single shape\");\n result[i] = shape[i];\n }\n }\n return result;\n }\n\n // https://pytorch.org/docs/stable/generated/torch.bucketize\n public static Tensor bucketize(Tensor input, Tensor boundaries, bool outInt32 = false, bool right = false)\n => input.bucketize(boundaries, outInt32, right);\n\n // https://pytorch.org/docs/stable/generated/torch.cartesian_prod\n /// <summary>\n /// Do cartesian product of the given sequence of tensors.\n /// </summary>\n /// <param name=\"tensors\"></param>\n public static Tensor cartesian_prod(IList<Tensor> tensors)\n {\n using var parray = new PinnedArray<IntPtr>();\n IntPtr tensorsRef = parray.CreateArray(tensors.Select(p => p.Handle).ToArray());\n\n var res = THSTensor_cartesian_prod(tensorsRef, parray.Array.Length);\n if (res == IntPtr.Zero) { torch.CheckForErrors(); }\n return new Tensor(res);\n }\n\n // https://pytorch.org/docs/stable/generated/torch.cartesian_prod\n /// <summary>\n /// Do cartesian product of the given sequence of tensors.\n /// </summary>\n /// <param name=\"tensors\"></param>\n public static Tensor cartesian_prod(params Tensor[] tensors) => cartesian_prod((IList<Tensor>)tensors);\n\n // https://pytorch.org/docs/stable/generated/torch.cdist\n /// <summary>\n /// Computes batched the p-norm distance between each pair of the two collections of row vectors.\n /// </summary>\n /// <param name=\"x1\">Input tensor of shape BxPxM</param>\n /// <param name=\"x2\">Input tensor of shape BxRxM</param>\n /// <param name=\"p\">p value for the p-norm distance to calculate between each vector (p > 0)</param>\n /// <param name=\"compute_mode\">\n /// use_mm_for_euclid_dist_if_necessary - will use matrix multiplication approach to calculate euclidean distance (p = 2) if P > 25 or R > 25\n /// use_mm_for_euclid_dist - will always use matrix multiplication approach to calculate euclidean distance (p = 2)\n /// donot_use_mm_for_euclid_dist - will never use matrix multiplication approach to calculate euclidean distance (p = 2)\n /// </param>\n /// <exception cref=\"ArgumentException\"></exception>\n public static Tensor cdist(\n Tensor x1,\n Tensor x2,\n double p = 2.0,\n compute_mode compute_mode = compute_mode.use_mm_for_euclid_dist_if_necessary)\n {\n if (p < 0)\n throw new ArgumentException($\"p must be non-negative\");\n\n var res = THSTensor_cdist(x1.Handle, x2.Handle, p, (long)compute_mode);\n if (res == IntPtr.Zero)\n CheckForErrors();\n return new Tensor(res);\n }\n\n // https://pytorch.org/docs/stable/generated/torch.clone\n public static Tensor clone(Tensor input) => input.clone();\n\n // https://pytorch.org/docs/stable/generated/torch.combinations\n /// <summary>\n /// Compute combinations of length r of the given tensor\n /// </summary>\n /// <param name=\"input\">1D vector.</param>\n /// <param name=\"r\">Number of elements to combine</param>\n /// <param name=\"with_replacement\">Whether to allow duplication in combination</param>\n /// <returns></returns>\n public static Tensor combinations(Tensor input, int r = 2, bool with_replacement = false)\n {\n if (input.ndim != 1)\n throw new ArgumentException($\"Expected a 1D vector, but got one with {input.ndim} dimensions.\");\n if (r < 0)\n throw new ArgumentException($\"r must be non-negative\");\n\n var res = THSTensor_combinations(input.Handle, r, with_replacement);\n if (res == IntPtr.Zero)\n CheckForErrors();\n return new Tensor(res);\n }\n\n\n\n // https://pytorch.org/docs/stable/generated/torch.corrcoef\n public static Tensor corrcoef(Tensor input) => input.corrcoef();\n\n // https://pytorch.org/docs/stable/generated/torch.cov\n /// <summary>\n /// Estimates the covariance matrix of the variables given by the input matrix, where rows are the variables and columns are the observations.\n /// </summary>\n /// <param name=\"input\">The input tensor</param>\n /// <param name=\"correction\">\n /// Difference between the sample size and sample degrees of freedom.\n /// Defaults to Bessel’s correction, correction = 1 which returns the unbiased estimate,\n /// even if both fweights and aweights are specified.\n /// Correction = 0 will return the simple average.\n /// </param>\n /// <param name=\"fweights\">\n /// A Scalar or 1D tensor of observation vector frequencies representing the number of times each observation should be repeated.\n /// Its numel must equal the number of columns of input.\n /// Must have integral dtype.</param>\n /// <param name=\"aweights\">A Scalar or 1D array of observation vector weights.\n /// These relative weights are typically large for observations considered “important” and smaller for\n /// observations considered less “important”.\n /// Its numel must equal the number of columns of input.\n /// Must have floating point dtype.</param>\n public static Tensor cov(Tensor input, long correction = 1, Tensor? fweights = null, Tensor? aweights = null)\n => input.cov(correction, fweights, aweights);\n\n // https://pytorch.org/docs/stable/generated/torch.cross\n /// <summary>\n /// Returns the cross product of vectors in dimension dim of input and other.\n /// input and other must have the same size, and the size of their dim dimension should be 3.\n /// </summary>\n public static Tensor cross(Tensor input, Scalar other, long dim = 0L) => input.cross(other, dim);\n\n // https://pytorch.org/docs/stable/generated/torch.cummax\n public static (Tensor values, Tensor indices) cummax(Tensor input, long dim) => input.cummax(dim);\n\n // https://pytorch.org/docs/stable/generated/torch.cummin\n public static (Tensor values, Tensor indices) cummin(Tensor input, long dim) => input.cummin(dim);\n\n // https://pytorch.org/docs/stable/generated/torch.cumprod\n public static Tensor cumprod(Tensor input, long dim, ScalarType? dtype = null) => input.cumprod(dim, dtype);\n\n // https://pytorch.org/docs/stable/generated/torch.cumsum\n /// <summary>\n /// Returns the cumulative sum of elements of input in the dimension dim.\n /// </summary>\n /// <param name=\"input\">The input tensor.</param>\n /// <param name=\"dim\">The dimension to do the operation over</param>\n /// <param name=\"type\">The desired data type of returned tensor. If specified, the input tensor is casted to dtype before the operation is performed.\n /// This is useful for preventing data type overflows.</param>\n public static Tensor cumsum(Tensor input, long dim, ScalarType? type = null) => input.cumsum(dim, type);\n\n // https://pytorch.org/docs/stable/generated/torch.diag\n /// <summary>\n /// If input is a vector (1-D tensor), then returns a 2-D square tensor with the elements of input as the diagonal.\n /// If input is a matrix (2-D tensor), then returns a 1-D tensor with the diagonal elements of input.\n /// </summary>\n /// <param name=\"input\">The input tensor.</param>\n /// <param name=\"diagonal\">\n /// The argument diagonal controls which diagonal to consider:\n /// If diagonal is 0, it is the main diagonal.\n /// If diagonal is greater than 0, it is above the main diagonal.\n /// If diagonal is less than 0, it is below the main diagonal.\n /// </param>\n public static Tensor diag(Tensor input, long diagonal = 0) => input.diag(diagonal);\n\n // https://pytorch.org/docs/stable/generated/torch.diag_embed\n /// <summary>\n /// Creates a tensor whose diagonals of certain 2D planes (specified by dim1 and dim2) are filled by input.\n /// To facilitate creating batched diagonal matrices, the 2D planes formed by the last two dimensions of the returned tensor are chosen by default.\n ///\n /// The argument offset controls which diagonal to consider:\n /// If offset is equal to 0, it is the main diagonal.\n /// If offset is greater than 0, it is above the main diagonal.\n /// If offset is less than 0, it is below the main diagonal.\n ///\n /// The size of the new matrix will be calculated to make the specified diagonal of the size of the last input dimension.Note that for offset other than 0,\n ///\n /// the order of dim1 and dim2 matters.Exchanging them is equivalent to changing the sign of offset.\n /// </summary>\n /// <param name=\"input\">The input tensor.</param>\n /// <param name=\"offset\">Which diagonal to consider.</param>\n /// <param name=\"dim1\">First dimension with respect to which to take diagonal. </param>\n /// <param name=\"dim2\">Second dimension with respect to which to take diagonal</param>\n public static Tensor diag_embed(Tensor input, long offset = 0L, long dim1 = -2L, long dim2 = -1L)\n => input.diag_embed(offset, dim1, dim2);\n\n // https://pytorch.org/docs/stable/generated/torch.diagflat\n /// <summary>\n /// If input is a vector (1-D tensor), then returns a 2-D square tensor with the elements of input as the diagonal.\n /// If input is a matrix (2-D tensor), then returns a 2-D tensor with diagonal elements equal to a flattened input.\n /// </summary>\n /// <param name=\"input\">The input tensor.</param>\n /// <param name=\"offset\">\n /// The argument diagonal controls which diagonal to consider:\n /// If diagonal is 0, it is the main diagonal.\n /// If diagonal is greater than 0, it is above the main diagonal.\n /// If diagonal is less than 0, it is below the main diagonal.\n /// </param>\n public static Tensor diagflat(Tensor input, long offset = 0) => input.diagflat(offset);\n\n // https://pytorch.org/docs/stable/generated/torch.diagonal\n /// <summary>\n /// Returns a partial view of input with the its diagonal elements with respect to dim1 and dim2 appended as a dimension at the end of the shape.\n /// The argument offset controls which diagonal to consider:\n ///\n /// If offset == 0, it is the main diagonal.\n /// If offset > 0, it is above the main diagonal.\n /// If offset < 0, it is below the main diagonal.\n /// </summary>\n /// <param name=\"input\">The input tensor</param>\n /// <param name=\"offset\">Which diagonal to consider. Default: 0 (main diagonal).</param>\n /// <param name=\"dim1\">First dimension with respect to which to take diagonal. Default: 0.</param>\n /// <param name=\"dim2\">Second dimension with respect to which to take diagonal. Default: 1.</param>\n /// <remarks>\n /// Applying torch.diag_embed() to the output of this function with the same arguments yields a diagonal matrix with the diagonal entries of the input.\n /// However, torch.diag_embed() has different default dimensions, so those need to be explicitly specified.\n /// </remarks>\n public static Tensor diagonal(Tensor input, long offset = 0, long dim1 = 0, long dim2 = 0) => input.diagonal(offset, dim1, dim2);\n\n // https://pytorch.org/docs/stable/generated/torch.diff\n /// <summary>\n /// Computes the n-th forward difference along the given dimension.\n /// </summary>\n /// <param name=\"input\">The input tensor.</param>\n /// <param name=\"n\">The number of times to recursively compute the difference</param>\n /// <param name=\"dim\">The dimension to compute the difference along. Default is the last dimension.</param>\n /// <param name=\"prepend\">\n /// Values to prepend or append to input along dim before computing the difference.\n /// Their dimensions must be equivalent to that of input, and their shapes must match input’s shape except on dim.\n /// </param>\n /// <param name=\"append\">\n /// Values to prepend or append to input along dim before computing the difference.\n /// Their dimensions must be equivalent to that of input, and their shapes must match input’s shape except on dim.\n /// </param>\n public static Tensor diff(Tensor input, long n = 1, long dim = -1, Tensor? prepend = null, Tensor? append = null) => input.diff(n, dim, prepend, append);\n\n // https://pytorch.org/docs/stable/generated/torch.einsum\n /// <summary>\n /// Sums the product of the elements of the input operands along dimensions specified using a notation based on the Einstein summation convention.\n /// </summary>\n /// <param name=\"equation\">The subscripts for the Einstein summation.</param>\n /// <param name=\"tensors\">The operands to compute the Einstein sum of.</param>\n /// <remarks>\n /// Einsum allows computing many common multi-dimensional linear algebraic array operations by representing them in a short-hand format based on the\n /// Einstein summation convention, given by equation.The details of this format are described below, but the general idea is to label every dimension\n /// of the input operands with some subscript and define which subscripts are part of the output. The output is then computed by summing the product\n /// of the elements of the operands along the dimensions whose subscripts are not part of the output.For example, matrix multiplication can be computed\n /// using einsum as torch.einsum(“ij,jk->ik”, A, B). Here, j is the summation subscript and i and k the output subscripts(see section below for more details on why).\n /// </remarks>\n /// <returns></returns>\n public static Tensor einsum(string equation, params Tensor[] tensors)\n {\n using var parray = new PinnedArray<IntPtr>();\n IntPtr tensorsRef = parray.CreateArray(tensors.Select(p => p.Handle).ToArray());\n\n var res = THSTensor_einsum(equation, tensorsRef, parray.Array.Length);\n if (res == IntPtr.Zero) { CheckForErrors(); }\n return new Tensor(res);\n }\n\n // https://pytorch.org/docs/stable/generated/torch.flatten\n /// <summary>\n /// Flattens input by reshaping it into a one-dimensional tensor.\n /// </summary>\n /// <param name=\"input\">The input tensor</param>\n /// <param name=\"start_dim\">The first dim to flatten</param>\n /// <param name=\"end_dim\">The last dim to flatten.</param>\n /// <remarks>Flattening a zero-dimensional tensor will return a one-dimensional view.</remarks>\n public static Tensor flatten(Tensor input, long start_dim = 0, long end_dim = -1) => input.flatten(start_dim, end_dim);\n\n // https://pytorch.org/docs/stable/generated/torch.flip\n public static Tensor flip(Tensor input, params long[] dims) => input.flip(dims);\n\n // https://pytorch.org/docs/stable/generated/torch.fliplr\n public static Tensor fliplr(Tensor input) => input.fliplr();\n\n // https://pytorch.org/docs/stable/generated/torch.flipud\n public static Tensor flipud(Tensor input) => input.flipud();\n\n // https://pytorch.org/docs/stable/generated/torch.kron\n /// <summary>\n /// Computes the Kronecker product of input and other.\n /// </summary>\n /// <param name=\"input\">The input tensor.</param>\n /// <param name=\"other\">The second tensor</param>\n public static Tensor kron(Tensor input, Tensor other) => input.kron(other);\n\n // https://pytorch.org/docs/stable/generated/torch.rot90\n /// <summary>\n /// Rotate a n-D tensor by 90 degrees in the plane specified by dims axis.\n /// Rotation direction is from the first towards the second axis if k is greater than 0,\n /// and from the second towards the first for k less than 0.\n /// </summary>\n /// <param name=\"input\">The input tensor</param>\n /// <param name=\"k\">The number of times to rotate.</param>\n /// <param name=\"dims\">Axes to rotate</param>\n public static Tensor rot90(Tensor input, long k = 1, (long, long)? dims = null) => input.rot90(k, dims);\n\n // https://pytorch.org/docs/stable/generated/torch.gcd\n /// <summary>\n /// Computes the element-wise greatest common divisor (GCD) of input and other.\n /// </summary>\n /// <param name=\"left\">The left-hand operand.</param>\n /// <param name=\"right\">The right-hand operand.</param>\n public static Tensor gcd(Tensor left, Tensor right) => left.gcd(right);\n\n // https://pytorch.org/docs/stable/generated/torch.gcd\n /// <summary>\n /// Computes the element-wise greatest common divisor (GCD) of input and other.\n /// </summary>\n /// <param name=\"left\">The left-hand operand.</param>\n /// <param name=\"right\">The right-hand operand.</param>\n public static Tensor gcd_(Tensor left, Tensor right) => left.gcd_(right);\n\n // https://pytorch.org/docs/stable/generated/torch.histc\n /// <summary>\n /// Computes the histogram of a tensor.\n /// The elements are sorted into equal width bins between min and max.If min and max are both zero, the minimum and maximum values of the data are used.\n /// Elements lower than min and higher than max are ignored.\n /// </summary>\n /// <param name=\"input\">The input tensor.</param>\n /// <param name=\"bins\">Number of histogram bins</param>\n /// <param name=\"min\">Lower end of the range (inclusive)</param>\n /// <param name=\"max\">Upper end of the range (inclusive)</param>\n public static Tensor histc(Tensor input, long bins = 100, long min = 0, long max = 0) => input.histc(bins, min, max);\n\n // https://pytorch.org/docs/stable/generated/torch.histogram\n [Obsolete(\"not implemented\", true)]\n static Tensor histogram(\n Tensor input,\n long bins,\n (float min, float max)? range = null,\n Tensor? weight = null,\n bool density = false)\n => throw new NotImplementedException();\n\n // https://pytorch.org/docs/stable/generated/torch.histogram\n [Obsolete(\"not implemented\", true)]\n static Tensor histogram(\n Tensor input,\n long[] bins,\n (float min, float max)? range = null,\n Tensor? weight = null,\n bool density = false)\n => throw new NotImplementedException();\n\n // https://pytorch.org/docs/stable/generated/torch.histogram\n [Obsolete(\"not implemented\", true)]\n static Tensor histogram(\n Tensor input,\n Tensor[] bins,\n (float min, float max)? range = null,\n Tensor? weight = null,\n bool density = false)\n => throw new NotImplementedException();\n\n // https://pytorch.org/docs/stable/generated/torch.histogramdd\n [Obsolete(\"not implemented\", true)]\n static (Tensor hist_values, Tensor[] edges) histogramdd(\n Tensor input,\n long bins,\n (float min, float max)? range = null,\n Tensor? weight = null,\n bool density = false)\n => throw new NotImplementedException();\n\n // https://pytorch.org/docs/stable/generated/torch.histogramdd\n [Obsolete(\"not implemented\", true)]\n static (Tensor hist_values, Tensor[] edges) histogramdd(\n Tensor input,\n long[] bins,\n (float min, float max)? range = null,\n Tensor? weight = null,\n bool density = false)\n => throw new NotImplementedException();\n\n // https://pytorch.org/docs/stable/generated/torch.histogramdd\n [Obsolete(\"not implemented\", true)]\n static (Tensor hist_values, Tensor[] edges) histogramdd(\n Tensor input,\n Tensor[] bins,\n (float min, float max)? range = null,\n Tensor? weight = null,\n bool density = false)\n => throw new NotImplementedException();\n\n // https://pytorch.org/docs/stable/generated/torch.meshgrid\n /// <summary>\n /// Creates grids of coordinates specified by the 1D inputs in tensors.\n /// This is helpful when you want to visualize data over some range of inputs.\n /// </summary>\n /// <returns></returns>\n /// <remarks>All tensors need to be of the same size.</remarks>\n static IEnumerable<Tensor> meshgrid(IEnumerable<Tensor> tensors, indexing indexing = indexing.ij)\n {\n var idx = indexing switch {\n indexing.ij => \"ij\",\n indexing.xy => \"xy\",\n _ => throw new ArgumentOutOfRangeException()\n };\n return meshgrid(tensors, idx);\n }\n\n // https://pytorch.org/docs/stable/generated/torch.meshgrid\n /// <summary>\n /// Creates grids of coordinates specified by the 1D inputs in tensors.\n /// This is helpful when you want to visualize data over some range of inputs.\n /// </summary>\n /// <returns></returns>\n /// <remarks>All tensors need to be of the same size.</remarks>\n public static Tensor[] meshgrid(IEnumerable<Tensor> tensors, string indexing = \"ij\")\n {\n IntPtr[] ptrArray;\n\n using (var parray = new PinnedArray<IntPtr>()) {\n IntPtr tensorsRef = parray.CreateArray(tensors.Select(p => p.Handle).ToArray());\n _ = THSTensor_meshgrid(tensorsRef, parray.Array.Length, indexing, parray.CreateArray);\n CheckForErrors();\n ptrArray = parray.Array;\n }\n return ptrArray.Select(x => new Tensor(x)).ToArray();\n }\n\n // https://pytorch.org/docs/stable/generated/torch.lcm\n /// <summary>\n /// Computes the element-wise least common multiple (LCM) of input and other.\n /// </summary>\n /// <param name=\"input\">The first input tensor.</param>\n /// <param name=\"other\">The second input tensor.</param>\n /// <remarks>Both input and other must have integer types.</remarks>\n public static Tensor lcm(Tensor input, Tensor other) => input.lcm(other);\n\n // https://pytorch.org/docs/stable/generated/torch.lcm\n /// <summary>\n /// Computes the element-wise least common multiple (LCM) of input and other in place.\n /// </summary>\n /// <param name=\"input\">The first input tensor.</param>\n /// <param name=\"other\">The second input tensor.</param>\n /// <remarks>Both input and other must have integer types.</remarks>\n public static Tensor lcm_(Tensor input, Tensor other) => input.lcm_(other);\n\n // https://pytorch.org/docs/stable/generated/torch.logcumsumexp\n /// <summary>\n /// Returns the logarithm of the cumulative summation of the exponentiation of elements of input in the dimension dim.\n /// </summary>\n /// <param name=\"input\">The input tensor.</param>\n /// <param name=\"dim\">The dimension to do the operation over</param>\n public static Tensor logcumsumexp(Tensor input, long dim) => input.logcumsumexp(dim);\n\n // https://pytorch.org/docs/stable/generated/torch.ravel\n public static Tensor ravel(Tensor input) => input.ravel();\n\n // https://pytorch.org/docs/stable/generated/torch.renorm\n /// <summary>\n /// Returns a tensor where each sub-tensor of input along dimension dim is normalized such that the p-norm of the sub-tensor is lower than the value maxnorm\n /// </summary>\n /// <param name=\"input\">The input tensor.</param>\n /// <param name=\"p\">The power for the norm computation</param>\n /// <param name=\"dim\">The dimension to slice over to get the sub-tensors</param>\n /// <param name=\"maxnorm\">The maximum norm to keep each sub-tensor under</param>\n public static Tensor renorm(Tensor input, float p, long dim, float maxnorm) => input.renorm(p, dim, maxnorm);\n\n // https://pytorch.org/docs/stable/generated/torch.repeat_interleave\n /// <summary>\n /// Repeat elements of a tensor.\n /// </summary>\n /// <param name=\"input\">The input tensor</param>\n /// <param name=\"repeats\">The number of repeats</param>\n /// <param name=\"dim\">The dimension to repeat</param>\n /// <param name=\"output_size\">The size of output</param>\n /// <returns></returns>\n public static Tensor repeat_interleave(Tensor input, long repeats, long? dim = null, long? output_size = null) => input.repeat_interleave(repeats, dim, output_size);\n\n // https://pytorch.org/docs/stable/generated/torch.repeat_interleave\n /// <summary>\n /// Repeat elements of a tensor.\n /// </summary>\n /// <param name=\"input\">The input tensor</param>\n /// <param name=\"repeats\">The number of repeats</param>\n /// <param name=\"dim\">The dimension to repeat</param>\n /// <param name=\"output_size\">The size of output</param>\n /// <returns></returns>\n public static Tensor repeat_interleave(Tensor input, Tensor repeats, long? dim = null, long? output_size = null) => input.repeat_interleave(repeats, dim, output_size);\n\n // https://pytorch.org/docs/stable/generated/torch.roll\n /// <summary>\n /// Roll the tensor along the given dimension(s).\n /// Elements that are shifted beyond the last position are re-introduced at the first position.\n /// If a dimension is not specified, the tensor will be flattened before rolling and then restored to the original shape.\n /// </summary>\n public static Tensor roll(Tensor input, long shifts, long? dims = null) => input.roll(shifts, dims);\n\n // https://pytorch.org/docs/stable/generated/torch.roll\n /// <summary>\n /// Roll the tensor along the given dimension(s).\n /// Elements that are shifted beyond the last position are re-introduced at the first position.\n /// If a dimension is not specified, the tensor will be flattened before rolling and then restored to the original shape.\n /// </summary>\n public static Tensor roll(Tensor input, (long, long) shifts, (long, long) dims) => input.roll(shifts, dims);\n\n // https://pytorch.org/docs/stable/generated/torch.roll\n /// <summary>\n /// Roll the tensor along the given dimension(s).\n /// Elements that are shifted beyond the last position are re-introduced at the first position.\n /// If a dimension is not specified, the tensor will be flattened before rolling and then restored to the original shape.\n /// </summary>\n public static Tensor roll(Tensor input, (long, long, long) shifts, (long, long, long) dims) => input.roll(shifts, dims);\n\n // https://pytorch.org/docs/stable/generated/torch.roll\n /// <summary>\n /// Roll the tensor along the given dimension(s).\n /// Elements that are shifted beyond the last position are re-introduced at the first position.\n /// If a dimension is not specified, the tensor will be flattened before rolling and then restored to the original shape.\n /// </summary>\n public static Tensor roll(Tensor input, long[] shifts, long[] dims) => input.roll(shifts, dims);\n\n // https://pytorch.org/docs/stable/generated/torch.roll\n /// <summary>\n /// Roll the tensor along the given dimension(s).\n /// Elements that are shifted beyond the last position are re-introduced at the first position.\n /// If a dimension is not specified, the tensor will be flattened before rolling and then restored to the original shape.\n /// </summary>\n public static Tensor roll(Tensor input, long[] shifts) => input.roll(shifts);\n\n // https://pytorch.org/docs/stable/generated/torch.roll\n /// <summary>\n /// Roll the tensor along the given dimension(s).\n /// Elements that are shifted beyond the last position are re-introduced at the first position.\n /// If a dimension is not specified, the tensor will be flattened before rolling and then restored to the original shape.\n /// </summary>\n public static Tensor roll(Tensor input, ReadOnlySpan<long> shifts, ReadOnlySpan<long> dims = default) => input.roll(shifts, dims);\n\n // https://pytorch.org/docs/stable/generated/torch.tensordot\n /// <summary>\n /// Returns a contraction of <paramref name=\"a\"/> and <paramref name=\"b\"/> over multiple dimensions.\n /// tensordot implements a generalized matrix product.\n /// </summary>\n /// <param name=\"a\">Left tensor to contract</param>\n /// <param name=\"b\">Right tensor to contract</param>\n /// <param name=\"dims\">number of dimensions to contract for <paramref name=\"a\"/> and <paramref name=\"b\"/></param>\n /// <returns>contraction</returns>\n public static Tensor tensordot(Tensor a, Tensor b, long dims = 2) => a.tensordot(b, dims);\n\n // https://pytorch.org/docs/stable/generated/torch.tensordot\n /// <summary>\n /// Returns a contraction of <paramref name=\"a\"/> and <paramref name=\"b\"/> over multiple dimensions.\n /// tensordot implements a generalized matrix product.\n /// </summary>\n /// <param name=\"a\">Left tensor to contract</param>\n /// <param name=\"b\">Right tensor to contract</param>\n /// <param name=\"dims1\">dimensions to contract for <paramref name=\"a\"/></param>\n /// <param name=\"dims2\">dimensions to contract for <paramref name=\"b\"/></param>\n /// <returns>contraction</returns>\n public static Tensor tensordot(Tensor a, Tensor b, long[] dims1, long[] dims2) => a.tensordot(b, dims1, dims2);\n\n // https://pytorch.org/docs/stable/generated/torch.tensordot\n /// <summary>\n /// Returns a contraction of <paramref name=\"a\"/> and <paramref name=\"b\"/> over multiple dimensions.\n /// tensordot implements a generalized matrix product.\n /// </summary>\n /// <param name=\"a\">Left tensor to contract</param>\n /// <param name=\"b\">Right tensor to contract</param>\n /// <param name=\"dims\">dimensions to contract for <paramref name=\"a\"/> and <paramref name=\"b\"/> respectively</param>\n /// <returns>contraction</returns>\n public static Tensor tensordot(Tensor a, Tensor b, (long, long)[] dims) => a.tensordot(b, dims);\n\n // https://pytorch.org/docs/stable/generated/torch.trace\n /// <summary>\n /// Returns the sum of the elements of the diagonal of the input 2-D matrix.\n /// </summary>\n /// <param name=\"input\">The input tensor</param>\n /// <returns></returns>\n public static Tensor trace(Tensor input) => input.trace();\n\n // https://pytorch.org/docs/stable/generated/torch.tril\n public static Tensor tril(Tensor input, long diagonal = 0) => input.tril(diagonal);\n\n public static Tensor tril_(Tensor input, long diagonal = 0) => input.tril_(diagonal);\n\n // https://pytorch.org/docs/stable/generated/torch.tril_indices\n public static Tensor tril_indices(\n long row,\n long col,\n long offset = 0L,\n ScalarType dtype = ScalarType.Int64,\n Device? device = null)\n {\n if (!torch.is_integral(dtype))\n throw new ArgumentException(\"dtype must be integral.\");\n\n if (device == null) {\n device = torch.CPU;\n }\n\n var res = NativeMethods.THSTensor_tril_indices(row, col, offset, (sbyte)dtype, (int)device.type, device.index);\n if (res == IntPtr.Zero)\n CheckForErrors();\n return new Tensor(res);\n }\n\n // https://pytorch.org/docs/stable/generated/torch.triu\n public static Tensor triu(Tensor input, long diagonal = 0L) => input.triu(diagonal);\n\n public static Tensor triu_(Tensor input, long diagonal = 0L) => input.triu_(diagonal);\n\n // https://pytorch.org/docs/stable/generated/torch.triu_indices\n public static Tensor triu_indices(\n long row,\n long col,\n long offset = 0L,\n ScalarType dtype = ScalarType.Int64,\n Device? device = null)\n {\n if (!torch.is_integral(dtype))\n throw new ArgumentException(\"dtype must be integral.\");\n\n if (device == null) {\n device = torch.CPU;\n }\n\n var res = NativeMethods.THSTensor_triu_indices(row, col, offset, (sbyte)dtype, (int)device.type, device.index);\n if (res == IntPtr.Zero)\n CheckForErrors();\n return new Tensor(res);\n }\n\n // https://pytorch.org/docs/stable/generated/torch.vander\n public static Tensor vander(Tensor x, long N = -1, bool increasing = false) => x.vander(N, increasing);\n\n // https://pytorch.org/docs/stable/generated/torch.view_as_real\n /// <summary>\n /// Returns a view of input as a real tensor.\n /// For an input complex tensor of size m1, m2, …, mi, this function returns a new real tensor of size m1, m2, …, mi, 2, where the last dimension of size 2 represents the real and imaginary components of complex numbers.\n /// </summary>\n /// <param name=\"input\">The input tensor</param>\n public static Tensor view_as_real(Tensor input) => input.view_as_real();\n\n // https://pytorch.org/docs/stable/generated/torch.view_as_complex\n /// <summary>\n /// Returns a view of input as a complex tensor.\n /// For an input complex tensor of size m1, m2, …, mi, 2, this function returns a new complex tensor of size m1, m2, …, mi where the last dimension of the input tensor is expected to represent the real and imaginary components of complex numbers.\n /// </summary>\n /// <param name=\"input\">The input tensor</param>\n public static Tensor view_as_complex(Tensor input) => input.view_as_complex();\n\n // https://pytorch.org/docs/stable/generated/torch.resolve_conj\n /// <summary>\n /// Returns a new tensor with materialized conjugation if input’s conjugate bit is set to True, else returns input.\n /// The output tensor will always have its conjugate bit set to False.\n /// </summary>\n /// <param name=\"input\">The input tensor.</param>\n public static Tensor resolve_conj(Tensor input) => input.resolve_conj();\n\n // https://pytorch.org/docs/stable/generated/torch.resolve_neg\n /// <summary>\n /// Returns a new tensor with materialized negation if input’s negative bit is set to True, else returns input.\n /// The output tensor will always have its negative bit set to False.\n /// </summary>\n public static Tensor resolve_neg(Tensor input) => input.resolve_neg();\n\n /// <summary>\n /// Returns true if the input's negative bit is set to True.\n /// </summary>\n public static Tensor is_neg(Tensor input) => input.is_neg();\n }\n}"},{"alpha_fraction":0.668683648109436,"alphanum_fraction":0.6760084629058838,"avg_line_length":37.448978424072266,"blob_id":"756fb9fdb47cc2aa7f658aed5896b784fbdc41e1","content_id":"5bb6d76619f601b1f67ab57fb823fbe53563bd27","detected_licenses":["MIT"],"is_generated":false,"is_vendor":false,"language":"C++","length_bytes":9420,"license_type":"permissive","max_line_length":224,"num_lines":245,"path":"/src/Native/LibTorchSharp/THSLoss.cpp","repo_name":"NiklasGustafsson/TorchSharp","src_encoding":"UTF-8","text":"// Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information.\n#include \"THSNN.h\"\n\n#include <torch/nn/init.h>\n\n\ntemplate<typename T>\nvoid ApplyReduction(T& opts, const int64_t reduction)\n{\n if (reduction == 0)\n opts = opts.reduction(torch::kNone);\n if (reduction == 1)\n opts = opts.reduction(torch::kMean);\n if (reduction == 2)\n opts = opts.reduction(torch::kSum);\n}\n\nTensor THSNN_binary_cross_entropy(const Tensor input, const Tensor target, const Tensor weight, const int64_t reduction)\n{\n CATCH_RETURN_Tensor(\n auto opts = torch::nn::functional::BinaryCrossEntropyFuncOptions();\n ApplyReduction(opts, reduction);\n if (weight != nullptr)\n opts = opts.weight(*weight);\n res = ResultTensor(torch::nn::functional::binary_cross_entropy(*input, *target, opts));\n )\n}\n\nTensor THSNN_binary_cross_entropy_with_logits(const Tensor input, const Tensor target, const Tensor weight, const int64_t reduction, const Tensor pos_weights_wrapper)\n{\n CATCH_RETURN_Tensor(\n auto opts = torch::nn::BCEWithLogitsLossOptions();\n ApplyReduction(opts, reduction);\n if (pos_weights_wrapper != nullptr)\n opts = opts.pos_weight(*pos_weights_wrapper);\n if (weight != nullptr)\n opts = opts.weight(*weight);\n res = ResultTensor(torch::nn::functional::binary_cross_entropy_with_logits(*input, *target, opts));\n )\n}\n\nTensor THSNN_cosine_embedding_loss(const Tensor input1, const Tensor input2, const Tensor target, const double margin, const int64_t reduction)\n{\n CATCH_RETURN_Tensor(\n auto opts = torch::nn::functional::CosineEmbeddingLossFuncOptions().margin(margin);\n ApplyReduction(opts, reduction);\n res = ResultTensor(torch::nn::functional::cosine_embedding_loss(*input1, *input2, *target, opts));\n )\n}\n\nTensor THSNN_cross_entropy(const Tensor input, const Tensor target, const Tensor weight, const int64_t ignore_index, const bool has_ii, const int64_t reduction, const double smoothing)\n{\n CATCH_RETURN_Tensor(\n auto opts = torch::nn::functional::CrossEntropyFuncOptions();\n ApplyReduction(opts, reduction);\n opts.label_smoothing(smoothing);\n if (has_ii)\n opts = opts.ignore_index(ignore_index);\n if (weight != nullptr)\n opts = opts.weight(*weight);\n res = ResultTensor(torch::nn::functional::cross_entropy(*input, *target, opts));\n )\n}\n\nTensor THSNN_hinge_embedding_loss(const Tensor input, const Tensor target, const double margin, const int64_t reduction)\n{\n CATCH_RETURN_Tensor(\n auto opts = torch::nn::functional::HingeEmbeddingLossFuncOptions().margin(margin);\n ApplyReduction(opts, reduction);\n res = ResultTensor(torch::nn::functional::hinge_embedding_loss(*input, *target, opts));\n )\n}\n\nTensor THSNN_huber_loss(const Tensor input, const Tensor target, const double delta, const int64_t reduction)\n{\n CATCH_RETURN_Tensor(\n auto opts = torch::nn::functional::HuberLossFuncOptions().delta(delta);\n ApplyReduction(opts, reduction);\n res = ResultTensor(torch::nn::functional::huber_loss(*input, *target, opts));\n )\n}\n\n\nTensor THSNN_kl_div_loss(const Tensor input, const Tensor target, const int64_t reduction, const bool log_target)\n{\n CATCH_RETURN_Tensor(\n auto opts = torch::nn::functional::KLDivFuncOptions().log_target(log_target);\n ApplyReduction(opts, reduction);\n\n res = ResultTensor(torch::nn::functional::kl_div(*input, *target, opts));\n )\n}\n\nTensor THSNN_l1_loss(const Tensor input, const Tensor target, const int64_t reduction)\n{\n CATCH_RETURN_Tensor(\n auto opts = torch::nn::functional::MSELossFuncOptions();\n ApplyReduction(opts, reduction);\n\n res = ResultTensor(torch::nn::functional::mse_loss(*input, *target, opts));\n )\n}\n\nTensor THSNN_margin_ranking_loss(const Tensor input1, const Tensor input2, const Tensor target, const double margin, const int64_t reduction)\n{\n CATCH_RETURN_Tensor(\n auto opts = torch::nn::functional::MarginRankingLossFuncOptions().margin(margin);\n ApplyReduction(opts, reduction);\n res = ResultTensor(torch::nn::functional::margin_ranking_loss(*input1, *input2, *target, opts));\n )\n}\n\nTensor THSNN_mse_loss(const Tensor input, const Tensor target, const int64_t reduction)\n{\n CATCH_RETURN_Tensor(\n auto opts = torch::nn::functional::MSELossFuncOptions();\n ApplyReduction(opts, reduction);\n\n res = ResultTensor(torch::nn::functional::mse_loss(*input, *target, opts));\n )\n}\n\nTensor THSNN_nll_loss(const Tensor input, const Tensor target, const Tensor weight, const int64_t reduction)\n{\n CATCH_RETURN_Tensor(\n auto opts = torch::nn::functional::NLLLossFuncOptions();\n ApplyReduction(opts, reduction);\n if (weight != nullptr)\n opts = opts.weight(*weight);\n\n res = ResultTensor(torch::nn::functional::nll_loss(*input, *target, opts));\n )\n}\n\nTensor THSNN_poisson_loss(const Tensor input, const Tensor target, const bool logInput, const bool full, const double eps, const int64_t reduction)\n{\n CATCH_RETURN_Tensor(\n auto opts = torch::nn::functional::PoissonNLLLossFuncOptions().log_input(logInput).full(full).eps(eps);\n ApplyReduction(opts, reduction);\n\n res = ResultTensor(torch::nn::functional::poisson_nll_loss(*input, *target, opts));\n )\n}\n\nTensor THSNN_smooth_l1_loss(const Tensor input, const Tensor target, const int64_t reduction, const double beta)\n{\n CATCH_RETURN_Tensor(\n auto opts = torch::nn::functional::SmoothL1LossFuncOptions();\n ApplyReduction(opts, reduction);\n\n res = ResultTensor(torch::nn::functional::smooth_l1_loss(*input, *target, opts, beta));\n )\n}\n\nTensor THSNN_soft_margin_loss(const Tensor input, const Tensor target, const int64_t reduction)\n{\n CATCH_RETURN_Tensor(\n auto opts = torch::nn::functional::SoftMarginLossFuncOptions();\n ApplyReduction(opts, reduction);\n\n res = ResultTensor(torch::nn::functional::soft_margin_loss(*input, *target, opts));\n )\n}\n\nTensor THSNN_ctc_loss(const Tensor log_probs, const Tensor targets, const Tensor input_lengths, const Tensor target_lengths, int64_t blank, bool zero_infinity, const int64_t reduction)\n{\n CATCH_RETURN_Tensor(\n auto opts = torch::nn::functional::CTCLossFuncOptions().blank(blank).zero_infinity(zero_infinity);\n ApplyReduction(opts, reduction);\n\n res = ResultTensor(torch::nn::functional::ctc_loss(*log_probs, *targets, *input_lengths, *target_lengths, opts));\n )\n}\n\nTensor THSNN_multilabel_margin_loss(const Tensor input, const Tensor target, const int64_t reduction)\n{\n CATCH_RETURN_Tensor(\n auto opts = torch::nn::functional::MultilabelMarginLossFuncOptions();\n ApplyReduction(opts, reduction);\n\n res = ResultTensor(torch::nn::functional::multilabel_margin_loss(*input, *target, opts));\n )\n}\n\nTensor THSNN_multilabel_soft_margin_loss(const Tensor input, const Tensor target, const Tensor weight, const int64_t reduction)\n{\n CATCH_RETURN_Tensor(\n auto opts = torch::nn::functional::MultilabelSoftMarginLossFuncOptions();\n ApplyReduction(opts, reduction);\n if (weight != nullptr)\n opts = opts.weight(*weight);\n\n res = ResultTensor(torch::nn::functional::multilabel_soft_margin_loss(*input, *target, opts));\n )\n}\n\nTensor THSNN_multi_margin_loss(const Tensor input, const Tensor target, const int64_t p, const double margin, const Tensor weight, const int64_t reduction)\n{\n CATCH_RETURN_Tensor(\n auto opts = torch::nn::functional::MultiMarginLossFuncOptions()\n .p(p)\n .margin(margin);\n ApplyReduction(opts, reduction);\n if (weight != nullptr)\n opts = opts.weight(*weight);\n\n res = ResultTensor(torch::nn::functional::multi_margin_loss(*input, *target, opts));\n )\n}\n\nTensor THSNN_triplet_margin_loss(const Tensor anchor, const Tensor positive, const Tensor negative, double margin, int64_t p, double eps, bool swap, const int64_t reduction)\n{\n CATCH_RETURN_Tensor(\n auto opts = torch::nn::functional::TripletMarginLossFuncOptions()\n .p(p)\n .eps(eps)\n .margin(margin)\n .swap(swap);\n ApplyReduction(opts, reduction);\n\n res = ResultTensor(torch::nn::functional::triplet_margin_loss(*anchor, *positive, *negative, opts));\n )\n}\n\nTensor THSNN_triplet_margin_with_distance_loss(const Tensor anchor, const Tensor positive, const Tensor negative, Tensor(*distance_function)(const Tensor x, const Tensor y), double margin, bool swap, const int64_t reduction)\n{\n CATCH_RETURN_Tensor(\n auto opts = torch::nn::functional::TripletMarginWithDistanceLossFuncOptions()\n .margin(margin)\n .swap(swap);\n\n ApplyReduction(opts, reduction);\n\n if (distance_function != nullptr) {\n opts = opts.distance_function(\n [=](const at::Tensor& x, const at::Tensor& y) -> const at::Tensor& {\n auto x1 = ResultTensor(x);\n auto y1 = ResultTensor(y);\n return *distance_function(x1, y1);\n });\n }\n\n res = ResultTensor(torch::nn::functional::triplet_margin_with_distance_loss(*anchor, *positive, *negative, opts));\n )\n}\n"},{"alpha_fraction":0.6013590097427368,"alphanum_fraction":0.601925253868103,"avg_line_length":35.79166793823242,"blob_id":"c930e90ec5bec98691f0c32da6c8193f72f3a84c","content_id":"7296a52a14c582cf2428d250f63f3215fede9115","detected_licenses":["MIT"],"is_generated":false,"is_vendor":false,"language":"C#","length_bytes":1766,"license_type":"permissive","max_line_length":130,"num_lines":48,"path":"/src/TorchSharp/NN/Identity.cs","repo_name":"NiklasGustafsson/TorchSharp","src_encoding":"UTF-8","text":"// Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information.\nusing System;\nusing static TorchSharp.torch;\nusing static TorchSharp.PInvoke.NativeMethods;\n\n#nullable enable\nnamespace TorchSharp\n{\n using Modules;\n\n namespace Modules\n {\n public sealed class Identity : torch.nn.Module<Tensor, Tensor>\n {\n internal Identity(IntPtr handle, IntPtr boxedHandle) : base(handle, boxedHandle) { }\n\n public override Tensor forward(Tensor tensor)\n {\n var res = THSNN_Identity_forward(handle, tensor.Handle);\n if (res == IntPtr.Zero) { torch.CheckForErrors(); }\n return new Tensor(res);\n }\n\n // Rather than spending cycles only to discover that this module has neither\n // parameters nor buffers, just shortcut the move completely.\n protected internal override nn.Module _to(Device device, ScalarType dtype) => this;\n protected internal override nn.Module _to(DeviceType deviceType, int deviceIndex = -1) => this;\n protected internal override nn.Module _to(ScalarType dtype) => this;\n }\n }\n\n public static partial class torch\n {\n public static partial class nn\n {\n /// <summary>\n /// A placeholder identity operator.\n /// </summary>\n /// <returns>The same tensor as is input.</returns>\n public static Identity Identity()\n {\n var res = THSNN_Identity_ctor(out var boxedHandle);\n if (res == IntPtr.Zero) { torch.CheckForErrors(); }\n return new Identity(res, boxedHandle);\n }\n }\n }\n}\n"},{"alpha_fraction":0.5044258832931519,"alphanum_fraction":0.5215146541595459,"avg_line_length":36.311927795410156,"blob_id":"9b952b0b986168d478b2a5b4a70fac663b9c9120","content_id":"215e4f74d52b55e633c8ae1d8847ff95cedcd414","detected_licenses":["MIT"],"is_generated":false,"is_vendor":false,"language":"C#","length_bytes":8134,"license_type":"permissive","max_line_length":197,"num_lines":218,"path":"/src/Examples/CIFAR10.cs","repo_name":"NiklasGustafsson/TorchSharp","src_encoding":"UTF-8","text":"// Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information.\nusing System;\nusing System.Diagnostics;\nusing static TorchSharp.torch.nn;\nusing static TorchSharp.torch.nn.functional;\nusing static TorchSharp.torch.utils.data;\n\nnamespace TorchSharp.Examples\n{\n /// <summary>\n /// Driver for various models trained and evaluated on the CIFAR10 small (32x32) color image data set.\n /// </summary>\n /// <remarks>\n /// The dataset for this example can be found at: https://www.cs.toronto.edu/~kriz/cifar.html\n /// Download the binary file, and place it in a dedicated folder, e.g. 'CIFAR10,' then edit\n /// the '_dataLocation' definition below to point at the right folder.\n ///\n /// Note: so far, CIFAR10 is supported, but not CIFAR100.\n /// </remarks>\n class CIFAR10\n {\n private static int _epochs = 8;\n private static int _trainBatchSize = 64;\n private static int _testBatchSize = 128;\n\n private readonly static int _logInterval = 25;\n private readonly static int _numClasses = 100;\n\n private readonly static int _timeout = 3600; // One hour by default.\n\n internal static void Main(string[] args)\n {\n var datasetPath = Environment.GetFolderPath(Environment.SpecialFolder.LocalApplicationData);\n\n torch.random.manual_seed(1);\n\n var device =\n // This worked on a GeForce RTX 2080 SUPER with 8GB, for all the available network architectures.\n // It may not fit with less memory than that, but it's worth modifying the batch size to fit in memory.\n torch.cuda.is_available() ? torch.CUDA :\n torch.CPU;\n\n if (device.type == DeviceType.CUDA) {\n _trainBatchSize *= 8;\n _testBatchSize *= 8;\n _epochs *= 16;\n }\n\n var modelName = args.Length > 0 ? args[0] : \"AlexNet\";\n var epochs = args.Length > 1 ? int.Parse(args[1]) : _epochs;\n var timeout = args.Length > 2 ? int.Parse(args[2]) : _timeout;\n\n Console.WriteLine();\n Console.WriteLine($\"\\tRunning {modelName} with CIFAR100 on {device.type.ToString()} for {epochs} epochs, terminating after {TimeSpan.FromSeconds(timeout)}.\");\n Console.WriteLine();\n\n Console.WriteLine($\"\\tCreating the model...\");\n\n Module<torch.Tensor, torch.Tensor> model = null;\n\n switch (modelName.ToLower()) {\n case \"alexnet\":\n model = new AlexNet(modelName, _numClasses, device);\n break;\n case \"mobilenet\":\n model = new MobileNet(modelName, _numClasses, device);\n break;\n case \"vgg11\":\n case \"vgg13\":\n case \"vgg16\":\n case \"vgg19\":\n model = new VGG(modelName, _numClasses, device);\n break;\n case \"resnet18\":\n model = ResNet.ResNet18(_numClasses, device);\n break;\n case \"resnet34\":\n _testBatchSize /= 4;\n model = ResNet.ResNet34(_numClasses, device);\n break;\n case \"resnet50\":\n _trainBatchSize /= 6;\n _testBatchSize /= 8;\n model = ResNet.ResNet50(_numClasses, device);\n break;\n#if false\n // The following is disabled, because they require big CUDA processors in order to run.\n case \"resnet101\":\n _trainBatchSize /= 6;\n _testBatchSize /= 8;\n model = ResNet.ResNet101(_numClasses, device);\n break;\n case \"resnet152\":\n _testBatchSize /= 4;\n model = ResNet.ResNet152(_numClasses, device);\n break;\n#endif\n }\n\n Console.WriteLine($\"\\tPreparing training and test data...\");\n Console.WriteLine();\n\n using (Dataset train_data = torchvision.datasets.CIFAR100(datasetPath, true, download: true),\n test_data = torchvision.datasets.CIFAR100(datasetPath, false, download: true))\n {\n using var train = new DataLoader(train_data, _trainBatchSize, device: device, shuffle: true);\n using var test = new DataLoader(test_data, _testBatchSize, device: device, shuffle: false);\n\n using (var optimizer = torch.optim.Adam(model.parameters(), 0.001)) {\n\n Stopwatch totalSW = new Stopwatch();\n totalSW.Start();\n\n for (var epoch = 1; epoch <= epochs; epoch++) {\n\n Stopwatch epchSW = new Stopwatch();\n epchSW.Start();\n\n Train(model, optimizer, torch.nn.NLLLoss(), train, epoch, _trainBatchSize, train_data.Count);\n Test(model, torch.nn.NLLLoss(), test, test_data.Count);\n\n epchSW.Stop();\n Console.WriteLine($\"Elapsed time for this epoch: {epchSW.Elapsed.TotalSeconds} s.\");\n\n if (totalSW.Elapsed.TotalSeconds > timeout) break;\n }\n\n totalSW.Stop();\n Console.WriteLine($\"Elapsed training time: {totalSW.Elapsed} s.\");\n }\n }\n\n model.Dispose();\n }\n\n private static void Train(\n Module<torch.Tensor, torch.Tensor> model,\n torch.optim.Optimizer optimizer,\n Loss<torch.Tensor, torch.Tensor, torch.Tensor> loss,\n DataLoader dataLoader,\n int epoch,\n long batchSize,\n long size)\n {\n model.train();\n\n int batchId = 1;\n long total = 0;\n long correct = 0;\n\n Console.WriteLine($\"Epoch: {epoch}...\");\n\n using (var d = torch.NewDisposeScope()) {\n\n foreach (var data in dataLoader) {\n\n optimizer.zero_grad();\n\n var target = data[\"label\"];\n var prediction = model.call(data[\"data\"]);\n var lsm = log_softmax(prediction, 1);\n var output = loss.call(lsm, target);\n\n output.backward();\n\n optimizer.step();\n\n total += target.shape[0];\n\n var predicted = prediction.argmax(1);\n correct += predicted.eq(target).sum().ToInt64();\n\n if (batchId % _logInterval == 0 || total == size) {\n Console.WriteLine($\"\\rTrain: epoch {epoch} [{total} / {size}] Loss: {output.ToSingle().ToString(\"0.000000\")} | Accuracy: { ((float)correct / total).ToString(\"0.000000\") }\");\n }\n\n batchId++;\n\n d.DisposeEverything();\n }\n }\n }\n\n private static void Test(\n Module<torch.Tensor, torch.Tensor> model,\n Loss<torch.Tensor, torch.Tensor, torch.Tensor> loss,\n DataLoader dataLoader,\n long size)\n {\n model.eval();\n\n double testLoss = 0;\n long correct = 0;\n int batchCount = 0;\n\n using (var d = torch.NewDisposeScope()) {\n\n foreach (var data in dataLoader) {\n\n var target = data[\"label\"];\n var prediction = model.call(data[\"data\"]);\n var lsm = log_softmax(prediction, 1);\n var output = loss.call(lsm, target);\n\n testLoss += output.ToSingle();\n batchCount += 1;\n\n var predicted = prediction.argmax(1);\n correct += predicted.eq(target).sum().ToInt64();\n\n d.DisposeEverything();\n }\n }\n\n Console.WriteLine($\"\\rTest set: Average loss {(testLoss / batchCount).ToString(\"0.0000\")} | Accuracy {((float)correct / size).ToString(\"0.0000\")}\");\n }\n }\n}\n"},{"alpha_fraction":0.40059059858322144,"alphanum_fraction":0.4575561583042145,"avg_line_length":32.95050811767578,"blob_id":"865a8c99e3cd4d61d62338f84b027274b2942e00","content_id":"a27582bbba6821bbbb2b559ed8bd9604fed713fe","detected_licenses":["MIT"],"is_generated":false,"is_vendor":false,"language":"C#","length_bytes":26753,"license_type":"permissive","max_line_length":240,"num_lines":788,"path":"/test/TorchSharpTest/LinearAlgebra.cs","repo_name":"NiklasGustafsson/TorchSharp","src_encoding":"UTF-8","text":"// Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information.\nusing System;\nusing System.Linq;\nusing System.Runtime.InteropServices;\nusing Xunit;\nusing static TorchSharp.torch;\n\n#nullable enable\n\nnamespace TorchSharp\n{\n [Collection(\"Sequential\")]\n public class LinearAlgebra\n {\n [Fact]\n [TestOf(nameof(tensordot))]\n public void TestTensorDot()\n {\n var a = arange(60).reshape(3, 4, 5);\n var b = arange(24).reshape(4, 3, 2);\n var res = tensordot(a, b, new []{ 1L, 0L }, new []{ 0L, 1L });\n\n var expected = from_array(new long[,]\n {\n {4400, 4730},\n {4532, 4874},\n {4664, 5018},\n {4796, 5162},\n {4928, 5306}\n });\n\n Assert.True(allclose(res, expected));\n }\n\n [Fact]\n [TestOf(nameof(lu))]\n public void TestLUSolve()\n {\n var A = randn(2, 3, 3);\n var b = randn(2, 3, 1);\n\n {\n var (A_LU, pivots, infos) = lu(A);\n\n Assert.NotNull(A_LU);\n Assert.NotNull(pivots);\n Assert.Null(infos);\n\n Assert.Equal(new long[] { 2, 3, 3 }, A_LU.shape);\n Assert.Equal(new long[] { 2, 3 }, pivots.shape);\n\n var x = lu_solve(b, A_LU, pivots);\n Assert.Equal(new long[] { 2, 3, 1 }, x.shape);\n\n var y = norm(bmm(A, x) - b);\n Assert.Empty(y.shape);\n }\n\n {\n var (A_LU, pivots, infos) = lu(A, get_infos: true);\n\n Assert.NotNull(A_LU);\n Assert.NotNull(pivots);\n Assert.NotNull(infos);\n\n Assert.Equal(new long[] { 2, 3, 3 }, A_LU.shape);\n Assert.Equal(new long[] { 2, 3 }, pivots.shape);\n Assert.Equal(new long[] { 2 }, infos.shape);\n\n var x = lu_solve(b, A_LU, pivots);\n Assert.Equal(new long[] { 2, 3, 1 }, x.shape);\n\n var y = norm(bmm(A, x) - b);\n Assert.Empty(y.shape);\n }\n }\n\n [Fact]\n [TestOf(nameof(lu_unpack))]\n public void TestLUUnpack()\n {\n var A = randn(2, 3, 3);\n\n {\n var (A_LU, pivots, infos) = lu(A);\n\n Assert.NotNull(A_LU);\n Assert.NotNull(pivots);\n Assert.Null(infos);\n\n var (P, A_L, A_U) = lu_unpack(A_LU, pivots);\n\n Assert.NotNull(P);\n Assert.NotNull(A_L);\n Assert.NotNull(A_U);\n\n Assert.Equal(new long[] { 2, 3, 3 }, P.shape);\n Assert.Equal(new long[] { 2, 3, 3 }, A_L!.shape);\n Assert.Equal(new long[] { 2, 3, 3 }, A_U!.shape);\n }\n }\n\n [Fact]\n [TestOf(nameof(Tensor.mul))]\n public void TestMul()\n {\n var x = ones(new long[] { 100, 100 });\n\n var y = x.mul(0.5f.ToScalar());\n\n var ydata = y.data<float>();\n var xdata = x.data<float>();\n\n for (int i = 0; i < 100; i++) {\n for (int j = 0; j < 100; j++) {\n Assert.Equal(ydata[i + j], xdata[i + j] * 0.5f);\n }\n }\n }\n\n void TestMmGen(Device device)\n {\n {\n var x1 = ones(new long[] { 1, 2 }, device: device);\n var x2 = ones(new long[] { 2, 1 }, device: device);\n\n var y = x1.mm(x2).to(DeviceType.CPU);\n\n var ydata = y.data<float>();\n\n Assert.Equal(2.0f, ydata[0]);\n }\n //System.Runtime.InteropServices.ExternalException : addmm for CUDA tensors only supports floating - point types.Try converting the tensors with.float() at C:\\w\\b\\windows\\pytorch\\aten\\src\\THC / generic / THCTensorMathBlas.cu:453\n if (device.type == DeviceType.CPU) {\n var x1 = ones(new long[] { 1, 2 }, int64, device: device);\n var x2 = ones(new long[] { 2, 1 }, int64, device: device);\n\n var y = x1.mm(x2).to(DeviceType.CPU);\n\n var ydata = y.data<long>();\n\n Assert.Equal(2L, ydata[0]);\n }\n }\n\n [Fact]\n [TestOf(nameof(CPU))]\n public void TestMmCpu()\n {\n TestMmGen(CPU);\n }\n\n [Fact]\n [TestOf(nameof(CUDA))]\n public void TestMmCuda()\n {\n if (cuda.is_available()) {\n TestMmGen(CUDA);\n }\n }\n\n void TestMVGen(Device device)\n {\n {\n var mat1 = ones(new long[] { 4, 3 }, device: device);\n var vec1 = ones(new long[] { 3 }, device: device);\n\n var y = mat1.mv(vec1).to(DeviceType.CPU);\n\n Assert.Equal(4, y.shape[0]);\n }\n }\n\n void TestAddMVGen(Device device)\n {\n {\n var x1 = ones(new long[] { 4 }, device: device);\n var mat1 = ones(new long[] { 4, 3 }, device: device);\n var vec1 = ones(new long[] { 3 }, device: device);\n\n var y = x1.addmv(mat1, vec1).to(DeviceType.CPU);\n\n Assert.Equal(4, y.shape[0]);\n }\n }\n\n [Fact]\n [TestOf(nameof(CPU))]\n public void TestMVCpu()\n {\n TestMVGen(CPU);\n }\n\n [Fact]\n [TestOf(nameof(CUDA))]\n public void TestMVCuda()\n {\n if (cuda.is_available()) {\n TestMVGen(CUDA);\n }\n }\n\n [Fact]\n public void TestAddMVCpu()\n {\n TestAddMVGen(CPU);\n }\n\n [Fact]\n [TestOf(nameof(CUDA))]\n public void TestAddMVCuda()\n {\n if (cuda.is_available()) {\n TestAddMVGen(CUDA);\n }\n }\n\n void TestAddRGen(Device device)\n {\n {\n var x1 = ones(new long[] { 4, 3 }, device: device);\n var vec1 = ones(new long[] { 4 }, device: device);\n var vec2 = ones(new long[] { 3 }, device: device);\n\n var y = x1.addr(vec1, vec2).to(DeviceType.CPU);\n\n Assert.Equal(new long[] { 4, 3 }, y.shape);\n }\n }\n\n [Fact]\n [TestOf(nameof(CPU))]\n public void TestAddRCpu()\n {\n TestAddRGen(CPU);\n }\n\n [Fact]\n [TestOf(nameof(CUDA))]\n public void TestAddRCuda()\n {\n if (cuda.is_available()) {\n TestAddRGen(CUDA);\n }\n }\n\n [Fact]\n [TestOf(nameof(Tensor.vdot))]\n public void VdotTest()\n {\n var a = new float[] { 1.0f, 2.0f, 3.0f };\n var b = new float[] { 1.0f, 2.0f, 3.0f };\n var expected = tensor(a.Zip(b).Select(x => x.First * x.Second).Sum());\n var res = tensor(a).vdot(tensor(b));\n Assert.True(res.allclose(expected));\n }\n\n [Fact]\n [TestOf(nameof(Tensor.vander))]\n public void VanderTest()\n {\n var x = tensor(new int[] { 1, 2, 3, 5 });\n {\n var res = x.vander();\n var expected = tensor(new long[] { 1, 1, 1, 1, 8, 4, 2, 1, 27, 9, 3, 1, 125, 25, 5, 1 }, 4, 4);\n Assert.Equal(expected, res);\n }\n {\n var res = x.vander(3);\n var expected = tensor(new long[] { 1, 1, 1, 4, 2, 1, 9, 3, 1, 25, 5, 1 }, 4, 3);\n Assert.Equal(expected, res);\n }\n {\n var res = x.vander(3, true);\n var expected = tensor(new long[] { 1, 1, 1, 1, 2, 4, 1, 3, 9, 1, 5, 25 }, 4, 3);\n Assert.Equal(expected, res);\n }\n }\n\n [Fact]\n [TestOf(nameof(linalg.vander))]\n public void LinalgVanderTest()\n {\n var x = tensor(new int[] { 1, 2, 3, 5 });\n {\n var res = linalg.vander(x);\n var expected = tensor(new long[] { 1, 1, 1, 1, 1, 2, 4, 8, 1, 3, 9, 27, 1, 5, 25, 125 }, 4, 4);\n Assert.Equal(expected, res);\n }\n {\n var res = linalg.vander(x, 3);\n var expected = tensor(new long[] { 1, 1, 1, 1, 2, 4, 1, 3, 9, 1, 5, 25 }, 4, 3);\n Assert.Equal(expected, res);\n }\n }\n\n [Fact]\n [TestOf(nameof(linalg.cholesky))]\n public void CholeskyTest()\n {\n var a = randn(new long[] { 3, 2, 2 }, float64);\n a = a.matmul(a.swapdims(-2, -1)); // Worked this in to get it tested. Alias for 'transpose'\n var l = linalg.cholesky(a);\n\n Assert.True(a.allclose(l.matmul(l.swapaxes(-2, -1)))); // Worked this in to get it tested. Alias for 'transpose'\n }\n\n [Fact]\n [TestOf(nameof(linalg.cholesky_ex))]\n public void CholeskyExTest()\n {\n var a = randn(new long[] { 3, 2, 2 }, float64);\n a = a.matmul(a.swapdims(-2, -1)); // Worked this in to get it tested. Alias for 'transpose'\n var (l, info) = linalg.cholesky_ex(a);\n\n Assert.True(a.allclose(l.matmul(l.swapaxes(-2, -1))));\n }\n\n [Fact]\n [TestOf(nameof(linalg.inv))]\n public void InvTest()\n {\n var a = randn(new long[] { 3, 2, 2 }, float64);\n var l = linalg.inv(a);\n\n Assert.Equal(a.shape, l.shape);\n }\n\n [Fact]\n [TestOf(nameof(linalg.inv_ex))]\n public void InvExTest()\n {\n var a = randn(new long[] { 3, 2, 2 }, float64);\n var (l, info) = linalg.inv_ex(a);\n\n Assert.Equal(a.shape, l.shape);\n }\n\n [Fact]\n [TestOf(nameof(linalg.cond))]\n public void CondTestF64()\n {\n {\n var a = randn(new long[] { 3, 3, 3 }, float64);\n // The following mostly checks that the runtime interop doesn't blow up.\n _ = linalg.cond(a);\n _ = linalg.cond(a, \"fro\");\n _ = linalg.cond(a, \"nuc\");\n _ = linalg.cond(a, 1);\n _ = linalg.cond(a, -1);\n _ = linalg.cond(a, 2);\n _ = linalg.cond(a, -2);\n _ = linalg.cond(a, Double.PositiveInfinity);\n _ = linalg.cond(a, Double.NegativeInfinity);\n }\n }\n\n [Fact]\n [TestOf(nameof(linalg.cond))]\n public void CondTestCF64()\n {\n {\n var a = randn(new long[] { 3, 3, 3 }, complex128);\n // The following mostly checks that the runtime interop doesn't blow up.\n _ = linalg.cond(a);\n _ = linalg.cond(a, \"fro\");\n _ = linalg.cond(a, \"nuc\");\n _ = linalg.cond(a, 1);\n _ = linalg.cond(a, -1);\n _ = linalg.cond(a, 2);\n _ = linalg.cond(a, -2);\n _ = linalg.cond(a, Double.PositiveInfinity);\n _ = linalg.cond(a, Double.NegativeInfinity);\n }\n }\n\n [Fact]\n [TestOf(nameof(linalg.qr))]\n public void QRTest()\n {\n var a = randn(new long[] { 4, 25, 25 });\n\n var l = linalg.qr(a);\n\n Assert.Equal(a.shape, l.Q.shape);\n Assert.Equal(a.shape, l.R.shape);\n }\n\n [Fact]\n [TestOf(nameof(linalg.solve))]\n public void SolveTest()\n {\n var A = randn(3, 3);\n var b = randn(3);\n var x = linalg.solve(A, b);\n Assert.True(A.matmul(x).allclose(b, rtol: 1e-03, atol: 1e-06));\n }\n\n [Fact]\n [TestOf(nameof(linalg.solve_triangular))]\n public void SolveTriangularTest()\n {\n {\n var A = randn(3, 3).triu_();\n var b = randn(3, 4);\n var x = linalg.solve_triangular(A, b, upper: true);\n Assert.True(A.matmul(x).allclose(b, rtol: 1e-03, atol: 1e-06));\n }\n {\n var A = randn(2, 3, 3).tril_();\n var b = randn(2, 3, 4);\n var x = linalg.solve_triangular(A, b, upper: false);\n Assert.True(A.matmul(x).allclose(b, rtol: 1e-03, atol: 1e-06));\n }\n {\n var A = randn(2, 4, 4).tril_();\n var b = randn(2, 3, 4);\n var x = linalg.solve_triangular(A, b, upper: false, left: false);\n Assert.True(x.matmul(A).allclose(b, rtol: 1e-03, atol: 1e-06));\n }\n }\n\n [Fact]\n [TestOf(nameof(linalg.svd))]\n public void SVDTest()\n {\n var a = randn(new long[] { 4, 25, 15 });\n\n var l = linalg.svd(a);\n\n Assert.Equal(new long[] { 4, 25, 25 }, l.U.shape);\n Assert.Equal(new long[] { 4, 15 }, l.S.shape);\n Assert.Equal(new long[] { 4, 15, 15 }, l.Vh.shape);\n\n l = linalg.svd(a, fullMatrices: false);\n\n Assert.Equal(a.shape, l.U.shape);\n Assert.Equal(new long[] { 4, 15 }, l.S.shape);\n Assert.Equal(new long[] { 4, 15, 15 }, l.Vh.shape);\n }\n\n\n [Fact]\n [TestOf(nameof(linalg.svdvals))]\n public void SVDValsTest()\n {\n var a = tensor(new double[] { -1.3490, -0.1723, 0.7730,\n -1.6118, -0.3385, -0.6490,\n 0.0908, 2.0704, 0.5647,\n -0.6451, 0.1911, 0.7353,\n 0.5247, 0.5160, 0.5110}, 5, 3);\n\n var l = linalg.svdvals(a);\n Assert.True(l.allclose(tensor(new double[] { 2.5138929972840613, 2.1086555338402455, 1.1064930672223237 }), rtol: 1e-04, atol: 1e-07));\n }\n\n [Fact]\n [TestOf(nameof(linalg.lstsq))]\n public void LSTSQTest()\n {\n var a = randn(new long[] { 4, 25, 15 });\n var b = randn(new long[] { 4, 25, 10 });\n\n var l = linalg.lstsq(a, b);\n\n Assert.Equal(new long[] { 4, 15, 10 }, l.Solution.shape);\n Assert.Equal(0, l.Residuals.shape[0]);\n Assert.Equal(new long[] { 4 }, l.Rank.shape);\n Assert.Equal(new long[] { 4, 15, 10 }, l.Solution.shape);\n Assert.Equal(0, l.SingularValues.shape[0]);\n }\n\n [Fact]\n [TestOf(nameof(linalg.lu))]\n public void LUTest()\n {\n var A = randn(2, 3, 3);\n var A_factor = linalg.lu(A);\n // For right now, pretty much just checking that it's not blowing up.\n Assert.Multiple(\n () => Assert.NotNull(A_factor.P),\n () => Assert.NotNull(A_factor.L),\n () => Assert.NotNull(A_factor.U)\n );\n }\n\n [Fact]\n [TestOf(nameof(linalg.lu_factor))]\n public void LUFactorTest()\n {\n var A = randn(2, 3, 3);\n var A_factor = linalg.lu_factor(A);\n // For right now, pretty much just checking that it's not blowing up.\n Assert.Multiple(\n () => Assert.NotNull(A_factor.LU),\n () => Assert.NotNull(A_factor.Pivots)\n );\n }\n\n [Fact]\n [TestOf(nameof(linalg.ldl_factor))]\n public void LDLFactorTest()\n {\n var A = randn(2, 3, 3);\n var A_factor = linalg.ldl_factor(A);\n // For right now, pretty much just checking that it's not blowing up.\n Assert.Multiple(\n () => Assert.NotNull(A_factor.LU),\n () => Assert.NotNull(A_factor.Pivots)\n );\n }\n\n [Fact]\n [TestOf(nameof(linalg.ldl_factor))]\n public void LDLFactorExTest()\n {\n var A = randn(2, 3, 3);\n var A_factor = linalg.ldl_factor_ex(A);\n // For right now, pretty much just checking that it's not blowing up.\n Assert.Multiple(\n () => Assert.NotNull(A_factor.LU),\n () => Assert.NotNull(A_factor.Pivots),\n () => Assert.NotNull(A_factor.Info)\n );\n }\n\n [Fact]\n [TestOf(nameof(Tensor.matrix_power))]\n public void MatrixPowerTest()\n {\n var a = randn(new long[] { 25, 25 });\n var b = a.matrix_power(3);\n Assert.Equal(new long[] { 25, 25 }, b.shape);\n }\n\n [Fact]\n [TestOf(nameof(Tensor.matrix_exp))]\n public void MatrixExpTest1()\n {\n var a = randn(new long[] { 25, 25 });\n var b = a.matrix_exp();\n Assert.Equal(new long[] { 25, 25 }, b.shape);\n\n var c = matrix_exp(a);\n Assert.Equal(new long[] { 25, 25 }, c.shape);\n }\n\n [Fact]\n [TestOf(nameof(matrix_exp))]\n public void MatrixExpTest2()\n {\n var a = randn(new long[] { 16, 25, 25 });\n var b = a.matrix_exp();\n Assert.Equal(new long[] { 16, 25, 25 }, b.shape);\n var c = matrix_exp(a);\n Assert.Equal(new long[] { 16, 25, 25 }, c.shape);\n }\n\n [Fact]\n [TestOf(nameof(linalg.matrix_rank))]\n public void MatrixRankTest()\n {\n var mr1 = linalg.matrix_rank(randn(4, 3, 2));\n Assert.Equal(new long[] { 4 }, mr1.shape);\n\n var mr2 = linalg.matrix_rank(randn(2, 4, 3, 2));\n Assert.Equal(new long[] { 2, 4 }, mr2.shape);\n\n // Really just testing that it doesn't blow up in interop for the following lines:\n\n mr2 = linalg.matrix_rank(randn(2, 4, 3, 2), atol: 1.0);\n Assert.Equal(new long[] { 2, 4 }, mr2.shape);\n\n mr2 = linalg.matrix_rank(randn(2, 4, 3, 2), atol: 1.0, rtol: 0.0);\n Assert.Equal(new long[] { 2, 4 }, mr2.shape);\n\n mr2 = linalg.matrix_rank(randn(2, 4, 3, 2), atol: tensor(1.0));\n Assert.Equal(new long[] { 2, 4 }, mr2.shape);\n\n mr2 = linalg.matrix_rank(randn(2, 4, 3, 2), atol: tensor(1.0), rtol: tensor(0.0));\n Assert.Equal(new long[] { 2, 4 }, mr2.shape);\n }\n\n [Fact]\n [TestOf(nameof(linalg.multi_dot))]\n public void MultiDotTest()\n {\n var a = randn(new long[] { 25, 25 });\n var b = randn(new long[] { 25, 25 });\n var c = randn(new long[] { 25, 25 });\n var d = linalg.multi_dot(new Tensor[] { a, b, c });\n Assert.Equal(new long[] { 25, 25 }, d.shape);\n }\n\n [Fact]\n [TestOf(nameof(linalg.det))]\n public void DeterminantTest()\n {\n {\n var a = tensor(\n new float[] { 0.9478f, 0.9158f, -1.1295f,\n 0.9701f, 0.7346f, -1.8044f,\n -0.2337f, 0.0557f, 0.6929f }, 3, 3);\n var l = linalg.det(a);\n Assert.True(l.allclose(tensor(0.09335048f)));\n }\n {\n var a = tensor(\n new float[] { 0.9254f, -0.6213f, -0.5787f, 1.6843f, 0.3242f, -0.9665f,\n 0.4539f, -0.0887f, 1.1336f, -0.4025f, -0.7089f, 0.9032f }, 3, 2, 2);\n var l = linalg.det(a);\n Assert.True(l.allclose(tensor(new float[] { 1.19910491f, 0.4099378f, 0.7385352f })));\n }\n }\n\n [Fact]\n [TestOf(nameof(linalg.matrix_norm))]\n public void MatrixNormTest()\n {\n {\n var a = arange(9, float32).view(3, 3);\n\n var b = linalg.matrix_norm(a);\n var c = linalg.matrix_norm(a, ord: -1);\n\n Assert.Equal(14.282857f, b.item<float>());\n Assert.Equal(9.0f, c.item<float>());\n }\n }\n\n [Fact]\n [TestOf(nameof(linalg.vector_norm))]\n public void VectorNormTest()\n {\n {\n var a = tensor(\n new float[] { -4.0f, -3.0f, -2.0f, -1.0f, 0, 1.0f, 2.0f, 3.0f, 4.0f });\n\n var b = linalg.vector_norm(a, ord: 3.5);\n var c = linalg.vector_norm(a.view(3, 3), ord: 3.5);\n\n Assert.Equal(5.4344883f, b.item<float>());\n Assert.Equal(5.4344883f, c.item<float>());\n }\n }\n\n [Fact]\n [TestOf(nameof(linalg.pinv))]\n public void PinvTest()\n {\n var mr1 = linalg.pinv(randn(4, 3, 5));\n Assert.Equal(new long[] { 4, 5, 3 }, mr1.shape);\n\n // Really just testing that it doesn't blow up in interop for the following lines:\n\n mr1 = linalg.pinv(randn(4, 3, 5), atol: 1.0);\n Assert.Equal(new long[] { 4, 5, 3 }, mr1.shape);\n\n mr1 = linalg.pinv(randn(4, 3, 5), atol: 1.0, rtol: 0.0);\n Assert.Equal(new long[] { 4, 5, 3 }, mr1.shape);\n\n mr1 = linalg.pinv(randn(4, 3, 5), atol: tensor(1.0));\n Assert.Equal(new long[] { 4, 5, 3 }, mr1.shape);\n\n mr1 = linalg.pinv(randn(4, 3, 5), atol: tensor(1.0), rtol: tensor(0.0));\n Assert.Equal(new long[] { 4, 5, 3 }, mr1.shape);\n }\n\n [Fact]\n [TestOf(nameof(linalg.eig))]\n public void EigTest32()\n {\n {\n var a = tensor(\n new float[] { 2.8050f, -0.3850f, -0.3850f, 3.2376f, -1.0307f, -2.7457f, -2.7457f, -1.7517f, 1.7166f }, 3, 3);\n\n var expected = tensor(\n new (float, float)[] { (3.44288778f, 0.0f), (2.17609453f, 0.0f), (-2.128083f, 0.0f) });\n\n {\n var (values, vectors) = linalg.eig(a);\n Assert.NotNull(vectors);\n Assert.True(values.allclose(expected));\n }\n }\n }\n\n [Fact]\n [TestOf(nameof(linalg.eigvals))]\n public void EighvalsTest32()\n {\n var a = tensor(\n new float[] { 2.8050f, -0.3850f, -0.3850f, 3.2376f, -1.0307f, -2.7457f, -2.7457f, -1.7517f, 1.7166f }, 3, 3);\n var expected = tensor(\n new (float, float)[] { (3.44288778f, 0.0f), (2.17609453f, 0.0f), (-2.128083f, 0.0f) });\n var l = linalg.eigvals(a);\n Assert.True(l.allclose(expected));\n }\n\n // TODO: (Skip = \"Intermittently failing on MacOS or Linux (note: may now be working, we need to recheck)\")\n [FactIgnoreOnPlatform(\"Intermittently fails\", \"OSX\", \"Linux\")]\n [TestOf(nameof(linalg.eigvals))]\n public void EighvalsTest64()\n {\n var a = tensor(\n new double[] { 2.8050f, -0.3850f, -0.3850f, 3.2376f, -1.0307f, -2.7457f, -2.7457f, -1.7517f, 1.7166f }, 3, 3);\n var expected = tensor(\n new System.Numerics.Complex[] { new System.Numerics.Complex(3.44288778f, 0.0f), new System.Numerics.Complex(2.17609453f, 0.0f), new System.Numerics.Complex(-2.128083f, 0.0f) });\n var l = linalg.eigvals(a);\n Assert.True(l.allclose(expected));\n }\n\n // TODO: (Skip = \"Intermittently failing on MacOS or Linux (note: may now be working, we need to recheck)\")\n [FactIgnoreOnPlatform(\"Intermittently fails\", \"OSX\", \"Linux\")]\n [TestOf(nameof(linalg.eigvalsh))]\n public void EighvalshTest32()\n {\n var a = tensor(\n new float[] { 2.8050f, -0.3850f, -0.3850f, 3.2376f, -1.0307f, -2.7457f,\n -2.7457f, -1.7517f, 1.7166f, 2.2207f, 2.2207f, -2.0898f }, 3, 2, 2);\n var expected = tensor(\n new float[] { 2.5797f, 3.46290016f, -4.16046524f, 1.37806475f, -3.11126733f, 2.73806715f }, 3, 2);\n var l = linalg.eigvalsh(a);\n Assert.True(l.allclose(expected));\n }\n\n // TODO: (Skip = \"Intermittently failing on MacOS or Linux (note: may now be working, we need to recheck)\")\n [FactIgnoreOnPlatform(\"Intermittently fails\", \"OSX\", \"Linux\")]\n [TestOf(nameof(linalg.eigvalsh))]\n public void EighvalshTest64()\n {\n var a = tensor(\n new double[] { 2.8050, -0.3850, -0.3850, 3.2376, -1.0307, -2.7457,\n -2.7457, -1.7517, 1.7166, 2.2207, 2.2207, -2.0898 }, 3, 2, 2);\n var expected = tensor(\n new double[] { 2.5797, 3.46290016, -4.16046524, 1.37806475, -3.11126733, 2.73806715 }, 3, 2);\n var l = linalg.eigvalsh(a);\n Assert.True(l.allclose(expected));\n }\n\n [Fact]\n [TestOf(nameof(linalg.norm))]\n public void LinalgNormTest()\n {\n {\n var a = tensor(\n new float[] { -4.0f, -3.0f, -2.0f, -1.0f, 0.0f, 1.0f, 2.0f, 3.0f, 4.0f });\n var b = a.reshape(3, 3);\n\n Assert.True(linalg.norm(a).allclose(tensor(7.7460f)));\n Assert.True(linalg.norm(b).allclose(tensor(7.7460f)));\n Assert.True(linalg.norm(b, \"fro\").allclose(tensor(7.7460f)));\n\n Assert.True(linalg.norm(a, float.PositiveInfinity).allclose(tensor(4.0f)));\n Assert.True(linalg.norm(b, float.PositiveInfinity).allclose(tensor(9.0f)));\n Assert.True(linalg.norm(a, float.NegativeInfinity).allclose(tensor(0.0f)));\n Assert.True(linalg.norm(b, float.NegativeInfinity).allclose(tensor(2.0f)));\n\n Assert.True(linalg.norm(a, 1).allclose(tensor(20.0f)));\n Assert.True(linalg.norm(b, 1).allclose(tensor(7.0f)));\n Assert.True(linalg.norm(a, -1).allclose(tensor(0.0f)));\n Assert.True(linalg.norm(b, -1).allclose(tensor(6.0f)));\n\n Assert.True(linalg.norm(a, 2).allclose(tensor(7.7460f)));\n Assert.True(linalg.norm(b, 2).allclose(tensor(7.3485f)));\n Assert.True(linalg.norm(a, 3).allclose(tensor(5.8480f)));\n Assert.True(linalg.norm(a, -2).allclose(tensor(0.0f)));\n Assert.True(linalg.norm(a, -3).allclose(tensor(0.0f)));\n }\n }\n\n [Fact]\n public void TestTrilIndex()\n {\n var a = tril_indices(3, 3);\n var expected = new long[] { 0, 1, 1, 2, 2, 2, 0, 0, 1, 0, 1, 2 };\n Assert.Equal(expected, a.data<long>().ToArray());\n }\n\n [Fact]\n public void TestTriuIndex()\n {\n var a = triu_indices(3, 3);\n var expected = new long[] { 0, 0, 0, 1, 1, 2, 0, 1, 2, 1, 2, 2 };\n Assert.Equal(expected, a.data<long>().ToArray());\n }\n }\n}\n"},{"alpha_fraction":0.6116677522659302,"alphanum_fraction":0.6152304410934448,"avg_line_length":58.092105865478516,"blob_id":"2193a8c2faa8681546edda60ec36f27244284a7a","content_id":"0473ddb98051c9ff5a8cf567bcdaa941706cee72","detected_licenses":["MIT"],"is_generated":false,"is_vendor":false,"language":"C#","length_bytes":4499,"license_type":"permissive","max_line_length":360,"num_lines":76,"path":"/src/TorchSharp/NN/MultiheadAttention.cs","repo_name":"NiklasGustafsson/TorchSharp","src_encoding":"UTF-8","text":"// Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See License.txt in the project root for license information.\nusing System;\nusing static TorchSharp.PInvoke.NativeMethods;\nusing static TorchSharp.torch;\n\n#nullable enable\nnamespace TorchSharp\n{\n using Modules;\n\n namespace Modules\n {\n public sealed class MultiheadAttention : torch.nn.Module<Tensor, Tensor, Tensor, Tensor?, bool, Tensor?, Tuple<Tensor,Tensor>>\n {\n internal MultiheadAttention(IntPtr handle, IntPtr boxedHandle) : base(handle, boxedHandle) { }\n\n /// <summary>\n /// Applies the MultiheadAttention function element-wise.\n /// </summary>\n /// <param name=\"query\">map a query and a set of key-value pairs to an output. See “Attention Is All You Need” for more details</param>\n /// <param name=\"key\"></param>\n /// <param name=\"value\"></param>\n /// <param name=\"key_padding_mask\">if provided, specified padding elements in the key will be ignored by the attention. When given a binary mask and a value is True, the corresponding value on the attention layer will be ignored. When given a byte mask and a value is non-zero, the corresponding value on the attention layer will be ignored</param>\n /// <param name=\"need_weights\">output attn_output_weights</param>\n /// <param name=\"attn_mask\">2D or 3D mask that prevents attention to certain positions. A 2D mask will be broadcasted for all the batches while a 3D mask allows to specify a different mask for the entries of each batch</param>\n /// <returns>attn_output, attn_ouput_weights</returns>\n\n public override Tuple<Tensor,Tensor> forward(Tensor query, Tensor key, Tensor value, Tensor? key_padding_mask, bool need_weights, Tensor? attn_mask)\n {\n THSNN_MultiheadAttention_forward(handle,\n query.Handle,\n key.Handle,\n value.Handle,\n key_padding_mask?.Handle ?? IntPtr.Zero,\n need_weights,\n attn_mask?.Handle ?? IntPtr.Zero,\n out var res1,\n out var res2);\n if (res1 == IntPtr.Zero || (need_weights && res2 == IntPtr.Zero)) { torch.CheckForErrors(); }\n return Tuple.Create(new Tensor(res1), new Tensor(res2));\n }\n\n public new Tuple<Tensor, Tensor> call(Tensor query, Tensor key, Tensor value, Tensor? key_padding_mask = null, bool need_weights = true, Tensor? attn_mask = null)\n {\n return base.call(query, key, value, key_padding_mask, need_weights, attn_mask);\n }\n }\n }\n\n public static partial class torch\n {\n public static partial class nn\n {\n /// <summary>\n /// Allows the model to jointly attend to information from different representation subspaces (based on the paper “Attention Is All You Need”).\n /// </summary>\n /// <param name=\"embedded_dim\">total dimension of the model</param>\n /// <param name=\"num_heads\">parallel attention heads</param>\n /// <param name=\"dropout\">a Dropout layer on attn_output_weights. Default: 0.0</param>\n /// <param name=\"bias\">add bias as module parameter. Default: true</param>\n /// <param name=\"add_bias_kv\">add bias to the key and value sequences at dim=0</param>\n /// <param name=\"add_zero_attn\">add a new batch of zeros to the key and value sequences at dim=1</param>\n /// <param name=\"kdim\">total number of features in key</param>\n /// <param name=\"vdim\">total number of features in value</param>\n /// <returns></returns>\n public static MultiheadAttention MultiheadAttention(long embedded_dim, long num_heads, double dropout = 0.0, bool bias = true, bool add_bias_kv = false, bool add_zero_attn = false, long? kdim=null, long? vdim=null)\n {\n var _kdim = kdim.HasValue ? kdim.Value : embedded_dim;\n var _vdim = vdim.HasValue ? vdim.Value : embedded_dim;\n var res = THSNN_MultiheadAttention_ctor(embedded_dim, num_heads, dropout, bias, add_bias_kv, add_zero_attn, _kdim, _vdim, out var boxedHandle);\n if (res == IntPtr.Zero) { torch.CheckForErrors(); }\n return new MultiheadAttention(res, boxedHandle);\n }\n }\n }\n}\n"},{"alpha_fraction":0.45609214901924133,"alphanum_fraction":0.472366064786911,"avg_line_length":47.0355339050293,"blob_id":"3e495b8f05fa894f7f63b7bc470422c63adcedc7","content_id":"f06f92fdc865ab213ff13ac5daa838dc5e738ebe","detected_licenses":["MIT"],"is_generated":false,"is_vendor":false,"language":"C#","length_bytes":9463,"license_type":"permissive","max_line_length":125,"num_lines":197,"path":"/src/TorchVision/AutoAugment.cs","repo_name":"NiklasGustafsson/TorchSharp","src_encoding":"UTF-8","text":"using System;\nusing System.Collections.Generic;\nusing System.Linq;\nusing static TorchSharp.torch;\nusing F = TorchSharp.torchvision.transforms.functional;\n\n#nullable enable\nnamespace TorchSharp\n{\n public static partial class torchvision\n {\n internal abstract class AutoAugmentBase\n {\n protected enum opType {\n ShearX,\n ShearY,\n TranslateX,\n TranslateY,\n Rotate,\n Brightness,\n Color,\n Contrast,\n Sharpness,\n Posterize,\n Solarize,\n AutoContrast,\n Equalize,\n Invert,\n Identity\n }\n\n protected static Tensor apply_op(\n Tensor img,\n opType op_name,\n double magnitude,\n InterpolationMode interpolation,\n IList<float>? fill)\n {\n switch(op_name) {\n case opType.ShearX:\n return F.affine(\n img,\n angle: 0.0f,\n translate: new[] { 0, 0 },\n scale: 1.0f,\n shear: new[] { (float)((180.0 / Math.PI) * Math.Atan(magnitude)), 0.0f },\n interpolation: interpolation,\n fill: fill?.FirstOrDefault());\n case opType.ShearY:\n return F.affine(\n img,\n angle: 0.0f,\n translate: new[] { 0, 0 },\n scale: 1.0f,\n shear: new[] { 0.0f, (float)((180.0 / Math.PI) * Math.Atan(magnitude)) },\n interpolation: interpolation,\n fill: fill?.FirstOrDefault());\n case opType.TranslateX:\n return F.affine(\n img,\n angle: 0.0f,\n translate: new[] { (int)(magnitude), 0 },\n scale: 1.0f,\n interpolation: interpolation,\n shear: new[] { 0.0f, 0.0f },\n fill: fill?.FirstOrDefault());\n case opType.TranslateY:\n return F.affine(\n img,\n angle: 0.0f,\n translate: new[] { 0, (int)(magnitude) },\n scale: 1.0f,\n interpolation: interpolation,\n shear: new[] { 0.0f, 0.0f },\n fill: fill?.FirstOrDefault());\n case opType.Rotate:\n return F.rotate(img, (float)magnitude, interpolation, fill: fill);\n case opType.Brightness:\n return F.adjust_brightness(img, 1.0 + magnitude);\n case opType.Color:\n return F.adjust_saturation(img, 1.0 + magnitude);\n case opType.Contrast:\n return F.adjust_contrast(img, 1.0 + magnitude);\n case opType.Sharpness:\n return F.adjust_sharpness(img, 1.0 + magnitude);\n case opType.Posterize:\n return F.posterize(img, (int)magnitude);\n case opType.Solarize:\n return F.solarize(img, magnitude);\n case opType.AutoContrast:\n return F.autocontrast(img);\n case opType.Equalize:\n return F.equalize(img);\n case opType.Invert:\n return F.invert(img);\n case opType.Identity:\n return img; // Pass\n default:\n throw new ArgumentException($\"The provided operator {op_name} is not recognized.\");\n }\n }\n }\n\n /* Original implementation from:\n * https://pytorch.org/vision/main/_modules/torchvision/transforms/autoaugment.html#RandAugment */\n internal class RandAugment : AutoAugmentBase, ITransform\n {\n public RandAugment(\n int num_ops = 2,\n int magnitude = 9,\n int num_magnitude_bins = 31,\n InterpolationMode interpolation = InterpolationMode.Nearest,\n IList<float>? fill = null)\n {\n this.num_ops = num_ops;\n this.magnitude = magnitude;\n this.num_magnitude_bins = num_magnitude_bins;\n this.interpolation = interpolation;\n this.fill = fill;\n }\n\n public Tensor call(Tensor img)\n {\n using var _ = torch.NewDisposeScope();\n var cDim = img.Dimensions - 3;\n var height = img.shape[cDim + 1];\n var width = img.shape[cDim + 2];\n var op_meta = augmentation_space(num_magnitude_bins, (height, width));\n for (int i = 0; i < num_ops; ++i) {\n var op_index = torch.randint(0, op_meta.Count, 1).ToInt32();\n var op_name = op_meta.Keys.ToList()[op_index];\n var (magnitudes, signed) = op_meta[op_name];\n var magnitude = magnitudes.Dimensions > 0 ? magnitudes[this.magnitude].ToDouble() : 0.0;\n\n if (signed && torch.randint(0, 2, 1).ToBoolean())\n magnitude *= -1.0;\n\n img = apply_op(img, op_name, magnitude, interpolation, this.fill);\n }\n return img.MoveToOuterDisposeScope();\n }\n\n private Dictionary<opType, (Tensor, bool)> augmentation_space(int num_bins, (long height, long width) image_size)\n {\n return new Dictionary<opType, (Tensor, bool)> {\n { opType.Identity, (torch.tensor(0.0), false) },\n { opType.ShearX, (torch.linspace(0.0, 0.3, num_bins), true) },\n { opType.ShearY, (torch.linspace(0.0, 0.3, num_bins), true) },\n { opType.TranslateX, (torch.linspace(0.0, 150.0 / 331.0 * image_size.height, num_bins), true) },\n { opType.TranslateY, (torch.linspace(0.0, 150.0 / 331.0 * image_size.width, num_bins), true) },\n { opType.Rotate, (torch.linspace(0.0, 30.0, num_bins), true) },\n { opType.Brightness, (torch.linspace(0.0, 0.9, num_bins), true) },\n { opType.Color, (torch.linspace(0.0, 0.9, num_bins), true) },\n { opType.Contrast, (torch.linspace(0.0, 0.9, num_bins), true) },\n { opType.Sharpness, (torch.linspace(0.0, 0.9, num_bins), true) },\n { opType.Posterize, (8 - (torch.arange(num_bins) / ((num_bins - 1) / 4)).round().@int(), false)},\n { opType.Solarize, (torch.linspace(255.0, 0.0, num_bins), false) },\n { opType.AutoContrast, (torch.tensor(0.0), false) },\n { opType.Equalize, (torch.tensor(0.0), false) }\n };\n }\n\n private readonly int num_ops;\n private readonly int magnitude;\n private readonly int num_magnitude_bins;\n private readonly InterpolationMode interpolation;\n private readonly IList<float>? fill;\n }\n\n public static partial class transforms\n {\n /// <summary>\n /// RandAugment data augmentation method based on\n /// \"RandAugment: Practical automated data augmentation with a reduced search space\"\n /// https://arxiv.org/abs/1909.13719\n /// The image is expected to be a torch Tensor, it should be of type torch.uint8, and it is expected\n /// to have [..., 1 or 3, H, W] shape, where ... means an arbitrary number of leading dimensions.\n /// </summary>\n /// <param name=\"num_ops\">Number of augmentation transformations to apply sequentially. Default: 2</param>\n /// <param name=\"magnitude\">Magnitude for all the transformations. Default: 9</param>\n /// <param name=\"num_magnitude_bins\">The number of different magnitude values. Default: 31</param>\n /// <param name=\"interpolation\">Desired interpolation enum defined by\n /// torchvision.transforms.InterpolationMode. Default: InterpolationMode.NEAREST.</param>\n /// <param name=\"fill\">Pixel fill value for the area outside the transformed\n /// image. If given a number, the value is used for all bands respectively. Default: null</param>\n static public ITransform RandAugment(\n int num_ops = 2,\n int magnitude = 9,\n int num_magnitude_bins = 31,\n InterpolationMode interpolation = InterpolationMode.Nearest,\n IList<float>? fill = null)\n {\n return new RandAugment(num_ops, magnitude, num_magnitude_bins, interpolation, fill);\n }\n }\n }\n}\n"},{"alpha_fraction":0.5736501812934875,"alphanum_fraction":0.5752227902412415,"avg_line_length":42.35606002807617,"blob_id":"0f4ed65c2fb91ee2821da415f1600f77afa9d99e","content_id":"4fbb086a3cb0d8e783ebdbf8b4ffb6cc6f417e50","detected_licenses":["MIT"],"is_generated":false,"is_vendor":false,"language":"C#","length_bytes":5725,"license_type":"permissive","max_line_length":149,"num_lines":132,"path":"/src/TorchSharp/Distributions/Exponential.cs","repo_name":"NiklasGustafsson/TorchSharp","src_encoding":"UTF-8","text":"// Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information.\nusing System;\nusing System.Collections.Generic;\nusing static TorchSharp.torch;\n\nnamespace TorchSharp\n{\n using Modules;\n\n namespace Modules\n {\n /// <summary>\n /// An Exponential distribution parameterized by `rate`.\n /// </summary>\n public class Exponential : torch.distributions.ExponentialFamily\n {\n /// <summary>\n /// The mean of the distribution.\n /// </summary>\n public override Tensor mean => rate.reciprocal();\n\n public override Tensor mode => torch.zeros_like(rate);\n\n /// <summary>\n /// The variance of the distribution\n /// </summary>\n public override Tensor variance => rate.pow(2);\n\n /// <summary>\n /// The standard deviation of the distribution\n /// </summary>\n public override Tensor stddev => rate.reciprocal();\n\n\n /// <summary>\n /// Constructor\n /// </summary>\n /// <param name=\"rate\">rate = 1 / scale of the distribution (often referred to as 'β')</param>\n /// <param name=\"generator\">An optional random number generator object.</param>\n public Exponential(Tensor rate, torch.Generator generator = null) : base(generator)\n {\n var locScale = torch.broadcast_tensors(rate);\n this.batch_shape = rate.size();\n this.rate = locScale[0].DetachFromDisposeScope();\n }\n\n private Tensor rate;\n\n /// <summary>\n /// Generates a sample_shape shaped reparameterized sample or sample_shape shaped batch of reparameterized samples\n /// if the distribution parameters are batched.\n /// </summary>\n /// <param name=\"sample_shape\">The sample shape.</param>\n public override Tensor rsample(params long[] sample_shape)\n {\n var shape = ExtendedShape(sample_shape);\n return rate.new_empty(shape).exponential_(generator: generator) / rate;\n }\n\n /// <summary>\n /// Returns the log of the probability density/mass function evaluated at `value`.\n /// </summary>\n /// <param name=\"value\"></param>\n public override Tensor log_prob(Tensor value) => WrappedTensorDisposeScope(() => rate.log() - rate * value);\n\n /// <summary>\n /// Returns entropy of distribution, batched over batch_shape.\n /// </summary>\n /// <returns></returns>\n public override Tensor entropy() => WrappedTensorDisposeScope(() => 1 - rate.log());\n\n /// <summary>\n /// Returns the cumulative density/mass function evaluated at `value`.\n /// </summary>\n /// <param name=\"value\"></param>\n public override Tensor cdf(Tensor value) => WrappedTensorDisposeScope(() => 1 - torch.exp(-rate * value));\n\n /// <summary>\n /// Returns the inverse cumulative density/mass function evaluated at `value`.\n /// </summary>\n /// <param name=\"value\"></param>\n public override Tensor icdf(Tensor value) => WrappedTensorDisposeScope(() => -torch.log(1 - value) / rate);\n\n /// <summary>\n /// Returns a new distribution instance (or populates an existing instance provided by a derived class) with batch dimensions expanded to\n /// `batch_shape`. This method calls `torch.Tensor.expand()` on the distribution's parameters. As such, this does not allocate new\n /// memory for the expanded distribution instance.\n /// </summary>\n /// <param name=\"batch_shape\">Tthe desired expanded size.</param>\n /// <param name=\"instance\">new instance provided by subclasses that need to override `.expand`.</param>\n public override distributions.Distribution expand(Size batch_shape, distributions.Distribution instance = null)\n {\n if (instance != null && !(instance is Exponential))\n throw new ArgumentException(\"expand(): 'instance' must be a Exponential distribution\");\n\n var r = rate.expand(batch_shape);\n\n var newDistribution = ((instance == null) ? new Exponential(r, generator) : instance) as Exponential;\n\n newDistribution.batch_shape = batch_shape;\n if (newDistribution == instance) {\n newDistribution.rate = r;\n }\n return newDistribution;\n }\n\n protected override IList<Tensor> NaturalParams => new Tensor[] { -rate };\n\n protected override Tensor MeanCarrierMeasure => torch.tensor(0, dtype:rate.dtype, device: rate.device);\n\n protected override Tensor LogNormalizer(params Tensor[] parameters) => -torch.log(-parameters[0]);\n }\n\n }\n\n public static partial class torch\n {\n public static partial class distributions\n {\n /// <summary>\n /// Creates a Exponential distribution parameterized by `rate`.\n /// </summary>\n /// <param name=\"rate\">rate = 1 / scale of the distribution (often referred to as 'β')</param>\n /// <param name=\"generator\">An optional random number generator object.</param>\n /// <returns></returns>\n public static Exponential Exponential(Tensor rate, torch.Generator generator = null)\n {\n return new Exponential(rate, generator);\n }\n }\n }\n}\n"},{"alpha_fraction":0.4319537281990051,"alphanum_fraction":0.4340375065803528,"avg_line_length":40.053096771240234,"blob_id":"7ca88a51d0afb4d1c39b18d05f17be1c96005708","content_id":"48f58f31f3a8f1ae7db47195c872176e18157155","detected_licenses":["MIT"],"is_generated":false,"is_vendor":false,"language":"C#","length_bytes":27834,"license_type":"permissive","max_line_length":200,"num_lines":678,"path":"/src/TorchSharp/Distributions/Transforms.cs","repo_name":"NiklasGustafsson/TorchSharp","src_encoding":"UTF-8","text":"// Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information.\nusing System;\nusing System.Collections.Generic;\nusing System.Linq;\n\nnamespace TorchSharp\n{\n\n public static partial class torch\n {\n public static partial class distributions\n {\n public static partial class transforms\n {\n\n /// <summary>\n /// Abstract class for invertable transformations with computable log det jacobians.\n ///\n /// They are primarily used in torch.distributions.TransformedDistribution.\n /// </summary>\n /// <remarks>\n /// Derived classes should implement one or both of 'forward()' or 'inverse()'.\n /// Derived classes that set `bijective=true` should also implement 'log_abs_det_jacobian()'\n /// </remarks>\n public abstract class Transform\n {\n protected bool _bijective = false;\n\n protected constraints.Constraint _domain;\n\n protected constraints.Constraint _codomain;\n\n protected Transform _inv = null;\n\n public virtual int event_dim {\n get {\n if (_domain.event_dim == codomain.event_dim)\n return _domain.event_dim;\n throw new InvalidOperationException(\"Please use either .domain.event_dim or .codomain.event_dim\");\n }\n }\n\n public virtual Transform inv {\n get {\n Transform result = null;\n if (this._inv != null)\n result = _inv;\n if (result == null) {\n result = new _InverseTransform(this);\n _inv = result;\n }\n return result;\n }\n }\n\n public virtual constraints.Constraint domain {\n get {\n return _domain;\n }\n }\n\n public virtual constraints.Constraint codomain {\n get {\n return _codomain;\n }\n }\n\n public virtual bool bijective {\n get {\n return _bijective;\n }\n }\n\n public Tensor sign {\n get {\n return _sign();\n }\n }\n\n protected internal virtual Tensor _sign()\n {\n throw new NotImplementedException();\n }\n\n protected internal abstract Tensor _call(Tensor x);\n\n protected internal abstract Tensor _inverse(Tensor y);\n\n protected internal virtual Tensor log_abs_det_jacobian(Tensor x, Tensor y)\n {\n throw new NotImplementedException();\n }\n\n public Tensor forward(Tensor x) => this._call(x);\n\n public virtual long[] forward_shape(long[] shape) => shape;\n\n public virtual long[] inverse_shape(long[] shape) => shape;\n\n protected internal static Tensor _sum_rightmost(Tensor value, int dim)\n {\n if (dim == 0) return value;\n var required_shape = new long[value.shape.Length - dim + 1];\n var i = 0;\n for (; i < value.shape.Length - dim; i++) required_shape[i] = value.shape[i];\n required_shape[i] = -1;\n return value.reshape(required_shape).sum(-1);\n }\n }\n\n\n internal class _InverseTransform : Transform\n {\n public _InverseTransform(torch.distributions.transforms.Transform transform)\n {\n this._inv = transform;\n }\n\n public override constraints.Constraint domain {\n get {\n return _inv.domain;\n }\n }\n\n public override constraints.Constraint codomain {\n get {\n return _inv.codomain;\n }\n }\n\n public override bool bijective {\n get {\n return _inv.bijective;\n }\n }\n\n public override int event_dim => base.event_dim;\n\n public override Transform inv => base.inv;\n\n protected internal override Tensor log_abs_det_jacobian(Tensor x, Tensor y)\n {\n return -_inv.log_abs_det_jacobian(y, x);\n }\n\n protected internal override Tensor _call(Tensor x)\n {\n return _inv._inverse(x);\n }\n\n protected internal override Tensor _inverse(Tensor y)\n {\n return _inv._call(y);\n }\n\n protected internal override Tensor _sign()\n {\n return _inv.sign;\n }\n\n public override long[] forward_shape(long[] shape)\n {\n return _inv.forward_shape(shape);\n }\n\n public override long[] inverse_shape(long[] shape)\n {\n return _inv.inverse_shape(shape);\n }\n }\n\n public class ComposeTransform : Transform\n {\n public ComposeTransform(IEnumerable<Transform> parts, int cache_size = 0)\n {\n if (parts == null) throw new ArgumentNullException(\"parts cannot be null\");\n\n _parts = parts.ToArray();\n _reverse_parts = parts.Reverse().ToArray();\n }\n\n private Transform[] _parts;\n private Transform[] _reverse_parts;\n\n public override int event_dim => base.event_dim;\n\n public override Transform inv {\n get {\n Transform i = _inv;\n\n if (i == null) {\n i = new ComposeTransform(_reverse_parts.Select(p => p.inv));\n _inv = i;\n }\n return _inv;\n }\n }\n\n public override constraints.Constraint domain {\n get {\n if (_parts == null) return constraints.real;\n\n var cnt = _parts.Length;\n var d = _parts[0].domain;\n\n var ed = _parts[cnt - 1].codomain.event_dim;\n foreach (var part in _reverse_parts) {\n ed += part.domain.event_dim - part.codomain.event_dim;\n ed = ed < part.domain.event_dim ? part.domain.event_dim : ed;\n }\n if (ed > d.event_dim) {\n d = constraints.independent(domain, ed - domain.event_dim);\n }\n return d;\n }\n }\n\n public override constraints.Constraint codomain {\n get {\n if (_parts == null) return constraints.real;\n\n var cnt = _parts.Length;\n var cod = _parts[cnt - 1].domain;\n\n var ed = _parts[0].domain.event_dim;\n foreach (var part in _parts) {\n ed += part.codomain.event_dim - part.domain.event_dim;\n ed = ed < part.codomain.event_dim ? part.codomain.event_dim : ed;\n }\n if (ed > cod.event_dim) {\n cod = constraints.independent(codomain, ed - codomain.event_dim);\n }\n return cod;\n }\n }\n\n public override bool bijective => _parts.All(p => p.bijective);\n\n protected internal override Tensor _sign()\n {\n Tensor s = 1;\n foreach (var p in _parts) s *= p.sign;\n return s;\n }\n\n protected internal override Tensor _call(Tensor x)\n {\n using var _ = torch.NewDisposeScope();\n foreach (var p in _parts) {\n x = p._call(x);\n }\n return x.MoveToOuterDisposeScope();\n }\n\n protected internal override Tensor _inverse(Tensor y)\n {\n throw new NotImplementedException();\n }\n\n protected internal override Tensor log_abs_det_jacobian(Tensor x, Tensor y)\n {\n if (_parts == null) {\n return torch.zeros_like(x);\n }\n\n using var _ = torch.NewDisposeScope();\n\n var xs = new List<Tensor>();\n xs.Add(x);\n for (int i = 0; i < _parts.Length-1; i++) {\n var part = _parts[i];\n xs.Add(part._call(xs[i]));\n }\n xs.Add(y);\n\n var terms = new List<Tensor>();\n var event_dim = domain.event_dim;\n\n for (int i = 0; i < _parts.Length - 1; i++) {\n var part = _parts[i];\n var x1 = xs[i];\n var y1 = xs[i + 1];\n\n terms.Add(_sum_rightmost(part.log_abs_det_jacobian(x1, y), event_dim - part.domain.event_dim));\n event_dim += part.codomain.event_dim - part.domain.event_dim;\n }\n\n Tensor result = terms[0];\n for (var i = 1; i < terms.Count; i++) {\n result = result + terms[i];\n }\n return result.MoveToOuterDisposeScope();\n }\n\n public override long[] forward_shape(long[] shape)\n {\n foreach (var p in _parts) {\n shape = p.forward_shape(shape);\n }\n return shape;\n }\n\n public override long[] inverse_shape(long[] shape)\n {\n foreach (var p in _reverse_parts) {\n shape = p.forward_shape(shape);\n }\n return shape;\n }\n }\n\n public class IndepdenentTransform : Transform\n {\n private Transform base_transform;\n private int reinterpreted_batch_dims;\n\n public IndepdenentTransform(Transform base_transform, int reinterpreted_batch_dims)\n {\n this.base_transform = base_transform;\n this.reinterpreted_batch_dims = reinterpreted_batch_dims;\n }\n\n public override int event_dim => base.event_dim;\n\n public override Transform inv => base.inv;\n\n public override constraints.Constraint domain => constraints.independent(base_transform.domain, reinterpreted_batch_dims);\n\n public override constraints.Constraint codomain => constraints.independent(base_transform.codomain, reinterpreted_batch_dims);\n\n public override bool bijective => base_transform.bijective;\n\n public override long[] forward_shape(long[] shape)\n {\n return base_transform.forward_shape(shape);\n }\n\n public override long[] inverse_shape(long[] shape)\n {\n return base_transform.inverse_shape(shape);\n }\n\n protected internal override Tensor _sign() => base_transform._sign();\n\n protected internal override Tensor log_abs_det_jacobian(Tensor x, Tensor y)\n {\n using var _ = torch.NewDisposeScope();\n var result = base_transform.log_abs_det_jacobian(x, y);\n result = _sum_rightmost(result, reinterpreted_batch_dims);\n return result.MoveToOuterDisposeScope();\n }\n\n protected internal override Tensor _call(Tensor x)\n {\n if (x.dim() < domain.event_dim)\n throw new ArgumentException(\"Too few dimensions on input\");\n return base_transform._call(x);\n }\n\n protected internal override Tensor _inverse(Tensor y)\n {\n if (y.dim() < codomain.event_dim)\n throw new ArgumentException(\"Too few dimensions on input\");\n return base_transform._inverse(y);\n }\n }\n\n public class ReshapeTransform : Transform\n {\n private Size in_shape;\n private Size out_shape;\n\n public ReshapeTransform(long[] in_shape, long[] out_shape)\n {\n this.in_shape = in_shape;\n this.out_shape = out_shape;\n if (this.in_shape.numel() != this.out_shape.numel())\n throw new ArgumentException(\"in_shape, out_shape have different numbers of elements\");\n\n }\n public override bool bijective => true;\n\n public override constraints.Constraint domain => constraints.independent(constraints.real, in_shape.Length);\n\n public override constraints.Constraint codomain => constraints.independent(constraints.real, out_shape.Length);\n\n public override long[] forward_shape(long[] shape)\n {\n return base.forward_shape(shape);\n }\n\n public override long[] inverse_shape(long[] shape)\n {\n return base.inverse_shape(shape);\n }\n\n protected internal override Tensor log_abs_det_jacobian(Tensor x, Tensor y)\n {\n long inLen = x.shape.Length - (x.dim() - in_shape.Length);\n var batch_shape = new long[inLen];\n for (var i = 0; i < inLen; i++) {\n batch_shape[i] = x.shape[i];\n }\n return x.new_zeros(batch_shape);\n }\n\n protected internal override Tensor _call(Tensor x)\n {\n long inLen = x.shape.Length - (x.dim() - in_shape.Length);\n long otLen = out_shape.Length;\n var batch_shape = new long[inLen+otLen];\n for (var i = 0; i < inLen; i++) {\n batch_shape[i] = x.shape[i];\n }\n for (var i = 0; i < otLen; i++) {\n batch_shape[i+inLen] = out_shape[i];\n }\n return x.reshape(batch_shape);\n }\n\n protected internal override Tensor _inverse(Tensor y)\n {\n long otLen = y.shape.Length - (y.dim() - out_shape.Length);\n long inLen = in_shape.Length;\n var batch_shape = new long[inLen + otLen];\n for (var i = 0; i < otLen; i++) {\n batch_shape[i] = y.shape[i];\n }\n for (var i = 0; i < inLen; i++) {\n batch_shape[i + otLen] = in_shape[i];\n }\n return y.reshape(batch_shape);\n }\n }\n\n public class ExpTransform : Transform\n {\n public override constraints.Constraint domain => constraints.real;\n\n public override constraints.Constraint codomain => constraints.positive;\n\n public override bool bijective => true;\n\n protected internal override Tensor _sign() => 1;\n\n protected internal override Tensor log_abs_det_jacobian(Tensor x, Tensor y) => x;\n protected internal override Tensor _call(Tensor x) => x.exp();\n\n protected internal override Tensor _inverse(Tensor y) => y.log();\n }\n\n public class LogTransform : Transform\n {\n public override constraints.Constraint domain => constraints.positive;\n\n public override constraints.Constraint codomain => constraints.real;\n\n public override bool bijective => true;\n\n protected internal override Tensor _sign() => 1;\n\n protected internal override Tensor log_abs_det_jacobian(Tensor x, Tensor y) => -x;\n\n protected internal override Tensor _call(Tensor x) => x.log();\n\n protected internal override Tensor _inverse(Tensor y) => y.exp();\n }\n\n public class PowerTransform : Transform\n {\n private Tensor exponent;\n\n public PowerTransform(Tensor exponent)\n {\n this.exponent = exponent;\n }\n\n public override constraints.Constraint domain => constraints.positive;\n\n public override constraints.Constraint codomain => constraints.positive;\n\n public override bool bijective => true;\n\n protected internal override Tensor _sign() => 1;\n\n public override long[] forward_shape(long[] shape)\n {\n return torch.broadcast_shapes(shape, exponent.shape).Shape;\n }\n\n public override long[] inverse_shape(long[] shape)\n {\n return torch.broadcast_shapes(shape, exponent.shape).Shape;\n }\n\n protected internal override Tensor log_abs_det_jacobian(Tensor x, Tensor y)\n {\n return torch.WrappedTensorDisposeScope(() => (exponent * y / x).abs().log());\n }\n\n protected internal override Tensor _call(Tensor x)\n {\n return x.pow(exponent);\n }\n\n protected internal override Tensor _inverse(Tensor y)\n {\n return y.pow(1 / exponent);\n }\n }\n\n public class SigmoidTransform : Transform\n {\n public override constraints.Constraint domain => constraints.real;\n\n public override constraints.Constraint codomain => constraints.unit_interval;\n\n public override bool bijective => true;\n\n protected internal override Tensor _sign() => 1;\n\n protected internal override Tensor log_abs_det_jacobian(Tensor x, Tensor y) => -nn.functional.softplus(-x) - nn.functional.softplus(x);\n\n protected internal override Tensor _call(Tensor x)\n {\n var finfo = torch.finfo(x.dtype);\n return torch.WrappedTensorDisposeScope(() => torch.clamp(torch.sigmoid(x), min: finfo.tiny, max: 1 - finfo.eps));\n }\n\n protected internal override Tensor _inverse(Tensor y)\n {\n using var _ = torch.NewDisposeScope();\n var finfo = torch.finfo(y.dtype);\n y = y.clamp(min: finfo.tiny, max: 1 - finfo.eps);\n return (y.log() - (-y).log1p()).MoveToOuterDisposeScope();\n }\n }\n\n public class SoftplusTransform : Transform\n {\n public override constraints.Constraint domain => constraints.real;\n\n public override constraints.Constraint codomain => constraints.positive;\n\n public override bool bijective => true;\n\n protected internal override Tensor _sign() => 1;\n\n protected internal override Tensor log_abs_det_jacobian(Tensor x, Tensor y) => -nn.functional.softplus(-x);\n\n protected internal override Tensor _call(Tensor x) => nn.functional.softplus(-x);\n\n protected internal override Tensor _inverse(Tensor y) => (-y).expm1().neg().log() + y;\n }\n\n public class SoftmaxTransform : Transform\n {\n public override constraints.Constraint domain => constraints.real_vector;\n\n public override constraints.Constraint codomain => constraints.simplex;\n\n public override bool bijective => true;\n\n protected internal override Tensor _call(Tensor x)\n {\n var logprobs = x;\n var probs = (logprobs - logprobs.max(-1, true).values).exp();\n return probs / probs.sum(-1, true);\n }\n\n protected internal override Tensor _inverse(Tensor y)\n {\n return y.log();\n }\n }\n\n\n public class TanhTransform : Transform\n {\n public override constraints.Constraint domain => constraints.real;\n\n public override constraints.Constraint codomain => constraints.interval(-1.0, 1.0);\n\n public override bool bijective => true;\n\n protected internal override Tensor _sign() => 1;\n\n protected internal override Tensor log_abs_det_jacobian(Tensor x, Tensor y) => torch.WrappedTensorDisposeScope(() => 2.0 * (Math.Log(2.0) - x - -nn.functional.softplus(-2.0 * x)));\n\n protected internal override Tensor _call(Tensor x) => x.tanh();\n\n protected internal override Tensor _inverse(Tensor y) => y.atanh();\n }\n\n public class AbsTransform : Transform\n {\n public override constraints.Constraint domain => constraints.real;\n\n public override constraints.Constraint codomain => constraints.positive;\n\n protected internal override Tensor _call(Tensor x) => x.abs();\n\n protected internal override Tensor _inverse(Tensor y) => y;\n }\n\n public class AffineTransform : Transform\n {\n private Tensor loc;\n private Tensor scale;\n private int _event_dim;\n\n public AffineTransform(double loc, double scale, int event_dim = 0)\n {\n this._event_dim = event_dim;\n this.loc = loc;\n this.scale = scale;\n }\n\n public AffineTransform (Tensor loc, Tensor scale, int event_dim = 0)\n {\n this._event_dim = event_dim;\n this.loc = loc;\n this.scale = scale;\n }\n\n public override int event_dim => _event_dim;\n\n public override bool bijective => true;\n\n public override constraints.Constraint domain =>\n _event_dim == 0 ? constraints.real : constraints.independent(constraints.real, event_dim);\n\n public override constraints.Constraint codomain =>\n _event_dim == 0 ? constraints.real : constraints.independent(constraints.real, event_dim);\n\n public override long[] forward_shape(long[] shape)\n {\n return torch.broadcast_shapes(shape, loc.shape, scale.shape).Shape;\n }\n\n public override long[] inverse_shape(long[] shape)\n {\n return torch.broadcast_shapes(shape, loc.shape, scale.shape).Shape;\n }\n\n protected internal override Tensor log_abs_det_jacobian(Tensor x, Tensor y)\n {\n using var _ = torch.NewDisposeScope();\n var shape = x.shape;\n var scale = this.scale;\n var result = torch.abs(scale).log();\n\n if (_event_dim > 0) {\n var result_size = new long[result.shape.Length - _event_dim + 1];\n int i = 0;\n for (; i < result.shape.Length - _event_dim; i++) result_size[i] = result.shape[i];\n result_size[i] = -1;\n result = result.view(result_size).sum(-1);\n var nshape = new long[shape.Length];\n for (; i < shape.Length - _event_dim; i++) nshape[i] = shape[i];\n shape = nshape;\n }\n return result.expand(shape).MoveToOuterDisposeScope();\n }\n\n protected internal override Tensor _call(Tensor x) => torch.WrappedTensorDisposeScope(() => loc + scale * x);\n\n protected internal override Tensor _inverse(Tensor y) => torch.WrappedTensorDisposeScope(() => (y - loc) / scale);\n\n protected internal override Tensor _sign()\n {\n return scale.sign();\n }\n }\n }\n }\n }\n}\n"},{"alpha_fraction":0.6935985088348389,"alphanum_fraction":0.7016780376434326,"avg_line_length":39.224998474121094,"blob_id":"f788fa7e0caaf70a7fa0398e2ae4b69a1eb8b438","content_id":"bc54870cd554a8893b69066c4bcce17356955df5","detected_licenses":["MIT"],"is_generated":false,"is_vendor":false,"language":"C#","length_bytes":1609,"license_type":"permissive","max_line_length":130,"num_lines":40,"path":"/src/TorchSharp/PInvoke/LibTorchSharp.THSData.cs","repo_name":"NiklasGustafsson/TorchSharp","src_encoding":"UTF-8","text":"// Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information.\n#nullable enable\nusing System;\nusing System.Runtime.InteropServices;\n\nnamespace TorchSharp.PInvoke\n{\n internal static partial class NativeMethods\n {\n#pragma warning disable CA2101\n [DllImport(\"LibTorchSharp\", CharSet = CharSet.Ansi, BestFitMapping = false, ThrowOnUnmappableChar = true)]\n internal static extern IntPtr THSData_loaderMNIST(\n [MarshalAs(UnmanagedType.LPStr)] string filename,\n long batchSize,\n [MarshalAs(UnmanagedType.U1)] bool isTrain);\n\n [DllImport(\"LibTorchSharp\", CharSet = CharSet.Ansi, BestFitMapping = false, ThrowOnUnmappableChar = true)]\n internal static extern IntPtr THSData_loaderCIFAR10(\n [MarshalAs(UnmanagedType.LPStr)] string path,\n long batchSize,\n [MarshalAs(UnmanagedType.U1)] bool isTrain);\n\n [DllImport(\"LibTorchSharp\")]\n internal static extern IntPtr THSData_current(IntPtr iterator, IntPtr data, IntPtr target);\n\n [DllImport(\"LibTorchSharp\")]\n [return: MarshalAs(UnmanagedType.U1)]\n internal static extern bool THSData_moveNext(IntPtr iterator);\n\n [DllImport(\"LibTorchSharp\")]\n internal static extern long THSData_size(IntPtr iterator);\n\n [DllImport(\"LibTorchSharp\")]\n internal static extern void THSData_reset(IntPtr iterator);\n\n [DllImport(\"LibTorchSharp\")]\n internal static extern void THSData_dispose(IntPtr iterator);\n }\n#pragma warning restore CA2101\n}\n"},{"alpha_fraction":0.5785619020462036,"alphanum_fraction":0.5825566053390503,"avg_line_length":34.78571319580078,"blob_id":"edbc10770974a49626d6a0d4677e3d08ee68af0b","content_id":"a9ee0ebe6bf3317ca1e3e115f971a3e9b27841df","detected_licenses":["MIT"],"is_generated":false,"is_vendor":false,"language":"C#","length_bytes":1502,"license_type":"permissive","max_line_length":130,"num_lines":42,"path":"/src/TorchVision/AdjustSaturation.cs","repo_name":"NiklasGustafsson/TorchSharp","src_encoding":"UTF-8","text":"// Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information.\nusing System;\nusing static TorchSharp.torch;\n\nnamespace TorchSharp\n{\n public static partial class torchvision\n {\n internal class AdjustSaturation : ITransform\n {\n internal AdjustSaturation(double saturation_factor)\n {\n if (saturation_factor < 0.0)\n throw new ArgumentException($\"The saturation factor ({saturation_factor}) must be non-negative.\");\n this.saturation_factor = saturation_factor;\n }\n\n public Tensor call(Tensor img)\n {\n return transforms.functional.adjust_saturation(img, saturation_factor);\n }\n\n private double saturation_factor;\n }\n\n public static partial class transforms\n {\n /// <summary>\n /// Adjust the color saturation of an image.\n /// </summary>\n /// <param name=\"saturation_factor\">\n /// How much to adjust the saturation. 0 will give a black and white image, 1 will give the original image\n /// while 2 will enhance the saturation by a factor of 2.\n /// </param>\n /// <returns></returns>\n static public ITransform AdjustSaturation(double saturation_factor)\n {\n return new AdjustSaturation(saturation_factor);\n }\n }\n }\n}"},{"alpha_fraction":0.5086056590080261,"alphanum_fraction":0.5247212052345276,"avg_line_length":39.3984375,"blob_id":"38cc94d081a917e7ddbdb63ab623d68c1a7148fb","content_id":"7d12f5cc60032d2d00e14ac07ff3af4e0fa61875","detected_licenses":["MIT"],"is_generated":false,"is_vendor":false,"language":"C#","length_bytes":15513,"license_type":"permissive","max_line_length":149,"num_lines":384,"path":"/src/TorchAudio/Modules/WaveRNN.cs","repo_name":"NiklasGustafsson/TorchSharp","src_encoding":"UTF-8","text":"// Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information.\n\n// A number of implementation details in this file have been translated from the Python version of torchaudio,\n// largely located in the files found in this folder:\n//\n// https://github.com/pytorch/audio/blob/c15eee23964098f88ab0afe25a8d5cd9d728af54/torchaudio/models/wavernn.py\n//\n// The origin has the following copyright notice and license:\n//\n// https://github.com/pytorch/audio/blob/main/LICENSE\n//\n\nusing System;\nusing System.Collections.Generic;\nusing static TorchSharp.torch;\nusing F = TorchSharp.torch.nn.functional;\n\n#nullable enable\nnamespace TorchSharp.Modules\n{\n /// <summary>\n /// This class is used to represent a WaveRNN module.\n /// </summary>\n public class WaveRNN : nn.Module<Tensor, Tensor, Tensor>\n {\n private readonly int _pad;\n public readonly nn.Module<Tensor, Tensor> fc;\n public readonly nn.Module<Tensor, Tensor> fc1;\n public readonly nn.Module<Tensor, Tensor> fc2;\n public readonly nn.Module<Tensor, Tensor> fc3;\n public readonly int hop_length;\n public readonly int kernel_size;\n public readonly int n_aux;\n public readonly int n_bits;\n public readonly int n_classes;\n public readonly int n_rnn;\n public readonly nn.Module<Tensor, Tensor> relu1;\n public readonly nn.Module<Tensor, Tensor> relu2;\n public readonly GRU rnn1;\n public readonly GRU rnn2;\n internal readonly UpsampleNetwork upsample;\n\n protected override void Dispose(bool disposing)\n {\n if (disposing) {\n fc.Dispose();\n fc1.Dispose();\n fc2.Dispose();\n fc3.Dispose();\n relu1.Dispose();\n relu2.Dispose();\n rnn1.Dispose();\n rnn2.Dispose();\n upsample.Dispose();\n }\n base.Dispose(disposing);\n }\n\n internal WaveRNN(\n string name,\n long[] upsample_scales,\n int n_classes,\n int hop_length,\n int n_res_block = 10,\n int n_rnn = 512,\n int n_fc = 512,\n int kernel_size = 5,\n int n_freq = 128,\n int n_hidden = 128,\n int n_output = 128) : base(name)\n {\n this.kernel_size = kernel_size;\n this._pad = (kernel_size % 2 == 1 ? kernel_size - 1 : kernel_size) / 2;\n this.n_rnn = n_rnn;\n this.n_aux = n_output / 4;\n this.hop_length = hop_length;\n this.n_classes = n_classes;\n this.n_bits = (int)(Math.Log(this.n_classes) / Math.Log(2) + 0.5);\n\n long total_scale = 1;\n foreach (var upsample_scale in upsample_scales) {\n total_scale *= upsample_scale;\n }\n if (total_scale != this.hop_length) {\n throw new ArgumentException($\"Expected: total_scale == hop_length, but found {total_scale} != {hop_length}\");\n }\n\n this.upsample = new UpsampleNetwork(\"upsamplenetwork\", upsample_scales, n_res_block, n_freq, n_hidden, n_output, kernel_size);\n this.fc = nn.Linear(n_freq + this.n_aux + 1, n_rnn);\n\n this.rnn1 = nn.GRU(n_rnn, n_rnn, batchFirst: true);\n this.rnn2 = nn.GRU(n_rnn + this.n_aux, n_rnn, batchFirst: true);\n\n this.relu1 = nn.ReLU(inplace: true);\n this.relu2 = nn.ReLU(inplace: true);\n\n this.fc1 = nn.Linear(n_rnn + this.n_aux, n_fc);\n this.fc2 = nn.Linear(n_fc + this.n_aux, n_fc);\n this.fc3 = nn.Linear(n_fc, this.n_classes);\n\n this.RegisterComponents();\n }\n\n /// <summary>\n /// Pass the input through the WaveRNN model.\n /// </summary>\n /// <param name=\"waveform\">The input waveform to the WaveRNN layer</param>\n /// <param name=\"specgram\">The input spectrogram to the WaveRNN layer</param>\n /// <returns></returns>\n /// <exception cref=\"ArgumentException\"></exception>\n public override Tensor forward(Tensor waveform, Tensor specgram)\n {\n if (waveform.size(1) != 1) {\n throw new ArgumentException(\"Require the input channel of waveform is 1\");\n }\n if (specgram.size(1) != 1) {\n throw new ArgumentException(\"Require the input channel of specgram is 1\");\n }\n // remove channel dimension until the end\n waveform = waveform.squeeze(1);\n specgram = specgram.squeeze(1);\n\n var batch_size = waveform.size(0);\n var h1 = torch.zeros(1, batch_size, this.n_rnn, dtype: waveform.dtype, device: waveform.device);\n var h2 = torch.zeros(1, batch_size, this.n_rnn, dtype: waveform.dtype, device: waveform.device);\n // output of upsample:\n // specgram: (n_batch, n_freq, (n_time - kernel_size + 1) * total_scale)\n // aux: (n_batch, n_output, (n_time - kernel_size + 1) * total_scale)\n Tensor aux;\n (specgram, aux) = this.upsample.call(specgram);\n specgram = specgram.transpose(1, 2);\n aux = aux.transpose(1, 2);\n\n var aux_idx = new long[5];\n for (int i = 0; i < aux_idx.Length; i++) {\n aux_idx[i] = this.n_aux * i;\n }\n var a1 = aux[TensorIndex.Colon, TensorIndex.Colon, TensorIndex.Slice(aux_idx[0], aux_idx[1])];\n var a2 = aux[TensorIndex.Colon, TensorIndex.Colon, TensorIndex.Slice(aux_idx[1], aux_idx[2])];\n var a3 = aux[TensorIndex.Colon, TensorIndex.Colon, TensorIndex.Slice(aux_idx[2], aux_idx[3])];\n var a4 = aux[TensorIndex.Colon, TensorIndex.Colon, TensorIndex.Slice(aux_idx[3], aux_idx[4])];\n\n var x = torch.cat(new Tensor[] { waveform.unsqueeze(-1), specgram, a1 }, dim: -1);\n x = this.fc.call(x);\n var res = x;\n (x, _) = this.rnn1.call(x, h1);\n\n x = x + res;\n res = x;\n x = torch.cat(new Tensor[] { x, a2 }, dim: -1);\n (x, _) = this.rnn2.call(x, h2);\n\n x = x + res;\n x = torch.cat(new Tensor[] { x, a3 }, dim: -1);\n x = this.fc1.call(x);\n x = this.relu1.call(x);\n\n x = torch.cat(new Tensor[] { x, a4 }, dim: -1);\n x = this.fc2.call(x);\n x = this.relu2.call(x);\n x = this.fc3.call(x);\n\n // bring back channel dimension\n return x.unsqueeze(1);\n }\n\n /// <summary>\n /// Inference method of WaveRNN.\n /// </summary>\n /// <param name=\"specgram\">Batch of spectrograms.</param>\n /// <param name=\"lengths\">Indicates the valid length of each audio in the batch.</param>\n /// <returns>The inferred waveform and the valid length in time axis of the output Tensor.</returns>\n public virtual (Tensor, Tensor?) infer(Tensor specgram, Tensor? lengths = null)\n {\n var device = specgram.device;\n var dtype = specgram.dtype;\n\n specgram = torch.nn.functional.pad(specgram, (this._pad, this._pad));\n Tensor aux;\n (specgram, aux) = this.upsample.call(specgram);\n if (lengths is not null) {\n lengths = lengths * this.upsample.total_scale;\n }\n\n var output = new List<Tensor>();\n long b_size = specgram.size(0);\n long seq_len = specgram.size(2);\n\n var h1 = torch.zeros(new long[] { 1, b_size, this.n_rnn }, device: device, dtype: dtype);\n var h2 = torch.zeros(new long[] { 1, b_size, this.n_rnn }, device: device, dtype: dtype);\n var x = torch.zeros(new long[] { b_size, 1 }, device: device, dtype: dtype);\n\n var aux_split = new Tensor[4];\n for (int i = 0; i < 4; i++) {\n aux_split[i] = aux[TensorIndex.Colon, TensorIndex.Slice(this.n_aux * i, this.n_aux * (i + 1)), TensorIndex.Colon];\n }\n\n for (int i = 0; i < seq_len; i++) {\n\n var m_t = specgram[TensorIndex.Colon, TensorIndex.Colon, i];\n\n var a1_t = aux_split[0][TensorIndex.Colon, TensorIndex.Colon, i];\n var a2_t = aux_split[1][TensorIndex.Colon, TensorIndex.Colon, i];\n var a3_t = aux_split[2][TensorIndex.Colon, TensorIndex.Colon, i];\n var a4_t = aux_split[3][TensorIndex.Colon, TensorIndex.Colon, i];\n\n x = torch.cat(new Tensor[] { x, m_t, a1_t }, dim: 1);\n x = this.fc.call(x);\n (_, h1) = this.rnn1.call(x.unsqueeze(1), h1);\n\n x = x + h1[0];\n var inp = torch.cat(new Tensor[] { x, a2_t }, dim: 1);\n (_, h2) = this.rnn2.call(inp.unsqueeze(1), h2);\n\n x = x + h2[0];\n x = torch.cat(new Tensor[] { x, a3_t }, dim: 1);\n x = F.relu(this.fc1.call(x));\n\n x = torch.cat(new Tensor[] { x, a4_t }, dim: 1);\n x = F.relu(this.fc2.call(x));\n\n var logits = this.fc3.call(x);\n\n var posterior = F.softmax(logits, dim: 1);\n\n x = torch.multinomial(posterior, 1).@float();\n // Transform label [0, 2 ** n_bits - 1] to waveform [-1, 1]\n\n x = 2 * x / ((1 << this.n_bits) - 1.0) - 1.0;\n\n output.Add(x);\n }\n return (torch.stack(output).permute(1, 2, 0), lengths);\n }\n\n private class ResBlock : nn.Module<Tensor, Tensor>\n {\n public nn.Module<Tensor, Tensor> resblock_model;\n\n protected override void Dispose(bool disposing)\n {\n if (disposing) {\n resblock_model.Dispose();\n }\n base.Dispose(disposing);\n }\n\n public ResBlock(string name, int n_freq = 128) : base(name)\n {\n this.resblock_model = nn.Sequential(\n nn.Conv1d(inputChannel: n_freq, outputChannel: n_freq, kernelSize: 1, bias: false),\n nn.BatchNorm1d(n_freq),\n nn.ReLU(inplace: true),\n nn.Conv1d(inputChannel: n_freq, outputChannel: n_freq, kernelSize: 1, bias: false),\n nn.BatchNorm1d(n_freq));\n RegisterComponents();\n }\n\n public override Tensor forward(Tensor specgram)\n {\n return this.resblock_model.call(specgram) + specgram;\n }\n }\n\n internal class MelResNet : nn.Module<Tensor, Tensor>\n {\n public readonly nn.Module<Tensor, Tensor> melresnet_model;\n\n protected override void Dispose(bool disposing)\n {\n if (disposing) {\n melresnet_model.Dispose();\n }\n base.Dispose(disposing);\n }\n\n public MelResNet(\n string name,\n int n_res_block = 10,\n int n_freq = 128,\n int n_hidden = 128,\n int n_output = 128,\n int kernel_size = 5) : base(name)\n {\n var modules = new List<nn.Module<Tensor, Tensor>>();\n modules.Add(nn.Conv1d(inputChannel: n_freq, outputChannel: n_hidden, kernelSize: kernel_size, bias: false));\n modules.Add(nn.BatchNorm1d(n_hidden));\n modules.Add(nn.ReLU(inplace: true));\n for (int i = 0; i < n_res_block; i++) {\n modules.Add(new ResBlock(\"resblock\", n_hidden));\n }\n modules.Add(nn.Conv1d(inputChannel: n_hidden, outputChannel: n_output, kernelSize: 1));\n this.melresnet_model = nn.Sequential(modules);\n RegisterComponents();\n }\n\n public override Tensor forward(Tensor specgram)\n {\n return this.melresnet_model.call(specgram);\n }\n }\n\n public class Stretch2d : nn.Module<Tensor, Tensor>\n {\n public long freq_scale;\n public long time_scale;\n\n public Stretch2d(string name, long time_scale, long freq_scale) : base(name)\n {\n this.freq_scale = freq_scale;\n this.time_scale = time_scale;\n this.RegisterComponents();\n }\n\n public override Tensor forward(Tensor specgram)\n {\n return specgram.repeat_interleave(this.freq_scale, -2).repeat_interleave(this.time_scale, -1);\n }\n }\n\n internal class UpsampleNetwork : nn.Module<Tensor, (Tensor,Tensor)>\n {\n public readonly long indent;\n public readonly MelResNet resnet;\n public readonly Stretch2d resnet_stretch;\n public readonly long total_scale;\n public readonly nn.Module<Tensor, Tensor> upsample_layers;\n\n protected override void Dispose(bool disposing)\n {\n if (disposing) {\n resnet.Dispose();\n resnet_stretch.Dispose();\n upsample_layers .Dispose();\n }\n base.Dispose(disposing);\n }\n\n public UpsampleNetwork(\n string name,\n long[] upsample_scales,\n int n_res_block = 10,\n int n_freq = 128,\n int n_hidden = 128,\n int n_output = 128,\n int kernel_size = 5) : base(name)\n {\n long total_scale = 1;\n foreach (var upsample_scale in upsample_scales) {\n total_scale *= upsample_scale;\n }\n this.total_scale = total_scale;\n\n this.indent = (kernel_size - 1) / 2 * total_scale;\n this.resnet = new MelResNet(\"melresnet\", n_res_block, n_freq, n_hidden, n_output, kernel_size);\n this.resnet_stretch = new Stretch2d(\"stretch2d\", total_scale, 1);\n\n var up_layers = new List<nn.Module<Tensor, Tensor>>();\n foreach (var scale in upsample_scales) {\n var stretch = new Stretch2d(\"stretch2d\", scale, 1);\n var conv = nn.Conv2d(inputChannel: 1, outputChannel: 1, kernelSize: (1, scale * 2 + 1), padding: (0, scale), bias: false);\n torch.nn.init.constant_(conv.weight, 1.0 / (scale * 2 + 1));\n up_layers.Add(stretch);\n up_layers.Add(conv);\n }\n this.upsample_layers = nn.Sequential(up_layers);\n this.RegisterComponents();\n }\n\n public override (Tensor, Tensor) forward(Tensor specgram)\n {\n var resnet_output = this.resnet.call(specgram).unsqueeze(1);\n resnet_output = this.resnet_stretch.call(resnet_output);\n resnet_output = resnet_output.squeeze(1);\n\n specgram = specgram.unsqueeze(1);\n var upsampling_output = this.upsample_layers.call(specgram);\n upsampling_output = upsampling_output.squeeze(1)[TensorIndex.Colon, TensorIndex.Colon, TensorIndex.Slice(this.indent, -this.indent)];\n\n return (upsampling_output, resnet_output);\n }\n }\n }\n}\n"},{"alpha_fraction":0.5384615659713745,"alphanum_fraction":0.5384615659713745,"avg_line_length":26.736841201782227,"blob_id":"42003a6ad9d51768ecf613f46814f4353df34c7e","content_id":"7cb598edad11660fbe13f0dbcfaf82eb971e374a","detected_licenses":["MIT"],"is_generated":false,"is_vendor":false,"language":"C#","length_bytes":1053,"license_type":"permissive","max_line_length":130,"num_lines":38,"path":"/src/TorchVision/Lambda.cs","repo_name":"NiklasGustafsson/TorchSharp","src_encoding":"UTF-8","text":"// Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information.\nusing System;\nusing static TorchSharp.torch;\n\n\nnamespace TorchSharp\n{\n public static partial class torchvision\n {\n internal class Lambda : ITransform\n {\n internal Lambda(Func<Tensor, Tensor> lambda)\n {\n this.lambda = lambda;\n }\n\n public Tensor call(Tensor img)\n {\n return lambda(img);\n }\n\n private Func<Tensor, Tensor> lambda;\n }\n\n public static partial class transforms\n {\n /// <summary>\n /// Apply a user-defined function as a transform. \n /// </summary>\n /// <param name=\"lambda\">Lambda/function to be used for transform.</param>\n /// <returns></returns>\n static public ITransform Lambda(Func<Tensor, Tensor> lambda)\n {\n return new Lambda(lambda);\n }\n }\n }\n}"},{"alpha_fraction":0.57302325963974,"alphanum_fraction":0.5801550149917603,"avg_line_length":42.58108139038086,"blob_id":"afb5d19e7142226a9e7999f8353927175bd9ef44","content_id":"363cb40d59f29b76c3c54271c99d91a6e40ac5a1","detected_licenses":["MIT"],"is_generated":false,"is_vendor":false,"language":"C#","length_bytes":3225,"license_type":"permissive","max_line_length":169,"num_lines":74,"path":"/src/TorchSharp/NN/Dropout2d.cs","repo_name":"NiklasGustafsson/TorchSharp","src_encoding":"UTF-8","text":"// Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information.\nusing System;\nusing static TorchSharp.torch;\nusing static TorchSharp.PInvoke.NativeMethods;\n\nnamespace TorchSharp\n{\n using Modules;\n\n namespace Modules\n {\n /// <summary>\n /// This class is used to represent a Dropout2d module.\n /// </summary>\n public sealed class Dropout2d : torch.nn.Module<Tensor, Tensor>\n {\n internal Dropout2d(double p = 0.5, bool inplace = false) : base(nameof(Dropout2d))\n {\n this.p = p;\n this.inplace = inplace;\n }\n\n public override Tensor forward(Tensor input)\n {\n var res = THSNN_dropout2d(input.Handle, p, this.training, inplace);\n if (res == IntPtr.Zero) { torch.CheckForErrors(); }\n return new Tensor(res);\n }\n\n // Rather than spending cycles only to discover that this module has neither\n // parameters nor buffers, just shortcut the move completely.\n protected internal override nn.Module _to(Device device, ScalarType dtype) => this;\n protected internal override nn.Module _to(DeviceType deviceType, int deviceIndex = -1) => this;\n protected internal override nn.Module _to(ScalarType dtype) => this;\n\n private bool inplace;\n private double p;\n }\n }\n\n public static partial class torch\n {\n public static partial class nn\n {\n /// <summary>\n /// Randomly zero out entire channels (a channel is a 2D feature map, e.g., the jj -th channel of the ii -th sample in the batched input is a 2D tensor).\n /// Each channel will be zeroed out independently on every forward call with probability p using samples from a Bernoulli distribution.\n /// </summary>\n /// <param name=\"p\">Probability of an element to be zeroed. Default: 0.5</param>\n /// <param name=\"inplace\">If set to true, will do this operation in-place. Default: false</param>\n /// <returns></returns>\n public static Dropout2d Dropout2d(double p = 0.5, bool inplace = false)\n {\n return new Dropout2d(p, inplace);\n }\n\n public static partial class functional\n {\n\n /// <summary>\n /// Randomly zero out entire channels (a channel is a 2D feature map, e.g., the jj -th channel of the ii -th sample in the batched input is a 2D tensor).\n /// Each channel will be zeroed out independently on every forward call with probability p using samples from a Bernoulli distribution.\n /// </summary>\n /// <returns></returns>\n public static Tensor dropout2d(Tensor input, double p = 0.5, bool training = true, bool inplace = false)\n {\n var res = THSNN_dropout2d(input.Handle, p, training, inplace);\n if (res == IntPtr.Zero) { torch.CheckForErrors(); }\n return new Tensor(res);\n }\n }\n }\n }\n}\n"},{"alpha_fraction":0.5326876640319824,"alphanum_fraction":0.5332929491996765,"avg_line_length":43.64864730834961,"blob_id":"507babe20d3e2fa97bff484f79d739eaeb463752","content_id":"d73678789bf3c90fe7b65d2d582c9d4e5bf56570","detected_licenses":["MIT"],"is_generated":false,"is_vendor":false,"language":"C#","length_bytes":4956,"license_type":"permissive","max_line_length":144,"num_lines":111,"path":"/src/TorchSharp/NN/Recurrent/GRUCell.cs","repo_name":"NiklasGustafsson/TorchSharp","src_encoding":"UTF-8","text":"// Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information.\nusing System;\nusing static TorchSharp.torch;\nusing static TorchSharp.torch.nn;\nusing static TorchSharp.PInvoke.NativeMethods;\n\n#nullable enable\nnamespace TorchSharp\n{\n using Modules;\n\n namespace Modules\n {\n public sealed class GRUCell : torch.nn.Module<Tensor, Tensor, Tensor>\n {\n internal GRUCell(IntPtr handle, IntPtr boxedHandle) : base(handle, boxedHandle) { }\n\n public new static GRUCell Load(string modelPath)\n {\n var res = Module<Tensor, Tensor>.Load(modelPath);\n return new GRUCell(res.handle.DangerousGetHandle(), IntPtr.Zero);\n }\n\n /// <summary>\n /// Apply the GRU cell to an input tensor.\n /// </summary>\n /// <param name=\"input\">Tensor of shape (batch, input_size) containing the features of the input sequence.</param>\n /// <param name=\"h0\">Tensor of shape (batch, hidden_size) containing the initial hidden state for each element in the batch.</param>\n /// <returns></returns>\n public override Tensor forward(Tensor input, Tensor? h0 = null)\n {\n var hN = THSNN_GRUCell_forward(handle, input.Handle, h0?.Handle ?? IntPtr.Zero);\n if (hN == IntPtr.Zero) { torch.CheckForErrors(); }\n return new Tensor(hN);\n }\n\n public Parameter? bias_ih {\n get {\n var res = THSNN_GRUCell_bias_ih(handle);\n if (res == IntPtr.Zero) { torch.CheckForErrors(); }\n return ((res == IntPtr.Zero) ? null : new Parameter(res));\n }\n set {\n THSNN_GRUCell_set_bias_ih(handle, (value is null ? IntPtr.Zero : value.Handle));\n torch.CheckForErrors();\n ConditionallyRegisterParameter(\"bias_ih\", value);\n }\n }\n\n public Parameter? bias_hh {\n get {\n var res = THSNN_GRUCell_bias_hh(handle);\n if (res == IntPtr.Zero) { torch.CheckForErrors(); }\n return ((res == IntPtr.Zero) ? null : new Parameter(res));\n }\n set {\n THSNN_GRUCell_set_bias_hh(handle, (value is null ? IntPtr.Zero : value.Handle));\n torch.CheckForErrors();\n ConditionallyRegisterParameter(\"bias_hh\", value);\n }\n }\n\n public Parameter? weight_ih {\n get {\n var res = THSNN_GRUCell_weight_ih(handle);\n if (res == IntPtr.Zero) { torch.CheckForErrors(); }\n return (res == IntPtr.Zero) ? null : new Parameter(res);\n }\n set {\n THSNN_GRUCell_set_weight_ih(handle, value is null ? IntPtr.Zero : value.Handle);\n torch.CheckForErrors();\n ConditionallyRegisterParameter(\"weight_ih\", value);\n }\n }\n\n public Parameter? weight_hh {\n get {\n var res = THSNN_GRUCell_weight_hh(handle);\n if (res == IntPtr.Zero) { torch.CheckForErrors(); }\n return (res == IntPtr.Zero) ? null : new Parameter(res);\n }\n set {\n THSNN_GRUCell_set_weight_hh(handle, value is null ? IntPtr.Zero : value.Handle);\n torch.CheckForErrors();\n ConditionallyRegisterParameter(\"weight_hh\", value);\n }\n }\n }\n }\n\n public static partial class torch\n {\n public static partial class nn\n {\n /// <summary>\n /// A gated recurrent unit (GRU) cell\n /// </summary>\n /// <param name=\"inputSize\">The number of expected features in the input x</param>\n /// <param name=\"hiddenSize\">The number of features in the hidden state h</param>\n /// <param name=\"device\">The desired device of the parameters and buffers in this module</param>\n /// <param name=\"dtype\">The desired floating point or complex dtype of the parameters and buffers in this module</param>\n /// <param name=\"bias\">If False, then the layer does not use bias weights b_ih and b_hh. Default: True</param>\n public static GRUCell GRUCell(long inputSize, long hiddenSize, bool bias = true, Device? device = null, ScalarType? dtype = null)\n {\n var res = THSNN_GRUCell_ctor(inputSize, hiddenSize, bias, out var boxedHandle);\n if (res == IntPtr.Zero) { torch.CheckForErrors(); }\n return new GRUCell(res, boxedHandle).MoveModule<GRUCell>(device, dtype);\n }\n }\n }\n}\n"},{"alpha_fraction":0.4594695568084717,"alphanum_fraction":0.4998132288455963,"avg_line_length":35.17567443847656,"blob_id":"3d59ed9521a3a99455658f06d901141a8195daeb","content_id":"3a76b9e32b6a1b432385cf0e61faed7f2949aa28","detected_licenses":["MIT"],"is_generated":false,"is_vendor":false,"language":"C#","length_bytes":2677,"license_type":"permissive","max_line_length":130,"num_lines":74,"path":"/src/Examples/AlexNet.cs","repo_name":"NiklasGustafsson/TorchSharp","src_encoding":"UTF-8","text":"// Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information.\n\nusing static TorchSharp.torch;\nusing static TorchSharp.torch.nn;\n\nnamespace TorchSharp.Examples\n{\n /// <summary>\n /// Modified version of original AlexNet to fix CIFAR10 32x32 images.\n /// </summary>\n class AlexNet : Module<Tensor, Tensor>\n {\n private readonly Module<Tensor, Tensor> features;\n private readonly Module<Tensor, Tensor> avgPool;\n private readonly Module<Tensor, Tensor> classifier;\n\n public AlexNet(string name, int numClasses, torch.Device device = null) : base(name)\n {\n features = Sequential(\n (\"c1\", Conv2d(3, 64, kernelSize: 3, stride: 2, padding: 1)),\n (\"r1\", ReLU(inplace: true)),\n (\"mp1\", MaxPool2d(kernelSize: new long[] { 2, 2 })),\n (\"c2\", Conv2d(64, 192, kernelSize: 3, padding: 1)),\n (\"r2\", ReLU(inplace: true)),\n (\"mp2\", MaxPool2d(kernelSize: new long[] { 2, 2 })),\n (\"c3\", Conv2d(192, 384, kernelSize: 3, padding: 1)),\n (\"r3\", ReLU(inplace: true)),\n (\"c4\", Conv2d(384, 256, kernelSize: 3, padding: 1)),\n (\"r4\", ReLU(inplace: true)),\n (\"c5\", Conv2d(256, 256, kernelSize: 3, padding: 1)),\n (\"r5\", ReLU(inplace: true)),\n (\"mp3\", MaxPool2d(kernelSize: new long[] { 2, 2 })));\n\n avgPool = AdaptiveAvgPool2d(new long[] { 2, 2 });\n\n classifier = Sequential(\n (\"d1\", Dropout()),\n (\"l1\", Linear(256 * 2 * 2, 4096)),\n (\"r1\", ReLU(inplace: true)),\n (\"d2\", Dropout()),\n (\"l2\", Linear(4096, 4096)),\n (\"r3\", ReLU(inplace: true)),\n (\"d3\", Dropout()),\n (\"l3\", Linear(4096, numClasses))\n );\n\n RegisterComponents();\n\n if (device != null && device.type == DeviceType.CUDA)\n this.to(device);\n }\n\n public override Tensor forward(Tensor input)\n {\n var f = features.call(input);\n var avg = avgPool.call(f);\n\n using (var x = avg.reshape(new long[] { avg.shape[0], 256 * 2 * 2 }))\n return classifier.call(x);\n }\n\n protected override void Dispose(bool disposing)\n {\n if (disposing) {\n features.Dispose();\n avgPool.Dispose();\n classifier.Dispose();\n ClearModules();\n }\n base.Dispose(disposing);\n }\n }\n\n}\n"},{"alpha_fraction":0.5558139681816101,"alphanum_fraction":0.561012327671051,"avg_line_length":44.6875,"blob_id":"df6a5e536593f6f6d695a6fe488fd38804d3c8f8","content_id":"bda99fb0923cb2af153b66be1db02d7bdc8a53f9","detected_licenses":["MIT"],"is_generated":false,"is_vendor":false,"language":"C#","length_bytes":7310,"license_type":"permissive","max_line_length":205,"num_lines":160,"path":"/src/TorchSharp/Tensor/Factories/empty.cs","repo_name":"NiklasGustafsson/TorchSharp","src_encoding":"UTF-8","text":"// Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information.\nusing System;\nusing static TorchSharp.PInvoke.NativeMethods;\n\n#nullable enable\nnamespace TorchSharp\n{\n public static partial class torch\n {\n /// <summary>\n /// Create a new tensor filled with empty\n /// </summary>\n public static Tensor empty(long[] size, ScalarType? dtype = null, Device? device = null, bool requires_grad = false, string[]? names = null)\n {\n return _empty(size, dtype, device, requires_grad, names: names);\n }\n\n /// <summary>\n /// Create a new tensor filled with empty\n /// </summary>\n public static Tensor empty(ReadOnlySpan<long> size, ScalarType? dtype = null, Device? device = null, bool requires_grad = false, string[]? names = null)\n {\n return _empty(size, dtype, device, requires_grad, names: names);\n }\n\n /// <summary>\n /// Create a new 1-D tensor filled with empty\n /// </summary>\n public static Tensor empty(long size, ScalarType? dtype = null, Device? device = null, bool requires_grad = false, string[]? names = null)\n {\n return _empty(stackalloc long[] { size }, dtype, device, requires_grad, names: names);\n }\n\n /// <summary>\n /// Create a new 2-D tensor filled with empty\n /// </summary>\n public static Tensor empty(long rows, long columns, ScalarType? dtype = null, Device? device = null, bool requires_grad = false, string[]? names = null)\n {\n return _empty(stackalloc long[] { rows, columns }, dtype, device, requires_grad, names: names);\n }\n\n /// <summary>\n /// Create a new 3-D tensor filled with empty\n /// </summary>\n public static Tensor empty(long dim0, long dim1, long dim2, ScalarType? dtype = null, Device? device = null, bool requires_grad = false, string[]? names = null)\n {\n return _empty(stackalloc long[] { dim0, dim1, dim2 }, dtype, device, requires_grad, names: names);\n }\n\n /// <summary>\n /// Create a new 4-D tensor filled with empty\n /// </summary>\n public static Tensor empty(long dim0, long dim1, long dim2, long dim3, ScalarType? dtype = null, Device? device = null, bool requires_grad = false, string[]? names = null)\n {\n return _empty(stackalloc long[] { dim0, dim1, dim2, dim3 }, dtype, device, requires_grad, names: names);\n }\n\n /// <summary>\n /// Create a new 1-D tensor filled with empty\n /// </summary>\n public static Tensor empty(int size, ScalarType? dtype = null, Device? device = null, bool requires_grad = false, string[]? names = null)\n {\n return _empty(stackalloc long[] { size }, dtype, device, requires_grad, names: names);\n }\n\n /// <summary>\n /// Create a new 2-D tensor filled with empty\n /// </summary>\n public static Tensor empty(int rows, int columns, ScalarType? dtype = null, Device? device = null, bool requires_grad = false, string[]? names = null)\n {\n return _empty(stackalloc long[] { rows, columns }, dtype, device, requires_grad, names: names);\n }\n\n /// <summary>\n /// Create a new 3-D tensor filled with empty\n /// </summary>\n public static Tensor empty(int dim0, int dim1, int dim2, ScalarType? dtype = null, Device? device = null, bool requires_grad = false, string[]? names = null)\n {\n return _empty(stackalloc long[] { dim0, dim1, dim2 }, dtype, device, requires_grad, names: names);\n }\n\n /// <summary>\n /// Create a new 4-D tensor filled with empty\n /// </summary>\n public static Tensor empty(int dim0, int dim1, int dim2, int dim3, ScalarType? dtype = null, Device? device = null, bool requires_grad = false, string[]? names = null)\n {\n return _empty(stackalloc long[] { dim0, dim1, dim2, dim3 }, dtype, device, requires_grad, names: names);\n }\n\n /// <summary>\n /// Returns a tensor filled with uninitialized data, with the same size as input.\n /// </summary>\n public static Tensor empty_like(Tensor input, ScalarType? dtype = null, Device? device = null, bool requires_grad = false, string[]? names = null) => input.empty_like(dtype, device, requires_grad);\n\n\n /// <summary>\n /// Returns a tensor filled with uninitialized data. The shape and strides of the tensor is defined by the variable argument size and stride respectively.\n /// </summary>\n public static Tensor empty_strided(long[] size, long[] strides, ScalarType? dtype = null, Device? device = null, bool requires_grad = false, string[]? names = null)\n {\n device = InitializeDevice(device);\n if (!dtype.HasValue) {\n // Determine the element type dynamically.\n dtype = get_default_dtype();\n }\n\n unsafe {\n fixed (long* psizes = size, pstrides = strides) {\n var handle = THSTensor_empty_strided((IntPtr)psizes, size.Length, (IntPtr)pstrides, strides.Length, (sbyte)dtype, (int)device.type, device.index, requires_grad);\n if (handle == IntPtr.Zero) {\n GC.Collect();\n GC.WaitForPendingFinalizers();\n handle = THSTensor_empty_strided((IntPtr)psizes, size.Length, (IntPtr)pstrides, strides.Length, (sbyte)dtype, (int)device.type, device.index, requires_grad);\n }\n if (handle == IntPtr.Zero) { CheckForErrors(); }\n var result = new Tensor(handle);\n\n if (names != null && names.Length > 0) {\n\n result.rename_(names);\n }\n\n return result;\n }\n }\n }\n\n /// <summary>\n /// Create a new tensor filled with empty\n /// </summary>\n private static Tensor _empty(ReadOnlySpan<long> size, ScalarType? dtype = null, Device? device = null, bool requires_grad = false, string[]? names = null)\n {\n device = InitializeDevice(device);\n if (!dtype.HasValue) {\n // Determine the element type dynamically.\n dtype = get_default_dtype();\n }\n\n unsafe {\n fixed (long* psizes = size) {\n var handle = THSTensor_empty((IntPtr)psizes, size.Length, (sbyte)dtype, (int)device.type, device.index, requires_grad);\n if (handle == IntPtr.Zero) {\n GC.Collect();\n GC.WaitForPendingFinalizers();\n handle = THSTensor_empty((IntPtr)psizes, size.Length, (sbyte)dtype, (int)device.type, device.index, requires_grad);\n }\n if (handle == IntPtr.Zero) { CheckForErrors(); }\n var result = new Tensor(handle);\n\n if (names != null && names.Length > 0) {\n\n result.rename_(names);\n }\n\n return result;\n }\n }\n }\n }\n}\n"},{"alpha_fraction":0.5628982782363892,"alphanum_fraction":0.5657304525375366,"avg_line_length":46.340782165527344,"blob_id":"bfc2ca728e291fa5ca1d78fb83443c90d0c9daa0","content_id":"ff4258652cde445a8cba21abb0041bde11ba568b","detected_licenses":["MIT"],"is_generated":false,"is_vendor":false,"language":"C#","length_bytes":8474,"license_type":"permissive","max_line_length":149,"num_lines":179,"path":"/src/TorchSharp/Distributions/Multinomial.cs","repo_name":"NiklasGustafsson/TorchSharp","src_encoding":"UTF-8","text":"// Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information.\nusing System;\nusing System.Collections.Generic;\nusing System.Linq;\nusing static TorchSharp.torch;\n\nnamespace TorchSharp\n{\n using Modules;\n\n namespace Modules\n {\n /// <summary>\n /// A Multinomial distribution parameterized by `probs` or `logits` (but not both).\n /// `total_count` must be broadcastable with `probs`/`logits`.\n /// </summary>\n public class Multinomial : torch.distributions.Distribution\n {\n /// <summary>\n /// The mean of the distribution.\n /// </summary>\n public override Tensor mean => total_count * probs;\n\n /// <summary>\n /// The variance of the distribution\n /// </summary>\n public override Tensor variance => WrappedTensorDisposeScope(() => total_count * probs * (1 - probs));\n\n /// <summary>\n /// Constructor\n /// </summary>\n /// <param name=\"total_count\">Number of Bernoulli trials</param>\n /// <param name=\"probs\">The probability of sampling '1'</param>\n /// <param name=\"logits\">The log-odds of sampling '1'</param>\n /// <param name=\"generator\">An optional random number generator object.</param>\n public Multinomial(int total_count, Tensor probs = null, Tensor logits = null, torch.Generator generator = null) : base(generator) \n {\n this.total_count = total_count;\n this.categorical = new Categorical(probs, logits);\n this.batch_shape = this.categorical.batch_shape;\n var ps = this.categorical.param_shape;\n this.event_shape = new long[] { ps[ps.Length-1] };\n }\n\n private Multinomial(int total_count, Categorical categorical, torch.Generator generator = null) : base(generator)\n {\n this.total_count = total_count;\n this.categorical = categorical;\n this.batch_shape = categorical.batch_shape;\n var ps = categorical.param_shape;\n this.event_shape = new long[] { ps[ps.Length - 1] };\n }\n\n /// <summary>\n /// Event probabilities\n /// </summary>\n public Tensor probs => categorical.probs;\n\n /// <summary>\n /// Event log-odds\n /// </summary>\n public Tensor logits => categorical.logits;\n\n\n public long[] param_shape => categorical.param_shape;\n\n private int total_count;\n private Categorical categorical;\n\n /// <summary>\n /// Generates a sample_shape shaped reparameterized sample or sample_shape shaped batch of reparameterized samples\n /// if the distribution parameters are batched.\n /// </summary>\n /// <param name=\"sample_shape\">The sample shape.</param>\n public override Tensor rsample(params long[] sample_shape)\n {\n var cShape = new List<long>(); cShape.Add(total_count); cShape.AddRange(sample_shape);\n\n using var _ = NewDisposeScope();\n var samples = categorical.sample(cShape.ToArray());\n var shifted_idx = Enumerable.Range(0, (int)samples.dim()).ToList();\n var tc = shifted_idx[0];\n shifted_idx.RemoveAt(0);\n shifted_idx.Add(tc);\n samples = samples.permute(shifted_idx.Select(i => (long)i).ToArray());\n var counts = samples.new_zeros(ExtendedShape(sample_shape));\n counts.scatter_add_(-1, samples, torch.ones_like(samples));\n return counts.type_as(probs).MoveToOuterDisposeScope();\n }\n\n /// <summary>\n /// Returns the log of the probability density/mass function evaluated at `value`.\n /// </summary>\n /// <param name=\"value\"></param>\n public override Tensor log_prob(Tensor value)\n {\n using var _ = NewDisposeScope();\n var bcast = torch.broadcast_tensors(logits, value);\n var l = bcast[0].clone();\n value = bcast[1];\n var log_factorial_n = torch.lgamma(value.sum(-1) + 1);\n var log_factorial_xs = torch.lgamma(value + 1).sum(-1);\n l[(value == 0) & (l == float.NegativeInfinity)] = torch.tensor(0.0f);\n var log_powers = (logits * value).sum(-1);\n return (log_factorial_n - log_factorial_xs + log_powers).MoveToOuterDisposeScope();\n }\n\n /// <summary>\n /// Returns a new distribution instance (or populates an existing instance provided by a derived class) with batch dimensions expanded to\n /// `batch_shape`. This method calls `torch.Tensor.expand()` on the distribution's parameters. As such, this does not allocate new\n /// memory for the expanded distribution instance.\n /// </summary>\n /// <param name=\"batch_shape\">Tthe desired expanded size.</param>\n /// <param name=\"instance\">new instance provided by subclasses that need to override `.expand`.</param>\n public override distributions.Distribution expand(Size batch_shape, distributions.Distribution instance = null)\n {\n if (instance != null && !(instance is Multinomial))\n throw new ArgumentException(\"expand(): 'instance' must be a Multinomial distribution\");\n\n var newDistribution = ((instance == null) ?\n new Multinomial(total_count, categorical.expand(batch_shape) as Categorical, generator) :\n instance) as Multinomial;\n\n if (newDistribution == instance) {\n newDistribution.total_count = total_count;\n newDistribution.categorical = categorical.expand(batch_shape) as Categorical;\n newDistribution.batch_shape = newDistribution.categorical.batch_shape;\n var ps = newDistribution.categorical.param_shape;\n newDistribution.event_shape = new long[] { ps[ps.Length - 1] };\n }\n\n return newDistribution;\n }\n\n /// <summary>\n /// Returns entropy of distribution, batched over batch_shape.\n /// </summary>\n /// <returns></returns>\n public override Tensor entropy()\n {\n throw new NotImplementedException();\n }\n }\n\n }\n public static partial class torch\n {\n public static partial class distributions\n {\n /// <summary>\n /// Creates a Multinomial distribution parameterized by `probs` or `logits` (but not both).\n /// `total_count` must be broadcastable with `probs`/`logits`.\n /// </summary>\n /// <param name=\"total_count\">Number of Bernoulli trials</param>\n /// <param name=\"probs\">The probability of sampling '1'</param>\n /// <param name=\"logits\">The log-odds of sampling '1'</param>\n /// <param name=\"generator\">An optional random number generator object.</param>\n /// <returns></returns>\n public static Multinomial Multinomial(int total_count, Tensor probs = null, Tensor logits = null, torch.Generator generator = null)\n {\n return new Multinomial(total_count, probs, logits, generator);\n }\n\n /// <summary>\n /// Creates an equal-probability multinomial distribution parameterized by the number of categories.\n /// `total_count` must be broadcastable with `probs`/`logits`.\n /// </summary>\n /// <param name=\"total_count\">Number of Bernoulli trials</param>\n /// <param name=\"categories\">The number of categories.</param>\n /// <param name=\"generator\">An optional random number generator object.</param>\n /// <returns></returns>\n public static Multinomial Multinomial(int total_count, int categories, torch.Generator generator = null)\n {\n var probs = torch.tensor(1.0 / categories).expand(categories);\n return new Multinomial(total_count, probs, null, generator);\n }\n }\n }\n}\n"},{"alpha_fraction":0.5633964538574219,"alphanum_fraction":0.565691351890564,"avg_line_length":36.91304397583008,"blob_id":"31f9c867490f7ed2e7c3be3aea7f9d3c48c95ee2","content_id":"d6cd833be9a51d1606f8b7a5ffc0dc74b9438c36","detected_licenses":["MIT"],"is_generated":false,"is_vendor":false,"language":"C#","length_bytes":1747,"license_type":"permissive","max_line_length":131,"num_lines":46,"path":"/src/TorchVision/Pad.cs","repo_name":"NiklasGustafsson/TorchSharp","src_encoding":"UTF-8","text":"// Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information.\n\nusing static TorchSharp.torch;\n\nnamespace TorchSharp\n{\n public static partial class torchvision\n {\n internal class Pad : ITransform\n {\n internal Pad(long[] pad, PaddingModes mode = PaddingModes.Constant, double value = 0)\n {\n this.pad = pad;\n this.mode = mode;\n this.value = value;\n }\n\n public Tensor call(Tensor input)\n {\n return TorchSharp.torch.nn.functional.pad(input, pad, mode, value);\n }\n\n private long[] pad;\n private PaddingModes mode;\n private double value;\n }\n\n public static partial class transforms\n {\n /// <summary>\n /// Pad the given image on all sides with the given “pad” value.\n /// </summary>\n /// <param name=\"padding\">\n /// Padding on each border. If a single int is provided this is used to pad all borders.\n /// If sequence of length 2 is provided this is the padding on left/right and top/bottom respectively.\n /// If a sequence of length 4 is provided this is the padding for the left, top, right and bottom borders respectively.\n /// </param>\n /// <param name=\"fill\">Pixel fill value for constant fill.</param>\n /// <param name=\"mode\">Type of padding.</param>\n static public ITransform Pad(long[] padding, PaddingModes mode = PaddingModes.Constant, double fill = 0)\n {\n return new Pad(padding, mode, fill);\n }\n }\n }\n}"},{"alpha_fraction":0.5328271985054016,"alphanum_fraction":0.5441445112228394,"avg_line_length":42.638038635253906,"blob_id":"71a9a10d8f19ee1c9c79dafe06a408654d4e63bf","content_id":"2ddb738f816682f3182214cbad2396ce1f779815","detected_licenses":["MIT"],"is_generated":false,"is_vendor":false,"language":"C#","length_bytes":14226,"license_type":"permissive","max_line_length":187,"num_lines":326,"path":"/src/TorchVision/dsets/CIFAR.cs","repo_name":"NiklasGustafsson/TorchSharp","src_encoding":"UTF-8","text":"// Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information.\nusing System;\nusing System.Collections.Generic;\nusing System.IO;\nusing System.Net;\nusing System.Net.Http;\nusing static TorchSharp.torch;\nusing static TorchSharp.torch.utils.data;\n\nnamespace TorchSharp \n{\n public static partial class torchvision\n {\n public static partial class datasets\n {\n /// <summary>\n /// CIFAR10 Dataset.\n /// </summary>\n /// <remarks>\n /// The dataset for this example can be found at: https://www.cs.toronto.edu/~kriz/cifar.html\n /// </remarks>\n /// <param name=\"root\">Root directory of dataset where directory CIFAR{10,100} exists or will be saved to if download is set to true.</param>\n /// <param name=\"train\">If true, creates dataset from training set, otherwise creates from test set</param>\n /// <param name=\"download\">If true, downloads the dataset from the internet and puts it in root directory. If dataset is already downloaded, it is not downloaded again</param>\n /// <param name=\"target_transform\">A transform that takes in the target tensor and transforms it.</param>\n /// <returns>A CIFAR dataset.</returns>\n public static Dataset CIFAR10(string root, bool train, bool download = false, torchvision.ITransform target_transform = null)\n {\n return new Modules.CIFAR10(root, train, \"https://www.cs.toronto.edu/~kriz/\", download, target_transform);\n }\n\n /// <summary>\n /// CIFAR10 Dataset.\n /// </summary>\n /// <remarks>\n /// The dataset for this example can be found at: https://www.cs.toronto.edu/~kriz/cifar.html\n /// </remarks>\n /// <param name=\"root\">Root directory of dataset where directory CIFAR{10,100} exists or will be saved to if download is set to true.</param>\n /// <param name=\"train\">If true, creates dataset from training set, otherwise creates from test set</param>\n /// <param name=\"download\">If true, downloads the dataset from the internet and puts it in root directory. If dataset is already downloaded, it is not downloaded again</param>\n /// <param name=\"target_transform\">A function that takes in the target tensor and transforms it.</param>\n /// <returns>A CIFAR dataset.</returns>\n public static Dataset CIFAR10(string root, bool train, bool download, Func<Tensor, Tensor> target_transform)\n {\n return new Modules.CIFAR10(root, train, \"https://www.cs.toronto.edu/~kriz/\", download, new torchvision.Lambda(target_transform));\n }\n\n /// <summary>\n /// CIFAR100 Dataset.\n /// </summary>\n /// <remarks>\n /// The dataset for this example can be found at: https://www.cs.toronto.edu/~kriz/cifar.html\n /// </remarks>\n /// <param name=\"root\">Root directory of dataset where directory CIFAR{10,100} exists or will be saved to if download is set to true.</param>\n /// <param name=\"train\">If true, creates dataset from training set, otherwise creates from test set</param>\n /// <param name=\"download\">If true, downloads the dataset from the internet and puts it in root directory. If dataset is already downloaded, it is not downloaded again</param>\n /// <param name=\"target_transform\">A transform that takes in the target and transforms it.</param>\n /// <returns>A CIFAR dataset.</returns>\n public static Dataset CIFAR100(string root, bool train, bool download = false, torchvision.ITransform target_transform = null)\n {\n return new Modules.CIFAR100(root, train, \"https://www.cs.toronto.edu/~kriz/\", download, target_transform);\n }\n\n /// <summary>\n /// CIFAR100 Dataset.\n /// </summary>\n /// <remarks>\n /// The dataset for this example can be found at: https://www.cs.toronto.edu/~kriz/cifar.html\n /// </remarks>\n /// <param name=\"root\">Root directory of dataset where directory CIFAR{10,100} exists or will be saved to if download is set to true.</param>\n /// <param name=\"train\">If true, creates dataset from training set, otherwise creates from test set</param>\n /// <param name=\"download\">If true, downloads the dataset from the internet and puts it in root directory. If dataset is already downloaded, it is not downloaded again</param>\n /// <param name=\"target_transform\">A function that takes in the target and transforms it.</param>\n /// <returns>A CIFAR dataset.</returns>\n public static Dataset CIFAR100(string root, bool train, bool download, Func<Tensor, Tensor> target_transform)\n {\n return new Modules.CIFAR100(root, train, \"https://www.cs.toronto.edu/~kriz/\", download, new torchvision.Lambda(target_transform));\n }\n }\n }\n\n namespace Modules\n {\n internal abstract class DatasetHelper : Dataset\n {\n protected void DownloadFile(string file, string target, string baseUrl)\n {\n var filePath = JoinPaths(target, file);\n\n var netPath = baseUrl.EndsWith('/') ? $\"{baseUrl}{file}\" : $\"{baseUrl}/{file}\";\n\n if (!File.Exists(filePath)) {\n lock (_httpClient) {\n using var s = _httpClient.GetStreamAsync(netPath).Result;\n using var fs = new FileStream(file, FileMode.CreateNew);\n s.CopyToAsync(fs).Wait();\n }\n }\n }\n\n protected static string JoinPaths(string directory, string file)\n {\n#if NETSTANDARD2_0_OR_GREATER\n return NSPath.Join(directory, file);\n#else\n return Path.Join(directory, file);\n#endif // NETSTANDARD2_0_OR_GREATER\n }\n\n // Sharing a client among all dataset downloads, based on this article's advice:\n // https://www.aspnetmonsters.com/2016/08/2016-08-27-httpclientwrong/\n //\n private static HttpClient _httpClient = new HttpClient();\n }\n\n internal abstract class CIFAR : DatasetHelper\n {\n protected void Download(string targetDir, string baseUrl, string dataset, string fileName)\n {\n if (!Directory.Exists(targetDir)) {\n Directory.CreateDirectory(targetDir);\n }\n\n DownloadFile(fileName, targetDir, baseUrl);\n\n if (!File.Exists(Path.Combine(targetDir, $\"{dataset}-binary.bin\"))) {\n DecompressFile(fileName, targetDir, targetDir);\n }\n }\n\n private static void DecompressFile(string file, string sourceDir, string targetDir)\n {\n Utils.Decompress.ExtractTGZ(Path.Combine(sourceDir, file), targetDir);\n }\n }\n\n internal class CIFAR10 : CIFAR, IDisposable\n {\n public CIFAR10(string root, bool train, string baseUrl, bool download, torchvision.ITransform transform)\n {\n var targetDir = JoinPaths(root, \"CIFAR10\");\n\n if (download) Download(targetDir, baseUrl, \"cifar-10\", \"cifar-10-binary.tar.gz\");\n\n this.transform = transform;\n\n var dataPath = Path.Combine(targetDir, \"cifar-10-batches-bin\");\n\n if (!train) {\n _count = ReadSingleFile(Path.Combine(dataPath, \"test_batch.bin\"));\n } else {\n _count += ReadSingleFile(Path.Combine(dataPath, \"data_batch_1.bin\"));\n _count += ReadSingleFile(Path.Combine(dataPath, \"data_batch_2.bin\"));\n _count += ReadSingleFile(Path.Combine(dataPath, \"data_batch_3.bin\"));\n _count += ReadSingleFile(Path.Combine(dataPath, \"data_batch_4.bin\"));\n _count += ReadSingleFile(Path.Combine(dataPath, \"data_batch_5.bin\"));\n }\n\n }\n\n private int ReadSingleFile(string path)\n {\n const int height = 32;\n const int width = 32;\n const int channels = 3;\n const int count = 10000;\n\n byte[] dataBytes = File.ReadAllBytes(path);\n\n var imgSize = channels * height * width + 1;\n\n if (dataBytes.Length != imgSize * count)\n throw new InvalidDataException($\"Not a proper CIFAR10 file: {path}\");\n\n // Go through the data and create tensors\n\n // A previous version of this relied on LINQ expressions, but it was about 20% slower.\n\n for (var i = 0; i < count; i++) {\n\n var imgStart = i * imgSize;\n\n labels.Add(tensor(dataBytes[imgStart], int64));\n\n var floats = new float[imgSize-1];\n for (int j = 0; j < imgSize-1; j++) {\n floats[j] = dataBytes[1 + j + imgStart] / 256.0f;\n }\n\n data.Add(tensor(floats, new long[] { channels, width, height }));\n }\n\n return count;\n }\n\n protected override void Dispose(bool disposing)\n {\n if (disposing) {\n data.ForEach(d => d.Dispose());\n labels.ForEach(d => d.Dispose());\n }\n }\n\n /// <summary>\n /// Get tensor according to index\n /// </summary>\n /// <param name=\"index\">Index for tensor</param>\n /// <returns>Tensors of index. DataLoader will catenate these tensors into batches.</returns>\n public override Dictionary<string, Tensor> GetTensor(long index)\n {\n var rdic = new Dictionary<string, Tensor>();\n if (transform is not null) {\n rdic.Add(\"data\", transform.call(data[(int)index]));\n }\n else {\n rdic.Add(\"data\", data[(int)index]);\n }\n rdic.Add(\"label\", labels[(int)index]);\n return rdic;\n }\n\n private List<Tensor> data = new();\n private List<Tensor> labels = new();\n\n private torchvision.ITransform transform;\n public override long Count => _count;\n\n private int _count = 0;\n }\n\n internal class CIFAR100 : CIFAR, IDisposable\n {\n public CIFAR100(string root, bool train, string baseUrl, bool download, torchvision.ITransform transform)\n {\n var targetDir = JoinPaths(root, \"CIFAR100\");\n\n if (download) Download(targetDir, baseUrl, \"cifar-100\", \"cifar-100-binary.tar.gz\");\n\n this.transform = transform;\n\n var dataPath = Path.Combine(targetDir, \"cifar-100-binary\");\n\n if (train) {\n _count = ReadSingleFile(Path.Combine(dataPath, \"train.bin\"));\n } else {\n _count = ReadSingleFile(Path.Combine(dataPath, \"test.bin\"));\n }\n\n }\n\n private int ReadSingleFile(string path)\n {\n const int height = 32;\n const int width = 32;\n const int channels = 3;\n\n byte[] dataBytes = File.ReadAllBytes(path);\n\n var imgSize = channels * height * width;\n var recSize = imgSize + 2;\n\n if (dataBytes.Length % recSize != 0)\n throw new InvalidDataException($\"Not a proper CIFAR100 file: {path}\");\n\n int count = dataBytes.Length / recSize;\n\n // Go through the data and create tensors\n\n for (var i = 0; i < count; i++) {\n\n var recStart = i * recSize;\n var imgStart = recStart + 2;\n\n // CIFAR100 has two labels -- one, which is 20 categories, one which is 100 entities.\n coarse_labels.Add(tensor(dataBytes[recStart], int64));\n fine_labels.Add(tensor(dataBytes[recStart+1], int64));\n\n var floats = new float[imgSize];\n for (int j = 0; j < imgSize; j++) {\n floats[j] = dataBytes[j + imgStart] / 256.0f;\n }\n\n data.Add(tensor(floats, new long[] { channels, width, height }));\n }\n\n return count;\n }\n\n protected override void Dispose(bool disposing)\n {\n if (disposing) {\n data.ForEach(d => d.Dispose());\n fine_labels.ForEach(d => d.Dispose());\n coarse_labels.ForEach(d => d.Dispose());\n }\n }\n\n /// <summary>\n /// Get tensor according to index\n /// </summary>\n /// <param name=\"index\">Index for tensor</param>\n /// <returns>Tensors of index. DataLoader will catenate these tensors into batches.</returns>\n public override Dictionary<string, Tensor> GetTensor(long index)\n {\n var rdic = new Dictionary<string, Tensor>();\n if (transform is not null) {\n rdic.Add(\"data\", transform.call(data[(int)index]));\n } else {\n rdic.Add(\"data\", data[(int)index]);\n }\n rdic.Add(\"label\", fine_labels[(int)index]);\n rdic.Add(\"categories\", coarse_labels[(int)index]);\n return rdic;\n }\n\n private List<Tensor> data = new();\n private List<Tensor> coarse_labels = new();\n private List<Tensor> fine_labels = new();\n\n private torchvision.ITransform transform;\n public override long Count => _count;\n\n private int _count = 0;\n }\n }\n}\n"},{"alpha_fraction":0.5431309938430786,"alphanum_fraction":0.5445367693901062,"avg_line_length":43.460227966308594,"blob_id":"c22a87526d411890b389f5ec241e9a22078d97f7","content_id":"3a1eb8dc8c227963ec0fb1856b3916823c52f617","detected_licenses":["MIT"],"is_generated":false,"is_vendor":false,"language":"C#","length_bytes":7825,"license_type":"permissive","max_line_length":149,"num_lines":176,"path":"/src/TorchSharp/Distributions/Bernoulli.cs","repo_name":"NiklasGustafsson/TorchSharp","src_encoding":"UTF-8","text":"// Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information.\nusing System;\nusing static TorchSharp.torch;\n\nnamespace TorchSharp\n{\n using Modules;\n\n namespace Modules\n {\n /// <summary>\n /// A Bernoulli distribution parameterized by `probs` or `logits` (but not both).\n /// </summary>\n public class Bernoulli : torch.distributions.Distribution\n {\n /// <summary>\n /// The mean of the distribution.\n /// </summary>\n public override Tensor mean => probs;\n\n /// <summary>\n /// The variance of the distribution\n /// </summary>\n public override Tensor variance =>\n WrappedTensorDisposeScope(() => probs * (1 - probs));\n\n /// <summary>\n /// Constructor\n /// </summary>\n /// <param name=\"p\"></param>\n /// <param name=\"l\"></param>\n /// <param name=\"generator\"></param>\n public Bernoulli(Tensor p = null, Tensor l = null, torch.Generator generator = null) : base(generator)\n {\n if ((p is null && l is null) || (p is not null && l is not null))\n throw new ArgumentException(\"One and only one of 'probs' and logits should be provided.\");\n\n this.batch_shape = p is null ? l.size() : p.size();\n this._probs = p?.alias().DetachFromDisposeScope();\n this._logits = l?.alias().DetachFromDisposeScope();\n }\n\n /// <summary>\n /// The probability of sampling 1\n /// </summary>\n public Tensor probs {\n get {\n return _probs ?? LogitsToProbs(_logits, true);\n }\n }\n\n /// <summary>\n /// The log-odds of sampling 1\n /// </summary>\n public Tensor logits {\n get {\n return _logits ?? ProbsToLogits(_probs, true);\n }\n }\n\n private Tensor _probs;\n private Tensor _logits;\n\n /// <summary>\n /// Generates a sample_shape shaped reparameterized sample or sample_shape shaped batch of reparameterized samples\n /// if the distribution parameters are batched.\n /// </summary>\n /// <param name=\"sample_shape\">The sample shape.</param>\n /// <returns></returns>\n public override Tensor rsample(params long[] sample_shape)\n {\n var shape = ExtendedShape(sample_shape);\n return torch.bernoulli(probs.expand(shape), generator);\n }\n\n /// <summary>\n /// Returns the log of the probability density/mass function evaluated at `value`.\n /// </summary>\n /// <param name=\"value\"></param>\n /// <returns></returns>\n\n public override Tensor log_prob(Tensor value)\n {\n var logitsValue = torch.broadcast_tensors(logits, value);\n return -torch.nn.functional.binary_cross_entropy_with_logits(logitsValue[0], logitsValue[1], reduction: nn.Reduction.None);\n }\n\n /// <summary>\n /// Returns entropy of distribution, batched over batch_shape.\n /// </summary>\n public override Tensor entropy()\n {\n return torch.nn.functional.binary_cross_entropy_with_logits(logits, probs, reduction: nn.Reduction.None);\n }\n\n /// <summary>\n /// Returns a new distribution instance (or populates an existing instance provided by a derived class) with batch dimensions expanded to\n /// `batch_shape`. This method calls `torch.Tensor.expand()` on the distribution's parameters. As such, this does not allocate new\n /// memory for the expanded distribution instance.\n /// </summary>\n /// <param name=\"batch_shape\">Tthe desired expanded size.</param>\n /// <param name=\"instance\">new instance provided by subclasses that need to override `.expand`.</param>\n /// <returns></returns>\n public override distributions.Distribution expand(Size batch_shape, distributions.Distribution instance = null)\n {\n if (instance != null && !(instance is Bernoulli))\n throw new ArgumentException(\"expand(): 'instance' must be a Bernoulli distribution\");\n\n var p = _probs?.expand(batch_shape);\n var l = _logits?.expand(batch_shape);\n\n var newDistribution = ((instance == null) ? new Bernoulli(p, l, generator) : instance) as Bernoulli;\n\n newDistribution.batch_shape = batch_shape;\n if (newDistribution == instance) {\n newDistribution._probs = p;\n newDistribution._logits = l;\n }\n return newDistribution;\n }\n }\n\n }\n public static partial class torch\n {\n public static partial class distributions\n {\n /// <summary>\n /// Creates a Bernoulli distribution parameterized by `probs` or `logits` (but not both).\n /// </summary>\n /// <param name=\"probs\">The probability of sampling '1'</param>\n /// <param name=\"logits\">The log-odds of sampling '1'</param>\n /// <param name=\"generator\">An optional random number generator object.</param>\n /// <returns></returns>\n public static Bernoulli Bernoulli(Tensor probs = null, Tensor logits = null, torch.Generator generator = null)\n {\n return new Bernoulli(probs, logits, generator);\n }\n\n /// <summary>\n /// Creates a Bernoulli distribution parameterized by `probs` or `logits` (but not both).\n /// </summary>\n /// <param name=\"probs\">The probability of sampling '1'</param>\n /// <param name=\"logits\">The log-odds of sampling '1'</param>\n /// <param name=\"generator\">An optional random number generator object.</param>\n /// <returns></returns>\n public static Bernoulli Bernoulli(float? probs, float? logits, torch.Generator generator = null)\n {\n if (probs.HasValue && !logits.HasValue)\n return new Bernoulli(torch.tensor(probs.Value), null, generator);\n else if (!probs.HasValue && logits.HasValue)\n return new Bernoulli(null, torch.tensor(logits.Value), generator);\n else\n throw new ArgumentException(\"One and only one of 'probs' and logits should be provided.\");\n }\n\n\n /// <summary>\n /// Creates a Bernoulli distribution parameterized by `probs` or `logits` (but not both).\n /// </summary>\n /// <param name=\"probs\">The probability of sampling '1'</param>\n /// <param name=\"logits\">The log-odds of sampling '1'</param>\n /// <param name=\"generator\">An optional random number generator object.</param>\n /// <returns></returns>\n public static Bernoulli Bernoulli(double? probs, double? logits, torch.Generator generator = null)\n {\n if (probs.HasValue && !logits.HasValue)\n return new Bernoulli(torch.tensor(probs.Value), null, generator);\n else if (!probs.HasValue && logits.HasValue)\n return new Bernoulli(null, torch.tensor(logits.Value), generator);\n else\n throw new ArgumentException(\"One and only one of 'probs' and 'logits' should be non-null\");\n }\n }\n }\n}\n"},{"alpha_fraction":0.5323854684829712,"alphanum_fraction":0.5331753492355347,"avg_line_length":36.264705657958984,"blob_id":"c6a3db3a9163cc9ddf2f4fec8ee53fbedc6e277a","content_id":"57c7b454cd46963008dfdf510b3fbc68102c98a5","detected_licenses":["MIT"],"is_generated":false,"is_vendor":false,"language":"C#","length_bytes":1266,"license_type":"permissive","max_line_length":130,"num_lines":34,"path":"/src/TorchAudio/Datasets/Utils.cs","repo_name":"NiklasGustafsson/TorchSharp","src_encoding":"UTF-8","text":"// Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information.\n\nusing System.IO;\nusing System.Text;\nusing ICSharpCode.SharpZipLib.GZip;\nusing ICSharpCode.SharpZipLib.Tar;\n\nnamespace TorchSharp\n{\n public static partial class torchaudio\n {\n public static partial class datasets\n {\n public static partial class utils\n {\n /// <summary>\n /// Extract an archive.\n /// </summary>\n /// <param name=\"from_path\">The path of the archive</param>\n /// <param name=\"to_path\">The path to extract the archive</param>\n internal static void extract_archive(string from_path, string to_path)\n {\n using (var fileStream = File.OpenRead(from_path)) {\n using (var inputStream = new GZipInputStream(fileStream)) {\n using (TarArchive tarArchive = TarArchive.CreateInputTarArchive(inputStream, Encoding.UTF8)) {\n tarArchive.ExtractContents(to_path);\n }\n }\n }\n }\n }\n }\n }\n}"},{"alpha_fraction":0.5554831624031067,"alphanum_fraction":0.5606974363327026,"avg_line_length":47.70634841918945,"blob_id":"1383722dd689bae865daa41fb40c5fff6871d414","content_id":"58d9229c4ecd53a0e84a7031efc92ec7b44819e9","detected_licenses":["MIT"],"is_generated":false,"is_vendor":false,"language":"C#","length_bytes":6137,"license_type":"permissive","max_line_length":228,"num_lines":126,"path":"/src/TorchSharp/NN/Normalization/InstanceNorm1d.cs","repo_name":"NiklasGustafsson/TorchSharp","src_encoding":"UTF-8","text":"// Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information.\nusing System;\nusing static TorchSharp.torch;\nusing static TorchSharp.PInvoke.NativeMethods;\n\n#nullable enable\nnamespace TorchSharp\n{\n using Modules;\n\n namespace Modules\n {\n\n /// <summary>\n /// This class is used to represent a InstanceNorm1D module.\n /// </summary>\n public sealed class InstanceNorm1d : torch.nn.Module<Tensor, Tensor>\n {\n internal InstanceNorm1d(IntPtr handle, IntPtr boxedHandle) : base(handle, boxedHandle)\n {\n }\n\n public override Tensor forward(Tensor tensor)\n {\n if (tensor.Dimensions < 2 || tensor.Dimensions > 3) throw new ArgumentException($\"Invalid number of dimensions for InstanceNorm argument: {tensor.Dimensions}\");\n var res = THSNN_InstanceNorm1d_forward(handle.DangerousGetHandle(), tensor.Handle);\n if (res == IntPtr.Zero) { torch.CheckForErrors(); }\n return new Tensor(res);\n }\n\n public Parameter? bias {\n get {\n var res = THSNN_InstanceNorm1d_bias(handle);\n if (res == IntPtr.Zero) { torch.CheckForErrors(); }\n return (res == IntPtr.Zero) ? null : new Parameter(res);\n }\n set {\n THSNN_InstanceNorm1d_set_bias(handle, (value is null ? IntPtr.Zero : value.Handle));\n torch.CheckForErrors();\n ConditionallyRegisterParameter(\"bias\", value);\n }\n }\n\n public Parameter? weight {\n get {\n var res = THSNN_InstanceNorm1d_weight(handle);\n if (res == IntPtr.Zero) { torch.CheckForErrors(); }\n return (res == IntPtr.Zero) ? null : new Parameter(res);\n }\n set {\n THSNN_InstanceNorm1d_set_weight(handle, value is null ? IntPtr.Zero : value.Handle);\n torch.CheckForErrors();\n ConditionallyRegisterParameter(\"weight\", value);\n }\n }\n\n public Tensor? running_mean {\n get {\n var res = THSNN_InstanceNorm1d_get_mean(handle);\n if (res == IntPtr.Zero) { torch.CheckForErrors(); return null; }\n return new Tensor(res);\n }\n set {\n THSNN_InstanceNorm1d_set_mean(handle, (value is null ? IntPtr.Zero : value.Handle));\n torch.CheckForErrors();\n ConditionallyRegisterBuffer(\"running_mean\", value);\n }\n }\n\n public Tensor? running_var {\n get {\n var res = THSNN_InstanceNorm1d_get_var(handle);\n if (res == IntPtr.Zero) { torch.CheckForErrors(); return null; }\n return new Tensor(res);\n }\n set {\n THSNN_InstanceNorm1d_set_var(handle, (value is null ? IntPtr.Zero : value.Handle));\n torch.CheckForErrors();\n ConditionallyRegisterBuffer(\"running_var\", value);\n }\n }\n\n public Tensor? num_batches_tracked {\n get {\n var res = THSNN_InstanceNorm1d_get_batches(handle);\n if (res == IntPtr.Zero) { torch.CheckForErrors(); return null; }\n return new Tensor(res);\n }\n }\n\n public void reset_running_stats()\n {\n THSNN_InstanceNorm1d_reset_stats(handle);\n torch.CheckForErrors();\n }\n }\n }\n\n public static partial class torch\n {\n public static partial class nn\n {\n /// <summary>\n /// Applies Instance Normalization over a 3D input (a mini-batch of 1D inputs with optional additional channel dimension) as described in the paper Instance Normalization: The Missing Ingredient for Fast Stylization.\n /// </summary>\n /// <param name=\"features\">C from an expected input of size (N,C,L) or LL from input of size (N, L)</param>\n /// <param name=\"eps\">A value added to the denominator for numerical stability. Default: 1e-5</param>\n /// <param name=\"momentum\">The value used for the running_mean and running_var computation. Can be set to None for cumulative moving average (i.e. simple average). Default: 0.1</param>\n /// <param name=\"affine\">A boolean value that when set to True, this module has learnable affine parameters. Default: true</param>\n /// <param name=\"track_running_stats\">A boolean value that when set to True, this module tracks the running mean and variance, and when set to False,\n /// this module does not track such statistics, and initializes statistics buffers running_mean and running_var as None.\n /// When these buffers are None, this module always uses batch statistics. in both training and eval modes. Default: true</param>\n /// <param name=\"device\">The desired device of the parameters and buffers in this module</param>\n /// <param name=\"dtype\">The desired floating point or complex dtype of the parameters and buffers in this module</param>\n /// <returns></returns>\n public static InstanceNorm1d InstanceNorm1d(long features, double eps = 1e-05, double momentum = 0.1, bool affine = false, bool track_running_stats = false, Device? device = null, ScalarType? dtype = null)\n {\n unsafe {\n var handle = THSNN_InstanceNorm1d_ctor(features, eps, momentum, affine, track_running_stats, out var boxedHandle);\n if (handle == IntPtr.Zero) { torch.CheckForErrors(); }\n return new InstanceNorm1d(handle, boxedHandle).MoveModule<InstanceNorm1d>(device, dtype);\n }\n }\n }\n }\n}\n"},{"alpha_fraction":0.32987022399902344,"alphanum_fraction":0.3371623456478119,"avg_line_length":48.14643478393555,"blob_id":"031d1bb0cbcbdde798f5af040f3ee43d6e38b953","content_id":"328427b1abab7ef4ae03ee2d3d43cc39418774eb","detected_licenses":["MIT"],"is_generated":false,"is_vendor":false,"language":"C#","length_bytes":25507,"license_type":"permissive","max_line_length":192,"num_lines":519,"path":"/src/TorchSharp/Utils/tensorboard/GifEncoder/Encoder.cs","repo_name":"NiklasGustafsson/TorchSharp","src_encoding":"UTF-8","text":"using System;\nusing System.IO;\nusing SkiaSharp;\n\nnamespace TorchSharp\n{\n public static partial class torch\n {\n public static partial class utils\n {\n public static partial class tensorboard\n {\n internal static partial class GifEncoder\n {\n /// <summary>\n /// Class AnimatedGifEncoder - Encodes a GIF file consisting of one or more frames.\n ///\n /// No copyright asserted on the source code of this class. May be used for any\n /// purpose, however, refer to the Unisys LZW patent for restrictions on use of\n /// the associated LZWEncoder class. Please forward any corrections to\n /// kweiner@fmsware.com.\n ///\n /// @author Kevin Weiner, FM Software\n /// @version 1.03 November 2003\n ///\n /// https://cs.android.com/android/platform/superproject/+/master:external/glide/third_party/gif_encoder/src/main/java/com/bumptech/glide/gifencoder/AnimatedGifEncoder.java\n /// </summary>\n internal class Encoder : IDisposable\n {\n protected int width; // image size\n protected int height;\n protected SKColor transparent = SKColor.Empty; // transparent color if given\n protected int transIndex; // transparent index in color table\n protected int repeat = -1; // no repeat\n protected int delay = 0; // frame delay (hundredths)\n protected bool started = false; // ready to output frames\n //\tprotected BinaryWriter bw;\n protected MemoryStream ms;\n //\t\tprotected FileStream fs;\n\n protected SKBitmap image; // current frame\n protected byte[] pixels; // BGR byte array from frame\n protected byte[] indexedPixels; // converted frame indexed to palette\n protected int colorDepth; // number of bit planes\n protected byte[] colorTab; // RGB palette\n protected bool[] usedEntry = new bool[256]; // active palette entries\n protected int palSize = 7; // color table size (bits-1)\n protected int dispose = -1; // disposal code (-1 = use default)\n protected bool closeStream = false; // close stream when finished\n protected bool firstFrame = true;\n protected bool sizeSet = false; // if false, get size from first frame\n protected int sample = 10; // default sample interval for quantizer\n private bool disposedValue;\n\n /// <summary>\n /// Sets the delay time between each frame, or changes it\n /// for subsequent frames (applies to last frame added).\n /// </summary>\n /// <param name=\"ms\"> delay time in milliseconds </param>\n public void SetDelay(int ms)\n {\n delay = (int)Math.Round(ms / 10.0f);\n }\n\n /// <summary>\n /// Sets the GIF frame disposal code for the last added frame\n /// and any subsequent frames. Default is 0 if no transparent\n /// color has been set, otherwise 2.\n /// </summary>\n /// <param name=\"code\"> disposal code. </param>\n public void SetDispose(int code)\n {\n if (code >= 0) {\n dispose = code;\n }\n }\n\n /// <summary>\n /// Sets the number of times the set of GIF frames\n /// should be played. Default is 1; 0 means play\n /// indefinitely. Must be invoked before the first\n /// image is added.\n /// </summary>\n /// <param name=\"iter\"> number of iterations. </param>\n public void SetRepeat(int iter)\n {\n if (iter >= 0) {\n repeat = iter;\n }\n }\n\n /// <summary>\n /// Sets the transparent color for the last added frame\n /// and any subsequent frames.\n /// Since all colors are subject to modification\n /// in the quantization process, the color in the final\n /// palette for each frame closest to the given color\n /// becomes the transparent color for that frame.\n /// May be set to null to indicate no transparent color.\n /// </summary>\n /// <param name=\"c\"> Color to be treated as transparent on display. </param>\n public void SetTransparent(SKColor c)\n {\n transparent = c;\n }\n\n /// <summary>\n /// Adds next GIF frame. The frame is not written immediately, but is\n /// actually deferred until the next frame is received so that timing\n /// data can be inserted. Invoking <code>finish()</code> flushes all\n /// frames. If <code>setSize</code> was not invoked, the size of the\n /// first image is used for all subsequent frames.\n /// </summary>\n /// <param name=\"im\"> BufferedImage containing frame to write. </param>\n /// <returns> true if successful. </returns>\n public bool AddFrame(SKBitmap im)\n {\n if ((im == null) || !started) {\n return false;\n }\n bool ok = true;\n try {\n if (!sizeSet) {\n // use first frame's size\n SetSize(im.Width, im.Height);\n }\n image = im;\n GetImagePixels(); // convert to correct format if necessary\n AnalyzePixels(); // build color table & map pixels\n if (firstFrame) {\n WriteLSD(); // logical screen descriptior\n WritePalette(); // global color table\n if (repeat >= 0) {\n // use NS app extension to indicate reps\n WriteNetscapeExt();\n }\n }\n WriteGraphicCtrlExt(); // write graphic control extension\n WriteImageDesc(); // image descriptor\n if (!firstFrame) {\n WritePalette(); // local color table\n }\n WritePixels(); // encode and write pixel data\n firstFrame = false;\n } catch (IOException) {\n ok = false;\n }\n\n return ok;\n }\n\n /// <summary>\n /// Flushes any pending data and closes output file.\n /// If writing to an OutputStream, the stream is not\n /// closed.\n /// </summary>\n /// <returns></returns>\n public bool Finish()\n {\n if (!started) return false;\n bool ok = true;\n started = false;\n try {\n ms.WriteByte(0x3b); // gif trailer\n ms.Flush();\n } catch (IOException) {\n ok = false;\n }\n\n // reset for subsequent use\n transIndex = 0;\n //\t\t\tfs = null;\n image = null;\n pixels = null;\n indexedPixels = null;\n colorTab = null;\n closeStream = false;\n firstFrame = true;\n\n return ok;\n }\n\n /// <summary>\n /// Sets frame rate in frames per second. Equivalent to\n /// <code>setDelay(1000/fps)</code>.\n /// </summary>\n /// <param name=\"fps\"> fps float frame rate (frames per second) </param>\n public void SetFrameRate(float fps)\n {\n if (fps != 0f) {\n delay = (int)Math.Round(100f / fps);\n }\n }\n\n /// <summary>\n /// Sets the GIF frame size. The default size is the\n /// size of the first frame added if this method is\n /// not invoked.\n /// </summary>\n /// <param name=\"w\"> frame width </param>\n /// <param name=\"h\"> frame width </param>\n public void SetSize(int w, int h)\n {\n if (started && !firstFrame) return;\n width = w;\n height = h;\n if (width < 1) width = 320;\n if (height < 1) height = 240;\n sizeSet = true;\n }\n\n /// <summary>\n /// Initiates GIF file creation on the given stream. The stream\n /// is not closed automatically.\n /// </summary>\n /// <param name=\"os\"> OutputStream on which GIF images are written. </param>\n /// <returns> false if initial write failed. </returns>\n public bool Start(MemoryStream os)\n {\n if (os == null) return false;\n bool ok = true;\n closeStream = false;\n ms = os;\n try {\n WriteString(\"GIF89a\"); // header\n } catch (IOException) {\n ok = false;\n }\n return started = ok;\n }\n\n /// <summary>\n /// Initiates writing of a GIF file to a memory stream.\n /// </summary>\n /// <returns></returns>\n public bool Start()\n {\n bool ok;\n try {\n ok = Start(new MemoryStream(10 * 1024));\n closeStream = true;\n } catch (IOException) {\n ok = false;\n }\n return started = ok;\n }\n\n /// <summary>\n /// Initiates writing of a GIF file with the specified name.\n /// </summary>\n /// <param name=\"file\"></param>\n /// <returns></returns>\n public bool Output(string file)\n {\n try {\n var fs = new FileStream(file, FileMode.OpenOrCreate, FileAccess.Write, FileShare.None);\n fs.Write(ms.ToArray(), 0, (int)ms.Length);\n fs.Close();\n } catch (IOException) {\n return false;\n }\n return true;\n }\n\n public MemoryStream Output()\n {\n return ms;\n }\n\n /// <summary>\n /// Analyzes image colors and creates color map.\n /// </summary>\n protected void AnalyzePixels()\n {\n int len = pixels.Length;\n int nPix = len / 3;\n indexedPixels = new byte[nPix];\n var nq = new NeuQuant(pixels, len, sample);\n // initialize quantizer\n colorTab = nq.Process(); // create reduced palette\n // convert map from BGR to RGB\n //\t\t\tfor (int i = 0; i < colorTab.Length; i += 3) \n //\t\t\t{\n //\t\t\t\tbyte temp = colorTab[i];\n //\t\t\t\tcolorTab[i] = colorTab[i + 2];\n //\t\t\t\tcolorTab[i + 2] = temp;\n //\t\t\t\tusedEntry[i / 3] = false;\n //\t\t\t}\n // map image pixels to new palette\n int k = 0;\n for (int i = 0; i < nPix; i++) {\n int index =\n nq.Map(pixels[k++] & 0xff,\n pixels[k++] & 0xff,\n pixels[k++] & 0xff);\n usedEntry[index] = true;\n indexedPixels[i] = (byte)index;\n }\n pixels = null;\n colorDepth = 8;\n palSize = 7;\n // get closest match to transparent color if specified\n if (transparent != SKColor.Empty) {\n //transIndex = FindClosest(transparent);\n transIndex = nq.Map(transparent.Blue, transparent.Green, transparent.Red);\n }\n }\n\n /// <summary>\n /// Returns index of palette color closest to c\n /// </summary>\n /// <param name=\"c\"></param>\n /// <returns></returns>\n protected int FindClosest(SKColor c)\n {\n if (colorTab == null) return -1;\n int r = c.Red;\n int g = c.Green;\n int b = c.Blue;\n int minpos = 0;\n int dmin = 256 * 256 * 256;\n int len = colorTab.Length;\n for (int i = 0; i < len;) {\n int dr = r - (colorTab[i++] & 0xff);\n int dg = g - (colorTab[i++] & 0xff);\n int db = b - (colorTab[i] & 0xff);\n int d = dr * dr + dg * dg + db * db;\n int index = i / 3;\n if (usedEntry[index] && (d < dmin)) {\n dmin = d;\n minpos = index;\n }\n i++;\n }\n return minpos;\n }\n\n /// <summary>\n /// Extracts image pixels into byte array \"pixels\"\n /// </summary>\n protected void GetImagePixels()\n {\n pixels = new byte[3 * image.Width * image.Height];\n int count = 0;\n\n for (int th = 0; th < image.Height; th++) {\n for (int tw = 0; tw < image.Width; tw++) {\n SKColor color = image.GetPixel(tw, th);\n pixels[count] = color.Red;\n count++;\n pixels[count] = color.Green;\n count++;\n pixels[count] = color.Blue;\n count++;\n }\n }\n\n //\t\tpixels = ((DataBufferByte) image.getRaster().getDataBuffer()).getData();\n }\n\n /// <summary>\n /// Writes Graphic Control Extension\n /// </summary>\n protected void WriteGraphicCtrlExt()\n {\n ms.WriteByte(0x21); // extension introducer\n ms.WriteByte(0xf9); // GCE label\n ms.WriteByte(4); // data block size\n int transp, disp;\n if (transparent == SKColor.Empty) {\n transp = 0;\n disp = 0; // dispose = no action\n } else {\n transp = 1;\n disp = 2; // force clear if using transparent color\n }\n if (dispose >= 0) {\n disp = dispose & 7; // user override\n }\n disp <<= 2;\n\n // packed fields\n ms.WriteByte(Convert.ToByte(0 | // 1:3 reserved\n disp | // 4:6 disposal\n 0 | // 7 user input - 0 = none\n transp)); // 8 transparency flag\n\n WriteShort(delay); // delay x 1/100 sec\n ms.WriteByte(Convert.ToByte(transIndex)); // transparent color index\n ms.WriteByte(0); // block terminator\n }\n\n /// <summary>\n /// Writes Image Descriptor\n /// </summary>\n protected void WriteImageDesc()\n {\n ms.WriteByte(0x2c); // image separator\n WriteShort(0); // image position x,y = 0,0\n WriteShort(0);\n WriteShort(width); // image size\n WriteShort(height);\n // packed fields\n if (firstFrame) {\n // no LCT - GCT is used for first (or only) frame\n ms.WriteByte(0);\n } else {\n // specify normal LCT\n ms.WriteByte(Convert.ToByte(0x80 | // 1 local color table 1=yes\n 0 | // 2 interlace - 0=no\n 0 | // 3 sorted - 0=no\n 0 | // 4-5 reserved\n palSize)); // 6-8 size of color table\n }\n }\n\n /// <summary>\n /// Writes Logical Screen Descriptor\n /// </summary>\n protected void WriteLSD()\n {\n // logical screen size\n WriteShort(width);\n WriteShort(height);\n // packed fields\n ms.WriteByte(Convert.ToByte(0x80 | // 1 : global color table flag = 1 (gct used)\n 0x70 | // 2-4 : color resolution = 7\n 0x00 | // 5 : gct sort flag = 0\n palSize)); // 6-8 : gct size\n\n ms.WriteByte(0); // background color index\n ms.WriteByte(0); // pixel aspect ratio - assume 1:1\n }\n\n /// <summary>\n /// Writes Netscape application extension to define repeat count.\n /// </summary>\n protected void WriteNetscapeExt()\n {\n ms.WriteByte(0x21); // extension introducer\n ms.WriteByte(0xff); // app extension label\n ms.WriteByte(11); // block size\n WriteString(\"NETSCAPE\" + \"2.0\"); // app id + auth code\n ms.WriteByte(3); // sub-block size\n ms.WriteByte(1); // loop sub-block id\n WriteShort(repeat); // loop count (extra iterations, 0=repeat forever)\n ms.WriteByte(0); // block terminator\n }\n\n /// <summary>\n /// Writes color table\n /// </summary>\n protected void WritePalette()\n {\n ms.Write(colorTab, 0, colorTab.Length);\n int n = (3 * 256) - colorTab.Length;\n for (int i = 0; i < n; i++) {\n ms.WriteByte(0);\n }\n }\n\n /// <summary>\n /// Encodes and writes pixel data\n /// </summary>\n protected void WritePixels()\n {\n var encoder = new LZWEncoder(indexedPixels, colorDepth);\n encoder.Encode(ms);\n }\n\n /// <summary>\n /// Write 16-bit value to output stream, LSB first\n /// </summary>\n /// <param name=\"value\"></param>\n protected void WriteShort(int value)\n {\n ms.WriteByte(Convert.ToByte(value & 0xff));\n ms.WriteByte(Convert.ToByte((value >> 8) & 0xff));\n }\n\n /// <summary>\n /// Writes string to output stream\n /// </summary>\n /// <param name=\"s\"></param>\n protected void WriteString(string s)\n {\n char[] chars = s.ToCharArray();\n for (int i = 0; i < chars.Length; i++) {\n ms.WriteByte((byte)chars[i]);\n }\n }\n\n protected virtual void Dispose(bool disposing)\n {\n if (!disposedValue) {\n if (disposing) {\n ms.Dispose();\n }\n\n disposedValue = true;\n }\n }\n\n ~Encoder()\n {\n Dispose(disposing: false);\n }\n\n public void Dispose()\n {\n Dispose(disposing: true);\n GC.SuppressFinalize(this);\n }\n }\n }\n }\n }\n }\n}\n"},{"alpha_fraction":0.4467984735965729,"alphanum_fraction":0.47587883472442627,"avg_line_length":32.08258819580078,"blob_id":"01b30a22e3773b7acca800c8b16d684a32688431","content_id":"cca0434fb0783d8358ad95514efcc85442a247c1","detected_licenses":["MIT"],"is_generated":false,"is_vendor":false,"language":"C#","length_bytes":14821,"license_type":"permissive","max_line_length":207,"num_lines":448,"path":"/test/TorchSharpTest/TestJIT.cs","repo_name":"NiklasGustafsson/TorchSharp","src_encoding":"UTF-8","text":"// Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information.\nusing System;\nusing System.IO;\nusing System.Linq;\nusing static TorchSharp.torch;\nusing static TorchSharp.torch.nn;\nusing Xunit;\n\n#nullable enable\n\nnamespace TorchSharp\n{\n [Collection(\"Sequential\")]\n public class TestJIT\n {\n [Fact]\n public void TestLoadJIT_Func()\n {\n // One linear layer followed by ReLU.\n using var m = torch.jit.load<Tensor, Tensor, Tensor>(@\"func.script.dat\");\n\n var sms = m.named_modules().ToArray();\n Assert.Empty(sms);\n\n var kids = m.named_children().ToArray();\n Assert.Empty(kids);\n\n var t = m.call(torch.ones(10), torch.ones(10));\n\n Assert.Equal(new long[] { 10 }, t.shape);\n Assert.Equal(torch.float32, t.dtype);\n Assert.True(torch.tensor(new float[] { 2, 2, 2, 2, 2, 2, 2, 2, 2, 2 }).allclose(t));\n }\n\n [Fact]\n public void TestLoadJIT_1()\n {\n var input = torch.ones(10);\n var expected = torch.tensor(new float[] { 0.313458264f, 0, 0.9996568f, 0, 0, 0 });\n\n // One linear layer followed by ReLU.\n var m = torch.jit.load<Tensor, Tensor>(@\"linrelu.script.dat\");\n if (torch.cuda.is_available()) {\n m = m.to(torch.CUDA);\n input = input.to(torch.CUDA);\n expected = expected.to(torch.CUDA);\n }\n\n var t = m.call(input);\n\n Assert.Equal(new long[] { 6 }, t.shape);\n Assert.Equal(torch.float32, t.dtype);\n Assert.True(expected.allclose(t));\n }\n\n [Fact]\n public void TestSaveJIT()\n {\n var location = \"TestSaveJIT.ts\";\n if (File.Exists(location)) File.Delete(location);\n\n try {\n\n // One linear layer followed by ReLU.\n using var m1 = torch.jit.load<Tensor, Tensor>(@\"linrelu.script.dat\");\n\n torch.jit.save(m1, location);\n using var m2 = torch.jit.load<Tensor, Tensor>(location);\n\n var t = m2.call(torch.ones(10));\n\n Assert.Equal(new long[] { 6 }, t.shape);\n Assert.Equal(torch.float32, t.dtype);\n Assert.True(torch.tensor(new float[] { 0.313458264f, 0, 0.9996568f, 0, 0, 0 }).allclose(t));\n\n } finally {\n if (File.Exists(location)) File.Delete(location);\n }\n }\n\n [Fact]\n public void TestLoadJIT_2()\n {\n // One linear layer followed by ReLU.\n using var m = torch.jit.load<Tensor, Tensor>(@\"scripted.script.dat\");\n var t = m.call(torch.ones(6));\n\n Assert.Equal(new long[] { 6 }, t.shape);\n Assert.Equal(torch.float32, t.dtype);\n Assert.True(torch.tensor(new float[] { 1.554085f, 1.01024628f, -1.35086036f, -1.84021854f, 0.0127189457f, 0.5994258f }).allclose(t));\n }\n\n [Fact]\n public void TestLoadJIT_3()\n {\n // Two linear layers, nested Sequential, ReLU in between.\n using var m = torch.jit.load<Tensor, Tensor>(@\"l1000_100_10.script.dat\");\n\n var sms = m.named_modules().ToArray();\n Assert.Equal(4, sms.Length);\n\n var kids = m.named_children().ToArray();\n Assert.Equal(2, kids.Length);\n\n var t = m.call(torch.ones(1000));\n\n Assert.Equal(new long[] { 10 }, t.shape);\n Assert.Equal(torch.float32, t.dtype);\n Assert.True(torch.tensor(new float[] { 0.564213157f, -0.04519982f, -0.005117342f, 0.395530462f, -0.3780813f, -0.004734449f, -0.3221216f, -0.289159119f, 0.268511474f, 0.180702567f }).allclose(t));\n\n Assert.Throws<System.Runtime.InteropServices.ExternalException>(() => m.call(torch.ones(100)));\n }\n\n [Fact]\n public void TestLoadJIT_4()\n {\n // Definitely not a TorchScript file. Let's see what the runtime does with it.\n Assert.Throws<System.Runtime.InteropServices.ExternalException>(() => torch.jit.load(@\"bug510.dat\"));\n }\n\n [Fact]\n public void TestSaveLoadJITCUDA()\n {\n if (torch.cuda.is_available()) {\n\n {\n using var m = torch.jit.load<Tensor, Tensor>(@\"linrelu.script.dat\");\n\n m.to(DeviceType.CUDA);\n var params0 = m.parameters().ToArray();\n foreach (var p in params0)\n Assert.Equal(DeviceType.CUDA, p.device_type);\n\n var t = m.call(torch.ones(10).cuda()).cpu();\n\n Assert.Equal(new long[] { 6 }, t.shape);\n Assert.Equal(torch.float32, t.dtype);\n Assert.True(torch.tensor(new float[] { 0.313458264f, 0, 0.9996568f, 0, 0, 0 }).allclose(t));\n }\n {\n using var m = torch.jit.load<Tensor, Tensor>(@\"linrelu.script.dat\", DeviceType.CUDA);\n\n var params0 = m.parameters().ToArray();\n foreach (var p in params0)\n Assert.Equal(DeviceType.CUDA, p.device_type);\n\n var t = m.call(torch.ones(10).cuda()).cpu();\n\n Assert.Equal(new long[] { 6 }, t.shape);\n Assert.Equal(torch.float32, t.dtype);\n Assert.True(torch.tensor(new float[] { 0.313458264f, 0, 0.9996568f, 0, 0, 0 }).allclose(t));\n }\n }\n }\n\n [Fact]\n public void TestJIT_TupleOut()\n {\n // def a(x, y):\n // return x + y, x - y\n //\n using var m = torch.jit.load<(Tensor, Tensor)>(@\"tuple_out.dat\");\n\n var x = torch.rand(3, 4);\n var y = torch.rand(3, 4);\n var output = m.call(x, y);\n\n Assert.Multiple(\n () => Assert.Equal(x.shape, output.Item1.shape),\n () => Assert.Equal(x.shape, output.Item2.shape),\n () => Assert.Equal(x + y, output.Item1),\n () => Assert.Equal(x - y, output.Item2)\n );\n }\n\n [Fact]\n public void TestJIT_TupleOutError()\n {\n // def a(x, y):\n // return x + y, x - y\n //\n using var m = torch.jit.load<(Tensor, Tensor)>(@\"func.script.dat\");\n\n var x = torch.rand(3, 4);\n var y = torch.rand(3, 4);\n Assert.Throws<InvalidCastException>(() => m.call(x, y));\n }\n\n [Fact]\n public void TestJIT_ListOut()\n {\n // def a(x, y):\n // return [x + y, x - y]\n //\n using var m = torch.jit.load<Tensor[]>(@\"list_out.dat\");\n\n var x = torch.rand(3, 4);\n var y = torch.rand(3, 4);\n var output = m.call(x, y);\n\n Assert.Multiple(\n () => Assert.Equal(x.shape, output[0].shape),\n () => Assert.Equal(x.shape, output[1].shape),\n () => Assert.Equal(x + y, output[0]),\n () => Assert.Equal(x - y, output[1])\n );\n }\n\n [Fact]\n public void TestJIT_ListOutError()\n {\n // def a(x, y):\n // return x + y, x - y\n //\n using var m = torch.jit.load<Tensor[]>(@\"func.script.dat\");\n\n var x = torch.rand(3, 4);\n var y = torch.rand(3, 4);\n Assert.Throws<InvalidCastException>(() => m.call(x, y));\n }\n\n\n\n [Fact]\n public void TestLoadJIT_Methods()\n {\n // class MyModule(nn.Module):\n // def __init__(self):\n // super().__init__()\n // self.p = nn.Parameter(torch.rand(10))\n // def forward(self, x: Tensor, y: Tensor) -> Tuple[Tensor, Tensor]:\n // return x + y, x - y\n //\n // @torch.jit.export\n // def predict(self, x: Tensor) -> Tensor:\n // return x + self.p\n // @torch.jit.export\n // def add_scalar(self, x: Tensor, i: int) -> Tensor:\n // return x + i\n\n using var m = new TestScriptModule(@\"exported.method.dat\");\n\n var x = torch.rand(3, 4);\n var y = torch.rand(3, 4);\n var output = m.call(x, y);\n\n Assert.Multiple(\n () => Assert.Equal(x.shape, output.Item1.shape),\n () => Assert.Equal(x.shape, output.Item2.shape),\n () => Assert.Equal(x + y, output.Item1),\n () => Assert.Equal(x - y, output.Item2)\n );\n\n var ones = m.add_scalar(torch.zeros(10), 1);\n\n Assert.Equal(torch.ones(10), ones);\n\n var a = torch.rand(10);\n var predict = m.predict(a);\n\n Assert.Multiple(\n () => Assert.NotEqual(a, predict)\n );\n }\n\n internal class TestScriptModule : Module<Tensor, Tensor, (Tensor, Tensor)>\n {\n internal TestScriptModule(string filename) : base(nameof(TestScriptModule))\n {\n m = torch.jit.load<(Tensor, Tensor)> (filename);\n }\n\n public override (Tensor, Tensor) forward(Tensor input1, Tensor input2)\n {\n return m.call(input1, input2);\n }\n\n public Tensor predict(Tensor input)\n {\n return m.invoke<Tensor>(\"predict\", input);\n }\n\n public Tensor add_scalar(Tensor input, int i)\n {\n return m.invoke<Tensor>(\"add_scalar\", input, i);\n }\n\n private torch.jit.ScriptModule<(Tensor, Tensor)> m;\n }\n\n [Fact]\n public void TestJITCompile_1()\n {\n string script = @\"\n def relu_script(a, b):\n return torch.relu(a + b)\n def relu6_script(a, b):\n return torch.relu6(a + b)\n def add_i(x: Tensor, i: int) -> Tensor:\n return x + i\n def add_d(x: Tensor, i: float) -> Tensor:\n return x + i\n def add_ii(x: int, i: int) -> Tuple[int,int]:\n return (x + i,x-i)\n\";\n\n using var cu = torch.jit.compile(script);\n\n Assert.NotNull(cu);\n\n var x = torch.randn(3, 4);\n var y = torch.randn(3, 4);\n\n var zeros = torch.zeros(3, 4);\n var ones = torch.ones(3, 4);\n\n var z = (Tensor)cu.invoke(\"relu_script\", x, y);\n Assert.Equal(torch.nn.functional.relu(x + y), z);\n z = cu.invoke<Tensor>(\"relu6_script\", x, y);\n Assert.Equal(torch.nn.functional.relu6(x + y), z);\n z = cu.invoke<Tensor>(\"add_i\", zeros, 1);\n Assert.Equal(ones, z);\n z = cu.invoke<Tensor>(\"add_d\", zeros, 1.0);\n Assert.Equal(ones, z);\n\n var ss = cu.invoke<(Scalar,Scalar)>(\"add_ii\", 3, 1);\n Assert.Multiple(\n () => Assert.Equal(4, ss.Item1.ToInt32()),\n () => Assert.Equal(2, ss.Item2.ToInt32())\n );\n }\n\n [Fact]\n public void TestJITCompile_2()\n {\n string script = @\"\n def none_script(a: Any, b: Any):\n return a\n def none_tuple(a: Any, b: Any):\n return (a, None)\n def tuple_tuple(a: Any, b: Any, c:Any):\n return (a, (b, None))\n def list_tuple(a: Any, b: Any, c:Any):\n return [a, (b, c)]\n def list_tuple_list(a: Any, b: Any, c:Any):\n return [a, (b, [c, None])]\n\";\n\n using var cu = torch.jit.compile(script);\n\n Assert.NotNull(cu);\n\n var x = torch.randn(3, 4);\n var y = torch.randn(3, 5);\n var w = torch.randn(3, 6);\n\n var z = cu.invoke(\"none_script\", null, null);\n Assert.Null(z);\n z = cu.invoke(\"none_script\", null, y);\n Assert.Null(z);\n z = cu.invoke(\"none_script\", x, null);\n Assert.NotNull(z);\n\n {\n var zArr = cu.invoke<(object, object)>(\"none_tuple\", null, null);\n Assert.Null(zArr.Item1);\n Assert.Null(zArr.Item2);\n }\n\n {\n var zArr = cu.invoke<(object, object)>(\"none_tuple\", x, null);\n Assert.NotNull(zArr.Item1);\n Assert.Null(zArr.Item2);\n }\n\n {\n var zArr = cu.invoke<(object, object)>(\"tuple_tuple\", x, y, w);\n Assert.NotNull(zArr.Item1);\n Assert.Equal(x, (Tensor)zArr.Item1);\n //Assert.NotNull(zArr.Item2);\n var (a,b) = ((object, object))zArr.Item2;\n Assert.NotNull(a);\n Assert.Null(b);\n }\n\n {\n var zArr = cu.invoke<object[]>(\"list_tuple\", x, y, w);\n Assert.NotNull(zArr);\n Assert.NotNull(zArr[0]);\n Assert.IsType<Tensor>(zArr[0]);\n Assert.Equal(x, zArr[0]);\n Assert.NotNull(zArr[1]);\n Assert.IsType<(Tensor,Tensor)>(zArr[1]);\n }\n\n {\n var zArr = cu.invoke<object[]>(\"list_tuple_list\", x, y, w);\n Assert.NotNull(z);\n Assert.NotNull(zArr[0]);\n Assert.Equal(x, zArr[0]);\n Assert.NotNull(zArr[1]);\n }\n }\n\n [Fact]//(Skip =\"Doesn't work yet.\")]\n public void TestJITCompile_3()\n {\n string script = @\"\n def list_first(a: List[Tensor]) -> Tensor:\n return a[0]\n def list_two(a: List[Tensor]) -> List[Tensor]:\n return [a[0],a[1]]\n def list_from_two(a: List[Tensor], b: List[Tensor]) -> List[Tensor]:\n return [a[0],a[1],b[0],b[1]]\n\";\n\n using var cu = torch.jit.compile(script);\n\n Assert.NotNull(cu);\n\n var x = torch.randn(3, 4);\n var y = torch.randn(3, 5);\n var w = torch.randn(3, 6);\n\n {\n var zArr = cu.invoke<Tensor>(\"list_first\", new[] { new[] { x, y } });\n Assert.NotNull(zArr);\n Assert.Equal(x, zArr);\n }\n {\n var zArr = cu.invoke<Tensor[]>(\"list_two\", new [] { new[] { x, w } });\n Assert.NotNull(zArr);\n Assert.Equal(2, zArr.Length);\n Assert.Equal(x, zArr[0]);\n Assert.Equal(w, zArr[1]);\n }\n {\n var zArr = cu.invoke<Tensor[]>(\"list_from_two\", new[] { x, y }, new[] { y, w });\n Assert.NotNull(zArr);\n Assert.Equal(4, zArr.Length);\n Assert.Equal(x, zArr[0]);\n Assert.Equal(y, zArr[1]);\n Assert.Equal(y, zArr[2]);\n Assert.Equal(w, zArr[3]);\n }\n }\n }\n}\n"},{"alpha_fraction":0.6138077974319458,"alphanum_fraction":0.6138077974319458,"avg_line_length":40.36800003051758,"blob_id":"d7dcc65168281c62ac0b6ac35ec7755c686f46cd","content_id":"162eda077cfa06d85cb15a1b17eb6feb4f9ba49a","detected_licenses":["MIT"],"is_generated":false,"is_vendor":false,"language":"C#","length_bytes":5171,"license_type":"permissive","max_line_length":140,"num_lines":125,"path":"/src/TorchSharp/Tensor/Factories/as_tensor.cs","repo_name":"NiklasGustafsson/TorchSharp","src_encoding":"UTF-8","text":"// Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information.\nusing System.Collections.Generic;\nusing System.Linq;\n\n#nullable enable\nnamespace TorchSharp\n{\n public static partial class torch\n {\n // as_tensor()\n\n public static Tensor as_tensor(Tensor data, ScalarType? dtype = null, Device? device = null)\n {\n if (dtype != null && device != null && (data.dtype != dtype || data.device != device)) {\n return data.to(dtype.Value, device).requires_grad_(data.requires_grad);\n } else if (dtype != null && data.dtype != dtype) {\n return data.to(dtype.Value).requires_grad_(data.requires_grad);\n } else if (device != null && data.device != device) {\n return data.to(device).requires_grad_(data.requires_grad);\n } else {\n return data.alias();\n }\n }\n\n public static Tensor as_tensor(IList<bool> rawArray, ScalarType? dtype = null, Device? device = null)\n {\n return torch.from_array(rawArray.ToArray(), dtype, device);\n }\n\n public static Tensor as_tensor(bool[] rawArray, torch.ScalarType? dtype = null, torch.Device? device = null)\n {\n return torch.from_array(rawArray, dtype, device);\n }\n\n public static Tensor as_tensor(IList<byte> rawArray, torch.ScalarType? dtype = null, torch.Device? device = null)\n {\n return torch.from_array(rawArray.ToArray(), dtype, device);\n }\n\n public static Tensor as_tensor(byte[] rawArray, torch.ScalarType? dtype = null, torch.Device? device = null)\n {\n return torch.from_array(rawArray, dtype, device);\n }\n\n public static Tensor as_tensor(IList<sbyte> rawArray, torch.ScalarType? dtype = null, torch.Device? device = null)\n {\n return torch.from_array(rawArray.ToArray(), dtype, device);\n }\n\n public static Tensor as_tensor(sbyte[] rawArray, torch.ScalarType? dtype = null, torch.Device? device = null)\n {\n return torch.from_array(rawArray, dtype, device);\n }\n\n public static Tensor as_tensor(IList<short> rawArray, torch.ScalarType? dtype = null, torch.Device? device = null)\n {\n return torch.from_array(rawArray.ToArray(), dtype, device);\n }\n\n public static Tensor as_tensor(short[] rawArray, torch.ScalarType? dtype = null, torch.Device? device = null)\n {\n return torch.from_array(rawArray, dtype, device);\n }\n\n public static Tensor as_tensor(IList<int> rawArray, torch.ScalarType? dtype = null, torch.Device? device = null)\n {\n return torch.from_array(rawArray.ToArray(), dtype, device);\n }\n\n public static Tensor as_tensor(int[] rawArray, torch.ScalarType? dtype = null, torch.Device? device = null)\n {\n return torch.from_array(rawArray, dtype, device);\n }\n\n public static Tensor as_tensor(IList<long> rawArray, torch.ScalarType? dtype = null, torch.Device? device = null)\n {\n return torch.from_array(rawArray.ToArray(), dtype, device);\n }\n\n public static Tensor as_tensor(long[] rawArray, torch.ScalarType? dtype = null, torch.Device? device = null)\n {\n return torch.from_array(rawArray, dtype, device);\n }\n\n public static Tensor as_tensor(IList<float> rawArray, torch.ScalarType? dtype = null, torch.Device? device = null)\n {\n return torch.from_array(rawArray.ToArray(), dtype, device);\n }\n\n public static Tensor as_tensor(float[] rawArray, torch.ScalarType? dtype = null, torch.Device? device = null)\n {\n return torch.from_array(rawArray, dtype, device);\n }\n\n public static Tensor as_tensor(IList<double> rawArray, torch.ScalarType? dtype = null, torch.Device? device = null)\n {\n return torch.from_array(rawArray.ToArray(), dtype, device);\n }\n\n public static Tensor as_tensor(double[] rawArray, torch.ScalarType? dtype = null, torch.Device? device = null)\n {\n return torch.from_array(rawArray, dtype, device);\n }\n\n public static Tensor as_tensor(IList<(float, float)> rawArray, torch.ScalarType? dtype = null, torch.Device? device = null)\n {\n return torch.from_array(rawArray.ToArray(), dtype, device);\n }\n\n public static Tensor as_tensor((float, float)[] rawArray, torch.ScalarType? dtype = null, torch.Device? device = null)\n {\n return torch.from_array(rawArray, dtype, device);\n }\n\n public static Tensor as_tensor(IList<System.Numerics.Complex> rawArray, torch.ScalarType? dtype = null, torch.Device? device = null)\n {\n return torch.from_array(rawArray.ToArray(), dtype, device);\n }\n\n public static Tensor as_tensor(System.Numerics.Complex[] rawArray, torch.ScalarType? dtype = null, torch.Device? device = null)\n {\n return torch.from_array(rawArray, dtype, device);\n }\n }\n}\n"},{"alpha_fraction":0.561601996421814,"alphanum_fraction":0.5667617917060852,"avg_line_length":52.5293083190918,"blob_id":"cd4abbdaec593fd0eabe05e42dc63a6a009f105f","content_id":"5e514bef5e2ed4c80d2c36d70b4cf99afee6e37e","detected_licenses":["MIT"],"is_generated":false,"is_vendor":false,"language":"C#","length_bytes":60300,"license_type":"permissive","max_line_length":244,"num_lines":1126,"path":"/src/TorchSharp/NN/Losses.cs","repo_name":"NiklasGustafsson/TorchSharp","src_encoding":"UTF-8","text":"// Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information.\nusing System;\nusing static TorchSharp.torch;\nusing static TorchSharp.torch.nn;\nusing static TorchSharp.PInvoke.NativeMethods;\n\n#nullable enable\n\nnamespace TorchSharp\n{\n using Modules;\n\n public abstract class Loss<T1, T2, TResult> : nn.Module<T1, T2, TResult>\n {\n public Loss(torch.nn.Reduction reduction = nn.Reduction.Mean) : base(nameof(Loss<T1, T2, TResult>))\n {\n this.reduction = reduction;\n }\n\n public torch.nn.Reduction reduction { get; }\n }\n\n public abstract class Loss<T1, T2, T3, TResult> : nn.Module<T1, T2, T3, TResult>\n {\n public Loss(torch.nn.Reduction reduction = nn.Reduction.Mean) : base(nameof(Loss<T1, T2, T3, TResult>))\n {\n this.reduction = reduction;\n }\n\n public torch.nn.Reduction reduction { get; }\n }\n public abstract class Loss<T1, T2, T3, T4, TResult> : nn.Module<T1, T2, T3, T4, TResult>\n {\n public Loss(torch.nn.Reduction reduction = nn.Reduction.Mean) : base(nameof(Loss<T1, T2, T3, T4, TResult>))\n {\n this.reduction = reduction;\n }\n\n public torch.nn.Reduction reduction { get; }\n }\n\n public abstract class WeightedLoss<T1, T2, TResult> : Loss<T1, T2, TResult>\n {\n public WeightedLoss(Tensor? weight = null, torch.nn.Reduction reduction = nn.Reduction.Mean) : base(reduction)\n {\n this.weight = weight;\n }\n\n public Tensor? weight { get; }\n }\n\n public abstract class WeightedLoss<T1, T2, T3, TResult> : Loss<T1, T2, T3, TResult>\n {\n public WeightedLoss(Tensor? weight = null, torch.nn.Reduction reduction = nn.Reduction.Mean) : base(reduction)\n {\n this.weight = weight;\n }\n\n public Tensor? weight { get; }\n }\n\n public abstract class WeightedLoss<T1, T2, T3, T4, TResult> : Loss<T1, T2, T3, T4, TResult>\n {\n public WeightedLoss(Tensor? weight = null, torch.nn.Reduction reduction = nn.Reduction.Mean) : base(reduction)\n {\n this.weight = weight;\n }\n\n public Tensor? weight { get; }\n }\n\n public static partial class torch\n {\n public static partial class nn\n {\n /// <summary>\n /// This criterion combines log_softmax and nll_loss in a single function.\n /// </summary>\n /// <param name=\"weight\">A manual rescaling weight if provided it’s repeated to match input tensor shape</param>\n /// <param name=\"ignore_index\">Specifies a target value that is ignored and does not contribute to the input gradient.</param>\n /// <param name=\"reduction\">Specifies the reduction to apply to the output</param>\n /// <returns></returns>\n public static Modules.CrossEntropyLoss CrossEntropyLoss(Tensor? weight = null, long? ignore_index = null, Reduction reduction = Reduction.Mean)\n {\n return new Modules.CrossEntropyLoss(weight, ignore_index, reduction);\n }\n\n /// <summary>\n /// Measures the Binary Cross Entropy between the target and the output.\n /// </summary>\n /// <param name=\"weight\">A manual rescaling weight if provided it’s repeated to match input tensor shape</param>\n /// <param name=\"reduction\">Specifies the reduction to apply to the output</param>\n /// <returns></returns>\n public static Modules.BCELoss BCELoss(Tensor? weight = null, Reduction reduction = Reduction.Mean)\n {\n return new Modules.BCELoss(weight, reduction);\n }\n\n /// <summary>\n /// Measures Binary Cross Entropy between target and output logits.\n /// </summary>\n /// <param name=\"weight\">A manual rescaling weight if provided it’s repeated to match input tensor shape</param>\n /// <param name=\"reduction\">Specifies the reduction to apply to the output</param>\n /// <param name=\"pos_weights\">A weight of positive examples. Must be a vector with length equal to the number of classes.</param>\n /// <returns></returns>\n public static Modules.BCEWithLogitsLoss BCEWithLogitsLoss(Tensor? weight = null, Reduction reduction = Reduction.Mean, Tensor? pos_weights = null)\n {\n return new Modules.BCEWithLogitsLoss(weight, reduction, pos_weights);\n }\n\n /// <summary>\n /// Measures the loss given two input tensor and a lable tensor with values 1 or -1.\n ///\n /// See: https://pytorch.org/docs/stable/generated/torch.nn.CosineEmbeddingLoss.html#torch.nn.CosineEmbeddingLoss\n /// </summary>\n /// <param name=\"margin\"> Should be a number from -1 to 1, 0 to 0.5 is suggested</param>\n /// <param name=\"reduction\">Specifies the reduction to apply to the output</param>\n /// <returns></returns>\n public static Modules.CosineEmbeddingLoss CosineEmbeddingLoss(double margin = 0.0, Reduction reduction = Reduction.Mean)\n {\n return new Modules.CosineEmbeddingLoss(margin, reduction);\n }\n\n\n /// <summary>\n /// The Connectionist Temporal Classification loss.\n ///\n /// Calculates loss between a continuous (unsegmented) time series and a target sequence.\n /// CTCLoss sums over the probability of possible alignments of input to target, producing a\n /// loss value which is differentiable with respect to each input node. The alignment of input to\n /// target is assumed to be “many-to-one”, which limits the length of the target sequence such that\n /// it must be less than the input length.\n /// </summary>\n /// <returns></returns>\n public static Modules.CTCLoss CTCLoss(long blank = 0, bool zero_infinity = false, Reduction reduction = Reduction.Mean)\n {\n return new Modules.CTCLoss(blank, zero_infinity, reduction);\n }\n\n /// <summary>\n /// Measures the loss given an input tensor x and a labels tensor y (containing 1 or -1).\n ///\n /// See: https://pytorch.org/docs/stable/generated/torch.nn.HingeEmbeddingLoss.html#torch.nn.HingeEmbeddingLoss\n /// </summary>\n /// <param name=\"margin\"> Should be a number from -1 to 1, 0 to 0.5 is suggested</param>\n /// <param name=\"reduction\">Specifies the reduction to apply to the output</param>\n /// <returns></returns>\n public static Modules.HingeEmbeddingLoss HingeEmbeddingLoss(double margin = 1.0, Reduction reduction = Reduction.Mean)\n {\n return new Modules.HingeEmbeddingLoss(margin, reduction);\n }\n\n /// <summary>\n /// Creates a criterion that uses a squared term if the absolute element-wise error falls below delta and a delta-scaled L1 term otherwise.\n ///\n /// See: https://pytorch.org/docs/stable/generated/torch.nn.HuberLoss.html#torch.nn.HuberLoss\n /// </summary>\n /// <param name=\"delta\">Specifies the threshold at which to change between delta-scaled L1 and L2 loss. The value must be positive. Default: 1.0</param>\n /// <param name=\"reduction\">Specifies the reduction to apply to the output</param>\n /// <returns></returns>\n public static Modules.HuberLoss HuberLoss(double delta = 1.0, Reduction reduction = Reduction.Mean)\n {\n return new Modules.HuberLoss(delta, reduction);\n }\n\n /// <summary>\n /// Creates a criterion that measures the loss given inputs x1, x2, two 1D mini-batch or 0D Tensors, and a label 1D mini-batch or 0D Tensor y (containing 1 or -1).\n ///\n /// See: https://pytorch.org/docs/stable/generated/torch.nn.MarginRankingLoss.html#torch.nn.MarginRankingLoss\n /// </summary>\n /// <param name=\"margin\">Has a default value of 0.</param>\n /// <param name=\"reduction\">Specifies the reduction to apply to the output</param>\n /// <returns></returns>\n public static Modules.MarginRankingLoss MarginRankingLoss(double margin = 0, Reduction reduction = Reduction.Mean)\n {\n return new Modules.MarginRankingLoss(margin, reduction);\n }\n\n /// <summary>\n /// Creates a criterion that optimizes a multi-label one-versus-all loss based on max-entropy, between input x and target y of size NxC.\n ///\n /// See: https://pytorch.org/docs/stable/generated/torch.nn.MultiLabelSoftMarginLoss.html#torch.nn.MultiLabelSoftMarginLoss\n /// </summary>\n /// <param name=\"weight\">A manual rescaling weight given to each class. If given, it has to be a Tensor of size C. Otherwise, it is treated as if having all ones.</param>\n /// <param name=\"reduction\">Specifies the reduction to apply to the output</param>\n /// <returns></returns>\n public static MultiLabelSoftMarginLoss MultiLabelSoftMarginLoss(Tensor? weight = null, Reduction reduction = Reduction.Mean)\n {\n return new MultiLabelSoftMarginLoss(weight, reduction);\n }\n\n /// <summary>\n /// Creates a criterion that optimizes a multi-class classification hinge loss.\n ///\n /// See: https://pytorch.org/docs/stable/generated/torch.nn.MultiMarginLoss.html#torch.nn.MultiMarginLoss\n /// </summary>\n /// <param name=\"p\">Has a default value of 1. 1 and 2 are the only supported values.</param>\n /// <param name=\"margin\">Has a default value of 1</param>\n /// <param name=\"weight\">A manual rescaling weight given to each class. If given, it has to be a Tensor of size C. Otherwise, it is treated as if having all ones.</param>\n /// <param name=\"reduction\">Specifies the reduction to apply to the output</param>\n public static MultiMarginLoss MultiMarginLoss(int p = 1, double margin = 1.0, Tensor? weight = null, Reduction reduction = Reduction.Mean)\n {\n return new MultiMarginLoss(p, margin, weight, reduction);\n }\n\n /// <summary>\n /// Measures the element-wise mean squared error.\n /// </summary>\n /// <param name=\"reduction\">Specifies the reduction to apply to the output</param>\n /// <returns></returns>\n public static MSELoss MSELoss(Reduction reduction = Reduction.Mean)\n {\n return new MSELoss(reduction);\n }\n\n /// <summary>\n /// Function that takes the mean element-wise absolute value difference.\n /// </summary>\n /// <param name=\"reduction\">Specifies the reduction to apply to the output</param>\n /// <returns></returns>\n public static L1Loss L1Loss(Reduction reduction = Reduction.Mean)\n {\n return new L1Loss(reduction);\n }\n\n /// <summary>\n /// The negative log likelihood loss.\n /// </summary>\n /// <param name=\"weight\">A manual rescaling weight if provided it’s repeated to match input tensor shape</param>\n /// <param name=\"reduction\">Specifies the reduction to apply to the output</param>\n /// <returns></returns>\n public static NLLLoss NLLLoss(Tensor? weight = null, Reduction reduction = Reduction.Mean)\n {\n return new NLLLoss(weight, reduction);\n }\n\n /// <summary>\n /// Poisson negative log likelihood loss.\n /// </summary>\n /// <param name=\"log_input\"></param>\n /// <param name=\"full\">Whether to compute full loss, i. e. to add the Stirling approximation term.</param>\n /// <param name=\"eps\">Small value to avoid evaluation of log(0)</param>\n /// <param name=\"reduction\">Specifies the reduction to apply to the output</param>\n /// <returns></returns>\n public static PoissonNLLLoss PoissonNLLLoss(bool log_input = true, bool full = false, float eps = 1e-8f, Reduction reduction = Reduction.Mean)\n {\n return new PoissonNLLLoss(log_input, full, eps, reduction);\n }\n\n /// <summary>\n /// The Kullback-Leibler divergence Loss\n /// </summary>\n /// <param name=\"log_target\">A flag indicating whether target is passed in the log space.</param>\n /// <param name=\"reduction\">Specifies the reduction to apply to the output</param>\n /// <returns></returns>\n public static KLDivLoss KLDivLoss(bool log_target = true, Reduction reduction = Reduction.Mean)\n {\n return new KLDivLoss(log_target, reduction);\n }\n\n /// <summary>\n /// Optimizes a two-class classification logistic loss between input tensor xx and target tensor yy (containing 1 or -1).\n /// </summary>\n /// <param name=\"reduction\">Specifies the reduction to apply to the output</param>\n /// <returns></returns>\n public static SoftMarginLoss SoftMarginLoss(Reduction reduction = Reduction.Mean)\n {\n return new SoftMarginLoss(reduction);\n }\n\n /// <summary>\n /// Creates a criterion that measures the triplet loss given an input tensors x1, x2, x3 and a margin with a value greater than 0.\n /// This is used for measuring a relative similarity between samples.\n ///\n /// See: https://pytorch.org/docs/stable/generated/torch.nn.TripletMarginLoss.html#torch.nn.TripletMarginLoss\n /// </summary>\n /// <param name=\"margin\">\n /// A nonnegative margin representing the minimum difference between the positive and negative distances required for the loss to be 0.\n /// Larger margins penalize cases where the negative examples are not distant enough from the anchors, relative to the positives.\n /// </param>\n /// <param name=\"p\">The norm degree for pairwise distance. </param>\n /// <param name=\"eps\"></param>\n /// <param name=\"swap\">\n /// If true, and if the positive example is closer to the negative example than the anchor is, swaps the positive example and the anchor in the loss computation.\n /// The distance swap is described in detail in the paper Learning shallow convolutional feature descriptors with triplet losses by V. Balntas, E. Riba et al.\n /// </param>\n /// <param name=\"reduction\">Specifies the reduction to apply to the output</param>\n /// <returns></returns>\n public static TripletMarginLoss TripletMarginLoss(double margin = 1.0, long p = 2, double eps = 1e-06, bool swap = false, Reduction reduction = Reduction.Mean)\n {\n return new TripletMarginLoss(margin, p, eps, swap, reduction);\n }\n\n /// <summary>\n /// Creates a criterion that measures the triplet loss given input tensors a, p, and n (representing anchor, positive, and negative examples, respectively),\n /// and a nonnegative, real-valued function (\"distance function\") used to compute the relationship between the anchor and positive example (\"positive distance\")\n /// and the anchor and negative example (\"negative distance\").\n /// </summary>\n /// <param name=\"distance\"> A nonnegative, real-valued function that quantifies the closeness of two tensors. If not specified, nn.PairwiseDistance will be used.</param>\n /// <param name=\"margin\">\n /// A nonnegative margin representing the minimum difference between the positive and negative distances required for the loss to be 0.\n /// Larger margins penalize cases where the negative examples are not distant enough from the anchors, relative to the positives.\n /// </param>\n /// <param name=\"swap\">\n /// If true, and if the positive example is closer to the negative example than the anchor is, swaps the positive example and the anchor in the loss computation.\n /// The distance swap is described in detail in the paper Learning shallow convolutional feature descriptors with triplet losses by V. Balntas, E. Riba et al.\n /// </param>\n /// <param name=\"reduction\">Specifies the reduction to apply to the output</param>\n /// <returns></returns>\n public static TripletMarginWithDistanceLoss TripletMarginWithDistanceLoss(Func<Tensor, Tensor, Tensor>? distance = null, double margin = 1.0, bool swap = false, Reduction reduction = Reduction.Mean)\n {\n return new TripletMarginWithDistanceLoss(distance, margin, swap, reduction);\n }\n\n /// <summary>\n /// Gaussian negative log likelihood loss.\n /// </summary>\n /// <param name=\"full\">Include the constant term in the loss calculation</param>\n /// <param name=\"eps\">Value used to clamp var (see note below), for stability.</param>\n /// <param name=\"reduction\">Specifies the reduction to apply to the output</param>\n /// <returns></returns>\n public static GaussianNLLLoss GaussianNLLLoss(bool full = false, float eps = 1e-8f, Reduction reduction = Reduction.Mean)\n {\n return new GaussianNLLLoss(full, eps, reduction);\n }\n\n /// <summary>\n /// Creates a criterion that optimizes a multi-class multi-classification hinge loss (margin-based loss) between input x (a 2D mini-batch Tensor)\n /// and output y (which is a 2D Tensor of target class indices).\n /// </summary>\n /// <param name=\"reduction\">Specifies the reduction to apply to the output</param>\n /// <returns></returns>\n public static MultiLabelMarginLoss MultiLabelMarginLoss(Reduction reduction = Reduction.Mean)\n {\n return new MultiLabelMarginLoss(reduction);\n }\n\n /// <summary>\n /// Function that uses a squared term if the absolute element-wise error falls below beta and an L1 term otherwise.\n /// </summary>\n /// <param name=\"beta\">Specifies the threshold at which to change between L1 and L2 loss. The value must be non-negative. Default: 1.0</param>\n /// <param name=\"reduction\">Specifies the reduction to apply to the output</param>\n /// <returns></returns>\n public static SmoothL1Loss SmoothL1Loss(Reduction reduction = Reduction.Mean, double beta = 1.0)\n {\n return new SmoothL1Loss(reduction, beta);\n }\n\n\n /// <summary>\n /// Class maintaing the supported loss functions.\n /// </summary>\n public static partial class functional\n {\n /// <summary>\n /// Function that measures Binary Cross Entropy between target and input logits.\n /// </summary>\n /// <param name=\"input\">Tensor of arbitrary shape as unnormalized scores (often referred to as logits).</param>\n /// <param name=\"target\">Tensor of the same shape as input with values between 0 and 1</param>\n /// <param name=\"weight\">A manual rescaling weight if provided it’s repeated to match input tensor shape</param>\n /// <param name=\"reduction\">Specifies the reduction to apply to the output</param>\n /// <param name=\"pos_weights\">A weight of positive examples. Must be a vector with length equal to the number of classes.</param>\n /// <returns></returns>\n public static Tensor binary_cross_entropy_with_logits(Tensor input, Tensor target, Tensor? weight = null, Reduction reduction = Reduction.Mean, Tensor? pos_weights = null)\n {\n var res = THSNN_binary_cross_entropy_with_logits(input.Handle, target.Handle, weight?.Handle ?? IntPtr.Zero, (long)reduction, pos_weights?.Handle ?? IntPtr.Zero);\n if (res == IntPtr.Zero) { torch.CheckForErrors(); }\n return new Tensor(res);\n }\n\n /// <summary>\n /// Function that measures the Binary Cross Entropy between the target and input probabilities.\n /// </summary>\n /// <param name=\"input\">Tensor of arbitrary shape as probabilities.</param>\n /// <param name=\"target\">Tensor of the same shape as input with values between 0 and 1</param>\n /// <param name=\"weight\">A manual rescaling weight if provided it’s repeated to match input tensor shape</param>\n /// <param name=\"reduction\">Specifies the reduction to apply to the output</param>\n /// <returns></returns>\n public static Tensor binary_cross_entropy(Tensor input, Tensor target, Tensor? weight = null, Reduction reduction = Reduction.Mean)\n {\n var res = THSNN_binary_cross_entropy(input.Handle, target.Handle, weight?.Handle ?? IntPtr.Zero, (long)reduction);\n if (res == IntPtr.Zero) { torch.CheckForErrors(); }\n return new Tensor(res);\n }\n\n /// <summary>\n /// Computes the cross entropy loss between input and target.\n /// </summary>\n /// <param name=\"input\">Tensor of arbitrary shape as unnormalized scores (often referred to as logits).</param>\n /// <param name=\"target\">Ground truth class indices or class probabilities; see Shape section below for supported shapes.</param>\n /// <param name=\"weight\">A manual rescaling weight if provided it’s repeated to match input tensor shape</param>\n /// <param name=\"ignore_index\">\n /// Specifies a target value that is ignored and does not contribute to the input gradient.\n /// Note that ignore_index is only applicable when the target contains class indices.\n /// </param>\n /// <param name=\"reduction\">Specifies the reduction to apply to the output</param>\n /// <param name=\"label_smoothing\">A float in [0.0, 1.0].\n /// Specifies the amount of smoothing when computing the loss, where 0.0 means no smoothing.\n /// The targets become a mixture of the original ground truth and a uniform distribution.</param>\n /// <returns></returns>\n public static Tensor cross_entropy(Tensor input, Tensor target, Tensor? weight = null, long ignore_index = -100, Reduction reduction = Reduction.Mean, double label_smoothing = 0.0)\n {\n var res = THSNN_cross_entropy(input.Handle, target.Handle, weight?.Handle ?? IntPtr.Zero, ignore_index, true, (long)reduction, label_smoothing);\n if (res == IntPtr.Zero) { torch.CheckForErrors(); }\n return new Tensor(res);\n }\n\n /// <summary>\n /// Poisson negative log likelihood loss.\n /// </summary>\n /// <param name=\"input\">Expectation of underlying Poisson distribution.</param>\n /// <param name=\"target\">Random sample target.</param>\n /// <param name=\"log_input\"></param>\n /// <param name=\"full\">Whether to compute full loss, i.e. to add the Stirling approximation term.</param>\n /// <param name=\"eps\">Small value to avoid evaluation of log(0)</param>\n /// <param name=\"reduction\">Specifies the reduction to apply to the output</param>\n /// <returns></returns>\n public static Tensor poisson_nll_loss(Tensor input, Tensor target, bool log_input = true, bool full = false, float eps = 1e-8f, Reduction reduction = Reduction.Mean)\n {\n var res = THSNN_poisson_loss(input.Handle, target.Handle, log_input, full, eps, (long)reduction);\n if (res == IntPtr.Zero) { torch.CheckForErrors(); }\n return new Tensor(res);\n }\n\n /// <summary>\n ///\n /// </summary>\n /// <param name=\"input1\">(N,D) or (D), where N is the batch size and D is the embedding dimension.</param>\n /// <param name=\"input2\">Same shape as input1</param>\n /// <param name=\"target\">N or ()</param>\n /// <param name=\"margin\">Should be a number from -1−1 to 11, 00 to 0.50.5 is suggested</param>\n /// <param name=\"reduction\">Specifies the reduction to apply to the output</param>\n /// <returns></returns>\n public static Tensor cosine_embedding_loss(Tensor input1, Tensor input2, Tensor target, double margin = 0.0, Reduction reduction = Reduction.Mean)\n {\n var res = THSNN_cosine_embedding_loss(input1.Handle, input2.Handle, target.Handle, margin, (long)reduction);\n if (res == IntPtr.Zero) { torch.CheckForErrors(); }\n return new Tensor(res);\n }\n\n /// <summary>\n /// Computes the Connectionist Temporal Classification loss.\n /// </summary>\n /// <param name=\"log_probs\">The logarithmized probabilities of the outputs.</param>\n /// <param name=\"targets\"></param>\n /// <param name=\"input_lengths\">Lengths of the inputs.</param>\n /// <param name=\"target_lengths\">Lengths of the targets.</param>\n /// <param name=\"blank\">Blank label.</param>\n /// <param name=\"zero_infinity\">Whether to zero infinite losses and the associated gradients.</param>\n /// <param name=\"reduction\">Specifies the reduction to apply to the output</param>\n /// <returns></returns>\n public static Tensor ctc_loss(Tensor log_probs, Tensor targets, Tensor input_lengths, Tensor target_lengths, long blank = 0, bool zero_infinity = false, Reduction reduction = Reduction.Mean)\n {\n var res = THSNN_ctc_loss(log_probs.Handle, targets.Handle, input_lengths.Handle, target_lengths.Handle, blank, zero_infinity, (long)reduction);\n if (res == IntPtr.Zero) { torch.CheckForErrors(); }\n return new Tensor(res);\n }\n\n /// <summary>\n /// Measures the loss given an input tensor x and a labels tensor y (containing 1 or -1).\n /// </summary>\n /// <param name=\"input\"></param>\n /// <param name=\"target\"></param>\n /// <param name=\"margin\"> Should be a number from -1 to 1, 0 to 0.5 is suggested</param>\n /// <param name=\"reduction\">Specifies the reduction to apply to the output</param>\n /// <returns></returns>\n public static Tensor hinge_embedding_loss(Tensor input, Tensor target, double margin = 0.0, Reduction reduction = Reduction.Mean)\n {\n var res = THSNN_hinge_embedding_loss(input.Handle, target.Handle, margin, (long)reduction);\n if (res == IntPtr.Zero) { torch.CheckForErrors(); }\n return new Tensor(res);\n }\n\n /// <summary>\n /// Function that uses a squared term if the absolute element-wise error falls below delta and a delta-scaled L1 term otherwise.\n /// </summary>\n /// <param name=\"input\"></param>\n /// <param name=\"target\"></param>\n /// <param name=\"delta\">Specifies the threshold at which to change between delta-scaled L1 and L2 loss. The value must be positive. Default: 1.0</param>\n /// <param name=\"reduction\">Specifies the reduction to apply to the output</param>\n /// <returns></returns>\n public static Tensor huber_loss(Tensor input, Tensor target, double delta = 1.0, Reduction reduction = Reduction.Mean)\n {\n var res = THSNN_huber_loss(input.Handle, target.Handle, delta, (long)reduction);\n if (res == IntPtr.Zero) { torch.CheckForErrors(); }\n return new Tensor(res);\n }\n\n /// <summary>\n /// Creates a criterion that measures the loss given inputs x1, x2, two 1D mini-batch or 0D Tensors, and a label 1D mini-batch or 0D Tensor y (containing 1 or -1).\n /// </summary>\n /// <param name=\"input1\"></param>\n /// <param name=\"input2\"></param>\n /// <param name=\"target\"></param>\n /// <param name=\"margin\">Has a default value of 0.</param>\n /// <param name=\"reduction\">Specifies the reduction to apply to the output</param>\n /// <returns></returns>\n public static Tensor margin_ranking_loss(Tensor input1, Tensor input2, Tensor target, double margin = 0.0, Reduction reduction = Reduction.Mean)\n {\n var res = THSNN_margin_ranking_loss(input1.Handle, input2.Handle, target.Handle, margin, (long)reduction);\n if (res == IntPtr.Zero) { torch.CheckForErrors(); }\n return new Tensor(res);\n }\n\n /// <summary>\n /// Creates a criterion that optimizes a multi-class multi-classification hinge loss (margin-based loss) between input x (a 2D mini-batch Tensor)\n /// and output y (which is a 2D Tensor of target class indices).\n /// </summary>\n /// <param name=\"input\"></param>\n /// <param name=\"target\"></param>\n /// <param name=\"reduction\">Specifies the reduction to apply to the output</param>\n /// <returns></returns>\n public static Tensor multi_label_margin_loss(Tensor input, Tensor target, Reduction reduction = Reduction.Mean)\n {\n var res = THSNN_multilabel_margin_loss(input.Handle, target.Handle, (long)reduction);\n if (res == IntPtr.Zero) { torch.CheckForErrors(); }\n return new Tensor(res);\n }\n\n /// <summary>\n /// Creates a criterion that optimizes a multi-label one-versus-all loss based on max-entropy, between input x and target y of size NxC.\n /// </summary>\n /// <param name=\"input\"></param>\n /// <param name=\"target\"></param>\n /// <param name=\"weight\">A manual rescaling weight if provided it’s repeated to match input tensor shape</param>\n /// <param name=\"reduction\">Specifies the reduction to apply to the output</param>\n /// <returns></returns>\n public static Tensor multilabel_soft_margin_loss(Tensor input, Tensor target, Tensor? weight = null,Reduction reduction = Reduction.Mean)\n {\n var res = THSNN_multilabel_soft_margin_loss(input.Handle, target.Handle, weight?.Handle ?? IntPtr.Zero, (long)reduction);\n if (res == IntPtr.Zero) { torch.CheckForErrors(); }\n return new Tensor(res);\n }\n\n /// <summary>\n /// Creates a criterion that optimizes a multi-class classification hinge loss.\n /// </summary>\n /// <param name=\"input\"></param>\n /// <param name=\"target\"></param>\n /// <param name=\"p\">Has a default value of 1. 1 and 2 are the only supported values.</param>\n /// <param name=\"margin\">Has a default value of 1</param>\n /// <param name=\"weight\">A manual rescaling weight if provided it’s repeated to match input tensor shape</param>\n /// <param name=\"reduction\">Specifies the reduction to apply to the output</param>\n /// <returns></returns>\n public static Tensor multi_margin_loss(Tensor input, Tensor target, int p = 1, double margin = 1.0, Tensor? weight = null, Reduction reduction = Reduction.Mean)\n {\n IntPtr h = (weight is null) ? IntPtr.Zero : weight.Handle;\n var res = THSNN_multi_margin_loss(input.Handle, target.Handle, p, margin, h, (long)reduction);\n if (res == IntPtr.Zero) { torch.CheckForErrors(); }\n return new Tensor(res);\n }\n\n /// <summary>\n ///\tMeasures the element-wise mean squared error.\n /// </summary>\n /// <param name=\"input\">Tensor of any shape.</param>\n /// <param name=\"target\">Tensor of the same shape as 'input'</param>\n /// <param name=\"reduction\">Specifies the reduction to apply to the output</param>\n /// <returns></returns>\n public static Tensor mse_loss(Tensor input, Tensor target, Reduction reduction = Reduction.Mean)\n {\n var res = THSNN_mse_loss(input.Handle, target.Handle, (long)reduction);\n if (res == IntPtr.Zero) { torch.CheckForErrors(); }\n return new Tensor(res);\n }\n\n /// <summary>\n /// Function that takes the mean element-wise absolute value difference.\n /// </summary>\n /// <param name=\"input\">Tensor of any shape.</param>\n /// <param name=\"target\">Tensor of the same shape as 'input'</param>\n /// <param name=\"reduction\">Specifies the reduction to apply to the output</param>\n /// <returns></returns>\n public static Tensor l1_loss(Tensor input, Tensor target, Reduction reduction = Reduction.Mean)\n {\n var res = THSNN_l1_loss(input.Handle, target.Handle, (long)reduction);\n if (res == IntPtr.Zero) { torch.CheckForErrors(); }\n return new Tensor(res);\n }\n\n /// <summary>\n /// Computes the negative log likelihood loss.\n /// </summary>\n /// <param name=\"input\"></param>\n /// <param name=\"target\"></param>\n /// <param name=\"weight\">A manual rescaling weight if provided it’s repeated to match input tensor shape</param>\n /// <param name=\"reduction\">Specifies the reduction to apply to the output</param>\n /// <returns></returns>\n public static Tensor nll_loss(Tensor input, Tensor target, Tensor? weight = null, Reduction reduction = Reduction.Mean)\n {\n var res = THSNN_nll_loss(input.Handle, target.Handle, weight?.Handle ?? IntPtr.Zero, (long)reduction);\n if (res == IntPtr.Zero) { torch.CheckForErrors(); }\n return new Tensor(res);\n }\n\n /// <summary>\n /// Gaussian negative log likelihood loss.\n /// </summary>\n /// <param name=\"input\"></param>\n /// <param name=\"target\"></param>\n /// <param name=\"variance\">Tensor of positive variance(s), one for each of the expectations in the input (heteroscedastic), or a single one (homoscedastic).</param>\n /// <param name=\"full\">Include the constant term in the loss calculation. </param>\n /// <param name=\"eps\">Value added to var, for stability</param>\n /// <param name=\"reduction\">Specifies the reduction to apply to the output</param>\n /// <returns></returns>\n public static Tensor gaussian_nll_loss(Tensor input, Tensor target, Tensor variance, bool full = false, float eps = 1e-6f, Reduction reduction = Reduction.Mean)\n {\n return new Modules.GaussianNLLLoss(full, eps, reduction).call(input, target, variance);\n }\n\n /// <summary>\n /// Computes the Kullback-Leibler divergence Loss\n /// </summary>\n /// <param name=\"input\"></param>\n /// <param name=\"target\"></param>\n /// <param name=\"log_target\">A flag indicating whether target is passed in the log space.</param>\n /// <param name=\"reduction\">Specifies the reduction to apply to the output</param>\n /// <returns></returns>\n public static Tensor kl_div(Tensor input, Tensor target, bool log_target = true, Reduction reduction = Reduction.Mean)\n {\n var res = THSNN_kl_div_loss(input.Handle, target.Handle, (long)reduction, log_target);\n if (res == IntPtr.Zero) { torch.CheckForErrors(); }\n return new Tensor(res);\n }\n\n /// <summary>\n /// Function that uses a squared term if the absolute element-wise error falls below beta and an L1 term otherwise.\n /// </summary>\n /// <param name=\"input\"></param>\n /// <param name=\"target\"></param>\n /// <param name=\"reduction\">Specifies the reduction to apply to the output</param>\n /// <param name=\"beta\">Specifies the threshold at which to change between L1 and L2 loss. The value must be non-negative.</param>\n /// <returns></returns>\n public static Tensor smooth_l1_loss(Tensor input, Tensor target, Reduction reduction = Reduction.Mean, double beta = 1.0)\n {\n var res = THSNN_smooth_l1_loss(input.Handle, target.Handle, (long)reduction, beta);\n if (res == IntPtr.Zero) { torch.CheckForErrors(); }\n return new Tensor(res);\n }\n\n /// <summary>\n /// Optimizes a two-class classification logistic loss between input tensor x and target tensor y (containing 1 or -1).\n /// </summary>\n /// <param name=\"input\"></param>\n /// <param name=\"target\"></param>\n /// <param name=\"reduction\">Specifies the reduction to apply to the output</param>\n /// <returns></returns>\n public static Tensor soft_margin_loss(Tensor input, Tensor target, Reduction reduction = Reduction.Mean)\n {\n var res = THSNN_soft_margin_loss(input.Handle, target.Handle, (long)reduction);\n if (res == IntPtr.Zero) { torch.CheckForErrors(); }\n return new Tensor(res);\n }\n\n /// <summary>\n /// Creates a criterion that measures the triplet loss given an input tensors x1, x2, x3 and a margin with a value greater than 0.\n /// This is used for measuring a relative similarity between samples.\n /// </summary>\n /// <param name=\"anchor\"></param>\n /// <param name=\"positive\"></param>\n /// <param name=\"negative\"></param>\n /// <param name=\"margin\">\n /// A nonnegative margin representing the minimum difference between the positive and negative distances required for the loss to be 0.\n /// Larger margins penalize cases where the negative examples are not distant enough from the anchors, relative to the positives.\n /// </param>\n /// <param name=\"p\">The norm degree for pairwise distance. </param>\n /// <param name=\"eps\"></param>\n /// <param name=\"swap\">\n /// If true, and if the positive example is closer to the negative example than the anchor is, swaps the positive example and the anchor in the loss computation.\n /// The distance swap is described in detail in the paper Learning shallow convolutional feature descriptors with triplet losses by V. Balntas, E. Riba et al.\n /// </param>\n /// <param name=\"reduction\">Specifies the reduction to apply to the output</param>\n /// <returns></returns>\n public static Tensor triplet_margin_loss(Tensor anchor, Tensor positive, Tensor negative, double margin = 1.0, long p = 2, double eps = 1e-06, bool swap = false, Reduction reduction = Reduction.Mean)\n {\n var res = THSNN_triplet_margin_loss(anchor.Handle, positive.Handle, negative.Handle, margin, p, eps, swap, (long)reduction);\n if (res == IntPtr.Zero) { torch.CheckForErrors(); }\n return new Tensor(res);\n }\n\n /// <summary>\n /// Creates a criterion that measures the triplet loss given input tensors a, p, and n (representing anchor, positive, and negative examples, respectively),\n /// and a nonnegative, real-valued function (\"distance function\") used to compute the relationship between the anchor and positive example (\"positive distance\")\n /// and the anchor and negative example (\"negative distance\").\n /// </summary>\n /// <param name=\"anchor\"></param>\n /// <param name=\"positive\"></param>\n /// <param name=\"negative\"></param>\n /// <param name=\"distance\"> A nonnegative, real-valued function that quantifies the closeness of two tensors. If not specified, nn.PairwiseDistance will be used.</param>\n /// <param name=\"margin\">\n /// A nonnegative margin representing the minimum difference between the positive and negative distances required for the loss to be 0.\n /// Larger margins penalize cases where the negative examples are not distant enough from the anchors, relative to the positives.\n /// </param>\n /// <param name=\"swap\">\n /// If true, and if the positive example is closer to the negative example than the anchor is, swaps the positive example and the anchor in the loss computation.\n /// The distance swap is described in detail in the paper Learning shallow convolutional feature descriptors with triplet losses by V. Balntas, E. Riba et al.\n /// </param>\n /// <param name=\"reduction\">Specifies the reduction to apply to the output</param>\n /// <returns></returns>\n public static Tensor triplet_margin_with_distance_loss(Tensor anchor, Tensor positive, Tensor negative, Func<Tensor, Tensor, Tensor>? distance = null, double margin = 1.0, bool swap = false, Reduction reduction = Reduction.Mean)\n {\n DistanceFunctionNative? func = null;\n\n if (distance != null) {\n func = (IntPtr x, IntPtr y) => {\n var x1 = new Tensor(x);\n var y1 = new Tensor(y);\n var res = distance(x1, y1);\n\n GC.SuppressFinalize(x1);\n GC.SuppressFinalize(y1);\n GC.SuppressFinalize(res);\n\n return res.Handle;\n };\n }\n var res = THSNN_triplet_margin_with_distance_loss(anchor.Handle, positive.Handle, negative.Handle, func, margin, swap, (long)reduction);\n if (res == IntPtr.Zero) { torch.CheckForErrors(); }\n return new Tensor(res);\n }\n }\n\n public enum Reduction : long\n {\n None = 0,\n Mean = 1,\n Sum = 2\n }\n }\n }\n\n namespace Modules\n {\n public sealed class CrossEntropyLoss : WeightedLoss<Tensor, Tensor, Tensor>\n {\n public CrossEntropyLoss(Tensor? weight = null, long? ignore_index = null, Reduction reduction = Reduction.Mean, double label_smoothing = 0.0) : base(weight, reduction)\n {\n this.ignore_index = ignore_index;\n this.label_smoothing = label_smoothing;\n }\n\n public override Tensor forward(Tensor input, Tensor target)\n {\n var ii = ignore_index.HasValue ? ignore_index.Value : -100;\n var res = THSNN_cross_entropy(input.Handle, target.Handle, weight?.Handle ?? IntPtr.Zero, ii, ignore_index.HasValue, (long)reduction, label_smoothing);\n if (res == IntPtr.Zero) { torch.CheckForErrors(); }\n return new Tensor(res);\n }\n\n public long? ignore_index { get; }\n public double label_smoothing { get; }\n }\n\n public sealed class BCELoss : WeightedLoss<Tensor, Tensor, Tensor>\n {\n public BCELoss(Tensor? weight = null, Reduction reduction = Reduction.Mean) : base(weight, reduction)\n {\n }\n\n public override Tensor forward(Tensor input, Tensor target)\n {\n var res = THSNN_binary_cross_entropy(input.Handle, target.Handle, weight?.Handle ?? IntPtr.Zero, (long)reduction);\n if (res == IntPtr.Zero) { torch.CheckForErrors(); }\n return new Tensor(res);\n }\n }\n\n public sealed class BCEWithLogitsLoss : WeightedLoss<Tensor, Tensor, Tensor>\n {\n public BCEWithLogitsLoss(Tensor? weight = null, Reduction reduction = Reduction.Mean, Tensor? pos_weights = null) : base(weight, reduction)\n {\n this.pos_weights = pos_weights;\n }\n\n public override Tensor forward(Tensor input, Tensor target)\n {\n var res = THSNN_binary_cross_entropy_with_logits(input.Handle, target.Handle, weight?.Handle ?? IntPtr.Zero, (long)reduction, pos_weights?.Handle ?? IntPtr.Zero);\n if (res == IntPtr.Zero) { torch.CheckForErrors(); }\n return new Tensor(res);\n }\n\n public Tensor? pos_weights { get; }\n }\n\n public sealed class CosineEmbeddingLoss : Loss<Tensor, Tensor, Tensor, Tensor>\n {\n public CosineEmbeddingLoss(double margin = 0.0, Reduction reduction = Reduction.Mean) : base(reduction)\n {\n this.margin = margin;\n }\n\n public override Tensor forward(Tensor input1, Tensor input2, Tensor target)\n {\n var res = THSNN_cosine_embedding_loss(input1.Handle, input2.Handle, target.Handle, margin, (long)reduction);\n if (res == IntPtr.Zero) { torch.CheckForErrors(); }\n return new Tensor(res);\n }\n\n public double margin { get; }\n }\n\n public sealed class CTCLoss : Loss<Tensor, Tensor, Tensor, Tensor, Tensor>\n {\n public CTCLoss(long blank = 0, bool zero_infinity = false, Reduction reduction = Reduction.Mean) : base(reduction)\n {\n this.blank = blank;\n this.zero_infinity = zero_infinity;\n }\n\n public override Tensor forward(Tensor log_probs, Tensor targets, Tensor input_lengths, Tensor target_lengths)\n {\n var res = THSNN_ctc_loss(log_probs.Handle, targets.Handle, input_lengths.Handle, target_lengths.Handle, blank, zero_infinity, (long)reduction);\n if (res == IntPtr.Zero) { torch.CheckForErrors(); }\n return new Tensor(res);\n }\n\n public long blank { get; }\n public bool zero_infinity { get; }\n }\n\n public sealed class HingeEmbeddingLoss : Loss<Tensor, Tensor, Tensor>\n {\n public HingeEmbeddingLoss(double margin = 0.0, Reduction reduction = Reduction.Mean) : base(reduction)\n {\n this.margin = margin;\n }\n\n public override Tensor forward(Tensor input, Tensor target)\n {\n var res = THSNN_hinge_embedding_loss(input.Handle, target.Handle, margin, (long)reduction);\n if (res == IntPtr.Zero) { torch.CheckForErrors(); }\n return new Tensor(res);\n }\n\n public double margin { get; }\n }\n\n public sealed class HuberLoss : Loss<Tensor, Tensor, Tensor>\n {\n public HuberLoss(double delta = 1.0, Reduction reduction = Reduction.Mean) : base(reduction)\n {\n this.delta = delta;\n }\n\n public override Tensor forward(Tensor input, Tensor target)\n {\n var res = THSNN_huber_loss(input.Handle, target.Handle, delta, (long)reduction);\n if (res == IntPtr.Zero) { torch.CheckForErrors(); }\n return new Tensor(res);\n }\n\n public double delta { get; }\n }\n\n public sealed class MarginRankingLoss : Loss<Tensor, Tensor, Tensor, Tensor>\n {\n public MarginRankingLoss(double margin = 0.0, Reduction reduction = Reduction.Mean) : base(reduction)\n {\n this.margin = margin;\n }\n\n public override Tensor forward(Tensor input1, Tensor input2, Tensor target)\n {\n var res = THSNN_margin_ranking_loss(input1.Handle, input2.Handle, target.Handle, margin, (long)reduction);\n if (res == IntPtr.Zero) { torch.CheckForErrors(); }\n return new Tensor(res);\n }\n\n public double margin { get; }\n }\n\n public sealed class MultiLabelMarginLoss : Loss<Tensor, Tensor, Tensor>\n {\n public MultiLabelMarginLoss(Reduction reduction = Reduction.Mean) : base(reduction)\n {\n }\n\n public override Tensor forward(Tensor input, Tensor target)\n {\n var res = THSNN_multilabel_margin_loss(input.Handle, target.Handle, (long)reduction);\n if (res == IntPtr.Zero) { torch.CheckForErrors(); }\n return new Tensor(res);\n }\n }\n\n public sealed class MultiLabelSoftMarginLoss : WeightedLoss<Tensor, Tensor, Tensor>\n {\n public MultiLabelSoftMarginLoss(Tensor? weight = null, Reduction reduction = Reduction.Mean) : base(weight, reduction)\n {\n }\n\n public override Tensor forward(Tensor input, Tensor target)\n {\n var res = THSNN_multilabel_soft_margin_loss(input.Handle, target.Handle, weight?.Handle ?? IntPtr.Zero, (long)reduction);\n if (res == IntPtr.Zero) { torch.CheckForErrors(); }\n return new Tensor(res);\n }\n }\n\n public sealed class MultiMarginLoss : WeightedLoss<Tensor, Tensor, Tensor>\n {\n public MultiMarginLoss(int p = 1, double margin = 1.0, Tensor? weight = null, Reduction reduction = Reduction.Mean) : base(weight, reduction)\n {\n this.margin = margin;\n this.p = p;\n }\n\n public override Tensor forward(Tensor input, Tensor target)\n {\n IntPtr h = (weight is null) ? IntPtr.Zero : weight.Handle;\n\n var res = THSNN_multi_margin_loss(input.Handle, target.Handle, p, margin, h, (long)reduction);\n if (res == IntPtr.Zero) { torch.CheckForErrors(); }\n return new Tensor(res);\n }\n\n public double margin { get; }\n public int p { get; }\n }\n\n public sealed class MSELoss : Loss<Tensor, Tensor, Tensor>\n {\n public MSELoss(Reduction reduction = Reduction.Mean) : base(reduction)\n {\n }\n\n public override Tensor forward(Tensor input, Tensor target)\n {\n var res = THSNN_mse_loss(input.Handle, target.Handle, (long)reduction);\n if (res == IntPtr.Zero) { torch.CheckForErrors(); }\n return new Tensor(res);\n }\n }\n\n public sealed class L1Loss : Loss<Tensor, Tensor, Tensor>\n {\n public L1Loss(Reduction reduction = Reduction.Mean) : base(reduction)\n {\n }\n\n public override Tensor forward(Tensor input, Tensor target)\n {\n var res = THSNN_l1_loss(input.Handle, target.Handle, (long)reduction);\n if (res == IntPtr.Zero) { torch.CheckForErrors(); }\n return new Tensor(res);\n }\n }\n\n public sealed class NLLLoss : WeightedLoss<Tensor, Tensor, Tensor>\n {\n public NLLLoss(Tensor? weight = null, Reduction reduction = Reduction.Mean) : base(weight, reduction)\n {\n }\n\n public override Tensor forward(Tensor input, Tensor target)\n {\n var res = THSNN_nll_loss(input.Handle, target.Handle, weight?.Handle ?? IntPtr.Zero, (long)reduction);\n if (res == IntPtr.Zero) { torch.CheckForErrors(); }\n return new Tensor(res);\n }\n }\n\n public sealed class PoissonNLLLoss : Loss<Tensor, Tensor, Tensor>\n {\n public PoissonNLLLoss(bool log_input = true, bool full = false, float eps = 1e-8f, Reduction reduction = Reduction.Mean) : base(reduction)\n {\n this.full = full;\n this.log_input = log_input;\n this.eps = eps;\n }\n\n public override Tensor forward(Tensor input, Tensor target)\n {\n var res = THSNN_poisson_loss(input.Handle, target.Handle, log_input, full, eps, (long)reduction);\n if (res == IntPtr.Zero) { torch.CheckForErrors(); }\n return new Tensor(res);\n }\n\n public bool log_input { get; }\n public bool full { get; }\n public float eps { get; }\n\n }\n\n public sealed class GaussianNLLLoss : Loss<Tensor, Tensor, Tensor, Tensor>\n {\n public GaussianNLLLoss(bool full = false, float eps = 1e-8f, Reduction reduction = Reduction.Mean) : base(reduction)\n {\n this.full = full;\n this.eps = eps;\n }\n\n public override Tensor forward(Tensor input, Tensor target, Tensor variance)\n {\n input = input.view(input.shape[0], -1);\n target = target.view(target.shape[0], -1);\n if (target.shape == input.shape) throw new ArgumentException(\"input and target must have the same shape\");\n\n variance = variance.view(target.shape[0], -1);\n if (variance.shape[1] != input.shape[1] && variance.shape[1] != 1) throw new ArgumentException(\"variance has the wrong shape\");\n\n if ((variance < 0).any().cpu().item<bool>()) throw new ArgumentException(\"variance has negative entry/entries\");\n\n using (var _ = torch.no_grad())\n variance = variance.clamp_min(eps);\n\n var loss = 0.5 * (variance.log() + (input - target).square() / variance).view(input.shape[0], -1).sum(dim: stackalloc long[] { 1 });\n\n if (full) {\n loss = loss + 0.5 * input.shape[1] * MathF.Log(2 * MathF.PI);\n }\n\n return (reduction == Reduction.Mean) ? loss.mean() : (reduction == Reduction.Sum) ? loss.sum() : loss;\n }\n\n public bool full { get; }\n public float eps { get; }\n\n }\n\n public sealed class KLDivLoss : Loss<Tensor, Tensor, Tensor>\n {\n public KLDivLoss(bool log_target = true, Reduction reduction = Reduction.Mean) : base(reduction)\n {\n this.log_target = log_target;\n }\n\n public override Tensor forward(Tensor input, Tensor target)\n {\n var res = THSNN_kl_div_loss(input.Handle, target.Handle, (long)reduction, log_target);\n if (res == IntPtr.Zero) { torch.CheckForErrors(); }\n return new Tensor(res);\n }\n\n public bool log_target { get; }\n }\n\n public sealed class SmoothL1Loss : Loss<Tensor, Tensor, Tensor>\n {\n public SmoothL1Loss(Reduction reduction = Reduction.Mean, double beta = 1.0) : base(reduction)\n {\n this.beta = beta;\n }\n\n public override Tensor forward(Tensor input, Tensor target)\n {\n var res = THSNN_smooth_l1_loss(input.Handle, target.Handle, (long)reduction, beta);\n if (res == IntPtr.Zero) { torch.CheckForErrors(); }\n return new Tensor(res);\n }\n\n public double beta { get; }\n }\n\n public sealed class SoftMarginLoss : Loss<Tensor, Tensor, Tensor>\n {\n public SoftMarginLoss(Reduction reduction = Reduction.Mean) : base(reduction)\n {\n }\n\n public override Tensor forward(Tensor input, Tensor target)\n {\n var res = THSNN_soft_margin_loss(input.Handle, target.Handle, (long)reduction);\n if (res == IntPtr.Zero) { torch.CheckForErrors(); }\n return new Tensor(res);\n }\n }\n\n public sealed class TripletMarginLoss : Loss<Tensor, Tensor, Tensor, Tensor>\n {\n public TripletMarginLoss(double margin = 1.0, long p = 2, double eps = 1e-06, bool swap = false, Reduction reduction = Reduction.Mean) : base(reduction)\n {\n this.margin = margin;\n this.p = p;\n this.eps = eps;\n this.swap = swap;\n }\n\n public override Tensor forward(Tensor anchor, Tensor positive, Tensor negative)\n {\n var res = THSNN_triplet_margin_loss(anchor.Handle, positive.Handle, negative.Handle, margin, p, eps, swap, (long)reduction);\n if (res == IntPtr.Zero) { torch.CheckForErrors(); }\n return new Tensor(res);\n }\n\n public double margin { get; }\n public long p { get; }\n double eps { get; }\n bool swap { get; }\n }\n\n public sealed class TripletMarginWithDistanceLoss : Loss<Tensor, Tensor, Tensor, Tensor>\n {\n public TripletMarginWithDistanceLoss(Func<Tensor, Tensor, Tensor>? distance = null, double margin = 1.0, bool swap = false, Reduction reduction = Reduction.Mean) : base(reduction)\n {\n if (distance != null) {\n this.distance = (IntPtr x, IntPtr y) => {\n var x1 = new Tensor(x);\n var y1 = new Tensor(y);\n var res = distance(x1, y1);\n\n GC.SuppressFinalize(x1);\n GC.SuppressFinalize(y1);\n GC.SuppressFinalize(res);\n\n return res.Handle;\n };\n }\n\n this.margin = margin;\n this.swap = swap;\n }\n\n public override Tensor forward(Tensor anchor, Tensor positive, Tensor negative)\n {\n var res = THSNN_triplet_margin_with_distance_loss(anchor.Handle, positive.Handle, negative.Handle, distance, margin, swap, (long)reduction);\n if (res == IntPtr.Zero) { torch.CheckForErrors(); }\n return new Tensor(res);\n }\n\n DistanceFunctionNative? distance { get; }\n public double margin { get; }\n bool swap { get; }\n }\n }\n}\n"},{"alpha_fraction":0.5615906715393066,"alphanum_fraction":0.571774959564209,"avg_line_length":38.653846740722656,"blob_id":"ecc96a1ede36505931832c95a316d4111d424671","content_id":"31eee07674fed9f8df8ff36a32f47beb74d70f29","detected_licenses":["MIT"],"is_generated":false,"is_vendor":false,"language":"C#","length_bytes":2062,"license_type":"permissive","max_line_length":135,"num_lines":52,"path":"/src/TorchVision/AdjustHue.cs","repo_name":"NiklasGustafsson/TorchSharp","src_encoding":"UTF-8","text":"// Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information.\n\nusing static TorchSharp.torch;\n\nnamespace TorchSharp\n{\n public static partial class torchvision\n {\n internal class AdjustHue : ITransform\n {\n internal AdjustHue(double hue_factor)\n {\n hue_factor %= 1.0;\n\n this.hue_factor = hue_factor;\n }\n\n public Tensor call(Tensor img)\n {\n return transforms.functional.adjust_hue(img, hue_factor);\n }\n\n private double hue_factor;\n }\n\n public static partial class transforms\n {\n /// <summary>\n /// Adjust hue of an image.\n /// The image hue is adjusted by converting the image to HSV and cyclically shifting the intensities in the hue channel(H).\n /// The image is then converted back to original image mode.\n /// </summary>\n /// <param name=\"hue_factor\">\n /// How much to shift the hue channel. 0 means no shift in hue.\n /// Hue is often defined in degrees, with 360 being a full turn on the color wheel.\n /// In this library, 1.0 is a full turn, which means that 0.5 and -0.5 give complete reversal of\n /// the hue channel in HSV space in positive and negative direction respectively.\n /// </param>\n /// <returns></returns>\n /// <remarks>\n /// Unlike Pytorch, TorchSharp will allow the hue_factor to lie outside the range [-0.5,0.5].\n /// A factor of 0.75 has the same effect as -.25\n /// Note that adjusting the hue is a very expensive operation, and may therefore not be suitable as a method\n /// for data augmentation when training speed is important.\n /// </remarks>\n static public ITransform AdjustHue(double hue_factor)\n {\n return new AdjustHue(hue_factor);\n }\n }\n }\n}\n"},{"alpha_fraction":0.5561426877975464,"alphanum_fraction":0.575957715511322,"avg_line_length":58.76315689086914,"blob_id":"df780ee75e893b03a70d7e2bd036027c655185c8","content_id":"020ace0222c83784e3e911a58cf2d52f7c5821d3","detected_licenses":["MIT"],"is_generated":false,"is_vendor":false,"language":"C#","length_bytes":2271,"license_type":"permissive","max_line_length":160,"num_lines":38,"path":"/test/TorchSharpTest/netstandardTests.cs","repo_name":"NiklasGustafsson/TorchSharp","src_encoding":"UTF-8","text":"using System.IO;\nusing Xunit;\n\nnamespace TorchSharp\n{\n public class NetStandardTests\n {\n [Theory]\n [InlineData(@\"c:\\users\\me\", @\"middle\", @\"c:\\users\\me\\middle\")]\n [InlineData(@\"c:\\users\\me\", null, @\"c:\\users\\me\")]\n [InlineData(null, @\"last\", @\"last\")]\n [InlineData(@\"c:\\users\\me\", \"\", @\"c:\\users\\me\")]\n [InlineData(\"\", @\"last\", @\"last\")]\n [InlineData(@\"c:\\users\\me\\\", @\"middle\\\", @\"c:\\users\\me\\middle\\\")]\n [InlineData(@\"c:\\users\\me\", @\"\\middle\\\", @\"c:\\users\\me\\middle\\\")]\n public static void TestNSPath2Parts(string s1, string s2, string expected) => Assert.Equal(expected, NSPath.Join(s1, s2));\n\n [Theory]\n [InlineData(@\"c:\\users\\me\", @\"middle\", @\"last\", @\"c:\\users\\me\\middle\\last\")]\n [InlineData(null, @\"middle\", @\"last\", @\"middle\\last\")]\n [InlineData(@\"c:\\users\\me\", null, @\"last\", @\"c:\\users\\me\\last\")]\n [InlineData(@\"c:\\users\\me\", @\"middle\", null, @\"c:\\users\\me\\middle\")]\n [InlineData(@\"c:\\users\\me\\\", @\"middle\", @\"last\", @\"c:\\users\\me\\middle\\last\")]\n [InlineData(@\"c:\\users\\me\", @\"\\middle\\\", @\"last\", @\"c:\\users\\me\\middle\\last\")]\n [InlineData(@\"c:\\users\\me\", @\"\\middle\", @\"\\last\", @\"c:\\users\\me\\middle\\last\")]\n public static void TestNSPath3Parts(string s1, string s2, string s3, string expected) => Assert.Equal(expected, NSPath.Join(s1, s2, s3));\n\n [Theory]\n [InlineData(@\"c:\\users\\me\", @\"middle1\", @\"middle2\", @\"last\", @\"c:\\users\\me\\middle1\\middle2\\last\")]\n [InlineData(null, @\"middle1\", @\"middle2\", @\"last\", @\"middle1\\middle2\\last\")]\n [InlineData(@\"c:\\users\\me\", null, @\"middle2\", @\"last\", @\"c:\\users\\me\\middle2\\last\")]\n [InlineData(@\"c:\\users\\me\", @\"middle1\", null, @\"last\", @\"c:\\users\\me\\middle1\\last\")]\n [InlineData(@\"c:\\users\\me\", @\"middle1\", @\"middle2\", null, @\"c:\\users\\me\\middle1\\middle2\")]\n [InlineData(@\"c:\\users\\me\\\", @\"middle1\\\", @\"middle2\\\", @\"last\", @\"c:\\users\\me\\middle1\\middle2\\last\")]\n [InlineData(@\"c:\\users\\me\", @\"\\middle1\", @\"\\middle2\", @\"\\last\", @\"c:\\users\\me\\middle1\\middle2\\last\")]\n public static void TestNSPath4Parts(string s1, string s2, string s3, string s4, string expected) => Assert.Equal(expected, NSPath.Join(s1, s2, s3, s4));\n }\n}\n"},{"alpha_fraction":0.5096489191055298,"alphanum_fraction":0.5152289867401123,"avg_line_length":32.34108352661133,"blob_id":"93365016f0dbdb79ada3eaf47eb318e8dbe5fa4f","content_id":"10c75c4193ca580119512807d978b587dd5cedde","detected_licenses":["MIT"],"is_generated":false,"is_vendor":false,"language":"C#","length_bytes":4301,"license_type":"permissive","max_line_length":149,"num_lines":129,"path":"/src/Examples.Utils/AG_NEWSReader.cs","repo_name":"NiklasGustafsson/TorchSharp","src_encoding":"UTF-8","text":"// Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information.\nusing System;\nusing System.Collections.Generic;\nusing System.IO;\nusing System.Linq;\nusing TorchSharp;\nusing static TorchSharp.torch;\n\nnamespace TorchText.Data\n{\n public class AG_NEWSReader : IDisposable\n {\n public static AG_NEWSReader AG_NEWS(string split, Device device, string root = \".data\")\n {\n var dataPath = Path.Combine(root, $\"{split}.csv\");\n return new AG_NEWSReader(dataPath, device);\n }\n\n private AG_NEWSReader(string path, Device device)\n {\n _path = path;\n _device = device;\n }\n\n private string _path;\n private Device _device;\n\n public IEnumerable<(int, string)> Enumerate()\n {\n return File.ReadLines(_path).Select(line => ParseLine(line));\n }\n\n public IEnumerable<(Tensor, Tensor, Tensor)> GetBatches(Func<string, IEnumerable<string>> tokenizer, Vocab.Vocab vocab, long batch_size)\n {\n // This data set fits in memory, so we will simply load it all and cache it between epochs.\n\n var inputs = new List<(int, string)>();\n\n if (_data == null) {\n\n _data = new List<(Tensor, Tensor, Tensor)>();\n\n var counter = 0;\n var lines = Enumerate().ToList();\n var left = lines.Count;\n\n foreach (var line in lines) {\n\n inputs.Add(line);\n left -= 1;\n\n if (++counter == batch_size || left == 0) {\n _data.Add(Batchifier(inputs, tokenizer, vocab));\n inputs.Clear();\n counter = 0;\n }\n }\n }\n\n return _data;\n }\n\n private List<(Tensor, Tensor, Tensor)> _data;\n private bool disposedValue;\n\n private (Tensor, Tensor, Tensor) Batchifier(IEnumerable<(int, string)> input, Func<string, IEnumerable<string>> tokenizer, Vocab.Vocab vocab)\n {\n var label_list = new List<long>();\n var text_list = new List<Tensor>();\n var offsets = new List<long>();\n offsets.Add(0);\n\n long last = 0;\n\n foreach (var (label, text) in input) {\n label_list.Add(label);\n var processed_text = torch.tensor(tokenizer(text).Select(t => (long)vocab[t]).ToArray(),dtype:torch.int64);\n text_list.Add(processed_text);\n last += processed_text.size(0);\n offsets.Add(last);\n }\n\n var labels = torch.tensor(label_list.ToArray(), dtype: torch.int64).to(_device);\n var texts = torch.cat(text_list.ToArray(), 0).to(_device);\n var offs = torch.tensor(offsets.Take(label_list.Count).ToArray(), dtype:torch.int64).to(_device);\n\n return (labels, texts, offs);\n }\n\n public (int, string) ParseLine(string line)\n {\n int label = 0;\n string text = \"\";\n\n int firstComma = line.IndexOf(\"\\\",\\\"\");\n label = int.Parse(line.Substring(1, firstComma - 1));\n text = line.Substring(firstComma + 2, line.Length - firstComma - 2);\n int secondComma = text.IndexOf(\"\\\",\\\"\");\n text = text.Substring(secondComma + 2, text.Length - secondComma - 2);\n int thirdComma = text.IndexOf(\"\\\",\\\"\");\n\n text = text.Substring(thirdComma + 2, text.Length - thirdComma - 3);\n\n return (label-1, text);\n }\n\n protected virtual void Dispose(bool disposing)\n {\n if (!disposedValue) {\n if (disposing && _data != null) {\n foreach (var (l, t, o) in _data) {\n l.Dispose();\n t.Dispose();\n o.Dispose();\n }\n }\n\n disposedValue = true;\n }\n }\n\n public void Dispose()\n {\n // Do not change this code. Put cleanup code in 'Dispose(bool disposing)' method\n Dispose(disposing: true);\n GC.SuppressFinalize(this);\n }\n }\n}\n"},{"alpha_fraction":0.5693780183792114,"alphanum_fraction":0.5707602500915527,"avg_line_length":48.24083709716797,"blob_id":"418b89fe66b2b9b3777dcd551b37920de68895c7","content_id":"750f8ebc1a36ab048ab363cec490dcf4d2c2b950","detected_licenses":["MIT"],"is_generated":false,"is_vendor":false,"language":"C#","length_bytes":9405,"license_type":"permissive","max_line_length":156,"num_lines":191,"path":"/src/TorchSharp/Distributions/NegativeBinomial.cs","repo_name":"NiklasGustafsson/TorchSharp","src_encoding":"UTF-8","text":"// Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information.\nusing System;\nusing static TorchSharp.torch;\nusing static TorchSharp.torch.nn.functional;\n\nnamespace TorchSharp\n{\n using Modules;\n\n namespace Modules\n {\n /// <summary>\n /// A NegativeBinomial distribution parameterized by total_count and either probs or logits (but not both).\n ///\n /// This is a distribution of the number of successful independent and identical Bernoulli trials\n /// before `total_count` failures are achieved. The probability of success of each Bernoulli trial is `probs`.\n /// </summary>\n public class NegativeBinomial : torch.distributions.Distribution\n {\n /// <summary>\n /// The mean of the distribution.\n /// </summary>\n public override Tensor mean => total_count * torch.exp(logits);\n\n /// <summary>\n /// Mode of the negative binomial distribution.\n /// </summary>\n public override Tensor mode =>\n WrappedTensorDisposeScope(() => ((total_count - 1) * logits.exp()).floor_().clamp(min: 0));\n\n /// <summary>\n /// The variance of the distribution\n /// </summary>\n public override Tensor variance =>\n WrappedTensorDisposeScope(() => mean / torch.sigmoid(-logits));\n\n public NegativeBinomial(Tensor total_count, Tensor p = null, Tensor l = null, torch.Generator generator = null) : base(generator)\n {\n this.batch_shape = p is null ? l.size() : p.size();\n this._probs = p ?? LogitsToProbs(l, true).DetachFromDisposeScope();\n this._logits = l ?? ProbsToLogits(p, true).DetachFromDisposeScope();\n this.generator = generator;\n\n var broadcast = (p is null) ? torch.broadcast_tensors(total_count, l) : torch.broadcast_tensors(total_count, p);\n this.total_count = broadcast[0].type_as(p ?? l).DetachFromDisposeScope();\n }\n\n /// <summary>\n /// Event probabilities\n /// </summary>\n public Tensor probs {\n get {\n return _probs;\n }\n }\n\n /// <summary>\n /// Event log-odds\n /// </summary>\n public Tensor logits {\n get {\n return _logits;\n }\n }\n\n private Tensor _probs;\n private Tensor _logits;\n private Tensor total_count;\n\n /// <summary>\n /// Generates a sample_shape shaped reparameterized sample or sample_shape shaped batch of reparameterized samples\n /// if the distribution parameters are batched.\n /// </summary>\n /// <param name=\"sample_shape\">The sample shape.</param>\n public override Tensor rsample(params long[] sample_shape)\n {\n using var scope = NewDisposeScope();\n using (var _ = torch.no_grad()) {\n var gamma = distributions.Gamma(concentration:total_count, rate: torch.exp(-_logits));\n var rate = gamma.sample(sample_shape);\n return torch.poisson(rate, generator).MoveToOuterDisposeScope();\n }\n }\n\n /// <summary>\n /// Returns the log of the probability density/mass function evaluated at `value`.\n /// </summary>\n /// <param name=\"value\"></param>\n public override Tensor log_prob(Tensor value)\n {\n using var _ = NewDisposeScope();\n var log_unnormalized_prob = (total_count * (-_logits).log_sigmoid() + value * logits.log_sigmoid());\n var log_normalization = (-torch.lgamma(total_count + value) + torch.lgamma(1.0 + value) + torch.lgamma(total_count));\n log_normalization = log_normalization.masked_fill(total_count + value == 0, 0);\n\n return (log_unnormalized_prob - log_normalization).MoveToOuterDisposeScope();\n }\n\n /// <summary>\n /// Returns entropy of distribution, batched over batch_shape.\n /// </summary>\n public override Tensor entropy()\n {\n throw new NotImplementedException(nameof(entropy));\n }\n\n /// <summary>\n /// Returns a new distribution instance (or populates an existing instance provided by a derived class) with batch dimensions expanded to\n /// `batch_shape`. This method calls `torch.Tensor.expand()` on the distribution's parameters. As such, this does not allocate new\n /// memory for the expanded distribution instance.\n /// </summary>\n /// <param name=\"batch_shape\">Tthe desired expanded size.</param>\n /// <param name=\"instance\">new instance provided by subclasses that need to override `.expand`.</param>\n public override distributions.Distribution expand(Size batch_shape, distributions.Distribution instance = null)\n {\n if (instance != null && !(instance is NegativeBinomial))\n throw new ArgumentException(\"expand(): 'instance' must be a NegativeBinomial distribution\");\n\n var newDistribution = ((instance == null) ?\n new NegativeBinomial(total_count.expand(batch_shape), p: _probs?.expand(batch_shape), l: logits?.expand(batch_shape), generator) :\n instance) as NegativeBinomial;\n\n newDistribution.batch_shape = batch_shape;\n if (newDistribution == instance) {\n newDistribution.total_count = total_count.expand(batch_shape);\n newDistribution._probs = _probs?.expand(batch_shape);\n newDistribution._logits = _logits?.expand(batch_shape);\n }\n return newDistribution;\n }\n }\n\n }\n public static partial class torch\n {\n public static partial class distributions\n {\n /// <summary>\n /// Creates a NegativeBinomial distribution parameterized by `probs` or `logits` (but not both).\n /// `total_count` must be broadcastable with `probs`/`logits`.\n /// </summary>\n /// <param name=\"total_count\">Number of Bernoulli trials</param>\n /// <param name=\"probs\">The probability of sampling '1'</param>\n /// <param name=\"logits\">The log-odds of sampling '1'</param>\n /// <param name=\"generator\">An optional random number generator object.</param>\n /// <returns></returns>\n public static NegativeBinomial NegativeBinomial(Tensor total_count, Tensor probs = null, Tensor logits = null, torch.Generator generator = null)\n {\n return new NegativeBinomial(total_count, probs, logits);\n }\n\n /// <summary>\n /// Creates a NegativeBinomial distribution parameterized by `probs` or `logits` (but not both).\n /// </summary>\n /// <param name=\"total_count\">Number of Bernoulli trials</param>\n /// <param name=\"probs\">The probability of sampling '1'</param>\n /// <param name=\"logits\">The log-odds of sampling '1'</param>\n /// <param name=\"generator\">An optional random number generator object.</param>\n /// <returns></returns>\n public static NegativeBinomial NegativeBinomial(int total_count, float? probs, float? logits, torch.Generator generator = null)\n {\n if (probs.HasValue && !logits.HasValue)\n return new NegativeBinomial(torch.tensor(total_count), torch.tensor(probs.Value), null);\n else if (!probs.HasValue && logits.HasValue)\n return new NegativeBinomial(torch.tensor(total_count), null, torch.tensor(logits.Value));\n else\n throw new ArgumentException(\"One and only one of 'probs' and logits should be provided.\");\n }\n\n\n /// <summary>\n /// Creates a NegativeBinomial distribution parameterized by `probs` or `logits` (but not both).\n /// </summary>\n /// <param name=\"total_count\">Number of Bernoulli trials</param>\n /// <param name=\"probs\">The probability of sampling '1'</param>\n /// <param name=\"logits\">The log-odds of sampling '1'</param>\n /// <param name=\"generator\">An optional random number generator object.</param>\n /// <returns></returns>\n public static NegativeBinomial NegativeBinomial(int total_count, double? probs, double? logits, torch.Generator generator = null)\n {\n if (probs.HasValue && !logits.HasValue)\n return new NegativeBinomial(torch.tensor(total_count), torch.tensor(probs.Value), null, generator);\n else if (!probs.HasValue && logits.HasValue)\n return new NegativeBinomial(torch.tensor(total_count), null, torch.tensor(logits.Value), generator);\n else\n throw new ArgumentException(\"One and only one of 'probs' and logits should be provided.\");\n }\n\n }\n }\n}\n"},{"alpha_fraction":0.5229358077049255,"alphanum_fraction":0.5275229215621948,"avg_line_length":15.769230842590332,"blob_id":"a1c0eb9defdf9b7a8cdaa900f081fd45b5cec240","content_id":"1917bda876596786b466ed006d6d1cc7b600558b","detected_licenses":["MIT"],"is_generated":false,"is_vendor":false,"language":"C#","length_bytes":218,"license_type":"permissive","max_line_length":44,"num_lines":13,"path":"/src/TorchSharp/Utils/tensorboard/Enums/HistogramBinSelector.cs","repo_name":"NiklasGustafsson/TorchSharp","src_encoding":"UTF-8","text":"namespace TorchSharp.Utils.tensorboard.Enums\n{\n public enum HistogramBinSelector : byte\n {\n Doane = 0,\n Rice,\n Scott,\n Sqrt,\n Stone,\n Sturges,\n Tensorflow\n }\n}\n"},{"alpha_fraction":0.5300502777099609,"alphanum_fraction":0.5330653190612793,"avg_line_length":38.79999923706055,"blob_id":"e82238826a2b03c1c547988fc25fc4909895cdca","content_id":"b4c2f030ee85ace47dd110d5d4f47ce4c538c8ae","detected_licenses":["MIT"],"is_generated":false,"is_vendor":false,"language":"C#","length_bytes":4975,"license_type":"permissive","max_line_length":144,"num_lines":125,"path":"/src/TorchSharp/Device.cs","repo_name":"NiklasGustafsson/TorchSharp","src_encoding":"UTF-8","text":"// Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information.\nusing System;\n\nnamespace TorchSharp\n{\n public static partial class torch\n {\n /// <summary>\n /// A torch.Device is an object representing the device on which a torch.Tensor is or will be allocated.\n /// </summary>\n public class Device\n {\n public DeviceType type { get; private set; } = DeviceType.CPU;\n public int index { get; private set; } = -1;\n\n /// <summary>\n /// Constructor\n /// </summary>\n /// <param name=\"description\">A device descriptor 'device[:index]'</param>\n public Device(string description)\n {\n var splits = description.Split(':');\n if (splits.Length == 1) {\n // Interpret as a device type\n type = (DeviceType)Enum.Parse(typeof(DeviceType), splits[0].ToUpper());\n } else if (splits.Length == 2) {\n // Interpret as a device type and index\n type = (DeviceType)Enum.Parse(typeof(DeviceType), splits[0].ToUpper());\n index = int.Parse(splits[1]);\n }\n }\n\n /// <summary>\n /// Constructor\n /// </summary>\n /// <param name=\"deviceType\">CPU or CUDA</param>\n /// <param name=\"index\">For CUDA, the device index</param>\n public Device(string deviceType, int index = -1)\n {\n type = (DeviceType)Enum.Parse(typeof(DeviceType), deviceType.ToUpper());\n this.index = index;\n }\n\n /// <summary>\n /// Constructor\n /// </summary>\n /// <param name=\"deviceType\">CPU or CUDA</param>\n /// <param name=\"index\">For CUDA, the device index</param>\n public Device(DeviceType deviceType, int index = -1)\n {\n type = deviceType;\n this.index = index;\n }\n\n /// <summary>\n /// Constructor\n /// </summary>\n /// <param name=\"index\">The CUDA device index</param>\n public Device(int index)\n {\n type = DeviceType.CUDA;\n this.index = index;\n }\n\n /// <summary>\n /// Return the device descriptor using the input format.\n /// </summary>\n /// <returns></returns>\n public override string ToString()\n {\n return type == DeviceType.CPU ? \"cpu\" : (index == -1) ? $\"{type.ToString().ToLower()}\" : $\"{type.ToString().ToLower()}:{index}\";\n }\n\n public static implicit operator Device(string description)\n {\n return new Device(description);\n }\n }\n\n /// <summary>\n /// Convenience declaration of a CPU device accessible everywhere.\n /// </summary>\n public static Device CPU = new Device(DeviceType.CPU, -1);\n\n /// <summary>\n /// Convenience declaration of a CUDA device accessible everywhere.\n /// </summary>\n public static Device CUDA = new Device(DeviceType.CUDA, -1);\n\n /// <summary>\n /// Convenience declaration of a META device accessible everywhere.\n /// </summary>\n public static Device META = new Device(DeviceType.META, -1);\n\n /// <summary>\n /// Factory for a device object, following the Pytorch API.\n /// </summary>\n /// <param name=\"description\">String description of the device, e.g. 'cpu' or 'cuda:0'</param>\n /// <returns></returns>\n public static Device device(string description) => new Device(description);\n\n /// <summary>\n /// Factory for a device object, following the Pytorch API.\n /// </summary>\n /// <param name=\"deviceType\">The device type, e.g. 'cpu' or 'cuda'</param>\n /// <param name=\"index\">The device index. Ignored for CPUs</param>\n /// <returns></returns>\n public static Device device(string deviceType, int index = -1) => new Device(deviceType, index);\n\n /// <summary>\n /// Factory for a device object, following the Pytorch API.\n /// </summary>\n /// <param name=\"deviceType\">The device type, e.g. DeviceType.CPU or DeviceType.CUDA.</param>\n /// <param name=\"index\">The device index. Ignored for CPUs</param>\n /// <returns></returns>\n public static Device device(DeviceType deviceType, int index = -1) => new Device(deviceType, index);\n\n /// <summary>\n /// Factory for a CUDA device object, following the Pytorch API.\n /// </summary>\n /// <param name=\"index\">The CUDA device ordinal.</param>\n /// <returns></returns>\n public static Device device(int index) => new Device(DeviceType.CUDA, index);\n }\n}\n"},{"alpha_fraction":0.5669390559196472,"alphanum_fraction":0.5686675310134888,"avg_line_length":45.79411697387695,"blob_id":"711085ff192d874631e58f284a7d7635b57e6909","content_id":"63a03c1790e18930c639520ec1f8093df3f9c6ab","detected_licenses":["MIT"],"is_generated":false,"is_vendor":false,"language":"C#","length_bytes":6364,"license_type":"permissive","max_line_length":156,"num_lines":136,"path":"/src/TorchSharp/Distributions/Cauchy.cs","repo_name":"NiklasGustafsson/TorchSharp","src_encoding":"UTF-8","text":"// Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information.\nusing System;\nusing static TorchSharp.torch;\n\nnamespace TorchSharp\n{\n using Modules;\n\n namespace Modules\n {\n /// <summary>\n /// A Cauchy (Lorentz) distribution. The distribution of the ratio of\n /// independent normally distributed random variables with means `0` follows a Cauchy distribution.\n /// </summary>\n public class Cauchy : torch.distributions.Distribution\n {\n /// <summary>\n /// The mode of the distribution.\n /// </summary>\n public override Tensor mode => loc;\n\n /// <summary>\n /// The mean of the distribution.\n /// </summary>\n public override Tensor mean => _mean;\n\n /// <summary>\n /// The variance of the distribution\n /// </summary>\n public override Tensor variance => _variance;\n\n /// <summary>\n /// Constructor\n /// </summary>\n /// <param name=\"loc\">Mode or median of the distribution.</param>\n /// <param name=\"scale\">Half width at half maximum.</param>\n /// <param name=\"generator\">An optional random number generator object.</param>\n public Cauchy(Tensor loc, Tensor scale, torch.Generator generator = null) : base(generator)\n {\n var locScale = torch.broadcast_tensors(loc, scale);\n this.loc = locScale[0].DetachFromDisposeScope();\n this.scale = locScale[1].DetachFromDisposeScope();\n this._mean = torch.full(ExtendedShape(), double.NaN, dtype: loc.dtype, device: loc.device).DetachFromDisposeScope();\n this._variance = torch.full(ExtendedShape(), double.PositiveInfinity, dtype: loc.dtype, device: loc.device).DetachFromDisposeScope();\n this.batch_shape = this.loc.size();\n }\n\n private Tensor loc;\n private Tensor scale;\n private Tensor _mean, _variance;\n\n /// <summary>\n /// Generates a sample_shape shaped reparameterized sample or sample_shape shaped batch of reparameterized samples\n /// if the distribution parameters are batched.\n /// </summary>\n /// <param name=\"sample_shape\">The sample shape.</param>\n /// <returns></returns>\n public override Tensor rsample(params long[] sample_shape)\n {\n var shape = ExtendedShape(sample_shape);\n var eps = loc.new_empty(shape).cauchy_(generator: generator);\n return loc + eps * scale;\n }\n\n /// <summary>\n /// Returns the log of the probability density/mass function evaluated at `value`.\n /// </summary>\n /// <param name=\"value\"></param>\n public override Tensor log_prob(Tensor value) =>\n WrappedTensorDisposeScope(() => -Math.Log(Math.PI) - scale.log() - (((value - loc) / scale).pow(2)).log1p()); \n\n /// <summary>\n /// Returns entropy of distribution, batched over batch_shape.\n /// </summary>\n /// <returns></returns>\n public override Tensor entropy() =>\n WrappedTensorDisposeScope(() => Math.Log(Math.PI * 4) + scale.log());\n\n /// <summary>\n /// Returns the cumulative density/mass function evaluated at `value`.\n /// </summary>\n /// <param name=\"value\"></param>\n public override Tensor cdf(Tensor value) =>\n WrappedTensorDisposeScope(() => torch.atan((value - loc) / scale) / Math.PI + 0.5);\n\n /// <summary>\n /// Returns the inverse cumulative density/mass function evaluated at `value`.\n /// </summary>\n /// <param name=\"value\"></param>\n public override Tensor icdf(Tensor value) =>\n WrappedTensorDisposeScope(() => torch.tan(Math.PI * (value - 0.5)) * scale + loc);\n\n\n /// <summary>\n /// Returns a new distribution instance (or populates an existing instance provided by a derived class) with batch dimensions expanded to\n /// `batch_shape`. This method calls `torch.Tensor.expand()` on the distribution's parameters. As such, this does not allocate new\n /// memory for the expanded distribution instance.\n /// </summary>\n /// <param name=\"batch_shape\">Tthe desired expanded size.</param>\n /// <param name=\"instance\">new instance provided by subclasses that need to override `.expand`.</param>\n public override distributions.Distribution expand(Size batch_shape, distributions.Distribution instance = null)\n {\n if (instance != null && !(instance is Cauchy))\n throw new ArgumentException(\"expand(): 'instance' must be a Cauchy distribution\");\n\n var newDistribution = ((instance == null) ? new Cauchy(loc.expand(batch_shape), scale.expand(batch_shape), generator) : instance) as Cauchy;\n\n newDistribution.batch_shape = batch_shape;\n if (newDistribution == instance) {\n newDistribution.loc = loc.expand(batch_shape);\n newDistribution.scale = scale.expand(batch_shape);\n }\n return newDistribution;\n }\n }\n\n }\n public static partial class torch\n {\n public static partial class distributions\n {\n /// <summary>\n /// Samples from a Cauchy (Lorentz) distribution. The distribution of the ratio of\n /// independent normally distributed random variables with means `0` follows a Cauchy distribution.\n /// </summary>\n /// <param name=\"loc\">Mode or median of the distribution.</param>\n /// <param name=\"scale\">Half width at half maximum.</param>\n /// <param name=\"generator\">An optional random number generator object.</param>\n /// <returns></returns>\n public static Cauchy Cauchy(Tensor loc, Tensor scale, torch.Generator generator = null)\n {\n return new Cauchy(loc, scale, generator);\n }\n }\n }\n}\n"},{"alpha_fraction":0.5930420756340027,"alphanum_fraction":0.5930420756340027,"avg_line_length":49.79452133178711,"blob_id":"3dcc95d41e74123530e4794bbb5b6165845dd10d","content_id":"620b8ac555def849e73efe782e978ca56a5db584","detected_licenses":["MIT"],"is_generated":false,"is_vendor":false,"language":"C#","length_bytes":3708,"license_type":"permissive","max_line_length":188,"num_lines":73,"path":"/src/TorchSharp/NN/TransformerDecoder.cs","repo_name":"NiklasGustafsson/TorchSharp","src_encoding":"UTF-8","text":"// Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information.\nusing System;\nusing static TorchSharp.torch;\nusing static TorchSharp.PInvoke.NativeMethods;\n\nnamespace TorchSharp\n{\n using Modules;\n\n namespace Modules\n {\n public sealed class TransformerDecoder : torch.nn.Module<Tensor, Tensor, Tensor, Tensor, Tensor, Tensor, Tensor>\n {\n internal TransformerDecoder(IntPtr handle, IntPtr boxedHandle) : base(handle, boxedHandle) { }\n\n /// <summary>\n /// Pass the inputs (and mask) through the decoder layers in turn.\n /// </summary>\n /// <param name=\"tgt\">The sequence to the decoder layer (required).</param>\n /// <param name=\"memory\">The sequence from the last layer of the encoder (required).</param>\n /// <param name=\"tgt_mask\">The mask for the tgt sequence (optional).</param>\n /// <param name=\"memory_mask\">The mask for the memory sequence (optional).</param>\n /// <param name=\"tgt_key_padding_mask\">The mask for the tgt keys per batch (optional).</param>\n /// <param name=\"memory_key_padding_mask\">The mask for the memory keys per batch (optional).</param>\n /// <returns></returns>\n public override Tensor forward(Tensor tgt, Tensor memory, Tensor tgt_mask, Tensor memory_mask = null, Tensor tgt_key_padding_mask = null, Tensor memory_key_padding_mask = null)\n {\n var res = THSNN_TransformerDecoder_forward(handle,\n tgt.Handle,\n memory.Handle,\n tgt_mask?.Handle ?? IntPtr.Zero,\n memory_mask?.Handle ?? IntPtr.Zero,\n tgt_key_padding_mask?.Handle ?? IntPtr.Zero,\n memory_key_padding_mask?.Handle ?? IntPtr.Zero);\n if (res == IntPtr.Zero) { torch.CheckForErrors(); }\n return new Tensor(res);\n }\n public new Tensor call(Tensor tgt, Tensor memory, Tensor tgt_mask, Tensor memory_mask = null, Tensor tgt_key_padding_mask = null, Tensor memory_key_padding_mask = null)\n {\n return base.call(tgt, memory, tgt_mask, memory_mask, tgt_key_padding_mask, memory_key_padding_mask);\n }\n\n /// <summary>\n /// Pass the inputs (and mask) through the decoder layers in turn.\n /// </summary>\n /// <param name=\"tgt\">The sequence to the decoder layer (required).</param>\n /// <param name=\"memory\">The sequence from the last layer of the encoder (required).</param>\n public Tensor call(Tensor tgt, Tensor memory)\n {\n return base.call(tgt, memory, null, null, null, null);\n }\n }\n }\n\n public static partial class torch\n {\n public static partial class nn\n {\n /// <summary>\n /// TransformerDecoder is a stack of N decoder layers\n /// </summary>\n /// <param name=\"decoder_layer\">An instance of the TransformerDecoderLayer class (required).</param>\n /// <param name=\"num_layers\">The number of sub-decoder-layers in the decoder (required).</param>\n /// <returns></returns>\n public static TransformerDecoder TransformerDecoder(TransformerDecoderLayer decoder_layer, long num_layers)\n {\n var res = THSNN_TransformerDecoder_ctor(decoder_layer.handle, num_layers, out var boxedHandle);\n if (res == IntPtr.Zero) { torch.CheckForErrors(); }\n return new TransformerDecoder(res, boxedHandle);\n }\n }\n }\n}\n"},{"alpha_fraction":0.6298515200614929,"alphanum_fraction":0.6306329965591431,"avg_line_length":57.181819915771484,"blob_id":"09a53f021f44c8559b840de351fee80d12dc393a","content_id":"c5b3ae593b7ef653a6554acb2d222fb153d09f60","detected_licenses":["MIT"],"is_generated":false,"is_vendor":false,"language":"C#","length_bytes":3839,"license_type":"permissive","max_line_length":288,"num_lines":66,"path":"/src/TorchVision/RandomRotation.cs","repo_name":"NiklasGustafsson/TorchSharp","src_encoding":"UTF-8","text":"// Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information.\nusing System;\nusing System.Collections.Generic;\nusing static TorchSharp.torch;\n\nnamespace TorchSharp\n{\n public static partial class torchvision\n {\n internal class RandomRotation : ITransform\n {\n public RandomRotation((double, double) degrees, InterpolationMode interpolation = InterpolationMode.Nearest, bool expand = false, (int, int)? center = null, IList<float> fill = null)\n {\n this.degrees = degrees;\n this.interpolation = interpolation;\n this.center = center;\n this.expand = expand;\n this.fill = fill;\n }\n\n public Tensor call(Tensor input)\n {\n var random = new Random();\n var angle = random.NextDouble() * (degrees.Item2 - degrees.Item1) + degrees.Item1;\n\n var rotate = torchvision.transforms.Rotate((float)angle, interpolation, expand, center, fill);\n return rotate.call(input);\n }\n\n private (double, double) degrees;\n private bool expand;\n private (int, int)? center;\n private IList<float> fill;\n private InterpolationMode interpolation;\n }\n\n public static partial class transforms\n {\n /// <summary>\n /// Rotate the image by a random angle. \n /// </summary>\n /// <param name=\"degrees\">Range of degrees to select from (-degrees, +degrees).</param>\n /// <param name=\"interpolation\">Desired interpolation enum. Default is `InterpolationMode.NEAREST`.</param>\n /// <param name=\"expand\">If true, expands the output to make it large enough to hold the entire rotated image. If false or omitted, make the output image the same size as the input image. Note that the expand flag assumes rotation around the center and no translation.</param>\n /// <param name=\"center\">Center of rotation, (x, y). Origin is the upper left corner.</param>\n /// <param name=\"fill\">Pixel fill value for the area outside the rotated. If given a number, the value is used for all bands respectively.</param>\n static public ITransform RandomRotation(double degrees, InterpolationMode interpolation = InterpolationMode.Nearest, bool expand = false, (int, int)? center = null, IList<float> fill = null)\n {\n return new RandomRotation((-degrees, degrees), interpolation, expand, center, fill);\n }\n\n /// <summary>\n ///Rotate the image by a random angle. \n /// </summary>\n /// <param name=\"degrees\">Range of degrees to select from</param>\n /// <param name=\"interpolation\">Desired interpolation enum. Default is `InterpolationMode.NEAREST`.</param>\n /// <param name=\"expand\">If true, expands the output to make it large enough to hold the entire rotated image. If false or omitted, make the output image the same size as the input image. Note that the expand flag assumes rotation around the center and no translation.</param>\n /// <param name=\"center\">Center of rotation, (x, y). Origin is the upper left corner.</param>\n /// <param name=\"fill\">Pixel fill value for the area outside the rotated. If given a number, the value is used for all bands respectively.</param>\n static public ITransform RandomRotation((double, double) degrees, InterpolationMode interpolation = InterpolationMode.Nearest, bool expand = false, (int, int)? center = null, IList<float> fill = null)\n {\n return new RandomRotation(degrees, interpolation, expand, center, fill);\n }\n }\n }\n}"},{"alpha_fraction":0.6136853694915771,"alphanum_fraction":0.62109375,"avg_line_length":50.55555725097656,"blob_id":"f802ca6420e5fcff10309b248ea52e530ad9e644","content_id":"31ad430b51ad7a021554cd667c9f57227d0f7df0","detected_licenses":["MIT"],"is_generated":false,"is_vendor":false,"language":"C#","length_bytes":7424,"license_type":"permissive","max_line_length":215,"num_lines":144,"path":"/src/TorchSharp/Tensor/Factories/tensor_long.cs","repo_name":"NiklasGustafsson/TorchSharp","src_encoding":"UTF-8","text":"// Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information.\nusing System;\nusing System.Collections.Generic;\nusing System.Diagnostics.Contracts;\nusing System.Linq;\nusing static TorchSharp.PInvoke.NativeMethods;\n\n#nullable enable\nnamespace TorchSharp\n{\n public static partial class torch\n {\n /// <summary>\n /// Create a scalar tensor from a single value\n /// </summary>\n public static Tensor tensor(long scalar, ScalarType? dtype = null, Device? device = null, bool requires_grad = false)\n {\n device = InitializeDevice(device);\n var handle = THSTensor_newInt64Scalar(scalar, (int)device.type, device.index, requires_grad);\n if (handle == IntPtr.Zero) { CheckForErrors(); }\n var tensor = new Tensor(handle);\n if (device is { }) {\n tensor = dtype.HasValue ? tensor.to(dtype.Value, device) : tensor.to(device);\n } else if (dtype.HasValue) {\n tensor = tensor.to_type(dtype.Value);\n }\n return tensor;\n }\n\n /// <summary>\n /// Create a tensor from an array of values, shaping it based on the shape passed in.\n /// </summary>\n [Pure]\n public static Tensor tensor(IList<long> rawArray, ReadOnlySpan<long> dimensions, ScalarType? dtype = null, Device? device = null, bool requires_grad = false, string[]? names = null)\n {\n return _tensor_i64(rawArray.ToArray(), dimensions, dtype, device, requires_grad, false, names);\n }\n\n /// <summary>\n /// Create a tensor from an array of values, shaping it based on the shape passed in.\n /// </summary>\n [Pure]\n public static Tensor tensor(long[] dataArray, ScalarType? dtype = null, Device? device = null, bool requires_grad = false, string[]? names = null)\n {\n return _tensor_i64(dataArray, stackalloc long[] { dataArray.LongLength }, dtype, device, requires_grad, names: names);\n }\n\n /// <summary>\n /// Create a tensor from an array of values, shaping it based on the shape passed in.\n /// </summary>\n [Pure]\n public static Tensor tensor(long[] dataArray, ReadOnlySpan<long> dimensions, ScalarType? dtype = null, Device? device = null, bool requires_grad = false, string[]? names = null)\n {\n return _tensor_i64(dataArray, dimensions, dtype, device, requires_grad, names: names);\n }\n\n [Pure]\n private static Tensor _tensor_i64(Array rawArray, ReadOnlySpan<long> dimensions, ScalarType? dtype, Device? device, bool requires_grad, bool clone = false, string[]? names = null) =>\n _tensor_generic(rawArray, dimensions, (sbyte)ScalarType.Int64, dtype, device, requires_grad, names: names);\n\n /// <summary>\n /// Create a 1-D tensor from an array of values, shaping it based on the input array.\n /// </summary>\n /// <remarks>The Torch runtime does not take ownership of the data, so there is no device argument.</remarks>\n [Pure]\n public static Tensor tensor(IList<long> rawArray, ScalarType? dtype = null, Device? device = null, bool requires_grad = false, string[]? names = null)\n {\n return tensor(rawArray, stackalloc long[] { (long)rawArray.Count }, dtype, device, requires_grad, names: names);\n }\n\n /// <summary>\n /// Create a tensor from an array of values, organizing it as a two-dimensional tensor.\n /// </summary>\n /// <remarks>\n /// The Torch runtime does not take ownership of the data, so there is no device argument.\n /// The input array must have rows * columns elements.\n /// </remarks>\n [Pure]\n public static Tensor tensor(IList<long> rawArray, long rows, long columns, ScalarType? dtype = null, Device? device = null, bool requires_grad = false, string[]? names = null)\n {\n return tensor(rawArray, stackalloc long[] { rows, columns }, dtype, device, requires_grad, names: names);\n }\n\n /// <summary>\n /// Create a tensor from an array of values, organizing it as a three-dimensional tensor.\n /// </summary>\n /// <remarks>\n /// The Torch runtime does not take ownership of the data, so there is no device argument.\n /// The input array must have dim0*dim1*dim2 elements.\n /// </remarks>\n [Pure]\n public static Tensor tensor(IList<long> rawArray, long dim0, long dim1, long dim2, ScalarType? dtype = null, Device? device = null, bool requires_grad = false, string[]? names = null)\n {\n return tensor(rawArray, stackalloc long[] { dim0, dim1, dim2 }, dtype, device, requires_grad, names: names);\n }\n\n /// <summary>\n /// Create a tensor from an array of values, organizing it as a four-dimensional tensor.\n /// </summary>\n /// <remarks>\n /// The Torch runtime does not take ownership of the data, so there is no device argument.\n /// The input array must have dim0*dim1*dim2*dim3 elements.\n /// </remarks>\n [Pure]\n public static Tensor tensor(IList<long> rawArray, long dim0, long dim1, long dim2, long dim3, ScalarType? dtype = null, Device? device = null, bool requires_grad = false, string[]? names = null)\n {\n return tensor(rawArray, stackalloc long[] { dim0, dim1, dim2, dim3 }, dtype, device, requires_grad, names: names);\n }\n\n /// <summary>\n /// Create a two-dimensional tensor from a two-dimensional array of values.\n /// </summary>\n [Pure]\n public static Tensor tensor(long[,] rawArray, ScalarType? dtype = null, Device? device = null, bool requires_grad = false, string[]? names = null)\n {\n return _tensor_i64(rawArray, stackalloc long[] { rawArray.GetLongLength(0), rawArray.GetLongLength(1) }, dtype, device, requires_grad, names: names);\n }\n\n /// <summary>\n /// Create a three-dimensional tensor from a three-dimensional array of values.\n /// </summary>\n [Pure]\n public static Tensor tensor(long[,,] rawArray, ScalarType? dtype = null, Device? device = null, bool requires_grad = false, string[]? names = null)\n {\n return _tensor_i64(rawArray, stackalloc long[] { rawArray.GetLongLength(0), rawArray.GetLongLength(1), rawArray.GetLongLength(2) }, dtype, device, requires_grad, names: names);\n }\n\n /// <summary>\n /// Create a four-dimensional tensor from a four-dimensional array of values.\n /// </summary>\n [Pure]\n public static Tensor tensor(long[,,,] rawArray, ScalarType? dtype = null, Device? device = null, bool requires_grad = false, string[]? names = null)\n {\n return _tensor_i64(rawArray, stackalloc long[] { rawArray.GetLongLength(0), rawArray.GetLongLength(1), rawArray.GetLongLength(2), rawArray.GetLongLength(3) }, dtype, device, requires_grad, names: names);\n }\n\n /// <summary>\n /// Cast a tensor to a 64-bit integer tensor.\n /// </summary>\n /// <param name=\"t\">The input tensor</param>\n /// <returns>'this' if the tensor is already a 64-bit integer tensor; otherwise, a new tensor.</returns>\n public static Tensor LongTensor(Tensor t) => t.to(ScalarType.Int64);\n }\n}\n"},{"alpha_fraction":0.5257009267807007,"alphanum_fraction":0.5257009267807007,"avg_line_length":22.83333396911621,"blob_id":"5a5330876f07ec5050be2ce1027629e6d0fefd45","content_id":"729959af11ba810ecedfb8f4c9fecfc6cdf9b2ad","detected_licenses":["MIT"],"is_generated":false,"is_vendor":false,"language":"C#","length_bytes":428,"license_type":"permissive","max_line_length":130,"num_lines":18,"path":"/src/TorchAudio/AudioEncoding.cs","repo_name":"NiklasGustafsson/TorchSharp","src_encoding":"UTF-8","text":"// Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information.\nnamespace TorchSharp\n{\n public static partial class torchaudio\n {\n /// <summary>\n /// Audio encoding\n /// </summary>\n public enum AudioEncoding\n {\n PCM_S,\n PCM_U,\n PCM_F,\n ULAW,\n ALAW\n }\n }\n}"},{"alpha_fraction":0.6038913130760193,"alphanum_fraction":0.6116005778312683,"avg_line_length":44.400001525878906,"blob_id":"c70c03e698bd8ffb8d2b36a6c5a308756f1f2acc","content_id":"d602ee499aaf0c25244b56b361ecf87b1047d2bf","detected_licenses":["MIT"],"is_generated":false,"is_vendor":false,"language":"C#","length_bytes":5448,"license_type":"permissive","max_line_length":130,"num_lines":120,"path":"/src/TorchAudio/Modules/Wav2Vec2Model.cs","repo_name":"NiklasGustafsson/TorchSharp","src_encoding":"UTF-8","text":"// Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information.\n\n// A number of implementation details in this file have been translated from the Python version of torchaudio,\n// largely located in the files found in this folder:\n//\n// https://github.com/pytorch/audio/blob/76fca37ac8941b72a509a6e58d623632efe04543/torchaudio/models/wav2vec2/model.py\n//\n// The origin has the following copyright notice and license:\n//\n// https://github.com/pytorch/audio/blob/main/LICENSE\n//\n\nusing static TorchSharp.torch;\n\n#nullable enable\nnamespace TorchSharp.Modules\n{\n /// <summary>\n /// Encoder model used in *wav2vec 2.0* [:footcite:`baevski2020wav2vec`].\n /// Note:\n /// To build the model, please use one of the factory functions.\n /// </summary>\n public partial class Wav2Vec2Model : nn.Module<Tensor, Tensor?, (Tensor, Tensor?)>\n {\n internal readonly FeatureExtractor feature_extractor;\n internal readonly Encoder encoder;\n private readonly nn.Module<Tensor, Tensor>? aux;\n\n /// <param name=\"name\"></param>\n /// <param name=\"feature_extractor\">Feature extractor that extracts feature vectors from raw audio Tensor.</param>\n /// <param name=\"encoder\">Encoder that converts the audio features into the sequence of probability\n /// distribution (in negative log-likelihood) over labels.</param>\n /// <param name=\"aux\">Auxiliary module. If provided, the output from encoder is passed to this module.</param>\n internal Wav2Vec2Model(\n string name,\n FeatureExtractor feature_extractor,\n Encoder encoder,\n nn.Module<Tensor, Tensor>? aux = null) : base(name)\n {\n this.feature_extractor = feature_extractor;\n this.encoder = encoder;\n this.aux = aux;\n RegisterComponents();\n }\n\n /// <summary>\n /// Extract feature vectors from raw waveforms\n /// \n /// This returns the list of outputs from the intermediate layers of\n /// transformer block in encoder.\n /// </summary>\n /// <param name=\"waveforms\">Audio tensor of shape `(batch, frames)`.</param>\n /// <param name=\"lengths\">Indicates the valid length of each audio in the batch.\n /// Shape: `(batch, )`.\n /// When the ``waveforms`` contains audios with different durations,\n /// by providing ``lengths`` argument, the model will compute\n /// the corresponding valid output lengths and apply proper mask in\n /// transformer attention layer.\n /// If ``None``, it is assumed that the entire audio waveform\n /// length is valid.</param>\n /// <param name=\"num_layers\">\n /// If given, limit the number of intermediate layers to go through.\n /// Providing `1` will stop the computation after going through one\n /// intermediate layers. If not given, the outputs from all the\n /// intermediate layers are returned.\n /// </param>\n /// <returns>\n /// List of Tensors\n /// Features from requested layers.\n /// Each Tensor is of shape: `(batch, time frame, feature dimension)`\n /// If ``lengths`` argument was provided, a Tensor of shape `(batch, )`\n /// is returned.\n /// It indicates the valid length in time axis of each feature Tensor.\n /// </returns>\n public (Tensor[], Tensor?) extract_features(\n Tensor waveforms,\n Tensor? lengths = null,\n int? num_layers = null)\n {\n Tensor x;\n (x, lengths) = this.feature_extractor.call(waveforms, lengths);\n var xs = this.encoder.extract_features(x, lengths, num_layers);\n return (xs, lengths);\n }\n\n /// <summary>\n /// Compute the sequence of probability distribution over labels.\n /// </summary>\n /// <param name=\"waveforms\">Audio tensor of shape `(batch, frames)`.\n /// waveforms (Tensor): Audio tensor of shape `(batch, frames)`.</param>\n /// <param name=\"lengths\">Indicates the valid length of each audio in the batch.\n /// Shape: `(batch, )`.\n /// When the ``waveforms`` contains audios with different durations,\n /// by providing ``lengths`` argument, the model will compute\n /// the corresponding valid output lengths and apply proper mask in\n /// transformer attention layer.\n /// If ``None``, it is assumed that all the audio in ``waveforms``\n /// have valid length. Default: ``None``.</param>\n /// <returns>\n /// The sequences of probability distribution (in logit) over labels.\n /// If ``lengths`` argument was provided, a Tensor of shape `(batch, )`\n /// is returned.\n /// It indicates the valid length in time axis of the output Tensor.\n /// </returns>\n public override (Tensor, Tensor?) forward(\n Tensor waveforms,\n Tensor? lengths = null)\n {\n Tensor x;\n (x, lengths) = this.feature_extractor.call(waveforms, lengths);\n x = this.encoder.call(x, lengths);\n if (this.aux != null) {\n x = this.aux.call(x);\n }\n return (x, lengths);\n }\n\n public new (Tensor, Tensor?) call(Tensor waveforms, Tensor? lengths = null) => base.call(waveforms, lengths);\n }\n}\n"},{"alpha_fraction":0.7279344797134399,"alphanum_fraction":0.7388535141944885,"avg_line_length":32.33333206176758,"blob_id":"b261bb4756fe03993c0b2cc038edac62327de90a","content_id":"ff42768386cb125603fdf87ad819937b16bf816a","detected_licenses":["MIT"],"is_generated":false,"is_vendor":false,"language":"C","length_bytes":1099,"license_type":"permissive","max_line_length":130,"num_lines":33,"path":"/src/Native/LibTorchSharp/THSAutograd.h","repo_name":"NiklasGustafsson/TorchSharp","src_encoding":"UTF-8","text":"// Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information.\n#pragma once\n\n#include \"../Stdafx.h\"\n\n#include \"Utils.h\"\n\n// Returns whether the grad is enabled or not.\nEXPORT_API(bool) THSAutograd_isGradEnabled();\n\n// Enables / disables grad.\nEXPORT_API(void) THSAutograd_setGrad(bool enabled);\n\n// Returns whether the grad is enabled or not.\nEXPORT_API(bool) THSAutograd_isAnomalyEnabled();\n\nEXPORT_API(bool) THSAutograd_shouldCheckNaN();\n\n// Enables / disables grad.\nEXPORT_API(void) THSAutograd_setAnomaly(bool enabled, bool check_nan);\n\nEXPORT_API(void) THSAutograd_grad(\n Tensor* outputs, const int64_t oLength,\n Tensor* inputs, const int64_t iLength,\n Tensor* grad_outs, const int64_t gLenght,\n bool retain_graph, bool create_graph, bool allow_unused,\n Tensor* (*allocator)(size_t length));\n\nEXPORT_API(void) THSAutograd_backward(\n Tensor* tensors, const int64_t tLength,\n Tensor* grad_tensors, const int64_t gtLength,\n bool retain_graph, bool create_graph,\n Tensor* inputs, const int64_t iLength);"},{"alpha_fraction":0.7593501210212708,"alphanum_fraction":0.7624157071113586,"avg_line_length":52.47541046142578,"blob_id":"a44f837a01472ea6a1f43fe990762e2cbc9bf4ba","content_id":"f1d28cebbb045c44d6775e968a8c07dbc1b1db5a","detected_licenses":["MIT"],"is_generated":false,"is_vendor":false,"language":"Markdown","length_bytes":3262,"license_type":"permissive","max_line_length":284,"num_lines":61,"path":"/CONTRIBUTING.md","repo_name":"NiklasGustafsson/TorchSharp","src_encoding":"UTF-8","text":"Contributing to TorchSharp\n==========================\n\nIf you are here, it means you are interested in helping us out. A hearty welcome and thank you! There are many ways you can contribute to the ML.NET project:\n\n* Offer PR's to fix bugs or implement new features.\n* Give us feedback and bug reports regarding the software or the documentation.\n* Improve our examples, tutorials, and documentation.\n\nThis document describes contribution guidelines that are specific to TorchSharp. Please read [.NET Core Guidelines](https://github.com/dotnet/coreclr/blob/main/Documentation/project-docs/contributing.md) for more general .NET Core contribution guidelines.\n\n## Developers\n\nSee the [Developer Guide](DEVGUIDE.md) for details about building and developing in this repo.\n\n\n## Pull Requests\n\nIf you send us a PR, whether for documentation, examples, or library code, we require that you sign a digital Contributor License Agreement (CLA), so that we know you have the right to contribute. Once you have signed it, future PRs should go through without further requests to sign.\n\n* **DO** use your own forked repository for all development, and submit cross-fork PRs.\n* **DO** resolve merge conflicts early, by merging recent changes to 'main' into your development fork before submitting a PR.\n* **DO** submit all code changes via pull requests (PRs). PRs will be reviewed and potentially merged by the repo maintainers after a peer review that includes at least one maintainer.\n* **DO** give PRs short-but-descriptive names (for example, \"Improve code coverage for System.Console by 10%\", not \"Fix #1234\")\n* **DO** refer to any relevant issues, and include [keywords](https://help.github.com/articles/closing-issues-via-commit-messages/) that automatically close issues when the PR is merged.\n* **DO** tag any users that should know about and/or review the change.\n* **DO** ensure each commit successfully builds. The entire PR must pass all tests in the Continuous Integration (CI) system before it'll be merged.\n* **DO** add a brief description to the RELEASENOTES.md file at the top under the heading of the upcoming release.\n* **DO** address PR feedback in an additional commit(s) rather than amending the existing commits, and only rebase/squash them when necessary. This makes it easier for reviewers to track changes.\n* **DO** assume that [\"Squash and Merge\"](https://github.com/blog/2141-squash-your-commits) will be used to merge your commit unless you request otherwise in the PR.\n* **DO NOT** fix merge conflicts using a merge commit. Prefer `git rebase`.\n* **DO NOT** mix independent, unrelated changes in one PR. Separate unrelated fixes into separate PRs.\n\n\n## A Useful Tip\n\nA useful tip from the Tensorflow.NET repo:\n\nAfter you fork, add dotnet/TorchSharp as 'upstream' to your local repo ...\n\n```git\ngit remote add upstream https://github.com/dotnet/TorchSharp.git\n```\n\nThis makes it easy to keep your fork up to date by regularly pulling and merging from upstream.\n\nAssuming that you do all your development off your main branch, keep your main updated\nwith these commands:\n\n```git\ngit checkout main\ngit pull upstream main\ngit push origin main\n```\n\nThen, you merge onto your dev branch:\n\n```git\ngit checkout <<your dev branch>>\ngit merge main\n```\n"},{"alpha_fraction":0.5750093460083008,"alphanum_fraction":0.5757575631141663,"avg_line_length":38.89552307128906,"blob_id":"b6eba5bf64249ca3e13f827da0a88f45ed369aff","content_id":"dc4fea3fe317eb299ba9f622acbbe26e50ef050c","detected_licenses":["MIT"],"is_generated":false,"is_vendor":false,"language":"C#","length_bytes":2673,"license_type":"permissive","max_line_length":139,"num_lines":67,"path":"/src/TorchSharp/NN/Activation/Softmax.cs","repo_name":"NiklasGustafsson/TorchSharp","src_encoding":"UTF-8","text":"// Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information.\nusing System;\nusing static TorchSharp.torch;\nusing static TorchSharp.PInvoke.NativeMethods;\n\nnamespace TorchSharp\n{\n using Modules;\n\n namespace Modules\n {\n /// <summary>\n /// This class is used to represent a Softmax module.\n /// </summary>\n public sealed class Softmax : torch.nn.Module<Tensor, Tensor>\n {\n internal Softmax(IntPtr handle, IntPtr boxedHandle) : base(handle, boxedHandle) { }\n\n public override Tensor forward(Tensor tensor)\n {\n var res = THSNN_Softmax_forward(handle, tensor.Handle);\n if (res == IntPtr.Zero) { torch.CheckForErrors(); }\n return new Tensor(res);\n }\n\n public override string GetName()\n {\n return typeof(Softmax).Name;\n }\n\n // Rather than spending cycles only to discover that this module has neither\n // parameters nor buffers, just shortcut the move completely.\n protected internal override nn.Module _to(Device device, ScalarType dtype) => this;\n protected internal override nn.Module _to(DeviceType deviceType, int deviceIndex = -1) => this;\n protected internal override nn.Module _to(ScalarType dtype) => this;\n }\n }\n\n public static partial class torch\n {\n public static partial class nn\n {\n /// <summary>\n /// Softmax\n /// </summary>\n /// <param name=\"dim\">A dimension along which Softmax will be computed (so every slice along dim will sum to 1)</param>\n /// <returns></returns>\n public static Softmax Softmax(long dim)\n {\n var handle = THSNN_Softmax_ctor(dim, out var boxedHandle);\n if (handle == IntPtr.Zero) { torch.CheckForErrors(); }\n return new Softmax(handle, boxedHandle);\n }\n\n public static partial class functional\n {\n /// <summary>\n /// Computes the softmax function for the input tensor.\n /// </summary>\n /// <param name=\"input\">The input tensor</param>\n /// <param name=\"dim\">A dimension along which softmax will be computed.</param>\n /// <param name=\"dtype\">The desired data type of returned tensor.</param>\n public static Tensor softmax(Tensor input, long dim, ScalarType? dtype = null) => torch.special.softmax(input, dim, dtype);\n }\n }\n }\n}\n"},{"alpha_fraction":0.5465657114982605,"alphanum_fraction":0.5545369982719421,"avg_line_length":34.6729850769043,"blob_id":"33fdeb6653b51580c7e58f804c8ada514b0af26a","content_id":"8fb175718ec201652a76b0d93010f49c06815762","detected_licenses":["MIT"],"is_generated":false,"is_vendor":false,"language":"C#","length_bytes":7527,"license_type":"permissive","max_line_length":208,"num_lines":211,"path":"/src/Examples/TextClassification.cs","repo_name":"NiklasGustafsson/TorchSharp","src_encoding":"UTF-8","text":"// Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information.\nusing System;\nusing System.IO;\nusing System.Linq;\nusing System.Collections.Generic;\nusing System.Diagnostics;\n\nusing static TorchSharp.torch;\nusing static TorchSharp.torch.nn;\n\nnamespace TorchSharp.Examples\n{\n\n /// <summary>\n /// This example is based on the PyTorch tutorial at:\n ///\n /// https://pytorch.org/tutorials/beginner/text_sentiment_ngrams_tutorial.html\n ///\n /// It relies on the AG_NEWS dataset, which can be downloaded in CSV form at:\n ///\n /// https://github.com/mhjabreel/CharCnn_Keras/tree/master/data/ag_news_csv\n ///\n /// Download the two files, and place them in a folder called \"AG_NEWS\" in\n /// accordance with the file path below (Windows only).\n ///\n /// </summary>\n public class TextClassification\n {\n private const long emsize = 200;\n\n private const long batch_size = 128;\n private const long eval_batch_size = 128;\n\n private const int epochs = 15;\n\n // This path assumes that you're running this on Windows.\n#if NET472_OR_GREATER\n private readonly static string _dataLocation = NSPath.Join(Environment.GetFolderPath(Environment.SpecialFolder.DesktopDirectory), \"..\", \"Downloads\", \"AG_NEWS\");\n#else\n private readonly static string _dataLocation = Path.Join(Environment.GetFolderPath(Environment.SpecialFolder.DesktopDirectory), \"..\", \"Downloads\", \"AG_NEWS\");\n#endif // NET472_OR_GREATER\n internal static void Main(string[] args)\n\n {\n torch.random.manual_seed(1);\n\n var cwd = Environment.CurrentDirectory;\n\n var device = torch.cuda.is_available() ? torch.CUDA : torch.CPU;\n Console.WriteLine($\"Running TextClassification on {device.type.ToString()}\");\n\n using (var reader = TorchText.Data.AG_NEWSReader.AG_NEWS(\"train\", (Device)device, _dataLocation)) {\n\n var dataloader = reader.Enumerate();\n\n var tokenizer = TorchText.Data.Utils.get_tokenizer(\"basic_english\");\n\n var counter = new TorchText.Vocab.Counter<string>();\n foreach (var (label, text) in dataloader) {\n counter.update(tokenizer(text));\n }\n\n var vocab = new TorchText.Vocab.Vocab(counter);\n\n var model = new TextClassificationModel(vocab.Count, emsize, 4).to((Device)device);\n\n var loss = CrossEntropyLoss();\n var lr = 5.0;\n var optimizer = torch.optim.SGD(model.parameters(), lr);\n var scheduler = torch.optim.lr_scheduler.StepLR(optimizer, 1, 0.2, last_epoch: 5);\n\n // This data set is small enough that we can get away with\n // collecting memory only once per epoch.\n\n using (var d = torch.NewDisposeScope()) {\n\n foreach (var epoch in Enumerable.Range(1, epochs)) {\n\n var sw = new Stopwatch();\n sw.Start();\n\n train(epoch, reader.GetBatches(tokenizer, vocab, batch_size), model, loss, optimizer);\n\n sw.Stop();\n\n var pgFirst = optimizer.ParamGroups.First();\n\n Console.WriteLine($\"\\nEnd of epoch: {epoch} | lr: {pgFirst.LearningRate:0.00} | time: {sw.Elapsed.TotalSeconds:0.0}s\\n\");\n scheduler.step();\n }\n }\n\n using (var d = torch.NewDisposeScope()) {\n\n using (var test_reader = TorchText.Data.AG_NEWSReader.AG_NEWS(\"test\", (Device)device, _dataLocation)) {\n\n var sw = new Stopwatch();\n sw.Start();\n\n var accuracy = evaluate(test_reader.GetBatches(tokenizer, vocab, eval_batch_size), model, loss);\n\n sw.Stop();\n\n Console.WriteLine($\"\\nEnd of training: test accuracy: {accuracy:0.00} | eval time: {sw.Elapsed.TotalSeconds:0.0}s\\n\");\n scheduler.step();\n }\n }\n }\n }\n\n static void train(int epoch, IEnumerable<(Tensor, Tensor, Tensor)> train_data, TextClassificationModel model, Loss<torch.Tensor, torch.Tensor, torch.Tensor> criterion, torch.optim.Optimizer optimizer)\n {\n model.train();\n\n double total_acc = 0.0;\n long total_count = 0;\n long log_interval = 250;\n\n var batch = 0;\n\n var batch_count = train_data.Count();\n\n foreach (var (labels, texts, offsets) in train_data) {\n\n optimizer.zero_grad();\n\n using (var predicted_labels = model.call(texts, offsets)) {\n\n var loss = criterion.call(predicted_labels, labels);\n loss.backward();\n torch.nn.utils.clip_grad_norm_(model.parameters().ToArray(), 0.5);\n optimizer.step();\n\n total_acc += (predicted_labels.argmax(1) == labels).sum().to(torch.CPU).item<long>();\n total_count += labels.size(0);\n }\n\n if (batch % log_interval == 0 && batch > 0) {\n var accuracy = total_acc / total_count;\n Console.WriteLine($\"epoch: {epoch} | batch: {batch} / {batch_count} | accuracy: {accuracy:0.00}\");\n }\n\n batch += 1;\n }\n }\n\n static double evaluate(IEnumerable<(Tensor, Tensor, Tensor)> test_data, TextClassificationModel model, Loss<Tensor, Tensor, Tensor> criterion)\n {\n model.eval();\n\n double total_acc = 0.0;\n long total_count = 0;\n\n foreach (var (labels, texts, offsets) in test_data) {\n\n using (var predicted_labels = model.call(texts, offsets)) {\n var loss = criterion.call(predicted_labels, labels);\n\n total_acc += (predicted_labels.argmax(1) == labels).sum().to(torch.CPU).item<long>();\n total_count += labels.size(0);\n }\n }\n\n return total_acc / total_count;\n }\n }\n\n class TextClassificationModel : Module<Tensor, Tensor>\n {\n private Modules.EmbeddingBag embedding;\n private Modules.Linear fc;\n\n public TextClassificationModel(long vocab_size, long embed_dim, long num_class) : base(\"TextClassification\")\n {\n embedding = EmbeddingBag(vocab_size, embed_dim, sparse: false);\n fc = Linear(embed_dim, num_class);\n InitWeights();\n\n RegisterComponents();\n }\n\n private void InitWeights()\n {\n var initrange = 0.5;\n\n init.uniform_(embedding.weight, -initrange, initrange);\n init.uniform_(fc.weight, -initrange, initrange);\n init.zeros_(fc.bias);\n }\n\n public override Tensor forward(Tensor t)\n {\n throw new NotImplementedException();\n }\n\n public Tensor call(Tensor input, Tensor offsets)\n {\n return fc.call(embedding.call(input, offsets));\n }\n\n protected override void Dispose(bool disposing)\n {\n if (disposing) {\n embedding.Dispose();\n fc.Dispose();\n ClearModules();\n }\n base.Dispose(disposing);\n }\n }\n}\n"},{"alpha_fraction":0.5338345766067505,"alphanum_fraction":0.5371407270431519,"avg_line_length":46.72010040283203,"blob_id":"ed558b1e96236a328e8cdee987a9c62078f697e6","content_id":"3dc444bec3e731c1427e33c81044d74f184015a5","detected_licenses":["MIT"],"is_generated":false,"is_vendor":false,"language":"C#","length_bytes":18761,"license_type":"permissive","max_line_length":207,"num_lines":393,"path":"/src/TorchSharp/Utils/tensorboard/SummaryWriter.cs","repo_name":"NiklasGustafsson/TorchSharp","src_encoding":"UTF-8","text":"// Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information.\n\nusing System;\nusing System.Collections.Generic;\nusing System.Diagnostics;\nusing System.IO;\nusing System.Threading;\nusing Google.Protobuf;\nusing Tensorboard;\n\nnamespace TorchSharp\n{\n public static partial class torch\n {\n public static partial class utils\n {\n public static partial class tensorboard\n {\n /// <summary>\n /// Writes entries directly to event files in the log_dir to be consumed by TensorBoard.\n /// \n /// The SummaryWriter class provides a high-level API to create an event file in a given directory and add summaries and events to it.The class updates the file contents asynchronously.\n ///\n /// This allows a training program to call methods to add data to the file directly from the training loop, without slowing down training.\n /// </summary>\n /// <param name=\"log_dir\">\n /// Save directory location. Default is runs/CURRENT_DATETIME_HOSTNAME, which changes after each run.\n /// Use hierarchical folder structure to compare between runs easily. e.g. pass in ‘runs/exp1’, ‘runs/exp2’, etc.\n /// for each new experiment to compare across them\n /// </param>\n /// <param name=\"filename_suffix\">Suffix added to all event filenames in the log_dir directory.</param>\n /// <param name=\"createRunName\">Create a time-based run name, even if log_dir is specified.</param>\n public static Modules.SummaryWriter SummaryWriter(string log_dir = null, string filename_suffix = null, bool createRunName = false)\n {\n if (createRunName && !string.IsNullOrEmpty(log_dir)) {\n log_dir = CreateRunName(log_dir);\n }\n\n return new Modules.SummaryWriter(log_dir, filename_suffix);\n }\n\n internal static string CreateRunName(string log_dir)\n {\n var now = DateTime.Now;\n var _months = System.Globalization.CultureInfo.InvariantCulture.DateTimeFormat.AbbreviatedMonthNames;\n var name = $\"{_months[now.Month - 1]}{now.Day}_{now.Hour}-{now.Minute}-{now.Second}_{System.Net.Dns.GetHostName()}\";\n log_dir = Path.Combine(log_dir, name);\n return log_dir;\n }\n }\n }\n }\n\n namespace Modules\n {\n /// <summary>\n /// Writes entries directly to event files in the log_dir to be consumed by TensorBoard.\n /// \n /// The SummaryWriter class provides a high-level API to create an event file in a given directory and add summaries and events to it.The class updates the file contents asynchronously.\n ///\n /// This allows a training program to call methods to add data to the file directly from the training loop, without slowing down training.\n /// </summary>\n public class SummaryWriter\n {\n internal SummaryWriter(string log_dir, string filename_suffix)\n {\n _suffix = filename_suffix;\n\n if (string.IsNullOrEmpty(log_dir)) {\n _log_dir = torch.utils.tensorboard.CreateRunName(\"runs\");\n } else {\n _log_dir = log_dir;\n }\n\n if (!Directory.Exists(_log_dir)) {\n Directory.CreateDirectory(_log_dir);\n }\n\n var fileName = Path.Combine(_log_dir, $\"events.out.tfevents.{DateTime.Now.Ticks}.{System.Net.Dns.GetHostName()}.{Process.GetCurrentProcess().Id}.{Interlocked.Increment(ref _global_uid)}\");\n if (!string.IsNullOrEmpty(_suffix)) {\n fileName = fileName + \".\" + _suffix;\n }\n\n if (File.Exists(fileName)) {\n File.Delete(fileName);\n }\n\n InitFile(fileName);\n\n _fileNames[\"__default__\"] = fileName;\n }\n\n /// <summary>\n /// The directory/folder where logging is made.\n /// </summary>\n public string LogDir { get { return _log_dir; } }\n\n /// <summary>\n /// Add scalar data to summary.\n /// </summary>\n /// <param name=\"tag\">Data identifier</param>\n /// <param name=\"scalar_value\">Value to save</param>\n /// <param name=\"global_step\">Global step value to record</param>\n /// <param name=\"walltime\">Optional override default walltime (DateTimeOffset.Now.ToUnixTimeSeconds())</param>\n public void add_scalar(string tag, float scalar_value, int global_step, long? walltime = null)\n {\n var fileName = InitDefaultFile();\n SetWalltime(ref walltime);\n\n var summary = new Summary();\n summary.Value.Add(new Summary.Types.Value() { SimpleValue = scalar_value, Tag = tag });\n var evnt = new Event() { Step = global_step, WallTime = walltime.Value, Summary = summary };\n\n WriteEvent(fileName, evnt);\n }\n\n /// <summary>\n /// Adds many scalar data points to summary.\n /// </summary>\n /// <param name=\"main_tag\">Data identifier</param>\n /// <param name=\"tag_scalar_dict\">Dictionary storing the tag and corresponding values</param>\n /// <param name=\"global_step\">Global step value to record</param>\n /// <param name=\"walltime\">Optional override default walltime (DateTimeOffset.Now.ToUnixTimeSeconds())</param>\n public void add_scalars(string main_tag, IDictionary<string, float> tag_scalar_dict, int global_step, long? walltime = null)\n {\n SetWalltime(ref walltime);\n\n foreach (var kv in tag_scalar_dict) {\n\n var key = kv.Key;\n var scalar_value = kv.Value;\n\n var summary = new Summary();\n summary.Value.Add(new Summary.Types.Value() { SimpleValue = scalar_value, Tag = main_tag });\n var evnt = new Event() { Step = global_step, WallTime = walltime.Value, Summary = summary };\n\n if (!_fileNames.TryGetValue(key, out var fileName)) {\n\n // We haven't logged this tag from this session, yet. Create a clean file.\n\n var fileDir = Path.Combine(_log_dir, $\"{main_tag}_{key}\");\n if (!Directory.Exists(fileDir)) {\n Directory.CreateDirectory(fileDir);\n }\n\n fileName = Path.Combine(fileDir, $\"events.out.tfevents.{DateTime.Now.Ticks}.{System.Net.Dns.GetHostName()}.{Process.GetCurrentProcess().Id}.{Interlocked.Increment(ref _global_uid)}\");\n\n if (File.Exists(fileName)) {\n File.Delete(fileName);\n }\n\n InitFile(fileName);\n\n _fileNames[key] = fileName;\n }\n\n WriteEvent(fileName, evnt);\n }\n }\n\n\n /// <summary>\n /// Adds many scalar data points to summary.\n /// </summary>\n /// <param name=\"main_tag\">Data identifier</param>\n /// <param name=\"tag_scalar_dict\">List of tuples storing the tag and corresponding values</param>\n /// <param name=\"global_step\">Global step value to record</param>\n /// <param name=\"walltime\">Optional override default walltime (DateTimeOffset.Now.ToUnixTimeSeconds())</param>\n public void add_scalars(string main_tag, IList<(string, float)> tag_scalar_dict, int global_step, long? walltime = null)\n {\n SetWalltime(ref walltime);\n\n foreach (var (key, scalar_value) in tag_scalar_dict) {\n\n var summary = new Summary();\n summary.Value.Add(new Summary.Types.Value() { SimpleValue = scalar_value, Tag = main_tag });\n var evnt = new Event() { Step = global_step, WallTime = walltime.Value, Summary = summary };\n\n if (!_fileNames.TryGetValue(key, out var fileName)) {\n\n var fileDir = Path.Combine(_log_dir, $\"{main_tag}_{key}\");\n if (!Directory.Exists(fileDir)) {\n Directory.CreateDirectory(fileDir);\n }\n\n fileName = Path.Combine(fileDir, $\"events.out.tfevents.{DateTime.Now.Ticks}.{System.Net.Dns.GetHostName()}.{Process.GetCurrentProcess().Id}.{Interlocked.Increment(ref _global_uid)}\");\n\n if (!File.Exists(fileName)) {\n InitFile(fileName);\n }\n\n _fileNames[key] = fileName;\n }\n\n WriteEvent(fileName, evnt);\n }\n }\n\n /// <summary>\n /// Add histogram to summary.\n ///\n /// https://pytorch.org/docs/stable/_modules/torch/utils/tensorboard/writer.html#SummaryWriter.add_histogram\n /// </summary>\n /// <param name=\"tag\"> Data identifier </param>\n /// <param name=\"values\"> Values to build histogram </param>\n /// <param name=\"global_step\"> Global step value to record </param>\n /// <param name=\"bins\"> This determines how the bins are made </param>\n /// <param name=\"walltime\"> Optional override default walltime (DateTimeOffset.Now.ToUnixTimeSeconds()) </param>\n /// <param name=\"max_bins\"></param>\n public void add_histogram(string tag,\n torch.Tensor values,\n int global_step,\n Utils.tensorboard.Enums.HistogramBinSelector bins = Utils.tensorboard.Enums.HistogramBinSelector.Tensorflow,\n long? walltime = null,\n long? max_bins = null)\n {\n static torch.Tensor default_bins()\n {\n double v = 1e-12;\n var buckets = new List<double>();\n var neg_buckets = new List<double>();\n while (v < 1e20) {\n buckets.Add(v);\n neg_buckets.Add(-v);\n v *= 1.1;\n }\n neg_buckets.Reverse();\n var result = new List<double>();\n result.AddRange(neg_buckets); result.Add(0); result.AddRange(buckets);\n return torch.tensor(result);\n }\n\n var fileName = InitDefaultFile();\n SetWalltime(ref walltime);\n Summary summary = bins == Utils.tensorboard.Enums.HistogramBinSelector.Tensorflow ?\n torch.utils.tensorboard.Summary.histogram(tag, values, default_bins(), max_bins) :\n torch.utils.tensorboard.Summary.histogram(tag, values, (HistogramBinSelector)(byte)bins, max_bins);\n var evnt = new Event() { Step = global_step, WallTime = walltime.Value, Summary = summary };\n WriteEvent(fileName, evnt);\n }\n\n /// <summary>\n /// Add batched image data to summary.\n ///\n /// https://pytorch.org/docs/stable/tensorboard.html#torch.utils.tensorboard.writer.SummaryWriter.add_image\n /// </summary>\n /// <param name=\"tag\"> Data identifier </param>\n /// <param name=\"img_tensor\"> Image data </param>\n /// <param name=\"global_step\"> Global step value to record </param>\n /// <param name=\"walltime\"> Optional override default walltime (DateTimeOffset.Now.ToUnixTimeSeconds()) </param>\n /// <param name=\"dataformats\"> Image data format specification of the form CHW, HWC, HW, WH, etc. </param>\n public void add_img(string tag, torch.Tensor img_tensor, int global_step, long? walltime = null, string dataformats = \"CHW\")\n {\n var fileName = InitDefaultFile();\n SetWalltime(ref walltime);\n Summary summary = torch.utils.tensorboard.Summary.image(tag, img_tensor, dataformats: dataformats);\n var evnt = new Event() { Step = global_step, WallTime = walltime.Value, Summary = summary };\n WriteEvent(fileName, evnt);\n }\n\n /// <summary>\n /// Add batched image data to summary.\n ///\n /// https://pytorch.org/docs/stable/tensorboard.html#torch.utils.tensorboard.writer.SummaryWriter.add_image\n /// </summary>\n /// <param name=\"tag\"> Data identifier </param>\n /// <param name=\"file_name\"> Image file </param>\n /// <param name=\"global_step\"> Global step value to record </param>\n /// <param name=\"walltime\"> Optional override default walltime (DateTimeOffset.Now.ToUnixTimeSeconds()) </param>\n public void add_img(string tag, string file_name, int global_step, long? walltime = null)\n {\n var fileName = InitDefaultFile();\n SetWalltime(ref walltime);\n Summary summary = torch.utils.tensorboard.Summary.image(tag, file_name);\n var evnt = new Event() { Step = global_step, WallTime = walltime.Value, Summary = summary };\n WriteEvent(fileName, evnt);\n }\n\n /// <summary>\n /// Add video data to summary.\n ///\n /// https://pytorch.org/docs/stable/tensorboard.html#torch.utils.tensorboard.writer.SummaryWriter.add_video\n /// </summary>\n /// <param name=\"tag\"> Data identifier </param>\n /// <param name=\"vid_tensor\">\n /// Video data\n ///\n /// Shape: (N,T,C,H,W). The values should lie in [0, 255] for type uint8 or [0, 1] for type float.\n /// </param>\n /// <param name=\"global_step\"> Global step value to record </param>\n /// <param name=\"fps\"> Frames per second </param>\n /// <param name=\"walltime\"> Optional override default walltime (DateTimeOffset.Now.ToUnixTimeSeconds()) </param>\n public void add_video(string tag, torch.Tensor vid_tensor, int global_step, int fps = 4, long? walltime = null)\n {\n var fileName = InitDefaultFile();\n SetWalltime(ref walltime);\n Summary summary = torch.utils.tensorboard.Summary.video(tag, vid_tensor, fps);\n var evnt = new Event() { Step = global_step, WallTime = walltime.Value, Summary = summary };\n WriteEvent(fileName, evnt);\n }\n\n /// <summary>\n /// Add text data to summary.\n /// \n /// https://pytorch.org/docs/stable/_modules/torch/utils/tensorboard/writer.html#SummaryWriter.add_text\n /// </summary>\n /// <param name=\"tag\"> Data identifier </param>\n /// <param name=\"text_string\"> String to save </param>\n /// <param name=\"global_step\"> Global step value to record </param>\n /// <param name=\"walltime\"> Optional override default walltime (DateTimeOffset.Now.ToUnixTimeSeconds()) </param>\n public void add_text(string tag, string text_string, int global_step, long? walltime = null)\n {\n var fileName = InitDefaultFile();\n SetWalltime(ref walltime);\n\n var evnt = new Event() { Step = global_step, WallTime = walltime.Value, Summary = torch.utils.tensorboard.Summary.text(tag, text_string) };\n WriteEvent(fileName, evnt);\n }\n\n private static void InitFile(string fileName)\n {\n var evnt = new Event() { FileVersion = \"brain.Event:2\", WallTime = DateTime.Now.Ticks };\n WriteEvent(fileName, evnt);\n }\n\n private string InitDefaultFile()\n {\n var fileName = _fileNames[\"__default__\"];\n\n if (!File.Exists(fileName)) {\n InitFile(fileName);\n }\n\n return fileName;\n }\n\n private static void SetWalltime(ref long? walltime)\n => walltime ??= DateTimeOffset.Now.ToUnixTimeSeconds();\n\n private static void WriteEvent(string fileName, Event evnt)\n {\n var bytes = evnt.ToByteArray();\n\n long header = bytes.LongLength;\n uint header_crc = GetMaskedCrc(header);\n uint footer_crc = GetMaskedCrc(bytes);\n\n using (var fStream = File.Open(fileName, FileMode.OpenOrCreate, FileAccess.Write, FileShare.Read)) {\n fStream.Seek(0, SeekOrigin.End);\n\n using (var writers = new BinaryWriter(fStream)) {\n writers.Write(header);\n writers.Write(header_crc);\n using (var stream = new Google.Protobuf.CodedOutputStream(fStream, true)) {\n evnt.WriteTo(stream);\n }\n fStream.Seek(0, SeekOrigin.End);\n writers.Write(footer_crc);\n fStream.Flush();\n }\n }\n }\n\n private string _log_dir = null;\n private Dictionary<string, string> _fileNames = new Dictionary<string, string>();\n\n private string _suffix = null;\n\n private int _global_uid;\n\n private static uint GetMaskedCrc(byte[] data)\n {\n var value = TorchSharp.Utils.CRC32C.process(data);\n // Rotate right by 15 bits and add a constant.\n return ((value >> 15) | (value << 17)) + 0xa282ead8U;\n }\n\n private static uint GetMaskedCrc(int data)\n {\n var value = TorchSharp.Utils.CRC32C.process(data);\n // Rotate right by 15 bits and add a constant.\n return ((value >> 15) | (value << 17)) + 0xa282ead8U;\n }\n\n private static uint GetMaskedCrc(long data)\n {\n var value = TorchSharp.Utils.CRC32C.process(data);\n // Rotate right by 15 bits and add a constant.\n return ((value >> 15) | (value << 17)) + 0xa282ead8U;\n }\n }\n }\n}"},{"alpha_fraction":0.4700726568698883,"alphanum_fraction":0.5074143409729004,"avg_line_length":34.14495086669922,"blob_id":"cc8c0fe28dfdbec371ce590d040e987f7c3d4f7b","content_id":"84f7bea9a0e3047583cf31c957a5583c58454fc2","detected_licenses":["MIT"],"is_generated":false,"is_vendor":false,"language":"C#","length_bytes":40732,"license_type":"permissive","max_line_length":173,"num_lines":1159,"path":"/test/TorchSharpTest/TestTorchTensorBugs.cs","repo_name":"NiklasGustafsson/TorchSharp","src_encoding":"UTF-8","text":"// Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information.\nusing System;\nusing System.Collections.Generic;\nusing System.Linq;\nusing System.IO;\n\nusing System.Threading;\nusing System.Runtime.CompilerServices;\n\nusing static TorchSharp.torch.nn;\nusing Xunit;\n\nusing static TorchSharp.torch;\nusing static TorchSharp.torchvision.models;\n\n\nusing System.Numerics;\n\n#nullable enable\n\nnamespace TorchSharp\n{\n // The tests in this file are all derived from reported GitHub Issues, serving\n // as regression tests.\n\n [Collection(\"Sequential\")]\n public class TestTorchTensorBugs\n {\n\n [Fact]\n public void ValidateAddInplace()\n {\n using var _ = NewDisposeScope();\n\n var x = torch.zeros(10, 10);\n var y = torch.ones(10).expand(10, 10);\n\n x.add_(y, 1);\n\n Assert.Equal(x, y);\n }\n\n [Fact(Skip = \"No longer throws an exception, and it doesn't seem like ever should have.\")]\n public void ValidateIssue145()\n {\n // Tensor.DataItem gives a hard crash on GPU tensor\n\n if (torch.cuda.is_available()) {\n\n using var _ = NewDisposeScope();\n\n var scalar = torch.tensor(3.14f, torch.CUDA);\n Assert.Throws<InvalidOperationException>(() => scalar.item<float>());\n var tensor = torch.zeros(new long[] { 10, 10 }, device: torch.CUDA);\n Assert.Throws<InvalidOperationException>(() => tensor.data<float>());\n Assert.Throws<InvalidOperationException>(() => { var _ = tensor.bytes; });\n }\n }\n\n class DoubleIt : nn.Module<Tensor, Tensor>\n {\n public DoubleIt() : base(\"double\") { }\n\n public override Tensor forward(Tensor t) => t * 2;\n }\n\n [Fact]\n public void ValidateIssue315_1()\n {\n using var _ = NewDisposeScope();\n\n // https://github.com/dotnet/TorchSharp/issues/315\n // custom module crash in GC thread\n\n // make Torch call our custom module by adding a ReLU in front of it\n using var net = nn.Sequential(\n (\"relu\", nn.ReLU()),\n (\"double\", new DoubleIt())\n );\n\n using var @in = torch.tensor(3);\n using var @out = net.call(@in);\n GC.Collect();\n GC.WaitForPendingFinalizers();\n }\n\n [Fact]\n public void ValidateIssue315_2()\n {\n using var _ = NewDisposeScope();\n\n Func<Tensor, Tensor, Tensor> distance =\n (x, y) => {\n return (x - y).abs();\n };\n\n using (Tensor anchor = torch.rand(new long[] { 15, 5 }, requires_grad: true).neg())\n using (Tensor positive = torch.randn(new long[] { 15, 5 }, requires_grad: true))\n using (Tensor negative = torch.randn(new long[] { 15, 5 })) {\n\n var output = nn.TripletMarginWithDistanceLoss(distance);\n using (var result = output.call(anchor, positive, negative)) { }\n }\n GC.Collect();\n GC.WaitForPendingFinalizers();\n }\n\n\n\n [Fact]\n public void ValidateIssue315_3()\n {\n using var _ = NewDisposeScope();\n\n var lin1 = Linear(1000, 100);\n var lin2 = Linear(100, 10);\n var seq = Sequential((\"lin1\", lin1), (\"relu1\", ReLU()), (\"lin2\", lin2));\n\n using var x = torch.randn(new long[] { 64, 1000 });\n using var y = torch.randn(new long[] { 64, 10 });\n\n double learning_rate = 0.00004f;\n var optimizer = torch.optim.LBFGS(seq.parameters(), learning_rate);\n var loss = nn.MSELoss(Reduction.Sum);\n\n Func<Tensor> closure = () => {\n using var eval = seq.call(x);\n var output = loss.call(eval, y);\n\n var l = output.ToSingle();\n\n optimizer.zero_grad();\n\n output.backward();\n return output;\n };\n\n optimizer.step(closure);\n\n GC.Collect();\n GC.WaitForPendingFinalizers();\n }\n\n\n private void ThreadFunc()\n {\n using var _ = NewDisposeScope();\n\n using var net = nn.Sequential(\n (\"relu\", nn.ReLU()),\n (\"double\", new DoubleIt())\n );\n\n using var @in = torch.tensor(3);\n\n for (var i = 0; i < 1000; i++) {\n using var @out = net.call(@in);\n }\n }\n\n [Fact]\n public void ValidateIssue315_4()\n {\n // Is CustomModule thread-safe?\n // See: https://github.com/pytorch/pytorch/issues/19029\n\n var threads = new List<Thread>();\n\n for (var i = 0; i < 10; i++) {\n var t = new Thread(ThreadFunc);\n threads.Add(t);\n }\n\n foreach (var t in threads) {\n t.Start();\n }\n foreach (var t in threads) {\n t.Join();\n }\n }\n\n class TestModule : Module<Tensor, Tensor>\n {\n public TestModule() : base(nameof(TestModule)) { }\n\n public override torch.Tensor forward(torch.Tensor t) => t.clone();\n\n public static void Reproduce()\n {\n Tensor t = torch.zeros(10);\n\n var seq = Make();\n GC.Collect();\n GC.WaitForPendingFinalizers();\n for (var i = 0; i < 100; i++) {\n GC.Collect();\n GC.WaitForPendingFinalizers();\n t = seq.call(t);\n }\n }\n\n [MethodImpl(MethodImplOptions.NoInlining)]\n static Module<Tensor, Tensor> Make() => Sequential((\"t\", new TestModule()), (\"d\", Linear(10, 10)));\n }\n\n [Fact]\n void ValidateIssue321()\n {\n TestModule.Reproduce();\n }\n\n [Fact]\n public void ValidateIssue353()\n {\n using var _ = NewDisposeScope();\n\n //\n // Just validating that the methods are there.\n //\n using var x = torch.zeros(3, 3);\n using var y = torch.ones(3, 3);\n\n var mx1 = x.max();\n Assert.Equal(0, mx1.item<float>());\n\n var mx2 = x.maximum(y);\n Assert.True(mx2.allclose(y));\n\n var mn1 = x.min();\n Assert.Equal(0, mn1.item<float>());\n\n var mn2 = x.minimum(y);\n Assert.True(mn2.allclose(x));\n }\n\n [Fact]\n public void ValidateIssue399_1()\n {\n using var _ = NewDisposeScope();\n\n // Create a contiguous 3x4 matrix and fill with auto-inc values starting at zero.\n // 0 1 2 3\n // 4 5 6 7\n // 8 9 10 11\n using var contig = torch.arange(12, int32).reshape(3, 4).contiguous();\n var data1 = contig.data<int>();\n\n // Create a 3x2 slice of this, which should contain:\n // 1 2\n // 5 6\n // 9 10\n using var sub = contig.slice(1, 1, 3, 1);\n var data2 = sub.data<int>();\n\n Assert.Equal(12, data1.Count);\n Assert.Equal(6, data2.Count);\n\n Assert.False(sub.is_contiguous());\n Assert.True(sub.contiguous().data<int>() == data2);\n Assert.False(sub.contiguous().data<int>() != data2);\n Assert.Equal(sub.contiguous().data<int>(), data2);\n Assert.Equal(sub.contiguous().data<int>().ToArray(), data2.ToArray());\n }\n\n [Fact]\n public void ValidateIssue399_2()\n {\n using var _ = NewDisposeScope();\n\n // Create a contiguous 3x4 matrix and fill with auto-inc values starting at zero.\n // 0 1 2 3\n // 4 5 6 7\n // 8 9 10 11\n using var contig = torch.arange(12, int32).reshape(3, 4).contiguous();\n var data1 = contig.data<int>();\n\n // Creating the transpose, which should look like:\n // 0 4 8\n // 1 5 9\n // 2 6 10\n // 3 7 11\n using var trans = contig.t();\n var data2 = trans.data<int>();\n\n Assert.False(trans.is_contiguous());\n\n Assert.Equal(12, data1.Count);\n Assert.Equal(12, data2.Count);\n\n Assert.Equal(6, data2[2, 1]);\n Assert.Equal(7, data2[3, 1]);\n\n Assert.True(trans.contiguous().data<int>() == data2);\n Assert.False(trans.contiguous().data<int>() != data2);\n Assert.Equal(trans.contiguous().data<int>(), data2);\n Assert.Equal(trans.contiguous().data<int>().ToArray(), data2.ToArray());\n }\n\n [Fact]\n public void ValidateIssue399_3()\n {\n using var _ = NewDisposeScope();\n\n using var contig = torch.arange(27, int32).reshape(3, 3, 3).contiguous();\n using var trans = contig.permute(2, 0, 1);\n\n Assert.False(trans.is_contiguous());\n Assert.Equal<int>(trans.contiguous().data<int>(), trans.data<int>());\n Assert.Equal<int>(trans.contiguous().data<int>().ToArray(), trans.data<int>().ToArray());\n }\n\n [Fact]\n public void ValidateIssue399_4()\n {\n using var _ = NewDisposeScope();\n\n using var contig = torch.arange(12, int32).reshape(3, 4).contiguous();\n using var flipped = contig.t().flip(1);\n var strides = flipped.stride();\n\n Assert.False(flipped.is_contiguous());\n Assert.Equal<int>(flipped.contiguous().data<int>(), flipped.data<int>());\n Assert.Equal<int>(flipped.contiguous().data<int>().ToArray(), flipped.data<int>().ToArray());\n }\n\n [Fact]\n public void ValidateIssue399_5()\n {\n using var _ = NewDisposeScope();\n\n using var contig = torch.arange(12, int32).reshape(3, 4).contiguous();\n using var strided = contig.as_strided(new long[] { 3, 2, 4 }, new long[] { 4, 0, 1 });\n\n Assert.False(strided.is_contiguous());\n Assert.Equal<int>(strided.contiguous().data<int>(), strided.data<int>());\n Assert.Equal(new long[] { 3, 2, 4 }, strided.shape);\n }\n\n [Fact]\n public void ValidateIssue399_6()\n {\n using var _ = NewDisposeScope();\n\n using var contig = torch.arange(3, int32).reshape(3, 1).contiguous();\n using var strided = contig.expand(3, 4);\n\n Assert.False(strided.is_contiguous());\n Assert.Equal<int>(strided.contiguous().data<int>(), strided.data<int>());\n Assert.Equal(new long[] { 3, 4 }, strided.shape);\n }\n\n [Fact]\n public void ValidateIssue399_7()\n {\n using var _ = NewDisposeScope();\n\n using var contig = torch.arange(27, int32).reshape(3, 3, 3).contiguous();\n using var trans = contig.permute(2, 0, 1);\n\n Assert.False(trans.is_contiguous());\n\n // Test the enumerators.\n var data1 = trans.contiguous().data<int>();\n var data2 = trans.data<int>();\n\n var expected = new int[] { 0, 3, 6, 9, 12, 15, 18, 21, 24, 1, 4, 7, 10, 13, 16, 19, 22, 25, 2, 5, 8, 11, 14, 17, 20, 23, 26 };\n\n long idx = 0;\n foreach (var value in data1) {\n Assert.Equal(expected[idx], value);\n idx += 1;\n }\n Assert.Equal(27, idx);\n\n idx = 0;\n foreach (var value in data2) {\n Assert.Equal(expected[idx], value);\n idx += 1;\n }\n Assert.Equal(27, idx);\n\n var arr1 = data1.AsEnumerable<int>().ToArray();\n var arr2 = data2.AsEnumerable<int>().ToArray();\n\n Assert.Equal(expected, arr1);\n Assert.Equal(arr1, arr2);\n }\n\n [Fact]\n public void ValidateIssue399_8()\n {\n using var _ = NewDisposeScope();\n\n // We need to test something that has rank 1, because the TensorAccessor uses\n // seprate enumeration logic for that.\n using var contig = torch.arange(48, int32).reshape(12, 4).contiguous();\n using var sub = contig.slice(1, 1, 2, 1).squeeze(1);\n\n var data1 = sub.contiguous().data<int>();\n var data2 = sub.data<int>();\n\n Assert.True(data1 == data2);\n Assert.False(data1 != data2);\n\n Assert.Equal(data1.ToArray(), data2.ToArray());\n\n var expected = new int[] { 1, 5, 9, 13, 17, 21, 25, 29, 33, 37, 41, 45 };\n\n long idx = 0;\n foreach (var value in data1) {\n Assert.Equal(expected[idx], value);\n idx += 1;\n }\n Assert.Equal(12, idx);\n\n idx = 0;\n foreach (var value in data2) {\n Assert.Equal(expected[idx], value);\n idx += 1;\n }\n Assert.Equal(12, idx);\n\n var arr1 = data1.AsEnumerable<int>().ToArray();\n var arr2 = data2.AsEnumerable<int>().ToArray();\n\n Assert.Equal(expected, arr1);\n Assert.Equal(arr1, arr2);\n }\n\n [Fact]\n public void ValidateRandThrowsOnWrongType()\n {\n using var _ = NewDisposeScope();\n\n Assert.Throws<ArgumentException>(() => torch.rand(3, 4, dtype: torch.int8));\n Assert.Throws<ArgumentException>(() => torch.rand(3, 4, dtype: torch.uint8));\n Assert.Throws<ArgumentException>(() => torch.rand(3, 4, dtype: torch.int16));\n Assert.Throws<ArgumentException>(() => torch.rand(3, 4, dtype: torch.int32));\n Assert.Throws<ArgumentException>(() => torch.rand(3, 4, dtype: torch.int64));\n Assert.Throws<ArgumentException>(() => torch.rand(3, 4, dtype: torch.@bool));\n Assert.Throws<ArgumentException>(() => torch.randn(3, 4, dtype: torch.int8));\n Assert.Throws<ArgumentException>(() => torch.randn(3, 4, dtype: torch.uint8));\n Assert.Throws<ArgumentException>(() => torch.randn(3, 4, dtype: torch.int16));\n Assert.Throws<ArgumentException>(() => torch.randn(3, 4, dtype: torch.int32));\n Assert.Throws<ArgumentException>(() => torch.randn(3, 4, dtype: torch.int64));\n Assert.Throws<ArgumentException>(() => torch.randn(3, 4, dtype: torch.@bool));\n }\n\n [Fact]\n public void ValidateIssue496()\n {\n using var _ = NewDisposeScope();\n\n var c2 = torch.nn.Conv2d(3, 16, kernelSize: (1, 7), stride: (1, 1), padding: (0, 3));\n var Win = torch.rand(16, 3, 8, 8);\n var s = c2.call(Win).shape;\n Assert.Equal(new long[] { 16, 16, 8, 8 }, s);\n }\n\n [Fact]\n public void ValidateIssue500()\n {\n using var _ = NewDisposeScope();\n\n using (var pool = BatchNorm1d(28)) {\n pool.eval();\n pool.call(torch.ones(1, 28));\n }\n using (var pool = BatchNorm1d(28))\n using (var seq = Sequential(pool)) {\n seq.eval();\n seq.call(torch.ones(1, 28));\n }\n using (var seq = new Module500()) {\n seq.eval();\n seq.call(torch.ones(1, 28));\n }\n }\n\n class Module500 : Module<Tensor, Tensor>\n {\n private Module<Tensor, Tensor> bn1 = BatchNorm1d(28);\n\n public Module500() : base(nameof(TestModule)) { RegisterComponents(); }\n\n public override torch.Tensor forward(torch.Tensor t) => bn1.call(t);\n }\n\n [Fact]\n public void ValidateIssue510()\n {\n using var _ = NewDisposeScope();\n\n var model = new Module510(1, 32);\n model.call(torch.randn(16, 1, 32));\n\n var w0 = model.get_parameter(\"stack.0.weight\").clone();\n var w1 = model.get_parameter(\"stack.1.weight\").clone();\n var b1 = model.get_parameter(\"stack.1.bias\").clone();\n var rm = model.get_buffer(\"stack.1.running_mean\").clone();\n var rv = model.get_buffer(\"stack.1.running_var\").clone();\n var nm = model.get_buffer(\"stack.1.num_batches_tracked\").clone();\n\n model.load(\"bug510.dat\");\n\n var w0_ = model.get_parameter(\"stack.0.weight\");\n var w1_ = model.get_parameter(\"stack.1.weight\");\n var b1_ = model.get_parameter(\"stack.1.bias\");\n var rm_ = model.get_buffer(\"stack.1.running_mean\");\n var rv_ = model.get_buffer(\"stack.1.running_var\");\n var nm_ = model.get_buffer(\"stack.1.num_batches_tracked\");\n\n Assert.NotEqual(w0, w0_);\n Assert.NotEqual(w1, w1_);\n Assert.NotEqual(b1, b1_);\n Assert.NotEqual(rm, rm_);\n Assert.NotEqual(rv, rv_);\n Assert.Equal(1, nm.item<long>());\n Assert.Equal(0, nm_.item<long>());\n }\n\n internal class Module510 : Module<Tensor, Tensor>\n {\n private readonly Module<Tensor, Tensor> stack;\n\n public Module510(int in_channels, int out_channels, int kernel_size = 3, int stride = 1, int padding = 0) : base(String.Empty)\n {\n var temp = BatchNorm1d(out_channels);\n this.stack = Sequential(\n Conv1d(in_channels, out_channels, 3, stride: stride, padding: padding, bias: false),\n temp,\n ReLU(inplace: true)\n );\n\n temp.weight = Parameter(torch.randn(temp.weight!.shape));\n temp.bias = Parameter(torch.randn(temp.bias!.shape));\n if (temp.running_mean is not null) temp.running_mean = torch.randn(temp.running_mean.shape);\n if (temp.running_var is not null) temp.running_var = torch.randn(temp.running_var.shape);\n\n this.RegisterComponents();\n }\n\n public override torch.Tensor forward(torch.Tensor t)\n {\n return this.stack.call(t);\n }\n }\n\n [Fact]\n public void ValidateIssue516()\n {\n if (torch.cuda.is_available()) {\n using var _ = NewDisposeScope();\n\n var model = new TestGradWarningModel();\n model.cuda();\n\n var optimizer = torch.optim.Adam(model.parameters());\n optimizer.zero_grad();\n\n var x = torch.ones(5, 3).cuda();\n var y = torch.ones(5, 4).cuda();\n\n var z = model.call(x);\n var lossFunc = torch.nn.CrossEntropyLoss();\n var loss = lossFunc.call(y, z);\n loss.backward();\n optimizer.step();\n\n var grad1 = optimizer.parameters().ToArray()[0].grad();\n Assert.NotNull(grad1);\n\n var grad2 = model.Weight.grad();\n Assert.NotNull(grad2);\n }\n }\n\n internal abstract class BaseModule : torch.nn.Module<Tensor, Tensor>\n {\n public int? InstanceId = null;\n\n protected BaseModule(string name) : base(name)\n {\n }\n }\n\n public class TestGradWarningModel : torch.nn.Module<Tensor, Tensor>\n {\n public readonly Modules.Parameter Weight;\n\n public TestGradWarningModel() : base(nameof(TestGradWarningModel))\n {\n Weight = torch.zeros(new long[] { 3, 4 }).AsParameter();\n RegisterComponents();\n }\n\n public override torch.Tensor forward(torch.Tensor t)\n {\n return torch.matmul(t, Weight);\n }\n }\n\n [Fact]\n public void Validate532()\n {\n using var _ = NewDisposeScope();\n var module = new Module532(1000, 100);\n\n var p = module.parameters().ToArray();\n var pB = module.batch.parameters().ToArray();\n var pC = module.conv.parameters().ToArray();\n\n Assert.Equal(pB.Length + pC.Length, p.Length);\n }\n\n internal class Module532 : Module<Tensor, Tensor>\n {\n public Module<Tensor, Tensor> conv;\n public Module<Tensor, Tensor> batch;\n private Module<Tensor, Tensor> seq;\n\n public Module532(int in_channels, int out_channels) : base(String.Empty)\n {\n conv = Conv1d(in_channels, out_channels, 3);\n batch = BatchNorm1d(out_channels);\n seq = Sequential(\n conv,\n batch,\n ReLU(inplace: true)\n );\n\n this.RegisterComponents();\n }\n\n public override torch.Tensor forward(torch.Tensor t)\n {\n return this.seq.call(t);\n }\n }\n\n\n\n [Fact]\n public void Validate538()\n {\n var module = new Module538(1000, 100);\n\n var sd = module.state_dict();\n\n Assert.Equal(7, sd.Count);\n\n if (File.Exists(\"bug538.dat\")) File.Delete(\"bug538.dat\");\n\n module.save(\"bug538.dat\");\n module.load(\"bug538.dat\");\n\n File.Delete(\"bug538.dat\");\n }\n\n internal class Module538 : Module<Tensor, Tensor>\n {\n private Module<Tensor, Tensor> seq;\n\n public Module538(int in_channels, int out_channels) : base(String.Empty)\n {\n seq = Sequential(Conv2d(1, 32, 3),\n BatchNorm2d(32),\n ReLU(),\n Flatten(),\n LogSoftmax(1)\n );\n\n RegisterComponents();\n }\n\n public override torch.Tensor forward(torch.Tensor t)\n {\n return this.seq.call(t);\n }\n }\n\n [Fact]\n public void GruModuleWorksWithoutPassingH0()\n {\n var device = torch.cuda.is_available() ? torch.CUDA : torch.CPU;\n using var _ = NewDisposeScope();\n using (Tensor input = torch.randn(new long[] { 5, 3, 10 }, device: device),\n h0 = torch.randn(new long[] { 1, 3, 20 }, device: device))\n using (var gru = GRU(10, 20)) {\n gru.to(device);\n var (output, hN) = gru.call(input);\n Assert.Equal(h0.shape, hN.shape);\n Assert.Equal(new long[] { input.shape[0], input.shape[1], 20 }, output.shape);\n }\n }\n\n [Fact]\n public void LstmModuleWorksWithoutPassingH0C0()\n {\n var device = torch.cuda.is_available() ? torch.CUDA : torch.CPU;\n using var _ = NewDisposeScope();\n using (Tensor input = torch.randn(new long[] { 5, 3, 10 }, device: device),\n h0 = torch.randn(new long[] { 1, 3, 20 }, device: device))\n using (var lstm = LSTM(10, 20)) {\n lstm.to(device);\n var (output, hN, hX) = lstm.call(input);\n Assert.Equal(h0.shape, hN.shape);\n Assert.Equal(h0.shape, hX.shape);\n Assert.Equal(new long[] { input.shape[0], input.shape[1], 20 }, output.shape);\n }\n }\n\n [Fact]\n public void RnnModuleWorksWithoutPassingH0()\n {\n var device = torch.cuda.is_available() ? torch.CUDA : torch.CPU;\n using var _ = NewDisposeScope();\n using (Tensor input = torch.randn(new long[] { 5, 3, 10 }, device: device),\n h0 = torch.randn(new long[] { 1, 3, 20 }, device: device))\n using (var rnn = RNN(10, 20)) {\n rnn.to(device);\n var (output, hN) = rnn.call(input);\n Assert.Equal(h0.shape, hN.shape);\n Assert.Equal(new long[] { input.shape[0], input.shape[1], 20 }, output.shape);\n }\n }\n\n [Fact]\n public void ValidateBug618()\n {\n if (torch.cuda.is_available()) {\n using var _ = NewDisposeScope();\n var mean = torch.randn(new long[] { 32, 3, 3 }, device: torch.CUDA);\n var tmp = torch.randn(new long[] { 3, 3 }, device: torch.CUDA);\n var log_std = tmp.expand_as(mean);\n var std = exp(log_std);\n var dist = torch.distributions.Normal(mean, std);\n dist.sample();// error\n }\n }\n\n [Fact]\n public void ValidateBug653()\n {\n var x = torch.linspace(0, 1, 100, dtype: torch.float32);\n var y = torch.linspace(0, 1, 100, dtype: torch.float64);\n var z = x.to(y);\n Assert.Equal(torch.float64, z.dtype);\n }\n\n [Fact]\n public void ValidateBug679()\n {\n // Per bug report.\n // https://github.com/dotnet/TorchSharp/issues/679\n //\n var dtype = torch.complex64;\n var spec = torch.rand(1, 1024, 500, dtype: dtype);\n Assert.Throws<System.Runtime.InteropServices.ExternalException>(() => torch.istft(spec, 512, 160, 400, null));\n\n spec = torch.rand(1, 512, 500, dtype: dtype);\n var x = torch.istft(spec, 512, 160, 400, null);\n\n spec = torch.rand(1, 257, 500, dtype: dtype);\n x = torch.istft(spec, 512, 160, 400, null);\n\n dtype = torch.complex128;\n spec = torch.rand(1, 1024, 500, dtype: dtype);\n Assert.Throws<System.Runtime.InteropServices.ExternalException>(() => torch.istft(spec, 512, 160, 400, null));\n\n spec = torch.rand(1, 512, 500, dtype: dtype);\n x = torch.istft(spec, 512, 160, 400, null);\n\n spec = torch.rand(1, 257, 500, dtype: dtype);\n x = torch.istft(spec, 512, 160, 400, null);\n }\n\n [Fact]\n public void ValidateBug715()\n {\n var resnet = resnet18();\n var resnetlist = resnet.named_children();\n var list = resnetlist.Take(6).Select(x => (x.name, (nn.Module<Tensor, Tensor>)x.module));\n var bone = nn.Sequential(list);\n\n var x = torch.zeros(1, 3, 64, 160);\n\n // This should not blow up.\n var tmp = bone.call(x);\n }\n\n [Fact]\n [TestOf(nameof(Modules.Categorical.probs))]\n public void ValidateBug836()\n {\n int nSamples = Convert.ToInt32(5e3);\n random.manual_seed(1);\n Tensor actionLogits = ones(nSamples, 2);\n Modules.Categorical distribution = new(logits: actionLogits);\n Tensor actions = distribution.sample();\n Tensor entropy = distribution.entropy();\n Tensor log_prob = distribution.log_prob(actions);\n var eMean = entropy.mean().ToSingle();\n var lMean = log_prob.mean().ToSingle();\n\n Assert.Equal(0.693147, eMean, 0.0001);\n Assert.Equal(-0.693147, lMean, 0.0001);\n }\n\n [Fact]\n [TestOf(nameof(torch.distributions.Bernoulli))]\n public void ValidateBug838()\n {\n int nSamples = Convert.ToInt32(5e6);\n random.manual_seed(1);\n Tensor actionLogits = rand(nSamples, 2);\n // This should not blow up.\n var distribution = distributions.Bernoulli(logits: actionLogits);\n }\n\n [Fact]\n public void ValidateMultiStepLR()\n {\n var gen = new Generator(4711);\n TestTraining.CreateLinearLayers(gen, out var lin1, out var lin2);\n TestTraining.CreateDataAndLabels(gen, out var x, out var y);\n\n var seq = Sequential((\"lin1\", lin1), (\"relu1\", ReLU()), (\"lin2\", lin2));\n\n double learning_rate = 0.1;\n var optimizer = torch.optim.SGD(seq.parameters(), learning_rate);\n var scheduler = torch.optim.lr_scheduler.MultiStepLR(optimizer, new int[] { 3, 5, 7 }, 0.9);\n\n optimizer.zero_grad();\n optimizer.step();\n scheduler.step(1);\n Assert.Equal(0.1, optimizer.ParamGroups.First().LearningRate);\n scheduler.step(2);\n Assert.Equal(0.1, optimizer.ParamGroups.First().LearningRate);\n scheduler.step(3);\n Assert.Equal(0.09, optimizer.ParamGroups.First().LearningRate, 0.00001);\n scheduler.step(4);\n Assert.Equal(0.09, optimizer.ParamGroups.First().LearningRate, 0.00001);\n scheduler.step(5);\n Assert.Equal(0.09 * 0.9, optimizer.ParamGroups.First().LearningRate, 0.00001);\n scheduler.step(6);\n scheduler.step(7);\n Assert.Equal(0.09 * 0.9 * 0.9, optimizer.ParamGroups.First().LearningRate, 0.00001);\n scheduler.step(8);\n Assert.Equal(0.09 * 0.9 * 0.9, optimizer.ParamGroups.First().LearningRate, 0.00001);\n scheduler.step(9);\n Assert.Equal(0.09 * 0.9 * 0.9, optimizer.ParamGroups.First().LearningRate, 0.00001);\n scheduler.step(10);\n Assert.Equal(0.09 * 0.9 * 0.9, optimizer.ParamGroups.First().LearningRate, 0.00001);\n }\n\n\n\n [Fact]\n public void ValidatePolynomialLR()\n {\n {\n // Linear decay\n var gen = new Generator(4711);\n TestTraining.CreateLinearLayers(gen, out var lin1, out var lin2);\n TestTraining.CreateDataAndLabels(gen, out var x, out var y);\n\n var seq = Sequential((\"lin1\", lin1), (\"relu1\", ReLU()), (\"lin2\", lin2));\n\n double learning_rate = 0.1;\n var optimizer = torch.optim.SGD(seq.parameters(), learning_rate);\n var scheduler = torch.optim.lr_scheduler.PolynomialLR(optimizer, 10, 1);\n\n optimizer.zero_grad();\n optimizer.step();\n scheduler.step(1);\n Assert.Equal(0.09, optimizer.ParamGroups.First().LearningRate, 0.00001);\n scheduler.step(2);\n Assert.Equal(0.08, optimizer.ParamGroups.First().LearningRate, 0.00001);\n scheduler.step(3);\n Assert.Equal(0.07, optimizer.ParamGroups.First().LearningRate, 0.00001);\n scheduler.step(4);\n Assert.Equal(0.06, optimizer.ParamGroups.First().LearningRate, 0.00001);\n scheduler.step(5);\n Assert.Equal(0.05, optimizer.ParamGroups.First().LearningRate, 0.00001);\n scheduler.step(6);\n scheduler.step(7);\n Assert.Equal(0.03, optimizer.ParamGroups.First().LearningRate, 0.00001);\n scheduler.step(8);\n Assert.Equal(0.02, optimizer.ParamGroups.First().LearningRate, 0.00001);\n scheduler.step(9);\n Assert.Equal(0.01, optimizer.ParamGroups.First().LearningRate, 0.00001);\n scheduler.step(10);\n Assert.Equal(0.0, optimizer.ParamGroups.First().LearningRate, 0.00001);\n }\n {\n // Squared decay\n var gen = new Generator(4711);\n TestTraining.CreateLinearLayers(gen, out var lin1, out var lin2);\n TestTraining.CreateDataAndLabels(gen, out var x, out var y);\n\n var seq = Sequential((\"lin1\", lin1), (\"relu1\", ReLU()), (\"lin2\", lin2));\n\n double learning_rate = 0.1;\n var optimizer = torch.optim.SGD(seq.parameters(), learning_rate);\n var scheduler = torch.optim.lr_scheduler.PolynomialLR(optimizer, 10, 2);\n\n optimizer.zero_grad();\n optimizer.step();\n scheduler.step(1);\n Assert.Equal(0.081, optimizer.ParamGroups.First().LearningRate, 0.00001);\n scheduler.step(2);\n Assert.Equal(0.064, optimizer.ParamGroups.First().LearningRate, 0.00001);\n scheduler.step(3);\n Assert.Equal(0.049, optimizer.ParamGroups.First().LearningRate, 0.00001);\n scheduler.step(4);\n Assert.Equal(0.036, optimizer.ParamGroups.First().LearningRate, 0.00001);\n scheduler.step(5);\n Assert.Equal(0.025, optimizer.ParamGroups.First().LearningRate, 0.00001);\n scheduler.step(6);\n scheduler.step(7);\n Assert.Equal(0.009, optimizer.ParamGroups.First().LearningRate, 0.00001);\n scheduler.step(8);\n Assert.Equal(0.004, optimizer.ParamGroups.First().LearningRate, 0.00001);\n scheduler.step(9);\n Assert.Equal(0.001, optimizer.ParamGroups.First().LearningRate, 0.00001);\n scheduler.step(10);\n Assert.Equal(0.0, optimizer.ParamGroups.First().LearningRate, 0.00001);\n }\n }\n\n\n [Fact]\n public void Validate845()\n {\n var module1 = new Module845(10, 10);\n var module2 = new Module845(10, 10);\n var module3 = new Module845(10, 10);\n\n var dev = torch.cuda.is_available() ? CUDA : CPU;\n\n module1.to(dev);\n module1.validate(float32, dev.type);\n\n module2.to(dev, float64);\n module2.validate(float64, dev.type);\n\n module3.to(float64);\n module3.validate(float64, CPU.type);\n }\n\n internal class Module845 : Module<Tensor, Tensor>\n {\n private Module<Tensor, Tensor> seq;\n\n public Module845(int in_channels, int out_channels) : base(String.Empty)\n {\n seq = Sequential(Conv2d(1, 32, 3),\n ReLU(),\n Flatten(),\n LogSoftmax(1)\n );\n\n register_buffer(\"test\", torch.ones(10, 10));\n RegisterComponents();\n }\n\n public override torch.Tensor forward(torch.Tensor t)\n {\n return this.seq.call(t);\n }\n\n public void validate(ScalarType expected, DeviceType devType)\n {\n foreach (var (name, buffer) in named_buffers()) {\n Assert.Equal(expected, buffer.dtype);\n Assert.Equal(devType, buffer.device_type);\n }\n }\n }\n\n [Fact]\n public void Validate851()\n {\n var a = torch.tensor(new long[] { 100, 200, 300, 400 }, new long[] { 1, 4 });\n a.print();\n var str = a.ToString(TorchSharp.TensorStringStyle.Numpy);\n Assert.Equal(\"[[100, 200, 300, 400]]\", str);\n }\n\n [Fact]\n public void Validate852()\n {\n float[] a = new float[12];\n var x = torch.as_tensor(a);\n }\n\n [Fact]\n public void Validate877()\n {\n if (torch.cuda.is_available()) {\n var device = torch.CUDA;\n torch.TryInitializeDeviceType(device.type);\n var train = new GitBlockTest(\"test\", device);\n\n var p0 = train.named_parameters().Select(p => p.name).ToArray();\n\n train.to(device);\n\n int named_parameters_1 = train.named_parameters().Count();\n train.to(torch.CUDA);\n int named_parameters_2 = train.named_parameters().Count();\n\n Assert.Equal(named_parameters_1, named_parameters_2);\n }\n }\n\n\n class GitTestCnn : Module<Tensor, Tensor>\n {\n private readonly TorchSharp.Modules.Sequential layers0;\n\n public GitTestCnn(string name, Device? device = null) : base(name)\n {\n var modules = new List<(string, Module<Tensor, Tensor>)>();\n modules.Add(($\"{name}-conv2d-1\", Conv2d(1, 4, kernelSize: (1L, 1L), stride: (1L, 1L), padding: (0L, 0L), paddingMode: PaddingModes.Replicate, bias: false)));\n layers0 = Sequential(modules);\n\n RegisterComponents();\n }\n\n public override Tensor forward(Tensor t)\n {\n var t1 = layers0.call(t).squeeze_(3);\n return t1;\n }\n }\n\n class GitTestGru : Module<Tensor, Tensor>\n {\n private TorchSharp.Modules.GRU layers1;\n private TorchSharp.Modules.GRU layers2;\n private Tensor init_h0;\n private Tensor init_h1;\n\n public GitTestGru(string name, Device? device = null) : base(name)\n {\n layers1 = nn.GRU(1, 4, batchFirst: true);\n layers2 = nn.GRU(4, 4, batchFirst: true);\n\n var state_size = new long[] { 1, 1, 4 };\n init_h0 = torch.nn.Parameter(torch.zeros(state_size, device: device));\n init_h1 = torch.nn.Parameter(torch.zeros(state_size, device: device));\n\n RegisterComponents();\n }\n\n public override Tensor forward(Tensor input)\n {\n var (rnn_output, states_h0) = layers1.call(input, init_h0);\n init_h0 = states_h0.detach_();\n var (_, states_h1) = layers2.call(rnn_output, init_h1);\n init_h1 = states_h1.detach_();\n var x2 = states_h1[-1];\n return x2;\n }\n }\n\n class GitBlockTest : Module<Tensor, Tensor>\n {\n private readonly Module<Tensor, Tensor> sequence_layers;\n\n public GitBlockTest(string name, Device? device = null) : base(name)\n {\n var modules = nn.ModuleDict<Module<Tensor, Tensor>>();\n modules.Add((\"cnn-1\", new GitTestCnn(\"GitTest\", device)));\n modules.Add((\"rnn-2\", new GitTestGru(\"GitTestGru\", device)));\n sequence_layers = Sequential(modules.values());\n RegisterComponents();\n }\n\n public override Tensor forward(Tensor input)\n {\n var t0 = sequence_layers.call(input);\n return t0;\n }\n }\n\n\n [Fact]\n public void Validate912()\n {\n var test = new Test_912(2);\n Dictionary<string, Tensor> sd = test.state_dict(); // No layer2.modules keyword\n Assert.Contains(sd.Keys, k => k.StartsWith(\"layer2.modules\"));\n }\n\n public class Test_912 : nn.Module\n {\n public nn.Module layer;\n public nn.Module layer2;\n\n public Test_912(int layernum) : base(\"Test_912\")\n {\n layer = nn.Linear(16, 2);\n layer2 = new Test2_912((from l in Enumerable.Range(0, layernum)\n select new Test3_912()).ToArray(),\n (from l in Enumerable.Range(0, layernum)\n select new Test3_912()).ToArray());\n this.RegisterComponents();\n }\n }\n public class Test2_912 : nn.Module\n {\n public new Modules.ModuleList<Test3_912> modules;\n public Modules.ModuleList<nn.Module> modules2;\n public nn.Module layer;\n public Test2_912(Test3_912[] ms, nn.Module[] ms2) : base(\"Test2_912\")\n {\n layer = nn.Linear(16, 2);\n modules = nn.ModuleList(ms);\n modules2 = nn.ModuleList(ms2);\n this.RegisterComponents();\n }\n }\n public class Test3_912 : nn.Module\n {\n public nn.Module layer;\n public Test3_912() : base(\"Test3_912\")\n {\n layer = nn.Linear(16, 2);\n this.RegisterComponents();\n }\n }\n\n [Fact]\n public void Validate971()\n {\n var t = torch.tensor(new double[] { 0, 1, 2 });\n var std_dev = t.std(false).item<double>();\n\n Assert.Equal(0.8165, std_dev, 0.0001);\n }\n\n [Fact(Skip = \"Takes too long to run to completion.\")]\n static void Validate1057()\n {\n if (torch.cuda_is_available()) {\n var device = torch.CUDA;\n\n for (int i = 0; i < 25; i++) {\n using (var _ = torch.NewDisposeScope()) {\n var data = torch.randn(200000, 3, 32, 32).to(device).requires_grad_(true);\n }\n }\n }\n }\n }\n}"},{"alpha_fraction":0.5044707655906677,"alphanum_fraction":0.505094587802887,"avg_line_length":40.10256576538086,"blob_id":"7c952ddcb84dd69e220e002731fd0d0550a52e77","content_id":"992f951b9f2d0357343dac1797d74c6ab75010c0","detected_licenses":["MIT"],"is_generated":false,"is_vendor":false,"language":"C#","length_bytes":4809,"license_type":"permissive","max_line_length":133,"num_lines":117,"path":"/src/TorchSharp/JIT/CompilationUnit.cs","repo_name":"NiklasGustafsson/TorchSharp","src_encoding":"UTF-8","text":"// Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information.\nusing System;\nusing TorchSharp.PInvoke;\nusing static TorchSharp.PInvoke.NativeMethods;\n\nnamespace TorchSharp\n{\n public static partial class torch\n {\n public static partial class jit\n {\n /// <summary>\n /// Represents a TorchScript compilation unit, i.e. a Python script file.\n /// </summary>\n /// <example>\n /// var cu = torch.jit.compile(@\"\n /// def relu_script(a, b):\n /// return torch.relu(a + b)\n /// \");\n ///\n /// var y = cu.invoke(\"relu_script\", torch.randn(10));\n /// </example>\n /// <remarks>\n /// Currently, scripts are limited to defining functions. Classes will be ignored.\n /// </remarks>\n public class CompilationUnit : IDisposable\n {\n internal CompilationUnit(IntPtr handle)\n {\n this.handle = handle;\n }\n\n ~CompilationUnit() => Dispose(false);\n\n /// <summary>\n /// Releases the storage.\n /// </summary>\n public void Dispose()\n {\n Dispose(true);\n GC.SuppressFinalize(this);\n }\n\n /// <summary>\n /// Implements the .NET Dispose pattern.\n /// </summary>\n protected virtual void Dispose(bool disposing)\n {\n if (disposing && handle != IntPtr.Zero) {\n handle = IntPtr.Zero;\n }\n }\n\n internal IntPtr handle;\n\n /// <summary>\n /// Invoke a function from the compilation unit.\n /// </summary>\n /// <param name=\"name\">The name of the function.</param>\n /// <param name=\"objs\">Function arguments.</param>\n public object invoke(string name, params object[] objs)\n {\n if (string.IsNullOrEmpty(name)) throw new ArgumentNullException(\"method name\");\n\n TensorOrScalar[] ptrArray = null;\n sbyte typeCode = 0;\n\n using (var parray = new IndexedPinnedArrays<TensorOrScalar>()) {\n\n var tRefsHandle = ScriptModule.DetermineArgumentTypeRefs(objs, out var count, parray);\n\n var allocated = parray.Count;\n\n THSJIT_CompilationUnit_Invoke(handle, name, tRefsHandle, count, parray.CreateArray, out typeCode, allocated);\n torch.CheckForErrors();\n ptrArray = parray[allocated];\n\n return ScriptModule.ProcessReturnValue(name, parray, ptrArray, typeCode);\n }\n }\n\n /// <summary>\n /// Invoke a function from the compilation unit.\n /// </summary>\n /// <typeparam name=\"TResult\">The return type of the TorchScript function.</typeparam>\n /// <param name=\"name\">The name of the function.</param>\n /// <param name=\"inputs\">Function arguments.</param>\n public TResult invoke<TResult>(string name, params object[] inputs) => (TResult)invoke(name, inputs);\n\n /// <summary>\n /// Invoke a function from the compilation unit.\n /// </summary>\n /// <typeparam name=\"T\">The type of all function arguments.</typeparam>\n /// <typeparam name=\"TResult\">The return type of the TorchScript function.</typeparam>\n /// <param name=\"name\">The name of the function.</param>\n /// <param name=\"inputs\">Function arguments.</param>\n public TResult invoke<T, TResult>(string name, params T[] inputs) => (TResult)invoke(name, inputs);\n }\n\n /// <summary>\n /// Create a TorchScript compilation unit containing TorchScript-compliant Python from a string.\n /// </summary>\n /// <param name=\"script\">A string with Python code expressing a set of TorchScript functions.</param>\n /// <returns></returns>\n public static CompilationUnit compile(string script)\n {\n if (string.IsNullOrEmpty(script))\n throw new ArgumentNullException(\"empty script\");\n\n var result = THSJIT_compile(script);\n if (result == IntPtr.Zero)\n CheckForErrors();\n return new CompilationUnit(result);\n }\n }\n }\n}\n"},{"alpha_fraction":0.5798611044883728,"alphanum_fraction":0.5819978713989258,"avg_line_length":39.25806427001953,"blob_id":"92d5292413ef9a197ebc4f6e473e64722d84a8c2","content_id":"68bff2b606ff3d1079d5939b2eb016ccdf3d80fe","detected_licenses":["MIT"],"is_generated":false,"is_vendor":false,"language":"C#","length_bytes":3744,"license_type":"permissive","max_line_length":149,"num_lines":93,"path":"/src/TorchSharp/Distributions/HalfCauchy.cs","repo_name":"NiklasGustafsson/TorchSharp","src_encoding":"UTF-8","text":"// Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information.\nusing System;\nusing static TorchSharp.torch;\n\nnamespace TorchSharp\n{\n using Modules;\n using static torch.distributions;\n\n namespace Modules\n {\n /// <summary>\n /// Creates a half-Cauchy distribution parameterized by `scale`\n /// </summary>\n public class HalfCauchy : TransformedDistribution\n {\n internal HalfCauchy(Tensor scale, torch.Generator generator = null) :\n base(Cauchy(torch.tensor(0).to(scale.dtype), scale, generator), new torch.distributions.transforms.AbsTransform(), generator)\n {\n this.scale = scale?.alias().DetachFromDisposeScope();\n }\n\n public Tensor scale { get; private set; }\n\n public override Tensor mean => torch.full(ExtendedShape(), double.PositiveInfinity, dtype: scale.dtype, device: scale.device);\n\n public override Tensor mode => torch.zeros_like(scale);\n\n public override Tensor variance => base_distribution.variance;\n\n public override Tensor log_prob(Tensor value)\n {\n using var _ = torch.NewDisposeScope();\n value = torch.as_tensor(value, scale.dtype, scale.device);\n var lp = base_distribution.log_prob(value) + Math.Log(2);\n lp = torch.where(value >= 0, lp, double.NegativeInfinity);\n return lp.MoveToOuterDisposeScope();\n }\n\n public override Tensor cdf(Tensor value)\n {\n return 2 * base_distribution.cdf(value) - 1;\n }\n\n public override Tensor icdf(Tensor value)\n {\n return base_distribution.icdf((value + 1) / 2);\n }\n\n /// <summary>\n /// Returns entropy of distribution, batched over batch_shape.\n /// </summary>\n public override Tensor entropy()\n {\n return base_distribution.entropy() - Math.Log(2);\n }\n\n /// <summary>\n /// Returns a new distribution instance (or populates an existing instance provided by a derived class) with batch dimensions expanded to\n /// `batch_shape`. This method calls `torch.Tensor.expand()` on the distribution's parameters. As such, this does not allocate new\n /// memory for the expanded distribution instance.\n /// </summary>\n /// <param name=\"batch_shape\">Tthe desired expanded size.</param>\n /// <param name=\"instance\">new instance provided by subclasses that need to override `.expand`.</param>\n public override Distribution expand(Size batch_shape, Distribution instance = null)\n {\n var newDistribution = ((instance == null)\n ? new HalfCauchy(scale.expand(batch_shape), generator)\n : instance) as HalfCauchy;\n return base.expand(batch_shape, newDistribution);\n }\n }\n }\n\n\n public static partial class torch\n {\n public static partial class distributions\n {\n /// <summary>\n /// Creates a half-Cauchy distribution parameterized by `scale`\n /// </summary>\n /// <param name=\"scale\">Scale parameter of the distribution.</param>\n /// <param name=\"generator\">An optional random number generator object.</param>\n /// <returns></returns>\n public static HalfCauchy HalfCauchy(Tensor scale, torch.Generator generator = null)\n {\n\n return new HalfCauchy(scale, generator);\n }\n }\n }\n}\n"},{"alpha_fraction":0.5810214281082153,"alphanum_fraction":0.5895333886146545,"avg_line_length":43.05555725097656,"blob_id":"2e9c9148c01ddcfbb74ec7390dc91c8e9407c38f","content_id":"e06eecafa156e8d3ec4894a11ff3aff1382bf5d6","detected_licenses":["MIT"],"is_generated":false,"is_vendor":false,"language":"C#","length_bytes":3172,"license_type":"permissive","max_line_length":169,"num_lines":72,"path":"/src/TorchSharp/NN/Dropout1d.cs","repo_name":"NiklasGustafsson/TorchSharp","src_encoding":"UTF-8","text":"// Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information.\nusing System;\nusing static TorchSharp.torch;\nusing static TorchSharp.PInvoke.NativeMethods;\n\nnamespace TorchSharp\n{\n using Modules;\n\n namespace Modules\n {\n /// <summary>\n /// This class is used to represent a Dropout2d module.\n /// </summary>\n public sealed class Dropout1d : torch.nn.Module<Tensor, Tensor>\n {\n internal Dropout1d(double p = 0.5, bool inplace = false) : base(nameof(Dropout1d))\n {\n this.p = p;\n this.inplace = inplace;\n }\n\n public override Tensor forward(Tensor tensor)\n {\n if (tensor.ndim != 3)\n throw new ArgumentException(\"tensor passed to Dropout1d must be of the shape (N,C,L\");\n return torch.nn.functional.dropout1d(tensor, this.p, this.training, this.inplace);\n }\n\n // Rather than spending cycles only to discover that this module has neither\n // parameters nor buffers, just shortcut the move completely.\n protected internal override nn.Module _to(Device device, ScalarType dtype) => this;\n protected internal override nn.Module _to(DeviceType deviceType, int deviceIndex = -1) => this;\n protected internal override nn.Module _to(ScalarType dtype) => this;\n\n private bool inplace;\n private double p;\n }\n }\n\n public static partial class torch\n {\n public static partial class nn\n {\n /// <summary>\n /// Randomly zero out entire channels (a channel is a 2D feature map, e.g., the jj -th channel of the ii -th sample in the batched input is a 2D tensor).\n /// Each channel will be zeroed out independently on every forward call with probability p using samples from a Bernoulli distribution.\n /// </summary>\n /// <param name=\"p\">Probability of an element to be zeroed. Default: 0.5</param>\n /// <param name=\"inplace\">If set to true, will do this operation in-place. Default: false</param>\n /// <returns></returns>\n public static Dropout1d Dropout1d(double p = 0.5, bool inplace = false)\n {\n return new Dropout1d(p, inplace);\n }\n\n public static partial class functional\n {\n\n /// <summary>\n /// Randomly zero out entire channels (a channel is a 2D feature map, e.g., the jj -th channel of the ii -th sample in the batched input is a 2D tensor).\n /// Each channel will be zeroed out independently on every forward call with probability p using samples from a Bernoulli distribution.\n /// </summary>\n /// <returns></returns>\n public static Tensor dropout1d(Tensor input, double p = 0.5, bool training = true, bool inplace = false)\n {\n return dropout2d(input.unsqueeze(-1), p, training, inplace).squeeze(-1);\n }\n }\n }\n }\n}\n"},{"alpha_fraction":0.653333306312561,"alphanum_fraction":0.653333306312561,"avg_line_length":29.882352828979492,"blob_id":"34210d34ce6bcbcf692763b5f024f55128db13dc","content_id":"22956e9b3b88cef5015cb37011ee0697624b5ebf","detected_licenses":["MIT"],"is_generated":false,"is_vendor":false,"language":"C#","length_bytes":525,"license_type":"permissive","max_line_length":130,"num_lines":17,"path":"/src/TorchVision/ITransform.cs","repo_name":"NiklasGustafsson/TorchSharp","src_encoding":"UTF-8","text":"// Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information.\n\nusing static TorchSharp.torch;\n\nnamespace TorchSharp\n{\n public static partial class torchvision\n {\n /// <summary>\n /// This is essentially an alias. We're keeping it because it was\n /// introduced and thus leaving it in will avoid a breaking change.\n /// </summary>\n public interface ITransform : nn.IModule<Tensor, Tensor>\n {\n }\n }\n}\n"},{"alpha_fraction":0.4776082932949066,"alphanum_fraction":0.48599597811698914,"avg_line_length":61.72922897338867,"blob_id":"3ef1848b6a52f29192832d6835a59db4e24c58b4","content_id":"dd43d825d25a8ffed0cae3943d69322edb6a62f2","detected_licenses":["MIT"],"is_generated":false,"is_vendor":false,"language":"C#","length_bytes":20387,"license_type":"permissive","max_line_length":198,"num_lines":325,"path":"/src/TorchSharp/Utils/tensorboard/Summary.cs","repo_name":"NiklasGustafsson/TorchSharp","src_encoding":"UTF-8","text":"// Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information.\nusing System;\nusing System.IO;\nusing Google.Protobuf;\nusing SkiaSharp;\n\nnamespace TorchSharp\n{\n public static partial class torch\n {\n public static partial class utils\n {\n public static partial class tensorboard\n {\n public static partial class Summary\n {\n private static int calc_scale_factor(Tensor tensor)\n => tensor.dtype == ScalarType.Byte || tensor.dtype == ScalarType.Int8 ? 1 : 255;\n\n /// <summary>\n /// Outputs a `Summary` protocol buffer with a histogram.\n /// The generated\n /// [`Summary`](https://www.tensorflow.org/code/tensorflow/core/framework/summary.proto)\n /// has one summary value containing a histogram for `values`.\n /// This op reports an `InvalidArgument` error if any value is not finite.\n ///\n /// https://github.com/pytorch/pytorch/blob/1.7/torch/utils/tensorboard/summary.py#L283\n /// </summary>\n /// <param name=\"name\"> A name for the generated node. Will also serve as a series name in TensorBoard. </param>\n /// <param name=\"values\"> A real numeric `Tensor`. Any shape. Values to use to build the histogram. </param>\n /// <param name=\"bins\"></param>\n /// <param name=\"max_bins\"></param>\n /// <returns> A scalar `Tensor` of type `string`. The serialized `Summary` protocol buffer. </returns>\n public static Tensorboard.Summary histogram(string name, Tensor values, Tensor bins, long? max_bins = null)\n {\n if (values.device.type != DeviceType.CPU)\n values = values.cpu();\n Tensorboard.HistogramProto hist = make_histogram(values, bins, max_bins);\n var summary = new Tensorboard.Summary();\n summary.Value.Add(new Tensorboard.Summary.Types.Value() { Tag = name, Histo = hist });\n return summary;\n }\n\n /// <summary>\n /// Outputs a `Summary` protocol buffer with a histogram.\n /// The generated\n /// [`Summary`](https://www.tensorflow.org/code/tensorflow/core/framework/summary.proto)\n /// has one summary value containing a histogram for `values`.\n /// This op reports an `InvalidArgument` error if any value is not finite.\n ///\n /// https://github.com/pytorch/pytorch/blob/1.7/torch/utils/tensorboard/summary.py#L283\n /// </summary>\n /// <param name=\"name\"> A name for the generated node. Will also serve as a series name in TensorBoard. </param>\n /// <param name=\"values\"> A real numeric `Tensor`. Any shape. Values to use to build the histogram. </param>\n /// <param name=\"bins\"></param>\n /// <param name=\"max_bins\"></param>\n /// <returns> A scalar `Tensor` of type `string`. The serialized `Summary` protocol buffer. </returns>\n public static Tensorboard.Summary histogram(string name, Tensor values, HistogramBinSelector bins, long? max_bins = null)\n {\n Tensorboard.HistogramProto hist = make_histogram(values, bins, max_bins);\n var summary = new Tensorboard.Summary();\n summary.Value.Add(new Tensorboard.Summary.Types.Value() { Tag = name, Histo = hist });\n return summary;\n }\n\n /// <summary>\n /// Convert values into a histogram proto using logic from histogram.cc.\n ///\n /// https://github.com/pytorch/pytorch/blob/1.7/torch/utils/tensorboard/summary.py#L304\n /// </summary>\n /// <param name=\"values\"></param>\n /// <param name=\"bins\"></param>\n /// <param name=\"max_bins\"></param>\n /// <returns></returns>\n /// <exception cref=\"ArgumentException\"></exception>\n public static Tensorboard.HistogramProto make_histogram(Tensor values, Tensor bins, long? max_bins = null)\n {\n if (values.numel() == 0)\n throw new ArgumentException(\"The input has no element.\");\n if (values.dtype != ScalarType.Float64)\n values = values.to_type(ScalarType.Float64);\n values = values.reshape(-1);\n (Tensor counts, Tensor limits) = torch.histogram(values, bins);\n return make_histogram(values, counts, limits, max_bins);\n }\n\n /// <summary>\n /// Convert values into a histogram proto using logic from histogram.cc.\n ///\n /// https://github.com/pytorch/pytorch/blob/1.7/torch/utils/tensorboard/summary.py#L304\n /// </summary>\n /// <param name=\"values\"></param>\n /// <param name=\"bins\"></param>\n /// <param name=\"max_bins\"></param>\n /// <returns></returns>\n /// <exception cref=\"ArgumentException\"></exception>\n public static Tensorboard.HistogramProto make_histogram(Tensor values, HistogramBinSelector bins, long? max_bins = null)\n {\n if (values.numel() == 0)\n throw new ArgumentException(\"The input has no element.\");\n if (values.dtype != ScalarType.Float64)\n values = values.to_type(ScalarType.Float64);\n values = values.reshape(-1);\n (Tensor counts, Tensor limits) = torch.histogram(values, bins);\n return make_histogram(values, counts, limits, max_bins);\n }\n\n private static Tensorboard.HistogramProto make_histogram(Tensor values, Tensor counts, Tensor limits, long? max_bins = null)\n {\n long num_bins = counts.shape[0];\n if (max_bins != null && num_bins > max_bins) {\n long subsampling = num_bins / max_bins.Value;\n long subsampling_remainder = num_bins % subsampling;\n if (subsampling_remainder != 0)\n counts = nn.functional.pad(counts, (0, subsampling - subsampling_remainder), PaddingModes.Constant, 0);\n counts = counts.reshape(-1, subsampling).sum(1);\n Tensor new_limits = empty(new long[] { counts.numel() + 1 }, limits.dtype);\n new_limits[TensorIndex.Slice(null, -1)] = limits[TensorIndex.Slice(null, -1, subsampling)];\n new_limits[-1] = limits[-1];\n limits = new_limits;\n }\n\n Tensor cum_counts = cumsum(greater(counts, 0), 0);\n Tensor search_value = empty(2, cum_counts.dtype);\n search_value[0] = 0;\n search_value[1] = cum_counts[-1] - 1;\n Tensor search_result = searchsorted(cum_counts, search_value, right: true);\n long start = search_result[0].item<long>();\n long end = search_result[1].item<long>() + 1;\n counts = start > 0 ? counts[TensorIndex.Slice(start - 1, end)] : concatenate(new Tensor[] { tensor(new[] { 0 }), counts[TensorIndex.Slice(stop: end)] });\n limits = limits[TensorIndex.Slice(start, end + 1)];\n\n if (counts.numel() == 0 || limits.numel() == 0)\n throw new ArgumentException(\"The histogram is empty, please file a bug report.\");\n\n Tensor sum_sq = values.dot(values);\n Tensorboard.HistogramProto histogramProto = new Tensorboard.HistogramProto() {\n Min = values.min().item<double>(),\n Max = values.max().item<double>(),\n Num = values.shape[0],\n Sum = values.sum().item<double>(),\n SumSquares = sum_sq.item<double>(),\n };\n histogramProto.BucketLimit.AddRange(limits.data<double>().ToArray());\n histogramProto.Bucket.AddRange(counts.data<double>().ToArray());\n return histogramProto;\n }\n\n /// <summary>\n /// Outputs a `Summary` protocol buffer with images.\n /// The summary has up to `max_images` summary values containing images. The\n /// images are built from `tensor` which must be 3-D with shape `[height, width,\n /// channels]` and where `channels` can be:\n /// * 1: `tensor` is interpreted as Grayscale.\n /// * 3: `tensor` is interpreted as RGB.\n /// * 4: `tensor` is interpreted as RGBA.\n /// The `name` in the outputted Summary.Value protobufs is generated based on the\n /// name, with a suffix depending on the max_outputs setting:\n /// * If `max_outputs` is 1, the summary value tag is '*name*/image'.\n /// * If `max_outputs` is greater than 1, the summary value tags are\n /// generated sequentially as '*name*/image/0', '*name*/image/1', etc.\n /// </summary>\n /// <param name=\"tag\"> A name for the generated node. Will also serve as a series name in TensorBoard. </param>\n /// <param name=\"tensor\">\n /// A 3-D `uint8` or `float32` `Tensor` of shape `[height, width,\n /// channels]` where `channels` is 1, 3, or 4.\n /// 'tensor' can either have values in [0, 1] (float32) or [0, 255] (uint8).\n /// The image() function will scale the image values to [0, 255] by applying\n /// a scale factor of either 1 (uint8) or 255 (float32). Out-of-range values\n /// will be clipped.\n /// </param>\n /// <param name=\"rescale\"> Rescale image size </param>\n /// <param name=\"dataformats\"> Image data format specification of the form CHW, HWC, HW, WH, etc. </param>\n /// <returns> A scalar `Tensor` of type `string`. The serialized `Summary` protocol buffer. </returns>\n public static Tensorboard.Summary image(string tag, Tensor tensor, double rescale = 1, string dataformats = \"NCHW\")\n {\n tensor = utils.convert_to_HWC(tensor, dataformats);\n int scale_factor = calc_scale_factor(tensor);\n tensor = tensor.to_type(ScalarType.Float32);\n tensor = (tensor * scale_factor).clip(0, 255).to_type(ScalarType.Byte);\n Tensorboard.Summary.Types.Image image = make_image(tensor, rescale);\n var summary = new Tensorboard.Summary();\n summary.Value.Add(new Tensorboard.Summary.Types.Value() { Tag = tag, Image = image });\n return summary;\n }\n\n /// <summary>\n /// Outputs a `Summary` protocol buffer with images.\n /// </summary>\n /// <param name=\"tag\"> A name for the generated node. Will also serve as a series name in TensorBoard. </param>\n /// <param name=\"file_name\"> local image filename </param>\n /// <param name=\"rescale\"> Rescale image size </param>\n /// <returns> A scalar `Tensor` of type `string`. The serialized `Summary` protocol buffer. </returns>\n public static Tensorboard.Summary image(string tag, string file_name, double rescale = 1)\n {\n using var img = SKBitmap.Decode(file_name);\n Tensorboard.Summary.Types.Image image = make_image(img, rescale);\n var summary = new Tensorboard.Summary();\n summary.Value.Add(new Tensorboard.Summary.Types.Value() { Tag = tag, Image = image });\n return summary;\n }\n\n /// <summary>\n /// Convert a tensor representation of an image to Image protobuf\n /// \n /// https://github.com/pytorch/pytorch/blob/master/torch/utils/tensorboard/summary.py#L481\n /// </summary>\n /// <param name=\"tensor\"> HWC(0~255) image tensor </param>\n /// <param name=\"rescale\"> Rescale image size </param>\n /// <returns></returns>\n public static Tensorboard.Summary.Types.Image make_image(Tensor tensor, double rescale = 1)\n {\n using SKBitmap skBmp = TensorToSKBitmap(tensor);\n return make_image(skBmp, rescale);\n }\n\n /// <summary>\n /// Convert an image to Image protobuf\n /// \n /// https://github.com/pytorch/pytorch/blob/master/torch/utils/tensorboard/summary.py#L495\n /// </summary>\n /// <param name=\"img\"> Image </param>\n /// <param name=\"rescale\"> Rescale image size </param>\n /// <returns></returns>\n internal static Tensorboard.Summary.Types.Image make_image(SKBitmap img, double rescale = 1)\n {\n using var image = img.Copy();\n byte[] bmpData = image.Resize(new SKSizeI((int)(image.Width * rescale), (int)(image.Height * rescale)), SKFilterQuality.High).Encode(SKEncodedImageFormat.Png, 100).ToArray();\n return new Tensorboard.Summary.Types.Image() { Height = image.Height, Width = image.Width, Colorspace = 4, EncodedImageString = ByteString.CopyFrom(bmpData) };\n }\n\n /// <summary>\n /// https://github.com/pytorch/pytorch/blob/master/torch/utils/tensorboard/summary.py#L509\n /// </summary>\n /// <param name=\"tag\"> A name for the generated node. Will also serve as a series name in TensorBoard. </param>\n /// <param name=\"tensor\"> Video data </param>\n /// <param name=\"fps\"> Frames per second </param>\n /// <returns></returns>\n public static Tensorboard.Summary video(string tag, Tensor tensor, int fps)\n {\n tensor = utils.prepare_video(tensor);\n int scale_factor = calc_scale_factor(tensor);\n tensor = tensor.to_type(ScalarType.Float32);\n tensor = (tensor * scale_factor).clip(0, 255).to_type(ScalarType.Byte);\n Tensorboard.Summary.Types.Image video = make_video(tensor, fps);\n var summary = new Tensorboard.Summary();\n summary.Value.Add(new Tensorboard.Summary.Types.Value() { Tag = tag, Image = video });\n return summary;\n }\n\n /// <summary>\n /// https://github.com/pytorch/pytorch/blob/master/torch/utils/tensorboard/summary.py#L520\n /// </summary>\n /// <param name=\"tensor\"> Video data </param>\n /// <param name=\"fps\"> Frames per second </param>\n /// <returns></returns>\n public static Tensorboard.Summary.Types.Image make_video(Tensor tensor, int fps)\n {\n int h = (int)tensor.shape[1];\n int w = (int)tensor.shape[2];\n using GifEncoder.Encoder encoder = new GifEncoder.Encoder();\n encoder.Start();\n encoder.SetRepeat(0);\n encoder.SetFrameRate(fps);\n foreach (var t in tensor.split(1)) {\n using SKBitmap bitmap = TensorToSKBitmap(t.squeeze());\n encoder.AddFrame(bitmap);\n }\n encoder.Finish();\n Stream stream = encoder.Output();\n stream.Position = 0;\n return new Tensorboard.Summary.Types.Image() { Height = h, Width = w, Colorspace = 4, EncodedImageString = ByteString.FromStream(stream) };\n }\n\n private static SKBitmap TensorToSKBitmap(Tensor tensor)\n {\n int h = (int)tensor.shape[0];\n int w = (int)tensor.shape[1];\n int c = (int)tensor.shape[2];\n\n byte[,,] data = tensor.cpu().data<byte>().ToNDArray() as byte[,,];\n var skBmp = new SKBitmap(w, h, SKColorType.Rgba8888, SKAlphaType.Opaque);\n int pixelSize = 4;\n unsafe {\n byte* pSkBmp = (byte*)skBmp.GetPixels().ToPointer();\n for (int i = 0; i < h; i++) {\n for (int j = 0; j < w; j++) {\n pSkBmp[j * pixelSize] = data[i, j, 0];\n pSkBmp[j * pixelSize + 1] = data[i, j, 1];\n pSkBmp[j * pixelSize + 2] = data[i, j, 2];\n pSkBmp[j * pixelSize + 3] = c == 4 ? data[i, j, 3] : (byte)255;\n }\n pSkBmp += skBmp.Info.RowBytes;\n }\n }\n\n return skBmp;\n }\n\n /// <summary>\n /// https://github.com/pytorch/pytorch/blob/master/torch/utils/tensorboard/summary.py#L630\n /// </summary>\n /// <param name=\"tag\"> Data identifier </param>\n /// <param name=\"text\"> String to save </param>\n /// <returns></returns>\n public static Tensorboard.Summary text(string tag, string text)\n {\n // TextPluginData(version=0).SerializeToString()\n // output: b''\n var pluginData = new Tensorboard.SummaryMetadata.Types.PluginData() { PluginName = \"text\", Content = ByteString.CopyFromUtf8(\"\") };\n var smd = new Tensorboard.SummaryMetadata() { PluginData = pluginData };\n var shapeProto = new Tensorboard.TensorShapeProto();\n shapeProto.Dim.Add(new Tensorboard.TensorShapeProto.Types.Dim() { Size = 1 });\n var tensor = new Tensorboard.TensorProto() { Dtype = Tensorboard.DataType.DtString, TensorShape = shapeProto };\n tensor.StringVal.Add(ByteString.CopyFromUtf8(text));\n\n var summary = new Tensorboard.Summary();\n summary.Value.Add(new Tensorboard.Summary.Types.Value() { Tag = tag + \"/text_summary\", Metadata = smd, Tensor = tensor });\n return summary;\n }\n }\n }\n }\n }\n}\n"},{"alpha_fraction":0.48758187890052795,"alphanum_fraction":0.488153338432312,"avg_line_length":35.39680099487305,"blob_id":"a578ff80f7aa6e51bd378213265001222032b3e8","content_id":"b066a165a737a33ddd61a1bbb171c37ada852dfc","detected_licenses":["MIT"],"is_generated":false,"is_vendor":false,"language":"C#","length_bytes":22749,"license_type":"permissive","max_line_length":186,"num_lines":625,"path":"/src/TorchSharp/Optimizers/Optimizer.cs","repo_name":"NiklasGustafsson/TorchSharp","src_encoding":"UTF-8","text":"// Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information.\nusing System;\nusing System.Collections.Generic;\nusing System.Linq;\nusing System.Runtime.InteropServices;\nusing static TorchSharp.torch;\nusing static TorchSharp.PInvoke.NativeMethods;\n\nnamespace TorchSharp\n{\n using System.IO;\n using Modules;\n using TorchSharp.Utils;\n\n public static partial class torch\n {\n public static partial class optim\n {\n /// <summary>\n /// Base class for all optimizers.\n /// </summary>\n public abstract partial class Optimizer : IDisposable\n {\n /// <summary>\n /// Class wrapping PyTorch's optimzer object reference.\n /// </summary>\n internal sealed class HType : SafeHandle\n {\n public HType(IntPtr preexistingHandle, bool ownsHandle) : base(IntPtr.Zero, ownsHandle)\n {\n SetHandle(preexistingHandle);\n }\n\n public override bool IsInvalid => handle == IntPtr.Zero;\n\n // This is just for marshalling\n internal HType() : base(IntPtr.Zero, true)\n {\n }\n\n protected override bool ReleaseHandle()\n {\n THSNN_Optimizer_dispose(this);\n return true;\n }\n\n protected override void Dispose(bool disposing)\n {\n if (disposing) {\n ReleaseHandle();\n }\n }\n }\n\n internal HType handle;\n\n /// <summary>\n /// Constructor used for optimizers implemented in native code.\n /// </summary>\n /// <param name=\"handle\"></param>\n protected Optimizer(IntPtr handle)\n {\n if (handle != IntPtr.Zero) {\n this.handle = new HType(handle, true);\n }\n }\n\n ~Optimizer()\n {\n Dispose(false);\n }\n\n /// <summary>\n /// Releases the storage.\n /// </summary>\n public void Dispose()\n {\n Dispose(true);\n GC.SuppressFinalize(this);\n }\n\n /// <summary>\n /// Implements the .NET Dispose pattern.\n /// </summary>\n protected virtual void Dispose(bool disposing)\n {\n if (disposing && handle != null && !handle.IsInvalid) {\n handle.Dispose();\n handle.SetHandleAsInvalid();\n }\n }\n\n /// <summary>\n /// Sets the gradients of all parameters to zero.\n /// </summary>\n public virtual void zero_grad()\n {\n THSNN_Optimizer_zero_grad(handle);\n torch.CheckForErrors();\n }\n\n [UnmanagedFunctionPointer(CallingConvention.Cdecl)]\n public delegate IntPtr LossClosure();\n\n /// <summary>\n /// Add a param group to the Optimizer s param_groups.\n /// </summary>\n /// <param name=\"param_group\"></param>\n /// <remarks>This can be useful when fine tuning a pre-trained network as frozen layers can be made trainable and added to the Optimizer as training progresses.</remarks>\n public virtual void add_param_group(ParamGroup param_group)\n {\n throw new NotImplementedException($\"add_param_group\");\n }\n\n\n /// <summary>\n /// Performs a single optimization step (parameter update).\n /// </summary>\n /// <param name=\"closure\">A closure that reevaluates the model and returns the loss. Optional for most optimizers.</param>\n /// <returns></returns>\n public virtual Tensor step(Func<Tensor> closure = null)\n {\n IntPtr res = (closure == null) ?\n THSNN_Optimizer_step(handle, null) :\n THSNN_Optimizer_step(handle, () => {\n return closure().DecoupleFromNativeHandle();\n });\n\n if (res == IntPtr.Zero)\n torch.CheckForErrors();\n\n return (res == IntPtr.Zero) ? null : new Tensor(res);\n }\n\n /// <summary>\n /// Get the parameters that the optimizer is handling.\n /// </summary>\n public virtual IEnumerable<Parameter> parameters()\n {\n IntPtr[] ptrArray;\n\n using (var pa = new PinnedArray<IntPtr>()) {\n THSNN_Optimizer_getParameters(handle, pa.CreateArray);\n torch.CheckForErrors();\n ptrArray = pa.Array;\n }\n return ptrArray.Select(x => new Parameter(x));\n }\n\n public class StateDictionary\n {\n internal StateDictionary() { Options = new List<OptimizerOptions>(); State = new List<OptimizerState>(); }\n\n public List<OptimizerOptions> Options { get; private set; }\n\n public List<OptimizerState> State { get; private set; }\n }\n\n public virtual IEnumerable<ILearningRateController> ParamGroups {\n get => _parameter_groups;\n }\n\n protected IList<ParamGroup> _parameter_groups;\n }\n\n /// <summary>\n /// This interfce is used by learning rate schedulers to access and control\n /// the rates used by optimizers.\n /// </summary>\n public interface ILearningRateController\n {\n /// <summary>\n /// The current LR\n /// </summary>\n double LearningRate { set; get; }\n\n /// <summary>\n /// The initial LR\n /// </summary>\n double InitialLearningRate { set; get; }\n }\n\n /// <summary>\n /// Indicates optimizers with support for momentum, which some LR schedulers require.\n /// </summary>\n public interface IMomentum\n {\n double Momentum { get; set; }\n }\n\n /// <summary>\n /// Indicates optimizers with support for betas instead of momentum.\n /// </summary>\n public interface IBetas\n {\n (double, double) Betas { get; set; }\n }\n }\n }\n\n namespace Modules\n {\n using static torch.optim;\n\n /// <summary>\n /// Base class to help with a couple of the things that managed-code implementations need.\n /// </summary>\n public abstract class OptimizerHelper : Optimizer\n {\n public OptimizerHelper() : base(IntPtr.Zero)\n {\n }\n\n\n protected enum TypeCode\n {\n Double = 0,\n Long = 1,\n Tensor = 2,\n Options = 3,\n State = 4,\n }\n\n /// <summary>\n /// Saves the optimizer state.\n /// </summary>\n /// <param name=\"location\">The name of a file where optimizer state data will be stored.</param>\n public void save_state_dict(string location)\n {\n using var stream = System.IO.File.Create(location);\n using (var writer = new System.IO.BinaryWriter(stream)) {\n\n save_state_dict(writer);\n writer.Flush();\n stream.Flush();\n }\n }\n\n /// <summary>\n /// Loads the optimizer state.\n /// </summary>\n /// <param name=\"location\">The name of a file where optimizer state data is stored.</param>\n /// <remarks>\n /// Optimizer state saved from PyTorch cannot be restored -- the file format is unique to TorchSharp.\n /// </remarks>\n public void load_state_dict(string location)\n {\n using var stream = System.IO.File.OpenRead(location);\n using (var reader = new System.IO.BinaryReader(stream)) {\n\n load_state_dict(reader);\n }\n }\n\n /// <summary>\n /// Saves the optimizer state.\n /// </summary>\n /// <param name=\"writer\">A binary writer connected to a stream where .</param>\n public void save_state_dict(System.IO.BinaryWriter writer)\n {\n // Save the name of the optimizer, so that we can avoid problems.\n writer.Write(this.GetType().Name);\n\n var sd = state_dict();\n\n writer.Encode(sd.Options.Count); // 4 bytes\n writer.Encode(sd.State.Count); // 4 bytes\n\n foreach (var opts in sd.Options) {\n\n opts.SaveStateDict(writer);\n }\n\n foreach (var state in sd.State) {\n state.SaveStateDict(writer);\n }\n }\n\n /// <summary>\n /// Loads the optimizer state.\n /// </summary>\n /// <param name=\"reader\">A binary reader connected to a stream containing saved optimizer state.</param>\n /// <remarks>\n /// Optimizer state saved from PyTorch cannot be restored -- the file format is unique to TorchSharp.\n /// </remarks>\n public void load_state_dict(System.IO.BinaryReader reader)\n {\n var optName = reader.ReadString();\n if (optName != this.GetType().Name) {\n throw new InvalidDataException($\"Mismatched optimizer type: expected '{this.GetType().Name}', but found '{optName}' in the loaded stream.\");\n }\n\n // First, figure out how many entries.\n var options = reader.Decode();\n var states = reader.Decode();\n\n var sd = state_dict();\n\n if (options != sd.Options.Count) throw new ArgumentException(\"Invalid optimizer state -- different number of parameter groups.\");\n if (states != sd.State.Count) throw new ArgumentException(\"Invalid optimizer state -- different number of states.\");\n\n for (var i = 0; i < options; i++) {\n var opts = sd.Options[i] as OptimizerOptions;\n opts.LoadStateDict(reader);\n }\n\n for (var i = 0; i < states; i++) {\n\n var state = sd.State[i] as OptimizerState;\n state.LoadStateDict(reader);\n }\n }\n\n /// <summary>\n /// Loads the optimizer state.\n /// </summary>\n /// <param name=\"state_dict\">Optimizer state. Should be an object returned from a call to state_dict().</param>\n /// <remarks>\n /// The format of the optimizer state dict is different from PyTorch's. Instead of a dictionary with two entries,\n /// the state is represented as record with two entries, both containing lists with state.\n /// </remarks>\n public virtual void load_state_dict(StateDictionary state_dict)\n {\n var sd = this.state_dict();\n\n if (state_dict.Options.Count != sd.Options.Count) throw new ArgumentException(\"Invalid optimizer state -- different number of parameter groups.\");\n if (state_dict.State.Count != sd.State.Count) throw new ArgumentException(\"Invalid optimizer state -- different number of states.\");\n\n for (var i = 0; i < state_dict.Options.Count; i++) {\n\n var st_opts = sd.Options[i] as OptimizerOptions;\n if (st_opts != state_dict.Options[i]) {\n st_opts.LoadStateDict(state_dict.Options[i]);\n }\n }\n\n for (var i = 0; i < state_dict.State.Count; i++) {\n\n var st_state = sd.State[i] as OptimizerState;\n if (st_state != state_dict.State[i]) {\n st_state.LoadStateDict(state_dict.State[i]);\n }\n }\n }\n\n /// <summary>\n /// Returns the state of the optimizer as a dict.\n /// </summary>\n /// <returns></returns>\n /// <remarks>\n /// The format of the optimizer state dict is different from PyTorch's. Instead of a dictionary with two entries,\n /// the state is represented as record with two entries, both containing lists with state.\n /// </remarks>\n public virtual StateDictionary state_dict()\n {\n var dict = new StateDictionary();\n\n int pgidx = -1;\n int pridx = 0;\n foreach (var pg in _parameter_groups) {\n\n dict.Options.Add(pg.Options);\n\n foreach (var p in pg.Parameters) {\n\n dict.State.Add(_state[p.Handle]);\n pridx++;\n }\n pgidx--;\n }\n return dict;\n }\n\n /// <summary>\n /// Move all the state to the indicated device.\n /// </summary>\n /// <param name=\"device\">The device to move all state to.</param>\n public void to(Device device)\n {\n foreach (var pg in _parameter_groups) {\n foreach (var p in pg.Parameters) {\n var state = _state[p.Handle];\n state.to(device);\n }\n }\n }\n\n /// <summary>\n /// Sets the gradients of all parameters to zero.\n /// </summary>\n public override void zero_grad()\n {\n foreach (var g in _parameter_groups) {\n\n foreach (var p in g.Parameters) {\n\n using var grad = p.grad();\n\n if (grad is null) continue;\n\n grad.zero_().Dispose();\n }\n }\n }\n\n /// <summary>\n /// Support routine for implementation of step() in all optimizers that support parameter groups.\n /// </summary>\n /// <typeparam name=\"T\">The ParamGroup type in use</typeparam>\n /// <param name=\"body\">The body of the step update.</param>\n /// <param name=\"loss_closure\">The closure, if any, for computing the loss.</param>\n /// <returns></returns>\n protected Tensor _step<T>(Action<T> body, Func<Tensor> loss_closure = null) where T : ParamGroup\n {\n Tensor loss = null;\n\n if (loss_closure != null) {\n using (var _ = torch.enable_grad())\n loss = loss_closure();\n }\n\n using (var _ = torch.no_grad()) {\n\n using (var d = torch.NewDisposeScope()) {\n\n foreach (var group in _parameter_groups) {\n\n body(group as T);\n\n }\n }\n }\n\n return loss;\n }\n\n /// <summary>\n /// Get the parameters that the optimizer is handling.\n /// </summary>\n public override IEnumerable<Parameter> parameters()\n {\n return _parameter_groups.SelectMany(pg => pg.Parameters);\n }\n\n /// <summary>\n /// Add a param group to the Optimizer s param_groups.\n /// </summary>\n /// <param name=\"param_group\"></param>\n /// <remarks>This can be useful when fine tuning a pre-trained network as frozen layers can be made trainable and added to the Optimizer as training progresses.</remarks>\n public override void add_param_group(ParamGroup param_group)\n {\n _parameter_groups.Add(param_group);\n }\n\n protected OptimizerOptions _defaults;\n\n protected Utils.OrderedDict<IntPtr, OptimizerState> _state = new Utils.OrderedDict<IntPtr, OptimizerState>();\n }\n\n /// <summary>\n /// Base class for optimizer options.\n /// </summary>\n public class OptimizerOptions\n {\n public double? LearningRate { get; set; }\n public double InitialLearningRate { get; set; }\n\n /// <summary>\n /// Save the optimizer options (param-group hyperparameters) to a stream.\n /// </summary>\n /// <param name=\"writer\">A binary writer connected to a stream open for writing.</param>\n public virtual void SaveStateDict(BinaryWriter writer)\n {\n writer.Write(InitialLearningRate);\n writer.Write(LearningRate.Value);\n }\n\n /// <summary>\n /// Load the optimizer options (param-group hyperparameters) from a stream.\n /// </summary>\n /// <param name=\"reader\">A binary reader connected to a stream open for reading.</param>\n public virtual void LoadStateDict(BinaryReader reader)\n {\n InitialLearningRate = reader.ReadDouble();\n LearningRate = reader.ReadDouble();\n }\n\n /// <summary>\n /// Load optimizer options (param-group hyperparameters) from another optimizer.\n /// </summary>\n /// <param name=\"source\">An optimizer options record.</param>\n public virtual void LoadStateDict(OptimizerOptions source)\n {\n InitialLearningRate = source.InitialLearningRate;\n LearningRate = source.LearningRate;\n }\n }\n\n /// <summary>\n /// Base class for optimizer options.\n /// </summary>\n public abstract class OptimizerState\n {\n /// <summary>\n /// Save the optimizer parameter state to a stream.\n /// </summary>\n /// <param name=\"writer\">A binary writer connected to a stream open for writing.</param>\n public abstract void SaveStateDict(BinaryWriter writer);\n\n /// <summary>\n /// Load the optimizer parameter state from a stream.\n /// </summary>\n /// <param name=\"reader\">A binary reader connected to a stream open for reading.</param>\n public abstract void LoadStateDict(BinaryReader reader);\n\n /// <summary>\n /// Load optimizer parameter state from another optimizer.\n /// </summary>\n /// <param name=\"source\">An optimizer state record.</param>\n public abstract void LoadStateDict(OptimizerState source);\n\n /// <summary>\n /// Useful for tests, allows comparison of one state with another.\n /// </summary>\n /// <param name=\"other\">The other optimizer state</param>\n /// <returns></returns>\n public virtual bool ApproximatelyEquals(OptimizerState other)\n {\n return false;\n }\n\n /// <summary>\n /// Move all the state to the indicated device.\n /// </summary>\n /// <param name=\"device\">The device to move all state to.</param>\n public virtual void to(Device device) { }\n\n protected static void LoadConditionalStateTensor(BinaryReader reader, ref Tensor result)\n {\n var hasTensor = reader.ReadBoolean();\n\n if (hasTensor) {\n TensorExtensionMethods.Load(ref result, reader);\n } else {\n if (result is not null)\n result.Dispose();\n result = null;\n }\n }\n\n protected static void SaveConditionalStateTensor(BinaryWriter writer, Tensor tensor)\n {\n if (tensor is not null) {\n writer.Write(true);\n tensor.Save(writer);\n } else {\n writer.Write(false);\n }\n }\n }\n\n /// <summary>\n /// Base class for parameter groups\n /// </summary>\n public class ParamGroup : ILearningRateController\n {\n public IEnumerable<Parameter> Parameters { get; set; }\n\n public OptimizerOptions Options { get; set; }\n\n public double LearningRate { get => Options.LearningRate.Value; set => Options.LearningRate = value; }\n public double InitialLearningRate { get => Options.InitialLearningRate; set => Options.InitialLearningRate = value; }\n }\n\n /// <summary>\n /// Generic-typed version of ParamGroup\n /// </summary>\n /// <typeparam name=\"TOptions\">The type of options used for the parameter group.</typeparam>\n public class ParamGroup<TOptions> : ParamGroup where TOptions : OptimizerOptions\n {\n /// <summary>\n /// Constructor\n /// </summary>\n public ParamGroup()\n {\n }\n\n /// <summary>\n /// Constructor\n /// </summary>\n /// <param name=\"parameters\">The parameters of the parameter group</param>\n /// <param name=\"options\">The options of the parameter group</param>\n public ParamGroup(IEnumerable<Parameter> parameters, TOptions options = null)\n {\n base.Parameters = parameters;\n base.Options = options;\n }\n\n /// <summary>\n /// Parameter group options / hyperparameters, used to control the optimizer algorithm.\n /// </summary>\n public new TOptions Options { get => (TOptions)base.Options; set => base.Options = value; }\n }\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n }\n}\n\n"},{"alpha_fraction":0.5719128251075745,"alphanum_fraction":0.5807909369468689,"avg_line_length":43.25,"blob_id":"a8e08fbc253d7fb6ad589ca2e079b3f927a8ce09","content_id":"76176217c1541cfdc23d0ae099f5fe31c6d8b0eb","detected_licenses":["MIT"],"is_generated":false,"is_vendor":false,"language":"C#","length_bytes":6197,"license_type":"permissive","max_line_length":149,"num_lines":140,"path":"/src/TorchSharp/Distributions/Beta.cs","repo_name":"NiklasGustafsson/TorchSharp","src_encoding":"UTF-8","text":"// Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information.\nusing System;\nusing System.Collections.Generic;\nusing static TorchSharp.torch;\n\nnamespace TorchSharp\n{\n using Modules;\n\n namespace Modules\n {\n /// <summary>\n /// A Beta distribution parameterized by concentration1 and concentration0.\n /// </summary>\n public class Beta : torch.distributions.ExponentialFamily\n {\n /// <summary>\n /// The mean of the distribution.\n /// </summary>\n public override Tensor mean => concentration1 / (concentration1 + concentration0);\n\n public override Tensor mode => dirichlet.mode[TensorIndex.Ellipsis, 0];\n\n /// <summary>\n /// The variance of the distribution\n /// </summary>\n public override Tensor variance {\n get {\n using var _ = NewDisposeScope();\n var total = concentration0 + concentration1;\n return (concentration1 * concentration0 / (total.pow(2) * (total + 1))).MoveToOuterDisposeScope();\n }\n }\n\n // Note that the order of the arguments is not a mistake -- the original source has them\n // ordered this way.\n\n public Beta(Tensor concentration1, Tensor concentration0, torch.Generator generator = null) : base(generator)\n {\n var bcast = torch.broadcast_tensors(concentration1, concentration0);\n this.concentration1 = bcast[0].DetachFromDisposeScope();\n this.concentration0 = bcast[1].DetachFromDisposeScope();\n this.dirichlet = new Dirichlet(torch.stack(bcast, -1), generator);\n this.batch_shape = this.dirichlet.batch_shape;\n }\n\n\n protected Dirichlet dirichlet;\n private Tensor concentration1;\n private Tensor concentration0;\n\n /// <summary>\n /// Generates a sample_shape shaped reparameterized sample or sample_shape shaped batch of reparameterized samples\n /// if the distribution parameters are batched.\n /// </summary>\n /// <param name=\"sample_shape\">The sample shape.</param>\n public override Tensor rsample(params long[] sample_shape)\n {\n return dirichlet.rsample(sample_shape).select(-1, 0);\n }\n\n /// <summary>\n /// Returns the log of the probability density/mass function evaluated at `value`.\n /// </summary>\n /// <param name=\"value\"></param>\n public override Tensor log_prob(Tensor value)\n {\n var heads_tails = torch.stack(new Tensor[] { value, 1.0 - value }, -1);\n return dirichlet.log_prob(heads_tails);\n }\n\n /// <summary>\n /// Returns entropy of distribution, batched over batch_shape.\n /// </summary>\n public override Tensor entropy()\n {\n return dirichlet.entropy();\n }\n\n /// <summary>\n /// Returns a new distribution instance (or populates an existing instance provided by a derived class) with batch dimensions expanded to\n /// `batch_shape`. This method calls `torch.Tensor.expand()` on the distribution's parameters. As such, this does not allocate new\n /// memory for the expanded distribution instance.\n /// </summary>\n /// <param name=\"batch_shape\">Tthe desired expanded size.</param>\n /// <param name=\"instance\">new instance provided by subclasses that need to override `.expand`.</param>\n public override distributions.Distribution expand(Size batch_shape, distributions.Distribution instance = null)\n {\n if (instance != null && !(instance is Beta))\n throw new ArgumentException(\"expand(): 'instance' must be a Beta distribution\");\n\n var c0 = concentration0.expand(batch_shape);\n var c1 = concentration1.expand(batch_shape);\n\n var newDistribution = ((instance == null) ? new Beta(c1, c0, generator) : instance) as Beta;\n\n newDistribution.batch_shape = batch_shape;\n if (newDistribution == instance) {\n newDistribution.dirichlet = new Dirichlet(torch.stack(new Tensor[] { c1, c0 }, -1));\n newDistribution.concentration1 = c1;\n newDistribution.concentration0 = c0;\n }\n return newDistribution;\n }\n\n protected override IList<Tensor> NaturalParams => new Tensor[] { concentration1, concentration0 };\n\n protected override Tensor MeanCarrierMeasure => new Tensor(IntPtr.Zero);\n\n protected override Tensor LogNormalizer(params Tensor[] parameters)\n {\n var x = parameters[0];\n var y = parameters[1];\n\n return torch.lgamma(x) + torch.lgamma(y) - torch.lgamma(x + y);\n }\n }\n\n }\n\n public static partial class torch\n {\n public static partial class distributions\n {\n /// <summary>\n /// Creates a Beta distribution parameterized by concentration1 and concentration0.\n /// </summary>\n /// <param name=\"concentration1\">1st concentration parameter of the distribution (often referred to as 'α')</param>\n /// <param name=\"concentration0\">2nd concentration parameter of the distribution (often referred to as 'β')</param>\n /// <param name=\"generator\">An optional random number generator object.</param>\n /// <returns></returns>\n /// <remarks>The order of the arguments is not a mistake -- the original source has them ordered this way.\n /// </remarks>\n public static Beta Beta(Tensor concentration1, Tensor concentration0, torch.Generator generator = null)\n {\n return new Beta(concentration1, concentration0, generator);\n }\n }\n }\n}\n"},{"alpha_fraction":0.5364921689033508,"alphanum_fraction":0.537065327167511,"avg_line_length":43.735042572021484,"blob_id":"81e18130097109206aa20ebfad3e4a9cb77a3ff5","content_id":"94bf275d8f44e544fe937d6d70b899a9e87e2369","detected_licenses":["MIT"],"is_generated":false,"is_vendor":false,"language":"C#","length_bytes":5234,"license_type":"permissive","max_line_length":195,"num_lines":117,"path":"/src/TorchSharp/NN/Recurrent/RNNCell.cs","repo_name":"NiklasGustafsson/TorchSharp","src_encoding":"UTF-8","text":"// Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information.\nusing System;\nusing static TorchSharp.torch;\nusing static TorchSharp.torch.nn;\nusing static TorchSharp.PInvoke.NativeMethods;\n\n#nullable enable\nnamespace TorchSharp\n{\n using Modules;\n\n namespace Modules\n {\n public sealed class RNNCell : torch.nn.Module<Tensor, Tensor, Tensor>\n {\n internal RNNCell(IntPtr handle, IntPtr boxedHandle) : base(handle, boxedHandle)\n {\n }\n\n public new static RNNCell Load(string modelPath)\n {\n var res = Module<Tensor, Tensor>.Load(modelPath);\n return new RNNCell(res.handle.DangerousGetHandle(), IntPtr.Zero);\n }\n\n /// <summary>\n /// Apply the RNN cell to an input tensor.\n /// </summary>\n /// <param name=\"input\">Tensor of shape (batch, input_size) containing the features of the input sequence.</param>\n /// <param name=\"h0\">Tensor of shape (batch, hidden_size) containing the initial hidden state for each element in the batch.</param>\n /// <returns></returns>\n public override Tensor forward(Tensor input, Tensor? h0 = null)\n {\n var hN = THSNN_RNNCell_forward(handle, input.Handle, h0?.Handle ?? IntPtr.Zero);\n if (hN == IntPtr.Zero) { torch.CheckForErrors(); }\n return new Tensor(hN);\n }\n\n public Parameter? bias_ih {\n get {\n var res = THSNN_RNNCell_bias_ih(handle);\n if (res == IntPtr.Zero) { torch.CheckForErrors(); }\n return ((res == IntPtr.Zero) ? null : new Parameter(res));\n }\n set {\n THSNN_RNNCell_set_bias_ih(handle, (value is null ? IntPtr.Zero : value.Handle));\n torch.CheckForErrors();\n ConditionallyRegisterParameter(\"bias_ih\", value);\n\n }\n }\n\n public Parameter? bias_hh {\n get {\n var res = THSNN_RNNCell_bias_hh(handle);\n if (res == IntPtr.Zero) { torch.CheckForErrors(); }\n return ((res == IntPtr.Zero) ? null : new Parameter(res));\n }\n set {\n THSNN_RNNCell_set_bias_hh(handle, (value is null ? IntPtr.Zero : value.Handle));\n torch.CheckForErrors();\n ConditionallyRegisterParameter(\"bias_hh\", value);\n\n }\n }\n\n public Parameter? weight_ih {\n get {\n var res = THSNN_RNNCell_weight_ih(handle);\n if (res == IntPtr.Zero) { torch.CheckForErrors(); }\n return (res == IntPtr.Zero) ? null : new Parameter(res);\n }\n set {\n THSNN_RNNCell_set_weight_ih(handle, value is null ? IntPtr.Zero : value.Handle);\n torch.CheckForErrors();\n ConditionallyRegisterParameter(\"weight_ih\", value);\n }\n }\n\n public Parameter? weight_hh {\n get {\n var res = THSNN_RNNCell_weight_hh(handle);\n if (res == IntPtr.Zero) { torch.CheckForErrors(); }\n return (res == IntPtr.Zero) ? null : new Parameter(res);\n }\n set {\n THSNN_RNNCell_set_weight_hh(handle, value is null ? IntPtr.Zero : value.Handle);\n torch.CheckForErrors();\n ConditionallyRegisterParameter(\"weight_hh\", value);\n }\n }\n }\n }\n\n public static partial class torch\n {\n public static partial class nn\n {\n /// <summary>\n /// An Elman RNN cell with tanh or ReLU non-linearity.\n /// </summary>\n /// <param name=\"inputSize\">The number of expected features in the input x</param>\n /// <param name=\"hiddenSize\">The number of features in the hidden state h</param>\n /// <param name=\"nonLinearity\">The non-linearity to use. Can be either 'tanh' or 'relu'. Default: 'tanh'</param>\n /// <param name=\"bias\">If False, then the layer does not use bias weights b_ih and b_hh. Default: True</param>\n /// <param name=\"device\">The desired device of the parameters and buffers in this module</param>\n /// <param name=\"dtype\">The desired floating point or complex dtype of the parameters and buffers in this module</param>\n /// <returns></returns>\n public static RNNCell RNNCell(long inputSize, long hiddenSize, NonLinearities nonLinearity = nn.NonLinearities.Tanh, bool bias = true, Device? device = null, ScalarType? dtype = null)\n {\n var res = THSNN_RNNCell_ctor(inputSize, hiddenSize, (long)nonLinearity, bias, out var boxedHandle);\n if (res == IntPtr.Zero) { torch.CheckForErrors(); }\n return new RNNCell(res, boxedHandle).MoveModule<RNNCell>(device, dtype);\n }\n }\n }\n}\n"},{"alpha_fraction":0.4843258559703827,"alphanum_fraction":0.48990610241889954,"avg_line_length":47.12702941894531,"blob_id":"1a6bd61fd5bb62cc7bd8207a5f7fa8e1292f0f29","content_id":"fdf7cb47cda8967cba8593dd67522429b2a15ad2","detected_licenses":["MIT"],"is_generated":false,"is_vendor":false,"language":"C#","length_bytes":82991,"license_type":"permissive","max_line_length":319,"num_lines":1724,"path":"/src/TorchSharp/NN/Module.cs","repo_name":"NiklasGustafsson/TorchSharp","src_encoding":"UTF-8","text":"// Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information.\nusing System;\nusing System.Collections.Generic;\nusing System.Diagnostics;\nusing System.Linq;\nusing System.Reflection;\nusing System.Runtime.InteropServices;\nusing TorchSharp.Modules;\nusing static TorchSharp.torch;\nusing static TorchSharp.Utils.LEB128Codec;\nusing static TorchSharp.PInvoke.NativeMethods;\n\nnamespace TorchSharp\n{\n public static partial class torch\n {\n public static partial class nn\n {\n /// <summary>\n /// Base class for all neural network modules.\n /// Your models should subclass this class.\n /// </summary>\n /// <remarks>\n /// Modules can also contain other Modules, allowing to nest them in a tree structure.\n /// You can assign the submodules as regular fields of the derived Module class. Submodules assigned\n /// to fields will be registered, and will have their parameters converted and moved when you call to(),\n /// and saved to disk when calling save().\n /// </remarks>\n public class Module : IDisposable\n {\n /// <summary>\n /// Class wrapping PyTorch's module object reference.\n /// </summary>\n protected internal sealed class HType : SafeHandle\n {\n public HType(IntPtr preexistingHandle, bool ownsHandle, Action<HType> dispose = null)\n : base(IntPtr.Zero, ownsHandle)\n {\n _dispose = dispose ?? THSNN_Module_dispose;\n SetHandle(preexistingHandle);\n }\n\n public override bool IsInvalid => handle == IntPtr.Zero;\n\n // This is just for marshalling\n internal HType() : base(IntPtr.Zero, true)\n {\n }\n\n protected override bool ReleaseHandle()\n {\n if (!IsInvalid) {\n _dispose(this);\n }\n SetHandle(IntPtr.Zero);\n return true;\n }\n\n protected override void Dispose(bool disposing)\n {\n if (disposing) {\n ReleaseHandle();\n }\n }\n\n private Action<HType> _dispose;\n }\n\n internal HType handle;\n\n /// Stores the AnyModule corresponding to this module.\n internal BoxedModule boxedModule;\n\n internal BoxedModule BoxedModule {\n get {\n if (boxedModule == null)\n throw new InvalidOperationException(\"A Sequential or Loaded module may not be added to a Sequential\");\n return boxedModule;\n }\n }\n\n internal Module(HType handle, IntPtr? boxedHandle)\n {\n this.handle = handle;\n boxedModule = boxedHandle.HasValue ? new BoxedModule(boxedHandle.Value) : null;\n\n if (handle.IsInvalid) return;\n register_p_and_b();\n }\n\n internal Module(IntPtr handle, IntPtr? boxedHandle, bool ownsHandle = true)\n {\n this.handle = new HType(handle, ownsHandle);\n boxedModule = boxedHandle.HasValue ? new BoxedModule(boxedHandle.Value) : null;\n\n if (handle == IntPtr.Zero) return;\n register_p_and_b();\n }\n\n private void register_p_and_b()\n {\n foreach (var (parameterName, parameter) in _named_parameters()) {\n register_parameter(parameterName, parameter);\n }\n foreach (var (bufferName, buffer) in _named_buffers()) {\n register_buffer(bufferName, buffer);\n }\n }\n\n ~Module() => Dispose(false);\n\n /// <summary>\n /// Releases the storage.\n /// </summary>\n public void Dispose()\n {\n Dispose(true);\n GC.SuppressFinalize(this);\n }\n\n /// <summary>\n /// Implements the .NET Dispose pattern.\n /// </summary>\n protected virtual void Dispose(bool disposing)\n {\n if (disposing && !handle.IsInvalid) {\n\n foreach (var (_, p) in named_buffers(false)) {\n p.Dispose();\n }\n foreach (var (_, b) in named_parameters(false)) {\n b.Dispose();\n }\n\n foreach (var (_, m) in named_modules()) {\n m.Dispose();\n }\n\n handle.Dispose();\n handle.SetHandleAsInvalid();\n boxedModule?.Dispose();\n }\n }\n\n /// <summary>\n /// Moves and converts the parameters and buffers.\n /// </summary>\n /// <param name=\"device\">The target device.</param>\n /// <param name=\"dtype\">The target element type.</param>\n protected internal virtual Module _to(Device device, ScalarType dtype)\n {\n if (device.type != DeviceType.CUDA) { device = new Device(device.type, -1); };\n\n if (device.type == DeviceType.CUDA && !torch.cuda.is_available()) throw new InvalidOperationException(\"CUDA is not available.\");\n\n InitializeDeviceType(device.type);\n THSNN_Module_to_device_dtype(handle, (sbyte)dtype, (int)device.type, device.index);\n CheckForErrors();\n\n _toEpilog(device, dtype);\n\n return this;\n }\n\n protected void _toEpilog(Device device, ScalarType dtype)\n {\n foreach (var (_, sm) in named_children()) sm._to(device, dtype);\n\n var alreadyHandled = new HashSet<IntPtr>();\n\n foreach (var field in GetType().GetFields(BindingFlags.NonPublic | BindingFlags.Public | BindingFlags.Instance)) {\n\n var fieldName = field.Name;\n var value = field.GetValue(this);\n\n switch (value) {\n // This order in which these cases are arranged is significant.\n case Parameter param when dtype == param.dtype && device.type == param.device_type && device.index == param.device_index:\n alreadyHandled.Add(param.handle);\n continue;\n\n case Parameter param: {\n var t = param.to(dtype, device);\n t.retain_grad();\n var p = new Parameter(t, param.requires_grad);\n field.SetValue(this, p);\n ConditionallyRegisterParameter(fieldName, p);\n alreadyHandled.Add(p.handle);\n break;\n }\n\n case Tensor tensor when (device.type != tensor.device_type || device.index != tensor.device_index): {\n var t = tensor.to(dtype, device);\n field.SetValue(this, t);\n ConditionallyRegisterBuffer(fieldName, t);\n alreadyHandled.Add(t.handle);\n break;\n }\n\n case Tensor tensor:\n alreadyHandled.Add(tensor.handle);\n break;\n }\n }\n\n foreach (var (name, param) in named_parameters(false).ToList()) {\n if (alreadyHandled.Contains(param.handle)) continue;\n var t = param.to(dtype, device);\n ConditionallyRegisterParameter(name, t);\n }\n\n foreach (var (name, buffer) in named_buffers(false).ToList()) {\n if (alreadyHandled.Contains(buffer.handle)) continue;\n var t = buffer.to(dtype, device);\n ConditionallyRegisterBuffer(name, t);\n }\n\n _deviceType = device.type;\n _deviceIndex = device.index;\n\n Debug.Assert(_deviceType == DeviceType.CUDA || _deviceIndex == -1);\n }\n\n\n /// <summary>\n /// Moves the parameters and buffers.\n /// </summary>\n /// <param name=\"deviceType\">The device type, e.g. 'CPU' or 'CUDA'.</param>\n /// <param name=\"deviceIndex\">The optional device index.</param>\n /// <returns></returns>\n protected internal virtual Module _to(DeviceType deviceType, int deviceIndex = -1)\n {\n if (deviceType != DeviceType.CUDA) deviceIndex = -1;\n\n if (deviceType == DeviceType.CUDA && !torch.cuda.is_available()) throw new InvalidOperationException(\"CUDA is not available.\");\n\n if (deviceType != _deviceType || deviceIndex != _deviceIndex) {\n\n InitializeDeviceType(deviceType);\n THSNN_Module_to_device(handle, (int)deviceType, deviceIndex);\n CheckForErrors();\n\n _toEpilog(deviceType, deviceIndex);\n }\n\n Debug.Assert(_deviceType == DeviceType.CUDA || _deviceIndex == -1);\n\n return this;\n }\n\n protected void _toEpilog(DeviceType deviceType, int deviceIndex)\n {\n foreach (var (_, sm) in named_children()) sm._to(deviceType, deviceIndex);\n\n var alreadyHandled = new HashSet<IntPtr>();\n\n foreach (var field in GetType().GetFields(BindingFlags.NonPublic | BindingFlags.Public | BindingFlags.Instance)) {\n\n var fieldName = field.Name;\n var value = field.GetValue(this);\n\n switch (value) {\n // This order in which these cases are arranged is significant.\n case Parameter param when deviceType == param.device_type && deviceIndex == param.device_index:\n alreadyHandled.Add(param.handle);\n continue;\n\n case Parameter param: {\n var t = param.to(deviceType, deviceIndex);\n t.retain_grad();\n var p = new Parameter(t, param.requires_grad);\n field.SetValue(this, p);\n ConditionallyRegisterParameter(fieldName, p);\n alreadyHandled.Add(p.handle);\n break;\n }\n\n case Tensor tensor when (deviceType != tensor.device_type || deviceIndex != tensor.device_index): {\n var t = tensor.to(deviceType, deviceIndex);\n field.SetValue(this, t);\n ConditionallyRegisterBuffer(fieldName, t);\n alreadyHandled.Add(t.handle);\n break;\n }\n\n case Tensor tensor:\n alreadyHandled.Add(tensor.handle);\n break;\n }\n }\n\n foreach (var (name, param) in named_parameters(false).ToList()) {\n if (alreadyHandled.Contains(param.handle)) continue;\n var t = param.to(deviceType, deviceIndex);\n ConditionallyRegisterParameter(name, t);\n }\n\n foreach (var (name, buffer) in named_buffers(false).ToList()) {\n if (alreadyHandled.Contains(buffer.handle)) continue;\n var t = buffer.to(deviceType, deviceIndex);\n ConditionallyRegisterBuffer(name, t);\n }\n\n _deviceType = deviceType;\n _deviceIndex = deviceIndex;\n }\n\n private DeviceType _deviceType = DeviceType.CPU;\n private int _deviceIndex = -1;\n\n /// <summary>\n /// Convert the parameters and buffers.\n /// </summary>\n /// <returns></returns>\n protected internal virtual Module _to(ScalarType dtype)\n {\n THSNN_Module_to_dtype(handle, (sbyte)dtype);\n CheckForErrors();\n\n _toEpilog(dtype);\n\n return this;\n }\n\n protected void _toEpilog(ScalarType dtype)\n {\n foreach (var (_, sm) in named_children()) sm._to(dtype);\n\n var alreadyHandled = new HashSet<IntPtr>();\n\n foreach (var field in GetType().GetFields(BindingFlags.NonPublic | BindingFlags.Public | BindingFlags.Instance)) {\n\n var fieldName = field.Name;\n var value = field.GetValue(this);\n\n switch (value) {\n // This order in which these cases are arranged is significant.\n case Parameter param when dtype == param.dtype:\n alreadyHandled.Add(param.handle);\n continue;\n\n case Parameter param: {\n var t = param.to(dtype);\n t.retain_grad();\n var p = new Parameter(t, param.requires_grad);\n field.SetValue(this, p);\n ConditionallyRegisterParameter(fieldName, p);\n alreadyHandled.Add(p.handle);\n break;\n }\n\n case Tensor tensor when dtype == tensor.dtype:\n alreadyHandled.Add(tensor.handle);\n continue;\n\n case Tensor tensor: {\n var t = tensor.to(dtype);\n field.SetValue(this, t);\n ConditionallyRegisterBuffer(fieldName, t);\n alreadyHandled.Add(t.handle);\n break;\n }\n }\n }\n\n foreach (var (name, param) in named_parameters(false).ToList()) {\n if (alreadyHandled.Contains(param.handle)) continue;\n var t = param.to(dtype);\n ConditionallyRegisterParameter(name, t);\n }\n\n foreach (var (name, buffer) in named_buffers(false).ToList()) {\n if (alreadyHandled.Contains(buffer.handle)) continue;\n var t = buffer.to(dtype);\n ConditionallyRegisterBuffer(name, t);\n }\n }\n\n /// <summary>\n /// Moves and converts the parameters and buffers.\n /// </summary>\n /// <param name=\"other\">The tensor serving as a template.</param>\n /// <returns></returns>\n public Module _to(Tensor other)\n {\n _to(other.dtype);\n return _to(other.device_type, other.device_index);\n }\n\n\n\n /// <summary>\n /// Applies a function recursively to every submodule as well as this.\n /// </summary>\n /// <param name=\"fn\">Function to be applied to each submodule</param>\n /// <returns></returns>\n public virtual Module apply(Action<Module> fn)\n {\n foreach (var (_, m) in _internal_submodules) m.apply(fn);\n fn(this);\n return this;\n }\n\n public static Module Load(string filename)\n {\n if (!System.IO.File.Exists(filename))\n throw new System.IO.FileNotFoundException(filename);\n\n var handle = THSNN_Module_load(filename);\n if (handle == IntPtr.Zero) { CheckForErrors(); }\n return new Module(handle, IntPtr.Zero);\n }\n\n public virtual void Save(string modelPath)\n => THSNN_Module_save(handle, modelPath);\n\n /// <summary>\n /// Sets the module in training mode.\n /// </summary>\n /// <remarks>\n /// This has any effect only on certain modules.See documentations of particular modules for details of their behaviors in training/evaluation mode, if they are affected, e.g.Dropout, BatchNorm, etc.\n /// </remarks>\n public virtual void train(bool train = true)\n {\n THSNN_Module_train(handle, train);\n CheckForErrors();\n foreach (var (_, m) in named_children()) { m.train(train); }\n }\n\n /// <summary>\n /// Sets the module in evaluation mode.\n /// </summary>\n /// <remarks>\n /// This has any effect only on certain modules.See documentations of particular modules for details of their behaviors in training/evaluation mode, if they are affected, e.g.Dropout, BatchNorm, etc.\n /// </remarks>\n public virtual void eval()\n {\n train(false);\n }\n\n /// <summary>\n /// Check whether the module is set to training or evaluation mode.\n /// </summary>\n public virtual bool training {\n get {\n var res = THSNN_Module_is_training(handle);\n CheckForErrors();\n return res;\n }\n }\n\n public virtual void zero_grad()\n {\n THSNN_Module_zero_grad(handle);\n CheckForErrors();\n }\n\n /// <summary>\n /// Returns an enumerable of module buffers, yielding both the name of the buffer as well as the buffer itself.\n /// </summary>\n /// <param name=\"recurse\">If true, then yields buffers of this module and all submodules. Otherwise, yields only buffers that are direct members of this module.</param>\n /// <returns>(string, torch.Tensor) – Tuple containing the name and buffer</returns>\n public virtual IEnumerable<(string name, Tensor buffer)> named_buffers(bool recurse = true)\n {\n var seen = new HashSet<IntPtr>();\n seen.Add(IntPtr.Zero); // Ignore invalid buffers.\n\n foreach (var nsm in _internal_buffers) {\n if (seen.Contains(nsm.Item2.handle)) continue;\n seen.Add(nsm.Item2.handle);\n yield return nsm;\n }\n\n if (!recurse) yield break;\n\n foreach (var (submoduleName, subModule) in _internal_submodules) {\n foreach (var (bufferName, buffer) in subModule.named_buffers(true)) {\n if (seen.Contains(buffer.handle)) continue;\n seen.Add(buffer.handle);\n yield return ($\"{submoduleName}.{bufferName}\", buffer);\n }\n }\n\n }\n\n /// <summary>\n /// Returns an enumerable of buffers.\n /// </summary>\n public virtual IEnumerable<Tensor> buffers(bool recurse = true) => named_buffers(recurse).Select(np => np.buffer);\n\n /// <summary>\n /// Returns an enumerable of immediate children modules, yielding both the name of the module as well as the module itself.\n /// </summary>\n /// <returns>(string, Module) – Tuple containing a name and child module</returns>\n public virtual IEnumerable<(string name, Module module)> named_children() => _internal_submodules;\n\n /// <summary>\n /// Returns an enumerable of all modules in the network, yielding both the name of the module as well as the module itself.\n /// </summary>\n /// <returns>(string, Module) – Tuple of name and module</returns>\n public virtual IEnumerable<(string name, Module module)> named_modules()\n {\n foreach (var nsm in _internal_submodules) {\n yield return nsm;\n }\n\n foreach (var (submoduleName, sm) in _internal_submodules) {\n foreach (var (n, p) in sm.named_modules()) {\n yield return ($\"{submoduleName}.{n}\", p);\n }\n }\n }\n\n /// <summary>\n /// Returns an enumerable of modules.\n /// </summary>\n public virtual IEnumerable<Module> modules() => named_modules().Select(np => np.module);\n\n /// <summary>\n /// Returns an enumerable of immediate modules.\n /// </summary>\n public virtual IEnumerable<Module> children() => named_children().Select(np => np.module);\n\n /// <summary>\n /// Returns a dictionary containing a whole state of the module.\n ///\n /// Both parameters and persistent buffers(e.g.running averages) are included.Keys are corresponding parameter and buffer names.\n /// Parameters and buffers set to null are not included.\n /// </summary>\n /// <param name=\"destination\">An optional dictionary where the state should be accumulated.</param>\n /// <param name=\"prefix\">A prefix string to use when entering the name of entries into the dictionary.</param>\n /// <returns></returns>\n public virtual Dictionary<string, Tensor> state_dict(Dictionary<string, Tensor> destination = null, string prefix = null)\n {\n destination ??= new Dictionary<string, Tensor>();\n\n foreach (var p in named_parameters()) {\n var key = string.IsNullOrEmpty(prefix) ? $\"{p.name}\" : $\"{prefix}.{p.name}\";\n destination.TryAdd(key, p.Item2);\n }\n\n foreach (var p in named_buffers()) {\n var key = string.IsNullOrEmpty(prefix) ? $\"{p.name}\" : $\"{prefix}.{p.name}\";\n destination.TryAdd(key, p.Item2);\n }\n\n foreach (var (n, p) in _internal_submodules) {\n var key = string.IsNullOrEmpty(prefix) ? $\"{n}\" : $\"{prefix}.{n}\";\n p.state_dict(destination, key);\n }\n\n return destination;\n }\n\n /// <summary>\n /// Copies parameters and buffers from state_dict into this module and its descendants.\n ///\n /// If strict is True, then the keys of state_dict must exactly match the keys returned by this module’s state_dict() function.\n /// </summary>\n /// <param name=\"source\">A dict containing parameters and persistent buffers.</param>\n /// <param name=\"strict\">Whether to strictly enforce that the keys in state_dict match the keys returned by this module’s state_dict() function.</param>\n /// <param name=\"skip\">A list of keys not to consider when loading the dictionary.</param>\n /// <returns></returns>\n public virtual (IList<string> missing_keys, IList<string> unexpected_keyes) load_state_dict(Dictionary<string, Tensor> source, bool strict = true, IList<string> skip = null)\n {\n List<string> missing = new List<string>();\n List<string> unexpected = new List<string>();\n skip ??= Array.Empty<string>();\n\n var destination = state_dict();\n\n foreach (var key in source.Keys) {\n if (skip.Contains(key)) continue;\n if (!destination.ContainsKey(key)) {\n unexpected.Add(key);\n }\n }\n\n foreach (var key in destination.Keys) {\n if (skip.Contains(key)) continue;\n if (!source.ContainsKey(key)) {\n missing.Add(key);\n }\n }\n\n if (strict && (missing.Count > 0 || unexpected.Count > 0))\n throw new InvalidOperationException(\"The loaded state_dict is not identical to the target dictionary.\");\n\n foreach (var key in source.Keys) {\n if (skip.Contains(key)) continue;\n if (destination.ContainsKey(key)) {\n destination[key].bytes = source[key].bytes;\n }\n }\n\n return (missing, unexpected);\n }\n\n protected virtual (string name, Parameter parameter)[] _named_parameters()\n {\n using var pa = new PinnedArray<IntPtr>();\n using var sa = new PinnedArray<IntPtr>();\n THSNN_Module_get_named_parameters(handle, pa.CreateArray, sa.CreateArray);\n CheckForErrors();\n var ptrArray = pa.Array;\n var strArray = sa.Array;\n\n return ptrArray.Select((x, i) => (Marshal.PtrToStringAnsi(strArray[i]), new Parameter(x))).ToArray();\n }\n\n protected virtual (string name, Tensor buffer)[] _named_buffers()\n {\n using var pa = new PinnedArray<IntPtr>();\n using var sa = new PinnedArray<IntPtr>();\n THSNN_Module_get_named_buffers(handle, pa.CreateArray, sa.CreateArray);\n CheckForErrors();\n var ptrArray = pa.Array;\n var strArray = sa.Array;\n\n return ptrArray.Select((x, i) => (Marshal.PtrToStringAnsi(strArray[i]), new Tensor(x))).ToArray();\n }\n\n /// <summary>\n /// Returns an enumerable of module parameters, yielding both the name of the parameter as well as the parameter itself.\n /// </summary>\n /// <param name=\"recurse\">If true, then yields parameters of this module and all submodules. Otherwise, yields only parameters that are direct members of this module.</param>\n /// <returns>(string, Parameter) – Tuple containing the name and parameter</returns>\n public virtual IEnumerable<(string name, Parameter parameter)> named_parameters(bool recurse = true)\n {\n var seen = new HashSet<IntPtr>();\n seen.Add(IntPtr.Zero); // Ignore invalid parameters.\n\n foreach (var nsm in _internal_params) {\n if (seen.Contains(nsm.Item2.handle)) continue;\n seen.Add(nsm.Item2.handle);\n yield return nsm;\n }\n\n if (!recurse) yield break;\n foreach (var (submoduleName, subModule) in _internal_submodules) {\n foreach (var (parameterName, parameter) in subModule.named_parameters(true)) {\n if (seen.Contains(parameter.handle)) continue;\n seen.Add(parameter.handle);\n yield return ($\"{submoduleName}.{parameterName}\", parameter);\n }\n }\n }\n\n protected virtual Parameter[] _parameters(bool recurse = true)\n {\n IntPtr[] ptrArray;\n\n using (var pa = new PinnedArray<IntPtr>()) {\n AllocatePinnedArray allocator = pa.CreateArray;\n THSNN_Module_get_parameters(handle, allocator, recurse);\n CheckForErrors();\n ptrArray = pa.Array;\n }\n return ptrArray.Select(x => new Parameter(x)).ToArray();\n }\n\n /// <summary>\n /// Returns an enumerable of module parameters.\n /// </summary>\n /// <param name=\"recurse\">If true, then yields parameters of this module and all submodules. Otherwise, yields only parameters that are direct members of this module.</param>\n /// <returns></returns>\n public virtual IEnumerable<Parameter> parameters(bool recurse = true)\n => named_parameters(recurse).Select(np => np.parameter);\n\n public virtual bool has_buffer(string target)\n {\n if (_internal_buffers.TryGetValue(target, out var buffer)) {\n return true;\n }\n\n var splits = target.Split('.');\n if (splits.Length <= 1) return false;\n foreach (var child in named_children().Where(nc => nc.name == splits[0])) {\n if (child.module.has_buffer(target.Remove(0, splits[0].Length + 1)))\n return true;\n }\n return false;\n }\n\n public virtual bool has_parameter(string target)\n {\n if (_internal_params.TryGetValue(target, out var parameter)) {\n return true;\n }\n\n var splits = target.Split('.');\n if (splits.Length <= 1) return false;\n foreach (var child in named_children().Where(nc => nc.name == splits[0])) {\n if (child.module.has_parameter(target.Remove(0, splits[0].Length + 1)))\n return true;\n }\n return false;\n }\n\n /// <summary>\n /// Returns the buffer given by target if it exists, otherwise throws an error.\n /// </summary>\n /// <param name=\"target\">The fully-qualified string name of the buffer to look for.</param>\n /// <returns>The tensor referenced by target</returns>\n public virtual Tensor get_buffer(string target)\n {\n if (target is null) throw new ArgumentNullException(\"target\");\n if (_internal_buffers.TryGetValue(target, out var buffer)) {\n return buffer;\n }\n\n var splits = target.Split('.');\n if (splits.Length <= 1) return null;\n foreach (var child in named_children().Where(nc => nc.name == splits[0])) {\n var p = child.module.get_buffer(target.Remove(0, splits[0].Length + 1));\n if (p is not null)\n return p;\n }\n return null;\n }\n\n /// <summary>\n /// Returns the parameter given by target if it exists, otherwise throws an error.\n /// </summary>\n /// <param name=\"target\">The fully-qualified string name of the Parameter to look for.</param>\n /// <returns>The Parameter referenced by target</returns>\n public virtual Parameter get_parameter(string target)\n {\n if (_internal_params.TryGetValue(target, out var parameter)) {\n return parameter;\n }\n\n var splits = target.Split('.');\n if (splits.Length <= 1) return null;\n foreach (var child in named_children().Where(nc => nc.name == splits[0])) {\n var p = child.module.get_parameter(target.Remove(0, splits[0].Length + 1));\n if (p is not null)\n return p;\n }\n return null;\n }\n\n /// <summary>\n /// Adds a buffer to the module.\n ///\n /// This is typically used to register a buffer that should not to be considered a model parameter.For example, BatchNorm’s running_mean is not a parameter,\n /// but is part of the module’s state.Buffers, by default, are persistent and will be saved alongside parameters.\n /// </summary>\n /// <param name=\"name\">Name of the buffer. The buffer can be accessed from this module using the given name</param>\n /// <param name=\"tensor\">\n /// Buffer to be registered. If null, then operations that run on buffers, such as cuda(), are ignored.\n /// If null, the buffer is not included in the module’s state_dict.</param>\n /// <exception cref=\"ArgumentNullException\"></exception>\n /// <exception cref=\"InvalidOperationException\"></exception>\n public virtual void register_buffer(string name, Tensor tensor)\n {\n if (tensor is null || tensor.handle == IntPtr.Zero)\n throw new ArgumentNullException(nameof(tensor), \"A null tensor cannot be registered as a buffer.\");\n\n if (!_internal_buffers.TryAdd(name, tensor))\n throw new InvalidOperationException($\"Tensor {name} is already registered.\");\n }\n\n /// <summary>\n /// Adds a parameter to the module.\n /// </summary>\n /// <param name=\"name\">Name of the parameter. The parameter can be accessed from this module using the given name</param>\n /// <param name=\"param\">\n /// Buffer to be registered.\n /// If null, then operations that run on buffers, such as cuda(), are ignored.\n /// If null, the buffer is not included in the module’s state_dict.</param>\n /// <exception cref=\"ArgumentNullException\"></exception>\n /// <exception cref=\"InvalidOperationException\"></exception>\n public virtual void register_parameter(string name, Parameter param)\n {\n if (param is null || param.handle == IntPtr.Zero)\n throw new ArgumentNullException(nameof(param), \"A null tensor cannot be registered as a parameter.\");\n\n if (!_internal_params.TryAdd(name, param))\n throw new InvalidOperationException($\"Parameter {name} is already registered.\");\n }\n\n /// <summary>\n /// Alias for register_module()\n /// </summary>\n /// <param name=\"name\">\n /// name of the child module.\n /// The child module can be accessed from this module using the given name\n /// </param>\n /// <param name=\"module\">child module to be added to the module.</param>\n /// <exception cref=\"ArgumentException\"></exception>\n /// <exception cref=\"InvalidOperationException\"></exception>\n public void add_module(string name, Module module)\n => register_module(name, module);\n\n /// <summary>\n /// Register a submodule.\n /// </summary>\n /// <param name=\"name\">Name of the submodule.</param>\n /// <param name=\"submodule\">The module to register.</param>\n /// <exception cref=\"ArgumentException\"></exception>\n /// <exception cref=\"InvalidOperationException\"></exception>\n public virtual void register_module(string name, Module submodule)\n {\n if (submodule is null || submodule.handle.IsInvalid) {\n if (_internal_submodules.ContainsKey(name)) {\n _internal_submodules.Remove(name);\n }\n } else {\n if (name.Contains(\".\")) {\n throw new ArgumentException($\"module name can't contain \\\".\\\", got: {name}\");\n }\n if (string.IsNullOrEmpty(name)) {\n throw new ArgumentException(\"module name can't be empty string \\\"\\\"\");\n }\n if (_internal_submodules.ContainsKey(name)) {\n throw new InvalidOperationException($\"Sub-module {name} is already registered.\");\n }\n\n submodule.RegisterComponents();\n\n _internal_submodules.Add(name, submodule);\n }\n }\n\n protected void ConditionallyRegisterParameter(string name, Tensor value)\n {\n if (value is null) {\n if (_internal_params.ContainsKey(name)) {\n _internal_params.Remove(name);\n }\n } else {\n var p = value is Parameter parameter\n ? parameter\n : new Parameter(value, requires_grad: true);\n\n if (_internal_params.ContainsKey(name)) {\n _internal_params[name] = p;\n } else {\n _internal_params.Add(name, p);\n }\n }\n }\n\n protected void ConditionallyRegisterBuffer(string name, Tensor value)\n {\n if (value is null) {\n if (_internal_buffers.ContainsKey(name)) {\n _internal_buffers.Remove(name);\n }\n } else {\n if (_internal_buffers.ContainsKey(name)) {\n _internal_buffers[name] = value;\n } else {\n _internal_buffers.Add(name, value);\n }\n }\n }\n\n public virtual string GetName()\n {\n var res = THSNN_Module_name(handle);\n CheckForErrors();\n return res;\n }\n\n /// <summary>\n /// Save the parameters and buffers of the module to a disk location.\n /// </summary>\n /// <param name=\"location\">The file path.</param>\n /// <param name=\"skip\">A list of keys not to consider when saving the weights.</param>\n /// <returns></returns>\n public Module save(string location, IList<string> skip = null)\n {\n using var stream = System.IO.File.Create(location);\n using var writer = new System.IO.BinaryWriter(stream);\n save(writer, skip);\n\n return this;\n }\n\n /// <summary>\n /// Save the parameters and buffers of the module to a disk location.\n /// </summary>\n /// <param name=\"writer\">A binary writer instance.</param>\n /// <param name=\"skip\">A list of keys not to consider when saving the weights.</param>\n /// <returns></returns>\n public Module save(System.IO.BinaryWriter writer, IList<string> skip = null)\n {\n var sd = state_dict();\n\n // First, write how many entries.\n save_state_dict(writer, sd, skip);\n\n return this;\n }\n\n /// <summary>\n /// Save the parameters and buffers of the module to a disk location.\n /// </summary>\n /// <param name=\"stream\">A writable stream instance.</param>\n /// <param name=\"skip\">A list of keys not to consider when saving the weights.</param>\n /// <returns></returns>\n public Module save(System.IO.Stream stream, IList<string> skip = null)\n {\n using var writer = new System.IO.BinaryWriter(stream);\n return save(writer, skip);\n }\n\n /// <summary>\n ///\n /// </summary>\n /// <param name=\"writer\">A binary writer instance.</param>\n /// <param name=\"skip\">A list of keys not to consider when saving the weights.</param>\n /// <param name=\"sd\">A dictionary containing all the buffers and parameters of the module.</param>\n public static void save_state_dict(System.IO.BinaryWriter writer, Dictionary<string, Tensor> sd, IList<string> skip = null)\n {\n if (skip is not null && skip.Count > 0) {\n // We need to make a copy, so that the passed-in 'sd' isn't modified.\n var tmp = new Dictionary<string, Tensor>();\n foreach (var kv in sd.Where(kv => !skip.Contains(kv.Key))) {\n tmp.Add(kv.Key, kv.Value);\n }\n sd = tmp;\n }\n\n writer.Encode(sd.Count); // 4 bytes\n\n foreach (var kvp in sd) {\n writer.Write(kvp.Key);\n kvp.Value.Save(writer);\n }\n }\n\n /// <summary>\n /// Load the parameters and buffers\n /// </summary>\n /// <param name=\"location\">The file path.</param>\n /// <param name=\"strict\">\n /// If true, will only load a module if it exactly corresponds to the current module's state.\n /// If false, will load the parameters and buffers that it finds in the saved file,\n /// leaving everything else alone.\n /// </param>\n /// <param name=\"skip\">A list of keys not to consider when loading the dictionary.</param>\n /// <returns>The module, with parameters and buffers loaded.</returns>\n /// <remarks>\n /// Using a skip list only prevents tensors in the target module from being modified, it\n /// does not alter any logic related to checking for matching tensor element types or entries.\n /// It may be necessary to also pass 'strict=false' to avoid exceptions.\n /// </remarks>\n public virtual Module load(string location, bool strict = true, IList<string> skip = null)\n {\n if (!System.IO.File.Exists(location))\n throw new System.IO.FileNotFoundException(location);\n\n using var stream = System.IO.File.OpenRead(location);\n using var reader = new System.IO.BinaryReader(stream);\n load(reader, strict, skip);\n\n return this;\n }\n\n /// <summary>\n /// Load the parameters and buffers\n /// </summary>\n /// <param name=\"reader\">A binary reader instance.</param>\n /// <param name=\"strict\">\n /// If true, will only load a module if it exactly corresponds to the current module's state.\n /// If false, will load the parameters and buffers that it finds in the saved file,\n /// leaving everything else alone.\n /// </param>\n /// <param name=\"skip\">A list of keys not to consider when loading the dictionary.</param>\n /// <returns>The module, with parameters and buffers loaded.</returns>\n /// <remarks>\n /// Using a skip list only prevents tensors in the target module from being modified, it\n /// does not alter any logic related to checking for matching tensor element types or entries.\n /// It may be necessary to also pass 'strict=false' to avoid exceptions.\n /// </remarks>\n public virtual Module load(System.IO.BinaryReader reader, bool strict = true, IList<string> skip = null)\n {\n skip ??= Array.Empty<string>();\n\n var dt = _deviceType;\n var di = _deviceIndex;\n\n if (dt != DeviceType.CPU) this.cpu();\n\n try {\n var sd = state_dict();\n\n // First, figure out how many entries.\n var streamEntries = reader.Decode();\n\n if (streamEntries != sd.Count && strict)\n throw new ArgumentException($\"Mismatched state_dict sizes: expected {sd.Count}, but found {streamEntries} entries.\");\n\n for (int i = 0; i < streamEntries; ++i) {\n var key = reader.ReadString();\n var found = sd.ContainsKey(key);\n if (!found && strict)\n throw new ArgumentException($\"Mismatched module state names: the target modules does not have a submodule or buffer named '{key}'\");\n\n if (found) {\n sd[key].Load(reader, skip: skip.Contains(key));\n }\n }\n } finally {\n if (dt != DeviceType.CPU) _to(dt, di);\n }\n\n return this;\n }\n\n /// <summary>\n /// Load the parameters and buffers\n /// </summary>\n /// <param name=\"stream\">A readable stream instance.</param>\n /// <param name=\"strict\">\n /// If true, will only load a module if it exactly corresponds to the current module's state.\n /// If false, will load the parameters and buffers that it finds in the saved file,\n /// leaving everything else alone.\n /// </param>\n /// <param name=\"skip\">A list of keys not to consider when loading the dictionary.</param>\n /// <returns>The module, with parameters and buffers loaded.</returns>\n /// <remarks>\n /// Using a skip list only prevents tensors in the target module from being modified, it\n /// does not alter any logic related to checking for matching tensor element types or entries.\n /// It may be necessary to also pass 'strict=false' to avoid exceptions.\n /// </remarks>\n public Module load(System.IO.Stream stream, bool strict = true, IList<string> skip = null)\n {\n using var reader = new System.IO.BinaryReader(stream);\n return load(reader, strict, skip);\n }\n\n /// <summary>\n /// Create a module and load its weights from disk.\n /// </summary>\n /// <typeparam name=\"T\"></typeparam>\n /// <param name=\"path\"></param>\n /// <returns></returns>\n public static Module Create<T>(string path)\n where T : Module, new()\n {\n var model = new T();\n return model.load(path);\n }\n\n /// <summary>\n /// Constructor for custom modules, i.e. those defined outside of TorchSharp.\n /// </summary>\n /// <param name=\"name\">The name of the module. Useful for debugging purposes, mostly.</param>\n protected Module(string name) : this(IntPtr.Zero, IntPtr.Zero)\n {\n this.name = name;\n\n IntPtr ForwardNative(IntPtr t)\n {\n var input = new Tensor(t);\n var output = ((nn.Module<Tensor, Tensor>)this).call(input);\n\n // handles must live on - we don't own them, but\n // the managed objects should go away.\n input.DecoupleFromNativeHandle();\n\n return output.DecoupleFromNativeHandle();\n }\n\n var res = THSNN_custom_module(name, ForwardNative, out var boxedHandle);\n CheckForErrors();\n handle = new HType(res, true);\n this._forwardNative = ForwardNative;\n boxedModule = new BoxedModule(boxedHandle);\n\n _init_parameters();\n }\n\n private void _init_parameters()\n { \n // In this case, the parameter registration was not done yet.\n foreach (var (parameterName, parameter) in _named_parameters()) {\n register_parameter(parameterName, parameter);\n }\n }\n\n protected virtual void RegisterComponents()\n {\n if (_areComponentsRegistered) return;\n\n foreach (var field in GetType().GetFields(BindingFlags.Public | BindingFlags.NonPublic | BindingFlags.Instance)) {\n\n var fieldName = field.Name;\n if (_internal_submodules.ContainsKey(fieldName) || _internal_params.ContainsKey(fieldName) || _internal_buffers.ContainsKey(fieldName)) continue;\n\n var value = field.GetValue(this);\n\n switch (value) {\n case Module module:\n register_module(fieldName, module);\n break;\n case Parameter param: // This test must come before the Tensor test\n register_parameter(fieldName, param);\n break;\n case Tensor tensor:\n register_buffer(fieldName, tensor);\n break;\n }\n }\n\n _areComponentsRegistered = true;\n }\n\n protected static (Device device, ScalarType dtype) GetDefaultDeviceAndType(Device device = null, ScalarType? dtype = null)\n {\n if (!dtype.HasValue)\n dtype = get_default_dtype();\n\n if (device == null)\n device = torch.CPU;\n\n return (device, dtype.Value);\n }\n\n internal T MoveModule<T>(Device device, ScalarType? dtype) where T : Module\n {\n T module = (T)this;\n\n return device != null ?\n (dtype.HasValue ? (T)module._to(device, dtype.Value) : (T)module._to(device.type, device.index)) :\n (dtype.HasValue ? (T)module._to(dtype.Value) : module);\n }\n\n protected void ClearModules() { _internal_submodules.clear(); }\n\n private bool _areComponentsRegistered;\n\n protected Utils.OrderedDict<string, Module> _internal_submodules = new Utils.OrderedDict<string, Module>();\n protected Utils.OrderedDict<string, Tensor> _internal_buffers = new Utils.OrderedDict<string, Tensor>();\n protected Utils.OrderedDict<string, Parameter> _internal_params = new Utils.OrderedDict<string, Parameter>();\n\n /// Keeps the callback delegate alive\n private ForwardFunctionC _forwardNative;\n protected string name;\n }\n\n internal class BoxedModule : IDisposable\n {\n internal sealed class HType : SafeHandle\n {\n public HType(IntPtr preexistingHandle, bool ownsHandle) : base(IntPtr.Zero, ownsHandle)\n => SetHandle(preexistingHandle);\n\n public override bool IsInvalid => handle == IntPtr.Zero;\n\n // This is just for marshalling\n internal HType() : base(IntPtr.Zero, true)\n {\n }\n\n protected override bool ReleaseHandle()\n {\n if (!IsInvalid) THSNN_AnyModule_dispose(this);\n handle = IntPtr.Zero;\n return true;\n }\n\n protected override void Dispose(bool disposing)\n {\n if (disposing) {\n ReleaseHandle();\n }\n }\n }\n\n internal HType handle;\n\n internal BoxedModule(IntPtr handle)\n {\n this.handle = new HType(handle, true);\n }\n\n ~BoxedModule() => Dispose(false);\n\n /// <summary>\n /// Releases the storage.\n /// </summary>\n public void Dispose()\n {\n Dispose(true);\n GC.SuppressFinalize(this);\n }\n\n /// <summary>\n /// Implements the .NET Dispose pattern.\n /// </summary>\n protected void Dispose(bool disposing)\n {\n if (disposing) {\n handle.Dispose();\n handle.SetHandleAsInvalid();\n }\n }\n }\n\n /// <summary>\n /// Interface for concrete modules with a forward() that takes a single argument.\n /// </summary>\n /// <typeparam name=\"T\">The argument type of the module's forward() function.</typeparam>\n /// <typeparam name=\"TResult\">The return type of the module's forward() function.</typeparam>\n public interface IModule<T, TResult>\n {\n public TResult call(T input1);\n }\n\n /// <summary>\n /// Interface for concrete modules with a forward() that takes two arguments.\n /// </summary>\n /// <typeparam name=\"T1\">The first argument type of the module's forward() function.</typeparam>\n /// <typeparam name=\"T2\">The second argument type of the module's forward() function.</typeparam>\n /// <typeparam name=\"TResult\">The return type of the module's forward() function.</typeparam>\n public interface IModule<T1, T2, TResult>\n {\n public abstract TResult call(T1 input1, T2 input2);\n }\n\n /// <summary>\n /// Interface for concrete modules with a forward() that takes three arguments.\n /// </summary>\n /// <typeparam name=\"T1\">The first argument type of the module's forward() function.</typeparam>\n /// <typeparam name=\"T2\">The second argument type of the module's forward() function.</typeparam>\n /// <typeparam name=\"T3\">The third argument type of the module's forward() function.</typeparam>\n /// <typeparam name=\"TResult\">The return type of the module's forward() function.</typeparam>\n public interface IModule<T1, T2, T3, TResult>\n {\n public abstract TResult call(T1 input1, T2 input2, T3 input3);\n }\n\n /// <summary>\n /// Interface for concrete modules with a forward() that takes four arguments.\n /// </summary>\n /// <typeparam name=\"T1\">The first argument type of the module's forward() function.</typeparam>\n /// <typeparam name=\"T2\">The second argument type of the module's forward() function.</typeparam>\n /// <typeparam name=\"T3\">The third argument type of the module's forward() function.</typeparam>\n /// <typeparam name=\"T4\">The fourth argument type of the module's forward() function.</typeparam>\n /// <typeparam name=\"TResult\">The return type of the module's forward() function.</typeparam>\n public interface IModule<T1, T2, T3, T4, TResult>\n {\n public abstract TResult call(T1 input1, T2 input2, T3 input3, T4 input4);\n }\n\n /// <summary>\n /// Interface for concrete modules with a forward() that takes five arguments.\n /// </summary>\n /// <typeparam name=\"T1\">The first argument type of the module's forward() function.</typeparam>\n /// <typeparam name=\"T2\">The second argument type of the module's forward() function.</typeparam>\n /// <typeparam name=\"T3\">The third argument type of the module's forward() function.</typeparam>\n /// <typeparam name=\"T4\">The fourth argument type of the module's forward() function.</typeparam>\n /// <typeparam name=\"T5\">The fifth argument type of the module's forward() function.</typeparam>\n /// <typeparam name=\"TResult\">The return type of the module's forward() function.</typeparam>\n public interface IModule<T1, T2, T3, T4, T5, TResult>\n {\n public abstract TResult call(T1 input1, T2 input2, T3 input3, T4 input4, T5 input5);\n }\n\n /// <summary>\n /// Interface for concrete modules with a forward() that takes six arguments.\n /// </summary>\n /// <typeparam name=\"T1\">The first argument type of the module's forward() function.</typeparam>\n /// <typeparam name=\"T2\">The second argument type of the module's forward() function.</typeparam>\n /// <typeparam name=\"T3\">The third argument type of the module's forward() function.</typeparam>\n /// <typeparam name=\"T4\">The fourth argument type of the module's forward() function.</typeparam>\n /// <typeparam name=\"T5\">The fifth argument type of the module's forward() function.</typeparam>\n /// <typeparam name=\"T6\">The sixth argument type of the module's forward() function.</typeparam>\n /// <typeparam name=\"TResult\">The return type of the module's forward() function.</typeparam>\n public interface IModule<T1, T2, T3, T4, T5, T6, TResult>\n {\n public abstract TResult call(T1 input1, T2 input2, T3 input3, T4 input4, T5 input5, T6 input6);\n }\n\n /// <summary>\n /// Represents a module that accepts 'hook' to the module logic.\n /// </summary>\n public class HookableModule<TPreHook,TPostHook> : Module\n {\n protected HookableModule(string name) : base(name) { }\n\n protected HookableModule(IntPtr handle, IntPtr boxedHandle) : base(handle, boxedHandle) { }\n internal HookableModule(HType handle, IntPtr? boxedHandle) : base(handle, boxedHandle) { }\n\n public HookRemover register_forward_hook(TPostHook hook)\n {\n var key = Guid.NewGuid().ToString();\n post_hooks.Add(key, hook);\n return new HookRemover(this, key);\n }\n\n public HookRemover register_forward_pre_hook(TPreHook hook)\n {\n var key = Guid.NewGuid().ToString();\n pre_hooks.Add(key, hook);\n return new HookRemover(this, key);\n }\n\n private void remove(string key)\n {\n if (pre_hooks.ContainsKey(key)) pre_hooks.Remove(key);\n if (post_hooks.ContainsKey(key)) post_hooks.Remove(key);\n }\n\n protected Dictionary<string, TPreHook> pre_hooks = new Dictionary<string, TPreHook>();\n protected Dictionary<string, TPostHook> post_hooks = new Dictionary<string, TPostHook>();\n\n /// <summary>\n /// Used to remove a specific hook, following the PyTorch API design.\n /// </summary>\n /// <remarks>The name and namespace of this class is not the same as in PyTorch, but serves the same purpose.</remarks>\n public class HookRemover\n {\n public HookRemover(HookableModule<TPreHook, TPostHook> module, string key)\n {\n this.module = module;\n this.key = key;\n }\n\n public void remove()\n {\n module.remove(key);\n }\n\n private HookableModule<TPreHook, TPostHook> module;\n private string key;\n }\n }\n\n /// <summary>\n /// Base class for concrete modules with a forward() that takes a single argument.\n /// </summary>\n /// <typeparam name=\"T\">The argument type of the module's forward() function.</typeparam>\n /// <typeparam name=\"TResult\">The return type of the module's forward() function.</typeparam>\n public abstract class Module<T, TResult> : HookableModule<Func<Module<T,TResult>, T, T>, Func<Module<T, TResult>, T, TResult, TResult>>, IModule<T, TResult>\n {\n protected Module(string name) : base(name) { }\n protected Module(IntPtr handle, IntPtr boxedHandle) : base(handle, boxedHandle) { }\n internal Module(HType handle, IntPtr? boxedHandle) : base(handle, boxedHandle) { }\n\n /// <summary>\n /// Invoke the logic of the module.\n /// </summary>\n /// <remarks>`forward` will not invoke any registered hooks for the module.</remarks>\n public abstract TResult forward(T input);\n\n /// <summary>\n /// Invoke the logic of the module.\n /// </summary>\n /// <remarks>`call` will invoke any registered hooks for the module.</remarks>\n public TResult call(T input)\n {\n // Call pre-hooks, if available.\n\n foreach (var hook in pre_hooks.Values) {\n var modified = hook(this, input);\n if (modified is not null)\n input = modified;\n }\n\n var result = forward(input);\n\n // Call post-hooks, if available.\n\n foreach (var hook in post_hooks.Values) {\n var modified = hook(this, input, result);\n if (modified is not null)\n result = modified;\n }\n\n return result;\n }\n }\n\n /// <summary>\n /// Base class for concrete modules with a forward() that takes two arguments.\n /// </summary>\n /// <typeparam name=\"T1\">The first argument type of the module's forward() function.</typeparam>\n /// <typeparam name=\"T2\">The second argument type of the module's forward() function.</typeparam>\n /// <typeparam name=\"TResult\">The return type of the module's forward() function.</typeparam>\n public abstract class Module<T1, T2, TResult> : HookableModule<Func<Module<T1, T2, TResult>, T1, T2, (T1, T2)?>, Func<Module<T1, T2, TResult>, T1, T2, TResult, TResult>>, IModule<T1, T2, TResult>\n {\n protected Module(string name) : base(name) { }\n protected Module(IntPtr handle, IntPtr boxedHandle) : base(handle, boxedHandle) { }\n internal Module(HType handle, IntPtr? boxedHandle) : base(handle, boxedHandle) { }\n\n /// <summary>\n /// Invoke the logic of the module.\n /// </summary>\n /// <remarks>`forward` will not invoke any registered hooks for the module.</remarks>\n public abstract TResult forward(T1 input1, T2 input2);\n\n /// <summary>\n /// Invoke the logic of the module.\n /// </summary>\n /// <remarks>`call` will invoke any registered hooks for the module.</remarks>\n public TResult call(T1 input1, T2 input2)\n {\n // Call pre-hooks, if available.\n\n foreach (var hook in pre_hooks.Values) {\n var modified = hook(this, input1, input2);\n if (modified.HasValue) {\n input1 = modified.Value.Item1;\n input2 = modified.Value.Item2;\n }\n }\n\n var result = forward(input1, input2);\n\n // Call post-hooks, if available.\n\n foreach (var hook in post_hooks.Values) {\n var modified = hook(this, input1, input2, result);\n if (modified is not null)\n result = modified;\n }\n\n return result;\n }\n }\n\n /// <summary>\n /// Base class for concrete modules with a forward() that takes three arguments.\n /// </summary>\n /// <typeparam name=\"T1\">The first argument type of the module's forward() function.</typeparam>\n /// <typeparam name=\"T2\">The second argument type of the module's forward() function.</typeparam>\n /// <typeparam name=\"T3\">The third argument type of the module's forward() function.</typeparam>\n /// <typeparam name=\"TResult\">The return type of the module's forward() function.</typeparam>\n public abstract class Module<T1, T2, T3, TResult> : HookableModule<Func<Module<T1, T2, T3, TResult>, T1, T2, T3, (T1, T2, T3)?>, Func<Module<T1, T2, T3, TResult>, T1, T2, T3, TResult, TResult>>, IModule<T1, T2, T3, TResult>\n {\n protected Module(string name) : base(name) { }\n protected Module(IntPtr handle, IntPtr boxedHandle) : base(handle, boxedHandle) { }\n internal Module(HType handle, IntPtr? boxedHandle) : base(handle, boxedHandle) { }\n\n /// <summary>\n /// Invoke the logic of the module.\n /// </summary>\n /// <remarks>`forward` will not invoke any registered hooks for the module.</remarks>\n public abstract TResult forward(T1 input1, T2 input2, T3 input3);\n\n /// <summary>\n /// Invoke the logic of the module.\n /// </summary>\n /// <remarks>`call` will invoke any registered hooks for the module.</remarks>\n public TResult call(T1 input1, T2 input2, T3 input3)\n {\n // Call pre-hooks, if available.\n\n foreach (var hook in pre_hooks.Values) {\n var modified = hook(this, input1, input2, input3);\n if (modified.HasValue) {\n input1 = modified.Value.Item1;\n input2 = modified.Value.Item2;\n input3 = modified.Value.Item3;\n }\n }\n\n var result = forward(input1, input2, input3);\n\n // Call post-hooks, if available.\n\n foreach (var hook in post_hooks.Values) {\n var modified = hook(this, input1, input2, input3, result);\n if (modified is not null)\n result = modified;\n }\n\n return result;\n }\n }\n\n /// <summary>\n /// Base class for concrete modules with a forward() that takes four arguments.\n /// </summary>\n /// <typeparam name=\"T1\">The first argument type of the module's forward() function.</typeparam>\n /// <typeparam name=\"T2\">The second argument type of the module's forward() function.</typeparam>\n /// <typeparam name=\"T3\">The third argument type of the module's forward() function.</typeparam>\n /// <typeparam name=\"T4\">The fourth argument type of the module's forward() function.</typeparam>\n /// <typeparam name=\"TResult\">The return type of the module's forward() function.</typeparam>\n public abstract class Module<T1, T2, T3, T4, TResult> : HookableModule<Func<Module<T1, T2, T3, T4, TResult>, T1, T2, T3, T4, (T1, T2, T3, T4)?>, Func<Module<T1, T2, T3, T4, TResult>, T1, T2, T3, T4, TResult, TResult>>, IModule<T1, T2, T3, T4, TResult>\n {\n protected Module(string name) : base(name) { }\n protected Module(IntPtr handle, IntPtr boxedHandle) : base(handle, boxedHandle) { }\n internal Module(HType handle, IntPtr? boxedHandle) : base(handle, boxedHandle) { }\n\n /// <summary>\n /// Invoke the logic of the module.\n /// </summary>\n /// <remarks>`forward` will not invoke any registered hooks for the module.</remarks>\n public abstract TResult forward(T1 input1, T2 input2, T3 input3, T4 input4);\n\n /// <summary>\n /// Invoke the logic of the module.\n /// </summary>\n /// <remarks>`call` will invoke any registered hooks for the module.</remarks>\n public TResult call(T1 input1, T2 input2, T3 input3, T4 input4)\n {\n // Call pre-hooks, if available.\n\n foreach (var hook in pre_hooks.Values) {\n var modified = hook(this, input1, input2, input3, input4);\n if (modified.HasValue) {\n input1 = modified.Value.Item1;\n input2 = modified.Value.Item2;\n input3 = modified.Value.Item3;\n input4 = modified.Value.Item4;\n }\n }\n\n var result = forward(input1, input2, input3, input4);\n\n // Call post-hooks, if available.\n\n foreach (var hook in post_hooks.Values) {\n var modified = hook(this, input1, input2, input3, input4, result);\n if (modified is not null)\n result = modified;\n }\n\n return result;\n }\n }\n\n /// <summary>\n /// Base class for concrete modules with a forward() that takes five arguments.\n /// </summary>\n /// <typeparam name=\"T1\">The first argument type of the module's forward() function.</typeparam>\n /// <typeparam name=\"T2\">The second argument type of the module's forward() function.</typeparam>\n /// <typeparam name=\"T3\">The third argument type of the module's forward() function.</typeparam>\n /// <typeparam name=\"T4\">The fourth argument type of the module's forward() function.</typeparam>\n /// <typeparam name=\"T5\">The fifth argument type of the module's forward() function.</typeparam>\n /// <typeparam name=\"TResult\">The return type of the module's forward() function.</typeparam>\n public abstract class Module<T1, T2, T3, T4, T5,TResult> : HookableModule<Func<Module<T1, T2, T3, T4, T5, TResult>, T1, T2, T3, T4, T5, (T1, T2, T3, T4, T5)?>, Func<Module<T1, T2, T3, T4, T5, TResult>, T1, T2, T3, T4, T5, TResult, TResult>>, IModule<T1, T2, T3, T4, T5, TResult>\n {\n protected Module(string name) : base(name) { }\n protected Module(IntPtr handle, IntPtr boxedHandle) : base(handle, boxedHandle) { }\n internal Module(HType handle, IntPtr? boxedHandle) : base(handle, boxedHandle) { }\n\n /// <summary>\n /// Invoke the logic of the module.\n /// </summary>\n /// <remarks>`forward` will not invoke any registered hooks for the module.</remarks>\n public abstract TResult forward(T1 input1, T2 input2, T3 input3, T4 input4, T5 input5);\n\n /// <summary>\n /// Invoke the logic of the module.\n /// </summary>\n /// <remarks>`call` will invoke any registered hooks for the module.</remarks>\n public TResult call(T1 input1, T2 input2, T3 input3, T4 input4, T5 input5)\n {\n // Call pre-hooks, if available.\n\n foreach (var hook in pre_hooks.Values) {\n var modified = hook(this, input1, input2, input3, input4, input5);\n if (modified.HasValue) {\n input1 = modified.Value.Item1;\n input2 = modified.Value.Item2;\n input3 = modified.Value.Item3;\n input4 = modified.Value.Item4;\n input5 = modified.Value.Item5;\n }\n }\n\n var result = forward(input1, input2, input3, input4, input5);\n\n // Call post-hooks, if available.\n\n foreach (var hook in post_hooks.Values) {\n var modified = hook(this, input1, input2, input3, input4, input5, result);\n if (modified is not null)\n result = modified;\n }\n\n return result;\n }\n }\n\n /// <summary>\n /// Base class for concrete modules with a forward() that takes six arguments.\n /// </summary>\n /// <typeparam name=\"T1\">The first argument type of the module's forward() function.</typeparam>\n /// <typeparam name=\"T2\">The second argument type of the module's forward() function.</typeparam>\n /// <typeparam name=\"T3\">The third argument type of the module's forward() function.</typeparam>\n /// <typeparam name=\"T4\">The fourth argument type of the module's forward() function.</typeparam>\n /// <typeparam name=\"T5\">The fifth argument type of the module's forward() function.</typeparam>\n /// <typeparam name=\"T6\">The sixth argument type of the module's forward() function.</typeparam>\n /// <typeparam name=\"TResult\">The return type of the module's forward() function.</typeparam>\n public abstract class Module<T1, T2, T3, T4, T5, T6, TResult> : HookableModule<Func<Module<T1, T2, T3, T4, T5, T6, TResult>, T1, T2, T3, T4, T5, T6, (T1, T2, T3, T4, T5, T6)?>, Func<Module<T1, T2, T3, T4, T5, T6, TResult>, T1, T2, T3, T4, T5, T6, TResult, TResult>>, IModule<T1, T2, T3, T4, T5, T6, TResult>\n {\n protected Module(string name) : base(name) { }\n protected Module(IntPtr handle, IntPtr boxedHandle) : base(handle, boxedHandle) { }\n internal Module(HType handle, IntPtr? boxedHandle) : base(handle, boxedHandle) { }\n\n /// <summary>\n /// Invoke the logic of the module.\n /// </summary>\n /// <remarks>`forward` will not invoke any registered hooks for the module.</remarks>\n public abstract TResult forward(T1 input1, T2 input2, T3 input3, T4 input4, T5 input5, T6 input6);\n\n /// <summary>\n /// Invoke the logic of the module.\n /// </summary>\n /// <remarks>`call` will invoke any registered hooks for the module.</remarks>\n public TResult call(T1 input1, T2 input2, T3 input3, T4 input4, T5 input5, T6 input6)\n {\n // Call pre-hooks, if available.\n\n foreach (var hook in pre_hooks.Values) {\n var modified = hook(this, input1, input2, input3, input4, input5, input6);\n if (modified.HasValue) {\n input1 = modified.Value.Item1;\n input2 = modified.Value.Item2;\n input3 = modified.Value.Item3;\n input4 = modified.Value.Item4;\n input5 = modified.Value.Item5;\n input6 = modified.Value.Item6;\n }\n }\n\n var result = forward(input1, input2, input3, input4, input5, input6);\n\n // Call post-hooks, if available.\n\n foreach (var hook in post_hooks.Values) {\n var modified = hook(this, input1, input2, input3, input4, input5, input6, result);\n if (modified is not null)\n result = modified;\n }\n\n return result;\n }\n }\n }\n }\n\n public static class ModuleExtensionMethods\n {\n /// <summary>\n /// Converts the parameters and buffers.\n /// </summary>\n /// <param name=\"module\">The module to move</param>\n /// <param name=\"type\">The target element type.</param>\n public static T to<T>(this T module, torch.ScalarType type) where T : torch.nn.Module\n {\n return (T)module._to(type);\n }\n\n /// <summary>\n /// Moves and converts the parameters and buffers.\n /// </summary>\n /// <param name=\"module\">The module to move</param>\n /// <param name=\"device\">The target device.</param>\n /// <param name=\"type\">The target element type.</param>\n public static T to<T>(this T module, torch.Device device, torch.ScalarType type) where T : torch.nn.Module\n {\n return (T)module._to(device, type);\n }\n\n /// <summary>\n /// Moves the parameters and buffers.\n /// </summary>\n /// <param name=\"module\">The module to move</param>\n /// <param name=\"deviceType\">The device type, e.g. 'CPU' or 'CUDA'.</param>\n /// <param name=\"deviceIndex\">The optional device index.</param>\n public static T to<T>(this T module, DeviceType deviceType, int deviceIndex = -1) where T : torch.nn.Module\n {\n return (T)module._to(deviceType, deviceIndex);\n }\n\n /// <summary>\n /// Moves the parameters and buffers.\n /// </summary>\n /// <param name=\"module\">The module to move</param>\"\n /// <param name=\"device\">The target device</param>\n /// <returns></returns>\n public static T to<T>(this T module, Device device) where T : torch.nn.Module => (T)module._to(device.type, device.index);\n\n /// <summary>\n /// Moves the parameters and buffers.\n /// </summary>\n /// <param name=\"module\">The module to move</param>\n /// <param name=\"device\">A string denoting the target device.</param>\n /// <returns></returns>\n /// <remarks>Relies on the Device constructor to parse the string.</remarks>\n public static T to<T>(this T module, string device) where T : torch.nn.Module\n {\n var dev = new Device(device);\n return (T)module._to(dev.type, dev.index);\n }\n\n /// <summary>\n /// Moves and converts the parameters and buffers.\n /// </summary>\n /// <param name=\"module\">The module to move</param>\n /// <param name=\"other\">The tensor serving as a template.</param>\n /// <returns></returns>\n public static T to<T>(this T module, Tensor other) where T : torch.nn.Module\n {\n return (T)module._to(other.device, other.dtype);\n }\n\n /// <summary>\n /// Moves all model parameters and buffers to the CPU.\n /// </summary>\n public static T cpu<T>(this T module) where T : torch.nn.Module => (T)module._to(DeviceType.CPU);\n\n /// <summary>\n /// Moves all model parameters and buffers to a GPU.\n ///\n /// This also makes associated parameters and buffers different objects.So it should be called before constructing optimizer if the module will live on GPU while being optimized.\n /// </summary>\n /// <param name=\"module\">The module to move</param>\n /// <param name=\"deviceIndex\">If specified, all parameters will be copied to that device</param>\n public static T cuda<T>(this T module, int deviceIndex = -1) where T : torch.nn.Module => (T)module._to(DeviceType.CUDA, deviceIndex);\n }\n\n internal delegate IntPtr ForwardFunctionC(IntPtr tensor);\n}\n"},{"alpha_fraction":0.6053891181945801,"alphanum_fraction":0.6099536418914795,"avg_line_length":43.980133056640625,"blob_id":"abdfbf99b0e55fea149856f90758f30f84d5495c","content_id":"43a50d336c83fb5ff2e832e8e059620343c00e6f","detected_licenses":["MIT"],"is_generated":false,"is_vendor":false,"language":"C#","length_bytes":13583,"license_type":"permissive","max_line_length":130,"num_lines":302,"path":"/src/TorchSharp/DisposeScope.cs","repo_name":"NiklasGustafsson/TorchSharp","src_encoding":"UTF-8","text":"// Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information.\n\nusing System;\nusing System.Collections.Generic;\nusing System.Linq;\nusing TorchSharp.Utils;\n\n#nullable enable\nnamespace TorchSharp\n{\n /// <summary>\n /// Keeps track of all disposables that are in the current scope - the dispose scopes can be nested and the\n /// nesting functionality is mainly managed by DisposeScopeManager.\n /// </summary>\n public sealed class DisposeScope : IDisposable\n {\n private readonly DisposeScopeManager _disposeScopeManager;\n\n public DisposeScope(DisposeScopeManager disposeScopeManager)\n {\n _disposeScopeManager = disposeScopeManager ?? throw new ArgumentNullException(nameof(disposeScopeManager));\n if (disposeScopeManager.DisposeScopeStack.Count > 0) {\n OuterScope = disposeScopeManager.DisposeScopeStack.Peek();\n }\n }\n\n /// <summary>\n /// The outer scope with relation to this scope.\n /// </summary>\n internal DisposeScope? OuterScope { get; }\n\n /// <summary>\n /// The disposables that are scheduled for disposing.\n /// </summary>\n /// TODO: There is a ReferenceEqualityComparer coming in .NET 6, use that!\n internal HashSet<IDisposable> Disposables { get; private set; } =\n new HashSet<IDisposable>(ReferenceEqualityComparer<IDisposable>.Default);\n\n /// <summary>\n /// A view of the disposables in the scope - this list will not be kept in synch with the disposables\n /// in the scope.\n /// </summary>\n public IReadOnlyList<IDisposable> DisposablesView => Disposables.ToList();\n\n /// <summary>\n /// The number of disposables currently held in the scope\n /// </summary>\n public int DisposablesCount => Disposables.Count;\n\n /// <summary>\n /// Includes a disposable in the scope - for tensors this is done automatically once the scope has been\n /// created. Use this method to add additional disposables that should be disposed, but you typically\n /// don't need to call this method.\n /// </summary>\n /// <param name=\"disposable\">The disposable to keep in the scope</param>\n /// <returns></returns>\n public T Include<T>(T disposable) where T : IDisposable\n {\n Disposables.Add(disposable);\n return disposable;\n }\n\n /// <summary>\n /// Excludes a set of tensors/disposables from the current dispose scope, and moves it up to the outer\n /// dispose scope, if one exists. See overloaded methods. If you wish to exclude a tensor from all sccopes,\n /// use Detach.\n /// </summary>\n public T MoveToOuter<T>(T disposable) where T : IDisposable\n {\n MoveToOuter(new IDisposable[] { disposable });\n return disposable;\n }\n\n /// <summary>\n /// Excludes a set of tensors/disposables from the current dispose scope, and moves it up to the outer\n /// dispose scope, if one exists. See overloaded methods. If you wish to exclude a tensor from all sccopes,\n /// use Detach.\n /// </summary>\n public (T1 first, T2 second) MoveToOuter<T1, T2>(T1 first, T2 second)\n where T1 : IDisposable where T2 : IDisposable\n {\n MoveToOuter(new IDisposable[] { first, second });\n return (first, second);\n }\n\n /// <summary>\n /// Excludes a set of tensors/disposables from the current dispose scope, and moves it up to the outer\n /// dispose scope, if one exists. See overloaded methods. If you wish to exclude a tensor from all sccopes,\n /// use Detach.\n /// </summary>\n public (T1 first, T2 second, T3 third) MoveToOuter<T1, T2, T3>(T1 first, T2 second, T3 third)\n where T1 : IDisposable where T2 : IDisposable where T3 : IDisposable\n {\n MoveToOuter(new IDisposable[] { first, second, third });\n return (first, second, third);\n }\n\n /// <summary>\n /// Excludes a set of tensors/disposables from the current dispose scope, and moves it up to the outer\n /// dispose scope, if one exists. See overloaded methods. If you wish to exclude a tensor from all sccopes,\n /// use Detach.\n /// </summary>\n public void MoveToOuter(params IDisposable[] disposables) =>\n MoveToOuter((IEnumerable<IDisposable>)disposables);\n\n /// <summary>\n /// Excludes a set of tensors/disposables from the current dispose scope, and moves it up to the outer\n /// dispose scope, if one exists. See overloaded methods. If you wish to exclude a tensor from all sccopes,\n /// use Detach.\n /// </summary>\n public void MoveToOuter(IEnumerable<IDisposable> disposables)\n {\n foreach (var disposable in disposables) {\n if (Disposables.Remove(disposable)) {\n AddToParent(disposable);\n }\n }\n }\n\n /// <summary>\n /// Detaches/excludes a set of tensors/disposables from the all dispose scopes, see overloaded methods. See MoveToOuter\n /// if you wish to move it to the outer dispose scope.\n /// </summary>\n public T Detach<T>(T disposable) where T : IDisposable\n {\n Detach(new IDisposable[] { disposable });\n return disposable;\n }\n\n /// <summary>\n /// Detaches/excludes a set of tensors/disposables from the all dispose scopes, see overloaded methods. See MoveToOuter\n /// if you wish to move it to the outer dispose scope.\n /// </summary>\n public (T1 first, T2 second) Detach<T1, T2>(T1 first, T2 second)\n where T1 : IDisposable where T2 : IDisposable\n {\n Detach(new IDisposable[] { first, second });\n return (first, second);\n }\n\n /// <summary>\n /// Detaches/excludes a set of tensors/disposables from the all dispose scopes, see overloaded methods. See MoveToOuter\n /// if you wish to move it to the outer dispose scope.\n /// </summary>\n public (T1 first, T2 second, T3 third) Detach<T1, T2, T3>(T1 first, T2 second, T3 third)\n where T1 : IDisposable where T2 : IDisposable where T3 : IDisposable\n {\n Detach(new IDisposable[] { first, second, third });\n return (first, second, third);\n }\n\n /// <summary>\n /// Detaches/excludes a set of tensors/disposables from the all dispose scopes, see overloaded methods. See MoveToOuter\n /// if you wish to move it to the outer dispose scope.\n /// </summary>\n public void Detach(params IDisposable[] disposables) => Detach((IEnumerable<IDisposable>)disposables);\n\n /// <summary>\n /// Detaches/excludes a set of tensors/disposables from the all dispose scopes, see overloaded methods. See MoveToOuter\n /// if you wish to move it to the outer dispose scope.\n /// </summary>\n public void Detach(IEnumerable<IDisposable> disposables)\n {\n foreach (var disposable in disposables) {\n if (Disposables.Remove(disposable)) {\n _disposeScopeManager.StatisticsInstance.DetachedFromScopeCount++;\n if (disposable is torch.Tensor tensor) {\n tensor.OwningDisposeScope = null;\n }\n }\n }\n }\n\n /// <summary>\n /// Disposes everything currently in the dispose scope.\n /// </summary>\n public void DisposeEverything() => DisposeEverythingBut(Enumerable.Empty<IDisposable>());\n\n /// <summary>\n /// As an intermediate step, you can dispose all the tensors/disposables currently scheduled for dispose, to\n /// clear up some memory without creating a new scope. Note that this doesn't permanently exclude the\n /// tensors from disposing, use Exclude for that. Also, excluded tensors don't need to be included\n /// here.\n /// </summary>\n public void DisposeEverythingBut(IEnumerable<IDisposable> inKeep)\n {\n // Avoiding multiple enumerations\n var oldList = Disposables;\n Disposables = inKeep.ToHashSet(ReferenceEqualityComparer<IDisposable>.Default);\n foreach (var disposable in oldList) {\n if (Disposables.Contains(disposable)) {\n continue;\n }\n\n if (disposable is torch.Tensor tensor) {\n // No need to have the disposable call back to the scope\n tensor.OwningDisposeScope = null;\n if (!tensor.IsInvalid) {\n _disposeScopeManager.StatisticsInstance.DisposedInScopeCount++;\n }\n } else {\n _disposeScopeManager.StatisticsInstance.DisposedInScopeCount++;\n }\n\n disposable.Dispose();\n }\n }\n\n /// <summary>\n /// As an intermediate step, you can dispose all the tensors/disposables currently scheduled for dispose, to\n /// clear up some memory without creating a new scope. Note that this doesn't permanently exclude the\n /// tensors from disposing, use Exclude for that. Also, excluded tensors don't need to be included\n /// here.\n /// </summary>\n public void DisposeEverythingBut(params IDisposable[] keep) =>\n DisposeEverythingBut((IEnumerable<IDisposable>)keep);\n\n /// <summary>\n /// As an intermediate step, you can dispose all the tensors/disposables currently scheduled for dispose, to\n /// clear up some memory without creating a new scope. Note that this doesn't permanently exclude the\n /// tensors from disposing, use Exclude for that. Also, excluded tensors don't need to be included\n /// here.\n /// </summary>\n public T DisposeEverythingBut<T>(T keep) where T : IDisposable\n {\n DisposeEverythingBut(new IDisposable[] { keep });\n return keep;\n }\n\n /// <summary>\n /// As an intermediate step, you can dispose all the tensors/disposables currently scheduled for dispose, to\n /// clear up some memory without creating a new scope. Note that this doesn't permanently exclude the\n /// tensors from disposing, use Exclude for that. Also, excluded tensors don't need to be included\n /// here.\n /// </summary>\n public (T1 first, T2 second) DisposeEverythingBut<T1, T2>(T1 first, T2 second)\n where T1 : IDisposable where T2 : IDisposable\n {\n DisposeEverythingBut(new IDisposable[] { first, second });\n return (first, second);\n }\n\n /// <summary>\n /// As an intermediate step, you can dispose all the tensors/disposables currently scheduled for dispose, to\n /// clear up some memory without creating a new scope. Note that this doesn't permanently exclude the\n /// tensors from disposing, use Exclude for that. Also, excluded tensors don't need to be included\n /// here.\n /// </summary>\n public (T1 first, T2 second, T3 third) DisposeEverythingBut<T1, T2, T3>(T1 first, T2 second, T3 third)\n where T1 : IDisposable where T2 : IDisposable where T3 : IDisposable\n {\n DisposeEverythingBut(new IDisposable[] { first, second, third });\n return (first, second, third);\n }\n\n /// <summary>\n /// Disposes of the DisposeScope and all the disposables in its list. You would typically not call this method,\n /// instead you should use a usings clause around the scope.\n /// </summary>\n public void Dispose()\n {\n DisposeEverything();\n _disposeScopeManager.RemoveDisposeScope(this);\n }\n\n /// <summary>\n /// A method that notifies the DisposeScope that a disposable was disposed, so that it can be removed from the\n /// tracked list. This will be called if a tensor is manually disposed, but you can also add your own\n /// disposables to the dispose scope. If you do, and dispose them manually, you should make sure to call this\n /// method.\n /// </summary>\n /// <param name=\"disposable\">The disposable that was disposed</param>\n public void MarkAsDisposed(IDisposable disposable)\n {\n _disposeScopeManager.StatisticsInstance.DisposedInScopeCount++;\n Disposables.Remove(disposable);\n if (disposable is torch.Tensor tensor) {\n tensor.OwningDisposeScope = null;\n }\n }\n\n /// <summary>\n /// Checks if the DisposeScope contains the disposable\n /// </summary>\n /// <param name=\"disposable\">The disposable that's searched for</param>\n /// <returns></returns>\n public bool Contains(IDisposable disposable) => Disposables.Contains(disposable);\n\n private void AddToParent(IDisposable disposable)\n {\n if (OuterScope != null) {\n OuterScope.Disposables.Add(disposable);\n } else {\n _disposeScopeManager.StatisticsInstance.DetachedFromScopeCount++;\n }\n\n if (disposable is torch.Tensor tensor) {\n tensor.OwningDisposeScope = OuterScope;\n }\n }\n }\n}"},{"alpha_fraction":0.6992481350898743,"alphanum_fraction":0.6992481350898743,"avg_line_length":19.538461685180664,"blob_id":"9ee620badae5b6b7cd767e709773fd5390f2d0f0","content_id":"42bd0ef72d3509ab1412d403281dade320d0ef64","detected_licenses":["MIT"],"is_generated":false,"is_vendor":false,"language":"C#","length_bytes":266,"license_type":"permissive","max_line_length":41,"num_lines":13,"path":"/src/TorchSharp/PInvoke/TensorOrScalar.cs","repo_name":"NiklasGustafsson/TorchSharp","src_encoding":"UTF-8","text":"using System;\nusing System.Runtime.InteropServices;\n\nnamespace TorchSharp.PInvoke\n{\n [StructLayout(LayoutKind.Sequential)]\n internal struct TensorOrScalar\n {\n public long TypeCode;\n public long ArrayIndex;\n public IntPtr Handle;\n }\n}"},{"alpha_fraction":0.6620370149612427,"alphanum_fraction":0.6620370149612427,"avg_line_length":23.11111068725586,"blob_id":"ba6474ae9b8689ae28d619a2dc0c38332292a822","content_id":"b70213e1548037a93fee73fab1f754b413015a65","detected_licenses":["MIT"],"is_generated":false,"is_vendor":false,"language":"C#","length_bytes":218,"license_type":"permissive","max_line_length":131,"num_lines":9,"path":"/src/TorchSharp/Tensor/Enums/indexing.cs","repo_name":"NiklasGustafsson/TorchSharp","src_encoding":"UTF-8","text":"// Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information.\nnamespace TorchSharp\n{\n public enum indexing\n {\n xy,\n ij\n }\n}"},{"alpha_fraction":0.593024730682373,"alphanum_fraction":0.6021500825881958,"avg_line_length":41.328041076660156,"blob_id":"4c1a0836e6290ab2b2539895653a6449ae2740fb","content_id":"2b9d44ceb74a59122ca83ac6d124e9295bb24f79","detected_licenses":["MIT"],"is_generated":false,"is_vendor":false,"language":"C#","length_bytes":23999,"license_type":"permissive","max_line_length":200,"num_lines":567,"path":"/src/TorchSharp/netstandard.cs","repo_name":"NiklasGustafsson/TorchSharp","src_encoding":"UTF-8","text":"// Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information.\n\n//\n// This file contains the implementation of functionality exist in .NET and not supported in netstandard.\n// The implementation is supported on Windows only. The goal mainly is to support .NET Framework.\n//\n\nusing System.Collections.Generic;\nusing System.IO;\nusing System.Reflection;\nusing System.Runtime.CompilerServices;\nusing System.Runtime.InteropServices;\n\nnamespace System\n{\n internal static partial class NSExtension\n {\n public static HashSet<TSource> ToHashSet<TSource>(this IEnumerable<TSource> source) => source.ToHashSet(comparer: null);\n\n public static HashSet<TSource> ToHashSet<TSource>(this IEnumerable<TSource> source, IEqualityComparer<TSource> comparer)\n {\n if (source == null)\n {\n throw new ArgumentNullException(nameof(source));\n }\n\n // Don't pre-allocate based on knowledge of size, as potentially many elements will be dropped.\n return new HashSet<TSource>(source, comparer);\n }\n\n public static bool TryAdd<TKey, TValue>(this IDictionary<TKey, TValue> dictionary, TKey key, TValue value)\n {\n if (!dictionary.TryGetValue(key, out _))\n {\n dictionary.Add(key, value);\n return true;\n }\n\n return false;\n }\n\n public static bool Contains(this string source, char c) => !string.IsNullOrEmpty(source) && source.IndexOf(c) >= 0;\n public static bool EndsWith(this string source, char c) => !string.IsNullOrEmpty(source) && source[source.Length - 1] == c;\n public static bool StartsWith(this string source, char c) => !string.IsNullOrEmpty(source) && source[0] == c;\n }\n\n // MathF emulation on platforms which don't support it natively.\n internal static class MathF\n {\n public const float PI = (float)Math.PI;\n\n [MethodImpl(MethodImplOptions.AggressiveInlining)] public static float Max(float x, float y) => Math.Max(x, y);\n [MethodImpl(MethodImplOptions.AggressiveInlining)] public static float Round(float x) => (float)Math.Round(x);\n [MethodImpl(MethodImplOptions.AggressiveInlining)] public static float Sqrt(float x) => (float)Math.Sqrt(x);\n [MethodImpl(MethodImplOptions.AggressiveInlining)] public static int Sign(float x) => Math.Sign(x);\n [MethodImpl(MethodImplOptions.AggressiveInlining)] public static float Abs(float x) => Math.Abs(x);\n [MethodImpl(MethodImplOptions.AggressiveInlining)] public static float Log10(float x) => log10f(x);\n [MethodImpl(MethodImplOptions.AggressiveInlining)] public static float Log2(float x) => log2f(x);\n [MethodImpl(MethodImplOptions.AggressiveInlining)] public static float Acos(float x) => acosf(x);\n [MethodImpl(MethodImplOptions.AggressiveInlining)] public static float Cos(float x) => cosf(x);\n [MethodImpl(MethodImplOptions.AggressiveInlining)] public static float Cosh(float x) => coshf(x);\n [MethodImpl(MethodImplOptions.AggressiveInlining)] public static float Atan(float x) => atanf(x);\n [MethodImpl(MethodImplOptions.AggressiveInlining)] public static float Tanh(float x) => tanhf(x);\n [MethodImpl(MethodImplOptions.AggressiveInlining)] public static float Pow(float x, float y) => powf(x, y);\n [MethodImpl(MethodImplOptions.AggressiveInlining)] public static float Sin(float x) => sinf(x);\n [MethodImpl(MethodImplOptions.AggressiveInlining)] public static float Sinh(float x) => sinhf(x);\n [MethodImpl(MethodImplOptions.AggressiveInlining)] public static float Asinh(float x) => asinhf(x);\n [MethodImpl(MethodImplOptions.AggressiveInlining)] public static float Tan(float x) => tanf(x);\n [MethodImpl(MethodImplOptions.AggressiveInlining)] public static float Acosh(float x) => acoshf(x);\n [MethodImpl(MethodImplOptions.AggressiveInlining)] public static float Atanh(float x) => atanhf(x);\n [MethodImpl(MethodImplOptions.AggressiveInlining)] public static float Asin(float x) => asinf(x);\n [MethodImpl(MethodImplOptions.AggressiveInlining)] public static float Log(float x) => logf(x);\n [MethodImpl(MethodImplOptions.AggressiveInlining)] public static float Exp(float x) => expf(x);\n [MethodImpl(MethodImplOptions.AggressiveInlining)] public static float Floor(float x) => floorf(x);\n [MethodImpl(MethodImplOptions.AggressiveInlining)] public static float Ceiling(float x) => ceilf(x);\n [MethodImpl(MethodImplOptions.AggressiveInlining)] public static float Truncate(float x) => truncf(x);\n\n public static float Log(float x, float y)\n {\n if (float.IsNaN(x))\n {\n return x; // IEEE 754-2008: NaN payload must be preserved\n }\n\n if (float.IsNaN(y))\n {\n return y; // IEEE 754-2008: NaN payload must be preserved\n }\n\n if (y == 1)\n {\n return float.NaN;\n }\n\n if ((x != 1) && ((y == 0) || float.IsPositiveInfinity(y)))\n {\n return float.NaN;\n }\n\n return Log(x) / Log(y);\n }\n\n public static float IEEERemainder(float x, float y)\n {\n if (float.IsNaN(x))\n {\n return x; // IEEE 754-2008: NaN payload must be preserved\n }\n\n if (float.IsNaN(y))\n {\n return y; // IEEE 754-2008: NaN payload must be preserved\n }\n\n float regularMod = x % y;\n\n if (float.IsNaN(regularMod))\n {\n return float.NaN;\n }\n\n if ((regularMod == 0) && IsNegative(x))\n {\n return NegativeZero;\n }\n\n float alternativeResult = (regularMod - (Abs(y) * Sign(x)));\n\n if (Abs(alternativeResult) == Abs(regularMod))\n {\n float divisionResult = x / y;\n float roundedResult = Round(divisionResult);\n\n if (Abs(roundedResult) > Abs(divisionResult))\n {\n return alternativeResult;\n }\n else\n {\n return regularMod;\n }\n }\n\n if (Abs(alternativeResult) < Abs(regularMod))\n {\n return alternativeResult;\n }\n else\n {\n return regularMod;\n }\n }\n\n internal const float NegativeZero = (float)-0.0;\n internal static bool IsNegative(float x) => (((ulong)BitConverter.DoubleToInt64Bits(x)) & 0x8000000000000000) == 0x8000000000000000;\n\n private const string CrtLibrary = \"ucrtbase.dll\";\n [DllImport(CrtLibrary)] private static extern float asinhf(float x);\n [DllImport(CrtLibrary)] private static extern float acosf(float x);\n [DllImport(CrtLibrary)] private static extern float cosf(float x);\n [DllImport(CrtLibrary)] private static extern float coshf(float x);\n [DllImport(CrtLibrary)] private static extern float atanf(float x);\n [DllImport(CrtLibrary)] private static extern float tanhf(float x);\n [DllImport(CrtLibrary)] private static extern float powf(float x, float y);\n [DllImport(CrtLibrary)] private static extern float sinf(float x);\n [DllImport(CrtLibrary)] private static extern float sinhf(float x);\n [DllImport(CrtLibrary)] private static extern float tanf(float x);\n [DllImport(CrtLibrary)] private static extern float logf(float x);\n [DllImport(CrtLibrary)] private static extern float acoshf(float x);\n [DllImport(CrtLibrary)] private static extern float atanhf(float x);\n [DllImport(CrtLibrary)] private static extern float asinf(float x);\n [DllImport(CrtLibrary)] private static extern float log10f(float x);\n [DllImport(CrtLibrary)] private static extern float log2f(float x);\n [DllImport(CrtLibrary)] private static extern float expf(float x);\n [DllImport(CrtLibrary)] private static extern float floorf(float x);\n [DllImport(CrtLibrary)] private static extern float ceilf(float x);\n [DllImport(CrtLibrary)] private static extern float truncf(float x);\n }\n\n internal static partial class NativeLibrary\n {\n [DllImport(\"kernel32.dll\", CharSet=CharSet.Unicode, SetLastError = true)]\n internal static extern IntPtr LoadLibraryEx(string lpFileName, IntPtr hReservedNull, int dwFlags);\n\n public static bool TryLoad(string libraryPath, out IntPtr handle)\n {\n if (!RuntimeInformation.IsOSPlatform(OSPlatform.Windows))\n {\n throw new NotSupportedException(\"This call is not supported on non-Windows platforms.\");\n }\n\n if (libraryPath == null)\n throw new ArgumentNullException(nameof(libraryPath));\n\n handle = LoadFromPath(libraryPath, throwOnError: false);\n return handle != IntPtr.Zero;\n }\n\n public static bool TryLoad(string libraryName, Assembly assembly, DllImportSearchPath? searchPath, out IntPtr handle)\n {\n if (!RuntimeInformation.IsOSPlatform(OSPlatform.Windows))\n {\n throw new NotSupportedException(\"This call is not supported on non-Windows platforms.\");\n }\n\n if (libraryName == null)\n throw new ArgumentNullException(nameof(libraryName));\n if (assembly == null)\n throw new ArgumentNullException(nameof(assembly));\n\n handle = LoadLibraryByName(libraryName,\n assembly,\n searchPath,\n throwOnError: false);\n return handle != IntPtr.Zero;\n }\n\n internal static IntPtr LoadLibraryByName(string libraryName, Assembly assembly, DllImportSearchPath? searchPath, bool throwOnError)\n {\n // First checks if a default dllImportSearchPathFlags was passed in, if so, use that value.\n // Otherwise checks if the assembly has the DefaultDllImportSearchPathsAttribute attribute.\n // If so, use that value.\n\n int searchPathFlags;\n bool searchAssemblyDirectory;\n if (searchPath.HasValue)\n {\n searchPathFlags = (int)(searchPath.Value & ~DllImportSearchPath.AssemblyDirectory);\n searchAssemblyDirectory = (searchPath.Value & DllImportSearchPath.AssemblyDirectory) != 0;\n }\n else\n {\n GetDllImportSearchPathFlags(assembly, out searchPathFlags, out searchAssemblyDirectory);\n }\n\n LoadLibErrorTracker errorTracker = default;\n IntPtr ret = LoadBySearch(assembly, searchAssemblyDirectory, searchPathFlags, ref errorTracker, libraryName);\n if (throwOnError && ret == IntPtr.Zero)\n {\n errorTracker.Throw(libraryName);\n }\n\n return ret;\n }\n\n internal static IntPtr LoadBySearch(Assembly callingAssembly, bool searchAssemblyDirectory, int dllImportSearchPathFlags, ref LoadLibErrorTracker errorTracker, string libraryName)\n {\n IntPtr ret;\n\n int loadWithAlteredPathFlags = 0;\n bool libNameIsRelativePath = !Path.IsPathRooted(libraryName);\n\n foreach (LibraryNameVariation libraryNameVariation in LibraryNameVariation.DetermineLibraryNameVariations(libraryName, libNameIsRelativePath))\n {\n string currLibNameVariation = libraryNameVariation.Prefix + libraryName + libraryNameVariation.Suffix;\n\n if (!libNameIsRelativePath)\n {\n int flags = loadWithAlteredPathFlags;\n if ((dllImportSearchPathFlags & (int)DllImportSearchPath.UseDllDirectoryForDependencies) != 0)\n {\n // LOAD_LIBRARY_SEARCH_DLL_LOAD_DIR is the only flag affecting absolute path. Don't OR the flags\n // unconditionally as all absolute path P/Invokes could then lose LOAD_WITH_ALTERED_SEARCH_PATH.\n flags |= dllImportSearchPathFlags;\n }\n\n ret = LoadLibraryHelper(currLibNameVariation, flags, ref errorTracker);\n if (ret != IntPtr.Zero)\n {\n return ret;\n }\n }\n else if ((callingAssembly != null) && searchAssemblyDirectory)\n {\n // Try to load the module alongside the assembly where the PInvoke was declared.\n // This only makes sense in dynamic scenarios (JIT/interpreter), so leaving this out for now.\n }\n\n ret = LoadLibraryHelper(currLibNameVariation, dllImportSearchPathFlags, ref errorTracker);\n if (ret != IntPtr.Zero)\n {\n return ret;\n }\n }\n\n return IntPtr.Zero;\n }\n\n internal static void GetDllImportSearchPathFlags(Assembly callingAssembly, out int searchPathFlags, out bool searchAssemblyDirectory)\n {\n var searchPath = DllImportSearchPath.AssemblyDirectory;\n\n foreach (CustomAttributeData cad in callingAssembly.CustomAttributes)\n {\n if (cad.AttributeType == typeof(DefaultDllImportSearchPathsAttribute))\n {\n searchPath = (DllImportSearchPath)cad.ConstructorArguments[0].Value!;\n }\n }\n\n searchPathFlags = (int)(searchPath & ~DllImportSearchPath.AssemblyDirectory);\n searchAssemblyDirectory = (searchPath & DllImportSearchPath.AssemblyDirectory) != 0;\n }\n\n private static IntPtr LoadFromPath(string libraryName, bool throwOnError)\n {\n LoadLibErrorTracker errorTracker = default;\n IntPtr ret = LoadLibraryHelper(libraryName, 0, ref errorTracker);\n if (throwOnError && ret == IntPtr.Zero)\n {\n errorTracker.Throw(libraryName);\n }\n\n return ret;\n }\n\n private static IntPtr LoadLibraryHelper(string libraryName, int flags, ref LoadLibErrorTracker errorTracker)\n {\n IntPtr ret = LoadLibraryEx(libraryName, IntPtr.Zero, flags);\n if (ret != IntPtr.Zero)\n {\n return ret;\n }\n\n int lastError = Marshal.GetLastWin32Error();\n if (lastError != LoadLibErrorTracker.ERROR_INVALID_PARAMETER)\n {\n errorTracker.TrackErrorCode(lastError);\n }\n\n return ret;\n }\n\n internal struct LoadLibErrorTracker\n {\n internal const int ERROR_INVALID_PARAMETER = 0x57;\n internal const int ERROR_MOD_NOT_FOUND = 126;\n internal const int ERROR_BAD_EXE_FORMAT = 193;\n\n private int _errorCode;\n\n public void Throw(string libraryName)\n {\n if (_errorCode == ERROR_BAD_EXE_FORMAT)\n {\n throw new BadImageFormatException();\n }\n\n throw new DllNotFoundException($\"Unable to native library '{libraryName}' or one of its dependencies.\");\n }\n\n public void TrackErrorCode(int errorCode)\n {\n _errorCode = errorCode;\n }\n\n }\n\n internal partial struct LibraryNameVariation\n {\n private const string LibraryNameSuffix = \".dll\";\n\n public string Prefix;\n public string Suffix;\n\n public LibraryNameVariation(string prefix, string suffix)\n {\n Prefix = prefix;\n Suffix = suffix;\n }\n\n internal static IEnumerable<LibraryNameVariation> DetermineLibraryNameVariations(string libName, bool isRelativePath, bool forOSLoader = false)\n {\n // This is a copy of the logic in DetermineLibNameVariations in dllimport.cpp in CoreCLR\n\n yield return new LibraryNameVariation(string.Empty, string.Empty);\n\n // Follow LoadLibrary rules if forOSLoader is true\n if (isRelativePath &&\n (!forOSLoader || libName.Contains('.') && !libName.EndsWith('.')) &&\n !libName.EndsWith(\".dll\", StringComparison.OrdinalIgnoreCase) &&\n !libName.EndsWith(\".exe\", StringComparison.OrdinalIgnoreCase))\n {\n yield return new LibraryNameVariation(string.Empty, LibraryNameSuffix);\n }\n }\n }\n }\n}\n\nnamespace System.Diagnostics.CodeAnalysis\n{\n [AttributeUsage(AttributeTargets.Parameter, Inherited = false)]\n internal sealed class MaybeNullWhenAttribute : Attribute\n {\n /// <summary>Initializes the attribute with the specified return value condition.</summary>\n /// <param name=\"returnValue\">\n /// The return value condition. If the method returns this value, the associated parameter may be null.\n /// </param>\n public MaybeNullWhenAttribute(bool returnValue) => ReturnValue = returnValue;\n\n /// <summary>Gets the return value condition.</summary>\n public bool ReturnValue { get; }\n }\n}\n\nnamespace System.Linq\n{\n internal static partial class NSEnumerable\n {\n public static IEnumerable<TResult> Zip<TFirst, TSecond, TResult>(this IEnumerable<TFirst> first, IEnumerable<TSecond> second, Func<TFirst, TSecond, TResult> resultSelector)\n {\n if (first is null)\n {\n throw new ArgumentNullException(nameof(first));\n }\n\n if (second is null)\n {\n throw new ArgumentNullException(nameof(second));\n }\n\n if (resultSelector is null)\n {\n throw new ArgumentNullException(nameof(resultSelector));\n }\n\n return ZipIterator(first, second, resultSelector);\n }\n\n public static IEnumerable<(TFirst First, TSecond Second)> Zip<TFirst, TSecond>(this IEnumerable<TFirst> first, IEnumerable<TSecond> second)\n {\n if (first is null)\n {\n throw new ArgumentNullException(nameof(first));\n }\n\n if (second is null)\n {\n throw new ArgumentNullException(nameof(second));\n }\n\n return ZipIterator(first, second);\n }\n\n public static IEnumerable<(TFirst First, TSecond Second, TThird Third)> Zip<TFirst, TSecond, TThird>(this IEnumerable<TFirst> first, IEnumerable<TSecond> second, IEnumerable<TThird> third)\n {\n if (first is null)\n {\n throw new ArgumentNullException(nameof(first));\n }\n\n if (second is null)\n {\n throw new ArgumentNullException(nameof(second));\n }\n\n if (third is null)\n {\n throw new ArgumentNullException(nameof(third));\n }\n\n return ZipIterator(first, second, third);\n }\n\n private static IEnumerable<(TFirst First, TSecond Second)> ZipIterator<TFirst, TSecond>(IEnumerable<TFirst> first, IEnumerable<TSecond> second)\n {\n using (IEnumerator<TFirst> e1 = first.GetEnumerator())\n using (IEnumerator<TSecond> e2 = second.GetEnumerator())\n {\n while (e1.MoveNext() && e2.MoveNext())\n {\n yield return (e1.Current, e2.Current);\n }\n }\n }\n\n private static IEnumerable<TResult> ZipIterator<TFirst, TSecond, TResult>(IEnumerable<TFirst> first, IEnumerable<TSecond> second, Func<TFirst, TSecond, TResult> resultSelector)\n {\n using (IEnumerator<TFirst> e1 = first.GetEnumerator())\n using (IEnumerator<TSecond> e2 = second.GetEnumerator())\n {\n while (e1.MoveNext() && e2.MoveNext())\n {\n yield return resultSelector(e1.Current, e2.Current);\n }\n }\n }\n\n private static IEnumerable<(TFirst First, TSecond Second, TThird Third)> ZipIterator<TFirst, TSecond, TThird>(IEnumerable<TFirst> first, IEnumerable<TSecond> second, IEnumerable<TThird> third)\n {\n using (IEnumerator<TFirst> e1 = first.GetEnumerator())\n using (IEnumerator<TSecond> e2 = second.GetEnumerator())\n using (IEnumerator<TThird> e3 = third.GetEnumerator())\n {\n while (e1.MoveNext() && e2.MoveNext() && e3.MoveNext())\n {\n yield return (e1.Current, e2.Current, e3.Current);\n }\n }\n }\n }\n}\n\nnamespace System.IO\n{\n internal class NSPath\n {\n internal const char DirectorySeparatorChar = '\\\\'; // Windows implementation\n internal const char AltDirectorySeparatorChar = '/';\n internal const string DirectorySeparatorCharAsString = \"\\\\\";\n\n public static string Join(string path1, string path2)\n {\n if (path1 is null || path1.Length == 0)\n return path2;\n if (path2 is null || path2.Length == 0)\n return path1;\n\n bool hasSeparator = IsDirectorySeparator(path1[path1.Length - 1]) || IsDirectorySeparator(path2[0]);\n return hasSeparator ? string.Concat(path1, path2) : string.Concat(path1, DirectorySeparatorCharAsString, path2);\n }\n\n [MethodImpl(MethodImplOptions.AggressiveInlining)]\n internal static bool IsDirectorySeparator(char c) => c == DirectorySeparatorChar || c == AltDirectorySeparatorChar;\n\n public static string Join(string path1, string path2, string path3)\n {\n if (path1 is null || path1.Length == 0)\n return Join(path2, path3);\n\n if (path2 is null || path2.Length == 0)\n return Join(path1, path3);\n\n if (path3 is null || path3.Length == 0)\n return Join(path1, path2);\n\n bool firstHasSeparator = IsDirectorySeparator(path1[path1.Length - 1]) || IsDirectorySeparator(path2[0]);\n bool secondHasSeparator = IsDirectorySeparator(path2[path2.Length - 1]) || IsDirectorySeparator(path3[0]);\n return path1 + (firstHasSeparator ? \"\" : DirectorySeparatorCharAsString) + path2 + (secondHasSeparator ? \"\" : DirectorySeparatorCharAsString) + path3;\n }\n\n public static string Join(string path1, string path2, string path3, string path4)\n {\n if (path1 is null || path1.Length == 0)\n return Join(path2, path3, path4);\n\n if (path2 is null || path2.Length == 0)\n return Join(path1, path3, path4);\n\n if (path3 is null || path3.Length == 0)\n return Join(path1, path2, path4);\n\n if (path4 is null || path4.Length == 0)\n return Join(path1, path2, path3);\n\n bool firstHasSeparator = IsDirectorySeparator(path1[path1.Length - 1]) || IsDirectorySeparator(path2[0]);\n bool secondHasSeparator = IsDirectorySeparator(path2[path2.Length - 1]) || IsDirectorySeparator(path3[0]);\n bool thirdHasSeparator = IsDirectorySeparator(path3[path3.Length - 1]) || IsDirectorySeparator(path4[0]);\n\n return path1 + (firstHasSeparator ? \"\" : DirectorySeparatorCharAsString) +\n path2 + (secondHasSeparator ? \"\" : DirectorySeparatorCharAsString) +\n path3 + (thirdHasSeparator ? \"\" : DirectorySeparatorCharAsString) +\n path4;\n }\n }\n}"},{"alpha_fraction":0.5804773569107056,"alphanum_fraction":0.5829253196716309,"avg_line_length":46.36231994628906,"blob_id":"11ef884f5387b362ebf9ed98ddb722bd0f865f42","content_id":"70a1858f09b10a684bd1d33112f2b9cd30986327","detected_licenses":["MIT"],"is_generated":false,"is_vendor":false,"language":"C#","length_bytes":6540,"license_type":"permissive","max_line_length":149,"num_lines":138,"path":"/src/TorchSharp/Distributions/Gamma.cs","repo_name":"NiklasGustafsson/TorchSharp","src_encoding":"UTF-8","text":"// Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information.\nusing System;\nusing System.Collections.Generic;\nusing static TorchSharp.torch;\n\nnamespace TorchSharp\n{\n using Modules;\n\n namespace Modules\n {\n /// <summary>\n /// A Gamma distribution parameterized by shape `concentration` and `rate`.\n /// </summary>\n public class Gamma : torch.distributions.ExponentialFamily\n {\n /// <summary>\n /// The mean of the distribution.\n /// </summary>\n public override Tensor mean => WrappedTensorDisposeScope(() => concentration / rate);\n\n public override Tensor mode => WrappedTensorDisposeScope(() => ((concentration - 1) / rate).clamp_(min: 0));\n\n /// <summary>\n /// The variance of the distribution\n /// </summary>\n public override Tensor variance => WrappedTensorDisposeScope(() => concentration / rate.pow(2));\n\n /// <summary>\n /// Constructor\n /// </summary>\n /// <param name=\"concentration\">Shape parameter of the distribution (often referred to as 'α')</param>\n /// <param name=\"rate\">rate = 1 / scale of the distribution (often referred to as 'β')</param>\n /// <param name=\"generator\">An optional random number generator object.</param>\n public Gamma(Tensor concentration, Tensor rate, torch.Generator generator = null) : base(generator)\n {\n var locScale = torch.broadcast_tensors(concentration, rate);\n this.concentration = locScale[0].DetachFromDisposeScope();\n this.rate = locScale[1].DetachFromDisposeScope();\n this.batch_shape = this.concentration.size();\n }\n\n protected Tensor concentration;\n private Tensor rate;\n\n /// <summary>\n /// Generates a sample_shape shaped reparameterized sample or sample_shape shaped batch of reparameterized samples\n /// if the distribution parameters are batched.\n /// </summary>\n /// <param name=\"sample_shape\">The sample shape.</param>\n public override Tensor rsample(params long[] sample_shape)\n {\n using var _ = torch.NewDisposeScope();\n var shape = ExtendedShape(sample_shape);\n var value = torch._standard_gamma(concentration.expand(shape), generator: generator) / rate.expand(shape);\n return value.detach().clamp_(min: torch.finfo(value.dtype).tiny).MoveToOuterDisposeScope();\n }\n\n /// <summary>\n /// Returns the log of the probability density/mass function evaluated at `value`.\n /// </summary>\n /// <param name=\"value\"></param>\n public override Tensor log_prob(Tensor value)\n {\n using var _ = torch.NewDisposeScope();\n value = torch.as_tensor(value, dtype: rate.dtype, device: rate.device);\n var result = concentration * rate.log() + (concentration - 1) * value.log() - rate * value - torch.lgamma(concentration);\n return result.MoveToOuterDisposeScope();\n }\n\n /// <summary>\n /// Returns entropy of distribution, batched over batch_shape.\n /// </summary>\n public override Tensor entropy() =>\n WrappedTensorDisposeScope(() =>\n concentration - rate.log() + concentration.lgamma() + (1.0 - concentration) * concentration.digamma());\n\n public override Tensor cdf(Tensor value) =>\n WrappedTensorDisposeScope(() => torch.special.gammainc(concentration, rate * value));\n\n /// <summary>\n /// Returns a new distribution instance (or populates an existing instance provided by a derived class) with batch dimensions expanded to\n /// `batch_shape`. This method calls `torch.Tensor.expand()` on the distribution's parameters. As such, this does not allocate new\n /// memory for the expanded distribution instance.\n /// </summary>\n /// <param name=\"batch_shape\">Tthe desired expanded size.</param>\n /// <param name=\"instance\">new instance provided by subclasses that need to override `.expand`.</param>\n public override distributions.Distribution expand(Size batch_shape, distributions.Distribution instance = null)\n {\n if (instance != null && !(instance is Gamma))\n throw new ArgumentException(\"expand(): 'instance' must be a Gamma distribution\");\n\n var c = concentration.expand(batch_shape);\n var r = rate.expand(batch_shape);\n\n var newDistribution = ((instance == null) ? new Gamma(c, r, generator) : instance) as Gamma;\n\n newDistribution.batch_shape = batch_shape;\n if (newDistribution == instance) {\n newDistribution.concentration = c;\n newDistribution.rate = r;\n }\n return newDistribution;\n }\n\n protected override IList<Tensor> NaturalParams => new Tensor[] { concentration - 1, rate };\n\n protected override Tensor MeanCarrierMeasure => torch.tensor(0, dtype:rate.dtype, device: rate.device);\n\n protected override Tensor LogNormalizer(params Tensor[] parameters)\n {\n var x = parameters[0];\n var y = parameters[1];\n\n return torch.lgamma(x + 1) + (x + 1) * torch.log(-y.reciprocal());\n }\n }\n\n }\n\n public static partial class torch\n {\n public static partial class distributions\n {\n /// <summary>\n /// Creates a Gamma distribution parameterized by shape `concentration` and `rate`.\n /// </summary>\n /// <param name=\"concentration\">Shape parameter of the distribution (often referred to as 'α')</param>\n /// <param name=\"rate\">rate = 1 / scale of the distribution (often referred to as 'β')</param>\n /// <param name=\"generator\">An optional random number generator object.</param>\n /// <returns></returns>\n public static Gamma Gamma(Tensor concentration, Tensor rate, torch.Generator generator = null)\n {\n return new Gamma(concentration, rate, generator);\n }\n }\n }\n}\n"},{"alpha_fraction":0.5351672172546387,"alphanum_fraction":0.5367302298545837,"avg_line_length":38.0121955871582,"blob_id":"e14378277b260b3c9865dfdbf5012e4192c33148","content_id":"9fb1d3d6e9980d8f133772689517c9375eb13e77","detected_licenses":["MIT"],"is_generated":false,"is_vendor":false,"language":"C#","length_bytes":3199,"license_type":"permissive","max_line_length":132,"num_lines":82,"path":"/src/TorchSharp/NN/Activation/PReLU.cs","repo_name":"NiklasGustafsson/TorchSharp","src_encoding":"UTF-8","text":"// Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information.\nusing System;\nusing static TorchSharp.torch;\nusing static TorchSharp.PInvoke.NativeMethods;\n\n#nullable enable\nnamespace TorchSharp\n{\n using Modules;\n\n namespace Modules\n {\n /// <summary>\n /// This class is used to represent a PReLU module.\n /// </summary>\n public sealed class PReLU : torch.nn.Module<Tensor, Tensor>\n {\n internal PReLU(IntPtr handle, IntPtr boxedHandle) : base(handle, boxedHandle) { }\n\n public override Tensor forward(Tensor tensor)\n {\n var res = THSNN_PReLU_forward(handle, tensor.Handle);\n if (res == IntPtr.Zero) { torch.CheckForErrors(); }\n return new Tensor(res);\n }\n\n public override string GetName()\n {\n return typeof(PReLU).Name;\n }\n\n public Parameter? weight {\n get {\n var res = THSNN_PReLU_weight(handle);\n if (res == IntPtr.Zero) { torch.CheckForErrors(); }\n return (res == IntPtr.Zero) ? null : new Parameter(res);\n }\n set {\n THSNN_PReLU_set_weight(handle, value!.Handle);\n torch.CheckForErrors();\n ConditionallyRegisterParameter(\"weight\", value);\n }\n }\n }\n }\n\n public static partial class torch\n {\n public static partial class nn\n {\n /// <summary>\n /// Parameterized Rectified Linear Unit\n /// </summary>\n /// <param name=\"num_parameters\">\n /// Number of 'a' to learn.\n /// Although it takes an int as input, there is only two values are legitimate: 1, or the number of channels at input.\n /// </param>\n /// <param name=\"init\">The initial value of 'a'.</param>\n /// <param name=\"device\">The desired device of the parameters and buffers in this module</param>\n /// <param name=\"dtype\">The desired floating point or complex dtype of the parameters and buffers in this module</param>\n public static PReLU PReLU(long num_parameters, double init = 0.25, Device? device = null, ScalarType? dtype = null)\n {\n var handle = THSNN_PReLU_ctor(num_parameters, init, out var boxedHandle);\n if (handle == IntPtr.Zero) { torch.CheckForErrors(); }\n return new PReLU(handle, boxedHandle).MoveModule<PReLU>(device, dtype);\n }\n\n public static partial class functional\n {\n /// <summary>\n /// Parameterized Rectified Linear Unit\n /// </summary>\n /// <param name=\"input\">The input tensor</param>\n /// <param name=\"weight\">Weight is expected to be a scalar or 1-D tensor.</param>\n public static Tensor prelu(Tensor input, Tensor weight)\n {\n return input.prelu(weight);\n }\n }\n }\n }\n}\n"},{"alpha_fraction":0.5407118201255798,"alphanum_fraction":0.5411993861198425,"avg_line_length":31.55555534362793,"blob_id":"60e9be32a739f1f7d85ad6a9c8d8a2ad42d85422","content_id":"8027843562e85e3f9a6f5fcab8fb4ee4cd531345","detected_licenses":["MIT"],"is_generated":false,"is_vendor":false,"language":"C#","length_bytes":2051,"license_type":"permissive","max_line_length":130,"num_lines":63,"path":"/src/TorchSharp/NN/Activation/LogSigmoid.cs","repo_name":"NiklasGustafsson/TorchSharp","src_encoding":"UTF-8","text":"// Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information.\nusing System;\nusing static TorchSharp.torch;\nusing static TorchSharp.PInvoke.NativeMethods;\n\nnamespace TorchSharp\n{\n using Modules;\n\n namespace Modules\n {\n /// <summary>\n /// This class is used to represent a LogSigmoid module.\n /// </summary>\n public sealed class LogSigmoid : torch.nn.Module<Tensor, Tensor>\n {\n internal LogSigmoid() : base(nameof(LogSigmoid)) { }\n\n public override Tensor forward(Tensor tensor)\n {\n return tensor.log_sigmoid();\n }\n\n public override string GetName()\n {\n return typeof(LogSigmoid).Name;\n }\n\n // Rather than spending cycles only to discover that this module has neither\n // parameters nor buffers, just shortcut the move completely.\n protected internal override nn.Module _to(Device device, ScalarType dtype) => this;\n protected internal override nn.Module _to(DeviceType deviceType, int deviceIndex = -1) => this;\n protected internal override nn.Module _to(ScalarType dtype) => this;\n }\n }\n public static partial class torch\n {\n public static partial class nn\n {\n /// <summary>\n /// LogSigmoid activation\n /// </summary>\n /// <returns></returns>\n public static LogSigmoid LogSigmoid()\n {\n return new LogSigmoid();\n }\n\n public static partial class functional\n {\n /// <summary>\n /// LogSigmoid activation\n /// </summary>\n /// <param name=\"x\">The input tensor</param>\n /// <returns></returns>\n public static Tensor logsigmoid(Tensor x)\n {\n return x.log_sigmoid();\n }\n }\n }\n }\n}\n"},{"alpha_fraction":0.5108544230461121,"alphanum_fraction":0.518925666809082,"avg_line_length":41.779762268066406,"blob_id":"98d550e3f7aecff71f2ae88945c3e5c0d3a88bc3","content_id":"a5eefcd5ecfff5b3f3d13fcd38f79aa7793a899b","detected_licenses":["MIT"],"is_generated":false,"is_vendor":false,"language":"C#","length_bytes":7186,"license_type":"permissive","max_line_length":178,"num_lines":168,"path":"/src/TorchAudio/Transforms/MelSpectrogram.cs","repo_name":"NiklasGustafsson/TorchSharp","src_encoding":"UTF-8","text":"// Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information.\n\n// A number of implementation details in this file have been translated from the Python version of torchaudio,\n// largely located in the files found in this folder:\n//\n// https://github.com/pytorch/audio/blob/bb77cbebb620a46fdc0dc7e6dae2253eef3f37e2/torchaudio/transforms/_transforms.py\n//\n// The origin has the following copyright notice and license:\n//\n// https://github.com/pytorch/audio/blob/main/LICENSE\n//\n\nusing System;\nusing static TorchSharp.torch;\nusing static TorchSharp.torch.nn;\nusing static TorchSharp.torchaudio;\n\nnamespace TorchSharp\n{\n namespace Transforms\n {\n public class MelSpectrogram : Module<Tensor, Tensor>\n {\n public readonly long sample_rate;\n public readonly long n_fft;\n public readonly long win_length;\n public readonly long hop_length;\n public readonly long pad;\n public readonly double power;\n public readonly bool normalized;\n public readonly long n_mels;\n public readonly double? f_max;\n public readonly double f_min;\n public readonly Spectrogram spectrogram;\n public readonly MelScale mel_scale;\n\n protected override void Dispose(bool disposing)\n {\n if (disposing) {\n spectrogram.Dispose();\n mel_scale.Dispose();\n }\n base.Dispose(disposing);\n }\n\n\n internal MelSpectrogram(\n string name,\n long sample_rate = 16000,\n long n_fft = 400,\n long? win_length = null,\n long? hop_length = null,\n double f_min = 0.0,\n double? f_max = null,\n long pad = 0,\n long n_mels = 128,\n WindowFunction window_fn = null,\n double power = 2.0,\n bool normalized = false,\n bool center = true,\n PaddingModes pad_mode = PaddingModes.Reflect,\n bool onesided = true,\n MelNorm norm = MelNorm.none,\n torchaudio.MelScale mel_scale = torchaudio.MelScale.htk) : base(name)\n {\n this.sample_rate = sample_rate;\n this.n_fft = n_fft;\n this.win_length = win_length ?? n_fft;\n this.hop_length = hop_length ?? (this.win_length / 2);\n this.pad = pad;\n this.power = power;\n this.normalized = normalized;\n this.n_mels = n_mels; // number of mel frequency bins\n this.f_max = f_max;\n this.f_min = f_min;\n this.spectrogram = torchaudio.transforms.Spectrogram(\n n_fft: this.n_fft,\n win_length: this.win_length,\n hop_length: this.hop_length,\n pad: this.pad,\n window_fn: window_fn,\n power: this.power,\n normalized: this.normalized,\n center: center,\n pad_mode: pad_mode,\n onesided: onesided);\n this.mel_scale = torchaudio.transforms.MelScale(\n this.n_mels, this.sample_rate, this.f_min, this.f_max, this.n_fft / 2 + 1, norm, mel_scale\n );\n this.RegisterComponents();\n }\n\n /// <param name=\"waveform\">Tensor of audio of dimension (..., time).</param>\n /// <returns>Mel frequency spectrogram of size (..., ``n_mels``, time).</returns>\n public override Tensor forward(Tensor waveform)\n {\n var specgram = this.spectrogram.call(waveform);\n var mel_specgram = this.mel_scale.call(specgram);\n return mel_specgram;\n }\n }\n }\n\n public partial class torchaudio\n {\n public partial class transforms\n {\n /// <summary>\n /// Create MelSpectrogram for a raw audio signal.\n /// </summary>\n /// <param name=\"sample_rate\">Sample rate of audio signal.</param>\n /// <param name=\"n_fft\">Size of FFT, creates ``n_fft / 2 + 1`` bins.</param>\n /// <param name=\"win_length\">Window size.</param>\n /// <param name=\"hop_length\">Length of hop between STFT windows.</param>\n /// <param name=\"f_min\">Minimum frequency.</param>\n /// <param name=\"f_max\">Maximum frequency.</param>\n /// <param name=\"pad\">Two sided padding of signal.</param>\n /// <param name=\"n_mels\">Number of mel filterbanks.</param>\n /// <param name=\"window_fn\">A function to create a window tensor that is applied/multiplied to each frame/window.</param>\n /// <param name=\"power\">Exponent for the magnitude spectrogram, (must be > 0) e.g., 1 for energy, 2 for power, etc.</param>\n /// <param name=\"normalized\">Whether to normalize by magnitude after stft.</param>\n /// <param name=\"center\">whether to pad :attr:`waveform` on both sides so that the :math:`t`-th frame is centered at time :math:`t \\times \\text{hop\\_length}`.</param>\n /// <param name=\"pad_mode\">controls the padding method used when :attr:`center` is ``true``.</param>\n /// <param name=\"onesided\">controls whether to return half of results to avoid redundancy.</param>\n /// <param name=\"norm\">If \"slaney\", divide the triangular mel weights by the width of the mel band (area normalization).</param>\n /// <param name=\"mel_scale\">Scale to use: ``htk`` or ``slaney``.</param>\n /// <returns></returns>\n\n public static Transforms.MelSpectrogram MelSpectrogram(\n long sample_rate = 16000,\n long n_fft = 400,\n long? win_length = null,\n long? hop_length = null,\n double f_min = 0.0,\n double? f_max = null,\n long pad = 0,\n long n_mels = 128,\n WindowFunction window_fn = null,\n double power = 2.0,\n bool normalized = false,\n bool center = true,\n PaddingModes pad_mode = PaddingModes.Reflect,\n bool onesided = true,\n MelNorm norm = MelNorm.none,\n torchaudio.MelScale mel_scale = torchaudio.MelScale.htk)\n {\n return new Transforms.MelSpectrogram(\n \"MelSpectrogram\",\n sample_rate,\n n_fft,\n win_length,\n hop_length,\n f_min,\n f_max,\n pad,\n n_mels,\n window_fn,\n power,\n normalized,\n center,\n pad_mode,\n onesided,\n norm,\n mel_scale);\n }\n }\n }\n}"},{"alpha_fraction":0.5135135054588318,"alphanum_fraction":0.5147420167922974,"avg_line_length":22.97058868408203,"blob_id":"891957cd125641035954ca760ce019810d3e1475","content_id":"46d408579eddb92837b0832097acd0a054c8be7f","detected_licenses":["MIT"],"is_generated":false,"is_vendor":false,"language":"C#","length_bytes":814,"license_type":"permissive","max_line_length":130,"num_lines":34,"path":"/src/TorchVision/VerticalFlip.cs","repo_name":"NiklasGustafsson/TorchSharp","src_encoding":"UTF-8","text":"// Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information.\n\nusing static TorchSharp.torch;\n\n\nnamespace TorchSharp\n{\n public static partial class torchvision\n {\n internal class VerticalFlip : ITransform\n {\n internal VerticalFlip()\n {\n }\n\n public Tensor call(Tensor input)\n {\n return input.flip(-2);\n }\n }\n\n public static partial class transforms\n {\n /// <summary>\n /// Flip the image vertically.\n /// </summary>\n /// <returns></returns>\n static public ITransform VerticalFlip()\n {\n return new VerticalFlip();\n }\n }\n }\n}"},{"alpha_fraction":0.5217657685279846,"alphanum_fraction":0.5223789215087891,"avg_line_length":31,"blob_id":"f09a9531aecef0e8c398851d5a03b15d5a9fb34a","content_id":"6d5b77751166e59c07d9554757a7d150c5bfcc24","detected_licenses":["MIT"],"is_generated":false,"is_vendor":false,"language":"C#","length_bytes":1631,"license_type":"permissive","max_line_length":130,"num_lines":51,"path":"/src/TorchVision/Resize.cs","repo_name":"NiklasGustafsson/TorchSharp","src_encoding":"UTF-8","text":"// Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information.\n\nusing static TorchSharp.torch;\n\nnamespace TorchSharp\n{\n public static partial class torchvision\n {\n internal class Resize : ITransform\n {\n internal Resize(int height, int width, int? maxSize)\n {\n this.height = height;\n this.width = width;\n this.maxSize = maxSize;\n }\n\n public Tensor call(Tensor input)\n {\n return transforms.functional.resize(input, height, width, maxSize);\n }\n\n private int height, width;\n private int? maxSize;\n }\n\n public static partial class transforms\n {\n /// <summary>\n /// Resize the input image to the given size.\n /// </summary>\n /// <param name=\"height\">Desired output height</param>\n /// <param name=\"width\">Desired output width</param>\n /// <returns></returns>\n static public ITransform Resize(int height, int width)\n {\n return new Resize(height, width, null);\n }\n\n /// <summary>\n /// Resize the input image to the given size.\n /// </summary>\n /// <param name=\"size\">Desired output size</param>\n /// <param name=\"maxSize\">Max size</param>\n static public ITransform Resize(int size, int? maxSize = null)\n {\n return new Resize(size, -1, maxSize);\n }\n }\n }\n}"},{"alpha_fraction":0.6404391527175903,"alphanum_fraction":0.6462336182594299,"avg_line_length":47.955223083496094,"blob_id":"67102487ac147824f8960fc83ee8e8387ae7e2d4","content_id":"55cb1bf196fd09b43ce0664645d063eb3a78a7c0","detected_licenses":["MIT"],"is_generated":false,"is_vendor":false,"language":"C#","length_bytes":3281,"license_type":"permissive","max_line_length":152,"num_lines":67,"path":"/src/TorchSharp/Tensor/torch.Tensors.cs","repo_name":"NiklasGustafsson/TorchSharp","src_encoding":"UTF-8","text":"// Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information.\n#nullable enable\nusing System;\nusing System.Diagnostics.Contracts;\n\nnamespace TorchSharp\n{\n // https://pytorch.org/docs/stable/torch#tensors\n public static partial class torch\n {\n // https://pytorch.org/docs/stable/generated/torch.is_tensor\n [Pure]public static bool is_tensor(object obj) => obj is Tensor;\n\n // https://pytorch.org/docs/stable/generated/torch.is_storage\n [Pure]public static bool is_storage(object obj) => obj is Storage;\n\n // https://pytorch.org/docs/stable/generated/torch.is_complex\n /// <summary>\n /// Returns True if the data type of input is a complex data type i.e., one of torch.complex64, and torch.complex128.\n /// </summary>\n /// <param name=\"input\">The input tensor</param>\n public static bool is_complex(Tensor input) => is_complex(input.dtype);\n\n // https://pytorch.org/docs/stable/generated/torch.is_floating_point\n /// <summary>\n /// Returns True if the data type of input is a floating point data type.\n /// </summary>\n /// <param name=\"input\">The input tensor</param>\n public static bool is_floating_point(Tensor input) => is_floating_point(input.dtype);\n\n // https://pytorch.org/docs/stable/generated/torch.is_nonzero\n /// <summary>\n /// Returns True if the input is a single element tensor which is not equal to zero after type conversions,\n /// i.e. not equal to torch.tensor([0.]) or torch.tensor([0]) or torch.tensor([False]).\n /// Throws an InvalidOperationException if torch.numel() != 1.\n /// </summary>\n /// <param name=\"input\">The input tensor</param>\n public static bool is_nonzero(Tensor input) => input.is_nonzero();\n\n // https://pytorch.org/docs/stable/generated/torch.set_default_dtype\n /// <summary>\n /// Sets the default floating point dtype to d. This dtype is:\n /// 1. The inferred dtype for python floats in torch.tensor().\n /// 2. Used to infer dtype for python complex numbers.\n /// The default complex dtype is set to torch.complex128 if default floating point dtype is torch.float64, otherwise it’s set to torch.complex64\n /// The default floating point dtype is initially torch.float32.\n /// </summary>\n /// <param name=\"dtype\"></param>\n public static void set_default_dtype(ScalarType dtype) { default_dtype = dtype; }\n\n // https://pytorch.org/docs/stable/generated/torch.get_default_dtype\n /// <summary>\n /// Get the current default floating point torch.dtype.\n /// </summary>\n /// <returns></returns>\n [Pure]public static ScalarType get_default_dtype() => default_dtype;\n\n // https://pytorch.org/docs/stable/generated/torch.set_default_tensor_type\n public static void set_default_tensor_type(Tensor t) => set_default_dtype(t.dtype);\n\n // https://pytorch.org/docs/stable/generated/torch.numel\n /// <summary>\n /// Get the number of elements in the input tensor.\n /// </summary>\n [Pure]public static long numel(Tensor input) => input.numel();\n }\n}"},{"alpha_fraction":0.5494467616081238,"alphanum_fraction":0.5608575344085693,"avg_line_length":32.627906799316406,"blob_id":"50b79906d4b177a753d089e6f7e0aa0e7edf69d5","content_id":"087aab7b722133307ceac79301aad328fd9c28bd","detected_licenses":["MIT"],"is_generated":false,"is_vendor":false,"language":"C#","length_bytes":2892,"license_type":"permissive","max_line_length":130,"num_lines":86,"path":"/src/TorchAudio/Transforms/GriffinLim.cs","repo_name":"NiklasGustafsson/TorchSharp","src_encoding":"UTF-8","text":"// Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information.\nusing System;\nusing static TorchSharp.torch;\nusing static TorchSharp.torchaudio;\n\n// A number of implementation details in this file have been translated from the Python version of torchaudio,\n// largely located in the files found in this folder:\n//\n// https://github.com/pytorch/audio/blob/bb77cbebb620a46fdc0dc7e6dae2253eef3f37e2/torchaudio/transforms/_transforms.py\n//\n// The origin has the following copyright notice and license:\n//\n// https://github.com/pytorch/audio/blob/main/LICENSE\n//\n\nnamespace TorchSharp.Transforms\n{\n public sealed class GriffinLim : nn.Module<Tensor, Tensor>, ITransform\n {\n public readonly long n_fft;\n public readonly int n_iter;\n public readonly long win_length;\n public readonly long hop_length;\n public readonly Tensor window;\n public readonly long? length;\n public readonly double power;\n public readonly double momentum;\n public readonly bool rand_init;\n\n protected override void Dispose(bool disposing)\n {\n if (disposing) {\n window.Dispose();\n }\n base.Dispose(disposing);\n }\n\n internal GriffinLim(\n string name,\n long n_fft = 400,\n int n_iter = 32,\n long? win_length = null,\n long? hop_length = null,\n WindowFunction window_fn = null,\n double power = 2.0,\n double momentum = 0.99,\n long? length = null,\n bool rand_init = true) : base(name)\n {\n if (momentum < 0 || 1 <= momentum) {\n throw new ArgumentOutOfRangeException($\"momentum must be in the range [0, 1). Found: {momentum}\");\n }\n\n this.n_fft = n_fft;\n this.n_iter = n_iter;\n this.win_length = win_length ?? n_fft;\n this.hop_length = hop_length ?? this.win_length / 2;\n if (window_fn != null) {\n this.window = window_fn(this.win_length);\n } else {\n this.window = torch.hann_window(this.win_length);\n }\n this.register_buffer(\"window\", this.window);\n this.length = length;\n this.power = power;\n this.momentum = momentum;\n this.rand_init = rand_init;\n }\n\n public override Tensor forward(Tensor specgram)\n {\n return torchaudio.functional.griffinlim(\n specgram,\n this.window,\n this.n_fft,\n this.hop_length,\n this.win_length,\n this.power,\n this.n_iter,\n this.momentum,\n this.length,\n this.rand_init\n );\n }\n }\n}\n"},{"alpha_fraction":0.5665773749351501,"alphanum_fraction":0.5825388431549072,"avg_line_length":50.86111068725586,"blob_id":"cefc3a6e112ef56095866801bcb6099e14efe450","content_id":"2aece98d00a06c7fc674dd709d4f24f128dd8641","detected_licenses":["MIT"],"is_generated":false,"is_vendor":false,"language":"C#","length_bytes":9335,"license_type":"permissive","max_line_length":182,"num_lines":180,"path":"/src/TorchVision/Ops/DropBlock.cs","repo_name":"NiklasGustafsson/TorchSharp","src_encoding":"UTF-8","text":"// Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information.\n\n// A number of implementation details in this file have been translated from the Python version of torchvision,\n// largely located in the files found in this folder:\n//\n// https://github.com/pytorch/vision/blob/a4f53308b2d0f1aa9191686e326f45c26053f686/torchvision/ops/drop_block.py\n//\n// The origin has the following copyright notice and license:\n//\n// https://github.com/pytorch/vision/blob/main/LICENSE\n//\n\nusing System;\nusing System.Collections.Generic;\nusing System.Reflection;\nusing System.Runtime.CompilerServices;\nusing TorchSharp.Modules;\nusing static TorchSharp.torch;\n\nnamespace TorchSharp\n{\n public static partial class torchvision\n {\n\n public static partial class ops\n {\n /// <summary>\n /// Implements DropBlock2d from `\"DropBlock: A regularization method for convolutional networks\"\n /// https://arxiv.org/abs/1810.12890\n /// </summary>\n /// <param name=\"input\">The input tensor of 4 dimensions with the first one beinng its batch.</param>\n /// <param name=\"p\">Probability of an element to be dropped.</param>\n /// <param name=\"block_size\">Size of the block to drop.</param>\n /// <param name=\"inplace\">If set to true, will do this operation in-place</param>\n /// <param name=\"eps\">A small value added to the denominator for numerical stability.</param>\n /// <param name=\"training\">Apply dropblock if is true</param>\n /// <returns>The randomly zeroed [N, C, H, W] tensor after dropblock</returns>\n /// <exception cref=\"ArgumentOutOfRangeException\">If p is not in the range [0,1]</exception>\n /// <exception cref=\"ArgumentException\">If the input tensor is not 4-dimensional</exception>\n public static Tensor drop_block2d(Tensor input, double p, long block_size, bool inplace = false, double eps = 1e-6, bool training = true)\n {\n if (p < 0 || p > 1) throw new ArgumentOutOfRangeException(nameof(p));\n if (input.ndim != 4) throw new ArgumentException($\"input should be 4 dimensional. Got {input.ndim} dimensions.\");\n if (!training || p == 0) return input.alias();\n\n var (N, C, H, W) = (input.shape[0], input.shape[1], input.shape[2], input.shape[3]);\n\n block_size = Math.Min(block_size, Math.Min(W, H));\n // compute the gamma of Bernoulli distribution\n var gamma = (p * H * W) / ((block_size * block_size) * ((H - block_size + 1) * (W - block_size + 1)));\n var noise = torch.empty(N, C, H - block_size + 1, W - block_size + 1, dtype: input.dtype, device: input.device);\n noise.bernoulli_(gamma);\n\n var pad = block_size / 2;\n noise = torch.nn.functional.pad(noise, (pad, pad, pad, pad), value: 0);\n noise = torch.nn.functional.max_pool2d(noise, stride: 1, kernelSize: block_size, padding: block_size / 2);\n noise = 1 - noise;\n\n var normalize_scale = noise.numel() / (eps + noise.sum());\n if (inplace)\n input.mul_(noise).mul_(normalize_scale);\n else\n input = input * noise * normalize_scale;\n return input;\n }\n\n /// <summary>\n /// Implements DropBlock3d from `\"DropBlock: A regularization method for convolutional networks\"\n /// https://arxiv.org/abs/1810.12890\n /// </summary>\n /// <param name=\"input\">The input tensor of 5 dimensions with the first one beinng its batch.</param>\n /// <param name=\"p\">Probability of an element to be dropped.</param>\n /// <param name=\"block_size\">Size of the block to drop.</param>\n /// <param name=\"inplace\">If set to true, will do this operation in-place</param>\n /// <param name=\"eps\">A small value added to the denominator for numerical stability.</param>\n /// <param name=\"training\">Apply dropblock if is true</param>\n /// <returns>The randomly zeroed [N, C, D, H, W] tensor after dropblock</returns>\n /// <exception cref=\"ArgumentOutOfRangeException\">If p is not in the range [0,1]</exception>\n /// <exception cref=\"ArgumentException\">If the input tensor is not 5-dimensional</exception>\n public static Tensor drop_block3d(Tensor input, double p, long block_size, bool inplace = false, double eps = 1e-6, bool training = true)\n {\n if (p < 0 || p > 1) throw new ArgumentOutOfRangeException(nameof(p));\n if (input.ndim != 5) throw new ArgumentException($\"input should be 5-dimensional. Got {input.ndim} dimensions.\");\n if (!training || p == 0) return input.alias();\n\n var (N, C, D, H, W) = (input.shape[0], input.shape[1], input.shape[2], input.shape[3], input.shape[4]);\n\n block_size = Math.Min(Math.Min(block_size, D), Math.Min(W, H));\n // compute the gamma of Bernoulli distribution\n var gamma = (p * D * H * W) / ((block_size * block_size * block_size) * ((D - block_size + 1) * (H - block_size + 1) * (W - block_size + 1)));\n var noise = torch.empty(new[] { N, C, D - block_size + 1, H - block_size + 1, W - block_size + 1 }, dtype: input.dtype, device: input.device);\n noise.bernoulli_(gamma);\n\n var pad = block_size / 2;\n var padding = new[] { pad, pad, pad, pad, pad, pad };\n noise = torch.nn.functional.pad(noise, padding, value: 0);\n noise = torch.nn.functional.max_pool3d(noise, strides: new long[] { 1, 1, 1 }, kernelSize: new[] { block_size, block_size, block_size }, padding: new long[] { pad });\n noise = 1 - noise;\n\n var normalize_scale = noise.numel() / (eps + noise.sum());\n if (inplace)\n input.mul_(noise).mul_(normalize_scale);\n else\n input = input * noise * normalize_scale;\n return input;\n }\n\n /// <summary>\n /// Implements DropBlock2d from `\"DropBlock: A regularization method for convolutional networks\"\n /// https://arxiv.org/abs/1810.12890\n /// </summary>\n public static DropBlock2d DropBlock2d(double p, long block_size, bool inplace = false, double eps = 1e-6) => new DropBlock2d(p, block_size, inplace, eps);\n\n /// <summary>\n /// Implements DropBlock3d from `\"DropBlock: A regularization method for convolutional networks\"\n /// https://arxiv.org/abs/1810.12890\n /// </summary>\n public static DropBlock3d DropBlock3d(double p, long block_size, bool inplace = false, double eps = 1e-6) => new DropBlock3d(p, block_size, inplace, eps);\n }\n }\n\n namespace Modules\n {\n public class DropBlock2d : torch.nn.Module<Tensor,Tensor>\n {\n public DropBlock2d(double p, long block_size, bool inplace = false, double eps = 1e-6) : base(nameof(DropBlock2d))\n {\n this.p = p;\n this.block_size = block_size;\n this.inplace = inplace;\n this.eps = eps;\n }\n\n\n public override Tensor forward(Tensor input)\n {\n return torchvision.ops.drop_block2d(input, p, block_size, inplace, eps, training);\n }\n\n // Rather than spending cycles only to discover that this module has neither\n // parameters nor buffers, just shortcut the move completely.\n protected override nn.Module _to(Device device, ScalarType dtype) => this;\n protected override nn.Module _to(DeviceType deviceType, int deviceIndex = -1) => this;\n protected override nn.Module _to(ScalarType dtype) => this;\n\n private bool inplace;\n private double p;\n private long block_size;\n private double eps;\n }\n\n public class DropBlock3d : torch.nn.Module<Tensor, Tensor>\n {\n public DropBlock3d(double p, long block_size, bool inplace = false, double eps = 1e-6) : base(nameof(DropBlock3d))\n {\n this.p = p;\n this.block_size = block_size;\n this.inplace = inplace;\n this.eps = eps;\n }\n\n\n public override Tensor forward(Tensor input)\n {\n return torchvision.ops.drop_block3d(input, p, block_size, inplace, eps, training);\n }\n\n // Rather than spending cycles only to discover that this module has neither\n // parameters nor buffers, just shortcut the move completely.\n protected override nn.Module _to(Device device, ScalarType dtype) => this;\n protected override nn.Module _to(DeviceType deviceType, int deviceIndex = -1) => this;\n protected override nn.Module _to(ScalarType dtype) => this;\n\n private bool inplace;\n private double p;\n private long block_size;\n private double eps;\n }\n }\n}\n"},{"alpha_fraction":0.7226585745811462,"alphanum_fraction":0.7297270894050598,"avg_line_length":26.165332794189453,"blob_id":"32f1bab56c75157b13ddf13c2ad0aade49a996e7","content_id":"96ee551e10b40f157239fe22efde491b5f2ad8ec","detected_licenses":["MIT"],"is_generated":false,"is_vendor":false,"language":"C++","length_bytes":10186,"license_type":"permissive","max_line_length":130,"num_lines":375,"path":"/src/Native/LibTorchSharp/THSSpecial.cpp","repo_name":"NiklasGustafsson/TorchSharp","src_encoding":"UTF-8","text":"// Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information.\n#include \"THSTensor.h\"\n\n#include <iostream>\n#include <fstream>\n\nTensor THSSpecial_airy_ai(const Tensor tensor)\n{\n CATCH_TENSOR(torch::special::airy_ai(*tensor));\n}\n\nTensor THSSpecial_airy_ai_out(const Tensor tensor, Tensor out)\n{\n CATCH_TENSOR(torch::special::airy_ai_out(*out, *tensor));\n}\n\nTensor THSSpecial_bessel_j0(const Tensor tensor)\n{\n CATCH_TENSOR(torch::special::bessel_j0(*tensor));\n}\n\nTensor THSSpecial_bessel_j0_out(const Tensor tensor, Tensor out)\n{\n CATCH_TENSOR(torch::special::bessel_j0_out(*out, *tensor));\n}\n\nTensor THSSpecial_bessel_j1(const Tensor tensor)\n{\n CATCH_TENSOR(torch::special::bessel_j1(*tensor));\n}\n\nTensor THSSpecial_bessel_j1_out(const Tensor tensor, Tensor out)\n{\n CATCH_TENSOR(torch::special::bessel_j1_out(*out, *tensor));\n}\n\nTensor THSSpecial_bessel_y0(const Tensor tensor)\n{\n CATCH_TENSOR(torch::special::bessel_y0(*tensor));\n}\n\nTensor THSSpecial_bessel_y0_out(const Tensor tensor, Tensor out)\n{\n CATCH_TENSOR(torch::special::bessel_y0_out(*out, *tensor));\n}\n\nTensor THSSpecial_bessel_y1(const Tensor tensor)\n{\n CATCH_TENSOR(torch::special::bessel_y1(*tensor));\n}\n\nTensor THSSpecial_bessel_y1_out(const Tensor tensor, Tensor out)\n{\n CATCH_TENSOR(torch::special::bessel_y1_out(*out, *tensor));\n}\n\nTensor THSSpecial_modified_bessel_i0(const Tensor tensor)\n{\n CATCH_TENSOR(torch::special::modified_bessel_i0(*tensor));\n}\n\nTensor THSSpecial_modified_bessel_i0_out(const Tensor tensor, Tensor out)\n{\n CATCH_TENSOR(torch::special::modified_bessel_i0_out(*out, *tensor));\n}\n\nTensor THSSpecial_modified_bessel_i1(const Tensor tensor)\n{\n CATCH_TENSOR(torch::special::modified_bessel_i1(*tensor));\n}\n\nTensor THSSpecial_modified_bessel_i1_out(const Tensor tensor, Tensor out)\n{\n CATCH_TENSOR(torch::special::modified_bessel_i1_out(*out, *tensor));\n}\n\nTensor THSSpecial_modified_bessel_k0(const Tensor tensor)\n{\n CATCH_TENSOR(torch::special::modified_bessel_k0(*tensor));\n}\n\nTensor THSSpecial_modified_bessel_k0_out(const Tensor tensor, Tensor out)\n{\n CATCH_TENSOR(torch::special::modified_bessel_k0_out(*out, *tensor));\n}\n\nTensor THSSpecial_modified_bessel_k1(const Tensor tensor)\n{\n CATCH_TENSOR(torch::special::modified_bessel_k1(*tensor));\n}\n\nTensor THSSpecial_modified_bessel_k1_out(const Tensor tensor, Tensor out)\n{\n CATCH_TENSOR(torch::special::modified_bessel_k1_out(*out, *tensor));\n}\n\nTensor THSSpecial_scaled_modified_bessel_k0(const Tensor tensor)\n{\n CATCH_TENSOR(torch::special::scaled_modified_bessel_k0(*tensor));\n}\n\nTensor THSSpecial_scaled_modified_bessel_k0_out(const Tensor tensor, Tensor out)\n{\n CATCH_TENSOR(torch::special::scaled_modified_bessel_k0_out(*out, *tensor));\n}\n\nTensor THSSpecial_scaled_modified_bessel_k1(const Tensor tensor)\n{\n CATCH_TENSOR(torch::special::scaled_modified_bessel_k1(*tensor));\n}\n\nTensor THSSpecial_scaled_modified_bessel_k1_out(const Tensor tensor, Tensor out)\n{\n CATCH_TENSOR(torch::special::scaled_modified_bessel_k1_out(*out, *tensor));\n}\n\nTensor THSSpecial_spherical_bessel_j0(const Tensor tensor)\n{\n CATCH_TENSOR(torch::special::spherical_bessel_j0(*tensor));\n}\n\nTensor THSSpecial_spherical_bessel_j0_out(const Tensor tensor, Tensor out)\n{\n CATCH_TENSOR(torch::special::spherical_bessel_j0_out(*out, *tensor));\n}\n\nTensor THSSpecial_chebyshev_polynomial_t(const Tensor x, const Tensor n)\n{\n CATCH_TENSOR(torch::special::chebyshev_polynomial_t(*x, *n));\n}\n\nTensor THSSpecial_chebyshev_polynomial_t_out(const Tensor x, const Tensor n, Tensor out)\n{\n CATCH_TENSOR(torch::special::chebyshev_polynomial_t_out(*out, *x, *n));\n}\n\nTensor THSSpecial_chebyshev_polynomial_u(const Tensor x, const Tensor n)\n{\n CATCH_TENSOR(torch::special::chebyshev_polynomial_u(*x, *n));\n}\n\nTensor THSSpecial_chebyshev_polynomial_u_out(const Tensor x, const Tensor n, Tensor out)\n{\n CATCH_TENSOR(torch::special::chebyshev_polynomial_u_out(*out, *x, *n));\n}\n\nTensor THSSpecial_chebyshev_polynomial_v(const Tensor x, const Tensor n)\n{\n CATCH_TENSOR(torch::special::chebyshev_polynomial_v(*x, *n));\n}\n\nTensor THSSpecial_chebyshev_polynomial_v_out(const Tensor x, const Tensor n, Tensor out)\n{\n CATCH_TENSOR(torch::special::chebyshev_polynomial_v_out(*out, *x, *n));\n}\n\nTensor THSSpecial_chebyshev_polynomial_w(const Tensor x, const Tensor n)\n{\n CATCH_TENSOR(torch::special::chebyshev_polynomial_w(*x, *n));\n}\n\nTensor THSSpecial_chebyshev_polynomial_w_out(const Tensor x, const Tensor n, Tensor out)\n{\n CATCH_TENSOR(torch::special::chebyshev_polynomial_w_out(*out, *x, *n));\n}\n\nTensor THSSpecial_shifted_chebyshev_polynomial_t(const Tensor x, const Tensor n)\n{\n CATCH_TENSOR(torch::special::shifted_chebyshev_polynomial_t(*x, *n));\n}\n\nTensor THSSpecial_shifted_chebyshev_polynomial_t_out(const Tensor x, const Tensor n, Tensor out)\n{\n CATCH_TENSOR(torch::special::shifted_chebyshev_polynomial_t_out(*out, *x, *n));\n}\n\nTensor THSSpecial_shifted_chebyshev_polynomial_u(const Tensor x, const Tensor n)\n{\n CATCH_TENSOR(torch::special::shifted_chebyshev_polynomial_u(*x, *n));\n}\n\nTensor THSSpecial_shifted_chebyshev_polynomial_u_out(const Tensor x, const Tensor n, Tensor out)\n{\n CATCH_TENSOR(torch::special::shifted_chebyshev_polynomial_u_out(*out, *x, *n));\n}\n\nTensor THSSpecial_shifted_chebyshev_polynomial_v(const Tensor x, const Tensor n)\n{\n CATCH_TENSOR(torch::special::shifted_chebyshev_polynomial_v(*x, *n));\n}\n\nTensor THSSpecial_shifted_chebyshev_polynomial_v_out(const Tensor x, const Tensor n, Tensor out)\n{\n CATCH_TENSOR(torch::special::shifted_chebyshev_polynomial_v_out(*out, *x, *n));\n}\n\nTensor THSSpecial_shifted_chebyshev_polynomial_w(const Tensor x, const Tensor n)\n{\n CATCH_TENSOR(torch::special::shifted_chebyshev_polynomial_w(*x, *n));\n}\n\nTensor THSSpecial_shifted_chebyshev_polynomial_w_out(const Tensor x, const Tensor n, Tensor out)\n{\n CATCH_TENSOR(torch::special::shifted_chebyshev_polynomial_w_out(*out, *x, *n));\n}\n\nTensor THSSpecial_hermite_polynomial_h(const Tensor x, const Tensor n)\n{\n CATCH_TENSOR(torch::special::hermite_polynomial_h(*x, *n));\n}\n\nTensor THSSpecial_hermite_polynomial_h_out(const Tensor x, const Tensor n, Tensor out)\n{\n CATCH_TENSOR(torch::special::hermite_polynomial_h_out(*out, *x, *n));\n}\n\nTensor THSSpecial_hermite_polynomial_he(const Tensor x, const Tensor n)\n{\n CATCH_TENSOR(torch::special::hermite_polynomial_he(*x, *n));\n}\n\nTensor THSSpecial_hermite_polynomial_he_out(const Tensor x, const Tensor n, Tensor out)\n{\n CATCH_TENSOR(torch::special::hermite_polynomial_he_out(*out, *x, *n));\n}\n\nTensor THSSpecial_laguerre_polynomial_l(const Tensor x, const Tensor n)\n{\n CATCH_TENSOR(torch::special::laguerre_polynomial_l(*x, *n));\n}\n\nTensor THSSpecial_laguerre_polynomial_l_out(const Tensor x, const Tensor n, Tensor out)\n{\n CATCH_TENSOR(torch::special::laguerre_polynomial_l_out(*out, *x, *n));\n}\n\nTensor THSSpecial_legendre_polynomial_p(const Tensor x, const Tensor n)\n{\n CATCH_TENSOR(torch::special::legendre_polynomial_p(*x, *n));\n}\n\nTensor THSSpecial_legendre_polynomial_p_out(const Tensor x, const Tensor n, Tensor out)\n{\n CATCH_TENSOR(torch::special::legendre_polynomial_p_out(*out, *x, *n));\n}\n\nTensor THSSpecial_entr(const Tensor tensor)\n{\n CATCH_TENSOR(torch::special::entr(*tensor));\n}\n\nTensor THSSpecial_erf(const Tensor tensor)\n{\n CATCH_TENSOR(torch::special::erf(*tensor));\n}\n\nTensor THSSpecial_erfc(const Tensor tensor)\n{\n CATCH_TENSOR(torch::special::erfc(*tensor));\n}\n\nTensor THSSpecial_erfcx(const Tensor tensor)\n{\n CATCH_TENSOR(torch::special::erfc(*tensor));\n}\n\nTensor THSSpecial_erfinv(const Tensor tensor)\n{\n CATCH_TENSOR(torch::special::erfinv(*tensor));\n}\n\nTensor THSSpecial_expit(const Tensor tensor)\n{\n CATCH_TENSOR(torch::special::expit(*tensor));\n}\n\nTensor THSSpecial_expm1(const Tensor tensor)\n{\n CATCH_TENSOR(torch::special::expm1(*tensor));\n}\n\nTensor THSSpecial_exp2(const Tensor tensor)\n{\n CATCH_TENSOR(torch::special::exp2(*tensor));\n}\n\nTensor THSSpecial_gammaln(const Tensor tensor)\n{\n CATCH_TENSOR(torch::special::gammaln(*tensor));\n}\n\nTensor THSSpecial_gammainc(const Tensor input, const Tensor other)\n{\n CATCH_TENSOR(torch::special::gammainc(*input, *other));\n}\n\nTensor THSSpecial_gammaincc(const Tensor input, const Tensor other)\n{\n CATCH_TENSOR(torch::special::gammaincc(*input, *other));\n}\n\nTensor THSSpecial_polygamma(const int64_t n, const Tensor tensor)\n{\n CATCH_TENSOR(torch::special::polygamma(n, *tensor));\n}\n\nTensor THSSpecial_digamma(const Tensor tensor)\n{\n CATCH_TENSOR(torch::special::digamma(*tensor));\n}\n\nTensor THSSpecial_multigammaln(const Tensor tensor, const int64_t p)\n{\n CATCH_TENSOR(torch::special::multigammaln(*tensor, p));\n}\n\nTensor THSSpecial_i0e(const Tensor tensor)\n{\n CATCH_TENSOR(torch::special::i0e(*tensor));\n}\n\nTensor THSSpecial_i0(const Tensor tensor)\n{\n CATCH_TENSOR(torch::special::i0(*tensor));\n}\n\nTensor THSSpecial_i1e(const Tensor tensor)\n{\n CATCH_TENSOR(torch::special::i1e(*tensor));\n}\n\nTensor THSSpecial_i1(const Tensor tensor)\n{\n CATCH_TENSOR(torch::special::i1(*tensor));\n}\n\nTensor THSSpecial_logit(const Tensor tensor)\n{\n CATCH_TENSOR(torch::special::logit(*tensor));\n}\n\nTensor THSSpecial_log_softmax(const Tensor tensor, int64_t dim, int8_t scalar_type)\n{\n CATCH_TENSOR(torch::special::log_softmax(*tensor, dim, c10::ScalarType(scalar_type)));\n}\n\nTensor THSSpecial_ndtr(const Tensor tensor)\n{\n CATCH_TENSOR(torch::special::ndtr(*tensor));\n}\n\nTensor THSSpecial_ndtri(const Tensor tensor)\n{\n CATCH_TENSOR(torch::special::ndtri(*tensor));\n}\n\nTensor THSSpecial_sinc(const Tensor tensor)\n{\n CATCH_TENSOR(torch::special::sinc(*tensor));\n}\n\nTensor THSSpecial_softmax(const Tensor tensor, int64_t dim, int8_t scalar_type)\n{\n CATCH_TENSOR(torch::special::softmax(*tensor, dim, c10::ScalarType(scalar_type)));\n}\n\nTensor THSSpecial_xlog1py(const Tensor input, const Tensor other)\n{\n CATCH_TENSOR(torch::special::xlog1py(*input, *other));\n}\n\nTensor THSSpecial_zeta(const Tensor input, const Tensor other)\n{\n CATCH_TENSOR(torch::special::zeta(*input, *other));\n}"},{"alpha_fraction":0.7142857313156128,"alphanum_fraction":0.7142857313156128,"avg_line_length":36.264705657958984,"blob_id":"496f723968283b1f04dc1fb493e887e3f5153cdb","content_id":"8e6d4f48c78ed238d3f71e1d89ff0fa2e025296b","detected_licenses":["MIT"],"is_generated":false,"is_vendor":false,"language":"C#","length_bytes":1267,"license_type":"permissive","max_line_length":130,"num_lines":34,"path":"/src/TorchSharp/PInvoke/LibTorchSharp.THSGenerator.cs","repo_name":"NiklasGustafsson/TorchSharp","src_encoding":"UTF-8","text":"// Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information.\n#nullable enable\nusing System;\nusing System.Runtime.InteropServices;\n\nnamespace TorchSharp.PInvoke\n{\n internal static partial class NativeMethods\n {\n [DllImport(\"LibTorchSharp\")]\n internal static extern long THSGenerator_initial_seed(IntPtr handle);\n\n [DllImport(\"LibTorchSharp\")]\n internal static extern IntPtr THSGenerator_get_rng_state(IntPtr handle);\n\n [DllImport(\"LibTorchSharp\")]\n internal static extern void THSGenerator_set_rng_state(IntPtr handle, IntPtr tensor);\n\n [DllImport(\"LibTorchSharp\")]\n internal static extern void THSGenerator_gen_manual_seed(IntPtr handle, long seed);\n\n [DllImport(\"LibTorchSharp\")]\n internal static extern IntPtr THSGenerator_new(ulong seed, long device_type, long device_index);\n\n [DllImport(\"LibTorchSharp\")]\n internal static extern IntPtr THSGenerator_default_generator();\n\n [DllImport(\"LibTorchSharp\")]\n internal static extern void THSGenerator_dispose(IntPtr handle);\n\n [DllImport(\"LibTorchSharp\")]\n internal static extern IntPtr THSGenerator_manual_seed(long seed);\n }\n}\n"},{"alpha_fraction":0.5340318083763123,"alphanum_fraction":0.537695050239563,"avg_line_length":47.06338119506836,"blob_id":"6454ac651613cae5c50718b7e2d57d09cff6a3c8","content_id":"53e6facfbd41664c6a26b43d990493026a0833e8","detected_licenses":["MIT"],"is_generated":false,"is_vendor":false,"language":"C#","length_bytes":13651,"license_type":"permissive","max_line_length":236,"num_lines":284,"path":"/src/TorchSharp/Tensor/Tensor.LinearAlgebra.cs","repo_name":"NiklasGustafsson/TorchSharp","src_encoding":"UTF-8","text":"// Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information.\nusing System;\nusing System.Linq;\nusing static TorchSharp.PInvoke.NativeMethods;\n\nnamespace TorchSharp\n{\n public static partial class torch\n {\n public partial class Tensor\n {\n // https://pytorch.org/docs/stable/generated/torch.tensordot\n /// <summary>\n /// Returns a contraction of a and b over multiple dimensions.\n /// tensordot implements a generalized matrix product.\n /// </summary>\n public Tensor tensordot(Tensor b, long[] dims1, long[] dims2)\n {\n IntPtr res;\n unsafe {\n fixed (long* pdims1 = dims1, pdims2 = dims2) {\n res = THSLinalg_tensordot(Handle, b.Handle,(IntPtr)pdims1, dims1.Length,(IntPtr)pdims2, dims2.Length);\n }\n }\n if (res == IntPtr.Zero) {\n CheckForErrors();\n }\n return new Tensor(res);\n }\n\n // https://pytorch.org/docs/stable/generated/torch.tensordot\n /// <summary>\n /// Returns a contraction of this tensor and <paramref name=\"b\"/> over multiple dimensions.\n /// tensordot implements a generalized matrix product.\n /// </summary>\n /// <param name=\"b\">Right tensor to contract</param>\n /// <param name=\"dims\">dimensions to contract for this tensor and <paramref name=\"b\"/> respectively</param>\n /// <returns>contraction</returns>\n public Tensor tensordot(Tensor b, (long, long)[] dims)\n => tensordot(b, dims.Select(t => t.Item1).ToArray(), dims.Select(t => t.Item2).ToArray());\n\n // https://pytorch.org/docs/stable/generated/torch.tensordot\n /// <summary>\n /// Returns a contraction of this tensor and <paramref name=\"b\"/> over multiple dimensions.\n /// tensordot implements a generalized matrix product.\n /// </summary>\n /// <param name=\"b\">Right tensor to contract</param>\n /// <param name=\"dims\">number of dimensions to contract for this tensor and <paramref name=\"b\"/></param>\n /// <returns>contraction</returns>\n public Tensor tensordot(Tensor b, long dims = 2)\n {\n if (dims < 0) throw new ArgumentOutOfRangeException(nameof(dims), dims, \"must be >= 0\");\n\n var list = new long[dims];\n for (long i = 0; i <= dims; i++) {\n list[i] = i;\n }\n\n return tensordot(b, list, list);\n }\n\n /// <summary>\n /// Computes the Cholesky decomposition of a symmetric positive-definite matrix AA or for batches of symmetric positive-definite matrices.\n /// </summary>\n /// <param name=\"upper\">If upper is true, the returned matrix U is upper-triangular. If upper is false, the returned matrix L is lower-triangular</param>\n /// <returns></returns>\n public Tensor cholesky(bool upper = false)\n {\n var res = THSTensor_cholesky(Handle, upper);\n if (res == IntPtr.Zero) { CheckForErrors(); }\n return new Tensor(res);\n }\n\n /// <summary>\n /// Computes the inverse of a symmetric positive-definite matrix AA using its Cholesky factor uu : returns matrix inv\n /// </summary>\n /// <param name=\"upper\"></param>\n /// <returns></returns>\n public Tensor cholesky_inverse(bool upper = false)\n {\n var res = THSTensor_cholesky_inverse(Handle, upper);\n if (res == IntPtr.Zero) { CheckForErrors(); }\n return new Tensor(res);\n }\n\n /// <summary>\n /// Solves a linear system of equations with a positive semidefinite matrix to be inverted given its Cholesky factor matrix u.\n /// </summary>\n /// <param name=\"input2\"></param>\n /// <param name=\"upper\"></param>\n /// <returns></returns>\n public Tensor cholesky_solve(Tensor input2, bool upper = false)\n {\n var res = THSTensor_cholesky_solve(Handle, input2.Handle, upper);\n if (res == IntPtr.Zero) { CheckForErrors(); }\n return new Tensor(res);\n }\n\n /// <summary>\n /// Computes the cross product of two 3-dimensional vectors.\n /// </summary>\n /// <remarks>\n /// Supports input of float, double, cfloat and cdouble dtypes. Also supports batches of vectors,\n /// for which it computes the product along the dimension dim. In this case, the output has the\n /// same batch dimensions as the inputs broadcast to a common shape.\n /// </remarks>\n public Tensor cross(Scalar other, long dim)\n {\n var res = THSTensor_cross(Handle, other.Handle, dim);\n if (res == IntPtr.Zero) { CheckForErrors(); }\n return new Tensor(res);\n }\n\n /// <summary>\n /// Computes the determinant of a square matrix.\n /// </summary>\n public Tensor det()\n {\n return linalg.det(this);\n }\n\n /// <summary>\n /// Calculates log determinant of a square matrix or batches of square matrices.\n /// </summary>\n /// <returns></returns>\n public Tensor logdet()\n {\n var shape = this.shape;\n var len = shape.Length;\n if (shape[len - 1] != shape[len - 2]) throw new ArgumentException(\"The input tensor is not square\");\n\n var res = THSTensor_logdet(Handle);\n if (res == IntPtr.Zero) { CheckForErrors(); }\n return new Tensor(res);\n }\n\n\n /// <summary>\n /// This is a low-level function for calling LAPACK’s geqrf directly.\n /// This function returns a namedtuple (a, tau) as defined in LAPACK documentation for geqrf.\n /// </summary>\n /// <remarks>\n /// Computes a QR decomposition of input. Both Q and R matrices are stored in the same output tensor a.\n /// The elements of R are stored on and above the diagonal. Elementary reflectors (or Householder vectors)\n /// implicitly defining matrix Q are stored below the diagonal. The results of this function can be used\n /// together with torch.linalg.householder_product() to obtain the Q matrix or with torch.ormqr(), which\n /// uses an implicit representation of the Q matrix, for an efficient matrix-matrix multiplication.\n /// </remarks>\n public (Tensor a, Tensor tau) geqrf()\n {\n var res = THSTensor_geqrf(Handle, out var tau);\n if (res == IntPtr.Zero || tau == IntPtr.Zero)\n torch.CheckForErrors();\n return (new Tensor(res), new Tensor(tau));\n }\n\n /// <summary>\n /// Matrix product of two tensors.\n /// </summary>\n /// <param name=\"target\"></param>\n /// <returns></returns>\n /// <remarks>\n /// The behavior depends on the dimensionality of the tensors as follows:\n /// 1. If both tensors are 1-dimensional, the dot product (scalar) is returned\n /// 2. If both arguments are 2-dimensional, the matrix-matrix product is returned.\n /// 3. If the first argument is 1-dimensional and the second argument is 2-dimensional, a 1 is prepended to its dimension for the purpose of the matrix multiply. After the matrix multiply, the prepended dimension is removed.\n /// 4. If the first argument is 2-dimensional and the second argument is 1-dimensional, the matrix-vector product is returned.\n /// 5. If both arguments are at least 1-dimensional and at least one argument is N-dimensional (where N > 2), then a batched matrix multiply is returned.\n /// </remarks>\n public Tensor matmul(Tensor target)\n {\n var res = THSTensor_matmul(Handle, target.Handle);\n if (res == IntPtr.Zero) { CheckForErrors(); }\n return new Tensor(res);\n }\n\n /// <summary>\n /// Performs a matrix multiplication of the matrices input and mat2.\n /// </summary>\n /// <param name=\"target\"></param>\n /// <returns></returns>\n public Tensor mm(Tensor target)\n {\n var res = THSTensor_mm(Handle, target.Handle);\n if (res == IntPtr.Zero) { CheckForErrors(); }\n return new Tensor(res);\n }\n\n /// <summary>\n /// Performs a matrix-vector product of the matrix input and the vector vec.\n /// </summary>\n /// <param name=\"target\"></param>\n /// <returns></returns>\n public Tensor mv(Tensor target)\n {\n var res = THSTensor_mv(Handle, target.Handle);\n if (res == IntPtr.Zero) { CheckForErrors(); }\n return new Tensor(res);\n }\n\n /// <summary>\n /// Computes the matrix exponential of a square matrix or of each square matrix in a batch.\n /// </summary>\n public Tensor matrix_exp()\n {\n var res = THSTensor_matrix_exp(Handle);\n if (res == IntPtr.Zero) { CheckForErrors(); }\n return new Tensor(res);\n }\n\n /// <summary>\n /// Computes the n-th power of a square matrix for an integer n.\n /// </summary>\n /// <param name=\"n\">The exponent</param>\n /// <returns></returns>\n /// <remarks>Input tensor must be of shape (*, m, m) where * is zero or more batch dimensions.</remarks>\n public Tensor matrix_power(int n)\n {\n var res = THSLinalg_matrix_power(Handle, n);\n if (res == IntPtr.Zero) { CheckForErrors(); }\n return new Tensor(res);\n }\n\n /// <summary>\n /// Computes the dot product of two 1D tensors.\n /// </summary>\n /// <returns></returns>\n /// <remarks>\n /// The vdot(a, b) function handles complex numbers differently than dot(a, b).\n /// If the first argument is complex, the complex conjugate of the first argument is used for the calculation of the dot product.\n /// </remarks>\n public Tensor vdot(Tensor target)\n {\n if (shape.Length != 1 || target.shape.Length != 1 || shape[0] != target.shape[0]) throw new InvalidOperationException(\"vdot arguments must have the same shape.\");\n var res = THSTensor_vdot(Handle, target.Handle);\n if (res == IntPtr.Zero) { CheckForErrors(); }\n return new Tensor(res);\n }\n\n /// <summary>\n /// Computes the dot product of two 1D tensors.\n /// </summary>\n /// <returns></returns>\n public Tensor dot(Tensor target)\n {\n if (shape.Length != 1 || target.shape.Length != 1 || shape[0] != target.shape[0]) throw new InvalidOperationException(\"dot arguments must have the same shape.\");\n var res = THSTensor_dot(Handle, target.Handle);\n if (res == IntPtr.Zero) { CheckForErrors(); }\n return new Tensor(res);\n }\n\n /// <summary>\n /// Computes the pseudoinverse (Moore-Penrose inverse) of a matrix.\n /// </summary>\n /// <param name=\"rcond\">The tolerance value to determine when is a singular value zero </param>\n /// <param name=\"hermitian\">Indicates whether A is Hermitian if complex or symmetric if real. </param>\n /// <remarks>Input should be tensor of shape (*, m, n) where * is zero or more batch dimensions.</remarks>\n /// <returns></returns>\n public Tensor pinverse(double rcond = 1e-15, bool hermitian = false)\n {\n var res = THSLinalg_pinverse(Handle, rcond, hermitian);\n if (res == IntPtr.Zero)\n CheckForErrors();\n return new Tensor(res);\n }\n\n /// <summary>\n /// Computes the matrix-matrix multiplication of a product of Householder matrices with a general matrix.\n /// </summary>\n /// <param name=\"tau\">Tensor of shape (*, min(mn, k)) where * is zero or more batch dimensions.</param>\n /// <param name=\"other\">Tensor of shape (*, m, n) where * is zero or more batch dimensions.</param>\n /// <param name=\"left\">Controls the order of multiplication.</param>\n /// <param name=\"transpose\">Controls whether the matrix Q is conjugate transposed or not.</param>\n /// <returns></returns>\n public Tensor ormqr(Tensor tau, Tensor other, bool left = true, bool transpose = false)\n {\n var res = THSTensor_ormqr(Handle, tau.handle, other.Handle, left, transpose);\n if (res == IntPtr.Zero)\n CheckForErrors();\n return new Tensor(res);\n }\n }\n }\n}"},{"alpha_fraction":0.7786961793899536,"alphanum_fraction":0.7956100702285767,"avg_line_length":91.2676773071289,"blob_id":"00c69f84566b348e4bd5aeff6dd0f6c071100d71","content_id":"330ceb1d259f0b53bcf94344456f73c4615bb3eb","detected_licenses":["MIT"],"is_generated":false,"is_vendor":false,"language":"C","length_bytes":54807,"license_type":"permissive","max_line_length":478,"num_lines":594,"path":"/src/Native/LibTorchSharp/THSNN.h","repo_name":"NiklasGustafsson/TorchSharp","src_encoding":"UTF-8","text":"// Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information.\n#pragma once\n\n#include \"../Stdafx.h\"\n\n#include \"torch/torch.h\"\n\n#include \"Utils.h\"\n\n// API.\n\nEXPORT_API(int) THSNN_Module_has_parameter(const NNModule module, const char* name);\nEXPORT_API(Tensor) THSNN_Module_get_parameter(const NNModule module, const char* name);\nEXPORT_API(void) THSNN_Module_get_named_parameters(const NNModule module, Tensor* (*allocator1)(size_t length), const char** (*allocator2)(size_t length));\nEXPORT_API(void) THSNN_Module_get_named_buffers(const NNModule module, Tensor* (*allocator1)(size_t length), const char** (*allocator2)(size_t length));\nEXPORT_API(void) THSNN_Module_get_named_children(const NNModule module, NNModule* (*allocator1)(size_t length), const char** (*allocator2)(size_t length));\nEXPORT_API(void) THSNN_Module_get_named_modules(const NNModule module, NNModule* (*allocator1)(size_t length), const char** (*allocator2)(size_t length));\nEXPORT_API(void) THSNN_Module_get_parameters(const NNModule module, Tensor* (*allocator1)(size_t length), bool recurse);\nEXPORT_API(int) THSNN_Module_is_training(NNModule module);\nEXPORT_API(void) THSNN_Module_train(NNModule module, bool on);\nEXPORT_API(long) THSNN_Module_children_size(const NNModule module);\nEXPORT_API(NNModule) THSNN_Module_child(const NNModule module, const int index);\nEXPORT_API(const char*) THSNN_Module_name(const NNModule module);\nEXPORT_API(void) THSNN_Module_zero_grad(const NNModule module);\nEXPORT_API(void) THSNN_Module_save(const NNModule module, const char* location);\nEXPORT_API(NNModule) THSNN_Module_load(const char* location);\nEXPORT_API(void) THSNN_Module_register_buffer(const NNModule module, const char* name, const Tensor submodule);\nEXPORT_API(void) THSNN_Module_register_parameter(const NNModule module, const char* name, const Tensor tensor, bool requires_grad);\nEXPORT_API(void) THSNN_Module_register_module(const NNModule module, const char* name, const NNModule submodule);\nEXPORT_API(void) THSNN_Module_dispose(const NNModule module);\nEXPORT_API(void) THSNN_Module_to_device(NNModule module, int64_t device, int64_t index);\nEXPORT_API(void) THSNN_Module_to_dtype(NNModule module, int8_t dtype);\nEXPORT_API(void) THSNN_Module_to_device_dtype(NNModule module, int8_t dtype, int64_t device, int64_t index);\n\nEXPORT_API(void) THSNN_AnyModule_dispose(const NNAnyModule module);\n//EXPORT_API(NNModule) THSNN_AnyModule_get(const NNAnyModule module);\n\nEXPORT_API(NNModule) THSNN_custom_module(const char* name, Tensor(*forward)(Tensor), NNAnyModule* outAsAnyModule);\n\n// Pooling\n\nEXPORT_API(NNModule) THSNN_MaxPool1d_ctor(const int64_t* kernelSize, const int64_t* stride, const int64_t* padding, const int64_t* dilation, bool ceil_mode, NNAnyModule* outAsAnyModule);\nEXPORT_API(Tensor) THSNN_MaxPool1d_forward(const NNModule module, const Tensor tensor);\nEXPORT_API(Tensor) THSNN_MaxPool1d_forward_with_indices(const NNModule module, const Tensor tensor, Tensor *indices);\n\nEXPORT_API(NNModule) THSNN_MaxPool2d_ctor(const int64_t* kernelSize, const int kernelSizeLength, const int64_t* stride, const int strideLength, const int64_t* padding, const int paddingLength, const int64_t* dilation, const int dilationLength, bool ceil_mode, NNAnyModule* outAsAnyModule);\nEXPORT_API(Tensor) THSNN_MaxPool2d_forward(const NNModule module, const Tensor tensor);\nEXPORT_API(Tensor) THSNN_MaxPool2d_forward_with_indices(const NNModule module, const Tensor tensor, Tensor* indices);\n\nEXPORT_API(NNModule) THSNN_MaxPool3d_ctor(const int64_t* kernelSize, const int kernelSizeLength, const int64_t* stride, const int strideLength, const int64_t* padding, const int paddingLength, const int64_t* dilation, const int dilationLength, bool ceil_mode, NNAnyModule* outAsAnyModule);\nEXPORT_API(Tensor) THSNN_MaxPool3d_forward(const NNModule module, const Tensor tensor);\nEXPORT_API(Tensor) THSNN_MaxPool3d_forward_with_indices(const NNModule module, const Tensor tensor, Tensor* indices);\n\nEXPORT_API(NNModule) THSNN_FractionalMaxPool2d_ctor(const int64_t* kernelSize, const int kernelSizeLength, const int64_t* outputSize, const int outputSizeLength, const double* outputRatio, const int outputRatioLength, NNAnyModule* outAsAnyModule);\nEXPORT_API(Tensor) THSNN_FractionalMaxPool2d_forward(const NNModule module, const Tensor tensor);\nEXPORT_API(Tensor) THSNN_FractionalMaxPool2d_forward_with_indices(const NNModule module, const Tensor tensor, Tensor* indices);\n\nEXPORT_API(NNModule) THSNN_FractionalMaxPool3d_ctor(const int64_t* kernelSize, const int kernelSizeLength, const int64_t* outputSize, const int outputSizeLength, const double* outputRatio, const int outputRatioLength, NNAnyModule* outAsAnyModule);\nEXPORT_API(Tensor) THSNN_FractionalMaxPool3d_forward(const NNModule module, const Tensor tensor);\nEXPORT_API(Tensor) THSNN_FractionalMaxPool3d_forward_with_indices(const NNModule module, const Tensor tensor, Tensor* indices);\n\nEXPORT_API(NNModule) THSNN_MaxUnpool1d_ctor(const int64_t* kernelSize, const int64_t* stride, const int64_t* padding, NNAnyModule* outAsAnyModule);\nEXPORT_API(Tensor) THSNN_MaxUnpool1d_forward(const NNModule module, const Tensor tensor, const Tensor indices, const int64_t* outputSize);\n\nEXPORT_API(NNModule) THSNN_MaxUnpool2d_ctor(const int64_t* kernelSize, const int kernelSizeLength, const int64_t* stride, const int strideLength, const int64_t* padding, const int paddingLength, NNAnyModule* outAsAnyModule);\nEXPORT_API(Tensor) THSNN_MaxUnpool2d_forward(const NNModule module, const Tensor tensor, const Tensor indices, const int64_t* outputSize, const int outputSizeLength);\n\nEXPORT_API(NNModule) THSNN_MaxUnpool3d_ctor(const int64_t* kernelSize, const int kernelSizeLength, const int64_t* stride, const int strideLength, const int64_t* padding, const int paddingLength, NNAnyModule* outAsAnyModule);\nEXPORT_API(Tensor) THSNN_MaxUnpool3d_forward(const NNModule module, const Tensor tensor, const Tensor indices, const int64_t* outputSize, const int outputSizeLength);\n\nEXPORT_API(NNModule) THSNN_AdaptiveAvgPool1d_ctor(const int64_t* sizes, const int length, NNAnyModule* outAsAnyModule);\nEXPORT_API(Tensor) THSNN_AdaptiveAvgPool1d_forward(const NNModule module, const Tensor tensor);\nEXPORT_API(NNModule) THSNN_AdaptiveAvgPool2d_ctor(const int64_t* sizes, const int length, NNAnyModule* outAsAnyModule);\nEXPORT_API(Tensor) THSNN_AdaptiveAvgPool2d_forward(const NNModule module, const Tensor tensor);\nEXPORT_API(NNModule) THSNN_AdaptiveAvgPool3d_ctor(const int64_t* sizes, const int length, NNAnyModule* outAsAnyModule);\nEXPORT_API(Tensor) THSNN_AdaptiveAvgPool3d_forward(const NNModule module, const Tensor tensor);\n\nEXPORT_API(NNModule) THSNN_AdaptiveMaxPool1d_ctor(const int64_t* sizes, const int length, NNAnyModule* outAsAnyModule);\nEXPORT_API(Tensor) THSNN_AdaptiveMaxPool1d_forward(const NNModule module, const Tensor tensor);\nEXPORT_API(NNModule) THSNN_AdaptiveMaxPool2d_ctor(const int64_t* sizes, const int length, NNAnyModule* outAsAnyModule);\nEXPORT_API(Tensor) THSNN_AdaptiveMaxPool2d_forward(const NNModule module, const Tensor tensor);\nEXPORT_API(NNModule) THSNN_AdaptiveMaxPool3d_ctor(const int64_t* sizes, const int length, NNAnyModule* outAsAnyModule);\nEXPORT_API(Tensor) THSNN_AdaptiveMaxPool3d_forward(const NNModule module, const Tensor tensor);\n\nEXPORT_API(NNModule) THSNN_AvgPool1d_ctor(const int64_t* kernelSize, const int64_t* stride, const int64_t* padding, bool ceil_mode, bool count_include_pad, int64_t divisor_override, NNAnyModule* outAsAnyModule);\nEXPORT_API(Tensor) THSNN_AvgPool1d_forward(const NNModule module, const Tensor tensor);\nEXPORT_API(NNModule) THSNN_AvgPool2d_ctor(const int64_t* kernelSize, const int kernelSizeLength, const int64_t* stride, const int strideLength, const int64_t* padding, const int paddingLength, bool ceil_mode, bool count_include_pad, int64_t divisor_override, NNAnyModule* outAsAnyModule);\nEXPORT_API(Tensor) THSNN_AvgPool2d_forward(const NNModule module, const Tensor tensor);\nEXPORT_API(NNModule) THSNN_AvgPool3d_ctor(const int64_t* kernelSize, const int kernelSizeLength, const int64_t* stride, const int strideLength, const int64_t* padding, const int paddingLength, bool ceil_mode, bool count_include_pad, int64_t divisor_override, NNAnyModule* outAsAnyModule);\nEXPORT_API(Tensor) THSNN_AvgPool3d_forward(const NNModule module, const Tensor tensor);\n\nEXPORT_API(NNModule) THSNN_LPPool1d_ctor(double norm_type, const int64_t* kernelSize, const int64_t* stride, bool ceil_mode, NNAnyModule* outAsAnyModule);\nEXPORT_API(Tensor) THSNN_LPPool1d_forward(const NNModule module, const Tensor tensor);\nEXPORT_API(NNModule) THSNN_LPPool2d_ctor(double norm_type, const int64_t* kernelSize, const int kernelSizeLength, const int64_t* stride, const int strideLength, bool ceil_mode, NNAnyModule* outAsAnyModule);\nEXPORT_API(Tensor) THSNN_LPPool2d_forward(const NNModule module, const Tensor tensor);\n\n// Padding\n\nEXPORT_API(NNModule) THSNN_ZeroPad2d_ctor(const int64_t padding, NNAnyModule* outAsAnyModule);\nEXPORT_API(NNModule) THSNN_ZeroPad2d_ctor_tuple(const int64_t padding_left, const int64_t padding_right, const int64_t padding_top, const int64_t padding_bottom, NNAnyModule* outAsAnyModule);\nEXPORT_API(Tensor) THSNN_ZeroPad2d_forward(const NNModule module, const Tensor tensor);\n\nEXPORT_API(NNModule) THSNN_ConstantPad1d_ctor(const double value, const int64_t padding, NNAnyModule* outAsAnyModule);\nEXPORT_API(NNModule) THSNN_ConstantPad1d_ctor_tuple(const double value, const int64_t padding_left, const int64_t padding_right, NNAnyModule* outAsAnyModule);\nEXPORT_API(Tensor) THSNN_ConstantPad1d_forward(const NNModule module, const Tensor tensor);\nEXPORT_API(NNModule) THSNN_ConstantPad2d_ctor(const double value, const int64_t padding, NNAnyModule* outAsAnyModule);\nEXPORT_API(NNModule) THSNN_ConstantPad2d_ctor_tuple(const double value, const int64_t padding_left, const int64_t padding_right, const int64_t padding_top, const int64_t padding_bottom, NNAnyModule* outAsAnyModule);\nEXPORT_API(Tensor) THSNN_ConstantPad2d_forward(const NNModule module, const Tensor tensor);\nEXPORT_API(NNModule) THSNN_ConstantPad3d_ctor(const double value, const int64_t padding, NNAnyModule* outAsAnyModule);\nEXPORT_API(NNModule) THSNN_ConstantPad3d_ctor_tuple(const double value, const int64_t padding_left, const int64_t padding_right, const int64_t padding_top, const int64_t padding_bottom, const int64_t padding_front, const int64_t padding_back, NNAnyModule* outAsAnyModule);\nEXPORT_API(Tensor) THSNN_ConstantPad3d_forward(const NNModule module, const Tensor tensor);\n\nEXPORT_API(NNModule) THSNN_ReplicationPad1d_ctor(const int64_t padding, NNAnyModule* outAsAnyModule);\nEXPORT_API(NNModule) THSNN_ReplicationPad1d_ctor_tuple(const int64_t padding_left, const int64_t padding_right, NNAnyModule* outAsAnyModule);\nEXPORT_API(Tensor) THSNN_ReplicationPad1d_forward(const NNModule module, const Tensor tensor);\nEXPORT_API(NNModule) THSNN_ReplicationPad2d_ctor(const int64_t padding, NNAnyModule* outAsAnyModule);\nEXPORT_API(NNModule) THSNN_ReplicationPad2d_ctor_tuple(const int64_t padding_left, const int64_t padding_right, const int64_t padding_top, const int64_t padding_bottom, NNAnyModule* outAsAnyModule);\nEXPORT_API(Tensor) THSNN_ReplicationPad2d_forward(const NNModule module, const Tensor tensor);\nEXPORT_API(NNModule) THSNN_ReplicationPad3d_ctor(const int64_t padding, NNAnyModule* outAsAnyModule);\nEXPORT_API(NNModule) THSNN_ReplicationPad3d_ctor_tuple(const int64_t padding_left, const int64_t padding_right, const int64_t padding_top, const int64_t padding_bottom, const int64_t padding_front, const int64_t padding_back, NNAnyModule* outAsAnyModule);\nEXPORT_API(Tensor) THSNN_ReplicationPad3d_forward(const NNModule module, const Tensor tensor);\n\nEXPORT_API(NNModule) THSNN_ReflectionPad1d_ctor(const int64_t padding, NNAnyModule* outAsAnyModule);\nEXPORT_API(NNModule) THSNN_ReflectionPad1d_ctor_tuple(const int64_t padding_left, const int64_t padding_right, NNAnyModule* outAsAnyModule);\nEXPORT_API(Tensor) THSNN_ReflectionPad1d_forward(const NNModule module, const Tensor tensor);\nEXPORT_API(NNModule) THSNN_ReflectionPad2d_ctor(const int64_t padding, NNAnyModule* outAsAnyModule);\nEXPORT_API(NNModule) THSNN_ReflectionPad2d_ctor_tuple(const int64_t padding_left, const int64_t padding_right, const int64_t padding_top, const int64_t padding_bottom, NNAnyModule* outAsAnyModule);\nEXPORT_API(Tensor) THSNN_ReflectionPad2d_forward(const NNModule module, const Tensor tensor);\nEXPORT_API(NNModule) THSNN_ReflectionPad3d_ctor(const int64_t padding, NNAnyModule* outAsAnyModule);\nEXPORT_API(NNModule) THSNN_ReflectionPad3d_ctor_tuple(const int64_t padding_left, const int64_t padding_right, const int64_t padding_top, const int64_t padding_bottom, const int64_t padding_front, const int64_t padding_back, NNAnyModule* outAsAnyModule);\nEXPORT_API(Tensor) THSNN_ReflectionPad3d_forward(const NNModule module, const Tensor tensor);\n\n// Convolution\n\nEXPORT_API(NNModule) THSNN_Conv1d_ctor(const int64_t inputChannel, const int64_t outputChannel, const int64_t kernelSize, const int64_t stride, const int64_t padding, const int64_t dilation, const int64_t paddingMode, const int64_t groups, const bool bias, NNAnyModule* outAsAnyModule);\nEXPORT_API(Tensor) THSNN_Conv1d_forward(const NNModule module, const Tensor tensor);\nEXPORT_API(Tensor) THSNN_Conv1d_bias(const NNModule module);\nEXPORT_API(void) THSNN_Conv1d_set_bias(const NNModule module, const Tensor bias);\nEXPORT_API(Tensor) THSNN_Conv1d_weight(const NNModule module);\nEXPORT_API(void) THSNN_Conv1d_set_weight(const NNModule module, const Tensor weight);\nEXPORT_API(NNModule) THSNN_Conv2d_ctor(const int64_t inputChannel, const int64_t outputChannel, const int64_t kernelSize, const int64_t stride, const int64_t padding, const int64_t dilation, const int64_t paddingMode, const int64_t groups, const bool bias, NNAnyModule* outAsAnyModule);\nEXPORT_API(NNModule) THSNN_Conv2d_ctor_1(const int64_t inputChannel, const int64_t outputChannel, const int64_t kernelX, const int64_t kernelY, const int64_t strideX, const int64_t strideY, const int64_t paddingX, const int64_t paddingY, const int64_t dilationX, const int64_t dilationY, const int64_t paddingMode, const int64_t groups, const bool bias, NNAnyModule* outAsAnyModule);\nEXPORT_API(Tensor) THSNN_Conv2d_forward(const NNModule module, const Tensor tensor);\nEXPORT_API(Tensor) THSNN_Conv2d_weight(const NNModule module);\nEXPORT_API(void) THSNN_Conv2d_set_weight(const NNModule module, const Tensor weight);\nEXPORT_API(Tensor) THSNN_Conv2d_bias(const NNModule module);\nEXPORT_API(void) THSNN_Conv2d_set_bias(const NNModule module, const Tensor bias);\nEXPORT_API(NNModule) THSNN_Conv3d_ctor(const int64_t inputChannel, const int64_t outputChannel, const int64_t kernelSize, const int64_t stride, const int64_t padding, const int64_t dilation, const int64_t paddingMode, const int64_t groups, const bool bias, NNAnyModule* outAsAnyModule);\nEXPORT_API(NNModule) THSNN_Conv3d_ctor_1(const int64_t inputChannel, const int64_t outputChannel, const int64_t kernelX, const int64_t kernelY, const int64_t kernelZ, const int64_t strideX, const int64_t strideY, const int64_t strideZ, const int64_t paddingX, const int64_t paddingY, const int64_t paddingZ, const int64_t dilationX, const int64_t dilationY, const int64_t dilationZ, const int64_t paddingMode, const int64_t groups, const bool bias, NNAnyModule* outAsAnyModule);\nEXPORT_API(Tensor) THSNN_Conv3d_forward(const NNModule module, const Tensor tensor);\nEXPORT_API(Tensor) THSNN_Conv3d_weight(const NNModule module);\nEXPORT_API(void) THSNN_Conv3d_set_weight(const NNModule module, const Tensor weight);\nEXPORT_API(Tensor) THSNN_Conv3d_bias(const NNModule module);\nEXPORT_API(void) THSNN_Conv3d_set_bias(const NNModule module, const Tensor bias);\n\nEXPORT_API(NNModule) THSNN_ConvTranspose1d_ctor(const int64_t inputChannel, const int64_t outputChannel, const int64_t kernelSize, const int64_t stride, const int64_t padding, const int64_t output_padding, const int64_t dilation, const int64_t paddingMode, const int64_t groups, const bool bias, NNAnyModule* outAsAnyModule);\nEXPORT_API(Tensor) THSNN_ConvTranspose1d_forward(const NNModule module, const Tensor tensor);\nEXPORT_API(Tensor) THSNN_ConvTranspose1d_bias(const NNModule module);\nEXPORT_API(void) THSNN_ConvTranspose1d_set_bias(const NNModule module, const Tensor bias);\nEXPORT_API(Tensor) THSNN_ConvTranspose1d_weight(const NNModule module);\nEXPORT_API(void) THSNN_ConvTranspose1d_set_weight(const NNModule module, const Tensor weight);\nEXPORT_API(NNModule) THSNN_ConvTranspose2d_ctor(const int64_t inputChannel, const int64_t outputChannel, const int64_t kernelSize, const int64_t stride, const int64_t padding, const int64_t output_padding, const int64_t dilation, const int64_t paddingMode, const int64_t groups, const bool bias, NNAnyModule* outAsAnyModule);\nEXPORT_API(Tensor) THSNN_ConvTranspose2d_forward(const NNModule module, const Tensor tensor);\nEXPORT_API(Tensor) THSNN_ConvTranspose2d_weight(const NNModule module);\nEXPORT_API(void) THSNN_ConvTranspose2d_set_weight(const NNModule module, const Tensor weight);\nEXPORT_API(Tensor) THSNN_ConvTranspose2d_bias(const NNModule module);\nEXPORT_API(void) THSNN_ConvTranspose2d_set_bias(const NNModule module, const Tensor bias);\nEXPORT_API(NNModule) THSNN_ConvTranspose3d_ctor(const int64_t inputChannel, const int64_t outputChannel, const int64_t kernelSize, const int64_t stride, const int64_t padding, const int64_t output_padding, const int64_t dilation, const int64_t paddingMode, const int64_t groups, const bool bias, NNAnyModule* outAsAnyModule);\nEXPORT_API(Tensor) THSNN_ConvTranspose3d_forward(const NNModule module, const Tensor tensor);\nEXPORT_API(Tensor) THSNN_ConvTranspose3d_weight(const NNModule module);\nEXPORT_API(void) THSNN_ConvTranspose3d_set_weight(const NNModule module, const Tensor weight);\nEXPORT_API(Tensor) THSNN_ConvTranspose3d_bias(const NNModule module);\nEXPORT_API(void) THSNN_ConvTranspose3d_set_bias(const NNModule module, const Tensor bias);\n\n// Normalization\n\nEXPORT_API(NNModule) THSNN_BatchNorm1d_ctor(const int64_t features, const double eps, const double momentum, const bool affine, const bool track_running_stats, NNAnyModule* outAsAnyModule);\nEXPORT_API(Tensor) THSNN_BatchNorm1d_forward(const NNModule module, const Tensor tensor);\nEXPORT_API(NNModule) THSNN_BatchNorm2d_ctor(const int64_t features, const double eps, const double momentum, const bool affine, const bool track_running_stats, NNAnyModule* outAsAnyModule);\nEXPORT_API(Tensor) THSNN_BatchNorm2d_forward(const NNModule module, const Tensor tensor);\nEXPORT_API(NNModule) THSNN_BatchNorm3d_ctor(const int64_t features, const double eps, const double momentum, const bool affine, const bool track_running_stats, NNAnyModule* outAsAnyModule);\nEXPORT_API(Tensor) THSNN_BatchNorm3d_forward(const NNModule module, const Tensor tensor);\n\nEXPORT_API(Tensor) THSNN_BatchNorm1d_bias(const NNModule module);\nEXPORT_API(void) THSNN_BatchNorm1d_set_bias(const NNModule module, const Tensor bias);\nEXPORT_API(Tensor) THSNN_BatchNorm1d_weight(const NNModule module);\nEXPORT_API(void) THSNN_BatchNorm1d_set_weight(const NNModule module, const Tensor weight);\n\nEXPORT_API(Tensor) THSNN_BatchNorm2d_bias(const NNModule module);\nEXPORT_API(void) THSNN_BatchNorm2d_set_bias(const NNModule module, const Tensor bias);\nEXPORT_API(Tensor) THSNN_BatchNorm2d_weight(const NNModule module);\nEXPORT_API(void) THSNN_BatchNorm2d_set_weight(const NNModule module, const Tensor weight);\n\nEXPORT_API(Tensor) THSNN_BatchNorm3d_bias(const NNModule module);\nEXPORT_API(void) THSNN_BatchNorm3d_set_bias(const NNModule module, const Tensor bias);\nEXPORT_API(Tensor) THSNN_BatchNorm3d_weight(const NNModule module);\nEXPORT_API(void) THSNN_BatchNorm3d_set_weight(const NNModule module, const Tensor weight);\n\nEXPORT_API(void) THSNN_BatchNorm1d_reset_stats(const NNModule module);\nEXPORT_API(void) THSNN_BatchNorm2d_reset_stats(const NNModule module);\nEXPORT_API(void) THSNN_BatchNorm3d_reset_stats(const NNModule module);\n\nEXPORT_API(Tensor) THSNN_BatchNorm1d_get_mean(const NNModule module);\nEXPORT_API(Tensor) THSNN_BatchNorm2d_get_mean(const NNModule module);\nEXPORT_API(Tensor) THSNN_BatchNorm3d_get_mean(const NNModule module);\n\nEXPORT_API(void) THSNN_BatchNorm1d_set_mean(const NNModule module, const Tensor weight);\nEXPORT_API(void) THSNN_BatchNorm2d_set_mean(const NNModule module, const Tensor weight);\nEXPORT_API(void) THSNN_BatchNorm3d_set_mean(const NNModule module, const Tensor weight);\n\nEXPORT_API(Tensor) THSNN_BatchNorm1d_get_var(const NNModule module);\nEXPORT_API(Tensor) THSNN_BatchNorm2d_get_var(const NNModule module);\nEXPORT_API(Tensor) THSNN_BatchNorm3d_get_var(const NNModule module);\n\nEXPORT_API(void) THSNN_BatchNorm1d_set_var(const NNModule module, const Tensor weight);\nEXPORT_API(void) THSNN_BatchNorm2d_set_var(const NNModule module, const Tensor weight);\nEXPORT_API(void) THSNN_BatchNorm3d_set_var(const NNModule module, const Tensor weight);\n\nEXPORT_API(Tensor) THSNN_BatchNorm1d_get_batches(const NNModule module);\nEXPORT_API(Tensor) THSNN_BatchNorm2d_get_batches(const NNModule module);\nEXPORT_API(Tensor) THSNN_BatchNorm3d_get_batches(const NNModule module);\n\nEXPORT_API(NNModule) THSNN_InstanceNorm1d_ctor(const int64_t features, const double eps, const double momentum, const bool affine, const bool track_running_stats, NNAnyModule* outAsAnyModule);\nEXPORT_API(Tensor) THSNN_InstanceNorm1d_forward(const NNModule module, const Tensor tensor);\nEXPORT_API(NNModule) THSNN_InstanceNorm2d_ctor(const int64_t features, const double eps, const double momentum, const bool affine, const bool track_running_stats, NNAnyModule* outAsAnyModule);\nEXPORT_API(Tensor) THSNN_InstanceNorm2d_forward(const NNModule module, const Tensor tensor);\nEXPORT_API(NNModule) THSNN_InstanceNorm3d_ctor(const int64_t features, const double eps, const double momentum, const bool affine, const bool track_running_stats, NNAnyModule* outAsAnyModule);\nEXPORT_API(Tensor) THSNN_InstanceNorm3d_forward(const NNModule module, const Tensor tensor);\n\nEXPORT_API(Tensor) THSNN_InstanceNorm1d_bias(const NNModule module);\nEXPORT_API(void) THSNN_InstanceNorm1d_set_bias(const NNModule module, const Tensor bias);\nEXPORT_API(Tensor) THSNN_InstanceNorm1d_weight(const NNModule module);\nEXPORT_API(void) THSNN_InstanceNorm1d_set_weight(const NNModule module, const Tensor weight);\n\nEXPORT_API(Tensor) THSNN_InstanceNorm2d_bias(const NNModule module);\nEXPORT_API(void) THSNN_InstanceNorm2d_set_bias(const NNModule module, const Tensor bias);\nEXPORT_API(Tensor) THSNN_InstanceNorm2d_weight(const NNModule module);\nEXPORT_API(void) THSNN_InstanceNorm2d_set_weight(const NNModule module, const Tensor weight);\n\nEXPORT_API(Tensor) THSNN_InstanceNorm3d_bias(const NNModule module);\nEXPORT_API(void) THSNN_InstanceNorm3d_set_bias(const NNModule module, const Tensor bias);\nEXPORT_API(Tensor) THSNN_InstanceNorm3d_weight(const NNModule module);\nEXPORT_API(void) THSNN_InstanceNorm3d_set_weight(const NNModule module, const Tensor weight);\n\nEXPORT_API(void) THSNN_InstanceNorm1d_reset_stats(const NNModule module);\nEXPORT_API(void) THSNN_InstanceNorm2d_reset_stats(const NNModule module);\nEXPORT_API(void) THSNN_InstanceNorm3d_reset_stats(const NNModule module);\n\nEXPORT_API(Tensor) THSNN_InstanceNorm1d_get_mean(const NNModule module);\nEXPORT_API(Tensor) THSNN_InstanceNorm2d_get_mean(const NNModule module);\nEXPORT_API(Tensor) THSNN_InstanceNorm3d_get_mean(const NNModule module);\n\nEXPORT_API(void) THSNN_InstanceNorm1d_set_mean(const NNModule module, const Tensor weight);\nEXPORT_API(void) THSNN_InstanceNorm2d_set_mean(const NNModule module, const Tensor weight);\nEXPORT_API(void) THSNN_InstanceNorm3d_set_mean(const NNModule module, const Tensor weight);\n\nEXPORT_API(Tensor) THSNN_InstanceNorm1d_get_var(const NNModule module);\nEXPORT_API(Tensor) THSNN_InstanceNorm2d_get_var(const NNModule module);\nEXPORT_API(Tensor) THSNN_InstanceNorm3d_get_var(const NNModule module);\n\nEXPORT_API(void) THSNN_InstanceNorm1d_set_var(const NNModule module, const Tensor weight);\nEXPORT_API(void) THSNN_InstanceNorm2d_set_var(const NNModule module, const Tensor weight);\nEXPORT_API(void) THSNN_InstanceNorm3d_set_var(const NNModule module, const Tensor weight);\n\nEXPORT_API(Tensor) THSNN_InstanceNorm1d_get_batches(const NNModule module);\nEXPORT_API(Tensor) THSNN_InstanceNorm2d_get_batches(const NNModule module);\nEXPORT_API(Tensor) THSNN_InstanceNorm3d_get_batches(const NNModule module);\n\n\n\nEXPORT_API(NNModule) THSNN_LayerNorm_ctor(const int64_t* norm_shape, const int64_t norm_shape_len, const double eps, const bool elementwise_affine, NNAnyModule* outAsAnyModule);\nEXPORT_API(Tensor) THSNN_LayerNorm_forward(const NNModule module, const Tensor tensor);\n\nEXPORT_API(Tensor) THSNN_LayerNorm_bias(const NNModule module);\nEXPORT_API(void) THSNN_LayerNorm_set_bias(const NNModule module, const Tensor bias);\nEXPORT_API(Tensor) THSNN_LayerNorm_weight(const NNModule module);\nEXPORT_API(void) THSNN_LayerNorm_set_weight(const NNModule module, const Tensor weight);\n\nEXPORT_API(NNModule) THSNN_GroupNorm_ctor(const int64_t num_groups, const int64_t num_channels, const double eps, const bool affine, NNAnyModule* outAsAnyModule);\nEXPORT_API(Tensor) THSNN_GroupNorm_forward(const NNModule module, const Tensor tensor);\n\nEXPORT_API(Tensor) THSNN_GroupNorm_bias(const NNModule module);\nEXPORT_API(void) THSNN_GroupNorm_set_bias(const NNModule module, const Tensor bias);\nEXPORT_API(Tensor) THSNN_GroupNorm_weight(const NNModule module);\nEXPORT_API(void) THSNN_GroupNorm_set_weight(const NNModule module, const Tensor weight);\n\nEXPORT_API(NNModule) THSNN_LocalResponseNorm_ctor(const int64_t size, const double alpha, const double beta, const double k, NNAnyModule* outAsAnyModule);\nEXPORT_API(Tensor) THSNN_LocalResponseNorm_forward(const NNModule module, const Tensor tensor);\n\nEXPORT_API(Tensor) THSNN_batch_norm(const Tensor input, const Tensor running_mean, const Tensor running_var, const Tensor weight, const Tensor bias, const bool training, const double momentum, const double eps);\nEXPORT_API(Tensor) THSNN_group_norm(const Tensor input, int64_t num_groups, const Tensor weight, const Tensor bias, const double eps);\nEXPORT_API(Tensor) THSNN_instance_norm(const Tensor input, const Tensor running_mean, const Tensor running_var, const Tensor weight, const Tensor bias, const bool use_input_stats, const double momentum, const double eps);\nEXPORT_API(Tensor) THSNN_layer_norm(const Tensor input, const int64_t* normalized_shape, const int64_t normalized_shape_len, const Tensor weight, const Tensor bias, const double eps);\nEXPORT_API(Tensor) THSNN_local_response_norm(const Tensor input, const int64_t size, const double alpha, const double beta, const double k);\n\n// Dropout\n\nEXPORT_API(NNModule) THSNN_Dropout_ctor(double probability, bool inplace, NNAnyModule* outAsAnyModule);\nEXPORT_API(Tensor) THSNN_Dropout_forward(const NNModule module, const Tensor tensor);\nEXPORT_API(NNModule) THSNN_Dropout1d_ctor(double probability, bool inplace, NNAnyModule* outAsAnyModule);\nEXPORT_API(Tensor) THSNN_Dropout1d_forward(const NNModule module, const Tensor tensor);\nEXPORT_API(NNModule) THSNN_Dropout2d_ctor(double probability, bool inplace, NNAnyModule* outAsAnyModule);\nEXPORT_API(Tensor) THSNN_Dropout2d_forward(const NNModule module, const Tensor tensor);\nEXPORT_API(NNModule) THSNN_Dropout3d_ctor(double probability, bool inplace, NNAnyModule* outAsAnyModule);\nEXPORT_API(Tensor) THSNN_Dropout3d_forward(const NNModule module, const Tensor tensor);\n\nEXPORT_API(NNModule) THSNN_AlphaDropout_ctor(double probability, bool inplace, NNAnyModule* outAsAnyModule);\nEXPORT_API(Tensor) THSNN_AlphaDropout_forward(const NNModule module, const Tensor tensor);\n\nEXPORT_API(NNModule) THSNN_FeatureAlphaDropout_ctor(double probability, NNAnyModule* outAsAnyModule);\nEXPORT_API(Tensor) THSNN_FeatureAlphaDropout_forward(const NNModule module, const Tensor tensor);\n\nEXPORT_API(Tensor) THSNN_dropout(const Tensor input, const double p, bool training, bool inplace);\nEXPORT_API(Tensor) THSNN_dropout2d(const Tensor input, const double p, bool training, bool inplace);\nEXPORT_API(Tensor) THSNN_dropout3d(const Tensor input, const double p, bool training, bool inplace);\n\nEXPORT_API(Tensor) THSNN_alpha_dropout(const Tensor input, const double p, bool training, bool inplace);\n\nEXPORT_API(Tensor) THSNN_feature_alpha_dropout(const Tensor input, const double p, bool training, bool inplace);\n\nEXPORT_API(Tensor) THSNN_fold(const Tensor input, const int64_t out1, const int64_t out2, const int64_t kernel1, const int64_t kernel2, const int64_t stride1, const int64_t stride2, const int64_t pad1, const int64_t pad2, const int64_t dil1, const int64_t dil2);\nEXPORT_API(Tensor) THSNN_unfold(const Tensor input, const int64_t kernel1, const int64_t kernel2, const int64_t stride1, const int64_t stride2, const int64_t pad1, const int64_t pad2, const int64_t dil1, const int64_t dil2);\n\n// Linear\n\nEXPORT_API(NNModule) THSNN_Identity_ctor(NNAnyModule* outAsAnyModule);\nEXPORT_API(Tensor) THSNN_Identity_forward(const NNModule module, const Tensor tensor);\nEXPORT_API(NNModule) THSNN_Linear_ctor(const int64_t input_size, const int64_t output_size, const bool with_bias, NNAnyModule* outAsAnyModule);\nEXPORT_API(Tensor) THSNN_Linear_forward(const NNModule module, const Tensor tensor);\nEXPORT_API(Tensor) THSNN_Linear_bias(const NNModule module);\nEXPORT_API(void) THSNN_Linear_set_bias(const NNModule module, const Tensor tensor);\nEXPORT_API(Tensor) THSNN_Linear_weight(const NNModule module);\nEXPORT_API(void) THSNN_Linear_set_weight(const NNModule module, const Tensor tensor);\n\nEXPORT_API(Tensor) THSNN_functional_linear(const Tensor input, const Tensor weights, const Tensor bias);\nEXPORT_API(Tensor) THSNN_functional_bilinear(const Tensor input1, const Tensor input2, const Tensor weights, const Tensor bias);\n\nEXPORT_API(NNModule) THSNN_Bilinear_ctor(const int64_t input_size_1, const int64_t input_size_2, const int64_t output_size, const bool with_bias, NNAnyModule* outAsAnyModule);\nEXPORT_API(Tensor) THSNN_Bilinear_forward(const NNModule module, const Tensor x1, const Tensor x2);\nEXPORT_API(Tensor) THSNN_Bilinear_bias(const NNModule module);\nEXPORT_API(void) THSNN_Bilinear_set_bias(const NNModule module, const Tensor tensor);\nEXPORT_API(Tensor) THSNN_Bilinear_weight(const NNModule module);\nEXPORT_API(void) THSNN_Bilinear_set_weight(const NNModule module, const Tensor tensor);\n\n// Vision -- Modules\n\nEXPORT_API(NNModule) THSNN_PixelShuffle_ctor(const int64_t upscale_factor, NNAnyModule* outAsAnyModule);\nEXPORT_API(Tensor) THSNN_PixelShuffle_forward(const NNModule module, const Tensor tensor);\nEXPORT_API(NNModule) THSNN_PixelUnshuffle_ctor(const int64_t downscale_factor, NNAnyModule* outAsAnyModule);\nEXPORT_API(Tensor) THSNN_PixelUnshuffle_forward(const NNModule module, const Tensor tensor);\nEXPORT_API(NNModule) THSNN_Upsample_ctor(const int64_t* size, const int size_len, const double* scale_factor, const int scale_factor_len, const int8_t mode, const int8_t align_corners, NNAnyModule* outAsAnyModule);\nEXPORT_API(Tensor) THSNN_Upsample_forward(const NNModule module, const Tensor tensor);\n\n// Vision -- Functions\n\nEXPORT_API(Tensor) THSNN_pad(const Tensor input, const int64_t* pad, const int pad_length, const int8_t mode, const double value);\nEXPORT_API(Tensor) THSNN_interpolate(const Tensor input, const int64_t* size, const int size_len, const double* scale_factor, const int scale_factor_len, const int8_t mode, const int8_t align_corners, const bool recompute_scale_factor, NNAnyModule* outAsAnyModule);\nEXPORT_API(Tensor) THSNN_grid_sample(const Tensor input, const Tensor grid, const int8_t mode, const int8_t padding_mode, const int8_t align_corners);\nEXPORT_API(Tensor) THSNN_affine_grid(const Tensor theta, const int64_t* size, const int size_len, const bool align_corners);\n\n// Activation functions\n\nEXPORT_API(NNModule) THSNN_CELU_ctor(const double alpha, const bool inplace, NNAnyModule* outAsAnyModule);\nEXPORT_API(Tensor) THSNN_CELU_forward(const NNModule module, const Tensor tensor);\nEXPORT_API(NNModule) THSNN_ELU_ctor(const double alpha, const bool inplace, NNAnyModule* outAsAnyModule);\nEXPORT_API(Tensor) THSNN_ELU_forward(const NNModule module, const Tensor tensor);\nEXPORT_API(NNModule) THSNN_GELU_ctor(NNAnyModule* outAsAnyModule);\nEXPORT_API(Tensor) THSNN_GELU_forward(const NNModule module, const Tensor tensor);\nEXPORT_API(NNModule) THSNN_GLU_ctor(const int64_t dim, NNAnyModule* outAsAnyModule);\nEXPORT_API(Tensor) THSNN_GLU_forward(const NNModule module, const Tensor tensor);\nEXPORT_API(NNModule) THSNN_Hardshrink_ctor(const double lambda, NNAnyModule* outAsAnyModule);\nEXPORT_API(Tensor) THSNN_Hardshrink_forward(const NNModule module, const Tensor tensor);\nEXPORT_API(NNModule) THSNN_Hardtanh_ctor(const double min_val, const double max_val, const bool inplace, NNAnyModule* outAsAnyModule);\nEXPORT_API(Tensor) THSNN_Hardtanh_forward(const NNModule module, const Tensor tensor);\nEXPORT_API(NNModule) THSNN_LeakyReLU_ctor(const double negative_sloope, const bool inplace, NNAnyModule* outAsAnyModule);\nEXPORT_API(Tensor) THSNN_LeakyReLU_forward(const NNModule module, const Tensor tensor);\nEXPORT_API(NNModule) THSNN_Mish_ctor(NNAnyModule* outAsAnyModule);\nEXPORT_API(Tensor) THSNN_Mish_forward(const NNModule module, const Tensor tensor);\nEXPORT_API(NNModule) THSNN_PReLU_ctor(const int64_t nparams, const double init, NNAnyModule* outAsAnyModule);\nEXPORT_API(Tensor) THSNN_PReLU_forward(const NNModule module, const Tensor tensor);\nEXPORT_API(Tensor) THSNN_PReLU_weight(const NNModule module);\nEXPORT_API(void) THSNN_PReLU_set_weight(const NNModule module, const Tensor weight);\nEXPORT_API(NNModule) THSNN_ReLU_ctor(bool inplace, NNAnyModule* outAsAnyModule);\nEXPORT_API(Tensor) THSNN_ReLU_forward(const NNModule module, const Tensor tensor);\nEXPORT_API(NNModule) THSNN_ReLU6_ctor(bool inplace, NNAnyModule* outAsAnyModule);\nEXPORT_API(Tensor) THSNN_ReLU6_forward(const NNModule module, const Tensor tensor);\nEXPORT_API(NNModule) THSNN_RReLU_ctor(const double lower, const double upper, const bool inplace, NNAnyModule* outAsAnyModule);\nEXPORT_API(Tensor) THSNN_RReLU_forward(const NNModule module, const Tensor tensor);\nEXPORT_API(NNModule) THSNN_LogSoftmax_ctor(int64_t dim, NNAnyModule* outAsAnyModule);\nEXPORT_API(Tensor) THSNN_LogSoftmax_forward(const NNModule module, const Tensor tensor);\nEXPORT_API(NNModule) THSNN_SELU_ctor(bool inplace, NNAnyModule* outAsAnyModule);\nEXPORT_API(Tensor) THSNN_SELU_forward(const NNModule module, const Tensor tensor);\nEXPORT_API(NNModule) THSNN_Sigmoid_ctor(NNAnyModule* outAsAnyModule);\nEXPORT_API(Tensor) THSNN_Sigmoid_forward(const NNModule module, const Tensor tensor);\nEXPORT_API(NNModule) THSNN_SiLU_ctor(NNAnyModule* outAsAnyModule);\nEXPORT_API(Tensor) THSNN_SiLU_forward(const NNModule module, const Tensor tensor);\nEXPORT_API(NNModule) THSNN_Softmax_ctor(const int64_t dim, NNAnyModule* outAsAnyModule);\nEXPORT_API(Tensor) THSNN_Softmax_forward(const NNModule module, const Tensor tensor);\nEXPORT_API(NNModule) THSNN_Softmax2d_ctor(NNAnyModule* outAsAnyModule);\nEXPORT_API(Tensor) THSNN_Softmax2d_forward(const NNModule module, const Tensor tensor);\nEXPORT_API(NNModule) THSNN_Softmin_ctor(const int64_t dim, NNAnyModule* outAsAnyModule);\nEXPORT_API(Tensor) THSNN_Softmin_forward(const NNModule module, const Tensor tensor);\nEXPORT_API(NNModule) THSNN_Softplus_ctor(const double beta, const double threshold, NNAnyModule* outAsAnyModule);\nEXPORT_API(Tensor) THSNN_Softplus_forward(const NNModule module, const Tensor tensor);\nEXPORT_API(NNModule) THSNN_Softshrink_ctor(const double lambda, NNAnyModule* outAsAnyModule);\nEXPORT_API(Tensor) THSNN_Softshrink_forward(const NNModule module, const Tensor tensor);\nEXPORT_API(NNModule) THSNN_Softsign_ctor(NNAnyModule* outAsAnyModule);\nEXPORT_API(Tensor) THSNN_Softsign_forward(const NNModule module, const Tensor tensor);\nEXPORT_API(NNModule) THSNN_Tanh_ctor(NNAnyModule* outAsAnyModule);\nEXPORT_API(Tensor) THSNN_Tanh_forward(const NNModule module, const Tensor tensor);\nEXPORT_API(NNModule) THSNN_Tanhshrink_ctor(NNAnyModule* outAsAnyModule);\nEXPORT_API(Tensor) THSNN_Tanhshrink_forward(const NNModule module, const Tensor tensor);\nEXPORT_API(NNModule) THSNN_Threshold_ctor(const double threshold, const double value, const bool inplace, NNAnyModule* outAsAnyModule);\nEXPORT_API(Tensor) THSNN_Threshold_forward(const NNModule module, const Tensor tensor);\n\n// Sparse\n\nEXPORT_API(NNModule) THSNN_Embedding_ctor(const int64_t num_embeddings, const int64_t embedding_dims, const int64_t padding_idx, bool has_pi, const double max_norm, const bool has_mn, const double norm_type, const bool scale_grad_by_freq, const bool sparse, NNAnyModule* outAsAnyModule);\nEXPORT_API(NNModule) THSNN_Embedding_from_pretrained(const Tensor embeddings, const bool freeze, const int64_t padding_idx, bool has_pi, const double max_norm, const bool has_mn, const double norm_type, const bool scale_grad_by_freq, const bool sparse, NNAnyModule* outAsAnyModule);\nEXPORT_API(Tensor) THSNN_Embedding_forward(const NNModule module, const Tensor weights);\nEXPORT_API(Tensor) THSNN_Embedding_weight(const NNModule module);\nEXPORT_API(void) THSNN_Embedding_set_weight(const NNModule module, const Tensor weights);\n\nEXPORT_API(NNModule) THSNN_EmbeddingBag_ctor(const int64_t num_embeddings, const int64_t embedding_dims, const double max_norm, const bool has_mn, const double norm_type, const bool scale_grad_by_freq, const int64_t mode, const bool sparse, const bool include_last_offset, const int64_t padding_idx, NNAnyModule* outAsAnyModule);\nEXPORT_API(NNModule) THSNN_EmbeddingBag_from_pretrained(const Tensor embeddings, const bool freeze, const double max_norm, const bool has_mn, const double norm_type, const bool scale_grad_by_freq, const int64_t mode, const bool sparse, const bool include_last_offset, const int64_t padding_idx, NNAnyModule* outAsAnyModule);\nEXPORT_API(Tensor) THSNN_EmbeddingBag_forward(const NNModule module, const Tensor input, const Tensor offsets, const Tensor per_sample_weights);\nEXPORT_API(Tensor) THSNN_EmbeddingBag_weight(const NNModule module);\nEXPORT_API(void) THSNN_EmbeddingBag_set_weight(const NNModule module, const Tensor weights);\n\n// Transformer\n\nEXPORT_API(NNModule) THSNN_Transformer_ctor(const int64_t d_model, const int64_t nhead, const int64_t num_encoder_layers, const int64_t num_decoder_layers, const int64_t dim_feedforward, const double dropout, const int64_t activation, NNAnyModule* outAsAnyModule);\nEXPORT_API(Tensor) THSNN_Transformer_forward(const NNModule module, const Tensor src, const Tensor tgt, const Tensor src_mask, const Tensor tgt_mask, const Tensor memory_mask, const Tensor src_key_padding_mask, const Tensor tgt_key_padding_mask, const Tensor memory_key_padding_mask);\nEXPORT_API(NNModule) THSNN_TransformerEncoderLayer_ctor(const int64_t d_model, const int64_t nhead, const int64_t dim_feedforward, const double dropout, const int64_t activation, NNAnyModule* outAsAnyModule);\nEXPORT_API(Tensor) THSNN_TransformerEncoderLayer_forward(const NNModule module, const Tensor src, const Tensor src_mask, const Tensor src_key_padding_mask);\nEXPORT_API(NNModule) THSNN_TransformerDecoderLayer_ctor(const int64_t d_model, const int64_t nhead, const int64_t dim_feedforward, const double dropout, const int64_t activation, NNAnyModule* outAsAnyModule);\nEXPORT_API(Tensor) THSNN_TransformerDecoderLayer_forward(const NNModule module, const Tensor tgt, const Tensor memory, const Tensor tgt_mask, const Tensor memory_mask, const Tensor tgt_key_padding_mask, const Tensor memory_key_padding_mask);\nEXPORT_API(NNModule) THSNN_TransformerEncoder_ctor(const NNModule encoder_layer, const int64_t num_layers, NNAnyModule* outAsAnyModule);\nEXPORT_API(Tensor) THSNN_TransformerEncoder_forward(const NNModule module, const Tensor src, const Tensor src_mask, const Tensor src_key_padding_mask);\nEXPORT_API(NNModule) THSNN_TransformerDecoder_ctor(const NNModule decoder_layer, const int64_t num_layers, NNAnyModule* outAsAnyModule);\nEXPORT_API(Tensor) THSNN_TransformerDecoder_forward(const NNModule module, const Tensor tgt, const Tensor memory, const Tensor tgt_mask, const Tensor memory_mask, const Tensor tgt_key_padding_mask, const Tensor memory_key_padding_mask);\nEXPORT_API(NNModule) THSNN_MultiheadAttention_ctor(const int64_t embeded_dim, const int64_t num_heads, const double dropout, const bool bias, const bool add_bias_kv, const bool add_zero_attn, const int64_t kdim, const int64_t vdim, NNAnyModule* outAsAnyModule);\nEXPORT_API(void) THSNN_MultiheadAttention_forward(const NNModule module, const Tensor query, const Tensor key, const Tensor value, const Tensor key_padding_mask, const bool need_weights, const Tensor attn_mask, Tensor& res1, Tensor& res2);\n\n\n// Recurrent\n\nEXPORT_API(NNModule) THSNN_RNN_ctor(const int64_t input_size, const int64_t hidden_size, const int64_t num_layers, const int64_t nonlinearity, const bool bias, const bool batchFirst, const double dropout, const bool bidirectional, NNAnyModule* outAsAnyModule);\nEXPORT_API(Tensor) THSNN_RNN_forward(const NNModule module, const Tensor input1, const Tensor input2, Tensor* h_n);\nEXPORT_API(PackedSequence) THSNN_RNN_forward_with_packed_input(const NNModule module, const PackedSequence input1, const Tensor input2, Tensor* h_n);\nEXPORT_API(NNModule) THSNN_GRU_ctor(const int64_t input_size, const int64_t hidden_size, const int64_t num_layers, const bool bias, const bool batchFirst, const double dropout, const bool bidirectional, NNAnyModule* outAsAnyModule);\nEXPORT_API(Tensor) THSNN_GRU_forward(const NNModule module, const Tensor input1, const Tensor input2, Tensor* h_n);\nEXPORT_API(PackedSequence) THSNN_GRU_forward_with_packed_input(const NNModule module, const PackedSequence input1, const Tensor input2, Tensor* h_n);\nEXPORT_API(NNModule) THSNN_LSTM_ctor(const int64_t input_size, const int64_t hidden_size, const int64_t num_layers, const bool bias, const bool batchFirst, const double dropout, const bool bidirectional, NNAnyModule* outAsAnyModule);\nEXPORT_API(Tensor) THSNN_LSTM_forward(const NNModule module, const Tensor input1, const Tensor h0, const Tensor c0, Tensor* h_n, Tensor* c_n);\nEXPORT_API(PackedSequence) THSNN_LSTM_forward_with_packed_input(const NNModule module, const PackedSequence input1, const Tensor h0, const Tensor c0, Tensor* h_n, Tensor* c_n);\n\nEXPORT_API(NNModule) THSNN_RNNCell_ctor(const int64_t input_size, const int64_t hidden_size, const int64_t nonlinearity, const bool bias, NNAnyModule* outAsAnyModule);\nEXPORT_API(Tensor) THSNN_RNNCell_forward(const NNModule module, const Tensor input1, const Tensor h0);\nEXPORT_API(NNModule) THSNN_GRUCell_ctor(const int64_t input_size, const int64_t hidden_size, const bool bias, NNAnyModule* outAsAnyModule);\nEXPORT_API(Tensor) THSNN_GRUCell_forward(const NNModule module, const Tensor input1, const Tensor h0);\nEXPORT_API(NNModule) THSNN_LSTMCell_ctor(const int64_t input_size, const int64_t hidden_size, const bool bias, NNAnyModule* outAsAnyModule);\nEXPORT_API(Tensor) THSNN_LSTMCell_forward(const NNModule module, const Tensor input1, const Tensor h0, const Tensor c0, Tensor* c_n);\n\nEXPORT_API(void) THSNN_RNN_flatten_parameters(const NNModule module);\nEXPORT_API(void) THSNN_GRU_flatten_parameters(const NNModule module);\nEXPORT_API(void) THSNN_LSTM_flatten_parameters(const NNModule module);\n\nEXPORT_API(Tensor) THSNN_RNN_bias_ih(const NNModule module, const int64_t idx);\nEXPORT_API(void) THSNN_RNN_set_bias_ih(const NNModule module, const Tensor bias, const int64_t idx);\nEXPORT_API(Tensor) THSNN_RNN_weight_ih(const NNModule module, const int64_t idx);\nEXPORT_API(void) THSNN_RNN_set_weight_ih(const NNModule module, const Tensor weight, const int64_t idx);\nEXPORT_API(Tensor) THSNN_RNN_bias_hh(const NNModule module, const int64_t idx);\nEXPORT_API(void) THSNN_RNN_set_bias_hh(const NNModule module, const Tensor bias, const int64_t idx);\nEXPORT_API(Tensor) THSNN_RNN_weight_hh(const NNModule module, const int64_t idx);\nEXPORT_API(void) THSNN_RNN_set_weight_hh(const NNModule module, const Tensor weight, const int64_t idx);\n\nEXPORT_API(Tensor) THSNN_RNNCell_bias_ih(const NNModule module);\nEXPORT_API(void) THSNN_RNNCell_set_bias_ih(const NNModule module, const Tensor bias);\nEXPORT_API(Tensor) THSNN_RNNCell_weight_ih(const NNModule module);\nEXPORT_API(void) THSNN_RNNCell_set_weight_ih(const NNModule module, const Tensor weight);\nEXPORT_API(Tensor) THSNN_RNNCell_bias_hh(const NNModule module);\nEXPORT_API(void) THSNN_RNNCell_set_bias_hh(const NNModule module, const Tensor bias);\nEXPORT_API(Tensor) THSNN_RNNCell_weight_hh(const NNModule module);\nEXPORT_API(void) THSNN_RNNCell_set_weight_hh(const NNModule module, const Tensor weight);\n\nEXPORT_API(Tensor) THSNN_LSTMCell_bias_ih(const NNModule module);\nEXPORT_API(void) THSNN_LSTMCell_set_bias_ih(const NNModule module, const Tensor bias);\nEXPORT_API(Tensor) THSNN_LSTMCell_weight_ih(const NNModule module);\nEXPORT_API(void) THSNN_LSTMCell_set_weight_ih(const NNModule module, const Tensor weight);\nEXPORT_API(Tensor) THSNN_LSTMCell_bias_hh(const NNModule module);\nEXPORT_API(void) THSNN_LSTMCell_set_bias_hh(const NNModule module, const Tensor bias);\nEXPORT_API(Tensor) THSNN_LSTMCell_weight_hh(const NNModule module);\nEXPORT_API(void) THSNN_LSTMCell_set_weight_hh(const NNModule module, const Tensor weight);\n\nEXPORT_API(Tensor) THSNN_GRUCell_bias_ih(const NNModule module);\nEXPORT_API(void) THSNN_GRUCell_set_bias_ih(const NNModule module, const Tensor bias);\nEXPORT_API(Tensor) THSNN_GRUCell_weight_ih(const NNModule module);\nEXPORT_API(void) THSNN_GRUCell_set_weight_ih(const NNModule module, const Tensor weight);\nEXPORT_API(Tensor) THSNN_GRUCell_bias_hh(const NNModule module);\nEXPORT_API(void) THSNN_GRUCell_set_bias_hh(const NNModule module, const Tensor bias);\nEXPORT_API(Tensor) THSNN_GRUCell_weight_hh(const NNModule module);\nEXPORT_API(void) THSNN_GRUCell_set_weight_hh(const NNModule module, const Tensor weight);\n\n// Containers\n\nEXPORT_API(NNModule) THSNN_Sequential_ctor();\nEXPORT_API(void) THSNN_Sequential_push_back(const NNModule module, const char* name, const NNAnyModule submodule);\nEXPORT_API(Tensor) THSNN_Sequential_forward(const NNModule module, const Tensor tensor);\n\n// Loss functions\n\nEXPORT_API(Tensor) THSNN_binary_cross_entropy(const Tensor input, const Tensor target, const Tensor weight, const int64_t reduction);\nEXPORT_API(Tensor) THSNN_binary_cross_entropy_with_logits(const Tensor input, const Tensor target, const Tensor weight, const int64_t reduction, const Tensor pos_weights_);\nEXPORT_API(Tensor) THSNN_cosine_embedding_loss(const Tensor input1, const Tensor input2, const Tensor target, const double margin, const int64_t reduction);\nEXPORT_API(Tensor) THSNN_cross_entropy(const Tensor input, const Tensor target, const Tensor weight, const int64_t ignore_index, const bool has_ii, const int64_t reduction, const double smoothing);\nEXPORT_API(Tensor) THSNN_ctc_loss(const Tensor log_probs, const Tensor targets, const Tensor input_lengths, const Tensor target_lengths, int64_t blank, bool zero_infinity, const int64_t reduction);\nEXPORT_API(Tensor) THSNN_hinge_embedding_loss(const Tensor input, const Tensor target, const double margin, const int64_t reduction);\nEXPORT_API(Tensor) THSNN_huber_loss(const Tensor input, const Tensor target, const double delta, const int64_t reduction);\nEXPORT_API(Tensor) THSNN_l1_loss(const Tensor input, const Tensor target, const int64_t reduction);\nEXPORT_API(Tensor) THSNN_margin_ranking_loss(const Tensor input1, const Tensor input2, const Tensor target, const double margin, const int64_t reduction);\nEXPORT_API(Tensor) THSNN_mse_loss(const Tensor input, const Tensor target, const int64_t reduction);\nEXPORT_API(Tensor) THSNN_multilabel_margin_loss(const Tensor input, const Tensor target, const int64_t reduction);\nEXPORT_API(Tensor) THSNN_multilabel_soft_margin_loss(const Tensor input, const Tensor target, const Tensor weight, const int64_t reduction);\nEXPORT_API(Tensor) THSNN_multi_margin_loss(const Tensor input, const Tensor target, const int64_t p, const double margin, const Tensor weight, const int64_t reduction);\nEXPORT_API(Tensor) THSNN_nll_loss(const Tensor input, const Tensor target, const Tensor weight, const int64_t reduction);\nEXPORT_API(Tensor) THSNN_poisson_loss(const Tensor input, const Tensor target, const bool logInput, const bool full, const double eps, const int64_t reduction);\nEXPORT_API(Tensor) THSNN_kl_div_loss(const Tensor input, const Tensor target, const int64_t reduction, const bool log_target);\nEXPORT_API(Tensor) THSNN_smooth_l1_loss(const Tensor input, const Tensor target, const int64_t reduction, const double beta);\nEXPORT_API(Tensor) THSNN_soft_margin_loss(const Tensor input, const Tensor target, const int64_t reduction);\nEXPORT_API(Tensor) THSNN_triplet_margin_loss(const Tensor anchor, const Tensor positive, const Tensor negative, double margin, int64_t p, double eps, bool swap, const int64_t reduction);\nEXPORT_API(Tensor) THSNN_triplet_margin_with_distance_loss(const Tensor anchor, const Tensor positive, const Tensor negative, Tensor (*distance_function)(const Tensor x, const Tensor y), double margin, bool swap, const int64_t reduction);\n\n// Optimizers\n\nEXPORT_API(Optimizer) THSNN_Adagrad_ctor(const Tensor* parameters, const int len, const double learning_rate, const double lr_decay, const double weight_decay, const double initial_accumulator_value, const double eps);\nEXPORT_API(Optimizer) THSNN_Adam_ctor(const Tensor* parameters, const int len, const double learning_rate, const double beta1, const double beta2, const double eps, const double weight_decay, const bool amsgrad);\nEXPORT_API(Optimizer) THSNN_AdamW_ctor(const Tensor* parameters, const int len, const double learning_rate, const double beta1, const double beta2, const double eps, const double weight_decay, const bool amsgrad);\nEXPORT_API(Optimizer) THSNN_LBFGS_ctor(const Tensor* parameters, const int len, const double lr, const int64_t max_iter, const int64_t max_eval, const double tolerange_grad, const double tolerance_change, const int64_t history_size);\nEXPORT_API(Optimizer) THSNN_RMSprop_ctor(const Tensor* parameters, const int length, const double learning_rate, const double alpha, const double eps, const double weight_decay, const double momentum, const bool centered);\nEXPORT_API(Optimizer) THSNN_SGD_ctor(const Tensor* parameters, const int length, const double learning_rate, const double momentum, const double dampening, const double weight_decay, const bool nesterov);\n\nEXPORT_API(void) THSNN_Adam_set_betas(const Optimizer optimizer, double beta1, double beta2);\nEXPORT_API(void) THSNN_AdamW_set_betas(const Optimizer optimizer, double beta1, double beta2);\nEXPORT_API(void) THSNN_RMSprop_set_momentum(const Optimizer optimizer, double momentum);\nEXPORT_API(void) THSNN_SGD_set_momentum(const Optimizer optimizer, double momentum);\n\nEXPORT_API(void) THSNN_Optimizer_zero_grad(const Optimizer optimizer);\nEXPORT_API(void) THSNN_Optimizer_getParameters(const Optimizer optimizer, Tensor* (*allocator)(size_t length));\nEXPORT_API(Tensor) THSNN_Optimizer_step(const Optimizer optimizer, Tensor(*loss_closure)());\n\nEXPORT_API(void) THSNN_Optimizer_dispose(const Optimizer optimizer);\n\nEXPORT_API(void) THSNN_Adagrad_set_lr(const Optimizer optimizer, const double lr);\nEXPORT_API(void) THSNN_Adam_set_lr(const Optimizer optimizer, const double lr);\nEXPORT_API(void) THSNN_AdamW_set_lr(const Optimizer optimizer, const double lr);\nEXPORT_API(void) THSNN_LBFGS_set_lr(const Optimizer optimizer, const double lr);\nEXPORT_API(void) THSNN_RMSprop_set_lr(const Optimizer optimizer, const double lr);\nEXPORT_API(void) THSNN_SGD_set_lr(const Optimizer optimizer, const double lr);\n\n// Misc.\n\nEXPORT_API(Tensor) THSNN_one_hot(const Tensor self, const int64_t num_classes);\n\nEXPORT_API(NNModule) THSNN_Flatten_ctor(const int64_t start_dim, const int64_t end_dim, NNAnyModule* outAsAnyModule);\nEXPORT_API(Tensor) THSNN_Flatten_forward(const NNModule module, const Tensor tensor);\n\nEXPORT_API(NNModule) THSNN_Unflatten_ctor(const int64_t dim, const int64_t* shape, const int64_t shape_len, NNAnyModule* outAsAnyModule);\nEXPORT_API(Tensor) THSNN_Unflatten_forward(const NNModule module, const Tensor tensor);\n\nEXPORT_API(NNModule) THSNN_CosineSimilarity_ctor(const int64_t dim, double eps, NNAnyModule* outAsAnyModule);\nEXPORT_API(Tensor) THSNN_CosineSimilarity_forward(const NNModule module, const Tensor input1, const Tensor input2);\n\nEXPORT_API(NNModule) THSNN_PairwiseDistance_ctor(double p, double eps, bool keep_dim, NNAnyModule* outAsAnyModule);\nEXPORT_API(Tensor) THSNN_PairwiseDistance_forward(const NNModule module, const Tensor input1, const Tensor input2);\n\nEXPORT_API(Tensor) THSNN_scaled_dot_product_attention(const Tensor query, const Tensor key, const Tensor value, const Tensor attention_mask, double p, bool casual);\n\n// Initializers\n\nEXPORT_API(void) THSNN_initUniform(Tensor twrapper, double low, double high);\nEXPORT_API(void) THSNN_initKaimingUniform(Tensor tensor, double a);\n\n// Utils RNN\n\nEXPORT_API(Tensor) THSNN_PackedSequence_data(PackedSequence sequence);\nEXPORT_API(Tensor) THSNN_PackedSequence_batch_sizes(PackedSequence sequence);\nEXPORT_API(Tensor) THSNN_PackedSequence_sorted_indices(PackedSequence sequence);\nEXPORT_API(Tensor) THSNN_PackedSequence_unsorted_indices(PackedSequence sequence);\nEXPORT_API(void) THSNN_PackedSequence_dispose(PackedSequence sequence);\nEXPORT_API(PackedSequence) THSNN_pack_padded_sequence(Tensor input, Tensor lengths, bool batch_first, bool enforce_sorted);\nEXPORT_API(void) THSNN_pad_packed_sequence(PackedSequence sequence, bool batch_first, double padding_value, int64_t total_length, Tensor* res1, Tensor* res2);\nEXPORT_API(Tensor) THSNN_pad_sequence(const Tensor* sequences, const int sequences_len, bool batch_first, double padding_value);\nEXPORT_API(PackedSequence) THSNN_pack_sequence(const Tensor* sequences, int sequences_len, bool enforce_sorted);\n"},{"alpha_fraction":0.5700336694717407,"alphanum_fraction":0.577104389667511,"avg_line_length":43.3283576965332,"blob_id":"e68621f6b0776cfa4e2e92b351544d06f20a8044","content_id":"16360fdc6c2ccad37f338ad9bc8092fca312bc52","detected_licenses":["MIT"],"is_generated":false,"is_vendor":false,"language":"C#","length_bytes":2970,"license_type":"permissive","max_line_length":142,"num_lines":67,"path":"/src/TorchSharp/NN/PairwiseDistance.cs","repo_name":"NiklasGustafsson/TorchSharp","src_encoding":"UTF-8","text":"// Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information.\nusing System;\nusing static TorchSharp.torch;\nusing static TorchSharp.PInvoke.NativeMethods;\n\nnamespace TorchSharp\n{\n using Modules;\n\n namespace Modules\n {\n /// <summary>\n /// Computes the pairwise distance between vectors using the p-norm.\n /// </summary>\n public sealed class PairwiseDistance : torch.nn.Module<Tensor, Tensor, Tensor>\n {\n internal PairwiseDistance(IntPtr handle, IntPtr boxedHandle) : base(handle, boxedHandle)\n {\n }\n\n public override Tensor forward(Tensor input1, Tensor input2)\n {\n var res = THSNN_PairwiseDistance_forward(handle, input1.Handle, input2.Handle);\n if (res == IntPtr.Zero) { torch.CheckForErrors(); }\n return new Tensor(res);\n }\n\n // Rather than spending cycles only to discover that this module has neither\n // parameters nor buffers, just shortcut the move completely.\n protected internal override nn.Module _to(Device device, ScalarType dtype) => this;\n protected internal override nn.Module _to(DeviceType deviceType, int deviceIndex = -1) => this;\n protected internal override nn.Module _to(ScalarType dtype) => this;\n }\n }\n\n public static partial class torch\n {\n public static partial class nn\n {\n public static PairwiseDistance PairwiseDistance(double p = 2.0, double eps = 1e-6, bool keep_dim = false)\n {\n var handle = THSNN_PairwiseDistance_ctor(p, eps, keep_dim, out var boxedHandle);\n if (handle == IntPtr.Zero) { torch.CheckForErrors(); }\n return new PairwiseDistance(handle, boxedHandle);\n }\n\n public static partial class functional\n {\n /// <summary>\n /// Computes the pairwise distance between vectors using the p-norm.\n /// </summary>\n /// <param name=\"input1\">(N,D) or (D) where N = batch dimension and D = vector dimension</param>\n /// <param name=\"input2\">(N, D) or (D), same shape as the Input1</param>\n /// <param name=\"p\">The norm degree. Default: 2</param>\n /// <param name=\"eps\">Small value to avoid division by zero.</param>\n /// <param name=\"keep_dim\">Determines whether or not to keep the vector dimension.</param>\n /// <returns></returns>\n public static Tensor pairwise_distance(Tensor input1, Tensor input2, double p = 2.0, double eps = 1e-6, bool keep_dim = false)\n {\n using (var f = nn.PairwiseDistance(p, eps, keep_dim)) {\n return f.call(input1, input2);\n }\n }\n }\n }\n }\n}\n"},{"alpha_fraction":0.6527947783470154,"alphanum_fraction":0.66431725025177,"avg_line_length":50.632911682128906,"blob_id":"a0e605a034a0040ae475d0c2351586a8ba26b61e","content_id":"f5ee6fe6e72a1a84e480c70db25fddb229c6d4e4","detected_licenses":["MIT"],"is_generated":false,"is_vendor":false,"language":"Markdown","length_bytes":16316,"license_type":"permissive","max_line_length":542,"num_lines":316,"path":"/docfx/articles/modules.md","repo_name":"NiklasGustafsson/TorchSharp","src_encoding":"UTF-8","text":"# Creating Your Own TorchSharp Modules\n\nUnfortunatly, the word 'Module' is one of the most overloaded terms in software. That said, the module concept is central to TorchSharp, which means we have to define its contextual meaning. \n\nIn the context of TorchSharp, it means the same as in PyTorch: the fundamental building block of all models is the 'Module' class. All neural network layers are derived from Module and the way to create a model for training and inference in your code is to create a new Module. Without it, back-propagation will not work.\n\n## Sequential\n\nFor the most basic network architecture, which actually covers a surprising breadth, there is a simple way to create modules -- the 'Sequential' class. This class is created from a list of Modules (i.e. model components). When data is passed to the model in the form of a tensor, the Sequential instance will invoke each submodule in the order they were passed when the Sequential instance was created, passing the output from each layer to the next. The output of the final submodule will be the output of the Sequential instance.\n\n```C#\nvar seq = Sequential((\"lin1\", Linear(100, 10)), (\"lin2\", Linear(10, 5)));\n...\nseq.forward(some_data);\n```\n\nThere is no real performance reason to use Sequential instead of rolling your own custom module, but there is much less code to write. That said, if using a custom module (up next) is your preference, for whatever reason, that's what you should do. It can be useful to create a custom module first, debug it, and then convert to using Sequential.\n\nSequential can only string together modules that take a single tensor and return a single tensor -- anything else needs to be a custom module.\n\n## Custom Modules\n\nA custom module is created by deriving a subclass from torch.nn.Module<T...,TResult>. The first generic parameters denote the input types of the module's forward() method:\n\n```C#\n private class TestModule1 : Module<Tensor,Tensor>\n {\n public TestModule1()\n : base(\"TestModule1\")\n {\n lin1 = Linear(100, 10);\n lin2 = Linear(10, 5);\n RegisterComponents();\n }\n\n public override Tensor forward(Tensor input)\n {\n using (var x = lin1.forward(input))\n return lin2.forward(x);\n }\n\n private Module lin1;\n private Module lin2;\n }\n```\n\nNote that the field names in the module class correspond to the names that were passed in to the Sequential constructor in the earlier section.\n\nCustom modules should always call `RegisterComponents()` once all submodules and buffers (see discussion later in this document) have been created. This will register the modules and its parameters with the native runtime, which is essential for it to function correctly. For this to work properly, each submodule should have its own private field (**not** property) in the class. The only exception are submodules that do not have trainable weights, such as activation functions like ReLU or tanh.\n\nThe `forward()` method contains the computation of the module. It can contain a mix of TorchSharp primitives, layers, as well as any .NET code. Note, however, that only TorchSharp APIs are capable of operating on data residing in CUDA memory. Therefore, if performance is of the essence, expressing all computation in terms of TorchSharp APIs is essential. Non-TorchSharp APIs should be limited to things that aren't related to the tensor data, things like logging, for example.\n\nIn PyTorch, the `forward()` method takes an arbitrary number of arguments, of any type, and supports using default arguments. \n\nIn TorchSharp, the signature of `forward()` is determined by the type parameter signature of the Module<> base class. In most cases, the input and output are both `torch.Tensor`. As noted above, the Sequential module collection class assumes that it is passing only a single tensor along between its layers. Therefore, any model that needs to pass multiple arguments between layers will have to be custom.\n\nGoing back to the code inside the `forward()` method -- please note that the local variable 'x' was declared in a using statement. This is important in order to deallocate the native memory associated with it as soon as it is no longer needed. For that reason, it is important to pull out temporaries like this into local variables, especially when the code is running on a GPU. (More on this at: [Dispose vs. GC in TorchSharp](memory.md)) \n\nIn other words, the following code is less memory efficient, because it delays reclaiming native memory until the next time GC is run:\n\n```C#\n public override Tensor forward(Tensor input)\n {\n return lin2.forward(lin1.forward(input));\n }\n```\n\n## Using Sequential Inside A Custome Module\n\nCustom modules are often combined with Sequential, which is especially useful when building long chains of repetitive blocks inside a custom module. In the C# TorchSharp examples, VGG, MobileNet, and ResNet demonstrate this well. \n\nTo illustrate, this is the code for MobileNet from the TorchSharp examples:\n\n```C#\n class MobileNet : Module\n {\n private readonly long[] planes = new long[] { 64, 128, 128, 256, 256, 512, 512, 512, 512, 512, 512, 1024, 1024 };\n private readonly long[] strides = new long[] { 1, 2, 1, 2, 1, 2, 1, 1, 1, 1, 1, 2, 1 };\n\n private readonly Module layers;\n\n public MobileNet(string name, int numClasses, Device device = null) : base(name)\n {\n var modules = new List<(string, Module)>();\n\n modules.Add((\"conv2d-first\", \n Conv2d(3, 32, kernelSize: 3, stride: 1, padding: 1, bias: false)));\n modules.Add((\"bnrm2d-first\", \n BatchNorm2d(32)));\n modules.Add((\"relu-first\", \n ReLU()));\n MakeLayers(modules, 32);\n modules.Add((\"avgpool\", \n AvgPool2d(new long[] { 2, 2 })));\n modules.Add((\"flatten\", \n Flatten()));\n modules.Add((\"linear\", \n Linear(planes[^1], numClasses)));\n\n layers = Sequential(modules);\n\n RegisterComponents();\n }\n\n private void MakeLayers(List<(string, Module)> modules, long in_planes)\n {\n\n for (var i = 0; i < strides.Length; i++) {\n var out_planes = planes[i];\n var stride = strides[i];\n\n modules.Add(($\"conv2d-{i}a\", \n Conv2d(in_planes, in_planes, kernelSize: 3, stride: stride, padding: 1, groups: in_planes, bias: false)));\n modules.Add(($\"bnrm2d-{i}a\", \n BatchNorm2d(in_planes)));\n modules.Add(($\"relu-{i}a\", \n ReLU()));\n modules.Add(($\"conv2d-{i}b\", \n Conv2d(in_planes, out_planes, kernelSize: 1L, stride: 1L, padding: 0L, bias: false)));\n modules.Add(($\"bnrm2d-{i}b\", \n BatchNorm2d(out_planes)));\n modules.Add(($\"relu-{i}b\", \n ReLU()));\n\n in_planes = out_planes;\n }\n }\n\n public override Tensor forward(Tensor input)\n {\n return layers.forward(input);\n }\n }\n```\n\n## ModuleList\n\nIn some circumstances, it's useful to define a dynamic number of modules in a custom module. It could be because you want to parameterize the network architecture, or dynamically choose which layers to run, or just that its tedious to define so many fields. This may be addressed by using a ModuleList to contain the submodules. Unlike Sequential, ModuleList itself does not suffice -- its `forward()` method will throw an exception if invoked, and you must iterate through the modules of the list directly in your `forward()` implementation.\n\nThe purpose is simply to provide a list implementation that automatically registers the submodules when components are registered. You have to iterate through the list in the `forward()` method:\n\n```C#\n private class TestModule1 : Module<Tensor,Tensor>\n {\n public TestModule1()\n : base(\"TestModule1\")\n {\n RegisterComponents();\n }\n\n public override Tensor forward(Tensor input)\n {\n for (int i = 0; i < submodules.Count; i++) { // Using 'for' instead of 'foreach' can be useful for debugging.\n input = submodules[i].forward(input); \n }\n }\n\n private ModuleList submodules = new ModuleList(Linear(100, 10), Linear(10, 5));\n }\n```\n\n## ModuleDict\n\nIn some circumstances, it's useful to define a dynamic number of modules in a custom module and use a dictionary to hold them. Like with `ModuleList`, it could be because you want to parameterize the network architecture, or dynamically choose which layers to run, or just that its tedious to define so many fields. This may be addressed by using a ModuleDict to contain the submodules.\n\nThe purpose is simply to provide a list implementation that automatically registers the submodules when components are registered. You have to iterate through the list in the `forward()` method:\n\n```C#\n private class TestModule1 : Module<Tensor,Tensor>\n {\n public TestModule1()\n : base(\"TestModule1\")\n {\n dict.Add(\"lin1\", Linear(100, 10));\n dict.Add(\"lin2\", Linear(10, 5));\n RegisterComponents();\n }\n\n public override Tensor forward(Tensor input)\n {\n using (var x = submodules[\"lin1\"].forward(input))\n return submodules[\"lin2\"].forward(x);\n }\n\n private ModuleDict submodules = new ModuleDict();\n }\n```\n\nModuleDict is an ordered dictionary, so you can also iterate through the dictionary as if it were a list. If so, you will get a sequence of tuples with the submodule name and module in each iteration.\n\n## Parameter\n\nMany modules are just compositions of existing modules, but sometimes it will implement a novel algorithm. In this case, the `forward()` method will not just pass tensors from one module to another, but actually use TorchSharp operators and functions to perform arithmetic directly. If this requires training of parameters, those parameters should be declared directly in the module. The Parameter class is a wrapper around tensor; its only purpose is to make sure that it is registered with the native runtime.\n\nFor example, a re-implementation of 'Linear' would look something like:\n\n```C#\n private class MyLinear : Module<Tensor,Tensor>\n {\n public MyLinear(long input_size, long output_size)\n : base(\"MyLinear\")\n {\n weights = Parameter(torch.randn(input_size, output_size));\n bias = Parameter(torch.zeros(output_size));\n RegisterComponents();\n }\n\n public override Tensor forward(Tensor input)\n {\n var mm = torch.matmul(input,weights);\n return mm.add_(bias);\n }\n\n private Parameter weights;\n private Parameter bias;\n }\n```\n\nIn this case, we're not relying on 'using' in the `forward()` method, because the \"temporary\" is reused as the target by the `add_()` function.\n\nParameter's dirty little secret is that it will clean out the tensor that is given to its constructor. So, `Parameter()` is preferrably used with another tensor factory (such as in the example above), or a cloned tensor. Once you have passes a tensor to the Parameter constructor, the original tensor is invalidated.\n\n## ParameterList\n\nMuch like ModuleList, ParameterList is a list of Parameter instances, which is automatically registered with the runtime if found as a field of a module instance.\n\n## ParameterDict\n\nMuch like ModuleDict, ParameterDict is a dictionary of Parameter instances, which is automatically registered with the runtime if found as a field of a module instance.\n\n## Buffers\n\nSometimes, a module needs to allocate tensor that are not trainable, i.e. their values are not modified during back-propagation. An example is a random dropout mask. These are referred to as 'buffers' as opposed to 'parameters' and are treated differently by `RegisterComponents()` -- even though they are not trainable, the native runtime still wants to know about them for other purposes, such as storing them to disk, so it is important to declare them in the module.\n\nEach buffer should be declared as a field of type 'Tensor' (not 'Parameter'). This will ensure that the buffer is registered properly when `RegisterComponents()` is called.\n\n\n## Modules, 'children()' and 'named_children()'\n\nIt is sometimes necessary to create a new model from an existing one and discard some of the final layers. The submodules will appear in the 'named_children' list in the same order that they are declared within the module itself, and when constructing a model based on the children, the layers may be reordered unless the submodules are declared in the same order that they are meant to be invoked.\n\nSo, for example:\n\n```C#\n private class TestModule1 : Module<Tensor,Tensor>\n {\n public TestModule1()\n : base(\"TestModule1\")\n {\n lin1 = Linear(100, 10);\n lin2 = Linear(10, 5);\n RegisterComponents();\n }\n\n public override Tensor forward(Tensor input)\n {\n using (var x = lin1.forward(input))\n return lin2.forward(x);\n }\n\n // Correct -- the layers are declared in the same order they are invoked.\n private Module lin1;\n private Module lin2;\n }\n\n private class TestModule2 : Module<Tensor,Tensor>\n {\n public TestModule2()\n : base(\"TestModule2\")\n {\n lin1 = Linear(100, 10);\n lin2 = Linear(10, 5);\n RegisterComponents();\n }\n\n public override Tensor forward(Tensor input)\n {\n using (var x = lin1.forward(input))\n return lin2.forward(x);\n }\n\n // Incorrect -- the layers are not declared in the same order they are invoked.\n private Module lin2;\n private Module lin1;\n }\n\n ...\n TestModule1 mod1 = ...\n TestModule2 mod2 = ...\n var seq1 = nn.Sequential(mod1.named_children());\n seq1.forward(t); // Does the same as mod1.forward(t)\n var seq2 = nn.Sequential(mod2.named_children());\n seq2.forward(t); // This probably blows up.\n```\n\n\n## Moving and Converting Modules\n\nThere are a few ways to move and/or convert the parameters and buffers in a module, whether custom or not. The actual methods that are used are declared as extension methods on 'Module,' but the implementation is found in three virtual methods declared as part of Module itself:\n\n```C#\nprotected virtual Module _to(ScalarType dtype)\nprotected virtual Module _to(DeviceType deviceType, int deviceIndex = -1)\nprotected virtual Module _to(Device device, ScalarType dtype)\n```\n\nThe most likely reason for overriding any or all of these is to \"hook\" calls to moves and conversions rather than implementing it differently. The Module base class already goes through all children modules, declared parameters and buffers, so it should generally not be necessary to implement these methods separately. In th \"hook\" scenario, it is advisable to end the body with a call to 'base._to(...)' like the TransformerModel in the SequenceToSequence example does:\n\n```C#\nprotected override Module _to(DeviceType deviceType, int deviceIndex = -1)\n{\n this.device = new Device(deviceType, deviceIndex);\n return base._to(deviceType, deviceIndex);\n}\n```\nIn this case, the model needs to know what device the parameters and buffers are, because it repeatedly generates a mask during training, and this mask must live on the same device as the model parameters.\n"},{"alpha_fraction":0.5411071181297302,"alphanum_fraction":0.548352837562561,"avg_line_length":58.48850631713867,"blob_id":"9cd3c2fae9978e47d1de2af7afa1ae7302e17649","content_id":"d920eb7bce4f4322441f9ea03d2345ecae27f9af","detected_licenses":["MIT"],"is_generated":false,"is_vendor":false,"language":"C#","length_bytes":10351,"license_type":"permissive","max_line_length":322,"num_lines":174,"path":"/src/TorchSharp/NN/Pooling/AvgPool3D.cs","repo_name":"NiklasGustafsson/TorchSharp","src_encoding":"UTF-8","text":"// Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information.\nusing System;\nusing static TorchSharp.torch;\nusing static TorchSharp.PInvoke.NativeMethods;\n\nnamespace TorchSharp\n{\n using Modules;\n\n namespace Modules\n {\n /// <summary>\n /// This class is used to represent a AvgPool3D module.\n /// </summary>\n public sealed class AvgPool3d : torch.nn.Module<Tensor, Tensor>\n {\n internal AvgPool3d(IntPtr handle, IntPtr boxedHandle) : base(handle, boxedHandle)\n {\n }\n\n public override Tensor forward(Tensor tensor)\n {\n var res = THSNN_AvgPool3d_forward(handle.DangerousGetHandle(), tensor.Handle);\n if (res == IntPtr.Zero) { torch.CheckForErrors(); }\n return new Tensor(res);\n }\n\n // Rather than spending cycles only to discover that this module has neither\n // parameters nor buffers, just shortcut the move completely.\n protected internal override nn.Module _to(Device device, ScalarType dtype) => this;\n protected internal override nn.Module _to(DeviceType deviceType, int deviceIndex = -1) => this;\n protected internal override nn.Module _to(ScalarType dtype) => this;\n }\n }\n\n public static partial class torch\n {\n public static partial class nn\n {\n /// <summary>\n /// Applies a 3D average pooling over an input signal composed of several input planes.\n /// </summary>\n /// <param name=\"kernel_size\">The size of the window</param>\n /// <param name=\"strides\">The stride of the window. Default value is kernel_size</param>\n /// <param name=\"padding\">implicit zero padding to be added on both sides</param>\n /// <param name=\"ceil_mode\">Whether to use ceil instead of floor to compute the output shape</param>\n /// <param name=\"count_include_pad\">Whether to include the zero-padding in the averaging calculation</param>\n /// <param name=\"divisor_override\">If specified, it will be used as divisor, otherwise size of the pooling region will be used</param>\n public static AvgPool3d AvgPool3d(long[] kernel_size, long[] strides = null, long[] padding = null, bool ceil_mode = false, bool count_include_pad = true, long? divisor_override = null)\n {\n unsafe {\n fixed (long* pkernelSize = kernel_size, pstrides = strides, ppadding = padding) {\n var handle = THSNN_AvgPool3d_ctor((IntPtr)pkernelSize, kernel_size.Length, (IntPtr)pstrides, (strides == null ? 0 : strides.Length), (IntPtr)ppadding, (padding == null ? 0 : padding.Length), ceil_mode, count_include_pad, divisor_override.HasValue ? divisor_override.Value : 0, out var boxedHandle);\n if (handle == IntPtr.Zero) { torch.CheckForErrors(); }\n return new AvgPool3d(handle, boxedHandle);\n }\n }\n }\n\n /// <summary>\n /// Applies a 3D average pooling over an input signal composed of several input planes.\n /// </summary>\n /// <param name=\"kernel_size\">The size of the window</param>\n /// <param name=\"stride\">The stride of the window. Default value is kernel_size</param>\n /// <param name=\"padding\">implicit zero padding to be added on both sides</param>\n /// <param name=\"ceil_mode\">Whether to use ceil instead of floor to compute the output shape</param>\n /// <param name=\"count_include_pad\">Whether to include the zero-padding in the averaging calculation</param>\n /// <param name=\"divisor_override\">If specified, it will be used as divisor, otherwise size of the pooling region will be used</param>\n public static unsafe AvgPool3d AvgPool3d((long, long, long) kernel_size, (long, long, long)? stride = null, (long, long, long)? padding = null, bool ceil_mode = false, bool count_include_pad = true, long? divisor_override = null)\n {\n long svalue1 = (stride == null) ? kernel_size.Item1 : stride.Value.Item1;\n long svalue2 = (stride == null) ? kernel_size.Item2 : stride.Value.Item2;\n long svalue3 = (stride == null) ? kernel_size.Item3 : stride.Value.Item3;\n\n long pvalue1 = (padding == null) ? 0 : stride.Value.Item1;\n long pvalue2 = (padding == null) ? 0 : stride.Value.Item2;\n long pvalue3 = (padding == null) ? 0 : stride.Value.Item3;\n\n long* pkernelSize = stackalloc long[3] { kernel_size.Item1, kernel_size.Item2, kernel_size.Item3 };\n long* pstrides = stackalloc long[3] { svalue1, svalue2, svalue3 };\n long* ppadding = stackalloc long[3] { pvalue1, pvalue2, pvalue3 };\n\n var handle = THSNN_AvgPool3d_ctor((IntPtr)pkernelSize, 3, (IntPtr)pstrides, 3, (IntPtr)ppadding, 3, ceil_mode, count_include_pad, divisor_override.HasValue ? divisor_override.Value : 0, out var boxedHandle);\n if (handle == IntPtr.Zero) { torch.CheckForErrors(); }\n return new AvgPool3d(handle, boxedHandle);\n }\n\n /// <summary>\n /// Applies a 3D average pooling over an input signal composed of several input planes.\n /// </summary>\n /// <param name=\"kernel_size\">The size of the window</param>\n /// <param name=\"stride\">The stride of the window. Default value is kernel_size</param>\n /// <param name=\"padding\">implicit zero padding to be added on both sides</param>\n /// <param name=\"ceil_mode\">Whether to use ceil instead of floor to compute the output shape</param>\n /// <param name=\"count_include_pad\">Whether to include the zero-padding in the averaging calculation</param>\n /// <param name=\"divisor_override\">If specified, it will be used as divisor, otherwise size of the pooling region will be used</param>\n public static unsafe AvgPool3d AvgPool3d(long kernel_size, long? stride = null, long? padding = null, bool ceil_mode = false, bool count_include_pad = true, long? divisor_override = null)\n {\n long svalue = (stride == null) ? kernel_size : stride.Value;\n long pvalue = (stride == null) ? 0 : padding.Value;\n\n long* pkernelSize = stackalloc long[3] { kernel_size, kernel_size, kernel_size };\n long* pstrides = stackalloc long[3] { svalue, svalue, svalue };\n long* ppadding = stackalloc long[3] { pvalue, pvalue, pvalue };\n\n var handle = THSNN_AvgPool3d_ctor((IntPtr)pkernelSize, 3, (IntPtr)pstrides, 3, (IntPtr)ppadding, 3, ceil_mode, count_include_pad, divisor_override.HasValue ? divisor_override.Value : 0, out var boxedHandle);\n if (handle == IntPtr.Zero) { torch.CheckForErrors(); }\n return new AvgPool3d(handle, boxedHandle);\n }\n\n public static partial class functional\n {\n /// <summary>\n /// Applies 3D average-pooling operation in kT x kH x kW regions by step size sT x sH x sW steps.\n /// </summary>\n /// <param name=\"input\">The input tensor.</param>\n /// <param name=\"kernelSizes\"></param>\n /// <param name=\"strides\"></param>\n /// <param name=\"paddings\"></param>\n /// <param name=\"ceil_mode\"></param>\n /// <param name=\"count_include_pad\"></param>\n /// <returns></returns>\n public static Tensor avg_pool3d(Tensor input, long[] kernelSizes,\n long[] strides = null,\n long[] paddings = null,\n bool ceil_mode = false,\n bool count_include_pad = true)\n {\n strides = (strides == null) ? new long[] { 1 } : strides;\n paddings = (paddings == null) ? new long[] { 0 } : paddings;\n unsafe {\n fixed (long* pkernelSize = kernelSizes, pstrides = strides, ppadding = paddings) {\n var res =\n THSTensor_avg_pool3d(input.Handle,\n (IntPtr)pkernelSize, kernelSizes.Length,\n (IntPtr)pstrides, strides.Length,\n (IntPtr)ppadding, paddings.Length,\n ceil_mode,\n count_include_pad);\n if (res == IntPtr.Zero) { torch.CheckForErrors(); }\n return new Tensor(res);\n }\n }\n }\n\n public static Tensor avg_pool3d_backward(Tensor input, Tensor originalInput,\n long[] kernelSizes,\n long[] strides = null,\n long[] paddings = null,\n bool ceil_mode = false,\n bool count_include_pad = true,\n long divisorOverride = 0)\n {\n strides = (strides == null) ? new long[] { 1 } : strides;\n paddings = (paddings == null) ? new long[] { 0 } : paddings;\n unsafe {\n fixed (long* pkernelSize = kernelSizes, pstrides = strides, ppadding = paddings) {\n var res =\n THSTensor_avg_pool3d_backward(input.Handle, originalInput.Handle,\n (IntPtr)pkernelSize, kernelSizes.Length,\n (IntPtr)pstrides, strides.Length,\n (IntPtr)ppadding, paddings.Length,\n ceil_mode,\n count_include_pad,\n divisorOverride);\n if (res == IntPtr.Zero) { torch.CheckForErrors(); }\n return new Tensor(res);\n }\n }\n }\n }\n }\n }\n}\n"},{"alpha_fraction":0.5389890670776367,"alphanum_fraction":0.5425450801849365,"avg_line_length":42.74444580078125,"blob_id":"1233e97601be87a90f079a1ffe1f4ea18dadb18c","content_id":"1ce8c12a69dc2aa49a1bf1b2d31e27d3088495c2","detected_licenses":["MIT"],"is_generated":false,"is_vendor":false,"language":"C#","length_bytes":7874,"license_type":"permissive","max_line_length":149,"num_lines":180,"path":"/src/TorchSharp/Distributions/Categorical.cs","repo_name":"NiklasGustafsson/TorchSharp","src_encoding":"UTF-8","text":"// Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information.\nusing System;\nusing System.Collections.Generic;\nusing System.Linq;\nusing static TorchSharp.torch;\n\nnamespace TorchSharp\n{\n using Modules;\n\n namespace Modules\n {\n public class Categorical : torch.distributions.Distribution\n {\n /// <summary>\n /// The mean of the distribution.\n /// </summary>\n public override Tensor mean => torch.full(ExtendedShape(), double.NaN, dtype: probs.dtype, device: probs.device);\n\n /// <summary>\n /// The variance of the distribution\n /// </summary>\n public override Tensor variance => torch.full(ExtendedShape(), double.NaN, dtype: probs.dtype, device: probs.device);\n\n /// <summary>\n /// Constructor\n /// </summary>\n /// <param name=\"probs\">Event probabilities</param>\n /// <param name=\"logits\">Even log-odds</param>\n /// <param name=\"generator\">An optional random number generator object.</param>\n public Categorical(Tensor probs = null, Tensor logits = null, torch.Generator generator = null) : base(generator)\n {\n var param = probs is null ? logits : probs;\n\n this._probs = (probs is not null) ? (probs / probs.sum(-1, keepdim: true)).DetachFromDisposeScope() : null;\n this._logits = (logits is not null) ? (logits - logits.logsumexp(-1, keepdim:true)).DetachFromDisposeScope() : null;\n this.num_events = param.size(-1);\n this.batch_shape = param.ndim > 1 ? param.shape.Take(param.shape.Length-1).ToArray() : new long[0];\n }\n\n /// <summary>\n /// Event probabilities\n /// </summary>\n public Tensor probs {\n get {\n return _probs ?? LogitsToProbs(_logits);\n }\n }\n\n /// <summary>\n /// Event log-odds\n /// </summary>\n public Tensor logits {\n get {\n return _logits ?? ProbsToLogits(_probs);\n }\n }\n\n /// <summary>\n /// The shape of the input parameter.\n /// </summary>\n public long[] param_shape {\n get {\n return _probs is null ? _logits.shape : _probs.shape;\n }\n }\n\n internal Tensor _probs;\n internal Tensor _logits;\n internal long num_events;\n\n /// <summary>\n /// Generates a sample_shape shaped reparameterized sample or sample_shape shaped batch of reparameterized samples\n /// if the distribution parameters are batched.\n /// </summary>\n /// <param name=\"sample_shape\">The sample shape.</param>\n public override Tensor rsample(params long[] sample_shape)\n {\n var probs_2d = probs.reshape(-1, num_events);\n var samples_2d = torch.multinomial(probs_2d, sample_shape.Aggregate<long, long>(1, (x, y) => x * y), true, generator).T;\n return samples_2d.reshape(ExtendedShape(sample_shape));\n }\n\n /// <summary>\n /// Returns the log of the probability density/mass function evaluated at `value`.\n /// </summary>\n /// <param name=\"value\"></param>\n public override Tensor log_prob(Tensor value)\n {\n value = value.@long().unsqueeze(-1);\n var valLogPmf = torch.broadcast_tensors(value, logits);\n value = valLogPmf[0][TensorIndex.Ellipsis, TensorIndex.Slice(null, 1)];\n return valLogPmf[1].gather(-1, value).squeeze(-1);\n }\n\n /// <summary>\n /// Returns entropy of distribution, batched over batch_shape.\n /// </summary>\n public override Tensor entropy()\n {\n var min_real = torch.finfo(logits.dtype).min;\n var logs = torch.clamp(logits, min: min_real);\n var p_log_p = logs * probs;\n return -p_log_p.sum(-1);\n }\n\n /// <summary>\n /// Returns a new distribution instance (or populates an existing instance provided by a derived class) with batch dimensions expanded to\n /// `batch_shape`. This method calls `torch.Tensor.expand()` on the distribution's parameters. As such, this does not allocate new\n /// memory for the expanded distribution instance.\n /// </summary>\n /// <param name=\"batch_shape\">Tthe desired expanded size.</param>\n /// <param name=\"instance\">new instance provided by subclasses that need to override `.expand`.</param>\n /// <returns></returns>\n public override distributions.Distribution expand(Size batch_shape, distributions.Distribution instance = null)\n {\n if (instance != null && !(instance is Categorical))\n throw new ArgumentException(\"expand(): 'instance' must be a Categorical distribution\");\n\n var param_shape = new List<long>();\n param_shape.AddRange(batch_shape);\n param_shape.Add(num_events);\n\n var shape = param_shape.ToArray();\n\n var p = _probs?.expand(shape);\n var l = _logits?.expand(shape);\n\n var newDistribution = ((instance == null) ? new Categorical(p, l, generator) : instance) as Categorical;\n\n newDistribution.batch_shape = batch_shape;\n if (newDistribution == instance) {\n newDistribution.num_events = this.num_events;\n newDistribution._probs = p;\n newDistribution._logits = l;\n }\n return newDistribution;\n }\n }\n\n }\n public static partial class torch\n {\n public static partial class distributions\n {\n /// <summary>\n /// Creates a Categorical distribution parameterized by `probs` or `logits` (but not both).\n ///\n /// Samples are integers from [0, K-1]` where `K` is probs.size(-1).\n /// \n /// If `probs` is 1-dimensional with length- `K`, each element is the relative probability\n /// of sampling the class at that index.\n /// \n /// If `probs` is N-dimensional, the first N-1 dimensions are treated as a batch of\n /// relative probability vectors.\n /// </summary>\n /// <param name=\"probs\">The probability of sampling '1'</param>\n /// <param name=\"logits\">The log-odds of sampling '1'</param>\n /// <param name=\"generator\">An optional random number generator object.</param>\n /// <returns></returns>\n public static Categorical Categorical(Tensor probs = null, Tensor logits = null, torch.Generator generator = null)\n {\n return new Categorical(probs, logits);\n }\n\n /// <summary>\n /// Creates an equal-probability categorical distribution parameterized by the number of categories.\n ///\n /// </summary>\n /// <param name=\"categories\">The number of categories.</param>\n /// <param name=\"generator\">An optional random number generator object.</param>\n /// <returns></returns>\n public static Categorical Categorical(int categories, torch.Generator generator = null)\n {\n var probs = torch.tensor(1.0 / categories).expand(categories);\n return new Categorical(probs, null, generator);\n }\n }\n }\n}\n"},{"alpha_fraction":0.7330131530761719,"alphanum_fraction":0.7362148761749268,"avg_line_length":52.03773498535156,"blob_id":"9b2476113534bcdcb768c38e889196bd1d5652e1","content_id":"c0d66ba2501804f7ee99e1f6fcb3ff88f1fd5ef3","detected_licenses":["MIT"],"is_generated":false,"is_vendor":false,"language":"C#","length_bytes":5622,"license_type":"permissive","max_line_length":195,"num_lines":106,"path":"/src/TorchSharp/PInvoke/LibTorchSharp.THSJIT.cs","repo_name":"NiklasGustafsson/TorchSharp","src_encoding":"UTF-8","text":"// Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information.\n#nullable enable\nusing System;\nusing System.Runtime.InteropServices;\n\nnamespace TorchSharp.PInvoke\n{\n#pragma warning disable CA2101\n internal static partial class NativeMethods\n {\n [DllImport(\"LibTorchSharp\", CharSet = CharSet.Ansi, BestFitMapping = false, ThrowOnUnmappableChar = true)]\n internal static extern void THSJIT_CompilationUnit_Invoke(IntPtr module, string name, IntPtr tensors, int length, AllocateIndexedPinnedArray allocator, out sbyte typeCode, int idx);\n\n [DllImport(\"LibTorchSharp\")]\n internal static extern void THSJIT_CompilationUnit_dispose(IntPtr handle);\n\n [DllImport(\"LibTorchSharp\", CharSet = CharSet.Ansi, BestFitMapping = false, ThrowOnUnmappableChar = true)]\n internal static extern IntPtr THSJIT_compile(string script);\n\n [DllImport(\"LibTorchSharp\")]\n internal static extern void THSJIT_Module_dispose(torch.nn.Module.HType handle);\n\n [DllImport(\"LibTorchSharp\")]\n internal static extern void THSJIT_Module_named_parameters(torch.nn.Module.HType module, AllocatePinnedArray allocator1, AllocatePinnedArray allocator2);\n\n [DllImport(\"LibTorchSharp\")]\n internal static extern void THSJIT_Module_named_buffers(torch.nn.Module.HType module, AllocatePinnedArray allocator1, AllocatePinnedArray allocator2);\n\n [DllImport(\"LibTorchSharp\")]\n internal static extern void THSJIT_Module_named_modules(torch.nn.Module.HType module, AllocatePinnedArray allocator1, AllocatePinnedArray allocator2);\n\n [DllImport(\"LibTorchSharp\")]\n internal static extern void THSJIT_Module_named_children(torch.nn.Module.HType module, AllocatePinnedArray allocator1, AllocatePinnedArray allocator2);\n\n [DllImport(\"LibTorchSharp\")]\n internal static extern long THSJIT_getNumModules(torch.nn.Module.HType module);\n\n [DllImport(\"LibTorchSharp\")]\n internal static extern int THSJIT_Module_num_inputs(torch.nn.Module.HType module);\n\n [DllImport(\"LibTorchSharp\")]\n internal static extern int THSJIT_Module_num_outputs(torch.nn.Module.HType module);\n\n [DllImport(\"LibTorchSharp\")]\n internal static extern void THSJIT_Module_train(torch.nn.Module.HType module, [MarshalAs(UnmanagedType.U1)] bool on);\n\n [DllImport(\"LibTorchSharp\")]\n internal static extern void THSJIT_Module_eval(torch.nn.Module.HType module);\n\n [DllImport(\"LibTorchSharp\")]\n [return: MarshalAs(UnmanagedType.U1)]\n internal static extern bool THSJIT_Module_is_training(torch.nn.Module.HType module);\n\n [DllImport(\"LibTorchSharp\")]\n internal static extern void THSJIT_Module_to_device(torch.nn.Module.HType module, long deviceType, long deviceIndex);\n\n [DllImport(\"LibTorchSharp\")]\n internal static extern void THSJIT_Module_to_device_dtype(torch.nn.Module.HType module, sbyte dtype, long deviceType, long deviceIndex);\n\n [DllImport(\"LibTorchSharp\")]\n internal static extern void THSJIT_Module_to_dtype(torch.nn.Module.HType module, sbyte dtype);\n\n [DllImport(\"LibTorchSharp\")]\n internal static extern IntPtr THSJIT_Module_getInputType(torch.nn.Module.HType module, int index);\n\n [DllImport(\"LibTorchSharp\")]\n internal static extern IntPtr THSJIT_getOutputType(torch.jit.Type.HType module, int index);\n\n [DllImport(\"LibTorchSharp\")]\n internal static extern void THSJIT_Module_forward(torch.nn.Module.HType module, IntPtr tensors, int length, AllocateIndexedPinnedArray allocator, out sbyte typeCode, int idx);\n\n [DllImport(\"LibTorchSharp\", CharSet = CharSet.Ansi, BestFitMapping = false, ThrowOnUnmappableChar = true)]\n internal static extern void THSJIT_Module_invoke(torch.nn.Module.HType module, string name, IntPtr tensors, int length, AllocateIndexedPinnedArray allocator, out sbyte typeCode, int idx);\n\n [DllImport(\"LibTorchSharp\", CharSet = CharSet.Ansi, BestFitMapping = false, ThrowOnUnmappableChar = true)]\n internal static extern IntPtr THSJIT_load(string filename, long deviceType, long deviceIndex);\n\n [DllImport(\"LibTorchSharp\", CharSet = CharSet.Ansi, BestFitMapping = false, ThrowOnUnmappableChar = true)]\n internal static extern void THSJIT_save(torch.nn.Module.HType handle, string filename);\n\n [DllImport(\"LibTorchSharp\")]\n internal static extern long THSJIT_TensorType_sizes(torch.jit.Type.HType handle, AllocatePinnedArray allocator);\n\n [DllImport(\"LibTorchSharp\")]\n internal static extern int THSJIT_getDimensionedTensorTypeDimensions(torch.jit.Type.HType handle);\n\n [DllImport(\"LibTorchSharp\", CharSet = CharSet.Ansi, BestFitMapping = false, ThrowOnUnmappableChar = true)]\n internal static extern string THSJIT_getDimensionedTensorDevice(torch.jit.Type.HType handle);\n\n [DllImport(\"LibTorchSharp\")]\n internal static extern sbyte THSJIT_TensorType_dtype(torch.jit.Type.HType handle);\n\n [DllImport(\"LibTorchSharp\")]\n internal static extern void THSJIT_Type_dispose(torch.jit.Type.HType handle);\n\n [DllImport(\"LibTorchSharp\")]\n internal static extern void THSJIT_TensorType_dispose(torch.jit.Type.HType handle);\n\n [DllImport(\"LibTorchSharp\")]\n internal static extern sbyte THSJIT_Type_kind(torch.jit.Type.HType handle);\n\n [DllImport(\"LibTorchSharp\")]\n internal static extern IntPtr THSJIT_Type_cast(torch.jit.Type.HType module);\n }\n#pragma warning restore CA2101\n}\n"},{"alpha_fraction":0.5343691110610962,"alphanum_fraction":0.5343691110610962,"avg_line_length":41.47999954223633,"blob_id":"cbf13b3685a0c3f831aa32a70641da9bec241dfd","content_id":"b5aa0c65356564de366b7f8807fd6b0434a013be","detected_licenses":["MIT"],"is_generated":false,"is_vendor":false,"language":"C#","length_bytes":4248,"license_type":"permissive","max_line_length":142,"num_lines":100,"path":"/src/TorchSharp/NN/Linear.cs","repo_name":"NiklasGustafsson/TorchSharp","src_encoding":"UTF-8","text":"// Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information.\nusing System;\nusing static TorchSharp.torch;\nusing static TorchSharp.torch.nn;\nusing static TorchSharp.PInvoke.NativeMethods;\n\n#nullable enable\nnamespace TorchSharp\n{\n using Modules;\n\n namespace Modules\n {\n public sealed class Linear : torch.nn.Module<Tensor, Tensor>\n {\n internal Linear(IntPtr handle, IntPtr boxedHandle) : base(handle, boxedHandle)\n {\n }\n\n public new static Linear Load(string modelPath)\n {\n var res = Module<Tensor, Tensor>.Load(modelPath);\n return new Linear(res.handle.DangerousGetHandle(), IntPtr.Zero);\n }\n\n public override Tensor forward(Tensor tensor)\n {\n var res = THSNN_Linear_forward(handle, tensor.Handle);\n if (res == IntPtr.Zero) { torch.CheckForErrors(); }\n return new Tensor(res);\n }\n\n public Parameter? bias {\n get {\n var res = THSNN_Linear_bias(handle);\n if (res == IntPtr.Zero) { torch.CheckForErrors(); }\n return ((res == IntPtr.Zero) ? null : new Parameter(res));\n }\n set {\n THSNN_Linear_set_bias(handle, value?.Handle ?? IntPtr.Zero);\n torch.CheckForErrors();\n ConditionallyRegisterParameter(\"bias\", value);\n }\n }\n\n public Parameter? weight {\n get {\n var res = THSNN_Linear_weight(handle);\n if (res == IntPtr.Zero) { torch.CheckForErrors(); }\n return (res == IntPtr.Zero) ? null : new Parameter(res);\n }\n set {\n THSNN_Linear_set_weight(handle, value!.Handle);\n torch.CheckForErrors();\n ConditionallyRegisterParameter(\"weight\", value);\n }\n }\n }\n }\n\n public static partial class torch\n {\n public static partial class nn\n {\n /// <summary>\n /// Applies a linear transformation to the incoming data.\n /// </summary>\n /// <param name=\"inputSize\">Size of each input sample</param>\n /// <param name=\"outputSize\">Size of each output sample</param>\n /// <param name=\"hasBias\">If set to false, the layer will not learn an additive bias.</param>\n /// <param name=\"device\">The desired device of the parameters and buffers in this module</param>\n /// <param name=\"dtype\">The desired floating point or complex dtype of the parameters and buffers in this module</param>\n public static Linear Linear(long inputSize, long outputSize, bool hasBias = true, Device? device = null, ScalarType? dtype = null)\n {\n var res = THSNN_Linear_ctor(inputSize, outputSize, hasBias, out var boxedHandle);\n if (res == IntPtr.Zero) { torch.CheckForErrors(); }\n\n return new Linear(res, boxedHandle).MoveModule<Linear>(device, dtype);\n }\n\n public static partial class functional\n {\n /// <summary>\n /// Applies a linear transformation to the incoming data.\n /// </summary>\n /// <param name=\"input\">Input tensor of shape (*,Hin)</param>\n /// <param name=\"weights\">Weights of shape (Hout,Hin) or (Hin)</param>\n /// <param name=\"bias\">Bias of shape (Hout) or ()</param>\n /// <returns>A tensor of shape (*,Hout) where '*' is the same as the subshape of the input.</returns>\n public static Tensor linear(Tensor input, Tensor weights, Tensor? bias = null)\n {\n IntPtr bPtr = bias?.Handle ?? IntPtr.Zero;\n var res = THSNN_functional_linear(input.Handle, weights.Handle, bPtr);\n if (res == IntPtr.Zero) { torch.CheckForErrors(); }\n return new Tensor(res);\n }\n }\n }\n }\n}\n"},{"alpha_fraction":0.5198135375976562,"alphanum_fraction":0.5225329995155334,"avg_line_length":37.43283462524414,"blob_id":"05cebbf97c1b1309357e33a13ff07075d87f68bc","content_id":"73b6ab1d7f6aad8634e678ec5e6b8145feddff75","detected_licenses":["MIT"],"is_generated":false,"is_vendor":false,"language":"C#","length_bytes":2574,"license_type":"permissive","max_line_length":125,"num_lines":67,"path":"/src/TorchVision/File.cs","repo_name":"NiklasGustafsson/TorchSharp","src_encoding":"UTF-8","text":"using System.IO;\nusing System.Threading.Tasks;\n\nusing static TorchSharp.torch;\n\nnamespace TorchSharp\n{\n public static partial class torchvision\n {\n partial class io\n {\n /// <summary>\n /// Reads a file into a <cref>Tensor</cref>.\n /// </summary>\n /// <param name=\"filename\">Path to the file.</param>\n /// <returns>\n /// One dimensional <cref>Tensor</cref> containing the bytes of the file.\n /// </returns>\n public static Tensor read_file(string filename)\n {\n return File.ReadAllBytes(filename);\n }\n\n /// <summary>\n /// Asynchronously reads a file into a <cref>Tensor</cref>.\n /// </summary>\n /// <param name=\"filename\">Path to the file.</param>\n /// <returns>\n /// A task that represents the asynchronous read operation.\n /// The value of the TResult parameter is a one dimensional <cref>Tensor</cref> containing the bytes of the file.\n /// </returns>\n public static async Task<Tensor> read_file_async(string filename)\n {\n byte[] data;\n\n using (FileStream stream = File.Open(filename, FileMode.Open)) {\n data = new byte[stream.Length];\n await stream.ReadAsync(data, 0, data.Length);\n }\n\n return data;\n }\n\n /// <summary>\n /// Writes a one dimensional <c>uint8</c> <cref>Tensor</cref> into a file.\n /// </summary>\n /// <param name=\"filename\">Path to the file.</param>\n /// <param name=\"data\">One dimensional <c>uint8</c> <cref>Tensor</cref>.</param>\n public static void write_file(string filename, Tensor data)\n {\n File.WriteAllBytes(filename, data.bytes.ToArray());\n }\n\n /// <summary>\n /// Asynchronously writes a one dimensional <c>uint8</c> <cref>Tensor</cref> into a file.\n /// </summary>\n /// <param name=\"filename\">Path to the file.</param>\n /// <param name=\"data\">One dimensional <c>uint8</c> <cref>Tensor</cref>.</param>\n public static async void write_file_async(string filename, Tensor data)\n {\n using (FileStream stream = File.Open(filename, FileMode.OpenOrCreate)) {\n await stream.WriteAsync(data.bytes.ToArray(), 0, (int)data.shape[0]);\n }\n }\n }\n }\n}"},{"alpha_fraction":0.5470979809761047,"alphanum_fraction":0.554709792137146,"avg_line_length":22.886363983154297,"blob_id":"1291b317d20afdd300825c6a6bc21fcd208c0ef6","content_id":"5167c75981ff5ea409b2f107ee7be550ada57ce9","detected_licenses":["MIT"],"is_generated":false,"is_vendor":false,"language":"C#","length_bytes":1051,"license_type":"permissive","max_line_length":130,"num_lines":44,"path":"/src/TorchSharp/Utils/BigEndianReader.cs","repo_name":"NiklasGustafsson/TorchSharp","src_encoding":"UTF-8","text":"// Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information.\nusing System;\nusing System.IO;\n\nnamespace TorchSharp.Utils\n{\n public class BigEndianReader\n {\n public BigEndianReader(BinaryReader baseReader)\n {\n mBaseReader = baseReader;\n }\n\n public int ReadInt32()\n {\n return BitConverter.ToInt32(ReadBigEndianBytes(4), 0);\n }\n\n public byte[] ReadBigEndianBytes(int count)\n {\n byte[] bytes = new byte[count];\n for (int i = count - 1; i >= 0; i--)\n bytes[i] = mBaseReader.ReadByte();\n\n return bytes;\n }\n\n public byte[] ReadBytes(int count)\n {\n return mBaseReader.ReadBytes(count);\n }\n\n public void Close()\n {\n mBaseReader.Close();\n }\n\n public Stream BaseStream {\n get { return mBaseReader.BaseStream; }\n }\n\n private BinaryReader mBaseReader;\n }\n}\n"},{"alpha_fraction":0.5101739168167114,"alphanum_fraction":0.5280209183692932,"avg_line_length":35.20164489746094,"blob_id":"fc47a1f549a0abf077c09ab9db5be28cc227455d","content_id":"6069186fba0a9c79dba88e5910b53498af703b82","detected_licenses":["MIT"],"is_generated":false,"is_vendor":false,"language":"C#","length_bytes":8797,"license_type":"permissive","max_line_length":133,"num_lines":243,"path":"/src/Examples/MNIST.cs","repo_name":"NiklasGustafsson/TorchSharp","src_encoding":"UTF-8","text":"// Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information.\nusing System;\nusing System.Diagnostics;\nusing static TorchSharp.torch;\n\nusing static TorchSharp.torch.nn;\nusing static TorchSharp.torch.utils.data;\n\nnamespace TorchSharp.Examples\n{\n /// <summary>\n /// Simple MNIST Convolutional model.\n /// </summary>\n /// <remarks>\n /// There are at least two interesting data sets to use with this example:\n ///\n /// 1. The classic MNIST set of 60000 images of handwritten digits.\n ///\n /// It is available at: http://yann.lecun.com/exdb/mnist/\n ///\n /// 2. The 'fashion-mnist' data set, which has the exact same file names and format as MNIST, but is a harder\n /// data set to train on. It's just as large as MNIST, and has the same 60/10 split of training and test\n /// data.\n /// It is available at: https://github.com/zalandoresearch/fashion-mnist/tree/master/data/fashion\n /// </remarks>\n public class MNIST\n {\n private static int _epochs = 1;\n private static int _trainBatchSize = 64;\n private static int _testBatchSize = 128;\n\n private readonly static int _logInterval = 100;\n\n internal static void Main(string[] args)\n {\n var dataset = args.Length > 0 ? args[0] : \"mnist\";\n var datasetPath = Environment.GetFolderPath(Environment.SpecialFolder.LocalApplicationData);\n\n torch.random.manual_seed(1);\n\n var cwd = Environment.CurrentDirectory;\n\n var device = torch.cuda.is_available() ? torch.CUDA : torch.CPU;\n Console.WriteLine($\"Running MNIST on {device.type.ToString()}\");\n Console.WriteLine($\"Dataset: {dataset}\");\n\n if (device.type == DeviceType.CUDA) {\n _trainBatchSize *= 4;\n _testBatchSize *= 4;\n }\n\n using var model = new Model(\"model\", device);\n\n var normImage = torchvision.transforms.Normalize(new double[] { 0.1307 }, new double[] { 0.3081 });\n\n using (Dataset train_data = torchvision.datasets.MNIST(datasetPath, true, download: true, target_transform: normImage),\n test_data = torchvision.datasets.MNIST(datasetPath, false, download: true, target_transform: normImage)) {\n\n TrainingLoop(\"mnist\", device, model, train_data, test_data);\n }\n }\n\n internal static void TrainingLoop(string dataset, Device device, Model model, Dataset train_data, Dataset test_data)\n {\n using var train = new DataLoader(train_data, _trainBatchSize, device: device, shuffle: true);\n using var test = new DataLoader(test_data, _testBatchSize, device: device, shuffle: false);\n\n if (device.type == DeviceType.CUDA) {\n _epochs *= 4;\n }\n\n var optimizer = torch.optim.Adam(model.parameters());\n\n var scheduler = torch.optim.lr_scheduler.StepLR(optimizer, 1, 0.75);\n\n Stopwatch sw = new Stopwatch();\n sw.Start();\n\n for (var epoch = 1; epoch <= _epochs; epoch++) {\n\n using (var d = torch.NewDisposeScope()) {\n\n Train(model, optimizer, torch.nn.NLLLoss(reduction: Reduction.Mean), train, epoch, train_data.Count);\n Test(model, torch.nn.NLLLoss(reduction: torch.nn.Reduction.Sum), test, test_data.Count);\n\n Console.WriteLine($\"End-of-epoch memory use: {GC.GetTotalMemory(false)}\");\n scheduler.step();\n }\n }\n\n sw.Stop();\n Console.WriteLine($\"Elapsed time: {sw.Elapsed.TotalSeconds:F1} s.\");\n\n Console.WriteLine(\"Saving model to '{0}'\", dataset + \".model.bin\");\n model.save(dataset + \".model.bin\");\n }\n\n internal class Model : Module<Tensor, Tensor>\n {\n private Module<Tensor, Tensor> conv1 = Conv2d(1, 32, 3);\n private Module<Tensor, Tensor> conv2 = Conv2d(32, 64, 3);\n private Module<Tensor, Tensor> fc1 = Linear(9216, 128);\n private Module<Tensor, Tensor> fc2 = Linear(128, 10);\n\n // These don't have any parameters, so the only reason to instantiate\n // them is performance, since they will be used over and over.\n private Module<Tensor, Tensor> pool1 = MaxPool2d(kernelSize: new long[] { 2, 2 });\n\n private Module<Tensor, Tensor> relu1 = ReLU();\n private Module<Tensor, Tensor> relu2 = ReLU();\n private Module<Tensor, Tensor> relu3 = ReLU();\n\n private Module<Tensor, Tensor> dropout1 = Dropout(0.25);\n private Module<Tensor, Tensor> dropout2 = Dropout(0.5);\n\n private Module<Tensor, Tensor> flatten = Flatten();\n private Module<Tensor, Tensor> logsm = LogSoftmax(1);\n\n public Model(string name, torch.Device device = null) : base(name)\n {\n RegisterComponents();\n\n if (device != null && device.type == DeviceType.CUDA)\n this.to(device);\n }\n\n public override Tensor forward(Tensor input)\n {\n // All these modules are private to the model and won't have hooks,\n // so we can use 'forward' instead of 'call'\n var l11 = conv1.forward(input);\n var l12 = relu1.forward(l11);\n\n var l21 = conv2.forward(l12);\n var l22 = relu2.forward(l21);\n var l23 = pool1.forward(l22);\n var l24 = dropout1.forward(l23);\n\n var x = flatten.forward(l24);\n\n var l31 = fc1.forward(x);\n var l32 = relu3.forward(l31);\n var l33 = dropout2.forward(l32);\n\n var l41 = fc2.forward(l33);\n\n return logsm.forward(l41);\n }\n\n protected override void Dispose(bool disposing)\n {\n if (disposing) {\n conv1.Dispose();\n conv2.Dispose();\n fc1.Dispose();\n fc2.Dispose();\n pool1.Dispose();\n relu1.Dispose();\n relu2.Dispose();\n relu3.Dispose();\n dropout1.Dispose();\n dropout2.Dispose();\n flatten.Dispose();\n logsm.Dispose();\n ClearModules();\n }\n base.Dispose(disposing);\n }\n }\n\n\n private static void Train(\n Model model,\n torch.optim.Optimizer optimizer,\n Loss<torch.Tensor, torch.Tensor, torch.Tensor> loss,\n DataLoader dataLoader,\n int epoch,\n long size)\n {\n model.train();\n\n int batchId = 1;\n long total = 0;\n\n Console.WriteLine($\"Epoch: {epoch}...\");\n\n using (var d = torch.NewDisposeScope()) {\n\n foreach (var data in dataLoader) {\n optimizer.zero_grad();\n\n var target = data[\"label\"];\n var prediction = model.call(data[\"data\"]);\n var output = loss.call(prediction, target);\n\n output.backward();\n\n optimizer.step();\n\n total += target.shape[0];\n\n if (batchId % _logInterval == 0 || total == size) {\n Console.WriteLine($\"\\rTrain: epoch {epoch} [{total} / {size}] Loss: {output.ToSingle():F4}\");\n }\n\n batchId++;\n\n d.DisposeEverything();\n }\n }\n }\n\n private static void Test(\n Model model,\n Loss<torch.Tensor, torch.Tensor, torch.Tensor> loss,\n DataLoader dataLoader,\n long size)\n {\n model.eval();\n\n double testLoss = 0;\n int correct = 0;\n\n using (var d = torch.NewDisposeScope()) {\n\n foreach (var data in dataLoader) {\n var prediction = model.call(data[\"data\"]);\n var output = loss.call(prediction, data[\"label\"]);\n testLoss += output.ToSingle();\n\n var pred = prediction.argmax(1);\n correct += pred.eq(data[\"label\"]).sum().ToInt32();\n\n d.DisposeEverything();\n }\n }\n\n Console.WriteLine($\"Size: {size}, Total: {size}\");\n\n Console.WriteLine($\"\\rTest set: Average loss {(testLoss / size):F4} | Accuracy {((double)correct / size):P2}\");\n }\n }\n}\n"},{"alpha_fraction":0.5167227983474731,"alphanum_fraction":0.5228196382522583,"avg_line_length":45.672088623046875,"blob_id":"3ca9ff687754f9a72e57785caafefbe648ab7028","content_id":"fa12f67122d842dcaabdd3d69cb85b1fab63bc5e","detected_licenses":["MIT"],"is_generated":false,"is_vendor":false,"language":"C#","length_bytes":17222,"license_type":"permissive","max_line_length":229,"num_lines":369,"path":"/src/TorchSharp/Optimizers/Rprop.cs","repo_name":"NiklasGustafsson/TorchSharp","src_encoding":"UTF-8","text":"// Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information.\nusing System;\nusing System.IO;\nusing System.Collections.Generic;\nusing System.Linq;\nusing static TorchSharp.torch;\n\nnamespace TorchSharp\n{\n using Modules;\n\n public static partial class torch\n {\n public static partial class optim\n {\n\n /// <summary>\n /// Implements the the resilient backpropagation algorithm.\n ///\n /// A Direct Adaptive Method for Faster Backpropagation Learning: The RPROP Algorithm\n /// http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.21.1417\n /// </summary>\n /// <param name=\"parameters\">Parameters to optimize. This optimizer requires the <b>named</b> parameters collection.</param>\n /// <param name=\"lr \">Learning rate</param>\n /// <param name=\"etaminus\">Multiplicative increase factor.</param>\n /// <param name=\"etaplus\">Multiplicative decrease factor.</param>\n /// <param name=\"min_step\">Minimum allowed step size.</param>\n /// <param name=\"max_step\">Maximum allowed step size.</param>\n /// <param name=\"maximize\">Maximize the params based on the objective, instead of minimizing.</param>\n public static Rprop Rprop(IEnumerable<Parameter> parameters, double lr = 1e-2, double etaminus = 0.5, double etaplus = 1.2, double min_step = 1e-6, double max_step = 50, bool maximize = false)\n {\n return new Rprop(parameters, lr, etaminus, etaplus, min_step, max_step, maximize);\n }\n\n /// <summary>\n /// Implements the the resilient backpropagation algorithm.\n ///\n /// A Direct Adaptive Method for Faster Backpropagation Learning: The RPROP Algorithm\n /// http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.21.1417\n /// </summary>\n /// <param name=\"parameters\">Parameters to optimize. This optimizer requires the <b>named</b> parameters collection.</param>\n /// <param name=\"lr \">Learning rate</param>\n /// <param name=\"etaminus\">Multiplicative increase factor.</param>\n /// <param name=\"etaplus\">Multiplicative decrease factor.</param>\n /// <param name=\"min_step\">Minimum allowed step size.</param>\n /// <param name=\"max_step\">Maximum allowed step size.</param>\n /// <param name=\"maximize\">Maximize the params based on the objective, instead of minimizing.</param>\n public static Rprop Rprop(IEnumerable<(string name, Parameter parameter)> parameters, double lr = 1e-2, double etaminus = 0.5, double etaplus = 1.2, double min_step = 1e-6, double max_step = 50, bool maximize = false)\n {\n return new Rprop(parameters.Select(np => np.parameter), lr, etaminus, etaplus, min_step, max_step, maximize);\n }\n\n /// <summary>\n /// Implements the the resilient backpropagation algorithm.\n ///\n /// A Direct Adaptive Method for Faster Backpropagation Learning: The RPROP Algorithm\n /// http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.21.1417\n /// </summary>\n /// <param name=\"parameters\">Parameters to optimize. This optimizer requires the <b>named</b> parameters collection.</param>\n /// <param name=\"lr \">Learning rate</param>\n /// <param name=\"etaminus\">Multiplicative increase factor.</param>\n /// <param name=\"etaplus\">Multiplicative decrease factor.</param>\n /// <param name=\"min_step\">Minimum allowed step size.</param>\n /// <param name=\"max_step\">Maximum allowed step size.</param>\n /// <param name=\"maximize\">Maximize the params based on the objective, instead of minimizing.</param>\n public static Rprop Rprop(IEnumerable<Rprop.ParamGroup> parameters, double lr = 1e-2, double etaminus = 0.5, double etaplus = 1.2, double min_step = 1e-6, double max_step = 50, bool maximize = false)\n {\n return new Rprop(parameters, lr, etaminus, etaplus, min_step, max_step, maximize);\n }\n }\n }\n\n namespace Modules\n {\n public class Rprop : OptimizerHelper\n {\n /// <summary>\n /// Implements Rprop algorithm (a variant of Adam based on infinity norm).\n ///\n /// It has been proposed in Adam: A Method for Stochastic Optimization.\n /// </summary>\n /// <param name=\"parameters\">Parameters to optimize.</param>\n /// <param name=\"lr \">Learning rate</param>\n /// <param name=\"etaminus\">Multiplicative increase factor.</param>\n /// <param name=\"etaplus\">Multiplicative decrease factor.</param>\n /// <param name=\"min_step\">Minimum allowed step size.</param>\n /// <param name=\"max_step\">Maximum allowed step size.</param>\n /// <param name=\"maximize\">Maximize the params based on the objective, instead of minimizing.</param>\n public Rprop(IEnumerable<Parameter> parameters, double lr = 0.01, double etaminus = 0.5, double etaplus = 1.2, double min_step = 1e-6, double max_step = 50, bool maximize = false)\n : this(new ParamGroup[] { new() { Parameters = parameters } }, lr, etaminus, etaplus, min_step, max_step, maximize)\n {\n }\n\n /// <summary>\n /// Implements Rprop algorithm (a variant of Adam based on infinity norm).\n ///\n /// It has been proposed in Adam: A Method for Stochastic Optimization.\n /// </summary>\n /// <param name=\"parameters\">Parameters to optimize.</param>\n /// <param name=\"lr \">Learning rate</param>\n /// <param name=\"etaminus\">Multiplicative increase factor.</param>\n /// <param name=\"etaplus\">Multiplicative decrease factor.</param>\n /// <param name=\"min_step\">Minimum allowed step size.</param>\n /// <param name=\"max_step\">Maximum allowed step size.</param>\n /// <param name=\"maximize\">Maximize the params based on the objective, instead of minimizing.</param>\n public Rprop(IEnumerable<ParamGroup> parameters, double lr = 1e-2, double etaminus = 0.5, double etaplus = 1.2, double min_step = 1e-6, double max_step = 50, bool maximize = false)\n {\n if (lr < 0.0) throw new ArgumentException($\"Invalid learning rate: {lr}\");\n\n var options = new Options {\n LearningRate = lr,\n InitialLearningRate = lr,\n maximize = maximize,\n etaminus = etaminus,\n etaplus = etaplus,\n min_step = min_step,\n max_step = max_step\n };\n\n _defaults = options;\n _parameter_groups = new List<Modules.ParamGroup>();\n\n foreach (var g in parameters) {\n add_param_group(g);\n }\n }\n\n /// <summary>\n /// Performs a single optimization step (parameter update).\n /// </summary>\n /// <param name=\"closure\">A closure that reevaluates the model and returns the loss. Optional for most optimizers.</param>\n /// <returns></returns>\n public override Tensor step(Func<Tensor> closure = null)\n {\n return _step<ParamGroup>(group => {\n\n var options = group.Options as Options;\n var maximize = options.maximize.Value;\n var etaminus = options.etaminus.Value;\n var etaplus = options.etaplus.Value;\n var min_step = options.min_step.Value;\n var max_step = options.max_step.Value;\n var lr = options.LearningRate.Value;\n\n foreach (var param in group.Parameters) {\n\n var grad = param.grad();\n\n if (grad is null) continue;\n\n if (grad.is_sparse) throw new ArgumentException(\"Rprop does not support sparse gradients\");\n\n if (maximize) grad = -grad;\n\n var state = (State)_state[param.handle];\n\n state.step += 1;\n\n grad = (max_step != 0)\n ? grad.add(param, alpha: max_step)\n : grad.alias();\n\n var sign = grad.mul(state.prev).sign();\n sign[sign.gt(0)] = (Tensor)etaplus;\n sign[sign.lt(0)] = (Tensor)etaminus;\n sign[sign.eq(0)] = (Tensor)1;\n\n state.step_size.mul_(sign).clamp_(min_step, max_step);\n\n grad = grad.clone();\n\n grad.index_put_(0, sign.eq(etaminus));\n\n param.addcmul_(grad.sign(), state.step_size, -1);\n\n state.prev.copy_(grad);\n }\n\n }, closure);\n }\n\n protected override void Dispose(bool disposing)\n {\n base.Dispose(disposing);\n foreach (var kvp in _state) {\n ((State)kvp.Item2).Dispose();\n }\n }\n /// <summary>\n /// Add a param group to the Optimizer s param_groups.\n /// </summary>\n /// <param name=\"param_group\"></param>\n /// <remarks>This can be useful when fine tuning a pre-trained network as frozen layers can be made trainable and added to the Optimizer as training progresses.</remarks>\n public override void add_param_group(Modules.ParamGroup param_group)\n {\n var def = _defaults as Options;\n if (param_group.Options is null) {\n param_group.Options = new Options();\n }\n\n var opt = param_group.Options as Options;\n\n // Make sure all the options are set.\n if (!opt.maximize.HasValue) opt.maximize = def.maximize;\n if (!opt.LearningRate.HasValue) opt.LearningRate = def.LearningRate;\n if (!opt.etaminus.HasValue) opt.etaminus = def.etaminus;\n if (!opt.etaplus.HasValue) opt.etaplus = def.etaplus;\n if (!opt.min_step.HasValue) opt.min_step = def.min_step;\n if (!opt.max_step.HasValue) opt.max_step = def.max_step;\n\n opt.InitialLearningRate = opt.LearningRate.Value;\n\n _parameter_groups.Add(param_group);\n\n foreach (var p in param_group.Parameters) {\n var state = new State();\n _state[p.Handle] = state;\n state.step = 0;\n state.prev = torch.zeros_like(p).DetachFromDisposeScope();\n state.step_size = p.new_empty(p.shape).fill_(opt.LearningRate).DetachFromDisposeScope();\n }\n }\n\n public sealed class State : OptimizerState, IDisposable\n {\n public long step;\n public Tensor prev;\n public Tensor step_size;\n\n public void Dispose()\n {\n Dispose(true);\n GC.SuppressFinalize(this);\n }\n\n private void Dispose(bool disposing)\n {\n if (disposing) {\n prev.Dispose();\n step_size.Dispose();\n }\n }\n\n /// <summary>\n /// Move all the state to the indicated device.\n /// </summary>\n /// <param name=\"device\">The device to move all state to.</param>\n public override void to(Device device)\n {\n prev = prev.to(device);\n step_size = step_size.to(device);\n }\n\n /// <summary>\n /// Load the optimizer parameter state from a stream.\n /// </summary>\n /// <param name=\"reader\">A binary reader connected to a stream open for reading.</param>\n public override void LoadStateDict(BinaryReader reader)\n {\n step = reader.ReadInt64();\n prev.Load(reader);\n step_size.Load(reader);\n }\n\n /// <summary>\n /// Save the optimizer parameter state to a stream.\n /// </summary>\n /// <param name=\"writer\">A binary writer connected to a stream open for writing.</param>\n public override void SaveStateDict(BinaryWriter writer)\n {\n writer.Write(this.step);\n prev.Save(writer);\n step_size.Save(writer);\n }\n\n /// <summary>\n /// Load optimizer parameter state from another optimizer.\n /// </summary>\n /// <param name=\"source\">An optimizer state record.</param>\n public override void LoadStateDict(OptimizerState source)\n {\n var st_state = source as State;\n prev.Dispose();\n step_size.Dispose();\n step = st_state.step;\n prev = st_state.prev;\n step_size = st_state.step_size;\n }\n\n /// <summary>\n /// Useful for tests, allows comparison of one state with another.\n /// </summary>\n /// <param name=\"other\">The other optimizer state</param>\n /// <returns></returns>\n public override bool ApproximatelyEquals(OptimizerState other)\n {\n var rhs = other as State;\n return (rhs is not null) && step == rhs.step && prev.allclose(rhs.prev) && step_size.allclose(rhs.step_size);\n }\n }\n\n\n public class Options : OptimizerOptions\n {\n public bool? maximize;\n public double? etaminus;\n public double? etaplus;\n public double? min_step;\n public double? max_step;\n\n /// <summary>\n /// Save the optimizer options (param-group hyperparameters) to a stream.\n /// </summary>\n /// <param name=\"writer\">A binary writer connected to a stream open for writing.</param>\n public override void SaveStateDict(BinaryWriter writer)\n {\n base.SaveStateDict(writer);\n writer.Write(maximize.Value);\n writer.Write(etaminus.Value);\n writer.Write(etaplus.Value);\n writer.Write(min_step.Value);\n writer.Write(max_step.Value);\n }\n\n /// <summary>\n /// Load the optimizer options (param-group hyperparameters) from a stream.\n /// </summary>\n /// <param name=\"reader\">A binary reader connected to a stream open for reading.</param>\n public override void LoadStateDict(BinaryReader reader)\n {\n base.LoadStateDict(reader);\n maximize = reader.ReadBoolean();\n etaminus = reader.ReadDouble();\n etaplus = reader.ReadDouble();\n min_step = reader.ReadDouble();\n max_step = reader.ReadDouble();\n }\n\n /// <summary>\n /// Load optimizer options (param-group hyperparameters) from another optimizer.\n /// </summary>\n /// <param name=\"source\">An optimizer options record.</param>\n public override void LoadStateDict(OptimizerOptions source)\n {\n base.LoadStateDict(source);\n var opts = source as Options;\n maximize = opts.maximize;\n etaminus = opts.etaminus;\n etaplus = opts.etaplus;\n min_step = opts.min_step;\n max_step = opts.max_step;\n }\n }\n\n public class ParamGroup : ParamGroup<Options>\n {\n public ParamGroup() { }\n\n public ParamGroup(IEnumerable<Parameter> parameters, Options options) : base(parameters, options) { }\n\n public ParamGroup(IEnumerable<Parameter> parameters, double lr = 1e-2, double etaminus = 0.5, double etaplus = 1.2, double min_step = 1e-6, double max_step = 50, bool maximize = false)\n : base(parameters, new Rprop.Options { LearningRate = lr, etaminus = etaminus, etaplus = etaplus, min_step = min_step, max_step = max_step, maximize = maximize })\n {\n }\n }\n\n }\n }\n}\n"},{"alpha_fraction":0.6332852840423584,"alphanum_fraction":0.6390489935874939,"avg_line_length":42.375,"blob_id":"cc01938f7ee6956e519cd65f24eea856cc481513","content_id":"b84e5428cfbfb1149564e57a989e885cbebc4912","detected_licenses":["MIT"],"is_generated":false,"is_vendor":false,"language":"C#","length_bytes":1388,"license_type":"permissive","max_line_length":130,"num_lines":32,"path":"/src/TorchSharp/Data/Loader.cs","repo_name":"NiklasGustafsson/TorchSharp","src_encoding":"UTF-8","text":"// Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information.\nusing static TorchSharp.PInvoke.NativeMethods;\n\nnamespace TorchSharp.Data\n{\n public class Loader\n {\n /// <summary>\n /// Create an iterator scanning the MNIST dataset.\n /// </summary>\n /// <param name=\"filename\">The position of the MNIST dataset</param>\n /// <param name=\"batchSize\">The required batch size</param>\n /// <param name=\"isTrain\">Wheter the iterator is for training or testing</param>\n /// <returns></returns>\n public static DataIterator MNIST(string filename, long batchSize, bool isTrain = true)\n {\n return new DataIterator(THSData_loaderMNIST(filename, batchSize, isTrain));\n }\n\n /// <summary>\n /// Create an iterator scanning the CIFAR10 dataset.\n /// </summary>\n /// <param name=\"path\">The position of the CIFAR10 dataset</param>\n /// <param name=\"batchSize\">The required batch size</param>\n /// <param name=\"isTrain\">Wheter the iterator is for training or testing</param>\n /// <returns></returns>\n public static DataIterator CIFAR10(string path, long batchSize, bool isTrain = true)\n {\n return new DataIterator(THSData_loaderCIFAR10(path, batchSize, isTrain));\n }\n }\n}\n"},{"alpha_fraction":0.5405641794204712,"alphanum_fraction":0.5449376702308655,"avg_line_length":43.40776824951172,"blob_id":"ca4125d90d0369aa18f6375d835108dbd2049394","content_id":"316f81ea0d8f3caf3cd191ca6e2d50bf9783ba39","detected_licenses":["MIT"],"is_generated":false,"is_vendor":false,"language":"C#","length_bytes":4573,"license_type":"permissive","max_line_length":166,"num_lines":103,"path":"/src/TorchSharp/NN/Bilinear.cs","repo_name":"NiklasGustafsson/TorchSharp","src_encoding":"UTF-8","text":"// Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information.\nusing System;\nusing static TorchSharp.torch;\nusing static TorchSharp.torch.nn;\nusing static TorchSharp.PInvoke.NativeMethods;\n\n#nullable enable\nnamespace TorchSharp\n{\n using Modules;\n\n namespace Modules\n {\n public sealed class Bilinear : Module<Tensor, Tensor, Tensor>\n {\n internal Bilinear(IntPtr handle, IntPtr boxedHandle) : base(handle, boxedHandle) { }\n\n public new static Bilinear Load(string modelPath)\n {\n var res = Module<Tensor, Tensor>.Load(modelPath);\n return new Bilinear(res.handle.DangerousGetHandle(), IntPtr.Zero);\n }\n\n public override Tensor forward(Tensor input1, Tensor input2)\n {\n var res = THSNN_Bilinear_forward(handle, input1.Handle, input2.Handle);\n if (res == IntPtr.Zero) { CheckForErrors(); }\n return new Tensor(res);\n }\n\n public Parameter? bias {\n get {\n var res = THSNN_Bilinear_bias(handle);\n if (res == IntPtr.Zero) { CheckForErrors(); }\n return ((res == IntPtr.Zero) ? null : new Parameter(res));\n }\n set {\n THSNN_Bilinear_set_bias(handle, value?.Handle ?? IntPtr.Zero);\n CheckForErrors();\n ConditionallyRegisterParameter(\"bias\", value);\n }\n }\n\n public Parameter? weight {\n get {\n var res = THSNN_Bilinear_weight(handle);\n if (res == IntPtr.Zero) { CheckForErrors(); }\n return (res == IntPtr.Zero) ? null : new Parameter(res);\n }\n set {\n THSNN_Bilinear_set_weight(handle, value?.Handle ?? IntPtr.Zero);\n CheckForErrors();\n ConditionallyRegisterParameter(\"weight\", value);\n }\n }\n }\n }\n\n public static partial class torch\n {\n public static partial class nn\n {\n\n /// <summary>\n /// Applies a bilinear transformation to the incoming data\n /// </summary>\n /// <param name=\"in1Features\">size of each first input sample</param>\n /// <param name=\"in2Features\">size of each second input sample</param>\n /// <param name=\"outputSize\">size of each output sample</param>\n /// <param name=\"hasBias\">If set to false, the layer will not learn an additive bias</param>\n /// <param name=\"device\">The desired device of the parameters and buffers in this module</param>\n /// <param name=\"dtype\">The desired floating point or complex dtype of the parameters and buffers in this module</param>\n /// <returns></returns>\n public static Bilinear Bilinear(long in1Features, long in2Features, long outputSize, bool hasBias = true, Device? device = null, ScalarType? dtype = null)\n {\n var res = THSNN_Bilinear_ctor(in1Features, in2Features, outputSize, hasBias, out var boxedHandle);\n if (res == IntPtr.Zero) { CheckForErrors(); }\n\n return new Bilinear(res, boxedHandle).MoveModule<Bilinear>(device, dtype);\n }\n\n public static partial class functional\n {\n /// <summary>\n /// Applies a bilinear transformation to the incoming data\n /// </summary>\n /// <param name=\"input1\">Input tensor of shape (N,*,H1)</param>\n /// <param name=\"input2\">Input tensor of shape (N,*,H2)</param>\n /// <param name=\"weight\">Weights of shape (Hout,H1, H2)</param>\n /// <param name=\"bias\">Optional bias of shape (Hout)</param>\n /// <returns>Tensor of shape (N,*,Hout)</returns>\n /// <remarks>The '*' sub-shape must be the same among the two inputs.</remarks>\n public static Tensor bilinear(Tensor input1, Tensor input2, Tensor weight, Tensor? bias = null)\n {\n IntPtr bPtr = bias?.Handle ?? IntPtr.Zero;\n var res = THSNN_functional_bilinear(input1.Handle, input2.Handle, weight.Handle, bPtr);\n if (res == IntPtr.Zero) { CheckForErrors(); }\n return new Tensor(res);\n }\n }\n }\n }\n}"},{"alpha_fraction":0.5852211713790894,"alphanum_fraction":0.5852211713790894,"avg_line_length":35.35293960571289,"blob_id":"9c68a4dd5c41496646d7643fb14f5c587fa6db5d","content_id":"81e9051d8a44d3aa96d8fdcb9e5464414611700a","detected_licenses":["MIT"],"is_generated":false,"is_vendor":false,"language":"C#","length_bytes":1858,"license_type":"permissive","max_line_length":130,"num_lines":51,"path":"/src/TorchSharp/NN/Parameter.cs","repo_name":"NiklasGustafsson/TorchSharp","src_encoding":"UTF-8","text":"// Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information.\nusing static TorchSharp.torch;\n\nnamespace TorchSharp\n{\n using Modules;\n\n namespace Modules\n {\n /// <summary>\n /// A kind of Tensor that is to be considered a module parameter.\n ///\n /// Parameters are Tensor subclasses, that have a very special property when used with Modules -\n /// when they’re assigned as Module attributes they are automatically added to the list of its parameters,\n /// and will appear e.g. in parameters() iterator.Assigning a Tensor doesn’t have such effect.This is because\n /// one might want to cache some temporary state, like last hidden state of the RNN, in the model.\n /// If there was no such class as Parameter, these temporaries would get registered too.\n /// </summary>\n public class Parameter : Tensor\n {\n /// <summary>\n /// Constructor\n /// </summary>\n /// <param name=\"data\">A tensor, which will become empty.</param>\n /// <param name=\"requires_grad\"></param>\n public Parameter(Tensor data, bool requires_grad = true) :\n base(data.with_requires_grad(requires_grad).MoveHandle())\n {\n }\n\n /// <summary>\n /// Constructor\n /// </summary>\n /// <param name=\"handle\">A tensor handle.</param>\n internal Parameter(System.IntPtr handle) : base(handle)\n {\n }\n\n };\n }\n\n public static partial class torch\n {\n public static partial class nn\n {\n public static Parameter Parameter(Tensor data, bool requires_grad = true) =>\n new Parameter(data, requires_grad);\n\n }\n }\n}\n"},{"alpha_fraction":0.6014527678489685,"alphanum_fraction":0.6024212837219238,"avg_line_length":35.24561309814453,"blob_id":"99567f0fa980537144825738fc31265fd504c8b7","content_id":"668fbdb002553e76864ecd6860f42a3f16cb0377","detected_licenses":["MIT"],"is_generated":false,"is_vendor":false,"language":"C#","length_bytes":2065,"license_type":"permissive","max_line_length":130,"num_lines":57,"path":"/src/TorchSharp/Tensor/torch.Parallelism.cs","repo_name":"NiklasGustafsson/TorchSharp","src_encoding":"UTF-8","text":"// Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information.\n#nullable enable\nusing System;\nusing System.Diagnostics.Contracts;\n\nusing static TorchSharp.PInvoke.NativeMethods;\n\nnamespace TorchSharp\n{\n // https://pytorch.org/docs/stable/torch#parallelism\n public static partial class torch\n {\n // https://pytorch.org/docs/stable/generated/torch.get_num_threads\n /// <summary>\n /// Returns the number of threads used for parallelizing CPU operations\n /// </summary>\n public static int get_num_threads()\n {\n var res = THSTorch_get_num_threads();\n if (res == -1) CheckForErrors();\n return res;\n }\n\n // https://pytorch.org/docs/stable/generated/torch.set_num_threads\n /// <summary>\n /// Sets the number of threads used for parallelizing CPU operations\n /// </summary>\n /// <param name=\"num\">The number of threads to use.</param>\n public static void set_num_threads(int num)\n {\n THSTorch_set_num_threads(num);\n CheckForErrors();\n }\n\n // https://pytorch.org/docs/stable/generated/torch.get_num_interop_threads\n /// <summary>\n /// Returns the number of threads used for inter-op parallelism on CPU (e.g. in JIT interpreter)\n /// </summary>\n public static int get_num_interop_threads()\n {\n var res = THSTorch_get_num_interop_threads();\n if (res == -1) CheckForErrors();\n return res;\n }\n\n // https://pytorch.org/docs/stable/generated/torch.set_num_interop_threads\n /// <summary>\n /// Sets the number of threads used for inter-op parallelism on CPU (e.g. in JIT interpreter)\n /// </summary>\n /// <param name=\"num\">The number of threads to use.</param>\n public static void set_num_interop_threads(int num)\n {\n THSTorch_set_num_interop_threads(num);\n CheckForErrors();\n }\n }\n}"},{"alpha_fraction":0.5210997462272644,"alphanum_fraction":0.5223785042762756,"avg_line_length":30.280000686645508,"blob_id":"a85bb779728d53a1490270762f68b3453e502036","content_id":"743e4c55c4553897b0d42e694da61adf8e4cbc04","detected_licenses":["MIT"],"is_generated":false,"is_vendor":false,"language":"C#","length_bytes":3128,"license_type":"permissive","max_line_length":185,"num_lines":100,"path":"/src/Examples.Utils/Vocab.cs","repo_name":"NiklasGustafsson/TorchSharp","src_encoding":"UTF-8","text":"// Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information.\nusing System;\nusing System.Collections;\nusing System.Collections.Generic;\nusing System.Diagnostics.CodeAnalysis;\nusing System.Linq;\nusing TorchSharp;\n\nusing static TorchSharp.torch.nn;\n\nnamespace TorchText.Vocab\n{\n /// <summary>\n /// This needs a permanent place.\n /// The functionality is based on the Python 'Counter' class.\n /// </summary>\n /// <typeparam name=\"T\"></typeparam>\n public class Counter<T> : IEnumerable<KeyValuePair<T, int>>\n {\n private Dictionary<T, int> _dict = new Dictionary<T, int>();\n\n public void update(T key)\n {\n if (_dict.TryGetValue(key, out int count)) {\n _dict[key] = count + 1;\n } else {\n _dict[key] = 1;\n }\n }\n public void update(IEnumerable<T> keys)\n {\n foreach (T key in keys) {\n update(key);\n }\n }\n public int this[T key] { get => _dict[key]; }\n\n public IEnumerator<KeyValuePair<T, int>> GetEnumerator()\n {\n return _dict.GetEnumerator();\n }\n\n IEnumerator IEnumerable.GetEnumerator()\n {\n return GetEnumerator();\n }\n }\n\n /// <summary>\n /// This belongs in its own package, 'TorchText'.\n /// For now, it's useful to keep it with the examples that use it.\n /// </summary>\n public class Vocab\n {\n public Vocab(Counter<string> counter, int? maxSize = null, int minFreq = 1, string[] specials = null, Func<torch.Tensor, torch.Tensor> unkInit = null, bool specialsFirst = true)\n {\n if (specials == null) specials = new string[] { \"<unk>\", \"<pad>\" };\n if (unkInit == null) unkInit = (t => init.zeros_(t.clone()));\n if (specialsFirst) {\n foreach (var sp in specials) {\n _dict.Add(sp, _last++);\n }\n }\n foreach (var kv in counter.Where(kv => kv.Value >= minFreq)) {\n if (!specials.Contains(kv.Key)) {\n _dict.Add(kv.Key, _last++);\n }\n if (_last > (maxSize ?? int.MaxValue))\n break;\n }\n if (!specialsFirst) {\n foreach (var sp in specials) {\n _dict.Add(sp, _last++);\n }\n }\n }\n\n public int this[string key] { get => _dict.TryGetValue(key, out int value) ? value : _dict[\"<unk>\"]; }\n\n public int Count => _dict.Count;\n\n public void Add(string key, int value)\n {\n _dict.Add(key, value);\n }\n\n public void Add(KeyValuePair<string, int> item)\n {\n Add(item.Key, item.Value);\n }\n\n public bool TryGetValue(string key, [MaybeNullWhen(false)] out int value)\n {\n return _dict.TryGetValue(key, out value);\n }\n\n private Dictionary<string, int> _dict = new Dictionary<string, int>();\n private int _last = 0;\n }\n}\n"},{"alpha_fraction":0.602297306060791,"alphanum_fraction":0.6036486625671387,"avg_line_length":51.85714340209961,"blob_id":"c08600650a03d8b2be994bffdd170809050e170c","content_id":"b8cda7aebb1c55fd1597fb8e94d59b3156025925","detected_licenses":["MIT"],"is_generated":false,"is_vendor":false,"language":"C#","length_bytes":7400,"license_type":"permissive","max_line_length":172,"num_lines":140,"path":"/src/TorchSharp/Distributions/RelaxedOneHotCategorical.cs","repo_name":"NiklasGustafsson/TorchSharp","src_encoding":"UTF-8","text":"// Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information.\nusing System;\nusing static TorchSharp.torch;\n\nnamespace TorchSharp\n{\n using Modules;\n using static torch.distributions;\n\n namespace Modules\n {\n /// <summary>\n /// Creates a RelaxedOneHotCategorical distribution, parametrized by `temperature`, and either `probs` or `logits` (but not both).\n /// This is a relaxed version of the `OneHotCategorical` distribution, so its samples are on simplex, and are reparametrizable..\n /// </summary>\n public class RelaxedOneHotCategorical : TransformedDistribution\n {\n /// <summary>\n /// Constructor\n /// </summary>\n /// <param name=\"temperature\">Relaxation temperature</param>\n /// <param name=\"probs\">the probability of sampling `1`</param>\n /// <param name=\"logits\">the log-odds of sampling `1`</param>\n /// <param name=\"generator\"></param>\n public RelaxedOneHotCategorical(Tensor temperature, Tensor probs = null, Tensor logits = null, torch.Generator generator = null) :\n base(ExpRelaxedCategorical(temperature, probs, logits, generator), new distributions.transforms.ExpTransform(), generator)\n {\n this._probs = probs?.alias().DetachFromDisposeScope();\n this._logits = logits?.alias().DetachFromDisposeScope();\n }\n\n private Tensor _probs;\n private Tensor _logits;\n\n private ExpRelaxedCategorical base_dist => this.base_distribution as ExpRelaxedCategorical;\n\n /// <summary>\n /// The probability of sampling 1\n /// </summary>\n public Tensor probs {\n get {\n return base_dist.probs;\n }\n }\n\n /// <summary>\n /// The log-odds of sampling 1\n /// </summary>\n public Tensor logits {\n get {\n return base_dist.logits;\n }\n }\n\n public Tensor temperature {\n get {\n return base_dist.logits;\n }\n }\n\n /// <summary>\n /// Returns a new distribution instance (or populates an existing instance provided by a derived class) with batch dimensions expanded to\n /// `batch_shape`. This method calls `torch.Tensor.expand()` on the distribution's parameters. As such, this does not allocate new\n /// memory for the expanded distribution instance.\n /// </summary>\n /// <param name=\"batch_shape\">Tthe desired expanded size.</param>\n /// <param name=\"instance\">new instance provided by subclasses that need to override `.expand`.</param>\n /// <returns></returns>\n public override distributions.Distribution expand(Size batch_shape, distributions.Distribution instance = null)\n {\n if (instance != null && !(instance is RelaxedOneHotCategorical))\n throw new ArgumentException(\"expand(): 'instance' must be a RelaxedOneHotCategorical distribution\");\n\n var newDistribution = ((instance == null) ? new RelaxedOneHotCategorical(temperature, _probs, _logits, generator) : instance) as RelaxedOneHotCategorical;\n\n newDistribution.batch_shape = batch_shape;\n return base.expand(batch_shape, newDistribution);\n }\n }\n\n }\n public static partial class torch\n {\n public static partial class distributions\n {\n /// <summary>\n /// Creates a RelaxedOneHotCategorical distribution, parametrized by `temperature`, and either `probs` or `logits` (but not both).\n /// This is a relaxed version of the `OneHotCategorical` distribution, so its samples are on simplex, and are reparametrizable..\n /// </summary>\n /// <param name=\"temperature\">Relaxation temperature</param>\n /// <param name=\"probs\">The probability of sampling '1'</param>\n /// <param name=\"logits\">The log-odds of sampling '1'</param>\n /// <param name=\"generator\">An optional random number generator object.</param>\n /// <returns></returns>\n public static RelaxedOneHotCategorical RelaxedOneHotCategorical(Tensor temperature, Tensor probs = null, Tensor logits = null, torch.Generator generator = null)\n {\n return new RelaxedOneHotCategorical(temperature, probs, logits, generator);\n }\n\n /// <summary>\n /// Creates a RelaxedOneHotCategorical distribution, parametrized by `temperature`, and either `probs` or `logits` (but not both).\n /// This is a relaxed version of the `OneHotCategorical` distribution, so its samples are on simplex, and are reparametrizable..\n /// </summary>\n /// <param name=\"temperature\">Relaxation temperature</param>\n /// <param name=\"probs\">The probability of sampling '1'</param>\n /// <param name=\"logits\">The log-odds of sampling '1'</param>\n /// <param name=\"generator\">An optional random number generator object.</param>\n /// <returns></returns>\n public static RelaxedOneHotCategorical RelaxedOneHotCategorical(Tensor temperature, float? probs, float? logits, torch.Generator generator = null)\n {\n if (probs.HasValue && !logits.HasValue)\n return new RelaxedOneHotCategorical(temperature, torch.tensor(probs.Value), null, generator);\n else if (!probs.HasValue && logits.HasValue)\n return new RelaxedOneHotCategorical(temperature, null, torch.tensor(logits.Value), generator);\n else\n throw new ArgumentException(\"One and only one of 'probs' and logits should be provided.\");\n }\n\n\n /// <summary>\n /// Creates a RelaxedOneHotCategorical distribution, parametrized by `temperature`, and either `probs` or `logits` (but not both).\n /// This is a relaxed version of the `OneHotCategorical` distribution, so its samples are on simplex, and are reparametrizable..\n /// </summary>\n /// <param name=\"temperature\">Relaxation temperature</param>\n /// <param name=\"probs\">The probability of sampling '1'</param>\n /// <param name=\"logits\">The log-odds of sampling '1'</param>\n /// <param name=\"generator\">An optional random number generator object.</param>\n /// <returns></returns>\n public static RelaxedOneHotCategorical RelaxedOneHotCategorical(Tensor temperature, double? probs, double? logits, torch.Generator generator = null)\n {\n if (probs.HasValue && !logits.HasValue)\n return new RelaxedOneHotCategorical(temperature, torch.tensor(probs.Value), null, generator);\n else if (!probs.HasValue && logits.HasValue)\n return new RelaxedOneHotCategorical(temperature, null, torch.tensor(logits.Value), generator);\n else\n throw new ArgumentException(\"One and only one of 'probs' and 'logits' should be non-null\");\n }\n }\n }\n}\n"},{"alpha_fraction":0.3323427438735962,"alphanum_fraction":0.8015121817588806,"avg_line_length":76.79247283935547,"blob_id":"e01bc550a7443bf4b763ac6faf9e99a8a516f859","content_id":"6f34e23b3016ad3384221af1257c309d74cd1f57","detected_licenses":["MIT"],"is_generated":false,"is_vendor":false,"language":"C","length_bytes":72347,"license_type":"permissive","max_line_length":87,"num_lines":930,"path":"/src/Native/LibTorchSharp/crc32c.c","repo_name":"NiklasGustafsson/TorchSharp","src_encoding":"UTF-8","text":"/* Part of CRC-32C library: https://crc32c.machinezoo.com/ */\n/*\n Copyright (c) 2013 - 2014, 2016 Mark Adler, Robert Vazan, Max Vysokikh\n\n This software is provided 'as-is', without any express or implied\n warranty. In no event will the author be held liable for any damages\n arising from the use of this software.\n\n Permission is granted to anyone to use this software for any purpose,\n including commercial applications, and to alter it and redistribute it\n freely, subject to the following restrictions:\n\n 1. The origin of this software must not be misrepresented; you must not\n claim that you wrote the original software. If you use this software\n in a product, an acknowledgment in the product documentation would be\n appreciated but is not required.\n 2. Altered source versions must be plainly marked as such, and must not be\n misrepresented as being the original software.\n 3. This notice may not be removed or altered from any source distribution.\n*/\n\n#ifndef _CRT_SECURE_NO_WARNINGS\n#define _CRT_SECURE_NO_WARNINGS\n#endif\n\n#include \"crc32c.h\"\n\n// ALTERED SOURCE VERSION\n//\n// Per #2 in the copyright notice above:\n// \n// The definition of 'NONMINMAX' below was altered from the original.\n\n//#define NOMINMAX\n\n#ifdef CRC32C_GCC\n#include <cpuid.h>\n#include <x86intrin.h>\n#else\n#include <intrin.h>\n#endif\n\n#define POLY 0x82f63b78\n#define LONG_SHIFT 8192\n#define SHORT_SHIFT 256\n\ntypedef const uint8_t *buffer;\n\nstatic union {\n uint32_t dword_table[16][256];\n uint64_t qword_array[2048];\n} table = {\n .qword_array = {\n 0xf26b830300000000, 0x1350f3f4e13b70f7, 0x35f1141cc79a971f, 0xd4ca64eb26a1e7e8,\n 0x78b2dbcc8ad958cf, 0x9989ab3b6be22838, 0xbf284cd34d43cfd0, 0x5e133c24ac78bf27,\n 0xe235446c105ec76f, 0x30e349bf165b798, 0x25afd373d7c45070, 0xc494a38436ff2087,\n 0x68ec1ca39a879fa0, 0x89d76c547bbcef57, 0xaf768bbc5d1d08bf, 0x4e4dfb4bbc267848,\n 0xd2d60ddd20bd8ede, 0x33ed7d2ac186fe29, 0x154c9ac2e72719c1, 0xf477ea35061c6936,\n 0x580f5512aa64d611, 0xb93425e54b5fa6e6, 0x9f95c20d6dfe410e, 0x7eaeb2fa8cc531f9,\n 0xc288cab230e349b1, 0x23b3ba45d1d83946, 0x5125dadf779deae, 0xe4292d5a1642ae59,\n 0x4851927dba3a117e, 0xa96ae28a5b016189, 0x8fcb05627da08661, 0x6ef075959c9bf696,\n 0xb3109ebf417b1dbc, 0x522bee48a0406d4b, 0x748a09a086e18aa3, 0x95b1795767dafa54,\n 0x39c9c670cba24573, 0xd8f2b6872a993584, 0xfe53516f0c38d26c, 0x1f682198ed03a29b,\n 0xa34e59d05125dad3, 0x42752927b01eaa24, 0x64d4cecf96bf4dcc, 0x85efbe3877843d3b,\n 0x2997011fdbfc821c, 0xc8ac71e83ac7f2eb, 0xee0d96001c661503, 0xf36e6f7fd5d65f4,\n 0x93ad106161c69362, 0x7296609680fde395, 0x5437877ea65c047d, 0xb50cf7894767748a,\n 0x197448aeeb1fcbad, 0xf84f38590a24bb5a, 0xdeeedfb12c855cb2, 0x3fd5af46cdbe2c45,\n 0x83f3d70e7198540d, 0x62c8a7f990a324fa, 0x44694011b602c312, 0xa55230e65739b3e5,\n 0x92a8fc1fb410cc2, 0xe811ff361a7a7c35, 0xceb018de3cdb9bdd, 0x2f8b6829dde0eb2a,\n 0x709db87b82f63b78, 0x91a6c88c63cd4b8f, 0xb7072f64456cac67, 0x563c5f93a457dc90,\n 0xfa44e0b4082f63b7, 0x1b7f9043e9141340, 0x3dde77abcfb5f4a8, 0xdce5075c2e8e845f,\n 0x60c37f1492a8fc17, 0x81f80fe373938ce0, 0xa759e80b55326b08, 0x466298fcb4091bff,\n 0xea1a27db1871a4d8, 0xb21572cf94ad42f, 0x2d80b0c4dfeb33c7, 0xccbbc0333ed04330,\n 0x502036a5a24bb5a6, 0xb11b46524370c551, 0x97baa1ba65d122b9, 0x7681d14d84ea524e,\n 0xdaf96e6a2892ed69, 0x3bc21e9dc9a99d9e, 0x1d63f975ef087a76, 0xfc5889820e330a81,\n 0x407ef1cab21572c9, 0xa145813d532e023e, 0x87e466d5758fe5d6, 0x66df162294b49521,\n 0xcaa7a90538cc2a06, 0x2b9cd9f2d9f75af1, 0xd3d3e1aff56bd19, 0xec064eed1e6dcdee,\n 0x31e6a5c7c38d26c4, 0xd0ddd53022b65633, 0xf67c32d80417b1db, 0x1747422fe52cc12c,\n 0xbb3ffd0849547e0b, 0x5a048dffa86f0efc, 0x7ca56a178ecee914, 0x9d9e1ae06ff599e3,\n 0x21b862a8d3d3e1ab, 0xc083125f32e8915c, 0xe622f5b7144976b4, 0x7198540f5720643,\n 0xab613a67590ab964, 0x4a5a4a90b831c993, 0x6cfbad789e902e7b, 0x8dc0dd8f7fab5e8c,\n 0x115b2b19e330a81a, 0xf0605bee020bd8ed, 0xd6c1bc0624aa3f05, 0x37faccf1c5914ff2,\n 0x9b8273d669e9f0d5, 0x7ab9032188d28022, 0x5c18e4c9ae7367ca, 0xbd23943e4f48173d,\n 0x105ec76f36e6f75, 0xe03e9c8112551f82, 0xc69f7b6934f4f86a, 0x27a40b9ed5cf889d,\n 0x8bdcb4b979b737ba, 0x6ae7c44e988c474d, 0x4c4623a6be2da0a5, 0xad7d53515f16d052,\n 0x13a2987700000000, 0x34e7a899274530ee, 0x5d28f9ab4e8a61dc, 0x7a6dc94569cf5132,\n 0x8eb65bcf9d14c3b8, 0xa9f36b21ba51f356, 0xc03c3a13d39ea264, 0xe7790afdf4db928a,\n 0x2c6769f63fc5f181, 0xb2259181880c16f, 0x62ed082a714f905d, 0x45a838c4560aa0b3,\n 0xb173aa4ea2d13239, 0x96369aa0859402d7, 0xfff9cb92ec5b53e5, 0xd8bcfb7ccb1e630b,\n 0x6c297b757f8be302, 0x4b6c4b9b58ced3ec, 0x22a31aa9310182de, 0x5e62a471644b230,\n 0xf13db8cde29f20ba, 0xd6788823c5da1054, 0xbfb7d911ac154166, 0x98f2e9ff8b507188,\n 0x53ec8af4404e1283, 0x74a9ba1a670b226d, 0x1d66eb280ec4735f, 0x3a23dbc6298143b1,\n 0xcef8494cdd5ad13b, 0xe9bd79a2fa1fe1d5, 0x8072289093d0b0e7, 0xa737187eb4958009,\n 0xecb55e73ff17c604, 0xcbf06e9dd852f6ea, 0xa23f3fafb19da7d8, 0x857a0f4196d89736,\n 0x71a19dcb620305bc, 0x56e4ad2545463552, 0x3f2bfc172c896460, 0x186eccf90bcc548e,\n 0xd370aff2c0d23785, 0xf4359f1ce797076b, 0x9dface2e8e585659, 0xbabffec0a91d66b7,\n 0x4e646c4a5dc6f43d, 0x69215ca47a83c4d3, 0xee0d96134c95e1, 0x27ab3d783409a50f,\n 0x933ebd71809c2506, 0xb47b8d9fa7d915e8, 0xddb4dcadce1644da, 0xfaf1ec43e9537434,\n 0xe2a7ec91d88e6be, 0x296f4e273acdd650, 0x40a01f1553028762, 0x67e52ffb7447b78c,\n 0xacfb4cf0bf59d487, 0x8bbe7c1e981ce469, 0xe2712d2cf1d3b55b, 0xc5341dc2d69685b5,\n 0x31ef8f48224d173f, 0x16aabfa6050827d1, 0x7f65ee946cc776e3, 0x5820de7a4b82460d,\n 0xe861628efbc3faf9, 0xcf245260dc86ca17, 0xa6eb0352b5499b25, 0x81ae33bc920cabcb,\n 0x7575a13666d73941, 0x523091d8419209af, 0x3bffc0ea285d589d, 0x1cbaf0040f186873,\n 0xd7a4930fc4060b78, 0xf0e1a3e1e3433b96, 0x992ef2d38a8c6aa4, 0xbe6bc23dadc95a4a,\n 0x4ab050b75912c8c0, 0x6df560597e57f82e, 0x43a316b1798a91c, 0x237f018530dd99f2,\n 0x97ea818c844819fb, 0xb0afb162a30d2915, 0xd960e050cac27827, 0xfe25d0beed8748c9,\n 0xafe4234195cda43, 0x2dbb72da3e19eaad, 0x447423e857d6bb9f, 0x6331130670938b71,\n 0xa82f700dbb8de87a, 0x8f6a40e39cc8d894, 0xe6a511d1f50789a6, 0xc1e0213fd242b948,\n 0x353bb3b526992bc2, 0x127e835b01dc1b2c, 0x7bb1d26968134a1e, 0x5cf4e2874f567af0,\n 0x1776a48a04d43cfd, 0x3033946423910c13, 0x59fcc5564a5e5d21, 0x7eb9f5b86d1b6dcf,\n 0x8a62673299c0ff45, 0xad2757dcbe85cfab, 0xc4e806eed74a9e99, 0xe3ad3600f00fae77,\n 0x28b3550b3b11cd7c, 0xff665e51c54fd92, 0x663934d7759baca0, 0x417c043952de9c4e,\n 0xb5a796b3a6050ec4, 0x92e2a65d81403e2a, 0xfb2df76fe88f6f18, 0xdc68c781cfca5ff6,\n 0x68fd47887b5fdfff, 0x4fb877665c1aef11, 0x2677265435d5be23, 0x13216ba12908ecd,\n 0xf5e98430e64b1c47, 0xd2acb4dec10e2ca9, 0xbb63e5eca8c17d9b, 0x9c26d5028f844d75,\n 0x5738b609449a2e7e, 0x707d86e763df1e90, 0x19b2d7d50a104fa2, 0x3ef7e73b2d557f4c,\n 0xca2c75b1d98eedc6, 0xed69455ffecbdd28, 0x84a6146d97048c1a, 0xa3e32483b041bcf4,\n 0xa541927e00000000, 0xea2ec0734f6f520d, 0x3b9f36649edea41a, 0x74f06469d1b1f617,\n 0x9d10acbb38513ec5, 0xd27ffeb6773e6cc8, 0x3ce08a1a68f9adf, 0x4ca15aace9e0c8d2,\n 0xd5e3eff470a27d8a, 0x9a8cbdf93fcd2f87, 0x4b3d4beeee7cd990, 0x45219e3a1138b9d,\n 0xedb2d13148f3434f, 0xa2dd833c079c1142, 0x736c752bd62de755, 0x3c0327269942b558,\n 0x4405696ae144fb14, 0xb6a3b67ae2ba919, 0xdadbcd707f9a5f0e, 0x95b49f7d30f50d03,\n 0x7c5457afd915c5d1, 0x333b05a2967a97dc, 0xe28af3b547cb61cb, 0xade5a1b808a433c6,\n 0x34a714e091e6869e, 0x7bc846edde89d493, 0xaa79b0fa0f382284, 0xe516e2f740577089,\n 0xcf62a25a9b7b85b, 0x43997828e6d8ea56, 0x92288e3f37691c41, 0xdd47dc3278064e4c,\n 0x622412a7c76580d9, 0x2d4b40aa880ad2d4, 0xfcfab6bd59bb24c3, 0xb395e4b016d476ce,\n 0x5a752c62ff34be1c, 0x151a7e6fb05bec11, 0xc4ab887861ea1a06, 0x8bc4da752e85480b,\n 0x12866f2db7c7fd53, 0x5de93d20f8a8af5e, 0x8c58cb3729195949, 0xc337993a66760b44,\n 0x2ad751e88f96c396, 0x65b803e5c0f9919b, 0xb409f5f21148678c, 0xfb66a7ff5e273581,\n 0x8360e9b326217bcd, 0xcc0fbbbe694e29c0, 0x1dbe4da9b8ffdfd7, 0x52d11fa4f7908dda,\n 0xbb31d7761e704508, 0xf45e857b511f1705, 0x25ef736c80aee112, 0x6a802161cfc1b31f,\n 0xf3c2943956830647, 0xbcadc63419ec544a, 0x6d1c3023c85da25d, 0x2273622e8732f050,\n 0xcb93aafc6ed23882, 0x84fcf8f121bd6a8f, 0x554d0ee6f00c9c98, 0x1a225cebbf63ce95,\n 0x2e66e53d8b277743, 0x6109b730c448254e, 0xb0b8412715f9d359, 0xffd7132a5a968154,\n 0x1637dbf8b3764986, 0x595889f5fc191b8b, 0x88e97fe22da8ed9c, 0xc7862def62c7bf91,\n 0x5ec498b7fb850ac9, 0x11abcabab4ea58c4, 0xc01a3cad655baed3, 0x8f756ea02a34fcde,\n 0x6695a672c3d4340c, 0x29faf47f8cbb6601, 0xf84b02685d0a9016, 0xb72450651265c21b,\n 0xcf221e296a638c57, 0x804d4c24250cde5a, 0x51fcba33f4bd284d, 0x1e93e83ebbd27a40,\n 0xf77320ec5232b292, 0xb81c72e11d5de09f, 0x69ad84f6ccec1688, 0x26c2d6fb83834485,\n 0xbf8063a31ac1f1dd, 0xf0ef31ae55aea3d0, 0x215ec7b9841f55c7, 0x6e3195b4cb7007ca,\n 0x87d15d662290cf18, 0xc8be0f6b6dff9d15, 0x190ff97cbc4e6b02, 0x5660ab71f321390f,\n 0xe90365e44c42f79a, 0xa66c37e9032da597, 0x77ddc1fed29c5380, 0x38b293f39df3018d,\n 0xd1525b217413c95f, 0x9e3d092c3b7c9b52, 0x4f8cff3beacd6d45, 0xe3ad36a5a23f48,\n 0x99a1186e3ce08a10, 0xd6ce4a63738fd81d, 0x77fbc74a23e2e0a, 0x4810ee79ed517c07,\n 0xa1f026ab04b1b4d5, 0xee9f74a64bdee6d8, 0x3f2e82b19a6f10cf, 0x7041d0bcd50042c2,\n 0x8479ef0ad060c8e, 0x4728ccfde2695e83, 0x96993aea33d8a894, 0xd9f668e77cb7fa99,\n 0x3016a0359557324b, 0x7f79f238da386046, 0xaec8042f0b899651, 0xe1a7562244e6c45c,\n 0x78e5e37adda47104, 0x378ab17792cb2309, 0xe63b4760437ad51e, 0xa954156d0c158713,\n 0x40b4ddbfe5f54fc1, 0xfdb8fb2aa9a1dcc, 0xde6a79a57b2bebdb, 0x91052ba83444b9d6,\n 0xdd45aab800000000, 0x62228939bf672381, 0xa6679b4b7b2231f3, 0x1900b8cac4451272,\n 0x2b01c95ef64463e6, 0x9466eadf49234067, 0x5023f8ad8d665215, 0xef44db2c32017194,\n 0x34211b85e964b13d, 0x8b463804560392bc, 0x4f032a76924680ce, 0xf06409f72d21a34f,\n 0xc26578631f20d2db, 0x7d025be2a047f15a, 0xb94749906402e328, 0x6206a11db65c0a9,\n 0xa60be33d725148b, 0xb5079db26842370a, 0x71428fc0ac072578, 0xce25ac41136006f9,\n 0xfc24ddd52161776d, 0x4343fe549e0654ec, 0x8706ec265a43469e, 0x3861cfa7e524651f,\n 0xe3040f0e3e41a5b6, 0x5c632c8f81268637, 0x98263efd45639445, 0x27411d7cfa04b7c4,\n 0x15406ce8c805c650, 0xaa274f697762e5d1, 0x6e625d1bb327f7a3, 0xd1057e9a0c40d422,\n 0x76e3f55faba65fe7, 0xc984d6de14c17c66, 0xdc1c4acd0846e14, 0xb2a6e72d6fe34d95,\n 0x80a796b95de23c01, 0x3fc0b538e2851f80, 0xfb85a74a26c00df2, 0x44e284cb99a72e73,\n 0x9f87446242c2eeda, 0x20e067e3fda5cd5b, 0xe4a5759139e0df29, 0x5bc256108687fca8,\n 0x69c32784b4868d3c, 0xd6a404050be1aebd, 0x12e11677cfa4bccf, 0xad8635f670c39f4e,\n 0xa1c6e1d47c834b6c, 0x1ea1c255c3e468ed, 0xdae4d02707a17a9f, 0x6583f3a6b8c6591e,\n 0x578282328ac7288a, 0xe8e5a1b335a00b0b, 0x2ca0b3c1f1e51979, 0x93c790404e823af8,\n 0x48a250e995e7fa51, 0xf7c573682a80d9d0, 0x3380611aeec5cba2, 0x8ce7429b51a2e823,\n 0xbee6330f63a399b7, 0x181108edcc4ba36, 0xc5c402fc1881a844, 0x7aa3217da7e68bc5,\n 0x8fe5638752a0c93f, 0x30824006edc7eabe, 0xf4c752742982f8cc, 0x4ba071f596e5db4d,\n 0x79a10061a4e4aad9, 0xc6c623e01b838958, 0x2833192dfc69b2a, 0xbde4121360a1b8ab,\n 0x6681d2babbc47802, 0xd9e6f13b04a35b83, 0x1da3e349c0e649f1, 0xa2c4c0c87f816a70,\n 0x90c5b15c4d801be4, 0x2fa292ddf2e73865, 0xebe780af36a22a17, 0x5480a32e89c50996,\n 0x58c0770c8585ddb4, 0xe7a7548d3ae2fe35, 0x23e246fffea7ec47, 0x9c85657e41c0cfc6,\n 0xae8414ea73c1be52, 0x11e3376bcca69dd3, 0xd5a6251908e38fa1, 0x6ac10698b784ac20,\n 0xb1a4c6316ce16c89, 0xec3e5b0d3864f08, 0xca86f7c217c35d7a, 0x75e1d443a8a47efb,\n 0x47e0a5d79aa50f6f, 0xf887865625c22cee, 0x3cc29424e1873e9c, 0x83a5b7a55ee01d1d,\n 0x24433c60f90696d8, 0x9b241fe14661b559, 0x5f610d938224a72b, 0xe0062e123d4384aa,\n 0xd2075f860f42f53e, 0x6d607c07b025d6bf, 0xa9256e757460c4cd, 0x16424df4cb07e74c,\n 0xcd278d5d106227e5, 0x7240aedcaf050464, 0xb605bcae6b401616, 0x9629f2fd4273597,\n 0x3b63eebbe6264403, 0x8404cd3a59416782, 0x4041df489d0475f0, 0xff26fcc922635671,\n 0xf36628eb2e238253, 0x4c010b6a9144a1d2, 0x884419185501b3a0, 0x37233a99ea669021,\n 0x5224b0dd867e1b5, 0xba45688c6700c234, 0x7e007afea345d046, 0xc167597f1c22f3c7,\n 0x1a0299d6c747336e, 0xa565ba57782010ef, 0x6120a825bc65029d, 0xde478ba40302211c,\n 0xec46fa3031035088, 0x5321d9b18e647309, 0x9764cbc34a21617b, 0x2803e842f54642fa,\n 0x38116fac00000000, 0x4833b0f47022df58, 0xd854d11ce045beb0, 0xa8760e44906761e8,\n 0xfd76643dc5670b91, 0x8d54bb65b545d4c9, 0x1d33da8d2522b521, 0x6d1105d555006a79,\n 0xb7330e7f8f2261d3, 0xc711d127ff00be8b, 0x5776b0cf6f67df63, 0x27546f971f45003b,\n 0x725405ee4a456a42, 0x276dab63a67b51a, 0x9211bb5eaa00d4f2, 0xe2336406da220baa,\n 0x23b9dafb1ba8b557, 0x539b05a36b8a6a0f, 0xc3fc644bfbed0be7, 0xb3debb138bcfd4bf,\n 0xe6ded16adecfbec6, 0x96fc0e32aeed619e, 0x69b6fda3e8a0076, 0x76b9b0824ea8df2e,\n 0xac9bbb28948ad484, 0xdcb96470e4a80bdc, 0x4cde059874cf6a34, 0x3cfcdac004edb56c,\n 0x69fcb0b951eddf15, 0x19de6fe121cf004d, 0x89b90e09b1a861a5, 0xf99bd151c18abefd,\n 0xf40050237516aae, 0x7f62da5a4773b5f6, 0xef05bbb2d714d41e, 0x9f2764eaa7360b46,\n 0xca270e93f236613f, 0xba05d1cb8214be67, 0x2a62b0231273df8f, 0x5a406f7b625100d7,\n 0x806264d1b8730b7d, 0xf040bb89c851d425, 0x6027da615836b5cd, 0x1005053928146a95,\n 0x45056f407d1400ec, 0x3527b0180d36dfb4, 0xa540d1f09d51be5c, 0xd5620ea8ed736104,\n 0x14e8b0552cf9dff9, 0x64ca6f0d5cdb00a1, 0xf4ad0ee5ccbc6149, 0x848fd1bdbc9ebe11,\n 0xd18fbbc4e99ed468, 0xa1ad649c99bc0b30, 0x31ca057409db6ad8, 0x41e8da2c79f9b580,\n 0x9bcad186a3dbbe2a, 0xebe80eded3f96172, 0x7b8f6f36439e009a, 0xbadb06e33bcdfc2,\n 0x5eadda1766bcb5bb, 0x2e8f054f169e6ae3, 0xbee864a786f90b0b, 0xcecabbfff6dbd453,\n 0x56b3baf06ea2d55c, 0x269165a81e800a04, 0xb6f604408ee76bec, 0xc6d4db18fec5b4b4,\n 0x93d4b161abc5decd, 0xe3f66e39dbe70195, 0x73910fd14b80607d, 0x3b3d0893ba2bf25,\n 0xd991db23e180b48f, 0xa9b3047b91a26bd7, 0x39d4659301c50a3f, 0x49f6bacb71e7d567,\n 0x1cf6d0b224e7bf1e, 0x6cd40fea54c56046, 0xfcb36e02c4a201ae, 0x8c91b15ab480def6,\n 0x4d1b0fa7750a600b, 0x3d39d0ff0528bf53, 0xad5eb117954fdebb, 0xdd7c6e4fe56d01e3,\n 0x887c0436b06d6b9a, 0xf85edb6ec04fb4c2, 0x6839ba865028d52a, 0x181b65de200a0a72,\n 0xc2396e74fa2801d8, 0xb21bb12c8a0ade80, 0x227cd0c41a6dbf68, 0x525e0f9c6a4f6030,\n 0x75e65e53f4f0a49, 0x777cbabd4f6dd511, 0xe71bdb55df0ab4f9, 0x9739040daf286ba1,\n 0x61e2d05e59f3bff2, 0x11c00f0629d160aa, 0x81a76eeeb9b60142, 0xf185b1b6c994de1a,\n 0xa485dbcf9c94b463, 0xd4a70497ecb66b3b, 0x44c0657f7cd10ad3, 0x34e2ba270cf3d58b,\n 0xeec0b18dd6d1de21, 0x9ee26ed5a6f30179, 0xe850f3d36946091, 0x7ea7d06546b6bfc9,\n 0x2ba7ba1c13b6d5b0, 0x5b85654463940ae8, 0xcbe204acf3f36b00, 0xbbc0dbf483d1b458,\n 0x7a4a6509425b0aa5, 0xa68ba513279d5fd, 0x9a0fdbb9a21eb415, 0xea2d04e1d23c6b4d,\n 0xbf2d6e98873c0134, 0xcf0fb1c0f71ede6c, 0x5f68d0286779bf84, 0x2f4a0f70175b60dc,\n 0xf56804dacd796b76, 0x854adb82bd5bb42e, 0x152dba6a2d3cd5c6, 0x650f65325d1e0a9e,\n 0x300f0f4b081e60e7, 0x402dd013783cbfbf, 0xd04ab1fbe85bde57, 0xa0686ea39879010f,\n 0xef306b1900000000, 0x34bccbdadb8ca0c3, 0x5dc55c6eb2f53777, 0x8649fcad697997b4,\n 0x8f3673066006181f, 0x54bad3c5bb8ab8dc, 0x3dc34471d2f32f68, 0xe64fe4b2097f8fab,\n 0x2f3c5b27c00c303e, 0xf4b0fbe41b8090fd, 0x9dc96c5072f90749, 0x4645cc93a975a78a,\n 0x4f3a4338a00a2821, 0x94b6e3fb7b8688e2, 0xfdcf744f12ff1f56, 0x2643d48cc973bf95,\n 0x6ac47d9485f4168d, 0xb148dd575e78b64e, 0xd8314ae3370121fa, 0x3bdea20ec8d8139,\n 0xac2658be5f20e92, 0xd14ec5483e7eae51, 0xb83752fc570739e5, 0x63bbf23f8c8b9926,\n 0xaac84daa45f826b3, 0x7144ed699e748670, 0x183d7addf70d11c4, 0xc3b1da1e2c81b107,\n 0xcace55b525fe3eac, 0x1142f576fe729e6f, 0x783b62c2970b09db, 0xa3b7c2014c87a918,\n 0xe13430f20e045beb, 0x3ab89031d588fb28, 0x53c10785bcf16c9c, 0x884da746677dcc5f,\n 0x813228ed6e0243f4, 0x5abe882eb58ee337, 0x33c71f9adcf77483, 0xe84bbf59077bd440,\n 0x213800ccce086bd5, 0xfab4a00f1584cb16, 0x93cd37bb7cfd5ca2, 0x48419778a771fc61,\n 0x413e18d3ae0e73ca, 0x9ab2b8107582d309, 0xf3cb2fa41cfb44bd, 0x28478f67c777e47e,\n 0x64c0267f8bf04d66, 0xbf4c86bc507ceda5, 0xd635110839057a11, 0xdb9b1cbe289dad2,\n 0x4c63e60ebf65579, 0xdf4a9ea3307af5ba, 0xb63309175903620e, 0x6dbfa9d4828fc2cd,\n 0xa4cc16414bfc7d58, 0x7f40b6829070dd9b, 0x16392136f9094a2f, 0xcdb581f52285eaec,\n 0xc4ca0e5e2bfa6547, 0x1f46ae9df076c584, 0x763f3929990f5230, 0xadb399ea4283f2f3,\n 0xf338dccf1c08b7d6, 0x28b47c0cc7841715, 0x41cdebb8aefd80a1, 0x9a414b7b75712062,\n 0x933ec4d07c0eafc9, 0x48b26413a7820f0a, 0x21cbf3a7cefb98be, 0xfa4753641577387d,\n 0x3334ecf1dc0487e8, 0xe8b84c320788272b, 0x81c1db866ef1b09f, 0x5a4d7b45b57d105c,\n 0x5332f4eebc029ff7, 0x88be542d678e3f34, 0xe1c7c3990ef7a880, 0x3a4b635ad57b0843,\n 0x76ccca4299fca15b, 0xad406a8142700198, 0xc439fd352b09962c, 0x1fb55df6f08536ef,\n 0x16cad25df9fab944, 0xcd46729e22761987, 0xa43fe52a4b0f8e33, 0x7fb345e990832ef0,\n 0xb6c0fa7c59f09165, 0x6d4c5abf827c31a6, 0x435cd0beb05a612, 0xdfb96dc8308906d1,\n 0xd6c6e26339f6897a, 0xd4a42a0e27a29b9, 0x6433d5148b03be0d, 0xbfbf75d7508f1ece,\n 0xfd3c8724120cec3d, 0x26b027e7c9804cfe, 0x4fc9b053a0f9db4a, 0x944510907b757b89,\n 0x9d3a9f3b720af422, 0x46b63ff8a98654e1, 0x2fcfa84cc0ffc355, 0xf443088f1b736396,\n 0x3d30b71ad200dc03, 0xe6bc17d9098c7cc0, 0x8fc5806d60f5eb74, 0x544920aebb794bb7,\n 0x5d36af05b206c41c, 0x86ba0fc6698a64df, 0xefc3987200f3f36b, 0x344f38b1db7f53a8,\n 0x78c891a997f8fab0, 0xa344316a4c745a73, 0xca3da6de250dcdc7, 0x11b1061dfe816d04,\n 0x18ce89b6f7fee2af, 0xc34229752c72426c, 0xaa3bbec1450bd5d8, 0x71b71e029e87751b,\n 0xb8c4a19757f4ca8e, 0x634801548c786a4d, 0xa3196e0e501fdf9, 0xd1bd36233e8d5d3a,\n 0xd8c2b98837f2d291, 0x34e194bec7e7252, 0x6a378eff8507e5e6, 0xb1bb2e3c5e8b4525,\n 0x68032cc800000000, 0xb8057558d0065990, 0xcde3e919a5e0c5d1, 0x1de5b08975e69c41,\n 0x262ed19b4e2dfd53, 0xf628880b9e2ba4c3, 0x83ce144aebcd3882, 0x53c84dda3bcb6112,\n 0xf458d66e9c5bfaa6, 0x245e8ffe4c5da336, 0x51b813bf39bb3f77, 0x81be4a2fe9bd66e7,\n 0xba752b3dd27607f5, 0x6a7372ad02705e65, 0x1f95eeec7796c224, 0xcf93b77ca7909bb4,\n 0x5558af753d5b83bd, 0x855ef6e5ed5dda2d, 0xf0b86aa498bb466c, 0x20be333448bd1ffc,\n 0x1b75522673767eee, 0xcb730bb6a370277e, 0xbe9597f7d696bb3f, 0x6e93ce670690e2af,\n 0xc90355d3a100791b, 0x19050c437106208b, 0x6ce3900204e0bcca, 0xbce5c992d4e6e55a,\n 0x872ea880ef2d8448, 0x5728f1103f2bddd8, 0x22ce6d514acd4199, 0xf2c834c19acb1809,\n 0x12b42bb27ab7077a, 0xc2b27222aab15eea, 0xb754ee63df57c2ab, 0x6752b7f30f519b3b,\n 0x5c99d6e1349afa29, 0x8c9f8f71e49ca3b9, 0xf9791330917a3ff8, 0x297f4aa0417c6668,\n 0x8eefd114e6ecfddc, 0x5ee9888436eaa44c, 0x2b0f14c5430c380d, 0xfb094d55930a619d,\n 0xc0c22c47a8c1008f, 0x10c475d778c7591f, 0x6522e9960d21c55e, 0xb524b006dd279cce,\n 0x2fefa80f47ec84c7, 0xffe9f19f97eadd57, 0x8a0f6ddee20c4116, 0x5a09344e320a1886,\n 0x61c2555c09c17994, 0xb1c40cccd9c72004, 0xc422908dac21bc45, 0x1424c91d7c27e5d5,\n 0xb3b452a9dbb77e61, 0x63b20b390bb127f1, 0x165497787e57bbb0, 0xc652cee8ae51e220,\n 0xfd99affa959a8332, 0x2d9ff66a459cdaa2, 0x58796a2b307a46e3, 0x887f33bbe07c1f73,\n 0x9d6d223cf56e0ef4, 0x4d6b7bac25685764, 0x388de7ed508ecb25, 0xe88bbe7d808892b5,\n 0xd340df6fbb43f3a7, 0x34686ff6b45aa37, 0x76a01abe1ea33676, 0xa6a6432ecea56fe6,\n 0x136d89a6935f452, 0xd130810ab933adc2, 0xa4d61d4bccd53183, 0x74d044db1cd36813,\n 0x4f1b25c927180901, 0x9f1d7c59f71e5091, 0xeafbe01882f8ccd0, 0x3afdb98852fe9540,\n 0xa036a181c8358d49, 0x7030f8111833d4d9, 0x5d664506dd54898, 0xd5d03dc0bdd31108,\n 0xee1b5cd28618701a, 0x3e1d0542561e298a, 0x4bfb990323f8b5cb, 0x9bfdc093f3feec5b,\n 0x3c6d5b27546e77ef, 0xec6b02b784682e7f, 0x998d9ef6f18eb23e, 0x498bc7662188ebae,\n 0x7240a6741a438abc, 0xa246ffe4ca45d32c, 0xd7a063a5bfa34f6d, 0x7a63a356fa516fd,\n 0xe7da25468fd9098e, 0x37dc7cd65fdf501e, 0x423ae0972a39cc5f, 0x923cb907fa3f95cf,\n 0xa9f7d815c1f4f4dd, 0x79f1818511f2ad4d, 0xc171dc46414310c, 0xdc114454b412689c,\n 0x7b81dfe01382f328, 0xab878670c384aab8, 0xde611a31b66236f9, 0xe6743a166646f69,\n 0x35ac22b35daf0e7b, 0xe5aa7b238da957eb, 0x904ce762f84fcbaa, 0x404abef22849923a,\n 0xda81a6fbb2828a33, 0xa87ff6b6284d3a3, 0x7f61632a17624fe2, 0xaf673abac7641672,\n 0x94ac5ba8fcaf7760, 0x44aa02382ca92ef0, 0x314c9e79594fb2b1, 0xe14ac7e98949eb21,\n 0x46da5c5d2ed97095, 0x96dc05cdfedf2905, 0xe33a998c8b39b544, 0x333cc01c5b3fecd4,\n 0x8f7a10e60f48dc6, 0xd8f1f89eb0f2d456, 0xad1764dfc5144817, 0x7d113d4f15121187,\n 0x493c7d2700000000, 0xdb4487699278fa4e, 0x6821ff4a211d826d, 0xfa590504b3657823,\n 0xb0779fd423b04da, 0x997f83b3d043fe94, 0x2a1afb90632686b7, 0xb86201def15e7cf9,\n 0xcd4a7493847609b4, 0x5f328edd160ef3fa, 0xec57f6fea56b8bd9, 0x7e2f0cb037137197,\n 0x8f717049c64d0d6e, 0x1d098a075435f720, 0xae6cf224e7508f03, 0x3c14086a7528754d,\n 0x443c18be0d006599, 0xd644e2f09f789fd7, 0x65219ad32c1de7f4, 0xf759609dbe651dba,\n 0x6071c644f3b6143, 0x947fe62add439b0d, 0x271a9e096e26e32e, 0xb5626447fc5e1960,\n 0xc04a110a89766c2d, 0x5232eb441b0e9663, 0xe1579367a86bee40, 0x732f69293a13140e,\n 0x827115d0cb4d68f7, 0x1009ef9e593592b9, 0xa36c97bdea50ea9a, 0x31146df3782810d4,\n 0x533cb6151a00cb32, 0xc1444c5b8878317c, 0x722134783b1d495f, 0xe059ce36a965b311,\n 0x1107b2cf583bcfe8, 0x837f4881ca4335a6, 0x301a30a279264d85, 0xa262caeceb5eb7cb,\n 0xd74abfa19e76c286, 0x453245ef0c0e38c8, 0xf6573dccbf6b40eb, 0x642fc7822d13baa5,\n 0x9571bb7bdc4dc65c, 0x70941354e353c12, 0xb46c3916fd504431, 0x2614c3586f28be7f,\n 0x5e3cd38c1700aeab, 0xcc4429c2857854e5, 0x7f2151e1361d2cc6, 0xed59abafa465d688,\n 0x1c07d756553baa71, 0x8e7f2d18c743503f, 0x3d1a553b7426281c, 0xaf62af75e65ed252,\n 0xda4ada389376a71f, 0x48322076010e5d51, 0xfb575855b26b2572, 0x692fa21b2013df3c,\n 0x9871dee2d14da3c5, 0xa0924ac4335598b, 0xb96c5c8ff05021a8, 0x2b14a6c16228dbe6,\n 0x7d3deb4334019664, 0xef45110da6796c2a, 0x5c20692e151c1409, 0xce5893608764ee47,\n 0x3f06ef99763a92be, 0xad7e15d7e44268f0, 0x1e1b6df4572710d3, 0x8c6397bac55fea9d,\n 0xf94be2f7b0779fd0, 0x6b3318b9220f659e, 0xd856609a916a1dbd, 0x4a2e9ad40312e7f3,\n 0xbb70e62df24c9b0a, 0x29081c6360346144, 0x9a6d6440d3511967, 0x8159e0e4129e329,\n 0x703d8eda3901f3fd, 0xe2457494ab7909b3, 0x51200cb7181c7190, 0xc358f6f98a648bde,\n 0x32068a007b3af727, 0xa07e704ee9420d69, 0x131b086d5a27754a, 0x8163f223c85f8f04,\n 0xf44b876ebd77fa49, 0x66337d202f0f0007, 0xd55605039c6a7824, 0x472eff4d0e12826a,\n 0xb67083b4ff4cfe93, 0x240879fa6d3404dd, 0x976d01d9de517cfe, 0x515fb974c2986b0,\n 0x673d20712e015d56, 0xf545da3fbc79a718, 0x4620a21c0f1cdf3b, 0xd45858529d642575,\n 0x250624ab6c3a598c, 0xb77edee5fe42a3c2, 0x41ba6c64d27dbe1, 0x96635c88df5f21af,\n 0xe34b29c5aa7754e2, 0x7133d38b380faeac, 0xc256aba88b6ad68f, 0x502e51e619122cc1,\n 0xa1702d1fe84c5038, 0x3308d7517a34aa76, 0x806daf72c951d255, 0x1215553c5b29281b,\n 0x6a3d45e8230138cf, 0xf845bfa6b179c281, 0x4b20c785021cbaa2, 0xd9583dcb906440ec,\n 0x28064132613a3c15, 0xba7ebb7cf342c65b, 0x91bc35f4027be78, 0x9b633911d25f4436,\n 0xee4b4c5ca777317b, 0x7c33b612350fcb35, 0xcf56ce31866ab316, 0x5d2e347f14124958,\n 0xac704886e54c35a1, 0x3e08b2c87734cfef, 0x8d6dcaebc451b7cc, 0x1f1530a556294d82,\n 0xf43ed64800000000, 0x19af0c29ed91da61, 0x2af1147bdecfc233, 0xc760ce1a335e1852,\n 0x4c4d24dfb873f297, 0xa1dcfebe55e228f6, 0x9282e6ec66bc30a4, 0x7f133c8d8b2deac5,\n 0x81354597750b93df, 0x6ca49ff6989a49be, 0x5ffa87a4abc451ec, 0xb26b5dc546558b8d,\n 0x3946b700cd786148, 0xd4d76d6120e9bb29, 0xe789753313b7a37b, 0xa18af52fe26791a,\n 0x1e29f1f6ea1727be, 0xf3b82b970786fddf, 0xc0e633c534d8e58d, 0x2d77e9a4d9493fec,\n 0xa65a03615264d529, 0x4bcbd900bff50f48, 0x7895c1528cab171a, 0x95041b33613acd7b,\n 0x6b2262299f1cb461, 0x86b3b848728d6e00, 0xb5eda01a41d37652, 0x587c7a7bac42ac33,\n 0xd35190be276f46f6, 0x3ec04adfcafe9c97, 0xd9e528df9a084c5, 0xe00f88ec14315ea4,\n 0x25fcefc5d1c2398d, 0xc86d35a43c53e3ec, 0xfb332df60f0dfbbe, 0x16a2f797e29c21df,\n 0x9d8f1d5269b1cb1a, 0x701ec7338420117b, 0x4340df61b77e0929, 0xaed105005aefd348,\n 0x50f77c1aa4c9aa52, 0xbd66a67b49587033, 0x8e38be297a066861, 0x63a964489797b200,\n 0xe8848e8d1cba58c5, 0x51554ecf12b82a4, 0x364b4cbec2759af6, 0xdbda96df2fe44097,\n 0xcfebc87b3bd51e33, 0x227a121ad644c452, 0x11240a48e51adc00, 0xfcb5d029088b0661,\n 0x77983aec83a6eca4, 0x9a09e08d6e3736c5, 0xa957f8df5d692e97, 0x44c622beb0f8f4f6,\n 0xbae05ba44ede8dec, 0x577181c5a34f578d, 0x642f999790114fdf, 0x89be43f67d8095be,\n 0x293a933f6ad7f7b, 0xef0273521b3ca51a, 0xdc5c6b002862bd48, 0x31cdb161c5f36729,\n 0x5256d3a3a66805eb, 0xbfc709c24bf9df8a, 0x8c99119078a7c7d8, 0x6108cbf195361db9,\n 0xea2521341e1bf77c, 0x7b4fb55f38a2d1d, 0x34eae307c0d4354f, 0xd97b39662d45ef2e,\n 0x275d407cd3639634, 0xcacc9a1d3ef24c55, 0xf992824f0dac5407, 0x1403582ee03d8e66,\n 0x9f2eb2eb6b1064a3, 0x72bf688a8681bec2, 0x41e170d8b5dfa690, 0xac70aab9584e7cf1,\n 0xb841f41d4c7f2255, 0x55d02e7ca1eef834, 0x668e362e92b0e066, 0x8b1fec4f7f213a07,\n 0x32068af40cd0c2, 0xeda3dceb199d0aa3, 0xdefdc4b92ac312f1, 0x336c1ed8c752c890,\n 0xcd4a67c23974b18a, 0x20dbbda3d4e56beb, 0x1385a5f1e7bb73b9, 0xfe147f900a2aa9d8,\n 0x753995558107431d, 0x98a84f346c96997c, 0xabf657665fc8812e, 0x46678d07b2595b4f,\n 0x8394ea2e77aa3c66, 0x6e05304f9a3be607, 0x5d5b281da965fe55, 0xb0caf27c44f42434,\n 0x3be718b9cfd9cef1, 0xd676c2d822481490, 0xe528da8a11160cc2, 0x8b900ebfc87d6a3,\n 0xf69f79f102a1afb9, 0x1b0ea390ef3075d8, 0x2850bbc2dc6e6d8a, 0xc5c161a331ffb7eb,\n 0x4eec8b66bad25d2e, 0xa37d51075743874f, 0x90234955641d9f1d, 0x7db29334898c457c,\n 0x6983cd909dbd1bd8, 0x841217f1702cc1b9, 0xb74c0fa34372d9eb, 0x5addd5c2aee3038a,\n 0xd1f03f0725cee94f, 0x3c61e566c85f332e, 0xf3ffd34fb012b7c, 0xe2ae27551690f11d,\n 0x1c885e4fe8b68807, 0xf119842e05275266, 0xc2479c7c36794a34, 0x2fd6461ddbe89055,\n 0xa4fbacd850c57a90, 0x496a76b9bd54a0f1, 0x7a346eeb8e0ab8a3, 0x97a5b48a639b62c2,\n 0xcb567ba500000000, 0x5816fa1e934081bb, 0xe83b0e22236d7587, 0x7b7b8f99b02df43c,\n 0x8d8c90ab46daeb0e, 0x1ecc1110d59a6ab5, 0xaee1e52c65b79e89, 0x3da16497f6f71f32,\n 0x46e3adb98db5d61c, 0xd5a32c021ef557a7, 0x658ed83eaed8a39b, 0xf6ce59853d982220,\n 0x3946b7cb6f3d12, 0x9379c70c582fbca9, 0x23543330e8024895, 0xb014b28b7b42c92e,\n 0xd5d1a16c1e87dac9, 0x469120d78dc75b72, 0xf6bcd4eb3deaaf4e, 0x65fc5550aeaa2ef5,\n 0x930b4a62585d31c7, 0x4bcbd9cb1db07c, 0xb0663fe57b304440, 0x2326be5ee870c5fb,\n 0x5864777093320cd5, 0xcb24f6cb00728d6e, 0x7b0902f7b05f7952, 0xe849834c231ff8e9,\n 0x1ebe9c7ed5e8e7db, 0x8dfe1dc546a86660, 0x3dd3e9f9f685925c, 0xae93684265c513e7,\n 0xf659ce373d0fb592, 0x65194f8cae4f3429, 0xd534bbb01e62c015, 0x46743a0b8d2241ae,\n 0xb08325397bd55e9c, 0x23c3a482e895df27, 0x93ee50be58b82b1b, 0xaed105cbf8aaa0,\n 0x7bec182bb0ba638e, 0xe8ac999023fae235, 0x58816dac93d71609, 0xcbc1ec17009797b2,\n 0x3d36f325f6608880, 0xae76729e6520093b, 0x1e5b86a2d50dfd07, 0x8d1b0719464d7cbc,\n 0xe8de14fe23886f5b, 0x7b9e9545b0c8eee0, 0xcbb3617900e51adc, 0x58f3e0c293a59b67,\n 0xae04fff065528455, 0x3d447e4bf61205ee, 0x8d698a77463ff1d2, 0x1e290bccd57f7069,\n 0x656bc2e2ae3db947, 0xf62b43593d7d38fc, 0x4606b7658d50ccc0, 0xd54636de1e104d7b,\n 0x23b129ece8e75249, 0xb0f1a8577ba7d3f2, 0xdc5c6bcb8a27ce, 0x939cddd058caa675,\n 0xb14910817a1f6b24, 0x2209913ae95fea9f, 0x9224650659721ea3, 0x164e4bdca329f18,\n 0xf793fb8f3cc5802a, 0x64d37a34af850191, 0xd4fe8e081fa8f5ad, 0x47be0fb38ce87416,\n 0x3cfcc69df7aabd38, 0xafbc472664ea3c83, 0x1f91b31ad4c7c8bf, 0x8cd132a147874904,\n 0x7a262d93b1705636, 0xe966ac282230d78d, 0x594b5814921d23b1, 0xca0bd9af015da20a,\n 0xafceca486498b1ed, 0x3c8e4bf3f7d83056, 0x8ca3bfcf47f5c46a, 0x1fe33e74d4b545d1,\n 0xe914214622425ae3, 0x7a54a0fdb102db58, 0xca7954c1012f2f64, 0x5939d57a926faedf,\n 0x227b1c54e92d67f1, 0xb13b9def7a6de64a, 0x11669d3ca401276, 0x9256e868590093cd,\n 0x64a1f75aaff78cff, 0xf7e176e13cb70d44, 0x47cc82dd8c9af978, 0xd48c03661fda78c3,\n 0x8c46a5134710deb6, 0x1f0624a8d4505f0d, 0xaf2bd094647dab31, 0x3c6b512ff73d2a8a,\n 0xca9c4e1d01ca35b8, 0x59dccfa6928ab403, 0xe9f13b9a22a7403f, 0x7ab1ba21b1e7c184,\n 0x1f3730fcaa508aa, 0x92b3f2b459e58911, 0x229e0688e9c87d2d, 0xb1de87337a88fc96,\n 0x472998018c7fe3a4, 0xd46919ba1f3f621f, 0x6444ed86af129623, 0xf7046c3d3c521798,\n 0x92c17fda5997047f, 0x181fe61cad785c4, 0xb1ac0a5d7afa71f8, 0x22ec8be6e9baf043,\n 0xd41b94d41f4def71, 0x475b156f8c0d6eca, 0xf776e1533c209af6, 0x643660e8af601b4d,\n 0x1f74a9c6d422d263, 0x8c34287d476253d8, 0x3c19dc41f74fa7e4, 0xaf595dfa640f265f,\n 0x59ae42c892f8396d, 0xcaeec37301b8b8d6, 0x7ac3374fb1954cea, 0xe983b6f422d5cd51,\n 0x9771f7c100000000, 0xbc7e6eb22b0f9973, 0xc16ec527561f32e6, 0xea615c547d10ab95,\n 0x3b4f920dac3e65cc, 0x10400b7e8731fcbf, 0x6d50a0ebfa21572a, 0x465f3998d12ece59,\n 0xcae14aa85d90bd69, 0xe1eed3db769f241a, 0x9cfe784e0b8f8f8f, 0xb7f1e13d208016fc,\n 0x66df2f64f1aed8a5, 0x4dd0b617daa141d6, 0x30c01d82a7b1ea43, 0x1bcf84f18cbe7330,\n 0x2c508d13bb217ad2, 0x75f1460902ee3a1, 0x7a4fbff5ed3e4834, 0x51402686c631d147,\n 0x806ee8df171f1f1e, 0xab6171ac3c10866d, 0xd671da3941002df8, 0xfd7e434a6a0fb48b,\n 0x71c0307ae6b1c7bb, 0x5acfa909cdbe5ec8, 0x27df029cb0aef55d, 0xcd09bef9ba16c2e,\n 0xddfe55b64a8fa277, 0xf6f1ccc561803b04, 0x8be167501c909091, 0xa0eefe23379f09e2,\n 0xe4df749473ae8355, 0xcfd0ede758a11a26, 0xb2c0467225b1b1b3, 0x99cfdf010ebe28c0,\n 0x48e11158df90e699, 0x63ee882bf49f7fea, 0x1efe23be898fd47f, 0x35f1bacda2804d0c,\n 0xb94fc9fd2e3e3e3c, 0x9240508e0531a74f, 0xef50fb1b78210cda, 0xc45f6268532e95a9,\n 0x1571ac3182005bf0, 0x3e7e3542a90fc283, 0x436e9ed7d41f6916, 0x686107a4ff10f065,\n 0x5ffe0e46c88ff987, 0x74f19735e38060f4, 0x9e13ca09e90cb61, 0x22eea5d3b59f5212,\n 0xf3c06b8a64b19c4b, 0xd8cff2f94fbe0538, 0xa5df596c32aeaead, 0x8ed0c01f19a137de,\n 0x26eb32f951f44ee, 0x29612a5cbe10dd9d, 0x547181c9c3007608, 0x7f7e18bae80fef7b,\n 0xae50d6e339212122, 0x855f4f90122eb851, 0xf84fe4056f3e13c4, 0xd3407d7644318ab7,\n 0x702cf16be75d06aa, 0x5b236818cc529fd9, 0x2633c38db142344c, 0xd3c5afe9a4dad3f,\n 0xdc1294a74b636366, 0xf71d0dd4606cfa15, 0x8a0da6411d7c5180, 0xa1023f323673c8f3,\n 0x2dbc4c02bacdbbc3, 0x6b3d57191c222b0, 0x7ba37ee4ecd28925, 0x50ace797c7dd1056,\n 0x818229ce16f3de0f, 0xaa8db0bd3dfc477c, 0xd79d1b2840ecece9, 0xfc92825b6be3759a,\n 0xcb0d8bb95c7c7c78, 0xe00212ca7773e50b, 0x9d12b95f0a634e9e, 0xb61d202c216cd7ed,\n 0x6733ee75f04219b4, 0x4c3c7706db4d80c7, 0x312cdc93a65d2b52, 0x1a2345e08d52b221,\n 0x969d36d001ecc111, 0xbd92afa32ae35862, 0xc082043657f3f3f7, 0xeb8d9d457cfc6a84,\n 0x3aa3531cadd2a4dd, 0x11acca6f86dd3dae, 0x6cbc61fafbcd963b, 0x47b3f889d0c20f48,\n 0x382723e94f385ff, 0x288deb4dbffc1c8c, 0x559d40d8c2ecb719, 0x7e92d9abe9e32e6a,\n 0xafbc17f238cde033, 0x84b38e8113c27940, 0xf9a325146ed2d2d5, 0xd2acbc6745dd4ba6,\n 0x5e12cf57c9633896, 0x751d5624e26ca1e5, 0x80dfdb19f7c0a70, 0x230264c2b4739303,\n 0xf22caa9b655d5d5a, 0xd92333e84e52c429, 0xa433987d33426fbc, 0x8f3c010e184df6cf,\n 0xb8a308ec2fd2ff2d, 0x93ac919f04dd665e, 0xeebc3a0a79cdcdcb, 0xc5b3a37952c254b8,\n 0x149d6d2083ec9ae1, 0x3f92f453a8e30392, 0x42825fc6d5f3a807, 0x698dc6b5fefc3174,\n 0xe533b58572424244, 0xce3c2cf6594ddb37, 0xb32c8763245d70a2, 0x98231e100f52e9d1,\n 0x490dd049de7c2788, 0x6202493af573befb, 0x1f12e2af8863156e, 0x341d7bdca36c8c1d,\n 0x3171d43000000000, 0x53927c5062e3a860, 0xf4b684f0c5c750c0, 0x96552c90a724f8a0,\n 0xbf1303418e62d771, 0xddf0ab21ec817f11, 0x7ad453814ba587b1, 0x1837fbe129462fd1,\n 0x28580c231929d813, 0x4abba4437bca7073, 0xed9f5ce3dcee88d3, 0x8f7cf483be0d20b3,\n 0xa63adb52974b0f62, 0xc4d97332f5a8a702, 0x63fd8b92528c5fa2, 0x11e23f2306ff7c2,\n 0x32264163253b026, 0x61c1cc7650b01846, 0xc6e534d6f794e0e6, 0xa4069cb695774886,\n 0x8d40b367bc316757, 0xefa31b07ded2cf37, 0x4887e3a779f63797, 0x2a644bc71b159ff7,\n 0x1a0bbc052b7a6835, 0x78e814654999c055, 0xdfccecc5eebd38f5, 0xbd2f44a58c5e9095,\n 0x94696b74a518bf44, 0xf68ac314c7fb1724, 0x51ae3bb460dfef84, 0x334d93d4023c47e4,\n 0x55d6b47c64a7604c, 0x37351c1c0644c82c, 0x9011e4bca160308c, 0xf2f24cdcc38398ec,\n 0xdbb4630deac5b73d, 0xb957cb6d88261f5d, 0x1e7333cd2f02e7fd, 0x7c909bad4de14f9d,\n 0x4cff6c6f7d8eb85f, 0x2e1cc40f1f6d103f, 0x89383cafb849e89f, 0xebdb94cfdaaa40ff,\n 0xc29dbb1ef3ec6f2e, 0xa07e137e910fc74e, 0x75aebde362b3fee, 0x65b943be54c8978e,\n 0x6785045a56f4d06a, 0x566ac3a3417780a, 0xa242549a933380aa, 0xc0a1fcfaf1d028ca,\n 0xe9e7d32bd896071b, 0x8b047b4bba75af7b, 0x2c2083eb1d5157db, 0x4ec32b8b7fb2ffbb,\n 0x7eacdc494fdd0879, 0x1c4f74292d3ea019, 0xbb6b8c898a1a58b9, 0xd98824e9e8f9f0d9,\n 0xf0ce0b38c1bfdf08, 0x922da358a35c7768, 0x35095bf804788fc8, 0x57eaf398669b27a8,\n 0xf83f14a8c94ec098, 0x9adcbcc8abad68f8, 0x3df844680c899058, 0x5f1bec086e6a3838,\n 0x765dc3d9472c17e9, 0x14be6bb925cfbf89, 0xb39a931982eb4729, 0xd1793b79e008ef49,\n 0xe116ccbbd067188b, 0x83f564dbb284b0eb, 0x24d19c7b15a0484b, 0x4632341b7743e02b,\n 0x6f741bca5e05cffa, 0xd97b3aa3ce6679a, 0xaab34b0a9bc29f3a, 0xc850e36af921375a,\n 0xca6ca48efb1d70be, 0xa88f0cee99fed8de, 0xfabf44e3eda207e, 0x6d485c2e5c39881e,\n 0x440e73ff757fa7cf, 0x26eddb9f179c0faf, 0x81c9233fb0b8f70f, 0xe32a8b5fd25b5f6f,\n 0xd3457c9de234a8ad, 0xb1a6d4fd80d700cd, 0x16822c5d27f3f86d, 0x7461843d4510500d,\n 0x5d27abec6c567fdc, 0x3fc4038c0eb5d7bc, 0x98e0fb2ca9912f1c, 0xfa03534ccb72877c,\n 0x9c9874e4ade9a0d4, 0xfe7bdc84cf0a08b4, 0x595f2424682ef014, 0x3bbc8c440acd5874,\n 0x12faa395238b77a5, 0x70190bf54168dfc5, 0xd73df355e64c2765, 0xb5de5b3584af8f05,\n 0x85b1acf7b4c078c7, 0xe7520497d623d0a7, 0x4076fc3771072807, 0x2295545713e48067,\n 0xbd37b863aa2afb6, 0x6930d3e6584107d6, 0xce142b46ff65ff76, 0xacf783269d865716,\n 0xaecbc4c29fba10f2, 0xcc286ca2fd59b892, 0x6b0c94025a7d4032, 0x9ef3c62389ee852,\n 0x20a913b311d8c783, 0x424abbd3733b6fe3, 0xe56e4373d41f9743, 0x878deb13b6fc3f23,\n 0xb7e21cd18693c8e1, 0xd501b4b1e4706081, 0x72254c1143549821, 0x10c6e47121b73041,\n 0x3980cba008f11f90, 0x5b6363c06a12b7f0, 0xfc479b60cd364f50, 0x9ea43300afd5e730,\n 0x30d2386500000000, 0x517648af61a470ca, 0xf39ad9f1c348e194, 0x923ea93ba2ec915e,\n 0xb3af8dbc837db5d9, 0xd20bfd76e2d9c513, 0x70e76c284035544d, 0x11431ce221912487,\n 0x33c5252603171d43, 0x526155ec62b36d89, 0xf08dc4b2c05ffcd7, 0x9129b478a1fb8c1d,\n 0xb0b890ff806aa89a, 0xd11ce035e1ced850, 0x73f0716b4322490e, 0x125401a1228639c4,\n 0x36fc02e3062e3a86, 0x57587229678a4a4c, 0xf5b4e377c566db12, 0x941093bda4c2abd8,\n 0xb581b73a85538f5f, 0xd425c7f0e4f7ff95, 0x76c956ae461b6ecb, 0x176d266427bf1e01,\n 0x35eb1fa0053927c5, 0x544f6f6a649d570f, 0xf6a3fe34c671c651, 0x97078efea7d5b69b,\n 0xb696aa798644921c, 0xd732dab3e7e0e2d6, 0x75de4bed450c7388, 0x147a3b2724a80342,\n 0x3c8e4d690c5c750c, 0x5d2a3da36df805c6, 0xffc6acfdcf149498, 0x9e62dc37aeb0e452,\n 0xbff3f8b08f21c0d5, 0xde57887aee85b01f, 0x7cbb19244c692141, 0x1d1f69ee2dcd518b,\n 0x3f99502a0f4b684f, 0x5e3d20e06eef1885, 0xfcd1b1becc0389db, 0x9d75c174ada7f911,\n 0xbce4e5f38c36dd96, 0xdd409539ed92ad5c, 0x7fac04674f7e3c02, 0x1e0874ad2eda4cc8,\n 0x3aa077ef0a724f8a, 0x5b0407256bd63f40, 0xf9e8967bc93aae1e, 0x984ce6b1a89eded4,\n 0xb9ddc236890ffa53, 0xd879b2fce8ab8a99, 0x7a9523a24a471bc7, 0x1b3153682be36b0d,\n 0x39b76aac096552c9, 0x58131a6668c12203, 0xfaff8b38ca2db35d, 0x9b5bfbf2ab89c397,\n 0xbacadf758a18e710, 0xdb6eafbfebbc97da, 0x79823ee149500684, 0x18264e2b28f4764e,\n 0x286ad27d18b8ea18, 0x49cea2b7791c9ad2, 0xeb2233e9dbf00b8c, 0x8a864323ba547b46,\n 0xab1767a49bc55fc1, 0xcab3176efa612f0b, 0x685f8630588dbe55, 0x9fbf6fa3929ce9f,\n 0x2b7dcf3e1baff75b, 0x4ad9bff47a0b8791, 0xe8352eaad8e716cf, 0x89915e60b9436605,\n 0xa8007ae798d24282, 0xc9a40a2df9763248, 0x6b489b735b9aa316, 0xaecebb93a3ed3dc,\n 0x2e44e8fb1e96d09e, 0x4fe098317f32a054, 0xed0c096fddde310a, 0x8ca879a5bc7a41c0,\n 0xad395d229deb6547, 0xcc9d2de8fc4f158d, 0x6e71bcb65ea384d3, 0xfd5cc7c3f07f419,\n 0x2d53f5b81d81cddd, 0x4cf785727c25bd17, 0xee1b142cdec92c49, 0x8fbf64e6bf6d5c83,\n 0xae2e40619efc7804, 0xcf8a30abff5808ce, 0x6d66a1f55db49990, 0xcc2d13f3c10e95a,\n 0x2436a77114e49f14, 0x4592d7bb7540efde, 0xe77e46e5d7ac7e80, 0x86da362fb6080e4a,\n 0xa74b12a897992acd, 0xc6ef6262f63d5a07, 0x6403f33c54d1cb59, 0x5a783f63575bb93,\n 0x2721ba3217f38257, 0x4685caf87657f29d, 0xe4695ba6d4bb63c3, 0x85cd2b6cb51f1309,\n 0xa45c0feb948e378e, 0xc5f87f21f52a4744, 0x6714ee7f57c6d61a, 0x6b09eb53662a6d0,\n 0x22189df712caa592, 0x43bced3d736ed558, 0xe1507c63d1824406, 0x80f40ca9b02634cc,\n 0xa165282e91b7104b, 0xc0c158e4f0136081, 0x622dc9ba52fff1df, 0x389b970335b8115,\n 0x210f80b411ddb8d1, 0x40abf07e7079c81b, 0xe2476120d2955945, 0x83e311eab331298f,\n 0xa272356d92a00d08, 0xc3d645a7f3047dc2, 0x613ad4f951e8ec9c, 0x9ea433304c9c56,\n 0x5407554600000000, 0xfc09ffcaa80eaa8c, 0x1f676af55f123e9, 0xa9f8dc23fdff8965,\n 0xffe51294abe247d2, 0x57ebb81803eced5e, 0xaa14317dfe13643b, 0x21a9bf1561dceb7,\n 0x62fac135228f955, 0xae21069ffa2653d9, 0x53de8ffa07d9dabc, 0xfbd02576afd77030,\n 0xadcdebc1f9cabe87, 0x5c3414d51c4140b, 0xf83cc828ac3b9d6e, 0x503262a4043537e2,\n 0xf056a7eca451f2aa, 0x58580d600c5f5826, 0xa5a78405f1a0d143, 0xda92e8959ae7bcf,\n 0x5bb4e03e0fb3b578, 0xf3ba4ab2a7bd1ff4, 0xe45c3d75a429691, 0xa64b695bf24c3c1d,\n 0xa27e5eb9f6790bff, 0xa70f4355e77a173, 0xf78f7d50a3882816, 0x5f81d7dc0b86829a,\n 0x99c196b5d9b4c2d, 0xa192b3e7f595e6a1, 0x5c6d3a82086a6fc4, 0xf463900ea064c548,\n 0x1948c6e34d4f93a5, 0xb1466c6fe5413929, 0x4cb9e50a18beb04c, 0xe4b74f86b0b01ac0,\n 0xb2aa8131e6add477, 0x1aa42bbd4ea37efb, 0xe75ba2d8b35cf79e, 0x4f5508541b525d12,\n 0x4b603fb61f676af0, 0xe36e953ab769c07c, 0x1e911c5f4a964919, 0xb69fb6d3e298e395,\n 0xe0827864b4852d22, 0x488cd2e81c8b87ae, 0xb5735b8de1740ecb, 0x1d7df101497aa447,\n 0xbd193449e91e610f, 0x15179ec54110cb83, 0xe8e817a0bcef42e6, 0x40e6bd2c14e1e86a,\n 0x16fb739b42fc26dd, 0xbef5d917eaf28c51, 0x430a5072170d0534, 0xeb04fafebf03afb8,\n 0xef31cd1cbb36985a, 0x473f6790133832d6, 0xbac0eef5eec7bbb3, 0x12ce447946c9113f,\n 0x44d38ace10d4df88, 0xecdd2042b8da7504, 0x1122a9274525fc61, 0xb92c03abed2b56ed,\n 0xce98720c9a9f274a, 0x6696d88032918dc6, 0x9b6951e5cf6e04a3, 0x3367fb696760ae2f,\n 0x657a35de317d6098, 0xcd749f529973ca14, 0x308b1637648c4371, 0x9885bcbbcc82e9fd,\n 0x9cb08b59c8b7de1f, 0x34be21d560b97493, 0xc941a8b09d46fdf6, 0x614f023c3548577a,\n 0x3752cc8b635599cd, 0x9f5c6607cb5b3341, 0x62a3ef6236a4ba24, 0xcaad45ee9eaa10a8,\n 0x6ac980a63eced5e0, 0xc2c72a2a96c07f6c, 0x3f38a34f6b3ff609, 0x973609c3c3315c85,\n 0xc12bc774952c9232, 0x69256df83d2238be, 0x94dae49dc0ddb1db, 0x3cd44e1168d31b57,\n 0x38e179f36ce62cb5, 0x90efd37fc4e88639, 0x6d105a1a39170f5c, 0xc51ef0969119a5d0,\n 0x93033e21c7046b67, 0x3b0d94ad6f0ac1eb, 0xc6f21dc892f5488e, 0x6efcb7443afbe202,\n 0x83d7e1a9d7d0b4ef, 0x2bd94b257fde1e63, 0xd626c24082219706, 0x7e2868cc2a2f3d8a,\n 0x2835a67b7c32f33d, 0x803b0cf7d43c59b1, 0x7dc4859229c3d0d4, 0xd5ca2f1e81cd7a58,\n 0xd1ff18fc85f84dba, 0x79f1b2702df6e736, 0x840e3b15d0096e53, 0x2c0091997807c4df,\n 0x7a1d5f2e2e1a0a68, 0xd213f5a28614a0e4, 0x2fec7cc77beb2981, 0x87e2d64bd3e5830d,\n 0x2786130373814645, 0x8f88b98fdb8fecc9, 0x727730ea267065ac, 0xda799a668e7ecf20,\n 0x8c6454d1d8630197, 0x246afe5d706dab1b, 0xd99577388d92227e, 0x719bddb4259c88f2,\n 0x75aeea5621a9bf10, 0xdda040da89a7159c, 0x205fc9bf74589cf9, 0x88516333dc563675,\n 0xde4cad848a4bf8c2, 0x764207082245524e, 0x8bbd8e6ddfbadb2b, 0x23b324e177b471a7,\n 0x678efd0100000000, 0xa8930703cf1dfa02, 0xfc597ff49bd782f5, 0x334485f654ca78f7,\n 0x55cd8e1a3243731b, 0x9ad07418fd5e8919, 0xce1a0cefa994f1ee, 0x107f6ed66890bec,\n 0x3081b376486e636, 0xcc15e135ab9b1c34, 0x98df99c2ff5164c3, 0x57c263c0304c9ec1,\n 0x314b682c56c5952d, 0xfe56922e99d86f2f, 0xaa9cead9cd1217d8, 0x658110db020fedda,\n 0xae83316dc90dcc6c, 0x619ecb6f0610366e, 0x3554b39852da4e99, 0xfa49499a9dc7b49b,\n 0x9cc04276fb4ebf77, 0x53ddb87434534575, 0x717c08360993d82, 0xc80a3a81af84c780,\n 0xca05d75bad8b2a5a, 0x5182d596296d058, 0x51d255ae365ca8af, 0x9ecfafacf94152ad,\n 0xf846a4409fc85941, 0x375b5e4250d5a343, 0x639126b5041fdbb4, 0xac8cdcb7cb0221b6,\n 0xf079132897f7ee29, 0x3f64e92a58ea142b, 0x6bae91dd0c206cdc, 0xa4b36bdfc33d96de,\n 0xc23a6033a5b49d32, 0xd279a316aa96730, 0x59ede2c63e631fc7, 0x96f018c4f17ee5c5,\n 0x94fff51ef371081f, 0x5be20f1c3c6cf21d, 0xf2877eb68a68aea, 0xc0358de9a7bb70e8,\n 0xa6bc8605c1327b04, 0x69a17c070e2f8106, 0x3d6b04f05ae5f9f1, 0xf276fef295f803f3,\n 0x3974df445efa2245, 0xf669254691e7d847, 0xa2a35db1c52da0b0, 0x6dbea7b30a305ab2,\n 0xb37ac5f6cb9515e, 0xc42a565da3a4ab5c, 0x90e02eaaf76ed3ab, 0x5ffdd4a8387329a9,\n 0x5df239723a7cc473, 0x92efc370f5613e71, 0xc625bb87a1ab4686, 0x93841856eb6bc84,\n 0x6fb14a69083fb768, 0xa0acb06bc7224d6a, 0xf466c89c93e8359d, 0x3b7b329e5cf5cf9f,\n 0x4d8d57a22a03aaa3, 0x8290ada0e51e50a1, 0xd65ad557b1d42856, 0x19472f557ec9d254,\n 0x7fce24b91840d9b8, 0xb0d3debbd75d23ba, 0xe419a64c83975b4d, 0x2b045c4e4c8aa14f,\n 0x290bb1944e854c95, 0xe6164b968198b697, 0xb2dc3361d552ce60, 0x7dc1c9631a4f3462,\n 0x1b48c28f7cc63f8e, 0xd455388db3dbc58c, 0x809f407ae711bd7b, 0x4f82ba78280c4779,\n 0x84809bcee30e66cf, 0x4b9d61cc2c139ccd, 0x1f57193b78d9e43a, 0xd04ae339b7c41e38,\n 0xb6c3e8d5d14d15d4, 0x79de12d71e50efd6, 0x2d146a204a9a9721, 0xe209902285876d23,\n 0xe0067df8878880f9, 0x2f1b87fa48957afb, 0x7bd1ff0d1c5f020c, 0xb4cc050fd342f80e,\n 0xd2450ee3b5cbf3e2, 0x1d58f4e17ad609e0, 0x49928c162e1c7117, 0x868f7614e1018b15,\n 0xda7ab98bbdf4448a, 0x1567438972e9be88, 0x41ad3b7e2623c67f, 0x8eb0c17ce93e3c7d,\n 0xe839ca908fb73791, 0x2724309240aacd93, 0x73ee48651460b564, 0xbcf3b267db7d4f66,\n 0xbefc5fbdd972a2bc, 0x71e1a5bf166f58be, 0x252bdd4842a52049, 0xea36274a8db8da4b,\n 0x8cbf2ca6eb31d1a7, 0x43a2d6a4242c2ba5, 0x1768ae5370e65352, 0xd8755451bffba950,\n 0x137775e774f988e6, 0xdc6a8fe5bbe472e4, 0x88a0f712ef2e0a13, 0x47bd0d102033f011,\n 0x213406fc46bafbfd, 0xee29fcfe89a701ff, 0xbae38409dd6d7908, 0x75fe7e0b1270830a,\n 0x77f193d1107f6ed0, 0xb8ec69d3df6294d2, 0xec2611248ba8ec25, 0x233beb2644b51627,\n 0x45b2e0ca223c1dcb, 0x8aaf1ac8ed21e7c9, 0xde65623fb9eb9f3e, 0x1178983d76f6653c,\n 0xf20c0dfe00000000, 0x13f860f3e1f46d0d, 0x3408a115c604aceb, 0xd5fccc1827f0c1e6,\n 0x7be922d989e52f27, 0x9a1d4fd46811422a, 0xbded8e324fe183cc, 0x5c19e33fae15eec1,\n 0xe42a2541162628bf, 0x5de484cf7d245b2, 0x222e89aad0228454, 0xc3dae4a731d6e959,\n 0x6dcf0a669fc30798, 0x8c3b676b7e376a95, 0xabcba68d59c7ab73, 0x4a3fcb80b833c67e,\n 0xde405c802c4c517e, 0x3fb4318dcdb83c73, 0x1844f06bea48fd95, 0xf9b09d660bbc9098,\n 0x57a573a7a5a97e59, 0xb6511eaa445d1354, 0x91a1df4c63add2b2, 0x7055b2418259bfbf,\n 0xc866743f3a6a79c1, 0x29921932db9e14cc, 0xe62d8d4fc6ed52a, 0xef96b5d91d9ab827,\n 0x41835b18b38f56e6, 0xa0773615527b3beb, 0x8787f7f3758bfa0d, 0x66739afe947f9700,\n 0xaa94af025898a2fc, 0x4b60c20fb96ccff1, 0x6c9003e99e9c0e17, 0x8d646ee47f68631a,\n 0x23718025d17d8ddb, 0xc285ed283089e0d6, 0xe5752cce17792130, 0x48141c3f68d4c3d,\n 0xbcb287bd4ebe8a43, 0x5d46eab0af4ae74e, 0x7ab62b5688ba26a8, 0x9b42465b694e4ba5,\n 0x3557a89ac75ba564, 0xd4a3c59726afc869, 0xf3530471015f098f, 0x12a7697ce0ab6482,\n 0x86d8fe7c74d4f382, 0x672c937195209e8f, 0x40dc5297b2d05f69, 0xa1283f9a53243264,\n 0xf3dd15bfd31dca5, 0xeec9bc561cc5b1a8, 0xc9397db03b35704e, 0x28cd10bddac11d43,\n 0x90fed6c362f2db3d, 0x710abbce8306b630, 0x56fa7a28a4f677d6, 0xb70e172545021adb,\n 0x191bf9e4eb17f41a, 0xf8ef94e90ae39917, 0xdf1f550f2d1358f1, 0x3eeb3802cce735fc,\n 0x433d4806b13145f8, 0xa2c9250b50c528f5, 0x8539e4ed7735e913, 0x64cd89e096c1841e,\n 0xcad8672138d46adf, 0x2b2c0a2cd92007d2, 0xcdccbcafed0c634, 0xed28a6c71f24ab39,\n 0x551b60b9a7176d47, 0xb4ef0db446e3004a, 0x931fcc526113c1ac, 0x72eba15f80e7aca1,\n 0xdcfe4f9e2ef24260, 0x3d0a2293cf062f6d, 0x1afae375e8f6ee8b, 0xfb0e8e7809028386,\n 0x6f7119789d7d1486, 0x8e8574757c89798b, 0xa975b5935b79b86d, 0x4881d89eba8dd560,\n 0xe694365f14983ba1, 0x7605b52f56c56ac, 0x20909ab4d29c974a, 0xc164f7b93368fa47,\n 0x795731c78b5b3c39, 0x98a35cca6aaf5134, 0xbf539d2c4d5f90d2, 0x5ea7f021acabfddf,\n 0xf0b21ee002be131e, 0x114673ede34a7e13, 0x36b6b20bc4babff5, 0xd742df06254ed2f8,\n 0x1ba5eafae9a9e704, 0xfa5187f7085d8a09, 0xdda146112fad4bef, 0x3c552b1cce5926e2,\n 0x9240c5dd604cc823, 0x73b4a8d081b8a52e, 0x54446936a64864c8, 0xb5b0043b47bc09c5,\n 0xd83c245ff8fcfbb, 0xec77af481e7ba2b6, 0xcb876eae398b6350, 0x2a7303a3d87f0e5d,\n 0x8466ed62766ae09c, 0x6592806f979e8d91, 0x42624189b06e4c77, 0xa3962c84519a217a,\n 0x37e9bb84c5e5b67a, 0xd61dd6892411db77, 0xf1ed176f03e11a91, 0x10197a62e215779c,\n 0xbe0c94a34c00995d, 0x5ff8f9aeadf4f450, 0x780838488a0435b6, 0x99fc55456bf058bb,\n 0x21cf933bd3c39ec5, 0xc03bfe363237f3c8, 0xe7cb3fd015c7322e, 0x63f52ddf4335f23,\n 0xa82abc1c5a26b1e2, 0x49ded111bbd2dcef, 0x6e2e10f79c221d09, 0x8fda7dfa7dd67004,\n }\n};\n\nstatic union {\n uint32_t dword_table[4][256];\n uint64_t qword_array[512];\n} long_shifts = {\n .qword_array = {\n 0xe040e0ac00000000, 0x252d5705c56db7a9, 0x6f77f90f8f3719a3, 0xaa1a4ea64a5aae0a,\n 0xfbc2a51b1b8245b7, 0x3eaf12b2deeff21e, 0x74f5bcb894b55c14, 0xb1980b1151d8ebbd,\n 0xd7446bc237048b6e, 0x1229dc6bf2693cc7, 0x58737261b83392cd, 0x9d1ec5c87d5e2564,\n 0xccc62e752c86ced9, 0x9ab99dce9eb7970, 0x43f137d6a3b1d77a, 0x869c807f66dc60d3,\n 0x8e49f6706e0916dc, 0x4b2441d9ab64a175, 0x17eefd3e13e0f7f, 0xc413587a2453b8d6,\n 0x95cbb3c7758b536b, 0x50a6046eb0e6e4c2, 0x1afcaa64fabc4ac8, 0xdf911dcd3fd1fd61,\n 0xb94d7d1e590d9db2, 0x7c20cab79c602a1b, 0x367a64bdd63a8411, 0xf317d314135733b8,\n 0xa2cf38a9428fd805, 0x67a28f0087e26fac, 0x2df8210acdb8c1a6, 0xe89596a308d5760f,\n 0x3c52cd14dc122db8, 0xf93f7abd197f9a11, 0xb365d4b75325341b, 0x7608631e964883b2,\n 0x27d088a3c790680f, 0xe2bd3f0a02fddfa6, 0xa8e7910048a771ac, 0x6d8a26a98dcac605,\n 0xb56467aeb16a6d6, 0xce3bf1d32e7b117f, 0x84615fd96421bf75, 0x410ce870a14c08dc,\n 0x10d403cdf094e361, 0xd5b9b46435f954c8, 0x9fe31a6e7fa3fac2, 0x5a8eadc7bace4d6b,\n 0x525bdbc8b21b3b64, 0x97366c6177768ccd, 0xdd6cc26b3d2c22c7, 0x180175c2f841956e,\n 0x49d99e7fa9997ed3, 0x8cb429d66cf4c97a, 0xc6ee87dc26ae6770, 0x3833075e3c3d0d9,\n 0x655f50a6851fb00a, 0xa032e70f407207a3, 0xea6849050a28a9a9, 0x2f05feaccf451e00,\n 0x7edd15119e9df5bd, 0xbbb0a2b85bf04214, 0xf1ea0cb211aaec1e, 0x3487bb1bd4c75bb7,\n 0x5d88cd2dbdc82d81, 0x98e57a8478a59a28, 0xd2bfd48e32ff3422, 0x17d26327f792838b,\n 0x460a889aa64a6836, 0x83673f336327df9f, 0xc93d9139297d7195, 0xc502690ec10c63c,\n 0x6a8c46438acca6ef, 0xafe1f1ea4fa11146, 0xe5bb5fe005fbbf4c, 0x20d6e849c09608e5,\n 0x710e03f4914ee358, 0xb463b45d542354f1, 0xfe391a571e79fafb, 0x3b54adfedb144d52,\n 0x3381dbf1d3c13b5d, 0xf6ec6c5816ac8cf4, 0xbcb6c2525cf622fe, 0x79db75fb999b9557,\n 0x28039e46c8437eea, 0xed6e29ef0d2ec943, 0xa73487e547746749, 0x6259304c8219d0e0,\n 0x485509fe4c5b033, 0xc1e8e73621a8079a, 0x8bb2493c6bf2a990, 0x4edffe95ae9f1e39,\n 0x1f071528ff47f584, 0xda6aa2813a2a422d, 0x90300c8b7070ec27, 0x555dbb22b51d5b8e,\n 0x819ae09561da0039, 0x44f7573ca4b7b790, 0xeadf936eeed199a, 0xcbc04e9f2b80ae33,\n 0x9a18a5227a58458e, 0x5f75128bbf35f227, 0x152fbc81f56f5c2d, 0xd0420b283002eb84,\n 0xb69e6bfb56de8b57, 0x73f3dc5293b33cfe, 0x39a97258d9e992f4, 0xfcc4c5f11c84255d,\n 0xad1c2e4c4d5ccee0, 0x687199e588317949, 0x222b37efc26bd743, 0xe7468046070660ea,\n 0xef93f6490fd316e5, 0x2afe41e0cabea14c, 0x60a4efea80e40f46, 0xa5c958434589b8ef,\n 0xf411b3fe14515352, 0x317c0457d13ce4fb, 0x7b26aa5d9b664af1, 0xbe4b1df45e0bfd58,\n 0xd8977d2738d79d8b, 0x1dfaca8efdba2a22, 0x57a06484b7e08428, 0x92cdd32d728d3381,\n 0xc31538902355d83c, 0x6788f39e6386f95, 0x4c222133ac62c19f, 0x894f969a690f7636,\n 0x7e7c2df300000000, 0x82847615fcf85be6, 0x8260eccefc1cc13d, 0x7e98b72800e49adb,\n 0x83a9d978fdd5f48b, 0x7f51829e012daf6d, 0x7fb5184501c935b6, 0x834d43a3fd316e50,\n 0x803bb214fe479fe7, 0x7cc3e9f202bfc401, 0x7c277329025b5eda, 0x80df28cffea3053c,\n 0x7dee469f03926b6c, 0x81161d79ff6a308a, 0x81f287a2ff8eaa51, 0x7d0adc440376f1b7,\n 0x871f64ccf963493f, 0x7be73f2a059b12d9, 0x7b03a5f1057f8802, 0x87fbfe17f987d3e4,\n 0x7aca904704b6bdb4, 0x8632cba1f84ee652, 0x86d6517af8aa7c89, 0x7a2e0a9c0452276f,\n 0x7958fb2b0724d6d8, 0x85a0a0cdfbdc8d3e, 0x85443a16fb3817e5, 0x79bc61f007c04c03,\n 0x848d0fa0faf12253, 0x78755446060979b5, 0x7891ce9d06ede36e, 0x8469957bfa15b888,\n 0x8956c97cf72ae48f, 0x75ae929a0bd2bf69, 0x754a08410b3625b2, 0x89b253a7f7ce7e54,\n 0x74833df70aff1004, 0x887b6611f6074be2, 0x889ffccaf6e3d139, 0x7467a72c0a1b8adf,\n 0x7711569b096d7b68, 0x8be90d7df595208e, 0x8b0d97a6f571ba55, 0x77f5cc400989e1b3,\n 0x8ac4a210f4b88fe3, 0x763cf9f60840d405, 0x76d8632d08a44ede, 0x8a2038cbf45c1538,\n 0x703580430e49adb0, 0x8ccddba5f2b1f656, 0x8c29417ef2556c8d, 0x70d11a980ead376b,\n 0x8de074c8f39c593b, 0x71182f2e0f6402dd, 0x71fcb5f50f809806, 0x8d04ee13f378c3e0,\n 0x8e721fa4f00e3257, 0x728a44420cf669b1, 0x726ede990c12f36a, 0x8e96857ff0eaa88c,\n 0x73a7eb2f0ddbc6dc, 0x8f5fb0c9f1239d3a, 0x8fbb2a12f1c707e1, 0x734371f40d3f5c07,\n 0x95c5921cebb9bfef, 0x693dc9fa1741e409, 0x69d9532117a57ed2, 0x952108c7eb5d2534,\n 0x68106697166c4b64, 0x94e83d71ea941082, 0x940ca7aaea708a59, 0x68f4fc4c1688d1bf,\n 0x6b820dfb15fe2008, 0x977a561de9067bee, 0x979eccc6e9e2e135, 0x6b669720151abad3,\n 0x9657f970e82bd483, 0x6aafa29614d38f65, 0x6a4b384d143715be, 0x96b363abe8cf4e58,\n 0x6ca6db2312daf6d0, 0x905e80c5ee22ad36, 0x90ba1a1eeec637ed, 0x6c4241f8123e6c0b,\n 0x91732fa8ef0f025b, 0x6d8b744e13f759bd, 0x6d6fee951313c366, 0x9197b573efeb9880,\n 0x92e144c4ec9d6937, 0x6e191f22106532d1, 0x6efd85f91081a80a, 0x9205de1fec79f3ec,\n 0x6f34b04f11489dbc, 0x93cceba9edb0c65a, 0x93287172ed545c81, 0x6fd02a9411ac0767,\n 0x62ef76931c935b60, 0x9e172d75e06b0086, 0x9ef3b7aee08f9a5d, 0x620bec481c77c1bb,\n 0x9f3a8218e146afeb, 0x63c2d9fe1dbef40d, 0x632643251d5a6ed6, 0x9fde18c3e1a23530,\n 0x9ca8e974e2d4c487, 0x6050b2921e2c9f61, 0x60b428491ec805ba, 0x9c4c73afe2305e5c,\n 0x617d1dff1f01300c, 0x9d854619e3f96bea, 0x9d61dcc2e31df131, 0x619987241fe5aad7,\n 0x9b8c3face5f0125f, 0x6774644a190849b9, 0x6790fe9119ecd362, 0x9b68a577e5148884,\n 0x6659cb271825e6d4, 0x9aa190c1e4ddbd32, 0x9a450a1ae43927e9, 0x66bd51fc18c17c0f,\n 0x65cba04b1bb78db8, 0x9933fbade74fd65e, 0x99d76176e7ab4c85, 0x652f3a901b531763,\n 0x981e54c0e6627933, 0x64e60f261a9a22d5, 0x640295fd1a7eb80e, 0x98face1be686e3e8,\n 0xd29f092f00000000, 0x724d6d80a0d264af, 0x96d7b6804448bfaf, 0x3605d22fe49adb00,\n 0x5a0e767188917f5e, 0xfadc12de28431bf1, 0x1e46c9deccd9c0f1, 0xbe94ad716c0ba45e,\n 0xc651816214ce884d, 0x6683e5cdb41cece2, 0x82193ecd508637e2, 0x22cb5a62f054534d,\n 0x4ec0fe3c9c5ff713, 0xee129a933c8d93bc, 0xa884193d81748bc, 0xaa5a253c78c52c13,\n 0xfb0219b5299d109a, 0x5bd07d1a894f7435, 0xbf4aa61a6dd5af35, 0x1f98c2b5cd07cb9a,\n 0x739366eba10c6fc4, 0xd341024401de0b6b, 0x37dbd944e544d06b, 0x9709bdeb4596b4c4,\n 0xefcc91f83d5398d7, 0x4f1ef5579d81fc78, 0xab842e57791b2778, 0xb564af8d9c943d7,\n 0x675deea6b5c2e789, 0xc78f8a0915108326, 0x23155109f18a5826, 0x83c735a651583c89,\n 0x81a5281b533a2134, 0x21774cb4f3e8459b, 0xc5ed97b417729e9b, 0x653ff31bb7a0fa34,\n 0x9345745dbab5e6a, 0xa9e633ea7b793ac5, 0x4d7ce8ea9fe3e1c5, 0xedae8c453f31856a,\n 0x956ba05647f4a979, 0x35b9c4f9e726cdd6, 0xd1231ff903bc16d6, 0x71f17b56a36e7279,\n 0x1dfadf08cf65d627, 0xbd28bba76fb7b288, 0x59b260a78b2d6988, 0xf96004082bff0d27,\n 0xa83838817aa731ae, 0x8ea5c2eda755501, 0xec70872e3eef8e01, 0x4ca2e3819e3deaae,\n 0x20a947dff2364ef0, 0x807b237052e42a5f, 0x64e1f870b67ef15f, 0xc4339cdf16ac95f0,\n 0xbcf6b0cc6e69b9e3, 0x1c24d463cebbdd4c, 0xf8be0f632a21064c, 0x586c6bcc8af362e3,\n 0x3467cf92e6f8c6bd, 0x94b5ab3d462aa212, 0x702f703da2b07912, 0xd0fd149202621dbd,\n 0x74eb4b47a6744268, 0xd4392fe806a626c7, 0x30a3f4e8e23cfdc7, 0x9071904742ee9968,\n 0xfc7a34192ee53d36, 0x5ca850b68e375999, 0xb8328bb66aad8299, 0x18e0ef19ca7fe636,\n 0x6025c30ab2baca25, 0xc0f7a7a51268ae8a, 0x246d7ca5f6f2758a, 0x84bf180a56201125,\n 0xe8b4bc543a2bb57b, 0x4866d8fb9af9d1d4, 0xacfc03fb7e630ad4, 0xc2e6754deb16e7b,\n 0x5d765bdd8fe952f2, 0xfda43f722f3b365d, 0x193ee472cba1ed5d, 0xb9ec80dd6b7389f2,\n 0xd5e7248307782dac, 0x7535402ca7aa4903, 0x91af9b2c43309203, 0x317dff83e3e2f6ac,\n 0x49b8d3909b27dabf, 0xe96ab73f3bf5be10, 0xdf06c3fdf6f6510, 0xad2208907fbd01bf,\n 0xc129acce13b6a5e1, 0x61fbc861b364c14e, 0x8561136157fe1a4e, 0x25b377cef72c7ee1,\n 0x27d16a73f54e635c, 0x87030edc559c07f3, 0x6399d5dcb106dcf3, 0xc34bb17311d4b85c,\n 0xaf40152d7ddf1c02, 0xf927182dd0d78ad, 0xeb08aa823997a3ad, 0x4bdace2d9945c702,\n 0x331fe23ee180eb11, 0x93cd869141528fbe, 0x77575d91a5c854be, 0xd785393e051a3011,\n 0xbb8e9d606911944f, 0x1b5cf9cfc9c3f0e0, 0xffc622cf2d592be0, 0x5f1446608d8b4f4f,\n 0xe4c7ae9dcd373c6, 0xae9e1e467c011769, 0x4a04c546989bcc69, 0xead6a1e93849a8c6,\n 0x86dd05b754420c98, 0x260f6118f4906837, 0xc295ba18100ab337, 0x6247deb7b0d8d798,\n 0x1a82f2a4c81dfb8b, 0xba50960b68cf9f24, 0x5eca4d0b8c554424, 0xfe1829a42c87208b,\n 0x92138dfa408c84d5, 0x32c1e955e05ee07a, 0xd65b325504c43b7a, 0x768956faa4165fd5,\n 0x4904f22100000000, 0xdb0d16639209e442, 0x68fb4c5421ffbe75, 0xfaf2a816b3f65a37,\n 0xafb8ecb43ff7cea, 0x98f26a89d1f698a8, 0x2b0430be6200c29f, 0xb90dd4fcf00926dd,\n 0xcefa0bf587fef9d4, 0x5cf3efb715f71d96, 0xef05b580a60147a1, 0x7d0c51c23408a3e3,\n 0x8d05771fc401853e, 0x1f0c935d5608617c, 0xacfac96ae5fe3b4b, 0x3ef32d2877f7df09,\n 0x431577780a118559, 0xd11c933a9818611b, 0x62eac90d2bee3b2c, 0xf0e32d4fb9e7df6e,\n 0xea0b9249eef9b3, 0x92e3efd0dbe71df1, 0x2115b5e7681147c6, 0xb31c51a5fa18a384,\n 0xc4eb8eac8def7c8d, 0x56e26aee1fe698cf, 0xe51430d9ac10c2f8, 0x771dd49b3e1926ba,\n 0x8714f246ce100067, 0x151d16045c19e425, 0xa6eb4c33efefbe12, 0x34e2a8717de65a50,\n 0x5d27f89314230ab2, 0xcf2e1cd1862aeef0, 0x7cd846e635dcb4c7, 0xeed1a2a4a7d55085,\n 0x1ed8847957dc7658, 0x8cd1603bc5d5921a, 0x3f273a0c7623c82d, 0xad2ede4ee42a2c6f,\n 0xdad9014793ddf366, 0x48d0e50501d41724, 0xfb26bf32b2224d13, 0x692f5b70202ba951,\n 0x99267dadd0228f8c, 0xb2f99ef422b6bce, 0xb8d9c3d8f1dd31f9, 0x2ad0279a63d4d5bb,\n 0x57367dca1e328feb, 0xc53f99888c3b6ba9, 0x76c9c3bf3fcd319e, 0xe4c027fdadc4d5dc,\n 0x14c901205dcdf301, 0x86c0e562cfc41743, 0x3536bf557c324d74, 0xa73f5b17ee3ba936,\n 0xd0c8841e99cc763f, 0x42c1605c0bc5927d, 0xf1373a6bb833c84a, 0x633ede292a3a2c08,\n 0x9337f8f4da330ad5, 0x13e1cb6483aee97, 0xb2c84681fbccb4a0, 0x20c1a2c369c550e2,\n 0x6142e74528461564, 0xf34b0307ba4ff126, 0x40bd593009b9ab11, 0xd2b4bd729bb04f53,\n 0x22bd9baf6bb9698e, 0xb0b47fedf9b08dcc, 0x34225da4a46d7fb, 0x914bc198d84f33b9,\n 0xe6bc1e91afb8ecb0, 0x74b5fad33db108f2, 0xc743a0e48e4752c5, 0x554a44a61c4eb687,\n 0xa543627bec47905a, 0x374a86397e4e7418, 0x84bcdc0ecdb82e2f, 0x16b5384c5fb1ca6d,\n 0x6b53621c2257903d, 0xf95a865eb05e747f, 0x4aacdc6903a82e48, 0xd8a5382b91a1ca0a,\n 0x28ac1ef661a8ecd7, 0xbaa5fab4f3a10895, 0x953a083405752a2, 0x9b5a44c1d25eb6e0,\n 0xecad9bc8a5a969e9, 0x7ea47f8a37a08dab, 0xcd5225bd8456d79c, 0x5f5bc1ff165f33de,\n 0xaf52e722e6561503, 0x3d5b0360745ff141, 0x8ead5957c7a9ab76, 0x1ca4bd1555a04f34,\n 0x7561edf73c651fd6, 0xe76809b5ae6cfb94, 0x549e53821d9aa1a3, 0xc697b7c08f9345e1,\n 0x369e911d7f9a633c, 0xa497755fed93877e, 0x17612f685e65dd49, 0x8568cb2acc6c390b,\n 0xf29f1423bb9be602, 0x6096f06129920240, 0xd360aa569a645877, 0x41694e14086dbc35,\n 0xb16068c9f8649ae8, 0x23698c8b6a6d7eaa, 0x909fd6bcd99b249d, 0x29632fe4b92c0df,\n 0x7f7068ae36749a8f, 0xed798ceca47d7ecd, 0x5e8fd6db178b24fa, 0xcc8632998582c0b8,\n 0x3c8f1444758be665, 0xae86f006e7820227, 0x1d70aa3154745810, 0x8f794e73c67dbc52,\n 0xf88e917ab18a635b, 0x6a87753823838719, 0xd9712f0f9075dd2e, 0x4b78cb4d027c396c,\n 0xbb71ed90f2751fb1, 0x297809d2607cfbf3, 0x9a8e53e5d38aa1c4, 0x887b7a741834586,\n }\n};\n\nstatic union {\n uint32_t dword_table[4][256];\n uint64_t qword_array[512];\n} short_shifts = {\n .qword_array = {\n 0xdcb17aa400000000, 0x603ff91dbc8e83b9, 0xa0400b277cf17183, 0x1cce889ec07ff23a,\n 0x255399a2f9e2e306, 0x99dd1a1b456c60bf, 0x59a2e82185139285, 0xe52c6b98399d113c,\n 0x2a98ca59f629b0fd, 0x961649e04aa73344, 0x5669bbda8ad8c17e, 0xeae73863365642c7,\n 0xd37a295f0fcb53fb, 0x6ff4aae6b345d042, 0xaf8b58dc733a2278, 0x1305db65cfb4a1c1,\n 0x350e6dafe9bf170b, 0x8980ee16553194b2, 0x49ff1c2c954e6688, 0xf5719f9529c0e531,\n 0xccec8ea9105df40d, 0x70620d10acd377b4, 0xb01dff2a6cac858e, 0xc937c93d0220637,\n 0xc327dd521f96a7f6, 0x7fa95eeba318244f, 0xbfd6acd16367d675, 0x3582f68dfe955cc,\n 0x3ac53e54e67444f0, 0x864bbded5afac749, 0x46344fd79a853573, 0xfabacc6e260bb6ca,\n 0xa232243d69258e7, 0xb6ada1fa6a1cdb5e, 0x76d253c0aa632964, 0xca5cd07916edaadd,\n 0xf3c1c1452f70bbe1, 0x4f4f42fc93fe3858, 0x8f30b0c65381ca62, 0x33be337fef0f49db,\n 0xfc0a92be20bbe81a, 0x408411079c356ba3, 0x80fbe33d5c4a9999, 0x3c756084e0c41a20,\n 0x5e871b8d9590b1c, 0xb966f20165d788a5, 0x7919003ba5a87a9f, 0xc59783821926f926,\n 0xe39c35483f2d4fec, 0x5f12b6f183a3cc55, 0x9f6d44cb43dc3e6f, 0x23e3c772ff52bdd6,\n 0x1a7ed64ec6cfacea, 0xa6f055f77a412f53, 0x668fa7cdba3edd69, 0xda01247406b05ed0,\n 0x15b585b5c904ff11, 0xa93b060c758a7ca8, 0x6944f436b5f58e92, 0xd5ca778f097b0d2b,\n 0xec5766b330e61c17, 0x50d9e50a8c689fae, 0x90a617304c176d94, 0x2c289489f099ee2d,\n 0x7479bd9ba8c8c73f, 0xc8f73e2214464486, 0x888cc18d439b6bc, 0xb4064fa168b73505,\n 0x8d9b5e9d512a2439, 0x3115dd24eda4a780, 0xf16a2f1e2ddb55ba, 0x4de4aca79155d603,\n 0x82500d665ee177c2, 0x3ede8edfe26ff47b, 0xfea17ce522100641, 0x422fff5c9e9e85f8,\n 0x7bb2ee60a70394c4, 0xc73c6dd91b8d177d, 0x7439fe3dbf2e547, 0xbbcd1c5a677c66fe,\n 0x9dc6aa904177d034, 0x21482929fdf9538d, 0xe137db133d86a1b7, 0x5db958aa8108220e,\n 0x64244996b8953332, 0xd8aaca2f041bb08b, 0x18d53815c46442b1, 0xa45bbbac78eac108,\n 0x6bef1a6db75e60c9, 0xd76199d40bd0e370, 0x171e6beecbaf114a, 0xab90e857772192f3,\n 0x920df96b4ebc83cf, 0x2e837ad2f2320076, 0xeefc88e8324df24c, 0x52720b518ec371f5,\n 0xa2ebe57c7e5a9fd8, 0x1e6566c5c2d41c61, 0xde1a94ff02abee5b, 0x62941746be256de2,\n 0x5b09067a87b87cde, 0xe78785c33b36ff67, 0x27f877f9fb490d5d, 0x9b76f44047c78ee4,\n 0x54c2558188732f25, 0xe84cd63834fdac9c, 0x28332402f4825ea6, 0x94bda7bb480cdd1f,\n 0xad20b6877191cc23, 0x11ae353ecd1f4f9a, 0xd1d1c7040d60bda0, 0x6d5f44bdb1ee3e19,\n 0x4b54f27797e588d3, 0xf7da71ce2b6b0b6a, 0x37a583f4eb14f950, 0x8b2b004d579a7ae9,\n 0xb2b611716e076bd5, 0xe3892c8d289e86c, 0xce4760f212f61a56, 0x72c9e34bae7899ef,\n 0xbd7d428a61cc382e, 0x1f3c133dd42bb97, 0xc18c33091d3d49ad, 0x7d02b0b0a1b3ca14,\n 0x449fa18c982edb28, 0xf811223524a05891, 0x386ed00fe4dfaaab, 0x84e053b658512912,\n 0x547df88f00000000, 0xfc860991a8fbf11e, 0x666c42541b94cd, 0xa89d9d5cfce065d3,\n 0xfc4ad115a837299a, 0x54b1200b00ccd884, 0xa85145d8fc2cbd57, 0xaab4c654d74c49,\n 0x1ffdd4a558225c5, 0xa9042c54fd79d4db, 0x55e449870199b108, 0xfd1fb899a9624016,\n 0xa9c8f4d0fdb50c5f, 0x13305ce554efd41, 0xfdd3601da9ae9892, 0x552891030155698c,\n 0xff79b305ab044b8a, 0x5782421b03ffba94, 0xab6227c8ff1fdf47, 0x399d6d657e42e59,\n 0x574e9a9f03336210, 0xffb56b81abc8930e, 0x3550e525728f6dd, 0xabaeff4cffd307c3,\n 0xaafb96c0fe866e4f, 0x20067de567d9f51, 0xfee0020daa9dfa82, 0x561bf31302660b9c,\n 0x2ccbf5a56b147d5, 0xaa374e44fe4ab6cb, 0x56d72b9702aad318, 0xfe2cda89aa512206,\n 0x799196a53e4e1e5, 0xaf62e874fb1f10fb, 0x53828da707ff7528, 0xfb797cb9af048436,\n 0xafae30f0fbd3c87f, 0x755c1ee53283961, 0xfbb5a43dafc85cb2, 0x534e55230733adac,\n 0x521b3caf0666c420, 0xfae0cdb1ae9d353e, 0x600a862527d50ed, 0xaefb597cfa86a1f3,\n 0xfa2c1535ae51edba, 0x52d7e42b06aa1ca4, 0xae3781f8fa4a7977, 0x6cc70e652b18869,\n 0xac9d52e0f8e0aa6f, 0x466a3fe501b5b71, 0xf886c62dacfb3ea2, 0x507d37330400cfbc,\n 0x4aa7b7a50d783f5, 0xac518a64f82c72eb, 0x50b1efb704cc1738, 0xf84a1ea9ac37e626,\n 0xf91f7725ad628faa, 0x51e4863b05997eb4, 0xad04e3e8f9791b67, 0x5ff12f65182ea79,\n 0x51285ebf0555a630, 0xf9d3afa1adae572e, 0x533ca72514e32fd, 0xadc83b6cf9b5c3e3,\n 0xf3b43b45a7c9c3ca, 0x5b4fca5b0f3232d4, 0xa7afaf88f3d25707, 0xf545e965b29a619,\n 0x5b8312df0ffeea50, 0xf378e3c1a7051b4e, 0xf9886125be57e9d, 0xa763770cf31e8f83,\n 0xa6361e80f24be60f, 0xecdef9e5ab01711, 0xf22d8a4da65072c2, 0x5ad67b530eab83dc,\n 0xe01371a5a7ccf95, 0xa6fac604f2873e8b, 0x5a1aa3d70e675b58, 0xf2e152c9a69caa46,\n 0x58b070cf0ccd8840, 0xf04b81d1a436795e, 0xcabe40258d61c8d, 0xa450151cf02ded93,\n 0xf0875955a4faa1da, 0x587ca84b0c0150c4, 0xa49ccd98f0e13517, 0xc673c86581ac409,\n 0xd32550a594fad85, 0xa5c9a414f1b45c9b, 0x5929c1c70d543948, 0xf1d230d9a5afc856,\n 0xa5057c90f178841f, 0xdfe8d8e59837501, 0xf11ee85da56310d2, 0x59e519430d98e1cc,\n 0xa050daa0f42d222f, 0x8ab2bbe5cd6d331, 0xf44b4e6da036b6e2, 0x5cb0bf7308cd47fc,\n 0x867f33a5c1a0bb5, 0xa09c0224f4e1faab, 0x5c7c67f708019f78, 0xf48796e9a0fa6e66,\n 0xf5d2ff65a1af07ea, 0x5d290e7b0954f6f4, 0xa1c96ba8f5b49327, 0x9329ab65d4f6239,\n 0x5de5d6ff09982e70, 0xf51e27e1a163df6e, 0x9fe42325d83babd, 0xa105b32cf5784ba3,\n 0xb54912a5f2969a5, 0xa3af6034f7d298bb, 0x5f4f05e70b32fd68, 0xf7b4f4f9a3c90c76,\n 0xa363b8b0f71e403f, 0xb9849ae5fe5b121, 0xf7782c7da305d4f2, 0x5f83dd630bfe25ec,\n 0x5ed6b4ef0aab4c60, 0xf62d45f1a250bd7e, 0xacd20225eb0d8ad, 0xa236d13cf64b29b3,\n 0xf6e19d75a29c65fa, 0x5e1a6c6b0a6794e4, 0xa2fa09b8f687f137, 0xa01f8a65e7c0029,\n 0x4a7ff16500000000, 0xde8013af94ffe2ca, 0x666c42002c13b365, 0xf293a0cab8ec51af,\n 0x125897af582766ca, 0x86a77565ccd88400, 0x3e4b24ca7434d5af, 0xaab4c600e0cb3765,\n 0xfa313cf1b04ecd94, 0x6ecede3b24b12f5e, 0xd6228f949c5d7ef1, 0x42dd6d5e08a29c3b,\n 0xa2165a3be869ab5e, 0x36e9b8f17c964994, 0x8e05e95ec47a183b, 0x1afa0b945085faf1,\n 0x2f0e1cbc6571edd9, 0xbbf1fe76f18e0f13, 0x31dafd949625ebc, 0x97e24d13dd9dbc76,\n 0x77297a763d568b13, 0xe3d698bca9a969d9, 0x5b3ac91311453876, 0xcfc52bd985badabc,\n 0x9f40d128d53f204d, 0xbbf33e241c0c287, 0xb353624df92c9328, 0x27ac80876dd371e2,\n 0xc767b7e28d184687, 0x5398552819e7a44d, 0xeb740487a10bf5e2, 0x7f8be64d35f41728,\n 0x809c2ad7cae3dbb2, 0x1463c81d5e1c3978, 0xac8f99b2e6f068d7, 0x38707b78720f8a1d,\n 0xd8bb4c1d92c4bd78, 0x4c44aed7063b5fb2, 0xf4a8ff78bed70e1d, 0x60571db22a28ecd7,\n 0x30d2e7437aad1626, 0xa42d0589ee52f4ec, 0x1cc1542656bea543, 0x883eb6ecc2414789,\n 0x68f58189228a70ec, 0xfc0a6343b6759226, 0x44e632ec0e99c389, 0xd019d0269a662143,\n 0xe5edc70eaf92366b, 0x711225c43b6dd4a1, 0xc9fe746b8381850e, 0x5d0196a1177e67c4,\n 0xbdcaa1c4f7b550a1, 0x2935430e634ab26b, 0x91d912a1dba6e3c4, 0x526f06b4f59010e,\n 0x55a30a9a1fdcfbff, 0xc15ce8508b231935, 0x79b0b9ff33cf489a, 0xed4f5b35a730aa50,\n 0xd846c5047fb9d35, 0x997b8e9ad3047fff, 0x2197df356be82e50, 0xb5683dffff17cc9a,\n 0xda5430f0902bc195, 0x4eabd23a04d4235f, 0xf6478395bc3872f0, 0x62b8615f28c7903a,\n 0x8273563ac80ca75f, 0x168cb4f05cf34595, 0xae60e55fe41f143a, 0x3a9f079570e0f6f0,\n 0x6a1afd6420650c01, 0xfee51faeb49aeecb, 0x46094e010c76bf64, 0xd2f6accb98895dae,\n 0x323d9bae78426acb, 0xa6c27964ecbd8801, 0x1e2e28cb5451d9ae, 0x8ad1ca01c0ae3b64,\n 0xbf25dd29f55a2c4c, 0x2bda3fe361a5ce86, 0x93366e4cd9499f29, 0x7c98c864db67de3,\n 0xe702bbe3ad7d4a86, 0x73fd59293982a84c, 0xcb110886816ef9e3, 0x5feeea4c15911b29,\n 0xf6b10bd4514e1d8, 0x9b94f277d1eb0312, 0x2378a3d8690752bd, 0xb7874112fdf8b077,\n 0x574c76771d338712, 0xc3b394bd89cc65d8, 0x7b5fc51231203477, 0xefa027d8a5dfd6bd,\n 0x10b7eb425ac81a27, 0x84480988ce37f8ed, 0x3ca4582776dba942, 0xa85bbaede2244b88,\n 0x48908d8802ef7ced, 0xdc6f6f4296109e27, 0x64833eed2efccf88, 0xf07cdc27ba032d42,\n 0xa0f926d6ea86d7b3, 0x3406c41c7e793579, 0x8cea95b3c69564d6, 0x18157779526a861c,\n 0xf8de401cb2a1b179, 0x6c21a2d6265e53b3, 0xd4cdf3799eb2021c, 0x403211b30a4de0d6,\n 0x75c6069b3fb9f7fe, 0xe139e451ab461534, 0x59d5b5fe13aa449b, 0xcd2a57348755a651,\n 0x2de16051679e9134, 0xb91e829bf36173fe, 0x1f2d3344b8d2251, 0x950d31fedf72c09b,\n 0xc588cb0f8ff73a6a, 0x517729c51b08d8a0, 0xe99b786aa3e4890f, 0x7d649aa0371b6bc5,\n 0x9dafadc5d7d05ca0, 0x9504f0f432fbe6a, 0xb1bc1ea0fbc3efc5, 0x2543fc6a6f3c0d0f,\n 0x25bbf5db00000000, 0x6ecc1e6d4b77ebb6, 0xb35422b796efd76c, 0xf823c901dd983cda,\n 0xd882df22833d829, 0x46ffc6446344339f, 0x9b67fa9ebedc0f45, 0xd0101128f5abe4f3,\n 0x75dc45895067b052, 0x3eabae3f1b105be4, 0xe33392e5c688673e, 0xa84479538dff8c88,\n 0x5def9da07854687b, 0x16987616332383cd, 0xcb004acceebbbf17, 0x8077a17aa5cc54a1,\n 0x8574957fa0cf60a4, 0xce037ec9ebb88b12, 0x139b42133620b7c8, 0x58eca9a57d575c7e,\n 0xad474d5688fcb88d, 0xe630a6e0c38b533b, 0x3ba89a3a1e136fe1, 0x70df718c55648457,\n 0xd513252df0a8d0f6, 0x9e64ce9bbbdf3b40, 0x43fcf2416647079a, 0x88b19f72d30ec2c,\n 0xfd20fd04d89b08df, 0xb65716b293ece369, 0x6bcf2a684e74dfb3, 0x20b8c1de05033405,\n 0x61c942624472b7b9, 0x2abea9d40f055c0f, 0xf726950ed29d60d5, 0xbc517eb899ea8b63,\n 0x49fa9a4b6c416f90, 0x28d71fd27368426, 0xdf154d27faaeb8fc, 0x9462a691b1d9534a,\n 0x31aef230141507eb, 0x7ad919865f62ec5d, 0xa741255c82fad087, 0xec36ceeac98d3b31,\n 0x199d2a193c26dfc2, 0x52eac1af77513474, 0x8f72fd75aac908ae, 0xc40516c3e1bee318,\n 0xc10622c6e4bdd71d, 0x8a71c970afca3cab, 0x57e9f5aa72520071, 0x1c9e1e1c3925ebc7,\n 0xe935faefcc8e0f34, 0xa242115987f9e482, 0x7fda2d835a61d858, 0x34adc635111633ee,\n 0x91619294b4da674f, 0xda167922ffad8cf9, 0x78e45f82235b023, 0x4cf9ae4e69425b95,\n 0xb9524abd9ce9bf66, 0xf225a10bd79e54d0, 0x2fbd9dd10a06680a, 0x64ca7667417183bc,\n 0xad5e9aa988e56f72, 0xe629711fc39284c4, 0x3bb14dc51e0ab81e, 0x70c6a673557d53a8,\n 0x856d4280a0d6b75b, 0xce1aa936eba15ced, 0x138295ec36396037, 0x58f57e5a7d4e8b81,\n 0xfd392afbd882df20, 0xb64ec14d93f53496, 0x6bd6fd974e6d084c, 0x20a11621051ae3fa,\n 0xd50af2d2f0b10709, 0x9e7d1964bbc6ecbf, 0x43e525be665ed065, 0x892ce082d293bd3,\n 0xd91fa0d282a0fd6, 0x46e611bb635de460, 0x9b7e2d61bec5d8ba, 0xd009c6d7f5b2330c,\n 0x25a222240019d7ff, 0x6ed5c9924b6e3c49, 0xb34df54896f60093, 0xf83a1efedd81eb25,\n 0x5df64a5f784dbf84, 0x1681a1e9333a5432, 0xcb199d33eea268e8, 0x806e7685a5d5835e,\n 0x75c59276507e67ad, 0x3eb279c01b098c1b, 0xe32a451ac691b0c1, 0xa85daeac8de65b77,\n 0xe92c2d10cc97d8cb, 0xa25bc6a687e0337d, 0x7fc3fa7c5a780fa7, 0x34b411ca110fe411,\n 0xc11ff539e4a400e2, 0x8a681e8fafd3eb54, 0x57f02255724bd78e, 0x1c87c9e3393c3c38,\n 0xb94b9d429cf06899, 0xf23c76f4d787832f, 0x2fa44a2e0a1fbff5, 0x64d3a19841685443,\n 0x9178456bb4c3b0b0, 0xda0faeddffb45b06, 0x7979207222c67dc, 0x4ce079b1695b8c6a,\n 0x49e34db46c58b86f, 0x294a602272f53d9, 0xdf0c9ad8fab76f03, 0x947b716eb1c084b5,\n 0x61d0959d446b6046, 0x2aa77e2b0f1c8bf0, 0xf73f42f1d284b72a, 0xbc48a94799f35c9c,\n 0x1984fde63c3f083d, 0x52f316507748e38b, 0x8f6b2a8aaad0df51, 0xc41cc13ce1a734e7,\n 0x31b725cf140cd014, 0x7ac0ce795f7b3ba2, 0xa758f2a382e30778, 0xec2f1915c994ecce,\n }\n};\n\n/* Table-driven software version as a fall-back. This is about 15 times slower\n than using the hardware instructions. This assumes little-endian integers,\n as is the case on Intel processors that the assembler code here is for. */\nuint32_t crc32c_append_sw(uint32_t crci, buffer input, size_t length)\n{\n buffer next = input;\n#ifdef _M_X64\n uint64_t crc;\n#else\n uint32_t crc;\n#endif\n\n crc = crci ^ 0xffffffff;\n#ifdef _M_X64\n while (length && ((uintptr_t)next & 7) != 0)\n {\n crc = table.dword_table[0][(crc ^ *next++) & 0xff] ^ (crc >> 8);\n --length;\n }\n while (length >= 16)\n {\n crc ^= *(uint64_t *)next;\n uint64_t high = *(uint64_t *)(next + 8);\n crc = table.dword_table[15][crc & 0xff]\n ^ table.dword_table[14][(crc >> 8) & 0xff]\n ^ table.dword_table[13][(crc >> 16) & 0xff]\n ^ table.dword_table[12][(crc >> 24) & 0xff]\n ^ table.dword_table[11][(crc >> 32) & 0xff]\n ^ table.dword_table[10][(crc >> 40) & 0xff]\n ^ table.dword_table[9][(crc >> 48) & 0xff]\n ^ table.dword_table[8][crc >> 56]\n ^ table.dword_table[7][high & 0xff]\n ^ table.dword_table[6][(high >> 8) & 0xff]\n ^ table.dword_table[5][(high >> 16) & 0xff]\n ^ table.dword_table[4][(high >> 24) & 0xff]\n ^ table.dword_table[3][(high >> 32) & 0xff]\n ^ table.dword_table[2][(high >> 40) & 0xff]\n ^ table.dword_table[1][(high >> 48) & 0xff]\n ^ table.dword_table[0][high >> 56];\n next += 16;\n length -= 16;\n }\n#else\n while (length && ((uintptr_t)next & 3) != 0)\n {\n crc = table.dword_table[0][(crc ^ *next++) & 0xff] ^ (crc >> 8);\n --length;\n }\n while (length >= 12)\n {\n crc ^= *(uint32_t *)next;\n uint32_t high = *(uint32_t *)(next + 4);\n uint32_t high2 = *(uint32_t *)(next + 8);\n crc = table.dword_table[11][crc & 0xff]\n ^ table.dword_table[10][(crc >> 8) & 0xff]\n ^ table.dword_table[9][(crc >> 16) & 0xff]\n ^ table.dword_table[8][crc >> 24]\n ^ table.dword_table[7][high & 0xff]\n ^ table.dword_table[6][(high >> 8) & 0xff]\n ^ table.dword_table[5][(high >> 16) & 0xff]\n ^ table.dword_table[4][high >> 24]\n ^ table.dword_table[3][high2 & 0xff]\n ^ table.dword_table[2][(high2 >> 8) & 0xff]\n ^ table.dword_table[1][(high2 >> 16) & 0xff]\n ^ table.dword_table[0][high2 >> 24];\n next += 12;\n length -= 12;\n }\n#endif\n while (length)\n {\n crc = table.dword_table[0][(crc ^ *next++) & 0xff] ^ (crc >> 8);\n --length;\n }\n return (uint32_t)crc ^ 0xffffffff;\n}\n\n/* Apply the zeros operator table to crc. */\nstatic inline uint32_t shift_crc(uint32_t shift_table[][256], uint32_t crc)\n{\n return shift_table[0][crc & 0xff]\n ^ shift_table[1][(crc >> 8) & 0xff]\n ^ shift_table[2][(crc >> 16) & 0xff]\n ^ shift_table[3][crc >> 24];\n}\n\nuint32_t crc32c_append(uint32_t crc, buffer input, size_t length)\n{\n\treturn crc32c_append_sw(crc, input, length);\n}\n"},{"alpha_fraction":0.43617162108421326,"alphanum_fraction":0.4375,"avg_line_length":50.21088409423828,"blob_id":"9007234a047b510c5ae12d8f58dbf856ddaec197","content_id":"1b6edba218b536f3c22e6d3126a1cf71b86ef6d6","detected_licenses":["MIT"],"is_generated":false,"is_vendor":false,"language":"C#","length_bytes":15056,"license_type":"permissive","max_line_length":228,"num_lines":294,"path":"/src/TorchSharp/DataLoader.cs","repo_name":"NiklasGustafsson/TorchSharp","src_encoding":"UTF-8","text":"// Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information.\n\nusing System;\nusing System.Collections;\nusing System.Collections.Generic;\nusing System.Diagnostics.SymbolStore;\nusing System.Linq;\nusing System.Threading;\nusing System.Threading.Tasks;\nusing TorchSharp.Utils;\n\nnamespace TorchSharp\n{\n public static partial class torch\n {\n public static partial class utils\n {\n public static partial class data\n {\n /// <summary>\n /// This class makes easier to create batch. Data set must implement Dataset interface\n /// </summary>\n public class DataLoader : DataLoader<Dictionary<string, torch.Tensor>, Dictionary<string, torch.Tensor>>\n {\n /// <summary>\n /// Pytorch style dataloader\n /// </summary>\n /// <param name=\"dataset\">Dataset for create batch</param>\n /// <param name=\"batchSize\">Size of batch</param>\n /// <param name=\"device\">device for output tensor</param>\n /// <param name=\"shuffler\">Shuffler for dataloader</param>\n /// <param name=\"num_worker\">Count of worker</param>\n /// <param name=\"drop_last\">\n /// Set to true to drop the last incomplete batch, if the dataset size is not divisible by the batch size.\n /// If alse and the size of dataset is not divisible by the batch size, then the last batch will be smaller.\n /// </param>\n public DataLoader(Dataset dataset, int batchSize, IEnumerable<long> shuffler, Device device = null, int num_worker = 1, bool drop_last = false)\n : base(dataset, batchSize, Collate, shuffler, device, num_worker, drop_last)\n {\n }\n\n /// <summary>\n /// Pytorch style dataloader\n /// </summary>\n /// <param name=\"dataset\">Dataset for create batch</param>\n /// <param name=\"batchSize\">Size of batch</param>\n /// <param name=\"shuffle\">true if shuffle dataset, false for not</param>\n /// <param name=\"device\">device for output tensor</param>\n /// <param name=\"seed\">Seed for generating shuffle</param>\n /// <param name=\"num_worker\">Count of worker</param>\n /// <param name=\"drop_last\">\n /// Set to true to drop the last incomplete batch, if the dataset size is not divisible by the batch size.\n /// If alse and the size of dataset is not divisible by the batch size, then the last batch will be smaller.\n /// </param>\n public DataLoader(Dataset dataset, int batchSize, bool shuffle = false, Device device = null, int? seed = null, int num_worker = 1, bool drop_last = false)\n : base(dataset, batchSize, Collate, shuffle, device, seed, num_worker, drop_last)\n {\n }\n\n private static Dictionary<string, torch.Tensor> Collate(IEnumerable<Dictionary<string, torch.Tensor>> dic, torch.Device device)\n {\n Dictionary<string, torch.Tensor> batch = new();\n foreach (var x in dic.First().Keys) {\n var t = cat(dic.Select(k => k[x].unsqueeze(0)).ToArray(), 0);\n if (t.device_type != device.type || t.device_index != device.index)\n t = t.to(device);\n batch[x] = t;\n }\n return batch;\n }\n }\n\n /// <summary>\n /// This class makes easier to create batch. Data set must implement Dataset interface\n /// </summary>\n public class DataLoader<T, S> : IEnumerable<S>, IDisposable\n {\n private Dataset<T> dataset;\n private int batchSize;\n private bool shuffle;\n private bool drop_last;\n private Device device;\n private IEnumerable<long> shuffler;\n private int num_worker;\n private Func<IEnumerable<T>, torch.Device, S> collate_fn;\n\n /// <summary>\n /// Pytorch style dataloader\n /// </summary>\n /// <param name=\"dataset\">Dataset for create batch</param>\n /// <param name=\"batchSize\">Size of batch</param>\n /// <param name=\"collate_fn\">Callback to merge items make to a batch</param>\n /// <param name=\"device\">device for output tensor</param>\n /// <param name=\"shuffler\">Shuffler for dataloader</param>\n /// <param name=\"num_worker\">Count of worker</param>\n /// <param name=\"drop_last\">\n /// Set to true to drop the last incomplete batch, if the dataset size is not divisible by the batch size.\n /// If alse and the size of dataset is not divisible by the batch size, then the last batch will be smaller.\n /// </param>\n public DataLoader(Dataset<T> dataset, int batchSize, Func<IEnumerable<T>, torch.Device, S> collate_fn, IEnumerable<long> shuffler, Device device = null, int num_worker = 1, bool drop_last = false)\n {\n this.dataset = dataset;\n this.batchSize = batchSize;\n this.shuffle = true;\n this.drop_last = drop_last;\n this.device = device ?? CPU;\n this.shuffler = shuffler;\n this.num_worker = num_worker;\n this.collate_fn = collate_fn;\n }\n\n /// <summary>\n /// Pytorch style dataloader\n /// </summary>\n /// <param name=\"dataset\">Dataset for create batch</param>\n /// <param name=\"batchSize\">Size of batch</param>\n /// <param name=\"collate_fn\">Callback to merge items to make a batch</param>\n /// <param name=\"shuffle\">true if shuffle dataset, false for not</param>\n /// <param name=\"device\">device for output tensor</param>\n /// <param name=\"seed\">Seed for generating shuffle</param>\n /// <param name=\"num_worker\">Count of worker</param>\n /// <param name=\"drop_last\">\n /// Set to true to drop the last incomplete batch, if the dataset size is not divisible by the batch size.\n /// If alse and the size of dataset is not divisible by the batch size, then the last batch will be smaller.\n /// </param>\n public DataLoader(Dataset<T> dataset, int batchSize, Func<IEnumerable<T>, torch.Device, S> collate_fn, bool shuffle = false, Device device = null, int? seed = null, int num_worker = 1, bool drop_last = false)\n {\n this.dataset = dataset;\n this.batchSize = batchSize;\n this.shuffle = shuffle;\n this.drop_last = drop_last;\n this.device = device ?? CPU;\n this.shuffler = seed is null ? new FisherYatesShuffler(dataset.Count) : new FisherYatesShuffler(dataset.Count, seed);\n this.num_worker = num_worker;\n this.collate_fn = collate_fn;\n }\n\n /// <summary>\n /// Generate enumerator\n /// </summary>\n /// <returns>Enumerator for batch</returns>\n public IEnumerator<S> GetEnumerator() =>\n new DataLoaderEnumerator(dataset, batchSize, shuffle, device, shuffler, num_worker, collate_fn);\n\n IEnumerator IEnumerable.GetEnumerator() => GetEnumerator();\n\n /// <summary>\n /// Size of batch\n /// </summary>\n public long Count => drop_last ? (dataset.Count / batchSize) : ((dataset.Count - 1) / batchSize + 1);\n\n private class DataLoaderEnumerator : IEnumerator<S>\n {\n private Dataset<T> dataset;\n private int batchSize;\n private Device device;\n private bool shuffle;\n private IEnumerable<long> shuffleEnumerable;\n private IEnumerator<long> shuffler;\n private long currentVal = 0;\n private int num_worker = 0;\n private IList<IDisposable> currentDisposables;\n private Func<IEnumerable<T>, torch.Device, S> collate_fn;\n public DataLoaderEnumerator(Dataset<T> dataset, int batchSize, bool shuffle, Device device, IEnumerable<long> shuffleEnumerable, int num_worker, Func<IEnumerable<T>, torch.Device, S> collate_fn)\n {\n this.dataset = dataset;\n this.batchSize = batchSize;\n this.device = device;\n this.shuffle = shuffle;\n this.shuffleEnumerable = shuffleEnumerable;\n if (num_worker < 1) num_worker = 1;\n this.num_worker = num_worker;\n this.collate_fn = collate_fn;\n Reset();\n }\n\n private bool MoveNextValue()\n {\n if (shuffle) {\n if (!shuffler.MoveNext()) return false;\n currentVal = shuffler.Current;\n return true;\n } else {\n currentVal++;\n return currentVal < dataset.Count;\n }\n }\n\n /// <summary>\n /// Get next batch\n /// </summary>\n /// <returns>true if batch created, false if batch has finished</returns>\n public bool MoveNext()\n {\n DisposeCurrent();\n using (var scope = DisposeScopeManager.NewDisposeScope()) {\n if (!MoveNextValue()) return false;\n\n var tensorIndexList = new List<long> {currentVal};\n for (int i = 1; i < batchSize; i++) {\n if (!MoveNextValue()) break;\n tensorIndexList.Add(currentVal);\n }\n\n var items = new List<T>(new T[tensorIndexList.Count]);\n var taskedBatchCount = 0;\n\n //Run Async\n var tasks = new List<Task>();\n foreach (var _ in Enumerable.Range(1, num_worker - 1))\n tasks.Add(new(ProcessPendingBatches));\n tasks.ForEach(x => x.Start());\n\n ProcessPendingBatches();\n\n foreach (var task in tasks)\n task.Wait();\n\n using (var collate_scope = DisposeScopeManager.NewDisposeScope()) {\n Current = collate_fn(items, device);\n currentDisposables = collate_scope.DisposablesView.ToList();\n collate_scope.Detach(currentDisposables);\n }\n\n return true;\n\n void ProcessPendingBatches()\n {\n while (true) {\n var idx = ScheduleBatch();\n if (idx is null) break;\n items[idx.Value.Item1] = dataset.GetTensor(idx.Value.Item2);\n }\n }\n\n (int, long)? ScheduleBatch()\n {\n var t = Interlocked.Increment(ref taskedBatchCount) - 1;\n if (t < tensorIndexList.Count)\n return (t, tensorIndexList[t]);\n return null;\n }\n }\n }\n\n /// <summary>\n /// Reset enumerator\n /// </summary>\n public void Reset()\n {\n DisposeCurrent();\n if(shuffle) shuffler = shuffleEnumerable.GetEnumerator();\n currentVal = -1;\n }\n\n /// <summary>\n /// Current tensor\n /// </summary>\n public S Current { get; private set; }\n\n object IEnumerator.Current => Current;\n\n public void Dispose()\n {\n DisposeCurrent();\n }\n\n private void DisposeCurrent()\n {\n if (currentDisposables is null) return;\n foreach(var x in currentDisposables)\n x.Dispose();\n currentDisposables = null;\n shuffler?.Dispose();\n }\n }\n\n public void Dispose()\n {\n Dispose(true);\n GC.SuppressFinalize(this);\n }\n\n protected virtual void Dispose(bool disposing)\n {\n if (disposing) {\n dataset.Dispose();\n }\n }\n }\n }\n }\n }\n}\n"},{"alpha_fraction":0.4713080823421478,"alphanum_fraction":0.47497737407684326,"avg_line_length":59.755645751953125,"blob_id":"cb594eb7a688753743a9bbb630116d874981c65e","content_id":"ea15e2ee6173d9d94d546999e79eb284fd27d307","detected_licenses":["MIT"],"is_generated":false,"is_vendor":false,"language":"C#","length_bytes":104943,"license_type":"permissive","max_line_length":364,"num_lines":1727,"path":"/src/TorchSharp/Optimizers/LRScheduler.cs","repo_name":"NiklasGustafsson/TorchSharp","src_encoding":"UTF-8","text":"// Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information.\nusing System;\nusing System.Collections.Generic;\nusing System.Diagnostics;\nusing System.Linq;\nusing System.Net.Security;\nusing TorchSharp.Modules;\n\n// All LR schedulers in this file are directly based on the Pytorch implementation at:\n//\n// https://github.com/pytorch/pytorch/blob/master/torch/optim/lr_scheduler.py\n//\n\nnamespace TorchSharp\n{\n public static partial class torch\n {\n public static partial class optim\n {\n public static partial class lr_scheduler\n {\n public abstract class LRScheduler\n {\n protected LRScheduler()\n {\n\n }\n\n protected LRScheduler(Optimizer optimizer, int last_epoch = -1, bool verbose = false)\n {\n _optimizer = optimizer;\n _last_epoch = last_epoch;\n _verbose = verbose;\n _base_lrs = optimizer.ParamGroups.Select(pg => pg.InitialLearningRate).ToList();\n _last_lrs = optimizer.ParamGroups.Select(pg => pg.LearningRate).ToList();\n\n if (last_epoch == -1) {\n foreach (var pg in optimizer.ParamGroups) {\n pg.InitialLearningRate = pg.LearningRate;\n }\n }\n }\n\n /// <summary>\n /// Advance the learning rate schedule. \n /// </summary>\n /// <remarks>Typically, this is done once per epoch or once every N batches.</remarks>\n public virtual void step()\n {\n step(null);\n }\n\n /// <summary>\n /// Advance the learning rate schedule. \n /// </summary>\n /// <remarks>Typically, this is done once per epoch or once every N batches.</remarks>\n public virtual void step(int? epoch)\n {\n _step_count += 1;\n _last_epoch = (epoch.HasValue) ? epoch.Value : _last_epoch + 1;\n\n // NOTE: It is super-important to use the 'get_lr()' method no more than once per step(), since\n // for many LR schedulers, it will modify the internal state of the scheduler,\n // as well as that of the controlled optimizer.\n var lr = get_lr().ToList();\n var pgs = _optimizer.ParamGroups.ToList();\n\n for (int i = 0; i < _base_lrs.Count; i++) {\n\n pgs[i].LearningRate = lr[i];\n if (_verbose && _last_lrs[i] != lr[i])\n Console.WriteLine($\"Adjusting learning rate to {lr[i]}\");\n _last_lrs[i] = lr[i];\n }\n }\n\n /// <summary>\n /// Advance the learning rate scheduler, passing in the current value of some metric.\n /// </summary>\n /// <remarks>\n /// The metric value is ignored by most LR schedulers.\n /// </remarks>\n public virtual void step(double current, int? epoch = null)\n {\n // Ignore the metric\n step(epoch);\n }\n\n /// <summary>\n /// Compute the current learning rate for the scheduler.\n /// </summary>\n protected virtual IEnumerable<double> get_lr() => _optimizer.ParamGroups.Select(pg => pg.LearningRate);\n\n /// <summary>\n /// Return last computed learning rate by current scheduler.\n /// </summary>\n public IEnumerable<double> get_last_lr() => _last_lrs;\n\n internal Optimizer _optimizer;\n internal int _last_epoch = -1;\n internal bool _verbose = false;\n protected int _step_count = 0;\n internal IList<double> _last_lrs;\n internal IList<double> _base_lrs;\n }\n\n public static partial class impl\n {\n /// <summary>\n /// Sets the learning rate of each parameter group to the initial lr times a given function.\n /// When last_epoch=-1, sets initial lr as lr.\n /// </summary>\n public class LambdaLR : LRScheduler\n {\n /// <summary>\n /// Constructor\n /// </summary>\n /// <param name=\"optimizer\">Wrapped optimizer.</param>\n /// <param name=\"lr_lambda\">A function which computes a multiplicative factor given an integer parameter epoch.</param>\n /// <param name=\"last_epoch\">The index of last epoch. Default: -1.</param>\n /// <param name=\"verbose\"> If true, prints a message to stdout for each update. Default: false.</param>\n /// <returns>A scheduler</returns>\n public LambdaLR(Optimizer optimizer, Func<int, double> lr_lambda, int last_epoch = -1, bool verbose = false) : base(optimizer, last_epoch, verbose)\n {\n if (optimizer == null) throw new ArgumentNullException(\"optimizer\");\n _lr_lambdas = Enumerable.Repeat(lr_lambda, optimizer.ParamGroups.Count()).ToList();\n\n step();\n }\n\n /// <summary>\n /// Constructor\n /// </summary>\n /// <param name=\"optimizer\">Wrapped optimizer.</param>\n /// <param name=\"lr_lambdas\">A list of functions, one for each paramater group, which computes a multiplicative factor given an integer parameter epoch.</param>\n /// <param name=\"last_epoch\">The index of last epoch. Default: -1.</param>\n /// <param name=\"verbose\"> If true, prints a message to stdout for each update. Default: false.</param>\n /// <returns>A scheduler</returns>\n public LambdaLR(Optimizer optimizer, IEnumerable<Func<int, double>> lr_lambdas, int last_epoch = -1, bool verbose = false) : base(optimizer, last_epoch, verbose)\n {\n if (optimizer == null) throw new ArgumentNullException(\"optimizer\");\n _lr_lambdas = lr_lambdas.ToList();\n\n step();\n }\n\n /// <summary>\n /// Compute the current learning rate for the scheduler.\n /// </summary>\n protected override IEnumerable<double> get_lr()\n {\n var pgs = _optimizer.ParamGroups.ToList();\n return Enumerable.Range(0, pgs.Count).Select(i => _base_lrs[i] * _lr_lambdas[i](_last_epoch));\n }\n\n private List<Func<int, double>> _lr_lambdas;\n }\n\n /// <summary>\n /// Multiply the learning rate of each parameter group by the factor given in the specified function.\n /// When last_epoch = -1, sets initial lr as lr.\n /// </summary>\n public class MultiplicativeLR : LRScheduler\n {\n /// <summary>\n /// Constructor\n /// </summary>\n /// <param name=\"optimizer\">Wrapped optimizer.</param>\n /// <param name=\"lr_lambdas\">A list of functions, one for each paramater group, which computes a multiplicative factor given an integer parameter epoch.</param>\n /// <param name=\"last_epoch\">The index of last epoch. Default: -1.</param>\n /// <param name=\"verbose\"> If true, prints a message to stdout for each update. Default: false.</param>\n /// <returns>A scheduler</returns>\n public MultiplicativeLR(Optimizer optimizer, IEnumerable<Func<int, double>> lr_lambdas, int last_epoch = -1, bool verbose = false) : base(optimizer, last_epoch, verbose)\n {\n if (optimizer == null) throw new ArgumentNullException(\"optimizer\");\n _lr_lambdas = lr_lambdas.ToList();\n\n step();\n }\n\n /// <summary>\n /// Constructor\n /// </summary>\n /// <param name=\"optimizer\">Wrapped optimizer.</param>\n /// <param name=\"lr_lambda\">A function which computes a multiplicative factor given an integer parameter epoch.</param>\n /// <param name=\"last_epoch\">The index of last epoch. Default: -1.</param>\n /// <param name=\"verbose\"> If true, prints a message to stdout for each update. Default: false.</param>\n /// <returns>A scheduler</returns>\n public MultiplicativeLR(Optimizer optimizer, Func<int, double> lr_lambda, int last_epoch = -1, bool verbose = false) : base(optimizer, last_epoch, verbose)\n {\n if (optimizer == null) throw new ArgumentNullException(\"optimizer\");\n _lr_lambdas = Enumerable.Repeat(lr_lambda, optimizer.ParamGroups.Count()).ToList();\n\n step();\n }\n\n /// <summary>\n /// Compute the current learning rate for the scheduler.\n /// </summary>\n protected override IEnumerable<double> get_lr()\n {\n var pgs = _optimizer.ParamGroups.ToList();\n return (_last_epoch > 0)\n ? Enumerable.Range(0, pgs.Count).Select(i => pgs[i].LearningRate * _lr_lambdas[i](_last_epoch))\n : Enumerable.Range(0, pgs.Count).Select(i => pgs[i].LearningRate);\n }\n\n private List<Func<int, double>> _lr_lambdas;\n }\n\n /// <summary>\n /// Decays the learning rate of each parameter group by gamma every step_size epochs.\n /// Notice that such decay can happen simultaneously with other changes to the learning rate from outside this scheduler.\n /// When last_epoch=-1, sets initial lr as lr.\n /// </summary>\n public class StepLR : LRScheduler\n {\n /// <summary>\n /// Constructor\n /// </summary>\n /// <param name=\"optimizer\">Wrapped optimizer.</param>\n /// <param name=\"step_size\">Period of learning rate decay.</param>\n /// <param name=\"gamma\">Multiplicative factor of learning rate decay. Default: 0.1.</param>\n /// <param name=\"last_epoch\">The index of last epoch. Default: -1.</param>\n /// <param name=\"verbose\"> If true, prints a message to stdout for each update. Default: false.</param>\n /// <returns>A scheduler</returns>\n public StepLR(Optimizer optimizer, int step_size, double gamma = 0.1, int last_epoch = -1, bool verbose = false) : base(optimizer, last_epoch, verbose)\n {\n if (optimizer == null) throw new ArgumentNullException(\"optimizer\");\n _step_size = step_size;\n _gamma = gamma;\n\n step();\n }\n\n /// <summary>\n /// Compute the current learning rate for the scheduler.\n /// </summary>\n protected override IEnumerable<double> get_lr()\n {\n return (_last_epoch == 0) || (_last_epoch % _step_size != 0)\n ? _optimizer.ParamGroups.Select(pg => pg.LearningRate)\n : _optimizer.ParamGroups.Select(pg => pg.LearningRate * _gamma);\n }\n\n private int _step_size;\n private double _gamma;\n }\n\n /// <summary>\n /// Decays the learning rate of each parameter group by gamma once the number of epoch reaches one of the milestones.\n /// Notice that such decay can happen simultaneously with other changes to the learning rate from outside this scheduler.\n /// When last_epoch=-1, sets initial lr as lr.\n /// </summary>\n public class MultiStepLR : LRScheduler\n {\n /// <summary>\n /// Constructor\n /// </summary>\n /// <param name=\"optimizer\">Wrapped optimizer.</param>\n /// <param name=\"milestones\">List of epoch indices. Must be increasing.</param>\n /// <param name=\"gamma\">Multiplicative factor of learning rate decay. Default: 0.1.</param>\n /// <param name=\"last_epoch\">The index of last epoch. Default: -1.</param>\n /// <param name=\"verbose\"> If true, prints a message to stdout for each update. Default: false.</param>\n /// <returns>A scheduler</returns>\n public MultiStepLR(Optimizer optimizer, IList<int> milestones, double gamma = 0.1, int last_epoch = -1, bool verbose = false) : base(optimizer, last_epoch, verbose)\n {\n if (optimizer == null) throw new ArgumentNullException(\"optimizer\");\n _milestones = milestones;\n _gamma = gamma;\n\n step();\n }\n\n /// <summary>\n /// Compute the current learning rate for the scheduler.\n /// </summary>\n protected override IEnumerable<double> get_lr()\n {\n var idx = _milestones.IndexOf(_last_epoch);\n return idx == -1\n ? _optimizer.ParamGroups.Select(pg => pg.LearningRate)\n : _optimizer.ParamGroups.Select(pg => pg.LearningRate * _gamma);\n }\n\n private IList<int> _milestones;\n private double _gamma;\n }\n\n /// <summary>\n /// Decays the learning rate of each parameter group using a polynomial function in the given total_iters.\n /// When last_epoch=-1, sets initial lr as lr.\n /// </summary>\n public class PolynomialLR : LRScheduler\n {\n /// <summary>\n /// Constructor\n /// </summary>\n /// <param name=\"optimizer\">Wrapped optimizer.</param>\n /// <param name=\"total_iters\">The number of steps that the scheduler decays the learning rate.</param>\n /// <param name=\"power\">The power of the polynomial.</param>\n /// <param name=\"last_epoch\">The index of last epoch. Default: -1.</param>\n /// <param name=\"verbose\"> If true, prints a message to stdout for each update. Default: false.</param>\n /// <returns>A scheduler</returns>\n public PolynomialLR(Optimizer optimizer, int total_iters = 5, int power = 1, int last_epoch = -1, bool verbose = false) : base(optimizer, last_epoch, verbose)\n {\n if (optimizer == null) throw new ArgumentNullException(\"optimizer\");\n _power = power;\n _total_iters = total_iters;\n step();\n }\n\n /// <summary>\n /// Compute the current learning rate for the scheduler.\n /// </summary>\n protected override IEnumerable<double> get_lr()\n {\n if (_last_epoch == 0 || _last_epoch > _total_iters) {\n return _optimizer.ParamGroups.Select(pg => pg.LearningRate);\n }\n else {\n var decay_factor = ((1.0 - _last_epoch / _total_iters) / (1.0 - (_last_epoch - 1) / _total_iters));\n if (_power != 1) {\n decay_factor = Math.Pow(decay_factor, _power);\n }\n return _optimizer.ParamGroups.Select(pg => pg.LearningRate * decay_factor);\n }\n }\n\n private double _total_iters;\n private int _power;\n }\n\n /// <summary>\n /// Decays the learning rate of each parameter group by gamma every epoch.\n /// Notice that such decay can happen simultaneously with other changes to the learning rate from outside this scheduler.\n /// When last_epoch=-1, sets initial lr as lr.\n /// </summary>\n public class ExponentialLR : LRScheduler\n {\n /// <summary>\n /// Constructor\n /// </summary>\n /// <param name=\"optimizer\">Wrapped optimizer.</param>\n /// <param name=\"gamma\">Multiplicative factor of learning rate decay. Default: 0.1.</param>\n /// <param name=\"last_epoch\">The index of last epoch. Default: -1.</param>\n /// <param name=\"verbose\"> If true, prints a message to stdout for each update. Default: false.</param>\n /// <returns>A scheduler</returns>\n public ExponentialLR(Optimizer optimizer, double gamma = 0.1, int last_epoch = -1, bool verbose = false) : base(optimizer, last_epoch, verbose)\n {\n if (optimizer == null) throw new ArgumentNullException(\"optimizer\");\n _gamma = gamma;\n\n step();\n }\n\n /// <summary>\n /// Compute the current learning rate for the scheduler.\n /// </summary>\n protected override IEnumerable<double> get_lr()\n {\n return (_last_epoch == 0)\n ? _optimizer.ParamGroups.Select(pg => pg.LearningRate)\n : _optimizer.ParamGroups.Select(pg => pg.LearningRate * _gamma);\n }\n\n private double _gamma;\n }\n\n /// <summary>\n /// Decays the learning rate of each parameter group by a small constant factor until the number of epoch reaches a pre-defined milestone: total_iters.\n /// Notice that such decay can happen simultaneously with other changes to the learning rate from outside this scheduler.\n /// When last_epoch=-1, sets initial lr as lr.\n /// </summary>\n public class ConstantLR : LRScheduler\n {\n /// <summary>\n /// Constructor\n /// </summary>\n /// <param name=\"optimizer\">Wrapped optimizer.</param>\n /// <param name=\"factor\">The number we multiply learning rate until the milestone.</param>\n /// <param name=\"total_iters\">The number of steps that the scheduler decays the learning rate.</param>\n /// <param name=\"last_epoch\">The index of last epoch. Default: -1.</param>\n /// <param name=\"verbose\"> If true, prints a message to stdout for each update. Default: false.</param>\n /// <returns>A scheduler</returns>\n public ConstantLR(Optimizer optimizer, double factor = 1.0 / 3, int total_iters = 5, int last_epoch = -1, bool verbose = false) : base(optimizer, last_epoch, verbose)\n {\n if (optimizer == null) throw new ArgumentNullException(\"optimizer\");\n _factor = factor;\n _total_iters = total_iters;\n\n step();\n }\n\n /// <summary>\n /// Compute the current learning rate for the scheduler.\n /// </summary>\n protected override IEnumerable<double> get_lr()\n {\n if (_last_epoch == 0) {\n return _optimizer.ParamGroups.Select(pg => pg.LearningRate * _factor);\n } else if (_last_epoch == _total_iters) {\n return _optimizer.ParamGroups.Select(pg => pg.LearningRate * (1.0 / _factor));\n } else {\n return _optimizer.ParamGroups.Select(pg => pg.LearningRate);\n }\n }\n\n private double _factor;\n private int _total_iters;\n }\n\n /// <summary>\n /// Decays the learning rate of each parameter group by linearly changing small multiplicative factor until the\n /// number of epoch reaches a pre-defined milestone: total_iters.\n /// Notice that such decay can happen simultaneously with other changes to the learning rate from outside this scheduler.\n /// When last_epoch=-1, sets initial lr as lr.\n /// </summary>\n public class LinearLR : LRScheduler\n {\n /// <summary>\n /// Constructor\n /// </summary>\n /// <param name=\"optimizer\">Wrapped optimizer.</param>\n /// <param name=\"start_factor\">The number we multiply learning rate in the first epoch. The multiplication factor changes towards end_factor in the following epochs.</param>\n /// <param name=\"end_factor\">The number we multiply learning rate at the end of linear changing process.</param>\n /// <param name=\"total_iters\">The number of steps that the scheduler decays the learning rate.</param>\n /// <param name=\"last_epoch\">The index of last epoch. Default: -1.</param>\n /// <param name=\"verbose\"> If true, prints a message to stdout for each update. Default: false.</param>\n /// <returns>A scheduler</returns>\n public LinearLR(Optimizer optimizer, double start_factor = 1.0 / 3, double end_factor = 5, int total_iters = 5, int last_epoch = -1, bool verbose = false) : base(optimizer, last_epoch, verbose)\n {\n if (optimizer == null) throw new ArgumentNullException(\"optimizer\");\n _start_factor = start_factor;\n _end_factor = end_factor;\n _total_iters = total_iters;\n\n step();\n }\n\n /// <summary>\n /// Compute the current learning rate for the scheduler.\n /// </summary>\n protected override IEnumerable<double> get_lr()\n {\n if (_last_epoch == 0) {\n return _optimizer.ParamGroups.Select(pg => pg.LearningRate * _start_factor);\n } else if (_last_epoch > _total_iters) {\n return _optimizer.ParamGroups.Select(pg => pg.LearningRate);\n } else {\n var factor = (1 + (_end_factor - _start_factor)) / (_total_iters * _start_factor + (_last_epoch - 1) * (_end_factor - _start_factor));\n return _optimizer.ParamGroups.Select(pg => pg.LearningRate * factor);\n }\n }\n\n private double _start_factor;\n private double _end_factor;\n private int _total_iters;\n }\n\n /// <summary>\n /// Receives the list of schedulers that is expected to be called sequentially during optimization process and milestone points that provides exact intervals to reflect which scheduler is supposed to be called at a given epoch.\n /// </summary>\n public class SequentialLR : LRScheduler\n {\n public SequentialLR(Optimizer optimizer, IEnumerable<LRScheduler> schedulers, IEnumerable<int> milestones, int last_epoch = -1) : base(optimizer, last_epoch + 1, false)\n {\n if (optimizer == null) throw new ArgumentNullException(\"optimizer\");\n\n foreach (var scheduler in schedulers) {\n if (scheduler._optimizer != optimizer)\n throw new ArgumentException($\"The SequentialLR scheduler expects all its sub-schedulers to be bound to the same optimizer.\");\n }\n\n if (milestones.Count() != schedulers.Count() - 1) {\n throw new ArgumentException($\"Received {schedulers.Count()} schedulers and {milestones.Count()} milestones. The SequentialLR scheduler expects the number of schedulers to be one more than the number of milestones. \");\n }\n\n _schedulers = schedulers.ToArray();\n _milestones = milestones.ToArray();\n\n foreach (var group in optimizer.ParamGroups) {\n group.LearningRate = group.InitialLearningRate;\n }\n foreach (var scheduler in _schedulers) {\n scheduler._last_epoch -= 1;\n }\n _schedulers[0].step();\n _last_lrs = _schedulers[0]._last_lrs;\n }\n\n /// <summary>\n /// Compute the current learning rate for the scheduler.\n /// </summary>\n protected override IEnumerable<double> get_lr()\n {\n\n var idx = _milestones.Length;\n for (; idx > 0 ;) {\n if (_milestones[idx-1] <= _last_epoch) break;\n idx -= 1;\n }\n\n var scheduler = _schedulers[idx];\n if (idx > 0 && _milestones[idx - 1] == _last_epoch)\n scheduler.step(0);\n else\n scheduler.step();\n\n return _optimizer.ParamGroups.Select(pg => pg.LearningRate);\n }\n\n private LRScheduler[] _schedulers;\n private int[] _milestones;\n }\n\n /// <summary>\n /// Set the learning rate of each parameter group using a cosine annealing schedule.\n /// When last_epoch=-1, sets initial lr as lr.\n /// </summary>\n public class CosineAnnealingLR : LRScheduler\n {\n /// <summary>\n /// Constructor\n /// </summary>\n /// <param name=\"optimizer\">Wrapped optimizer.</param>\n /// <param name=\"T_max\">Maximum number of iterations.</param>\n /// <param name=\"eta_min\">Minimum learning rate.</param>\n /// <param name=\"last_epoch\">The index of last epoch. Default: -1.</param>\n /// <param name=\"verbose\"> If true, prints a message to stdout for each update. Default: false.</param>\n /// <returns>A scheduler</returns>\n public CosineAnnealingLR(Optimizer optimizer, double T_max, double eta_min = 0, int last_epoch = -1, bool verbose = false) : base(optimizer, last_epoch, verbose)\n {\n if (optimizer == null) throw new ArgumentNullException(\"optimizer\");\n _T_max = T_max;\n _eta_min = eta_min;\n\n step();\n }\n\n /// <summary>\n /// Compute the current learning rate for the scheduler.\n /// </summary>\n protected override IEnumerable<double> get_lr()\n {\n if (_last_epoch == 0) {\n return _optimizer.ParamGroups.Select(pg => pg.LearningRate);\n } else if ((_last_epoch - 1 - _T_max) % (2 * _T_max) == 0) {\n var pgs = _optimizer.ParamGroups.ToList();\n return Enumerable.Range(0, pgs.Count).Select(i => pgs[i].LearningRate + (_base_lrs[i] - _eta_min) * (1 - Math.Cos(Math.PI / _T_max)) / 2);\n } else {\n return _optimizer.ParamGroups.Select(pg => (1 + Math.Cos(Math.PI * _last_epoch / _T_max)) /\n (1 + Math.Cos(Math.PI * (_last_epoch - 1) / _T_max)) * (pg.LearningRate - _eta_min) + _eta_min);\n }\n }\n\n private double _T_max;\n private double _eta_min;\n }\n\n /// <summary>\n /// Set the learning rate of each parameter group using a cosine annealing schedule.\n /// When last_epoch=-1, sets initial lr as lr.\n /// </summary>\n public class ReduceLROnPlateau : LRScheduler\n {\n /// <summary>\n /// Constructor\n /// </summary>\n /// <returns>A scheduler</returns>\n public ReduceLROnPlateau(Optimizer optimizer, string mode = \"min\", double factor = 0.1, int patience = 10, double threshold = 1e-4, string threshold_mode = \"rel\", int cooldown = 0, IList<double> min_lr = null, double eps = 1e-8, bool verbose = false)\n :base(optimizer, -1, verbose)\n {\n if (optimizer == null) throw new ArgumentNullException(\"optimizer\");\n if (factor >= 1.0) throw new ArgumentException(\"Factor should be < 1.0\");\n\n switch (mode) {\n case \"min\":\n case \"max\":\n break;\n default:\n throw new ArgumentException($\"mode {mode} is unknown!\");\n }\n switch (threshold_mode) {\n case \"rel\":\n case \"abs\":\n break;\n default:\n throw new ArgumentException($\"threshold mode {threshold_mode} is unknown!\");\n }\n\n this._optimizer = optimizer;\n this.factor = factor;\n\n var pgLength = optimizer.ParamGroups.Count();\n if (min_lr == null || min_lr.Count == 0) {\n this.min_lrs = Enumerable.Repeat(0.0, pgLength).ToList();\n }\n else if (min_lr.Count == 1) {\n this.min_lrs = Enumerable.Repeat(min_lr[0], pgLength).ToList();\n }\n else {\n this.min_lrs = min_lr.ToList();\n }\n\n this.patience = patience;\n this.cooldown = cooldown;\n this.cooldown_counter = 0;\n this.mode = mode;\n this.threshold = threshold;\n this.threshold_mode = threshold_mode;\n this.best = -1;\n this.num_bad_epochs = -1;\n this.eps = eps;\n\n this.mode_worst = (mode == \"min\") ? double.PositiveInfinity : double.NegativeInfinity;\n\n reset();\n }\n\n /// <summary>\n /// Advance the LR scheduler one epoch.\n /// Unlike other LR schedulers, you're supposed to pass in the most recent value of the metric\n /// that is used to decided whether to modify the learning rates. \n /// </summary>\n /// <param name=\"current\">The current value of the metric that we're interested in imvproving.</param>\n /// <param name=\"epoch\">The current epoch.</param>\n public override void step(double current, int? epoch = null)\n {\n if (epoch == null) {\n epoch = _last_epoch + 1;\n }\n _last_epoch = epoch.Value;\n\n if (is_better(current, best)) {\n best = current;\n num_bad_epochs = 0;\n }\n else {\n num_bad_epochs += 1;\n }\n\n if (cooldown_counter > 0) {\n cooldown_counter -= 1;\n num_bad_epochs = 0;\n }\n\n if (num_bad_epochs > patience) {\n reduce_lr(_last_epoch);\n cooldown_counter = cooldown;\n num_bad_epochs = 0;\n }\n }\n\n public override void step()\n {\n throw new InvalidOperationException(\"step() should not be used with the ReduceLROnPlateau scheduler. Use step(double, int?), instead.\");\n }\n\n public override void step(int? epoch)\n {\n throw new InvalidOperationException(\"step(int?) should not be used with the ReduceLROnPlateau scheduler. Use step(double, int?), instead.\");\n }\n\n private bool is_better(double a, double best)\n {\n if (mode == \"min\" && threshold_mode == \"rel\") {\n return a < best * (1 - threshold);\n }\n else if (mode == \"min\" && threshold_mode == \"abs\") {\n return a < best - threshold;\n }\n else if (mode == \"max\" && threshold_mode == \"rel\") {\n return a > best * (threshold + 1);\n }\n else {\n return a > best + threshold;\n }\n }\n\n private void reduce_lr(long epoch)\n {\n var pgs = _optimizer.ParamGroups.ToList();\n\n for (var i = 0; i < pgs.Count; i++) {\n var param_group = pgs[i];\n\n var old_lr = param_group.LearningRate;\n var new_lr = Math.Max(old_lr * factor, min_lrs[i]);\n if (old_lr - new_lr > eps) {\n param_group.LearningRate = new_lr;\n if (_verbose) {\n Console.WriteLine($\"Epoch {epoch}: reducing learning rate of group {i} to {new_lr:g4}.\");\n }\n }\n }\n }\n\n private void reset()\n {\n best = mode_worst;\n cooldown_counter = 0;\n num_bad_epochs = 0;\n }\n\n private int patience;\n private int cooldown;\n private int cooldown_counter;\n private string mode;\n private double threshold;\n private string threshold_mode;\n private double best;\n private double mode_worst;\n private int num_bad_epochs;\n private double eps;\n\n private double factor;\n private List<double> min_lrs;\n }\n\n /// <summary>\n /// Sets the learning rate of each parameter group according to cyclical learning rate policy(CLR).\n ///\n /// The policy cycles the learning rate between two boundaries with a constant frequency, as detailed in\n /// the paper `Cyclical Learning Rates for Training Neural Networks`_.\n /// \n /// The distance between the two boundaries can be scaled on a per-iteration or per-cycle basis.\n /// </summary>\n public class CyclicLR : LRScheduler\n {\n public enum Mode\n {\n Triangular,\n Triangular2,\n ExpRange\n }\n public enum ScaleMode\n {\n Cycle,\n Iterations\n }\n\n /// <summary>\n /// Constructor\n /// </summary>\n public CyclicLR(Optimizer optimizer,\n double base_lr,\n double max_lr,\n int step_size_up = 2000,\n int step_size_down = -1,\n Mode mode = Mode.Triangular,\n double gamma = 1.0,\n Func<double, double> scale_fn = null,\n ScaleMode scale_mode = ScaleMode.Cycle,\n bool cycle_momentum = true,\n double base_momentum = 0.8,\n double max_momentum = 0.9,\n int last_epoch = -1,\n bool verbose = false) : base(optimizer, last_epoch, verbose)\n {\n if (optimizer == null) throw new ArgumentNullException(\"optimizer\");\n\n var pgCount = optimizer.ParamGroups.Count();\n\n Initialize(\n optimizer,\n Enumerable.Repeat(base_lr, pgCount),\n Enumerable.Repeat(max_lr, pgCount),\n step_size_up,\n step_size_down,\n mode,\n gamma,\n scale_fn,\n scale_mode,\n cycle_momentum,\n Enumerable.Repeat(base_momentum, pgCount),\n Enumerable.Repeat(max_momentum, pgCount));\n }\n\n /// <summary>\n /// Constructor\n /// </summary>\n public CyclicLR(Optimizer optimizer,\n IEnumerable<double> base_lr,\n IEnumerable<double> max_lr,\n int step_size_up = 2000,\n int step_size_down = -1,\n Mode mode = Mode.Triangular,\n double gamma = 1.0,\n Func<double, double> scale_fn = null,\n ScaleMode scale_mode = ScaleMode.Cycle,\n bool cycle_momentum = true,\n IEnumerable<double> base_momentum = null,\n IEnumerable<double> max_momentum = null,\n int last_epoch = -1,\n bool verbose = false) : base(optimizer, last_epoch, verbose)\n {\n if (optimizer == null) throw new ArgumentNullException(\"optimizer\");\n\n Initialize(\n optimizer,\n base_lr,\n max_lr,\n step_size_up,\n step_size_down,\n mode,\n gamma,\n scale_fn,\n scale_mode,\n cycle_momentum,\n base_momentum,\n max_momentum);\n }\n\n private void Initialize(Optimizer optimizer, IEnumerable<double> base_lr, IEnumerable<double> max_lr, int step_size_up, int step_size_down, Mode mode, double gamma, Func<double, double> scale_fn, ScaleMode scale_mode, bool cycle_momentum, IEnumerable<double> base_momentum, IEnumerable<double> max_momentum)\n {\n double down = (step_size_down == -1) ? step_size_up : step_size_down;\n _total_size = step_size_up + down;\n _step_ratio = step_size_up / _total_size;\n\n var pgs = optimizer.ParamGroups.ToList();\n\n _max_lrs = max_lr.ToList();\n _base_lrs = base_lr.ToList();\n\n if (_last_epoch == -1) {\n for (int i = 0; i < pgs.Count; i++) {\n pgs[i].LearningRate = _base_lrs[i];\n }\n }\n\n _mode = mode;\n _gamma = gamma;\n _cycle_momentum = cycle_momentum;\n\n if (cycle_momentum) {\n var momentum = optimizer as IMomentum;\n if (momentum == null && cycle_momentum) throw new ArgumentException($\"optimizer must support momentum with `cycle_momentum` option enabled\");\n\n _base_momentum = (base_momentum is null) ? Enumerable.Repeat(0.8, pgs.Count).ToList() : base_momentum.ToList();\n _max_momentum = (max_momentum is null) ? Enumerable.Repeat(0.9, pgs.Count).ToList() : max_momentum.ToList();\n\n if (_last_epoch == -1) {\n for (int i = 0; i < pgs.Count; i++) {\n (pgs[i] as IMomentum).Momentum = _base_momentum[i];\n }\n }\n }\n\n\n if (scale_fn == null) {\n switch (mode) {\n case Mode.Triangular:\n _scale_func = x => 1.0;\n _scale_mode = ScaleMode.Cycle;\n break;\n case Mode.Triangular2:\n _scale_func = x => 1.0 / (Math.Pow(2, x - 1));\n _scale_mode = ScaleMode.Cycle;\n break;\n case Mode.ExpRange:\n _scale_func = x => Math.Pow(this._gamma, x);\n _scale_mode = ScaleMode.Cycle;\n break;\n }\n } else {\n _scale_func = scale_fn;\n _scale_mode = scale_mode;\n }\n\n step();\n }\n\n /// <summary>\n /// Compute the current learning rate for the scheduler.\n /// </summary>\n protected override IEnumerable<double> get_lr()\n {\n var pgs = _optimizer.ParamGroups.ToList();\n\n var cycle = Math.Floor(1.0 + _last_epoch / _total_size);\n var x = 1.0 + _last_epoch / _total_size - cycle;\n\n var scale_factor = (x <= _step_ratio) ? x / _step_ratio : (x - 1) / (_step_ratio - 1);\n\n return Enumerable.Range(0, pgs.Count).Select(i => {\n\n var base_height = (_max_lrs[i] - _base_lrs[i]) * scale_factor;\n\n var computed_lr = (_scale_mode == ScaleMode.Cycle)\n ? _base_lrs[i] + base_height * _scale_func(cycle)\n : _base_lrs[i] + base_height * _scale_func(_last_epoch);\n\n if (_cycle_momentum) {\n\n base_height = (_max_momentum[i] - _base_momentum[i]) * scale_factor;\n\n (pgs[i] as IMomentum).Momentum = (_scale_mode == ScaleMode.Cycle)\n ? _max_momentum[i] + base_height * _scale_func(cycle)\n : _max_momentum[i] + base_height * _scale_func(_last_epoch);\n\n }\n\n return computed_lr;\n });\n }\n\n private double _total_size;\n private double _step_ratio;\n\n private Func<double, double> _scale_func;\n private ScaleMode _scale_mode;\n\n private bool _cycle_momentum;\n\n private List<double> _max_lrs;\n\n private List<double> _base_momentum;\n private List<double> _max_momentum;\n\n private Mode _mode;\n private double _gamma;\n }\n\n /// <summary>\n /// Sets the learning rate of each parameter group according to the 1cycle learning rate policy.\n /// </summary>\n public class OneCycleLR : LRScheduler\n {\n public enum AnnealStrategy\n {\n Cos,\n Linear\n }\n\n /// <summary>\n /// Constructor\n /// </summary>\n public OneCycleLR(Optimizer optimizer,\n double max_lr,\n int total_steps = -1,\n int epochs = -1,\n int steps_per_epoch = -1,\n double pct_start = 0.3,\n AnnealStrategy anneal_strategy = AnnealStrategy.Cos,\n bool cycle_momentum = true,\n double base_momentum = 0.85,\n double max_momentum = 0.95,\n double div_factor = 25,\n double final_div_factor = 1e4,\n bool three_phase = false,\n int last_epoch = -1, bool verbose = false) : base(optimizer, last_epoch, verbose)\n {\n if (optimizer == null) throw new ArgumentNullException(\"optimizer\");\n\n var pgCount = _optimizer.ParamGroups.Count();\n\n Initialize(optimizer,\n Enumerable.Repeat(max_lr, pgCount),\n total_steps,\n epochs,\n steps_per_epoch,\n pct_start, anneal_strategy,\n cycle_momentum,\n Enumerable.Repeat(base_momentum, pgCount),\n Enumerable.Repeat(max_momentum, pgCount),\n div_factor,\n final_div_factor,\n three_phase,\n last_epoch);\n }\n\n /// <summary>\n /// Constructor\n /// </summary>\n public OneCycleLR(Optimizer optimizer,\n IEnumerable<double> max_lr,\n int total_steps = -1,\n int epochs = -1,\n int steps_per_epoch = -1,\n double pct_start = 0.3,\n AnnealStrategy anneal_strategy = AnnealStrategy.Cos,\n bool cycle_momentum = true,\n IEnumerable<double> base_momentum = null,\n IEnumerable<double> max_momentum = null,\n double div_factor = 25,\n double final_div_factor = 1e4,\n bool three_phase = false,\n int last_epoch = -1, bool verbose = false) : base(optimizer, last_epoch, verbose)\n {\n if (optimizer == null) throw new ArgumentNullException(\"optimizer\");\n\n Initialize(optimizer,\n max_lr,\n total_steps,\n epochs,\n steps_per_epoch,\n pct_start,\n anneal_strategy,\n cycle_momentum,\n base_momentum,\n max_momentum,\n div_factor,\n final_div_factor,\n three_phase,\n last_epoch);\n }\n\n private void Initialize(Optimizer optimizer, IEnumerable<double> max_lr, int total_steps, int epochs, int steps_per_epoch, double pct_start, AnnealStrategy anneal_strategy, bool cycle_momentum, IEnumerable<double> base_momentum, IEnumerable<double> max_momentum, double div_factor, double final_div_factor, bool three_phase, int last_epoch)\n {\n _cycle_momentum = cycle_momentum;\n\n var pgs = optimizer.ParamGroups.ToList();\n\n if (cycle_momentum) {\n var _momentum = optimizer as IMomentum;\n var _betas = optimizer as IBetas;\n if (_momentum == null && _betas == null) throw new ArgumentException($\"optimizer must support momentum with `cycle_momentum` option enabled\");\n }\n\n if (pct_start < 0 || pct_start > 1) throw new ArgumentException($\"Expected float between 0 and 1 for pct_start, but got {pct_start}\");\n\n if (total_steps == -1 && epochs == -1 && steps_per_epoch == -1) {\n throw new ArgumentException(\"You must define either total_steps OR (epochs AND steps_per_epoch)\");\n } else if (total_steps != -1) {\n if (total_steps <= 0) throw new ArgumentException($\"Expected positive integer total_steps, but got {total_steps}\");\n _total_steps = total_steps;\n } else {\n if (epochs <= 0) throw new ArgumentException($\"Expected positive integer epochs, but got {epochs}\");\n if (steps_per_epoch <= 0) throw new ArgumentException($\"Expected positive integer steps_per_epoch, but got {steps_per_epoch}\");\n _total_steps = epochs * steps_per_epoch;\n }\n\n var mlr = max_lr.ToList();\n\n _betas = pgs.Select(pg => pg as IBetas).ToList();\n _momentum = pgs.Select(pg => pg as IMomentum).ToList();\n\n var initial_lrs = max_lr.Select(mlr => mlr / div_factor).ToList();\n\n _phase_values[\"initial_lr\"] = initial_lrs;\n _phase_values[\"max_lr\"] = mlr;\n _phase_values[\"min_lr\"] = initial_lrs.Select(ilr => ilr / final_div_factor).ToList();\n\n _annealing_func = (anneal_strategy == AnnealStrategy.Cos)\n ? (start, end, pct) => end + (start - end) / 2.0 * (Math.Cos(Math.PI * pct) + 1)\n : (start, end, pct) => (end - start) * pct + start;\n\n _schedule_phases = three_phase\n ? new PhaseDescriptor[] {\n new PhaseDescriptor { end_step = pct_start * _total_steps - 1, start_lr = \"initial_lr\", end_lr = \"max_lr\", start_momentum = \"max_momentum\", end_momentum = \"base_momentum\" },\n new PhaseDescriptor { end_step = 2 * pct_start * _total_steps - 2, start_lr = \"max_lr\", end_lr = \"initial_lr\", start_momentum = \"base_momentum\", end_momentum = \"max_momentum\" },\n new PhaseDescriptor { end_step = _total_steps - 1, start_lr = \"initial_lr\", end_lr = \"min_lr\", start_momentum = \"max_momentum\", end_momentum = \"max_momentum\" }\n }\n : new PhaseDescriptor[] {\n new PhaseDescriptor { end_step = pct_start * _total_steps - 1, start_lr = \"initial_lr\", end_lr = \"max_lr\", start_momentum = \"max_momentum\", end_momentum = \"base_momentum\" },\n new PhaseDescriptor { end_step = _total_steps - 1, start_lr = \"max_lr\", end_lr = \"min_lr\", start_momentum = \"base_momentum\", end_momentum = \"max_momentum\" },\n };\n\n if (last_epoch == -1) {\n\n if (cycle_momentum) {\n\n var _base_momentum = (base_momentum is null) ? Enumerable.Repeat(0.85, pgs.Count).ToList() : base_momentum.ToList();\n var _max_momentum = (max_momentum is null) ? Enumerable.Repeat(0.95, pgs.Count).ToList() : max_momentum.ToList();\n\n for (int i = 0; i < pgs.Count; i++) {\n if (_betas[i] != null) {\n var (_, beta2) = _betas[i].Betas;\n _betas[i].Betas = (_max_momentum[i], beta2);\n } else {\n if (_momentum[i] != null)\n _momentum[i].Momentum = _max_momentum[i];\n }\n }\n _phase_values[\"max_momentum\"] = _max_momentum;\n _phase_values[\"base_momentum\"] = _base_momentum;\n }\n }\n step();\n }\n\n /// <summary>\n /// Compute the current learning rate for the scheduler.\n /// </summary>\n protected override IEnumerable<double> get_lr()\n {\n var step_num = _last_epoch;\n if (step_num > _total_steps) {\n throw new InvalidOperationException($\"Tried to step {step_num + 1} times. The specified number of total steps is {_total_steps}\");\n }\n\n var pgs = _optimizer.ParamGroups.ToList();\n\n return Enumerable.Range(0, pgs.Count).Select(i => {\n\n double start_step = 0;\n double computed_lr = 0;\n double computed_momentum = 0;\n\n for (var j = 0; j < _schedule_phases.Length; j++) {\n\n var phase = _schedule_phases[j];\n var end_step = phase.end_step;\n\n if (step_num <= end_step || i == _schedule_phases.Length - 1) {\n var pct = (step_num - start_step) / (end_step - start_step);\n computed_lr = _annealing_func(_phase_values[phase.start_lr][i], _phase_values[phase.end_lr][i], pct);\n if (_cycle_momentum) {\n computed_momentum = _annealing_func(_phase_values[phase.start_momentum][i], _phase_values[phase.end_momentum][i], pct);\n }\n break;\n }\n start_step = phase.end_step;\n }\n\n if (_cycle_momentum) {\n if (_betas[i] != null) {\n var (_, beta2) = _betas[i].Betas;\n _betas[i].Betas = (computed_momentum, beta2);\n } else {\n _momentum[i].Momentum = computed_momentum;\n }\n }\n return computed_lr;\n });\n }\n\n private Func<double, double, double, double> _annealing_func;\n\n private PhaseDescriptor[] _schedule_phases;\n private Dictionary<string, List<double>> _phase_values = new Dictionary<string, List<double>>();\n\n private bool _cycle_momentum;\n\n private List<IBetas> _betas;\n private List<IMomentum> _momentum;\n\n private int _total_steps;\n\n private class PhaseDescriptor\n {\n public double end_step;\n public string start_lr;\n public string end_lr;\n public string start_momentum;\n public string end_momentum;\n }\n }\n\n /// <summary>\n /// Chains list of learning rate schedulers. It takes a list of chainable learning rate schedulers and applies step() to all of them.\n /// </summary>\n public class ChainedLR : LRScheduler\n {\n /// <summary>\n /// Constructor\n /// </summary>\n /// <param name=\"schedulers\">List of chained schedulers.</param>\n /// <returns>A scheduler</returns>\n public ChainedLR(IEnumerable<LRScheduler> schedulers)\n {\n _schedulers = schedulers;\n }\n\n public override void step()\n {\n foreach (var sched in _schedulers) {\n sched.step();\n }\n }\n\n private IEnumerable<LRScheduler> _schedulers;\n }\n }\n\n /// <summary>\n /// Decays the learning rate of each parameter group by gamma every step_size epochs.\n /// Notice that such decay can happen simultaneously with other changes to the learning rate from outside this scheduler.\n /// When last_epoch=-1, sets initial lr as lr.\n /// </summary>\n /// <param name=\"optimizer\">Wrapped optimizer.</param>\n /// <param name=\"step_size\">Period of learning rate decay.</param>\n /// <param name=\"gamma\">Multiplicative factor of learning rate decay. Default: 0.1.</param>\n /// <param name=\"last_epoch\">\n /// The index of the last batch. This parameter is used when resuming a training job.Since `step()` should be invoked after each\n /// batch instead of after each epoch, this number represents the total number of *batches* computed, not the total number of epochs computed.\n /// When last_epoch = -1, the schedule is started from the beginning.\n /// </param>\n /// <param name=\"verbose\"> If true, prints a message to stdout for each update. Default: false.</param>\n /// <returns>A scheduler</returns>\n public static LRScheduler StepLR(Optimizer optimizer, int step_size, double gamma = 0.1, int last_epoch = -1, bool verbose = false)\n {\n return new impl.StepLR(optimizer, step_size, gamma, last_epoch, verbose);\n }\n\n /// <summary>\n /// Sets the learning rate of each parameter group to the initial lr times a given function.\n /// When last_epoch=-1, sets initial lr as lr.\n /// </summary>\n /// <param name=\"optimizer\">Wrapped optimizer.</param>\n /// <param name=\"lr_lambda\">A function which computes a multiplicative factor given an integer parameter epoch.</param>\n /// <param name=\"last_epoch\">\n /// The index of the last batch. This parameter is used when resuming a training job.Since `step()` should be invoked after each\n /// batch instead of after each epoch, this number represents the total number of *batches* computed, not the total number of epochs computed.\n /// When last_epoch = -1, the schedule is started from the beginning.\n /// </param>\n /// <param name=\"verbose\"> If true, prints a message to stdout for each update. Default: false.</param>\n /// <returns>A scheduler</returns>\n public static LRScheduler LambdaLR(Optimizer optimizer, Func<int, double> lr_lambda, int last_epoch = -1, bool verbose = false)\n {\n return new impl.LambdaLR(optimizer, lr_lambda, last_epoch, verbose);\n\n }\n\n /// <summary>\n /// Decays the learning rate of each parameter group by gamma once the number of epoch reaches one of the milestones.\n /// Notice that such decay can happen simultaneously with other changes to the learning rate from outside this scheduler.\n /// When last_epoch=-1, sets initial lr as lr.\n /// </summary>\n /// <param name=\"optimizer\">Wrapped optimizer.</param>\n /// <param name=\"milestones\">List of epoch indices. Must be increasing.</param>\n /// <param name=\"gamma\">Multiplicative factor of learning rate decay. Default: 0.1.</param>\n /// <param name=\"last_epoch\">\n /// The index of the last batch. This parameter is used when resuming a training job.Since `step()` should be invoked after each\n /// batch instead of after each epoch, this number represents the total number of *batches* computed, not the total number of epochs computed.\n /// When last_epoch = -1, the schedule is started from the beginning.\n /// </param>\n /// <param name=\"verbose\"> If true, prints a message to stdout for each update. Default: false.</param>\n /// <returns>A scheduler</returns>\n public static LRScheduler MultiStepLR(Optimizer optimizer, IList<int> milestones, double gamma = 0.1, int last_epoch = -1, bool verbose = false)\n {\n return new impl.MultiStepLR(optimizer, milestones, gamma, last_epoch, verbose);\n\n }\n\n /// <summary>\n /// Constructor\n /// </summary>\n /// <param name=\"optimizer\">Wrapped optimizer.</param>\n /// <param name=\"total_iters\">The number of steps that the scheduler decays the learning rate.</param>\n /// <param name=\"power\">The power of the polynomial.</param>\n /// <param name=\"last_epoch\">The index of last epoch. Default: -1.</param>\n /// <param name=\"verbose\"> If true, prints a message to stdout for each update. Default: false.</param>\n /// <returns>A scheduler</returns>\n public static LRScheduler PolynomialLR(Optimizer optimizer, int total_iters = 5, int power = 1, int last_epoch = -1, bool verbose = false)\n {\n return new impl.PolynomialLR(optimizer, total_iters, power, last_epoch, verbose);\n }\n\n /// <summary>\n /// Multiply the learning rate of each parameter group by the factor given in the specified function.\n /// When last_epoch = -1, sets initial lr as lr.\n /// </summary>\n /// <param name=\"optimizer\">Wrapped optimizer.</param>\n /// <param name=\"lr_lambda\">A function which computes a multiplicative factor given an integer parameter epoch.</param>\n /// <param name=\"last_epoch\">\n /// The index of the last batch. This parameter is used when resuming a training job.Since `step()` should be invoked after each\n /// batch instead of after each epoch, this number represents the total number of *batches* computed, not the total number of epochs computed.\n /// When last_epoch = -1, the schedule is started from the beginning.\n /// </param>\n /// <param name=\"verbose\"> If true, prints a message to stdout for each update. Default: false.</param>\n /// <returns>A scheduler</returns>\n public static LRScheduler MultiplicativeLR(Optimizer optimizer, Func<int, double> lr_lambda, int last_epoch = -1, bool verbose = false)\n {\n return new impl.MultiplicativeLR(optimizer, lr_lambda, last_epoch, verbose);\n\n }\n\n /// <summary>\n /// Decays the learning rate of each parameter group by gamma every epoch.\n /// Notice that such decay can happen simultaneously with other changes to the learning rate from outside this scheduler.\n /// When last_epoch=-1, sets initial lr as lr.\n /// </summary>\n /// <param name=\"optimizer\">Wrapped optimizer.</param>\n /// <param name=\"gamma\">Multiplicative factor of learning rate decay. Default: 0.1.</param>\n /// <param name=\"last_epoch\">\n /// The index of the last batch. This parameter is used when resuming a training job.Since `step()` should be invoked after each\n /// batch instead of after each epoch, this number represents the total number of *batches* computed, not the total number of epochs computed.\n /// When last_epoch = -1, the schedule is started from the beginning.\n /// </param>\n /// <param name=\"verbose\"> If true, prints a message to stdout for each update. Default: false.</param>\n /// <returns>A scheduler</returns>\n public static LRScheduler ExponentialLR(Optimizer optimizer, double gamma = 0.1, int last_epoch = -1, bool verbose = false)\n {\n return new impl.ExponentialLR(optimizer, gamma, last_epoch, verbose);\n }\n\n /// <summary>\n /// Decays the learning rate of each parameter group by a small constant factor until the number of epoch reaches a pre-defined milestone: total_iters.\n /// Notice that such decay can happen simultaneously with other changes to the learning rate from outside this scheduler.\n /// When last_epoch=-1, sets initial lr as lr.\n /// </summary>\n /// <param name=\"optimizer\">Wrapped optimizer.</param>\n /// <param name=\"factor\">The number we multiply learning rate until the milestone.</param>\n /// <param name=\"total_iters\">The number of steps that the scheduler decays the learning rate.</param>\n /// <param name=\"last_epoch\">\n /// The index of the last batch. This parameter is used when resuming a training job.Since `step()` should be invoked after each\n /// batch instead of after each epoch, this number represents the total number of *batches* computed, not the total number of epochs computed.\n /// When last_epoch = -1, the schedule is started from the beginning.\n /// </param>\n /// <param name=\"verbose\">If true, prints a message to stdout for each update. Default: false.</param>\n /// <returns>A scheduler</returns>\n public static LRScheduler ConstantLR(Optimizer optimizer, double factor = 1.0 / 3, int total_iters = 5, int last_epoch = -1, bool verbose = false)\n {\n return new impl.ConstantLR(optimizer, factor, total_iters, last_epoch, verbose);\n }\n\n /// <summary>\n /// Uses a list of schedulers, chosen based on the epoch. A sequence of milestones determines when a switch occurs from one scheduler to the next.\n /// </summary>\n /// <param name=\"optimizer\">Wrapped optimizer. All sub-schedulers must be bound to the same optimizer.</param>\n /// <param name=\"schedulers\">List of chained schedulers. Should be one more than the number of milestones.</param>\n /// <param name=\"milestones\">List of integers reflecting the milestone points.</param>\n /// <param name=\"last_epoch\">The index of last epoch. Default: -1.</param>\n /// <returns></returns>\n /// <remarks>The 'verbose' flag should be passed to each individual sub-scheduler.</remarks>\n public static LRScheduler SequentialLR(Optimizer optimizer, IEnumerable<LRScheduler> schedulers, IEnumerable<int> milestones, int last_epoch = -1)\n {\n return new impl.SequentialLR(optimizer, schedulers, milestones, last_epoch);\n }\n\n /// <summary>\n /// Decays the learning rate of each parameter group by linearly changing small multiplicative factor until the\n /// number of epoch reaches a pre-defined milestone: total_iters.\n /// </summary>\n /// <param name=\"optimizer\">Wrapped optimizer.</param>\n /// <param name=\"start_factor\">The number we multiply learning rate in the first epoch. The multiplication factor changes towards end_factor in the following epochs.</param>\n /// <param name=\"end_factor\">The number we multiply learning rate at the end of linear changing process.</param>\n /// <param name=\"total_iters\">The number of steps that the scheduler decays the learning rate.</param>\n /// <param name=\"last_epoch\">\n /// The index of the last batch. This parameter is used when resuming a training job.Since `step()` should be invoked after each\n /// batch instead of after each epoch, this number represents the total number of *batches* computed, not the total number of epochs computed.\n /// When last_epoch = -1, the schedule is started from the beginning.\n /// </param>\n /// <param name=\"verbose\">If true, prints a message to stdout for each update. Default: false.</param>\n /// <returns>A scheduler</returns>\n public static LRScheduler LinearLR(Optimizer optimizer, double start_factor = 1.0 / 3, double end_factor = 5, int total_iters = 5, int last_epoch = -1, bool verbose = false)\n {\n return new impl.LinearLR(optimizer, start_factor, end_factor, total_iters, last_epoch, verbose);\n }\n\n /// <summary>\n /// Chains list of learning rate schedulers. It takes a list of chainable learning rate schedulers and applies step() to all of them.\n /// </summary>\n ///<param name=\"schedulers\">List of chained schedulers.</param>\n /// <returns>A scheduler</returns>\n public static LRScheduler ChainedLR(IEnumerable<LRScheduler> schedulers)\n {\n return new impl.ChainedLR(schedulers);\n }\n\n /// <summary>\n /// Sets the learning rate using a cosine annealing schedule.\n /// </summary>\n /// <param name=\"optimizer\">Wrapped optimizer.</param>\n /// <param name=\"T_max\">Maximum number of iterations.</param>\n /// <param name=\"eta_min\">Minimum learning rate.</param>\n /// <param name=\"last_epoch\">\n /// The index of the last batch. This parameter is used when resuming a training job.Since `step()` should be invoked after each\n /// batch instead of after each epoch, this number represents the total number of *batches* computed, not the total number of epochs computed.\n /// When last_epoch = -1, the schedule is started from the beginning.\n /// </param>\n /// <param name=\"verbose\"> If true, prints a message to stdout for each update. Default: false.</param>\n /// <returns>A scheduler</returns>\n public static LRScheduler CosineAnnealingLR(Optimizer optimizer, double T_max, double eta_min = 0, int last_epoch = -1, bool verbose = false)\n {\n return new impl.CosineAnnealingLR(optimizer, T_max, eta_min, last_epoch, verbose);\n }\n\n /// <summary>\n /// Sets the learning rate of each parameter group according to the\n /// 1cycle learning rate policy.The 1cycle policy anneals the learning\n /// rate from an initial learning rate to some maximum learning rate and then\n /// from that maximum learning rate to some minimum learning rate much lower\n /// than the initial learning rate.\n /// \n /// This policy was initially described in the paper `Super-Convergence:\n /// Very Fast Training of Neural Networks Using Large Learning Rates`_.\n ///\n /// The 1cycle learning rate policy changes the learning rate after every batch.\n /// `step` should be called after a batch has been used for training.\n ///\n /// This scheduler is not chainable. \n /// </summary>\n /// <param name=\"optimizer\">Wrapped optimizer.</param>\n /// <param name=\"max_lr\">Upper learning rate boundaries in the cycle</param>\n /// <param name=\"total_steps\">\n /// The total number of steps in the cycle.\n /// Note that if a value is not provided here, then it must be inferred by providing a value for epochs and steps_per_epoch.\n /// </param>\n /// <param name=\"epochs\">\n /// The number of epochs to train for. This is used along\n /// with steps_per_epoch in order to infer the total number of steps in the cycle\n /// if a value for total_steps is not provided.\n /// </param>\n /// <param name=\"steps_per_epoch\">\n /// The number of steps per epoch to train for. This is\n /// used along with epochs in order to infer the total number of steps in the\n /// cycle if a value for total_steps is not provided.\n /// </param>\n /// <param name=\"pct_start\">The percentage of the cycle (in number of steps) spent increasing the learning rate.</param>\n /// <param name=\"anneal_strategy\">Specifies the annealing strategy: \"cos\" for cosine annealing, \"linear\" for linear annealing.</param>\n /// <param name=\"cycle_momentum\">If true, momentum is cycled inversely to learning rate between 'base_momentum' and 'max_momentum'.</param>\n /// <param name=\"base_momentum\">\n /// Lower momentum boundaries in the cycle\n /// for each parameter group.Note that momentum is cycled inversely\n /// to learning rate; at the peak of a cycle, momentum is\n /// 'base_momentum' and learning rate is 'max_lr'.\n /// </param>\n /// <param name=\"max_momentum\">\n /// Upper momentum boundaries in the cycle for each parameter group.\n /// Functionally, it defines the cycle amplitude(max_momentum - base_momentum).\n /// Note that momentum is cycled inversely to learning rate; at the start of a cycle, momentum is 'max_momentum'\n /// and learning rate is 'base_lr'\n /// </param>\n /// <param name=\"div_factor\">Determines the initial learning rate via initial_lr = max_lr/div_factor</param>\n /// <param name=\"final_div_factor\">Determines the minimum learning rate via min_lr = initial_lr/final_div_factor</param>\n /// <param name=\"three_phase\">\n /// If ``True``, use a third phase of the schedule to annihilate the\n /// learning rate according to 'final_div_factor' instead of modifying the second\n /// phase (the first two phases will be symmetrical about the step indicated by 'pct_start').\n /// </param>\n /// <param name=\"last_epoch\">\n /// The index of the last batch. This parameter is used when resuming a training job.Since `step()` should be invoked after each\n /// batch instead of after each epoch, this number represents the total number of *batches* computed, not the total number of epochs computed.\n /// When last_epoch = -1, the schedule is started from the beginning.\n /// </param>\n /// <param name=\"verbose\"> If true, prints a message to stdout for each update. Default: false.</param>\n /// <returns>A scheduler</returns>\n /// <remarks>\n /// Note also that the total number of steps in the cycle can be determined in one\n /// of two ways (listed in order of precedence):\n ///\n /// #. A value for total_steps is explicitly provided.\n /// #. A number of epochs (epochs) and a number of steps per epoch\n /// (steps_per_epoch) are provided.\n /// In this case, the number of total steps is inferred by\n /// total_steps = epochs * steps_per_epoch\n ///\n /// You must either provide a value for total_steps or provide a value for both\n /// epochs and steps_per_epoch.\n ///\n /// The default behaviour of this scheduler follows the fastai implementation of 1cycle, which\n /// claims that \"unpublished work has shown even better results by using only two phases\". To\n /// mimic the behaviour of the original paper instead, set ``three_phase= True``.\n /// </remarks>\n public static LRScheduler OneCycleLR(Optimizer optimizer,\n double max_lr,\n int total_steps = -1,\n int epochs = -1,\n int steps_per_epoch = -1,\n double pct_start = 0.3,\n impl.OneCycleLR.AnnealStrategy anneal_strategy = impl.OneCycleLR.AnnealStrategy.Cos,\n bool cycle_momentum = true,\n double base_momentum = 0.85,\n double max_momentum = 0.95,\n double div_factor = 25,\n double final_div_factor = 1e4,\n bool three_phase = false,\n int last_epoch = -1, bool verbose = false)\n {\n return new impl.OneCycleLR(optimizer, max_lr, total_steps, epochs, steps_per_epoch, pct_start, anneal_strategy, cycle_momentum, base_momentum, max_momentum, div_factor, final_div_factor, three_phase, last_epoch, verbose);\n }\n\n /// <summary>\n /// Sets the learning rate of each parameter group according to the\n /// 1cycle learning rate policy.The 1cycle policy anneals the learning\n /// rate from an initial learning rate to some maximum learning rate and then\n /// from that maximum learning rate to some minimum learning rate much lower\n /// than the initial learning rate.\n /// \n /// This policy was initially described in the paper `Super-Convergence:\n /// Very Fast Training of Neural Networks Using Large Learning Rates`_.\n ///\n /// The 1cycle learning rate policy changes the learning rate after every batch.\n /// `step` should be called after a batch has been used for training.\n ///\n /// This scheduler is not chainable. \n /// </summary>\n /// <param name=\"optimizer\">Wrapped optimizer.</param>\n /// <param name=\"max_lr\">Upper learning rate boundaries in the cycle</param>\n /// <param name=\"total_steps\">\n /// The total number of steps in the cycle.\n /// Note that if a value is not provided here, then it must be inferred by providing a value for epochs and steps_per_epoch.\n /// </param>\n /// <param name=\"epochs\">\n /// The number of epochs to train for. This is used along\n /// with steps_per_epoch in order to infer the total number of steps in the cycle\n /// if a value for total_steps is not provided.\n /// </param>\n /// <param name=\"steps_per_epoch\">\n /// The number of steps per epoch to train for. This is\n /// used along with epochs in order to infer the total number of steps in the\n /// cycle if a value for total_steps is not provided.\n /// </param>\n /// <param name=\"pct_start\">The percentage of the cycle (in number of steps) spent increasing the learning rate.</param>\n /// <param name=\"anneal_strategy\">Specifies the annealing strategy: \"cos\" for cosine annealing, \"linear\" for linear annealing.</param>\n /// <param name=\"cycle_momentum\">If true, momentum is cycled inversely to learning rate between 'base_momentum' and 'max_momentum'.</param>\n /// <param name=\"base_momentum\">\n /// Lower momentum boundaries in the cycle\n /// for each parameter group.Note that momentum is cycled inversely\n /// to learning rate; at the peak of a cycle, momentum is\n /// 'base_momentum' and learning rate is 'max_lr'.\n /// </param>\n /// <param name=\"max_momentum\">\n /// Upper momentum boundaries in the cycle for each parameter group.\n /// Functionally, it defines the cycle amplitude(max_momentum - base_momentum).\n /// Note that momentum is cycled inversely to learning rate; at the start of a cycle, momentum is 'max_momentum'\n /// and learning rate is 'base_lr'\n /// </param>\n /// <param name=\"div_factor\">Determines the initial learning rate via initial_lr = max_lr/div_factor</param>\n /// <param name=\"final_div_factor\">Determines the minimum learning rate via min_lr = initial_lr/final_div_factor</param>\n /// <param name=\"three_phase\">\n /// If ``True``, use a third phase of the schedule to annihilate the\n /// learning rate according to 'final_div_factor' instead of modifying the second\n /// phase (the first two phases will be symmetrical about the step indicated by 'pct_start').\n /// </param>\n /// <param name=\"last_epoch\">\n /// The index of the last batch. This parameter is used when resuming a training job.Since `step()` should be invoked after each\n /// batch instead of after each epoch, this number represents the total number of *batches* computed, not the total number of epochs computed.\n /// When last_epoch = -1, the schedule is started from the beginning.\n /// </param>\n /// <param name=\"verbose\"> If true, prints a message to stdout for each update. Default: false.</param>\n /// <returns>A scheduler</returns>\n /// <remarks>\n /// Note also that the total number of steps in the cycle can be determined in one\n /// of two ways (listed in order of precedence):\n ///\n /// #. A value for total_steps is explicitly provided.\n /// #. A number of epochs (epochs) and a number of steps per epoch\n /// (steps_per_epoch) are provided.\n /// In this case, the number of total steps is inferred by\n /// total_steps = epochs * steps_per_epoch\n ///\n /// You must either provide a value for total_steps or provide a value for both\n /// epochs and steps_per_epoch.\n ///\n /// The default behaviour of this scheduler follows the fastai implementation of 1cycle, which\n /// claims that \"unpublished work has shown even better results by using only two phases\". To\n /// mimic the behaviour of the original paper instead, set ``three_phase= True``.\n /// </remarks>\n public static LRScheduler OneCycleLR(Optimizer optimizer,\n IEnumerable<double> max_lr,\n int total_steps = -1,\n int epochs = -1,\n int steps_per_epoch = -1,\n double pct_start = 0.3,\n impl.OneCycleLR.AnnealStrategy anneal_strategy = impl.OneCycleLR.AnnealStrategy.Cos,\n bool cycle_momentum = true,\n IEnumerable<double> base_momentum = null,\n IEnumerable<double> max_momentum = null,\n double div_factor = 25,\n double final_div_factor = 1e4,\n bool three_phase = false,\n int last_epoch = -1, bool verbose = false)\n {\n return new impl.OneCycleLR(optimizer, max_lr, total_steps, epochs, steps_per_epoch, pct_start, anneal_strategy, cycle_momentum, base_momentum, max_momentum, div_factor, final_div_factor, three_phase, last_epoch, verbose);\n }\n\n /// <summary>\n /// Sets the learning rate of each parameter group according to\n /// cyclical learning rate policy(CLR). The policy cycles the learning\n /// rate between two boundaries with a constant frequency, as detailed in\n /// the paper `Cyclical Learning Rates for Training Neural Networks`_.\n /// The distance between the two boundaries can be scaled on a per-iteration\n /// or per-cycle basis.\n ///\n /// Cyclical learning rate policy changes the learning rate after every batch.\n /// `step` should be called after a batch has been used for training.\n ///\n /// This class has three built-in policies, as put forth in the paper:\n /// * \"triangular\": A basic triangular cycle without amplitude scaling.\n /// * \"triangular2\": A basic triangular cycle that scales initial amplitude by half each cycle.\n /// * \"exp_range\": A cycle that scales initial amplitude by gamma^(cycle iterations).\n /// }`\n /// at each cycle iteration.\n /// /// </summary>\n /// <param name=\"optimizer\">Wrapped optimizer.</param>\n /// <param name=\"base_lr\">Initial learning rate which is the lower boundary in the cycle</param>\n /// <param name=\"max_lr\">\n /// Upper learning rate boundaries in the cycle for each parameter group.\n /// Functionally, it defines the cycle amplitude(max_lr - base_lr).\n /// The lr at any cycle is the sum of base_lr and some scaling of the amplitude; therefore \n /// max_lr may not actually be reached depending on the scaling function.\n /// </param>\n /// <param name=\"step_size_up\">Number of training iterations in the increasing half of a cycle.</param>\n /// <param name=\"step_size_down\">\n /// Number of training iterations in the decreasing half of a cycle.\n /// If step_size_down is -1, it is set to step_size_up.\n /// </param>\n /// <param name=\"mode\">Values correspond to policies detailed above. If scale_fn is non-null, this argument is ignored.</param>\n /// <param name=\"gamma\">Constant in 'exp_range' scaling function</param>\n /// <param name=\"scale_fn\">Custom scaling policy defined by a single argument lambda function. If specified, then 'mode' is ignored.</param>\n /// <param name=\"scale_mode\">Defines whether scale_fn is evaluated on cycle number or cycle iterations(training iterations since start of cycle)</param>\n /// <param name=\"cycle_momentum\">If true, momentum is cycled inversely to learning rate between 'base_momentum' and 'max_momentum'.</param>\n /// <param name=\"base_momentum\">Lower momentum boundaries in the cycle. Note that momentum is cycled inversely to learning rate</param>\n /// <param name=\"max_momentum\">\n /// Upper momentum boundaries in the cycle.\n /// Functionally, it defines the cycle amplitude(max_momentum - base_momentum).\n /// The momentum at any cycle is the difference of max_momentum and some scaling of the amplitude; therefore\n /// base_momentum may not actually be reached depending on the scaling function.\n /// </param>\n /// <param name=\"last_epoch\">\n /// The index of the last batch. This parameter is used when resuming a training job.Since `step()` should be invoked after each\n /// batch instead of after each epoch, this number represents the total number of *batches* computed, not the total number of epochs computed.\n /// When last_epoch = -1, the schedule is started from the beginning.\n /// </param>\n /// <param name=\"verbose\"> If true, prints a message to stdout for each update. Default: false.</param>\n /// <returns>A scheduler</returns>\n public static LRScheduler CyclicLR(Optimizer optimizer,\n double base_lr,\n double max_lr,\n int step_size_up = 2000,\n int step_size_down = -1,\n impl.CyclicLR.Mode mode = impl.CyclicLR.Mode.Triangular,\n double gamma = 1.0,\n Func<double, double> scale_fn = null,\n impl.CyclicLR.ScaleMode scale_mode = impl.CyclicLR.ScaleMode.Cycle,\n bool cycle_momentum = true,\n double base_momentum = 0.8,\n double max_momentum = 0.9,\n int last_epoch = -1,\n bool verbose = false)\n {\n return new impl.CyclicLR(optimizer, base_lr, max_lr, step_size_up, step_size_down, mode, gamma, scale_fn, scale_mode, cycle_momentum, base_momentum, max_momentum, last_epoch, verbose);\n }\n\n /// <summary>\n /// Sets the learning rate of each parameter group according to\n /// cyclical learning rate policy(CLR). The policy cycles the learning\n /// rate between two boundaries with a constant frequency, as detailed in\n /// the paper `Cyclical Learning Rates for Training Neural Networks`_.\n /// The distance between the two boundaries can be scaled on a per-iteration\n /// or per-cycle basis.\n ///\n /// Cyclical learning rate policy changes the learning rate after every batch.\n /// `step` should be called after a batch has been used for training.\n ///\n /// This class has three built-in policies, as put forth in the paper:\n /// * \"triangular\": A basic triangular cycle without amplitude scaling.\n /// * \"triangular2\": A basic triangular cycle that scales initial amplitude by half each cycle.\n /// * \"exp_range\": A cycle that scales initial amplitude by gamma^(cycle iterations).\n /// }`\n /// at each cycle iteration.\n /// /// </summary>\n /// <param name=\"optimizer\">Wrapped optimizer.</param>\n /// <param name=\"base_lr\">Initial learning rate which is the lower boundary in the cycle</param>\n /// <param name=\"max_lr\">\n /// Upper learning rate boundaries in the cycle for each parameter group.\n /// Functionally, it defines the cycle amplitude(max_lr - base_lr).\n /// The lr at any cycle is the sum of base_lr and some scaling of the amplitude; therefore \n /// max_lr may not actually be reached depending on the scaling function.\n /// </param>\n /// <param name=\"step_size_up\">Number of training iterations in the increasing half of a cycle.</param>\n /// <param name=\"step_size_down\">\n /// Number of training iterations in the decreasing half of a cycle.\n /// If step_size_down is -1, it is set to step_size_up.\n /// </param>\n /// <param name=\"mode\">Values correspond to policies detailed above. If scale_fn is non-null, this argument is ignored.</param>\n /// <param name=\"gamma\">Constant in 'exp_range' scaling function</param>\n /// <param name=\"scale_fn\">Custom scaling policy defined by a single argument lambda function. If specified, then 'mode' is ignored.</param>\n /// <param name=\"scale_mode\">Defines whether scale_fn is evaluated on cycle number or cycle iterations(training iterations since start of cycle)</param>\n /// <param name=\"cycle_momentum\">If true, momentum is cycled inversely to learning rate between 'base_momentum' and 'max_momentum'.</param>\n /// <param name=\"base_momentum\">Lower momentum boundaries in the cycle. Note that momentum is cycled inversely to learning rate</param>\n /// <param name=\"max_momentum\">\n /// Upper momentum boundaries in the cycle.\n /// Functionally, it defines the cycle amplitude(max_momentum - base_momentum).\n /// The momentum at any cycle is the difference of max_momentum and some scaling of the amplitude; therefore\n /// base_momentum may not actually be reached depending on the scaling function.\n /// </param>\n /// <param name=\"last_epoch\">\n /// The index of the last batch. This parameter is used when resuming a training job.Since `step()` should be invoked after each\n /// batch instead of after each epoch, this number represents the total number of *batches* computed, not the total number of epochs computed.\n /// When last_epoch = -1, the schedule is started from the beginning.\n /// </param>\n /// <param name=\"verbose\"> If true, prints a message to stdout for each update. Default: false.</param>\n /// <returns>A scheduler</returns>\n public static LRScheduler CyclicLR(Optimizer optimizer,\n IEnumerable<double> base_lr,\n IEnumerable<double> max_lr,\n int step_size_up = 2000,\n int step_size_down = -1,\n impl.CyclicLR.Mode mode = impl.CyclicLR.Mode.Triangular,\n double gamma = 1.0,\n Func<double, double> scale_fn = null,\n impl.CyclicLR.ScaleMode scale_mode = impl.CyclicLR.ScaleMode.Cycle,\n bool cycle_momentum = true,\n IEnumerable<double> base_momentum = null,\n IEnumerable<double> max_momentum = null,\n int last_epoch = -1,\n bool verbose = false)\n {\n return new impl.CyclicLR(optimizer, base_lr, max_lr, step_size_up, step_size_down, mode, gamma, scale_fn, scale_mode, cycle_momentum, base_momentum, max_momentum, last_epoch, verbose);\n }\n\n /// <summary>\n /// Reduce learning rate when a metric has stopped improving.\n /// Models often benefit from reducing the learning rate by a factor of 2-10 once learning stagnates.\n /// This scheduler reads a metrics quantity and if no improvement is seen for a ‘patience’ number of epochs, the learning rate is reduced.\n /// </summary>\n /// <param name=\"optimizer\">Wrapped optimizer.</param>\n /// <param name=\"mode\">\n /// One of min, max. In min mode, lr will be reduced when the quantity monitored has stopped decreasing;\n /// in max mode it will be reduced when the quantity monitored has stopped increasing. Default: ‘min’\n /// </param>\n /// <param name=\"factor\">Factor by which the learning rate will be reduced. new_lr = lr * factor.</param>\n /// <param name=\"patience\">\n /// Number of epochs with no improvement after which learning rate will be reduced. For example, if patience = 2,\n /// then we will ignore the first 2 epochs with no improvement, and will only decrease the LR after the 3rd epoch if\n /// the loss still hasn’t improved then. Default: 10.</param>\n /// <param name=\"threshold\">Threshold for measuring the new optimum, to only focus on significant changes. </param>\n /// <param name=\"threshold_mode\">\n /// One of rel, abs. In rel mode, dynamic_threshold = best * ( 1 + threshold ) in ‘max’ mode or best * ( 1 - threshold ) in min mode. In abs mode, dynamic_threshold = best + threshold in max mode or best - threshold in min mode. Default: ‘rel’.\n /// </param>\n /// <param name=\"cooldown\">Number of epochs to wait before resuming normal operation after lr has been reduced.</param>\n /// <param name=\"min_lr\">A scalar or a list of scalars. A lower bound on the learning rate of all param groups or each group respectively. Default: 0.</param>\n /// <param name=\"eps\">Minimal decay applied to lr. If the difference between new and old lr is smaller than eps, the update is ignored. Default: 1e-8.</param>\n /// <param name=\"verbose\">Indicates whether to print a message to stdout for each update</param>\n public static LRScheduler ReduceLROnPlateau(Optimizer optimizer, string mode = \"min\", double factor = 0.1, int patience = 10, double threshold = 1e-4, string threshold_mode = \"rel\", int cooldown = 0, IList<double> min_lr = null, double eps = 1e-8, bool verbose = false)\n {\n return new impl.ReduceLROnPlateau(optimizer, mode, factor, patience, threshold, threshold_mode, cooldown, min_lr, eps, verbose);\n }\n }\n }\n }\n}\n"},{"alpha_fraction":0.7059859037399292,"alphanum_fraction":0.7059859037399292,"avg_line_length":39.64285659790039,"blob_id":"1755d0a70f8e09c480d5933b1fdac547058547db","content_id":"5e2c09e91c5415828c8995690ac86947ab7b5fd7","detected_licenses":["MIT"],"is_generated":false,"is_vendor":false,"language":"C#","length_bytes":570,"license_type":"permissive","max_line_length":131,"num_lines":14,"path":"/src/TorchSharp/Tensor/torch.Serialization.cs","repo_name":"NiklasGustafsson/TorchSharp","src_encoding":"UTF-8","text":"// Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information.\n#nullable enable\nnamespace TorchSharp\n{\n // https://pytorch.org/docs/stable/torch#serialization\n public static partial class torch\n {\n // https://pytorch.org/docs/stable/generated/torch.save\n public static void save(Tensor t, string location) => t.save(location);\n\n // https://pytorch.org/docs/stable/generated/torch.load\n public static Tensor load(string location) => Tensor.load(location);\n }\n}"},{"alpha_fraction":0.6069402098655701,"alphanum_fraction":0.6089553833007812,"avg_line_length":47.78291320800781,"blob_id":"8e4cc20afc504632ff9d714d4b4e5cde5a35d44b","content_id":"0fccbd8ce82bb250c0ad0424b5030b0fff70e45d","detected_licenses":["MIT"],"is_generated":false,"is_vendor":false,"language":"C#","length_bytes":83377,"license_type":"permissive","max_line_length":197,"num_lines":1709,"path":"/src/TorchSharp/Tensor/torch.PointwiseOps.cs","repo_name":"NiklasGustafsson/TorchSharp","src_encoding":"UTF-8","text":"// Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information.\n#nullable enable\nusing System;\nusing System.Collections.Generic;\nusing System.Diagnostics.Contracts;\nusing ICSharpCode.SharpZipLib.BZip2;\n\nnamespace TorchSharp\n{\n public static partial class torch\n {\n // https://pytorch.org/docs/stable/generated/torch.abs\n /// <summary>\n /// Compute the absolute value of each element in the tensor\n /// </summary>\n /// <param name=\"input\">The input tensor.</param>\n [Pure]public static Tensor abs(Tensor input) => input.abs();\n\n // https://pytorch.org/docs/stable/generated/torch.abs\n /// <summary>\n /// Compute the absolute value of each element in the tensor, in-place\n /// </summary>\n /// <param name=\"input\">The input tensor.</param>\n public static Tensor abs_(Tensor input) => input.abs_();\n\n // https://pytorch.org/docs/stable/generated/torch.absolute\n /// <summary>\n /// Compute the absolute value of each element in the tensor\n /// </summary>\n /// <param name=\"input\">The input tensor.</param>\n [Pure]public static Tensor absolute(Tensor input) => input.absolute();\n\n // https://pytorch.org/docs/stable/generated/torch.absolute\n /// <summary>\n /// Compute the absolute value of each element in the tensor, in-place\n /// </summary>\n /// <param name=\"input\">The input tensor.</param>\n public static Tensor absolute_(Tensor input) => input.absolute_();\n\n // https://pytorch.org/docs/stable/generated/torch.acos\n /// <summary>\n /// Computes the arccosine of the elements of input.\n /// </summary>\n /// <returns></returns>\n [Pure]public static Tensor acos(Tensor input) => input.acos();\n\n // https://pytorch.org/docs/stable/generated/torch.acos\n /// <summary>\n /// Computes the arccosine of the elements of input.\n /// </summary>\n /// <returns></returns>\n public static Tensor acos_(Tensor input) => input.acos_();\n\n // https://pytorch.org/docs/stable/generated/torch.arccos\n /// <summary>\n /// Computes the arccosine of the elements of input.\n /// </summary>\n /// <returns></returns>\n [Pure]public static Tensor arccos(Tensor input) => input.arccos();\n\n // https://pytorch.org/docs/stable/generated/torch.arccos\n /// <summary>\n /// Computes the arccosine of the elements of input.\n /// </summary>\n /// <returns></returns>\n public static Tensor arccos_(Tensor input) => input.arccos_();\n\n // https://pytorch.org/docs/stable/generated/torch.acosh\n /// <summary>\n /// Computes the hyperbolic arccosine of the elements of input.\n /// </summary>\n /// <returns></returns>\n [Pure]public static Tensor acosh(Tensor input) => input.acosh();\n\n // https://pytorch.org/docs/stable/generated/torch.acosh\n /// <summary>\n /// Computes the hyperbolic arccosine of the elements of input.\n /// </summary>\n /// <returns></returns>\n public static Tensor acosh_(Tensor input) => input.acosh_();\n\n // https://pytorch.org/docs/stable/generated/torch.arccosh\n /// <summary>\n /// Computes the hyperbolic arccosine of the elements of input.\n /// </summary>\n /// <returns></returns>\n [Pure]public static Tensor arccosh(Tensor input) => input.arccosh();\n\n // https://pytorch.org/docs/stable/generated/torch.arccosh\n /// <summary>\n /// Computes the hyperbolic arccosine of the elements of input.\n /// </summary>\n /// <returns></returns>\n public static Tensor arccosh_(Tensor input) => input.arccosh_();\n\n // https://pytorch.org/docs/stable/generated/torch.add\n /// <summary>\n /// Add two tensors, element-wise\n /// </summary>\n /// <param name=\"left\">The left-hand operand.</param>\n /// <param name=\"right\">The right-hand operand.</param>\n /// <returns></returns>\n [Pure]public static Tensor add(Tensor left, Tensor right) => left.add(right);\n\n // https://pytorch.org/docs/stable/generated/torch.add\n /// <summary>\n /// Add a scalar value to each element in the target tensor.\n /// </summary>\n /// <param name=\"left\">The left-hand operand.</param>\n /// <param name=\"right\">The right-hand operand.</param>\n [Pure]public static Tensor add(Tensor left, Scalar right) => left.add(right);\n\n // https://pytorch.org/docs/stable/generated/torch.add\n /// <summary>\n /// Add two tensors, element-wise, scaling the second operator by 'alpha'\n /// </summary>\n /// <param name=\"left\">The left-hand operand.</param>\n /// <param name=\"right\">The right-hand operand.</param>\n /// <param name=\"alpha\">RHS scale factor.</param>\n [Pure]public static Tensor add(Tensor left, Tensor right, Scalar alpha) => left.add(right, alpha);\n\n // https://pytorch.org/docs/stable/generated/torch.add\n /// <summary>\n /// Add a scalar value to each element in the target tensor, scaled by 'alpha'\n /// </summary>\n /// <param name=\"left\">The left-hand operand.</param>\n /// <param name=\"right\">The right-hand operand.</param>\n /// <param name=\"alpha\">RHS scale factor.</param>\n [Pure]public static Tensor add(Tensor left, Scalar right, Scalar alpha) => left.add(right, alpha);\n\n // https://pytorch.org/docs/stable/generated/torch.add\n /// <summary>\n /// Add two tensors, element-wise, in place\n /// </summary>\n /// <param name=\"left\">The left-hand operand.</param>\n /// <param name=\"right\">The right-hand operand.</param>\n public static Tensor add_(Tensor left, Tensor right) => left.add_(right);\n\n // https://pytorch.org/docs/stable/generated/torch.add\n /// <summary>\n /// Add a scalar value to each element in the target tensor, in place.\n /// </summary>\n /// <param name=\"left\">The left-hand operand.</param>\n /// <param name=\"right\">The right-hand operand.</param>\n public static Tensor add_(Tensor left, Scalar right) => left.add_(right);\n\n // https://pytorch.org/docs/stable/generated/torch.add\n /// <summary>\n /// Add two tensors, element-wise, scaling the second operator by 'alpha', in place\n /// </summary>\n /// <param name=\"left\">The left-hand operand.</param>\n /// <param name=\"right\">The right-hand operand.</param>\n /// <param name=\"alpha\">RHS scale factor.</param>\n public static Tensor add_(Tensor left, Tensor right, Scalar alpha) => left.add_(right, alpha);\n\n // https://pytorch.org/docs/stable/generated/torch.add\n /// <summary>\n /// Add a scalar value to each element in the target tensor, scaled by 'alpha', in place\n /// </summary>\n /// <param name=\"left\">The left-hand operand.</param>\n /// <param name=\"right\">The right-hand operand.</param>\n /// <param name=\"alpha\">RHS scale factor.</param>\n public static Tensor add_(Tensor left, Scalar right, Scalar alpha) => left.add_(right, alpha);\n\n // https://pytorch.org/docs/stable/generated/torch.addcdiv\n /// <summary>\n /// Performs the element-wise division of tensor1 by tensor2, multiply the result by the scalar value and add it to input.\n /// </summary>\n /// <param name=\"input\">The input tensor.</param>\n /// <param name=\"tensor1\">First tensor</param>\n /// <param name=\"tensor2\">Second tensor</param>\n /// <param name=\"value\">Scale factor</param>\n [Pure]public static Tensor addcdiv(Tensor input, Tensor tensor1, Tensor tensor2, Scalar value)\n => input.addcdiv(tensor1, tensor2, value);\n\n // https://pytorch.org/docs/stable/generated/torch.addcdiv\n /// <summary>\n /// Performs the element-wise division of tensor1 by tensor2, multiply the result by the scalar value and add it to input.\n /// In-place version of <see cref=\"addcdiv\"/>.\n /// </summary>\n /// <param name=\"input\">The input tensor.</param>\n /// <param name=\"tensor1\">First tensor</param>\n /// <param name=\"tensor2\">Second tensor</param>\n /// <param name=\"value\">Scale factor</param>\n public static Tensor addcdiv_(Tensor input, Tensor tensor1, Tensor tensor2, Scalar value)\n => input.addcdiv_(tensor1, tensor2, value);\n\n // https://pytorch.org/docs/stable/generated/torch.addcmul\n /// <summary>\n /// Performs the element-wise multiplication of tensor1 by tensor2, multiply the result by the scalar value and add it to input.\n /// </summary>\n /// <param name=\"input\">The input tensor.</param>\n /// <param name=\"tensor1\">First tensor</param>\n /// <param name=\"tensor2\">Second tensor</param>\n /// <param name=\"value\">Scale factor</param>\n [Pure]public static Tensor addcmul(Tensor input, Tensor tensor1, Tensor tensor2, Scalar value)\n => input.addcmul(tensor1, tensor2, value);\n\n // https://pytorch.org/docs/stable/generated/torch.addcmul\n /// <summary>\n /// Performs the element-wise divismultiplicationion of tensor1 by tensor2, multiply the result by the scalar value and add it to input.\n /// In-place version of <see cref=\"addcmul\"/>.\n /// </summary>\n /// <param name=\"input\">The input tensor.</param>\n /// <param name=\"tensor1\">First tensor</param>\n /// <param name=\"tensor2\">Second tensor</param>\n /// <param name=\"value\">Scale factor</param>\n public static Tensor addcmul_(Tensor input, Tensor tensor1, Tensor tensor2, Scalar value)\n => input.addcmul_(tensor1, tensor2, value);\n\n // https://pytorch.org/docs/stable/generated/torch.angle\n /// <summary>\n /// Computes the element-wise angle (in radians) of the given input tensor.\n /// </summary>\n /// <returns></returns>\n /// <remarks>\n /// Starting in Torch 1.8, angle returns pi for negative real numbers, zero for non-negative real numbers, and propagates NaNs.\n /// Previously the function would return zero for all real numbers and not propagate floating-point NaNs.\n /// </remarks>\n [Pure]public static Tensor angle(Tensor input) => input.angle();\n\n // https://pytorch.org/docs/stable/generated/torch.asin\n /// <summary>\n /// Computes the arcsine of the elements of input.\n /// </summary>\n /// <returns></returns>\n [Pure]public static Tensor asin(Tensor input) => input.asin();\n\n // https://pytorch.org/docs/stable/generated/torch.asin\n /// <summary>\n /// Computes the arcsine of the elements of input, in place.\n /// </summary>\n /// <returns></returns>\n public static Tensor asin_(Tensor input) => input.asin_();\n\n // https://pytorch.org/docs/stable/generated/torch.arcsin\n /// <summary>\n /// Computes the arcsine of the elements of input.\n /// </summary>\n /// <returns></returns>\n [Pure]public static Tensor arcsin(Tensor input) => input.arcsin();\n\n // https://pytorch.org/docs/stable/generated/torch.arcsin\n /// <summary>\n /// Computes the arcsine of the elements of input.\n /// </summary>\n /// <returns></returns>\n public static Tensor arcsin_(Tensor input) => input.arcsin_();\n\n // https://pytorch.org/docs/stable/generated/torch.asinh\n /// <summary>\n /// Computes the hyperbolic arcsine of the elements of input.\n /// </summary>\n /// <returns></returns>\n [Pure]public static Tensor asinh(Tensor input) => input.asinh();\n\n // https://pytorch.org/docs/stable/generated/torch.asinh\n /// <summary>\n /// Computes the hyperbolic arcsine of the elements of input.\n /// </summary>\n /// <returns></returns>\n public static Tensor asinh_(Tensor input) => input.asinh_();\n\n // https://pytorch.org/docs/stable/generated/torch.arcsinh\n /// <summary>\n /// Computes the hyperbolic arcsine of the elements of input.\n /// </summary>\n /// <returns></returns>\n [Pure]public static Tensor arcsinh(Tensor input) => input.arcsinh();\n\n // https://pytorch.org/docs/stable/generated/torch.arcsinh\n /// <summary>\n /// Computes the hyperbolic arcsine of the elements of input.\n /// </summary>\n /// <returns></returns>\n public static Tensor arcsinh_(Tensor input) => input.arcsinh_();\n\n // https://pytorch.org/docs/stable/generated/torch.atan\n /// <summary>\n /// Computes the arctangent of the elements of input.\n /// </summary>\n /// <returns></returns>\n [Pure]public static Tensor atan(Tensor input) => input.atan();\n\n // https://pytorch.org/docs/stable/generated/torch.atan\n /// <summary>\n /// Computes the arctangent of the elements of input.\n /// </summary>\n /// <returns></returns>\n public static Tensor atan_(Tensor input) => input.atan_();\n\n // https://pytorch.org/docs/stable/generated/torch.arctan\n /// <summary>\n /// Computes the arctangent of the elements of input.\n /// </summary>\n /// <returns></returns>\n [Pure]public static Tensor arctan(Tensor input) => input.arctan();\n\n // https://pytorch.org/docs/stable/generated/torch.arctan\n /// <summary>\n /// Computes the arctangent of the elements of input.\n /// </summary>\n /// <returns></returns>\n public static Tensor arctan_(Tensor input) => input.arctan_();\n\n // https://pytorch.org/docs/stable/generated/torch.atanh\n /// <summary>\n /// Computes the hyperbolic arctangent of the elements of input.\n /// </summary>\n /// <returns></returns>\n [Pure]public static Tensor atanh(Tensor input) => input.atanh();\n\n // https://pytorch.org/docs/stable/generated/torch.atanh\n /// <summary>\n /// Computes the hyperbolic arctangent of the elements of input.\n /// </summary>\n /// <returns></returns>\n public static Tensor atanh_(Tensor input) => input.atanh_();\n\n // https://pytorch.org/docs/stable/generated/torch.arctanh\n /// <summary>\n /// Computes the hyperbolic arctangent of the elements of input.\n /// </summary>\n /// <returns></returns>\n [Pure]public static Tensor arctanh(Tensor input) => input.arctanh();\n\n /// <summary>\n /// Computes the hyperbolic arctangent of the elements of input.\n /// </summary>\n /// <returns></returns>\n public static Tensor arctanh_(Tensor input) => input.arctanh_();\n\n // https://pytorch.org/docs/stable/generated/torch.atan2\n /// <summary>\n /// Element-wise arctangent of input / other with consideration of the quadrant.\n /// </summary>\n /// <returns></returns>\n [Pure]public static Tensor atan2(Tensor input, Tensor other) => input.atan2(other);\n\n /// <summary>\n /// Element-wise arctangent of input / other with consideration of the quadrant.\n /// </summary>\n /// <returns></returns>\n public static Tensor atan2_(Tensor input, Tensor other) => input.atan2_(other);\n\n // https://pytorch.org/docs/stable/generated/torch.arctan2\n /// <summary>\n /// Element-wise arctangent of input / other with consideration of the quadrant.\n /// </summary>\n /// <returns></returns>\n [Pure]public static Tensor arctan2(Tensor input, Tensor other) => input.arctan2(other);\n\n // https://pytorch.org/docs/stable/generated/torch.arctan2\n /// <summary>\n /// Element-wise arctangent of input / other with consideration of the quadrant.\n /// </summary>\n /// <returns></returns>\n public static Tensor arctan2_(Tensor input, Tensor other) => input.arctan2_(other);\n\n // https://pytorch.org/docs/stable/generated/torch.arctan\n /// <summary>\n /// Element-wise arctangent of input / other with consideration of the quadrant.\n /// </summary>\n /// <returns></returns>\n [Pure]public static Tensor arctan(Tensor input, Tensor other) => input.arctan(other);\n\n // https://pytorch.org/docs/stable/generated/torch.arctan\n /// <summary>\n /// Element-wise arctangent of input / other with consideration of the quadrant.\n /// </summary>\n /// <returns></returns>\n public static Tensor arctan_(Tensor input, Tensor other) => input.arctan_(other);\n\n // https://pytorch.org/docs/stable/generated/torch.bitwise_not\n /// <summary>\n /// Element-wise bitwise NOT\n /// </summary>\n /// <param name=\"input\">The input tensor.</param>\n [Pure]public static Tensor bitwise_not(Tensor input) => input.bitwise_not();\n\n // https://pytorch.org/docs/stable/generated/torch.bitwise_not\n /// <summary>\n /// Element-wise bitwise NOT, in place.\n /// </summary>\n /// <param name=\"input\">The input tensor.</param>\n public static Tensor bitwise_not_(Tensor input) => input.bitwise_not_();\n\n // https://pytorch.org/docs/stable/generated/torch.bitwise_and\n /// <summary>\n /// Element-wise bitwise AND\n /// </summary>\n /// <param name=\"left\">Left-hand operand.</param>\n /// <param name=\"right\">Right-hand operand.</param>\n [Pure]public static Tensor bitwise_and(Tensor left, Tensor right) => left.bitwise_and(right);\n\n // https://pytorch.org/docs/stable/generated/torch.bitwise_and\n /// <summary>\n /// Element-wise bitwise AND, in place.\n /// </summary>\n /// <param name=\"left\">Left-hand operand.</param>\n /// <param name=\"right\">Right-hand operand.</param>\n public static Tensor bitwise_and_(Tensor left, Tensor right) => left.bitwise_and_(right);\n\n // https://pytorch.org/docs/stable/generated/torch.bitwise_or\n /// <summary>\n /// Element-wise bitwise OR\n /// </summary>\n /// <param name=\"left\">Left-hand operand.</param>\n /// <param name=\"right\">Right-hand operand.</param>\n [Pure]public static Tensor bitwise_or(Tensor left, Tensor right) => left.bitwise_or(right);\n\n // https://pytorch.org/docs/stable/generated/torch.bitwise_or\n /// <summary>\n /// Element-wise bitwiseXOR, in place.\n /// </summary>\n /// <param name=\"left\">Left-hand operand.</param>\n /// <param name=\"right\">Right-hand operand.</param>\n public static Tensor bitwise_or_(Tensor left, Tensor right) => left.bitwise_or_(right);\n\n // https://pytorch.org/docs/stable/generated/torch.bitwise_xor\n /// <summary>\n /// Element-wise bitwise XOR\n /// </summary>\n /// <param name=\"left\">Left-hand operand.</param>\n /// <param name=\"right\">Right-hand operand.</param>\n [Pure]public static Tensor bitwise_xor(Tensor left, Tensor right) => left.bitwise_xor(right);\n\n // https://pytorch.org/docs/stable/generated/torch.bitwise_xor\n /// <summary>\n /// Element-wise bitwise XOR, in place.\n /// </summary>\n /// <param name=\"left\">Left-hand operand.</param>\n /// <param name=\"right\">Right-hand operand.</param>\n public static Tensor bitwise_xor_(Tensor left, Tensor right) => left.bitwise_xor_(right);\n\n // https://pytorch.org/docs/stable/generated/torch.bitwise_left_shift\n /// <summary>\n /// Element-wise bitwise left shift\n /// </summary>\n /// <param name=\"left\">Left-hand operand.</param>\n /// <param name=\"right\">Right-hand operand.</param>\n public static Tensor bitwise_left_shift(Tensor left, Tensor right) => left.bitwise_left_shift(right);\n\n // https://pytorch.org/docs/stable/generated/torch.bitwise_left_shift\n /// <summary>\n /// Element-wise bitwise left shift, in place.\n /// </summary>\n /// <param name=\"left\">Left-hand operand.</param>\n /// <param name=\"right\">Right-hand operand.</param>\n public static Tensor bitwise_left_shift_(Tensor left, Tensor right) => left.bitwise_left_shift_(right);\n\n // https://pytorch.org/docs/stable/generated/torch.bitwise_right_shift\n /// <summary>\n /// Element-wise bitwise right shift\n /// </summary>\n /// <param name=\"left\">Left-hand operand.</param>\n /// <param name=\"right\">Right-hand operand.</param>\n [Pure]public static Tensor bitwise_right_shift(Tensor left, Tensor right) => left.bitwise_right_shift(right);\n\n // https://pytorch.org/docs/stable/generated/torch.bitwise_right_shift\n /// <summary>\n /// Element-wise bitwise right shift, in place.\n /// </summary>\n /// <param name=\"left\">Left-hand operand.</param>\n /// <param name=\"right\">Right-hand operand.</param>\n public static Tensor bitwise_right_shift_(Tensor left, Tensor right) => left.bitwise_right_shift_(right);\n\n // https://pytorch.org/docs/stable/generated/torch.ceil\n /// <summary>\n /// Returns a new tensor with the ceil of the elements of input, the smallest integer greater than or equal to each element.\n /// </summary>\n /// <param name=\"input\">The input tensor.</param>\n public static Tensor ceil(Tensor input) => input.ceil();\n\n // https://pytorch.org/docs/stable/generated/torch.ceil\n /// <summary>\n /// Replaces each element of the input with the smallest integer greater than or equal to the element.\n /// </summary>\n /// <param name=\"input\">The input tensor.</param>\n public static Tensor ceil_(Tensor input) => input.ceil_();\n\n // https://pytorch.org/docs/stable/generated/torch.clamp\n /// <summary>\n /// Clamps all elements in input into the range [ min, max ].\n /// </summary>\n /// <param name=\"input\">The input tensor</param>\n /// <param name=\"min\">The minimum value</param>\n /// <param name=\"max\">The maximum value</param>\n public static Tensor clamp(Tensor input, Scalar? min = null, Scalar? max = null) => input.clamp(min, max);\n\n // https://pytorch.org/docs/stable/generated/torch.clamp\n /// <summary>\n /// Clamps all elements in input into the range [ min, max ] in place.\n /// </summary>\n /// <param name=\"input\">The input tensor</param>\n /// <param name=\"min\">The minimum value</param>\n /// <param name=\"max\">The maximum value</param>\n public static Tensor clamp_(Tensor input, Scalar? min = null, Scalar? max = null) => input.clamp_(min, max);\n\n // https://pytorch.org/docs/stable/generated/torch.clamp\n /// <summary>\n /// Clamps all elements in input into the range [ min, max ].\n /// </summary>\n /// <param name=\"input\">The input tensor</param>\n /// <param name=\"min\">The minimum value</param>\n /// <param name=\"max\">The maximum value</param>\n public static Tensor clamp(Tensor input, Tensor? min = null, Tensor? max = null) => input.clamp(min, max);\n\n // https://pytorch.org/docs/stable/generated/torch.clamp\n /// <summary>\n /// Clamps all elements in input into the range [ min, max ] in place.\n /// </summary>\n /// <param name=\"input\">The input tensor</param>\n /// <param name=\"min\">The minimum value</param>\n /// <param name=\"max\">The maximum value</param>\n public static Tensor clamp_(Tensor input, Tensor? min = null, Tensor? max = null) => input.clamp_(min, max);\n\n // https://pytorch.org/docs/stable/generated/torch.clip\n public static Tensor clip(Tensor input, Scalar? min = null, Scalar? max = null) => input.clip(min, max);\n\n // https://pytorch.org/docs/stable/generated/torch.conj_physical\n /// <summary>\n /// Computes the element-wise conjugate of the given input tensor. If input has a non-complex <see cref=\"DeviceType\">dtype</see>, this function just returns input.\n /// </summary>\n /// <param name=\"input\">The input tensor.</param>\n [Pure]public static Tensor conj_physical(Tensor input) => input.conj_physical();\n\n // https://pytorch.org/docs/stable/generated/torch.conj_physical\n /// <summary>\n /// In-place version of conj_physical\n /// </summary>\n /// <param name=\"input\">The input tensor.</param>\n public static Tensor conj_physical_(Tensor input) => input.conj_physical_();\n\n // https://pytorch.org/docs/stable/generated/torch.copysign\n /// <summary>\n /// Create a new floating-point tensor with the magnitude of input and the sign of other, elementwise.\n /// Supports broadcasting to a common shape, and integer and float inputs.\n /// </summary>\n /// <param name=\"input\">magnitudes</param>\n /// <param name=\"other\">contains value(s) whose signbit(s) are applied to the magnitudes in <paramref name=\"input\"/>.</param>\n /// <returns>the output tensor</returns>\n [Pure]public static Tensor copysign(Tensor input, Tensor other) => input.copysign(other);\n\n // https://pytorch.org/docs/stable/generated/torch.cos\n /// <summary>\n /// Computes the cosine of the elements of input.\n /// </summary>\n /// <returns></returns>\n [Pure]public static Tensor cos(Tensor input) => input.cos();\n\n /// <summary>\n /// Computes the cosine of the elements of input.\n /// </summary>\n /// <returns></returns>\n public static Tensor cos_(Tensor input) => input.cos_();\n\n // https://pytorch.org/docs/stable/generated/torch.cosh\n /// <summary>\n /// Computes the hyperbolic cosine of the elements of input.\n /// </summary>\n /// <returns></returns>\n public static Tensor cosh(Tensor input) => input.cosh();\n\n // https://pytorch.org/docs/stable/generated/torch.cosh\n /// <summary>\n /// Computes the hyperbolic cosine of the elements of input.\n /// </summary>\n /// <returns></returns>\n public static Tensor cosh_(Tensor input) => input.cosh_();\n\n // https://pytorch.org/docs/stable/generated/torch.deg2rad\n [Pure]public static Tensor deg2rad(Tensor input) => input.deg2rad();\n\n // https://pytorch.org/docs/stable/generated/torch.div\n /// <summary>\n /// Divides each element of the input by the corresponding element of other.\n /// </summary>\n /// <param name=\"left\">Numerator</param>\n /// <param name=\"right\">Denominator</param>\n /// <param name=\"rounding_mode\">Rounding mode.</param>\n public static Tensor div(Tensor left, Tensor right, RoundingMode rounding_mode = RoundingMode.None) => left.div(right);\n\n // https://pytorch.org/docs/stable/generated/torch.div\n /// <summary>\n /// Divides each element of the input by a scalar value.\n /// </summary>\n /// <param name=\"left\">Numerator</param>\n /// <param name=\"right\">Denominator</param>\n /// <param name=\"rounding_mode\">Rounding mode.</param>\n public static Tensor div(Tensor left, Scalar right, RoundingMode rounding_mode = RoundingMode.None) => left.div(right);\n\n // https://pytorch.org/docs/stable/generated/torch.div\n /// <summary>\n /// Divides each element of the input by the corresponding element of other.\n /// </summary>\n /// <param name=\"left\">Numerator</param>\n /// <param name=\"right\">Denominator</param>\n /// <param name=\"rounding_mode\">Rounding mode.</param>\n public static Tensor div_(Tensor left, Scalar right, RoundingMode rounding_mode = RoundingMode.None) => left.div_(right, rounding_mode);\n\n // https://pytorch.org/docs/stable/generated/torch.div\n /// <summary>\n /// Divides each element of the input by the corresponding element of other.\n /// </summary>\n /// <param name=\"left\">Numerator</param>\n /// <param name=\"right\">Denominator</param>\n /// <param name=\"rounding_mode\">Rounding mode.</param>\n public static Tensor div_(Tensor left, Tensor right, RoundingMode rounding_mode = RoundingMode.None) => left.div_(right);\n\n // https://pytorch.org/docs/stable/generated/torch.divide\n /// <summary>\n /// Divides each element of the input by the corresponding element of other.\n /// </summary>\n /// <param name=\"left\">Numerator</param>\n /// <param name=\"right\">Denominator</param>\n /// <param name=\"rounding_mode\">Rounding mode.</param>\n public static Tensor divide(Tensor left, Tensor right, RoundingMode rounding_mode = RoundingMode.None) => left.div(right);\n\n // https://pytorch.org/docs/stable/generated/torch.divide\n /// <summary>\n /// Divides each element of the input by a scalar value.\n /// </summary>\n /// <param name=\"left\">Numerator</param>\n /// <param name=\"right\">Denominator</param>\n /// <param name=\"rounding_mode\">Rounding mode.</param>\n public static Tensor divide(Tensor left, Scalar right, RoundingMode rounding_mode = RoundingMode.None) => left.div(right);\n\n // https://pytorch.org/docs/stable/generated/torch.divide\n /// <summary>\n /// Divides each element of the input by the corresponding element of other.\n /// </summary>\n /// <param name=\"left\">Numerator</param>\n /// <param name=\"right\">Denominator</param>\n /// <param name=\"rounding_mode\">Rounding mode.</param>\n public static Tensor divide_(Tensor left, Tensor right, RoundingMode rounding_mode = RoundingMode.None) => left.div_(right);\n\n // https://pytorch.org/docs/stable/generated/torch.divide\n /// <summary>\n /// Divides each element of the input by the corresponding element of other.\n /// </summary>\n /// <param name=\"left\">Numerator</param>\n /// <param name=\"right\">Denominator</param>\n public static Tensor divide_(Tensor left, Scalar right) => left.div_(right);\n\n // https://pytorch.org/docs/stable/generated/torch.digamma\n /// <summary>\n /// Computes the logarithmic derivative of the gamma function on input.\n /// </summary>\n /// <param name=\"input\">The input tensor.</param>\n public static Tensor digamma(Tensor input) => input.digamma();\n\n // https://pytorch.org/docs/stable/generated/torch.digamma\n /// <summary>\n /// Computes the logarithmic derivative of the gamma function on input, in place.\n /// </summary>\n /// <param name=\"input\">The input tensor.</param>\n public static Tensor digamma_(Tensor input) => input.digamma_();\n\n // https://pytorch.org/docs/stable/generated/torch.erf\n /// <summary>\n /// Computes the error function of the input.\n /// </summary>\n public static Tensor erf(Tensor input) => input.erf();\n\n // https://pytorch.org/docs/stable/generated/torch.erf\n /// <summary>\n /// Computes the error function of the input in place.\n /// </summary>\n public static Tensor erf_(Tensor input) => input.erf_();\n\n // https://pytorch.org/docs/stable/generated/torch.erfc\n /// <summary>\n /// Computes the error function of the input.\n /// </summary>\n public static Tensor erfc(Tensor input) => input.erfc();\n\n // https://pytorch.org/docs/stable/generated/torch.erfc\n /// <summary>\n /// Computes the error function of the input in place.\n /// </summary>\n public static Tensor erfc_(Tensor input) => input.erfc_();\n\n // https://pytorch.org/docs/stable/generated/torch.erfinv\n /// <summary>\n /// Computes the error function of the input.\n /// </summary>\n public static Tensor erfinv(Tensor input) => input.erf();\n\n // https://pytorch.org/docs/stable/generated/torch.erfinv\n /// <summary>\n /// Computes the error function of the input in place.\n /// </summary>\n public static Tensor erfinv_(Tensor input) => input.erfinv_();\n\n // https://pytorch.org/docs/stable/generated/torch.exp\n /// <summary>\n /// Returns a new tensor with the exponential of the elements of the input tensor input.\n /// </summary>\n /// <param name=\"input\">The input tensor.</param>\n public static Tensor exp(Tensor input) => input.exp();\n\n // https://pytorch.org/docs/stable/generated/torch.exp\n /// <summary>\n /// Replaces each element of the input with the exponential of the elements of the input tensor input.\n /// </summary>\n /// <param name=\"input\">The input tensor.</param>\n public static Tensor exp_(Tensor input) => input.exp_();\n\n // https://pytorch.org/docs/stable/generated/torch.exp2\n /// <summary>\n /// Computes the base 2 exponential function of input.\n /// </summary>\n /// <param name=\"input\">The input tensor.</param>\n public static Tensor exp2(Tensor input) => input.exp2();\n\n // https://pytorch.org/docs/stable/generated/torch.expm1\n /// <summary>\n /// Returns a new tensor with the exponential of the elements minus 1 of input.\n /// </summary>\n /// <param name=\"input\">The input tensor.</param>\n public static Tensor expm1(Tensor input) => input.expm1();\n\n // https://pytorch.org/docs/stable/generated/torch.expm1\n /// <summary>\n /// Replaces each element with the exponential of the element minus 1 of input.\n /// </summary>\n /// <param name=\"input\">The input tensor.</param>\n public static Tensor expm1_(Tensor input) => input.expm1_();\n\n // https://pytorch.org/docs/stable/generated/torch.fake_quantize_per_channel_affine\n /// <summary>\n /// Returns a new tensor with the data in <paramref name=\"input\"/> fake quantized per channel using\n /// <paramref name=\"scale\"/>, <paramref name=\"zero_point\"/>, <paramref name=\"quant_min\"/> and <paramref name=\"quant_max\"/>,\n /// across the channel specified by <paramref name=\"axis\"/>.\n /// </summary>\n /// <param name=\"input\">the input value(s) (float32)</param>\n /// <param name=\"scale\">quantization scale, per channel (float32)</param>\n /// <param name=\"zero_point\">quantization zero_point, per channel (torch.int32, torch.half, or torch.float32)</param>\n /// <param name=\"axis\">channel axis</param>\n /// <param name=\"quant_min\">lower bound of the quantized domain</param>\n /// <param name=\"quant_max\">upper bound of the quantized domain</param>\n /// <returns>A newly fake_quantized per channel torch.float32 tensor</returns>\n [Pure, Obsolete(\"not implemented\", true)]\n public static Tensor fake_quantize_per_channel_affine(Tensor input, Tensor scale, Tensor zero_point, int axis, long quant_min, long quant_max)\n => throw new NotImplementedException();\n\n // https://pytorch.org/docs/stable/generated/torch.fake_quantize_per_tensor_affine\n /// <summary>\n /// Returns a new tensor with the data in <paramref name=\"input\"/> fake quantized per channel using\n /// <paramref name=\"scale\"/>, <paramref name=\"zero_point\"/>, <paramref name=\"quant_min\"/> and <paramref name=\"quant_max\"/>,\n /// across the channel specified by axis.\n /// </summary>\n /// <param name=\"input\">the input value(s) (float32)</param>\n /// <param name=\"scale\">quantization scale, per channel (float32)</param>\n /// <param name=\"zero_point\">quantization zero_point, per channel (torch.int32, torch.half, or torch.float32)</param>\n /// <param name=\"quant_min\">lower bound of the quantized domain</param>\n /// <param name=\"quant_max\">upper bound of the quantized domain</param>\n /// <returns>A newly fake_quantized per channel torch.float32 tensor</returns>\n [Pure, Obsolete(\"not implemented\", true)]\n public static Tensor fake_quantize_per_tensor_affine(Tensor input, Tensor scale, Tensor zero_point, long quant_min, long quant_max)\n => throw new NotImplementedException();\n\n // https://pytorch.org/docs/stable/generated/torch.fix\n /// <summary>\n /// Returns a new tensor with the truncated integer values of the elements of input.\n /// </summary>\n /// <param name=\"input\">The input tensor.</param>\n [Pure]public static Tensor fix(Tensor input) => input.fix();\n\n // https://pytorch.org/docs/stable/generated/torch.fix\n /// <summary>\n /// Replaces each element with the truncated integer values of the elements of input.\n /// </summary>\n /// <param name=\"input\">The input tensor.</param>\n public static Tensor fix_(Tensor input) => input.fix_();\n\n // https://pytorch.org/docs/stable/generated/torch.float_power\n /// <summary>\n /// Raises input to the power of exponent, elementwise, in double precision.\n /// </summary>\n /// <param name=\"input\">The input tensor.</param>\n /// <param name=\"target\">The exponent.</param>\n /// <remarks> If neither input is complex returns a torch.float64 tensor, and if one or more inputs is complex returns a torch.complex128 tensor.</remarks>\n [Pure]public static Tensor float_power(Tensor input, Tensor target) => input.float_power(target);\n\n // https://pytorch.org/docs/stable/generated/torch.floor\n /// <summary>\n /// Returns a new tensor with the floor of the elements of input, the largest integer less than or equal to each element.\n /// </summary>\n /// <param name=\"input\">The input tensor.</param>\n [Pure]public static Tensor floor(Tensor input) => input.floor();\n\n // https://pytorch.org/docs/stable/generated/torch.floor\n /// <summary>\n /// Replaces each element with the floor of the input, the largest integer less than or equal to each element.\n /// </summary>\n /// <param name=\"input\">The input tensor.</param>\n public static Tensor floor_(Tensor input) => input.floor_();\n\n // https://pytorch.org/docs/stable/generated/torch.floor_divide\n /// <summary>\n /// Computes input divided by other, elementwise, and floors the result.\n /// Supports broadcasting to a common shape, type promotion, and integer and float inputs.\n /// </summary>\n /// <param name=\"input\">the dividend</param>\n /// <param name=\"other\">the divisor</param>\n /// <returns>the output tensor</returns>\n [Pure]\n public static Tensor floor_divide(Tensor input, Tensor other) => input.floor_divide(other);\n\n // https://pytorch.org/docs/stable/generated/torch.floor_divide\n /// <summary>\n /// Computes input divided by other, elementwise, and floors the result.\n /// Supports broadcasting to a common shape, type promotion, and integer and float inputs.\n /// </summary>\n /// <param name=\"input\">the dividend</param>\n /// <param name=\"other\">the divisor</param>\n /// <returns>the output tensor</returns>\n public static Tensor floor_divide_(Tensor input, Tensor other) => input.floor_divide_(other);\n\n // https://pytorch.org/docs/stable/generated/torch.fmod\n /// <summary>\n /// Computes the element-wise remainder of division.\n /// </summary>\n [Pure]public static Tensor fmod(Tensor left, Tensor right) => left.fmod(right);\n\n // https://pytorch.org/docs/stable/generated/torch.fmod\n /// <summary>\n /// Computes the element-wise remainder of division, in place.\n /// </summary>\n /// <param name=\"left\">Numerator</param>\n /// <param name=\"right\">Denominator</param>\n public static Tensor fmod_(Tensor left, Tensor right) => left.fmod_(right);\n\n // https://pytorch.org/docs/stable/generated/torch.fmod\n /// <summary>\n /// Computes the element-wise remainder of division.\n /// </summary>\n /// <param name=\"left\">Numerator</param>\n /// <param name=\"right\">Denominator</param>\n [Pure]public static Tensor fmod(Tensor left, Scalar right) => left.fmod(right);\n\n // https://pytorch.org/docs/stable/generated/torch.fmod\n /// <summary>\n /// Computes the element-wise remainder of division, in place.\n /// </summary>\n /// <param name=\"left\">Numerator</param>\n /// <param name=\"right\">Denominator</param>\n public static Tensor fmod_(Tensor left, Scalar right) => left.fmod_(right);\n\n // https://pytorch.org/docs/stable/generated/torch.frac\n /// <summary>\n /// Computes the fractional portion of each element in input.\n /// </summary>\n /// <param name=\"input\">The input tensor.</param>\n [Pure]public static Tensor frac(Tensor input) => input.frac();\n\n // https://pytorch.org/docs/stable/generated/torch.frac\n /// <summary>\n /// Computes the fractional portion of each element in input, in-place.\n /// </summary>\n /// <param name=\"input\">The input tensor.</param>\n public static Tensor frac_(Tensor input) => input.frac_();\n\n // https://pytorch.org/docs/stable/generated/torch.frexp\n /// <summary>\n /// Decomposes input into mantissa and exponent tensors\n /// </summary>\n /// <param name=\"input\">The input tensor.</param>\n [Pure]public static (Tensor Mantissa, Tensor Exponent) frexp(Tensor input) => input.frexp();\n\n // https://pytorch.org/docs/stable/generated/torch.gradient\n [Pure, Obsolete(\"not implemented\", true)]\n public static ICollection<Tensor> gradient(Tensor input, int spacing = 1, long? dim = null, int edge_order = 1)\n => throw new NotImplementedException();\n\n // https://pytorch.org/docs/stable/generated/torch.gradient\n [Pure, Obsolete(\"not implemented\", true)]\n public static ICollection<Tensor> gradient(Tensor input, int spacing = 1, long[]? dims = null, int edge_order = 1)\n => throw new NotImplementedException();\n\n // https://pytorch.org/docs/stable/generated/torch.imag\n [Pure]public static Tensor imag(Tensor input) => input.imag;\n\n // https://pytorch.org/docs/stable/generated/torch.ldexp\n /// <summary>\n /// Multiplies input by pow(2,other).\n /// </summary>\n /// <param name=\"input\">The input tensor.</param>\n /// <param name=\"other\">A tensor of exponents, typically integers</param>\n /// <remarks>Typically this function is used to construct floating point numbers by multiplying mantissas in input with integral powers of two created from the exponents in other.</remarks>\n [Pure]public static Tensor ldexp(Tensor input, Tensor other) => input.ldexp(other);\n\n // https://pytorch.org/docs/stable/generated/torch.ldexp\n /// <summary>\n /// Multiplies input by pow(2,other) in place.\n /// </summary>\n /// <param name=\"input\">The input tensor.</param>\n /// <param name=\"other\">A tensor of exponents, typically integers</param>\n /// <remarks>Typically this function is used to construct floating point numbers by multiplying mantissas in input with integral powers of two created from the exponents in other.</remarks>\n public static Tensor ldexp_(Tensor input, Tensor other) => input.ldexp_(other);\n\n // https://pytorch.org/docs/stable/generated/torch.lerp\n /// <summary>\n /// Does a linear interpolation of two tensors start (given by input)\n /// and end based on a scalar or tensor weight and returns the resulting out tensor.\n /// </summary>\n /// <remarks>\n /// The shapes of start and end must be broadcastable.\n /// If weight is a tensor, then the shapes of weight, start, and end must be broadcastable.\n /// </remarks>\n /// <param name=\"input\">the tensor with the starting points</param>\n /// <param name=\"end\">the tensor with the ending points</param>\n /// <param name=\"weight\">the weight for the interpolation formula</param>\n /// <returns>the output tensor</returns>\n [Pure]public static Tensor lerp(Tensor input, Tensor end, Tensor weight) => input.lerp(end, weight);\n\n // https://pytorch.org/docs/stable/generated/torch.lgamma\n /// <summary>\n /// Computes the logarithm of the gamma function on input.\n /// </summary>\n /// <param name=\"input\">The input tensor.</param>\n [Pure]public static Tensor lgamma(Tensor input) => input.lgamma();\n\n // https://pytorch.org/docs/stable/generated/torch.lgamma\n /// <summary>\n /// Computes the logarithm of the gamma function on input, in place.\n /// </summary>\n /// <param name=\"input\">The input tensor.</param>\n public static Tensor lgamma_(Tensor input) => input.lgamma();\n\n // https://pytorch.org/docs/stable/generated/torch.log\n /// <summary>\n /// Returns a new tensor with the natural logarithm of the input elements.\n /// </summary>\n /// <param name=\"input\">The input tensor.</param>\n [Pure]public static Tensor log(Tensor input) => input.log();\n\n // https://pytorch.org/docs/stable/generated/torch.log\n /// <summary>\n /// Replaces each elements with the natural logarithm of the input.\n /// </summary>\n /// <param name=\"input\">The input tensor.</param>\n public static Tensor log_(Tensor input) => input.log_();\n\n // https://pytorch.org/docs/stable/generated/torch.log10\n /// <summary>\n /// Returns a new tensor with the logarithm to the base 10 of the elements of input.\n /// </summary>\n /// <param name=\"input\">The input tensor.</param>\n [Pure]public static Tensor log10(Tensor input) => input.log10();\n\n // https://pytorch.org/docs/stable/generated/torch.log10\n /// <summary>\n /// Replaces each elements with the logarithm to the base 10 of the elements of input.\n /// </summary>\n /// <param name=\"input\">The input tensor.</param>\n public static Tensor log10_(Tensor input) => input.log10_();\n\n // https://pytorch.org/docs/stable/generated/torch.log1p\n /// <summary>\n /// Returns a new tensor with the natural logarithm of (1 + input).\n /// </summary>\n /// <param name=\"input\">The input tensor.</param>\n [Pure]public static Tensor log1p(Tensor input) => input.log1p();\n\n // https://pytorch.org/docs/stable/generated/torch.log1p\n /// <summary>\n /// Replaces each elements with the natural logarithm of (1 + input), in place.\n /// </summary>\n /// <param name=\"input\">The input tensor.</param>\n public static Tensor log1p_(Tensor input) => input.log1p_();\n\n // https://pytorch.org/docs/stable/generated/torch.log2\n /// <summary>\n /// Returns a new tensor with the logarithm to the base 10 of the elements of input.\n /// </summary>\n /// <param name=\"input\">The input tensor.</param>\n [Pure]public static Tensor log2(Tensor input) => input.log2();\n\n // https://pytorch.org/docs/stable/generated/torch.log2\n /// <summary>\n /// Replaces each elements with the logarithm to the base 10 of the elements of input.\n /// </summary>\n /// <param name=\"input\">The input tensor.</param>\n public static Tensor log2_(Tensor input) => input.log2_();\n\n // https://pytorch.org/docs/stable/generated/torch.logaddexp\n /// <summary>\n /// Logarithm of the sum of exponentiations of the inputs.\n /// </summary>\n /// <param name=\"left\">The left-hand operand.</param>\n /// <param name=\"right\">The right-hand operand.</param>\n [Pure]public static Tensor logaddexp(Tensor left, Tensor right) => left.logaddexp(right);\n\n // https://pytorch.org/docs/stable/generated/torch.logaddexp2\n /// <summary>\n /// Logarithm of the sum of exponentiations of the inputs in base-2.\n /// </summary>\n /// <param name=\"left\">The left-hand operand.</param>\n /// <param name=\"right\">The right-hand operand.</param>\n [Pure]public static Tensor logaddexp2(Tensor left, Tensor right) => left.logaddexp2(right);\n\n // https://pytorch.org/docs/stable/generated/torch.logical_and\n /// <summary>\n /// Element-wise logical AND\n /// </summary>\n /// <param name=\"left\">The left-hand operand.</param>\n /// <param name=\"right\">The right-hand operand.</param>\n [Pure]public static Tensor logical_and(Tensor left, Tensor right) => left.logical_and(right);\n\n // https://pytorch.org/docs/stable/generated/torch.logical_and\n /// <summary>\n /// Element-wise logical AND, in place.\n /// </summary>\n /// <param name=\"left\">The left-hand operand.</param>\n /// <param name=\"right\">The right-hand operand.</param>\n public static Tensor logical_and_(Tensor left, Tensor right) => left.logical_and_(right);\n\n // https://pytorch.org/docs/stable/generated/torch.logical_not\n /// <summary>\n /// Element-wise logical NOT\n /// </summary>\n /// <param name=\"input\">The input tensor.</param>\n public static Tensor logical_not(Tensor input) => input.logical_not();\n\n // https://pytorch.org/docs/stable/generated/torch.logical_or\n /// <summary>\n /// Element-wise logical OR\n /// </summary>\n /// <param name=\"left\">The left-hand operand.</param>\n /// <param name=\"right\">The right-hand operand.</param>\n [Pure]public static Tensor logical_or(Tensor left, Tensor right) => left.logical_or(right);\n\n // https://pytorch.org/docs/stable/generated/torch.logical_or\n /// <summary>\n /// Element-wise logicalXOR, in place.\n /// </summary>\n /// <param name=\"left\">The left-hand operand.</param>\n /// <param name=\"right\">The right-hand operand.</param>\n public static Tensor logical_or_(Tensor left, Tensor right) => left.logical_or_(right);\n\n // https://pytorch.org/docs/stable/generated/torch.logical_xor\n /// <summary>\n /// Element-wise logical XOR\n /// </summary>\n /// <param name=\"left\">The left-hand operand.</param>\n /// <param name=\"right\">The right-hand operand.</param>\n [Pure]public static Tensor logical_xor(Tensor left, Tensor right) => left.logical_xor(right);\n\n // https://pytorch.org/docs/stable/generated/torch.logical_xor\n /// <summary>\n /// Element-wise logical XOR\n /// </summary>\n /// <param name=\"left\">The left-hand operand.</param>\n /// <param name=\"right\">The right-hand operand.</param>\n public static Tensor logical_xor_(Tensor left, Tensor right) => left.logical_xor_(right);\n\n // https://pytorch.org/docs/stable/generated/torch.logit\n /// <summary>\n /// Returns a new tensor with the logit of the elements of input.\n /// input is clamped to [eps, 1 - eps] when eps is not null\n /// </summary>\n /// <param name=\"input\">The input tensor.</param>\n /// <param name=\"eps\">The epsilon for input clamp bound.</param>\n [Pure]public static Tensor logit(Tensor input, double? eps = null) => input.logit(eps);\n\n // https://pytorch.org/docs/stable/generated/torch.hypot\n /// <summary>\n /// Element-wise: given the legs of a right triangle, return its hypotenuse.\n /// </summary>\n /// <param name=\"left\">The left-hand operand.</param>\n /// <param name=\"right\">The right-hand operand.</param>\n [Pure]public static Tensor hypot(Tensor left, Tensor right) => left.hypot(right);\n\n // https://pytorch.org/docs/stable/generated/torch.i0\n /// <summary>\n /// Alias for <see cref=\"torch.special.i0\"/>.\n /// </summary>\n /// <param name=\"input\"></param>\n /// <returns></returns>\n [Pure]public static Tensor i0(Tensor input) => special.i0(input);\n\n // https://pytorch.org/docs/stable/generated/torch.igamma\n /// <summary>\n /// Alias for <see cref=\"torch.special.gammainc\"/>.\n /// </summary>\n /// <param name=\"input\"></param>\n /// <param name=\"other\"></param>\n /// <returns></returns>\n [Pure]public static Tensor igamma(Tensor input, Tensor other) => special.gammainc(input, other);\n\n // https://pytorch.org/docs/stable/generated/torch.igammac\n /// <summary>\n /// Alias for <see cref=\"torch.special.gammaincc\"/>\".\n /// </summary>\n /// <param name=\"input\"></param>\n /// <param name=\"other\"></param>\n /// <returns></returns>\n [Pure]public static Tensor igammac(Tensor input, Tensor other) => special.gammaincc(input, other);\n\n // https://pytorch.org/docs/stable/generated/torch.mul\n /// <summary>\n /// Multiplies each element of the input by the corresponding element of other.\n /// </summary>\n /// <param name=\"left\">The left-hand operand.</param>\n /// <param name=\"right\">The right-hand operand.</param>\n public static Tensor mul(Tensor left, Tensor right) => left.mul(right);\n\n // https://pytorch.org/docs/stable/generated/torch.mul\n /// <summary>\n /// Divides each element of the input by a scalar value.\n /// </summary>\n /// <param name=\"left\">The left-hand operand.</param>\n /// <param name=\"right\">The right-hand operand.</param>\n public static Tensor mul(Tensor left, Scalar right) => left.mul(right);\n\n // https://pytorch.org/docs/stable/generated/torch.mul\n /// <summary>\n /// Divides each element of the input by the corresponding element of other.\n /// </summary>\n /// <param name=\"left\">The left-hand operand.</param>\n /// <param name=\"right\">The right-hand operand.</param>\n public static Tensor mul_(Tensor left, Tensor right) => left.mul_(right);\n\n // https://pytorch.org/docs/stable/generated/torch.mul\n /// <summary>\n /// Divides each element of the input by the corresponding element of other.\n /// </summary>\n /// <param name=\"left\">The left-hand operand.</param>\n /// <param name=\"right\">The right-hand operand.</param>\n public static Tensor mul_(Tensor left, Scalar right) => left.mul_(right);\n\n // https://pytorch.org/docs/stable/generated/torch.multiply\n /// <summary>\n /// Multiplies each element of the input by the corresponding element of other.\n /// </summary>\n /// <param name=\"left\">The left-hand operand.</param>\n /// <param name=\"right\">The right-hand operand.</param>\n public static Tensor multiply(Tensor left, Tensor right) => left.mul(right);\n\n // https://pytorch.org/docs/stable/generated/torch.multiply\n /// <summary>\n /// Divides each element of the input by a scalar value.\n /// </summary>\n /// <param name=\"left\">The left-hand operand.</param>\n /// <param name=\"right\">The right-hand operand.</param>\n public static Tensor multiply(Tensor left, Scalar right) => left.mul(right);\n\n // https://pytorch.org/docs/stable/generated/torch.multiply\n /// <summary>\n /// Divides each element of the input by the corresponding element of other.\n /// </summary>\n /// <param name=\"left\">The left-hand operand.</param>\n /// <param name=\"right\">The right-hand operand.</param>\n public static Tensor multiply_(Tensor left, Tensor right) => left.mul_(right);\n\n // https://pytorch.org/docs/stable/generated/torch.multiply\n /// <summary>\n /// Divides each element of the input by the corresponding element of other.\n /// </summary>\n /// <param name=\"left\">The left-hand operand.</param>\n /// <param name=\"right\">The right-hand operand.</param>\n public static Tensor multiply_(Tensor left, Scalar right) => left.mul_(right);\n\n // https://pytorch.org/docs/stable/generated/torch.mvlgamma\n /// <summary>\n /// Computes the multivariate log-gamma function) with dimension pp element-wise\n /// </summary>\n /// <param name=\"input\">The input tensor.</param>\n /// <param name=\"p\">The number of dimensions</param>\n /// <returns></returns>\n public static Tensor mvlgamma(Tensor input, long p) => input.mvlgamma(p);\n\n // https://pytorch.org/docs/stable/generated/torch.mvlgamma\n /// <summary>\n /// Computes the multivariate log-gamma function) with dimension pp element-wise, in place.\n /// </summary>\n /// <param name=\"input\">The input tensor.</param>\n /// <param name=\"p\">The number of dimensions</param>\n /// <returns></returns>\n public static Tensor mvlgamma_(Tensor input, long p) => input.mvlgamma_(p);\n\n // https://pytorch.org/docs/stable/generated/torch.nan_to_num\n /// <summary>\n /// Replaces NaN, positive infinity, and negative infinity values in input with the values specified by nan,\n /// posinf, and neginf, respectively. By default, NaNs are replaced with zero,\n /// positive infinity is replaced with the greatest finite value representable by input’s dtype,\n /// and negative infinity is replaced with the least finite value representable by input’s dtype.\n /// </summary>\n /// <param name=\"input\">the input tensor</param>\n /// <param name=\"nan\">the value to replace NaNs with. Default is zero.</param>\n /// <param name=\"posinf\">\n /// if a Number, the value to replace positive infinity values with.\n /// If None, positive infinity values are replaced with the greatest finite value representable by input’s dtype.\n /// Default is null.\n /// </param>\n /// <param name=\"neginf\">\n /// if a Number, the value to replace negative infinity values with.\n /// If None, negative infinity values are replaced with the lowest finite value representable by input’s dtype.\n /// Default is null.\n /// </param>\n /// <returns></returns>\n public static Tensor nan_to_num(Tensor input, double nan = 0d, double? posinf = null, double? neginf = null)\n => input.nan_to_num(nan, posinf, neginf);\n\n // https://pytorch.org/docs/stable/generated/torch.neg\n /// <summary>\n /// Negation\n /// </summary>\n /// <param name=\"input\">The input tensor.</param>\n public static Tensor neg(Tensor input) => input.neg();\n\n // https://pytorch.org/docs/stable/generated/torch.neg\n /// <summary>\n /// In-place negation\n /// </summary>\n /// <param name=\"input\">The input tensor.</param>\n public static Tensor neg_(Tensor input) => input.neg_();\n\n // https://pytorch.org/docs/stable/generated/torch.negative\n /// <summary>\n /// Negation\n /// </summary>\n /// <param name=\"input\">The input tensor.</param>\n public static Tensor negative(Tensor input) => input.neg();\n\n // https://pytorch.org/docs/stable/generated/torch.negative\n /// <summary>\n /// In-place negation\n /// </summary>\n /// <param name=\"input\">The input tensor.</param>\n public static Tensor negative_(Tensor input) => input.neg_();\n\n\n // https://pytorch.org/docs/stable/generated/torch.nextafter\n public static Tensor nextafter(Tensor input, Tensor other) => input.nextafter(other);\n\n // https://pytorch.org/docs/stable/generated/torch.polygamma\n /// <summary>\n /// Computes the Nth derivative of the digamma function on input.\n /// </summary>\n /// <param name=\"input\">The input tensor.</param>\n /// <param name=\"p\">The number of dimensions</param>\n /// <returns></returns>\n public static Tensor polygamma(Tensor input, long p) => input.polygamma(p);\n\n // https://pytorch.org/docs/stable/generated/torch.polygamma\n /// <summary>\n /// Computes the Nth derivative of the digamma function on input, in-place.\n /// </summary>\n /// <param name=\"input\">The input tensor.</param>\n /// <param name=\"p\">The number of dimensions</param>\n public static Tensor polygamma_(Tensor input, long p) => input.polygamma_(p);\n\n // https://pytorch.org/docs/stable/generated/torch.positive\n public static Tensor positive(Tensor input) => input.positive();\n\n // https://pytorch.org/docs/stable/generated/torch.pow\n /// <summary>\n /// Takes the power of each element in input with exponent and returns a tensor with the result.\n /// </summary>\n /// <param name=\"left\">The left-hand operand.</param>\n /// <param name=\"exponent\">The right-hand operand.</param>\n [Pure]public static Tensor pow(Tensor left, Tensor exponent) => left.pow(exponent);\n\n // https://pytorch.org/docs/stable/generated/torch.pow\n /// <summary>\n /// Takes the power of each element in input with exponent and returns a tensor with the result.\n /// </summary>\n /// <param name=\"left\">The left-hand operand.</param>\n /// <param name=\"exponent\">The right-hand operand.</param>\n [Pure]public static Tensor pow(Tensor left, Scalar exponent) => left.pow(exponent);\n\n // https://pytorch.org/docs/stable/generated/torch.pow\n /// <summary>\n /// Replaces each element in input with the power of the element and the exponent.\n /// </summary>\n /// <param name=\"left\">The left-hand operand.</param>\n /// <param name=\"exponent\">The right-hand operand.</param>\n public static Tensor pow_(Tensor left, Tensor exponent) => left.pow_(exponent);\n\n // https://pytorch.org/docs/stable/generated/torch.pow\n /// <summary>\n /// Replaces each element in input with the power of the element and the exponent.\n /// </summary>\n /// <param name=\"left\">The left-hand operand.</param>\n /// <param name=\"exponent\">The right-hand operand.</param>\n public static Tensor pow_(Tensor left, Scalar exponent) => left.pow_(exponent);\n\n // https://pytorch.org/docs/stable/generated/torch.quantized_batch_norm\n /// <summary>\n /// Applies batch normalization on a 4D (NCHW) quantized tensor.\n /// </summary>\n /// <param name=\"input\">quantized tensor</param>\n /// <param name=\"weight\">float tensor that corresponds to the gamma, size C</param>\n /// <param name=\"bias\">float tensor that corresponds to the beta, size C</param>\n /// <param name=\"mean\">float mean value in batch normalization, size C</param>\n /// <param name=\"var\">float tensor for variance, size C</param>\n /// <param name=\"eps\">a value added to the denominator for numerical stability.</param>\n /// <param name=\"output_scale\">output quantized tensor scale</param>\n /// <param name=\"output_zero_point\">output quantized tensor zero_point</param>\n /// <returns>A quantized tensor with batch normalization applied.</returns>\n [Pure, Obsolete(\"not implemented\", true)]\n public static Tensor quantized_batch_norm(Tensor input, Tensor? weight, Tensor? bias, Tensor mean, Tensor @var, double eps, double output_scale, long output_zero_point)\n => throw new NotImplementedException();\n\n // https://pytorch.org/docs/stable/generated/torch.quantized_max_pool1d\n /// <summary>\n /// Applies a 1D max pooling over an input quantized tensor composed of several input planes.\n /// </summary>\n /// <param name=\"input\">quantized tensor</param>\n /// <param name=\"kernel_size\">the size of the sliding window</param>\n /// <param name=\"stride\">the stride of the sliding window</param>\n /// <param name=\"padding\">padding to be added on both sides, must be >= 0 and <= kernel_size / 2</param>\n /// <param name=\"dilation\">the stride between elements within a sliding window, must be > 0. Default 1</param>\n /// <param name=\"ceil_mode\">If <value>true</value>, will use ceil instead of floor to compute the output shape. Defaults to <value>false</value>.</param>\n /// <returns>A quantized tensor with max_pool1d applied.</returns>\n [Pure, Obsolete(\"not implemented\", true)]\n public static Tensor quantized_max_pool1d(Tensor input, long[] kernel_size, long[]? stride, long[]? padding, long dilation=1, bool ceil_mode=false)\n => throw new NotImplementedException();\n\n // https://pytorch.org/docs/stable/generated/torch.quantized_max_pool2d\n /// <summary>\n /// Applies a 2D max pooling over an input quantized tensor composed of several input planes.\n /// </summary>\n /// <param name=\"input\">quantized tensor</param>\n /// <param name=\"kernel_size\">the size of the sliding window</param>\n /// <param name=\"stride\">the stride of the sliding window</param>\n /// <param name=\"padding\">padding to be added on both sides, must be >= 0 and <= kernel_size / 2</param>\n /// <param name=\"dilation\">The stride between elements within a sliding window, must be > 0. Default 1</param>\n /// <param name=\"ceil_mode\">If <value>true</value>, will use ceil instead of floor to compute the output shape. Defaults to <value>false</value>.</param>\n /// <returns>A quantized tensor with max_pool2d applied.</returns>\n [Pure, Obsolete(\"not implemented\", true)]\n public static Tensor quantized_max_pool2d(Tensor input, long[] kernel_size, long[]? stride, long[]? padding, long dilation=1, bool ceil_mode=false)\n => throw new NotImplementedException();\n\n // https://pytorch.org/docs/stable/generated/torch.rad2deg\n /// <summary>\n /// Returns a new tensor with each of the elements of input converted from angles in radians to degrees.\n /// </summary>\n /// <param name=\"input\">The input tensor.</param>\n /// <returns>tensor with angles in radians</returns>\n public static Tensor rad2deg(Tensor input) => input.rad2deg();\n\n // https://pytorch.org/docs/stable/generated/torch.real\n /// <summary>\n /// Returns a new tensor containing real values of the self tensor.\n /// The returned tensor and self share the same underlying storage.\n /// </summary>\n /// <param name=\"input\">the input tensor.</param>\n /// <returns>tensor containing real values</returns>\n [Pure]public static Tensor real(Tensor input) => input.real;\n\n // https://pytorch.org/docs/stable/generated/torch.reciprocal\n /// <summary>\n /// Returns a new tensor with the reciprocal of the elements of input\n /// </summary>\n /// <param name=\"input\">The input tensor.</param>\n [Pure]public static Tensor reciprocal(Tensor input) => input.reciprocal();\n\n // https://pytorch.org/docs/stable/generated/torch.reciprocal\n /// <summary>\n /// Replaces each element with the reciprocal of the input\n /// </summary>\n /// <param name=\"input\">The input tensor.</param>\n public static Tensor reciprocal_(Tensor input) => input.reciprocal_();\n\n // https://pytorch.org/docs/stable/generated/torch.remainder\n /// <summary>\n /// Computes the element-wise remainder of division.\n /// </summary>\n /// <param name=\"left\">Numerator</param>\n /// <param name=\"right\">Denominator</param>\n [Pure]public static Tensor remainder(Tensor left, Tensor right) => left.remainder(right);\n\n // https://pytorch.org/docs/stable/generated/torch.remainder\n /// <summary>\n /// Computes the element-wise remainder of division.\n /// </summary>\n /// <param name=\"left\">Numerator</param>\n /// <param name=\"right\">Denominator</param>\n [Pure]public static Tensor remainder(Tensor left, Scalar right) => left.remainder(right);\n\n // https://pytorch.org/docs/stable/generated/torch.remainder\n /// <summary>\n /// Computes the element-wise remainder of division, in place\n /// </summary>\n /// <param name=\"left\">Numerator</param>\n /// <param name=\"right\">Denominator</param>\n public static Tensor remainder_(Tensor left, Tensor right) => left.remainder_(right);\n\n // https://pytorch.org/docs/stable/generated/torch.remainder\n /// <summary>\n /// Computes the element-wise remainder of division, in place\n /// </summary>\n /// <param name=\"left\">Numerator</param>\n /// <param name=\"right\">Denominator</param>\n public static Tensor remainder_(Tensor left, Scalar right) => left.remainder_(right);\n\n // https://pytorch.org/docs/stable/generated/torch.round\n /// <summary>\n /// Returns a new tensor with each of the elements of input rounded to the closest value with the given number of decimals.\n /// </summary>\n /// <param name=\"input\">The input tensor.</param>\n /// <param name=\"decimals\">Number of decimal places to round to (default: 0). If decimals is negative, it specifies the number of positions to the left of the decimal point.</param>\n [Pure]public static Tensor round(Tensor input, long decimals = 0L) => input.round(decimals);\n\n // https://pytorch.org/docs/stable/generated/torch.round\n /// <summary>\n /// Replaces each of the elements of input with the element rounded to the closest value with the given number of decimals.\n /// </summary>\n /// <param name=\"input\">The input tensor.</param>\n /// <param name=\"decimals\">Number of decimal places to round to (default: 0). If decimals is negative, it specifies the number of positions to the left of the decimal point.</param>\n public static Tensor round_(Tensor input, long decimals = 0L) => input.round_(decimals);\n\n // https://pytorch.org/docs/stable/generated/torch.rsqrt\n /// <summary>\n /// Returns a new tensor with the reciprocal of the square-root of each of the elements of input.\n /// </summary>\n /// <param name=\"input\">The input tensor.</param>\n [Pure]public static Tensor rsqrt(Tensor input) => input.rsqrt();\n\n // https://pytorch.org/docs/stable/generated/torch.rsqrt\n /// <summary>\n /// Replaces each of the elements of input with the reciprocal of the square-root of each of the elements of input.\n /// </summary>\n /// <param name=\"input\">The input tensor.</param>\n public static Tensor rsqrt_(Tensor input) => input.rsqrt_();\n\n // https://pytorch.org/docs/stable/generated/torch.sigmoid\n /// <summary>\n /// Computes the logistic sigmoid function of the elements of input.\n /// </summary>\n /// <param name=\"input\">The input tensor.</param>\n [Pure]public static Tensor sigmoid(Tensor input) => input.sigmoid();\n\n // https://pytorch.org/docs/stable/generated/torch.sigmoid\n /// <summary>\n /// Computes the logistic sigmoid function of the elements of input.\n /// </summary>\n /// <param name=\"input\">The input tensor.</param>\n public static Tensor sigmoid_(Tensor input) => input.sigmoid_();\n\n // https://pytorch.org/docs/stable/generated/torch.sign\n /// <summary>\n /// Returns a new tensor with the signs (-1, 0, 1) of the elements of input.\n /// </summary>\n /// <param name=\"input\">The input tensor.</param>\n [Pure]public static Tensor sign(Tensor input) => input.sign();\n\n // https://pytorch.org/docs/stable/generated/torch.sign\n /// <summary>\n /// Returns a new tensor with the signs (-1, 0, 1) of the elements of input.\n /// </summary>\n /// <param name=\"input\">The input tensor.</param>\n [Pure] public static Tensor sign_(Tensor input) => input.sign_();\n\n // https://pytorch.org/docs/stable/generated/torch.sgn\n /// <summary>\n /// This function is an extension of torch.sign() to complex tensors.\n /// It computes a new tensor whose elements have the same angles as the corresponding\n /// elements of input and absolute values (i.e. magnitudes) of one for complex tensors\n /// and is equivalent to torch.sign() for non-complex tensors.\n /// </summary>\n /// <param name=\"input\">the input tensor.</param>\n /// <returns>the output tensor.</returns>\n [Pure]\n public static Tensor sgn(Tensor input) => input.sgn();\n\n // https://pytorch.org/docs/stable/generated/torch.sgn\n /// <summary>\n /// This function is an extension of torch.sign() to complex tensors.\n /// It computes a new tensor whose elements have the same angles as the corresponding\n /// elements of input and absolute values (i.e. magnitudes) of one for complex tensors\n /// and is equivalent to torch.sign() for non-complex tensors.\n /// </summary>\n /// <param name=\"input\">the input tensor.</param>\n /// <returns>the output tensor.</returns>\n [Pure]\n public static Tensor sgn_(Tensor input) => input.sgn_();\n\n // https://pytorch.org/docs/stable/generated/torch.signbit\n /// <summary>\n /// Tests whether each element of input has its sign bit set (is less than zero) or not.\n /// </summary>\n /// <param name=\"input\">The input tensor.</param>\n /// <returns>A boolean tensor of the same shape as the input.</returns>\n [Pure]public static Tensor signbit(Tensor input) => input.signbit();\n\n // https://pytorch.org/docs/stable/generated/torch.sin\n /// <summary>\n /// Computes the sine of the elements of input.\n /// </summary>\n /// <returns></returns>\n [Pure]public static Tensor sin(Tensor input) => input.sin();\n\n // https://pytorch.org/docs/stable/generated/torch.sin\n /// <summary>\n /// Computes the sine of the elements of input.\n /// </summary>\n /// <returns></returns>\n public static Tensor sin_(Tensor input) => input.sin_();\n\n // https://pytorch.org/docs/stable/generated/torch.sinc\n /// <summary>\n /// Computes the normalized sinc of input.\n /// </summary>\n /// <returns></returns>\n [Pure]public static Tensor sinc(Tensor input) => input.sinc();\n\n // https://pytorch.org/docs/stable/generated/torch.sinc\n /// <summary>\n /// Computes the normalized sinc of input, in place.\n /// </summary>\n /// <returns></returns>\n public static Tensor sinc_(Tensor input) => input.sinc_();\n\n // https://pytorch.org/docs/stable/generated/torch.sinh\n /// <summary>\n /// Computes the hyperbolic sine of the elements of input.\n /// </summary>\n /// <returns></returns>\n [Pure]public static Tensor sinh(Tensor input) => input.sinh();\n\n // https://pytorch.org/docs/stable/generated/torch.sinh\n /// <summary>\n /// Computes the hyperbolic sine of the elements of input.\n /// </summary>\n /// <returns></returns>\n public static Tensor sinh_(Tensor input) => input.sinh_();\n\n // https://pytorch.org/docs/stable/generated/torch.sqrt\n /// <summary>\n /// Computes the element-wise square root\n /// </summary>\n /// <param name=\"input\">The input tensor.</param>\n [Pure]public static Tensor sqrt(Tensor input) => input.sqrt();\n\n // https://pytorch.org/docs/stable/generated/torch.sqrt\n /// <summary>\n /// Computes the element-wise square root, in place\n /// </summary>\n /// <param name=\"input\">The input tensor.</param>\n public static Tensor sqrt_(Tensor input) => input.sqrt_();\n\n // https://pytorch.org/docs/stable/generated/torch.square\n /// <summary>\n /// Computes the element-wise square\n /// </summary>\n /// <param name=\"input\">The input tensor.</param>\n [Pure]public static Tensor square(Tensor input) => input.square();\n\n // https://pytorch.org/docs/stable/generated/torch.sub\n /// <summary>\n /// Element-wise subtraction\n /// </summary>\n /// <param name=\"left\">The left-hand operand.</param>\n /// <param name=\"right\">The right-hand operand.</param>\n [Pure]public static Tensor sub(Tensor left, Tensor right) => left.sub(right);\n\n // https://pytorch.org/docs/stable/generated/torch.sub\n /// <summary>\n /// Element-wise subtraction\n /// </summary>\n /// <param name=\"left\">The left-hand operand.</param>\n /// <param name=\"right\">The right-hand operand.</param>\n [Pure]public static Tensor sub(Tensor left, Scalar right) => left.sub(right);\n\n // https://pytorch.org/docs/stable/generated/torch.sub\n /// <summary>\n /// Element-wise subtraction\n /// </summary>\n /// <param name=\"left\">The left-hand operand.</param>\n /// <param name=\"right\">The right-hand operand.</param>\n public static Tensor sub_(Tensor left, Tensor right) => left.sub_(right);\n\n // https://pytorch.org/docs/stable/generated/torch.sub\n /// <summary>\n /// Element-wise subtraction\n /// </summary>\n /// <param name=\"left\">The left-hand operand.</param>\n /// <param name=\"right\">The right-hand operand.</param>\n public static Tensor sub_(Tensor left, Scalar right) => left.sub_(right);\n\n // https://pytorch.org/docs/stable/generated/torch.subtract\n /// <summary>\n /// Element-wise subtraction\n /// </summary>\n /// <param name=\"left\">The left-hand operand.</param>\n /// <param name=\"right\">The right-hand operand.</param>\n [Pure]public static Tensor subtract(Tensor left, Tensor right) => left.subtract(right);\n\n // https://pytorch.org/docs/stable/generated/torch.subtract\n /// <summary>\n /// Element-wise subtraction\n /// </summary>\n /// <param name=\"left\">The left-hand operand.</param>\n /// <param name=\"right\">The right-hand operand.</param>\n [Pure]public static Tensor subtract(Tensor left, Scalar right) => left.subtract(right);\n\n // https://pytorch.org/docs/stable/generated/torch.subtract\n /// <summary>\n /// Element-wise subtraction\n /// </summary>\n /// <returns></returns>\n public static Tensor subtract_(Tensor left, Tensor right) => left.subtract_(right);\n\n // https://pytorch.org/docs/stable/generated/torch.subtract\n /// <summary>\n /// Element-wise subtraction\n /// </summary>\n /// <param name=\"left\">The left-hand operand.</param>\n /// <param name=\"right\">The right-hand operand.</param>\n public static Tensor subtract_(Tensor left, Scalar right) => left.subtract_(right);\n\n // https://pytorch.org/docs/stable/generated/torch.tan\n /// <summary>\n /// Computes the tangent of the elements of input.\n /// </summary>\n /// <returns></returns>\n [Pure]public static Tensor tan(Tensor input) => input.tan();\n\n // https://pytorch.org/docs/stable/generated/torch.tan\n /// <summary>\n /// Computes the tangent of the elements of input. In-place version.\n /// </summary>\n /// <returns></returns>\n public static Tensor tan_(Tensor input) => input.tan_();\n\n // https://pytorch.org/docs/stable/generated/torch.tanh\n /// <summary>\n /// Computes the hyperbolic tangent of the elements of input.\n /// </summary>\n /// <returns></returns>\n [Pure]\n public static Tensor tanh(Tensor input) => input.tanh();\n\n // https://pytorch.org/docs/stable/generated/torch.tanh\n /// <summary>\n /// Computes the hyperbolic tangent of the elements of input.\n /// </summary>\n /// <returns></returns>\n public static Tensor tanh_(Tensor input) => input.tanh_();\n\n // https://pytorch.org/docs/stable/generated/torch.true_divide\n /// <summary>\n /// Alias for torch.div() with rounding_mode=None.\n /// </summary>\n [Pure]\n public static Tensor true_divide(Tensor dividend, Tensor divisor) => dividend.true_divide(divisor);\n\n // https://pytorch.org/docs/stable/generated/torch.true_divide\n /// <summary>\n /// Alias for torch.div_() with rounding_mode=None.\n /// </summary>\n public static Tensor true_divide_(Tensor dividend, Tensor divisor) => dividend.true_divide_(divisor);\n\n // https://pytorch.org/docs/stable/generated/torch.trunc\n /// <summary>\n /// Returns a new tensor with the truncated integer values of the elements of input.\n /// </summary>\n /// <param name=\"input\">The input tensor.</param>\n [Pure]\n public static Tensor trunc(Tensor input) => input.trunc();\n\n // https://pytorch.org/docs/stable/generated/torch.trunc\n /// <summary>\n /// Replaces each element with the truncated integer values of the elements of input.\n /// </summary>\n /// <param name=\"input\">The input tensor.</param>\n public static Tensor trunc_(Tensor input) => input.trunc_();\n\n // https://pytorch.org/docs/stable/generated/torch.xlogy\n /// <summary>\n /// Computes x * log(y)\n /// </summary>\n /// <param name=\"x\">The 'x' operand.</param>\n /// <param name=\"y\">The 'y' operand.</param>\n [Pure]\n public static Tensor xlogy(Tensor x, Tensor y) => x.xlogy(y);\n\n // https://pytorch.org/docs/stable/generated/torch.xlogy\n /// <summary>\n /// Computes x * log(y) in place\n /// </summary>\n /// <param name=\"x\">The 'x' operand.</param>\n /// <param name=\"y\">The 'y' operand.</param>\n public static Tensor xlogy_(Tensor x, Tensor y) => x.xlogy_(y);\n\n // https://pytorch.org/docs/stable/generated/torch.xlogy\n /// <summary>\n /// Computes x * log(y)\n /// </summary>\n /// <param name=\"x\">The 'x' operand.</param>\n /// <param name=\"y\">The 'y' operand.</param>\n [Pure]\n public static Tensor xlogy(Tensor x, Scalar y) => x.xlogy(y);\n\n // https://pytorch.org/docs/stable/generated/torch.xlogy\n /// <summary>\n /// Computes x * log(y) in place\n /// </summary>\n /// <param name=\"x\">The 'x' operand.</param>\n /// <param name=\"y\">The 'y' operand.</param>\n public static Tensor xlogy_(Tensor x, Scalar y) => x.xlogy_(y);\n }\n}"},{"alpha_fraction":0.7084447741508484,"alphanum_fraction":0.7176156044006348,"avg_line_length":57.17777633666992,"blob_id":"31a107ecd71f9c25089128952e275abe1991c603","content_id":"8884e02790c5ebc92391c4c96db1a2fae3d199ab","detected_licenses":["MIT"],"is_generated":false,"is_vendor":false,"language":"C#","length_bytes":2617,"license_type":"permissive","max_line_length":206,"num_lines":45,"path":"/src/TorchVision/LibTorchSharp.cs","repo_name":"NiklasGustafsson/TorchSharp","src_encoding":"UTF-8","text":"// Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information.\nusing System;\nusing System.Runtime.InteropServices;\n\nnamespace TorchSharp\n{\n internal static class NativeMethods\n {\n /* Tensor THSVision_AdjustHue(const Tensor i, const double hue_factor) */\n [DllImport(\"LibTorchSharp\")]\n internal static extern IntPtr THSVision_AdjustHue(IntPtr img, double hue_factor);\n\n // EXPORT_API(Tensor) THSVision_ApplyGridTransform(Tensor img, Tensor grid, const int8_t m, const float* fill, const int64_t fill_length);\n [DllImport(\"LibTorchSharp\")]\n internal static extern IntPtr THSVision_ApplyGridTransform(IntPtr img, IntPtr grid, sbyte mode, IntPtr fill,\n long fill_length);\n\n /* Tensor THSVision_GenerateAffineGrid(Tensor theta, const int64_t w, const int64_t h, const int64_t ow, const int64_t oh); */\n [DllImport(\"LibTorchSharp\")]\n internal static extern IntPtr THSVision_GenerateAffineGrid(IntPtr theta, long w, long h, long ow, long oh);\n\n /* Tensor THSVision_ComputeOutputSize(const float* matrix, const int64_t matrix_length, const int64_t w, const int64_t h); */\n [DllImport(\"LibTorchSharp\")]\n internal static extern void THSVision_ComputeOutputSize(IntPtr matrix, long matrix_length, long w, long h,\n out int first, out int second);\n\n /* Tensor THSVision_PerspectiveGrid(const float* coeffs, const int64_t coeffs_length, const int64_t ow, const int64_t oh, const int8_t scalar_type, const int device_type, const int device_index); */\n [DllImport(\"LibTorchSharp\")]\n internal static extern IntPtr THSVision_PerspectiveGrid(IntPtr coeffs, long coeffs_length, long ow, long oh,\n sbyte dtype, int device_type, int device_index);\n\n /* Tensor THSVision_ScaleChannel(Tensor ic); */\n [DllImport(\"LibTorchSharp\")]\n internal static extern IntPtr THSVision_ScaleChannel(IntPtr img);\n\n [DllImport(\"LibTorchSharp\")]\n internal static extern void THSVision_BRGA_RGB(IntPtr inputBytes, IntPtr redBytes, IntPtr greenBytes, IntPtr blueBytes, long inputChannelCount, long imageSize);\n\n [DllImport(\"LibTorchSharp\")]\n internal static extern void THSVision_BRGA_RGBA(IntPtr inputBytes, IntPtr redBytes, IntPtr greenBytes, IntPtr blueBytes, IntPtr alphaBytes, long inputChannelCount, long imageSize);\n\n [DllImport(\"LibTorchSharp\")]\n internal static extern void THSVision_RGB_BRGA(IntPtr inputBytes, IntPtr outBytes, long inputChannelCount, long imageSize);\n }\n}"},{"alpha_fraction":0.49823907017707825,"alphanum_fraction":0.49948206543922424,"avg_line_length":30.34415626525879,"blob_id":"eb584a341ac54b0943aa3f4b4c541f09749788e2","content_id":"129753d35374ef60700acd8c42f4407bee937305","detected_licenses":["MIT"],"is_generated":false,"is_vendor":false,"language":"C#","length_bytes":4827,"license_type":"permissive","max_line_length":156,"num_lines":154,"path":"/src/TorchSharp/NN/ParameterList.cs","repo_name":"NiklasGustafsson/TorchSharp","src_encoding":"UTF-8","text":"// Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information.\nusing System.Collections;\nusing System.Collections.Generic;\nusing static TorchSharp.torch.nn;\n\nnamespace TorchSharp\n{\n using System.Linq;\n using Modules;\n\n namespace Modules\n {\n /// <summary>\n /// Holds parameters in a list.\n /// ParameterList can be indexed like a regular list, but parameters it contains are properly registered, and will be visible by all Module methods.\n /// </summary>\n public class ParameterList : Module, IList<Parameter>\n {\n public ParameterList(params Parameter[] parameters) : base(nameof(ParameterList))\n {\n if (parameters != null && parameters.Length > 0) {\n _list.AddRange(parameters);\n }\n }\n\n protected override void RegisterComponents()\n {\n if (_registered) return;\n\n for (int i = 0; i < _list.Count; i++) {\n register_parameter($\"{i}\", _list[i]);\n }\n _registered = true;\n }\n\n public override IEnumerable<(string name, Parameter parameter)> named_parameters(bool recurse = true)\n {\n return Enumerable.Range(0, _list.Count).Select(i => ($\"{i}\", _list[i]));\n }\n\n public override bool has_parameter(string target)\n {\n return int.TryParse(target, out int idx) && idx > -1 && idx < _list.Count && _list[idx] is not null;\n }\n\n public override Parameter get_parameter(string target)\n {\n if (int.TryParse(target, out int idx) && idx > -1 && idx < _list.Count) {\n return _list[idx];\n }\n return null;\n }\n\n private bool _registered = false;\n\n public Parameter this[int index] {\n get => _list[index];\n set => _list[index] = value;\n }\n\n public int Count => _list.Count;\n\n public bool IsReadOnly => false;\n\n public void Add(Parameter item)\n {\n _list.Add(item);\n }\n\n // The following two funtions are here because PyTorch defines them.\n\n /// <summary>\n /// Appends parameters from an enumeration to the end of the list.\n /// </summary>\n /// <param name=\"parameters\"></param>\n public void extend(IEnumerable<Parameter> parameters)\n {\n _list.AddRange(parameters);\n }\n\n /// <summary>\n /// Appends zero or more parameters to the end of the list.\n /// </summary>\n public void append(params Parameter[] parameters)\n {\n if (parameters != null && parameters.Length > 0) {\n _list.AddRange(parameters);\n }\n }\n\n public void Clear()\n {\n _list.Clear();\n }\n\n public bool Contains(Parameter item)\n {\n return _list.Contains(item);\n }\n\n public void CopyTo(Parameter[] array, int arrayIndex)\n {\n _list.CopyTo(array, arrayIndex);\n }\n\n public IEnumerator<Parameter> GetEnumerator()\n {\n return _list.GetEnumerator();\n }\n\n public int IndexOf(Parameter item)\n {\n return _list.IndexOf(item);\n }\n\n public void Insert(int index, Parameter item)\n {\n _list.Insert(index, item);\n }\n\n public bool Remove(Parameter item)\n {\n return _list.Remove(item);\n }\n\n public void RemoveAt(int index)\n {\n _list.RemoveAt(index);\n }\n\n IEnumerator IEnumerable.GetEnumerator()\n {\n return GetEnumerator();\n }\n\n private List<Parameter> _list = new List<Parameter>();\n }\n }\n\n public static partial class torch\n {\n public static partial class nn\n {\n /// <summary>\n /// Create a ParameterList instance from an array of parameter tensors.\n /// </summary>\n /// <param name=\"parameters\">A list of modules.</param>\n /// <remarks>\n /// ParameterList can be indexed like a regular list, but the parameters it contains are properly registered.\n /// </remarks>\n public static ParameterList ParameterList(params Parameter[] parameters) => new ParameterList(parameters);\n }\n }\n}\n"},{"alpha_fraction":0.546951413154602,"alphanum_fraction":0.5482556223869324,"avg_line_length":42.50354766845703,"blob_id":"496925f4af86e870ef9a3b5bfee49b255b170f58","content_id":"047751d0afae96ee1eccf0b0ca4d297d24339362","detected_licenses":["MIT"],"is_generated":false,"is_vendor":false,"language":"C#","length_bytes":6134,"license_type":"permissive","max_line_length":149,"num_lines":141,"path":"/src/TorchSharp/Distributions/Uniform.cs","repo_name":"NiklasGustafsson/TorchSharp","src_encoding":"UTF-8","text":"// Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information.\nusing System;\nusing static TorchSharp.torch;\n\nnamespace TorchSharp\n{\n using Modules;\n\n namespace Modules\n {\n /// <summary>\n /// Generates uniformly distributed random samples from the half-open interval [low, high[.\n /// </summary>\n public class Uniform : torch.distributions.Distribution\n {\n /// <summary>\n /// The mean of the distribution.\n /// </summary>\n public override Tensor mean =>\n WrappedTensorDisposeScope(() => (high + low) / 2);\n\n public override Tensor mode => double.NaN * high;\n\n /// <summary>\n /// The variance of the distribution\n /// </summary>\n public override Tensor variance =>\n WrappedTensorDisposeScope(() => (high - low).pow(2) / 12);\n\n /// <summary>\n /// Constructor\n /// </summary>\n /// <param name=\"low\">Lower bound (inclusive)</param>\n /// <param name=\"high\">Upper bound (exclusive)</param>\n /// <param name=\"generator\">An optional random number generator object.</param>\n public Uniform(Tensor low, Tensor high, torch.Generator generator = null) : base(generator, low.size())\n {\n var lowHigh = torch.broadcast_tensors(low, high);\n this.low = lowHigh[0].DetachFromDisposeScope();\n this.high = lowHigh[1].DetachFromDisposeScope();\n }\n\n private Tensor high;\n private Tensor low;\n\n /// <summary>\n /// Generates a sample_shape shaped reparameterized sample or sample_shape shaped batch of reparameterized samples\n /// if the distribution parameters are batched.\n /// </summary>\n /// <param name=\"sample_shape\">The sample shape.</param>\n public override Tensor rsample(params long[] sample_shape)\n {\n using var _ = torch.NewDisposeScope();\n var shape = ExtendedShape(sample_shape);\n var rand = torch.rand(shape, dtype: low.dtype, device: low.device, generator: generator);\n return (low + rand * (high - low)).MoveToOuterDisposeScope();\n }\n\n /// <summary>\n /// Returns the log of the probability density/mass function evaluated at `value`.\n /// </summary>\n /// <param name=\"value\"></param>\n public override Tensor log_prob(Tensor value)\n {\n using var _ = torch.NewDisposeScope();\n var lb = low.le(value).type_as(low);\n var ub = high.gt(value).type_as(low);\n return (torch.log(lb.mul(ub)) - torch.log(high - low)).MoveToOuterDisposeScope();\n }\n\n /// <summary>\n /// Returns the cumulative density/mass function evaluated at `value`.\n /// </summary>\n /// <param name=\"value\"></param>\n public override Tensor cdf(Tensor value)\n {\n return torch.WrappedTensorDisposeScope(() => ((value - low) / (high - low)).clamp_(0, 1));\n }\n\n /// <summary>\n /// Returns the inverse cumulative density/mass function evaluated at `value`.\n /// </summary>\n /// <param name=\"value\"></param>\n public override Tensor icdf(Tensor value)\n {\n return torch.WrappedTensorDisposeScope(() => (value * (high - low)).add_(low));\n }\n\n /// <summary>\n /// Returns entropy of distribution, batched over batch_shape.\n /// </summary>\n public override Tensor entropy()\n {\n return torch.WrappedTensorDisposeScope(() => (high - low).log_());\n }\n\n /// <summary>\n /// Returns a new distribution instance (or populates an existing instance provided by a derived class) with batch dimensions expanded to\n /// `batch_shape`. This method calls `torch.Tensor.expand()` on the distribution's parameters. As such, this does not allocate new\n /// memory for the expanded distribution instance.\n /// </summary>\n /// <param name=\"batch_shape\">Tthe desired expanded size.</param>\n /// <param name=\"instance\">new instance provided by subclasses that need to override `.expand`.</param>\n public override distributions.Distribution expand(Size batch_shape, distributions.Distribution instance = null)\n {\n if (instance != null && !(instance is Uniform))\n throw new ArgumentException(\"expand(): 'instance' must be a Uniform distribution\");\n\n var newDistribution = ((instance == null) ?\n new Uniform(low.expand(batch_shape), high.expand(batch_shape), generator) :\n instance) as Uniform;\n\n newDistribution.batch_shape = batch_shape;\n if (newDistribution == instance)\n {\n newDistribution.low = low.expand(batch_shape);\n newDistribution.high = high.expand(batch_shape);\n }\n return newDistribution;\n }\n }\n\n }\n public static partial class torch\n {\n public static partial class distributions\n {\n /// <summary>\n /// Generates uniformly distributed random samples from the half-open interval [low, high[.\n /// </summary>\n /// <param name=\"low\">Lower bound (inclusive)</param>\n /// <param name=\"high\">Upper bound (exclusive)</param>\n /// <param name=\"generator\">An optional random number generator object.</param>\n /// <returns></returns>\n public static Uniform Uniform(Tensor low, Tensor high, torch.Generator generator = null)\n {\n return new Uniform(low, high, generator);\n }\n }\n }\n}\n"},{"alpha_fraction":0.6966068148612976,"alphanum_fraction":0.7001996040344238,"avg_line_length":36.01970291137695,"blob_id":"d5e6f8e37ec41423493c2d075b54a69f00e71124","content_id":"42fd493ee202f49d0720ccfed9dc2240296b579e","detected_licenses":["MIT"],"is_generated":false,"is_vendor":false,"language":"C++","length_bytes":7515,"license_type":"permissive","max_line_length":234,"num_lines":203,"path":"/src/Native/LibTorchSharp/THSOptimizers.cpp","repo_name":"NiklasGustafsson/TorchSharp","src_encoding":"UTF-8","text":"// Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information.\n#include \"THSNN.h\"\n\n#include <torch/nn/init.h>\n\nvoid THSNN_Optimizer_getParameters(const Optimizer optimizer, Tensor* (*allocator)(size_t length))\n{\n auto parameters = (*optimizer)->parameters();\n Tensor* result = allocator(parameters.size());\n\n for (size_t i = 0; i < parameters.size(); i++)\n {\n result[i] = ResultTensor(parameters[i]);\n }\n}\n\nTensor THSNN_Optimizer_step(const Optimizer optimizer, Tensor(*loss_closure)())\n{\n CATCH_TENSOR((loss_closure == nullptr) ? (*optimizer)->step() : (*optimizer)->step([loss_closure]() -> at::Tensor { return *(loss_closure()); }));\n}\n\nvoid THSNN_Optimizer_zero_grad(const Optimizer optimizer)\n{\n (*optimizer)->zero_grad();\n auto defaults = (*optimizer)->defaults();\n}\n\nOptimizer THSNN_Adagrad_ctor(const Tensor* parameters, const int length, const double learning_rate, const double lr_decay, const double weight_decay, const double initial_accumulator_value, const double eps)\n{\n auto params = toTensors<at::Tensor>((torch::Tensor**)parameters, length);\n auto options = torch::optim::AdagradOptions(learning_rate)\n .lr_decay(lr_decay)\n .weight_decay(weight_decay)\n .initial_accumulator_value(initial_accumulator_value)\n .eps(eps);\n\n return new std::shared_ptr<torch::optim::Optimizer>(std::make_shared<torch::optim::Adagrad>(torch::optim::Adagrad(params, options)));\n}\n\nOptimizer THSNN_Adam_ctor(const Tensor* parameters, const int length, const double learning_rate, const double beta1, const double beta2, const double eps, const double weight_decay, const bool amsgrad)\n{\n auto params = toTensors<at::Tensor>((torch::Tensor**)parameters, length);\n auto options = torch::optim::AdamOptions(learning_rate)\n .betas(std::make_tuple(beta1, beta2))\n .eps(eps)\n .weight_decay(weight_decay)\n .amsgrad(amsgrad);\n\n return new std::shared_ptr<torch::optim::Optimizer>(std::make_shared<torch::optim::Adam>(torch::optim::Adam(params, options)));\n}\n\nOptimizer THSNN_AdamW_ctor(const Tensor* parameters, const int length, const double learning_rate, const double beta1, const double beta2, const double eps, const double weight_decay, const bool amsgrad)\n{\n auto params = toTensors<at::Tensor>((torch::Tensor**)parameters, length);\n auto options = torch::optim::AdamWOptions(learning_rate)\n .betas(std::make_tuple(beta1, beta2))\n .eps(eps)\n .weight_decay(weight_decay)\n .amsgrad(amsgrad);\n\n return new std::shared_ptr<torch::optim::Optimizer>(std::make_shared<torch::optim::AdamW>(torch::optim::AdamW(params, options)));\n}\n\nOptimizer THSNN_LBFGS_ctor(const Tensor* parameters, const int length, const double learning_rate, const int64_t max_iter, const int64_t max_eval, const double tolerange_grad, const double tolerance_change, const int64_t history_size)\n{\n auto params = toTensors<at::Tensor>((torch::Tensor**)parameters, length);\n auto options = torch::optim::LBFGSOptions(learning_rate)\n .max_iter(max_iter)\n .max_eval(max_eval)\n .tolerance_grad(tolerange_grad)\n .tolerance_change(tolerance_change)\n .history_size(history_size);\n\n return new std::shared_ptr<torch::optim::Optimizer>(std::make_shared<torch::optim::LBFGS>(torch::optim::LBFGS(params, options)));\n}\n\nOptimizer THSNN_RMSprop_ctor(const Tensor* parameters, const int length, const double learning_rate, const double alpha, const double eps, const double weight_decay, const double momentum, const bool centered)\n{\n auto params = toTensors<at::Tensor>((torch::Tensor**)parameters, length);\n\n auto options = torch::optim::RMSpropOptions(learning_rate)\n .alpha(alpha)\n .eps(eps)\n .weight_decay(weight_decay)\n .momentum(momentum)\n .centered(centered);\n\n return new std::shared_ptr<torch::optim::Optimizer>(std::make_shared<torch::optim::RMSprop>(torch::optim::RMSprop(params, options)));\n}\n\nOptimizer THSNN_SGD_ctor(const Tensor* parameters, const int length, const double learning_rate, const double momentum, const double dampening, const double weight_decay, const bool nesterov)\n{\n auto params = toTensors<at::Tensor>((torch::Tensor**)parameters, length);\n auto opts = torch::optim::SGDOptions(learning_rate)\n .momentum(momentum)\n .dampening(dampening)\n .weight_decay(weight_decay)\n .nesterov(nesterov);\n\n return new std::shared_ptr<torch::optim::Optimizer>(std::make_shared<torch::optim::SGD>(torch::optim::SGD(params, opts)));\n}\n\n\n// Scheduler integration\n\ntemplate<typename OptionsType>\nvoid SetLearningRate(const Optimizer optimizer, const double lr)\n{\n auto options = dynamic_cast<OptionsType*>(&(*optimizer)->defaults());\n options->lr(lr);\n\n auto& pgs = (*optimizer)->param_groups();\n for (auto pg = pgs.begin(); pg < pgs.end(); ++pg)\n {\n options = dynamic_cast<OptionsType*>(&(pg->options()));\n options->lr(lr);\n }\n}\n\ntemplate<typename OptionsType>\nvoid SetMomentum(const Optimizer optimizer, const double momentum)\n{\n auto options = dynamic_cast<OptionsType*>(&(*optimizer)->defaults());\n options->momentum(momentum);\n\n auto& pgs = (*optimizer)->param_groups();\n for (auto pg = pgs.begin(); pg < pgs.end(); ++pg)\n {\n options = dynamic_cast<OptionsType*>(&(pg->options()));\n options->momentum(momentum);\n }\n}\n\ntemplate<typename OptionsType>\nvoid SetBetas(const Optimizer optimizer, const double beta1, const double beta2)\n{\n auto betas = std::make_tuple(beta1, beta2);\n\n auto options = dynamic_cast<OptionsType*>(&(*optimizer)->defaults());\n options->betas(betas);\n\n auto& pgs = (*optimizer)->param_groups();\n for (auto pg = pgs.begin(); pg < pgs.end(); ++pg)\n {\n options = dynamic_cast<OptionsType*>(&(pg->options()));\n options->betas(betas);\n }\n}\n\nvoid THSNN_Adagrad_set_lr(const Optimizer optimizer, const double lr)\n{\n SetLearningRate<torch::optim::AdagradOptions>(optimizer, lr);\n}\n\nvoid THSNN_Adam_set_lr(const Optimizer optimizer, const double lr)\n{\n SetLearningRate<torch::optim::AdamOptions>(optimizer, lr);\n}\n\nvoid THSNN_AdamW_set_lr(const Optimizer optimizer, const double lr)\n{\n SetLearningRate<torch::optim::AdamWOptions>(optimizer, lr);\n}\n\nvoid THSNN_RMSprop_set_lr(const Optimizer optimizer, const double lr)\n{\n SetLearningRate<torch::optim::RMSpropOptions>(optimizer, lr);\n}\n\nvoid THSNN_LBFGS_set_lr(const Optimizer optimizer, const double lr)\n{\n SetLearningRate<torch::optim::LBFGSOptions>(optimizer, lr);\n}\n\nvoid THSNN_SGD_set_lr(const Optimizer optimizer, const double lr)\n{\n SetLearningRate<torch::optim::SGDOptions>(optimizer, lr);\n}\n\nvoid THSNN_Optimizer_dispose(const Optimizer optimizer)\n{\n delete optimizer; // NOTE: this reduces the ref count on the shared_ptr\n}\n\nvoid THSNN_Adam_set_betas(const Optimizer optimizer, double beta1, double beta2)\n{\n SetBetas<torch::optim::AdamOptions>(optimizer, beta1, beta2);\n}\n\nvoid THSNN_AdamW_set_betas(const Optimizer optimizer, double beta1, double beta2)\n{\n SetBetas<torch::optim::AdamWOptions>(optimizer, beta1, beta2);\n}\n\nvoid THSNN_RMSprop_set_momentum(const Optimizer optimizer, double momentum)\n{\n SetMomentum<torch::optim::RMSpropOptions>(optimizer, momentum);\n}\n\nvoid THSNN_SGD_set_momentum(const Optimizer optimizer, double momentum)\n{\n SetMomentum<torch::optim::SGDOptions>(optimizer, momentum);\n}\n"},{"alpha_fraction":0.6264303922653198,"alphanum_fraction":0.6299987435340881,"avg_line_length":52.64521408081055,"blob_id":"caad04d8ac7c7c6fb283494d9275cc86e2166e83","content_id":"8e062c7863cbfdf170583d1ac3ab11463e5df032","detected_licenses":["MIT"],"is_generated":false,"is_vendor":false,"language":"C#","length_bytes":32509,"license_type":"permissive","max_line_length":183,"num_lines":606,"path":"/src/TorchSharp/Tensor/torch.IndexingSlicingJoiningMutatingOps.cs","repo_name":"NiklasGustafsson/TorchSharp","src_encoding":"UTF-8","text":"// Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information.\n#nullable enable\nusing System;\nusing System.Collections.Generic;\nusing System.Diagnostics.Contracts;\nusing System.Linq;\nusing static TorchSharp.PInvoke.NativeMethods;\n\nnamespace TorchSharp\n{\n // https://pytorch.org/docs/stable/torch#indexing-slicing-joining-mutating-ops\n public static partial class torch\n {\n // https://pytorch.org/docs/stable/generated/torch.adjoint\n /// <summary>\n /// Returns a view of the tensor conjugated and with the last two dimensions transposed.\n /// </summary>\n /// <param name=\"input\">The input tensor</param>\n public static Tensor adjoint(Tensor input) => input.adjoint();\n\n // https://pytorch.org/docs/stable/generated/torch.argwhere\n /// <summary>\n /// Returns a tensor containing the indices of all non-zero elements of input.\n /// Each row in the result contains the indices of a non-zero element in input.\n /// The result is sorted lexicographically, with the last index changing the fastest (C-style).\n /// If input has n dimensions, then the resulting indices tensor out is of size (z×n), where\n /// z is the total number of non-zero elements in the input tensor.\n /// </summary>\n public static Tensor argwhere(Tensor input) => input.argwhere();\n\n // https://pytorch.org/docs/stable/generated/torch.cat\n /// <summary>\n /// Concatenates the given sequence of tensors in the given dimension.\n /// </summary>\n /// <param name=\"tensors\">A sequence of tensors of the same type. Non-empty tensors provided must have the same shape, except in the cat dimension.</param>\n /// <param name=\"dim\">The dimension over which the tensors are concatenated</param>\n /// <remarks> All tensors must either have the same shape (except in the concatenating dimension) or be empty.</remarks>\n public static Tensor cat(IList<Tensor> tensors, long dim = 0)\n {\n switch (tensors.Count)\n {\n case <=0: throw new ArgumentException(nameof(tensors));\n case 1: return tensors[0];\n }\n\n using var parray = new PinnedArray<IntPtr>();\n IntPtr tensorsRef = parray.CreateArray(tensors.Select(p => p.Handle).ToArray());\n\n var res = THSTensor_cat(tensorsRef, parray.Array.Length, dim);\n if (res == IntPtr.Zero) CheckForErrors();\n return new Tensor(res);\n }\n\n // https://pytorch.org/docs/stable/generated/torch.concat\n /// <summary>\n /// Alias of torch.cat()\n /// </summary>\n /// <param name=\"tensors\">A sequence of tensors of the same type. Non-empty tensors provided must have the same shape, except in the cat dimension.</param>\n /// <param name=\"dim\">The dimension over which the tensors are concatenated</param>\n /// <remarks> All tensors must either have the same shape (except in the concatenating dimension) or be empty.</remarks>\n public static Tensor concat(IList<Tensor> tensors, long dim = 0) => torch.cat(tensors, dim);\n\n // https://pytorch.org/docs/stable/generated/torch.conj\n /// <summary>\n /// Returns a view of input with a flipped conjugate bit. If input has a non-complex dtype, this function just returns input.\n /// </summary>\n /// <param name=\"input\">The input tensor.</param>\n public static Tensor conj(Tensor input) => input.conj();\n\n // https://pytorch.org/docs/stable/generated/torch.chunk\n public static Tensor[] chunk(Tensor input, long chunks, long dim = 0)\n => input.chunk(chunks, dim);\n\n // https://pytorch.org/docs/stable/generated/torch.dsplit\n public static Tensor[] dsplit(Tensor input, Tensor indices_or_sections)\n => input.dsplit(indices_or_sections);\n\n // https://pytorch.org/docs/stable/generated/torch.dsplit\n public static Tensor[] dsplit(Tensor input, long size)\n => input.dsplit(size);\n\n // https://pytorch.org/docs/stable/generated/torch.dsplit\n public static Tensor[] dsplit(Tensor input, long[] indices_or_sections)\n => input.dsplit(indices_or_sections);\n\n // https://pytorch.org/docs/stable/generated/torch.dsplit\n public static Tensor[] dsplit(Tensor input, (long, long) indices_or_sections)\n => input.dsplit(indices_or_sections);\n\n // https://pytorch.org/docs/stable/generated/torch.dsplit\n public static Tensor[] dsplit(Tensor input, (long, long, long) indices_or_sections)\n => input.dsplit(indices_or_sections);\n\n // https://pytorch.org/docs/stable/generated/torch.dsplit\n public static Tensor[] dsplit(Tensor input, (long, long, long, long) indices_or_sections)\n => input.dsplit(indices_or_sections);\n\n // https://pytorch.org/docs/stable/generated/torch.dstack\n /// <summary>\n /// Stack tensors in sequence depthwise (along third axis).\n /// </summary>\n /// <param name=\"tensors\"></param>\n /// <returns></returns>\n /// <remarks>This is equivalent to concatenation along the third axis after 1-D and 2-D tensors have been reshaped by torch.atleast_3d().</remarks>\n public static Tensor dstack(params Tensor[] tensors)\n => dstack((IEnumerable<Tensor>)tensors);\n\n // https://pytorch.org/docs/stable/generated/torch.dstack\n /// <summary>\n /// Stack tensors in sequence depthwise (along third axis).\n /// </summary>\n /// <param name=\"tensors\"></param>\n /// <returns></returns>\n /// <remarks>This is equivalent to concatenation along the third axis after 1-D and 2-D tensors have been reshaped by torch.atleast_3d().</remarks>\n public static Tensor dstack(IList<Tensor> tensors)\n {\n using (var parray = new PinnedArray<IntPtr>()) {\n IntPtr tensorsRef = parray.CreateArray(tensors.Select(p => p.Handle).ToArray());\n\n var res = THSTensor_dstack(tensorsRef, parray.Array.Length);\n if (res == IntPtr.Zero) { torch.CheckForErrors(); }\n return new Tensor(res);\n }\n }\n\n /// <summary>\n /// Stack tensors in sequence depthwise (along third axis).\n /// </summary>\n /// <param name=\"tensors\"></param>\n /// <returns></returns>\n /// <remarks>This is equivalent to concatenation along the third axis after 1-D and 2-D tensors have been reshaped by torch.atleast_3d().</remarks>\n public static Tensor dstack(IEnumerable<Tensor> tensors)\n {\n using var parray = new PinnedArray<IntPtr>();\n IntPtr tensorsRef = parray.CreateArray(tensors.Select(p => p.Handle).ToArray());\n var res = THSTensor_dstack(tensorsRef, parray.Array.Length);\n if (res == IntPtr.Zero) { CheckForErrors(); }\n return new Tensor(res);\n }\n \n // https://pytorch.org/docs/stable/generated/torch.gather\n /// <summary>\n /// Gathers values along an axis specified by dim.\n /// </summary>\n public static Tensor gather(Tensor input, long dim, Tensor index) => input.gather(dim, index);\n\n // https://pytorch.org/docs/stable/generated/torch.gather\n // TODO: implement parameter sparse_grad\n public static Tensor gather(Tensor input, long dim, Tensor index, bool sparse_grad=false)\n => input.gather(dim, index);\n\n // https://pytorch.org/docs/stable/generated/torch.hsplit\n public static Tensor[] hsplit(Tensor input, Tensor indices_or_sections)\n => input.hsplit(indices_or_sections);\n\n // https://pytorch.org/docs/stable/generated/torch.hsplit\n public static Tensor[] hsplit(Tensor input, long indices_or_sections)\n => input.hsplit(indices_or_sections);\n\n // https://pytorch.org/docs/stable/generated/torch.hsplit\n public static Tensor[] hsplit(Tensor input, long[] indices_or_sections)\n => input.hsplit(indices_or_sections);\n\n // https://pytorch.org/docs/stable/generated/torch.hsplit\n public static Tensor[] hsplit(Tensor input, (long, long) indices_or_sections)\n => input.hsplit(new[]{\n indices_or_sections.Item1,\n indices_or_sections.Item2\n });\n\n // https://pytorch.org/docs/stable/generated/torch.hsplit\n public static Tensor[] hsplit(Tensor input, (long, long, long) indices_or_sections)\n => input.hsplit(new[]{\n indices_or_sections.Item1,\n indices_or_sections.Item2,\n indices_or_sections.Item3\n });\n\n // https://pytorch.org/docs/stable/generated/torch.hsplit\n public static Tensor[] hsplit(Tensor input, (long, long, long, long) indices_or_sections)\n => input.hsplit(new[]{\n indices_or_sections.Item1,\n indices_or_sections.Item2,\n indices_or_sections.Item3,\n indices_or_sections.Item4\n });\n\n // https://pytorch.org/docs/stable/generated/torch.hstack\n /// <summary>\n /// Stack tensors in sequence horizontally (column wise).\n /// </summary>\n /// <param name=\"tensors\"></param>\n /// <returns></returns>\n public static Tensor hstack(IList<Tensor> tensors)\n {\n using var parray = new PinnedArray<IntPtr>();\n IntPtr tensorsRef = parray.CreateArray(tensors.Select(p => p.Handle).ToArray());\n\n var res = THSTensor_hstack(tensorsRef, parray.Array.Length);\n if (res == IntPtr.Zero) { torch.CheckForErrors(); }\n return new Tensor(res);\n }\n\n // https://pytorch.org/docs/stable/generated/torch.hstack\n /// <summary>\n /// Stack tensors in sequence horizontally (column wise).\n /// </summary>\n /// <param name=\"tensors\"></param>\n /// <returns></returns>\n public static Tensor hstack(params Tensor[] tensors)\n {\n return hstack((IEnumerable<Tensor>)tensors);\n }\n\n // https://pytorch.org/docs/stable/generated/torch.hstack\n /// <summary>\n /// Stack tensors in sequence horizontally (column wise).\n /// </summary>\n /// <param name=\"tensors\"></param>\n /// <returns></returns>\n public static Tensor hstack(IEnumerable<Tensor> tensors)\n {\n using var parray = new PinnedArray<IntPtr>();\n IntPtr tensorsRef = parray.CreateArray(tensors.Select(p => p.Handle).ToArray());\n\n var res = THSTensor_hstack(tensorsRef, parray.Array.Length);\n if (res == IntPtr.Zero) { CheckForErrors(); }\n return new Tensor(res);\n }\n\n // https://pytorch.org/docs/stable/generated/torch.index_add\n /// <summary>\n /// Accumulate the elements of alpha times source into the input tensor by adding to the indices in the order given in index.\n ///\n /// For example, if dim == 0, index[i] == j, and alpha=-1, then the ith row of source is subtracted from the jth row of the input tensor.\n /// The dimth dimension of source must have the same size as the length of index(which must be a vector), and all other dimensions must match self, or an error will be raised.\n /// </summary>\n /// <param name=\"input\">The input tensor</param>\n /// <param name=\"dim\">Dimension along which to index</param>\n /// <param name=\"index\">Indices of source to select from, should have dtype either torch.int64 or torch.int32</param>\n /// <param name=\"source\">The tensor containing values to add</param>\n /// <param name=\"alpha\">The scalar multiplier for source</param>\n /// <returns></returns>\n public static Tensor index_add(Tensor input, long dim, Tensor index, Tensor source, Scalar alpha)\n => input.index_add(dim, index, source, alpha);\n\n // https://pytorch.org/docs/stable/generated/torch.index_add\n /// <summary>\n /// Accumulate, in place, the elements of alpha times source into the input tensor by adding to the indices in the order given in index.\n ///\n /// For example, if dim == 0, index[i] == j, and alpha=-1, then the ith row of source is subtracted from the jth row of the input tensor.\n /// The dimth dimension of source must have the same size as the length of index(which must be a vector), and all other dimensions must match self, or an error will be raised.\n /// </summary>\n /// <param name=\"input\">The input tensor</param>\n /// <param name=\"dim\">Dimension along which to index</param>\n /// <param name=\"index\">Indices of source to select from, should have dtype either torch.int64 or torch.int32</param>\n /// <param name=\"source\">The tensor containing values to add</param>\n /// <param name=\"alpha\">The scalar multiplier for source</param>\n /// <returns></returns>\n public static Tensor index_add_(Tensor input, long dim, Tensor index, Tensor source, Scalar alpha)\n => input.index_add_(dim, index, source, alpha);\n\n // https://pytorch.org/docs/stable/generated/torch.index_copy\n /// <summary>\n /// Copies the elements of the source tensor into the input tensor by selecting the indices in the order given in index.\n ///\n /// For example, if dim == 0 and index[i] == j, then the ith row of tensor is copied to the jth row of the input tensor.\n /// The dimth dimension of source must have the same size as the length of index(which must be a vector), and all other dimensions must match self, or an error will be raised.\n /// </summary>\n /// <param name=\"input\">The input tensor</param>\n /// <param name=\"dim\">Dimension along which to index</param>\n /// <param name=\"index\">Indices of source to select from, should have dtype either torch.int64 or torch.int32</param>\n /// <param name=\"source\">The tensor containing values to copy</param>\n /// <returns></returns>\n public static Tensor index_copy(Tensor input, long dim, Tensor index, Tensor source)\n => input.index_copy(dim, index, source);\n\n // https://pytorch.org/docs/stable/generated/torch.index_copy\n /// <summary>\n /// Copies, in place, the elements of the source tensor into the input tensor by selecting the indices in the order given in index.\n ///\n /// For example, if dim == 0 and index[i] == j, then the ith row of tensor is copied to the jth row of the input tensor.\n /// The dimth dimension of source must have the same size as the length of index(which must be a vector), and all other dimensions must match self, or an error will be raised.\n /// </summary>\n /// <param name=\"input\">The input tensor</param>\n /// <param name=\"dim\">Dimension along which to index</param>\n /// <param name=\"index\">Indices of source to select from, should have dtype either torch.int64 or torch.int32</param>\n /// <param name=\"source\">The tensor containing values to copy</param>\n /// <returns></returns>\n public static Tensor index_copy_(Tensor input, long dim, Tensor index, Tensor source)\n => input.index_copy_(dim, index, source);\n\n // https://pytorch.org/docs/stable/generated/torch.index_reduce\n [Obsolete(\"not implemented\", true)]\n public static Tensor index_reduce(Tensor input, long dim, Tensor index, Tensor source, Reduce reduce, bool include_self=true)\n => throw new NotImplementedException();\n\n // https://pytorch.org/docs/stable/generated/torch.index_select\n /// <summary>\n /// Returns a new tensor which indexes the input tensor along dimension dim using the entries in index which is a LongTensor.\n /// </summary>\n public static Tensor index_select(Tensor input, long dim, Tensor index)\n => input.index_select(dim, index);\n\n // https://pytorch.org/docs/stable/generated/torch.masked_select\n public static Tensor masked_select(Tensor input, Tensor mask)\n => input.masked_select(mask);\n\n // https://pytorch.org/docs/stable/generated/torch.movedim\n public static Tensor movedim(Tensor input, long source, long destination)\n => input.movedim(new[]{source}, new[]{destination});\n\n // https://pytorch.org/docs/stable/generated/torch.movedim\n static Tensor movedim(Tensor input, (long, long) source, (long, long) destination)\n => input.movedim(\n new[]{source.Item1, source.Item2},\n new[]{destination.Item1, destination.Item2});\n\n // https://pytorch.org/docs/stable/generated/torch.movedim\n static Tensor movedim(Tensor input, (long, long, long) source, (long, long, long) destination)\n => input.movedim(\n new[]{source.Item1, source.Item2, source.Item3},\n new[]{destination.Item1, destination.Item2, destination.Item3});\n\n // https://pytorch.org/docs/stable/generated/torch.movedim\n static Tensor movedim(Tensor input, (long, long, long, long) source, (long, long, long, long) destination)\n => input.movedim(\n new[]{source.Item1, source.Item2, source.Item3, source.Item4},\n new[]{destination.Item1, destination.Item2, destination.Item3, destination.Item4});\n\n // https://pytorch.org/docs/stable/generated/torch.movedim\n public static Tensor movedim(Tensor input, long[] source, long[] destination)\n => input.movedim(source, destination);\n\n // https://pytorch.org/docs/stable/generated/torch.moveaxis\n public static Tensor moveaxis(Tensor input, long source, long destination)\n => input.moveaxis(new[]{source}, new[]{destination});\n\n // https://pytorch.org/docs/stable/generated/torch.moveaxis\n public static Tensor moveaxis(Tensor input, (long, long) source, (long, long) destination)\n => input.moveaxis(\n new[]{source.Item1, source.Item2 },\n new[]{ destination.Item1, destination.Item2 });\n\n // https://pytorch.org/docs/stable/generated/torch.moveaxis\n public static Tensor moveaxis(Tensor input, (long, long, long) source, (long, long, long) destination)\n => input.moveaxis(\n new[]{source.Item1, source.Item2, source.Item3 },\n new[]{ destination.Item1, destination.Item2, destination.Item3 });\n\n public static Tensor moveaxis(Tensor input, (long, long, long, long) source, (long, long, long, long) destination)\n => input.moveaxis(\n new[]{source.Item1, source.Item2, source.Item3, source.Item4 },\n new[]{ destination.Item1, destination.Item2, destination.Item3, destination.Item4 });\n\n public static Tensor moveaxis(Tensor input, long[] source, long[] destination)\n => input.moveaxis(source, destination);\n\n // https://pytorch.org/docs/stable/generated/torch.narrow\n public static Tensor narrow(Tensor input, long dim, long start, long length)\n => input.narrow(dim, start, length);\n\n // https://pytorch.org/docs/stable/generated/torch.nonzero\n /// <summary>\n /// Returns a tensor containing the indices of all non-zero elements of input.\n /// Each row in the result contains the indices of a non-zero element in input.\n /// The result is sorted lexicographically, with the last index changing the fastest (C-style).\n /// </summary>\n public static Tensor nonzero(Tensor input) => input.nonzero();\n\n // https://pytorch.org/docs/stable/generated/torch.permute\n /// <summary>\n /// Returns a view of the original tensor with its dimensions permuted.\n /// </summary>\n /// <param name=\"input\">The input tensor.</param>\n /// <param name=\"permutation\">The desired ordering of dimensions</param>\n public static Tensor permute(Tensor input, params long[] permutation) => input.permute(permutation);\n\n // https://pytorch.org/docs/stable/generated/torch.reshape\n /// <summary>\n /// Returns a tensor with the same data and number of elements as the input but with the specified shape.\n /// </summary>\n /// <param name=\"input\">The input tensor</param>\n /// <param name=\"shape\">The new tensor shape.</param>\n public static Tensor reshape(Tensor input, params long[] shape) => input.reshape(shape);\n\n // https://pytorch.org/docs/stable/generated/torch.select\n public static Tensor select(Tensor input, long dim, long index)\n => input.select(dim, index);\n\n // https://pytorch.org/docs/stable/generated/torch.scatter\n /// <summary>\n /// Writes all values from the tensor src into input at the indices specified in the index tensor. For each\n /// value in src, its output index is specified by its index in src for dimension != dim and by the #\n /// corresponding value in index for dimension = dim.\n /// </summary>\n public static Tensor scatter(Tensor input, long dim, Tensor index, Tensor src)\n => input.scatter(dim, index, src);\n\n // https://pytorch.org/docs/stable/generated/torch.scatter\n /// <summary>\n /// Writes all values from the tensor src into input at the indices specified in the index tensor. For each\n /// value in src, its output index is specified by its index in src for dimension != dim and by the #\n /// corresponding value in index for dimension = dim.\n /// </summary>\n public static Tensor scatter_(Tensor input, long dim, Tensor index, Tensor src)\n => input.scatter_(dim, index, src);\n\n // https://pytorch.org/docs/stable/generated/torch.diagonal_scatter\n /// <summary>\n /// Embeds the values of the src tensor into input along the diagonal elements of input, with respect to dim1 and dim2.\n /// </summary>\n /// <param name=\"input\">The input tensor.</param>\n /// <param name=\"src\">The tensor to embed into 'this'.</param>\n /// <param name=\"offset\">Which diagonal to consider. Default: main diagonal.</param>\n /// <param name=\"dim1\">First dimension with respect to which to take diagonal.</param>\n /// <param name=\"dim2\">Second dimension with respect to which to take diagonal.</param>\n public static Tensor diagonal_scatter(Tensor input, Tensor src, long offset = 0L, long dim1 = 0L, long dim2 = 1L) => input.diagonal_scatter(src, offset, dim1, dim2);\n\n // https://pytorch.org/docs/stable/generated/torch.select_scatter\n /// <summary>\n /// Embeds the values of the src tensor into input at the given index. This function returns a tensor with fresh storage; it does not create a view.\n /// </summary>\n /// <param name=\"input\">The input tensor.</param>\n /// <param name=\"src\">The tensor to embed into 'this'</param>\n /// <param name=\"dim\">The dimension to insert the slice into</param>\n /// <param name=\"index\">The index to select with</param>\n /// <remarks>This function returns a tensor with fresh storage; it does not create a view.</remarks>\n public static Tensor select_scatter(Tensor input, Tensor src, long dim, long index) => input.select_scatter(src, dim, index);\n\n // https://pytorch.org/docs/stable/generated/torch.slice_scatter\n /// <summary>\n /// Embeds the values of the src tensor into input at the given dimension.\n /// </summary>\n /// <param name=\"input\">The input tensor.</param>\n /// <param name=\"src\">The tensor to embed into 'this'.</param>\n /// <param name=\"dim\">The dimension to insert the slice into</param>\n /// <param name=\"start\">The start index of where to insert the slice</param>\n /// <param name=\"end\">The end index of where to insert the slice</param>\n /// <param name=\"step\">How many elements to skip</param>\n public static Tensor slice_scatter(Tensor input, Tensor src, long dim = 0L, long? start = null, long? end = null, long step = 1L)\n => input.slice_scatter(src, dim, start, end, step);\n\n // https://pytorch.org/docs/stable/generated/torch.scatter_add\n /// <summary>\n /// Adds all values from the tensor other into input at the indices specified in the index tensor in a similar fashion as scatter_().\n /// For each value in src, it is added to an index in self which is specified by its index in src for dimension != dim and by the\n /// corresponding value in index for dimension = dim.\n /// </summary>\n public static Tensor scatter_add(Tensor input, long dim, Tensor index, Tensor src)\n => input.scatter_add(dim, index, src);\n\n // https://pytorch.org/docs/stable/generated/torch.scatter_reduce\n [Obsolete(\"not implemented\", true)]\n static Tensor scatter_reduce(\n Tensor input,\n long dim,\n Tensor index,\n Tensor src,\n Reduce reduce,\n bool include_self = true)\n => throw new NotImplementedException();\n\n // https://pytorch.org/docs/stable/generated/torch.split\n public static Tensor[] split(Tensor tensor, long[] split_size_or_sections, long dim = 0L)\n => tensor.split(split_size_or_sections, dim);\n\n // https://pytorch.org/docs/stable/generated/torch.stack\n /// <summary>\n /// Concatenates a sequence of tensors along a new dimension.\n /// </summary>\n /// <returns></returns>\n /// <remarks>All tensors need to be of the same size.</remarks>\n public static Tensor stack(IEnumerable<Tensor> tensors, long dim = 0)\n {\n using var parray = new PinnedArray<IntPtr>();\n IntPtr tensorsRef = parray.CreateArray(tensors.Select(p => p.Handle).ToArray());\n\n var res = THSTensor_stack(tensorsRef, parray.Array.Length, dim);\n if (res == IntPtr.Zero) { CheckForErrors(); }\n return new Tensor(res);\n }\n\n // https://pytorch.org/docs/stable/generated/torch.swapaxes\n public static Tensor swapaxes(Tensor input, long axis0, long axis1)\n => input.swapaxes(axis0, axis1);\n\n // https://pytorch.org/docs/stable/generated/torch.swapdims\n public static Tensor swapdims(Tensor input, long dim0, long dim1)\n => input.swapdims(dim0, dim1);\n\n // https://pytorch.org/docs/stable/generated/torch.t\n public static Tensor t(Tensor input)\n => input.t();\n\n // https://pytorch.org/docs/stable/generated/torch.take\n public static Tensor take(Tensor input, Tensor index)\n => input.take(index);\n\n // https://pytorch.org/docs/stable/generated/torch.take_along_dim\n public static Tensor take_along_dim(Tensor input, Tensor indices, long dim = 0L)\n => input.take_along_dim(indices, dim);\n\n // https://pytorch.org/docs/stable/generated/torch.take_along_dim\n public static Tensor take_along_dim(Tensor input, IEnumerable<long> indices, long dim = 0L)\n => input.take_along_dim(indices, dim);\n\n // https://pytorch.org/docs/stable/generated/torch.tensor_split\n public static Tensor[] tensor_split(Tensor input, long indices_or_sections, long dim = 0L)\n => input.tensor_split(indices_or_sections, dim);\n\n // https://pytorch.org/docs/stable/generated/torch.tensor_split\n public static Tensor[] tensor_split(Tensor input, long[] indices_or_sections, long dim = 0L)\n => input.tensor_split(indices_or_sections, dim);\n\n // https://pytorch.org/docs/stable/generated/torch.tensor_split\n public static Tensor[] tensor_split(Tensor input, Tensor indices_or_sections, long dim = 0L)\n => input.tensor_split(indices_or_sections, dim);\n\n // https://pytorch.org/docs/stable/generated/torch.tile\n /// <summary>\n /// Constructs a tensor by repeating the elements of input. The dims argument specifies the number of repetitions in each dimension.\n /// </summary>\n /// <param name=\"input\">The input tensor</param>\n /// <param name=\"dims\">The number of repetitions per dimension.</param>\n public static Tensor tile(Tensor input, long[] dims) => input.tile(dims);\n\n // https://pytorch.org/docs/stable/generated/torch.transpose\n public static Tensor transpose(Tensor input, long dim0, long dim1)\n => input.transpose(dim0, dim1);\n\n // https://pytorch.org/docs/stable/generated/torch.unbind\n public static Tensor[] unbind(Tensor input, long dim = 0L)\n => input.unbind(dim);\n\n // https://pytorch.org/docs/stable/generated/torch.unsqueeze\n /// <summary>\n /// Returns a new tensor with a dimension of size one inserted at the specified position.\n /// The returned tensor shares the same underlying data with this tensor.\n /// </summary>\n public static Tensor unsqueeze(Tensor input, long dim)\n => input.unsqueeze(dim);\n\n // https://pytorch.org/docs/stable/generated/torch.unsqueeze\n /// <summary>\n /// Returns a new tensor with a dimension of size one inserted at the specified position.\n /// The returned tensor shares the same underlying data with this tensor.\n /// </summary>\n public static Tensor unsqueeze_(Tensor input, long dim)\n => input.unsqueeze_(dim);\n\n // https://pytorch.org/docs/stable/generated/torch.vsplit\n public static Tensor[] vsplit(Tensor input, long[] indices_or_sections)\n => input.vsplit(indices_or_sections);\n\n // https://pytorch.org/docs/stable/generated/torch.vstack\n /// <summary>\n /// Stack tensors in sequence vertically (row wise).\n /// </summary>\n /// <param name=\"tensors\"></param>\n /// <returns></returns>\n public static Tensor vstack(IList<Tensor> tensors)\n {\n using var parray = new PinnedArray<IntPtr>();\n IntPtr tensorsRef = parray.CreateArray(tensors.Select(p => p.Handle).ToArray());\n\n var res = THSTensor_vstack(tensorsRef, parray.Array.Length);\n if (res == IntPtr.Zero) { CheckForErrors(); }\n return new Tensor(res);\n }\n\n // https://pytorch.org/docs/stable/generated/torch.where\n /// <summary>\n /// Return a tensor of elements selected from either x or y, depending on condition.\n /// </summary>\n /// <param name=\"condition\">When true, yield x, otherwise yield y.</param>\n /// <param name=\"x\">Values selected at indices where condition is true</param>\n /// <param name=\"y\">Values selected at indices where condition is false</param>\n /// <returns></returns>\n public static Tensor where(Tensor condition, Tensor x, Tensor y) => x.where(condition, y);\n\n // https://pytorch.org/docs/stable/generated/torch.where\n /// <summary>\n /// Returns a tuple of 1-D tensors, one for each dimension in input, each containing the indices (in that dimension) of all non-zero elements of input .\n /// If input has nn dimensions, then the resulting tuple contains nn tensors of size zz, where zz is the total number of non-zero elements in the input tensor.\n /// As a special case, when input has zero dimensions and a nonzero scalar value, it is treated as a one-dimensional tensor with one element.\n /// </summary>\n /// <param name=\"condition\">The input tensor</param>\n /// <returns></returns>\n /// <exception cref=\"ArgumentException\"></exception>\n public static Tensor[] where(Tensor condition)\n {\n if (condition.dtype != ScalarType.Bool) throw new ArgumentException(\"The condition to 'where' must be a boolean tensor.\");\n\n IntPtr[] ptrArray;\n\n using (var pa = new PinnedArray<IntPtr>()) {\n THSTensor_where_list(condition.Handle, pa.CreateArray);\n CheckForErrors();\n ptrArray = pa.Array;\n }\n\n return ptrArray.Select(x => new Tensor(x)).ToArray();\n }\n }\n}"},{"alpha_fraction":0.5130791664123535,"alphanum_fraction":0.5197653770446777,"avg_line_length":45.08648681640625,"blob_id":"e8830d173acb3a031c7329a7cb33cf9190070171","content_id":"e0b4c495849cdc10d11ff96aad3d0ee6cbd35d5c","detected_licenses":["MIT"],"is_generated":false,"is_vendor":false,"language":"C#","length_bytes":8525,"license_type":"permissive","max_line_length":130,"num_lines":185,"path":"/src/TorchAudio/Transforms.cs","repo_name":"NiklasGustafsson/TorchSharp","src_encoding":"UTF-8","text":"// Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information.\nusing System;\nusing static TorchSharp.torch;\nusing TorchSharp.Transforms;\n\n// A number of implementation details in this file have been translated from the Python version of torchaudio,\n// largely located in the files found in this folder:\n//\n// https://github.com/pytorch/audio/blob/bb77cbebb620a46fdc0dc7e6dae2253eef3f37e2/torchaudio/transforms/_transforms.py\n//\n// The origin has the following copyright notice and license:\n//\n// https://github.com/pytorch/audio/blob/main/LICENSE\n//\n\nnamespace TorchSharp\n{\n public static partial class torchaudio\n {\n public delegate torch.Tensor WindowFunction(long win_length);\n\n public static partial class transforms\n {\n /// <summary>\n /// Compute spectrograms from audio signals.\n /// </summary>\n /// <param name=\"n_fft\">The size of Fourier transform</param>\n /// <param name=\"hop_length\">The hop length</param>\n /// <param name=\"win_length\">The window length</param>\n /// <param name=\"pad\">Padding on the sides</param>\n /// <param name=\"window_fn\">The callback to create a window function</param>\n /// <param name=\"window\">The window function</param>\n /// <param name=\"power\">Exponent for the magnitude spectrogram</param>\n /// <param name=\"normalized\">Whether the output is normalized, or not.</param>\n /// <param name=\"center\">Whether the t-th frame is centered around t * hop_window, or not.</param>\n /// <param name=\"pad_mode\">The padding mode used when center is true.</param>\n /// <param name=\"onesided\">Whether the output is onesided or not.</param>\n /// <param name=\"return_complex\">Deprecated and not used.</param>\n /// <returns>ITransform to compute spectrograms of audio signals</returns>\n public static Spectrogram Spectrogram(\n long n_fft = 400,\n long? win_length = null,\n long? hop_length = null,\n long pad = 0,\n WindowFunction window_fn = null,\n Tensor window = null,\n double? power = 2.0,\n bool normalized = false,\n bool center = true,\n PaddingModes pad_mode = PaddingModes.Reflect,\n bool onesided = true,\n bool? return_complex = null)\n {\n return new Spectrogram(\n \"Spectrogram\",\n n_fft: n_fft,\n hop_length: hop_length,\n win_length: win_length,\n pad: pad,\n window_fn: window_fn,\n window: window,\n power: power,\n normalized: normalized,\n center: center,\n pad_mode: pad_mode,\n onesided: onesided,\n return_complex: return_complex);\n }\n\n /// <summary>\n /// Compute inverse of spectrogram.\n /// </summary>\n /// <param name=\"n_fft\">The size of Fourier transform</param>\n /// <param name=\"hop_length\">The hop length</param>\n /// <param name=\"win_length\">The window length</param>\n /// <param name=\"pad\">Padding on the sides</param>\n /// <param name=\"window_fn\">The callback to create a window function</param>\n /// <param name=\"window\">The window function</param>\n /// <param name=\"normalized\">Whether the output is normalized, or not.</param>\n /// <param name=\"center\">Whether the t-th frame is centered around t * hop_window, or not.</param>\n /// <param name=\"pad_mode\">The padding mode used when center is true.</param>\n /// <param name=\"onesided\">Whether the output is onesided or not.</param>\n /// <returns>ITransform to compute inverse of spectrogram</returns>\n public static InverseSpectrogram InverseSpectrogram(\n long n_fft = 400,\n long? win_length = null,\n long? hop_length = null,\n long pad = 0,\n WindowFunction window_fn = null,\n Tensor window = null,\n bool normalized = false,\n bool center = true,\n PaddingModes pad_mode = PaddingModes.Reflect,\n bool onesided = true)\n {\n return new InverseSpectrogram(\n \"InverseSpectrogram\",\n n_fft: n_fft,\n hop_length: hop_length,\n win_length: win_length,\n pad: pad,\n window_fn: window_fn,\n window: window,\n normalized: normalized,\n center: center,\n pad_mode: pad_mode,\n onesided: onesided);\n }\n\n /// <summary>\n /// Resample the waveform\n /// </summary>\n /// <param name=\"orig_freq\">The source sampling rate</param>\n /// <param name=\"new_freq\">The destination sampling rate</param>\n /// <param name=\"lowpass_filter_width\">The width of the filter</param>\n /// <param name=\"rolloff\">The roll-off frequency</param>\n /// <param name=\"resampling_method\">The resampling method</param>\n /// <param name=\"beta\">Beta for Keizer window</param>\n /// <param name=\"device\">The device</param>\n /// <param name=\"dtype\">The scalar type</param>\n /// <returns>The resampled waveform</returns>\n /// <exception cref=\"ArgumentOutOfRangeException\"></exception>\n public static Resample Resample(\n int orig_freq = 16000,\n int new_freq = 16000,\n ResamplingMethod resampling_method = ResamplingMethod.sinc_interpolation,\n int lowpass_filter_width = 6,\n double rolloff = 0.99,\n double? beta = null,\n torch.Device device = null,\n torch.ScalarType? dtype = null)\n {\n return new Resample(\n \"Resample\",\n orig_freq,\n new_freq,\n resampling_method,\n lowpass_filter_width,\n rolloff,\n beta,\n device,\n dtype);\n }\n\n /// <summary>\n /// Compute waveform from a linear scale magnitude spectrogram using the Griffin-Lim transformation.\n /// </summary>\n /// <param name=\"n_fft\">Size of FFT, creates ``n_fft // 2 + 1`` bins.</param>\n /// <param name=\"n_iter\">Number of iteration for phase recovery process.</param>\n /// <param name=\"win_length\">Window size.</param>\n /// <param name=\"hop_length\">Length of hop between STFT windows.</param>\n /// <param name=\"window_fn\">A function to create a window tensor\n /// that is applied/multiplied to each frame/window.</param>\n /// <param name=\"power\">Exponent for the magnitude spectrogram,\n /// (must be > 0) e.g., 1 for energy, 2 for power, etc.</param>\n /// <param name=\"momentum\">The momentum parameter for fast Griffin-Lim.</param>\n /// <param name=\"length\">Array length of the expected output.</param>\n /// <param name=\"rand_init\">Initializes phase randomly if True and to zero otherwise.</param>\n /// <returns></returns>\n public static GriffinLim GriffinLim(\n int n_fft = 400,\n int n_iter = 32,\n long? win_length = null,\n long? hop_length = null,\n WindowFunction window_fn = null,\n double power = 2.0,\n double momentum = 0.99,\n int? length = null,\n bool rand_init = true)\n {\n return new GriffinLim(\n \"GriffinLim\",\n n_fft,\n n_iter,\n win_length,\n hop_length,\n window_fn,\n power,\n momentum,\n length,\n rand_init);\n }\n }\n }\n}"},{"alpha_fraction":0.5553860068321228,"alphanum_fraction":0.5590479373931885,"avg_line_length":39.456790924072266,"blob_id":"5b0d4fdac7d305df1ea68710fcefcdd3e307543d","content_id":"5df3b78737001a5b6cc9d47e0ecdfe7122d66648","detected_licenses":["MIT"],"is_generated":false,"is_vendor":false,"language":"C#","length_bytes":3277,"license_type":"permissive","max_line_length":172,"num_lines":81,"path":"/src/TorchSharp/Distributions/Pareto.cs","repo_name":"NiklasGustafsson/TorchSharp","src_encoding":"UTF-8","text":"// Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information.\nusing System;\nusing static TorchSharp.torch;\n\nnamespace TorchSharp\n{\n using Modules;\n using static torch.distributions;\n\n namespace Modules\n {\n public class Pareto : TransformedDistribution\n {\n internal Pareto(Tensor scale, Tensor alpha, Distribution base_distribution, torch.distributions.transforms.Transform[] transforms, Generator generator = null) :\n base(base_distribution, transforms, generator)\n {\n this.scale = scale.alias().DetachFromDisposeScope();\n this.alpha = alpha.alias().DetachFromDisposeScope();\n }\n\n public Tensor scale { get; private set; }\n\n public Tensor alpha { get; private set; }\n\n public override Tensor mean {\n get {\n using var _ = torch.NewDisposeScope();\n var a = alpha.clamp(min: 1);\n return (a * scale / (a - 1)).MoveToOuterDisposeScope();\n }\n }\n\n public override Tensor mode => scale;\n\n public override Tensor variance {\n get {\n using var _ = torch.NewDisposeScope();\n var a = alpha.clamp(min: 2);\n return (scale.pow(2) * a / ((a - 1).pow(2) * (a - 2))).MoveToOuterDisposeScope();\n }\n }\n\n public override Distribution expand(Size batch_shape, Distribution instance = null)\n {\n var newDistribution = ((instance == null)\n ? torch.distributions.Pareto(scale.expand(batch_shape), alpha.expand(batch_shape), generator)\n : instance) as Pareto;\n return base.expand(batch_shape, newDistribution);\n }\n\n public override Tensor entropy() => torch.WrappedTensorDisposeScope(() => (scale / alpha).log() + 1 + alpha.reciprocal());\n }\n }\n\n\n public static partial class torch\n {\n public static partial class distributions\n {\n /// <summary>\n /// Samples from a Pareto Type 1 distribution.\n /// </summary>\n /// <param name=\"scale\">Scale parameter of the distribution.</param>\n /// <param name=\"alpha\">Shape parameter of the distribution</param>\n /// <param name=\"generator\">An optional random number generator object.</param>\n /// <returns></returns>\n public static Pareto Pareto(Tensor scale, Tensor alpha, torch.Generator generator = null)\n {\n var scaleAlpha = torch.broadcast_tensors(scale, alpha);\n scale = scaleAlpha[0];\n alpha = scaleAlpha[1];\n var base_dist = Exponential(alpha, generator);\n var transforms = new torch.distributions.transforms.Transform[] {\n new torch.distributions.transforms.ExpTransform(),\n new torch.distributions.transforms.AffineTransform(loc:0, scale:scale)\n };\n return new Pareto(scale, alpha, base_dist, transforms, generator);\n }\n }\n }\n}\n"},{"alpha_fraction":0.5217464566230774,"alphanum_fraction":0.5250704288482666,"avg_line_length":46.97297286987305,"blob_id":"06c0944d9719839e4b8f2522cbd24ac54eec1855","content_id":"250d9ac5ed563bf8e629b51eda263aaf0c37abfd","detected_licenses":["MIT"],"is_generated":false,"is_vendor":false,"language":"C#","length_bytes":17750,"license_type":"permissive","max_line_length":236,"num_lines":370,"path":"/src/TorchSharp/Optimizers/SGD.cs","repo_name":"NiklasGustafsson/TorchSharp","src_encoding":"UTF-8","text":"// Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information.\nusing System;\nusing System.Collections.Generic;\nusing System.Linq;\nusing static TorchSharp.torch;\n\nnamespace TorchSharp\n{\n using System.IO;\n using Modules;\n\n public static partial class torch\n {\n public static partial class optim\n {\n /// <summary>\n /// Implements the stochastic gradient descent (optionally with momentum).\n ///\n /// The use of momentum is covered in:\n /// http://www.cs.toronto.edu/%7Ehinton/absps/momentum.pdf\n /// </summary>\n /// <param name=\"parameters\">Parameters to optimize. This optimizer requires the <b>named</b> parameters collection.</param>\n /// <param name=\"learningRate\">Learning rate</param>\n /// <param name=\"momentum\">Momentum factor (default: 0)</param>\n /// <param name=\"dampening\">Dampening for momentum (default: 0)</param>\n /// <param name=\"weight_decay\">Weight decay (L2 penalty) (default: 0)</param>\n /// <param name=\"nesterov\">Enables Nesterov momentum (default: False)</param>\n /// <param name=\"maximize\"></param>\n /// <returns></returns>\n public static Modules.SGD SGD(IEnumerable<Parameter> parameters, double learningRate, double momentum = 0, double dampening = 0, double weight_decay = 0, bool nesterov = false, bool maximize = false)\n {\n return new Modules.SGD(parameters, learningRate, momentum, dampening, weight_decay, nesterov, maximize);\n }\n\n /// <summary>\n /// Implements the stochastic gradient descent (optionally with momentum).\n ///\n /// The use of momentum is covered in:\n /// http://www.cs.toronto.edu/%7Ehinton/absps/momentum.pdf\n /// </summary>\n /// <param name=\"parameters\">Parameters to optimize. This optimizer requires the <b>named</b> parameters collection.</param>\n /// <param name=\"learningRate\">Learning rate</param>\n /// <param name=\"momentum\">Momentum factor (default: 0)</param>\n /// <param name=\"dampening\">Dampening for momentum (default: 0)</param>\n /// <param name=\"weight_decay\">Weight decay (L2 penalty) (default: 0)</param>\n /// <param name=\"nesterov\">Enables Nesterov momentum (default: False)</param>\n /// <param name=\"maximize\"></param>\n /// <returns></returns>\n public static Modules.SGD SGD(IEnumerable<(string name, Parameter parameter)> parameters, double learningRate, double momentum = 0, double dampening = 0, double weight_decay = 0, bool nesterov = false, bool maximize = false)\n {\n return new Modules.SGD(parameters.Select(np => np.parameter), learningRate, momentum, dampening, weight_decay, nesterov, maximize);\n }\n\n /// <summary>\n /// Implements the stochastic gradient descent (optionally with momentum).\n ///\n /// The use of momentum is covered in:\n /// http://www.cs.toronto.edu/%7Ehinton/absps/momentum.pdf\n /// </summary>\n /// <param name=\"parameters\">Parameters to optimize. This optimizer requires the <b>named</b> parameters collection.</param>\n /// <param name=\"learningRate\">Learning rate</param>\n /// <param name=\"momentum\">Momentum factor (default: 0)</param>\n /// <param name=\"dampening\">Dampening for momentum (default: 0)</param>\n /// <param name=\"weight_decay\">Weight decay (L2 penalty) (default: 0)</param>\n /// <param name=\"nesterov\">Enables Nesterov momentum (default: False)</param>\n /// <param name=\"maximize\"></param>\n /// <returns></returns>\n public static Modules.SGD SGD(IEnumerable<SGD.ParamGroup> parameters, double learningRate, double momentum = 0.0, double dampening = 0, double weight_decay = 0, bool nesterov = false, bool maximize = false)\n {\n return new Modules.SGD(parameters, learningRate, momentum, dampening, weight_decay, nesterov, maximize);\n }\n }\n }\n namespace Modules\n {\n using static torch.optim;\n\n public class SGD : OptimizerHelper, IMomentum\n {\n /// <summary>\n /// Implements stochastic gradient descent (optionally with momentum).\n /// </summary>\n /// <param name=\"parameters\">Parameters to optimize. This optimizer requires the <b>named</b> parameters collection.</param>\n /// <param name=\"lr\">Learning rate</param>\n /// <param name=\"momentum\">Momentum factor (default: 0)</param>\n /// <param name=\"dampening\">Dampening for momentum (default: 0)</param>\n /// <param name=\"weight_decay\">Weight decay (L2 penalty) (default: 0)</param>\n /// <param name=\"nesterov\">Enables Nesterov momentum (default: False)</param>\n /// <param name=\"maximize\"></param>\n /// <returns></returns>\n public SGD(IEnumerable<Parameter> parameters, double lr, double momentum = 0.0, double dampening = 0, double weight_decay = 0, bool nesterov = false, bool maximize = false)\n : this(new ParamGroup[] { new ParamGroup { Parameters = parameters } }, lr, momentum, dampening, weight_decay, nesterov, maximize)\n {\n }\n\n /// <summary>\n /// Implements stochastic gradient descent (optionally with momentum).\n /// </summary>\n /// <param name=\"parameters\">Parameters to optimize. This optimizer requires the <b>named</b> parameters collection.</param>\n /// <param name=\"lr\">Learning rate</param>\n /// <param name=\"momentum\">Momentum factor (default: 0)</param>\n /// <param name=\"dampening\">Dampening for momentum (default: 0)</param>\n /// <param name=\"weight_decay\">Weight decay (L2 penalty) (default: 0)</param>\n /// <param name=\"nesterov\">Enables Nesterov momentum (default: False)</param>\n /// <param name=\"maximize\"></param>\n /// <returns></returns>\n public SGD(IEnumerable<ParamGroup> parameters, double lr, double momentum = 0.0, double dampening = 0, double weight_decay = 0, bool nesterov = false, bool maximize = false)\n {\n if (lr < 0.0) throw new ArgumentException($\"Invalid learning rate: {lr}\");\n if (momentum < 0.0) throw new ArgumentException($\"Invalid momentum value: {momentum}\");\n if (weight_decay < 0.0) throw new ArgumentException($\"Invalid weight_decay value: {weight_decay}\");\n if (nesterov && (momentum <= 0 || dampening != 0)) throw new ArgumentException(\"Nesterov momentum requires a momentum and zero dampening\");\n\n var options = new Options {\n LearningRate = lr,\n InitialLearningRate = lr,\n dampening = dampening,\n maximize = maximize,\n momentum = momentum,\n nesterov = nesterov,\n weight_decay = weight_decay\n };\n\n _defaults = options;\n _parameter_groups = new List<Modules.ParamGroup>();\n\n foreach (var g in parameters) {\n add_param_group(g);\n }\n }\n\n /// <summary>\n /// Performs a single optimization step (parameter update).\n /// </summary>\n /// <param name=\"closure\">A closure that reevaluates the model and returns the loss. Optional for most optimizers.</param>\n /// <returns></returns>\n public override Tensor step(Func<Tensor> closure = null)\n {\n return _step<ParamGroup>(group => {\n\n var options = group.Options;\n var momentum = options.momentum.Value;\n var dampening = options.dampening.Value;\n var weight_decay = options.weight_decay.Value;\n var nesterov = options.nesterov.Value;\n var maximize = options.maximize.Value;\n var lr = options.LearningRate.Value;\n\n foreach (var param in group.Parameters) {\n\n var state = (State)_state[param.handle];\n\n var grad = param.grad();\n\n if (grad is null) continue;\n\n if (weight_decay != 0) {\n grad = grad.add(param, alpha: weight_decay);\n }\n\n if (momentum != 0) {\n var buf = state.momentum_buffer;\n\n if (buf is null) {\n buf = grad.clone().detach().DetachFromDisposeScope();\n state.momentum_buffer = buf;\n } else {\n buf.mul_(momentum).add_(grad, alpha: (1 - dampening));\n }\n\n if (nesterov) {\n grad = grad.add(buf, alpha: momentum);\n } else {\n grad = buf;\n }\n\n state.momentum_buffer = buf;\n }\n\n var alpha = maximize ? lr : -lr;\n param.add_(grad, alpha: alpha);\n\n }\n }, closure);\n }\n\n protected override void Dispose(bool disposing)\n {\n base.Dispose(disposing);\n foreach (var kvp in _state) {\n var st = (State)kvp.Item2;\n if (st.momentum_buffer is not null) {\n st.momentum_buffer.Dispose();\n }\n }\n _state.Clear();\n }\n\n public sealed class State : OptimizerState, IDisposable\n {\n public Tensor momentum_buffer;\n\n public void Dispose()\n {\n Dispose(true);\n GC.SuppressFinalize(this);\n }\n\n private void Dispose(bool disposing)\n {\n if (disposing) {\n momentum_buffer?.Dispose();\n }\n }\n\n /// <summary>\n /// Move all the state to the indicated device.\n /// </summary>\n /// <param name=\"device\">The device to move all state to.</param>\n public override void to(Device device)\n {\n momentum_buffer = momentum_buffer?.to(device);\n }\n\n /// <summary>\n /// Load the optimizer parameter state from a stream.\n /// </summary>\n /// <param name=\"reader\">A binary reader connected to a stream open for reading.</param>\n public override void LoadStateDict(BinaryReader reader)\n {\n LoadConditionalStateTensor(reader, ref momentum_buffer);\n }\n\n /// <summary>\n /// Save the optimizer parameter state to a stream.\n /// </summary>\n /// <param name=\"writer\">A binary writer connected to a stream open for writing.</param>\n public override void SaveStateDict(BinaryWriter writer)\n {\n SaveConditionalStateTensor(writer, momentum_buffer);\n }\n\n /// <summary>\n /// Load optimizer parameter state from another optimizer.\n /// </summary>\n /// <param name=\"source\">An optimizer state record.</param>\n public override void LoadStateDict(OptimizerState source)\n {\n var st_state = source as State;\n if (momentum_buffer is not null) {\n momentum_buffer.Dispose();\n }\n momentum_buffer = st_state.momentum_buffer;\n }\n\n /// <summary>\n /// Useful for tests, allows comparison of one state with another.\n /// </summary>\n /// <param name=\"other\">The other optimizer state</param>\n /// <returns></returns>\n public override bool ApproximatelyEquals(OptimizerState other)\n {\n var rhs = other as State;\n return (rhs is not null) && (momentum_buffer is null || momentum_buffer.allclose(rhs.momentum_buffer));\n }\n }\n\n /// <summary>\n /// Add a param group to the Optimizer s param_groups.\n /// </summary>\n /// <param name=\"param_group\"></param>\n /// <remarks>This can be useful when fine tuning a pre-trained network as frozen layers can be made trainable and added to the Optimizer as training progresses.</remarks>\n public override void add_param_group(Modules.ParamGroup param_group)\n {\n var def = _defaults as Options;\n if (param_group.Options is null) {\n param_group.Options = new Options();\n }\n\n var opt = param_group.Options as Options;\n\n // Make sure all the options are set.\n if (!opt.LearningRate.HasValue) opt.LearningRate = def.LearningRate;\n if (!opt.momentum.HasValue) opt.momentum = def.momentum;\n if (!opt.dampening.HasValue) opt.dampening = def.dampening;\n if (!opt.weight_decay.HasValue) opt.weight_decay = def.weight_decay;\n if (!opt.nesterov.HasValue) opt.nesterov = def.nesterov;\n if (!opt.maximize.HasValue) opt.maximize = def.maximize;\n\n opt.InitialLearningRate = opt.LearningRate.Value;\n\n _parameter_groups.Add(param_group);\n\n foreach (var p in param_group.Parameters) {\n var state = new State();\n _state.Add((p.Handle,state));\n state.momentum_buffer = null;\n }\n }\n\n public class Options : Modules.OptimizerOptions\n {\n public double? momentum;\n public double? dampening;\n public double? weight_decay;\n public bool? nesterov;\n public bool? maximize;\n\n /// <summary>\n /// Load the optimizer options (param-group hyperparameters) from a stream.\n /// </summary>\n /// <param name=\"reader\">A binary reader connected to a stream open for reading.</param>\n public override void LoadStateDict(BinaryReader reader)\n {\n base.LoadStateDict(reader);\n momentum = reader.ReadDouble();\n dampening = reader.ReadDouble();\n weight_decay = reader.ReadDouble();\n nesterov = reader.ReadBoolean();\n maximize = reader.ReadBoolean();\n }\n\n /// <summary>\n /// Load optimizer options (param-group hyperparameters) from another optimizer.\n /// </summary>\n /// <param name=\"source\">An optimizer options record.</param>\n public override void LoadStateDict(OptimizerOptions source)\n {\n base.LoadStateDict(source);\n var opts = source as Options;\n momentum = opts.momentum;\n dampening = opts.dampening;\n weight_decay = opts.weight_decay;\n nesterov = opts.nesterov;\n maximize = opts.maximize;\n }\n\n /// <summary>\n /// Save the optimizer options (param-group hyperparameters) to a stream.\n /// </summary>\n /// <param name=\"writer\">A binary writer connected to a stream open for writing.</param>\n public override void SaveStateDict(BinaryWriter writer)\n {\n base.SaveStateDict(writer);\n writer.Write(momentum.Value);\n writer.Write(dampening.Value);\n writer.Write(weight_decay.Value);\n writer.Write(nesterov.Value);\n writer.Write(maximize.Value);\n }\n }\n\n public class ParamGroup : ParamGroup<Options>, IMomentum\n {\n public ParamGroup() { }\n\n public ParamGroup(IEnumerable<Parameter> parameters, Options options) : base(parameters, options) { }\n\n public ParamGroup(IEnumerable<Parameter> parameters, double lr = 1e-3, double momentum = 0.0, double dampening = 0, double weight_decay = 0, bool nesterov = false, bool maximize = false)\n : base(parameters, new SGD.Options { LearningRate = lr, dampening = dampening, momentum = momentum, weight_decay = weight_decay, nesterov = nesterov, maximize = maximize })\n {\n }\n\n public double Momentum { get => Options.momentum.Value; set => Options.momentum = value; }\n }\n\n public double Momentum { get => (_defaults as Options).momentum.Value; set => (_defaults as Options).momentum = value; }\n }\n }\n}\n"},{"alpha_fraction":0.535237193107605,"alphanum_fraction":0.5380009412765503,"avg_line_length":30.463768005371094,"blob_id":"5828959b8aa25fc858d2d99eeea6656e4b456a29","content_id":"aedcf10155a9f7cad45aa0cab880239f49cc9582","detected_licenses":["MIT"],"is_generated":false,"is_vendor":false,"language":"C#","length_bytes":2171,"license_type":"permissive","max_line_length":161,"num_lines":69,"path":"/src/TorchSharp/Utils/TensorDataset.cs","repo_name":"NiklasGustafsson/TorchSharp","src_encoding":"UTF-8","text":"// Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information.\n\nusing System;\nusing System.IO;\nusing System.Collections.Generic;\n\nnamespace TorchSharp\n{\n using System.Data;\n using System.Linq;\n using Modules;\n\n public static partial class torch\n {\n public static partial class utils\n {\n public static partial class data {\n\n /// <summary>\n /// Dataset wrapping tensors.\n ///\n /// Each sample will be retrieved by indexing tensors along the first dimension.\n /// </summary>\n /// <param name=\"tensors\">Tensors that have the same size of the first dimension.</param>\n public static TensorDataset TensorDataset(params torch.Tensor[] tensors) => new TensorDataset(tensors);\n }\n }\n }\n\n namespace Modules\n {\n public class TensorDataset : torch.utils.data.Dataset<IList<torch.Tensor>>\n {\n internal TensorDataset(torch.Tensor[] tensors)\n {\n if (tensors is null || tensors.Length == 0) throw new ArgumentNullException(nameof(tensors));\n long size1 = tensors[0].shape[0];\n if (!tensors.All(t => t.shape[0] == size1)) throw new ArgumentException(\"All tensors must have the same first dimension size.\", nameof(tensors));\n\n _tensors.AddRange(tensors);\n }\n\n /// <summary>\n /// Indexer\n /// </summary>\n public IList<torch.Tensor> this[long index] {\n\n get {\n return _tensors.Select(t => t[index]).ToList();\n }\n }\n\n /// <summary>\n /// Length of the dataset\n /// </summary>\n public override long Count {\n get { return _tensors.Count; }\n }\n\n public override IList<torch.Tensor> GetTensor(long index)\n {\n return this[index];\n }\n\n private List<torch.Tensor> _tensors = new List<torch.Tensor>();\n }\n\n }\n}\n"},{"alpha_fraction":0.532310426235199,"alphanum_fraction":0.5464738607406616,"avg_line_length":42.46154022216797,"blob_id":"62b29d7481339376d2112fe2571fcbda72356a57","content_id":"6324757a29cdfd9ec0100fc63f5b275327869d6c","detected_licenses":["MIT"],"is_generated":false,"is_vendor":false,"language":"C#","length_bytes":3389,"license_type":"permissive","max_line_length":130,"num_lines":78,"path":"/src/TorchAudio/Datasets/YesnoDataset.cs","repo_name":"NiklasGustafsson/TorchSharp","src_encoding":"UTF-8","text":"// Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information.\nusing System;\nusing System.IO;\nusing System.Linq;\n\nnamespace TorchSharp\n{\n public static partial class torchaudio\n {\n public static partial class datasets\n {\n private class YesnoDataset : torch.utils.data.Dataset<YesnoDatasetItem>\n {\n public const string ArchiveChecksum = \"c3f49e0cca421f96b75b41640749167b52118f232498667ca7a5f9416aef8e73\";\n\n private readonly string[] audioPathList;\n\n public YesnoDataset(string directoryPathInArchive)\n {\n audioPathList = Directory.EnumerateFiles(directoryPathInArchive, \"*.wav\").ToArray();\n }\n\n public override long Count => audioPathList.LongLength;\n\n public override YesnoDatasetItem GetTensor(long index)\n {\n var (waveform, sample_rate) = torchaudio.load(audioPathList[index]);\n return new() {\n waveform = waveform,\n sample_rate = sample_rate,\n labels = null\n };\n }\n }\n\n /// <summary>\n /// Create a Yesno dataset\n /// </summary>\n /// <param name=\"root\">The path to the dataset</param>\n /// <param name=\"url\">The URL to download the dataset from</param>\n /// <param name=\"folder_in_archive\">The top directory of the dataset</param>\n /// <param name=\"download\">True to download the dataset</param>\n /// <returns>The dataset</returns>\n /// <exception cref=\"InvalidDataException\"></exception>\n public static torch.utils.data.Dataset<YesnoDatasetItem> YESNO(\n string root,\n string url = \"http://www.openslr.org/resources/1/waves_yesno.tar.gz\",\n string folder_in_archive = \"waves_yesno\",\n bool download = false)\n {\n var directoryPathInArchive = Path.Combine(root, folder_in_archive);\n var archiveFileName = GetFileNameFromUrl(url);\n var archivePath = Path.Combine(root, archiveFileName);\n if (download) {\n if (!Directory.Exists(directoryPathInArchive)) {\n if (!File.Exists(archivePath)) {\n torch.hub.download_url_to_file(url, archivePath, YesnoDataset.ArchiveChecksum);\n }\n utils.extract_archive(archivePath, root);\n }\n }\n if (!Directory.Exists(directoryPathInArchive)) {\n throw new InvalidDataException(\"Dataset not found. Please use `download=true` to download it.\");\n }\n return new YesnoDataset(directoryPathInArchive);\n }\n\n private static string GetFileNameFromUrl(string url)\n {\n int index = url.LastIndexOf('/');\n if (index < 0) throw new ArgumentException();\n var fileName = url.Substring(index + 1);\n if (string.IsNullOrWhiteSpace(fileName)) throw new ArgumentException();\n return fileName;\n }\n }\n }\n}"},{"alpha_fraction":0.8063063025474548,"alphanum_fraction":0.8063063025474548,"avg_line_length":23.77777862548828,"blob_id":"b3676c49ea15108b35c41a4b93af7e419eac305b","content_id":"7a1bee53a7cc355bdf0a9996f7d9d5036ffc180c","detected_licenses":["MIT"],"is_generated":false,"is_vendor":false,"language":"C#","length_bytes":224,"license_type":"permissive","max_line_length":70,"num_lines":9,"path":"/src/TorchSharp/DistanceFunctionNative.cs","repo_name":"NiklasGustafsson/TorchSharp","src_encoding":"UTF-8","text":"#nullable enable\nusing System;\nusing System.Runtime.InteropServices;\n\nnamespace TorchSharp\n{\n [UnmanagedFunctionPointer(CallingConvention.Cdecl)]\n public delegate IntPtr DistanceFunctionNative(IntPtr x, IntPtr y);\n}"},{"alpha_fraction":0.7151066064834595,"alphanum_fraction":0.7199258804321289,"avg_line_length":38.37956237792969,"blob_id":"714c6ff073ebd7e04b490bd8a6ff7d33dd44f572","content_id":"7b423e5a44cf0b990a3762d92384a44cb0e58628","detected_licenses":["MIT"],"is_generated":false,"is_vendor":false,"language":"Markdown","length_bytes":5395,"license_type":"permissive","max_line_length":410,"num_lines":137,"path":"/docfx/articles/torchscript.md","repo_name":"NiklasGustafsson/TorchSharp","src_encoding":"UTF-8","text":"# TorchScript\n\nTorchSchript is a PyTorch technology that lets you save a subset of PyTorch-based Python code without a dependency on a Python runtime. Such models can be loaded into native code, and therefore into .NET code.\n\nTorchScript is very powerful, because it allows you to save the logic and the weights of a model together, and it furthermore allows the module to be loaded into another program, __without any dependencies on the Python runtime.__ Thus, you can load a model that has been serialized using TorchScript and have it behave as any TorchScript module -- you can use it for training, or you can use it for inference.\n\n## Loading TorchScript Modules\n\nStarting with release 0.96.9, you can load TorchScript modules and functions that have been either traced or scripted in Pytorch. It is, however, not yet possible to create a TorchScript module from scratch using TorchSharp. \n\nIn Python, a TorchScript module can be a class derived from 'nn.Module,' or a function that operates on tensors and stays within the constraints that TorchScript places on it. The script can be formed by tracing or by compiling the code. Refer to the [Pytorch JIT](https://pytorch.org/docs/stable/jit.html) docs for information on the details.\n\nFor example, the following Python code creates a TorchScript module. Note the use of type annotations\n\n```Python\nfrom typing import Tuple\nimport torch\nfrom torch import nn\nfrom torch import Tensor\n\nclass MyModule(nn.Module):\n def __init__(self):\n super().__init__()\n self.p = nn.Parameter(torch.rand(10))\n\n def forward(self, x: Tensor, y: Tensor) -> Tuple[Tensor, Tensor]:\n return x + y, x - y\n \n @torch.jit.export\n def predict(self, x: Tensor) -> Tensor:\n return x + self.p\n\n @torch.jit.export\n def add_scalar(self, x: Tensor, i: int) -> Tensor:\n return x + i\n\nm = MyModule()\n\nm = torch.jit.script(m)\nm.save(\"exported.method.dat\")\n```\n\nThe following can also be used to create a TorchScript script:\n\n```Python\n@torch.jit.script\ndef a(x: Tensor, y: Tensor):\n return x + y, x - y\n```\n\nOnce you have a TorchScript file, you can load it into TorchSharp using:\n\n```C#\nvar m = torch.jit.load(\"file-name\");\n```\n\nIt returns a ScriptModule, which behaves just like any other TorchSharp module. Whether the original script came from a module or a function, it is deserialized as a module. You can use it for training of inference by calling either `train()` or `eval()`. ScriptModules always start out on the CPU, so you have to call `cuda()` in order to move it to a GPU.\n\nNote that if you used __tracing__ to create the TorchScript file in Pytorch, submodules that behave differently in training and eval modes will behave according to the mode they were traced in.\n\nIf you use the script module to train, you may want / need to save it afterwards. \n\nThat is easily done using `save()`:\n\n```C#\ntorch.jit.save(m, \"file-name\");\n```\n\nThere's also a type-safe version of `load()` that returns a script module that implements IModule<T1...,TResult>. For the above example(s) `torch.jit.load<Tensor,Tensor,(Tensor, Tensor)>` is the appropriate one to use:\n\n```C#\nvar m = torch.jit.load<Tensor,Tensor,Tensor>(\"file-name\");\n```\n\nWhile it is possible to save a modified ScriptModule from TorchSharp, it is not (yet) possible to create one _from scratch_ using either tracing or scripting.\n\nScript modules have a main `forward()` method, even if they were created from a function rather than a Module class. However, if the module was created from a class, and other methods have the 'torch.jit.export' annotation, then those methods are also callable from TorchSharp:\n\n```C#\nvar x = torch.rand(10);\nvar y = torch.rand(10);\nvar t0 = m.forward(x,y);\nvar t1 = m.invoke(\"predict\", x);\nvar t2 = m.invoke(\"add_scalar\", x, 3.14);\n```\n\nNote how methods other than `forward()` must be invoked with their name passed as a string. It may be useful to wrap such modules in a hand-written class, like:\n\n```C#\nclass TestScriptModule : Module<Tensor, Tensor, (Tensor, Tensor)>\n{\n internal TestScriptModule(string filename) : base(nameof(TestScriptModule))\n {\n m = torch.jit.load<(Tensor, Tensor)> (filename);\n }\n\n public override (Tensor, Tensor) forward(Tensor input1, Tensor input2)\n {\n return m.forward(input1, input2);\n }\n\n public Tensor predict(Tensor input)\n {\n return m.invoke<Tensor>(\"predict\", input);\n }\n\n public Tensor add_scalar(Tensor input, int i)\n {\n return m.invoke<Tensor>(\"add_scalar\", input, i);\n }\n\n private torch.jit.ScriptModule<Tensor, Tensor, (Tensor, Tensor)> m;\n}\n```\n\n## Compiling TorchScript from text\n\nBesides the ability to load TorchScript modules from files, TorchSharp also offers the ability to create them from Python source code directly using the `torch.jit.compile()` method. This results not in a ScriptModule, but a CompilationUnit, which can contain methods, classes, etc. \n\nRight now, TorchSharp only offers support for using methods compiled from source:\n\n```C#\nstring script = @\"\n def relu_script(a, b):\n return torch.relu(a + b)\n def add_i(x: Tensor, i: int) -> Tensor:\n return x + i\n\";\n\nusing var cu = torch.jit.compile(script);\n\nvar x = torch.randn(3, 4);\nvar y = torch.randn(3, 4);\n\nvar z = (Tensor)cu.invoke(\"relu_script\", x, y); // Return type is 'object'\nz = cu.invoke<Tensor>(\"add_i\", x, 1); // Type-safe return type.\n```\n"},{"alpha_fraction":0.5667069554328918,"alphanum_fraction":0.5806126594543457,"avg_line_length":43.702701568603516,"blob_id":"5de9405d5b0eed44bda48eef78b1bbc45385a791","content_id":"57524da88b14e80e9703b708a438387be278e80f","detected_licenses":["MIT"],"is_generated":false,"is_vendor":false,"language":"C#","length_bytes":4962,"license_type":"permissive","max_line_length":163,"num_lines":111,"path":"/src/TorchVision/Ops/SqueezeExcitation.cs","repo_name":"NiklasGustafsson/TorchSharp","src_encoding":"UTF-8","text":"// Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information.\n\n// A number of implementation details in this file have been translated from the Python version of torchvision,\n// largely located in the files found in this folder:\n//\n// https://github.com/pytorch/vision/blob/a4f53308b2d0f1aa9191686e326f45c26053f686/torchvision/ops/misc.py\n//\n// The origin has the following copyright notice and license:\n//\n// https://github.com/pytorch/vision/blob/main/LICENSE\n//\n\nusing System;\nusing System.Collections.Generic;\nusing System.Linq;\nusing static TorchSharp.torch;\n\n#nullable enable\nnamespace TorchSharp\n{\n public static partial class torchvision\n {\n public static partial class ops\n {\n /// <summary>\n /// This block implements the Squeeze-and-Excitation block from https://arxiv.org/abs/1709.01507\n /// Parameters ``activation``, and ``scale_activation`` correspond to ``delta`` and ``sigma`` in eq. 3.\n /// </summary>\n /// <param name=\"input_channels\">Number of channels in the input image</param>\n /// <param name=\"squeeze_channels\">Number of squeeze channels</param>\n /// <param name=\"activation\">`delta` activation.</param>\n /// <param name=\"scale_activation\">`sigma` activation</param>\n public static SqueezeExcitation SqueezeExcitation(\n long input_channels,\n long squeeze_channels,\n Func<nn.Module<Tensor, Tensor>>? activation = null,\n Func<nn.Module<Tensor, Tensor>>? scale_activation = null) => new SqueezeExcitation(input_channels, squeeze_channels, activation, scale_activation);\n }\n\n /// <summary>\n /// This block implements the Squeeze-and-Excitation block from https://arxiv.org/abs/1709.01507\n /// Parameters ``activation``, and ``scale_activation`` correspond to ``delta`` and ``sigma`` in eq. 3.\n /// </summary>\n public class SqueezeExcitation : torch.nn.Module<Tensor, Tensor>\n {\n private readonly nn.Module<Tensor, Tensor> avgpool;\n private readonly nn.Module<Tensor, Tensor> fc1;\n private readonly nn.Module<Tensor, Tensor> fc2;\n private readonly nn.Module<Tensor, Tensor> activation;\n private readonly nn.Module<Tensor, Tensor> scale_activation;\n\n private long input_channels;\n\n /// <summary>\n /// Constructor\n /// </summary>\n /// <param name=\"input_channels\">Number of channels in the input image</param>\n /// <param name=\"squeeze_channels\">Number of squeeze channels</param>\n /// <param name=\"activation\">``delta`` activation</param>\n /// <param name=\"scale_activation\">``sigma`` activation.</param>\n public SqueezeExcitation(\n long input_channels,\n long squeeze_channels,\n Func<nn.Module<Tensor, Tensor>>? activation = null,\n Func<nn.Module<Tensor, Tensor>>? scale_activation = null) : base(nameof(SqueezeExcitation))\n {\n this.input_channels = input_channels;\n\n this.avgpool = torch.nn.AdaptiveAvgPool2d(1);\n this.fc1 = torch.nn.Conv2d(input_channels, squeeze_channels, 1);\n this.fc2 = torch.nn.Conv2d(squeeze_channels, input_channels, 1);\n this.activation = activation is null ? nn.ReLU() : activation();\n this.scale_activation = scale_activation is null ? nn.Sigmoid() : scale_activation();\n\n RegisterComponents();\n }\n\n private Tensor _scale(Tensor input)\n {\n var scale = this.avgpool.call(input);\n scale = this.fc1.call(scale);\n scale = this.activation.call(scale);\n scale = this.fc2.call(scale);\n return this.scale_activation.call(scale);\n }\n\n public override Tensor forward(Tensor input)\n {\n if ((input.ndim == 4 && input.shape[1] == input_channels) ||\n (input.ndim == 3 && input.shape[0] == input_channels)) {\n using var _ = NewDisposeScope();\n var scale = this._scale(input);\n return (scale * input).MoveToOuterDisposeScope();\n }\n throw new ArgumentException(\"Expected 3D (unbatched) or 4D (batched) input to SqueezeExcitation\");\n }\n\n protected override void Dispose(bool disposing)\n {\n if (disposing) {\n avgpool.Dispose();\n fc1.Dispose();\n fc2.Dispose();\n activation.Dispose();\n scale_activation.Dispose();\n }\n base.Dispose(disposing);\n }\n }\n }\n}\n"},{"alpha_fraction":0.5585940480232239,"alphanum_fraction":0.5642493963241577,"avg_line_length":48.889286041259766,"blob_id":"e5c4914f0bb26e0303e461ff7c2cb091ed78b6c2","content_id":"f5e50f15ff6020d805fa60d4cf0a85deab505cda","detected_licenses":["MIT"],"is_generated":false,"is_vendor":false,"language":"C#","length_bytes":13969,"license_type":"permissive","max_line_length":200,"num_lines":280,"path":"/src/TorchSharp/Distributions/MultiVariateNormal.cs","repo_name":"NiklasGustafsson/TorchSharp","src_encoding":"UTF-8","text":"// Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information.\nusing System;\nusing System.Collections.Generic;\nusing System.Linq;\nusing static TorchSharp.torch;\n\nnamespace TorchSharp\n{\n using System.Numerics;\n using Modules;\n\n namespace Modules\n {\n /// <summary>\n /// A multivariate normal (also called Gaussian) distribution parameterized by a mean vector and a covariance matrix.\n ///\n /// The multivariate normal distribution can be parameterized either in terms of a positive definite covariance matrix\n /// or a positive definite precision matrix or a lower-triangular matrix with positive-valued diagonal entries. This triangular matrix\n /// can be obtained via Cholesky decomposition of the covariance.\n /// </summary>\n public class MultivariateNormal : torch.distributions.Distribution\n {\n /// <summary>\n /// The mean of the distribution.\n /// </summary>\n public override Tensor mean => loc;\n\n /// <summary>\n /// The mode of the distribution.\n /// </summary>\n public override Tensor mode => loc;\n\n /// <summary>\n /// The variance of the distribution\n /// </summary>\n public override Tensor variance =>\n WrappedTensorDisposeScope(() => _unbroadcasted_scale_tril.pow(2).sum(-1).expand(batch_shape + event_shape));\n\n /// <summary>\n /// Constructor\n /// </summary>\n /// <param name=\"loc\"></param>\n /// <param name=\"covariance_matrix\">Positive-definite covariance matrix</param>\n /// <param name=\"precision_matrix\">Positive-definite precision matrix</param>\n /// <param name=\"scale_tril\">The lower-triangular factor of covariance, with positive-valued diagonal</param>\n /// <param name=\"generator\">An optional random number generator object.</param>\n /// <remarks>\n /// Only one of `covariance_matrix` or `precision_matrix` or `scale_tril` may be specified.\n ///\n /// Using `scale_tril` will be more efficient: all computations internally are based on `scale_tril`.\n /// If `covariance_matrix` or `precision_matrix` is passed instead, it is only used to compute\n /// the corresponding lower triangular matrices using a Cholesky decomposition.\n /// </remarks>\n public MultivariateNormal(Tensor loc, Tensor covariance_matrix = null, Tensor precision_matrix = null, Tensor scale_tril = null, torch.Generator generator = null) : base(generator)\n {\n var argCount = 0;\n argCount += (covariance_matrix is null ? 0 : 1);\n argCount += (precision_matrix is null ? 0 : 1);\n argCount += (scale_tril is null ? 0 : 1);\n\n if (argCount != 1)\n throw new ArgumentException(\"Exactly one of covariance_matrix or precision_matrix or scale_tril may be specified.\");\n\n using var _ = NewDisposeScope();\n\n if (scale_tril is not null) {\n if (scale_tril.dim() < 2) {\n throw new ArgumentException(\"scale_tril matrix must be at least two-dimensional, with optional leading batch dimensions\");\n }\n batch_shape = torch.broadcast_shapes(TakeAllBut(scale_tril.shape, 2), TakeAllBut(loc.shape, 1));\n this.scale_tril = scale_tril.expand(batch_shape + (-1, -1)).DetachFromDisposeScope();\n _unbroadcasted_scale_tril = scale_tril;\n } else if (covariance_matrix is not null) {\n if (covariance_matrix.dim() < 2) {\n throw new ArgumentException(\"covariance_matrix matrix must be at least two-dimensional, with optional leading batch dimensions\");\n }\n batch_shape = torch.broadcast_shapes(TakeAllBut(covariance_matrix.shape, 2).ToArray(), TakeAllBut(loc.shape, 1).ToArray());\n this.covariance_matrix = covariance_matrix.expand(batch_shape + (-1, -1)).DetachFromDisposeScope();\n _unbroadcasted_scale_tril = torch.linalg.cholesky(covariance_matrix).DetachFromDisposeScope();\n } else {\n if (precision_matrix.dim() < 2) {\n throw new ArgumentException(\"precision_matrix matrix must be at least two-dimensional, with optional leading batch dimensions\");\n }\n batch_shape = torch.broadcast_shapes(TakeAllBut(precision_matrix.shape, 2).ToArray(), TakeAllBut(loc.shape, 1).ToArray());\n this.precision_matrix = precision_matrix.expand(batch_shape + (-1, -1)).DetachFromDisposeScope();\n _unbroadcasted_scale_tril = PrecisionToScaleTril(precision_matrix).DetachFromDisposeScope();\n }\n\n this.loc = loc.expand(batch_shape + -1).DetachFromDisposeScope();\n\n this.event_shape = loc.shape[loc.shape.Length-1];\n }\n\n private MultivariateNormal(torch.Generator generator = null) : base(generator) { }\n\n private Tensor loc;\n private Tensor scale_tril;\n private Tensor precision_matrix;\n private Tensor covariance_matrix;\n\n private Tensor _unbroadcasted_scale_tril;\n\n /// <summary>\n /// Generates a sample_shape shaped reparameterized sample or sample_shape shaped batch of reparameterized samples\n /// if the distribution parameters are batched.\n /// </summary>\n /// <param name=\"sample_shape\">The sample shape.</param>\n public override Tensor rsample(params long[] sample_shape)\n {\n using var _ = NewDisposeScope();\n\n var shape = ExtendedShape(sample_shape);\n var eps = torch.empty(shape, dtype: loc.dtype, device: loc.device).normal_(generator:generator);\n return (loc + BatchMV(_unbroadcasted_scale_tril, eps)).MoveToOuterDisposeScope();\n }\n\n /// <summary>\n /// Returns the log of the probability density/mass function evaluated at `value`.\n /// </summary>\n /// <param name=\"value\"></param>\n public override Tensor log_prob(Tensor value)\n {\n using var _ = NewDisposeScope();\n\n var diff = value - loc;\n var M = BatchMahalanobis(_unbroadcasted_scale_tril, diff);\n var half_log_det = _unbroadcasted_scale_tril.diagonal(dim1: -2, dim2: -1).log().sum(-1);\n return (-0.5 * (event_shape[0] * Math.Log(2 * Math.PI) + M) - half_log_det).MoveToOuterDisposeScope();\n }\n\n /// <summary>\n /// Returns a new distribution instance (or populates an existing instance provided by a derived class) with batch dimensions expanded to\n /// `batch_shape`. This method calls `torch.Tensor.expand()` on the distribution's parameters. As such, this does not allocate new\n /// memory for the expanded distribution instance.\n /// </summary>\n /// <param name=\"batch_shape\">Tthe desired expanded size.</param>\n /// <param name=\"instance\">new instance provided by subclasses that need to override `.expand`.</param>\n public override distributions.Distribution expand(Size batch_shape, distributions.Distribution instance = null)\n {\n if (instance != null && !(instance is MultivariateNormal))\n throw new ArgumentException(\"expand(): 'instance' must be a MultivariateNormal distribution\");\n\n var newDistribution = ((instance == null) ? new MultivariateNormal(generator) : instance) as MultivariateNormal;\n\n var loc_shape = batch_shape + event_shape;\n var cov_shape = batch_shape + event_shape + event_shape;\n\n newDistribution.loc = loc.expand(loc_shape);\n newDistribution._unbroadcasted_scale_tril = _unbroadcasted_scale_tril;\n newDistribution.scale_tril = scale_tril?.expand(cov_shape);\n newDistribution.covariance_matrix = covariance_matrix?.expand(cov_shape).DetachFromDisposeScope();\n newDistribution.precision_matrix = precision_matrix?.expand(cov_shape).DetachFromDisposeScope();\n\n newDistribution.batch_shape = batch_shape;\n newDistribution.event_shape = event_shape;\n\n return newDistribution;\n }\n\n /// <summary>\n /// Returns entropy of distribution, batched over batch_shape.\n /// </summary>\n /// <returns></returns>\n public override Tensor entropy()\n {\n using var _ = NewDisposeScope();\n\n var half_log_det = _unbroadcasted_scale_tril.diagonal(dim1: -2, dim2: -1).log().sum(-1);\n var H = 0.5 * event_shape[0] * (1 + Math.Log(2 * Math.PI)) + half_log_det;\n\n return ((batch_shape.Length == 0) ? H : H.expand(batch_shape)).MoveToOuterDisposeScope();\n }\n\n private Tensor BatchMV(Tensor bmat, Tensor bvec)\n {\n using var _ = NewDisposeScope();\n return torch.matmul(bmat, bvec.unsqueeze(-1)).squeeze(-1).MoveToOuterDisposeScope();\n }\n\n private IEnumerable<long> Range(long start, long end, long step = 1)\n {\n var result = new List<long>();\n for (long i = start; i < end; i += step) {\n result.Add(i);\n }\n return result;\n }\n\n private long[] TakeAllBut(long[] input, int count)\n {\n var result = new long[input.Length - count];\n for (int i = 0; i < result.Length; i++)\n result[i] = input[i];\n return result;\n }\n\n private Tensor BatchMahalanobis(Tensor bL, Tensor bx)\n {\n using var _ = NewDisposeScope();\n\n var n = bx.size(-1);\n var bx_batch_shape = TakeAllBut(bx.shape, 1);\n\n var bx_batch_dims = bx_batch_shape.Length;\n var bL_batch_dims = bL.dim() - 2;\n int outer_batch_dims = bx_batch_dims - (int)bL_batch_dims;\n var old_batch_dims = outer_batch_dims + bL_batch_dims;\n var new_batch_dims = outer_batch_dims + 2 * bL_batch_dims;\n\n var bx_new_shape = TakeAllBut(bx.shape, outer_batch_dims).ToList();\n\n for (int i = 0; i < bL.ndim - 2; i++) {\n var sL = bL.shape[i];\n var sx = bx.shape[outer_batch_dims + i];\n bx_new_shape.Add(sx / sL);\n bx_new_shape.Add(sL);\n }\n bx_new_shape.Add(n);\n bx = bx.reshape(bx_new_shape.ToArray());\n\n List<long> permute_dims = new List<long>();\n permute_dims.AddRange(Range(0, outer_batch_dims));\n permute_dims.AddRange(Range(outer_batch_dims, new_batch_dims, 2));\n permute_dims.AddRange(Range(outer_batch_dims + 1, new_batch_dims, 2));\n permute_dims.Add(new_batch_dims);\n\n bx = bx.permute(permute_dims.ToArray());\n\n var flat_L = bL.reshape(-1, n, n);\n var flat_x = bx.reshape(-1, flat_L.size(0), n);\n var flat_x_swap = flat_x.permute(1, 2, 0);\n\n var M_swap = torch.linalg.solve_triangular(flat_L, flat_x_swap, upper: false).pow(2).sum(-2);\n var M = M_swap.t();\n\n var permuted_M = M.reshape(TakeAllBut(bx.shape, 1));\n var permuted_inv_dims = Range(0, outer_batch_dims).ToList();\n for (int i = 0; i < bL_batch_dims; i++) {\n permuted_inv_dims.Add(outer_batch_dims + i);\n permuted_inv_dims.Add(old_batch_dims + i);\n }\n\n var reshaped_M = permuted_M.permute(permuted_inv_dims);\n\n return reshaped_M.reshape(bx_batch_shape).MoveToOuterDisposeScope();\n }\n\n private Tensor PrecisionToScaleTril(Tensor P)\n {\n using var _ = NewDisposeScope();\n var Lf = torch.linalg.cholesky(torch.flip(P, -2, -1));\n var L_inv = torch.transpose(torch.flip(Lf, -2, -1), -2, -1);\n var Id = torch.eye(P.shape[P.shape.Length - 1], dtype: P.dtype, device: P.device);\n var L = torch.linalg.solve_triangular(L_inv, Id, upper: false);\n return L.MoveToOuterDisposeScope();\n }\n }\n\n }\n public static partial class torch\n {\n public static partial class distributions\n {\n /// <summary>\n /// Creates a MultivariateNormal distribution parameterized by `probs` or `logits` (but not both).\n /// `total_count` must be broadcastable with `probs`/`logits`.\n /// </summary>\n /// <param name=\"loc\"></param>\n /// <param name=\"covariance_matrix\"></param>\n /// <param name=\"precision_matrix\"></param>\n /// <param name=\"scale_tril\"></param>\n /// <param name=\"generator\">An optional random number generator object.</param>\n /// <returns></returns>\n public static MultivariateNormal MultivariateNormal(Tensor loc, Tensor covariance_matrix = null, Tensor precision_matrix = null, Tensor scale_tril = null, torch.Generator generator = null)\n {\n return new MultivariateNormal(loc, covariance_matrix, precision_matrix, scale_tril, generator);\n }\n }\n }\n}\n"},{"alpha_fraction":0.5440872311592102,"alphanum_fraction":0.5500094294548035,"avg_line_length":60.491329193115234,"blob_id":"53720992fcb8093412dc8d366d603608250ddc7b","content_id":"9536603d057629a83ddea7d3f52cc6a7d0e0da9d","detected_licenses":["MIT"],"is_generated":false,"is_vendor":false,"language":"C#","length_bytes":10638,"license_type":"permissive","max_line_length":306,"num_lines":173,"path":"/src/TorchSharp/NN/Pooling/MaxPool3D.cs","repo_name":"NiklasGustafsson/TorchSharp","src_encoding":"UTF-8","text":"// Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information.\nusing System;\nusing System.Linq;\nusing static TorchSharp.torch;\nusing static TorchSharp.PInvoke.NativeMethods;\n\nnamespace TorchSharp\n{\n using Modules;\n\n namespace Modules\n {\n /// <summary>\n /// This class is used to represent a MaxPool3D module.\n /// </summary>\n public sealed class MaxPool3d : torch.nn.Module<Tensor, Tensor>\n {\n internal MaxPool3d(IntPtr handle, IntPtr boxedHandle) : base(handle, boxedHandle)\n {\n }\n\n public override Tensor forward(Tensor tensor)\n {\n var res = THSNN_MaxPool3d_forward(handle, tensor.Handle);\n if (res == IntPtr.Zero) { torch.CheckForErrors(); }\n return new Tensor(res);\n }\n\n public (Tensor Values, Tensor Indices) forward_with_indices(Tensor tensor)\n {\n var res = THSNN_MaxPool3d_forward_with_indices(handle, tensor.Handle, out var indices);\n if (res == IntPtr.Zero || indices == IntPtr.Zero) { torch.CheckForErrors(); }\n return (new Tensor(res), new Tensor(indices));\n }\n\n // Rather than spending cycles only to discover that this module has neither\n // parameters nor buffers, just shortcut the move completely.\n protected internal override nn.Module _to(Device device, ScalarType dtype) => this;\n protected internal override nn.Module _to(DeviceType deviceType, int deviceIndex = -1) => this;\n protected internal override nn.Module _to(ScalarType dtype) => this;\n }\n }\n\n public static partial class torch\n {\n public static partial class nn\n {\n /// <summary>\n /// Applies a 3D max pooling over an input signal composed of several input planes.\n /// </summary>\n /// <param name=\"kernelSize\">The size of the sliding window, must be > 0.</param>\n /// <param name=\"stride\">The stride of the sliding window, must be > 0. Default value is kernel_size.</param>\n /// <param name=\"padding\">Implicit negative infinity padding to be added on both sides, must be >= 0 and less than or equal to kernel_size / 2</param>\n /// <param name=\"dilation\">The stride between elements within a sliding window, must be > 0.</param>\n /// <param name=\"ceilMode\">If true, will use ceil instead of floor to compute the output shape. This ensures that every element in the input tensor is covered by a sliding window.</param>\n /// <returns></returns>\n public static MaxPool3d MaxPool3d(long kernelSize, long? stride = null, long? padding = null, long? dilation = null, bool ceilMode = false)\n {\n var pStride = stride.HasValue ? new long[] { stride.Value, stride.Value, stride.Value } : null;\n var pPadding = padding.HasValue ? new long[] { padding.Value, padding.Value, padding.Value } : null;\n var pDilation = dilation.HasValue ? new long[] { dilation.Value, dilation.Value, dilation.Value } : null;\n return MaxPool3d(new long[] { kernelSize, kernelSize, kernelSize }, pStride, pPadding, pDilation, ceilMode);\n }\n\n /// <summary>\n /// Applies a 3D max pooling over an input signal composed of several input planes.\n /// </summary>\n /// <param name=\"kernelSize\">The size of the sliding window, must be > 0.</param>\n /// <param name=\"stride\">The stride of the sliding window, must be > 0. Default value is kernel_size.</param>\n /// <param name=\"padding\">Implicit negative infinity padding to be added on both sides, must be >= 0 and less than or equal to kernel_size / 2</param>\n /// <param name=\"dilation\">The stride between elements within a sliding window, must be > 0.</param>\n /// <param name=\"ceilMode\">If true, will use ceil instead of floor to compute the output shape. This ensures that every element in the input tensor is covered by a sliding window.</param>\n /// <returns></returns>\n public static MaxPool3d MaxPool3d((long, long, long) kernelSize, (long, long, long)? stride = null, (long, long, long)? padding = null, (long, long, long)? dilation = null, bool ceilMode = false)\n {\n var pStride = stride.HasValue ? new long[] { stride.Value.Item1, stride.Value.Item2, stride.Value.Item3 } : null;\n var pPadding = padding.HasValue ? new long[] { padding.Value.Item1, padding.Value.Item2, padding.Value.Item3 } : null;\n var pDilation = dilation.HasValue ? new long[] { dilation.Value.Item1, dilation.Value.Item2, dilation.Value.Item3 } : null;\n return MaxPool3d(new long[] { kernelSize.Item1, kernelSize.Item2, kernelSize.Item3 }, pStride, pPadding, pDilation, ceilMode);\n }\n\n /// <summary>\n /// Applies a 3D max pooling over an input signal composed of several input planes.\n /// </summary>\n /// <param name=\"kernelSize\">The size of the sliding window, must be > 0.</param>\n /// <param name=\"strides\">The stride of the sliding window, must be > 0. Default value is kernel_size.</param>\n /// <param name=\"padding\">Implicit negative infinity padding to be added on both sides, must be >= 0 and less than or equal to kernel_size / 2</param>\n /// <param name=\"dilation\">The stride between elements within a sliding window, must be > 0.</param>\n /// <param name=\"ceilMode\">If true, will use ceil instead of floor to compute the output shape. This ensures that every element in the input tensor is covered by a sliding window.</param>\n /// <returns></returns>\n public static MaxPool3d MaxPool3d(long[] kernelSize, long[] strides = null, long[] padding = null, long[] dilation = null, bool ceilMode = false)\n {\n unsafe {\n fixed (long* pkernelSize = kernelSize, pstrides = strides, pPadding = padding, pDilation = dilation) {\n var handle = THSNN_MaxPool3d_ctor((IntPtr)pkernelSize, kernelSize.Length, (IntPtr)pstrides, (strides == null ? 0 : strides.Length), (IntPtr)pPadding, (padding == null ? 0 : padding.Length), (IntPtr)pDilation, (dilation == null ? 0 : dilation.Length), ceilMode, out var boxedHandle);\n if (handle == IntPtr.Zero) { torch.CheckForErrors(); }\n return new MaxPool3d(handle, boxedHandle);\n }\n }\n }\n\n public static partial class functional\n {\n /// <summary>\n /// Applies a 3D max pooling over an input signal composed of several input planes.\n /// </summary>\n /// <param name=\"input\">The input tensor.</param>\n /// <param name=\"kernelSize\"></param>\n /// <param name=\"strides\"></param>\n /// <param name=\"padding\"></param>\n /// <param name=\"dilation\"></param>\n /// <param name=\"ceil_mode\"></param>\n /// <returns></returns>\n public static Tensor max_pool3d(Tensor input, long[] kernelSize, long[] strides = null,\n long[] padding = null, long[] dilation = null, bool ceil_mode = false)\n {\n strides = strides ?? kernelSize.Select(x => 1L).ToArray();\n padding = padding ?? kernelSize.Select(x => 0L).ToArray();\n dilation = dilation ?? kernelSize.Select(x => 1L).ToArray();\n unsafe {\n fixed (long* pkernelSize = kernelSize, pstrides = strides, ppadding = padding, pdilation = dilation) {\n var res =\n THSTensor_max_pool3d(input.Handle,\n (IntPtr)pkernelSize, kernelSize.Length,\n (IntPtr)pstrides, strides.Length,\n (IntPtr)ppadding, padding.Length,\n (IntPtr)pdilation, dilation.Length,\n ceil_mode);\n if (res == IntPtr.Zero) { torch.CheckForErrors(); }\n return new Tensor(res);\n }\n }\n }\n\n /// <summary>\n /// Applies a 3D max pooling over an input signal composed of several input planes.\n /// </summary>\n /// <param name=\"input\">The input tensor.</param>\n /// <param name=\"kernelSize\"></param>\n /// <param name=\"strides\"></param>\n /// <param name=\"padding\"></param>\n /// <param name=\"dilation\"></param>\n /// <param name=\"ceil_mode\"></param>\n /// <returns></returns>\n public static (Tensor output, Tensor indices) max_pool3d_with_indices(Tensor input, long[] kernelSize, long[] strides = null,\n long[] padding = null, long[] dilation = null, bool ceil_mode = false)\n {\n strides = strides ?? kernelSize.Select(x => 1L).ToArray();\n padding = padding ?? kernelSize.Select(x => 0L).ToArray();\n dilation = dilation ?? kernelSize.Select(x => 1L).ToArray();\n IntPtr[] ptrArray;\n\n using (var pa = new PinnedArray<IntPtr>()) {\n unsafe {\n fixed (long* pkernelSize = kernelSize, pstrides = strides, ppadding = padding, pdilation = dilation) {\n THSTensor_max_pool3d_with_indices(input.Handle,\n pa.CreateArray,\n (IntPtr)pkernelSize, kernelSize.Length,\n (IntPtr)pstrides, strides.Length,\n (IntPtr)ppadding, padding.Length,\n (IntPtr)pdilation, dilation.Length,\n ceil_mode);\n torch.CheckForErrors();\n }\n }\n ptrArray = pa.Array;\n }\n return (new Tensor(ptrArray[0]), new Tensor(ptrArray[1]));\n }\n }\n }\n }\n}\n"},{"alpha_fraction":0.5308775901794434,"alphanum_fraction":0.5373781323432922,"avg_line_length":33.8301887512207,"blob_id":"4db15dc01e676a64ce9aaf92ee908990b730c2b0","content_id":"89cd0b5ae63ef42ef217d2a0532ebe9f52cd96df","detected_licenses":["MIT"],"is_generated":false,"is_vendor":false,"language":"C#","length_bytes":1846,"license_type":"permissive","max_line_length":130,"num_lines":53,"path":"/src/TorchVision/AdjustGamma.cs","repo_name":"NiklasGustafsson/TorchSharp","src_encoding":"UTF-8","text":"// Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information.\nusing System;\nusing static TorchSharp.torch;\n\nnamespace TorchSharp\n{\n public static partial class torchvision\n {\n internal class AdjustGamma : ITransform\n {\n internal AdjustGamma(double gamma, double gain = 1.0)\n {\n if (gamma < 0.0)\n throw new ArgumentException($\"The saturation factor ({gamma}) must be non-negative.\");\n this.gamma = gamma;\n this.gain = gain;\n }\n\n public Tensor call(Tensor img)\n {\n var dtype = img.dtype;\n if (!torch.is_floating_point(img))\n img = transforms.ConvertImageDtype(torch.float32).call(img);\n\n img = (gain * img.pow(gamma)).clamp(0, 1);\n\n return transforms.ConvertImageDtype(dtype).call(img); ;\n }\n\n private double gamma;\n private double gain;\n }\n\n public static partial class transforms\n {\n /// <summary>\n /// Perform gamma correction on an image.\n ///\n /// See: https://en.wikipedia.org/wiki/Gamma_correction\n /// </summary>\n /// <param name=\"gamma\">\n /// Non negative real number.\n /// gamma larger than 1 make the shadows darker, while gamma smaller than 1 make dark regions lighter.\n /// </param>\n /// <param name=\"gain\">The constant multiplier in the gamma correction equation.</param>\n /// <returns></returns>\n static public ITransform AdjustGamma(double gamma, double gain = 1.0)\n {\n return new AdjustGamma(gamma);\n }\n }\n }\n}\n"},{"alpha_fraction":0.580381453037262,"alphanum_fraction":0.5953678488731384,"avg_line_length":29.625,"blob_id":"608516b7c52ad86a70df5288f81f0524677cd051","content_id":"7fa461630b74088004d0f3bbabb95c874d13ee92","detected_licenses":["MIT"],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":734,"license_type":"permissive","max_line_length":92,"num_lines":24,"path":"/test/TorchSharpTest/bug510.py","repo_name":"NiklasGustafsson/TorchSharp","src_encoding":"UTF-8","text":"import torch\nimport src.Python.exportsd as exportsd\n\nclass BasicConv1d(torch.nn.Module):\n def __init__(self, in_channels, out_channels, **kwargs):\n super().__init__()\n \n self.stack = torch.nn.Sequential(\n torch.nn.Conv1d(in_channels, out_channels, kernel_size=3, bias=False, **kwargs),\n torch.nn.BatchNorm1d(out_channels),\n torch.nn.ReLU(inplace=True)\n )\n \n def forward(self, x):\n return self.stack(x)\n\nif __name__ == '__main__':\n # Create model\n model = BasicConv1d(1, 32)\n \n #Export model to .dat file for ingestion into TorchSharp\n f = open(\"bug510.dat\", \"wb\")\n exportsd.save_state_dict(model.to(\"cpu\").state_dict(), f)\n f.close()"},{"alpha_fraction":0.44665011763572693,"alphanum_fraction":0.44665011763572693,"avg_line_length":28.512195587158203,"blob_id":"e3a9f6c8e6e1a651fbdac3d68b6919beb5581516","content_id":"a09356d4a6b9af6a4b6731b665d0f9f32fd346a1","detected_licenses":["MIT"],"is_generated":false,"is_vendor":false,"language":"C#","length_bytes":1209,"license_type":"permissive","max_line_length":130,"num_lines":41,"path":"/src/TorchAudio/Datasets/SpeechCommandsDatasetItem.cs","repo_name":"NiklasGustafsson/TorchSharp","src_encoding":"UTF-8","text":"// Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information.\n\nnamespace TorchSharp\n{\n public static partial class torchaudio\n {\n public static partial class datasets\n {\n /// <summary>\n /// An item in SPEECHCOMMANDS dataset.\n /// </summary>\n public class SpeechCommandsDatasetItem\n {\n /// <summary>\n /// Samples of the audio clip\n /// </summary>\n public torch.Tensor waveform;\n\n /// <summary>\n /// Sampling rate of the audio clip\n /// </summary>\n public int sample_rate;\n\n /// <summary>\n /// Labels of the audio clip, 'bird', 'yes', ...\n /// </summary>\n public string label;\n\n /// <summary>\n /// Speaker ID\n /// </summary>\n public string speaker_id;\n\n /// <summary>\n /// Utterance number\n /// </summary>\n public int utterance_number;\n }\n }\n }\n}"},{"alpha_fraction":0.5736129283905029,"alphanum_fraction":0.6063783168792725,"avg_line_length":27.612499237060547,"blob_id":"34a02e4e1e1660cbb2b0e026c56ea2b3043be64f","content_id":"ddd5432443e86bd624c76149ec46132af1999499","detected_licenses":["MIT"],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":2289,"license_type":"permissive","max_line_length":129,"num_lines":80,"path":"/src/Python/importsd.py","repo_name":"NiklasGustafsson/TorchSharp","src_encoding":"UTF-8","text":"#\n# Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information.\n#\nimport torch\nimport leb128\nimport numpy as np\nfrom collections import OrderedDict\n\n\n_DTYPE_SIZE_MAP = {\n np.uint8: 1,\n np.int8: 1,\n np.int16: 2,\n np.int32: 4,\n np.int64: 8,\n np.float16: 2,\n np.float32: 4,\n np.float64: 8,\n}\n\n\ndef _get_elem_type(type_num: int):\n if type_num == 0:\n return np.uint8\n elif type_num == 1:\n return np.int8\n elif type_num == 2:\n return np.int16\n elif type_num == 3:\n return np.int32\n elif type_num == 4:\n return np.int64\n elif type_num == 5:\n return np.float16\n elif type_num == 6:\n return np.float32\n elif type_num == 7:\n return np.float64\n elif type_num == 11:\n # return torch.bool\n raise NotImplemented(\"Unsupported data type\")\n elif type_num == 15:\n # return torch.bfloat16\n raise NotImplemented(\"Unsupported data type\")\n elif type_num == 4711:\n raise NotImplemented(\"Unsupported data type\")\n else:\n raise ValueError(\"cannot decode the data type\")\n\n\ndef load_state_dict(stream):\n \"\"\"\n Loads a PyTorch state dictionary using the format that saved by TorchSharp.\n\n :param stream: An write stream opened for binary I/O.\n :return sd: A dictionary can be loaded by 'model.load_state_dict()'\n \"\"\"\n sd = OrderedDict()\n dict_len, _ = leb128.u.decode_reader(stream)\n for i in range(dict_len):\n key_len, _ = leb128.u.decode_reader(stream)\n key_name = stream.read(key_len).decode(\"utf-8\")\n\n ele_type, _ = leb128.u.decode_reader(stream)\n buffer_dtype = _get_elem_type(ele_type)\n\n buffer_shape_len, _ = leb128.u.decode_reader(stream)\n buffer_shape = tuple(leb128.u.decode_reader(stream)[0] for _ in range(buffer_shape_len))\n if buffer_shape:\n data_size = np.prod(buffer_shape)\n else:\n data_size = 1\n\n data_size_bytes = data_size * _DTYPE_SIZE_MAP[buffer_dtype]\n sd[key_name] = torch.from_numpy(\n np.frombuffer(\n stream.read(data_size_bytes), dtype=buffer_dtype, count=data_size\n ).reshape(buffer_shape)\n )\n return sd\n"},{"alpha_fraction":0.3953971564769745,"alphanum_fraction":0.41745245456695557,"avg_line_length":49.055999755859375,"blob_id":"4694733fc0957452a403e0919fee2d2ebba91426","content_id":"edfccd899c6ed72317f94aa63570c01c0a664d63","detected_licenses":["MIT"],"is_generated":false,"is_vendor":false,"language":"C#","length_bytes":6257,"license_type":"permissive","max_line_length":218,"num_lines":125,"path":"/src/TorchSharp/Utils/tensorboard/Utils.cs","repo_name":"NiklasGustafsson/TorchSharp","src_encoding":"UTF-8","text":"// Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information.\n\nusing System;\nusing System.Diagnostics;\nusing System.Linq;\n\nnamespace TorchSharp\n{\n public static partial class torch\n {\n public static partial class utils\n {\n public static partial class tensorboard\n {\n internal static partial class utils\n {\n /// <summary>\n /// Converts a 5D tensor [batchsize, time(frame), channel(color), height, width]\n /// into 4D tensor with dimension[time(frame), new_width, new_height, channel].\n /// A batch of images are spreaded to a grid, which forms a frame.\n /// e.g. Video with batchsize 16 will have a 4x4 grid.\n ///\n /// https://github.com/pytorch/pytorch/blob/master/torch/utils/tensorboard/_utils.py#L110\n /// </summary>\n /// <param name=\"V\"></param>\n /// <returns></returns>\n public static Tensor prepare_video(Tensor V)\n {\n long b = V.shape[0];\n long t = V.shape[1];\n long c = V.shape[2];\n long h = V.shape[3];\n long w = V.shape[4];\n\n if (V.dtype == ScalarType.Int8 || V.dtype == ScalarType.Byte)\n V = V.to_type(ScalarType.Float32) / 255.0;\n\n bool is_power2(long num)\n => num != 0 && ((num & (num - 1)) == 0);\n int bit_length(long value)\n => Convert.ToString(value, 2).Length;\n\n if (!is_power2(V.shape[0])) {\n int len_addition = Convert.ToInt32(Math.Pow(2, bit_length(V.shape[0])) - V.shape[0]);\n V = cat(new Tensor[] { V, zeros(new long[] { len_addition, t, c, h, w }, device: V.device) });\n }\n\n long n_rows = Convert.ToInt32(Math.Pow(2, (bit_length(V.shape[0]) - 1) / 2));\n long n_cols = V.shape[0] / n_rows;\n\n V = V.reshape(n_rows, n_cols, t, c, h, w);\n V = V.permute(2, 0, 4, 1, 5, 3);\n V = V.reshape(t, n_rows * h, n_cols * w, c);\n return V;\n }\n\n /// <summary>\n /// https://github.com/pytorch/pytorch/blob/6c30dc6ceed5542351b3be4f8043b28020f93f3a/torch/utils/tensorboard/_utils.py#L69\n /// </summary>\n /// <param name=\"I\"></param>\n /// <param name=\"ncols\"></param>\n /// <returns></returns>\n public static Tensor make_grid(Tensor I, long ncols = 8)\n {\n if (I.shape[1] == 1)\n I = I.expand(-1, 3);\n Trace.Assert(I.ndim == 4 && I.shape[1] == 3);\n long nimg = I.shape[0];\n long H = I.shape[2];\n long W = I.shape[3];\n ncols = Math.Min(ncols, nimg);\n long nrows = (long)Math.Ceiling((double)nimg / (double)ncols);\n Tensor canvas = zeros(new long[] { 3, H * nrows, W * ncols }, I.dtype);\n long i = 0;\n for (long r = 0; r < nrows; r++) {\n for (long c = 0; c < ncols; c++) {\n if (i >= nimg)\n break;\n canvas.narrow(1, r * H, H).narrow(2, c * W, W).copy_(I[i]);\n i++;\n }\n }\n return canvas;\n }\n\n /// <summary>\n /// https://github.com/pytorch/pytorch/blob/6c30dc6ceed5542351b3be4f8043b28020f93f3a/torch/utils/tensorboard/_utils.py#L95\n /// </summary>\n /// <param name=\"tensor\"> Image data </param>\n /// <param name=\"input_format\"> Image data format specification of the form NCHW, NHWC, CHW, HWC, HW, WH, etc. </param>\n /// <returns></returns>\n public static Tensor convert_to_HWC(Tensor tensor, string input_format)\n {\n Trace.Assert(tensor.shape.Length == input_format.Length, $\"size of input tensor and input format are different. tensor shape: ({string.Join(\", \", tensor.shape)}), input_format: {input_format}\");\n input_format = input_format.ToUpper();\n\n if (input_format.Length == 4) {\n long[] index = \"NCHW\".Select(c => Convert.ToInt64(input_format.IndexOf(c))).ToArray();\n Tensor tensor_NCHW = tensor.permute(index);\n Tensor tensor_CHW = make_grid(tensor_NCHW);\n return tensor_CHW.permute(1, 2, 0);\n }\n\n if (input_format.Length == 3) {\n long[] index = \"HWC\".Select(c => Convert.ToInt64(input_format.IndexOf(c))).ToArray();\n Tensor tensor_HWC = tensor.permute(index);\n if (tensor_HWC.shape[2] == 1)\n tensor_HWC = tensor_HWC.expand(-1, -1, 3);\n return tensor_HWC;\n }\n\n if (input_format.Length == 2) {\n long[] index = \"HW\".Select(c => Convert.ToInt64(input_format.IndexOf(c))).ToArray();\n tensor = tensor.permute(index);\n tensor = tensor.unsqueeze(-1).expand(-1, -1, 3);\n return tensor;\n }\n\n throw new NotImplementedException();\n }\n }\n }\n }\n }\n}\n"},{"alpha_fraction":0.6045751571655273,"alphanum_fraction":0.6078431606292725,"avg_line_length":20.85714340209961,"blob_id":"0123336db26ce17a05ed88e8a31040e8714237c9","content_id":"292e094435f6d089b5204d844ebb4fdb7350fa89","detected_licenses":["MIT"],"is_generated":false,"is_vendor":false,"language":"C#","length_bytes":306,"license_type":"permissive","max_line_length":130,"num_lines":14,"path":"/src/TorchSharp/Tensor/Enums/HistogramBinSelector.cs","repo_name":"NiklasGustafsson/TorchSharp","src_encoding":"UTF-8","text":"// Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information.\n\nnamespace TorchSharp\n{\n public enum HistogramBinSelector : byte\n {\n Doane = 0,\n Rice,\n Scott,\n Sqrt,\n Stone,\n Sturges\n }\n}\n"},{"alpha_fraction":0.6214463710784912,"alphanum_fraction":0.6214463710784912,"avg_line_length":40.79166793823242,"blob_id":"0762788f0bdbebeec9b9664996eb7f36d1ea24df","content_id":"11598e5f7e2b77d8d49af18007d53285fa69c91a","detected_licenses":["MIT"],"is_generated":false,"is_vendor":false,"language":"C#","length_bytes":2009,"license_type":"permissive","max_line_length":196,"num_lines":48,"path":"/src/TorchVision/Perspective.cs","repo_name":"NiklasGustafsson/TorchSharp","src_encoding":"UTF-8","text":"// Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information.\nusing System;\nusing System.Collections.Generic;\nusing static TorchSharp.torch;\n\n\nnamespace TorchSharp\n{\n public static partial class torchvision\n {\n internal class Perspective : ITransform\n {\n internal Perspective(IList<IList<int>> startpoints, IList<IList<int>> endpoints, InterpolationMode interpolation, IList<float> fill = null)\n {\n if (interpolation != InterpolationMode.Nearest && interpolation != InterpolationMode.Bilinear)\n throw new ArgumentException($\"Invalid interpolation mode for 'Perspective': {interpolation}. Use 'nearest' or 'bilinear'.\");\n\n this.startpoints = startpoints;\n this.endpoints = endpoints;\n this.interpolation = interpolation;\n this.fill = fill;\n }\n\n public Tensor call(Tensor input)\n {\n return transforms.functional.perspective(input, startpoints, endpoints, interpolation, fill);\n }\n\n private IList<IList<int>> startpoints;\n private IList<IList<int>> endpoints;\n private InterpolationMode interpolation;\n private IList<float> fill;\n }\n\n public static partial class transforms\n {\n /// <summary>\n /// Perform perspective transform of the given image.\n /// The image is expected to have […, H, W] shape, where … means an arbitrary number of leading dimensions.\n /// </summary>\n /// <returns></returns>\n static public ITransform Perspective(IList<IList<int>> startpoints, IList<IList<int>> endpoints, InterpolationMode interpolation = InterpolationMode.Bilinear, IList<float> fill = null)\n {\n return new Perspective(startpoints, endpoints, interpolation, fill);\n }\n }\n }\n}"},{"alpha_fraction":0.5039065480232239,"alphanum_fraction":0.5209760665893555,"avg_line_length":49.995094299316406,"blob_id":"2eac9d5f5a46248685f2e0372cdfd0e55079691d","content_id":"77713e97cb7712d6292e39e6f2cf8a01f73fc83b","detected_licenses":["MIT"],"is_generated":false,"is_vendor":false,"language":"C#","length_bytes":51964,"license_type":"permissive","max_line_length":201,"num_lines":1019,"path":"/src/TorchVision/models/ResNet.cs","repo_name":"NiklasGustafsson/TorchSharp","src_encoding":"UTF-8","text":"// Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information.\nusing System;\nusing System.Collections.Generic;\nusing System.Text.RegularExpressions;\nusing static TorchSharp.torch;\nusing static TorchSharp.torch.nn;\n\n#nullable enable\nnamespace TorchSharp\n{\n public static partial class torchvision\n {\n public static partial class models\n {\n /// <summary>\n /// ResNet-18\n /// </summary>\n /// <param name=\"num_classes\">The number of output classes.</param>\n /// <param name=\"zero_init_residual\">Whether to zero-initalize the residual block's norm layers.</param>\n /// <param name=\"groups\">The number of groups.</param>\n /// <param name=\"width_per_group\">The width of each group.</param>\n /// <param name=\"replace_stride_with_dilation\">Each element in the tuple indicates if we should replace the 2x2 stride with a dilated convolution instead</param>\n /// <param name=\"norm_layer\">The normalization layer to use -- a function creating a layer.</param>\n /// <param name=\"weights_file\">The location of a file containing pre-trained weights for the model.</param>\n /// <param name=\"skipfc\">If true, the last linear layer of the classifier will not be loaded from the weights file.</param>\n /// <param name=\"device\">The device to locate the model on.</param>\n /// <remarks>\n /// Pre-trained weights may be retrieved by using Pytorch and saving the model state-dict\n /// using the exportsd.py script, then loading into the .NET instance:\n ///\n /// from torchvision import models\n /// import exportsd\n /// \n /// model = models.resnet18(pretrained=True)\n /// f = open(\"model_weights.dat\", \"wb\")\n /// exportsd.save_state_dict(model.state_dict(), f)\n /// f.close()\n ///\n /// See also: https://github.com/dotnet/TorchSharp/blob/main/docfx/articles/saveload.md\n ///\n /// In order for the weights to be loaded, the number of classes has to be the same as\n /// in the pre-trained model, which is 1000.\n ///\n /// It is also possible to skip loading the last linear layer and use it for transfer-learning\n /// with a different number of output classes. To do so, pass skipfc=true.\n ///\n /// All pre-trained models expect input images normalized in the same way, i.e. mini-batches of 3-channel RGB\n /// images of shape (3 x H x W), where H and W are expected to be at least 224. The images have to be loaded\n /// in to a range of [0, 1] and then normalized using mean = [0.485, 0.456, 0.406] and std = [0.229, 0.224, 0.225].\n /// </remarks>\n public static Modules.ResNet resnet18(\n int num_classes = 1000,\n bool zero_init_residual = false,\n int groups = 1,\n int width_per_group = 64,\n (bool, bool, bool)? replace_stride_with_dilation = null,\n Func<int, Module<Tensor, Tensor>>? norm_layer = null,\n string? weights_file = null,\n bool skipfc = true,\n Device? device = null)\n {\n return Modules.ResNet.ResNet18(num_classes,\n zero_init_residual,\n groups,\n width_per_group,\n replace_stride_with_dilation,\n norm_layer,\n weights_file, skipfc, device);\n }\n\n /// <summary>\n /// ResNet-34\n /// </summary>\n /// <param name=\"num_classes\">The number of output classes.</param>\n /// <param name=\"zero_init_residual\">Whether to zero-initalize the residual block's norm layers.</param>\n /// <param name=\"groups\">The number of groups.</param>\n /// <param name=\"width_per_group\">The width of each group.</param>\n /// <param name=\"replace_stride_with_dilation\">Each element in the tuple indicates if we should replace the 2x2 stride with a dilated convolution instead</param>\n /// <param name=\"norm_layer\">The normalization layer to use -- a function creating a layer.</param>\n /// <param name=\"weights_file\">The location of a file containing pre-trained weights for the model.</param>\n /// <param name=\"skipfc\">If true, the last linear layer of the classifier will not be loaded from the weights file.</param>\n /// <param name=\"device\">The device to locate the model on.</param>\n /// <remarks>\n /// Pre-trained weights may be retrieved by using Pytorch and saving the model state-dict\n /// using the exportsd.py script, then loading into the .NET instance:\n ///\n /// from torchvision import models\n /// import exportsd\n /// \n /// model = models.resnet34(pretrained=True)\n /// f = open(\"model_weights.dat\", \"wb\")\n /// exportsd.save_state_dict(model.state_dict(), f)\n /// f.close()\n ///\n /// See also: https://github.com/dotnet/TorchSharp/blob/main/docfx/articles/saveload.md\n ///\n /// In order for the weights to be loaded, the number of classes has to be the same as\n /// in the pre-trained model, which is 1000.\n ///\n /// It is also possible to skip loading the last linear layer and use it for transfer-learning\n /// with a different number of output classes. To do so, pass skipfc=true.\n ///\n /// All pre-trained models expect input images normalized in the same way, i.e. mini-batches of 3-channel RGB\n /// images of shape (3 x H x W), where H and W are expected to be at least 224. The images have to be loaded\n /// in to a range of [0, 1] and then normalized using mean = [0.485, 0.456, 0.406] and std = [0.229, 0.224, 0.225].\n /// </remarks>\n public static Modules.ResNet resnet34(\n int num_classes = 1000,\n bool zero_init_residual = false,\n int groups = 1,\n int width_per_group = 64,\n (bool, bool, bool)? replace_stride_with_dilation = null,\n Func<int, Module<Tensor, Tensor>>? norm_layer = null,\n string? weights_file = null,\n bool skipfc = true,\n Device? device = null)\n {\n return Modules.ResNet.ResNet34(num_classes,\n zero_init_residual,\n groups,\n width_per_group,\n replace_stride_with_dilation,\n norm_layer,\n weights_file, skipfc, device);\n }\n\n /// <summary>\n /// ResNet-50\n /// </summary>\n /// <param name=\"num_classes\">The number of output classes.</param>\n /// <param name=\"zero_init_residual\">Whether to zero-initalize the residual block's norm layers.</param>\n /// <param name=\"groups\">The number of groups.</param>\n /// <param name=\"width_per_group\">The width of each group.</param>\n /// <param name=\"replace_stride_with_dilation\">Each element in the tuple indicates if we should replace the 2x2 stride with a dilated convolution instead</param>\n /// <param name=\"norm_layer\">The normalization layer to use -- a function creating a layer.</param>\n /// <param name=\"weights_file\">The location of a file containing pre-trained weights for the model.</param>\n /// <param name=\"skipfc\">If true, the last linear layer of the classifier will not be loaded from the weights file.</param>\n /// <param name=\"device\">The device to locate the model on.</param>\n /// <remarks>\n /// Pre-trained weights may be retrieved by using Pytorch and saving the model state-dict\n /// using the exportsd.py script, then loading into the .NET instance:\n ///\n /// from torchvision import models\n /// import exportsd\n /// \n /// model = models.resnet50(pretrained=True)\n /// f = open(\"model_weights.dat\", \"wb\")\n /// exportsd.save_state_dict(model.state_dict(), f)\n /// f.close()\n ///\n /// See also: https://github.com/dotnet/TorchSharp/blob/main/docfx/articles/saveload.md\n ///\n /// In order for the weights to be loaded, the number of classes has to be the same as\n /// in the pre-trained model, which is 1000.\n ///\n /// It is also possible to skip loading the last linear layer and use it for transfer-learning\n /// with a different number of output classes. To do so, pass skipfc=true.\n ///\n /// All pre-trained models expect input images normalized in the same way, i.e. mini-batches of 3-channel RGB\n /// images of shape (3 x H x W), where H and W are expected to be at least 224. The images have to be loaded\n /// in to a range of [0, 1] and then normalized using mean = [0.485, 0.456, 0.406] and std = [0.229, 0.224, 0.225].\n /// </remarks>\n public static Modules.ResNet resnet50(\n int num_classes = 1000,\n bool zero_init_residual = false,\n int groups = 1,\n int width_per_group = 64,\n (bool, bool, bool)? replace_stride_with_dilation = null,\n Func<int, Module<Tensor, Tensor>>? norm_layer = null,\n string? weights_file = null,\n bool skipfc = true,\n Device? device = null)\n {\n return Modules.ResNet.ResNet50(num_classes,\n zero_init_residual,\n groups,\n width_per_group,\n replace_stride_with_dilation,\n norm_layer,\n weights_file, skipfc, device);\n }\n\n /// <summary>\n /// Wide ResNet-50-2 model from 'Wide Residual Networks' https://arxiv.org/abs/1605.07146_\n /// </summary>\n /// <param name=\"num_classes\">The number of output classes.</param>\n /// <param name=\"zero_init_residual\">Whether to zero-initalize the residual block's norm layers.</param>\n /// <param name=\"groups\">The number of groups.</param>\n /// <param name=\"replace_stride_with_dilation\">Each element in the tuple indicates if we should replace the 2x2 stride with a dilated convolution instead</param>\n /// <param name=\"norm_layer\">The normalization layer to use -- a function creating a layer.</param>\n /// <param name=\"weights_file\">The location of a file containing pre-trained weights for the model.</param>\n /// <param name=\"skipfc\">If true, the last linear layer of the classifier will not be loaded from the weights file.</param>\n /// <param name=\"device\">The device to locate the model on.</param>\n /// <remarks>\n /// Pre-trained weights may be retrieved by using Pytorch and saving the model state-dict\n /// using the exportsd.py script, then loading into the .NET instance:\n ///\n /// from torchvision import models\n /// import exportsd\n /// \n /// model = models.wide_resnet50_2(pretrained=True)\n /// f = open(\"model_weights.dat\", \"wb\")\n /// exportsd.save_state_dict(model.state_dict(), f)\n /// f.close()\n ///\n /// See also: https://github.com/dotnet/TorchSharp/blob/main/docfx/articles/saveload.md\n ///\n /// In order for the weights to be loaded, the number of classes has to be the same as\n /// in the pre-trained model, which is 1000.\n ///\n /// It is also possible to skip loading the last linear layer and use it for transfer-learning\n /// with a different number of output classes. To do so, pass skipfc=true.\n ///\n /// All pre-trained models expect input images normalized in the same way, i.e. mini-batches of 3-channel RGB\n /// images of shape (3 x H x W), where H and W are expected to be at least 224. The images have to be loaded\n /// in to a range of [0, 1] and then normalized using mean = [0.485, 0.456, 0.406] and std = [0.229, 0.224, 0.225].\n /// </remarks>\n public static Modules.ResNet wide_resnet50_2(\n int num_classes = 1000,\n bool zero_init_residual = false,\n int groups = 1,\n (bool, bool, bool)? replace_stride_with_dilation = null,\n Func<int, Module<Tensor, Tensor>>? norm_layer = null,\n string? weights_file = null,\n bool skipfc = true,\n Device? device = null)\n {\n return Modules.ResNet.ResNet50(num_classes,\n zero_init_residual,\n groups,\n 64 * 2,\n replace_stride_with_dilation,\n norm_layer,\n weights_file, skipfc, device);\n }\n\n /// <summary>\n /// ResNeXt-50 32x4d model from\n /// `Aggregated Residual Transformation for Deep Neural Networks https://arxiv.org/abs/1611.05431\n /// </summary>\n /// <param name=\"num_classes\">The number of output classes.</param>\n /// <param name=\"zero_init_residual\">Whether to zero-initalize the residual block's norm layers.</param>\n /// <param name=\"replace_stride_with_dilation\">Each element in the tuple indicates if we should replace the 2x2 stride with a dilated convolution instead</param>\n /// <param name=\"norm_layer\">The normalization layer to use -- a function creating a layer.</param>\n /// <param name=\"weights_file\">The location of a file containing pre-trained weights for the model.</param>\n /// <param name=\"skipfc\">If true, the last linear layer of the classifier will not be loaded from the weights file.</param>\n /// <param name=\"device\">The device to locate the model on.</param>\n /// <remarks>\n /// Pre-trained weights may be retrieved by using Pytorch and saving the model state-dict\n /// using the exportsd.py script, then loading into the .NET instance:\n ///\n /// from torchvision import models\n /// import exportsd\n /// \n /// model = models.resnext50_32x4d(pretrained=True)\n /// f = open(\"model_weights.dat\", \"wb\")\n /// exportsd.save_state_dict(model.state_dict(), f)\n /// f.close()\n ///\n /// See also: https://github.com/dotnet/TorchSharp/blob/main/docfx/articles/saveload.md\n ///\n /// In order for the weights to be loaded, the number of classes has to be the same as\n /// in the pre-trained model, which is 1000.\n ///\n /// It is also possible to skip loading the last linear layer and use it for transfer-learning\n /// with a different number of output classes. To do so, pass skipfc=true.\n ///\n /// All pre-trained models expect input images normalized in the same way, i.e. mini-batches of 3-channel RGB\n /// images of shape (3 x H x W), where H and W are expected to be at least 224. The images have to be loaded\n /// in to a range of [0, 1] and then normalized using mean = [0.485, 0.456, 0.406] and std = [0.229, 0.224, 0.225].\n /// </remarks>\n public static Modules.ResNet resnext50_32x4d(\n int num_classes = 1000,\n bool zero_init_residual = false,\n (bool, bool, bool)? replace_stride_with_dilation = null,\n Func<int, Module<Tensor, Tensor>>? norm_layer = null,\n string? weights_file = null,\n bool skipfc = true,\n Device? device = null)\n {\n return Modules.ResNet.ResNet50(num_classes,\n zero_init_residual,\n 32,\n 4,\n replace_stride_with_dilation,\n norm_layer,\n weights_file, skipfc, device);\n }\n\n /// <summary>\n /// ResNet-101\n /// </summary>\n /// <param name=\"num_classes\">The number of output classes.</param>\n /// <param name=\"zero_init_residual\">Whether to zero-initalize the residual block's norm layers.</param>\n /// <param name=\"groups\">The number of groups.</param>\n /// <param name=\"width_per_group\">The width of each group.</param>\n /// <param name=\"replace_stride_with_dilation\">Each element in the tuple indicates if we should replace the 2x2 stride with a dilated convolution instead</param>\n /// <param name=\"norm_layer\">The normalization layer to use -- a function creating a layer.</param>\n /// <param name=\"weights_file\">The location of a file containing pre-trained weights for the model.</param>\n /// <param name=\"skipfc\">If true, the last linear layer of the classifier will not be loaded from the weights file.</param>\n /// <param name=\"device\">The device to locate the model on.</param>\n /// <remarks>\n /// Pre-trained weights may be retrieved by using Pytorch and saving the model state-dict\n /// using the exportsd.py script, then loading into the .NET instance:\n ///\n /// from torchvision import models\n /// import exportsd\n /// \n /// model = models.resnet101(pretrained=True)\n /// f = open(\"model_weights.dat\", \"wb\")\n /// exportsd.save_state_dict(model.state_dict(), f)\n /// f.close()\n ///\n /// See also: https://github.com/dotnet/TorchSharp/blob/main/docfx/articles/saveload.md\n ///\n /// In order for the weights to be loaded, the number of classes has to be the same as\n /// in the pre-trained model, which is 1000.\n ///\n /// It is also possible to skip loading the last linear layer and use it for transfer-learning\n /// with a different number of output classes. To do so, pass skipfc=true.\n ///\n /// All pre-trained models expect input images normalized in the same way, i.e. mini-batches of 3-channel RGB\n /// images of shape (3 x H x W), where H and W are expected to be at least 224. The images have to be loaded\n /// in to a range of [0, 1] and then normalized using mean = [0.485, 0.456, 0.406] and std = [0.229, 0.224, 0.225].\n /// </remarks>\n public static Modules.ResNet resnet101(\n int num_classes = 1000,\n bool zero_init_residual = false,\n int groups = 1,\n int width_per_group = 64,\n (bool, bool, bool)? replace_stride_with_dilation = null,\n Func<int, Module<Tensor, Tensor>>? norm_layer = null,\n string? weights_file = null,\n bool skipfc = true,\n Device? device = null)\n {\n return Modules.ResNet.ResNet101(num_classes,\n zero_init_residual,\n groups,\n width_per_group,\n replace_stride_with_dilation,\n norm_layer,\n weights_file, skipfc, device);\n }\n\n /// <summary>\n /// ResNeXt-101 32x8d model from\n /// `Aggregated Residual Transformation for Deep Neural Networks https://arxiv.org/abs/1611.05431\n /// </summary>\n /// <param name=\"num_classes\">The number of output classes.</param>\n /// <param name=\"zero_init_residual\">Whether to zero-initalize the residual block's norm layers.</param>\n /// <param name=\"replace_stride_with_dilation\">Each element in the tuple indicates if we should replace the 2x2 stride with a dilated convolution instead</param>\n /// <param name=\"norm_layer\">The normalization layer to use -- a function creating a layer.</param>\n /// <param name=\"weights_file\">The location of a file containing pre-trained weights for the model.</param>\n /// <param name=\"skipfc\">If true, the last linear layer of the classifier will not be loaded from the weights file.</param>\n /// <param name=\"device\">The device to locate the model on.</param>\n /// <remarks>\n /// Pre-trained weights may be retrieved by using Pytorch and saving the model state-dict\n /// using the exportsd.py script, then loading into the .NET instance:\n ///\n /// from torchvision import models\n /// import exportsd\n /// \n /// model = models.resnext101_32x8d(pretrained=True)\n /// f = open(\"model_weights.dat\", \"wb\")\n /// exportsd.save_state_dict(model.state_dict(), f)\n /// f.close()\n ///\n /// See also: https://github.com/dotnet/TorchSharp/blob/main/docfx/articles/saveload.md\n ///\n /// In order for the weights to be loaded, the number of classes has to be the same as\n /// in the pre-trained model, which is 1000.\n ///\n /// It is also possible to skip loading the last linear layer and use it for transfer-learning\n /// with a different number of output classes. To do so, pass skipfc=true.\n ///\n /// All pre-trained models expect input images normalized in the same way, i.e. mini-batches of 3-channel RGB\n /// images of shape (3 x H x W), where H and W are expected to be at least 224. The images have to be loaded\n /// in to a range of [0, 1] and then normalized using mean = [0.485, 0.456, 0.406] and std = [0.229, 0.224, 0.225].\n /// </remarks>\n public static Modules.ResNet resnext101_32x8d(\n int num_classes = 1000,\n bool zero_init_residual = false,\n (bool, bool, bool)? replace_stride_with_dilation = null,\n Func<int, Module<Tensor, Tensor>>? norm_layer = null,\n string? weights_file = null,\n bool skipfc = true,\n Device? device = null)\n {\n return Modules.ResNet.ResNet50(num_classes,\n zero_init_residual,\n 32,\n 8,\n replace_stride_with_dilation,\n norm_layer,\n weights_file, skipfc, device);\n }\n\n /// <summary>\n /// ResNeXt-101 64x4d model from\n /// `Aggregated Residual Transformation for Deep Neural Networks https://arxiv.org/abs/1611.05431\n /// </summary>\n /// <param name=\"num_classes\">The number of output classes.</param>\n /// <param name=\"zero_init_residual\">Whether to zero-initalize the residual block's norm layers.</param>\n /// <param name=\"replace_stride_with_dilation\">Each element in the tuple indicates if we should replace the 2x2 stride with a dilated convolution instead</param>\n /// <param name=\"norm_layer\">The normalization layer to use -- a function creating a layer.</param>\n /// <param name=\"weights_file\">The location of a file containing pre-trained weights for the model.</param>\n /// <param name=\"skipfc\">If true, the last linear layer of the classifier will not be loaded from the weights file.</param>\n /// <param name=\"device\">The device to locate the model on.</param>\n /// <remarks>\n /// Pre-trained weights may be retrieved by using Pytorch and saving the model state-dict\n /// using the exportsd.py script, then loading into the .NET instance:\n ///\n /// from torchvision import models\n /// import exportsd\n /// \n /// model = models.resnext101_32x8d(pretrained=True)\n /// f = open(\"model_weights.dat\", \"wb\")\n /// exportsd.save_state_dict(model.state_dict(), f)\n /// f.close()\n ///\n /// See also: https://github.com/dotnet/TorchSharp/blob/main/docfx/articles/saveload.md\n ///\n /// In order for the weights to be loaded, the number of classes has to be the same as\n /// in the pre-trained model, which is 1000.\n ///\n /// It is also possible to skip loading the last linear layer and use it for transfer-learning\n /// with a different number of output classes. To do so, pass skipfc=true.\n ///\n /// All pre-trained models expect input images normalized in the same way, i.e. mini-batches of 3-channel RGB\n /// images of shape (3 x H x W), where H and W are expected to be at least 224. The images have to be loaded\n /// in to a range of [0, 1] and then normalized using mean = [0.485, 0.456, 0.406] and std = [0.229, 0.224, 0.225].\n /// </remarks>\n public static Modules.ResNet resnext101_64x4d(\n int num_classes = 1000,\n bool zero_init_residual = false,\n (bool, bool, bool)? replace_stride_with_dilation = null,\n Func<int, Module<Tensor, Tensor>>? norm_layer = null,\n string? weights_file = null,\n bool skipfc = true,\n Device? device = null)\n {\n return Modules.ResNet.ResNet50(num_classes,\n zero_init_residual,\n 64,\n 4,\n replace_stride_with_dilation,\n norm_layer,\n weights_file, skipfc, device);\n }\n\n /// <summary>\n /// Wide ResNet-101-2 model from 'Wide Residual Networks' https://arxiv.org/abs/1605.07146\n /// </summary>\n /// <param name=\"num_classes\">The number of output classes.</param>\n /// <param name=\"zero_init_residual\">Whether to zero-initalize the residual block's norm layers.</param>\n /// <param name=\"groups\">The number of groups.</param>\n /// <param name=\"replace_stride_with_dilation\">Each element in the tuple indicates if we should replace the 2x2 stride with a dilated convolution instead</param>\n /// <param name=\"norm_layer\">The normalization layer to use -- a function creating a layer.</param>\n /// <param name=\"weights_file\">The location of a file containing pre-trained weights for the model.</param>\n /// <param name=\"skipfc\">If true, the last linear layer of the classifier will not be loaded from the weights file.</param>\n /// <param name=\"device\">The device to locate the model on.</param>\n /// <remarks>\n /// Pre-trained weights may be retrieved by using Pytorch and saving the model state-dict\n /// using the exportsd.py script, then loading into the .NET instance:\n ///\n /// from torchvision import models\n /// import exportsd\n /// \n /// model = models.resnet101(pretrained=True)\n /// f = open(\"model_weights.dat\", \"wb\")\n /// exportsd.save_state_dict(model.state_dict(), f)\n /// f.close()\n ///\n /// See also: https://github.com/dotnet/TorchSharp/blob/main/docfx/articles/saveload.md\n ///\n /// In order for the weights to be loaded, the number of classes has to be the same as\n /// in the pre-trained model, which is 1000.\n ///\n /// It is also possible to skip loading the last linear layer and use it for transfer-learning\n /// with a different number of output classes. To do so, pass skipfc=true.\n ///\n /// All pre-trained models expect input images normalized in the same way, i.e. mini-batches of 3-channel RGB\n /// images of shape (3 x H x W), where H and W are expected to be at least 224. The images have to be loaded\n /// in to a range of [0, 1] and then normalized using mean = [0.485, 0.456, 0.406] and std = [0.229, 0.224, 0.225].\n /// </remarks>\n public static Modules.ResNet wide_resnet101_2(\n int num_classes = 1000,\n bool zero_init_residual = false,\n int groups = 1,\n (bool, bool, bool)? replace_stride_with_dilation = null,\n Func<int, Module<Tensor, Tensor>>? norm_layer = null,\n string? weights_file = null,\n bool skipfc = true,\n Device? device = null)\n {\n return Modules.ResNet.ResNet101(num_classes,\n zero_init_residual,\n groups,\n 64*2,\n replace_stride_with_dilation,\n norm_layer,\n weights_file, skipfc, device);\n }\n\n /// <summary>\n /// ResNet-152\n /// </summary>\n /// <param name=\"num_classes\">The number of output classes.</param>\n /// <param name=\"zero_init_residual\">Whether to zero-initalize the residual block's norm layers.</param>\n /// <param name=\"groups\">The number of groups.</param>\n /// <param name=\"width_per_group\">The width of each group.</param>\n /// <param name=\"replace_stride_with_dilation\">Each element in the tuple indicates if we should replace the 2x2 stride with a dilated convolution instead</param>\n /// <param name=\"norm_layer\">The normalization layer to use -- a function creating a layer.</param>\n /// <param name=\"weights_file\">The location of a file containing pre-trained weights for the model.</param>\n /// <param name=\"skipfc\">If true, the last linear layer of the classifier will not be loaded from the weights file.</param>\n /// <param name=\"device\">The device to locate the model on.</param>\n /// <remarks>\n /// Pre-trained weights may be retrieved by using Pytorch and saving the model state-dict\n /// using the exportsd.py script, then loading into the .NET instance:\n ///\n /// from torchvision import models\n /// import exportsd\n /// \n /// model = models.resnet152(pretrained=True)\n /// f = open(\"model_weights.dat\", \"wb\")\n /// exportsd.save_state_dict(model.state_dict(), f)\n /// f.close()\n ///\n /// See also: https://github.com/dotnet/TorchSharp/blob/main/docfx/articles/saveload.md\n ///\n /// In order for the weights to be loaded, the number of classes has to be the same as\n /// in the pre-trained model, which is 1000.\n ///\n /// It is also possible to skip loading the last linear layer and use it for transfer-learning\n /// with a different number of output classes. To do so, pass skipfc=true.\n ///\n /// All pre-trained models expect input images normalized in the same way, i.e. mini-batches of 3-channel RGB\n /// images of shape (3 x H x W), where H and W are expected to be at least 224. The images have to be loaded\n /// in to a range of [0, 1] and then normalized using mean = [0.485, 0.456, 0.406] and std = [0.229, 0.224, 0.225].\n /// </remarks>\n public static Modules.ResNet resnet152(\n int num_classes = 1000,\n bool zero_init_residual = false,\n int groups = 1,\n int width_per_group = 64,\n (bool, bool, bool)? replace_stride_with_dilation = null,\n Func<int, Module<Tensor, Tensor>>? norm_layer = null,\n string? weights_file = null,\n bool skipfc = true,\n Device? device = null)\n {\n return Modules.ResNet.ResNet152(num_classes,\n zero_init_residual,\n groups,\n width_per_group,\n replace_stride_with_dilation,\n norm_layer,\n weights_file, skipfc, device);\n }\n }\n }\n\n namespace Modules\n {\n public class ResNet : Module<Tensor, Tensor>\n {\n // The code here is based on\n // https://github.com/pytorch/vision/blob/main/torchvision/models/resnet.py\n // Licence and copypright notice at: https://github.com/pytorch/vision/blob/main/LICENSE\n\n private readonly Module<Tensor, Tensor> conv1;\n private readonly Module<Tensor, Tensor> bn1;\n private readonly Module<Tensor, Tensor> relu;\n private readonly Module<Tensor, Tensor> maxpool;\n\n private readonly Sequential layer1 = Sequential();\n private readonly Sequential layer2 = Sequential();\n private readonly Sequential layer3 = Sequential();\n private readonly Sequential layer4 = Sequential();\n\n private readonly Module<Tensor, Tensor> avgpool;\n private readonly Module<Tensor, Tensor> flatten;\n private readonly Module<Tensor, Tensor> fc;\n\n private readonly Func<int, Module<Tensor, Tensor>> norm_layer;\n\n private int in_planes = 64;\n private int dilation;\n private int groups;\n private int base_width;\n\n protected override void Dispose(bool disposing)\n {\n if (disposing) {\n conv1.Dispose();\n bn1.Dispose();\n relu.Dispose();\n maxpool.Dispose();\n avgpool.Dispose();\n flatten.Dispose();\n fc.Dispose();\n layer1.Dispose(); layer2.Dispose();\n layer3.Dispose(); layer4.Dispose();\n }\n base.Dispose(disposing);\n }\n\n public static ResNet ResNet18(\n int numClasses = 1000,\n bool zero_init_residual = false,\n int groups = 1,\n int width_per_group = 64,\n (bool, bool, bool)? replace_stride_with_dilation = null,\n Func<int, Module<Tensor, Tensor>>? norm_layer = null,\n string? weights_file = null,\n bool skipfc = true,\n Device? device = null)\n {\n return new ResNet(\n \"ResNet18\",\n (in_planes, planes, stride, downsample, groups, base_width, dilation, norm_layer) => new BasicBlock(in_planes, planes, stride, downsample, groups, base_width, dilation, norm_layer),\n BasicBlock.expansion, new int[] { 2, 2, 2, 2 },\n numClasses,\n zero_init_residual,\n groups,\n width_per_group,\n replace_stride_with_dilation,\n norm_layer,\n weights_file,\n skipfc,\n device);\n }\n\n public static ResNet ResNet34(\n int numClasses = 1000,\n bool zero_init_residual = false,\n int groups = 1,\n int width_per_group = 64,\n (bool, bool, bool)? replace_stride_with_dilation = null,\n Func<int, Module<Tensor, Tensor>>? norm_layer = null,\n string? weights_file = null,\n bool skipfc = true,\n Device? device = null)\n {\n return new ResNet(\n \"ResNet34\",\n (in_planes, planes, stride, downsample, groups, base_width, dilation, norm_layer) => new BasicBlock(in_planes, planes, stride, downsample, groups, base_width, dilation, norm_layer),\n BasicBlock.expansion, new int[] { 3, 4, 6, 3 },\n numClasses,\n zero_init_residual,\n groups,\n width_per_group,\n replace_stride_with_dilation,\n norm_layer,\n weights_file,\n skipfc,\n device);\n }\n\n public static ResNet ResNet50(\n int numClasses = 1000,\n bool zero_init_residual = false,\n int groups = 1,\n int width_per_group = 64,\n (bool, bool, bool)? replace_stride_with_dilation = null,\n Func<int, Module<Tensor, Tensor>>? norm_layer = null,\n string? weights_file = null,\n bool skipfc = true,\n Device? device = null)\n {\n return new ResNet(\n \"ResNet50\",\n (in_planes, planes, stride, downsample, groups, base_width, dilation, norm_layer) => new Bottleneck(in_planes, planes, stride, downsample, groups, base_width, dilation, norm_layer),\n Bottleneck.expansion, new int[] { 3, 4, 6, 3 },\n numClasses,\n zero_init_residual,\n groups,\n width_per_group,\n replace_stride_with_dilation,\n norm_layer,\n weights_file,\n skipfc,\n device);\n }\n\n public static ResNet ResNet101(\n int numClasses = 1000,\n bool zero_init_residual = false,\n int groups = 1,\n int width_per_group = 64,\n (bool, bool, bool)? replace_stride_with_dilation = null,\n Func<int, Module<Tensor, Tensor>>? norm_layer = null,\n string? weights_file = null,\n bool skipfc = true,\n Device? device = null)\n {\n return new ResNet(\n \"ResNet101\",\n (in_planes, planes, stride, downsample, groups, base_width, dilation, norm_layer) => new Bottleneck(in_planes, planes, stride, downsample, groups, base_width, dilation, norm_layer),\n Bottleneck.expansion, new int[] { 3, 4, 23, 3 },\n numClasses,\n zero_init_residual,\n groups,\n width_per_group,\n replace_stride_with_dilation,\n norm_layer,\n weights_file,\n skipfc,\n device);\n }\n\n public static ResNet ResNet152(\n int numClasses = 1000,\n bool zero_init_residual = false,\n int groups = 1,\n int width_per_group = 64,\n (bool, bool, bool)? replace_stride_with_dilation = null,\n Func<int, Module<Tensor, Tensor>>? norm_layer = null,\n string? weights_file = null,\n bool skipfc = true,\n Device? device = null)\n {\n return new ResNet(\n \"ResNet152\",\n (in_planes, planes, stride, downsample, groups, base_width, dilation, norm_layer) => new Bottleneck(in_planes, planes, stride, downsample, groups, base_width, dilation, norm_layer),\n Bottleneck.expansion, new int[] { 3, 8, 36, 3 },\n numClasses,\n zero_init_residual,\n groups,\n width_per_group,\n replace_stride_with_dilation,\n norm_layer,\n weights_file,\n skipfc,\n device);\n }\n\n public delegate Module<Tensor, Tensor> BlockFunc(\n int inplanes,\n int planes,\n int stride = 1,\n Module<Tensor, Tensor>? downsample = null,\n int groups = 1,\n int base_width = 64,\n int dilation = 1,\n Func<int, Module<Tensor, Tensor>>? norm_layer = null);\n\n public ResNet(string name,\n BlockFunc block,\n int expansion,\n IList<int> layers,\n int numClasses = 1000,\n bool zero_init_residual = false,\n int groups = 1,\n int width_per_group = 64,\n (bool, bool, bool)? replace_stride_with_dilation = null,\n Func<int, Module<Tensor, Tensor>>? norm_layer = null,\n string? weights_file = null,\n bool skipfc = true,\n Device? device = null) : base(name)\n {\n norm_layer = (norm_layer is not null) ? norm_layer : (planes) => BatchNorm2d(planes);\n this.norm_layer = norm_layer;\n\n this.in_planes = 64;\n this.dilation = 1;\n this.groups = groups;\n this.base_width = width_per_group;\n\n var rswd = replace_stride_with_dilation.HasValue ? replace_stride_with_dilation.Value : (false, false, false);\n\n conv1 = Conv2d(3, in_planes, kernelSize: 7, stride: 2, padding: 3, bias: false);\n bn1 = norm_layer(in_planes);\n relu = ReLU(inplace: true);\n maxpool = MaxPool2d(kernelSize: 3, stride: 2, padding: 1);\n\n MakeLayer(layer1, block, expansion, 64, layers[0], 1);\n MakeLayer(layer2, block, expansion, 128, layers[1], 2, rswd.Item1);\n MakeLayer(layer3, block, expansion, 256, layers[2], 2, rswd.Item2);\n MakeLayer(layer4, block, expansion, 512, layers[3], 2, rswd.Item3);\n\n avgpool = nn.AdaptiveAvgPool2d(new long[] { 1, 1 });\n flatten = Flatten();\n fc = Linear(512 * expansion, numClasses);\n\n RegisterComponents();\n\n if (string.IsNullOrEmpty(weights_file)) {\n\n foreach (var (_, m) in named_modules()) {\n switch (m) {\n case TorchSharp.Modules.Conv2d conv:\n torch.nn.init.kaiming_normal_(conv.weight, mode: init.FanInOut.FanOut, nonlinearity: init.NonlinearityType.ReLU);\n break;\n case TorchSharp.Modules.BatchNorm2d bn:\n torch.nn.init.constant_(bn.weight, 1);\n torch.nn.init.constant_(bn.bias, 0);\n break;\n case TorchSharp.Modules.GroupNorm gn:\n torch.nn.init.constant_(gn.weight, 1);\n torch.nn.init.constant_(gn.bias, 0);\n break;\n }\n }\n\n if (zero_init_residual) {\n foreach (var (_, m) in named_modules()) {\n\n switch (m) {\n case BasicBlock bb:\n if (bb.bn2 is TorchSharp.Modules.BatchNorm2d bb2d) {\n torch.nn.init.constant_(bb2d.weight, 0);\n }\n break;\n case Bottleneck bn:\n if (bn.bn3 is TorchSharp.Modules.BatchNorm2d bn2d) {\n torch.nn.init.constant_(bn2d.weight, 0);\n }\n break;\n }\n }\n\n }\n\n } else {\n\n this.load(weights_file, skip: skipfc ? new[] { \"fc.weight\", \"fc.bias\" } : null);\n }\n\n if (device != null && device.type == DeviceType.CUDA)\n this.to(device);\n }\n\n private void MakeLayer(Sequential modules, BlockFunc block, int expansion, int planes, int blocks, int stride, bool dilate = false)\n {\n Sequential? downsample = null;\n var previous_dilation = this.dilation;\n\n if (dilate) {\n this.dilation *= stride;\n stride = 1;\n }\n\n if (stride != 1 || in_planes != planes * expansion) {\n downsample = Sequential(\n Conv2d(in_planes, planes * expansion, kernelSize: 1, stride: stride, bias: false),\n norm_layer(planes * expansion)\n );\n }\n\n modules.append(block(in_planes, planes, stride, downsample, groups, base_width, previous_dilation, norm_layer));\n\n this.in_planes = planes * expansion;\n\n for (int i = 1; i < blocks; i++) {\n modules.append(block(in_planes, planes, 1, null, groups, base_width, dilation, norm_layer));\n }\n }\n\n public override Tensor forward(Tensor input)\n {\n using (var scope = NewDisposeScope()) {\n\n var x = maxpool.call(relu.call(bn1.call(conv1.call(input))));\n\n x = layer1.call(x);\n x = layer2.call(x);\n x = layer3.call(x);\n x = layer4.call(x);\n\n var res = fc.call(flatten.call(avgpool.call(x)));\n scope.MoveToOuter(res);\n return res;\n }\n }\n\n class BasicBlock : Module<Tensor, Tensor>\n {\n public BasicBlock(\n int in_planes,\n int planes,\n int stride,\n Module<Tensor, Tensor>? downsample = null,\n int groups = 1,\n int base_width = 64,\n int dilation = 1,\n Func<int, Module<Tensor, Tensor>>? norm_layer = null) : base(\"BasicBlock\")\n {\n if (groups != 1 || base_width != 64) throw new ArgumentException(\"BasicBlock only supports groups=1 and base_width=64\");\n if (dilation > 1) throw new NotImplementedException(\"dilation > 1 not supported in BasicBlock\");\n\n if (norm_layer is null) {\n norm_layer = (planes) => BatchNorm2d(planes);\n }\n\n conv1 = Conv2d(in_planes, planes, kernelSize: 3, stride: stride, padding: 1, bias: false);\n bn1 = norm_layer(planes);\n relu1 = ReLU(inplace: true);\n conv2 = Conv2d(planes, planes, kernelSize: 3, stride: 1, padding: 1, bias: false);\n bn2 = norm_layer(planes);\n this.downsample = downsample;\n\n RegisterComponents();\n }\n\n public override Tensor forward(Tensor input)\n {\n var identity = input;\n\n var x = relu1.call(bn1.call(conv1.call(input)));\n x = bn2.call(conv2.call(x));\n\n if (downsample is not null) {\n identity = downsample.call(input);\n }\n\n return x.add_(identity).relu_();\n }\n\n protected override void Dispose(bool disposing)\n {\n if (disposing) {\n conv1.Dispose();\n bn1.Dispose();\n conv2.Dispose();\n bn2.Dispose();\n relu1.Dispose();\n downsample?.Dispose();\n }\n base.Dispose(disposing);\n }\n\n public static int expansion = 1;\n\n private readonly Module<Tensor, Tensor> conv1;\n private readonly Module<Tensor, Tensor> bn1;\n private readonly Module<Tensor, Tensor> conv2;\n internal readonly Module<Tensor, Tensor> bn2;\n private readonly Module<Tensor, Tensor> relu1;\n private readonly Module<Tensor, Tensor>? downsample;\n }\n\n class Bottleneck : Module<Tensor, Tensor>\n {\n public Bottleneck(\n int in_planes,\n int planes,\n int stride,\n Module<Tensor, Tensor>? downsample = null,\n int groups = 1,\n int base_width = 64,\n int dilation = 1,\n Func<int, Module<Tensor, Tensor>>? norm_layer = null) : base(\"Bottleneck\")\n {\n if (norm_layer is null) {\n norm_layer = (planes) => BatchNorm2d(planes);\n }\n\n var width = (int)(planes * (base_width / 64.0)) * groups;\n\n conv1 = Conv2d(in_planes, width, kernelSize: 1, bias: false);\n bn1 = norm_layer(width);\n relu1 = ReLU(inplace: true);\n conv2 = Conv2d(width, width, kernelSize: 3, stride: stride, groups: groups, padding: dilation, dilation: dilation, bias: false);\n bn2 = norm_layer(width);\n relu2 = ReLU(inplace: true);\n conv3 = Conv2d(width, expansion * planes, kernelSize: 1, bias: false);\n bn3 = norm_layer(expansion * planes);\n\n this.downsample = downsample;\n\n RegisterComponents();\n }\n\n public override Tensor forward(Tensor input)\n {\n var identity = input;\n\n var x = relu1.call(bn1.call(conv1.call(input)));\n x = relu2.call(bn2.call(conv2.call(x)));\n x = bn3.call(conv3.call(x));\n\n if (downsample is not null) {\n identity = downsample.call(input);\n }\n\n return x.add_(identity).relu_();\n }\n\n protected override void Dispose(bool disposing)\n {\n if (disposing) {\n conv1.Dispose();\n bn1.Dispose();\n conv2.Dispose(); conv3.Dispose();\n bn2.Dispose(); bn3.Dispose();\n relu1.Dispose(); relu2.Dispose();\n downsample?.Dispose();\n }\n base.Dispose(disposing);\n }\n\n public static int expansion = 4;\n\n private readonly Module<Tensor, Tensor> conv1;\n private readonly Module<Tensor, Tensor> bn1;\n private readonly Module<Tensor, Tensor> conv2;\n private readonly Module<Tensor, Tensor> bn2;\n private readonly Module<Tensor, Tensor> conv3;\n internal readonly Module<Tensor, Tensor> bn3;\n private readonly Module<Tensor, Tensor> relu1;\n private readonly Module<Tensor, Tensor> relu2;\n\n private readonly Module<Tensor, Tensor>? downsample;\n }\n }\n }\n}\n"},{"alpha_fraction":0.5336246490478516,"alphanum_fraction":0.5448733568191528,"avg_line_length":38.47923278808594,"blob_id":"09dd025be8f42bfcd6fbf9b07ec06d78f9dd6fc7","content_id":"1988b7d792a81e64ac14dddb1f689eedd5522658","detected_licenses":["MIT"],"is_generated":false,"is_vendor":false,"language":"C#","length_bytes":12357,"license_type":"permissive","max_line_length":183,"num_lines":313,"path":"/src/Examples/SequenceToSequence.cs","repo_name":"NiklasGustafsson/TorchSharp","src_encoding":"UTF-8","text":"// Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information.\nusing System;\nusing System.IO;\nusing System.Linq;\nusing System.Collections.Generic;\nusing System.Diagnostics;\nusing static TorchSharp.torch;\nusing static TorchSharp.torch.nn;\n\nnamespace TorchSharp.Examples\n{\n\n /// <summary>\n /// This example is based on the PyTorch tutorial at:\n ///\n /// https://pytorch.org/tutorials/beginner/transformer_tutorial.html\n ///\n /// It relies on the WikiText2 dataset, which can be downloaded at:\n ///\n /// https://s3.amazonaws.com/research.metamind.io/wikitext/wikitext-2-v1.zip\n ///\n /// After downloading, extract the files using the defaults (Windows only).\n /// </summary>\n public class SequenceToSequence\n {\n // This path assumes that you're running this on Windows.\n#if NET472_OR_GREATER\n private readonly static string _dataLocation = NSPath.Join(Environment.GetFolderPath(Environment.SpecialFolder.DesktopDirectory), \"..\", \"Downloads\", \"wikitext-2-v1\");\n#else\n private readonly static string _dataLocation = Path.Join(Environment.GetFolderPath(Environment.SpecialFolder.DesktopDirectory), \"..\", \"Downloads\", \"wikitext-2-v1\");\n#endif // NET472_OR_GREATER\n\n private const long emsize = 200;\n private const long nhid = 200;\n private const long nlayers = 2;\n private const long nhead = 2;\n private const double dropout = 0.2;\n\n private const int batch_size = 64;\n private const int eval_batch_size = 32;\n\n private const int epochs = 10;\n\n internal static void Main(string[] args)\n\n {\n torch.random.manual_seed(1);\n\n var cwd = Environment.CurrentDirectory;\n\n var device = torch.cuda.is_available() ? torch.CUDA : torch.CPU;\n Console.WriteLine($\"Running SequenceToSequence on {device.type.ToString()} for {epochs} epochs.\");\n\n var vocab_iter = TorchText.Datasets.WikiText2(\"train\", _dataLocation);\n var tokenizer = TorchText.Data.Utils.get_tokenizer(\"basic_english\");\n\n var counter = new TorchText.Vocab.Counter<string>();\n foreach (var item in vocab_iter) {\n counter.update(tokenizer(item));\n }\n\n var vocab = new TorchText.Vocab.Vocab(counter);\n\n var (train_iter, valid_iter, test_iter) = TorchText.Datasets.WikiText2(_dataLocation);\n\n var train_data = Batchify(ProcessInput(train_iter, tokenizer, vocab), batch_size).to((Device)device);\n var valid_data = Batchify(ProcessInput(valid_iter, tokenizer, vocab), eval_batch_size).to((Device)device);\n var test_data = Batchify(ProcessInput(test_iter, tokenizer, vocab), eval_batch_size).to((Device)device);\n\n var bptt = 32;\n\n var ntokens = vocab.Count;\n\n var model = new TransformerModel(ntokens, emsize, nhead, nhid, nlayers, dropout).to((Device)device);\n var loss = CrossEntropyLoss();\n var lr = 2.50;\n var optimizer = torch.optim.SGD(model.parameters(), lr);\n var scheduler = torch.optim.lr_scheduler.StepLR(optimizer, 1, 0.95, last_epoch: 15);\n\n var totalTime = new Stopwatch();\n totalTime.Start();\n\n foreach (var epoch in Enumerable.Range(1, epochs)) {\n\n var sw = new Stopwatch();\n sw.Start();\n\n train(epoch, train_data, model, loss, bptt, ntokens, optimizer);\n\n var val_loss = evaluate(valid_data, model, loss, bptt, ntokens, optimizer);\n sw.Stop();\n\n var pgFirst = optimizer.ParamGroups.First();\n Console.WriteLine($\"\\nEnd of epoch: {epoch} | lr: {pgFirst.LearningRate:0.00} | time: {sw.Elapsed.TotalSeconds:0.0}s | loss: {val_loss:0.00}\\n\");\n scheduler.step();\n }\n\n var tst_loss = evaluate(test_data, model, loss, bptt, ntokens, optimizer);\n totalTime.Stop();\n\n Console.WriteLine($\"\\nEnd of training | time: {totalTime.Elapsed.TotalSeconds:0.0}s | loss: {tst_loss:0.00}\\n\");\n }\n\n private static void train(int epoch, Tensor train_data, TransformerModel model, Loss<Tensor, Tensor, Tensor> criterion, int bptt, int ntokens, torch.optim.Optimizer optimizer)\n {\n model.train();\n\n using (var d = torch.NewDisposeScope()) {\n\n var total_loss = 0.0f;\n\n var batch = 0;\n var log_interval = 200;\n\n var src_mask = model.GenerateSquareSubsequentMask(bptt);\n\n var tdlen = train_data.shape[0];\n\n\n for (int i = 0; i < tdlen - 1; batch++, i += bptt) {\n\n var (data, targets) = GetBatch(train_data, i, bptt);\n optimizer.zero_grad();\n\n if (data.shape[0] != bptt) {\n src_mask = model.GenerateSquareSubsequentMask(data.shape[0]);\n }\n\n using (var output = model.call(data, src_mask)) {\n var loss = criterion.call(output.view(-1, ntokens), targets);\n loss.backward();\n torch.nn.utils.clip_grad_norm_(model.parameters().ToArray(), 0.5);\n optimizer.step();\n\n total_loss += loss.to(torch.CPU).item<float>();\n }\n\n if ((batch % log_interval == 0 && batch > 0) || (batch == tdlen / bptt)) {\n var cur_loss = total_loss / log_interval;\n Console.WriteLine($\"epoch: {epoch} | batch: {batch} / {tdlen / bptt} | loss: {cur_loss:0.00}\");\n total_loss = 0;\n }\n\n d.DisposeEverythingBut(src_mask);\n }\n }\n }\n\n private static double evaluate(Tensor eval_data, TransformerModel model, Loss<Tensor, Tensor, Tensor> criterion, int bptt, int ntokens, torch.optim.Optimizer optimizer)\n {\n model.eval();\n\n using (var d = torch.NewDisposeScope()) {\n\n var src_mask = model.GenerateSquareSubsequentMask(bptt);\n\n var total_loss = 0.0f;\n var batch = 0;\n\n\n for (int i = 0; i < eval_data.shape[0] - 1; batch++, i += bptt) {\n\n var (data, targets) = GetBatch(eval_data, i, bptt);\n if (data.shape[0] != bptt) {\n src_mask = model.GenerateSquareSubsequentMask(data.shape[0]);\n }\n using (var output = model.call(data, src_mask)) {\n var loss = criterion.call(output.view(-1, ntokens), targets);\n total_loss += data.shape[0] * loss.to(torch.CPU).item<float>();\n }\n\n data.Dispose();\n targets.Dispose();\n\n d.DisposeEverythingBut(src_mask);\n }\n\n return total_loss / eval_data.shape[0];\n }\n }\n\n static Tensor ProcessInput(IEnumerable<string> iter, Func<string, IEnumerable<string>> tokenizer, TorchText.Vocab.Vocab vocab)\n {\n List<Tensor> data = new List<Tensor>();\n foreach (var item in iter) {\n List<long> itemData = new List<long>();\n foreach (var token in tokenizer(item)) {\n itemData.Add(vocab[token]);\n }\n data.Add(torch.tensor(itemData.ToArray(), torch.int64));\n }\n\n var result = torch.cat(data.Where(t => t.NumberOfElements > 0).ToList(), 0);\n return result;\n }\n\n static Tensor Batchify(Tensor data, int batch_size)\n {\n var nbatch = data.shape[0] / batch_size;\n return data.narrow(0, 0, nbatch * batch_size).view(batch_size, -1).t().contiguous();\n }\n\n static (Tensor, Tensor) GetBatch(Tensor source, int index, int bptt)\n {\n var len = Math.Min(bptt, source.shape[0] - 1 - index);\n var data = source[TensorIndex.Slice(index, index + len)];\n var target = source[TensorIndex.Slice(index + 1, index + 1 + len)].reshape(-1);\n return (data, target);\n }\n\n class TransformerModel : Module<Tensor, Tensor, Tensor>\n {\n private Modules.TransformerEncoder transformer_encoder;\n private PositionalEncoding pos_encoder;\n private Modules.Embedding encoder;\n private Modules.Linear decoder;\n\n private long ninputs;\n private Device device;\n\n public TransformerModel(long ntokens, long ninputs, long nheads, long nhidden, long nlayers, double dropout = 0.5) : base(\"Transformer\")\n {\n this.ninputs = ninputs;\n\n pos_encoder = new PositionalEncoding(ninputs, dropout);\n var encoder_layers = TransformerEncoderLayer(ninputs, nheads, nhidden, dropout);\n transformer_encoder = TransformerEncoder(encoder_layers, nlayers);\n encoder = Embedding(ntokens, ninputs);\n decoder = Linear(ninputs, ntokens);\n InitWeights();\n\n RegisterComponents();\n }\n\n public Tensor GenerateSquareSubsequentMask(long size)\n {\n var mask = (torch.ones(new long[] { size, size }) == 1).triu().transpose(0, 1);\n return mask.to_type(ScalarType.Float32)\n .masked_fill(mask == 0, float.NegativeInfinity)\n .masked_fill(mask == 1, 0.0f).to(device);\n }\n\n private void InitWeights()\n {\n var initrange = 0.1;\n\n init.uniform_(encoder.weight, -initrange, initrange);\n init.zeros_(decoder.bias);\n init.uniform_(decoder.weight, -initrange, initrange);\n }\n\n public override Tensor forward(Tensor t, Tensor mask)\n {\n var src = pos_encoder.call(encoder.call(t) * MathF.Sqrt(ninputs));\n var enc = transformer_encoder.call(src, mask);\n return decoder.call(enc);\n }\n\n protected override void Dispose(bool disposing)\n {\n if (disposing) {\n transformer_encoder.Dispose();\n pos_encoder.Dispose();\n encoder.Dispose();\n decoder.Dispose();\n ClearModules();\n }\n base.Dispose(disposing);\n }\n\n protected override Module _to(DeviceType deviceType, int deviceIndex = -1)\n {\n this.device = new Device(deviceType, deviceIndex);\n return base._to(deviceType, deviceIndex);\n }\n }\n\n class PositionalEncoding : Module<Tensor, Tensor>\n {\n private Module<Tensor, Tensor> dropout;\n private Tensor pe;\n\n public PositionalEncoding(long dmodel, double dropout, int maxLen = 5000) : base(\"PositionalEncoding\")\n {\n this.dropout = Dropout(dropout);\n var pe = torch.zeros(new long[] { maxLen, dmodel });\n var position = torch.arange(0, maxLen, 1).unsqueeze(1);\n var divTerm = (torch.arange(0, dmodel, 2) * (-Math.Log(10000.0) / dmodel)).exp();\n pe[TensorIndex.Ellipsis, TensorIndex.Slice(0, null, 2)] = (position * divTerm).sin();\n pe[TensorIndex.Ellipsis, TensorIndex.Slice(1, null, 2)] = (position * divTerm).cos();\n this.pe = pe.unsqueeze(0).transpose(0, 1);\n\n RegisterComponents();\n }\n\n public override Tensor forward(Tensor t)\n {\n using var x = t + pe[TensorIndex.Slice(null, t.shape[0]), TensorIndex.Slice()];\n return dropout.call(x);\n }\n\n protected override void Dispose(bool disposing)\n {\n if (disposing) {\n dropout.Dispose();\n pe.Dispose();\n ClearModules();\n }\n base.Dispose(disposing);\n }\n }\n }\n}\n"},{"alpha_fraction":0.5054945349693298,"alphanum_fraction":0.5054945349693298,"avg_line_length":28.1200008392334,"blob_id":"2b4624bb37ccd65df9f08b938db248eb4d789c20","content_id":"0b7dec35775bd78675d10d5a0ff10d99ab80f657","detected_licenses":["MIT"],"is_generated":false,"is_vendor":false,"language":"C#","length_bytes":1456,"license_type":"permissive","max_line_length":130,"num_lines":50,"path":"/src/TorchVision/Compose.cs","repo_name":"NiklasGustafsson/TorchSharp","src_encoding":"UTF-8","text":"// Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information.\n\nusing System;\nusing static TorchSharp.torch;\n\nnamespace TorchSharp\n{\n public static partial class torchvision\n {\n internal class ComposedTransforms : IDisposable, ITransform\n {\n public ComposedTransforms(ITransform[] transforms)\n {\n this.transforms = transforms;\n }\n\n public void Dispose()\n {\n foreach (var t in transforms) {\n if (t is IDisposable) {\n ((IDisposable)t).Dispose();\n }\n }\n }\n\n public Tensor call(Tensor input)\n {\n foreach (var t in transforms) {\n input = t.call(input);\n }\n return input;\n }\n\n private ITransform[] transforms;\n }\n\n public static partial class transforms\n {\n /// <summary>\n /// Composes several transforms together.\n /// </summary>\n /// <param name=\"transforms\">A list of transforms to compose serially.</param>\n /// <returns></returns>\n static public ITransform Compose(params ITransform[] transforms)\n {\n return new ComposedTransforms(transforms);\n }\n }\n }\n}\n"},{"alpha_fraction":0.6159601211547852,"alphanum_fraction":0.6221945285797119,"avg_line_length":36.015384674072266,"blob_id":"442c53a4e01cfe622efca8238cb98b161d1bac5c","content_id":"df0cb54044067fb7e1f625999bf57b0e5a1476df","detected_licenses":["MIT"],"is_generated":false,"is_vendor":false,"language":"C#","length_bytes":2406,"license_type":"permissive","max_line_length":130,"num_lines":65,"path":"/src/TorchSharp/Utils/Decompress.cs","repo_name":"NiklasGustafsson/TorchSharp","src_encoding":"UTF-8","text":"// Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information.\nusing System;\nusing System.IO;\nusing System.Linq;\nusing System.Threading;\nusing System.Threading.Tasks;\nusing ICSharpCode.SharpZipLib.Core;\nusing ICSharpCode.SharpZipLib.GZip;\nusing ICSharpCode.SharpZipLib.Tar;\n\n// This code was inspired by code found in the SciSharpStack-Examples repository located at:\n//\n// https://github.com/SciSharp/SciSharp-Stack-Examples\n//\n// Original License: https://github.com/SciSharp/SciSharp-Stack-Examples/blob/master/LICENSE\n//\n// Original copyright information was not found at the above location.\n\nnamespace TorchSharp.Utils\n{\n public static class Decompress\n {\n public static void DecompressGZipFile(string gzipFileName, string targetDir)\n {\n byte[] buf = new byte[4096];\n\n using (var fs = File.OpenRead(gzipFileName))\n using (var gzipStream = new GZipInputStream(fs)) {\n\n string fnOut = Path.Combine(targetDir, Path.GetFileNameWithoutExtension(gzipFileName));\n\n using (var fsOut = File.Create(fnOut)) {\n StreamUtils.Copy(gzipStream, fsOut, buf);\n }\n }\n }\n public static void ExtractTGZ(string gzArchiveName, string destFolder)\n {\n var flag = gzArchiveName.Split(Path.DirectorySeparatorChar).Last().Split('.').First() + \".bin\";\n if (File.Exists(Path.Combine(destFolder, flag))) return;\n\n Console.WriteLine($\"Extracting.\");\n var task = Task.Run(() => {\n using (var inStream = File.OpenRead(gzArchiveName)) {\n using (var gzipStream = new GZipInputStream(inStream)) {\n#pragma warning disable CS0618 // Type or member is obsolete\n using (TarArchive tarArchive = TarArchive.CreateInputTarArchive(gzipStream))\n#pragma warning restore CS0618 // Type or member is obsolete\n tarArchive.ExtractContents(destFolder);\n }\n }\n });\n\n while (!task.IsCompleted) {\n Thread.Sleep(200);\n Console.Write(\".\");\n }\n\n File.Create(Path.Combine(destFolder, flag));\n Console.WriteLine(\"\");\n Console.WriteLine(\"Extraction completed.\");\n }\n\n }\n}\n"},{"alpha_fraction":0.4355202317237854,"alphanum_fraction":0.43863415718078613,"avg_line_length":34.613765716552734,"blob_id":"1a9ef997c198e7cf1935de02ed247328a8bd3182","content_id":"35515c05407e574f9952002c50b3beef28ed453a","detected_licenses":["MIT"],"is_generated":false,"is_vendor":false,"language":"C#","length_bytes":18626,"license_type":"permissive","max_line_length":150,"num_lines":523,"path":"/src/TorchSharp/Tensor/Storage.cs","repo_name":"NiklasGustafsson/TorchSharp","src_encoding":"UTF-8","text":"// Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information.\nusing System;\nusing System.Collections;\nusing System.Collections.Generic;\nusing System.Diagnostics;\nusing static TorchSharp.PInvoke.NativeMethods;\n\nnamespace TorchSharp\n{\n public static partial class torch\n {\n /// <summary>\n /// A torch.Storage is a contiguous, one-dimensional array of a single data type.\n /// Every tensor has a corresponding storage of the same data type.\n /// </summary>\n public abstract class Storage\n {\n protected Storage()\n {\n }\n\n protected Storage(Tensor tensor)\n {\n _tensor = tensor;\n data_ptr();\n }\n\n protected Storage(Tensor tensor, IntPtr data_ptr)\n {\n _tensor = tensor;\n _tensor_data_ptr = (data_ptr == IntPtr.Zero) ? this.data_ptr() : data_ptr;\n }\n\n internal static Storage<T> Create<T>(Tensor tensor) where T : unmanaged\n {\n var type = typeof(T);\n switch (type) {\n case Type _ when type == typeof(byte):\n return new Storage<T>(tensor.@byte());\n case Type _ when type == typeof(bool):\n return new Storage<T>(tensor.@bool());\n case Type _ when type == typeof(int):\n return new Storage<T>(tensor.@int());\n case Type _ when type == typeof(long):\n return new Storage<T>(tensor.@long());\n case Type _ when type == typeof(float):\n return new Storage<T>(tensor.@float());\n case Type _ when type == typeof(double):\n return new Storage<T>(tensor.@double());\n case Type _ when type == typeof((float,float)):\n return new Storage<T>(tensor.to_type(ScalarType.ComplexFloat32));\n case Type _ when type == typeof(System.Numerics.Complex):\n return new Storage<T>(tensor.to_type(ScalarType.ComplexFloat64));\n default:\n throw new NotSupportedException();\n }\n }\n\n protected static Tensor CreateTypedTensor<T>(ScalarType dtype, IList<T> rawArray)\n {\n switch (dtype) {\n case ScalarType.Int8:\n return torch.tensor(rawArray as IList<byte>);\n case ScalarType.Bool:\n return torch.tensor(rawArray as IList<bool>);\n case ScalarType.Int32:\n return torch.tensor(rawArray as IList<int>);\n case ScalarType.Int64:\n return torch.tensor(rawArray as IList<long>);\n case ScalarType.Float32:\n return torch.tensor(rawArray as IList<float>);\n case ScalarType.Float64:\n return torch.tensor(rawArray as IList<double>);\n case ScalarType.ComplexFloat32:\n return torch.tensor(rawArray as IList<(float, float)>);\n case ScalarType.ComplexFloat64:\n return torch.tensor(rawArray as IList<System.Numerics.Complex>);\n default:\n throw new NotSupportedException();\n }\n }\n\n protected torch.Tensor _tensor; // Keeping it alive.\n protected IntPtr _tensor_data_ptr;\n\n /// <summary>\n /// Convert to bool storage.\n /// </summary>\n /// <returns></returns>\n public Storage<bool> @bool() => _tensor.to_type(ScalarType.Bool).storage<bool>();\n\n /// <summary>\n /// Convert to byte storage.\n /// </summary>\n public Storage<byte> @byte() => _tensor.to_type(ScalarType.Byte).storage<byte>();\n\n /// <summary>\n /// Convert to char storage.\n /// </summary>\n public Storage<char> @char() => _tensor.to_type(ScalarType.Int8).storage<char>();\n\n /// <summary>\n /// Convert to int storage.\n /// </summary>\n public Storage<int> @int() => _tensor.to_type(ScalarType.Int32).storage<int>();\n\n /// <summary>\n /// Convert to long storage.\n /// </summary>\n public Storage<long> @long() => _tensor.to_type(ScalarType.Int64).storage<long>();\n\n /// <summary>\n /// Convert to float storage.\n /// </summary>\n public Storage<float> @float() => _tensor.to_type(ScalarType.Float32).storage<float>();\n\n /// <summary>\n /// Convert to double storage.\n /// </summary>\n public Storage<double> @double() => _tensor.to_type(ScalarType.Float64).storage<double>();\n\n /// <summary>\n /// Convert to 32-bit complex storage.\n /// </summary>\n public Storage<(float,float)> complex_float() => _tensor.to_type(ScalarType.ComplexFloat32).storage<(float, float)>();\n\n /// <summary>\n /// Convert to 64-bit complex storage.\n /// </summary>\n public Storage<System.Numerics.Complex> complex_double() => _tensor.to_type(ScalarType.ComplexFloat64).storage<System.Numerics.Complex>();\n\n /// <summary>\n /// The size of each storage element.\n /// </summary>\n /// <returns></returns>\n public abstract int element_size();\n\n /// <summary>\n /// A pointer to the raw data in memory.\n /// </summary>\n /// <returns></returns>\n protected IntPtr data_ptr()\n {\n if (_tensor_data_ptr != IntPtr.Zero)\n return _tensor_data_ptr;\n\n var res = THSStorage_data_ptr(_tensor.Handle);\n if (res == IntPtr.Zero) { CheckForErrors(); }\n _tensor_data_ptr = res;\n return res;\n }\n\n /// <summary>\n /// The number of bytes allocated to the storage.\n /// </summary>\n /// <returns></returns>\n public ulong nbytes()\n {\n var res = THSStorage_nbytes(_tensor.Handle);\n CheckForErrors();\n return res;\n }\n }\n\n /// <summary>\n /// A torch.Storage is a contiguous, one-dimensional array of a single data type.\n /// Every tensor has a corresponding storage of the same data type.\n /// </summary>\n public sealed class Storage<T> : torch.Storage, IDisposable, IEnumerable<T> where T : unmanaged\n {\n internal Storage(torch.Tensor tensor, int count = -1) : base(tensor, IntPtr.Zero)\n {\n _count = count;\n }\n\n internal Storage(IntPtr data_ptr, ScalarType dtype, int count = -1) : base(null, data_ptr)\n {\n _count = count;\n _tensor = CreateTypedTensor<T>(dtype, this.ToArray());\n }\n\n internal Storage(T value) : base()\n {\n _scalarValue = value;\n }\n\n private T _scalarValue = default(T);\n\n /// <summary>\n /// Size of each storage element.\n /// </summary>\n /// <returns></returns>\n public override int element_size()\n {\n ValidateNotScalar();\n unsafe {\n return sizeof(T);\n }\n }\n\n /// <summary>\n /// The device where the storage is located.\n /// </summary>\n public Device device {\n get {\n ValidateNotScalar();\n return _tensor.device;\n }\n }\n\n /// <summary>\n /// Move the storage to a CUDA device.\n /// </summary>\n public Storage<T> cuda(Device device = null)\n {\n ValidateNotScalar();\n return _tensor.cuda(device).storage<T>();\n }\n\n /// <summary>\n /// Move the storage to the CPU.\n /// </summary>\n public Storage<T> cpu()\n {\n ValidateNotScalar();\n return _tensor.cpu().storage<T>();\n }\n\n /// <summary>\n /// Convert the storage instance to a .NET array, copying its data.\n /// </summary>\n /// <returns></returns>\n public T[] ToArray()\n {\n ValidateNotScalar();\n var result = new T[Count];\n CopyTo(result, 0);\n return result;\n }\n\n /// <summary>\n /// Accesses a single element of a storage.\n /// </summary>\n /// <param name=\"index\">The index of the element.</param>\n /// <returns></returns>\n public T this[int index] {\n get {\n ValidateNotScalar();\n CheckIndex(index);\n unsafe {\n T* ptr = (T*)_tensor_data_ptr;\n return ptr[index];\n }\n }\n set {\n ValidateNotScalar();\n CheckIndex(index);\n unsafe {\n T* ptr = (T*)_tensor_data_ptr;\n ptr[index] = value;\n }\n }\n }\n\n /// <summary>\n /// Accesses a slice of a storage instance.\n /// </summary>\n /// <param name=\"range\">The range, expressed as a tuple [start,end)</param>\n public Storage<T> this[(int? start, int? end) range] {\n get {\n ValidateNotScalar();\n if (range.end < range.start)\n throw new ArgumentException(\"end < start\");\n var start = CheckIndex(range.start.HasValue? range.start.Value : 0);\n var end = CheckIndex(range.end.HasValue ? range.end.Value : Count);\n var count = end - start;\n unsafe {\n var ptr = _tensor_data_ptr + sizeof(T) * start;\n return new Storage<T>(ptr, _tensor.dtype, count);\n }\n }\n set {\n ValidateNotScalar();\n if (value._tensor is not null || value._tensor_data_ptr != IntPtr.Zero)\n throw new InvalidOperationException();\n if (range.end < range.start)\n throw new ArgumentException(\"end < start\");\n var v = value._scalarValue;\n var start = CheckIndex(range.start.HasValue ? range.start.Value : 0);\n var end = CheckIndex(range.end.HasValue ? range.end.Value : Count);\n unsafe {\n T* ptr = (T*)_tensor_data_ptr;\n for (int i = start; i < end; i++)\n ptr[i] = v;\n }\n }\n }\n\n#if !NETSTANDARD2_0_OR_GREATER\n /// <summary>\n /// Accesses a slice of a storage instance.\n /// </summary>\n /// <param name=\"range\">The range.</param>\n public Storage<T> this[System.Range range] {\n get {\n var start = CheckIndex(range.Start);\n var end = CheckIndex(range.End);\n return this[(start, end)];\n }\n set {\n var start = CheckIndex(range.Start);\n var end = CheckIndex(range.End);\n this[(start, end)] = value;\n }\n }\n\n private int CheckIndex(System.Index index)\n {\n var i = index.IsFromEnd ? Count - index.Value : index.Value;\n if (i < 0 || i >= Count)\n throw new IndexOutOfRangeException();\n return i;\n }\n#endif\n\n private int CheckIndex(int index) {\n if (index < 0 || index >= Count)\n throw new IndexOutOfRangeException();\n return index;\n }\n\n private void ValidateNotScalar()\n {\n if (_tensor is null &&_tensor_data_ptr == IntPtr.Zero)\n throw new InvalidOperationException(\"Invalid use of a scalar Storage instance.\");\n }\n\n /// <summary>\n /// The number of elements in the storage.\n /// </summary>\n public int Count {\n get {\n ValidateNotScalar();\n if (_count == -1) {\n unsafe {\n _count = (int)nbytes() / sizeof(T);\n }\n }\n return _count;\n }\n }\n\n private int _count = -1;\n\n public static implicit operator Storage<T>(T value)\n {\n return new Storage<T>(value);\n }\n\n /// <summary>\n /// Copy data from an array into a storage instance.\n /// </summary>\n /// <param name=\"array\"></param>\n public void copy_(IList<T> array)\n {\n ValidateNotScalar();\n CopyFrom(array, 0);\n }\n\n /// <summary>\n /// Fill a storage instance with a single value.\n /// </summary>\n /// <param name=\"value\"></param>\n public void fill_(T value)\n {\n ValidateNotScalar();\n unsafe {\n var ptr = (T*)data_ptr();\n for (int i = 0; i < Count; i++)\n ((T*)ptr)[i] = value;\n }\n }\n\n /// <summary>\n /// Convert the storage to a .NET list.\n /// </summary>\n /// <returns></returns>\n public IList<T> tolist()\n {\n ValidateNotScalar();\n var result = new T[Count];\n CopyTo(result, 0);\n return result;\n }\n\n public bool IsReadOnly => false;\n\n public bool Contains(T item)\n {\n ValidateNotScalar();\n unsafe {\n var ptr = (T*)data_ptr();\n for (int i = 0; i < Count; i++)\n if (((T*)ptr)[i].Equals(item)) return true;\n }\n return false;\n }\n\n /// <summary>\n /// Copy the contents of the Storage instance into the provided array, starting at 'arrayIndex'\n /// </summary>\n /// <param name=\"array\">A target array.</param>\n /// <param name=\"arrayIndex\">The first offset in the array to write data to.</param>\n public void CopyTo(IList<T> array, int arrayIndex)\n {\n ValidateNotScalar();\n\n unsafe {\n var ptr = (T*)data_ptr();\n for (int i = 0; i < Count && i < array.Count-arrayIndex; i++)\n array[i + arrayIndex] = ((T*)ptr)[i];\n }\n }\n\n /// <summary>\n /// Copy the contents of the provided array into the Storage instance, starting at 'arrayIndex' in the array.\n /// </summary>\n /// <param name=\"array\">A source array.</param>\n /// <param name=\"arrayIndex\">The first offset in the array to read data from.</param>\n public void CopyFrom(IList<T> array, int arrayIndex)\n {\n ValidateNotScalar();\n\n unsafe {\n var ptr = (T*)data_ptr();\n for (int i = 0; i < Count && i < array.Count-arrayIndex; i++)\n ((T*)ptr)[i] = array[i+arrayIndex];\n }\n }\n\n /// <summary>\n /// Look up a value and return the first index where it can be found.\n /// </summary>\n /// <param name=\"item\">The item to look for.</param>\n /// <returns></returns>\n public int IndexOf(T item)\n {\n unsafe {\n var ptr = (T*)data_ptr();\n for (int i = 0; i < Count; i++)\n if (((T*)ptr)[i].Equals(item)) return i;\n }\n return -1;\n }\n\n public IEnumerator<T> GetEnumerator()\n {\n ValidateNotScalar();\n return new StorageEnumerator(this);\n }\n\n IEnumerator IEnumerable.GetEnumerator()\n {\n ValidateNotScalar();\n return new StorageEnumerator(this);\n }\n\n public void Dispose()\n {\n _tensor_data_ptr = IntPtr.Zero;\n _tensor = null;\n }\n\n private struct StorageEnumerator: IEnumerator<T>\n {\n private Storage<T> _span;\n private readonly long _count;\n\n // State.\n private long _index;\n private T _current;\n\n public StorageEnumerator(Storage<T> span)\n {\n _span = span;\n _count = span.Count;\n Debug.Assert(_count > 0);\n _index = -1;\n _current = default;\n }\n\n public T Current => _current;\n\n object IEnumerator.Current => Current;\n\n public void Dispose()\n {\n _span = null;\n Reset();\n }\n\n public bool MoveNext()\n {\n if (_index >= _count - 1) {\n Reset();\n return false;\n }\n\n _index += 1;\n\n unsafe { _current = ((T*)_span._tensor_data_ptr)[_index]; }\n return true;\n }\n\n public void Reset()\n {\n _index = -1;\n _current = default;\n }\n }\n }\n }\n}\n"},{"alpha_fraction":0.581882894039154,"alphanum_fraction":0.5842563509941101,"avg_line_length":37.89230728149414,"blob_id":"cd9081aadae5815e1138b136ef3a7ccc19016018","content_id":"ff812dfb33350b9627983246cb8264f1a973c120","detected_licenses":["MIT"],"is_generated":false,"is_vendor":false,"language":"C#","length_bytes":2528,"license_type":"permissive","max_line_length":164,"num_lines":65,"path":"/src/TorchSharp/Distributions/LogNormal.cs","repo_name":"NiklasGustafsson/TorchSharp","src_encoding":"UTF-8","text":"// Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information.\n\nusing static TorchSharp.torch;\n\nnamespace TorchSharp\n{\n using Modules;\n using static torch.distributions;\n\n namespace Modules\n {\n public class LogNormal : TransformedDistribution\n {\n internal LogNormal(Tensor loc, Tensor scale, torch.Generator generator = null) :\n base(Normal(loc, scale, generator), new torch.distributions.transforms.Transform[] { new torch.distributions.transforms.ExpTransform() }, generator)\n {\n this.loc = loc.alias().DetachFromDisposeScope();\n this.scale = scale.alias().DetachFromDisposeScope();\n }\n\n public Tensor loc { get; private set; }\n\n public Tensor scale { get; private set; }\n\n public override Tensor mean => torch.WrappedTensorDisposeScope(() => (loc + scale.pow(2) / 2).exp());\n\n public override Tensor mode => torch.WrappedTensorDisposeScope(() => (loc - scale.square()).exp());\n\n public override Tensor variance => torch.WrappedTensorDisposeScope(() => (scale.pow(2).exp() - 1) * (2 * loc + scale.pow(2)).exp());\n\n public override Tensor entropy()\n {\n return base_distribution.entropy() + loc;\n }\n\n public override Distribution expand(Size batch_shape, Distribution instance = null)\n {\n var newDistribution = ((instance == null)\n ? new LogNormal(loc.expand(batch_shape), scale.expand(batch_shape), generator)\n : instance) as LogNormal;\n return base.expand(batch_shape, newDistribution);\n }\n }\n }\n\n\n public static partial class torch\n {\n public static partial class distributions\n {\n /// <summary>\n /// Creates a log-normal distribution parameterized by `loc` and `scale`\n /// </summary>\n /// <param name=\"loc\">Mode or median of the distribution.</param>\n /// <param name=\"scale\">Standard deviation.</param>\n /// <param name=\"generator\">An optional random number generator object.</param>\n /// <returns></returns>\n public static LogNormal LogNormal(Tensor loc, Tensor scale, torch.Generator generator = null)\n {\n \n return new LogNormal(loc, scale, generator);\n }\n }\n }\n}\n"},{"alpha_fraction":0.5458303689956665,"alphanum_fraction":0.5670561790466309,"avg_line_length":28.321678161621094,"blob_id":"c8a157aed65a0f581f3793d3d30a8e2b9722cbfe","content_id":"9bd62666271cf80ade6d0671f7dd3e7953d05d71","detected_licenses":["MIT"],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":12579,"license_type":"permissive","max_line_length":129,"num_lines":429,"path":"/src/Python/exportsd.py","repo_name":"NiklasGustafsson/TorchSharp","src_encoding":"UTF-8","text":"#\n# Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information.\n#\nimport io\nimport torch\nimport leb128\nimport numpy as np\n\ndef _elem_type(t):\n dt = t.dtype\n\n if dt == torch.uint8:\n return 0\n elif dt == torch.int8:\n return 1\n elif dt == torch.int16:\n return 2\n elif dt == torch.int32:\n return 3\n elif dt == torch.int64:\n return 4\n elif dt == torch.float16:\n return 5\n elif dt == torch.float32:\n return 6\n elif dt == torch.float64:\n return 7\n elif dt == torch.bool:\n return 11\n elif dt == torch.bfloat16:\n return 15\n else:\n return 4711\n\ndef _write_tensor(t, stream):\n stream.write(leb128.u.encode(_elem_type(t)))\n stream.write(leb128.u.encode(len(t.shape)))\n for s in t.shape:\n stream.write(leb128.u.encode(s))\n stream.write(t.numpy().tobytes())\n\ndef save_state_dict(sd, stream):\n \"\"\"\n Saves a PyToch state dictionary using the format that TorchSharp can\n read.\n\n :param sd: A dictionary produced by 'model.state_dict()'\n :param stream: An write stream opened for binary I/O.\n \"\"\"\n stream.write(leb128.u.encode(len(sd)))\n for entry in sd:\n stream.write(leb128.u.encode(len(entry)))\n stream.write(bytes(entry, 'utf-8'))\n _write_tensor(sd[entry], stream)\n\ndef _write_encoded_int64(value, stream):\n stream.write(leb128.u.encode(value))\n\ndef _write_optim_name(name, stream):\n _write_encoded_int64(len(name),stream)\n stream.write(bytes(name, 'utf-8'))\n\ndef _write_bool(value, stream):\n stream.write(value.to_bytes(1, 'little'))\n\ndef _write_int32(value, stream):\n stream.write(value.to_bytes(4, 'little'))\n\ndef _write_int64(value, stream):\n stream.write(value.to_bytes(8, 'little'))\n\ndef _write_conditional_state_tensor(name, state, stream):\n if name not in state:\n _write_bool(False, stream)\n else: \n buf = state[name]\n if buf == None:\n _write_bool(False, stream)\n else:\n _write_bool(True, stream)\n _write_tensor(buf, stream)\n\ndef save_sgd(optim, stream):\n\n sd = optim.state_dict()\n\n _write_optim_name('SGD', stream)\n\n _write_encoded_int64(len(optim.param_groups), stream)\n _write_encoded_int64(len(optim.state), stream)\n\n # Write options\n\n for pg in optim.param_groups:\n floats = np.empty(5,dtype=np.float64)\n floats[0] = pg['lr'] \n floats[1] = pg['lr']\n floats[2] = pg['momentum']\n floats[3] = pg['dampening']\n floats[4] = pg['weight_decay']\n stream.write(floats.tobytes())\n _write_bool(pg['nesterov'], stream)\n _write_bool(pg['maximize'], stream)\n\n # Write state\n for group in optim.param_groups:\n for p in group['params']:\n st = optim.state[p]\n _write_conditional_state_tensor('momentum_buffer', st, stream) \n\ndef save_asgd(optim, stream):\n\n sd = optim.state_dict()\n\n _write_optim_name('ASGD', stream)\n\n _write_encoded_int64(len(optim.param_groups), stream)\n _write_encoded_int64(len(optim.state), stream)\n\n # Write options\n\n for pg in optim.param_groups:\n floats = np.empty(2,dtype=np.float64)\n floats[0] = pg['lr'] \n floats[1] = pg['lr']\n stream.write(floats.tobytes())\n _write_bool(pg['maximize'], stream)\n floats = np.empty(4,dtype=np.float64)\n floats[0] = pg['lambd']\n floats[1] = pg['alpha']\n floats[2] = pg['weight_decay']\n floats[3] = pg['t0']\n stream.write(floats.tobytes())\n\n # Write state\n for group in optim.param_groups:\n for p in group['params']:\n st = optim.state[p]\n _write_int64(int(st[\"step\"].item()), stream)\n floats = np.empty(2,dtype=np.float64)\n floats[0] = st['eta'] \n floats[1] = st['mu']\n stream.write(floats.tobytes())\n _write_tensor(st['ax'], stream)\n\ndef save_rprop(optim, stream):\n\n sd = optim.state_dict()\n\n _write_optim_name('Rprop', stream)\n\n _write_encoded_int64(len(optim.param_groups), stream)\n _write_encoded_int64(len(optim.state), stream)\n\n # Write options\n\n for pg in optim.param_groups:\n floats = np.empty(2,dtype=np.float64)\n floats[0] = pg['lr'] \n floats[1] = pg['lr']\n stream.write(floats.tobytes())\n _write_bool(pg['maximize'], stream)\n floats = np.empty(4,dtype=np.float64)\n etaminus, etaplus = pg['etas']\n min_step, max_step = pg['step_sizes']\n floats[0] = etaminus\n floats[1] = etaplus\n floats[2] = min_step\n floats[3] = max_step\n stream.write(floats.tobytes())\n\n # Write state\n for group in optim.param_groups:\n for p in group['params']:\n st = optim.state[p]\n _write_int64(st[\"step\"], stream)\n _write_tensor(st['prev'], stream)\n _write_tensor(st['step_size'], stream)\n\ndef save_rmsprop(optim, stream):\n\n sd = optim.state_dict()\n\n _write_optim_name('RMSProp', stream)\n\n _write_encoded_int64(len(optim.param_groups), stream)\n _write_encoded_int64(len(optim.state), stream)\n\n # Write options\n\n for pg in optim.param_groups:\n floats = np.empty(2,dtype=np.float64)\n floats[0] = pg['lr'] \n floats[1] = pg['lr']\n stream.write(floats.tobytes())\n _write_bool(pg['maximize'], stream)\n floats = np.empty(4,dtype=np.float64)\n floats[0] = pg['momentum']\n floats[1] = pg['alpha']\n floats[2] = pg['eps']\n floats[3] = pg['weight_decay']\n stream.write(floats.tobytes())\n _write_bool(pg['centered'], stream)\n\n # Write state\n for group in optim.param_groups:\n for p in group['params']:\n st = optim.state[p]\n _write_int64(st[\"step\"], stream)\n _write_tensor(st['square_avg'], stream)\n _write_conditional_state_tensor('momentum_buffer', st, stream)\n _write_conditional_state_tensor('grad_avg', st, stream)\n\ndef save_adam(optim, stream):\n\n sd = optim.state_dict()\n\n _write_optim_name('Adam', stream)\n\n _write_encoded_int64(len(optim.param_groups), stream)\n _write_encoded_int64(len(optim.state), stream)\n\n # Write options\n\n for pg in optim.param_groups:\n floats = np.empty(6,dtype=np.float64)\n floats[0] = pg['lr'] \n floats[1] = pg['lr']\n beta1, beta2 = pg['betas']\n floats[2] = beta1\n floats[3] = beta2\n floats[4] = pg['eps']\n floats[5] = pg['weight_decay']\n stream.write(floats.tobytes())\n _write_bool(pg['amsgrad'], stream)\n _write_bool(pg['maximize'], stream)\n\n # Write state\n for group in optim.param_groups:\n for p in group['params']:\n st = optim.state[p]\n _write_int64(int(st[\"step\"].item()), stream)\n _write_tensor(st['exp_avg'], stream)\n _write_tensor(st['exp_avg_sq'], stream)\n _write_conditional_state_tensor('max_exp_avg_sq', st, stream)\n\ndef save_adamw(optim, stream):\n\n sd = optim.state_dict()\n\n _write_optim_name('AdamW', stream)\n\n _write_encoded_int64(len(optim.param_groups), stream)\n _write_encoded_int64(len(optim.state), stream)\n\n # Write options\n\n for pg in optim.param_groups:\n floats = np.empty(6,dtype=np.float64)\n floats[0] = pg['lr'] \n floats[1] = pg['lr']\n beta1, beta2 = pg['betas']\n floats[2] = beta1\n floats[3] = beta2\n floats[4] = pg['eps']\n floats[5] = pg['weight_decay']\n stream.write(floats.tobytes())\n _write_bool(pg['amsgrad'], stream)\n _write_bool(pg['maximize'], stream)\n\n # Write state\n for group in optim.param_groups:\n for p in group['params']:\n st = optim.state[p]\n _write_int64(int(st[\"step\"].item()), stream)\n _write_tensor(st['exp_avg'], stream)\n _write_tensor(st['exp_avg_sq'], stream)\n _write_conditional_state_tensor('max_exp_avg_sq', st, stream)\n\ndef save_nadam(optim, stream):\n\n sd = optim.state_dict()\n\n _write_optim_name('NAdam', stream)\n\n _write_encoded_int64(len(optim.param_groups), stream)\n _write_encoded_int64(len(optim.state), stream)\n\n # Write options\n\n for pg in optim.param_groups:\n floats = np.empty(7,dtype=np.float64)\n floats[0] = pg['lr'] \n floats[1] = pg['lr']\n beta1, beta2 = pg['betas']\n floats[2] = beta1\n floats[3] = beta2\n floats[4] = pg['eps']\n floats[5] = pg['weight_decay']\n floats[6] = pg['momentum_decay']\n stream.write(floats.tobytes())\n\n # Write state\n for group in optim.param_groups:\n for p in group['params']:\n st = optim.state[p]\n _write_int64(int(st[\"step\"].item()), stream)\n floats = np.empty(1,dtype=np.float64)\n floats[0] = float(st[\"mu_product\"].item()) \n stream.write(floats.tobytes())\n _write_tensor(st['exp_avg'], stream)\n _write_tensor(st['exp_avg_sq'], stream)\n\ndef save_radam(optim, stream):\n\n sd = optim.state_dict()\n\n _write_optim_name('RAdam', stream)\n\n _write_encoded_int64(len(optim.param_groups), stream)\n _write_encoded_int64(len(optim.state), stream)\n\n # Write options\n\n for pg in optim.param_groups:\n floats = np.empty(6,dtype=np.float64)\n floats[0] = pg['lr'] \n floats[1] = pg['lr']\n beta1, beta2 = pg['betas']\n floats[2] = beta1\n floats[3] = beta2\n floats[4] = pg['eps']\n floats[5] = pg['weight_decay']\n stream.write(floats.tobytes())\n\n # Write state\n for group in optim.param_groups:\n for p in group['params']:\n st = optim.state[p]\n _write_int64(int(st[\"step\"].item()), stream)\n _write_tensor(st['exp_avg'], stream)\n _write_tensor(st['exp_avg_sq'], stream)\n\ndef save_adamax(optim, stream):\n\n sd = optim.state_dict()\n\n _write_optim_name('Adamax', stream)\n\n _write_encoded_int64(len(optim.param_groups), stream)\n _write_encoded_int64(len(optim.state), stream)\n\n # Write options\n\n for pg in optim.param_groups:\n floats = np.empty(6,dtype=np.float64)\n floats[0] = pg['lr'] \n floats[1] = pg['lr']\n beta1, beta2 = pg['betas']\n floats[2] = beta1\n floats[3] = beta2\n floats[4] = pg['eps']\n floats[5] = pg['weight_decay']\n stream.write(floats.tobytes())\n\n # Write state\n for group in optim.param_groups:\n for p in group['params']:\n st = optim.state[p]\n _write_int64(int(st[\"step\"].item()), stream)\n _write_tensor(st['exp_avg'], stream)\n _write_tensor(st['exp_inf'], stream)\n\ndef save_adadelta(optim, stream):\n\n sd = optim.state_dict()\n\n _write_optim_name('Adadelta', stream)\n\n _write_encoded_int64(len(optim.param_groups), stream)\n _write_encoded_int64(len(optim.state), stream)\n\n # Write options\n\n for pg in optim.param_groups:\n floats = np.empty(5,dtype=np.float64)\n floats[0] = pg['lr'] \n floats[1] = pg['lr']\n floats[2] = pg['rho']\n floats[3] = pg['eps']\n floats[4] = pg['weight_decay']\n stream.write(floats.tobytes())\n _write_bool(pg['maximize'], stream)\n\n # Write state\n for group in optim.param_groups:\n for p in group['params']:\n st = optim.state[p]\n _write_int64(st[\"step\"], stream)\n _write_tensor(st['square_avg'], stream)\n _write_tensor(st['acc_delta'], stream)\n\ndef save_adagrad(optim, stream):\n\n sd = optim.state_dict()\n\n _write_optim_name('Adagrad', stream)\n\n _write_encoded_int64(len(optim.param_groups), stream)\n _write_encoded_int64(len(optim.state), stream)\n\n # Write options\n\n for pg in optim.param_groups:\n floats = np.empty(6,dtype=np.float64)\n floats[0] = pg['lr'] \n floats[1] = pg['lr']\n floats[2] = pg['lr_decay']\n floats[3] = pg['initial_accumulator_value']\n floats[4] = pg['eps']\n floats[5] = pg['weight_decay']\n stream.write(floats.tobytes())\n\n # Write state\n for group in optim.param_groups:\n for p in group['params']:\n st = optim.state[p]\n _write_int64(int(st[\"step\"].item()), stream)\n _write_tensor(st['sum'], stream)\n"},{"alpha_fraction":0.7192075848579407,"alphanum_fraction":0.7245908975601196,"avg_line_length":54.61676788330078,"blob_id":"fb91138d748b56a2c3c40b3b90d26bbd2ecaf663","content_id":"4a0298a15dfd95f114145473bfd8ef4981349693","detected_licenses":["MIT"],"is_generated":false,"is_vendor":false,"language":"C#","length_bytes":9288,"license_type":"permissive","max_line_length":244,"num_lines":167,"path":"/src/TorchSharp/PInvoke/LibTorchSharp.THSLinalg.cs","repo_name":"NiklasGustafsson/TorchSharp","src_encoding":"UTF-8","text":"// Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information.\n#nullable enable\nusing System;\nusing System.Runtime.InteropServices;\nusing static TorchSharp.torch;\n\nnamespace TorchSharp.PInvoke\n{\n#pragma warning disable CA2101\n internal static partial class NativeMethods\n {\n [DllImport(\"LibTorchSharp\")]\n internal static extern IntPtr THSLinalg_cholesky(IntPtr tensor);\n\n [DllImport(\"LibTorchSharp\")]\n internal static extern IntPtr THSLinalg_cholesky_ex(IntPtr tensor, [MarshalAs(UnmanagedType.U1)] bool check_errors, out IntPtr pInfo);\n\n [DllImport(\"LibTorchSharp\")]\n internal static extern IntPtr THSLinalg_cond_int(IntPtr tensor, int p);\n\n [DllImport(\"LibTorchSharp\")]\n internal static extern IntPtr THSLinalg_cond_float(IntPtr tensor, double p);\n\n [DllImport(\"LibTorchSharp\", CharSet = CharSet.Ansi, BestFitMapping = false, ThrowOnUnmappableChar = true)]\n internal static extern IntPtr THSLinalg_cond_str(IntPtr tensor, [MarshalAs(UnmanagedType.LPStr)] string p);\n\n [DllImport(\"LibTorchSharp\")]\n internal static extern IntPtr THSLinalg_cond_none(IntPtr tensor);\n\n [DllImport(\"LibTorchSharp\")]\n internal static extern IntPtr THSLinalg_cross(IntPtr input, IntPtr other, long dim);\n\n [DllImport(\"LibTorchSharp\")]\n internal static extern IntPtr THSLinalg_det(IntPtr tensor);\n\n [DllImport(\"LibTorchSharp\")]\n internal static extern IntPtr THSLinalg_slogdet(IntPtr tensor, out IntPtr pLogabsdet);\n\n [DllImport(\"LibTorchSharp\")]\n internal static extern IntPtr THSLinalg_eig(IntPtr tensor, out IntPtr pEigenvectors);\n\n [DllImport(\"LibTorchSharp\")]\n internal static extern IntPtr THSTensor_geqrf(IntPtr tensor, out IntPtr tau);\n\n [DllImport(\"LibTorchSharp\")]\n internal static extern IntPtr THSLinalg_eigh(IntPtr tensor, byte UPLO, out IntPtr pEigenvectors);\n\n [DllImport(\"LibTorchSharp\")]\n internal static extern IntPtr THSLinalg_eigvals(IntPtr tensor);\n\n [DllImport(\"LibTorchSharp\")]\n internal static extern IntPtr THSLinalg_eigvalsh(IntPtr tensor, byte UPLO);\n\n [DllImport(\"LibTorchSharp\")]\n internal static extern IntPtr THSLinalg_householder_product(IntPtr tensor, IntPtr tau);\n\n [DllImport(\"LibTorchSharp\")]\n internal static extern IntPtr THSLinalg_inv(IntPtr tensor);\n\n [DllImport(\"LibTorchSharp\")]\n internal static extern IntPtr THSLinalg_inv_ex(IntPtr tensor, [MarshalAs(UnmanagedType.U1)] bool check_errors, out IntPtr pInfo);\n\n [DllImport(\"LibTorchSharp\")]\n internal static extern IntPtr THSLinalg_lstsq_none(IntPtr tensor, IntPtr other, out IntPtr pResiduals, out IntPtr pRank, out IntPtr pSingularValues);\n\n [DllImport(\"LibTorchSharp\")]\n internal static extern IntPtr THSLinalg_lstsq_rcond(IntPtr tensor, IntPtr other, double rcond, out IntPtr pResiduals, out IntPtr pRank, out IntPtr pSingularValues);\n\n [DllImport(\"LibTorchSharp\")]\n internal static extern IntPtr THSLinalg_ldl_factor(IntPtr A, [MarshalAs(UnmanagedType.U1)] bool hermitian, out IntPtr pivots);\n [DllImport(\"LibTorchSharp\")]\n internal static extern IntPtr THSLinalg_ldl_factor_ex(IntPtr A, [MarshalAs(UnmanagedType.U1)] bool hermitian, [MarshalAs(UnmanagedType.U1)] bool check_errors, out IntPtr pivots, out IntPtr info);\n [DllImport(\"LibTorchSharp\")]\n internal static extern IntPtr THSLinalg_ldl_solve(IntPtr LD, IntPtr pivots, IntPtr B, [MarshalAs(UnmanagedType.U1)] bool hermitian);\n\n [DllImport(\"LibTorchSharp\")]\n internal static extern IntPtr THSLinalg_lu(IntPtr tensor, [MarshalAs(UnmanagedType.U1)] bool pivot, out IntPtr pL, out IntPtr pU);\n\n [DllImport(\"LibTorchSharp\")]\n internal static extern IntPtr THSLinalg_lu_factor(IntPtr tensor, [MarshalAs(UnmanagedType.U1)] bool pivot, out IntPtr pPivots);\n\n [DllImport(\"LibTorchSharp\")]\n internal static extern IntPtr THSLinalg_matrix_norm_fronuc(IntPtr tensor, byte fronuc, IntPtr dim, int dim_length, [MarshalAs(UnmanagedType.U1)] bool keepdim);\n\n [DllImport(\"LibTorchSharp\")]\n internal static extern IntPtr THSLinalg_matrix_norm(IntPtr tensor, IntPtr ord, IntPtr dim, int dim_length, [MarshalAs(UnmanagedType.U1)] bool keepdim);\n\n [DllImport(\"LibTorchSharp\")]\n internal static extern IntPtr THSLinalg_matrix_rank(IntPtr tensor, double atol, [MarshalAs(UnmanagedType.U1)] bool has_atol, double rtol, [MarshalAs(UnmanagedType.U1)] bool has_rtol, [MarshalAs(UnmanagedType.U1)] bool hermitian);\n\n [DllImport(\"LibTorchSharp\")]\n internal static extern IntPtr THSLinalg_matrix_rank_tensor(IntPtr tensor, IntPtr atol, IntPtr rtol, [MarshalAs(UnmanagedType.U1)] bool hermitian);\n\n [DllImport(\"LibTorchSharp\")]\n internal static extern IntPtr THSLinalg_dot(IntPtr tensor, int len);\n\n [DllImport(\"LibTorchSharp\")]\n internal static extern IntPtr THSLinalg_multi_dot(IntPtr tensor, int len);\n\n [DllImport(\"LibTorchSharp\", CharSet = CharSet.Ansi, BestFitMapping = false, ThrowOnUnmappableChar = true)]\n internal static extern IntPtr THSLinalg_norm_str(IntPtr tensor, [MarshalAs(UnmanagedType.LPStr)] string p, IntPtr dim, int dim_length, [MarshalAs(UnmanagedType.U1)] bool keepdim);\n\n [DllImport(\"LibTorchSharp\")]\n internal static extern IntPtr THSLinalg_norm_float(IntPtr tensor, double p, IntPtr dim, int dim_length, [MarshalAs(UnmanagedType.U1)] bool keepdim);\n\n [DllImport(\"LibTorchSharp\")]\n internal static extern IntPtr THSLinalg_norm_int(IntPtr tensor, int p, IntPtr dim, int dim_length, [MarshalAs(UnmanagedType.U1)] bool keepdim);\n\n [DllImport(\"LibTorchSharp\")]\n internal static extern IntPtr THSLinalg_norm_opt(IntPtr tensor, IntPtr dim, int dim_length, [MarshalAs(UnmanagedType.U1)] bool keepdim);\n\n [DllImport(\"LibTorchSharp\")]\n internal static extern IntPtr THSLinalg_pinv(IntPtr tensor, double atol, [MarshalAs(UnmanagedType.U1)] bool has_atol, double rtol, [MarshalAs(UnmanagedType.U1)] bool has_rtol, [MarshalAs(UnmanagedType.U1)] bool hermitian);\n\n [DllImport(\"LibTorchSharp\")]\n internal static extern IntPtr THSLinalg_pinv_tensor(IntPtr tensor, IntPtr atol, IntPtr rtol, [MarshalAs(UnmanagedType.U1)] bool hermitian);\n\n [DllImport(\"LibTorchSharp\")]\n internal static extern IntPtr THSLinalg_qr(IntPtr tensor, byte mode, out IntPtr pR);\n\n [DllImport(\"LibTorchSharp\")]\n internal static extern IntPtr THSLinalg_solve(IntPtr tensor, IntPtr other, [MarshalAs(UnmanagedType.U1)] bool left);\n\n [DllImport(\"LibTorchSharp\")]\n internal static extern IntPtr THSLinalg_solve_ex(IntPtr tensor, IntPtr other, [MarshalAs(UnmanagedType.U1)] bool left, [MarshalAs(UnmanagedType.U1)] bool check_errors, out IntPtr infos);\n\n [DllImport(\"LibTorchSharp\")]\n internal static extern IntPtr THSLinalg_solve_triangular(IntPtr tensor, IntPtr other, [MarshalAs(UnmanagedType.U1)] bool upper, [MarshalAs(UnmanagedType.U1)] bool left, [MarshalAs(UnmanagedType.U1)] bool unitriangular);\n\n [DllImport(\"LibTorchSharp\")]\n internal static extern IntPtr THSLinalg_solve_triangular_out(IntPtr tensor, IntPtr other, [MarshalAs(UnmanagedType.U1)] bool upper, [MarshalAs(UnmanagedType.U1)] bool left, [MarshalAs(UnmanagedType.U1)] bool unitriangular, IntPtr _out);\n\n [DllImport(\"LibTorchSharp\")]\n internal static extern IntPtr THSLinalg_svd(IntPtr tensor, [MarshalAs(UnmanagedType.U1)] bool fullMatrices, out IntPtr pS, out IntPtr pVh);\n\n [DllImport(\"LibTorchSharp\")]\n internal static extern IntPtr THSLinalg_svdvals(IntPtr tensor);\n\n [DllImport(\"LibTorchSharp\")]\n internal static extern IntPtr THSLinalg_tensorinv(IntPtr tensor, long ind);\n\n [DllImport(\"LibTorchSharp\")]\n internal static extern IntPtr THSLinalg_tensorsolve(IntPtr tensor, IntPtr other, IntPtr dim, int dim_length);\n\n [DllImport(\"LibTorchSharp\")]\n internal static extern IntPtr THSLinalg_vector_norm(IntPtr tensor, IntPtr ord, IntPtr dim, int dim_length, [MarshalAs(UnmanagedType.U1)] bool keepdim);\n\n [DllImport(\"LibTorchSharp\")]\n internal static extern IntPtr THSLinalg_matrix_power(IntPtr tensor, long n);\n\n [DllImport(\"LibTorchSharp\")]\n internal static extern IntPtr THSLinalg_pinverse(IntPtr tensor, double rcond, [MarshalAs(UnmanagedType.U1)] bool hermitian);\n\n [DllImport(\"LibTorchSharp\")]\n internal static extern IntPtr THSLinalg_vander(IntPtr tensor, long N);\n\n [DllImport(\"LibTorchSharp\")]\n internal static extern IntPtr THSLinalg_vecdot(IntPtr x, IntPtr y, long dim, IntPtr output);\n\n [DllImport(\"LibTorchSharp\")]\n internal static extern IntPtr THSLinalg_lu_solve(IntPtr B, IntPtr LU, IntPtr pivots, [MarshalAs(UnmanagedType.U1)] bool left, [MarshalAs(UnmanagedType.U1)] bool adjoint, IntPtr output);\n\n [DllImport(\"LibTorchSharp\")]\n internal static extern IntPtr THSLinalg_tensordot(IntPtr input1, IntPtr input2, IntPtr dims1, int dims1_length, IntPtr dims2, int dims2_length);\n }\n#pragma warning restore CA2101\n}\n"},{"alpha_fraction":0.7365591526031494,"alphanum_fraction":0.7708333134651184,"avg_line_length":56.269229888916016,"blob_id":"b80a330e1aa66c6562079c4f47a4e57a164dcbd5","content_id":"c4d00be17cffa18409e0d993ccf868be25442bb2","detected_licenses":["MIT"],"is_generated":false,"is_vendor":false,"language":"C","length_bytes":1488,"license_type":"permissive","max_line_length":204,"num_lines":26,"path":"/src/Native/LibTorchSharp/THSVision.h","repo_name":"NiklasGustafsson/TorchSharp","src_encoding":"UTF-8","text":"// Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information.\n#pragma once\n\n#include \"../Stdafx.h\"\n\n#include \"torch/torch.h\"\n\n#include \"Utils.h\"\n\n// API\n\nEXPORT_API(Tensor) THSVision_AdjustHue(const Tensor img, const double hue_factor);\n\nEXPORT_API(Tensor) THSVision_GenerateAffineGrid(Tensor theta, const int64_t w, const int64_t h, const int64_t ow, const int64_t oh);\nEXPORT_API(Tensor) THSVision_ApplyGridTransform(Tensor i, Tensor g, const int8_t m, const float* fill, const int64_t fill_length);\n\nEXPORT_API(Tensor) THSVision_PerspectiveGrid(const float* coeffs, const int64_t coeffs_length, const int64_t ow, const int64_t oh, const int8_t scalar_type, const int device_type, const int device_index);\n\nEXPORT_API(Tensor) THSVision_ScaleChannel(Tensor ic);\n\nEXPORT_API(void) THSVision_ComputeOutputSize(const float* matrix, const int64_t matrix_length, const int64_t w, const int64_t h, int32_t* first, int32_t* second);\n\nEXPORT_API(void) THSVision_BRGA_RGB(const uint8_t* inputBytes, uint8_t* redBytes, uint8_t* greenBytes, uint8_t* blueBytes, int64_t inputChannelCount, int64_t imageSize);\nEXPORT_API(void) THSVision_BRGA_RGBA(const uint8_t* inputBytes, uint8_t* redBytes, uint8_t* greenBytes, uint8_t* blueBytes, uint8_t* alphaBytes, int64_t inputChannelCount, int64_t imageSize);\n\nEXPORT_API(void) THSVision_RGB_BRGA(const uint8_t* inputBytes, uint8_t* outBytes, int64_t inputChannelCount, int64_t imageSize);"},{"alpha_fraction":0.6682669520378113,"alphanum_fraction":0.6692270636558533,"avg_line_length":35.561405181884766,"blob_id":"93f1a1cf7619ff5937db70a53389a95d5d228d07","content_id":"0a4c959fc485f6ae6ab2fa23bf807cb501e79c00","detected_licenses":["MIT"],"is_generated":false,"is_vendor":false,"language":"C#","length_bytes":2083,"license_type":"permissive","max_line_length":130,"num_lines":57,"path":"/src/TorchSharp/DisposeScopeManager.cs","repo_name":"NiklasGustafsson/TorchSharp","src_encoding":"UTF-8","text":"// Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information.\n\nusing System;\nusing System.Collections.Generic;\nusing System.Diagnostics;\n\n#nullable enable\nnamespace TorchSharp\n{\n /// <summary>\n /// Manages dispose scopes, that can make automatic tensor disposal easier. Note that the\n /// DisposeManager is thread local. The DisposeScopeManager can also manage other disposables, such as training\n /// batches and the like.\n /// </summary>\n public class DisposeScopeManager\n {\n [ThreadStatic] private static DisposeScopeManager? _threadSingleton;\n internal ThreadDisposeScopeStatistics StatisticsInstance { get; } = new ThreadDisposeScopeStatistics();\n\n internal static DisposeScopeManager ThreadSingleton => (_threadSingleton ??= new DisposeScopeManager());\n internal Stack<DisposeScope> DisposeScopeStack { get; } = new();\n\n public static ThreadDisposeScopeStatistics Statistics => ThreadSingleton.StatisticsInstance;\n\n internal DisposeScope? RegisterOnCurrentDisposeScope(IDisposable disposable)\n {\n if (DisposeScopeStack.Count == 0) {\n StatisticsInstance.CreatedOutsideScopeCount++;\n return null;\n }\n\n StatisticsInstance.CreatedInScopeCount++;\n var current = DisposeScopeStack.Peek();\n current.Include(disposable);\n return current;\n }\n\n internal static DisposeScope NewDisposeScope()\n {\n return ThreadSingleton.InnerNewDisposeScope();\n }\n\n internal void RemoveDisposeScope(DisposeScope disposeScope)\n {\n Debug.Assert(DisposeScopeStack.Count > 0);\n Debug.Assert(DisposeScopeStack.Peek() == disposeScope);\n DisposeScopeStack.Pop();\n }\n\n private DisposeScope InnerNewDisposeScope()\n {\n var disposeScope = new DisposeScope(this);\n DisposeScopeStack.Push(disposeScope);\n return disposeScope;\n }\n }\n}"},{"alpha_fraction":0.4377593398094177,"alphanum_fraction":0.440248966217041,"avg_line_length":26.089887619018555,"blob_id":"73f93464bf6cbbb6b2dd3326ff48d7517cbea865","content_id":"4134ccc6837798d0cb8b902c375dab3841707fea","detected_licenses":["MIT"],"is_generated":false,"is_vendor":false,"language":"C#","length_bytes":2410,"license_type":"permissive","max_line_length":81,"num_lines":89,"path":"/src/TorchSharp/Utils/FisherYatesShuffler.cs","repo_name":"NiklasGustafsson/TorchSharp","src_encoding":"UTF-8","text":"using System;\nusing System.Collections;\nusing System.Collections.Generic;\n\nnamespace TorchSharp.Utils\n{\n public class FisherYatesShuffler : IEnumerable<long>\n {\n private long size;\n private int? seed;\n\n public FisherYatesShuffler(long size, int? seed = null)\n {\n this.size = size;\n this.seed = seed;\n }\n\n public IEnumerator<long> GetEnumerator()\n => seed == null\n ? new FisherYatesShufflerEnumerable(size)\n : new FisherYatesShufflerEnumerable(size, seed.Value);\n\n IEnumerator IEnumerable.GetEnumerator()\n {\n return GetEnumerator();\n }\n\n private class FisherYatesShufflerEnumerable : IEnumerator<long>\n {\n public FisherYatesShufflerEnumerable(long size)\n {\n this.size = size;\n this.seed = null;\n Reset();\n }\n\n public FisherYatesShufflerEnumerable(long size, int seed)\n {\n this.size = size;\n this.seed = seed;\n Reset();\n }\n\n private int? seed;\n private long size;\n private Dictionary<long, long> indexs = new();\n private Random r;\n private long current = -1;\n\n public bool MoveNext()\n {\n current++;\n return current < size;\n }\n\n public void Reset()\n {\n r = seed == null ? new Random() : new Random(seed.Value);\n current = -1;\n indexs.Clear();\n for (var i = 0L; i < size; i++) {\n var rndidx = GetRandomLong(i);\n if (rndidx == i)\n indexs[i] = i;\n else {\n indexs[i] = indexs[rndidx];\n indexs[rndidx] = i;\n }\n }\n }\n\n public long Current => indexs[current];\n\n object IEnumerator.Current => Current;\n\n public void Dispose()\n {\n /* Ignore */\n }\n\n private long GetRandomLong(long l)\n {\n unchecked {\n return (((long) r.Next() << 32) + (uint) r.Next()) % (l + 1);\n }\n }\n }\n }\n}"},{"alpha_fraction":0.4176732301712036,"alphanum_fraction":0.424348384141922,"avg_line_length":33.206520080566406,"blob_id":"cac90bf025e2cfd5542bf0f7d098a570ad2b99f9","content_id":"c54bbf60fdbfe161fff37cc08c13cd4037269ee6","detected_licenses":["MIT"],"is_generated":false,"is_vendor":false,"language":"C#","length_bytes":3146,"license_type":"permissive","max_line_length":130,"num_lines":92,"path":"/src/TorchSharp/ConsoleProgressBar.cs","repo_name":"NiklasGustafsson/TorchSharp","src_encoding":"UTF-8","text":"// Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information.\nusing System;\nusing System.Diagnostics;\n\nnamespace TorchSharp\n{\n internal class ConsoleProgressBar : IProgressBar\n {\n private const long DisplayEveryMilliseconds = 100;\n\n private readonly int _displayWidth;\n private readonly Stopwatch _stopWatch;\n private bool _hidden;\n private long _value;\n private long? _maximum;\n\n internal ConsoleProgressBar(bool hidden)\n {\n this._hidden = hidden;\n try {\n // This fails when console is not available.\n this._displayWidth = Console.BufferWidth;\n } catch {\n this._displayWidth = 80;\n }\n this._stopWatch = new Stopwatch();\n this._stopWatch.Start();\n }\n\n public long Value {\n set {\n if (value > this._maximum) {\n value = this._maximum.Value;\n }\n this._value = value;\n Display();\n }\n get {\n return _value;\n }\n }\n\n public long? Maximum {\n get {\n return _maximum;\n }\n set {\n if (value < this._value) {\n this._value = value.Value;\n }\n this._maximum = value;\n Display();\n }\n }\n\n public void Dispose()\n {\n if (!_hidden) {\n Console.Error.WriteLine();\n }\n }\n\n private void Display()\n {\n if (!_hidden) {\n if (_value == 0 || _value == _maximum || this._stopWatch.ElapsedMilliseconds > DisplayEveryMilliseconds) {\n this._stopWatch.Restart();\n if (this.Maximum == null) {\n Console.Error.Write(\"\\r{0}\", _value);\n } else {\n string left = string.Format(\"{0,3}%[\", 100 * _value / _maximum);\n string right = string.Format(\"] {0}/{1}\", _value, _maximum);\n int barContainerWidth = this._displayWidth - left.Length - right.Length - 1;\n string center = string.Empty;\n if (barContainerWidth > 0) {\n int barWidth = (int)(barContainerWidth * _value / _maximum);\n if (barWidth > 0) {\n center = new string('=', barWidth);\n if (this._displayWidth - barWidth > 0) {\n center += new string(' ', barContainerWidth - barWidth);\n }\n } else {\n center = new string(' ', barContainerWidth);\n }\n }\n Console.Error.Write(\"\\r{0}{1}{2}\", left, center, right);\n }\n }\n }\n }\n }\n}"},{"alpha_fraction":0.4960475564002991,"alphanum_fraction":0.5081413388252258,"avg_line_length":48.42070007324219,"blob_id":"4097f97cca7412692aa61b51b9535d9e70bdc698","content_id":"fdbfb37be80812573ab6f389dc6981f05e264a41","detected_licenses":["MIT"],"is_generated":false,"is_vendor":false,"language":"C#","length_bytes":29602,"license_type":"permissive","max_line_length":319,"num_lines":599,"path":"/src/TorchAudio/Functional.cs","repo_name":"NiklasGustafsson/TorchSharp","src_encoding":"UTF-8","text":"// Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information.\nusing System;\nusing System.Collections.Generic;\nusing static TorchSharp.torch;\nusing System.Diagnostics;\n\n// A number of implementation details in this file have been translated from the Python version of torchaudio,\n// largely located in the files found in this folder:\n//\n// https://github.com/pytorch/audio/blob/39c2c0a77e11b82ce152d60df3a92f6854d5f52b/torchaudio/functional/functional.py\n//\n// The origin has the following copyright notice and license:\n//\n// https://github.com/pytorch/audio/blob/main/LICENSE\n//\n\nnamespace TorchSharp\n{\n public static partial class torchaudio\n {\n public static partial class functional\n {\n /// <summary>\n /// Compute spectrograms from audio signals.\n /// </summary>\n /// <param name=\"waveform\">The audio signal tensor</param>\n /// <param name=\"pad\">Padding on the sides</param>\n /// <param name=\"window\">The window function</param>\n /// <param name=\"n_fft\">The size of Fourier transform</param>\n /// <param name=\"hop_length\">The hop length</param>\n /// <param name=\"win_length\">The window length</param>\n /// <param name=\"power\">Exponent for the magnitude spectrogram</param>\n /// <param name=\"normalized\">Whether the output is normalized, or not.</param>\n /// <param name=\"center\">Whether the t-th frame is centered around t * hop_window, or not.</param>\n /// <param name=\"pad_mode\">The padding mode used when center is true.</param>\n /// <param name=\"onesided\">Whether the output is onesided or not.</param>\n /// <param name=\"return_complex\">Deprecated and not used.</param>\n /// <returns>Spectrograms of audio signals</returns>\n public static torch.Tensor spectrogram(torch.Tensor waveform, long pad, torch.Tensor window, long n_fft, long hop_length, long win_length, double? power, bool normalized, bool center = true, PaddingModes pad_mode = PaddingModes.Reflect, bool onesided = true, bool? return_complex = null)\n {\n using (var d = torch.NewDisposeScope()) {\n\n if (pad > 0) {\n // The original torchaudio doesn't have `torch.no_grad()' here to\n // avoid issues with JIT.\n // https://github.com/pytorch/audio/commit/a420cced7e60fcf9ba6efcff3a2e8bee3ac67d05#diff-ab14255549624af556aa0d1dfaf83241a95e05c3dd6a668cd2607655839f7a09\n using (torch.no_grad()) {\n waveform = torch.nn.functional.pad(waveform, new long[] { pad, pad }, PaddingModes.Constant);\n }\n }\n\n // pack batch\n var shape = waveform.size();\n waveform = waveform.reshape(-1, shape[shape.Length - 1]);\n\n // default values are consistent with librosa.core.spectrum._spectrogram\n var spec_f = torch.stft(\n input: waveform,\n n_fft: n_fft,\n hop_length: hop_length,\n win_length: win_length,\n window: window,\n center: center,\n pad_mode: pad_mode,\n normalized: false,\n onesided: onesided,\n return_complex: true);\n\n // unpack batch\n var spec_shape = new long[shape.Length + spec_f.dim() - 2];\n Array.Copy(shape, spec_shape, shape.Length - 1);\n Array.Copy(spec_f.shape, 1, spec_shape, shape.Length - 1, spec_f.dim() - 1);\n spec_f = spec_f.reshape(spec_shape);\n\n if (normalized) {\n spec_f /= window.pow(2.0).sum().sqrt();\n }\n\n if (power.HasValue) {\n if (power.Value == 1.0) {\n spec_f = spec_f.abs();\n } else {\n spec_f = spec_f.abs().pow(power.Value);\n }\n }\n\n return d.MoveToOuter(spec_f);\n }\n }\n\n /// <summary>\n /// Compute inverse of spectrogram.\n /// </summary>\n /// <param name=\"spectrogram\">The spectrogram tensor</param>\n /// <param name=\"length\">The length of the output tensor</param>\n /// <param name=\"pad\">Padding on the sides</param>\n /// <param name=\"window\">The window function</param>\n /// <param name=\"n_fft\">The size of Fourier transform</param>\n /// <param name=\"hop_length\">The hop length</param>\n /// <param name=\"win_length\">The window length</param>\n /// <param name=\"normalized\">Whether the output is normalized, or not.</param>\n /// <param name=\"center\">Whether the t-th frame is centered around t * hop_window, or not.</param>\n /// <param name=\"pad_mode\">The padding mode used when center is true.</param>\n /// <param name=\"onesided\">Whether the output is onesided or not.</param>\n /// <returns>Inverse of spectrogram</returns>\n public static torch.Tensor inverse_spectrogram(torch.Tensor spectrogram, long? length, long pad, torch.Tensor window, long n_fft, long hop_length, long win_length, bool normalized, bool center = true, PaddingModes pad_mode = PaddingModes.Reflect, bool onesided = true)\n {\n if (!spectrogram.is_complex()) {\n throw new ArgumentException(\"Expected `spectrogram` to be complex dtype.\");\n }\n\n using (var d = torch.NewDisposeScope()) {\n\n if (normalized) {\n spectrogram = spectrogram * window.pow(2.0).sum().sqrt();\n }\n\n // pack batch\n var shape = spectrogram.size();\n spectrogram = spectrogram.reshape(-1, shape[shape.Length - 2], shape[shape.Length - 1]);\n\n // default values are consistent with librosa.core.spectrum._spectrogram\n var waveform = torch.istft(\n input: spectrogram,\n n_fft: n_fft,\n hop_length: hop_length,\n win_length: win_length,\n window: window,\n center: center,\n normalized: false,\n onesided: onesided,\n length: length.HasValue ? length.Value + 2 * pad : -1,\n return_complex: false\n );\n\n if (length.HasValue && pad > 0) {\n // remove padding from front and back\n waveform = waveform[TensorIndex.Colon, TensorIndex.Slice(pad, -pad)];\n }\n\n // unpack batch\n var waveform_shape = new long[shape.Length - 1];\n Array.Copy(shape, waveform_shape, shape.Length - 2);\n waveform_shape[waveform_shape.Length - 1] = waveform.shape[waveform.dim() - 1];\n waveform = waveform.reshape(waveform_shape);\n\n return d.MoveToOuter(waveform);\n }\n }\n\n private static ScalarType _get_complex_dtype(torch.ScalarType real_dtype)\n {\n if (real_dtype == ScalarType.Float64)\n return ScalarType.ComplexFloat64;\n if (real_dtype == ScalarType.Float32)\n return ScalarType.ComplexFloat32;\n if (real_dtype == ScalarType.Float16)\n return ScalarType.ComplexFloat32;\n throw new ArgumentException($\"Unexpected dtype {real_dtype}\");\n }\n\n /// <summary>\n /// Compute waveform from a linear scale magnitude spectrogram using the Griffin-Lim transformation.\n /// </summary>\n /// <param name=\"specgram\">A magnitude-only STFT spectrogram of dimension `(..., freq, frames)` where freq is ``n_fft // 2 + 1``.</param>\n /// <param name=\"window\">Window tensor that is applied/multiplied to each frame/window</param>\n /// <param name=\"n_fft\">Size of FFT, creates ``n_fft // 2 + 1`` bins</param>\n /// <param name=\"hop_length\">Length of hop between STFT windows.</param>\n /// <param name=\"win_length\">Window size.</param>\n /// <param name=\"power\">Exponent for the magnitude spectrogram, (must be > 0) e.g., 1 for energy, 2 for power, etc.</param>\n /// <param name=\"n_iter\">Number of iteration for phase recovery process.</param>\n /// <param name=\"momentum\">The momentum parameter for fast Griffin-Lim.</param>\n /// <param name=\"length\">Array length of the expected output.</param>\n /// <param name=\"rand_init\">Initializes phase randomly if True, to zero otherwise.</param>\n /// <returns></returns>\n /// <exception cref=\"ArgumentOutOfRangeException\"></exception>\n public static Tensor griffinlim(Tensor specgram, Tensor window, long n_fft, long hop_length, long win_length, double power, int n_iter, double momentum, long? length, bool rand_init)\n {\n if (momentum < 0.0 || 1.0 <= momentum) {\n throw new ArgumentOutOfRangeException($\"momentum must be in range [0, 1). Found: {momentum}\");\n }\n momentum = momentum / (1 + momentum);\n\n // pack batch\n var shape = specgram.size();\n specgram = specgram.reshape(new long[] { -1, shape[shape.Length - 2], shape[shape.Length - 1] });\n\n specgram = specgram.pow(1 / power);\n\n // initialize the phase\n Tensor angles;\n if (rand_init) {\n angles = torch.rand(specgram.size(), dtype: _get_complex_dtype(specgram.dtype), device: specgram.device);\n } else {\n angles = torch.full(specgram.size(), 1, dtype: _get_complex_dtype(specgram.dtype), device: specgram.device);\n }\n\n // And initialize the previous iterate to 0\n var tprev = torch.tensor(0.0, dtype: specgram.dtype, device: specgram.device);\n for (int i = 0; i < n_iter; i++) {\n // Invert with our current estimate of the phases\n var inverse = torch.istft(\n specgram * angles, n_fft: n_fft, hop_length: hop_length, win_length: win_length, window: window, length: length ?? -1\n );\n\n // Rebuild the spectrogram\n var rebuilt = torch.stft(\n input: inverse,\n n_fft: n_fft,\n hop_length: hop_length,\n win_length: win_length,\n window: window,\n center: true,\n pad_mode: PaddingModes.Reflect,\n normalized: false,\n onesided: true,\n return_complex: true);\n\n // Update our phase estimates\n angles = rebuilt;\n if (momentum > 0.0) {\n angles = angles - tprev.mul_(momentum);\n }\n angles = angles.div(angles.abs().add(1e-16));\n\n // Store the previous iterate\n tprev = rebuilt;\n }\n\n // Return the final phase estimates\n var waveform = torch.istft(\n specgram * angles, n_fft: n_fft, hop_length: hop_length, win_length: win_length, window: window, length: length ?? -1\n );\n\n // unpack batch\n var new_shape = new long[shape.Length - 1];\n Array.Copy(shape, new_shape, shape.Length - 2);\n new_shape[new_shape.Length - 1] = waveform.shape[waveform.dim() - 1];\n waveform = waveform.reshape(new_shape);\n\n return waveform;\n }\n\n /// <summary>\n /// Turn a spectrogram from the power/amplitude scale to the decibel scale.\n /// </summary>\n /// <param name=\"x\">Input spectrogram(s) before being converted to decibel scale.</param>\n /// <param name=\"multiplier\">Use 10. for power and 20. for amplitude</param>\n /// <param name=\"amin\">Number to clamp x</param>\n /// <param name=\"db_multiplier\">Log10(max(reference value and amin))</param>\n /// <param name=\"top_db\">Minimum negative cut-off in decibels.</param>\n /// <returns>Output tensor in decibel scale</returns>\n public static Tensor amplitude_to_DB(Tensor x, double multiplier, double amin, double db_multiplier, double? top_db = null)\n {\n var x_db = multiplier * torch.log10(torch.clamp(x, min: amin));\n x_db -= multiplier * db_multiplier;\n\n if (top_db != null) {\n // Expand batch\n var shape = x_db.size();\n var packed_channels = x_db.dim() > 2 ? shape[shape.Length - 3] : 1;\n x_db = x_db.reshape(-1, packed_channels, shape[shape.Length - 2], shape[shape.Length - 1]);\n\n x_db = torch.maximum(x_db, (x_db.amax(dims: new long[] { -3, -2, -1 }) - top_db).view(-1, 1, 1, 1));\n\n // Repack batch\n x_db = x_db.reshape(shape);\n }\n return x_db;\n }\n\n /// <summary>\n /// Turn a tensor from the decibel scale to the power/amplitude scale.\n /// </summary>\n /// <param name=\"x\">Input tensor before being converted to power/amplitude scale.</param>\n /// <param name=\"ref\">Reference which the output will be scaled by.</param>\n /// <param name=\"power\">If power equals 1, will compute DB to power. If 0.5, will compute DB to amplitude.</param>\n /// <returns>Output tensor in power/amplitude scale.</returns>\n public static Tensor DB_to_amplitude(Tensor x, double @ref, double power)\n {\n return @ref * torch.pow(torch.pow(10.0, 0.1 * x), power);\n }\n\n private static double _hz_to_mel(double freq, MelScale mel_scale = MelScale.htk)\n {\n if (mel_scale == MelScale.htk) {\n return 2595.0 * Math.Log10(1.0 + freq / 700.0);\n }\n\n // Fill in the linear part\n var f_min = 0.0;\n var f_sp = 200.0 / 3;\n\n var mels = (freq - f_min) / f_sp;\n\n // Fill in the log-scale part\n var min_log_hz = 1000.0;\n var min_log_mel = (min_log_hz - f_min) / f_sp;\n var logstep = Math.Log(6.4) / 27.0;\n\n if (freq >= min_log_hz) {\n mels = min_log_mel + Math.Log(freq / min_log_hz) / logstep;\n }\n\n return mels;\n }\n\n private static Tensor _mel_to_hz(Tensor mels, MelScale mel_scale = MelScale.htk)\n {\n if (mel_scale == MelScale.htk) {\n return 700.0 * (torch.pow(10.0, mels / 2595.0) - 1.0);\n }\n\n // Fill in the linear scale\n var f_min = 0.0;\n var f_sp = 200.0 / 3;\n\n var freqs = f_min + f_sp * mels;\n\n // And now the nonlinear scale\n var min_log_hz = 1000.0;\n var min_log_mel = (min_log_hz - f_min) / f_sp;\n var logstep = Math.Log(6.4) / 27.0;\n\n var log_t = mels >= min_log_mel;\n freqs[log_t] = min_log_hz * torch.exp(logstep * (mels[log_t] - min_log_mel));\n\n return freqs;\n }\n\n private static Tensor _create_triangular_filterbank(Tensor all_freqs, Tensor f_pts)\n {\n // Adopted from Librosa\n // calculate the difference between each filter mid point and each stft freq point in hertz\n var f_diff = f_pts[TensorIndex.Slice(1, null)] - f_pts[TensorIndex.Slice(null, -1)]; // (n_filter + 1)\n var slopes = f_pts.unsqueeze(0) - all_freqs.unsqueeze(1); // (n_freqs, n_filter + 2)\n // create overlapping triangles\n var zero = torch.zeros(1);\n var down_slopes = (-1.0 * slopes[TensorIndex.Colon, TensorIndex.Slice(null, -2)]) / f_diff[TensorIndex.Slice(null, -1)]; // (n_freqs, n_filter)\n var up_slopes = slopes[TensorIndex.Colon, TensorIndex.Slice(2, null)] / f_diff[TensorIndex.Slice(1, null)]; // (n_freqs, n_filter)\n var fb = torch.maximum(zero, torch.minimum(down_slopes, up_slopes));\n\n return fb;\n }\n\n /// <summary>\n /// Create a frequency bin conversion matrix.\n /// </summary>\n /// <param name=\"n_freqs\">Number of frequencies to highlight/apply</param>\n /// <param name=\"f_min\">Minimum frequency(Hz)</param>\n /// <param name=\"f_max\">Maximum frequency(Hz)</param>\n /// <param name=\"n_mels\">Number of mel filterbanks</param>\n /// <param name=\"sample_rate\">Sample rate of the audio waveform</param>\n /// <param name=\"norm\">If MelNorm.slaney, divide the triangular mel weights by the width of the mel band</param>\n /// <param name=\"mel_scale\">Scale to use</param>\n /// <returns>Triangular filter banks</returns>\n public static Tensor melscale_fbanks(long n_freqs, double f_min, double f_max, long n_mels, long sample_rate, MelNorm norm = MelNorm.none, MelScale mel_scale = MelScale.htk)\n {\n // freq bins\n var all_freqs = torch.linspace(0, sample_rate / 2, n_freqs);\n\n // calculate mel freq bins\n var m_min = _hz_to_mel(f_min, mel_scale: mel_scale);\n var m_max = _hz_to_mel(f_max, mel_scale: mel_scale);\n\n var m_pts = torch.linspace(m_min, m_max, n_mels + 2);\n var f_pts = _mel_to_hz(m_pts, mel_scale: mel_scale);\n\n // create filterbank\n var fb = _create_triangular_filterbank(all_freqs, f_pts);\n\n if (norm == MelNorm.slaney) {\n // Slaney-style mel is scaled to be approx constant energy per channel\n var enorm = 2.0 / (f_pts[TensorIndex.Slice(2, n_mels + 2)] - f_pts[TensorIndex.Slice(null, n_mels)]);\n fb *= enorm.unsqueeze(0);\n }\n\n if ((fb.max(dim: 0).values == 0.0).any().item<bool>()) {\n Debug.Print(\n \"At least one mel filterbank has all zero values. \" +\n $\"The value for `n_mels` ({n_mels}) may be set too high. \" +\n $\"Or, the value for `n_freqs` ({n_freqs}) may be set too low.\"\n );\n }\n\n return fb;\n }\n\n /// <summary>\n /// Creates a linear triangular filterbank.\n /// </summary>\n /// <param name=\"n_freqs\">Number of frequencies to highlight/apply</param>\n /// <param name=\"f_min\">Minimum frequency (Hz)</param>\n /// <param name=\"f_max\">Maximum frequency (Hz)</param>\n /// <param name=\"n_filter\">Number of (linear) triangular filter</param>\n /// <param name=\"sample_rate\">Sample rate of the audio waveform</param>\n /// <returns>Triangular filter banks</returns>\n public static Tensor linear_fbanks(int n_freqs, double f_min, double f_max, int n_filter, int sample_rate)\n {\n // freq bins\n var all_freqs = torch.linspace(0, sample_rate / 2, n_freqs);\n\n // filter mid-points\n var f_pts = torch.linspace(f_min, f_max, n_filter + 2);\n\n // create filterbank\n var fb = _create_triangular_filterbank(all_freqs, f_pts);\n\n return fb;\n }\n\n /// <summary>\n /// Create a DCT transformation matrix with shape (``n_mels``, ``n_mfcc``)\n /// </summary>\n /// <param name=\"n_mfcc\">Number of mfc coefficients to retain</param>\n /// <param name=\"n_mels\">Number of mel filterbanks</param>\n /// <param name=\"norm\">Norm to use</param>\n /// <returns>The transformation matrix</returns>\n public static Tensor create_dct(int n_mfcc, int n_mels, DCTNorm norm)\n {\n // http://en.wikipedia.org/wiki/Discrete_cosine_transform#DCT-II\n var n = torch.arange((float)n_mels);\n var k = torch.arange((float)n_mfcc).unsqueeze(1);\n var dct = torch.cos(Math.PI / n_mels * (n + 0.5) * k);\n\n if (norm == DCTNorm.none) {\n dct *= 2.0;\n } else {\n dct[0] *= 1.0 / Math.Sqrt(2.0);\n dct *= Math.Sqrt(2.0 / n_mels);\n }\n return dct.t();\n }\n\n /// <summary>\n /// Encode signal based on mu-law companding.\n /// </summary>\n /// <param name=\"x\">Input tensor</param>\n /// <param name=\"quantization_channels\">Number of channels</param>\n /// <returns>Input after mu-law encoding</returns>\n /// <exception cref=\"ArgumentException\"></exception>\n public static Tensor mu_law_encoding(Tensor x, int quantization_channels)\n {\n if (!x.is_floating_point()) {\n throw new ArgumentException(\"The input Tensor must be of floating type. \");\n }\n var mu = torch.tensor(quantization_channels - 1.0, dtype: x.dtype);\n var x_mu = torch.sign(x) * torch.log1p(mu * torch.abs(x)) / torch.log1p(mu);\n x_mu = ((x_mu + 1) / 2 * mu + 0.5).to(torch.int64);\n return x_mu;\n }\n\n /// <summary>\n /// Decode mu-law encoded signal.\n /// </summary>\n /// <param name=\"x_mu\">Input tensor</param>\n /// <param name=\"quantization_channels\">Number of channels</param>\n /// <returns>Input after mu-law decoding</returns>\n public static Tensor mu_law_decoding(Tensor x_mu, int quantization_channels)\n {\n if (!x_mu.is_floating_point()) {\n x_mu = x_mu.to(torch.@float);\n }\n var mu = torch.tensor(quantization_channels - 1.0, dtype: x_mu.dtype);\n var x = x_mu / mu * 2 - 1.0;\n x = torch.sign(x) * (torch.exp(torch.abs(x) * torch.log1p(mu)) - 1.0) / mu;\n return x;\n }\n\n /// <summary>\n /// Resample the waveform\n /// </summary>\n /// <param name=\"waveform\">The input waveform</param>\n /// <param name=\"orig_freq\">The source sampling rate</param>\n /// <param name=\"new_freq\">The destination sampling rate</param>\n /// <param name=\"lowpass_filter_width\">The width of the filter</param>\n /// <param name=\"rolloff\">The roll-off frequency</param>\n /// <param name=\"resampling_method\">The resampling method</param>\n /// <param name=\"beta\">Beta for Keizer window</param>\n /// <returns>The resampled waveform</returns>\n /// <exception cref=\"ArgumentOutOfRangeException\"></exception>\n public static torch.Tensor resample(torch.Tensor waveform, int orig_freq, int new_freq, int lowpass_filter_width = 6, double rolloff = 0.99, ResamplingMethod resampling_method = ResamplingMethod.sinc_interpolation, double? beta = null)\n {\n if (orig_freq <= 0 || new_freq <= 0) {\n throw new ArgumentOutOfRangeException();\n }\n\n using (var d = torch.NewDisposeScope()) {\n if (orig_freq == new_freq) {\n return d.MoveToOuter(waveform.alias());\n }\n\n int gcd = Gcd(orig_freq, new_freq);\n\n var (kernel, width) = _get_sinc_resample_kernel(\n orig_freq,\n new_freq,\n gcd,\n lowpass_filter_width,\n rolloff,\n resampling_method,\n beta,\n waveform.device,\n waveform.dtype);\n var resampled = _apply_sinc_resample_kernel(waveform, orig_freq, new_freq, gcd, kernel, width);\n return d.MoveToOuter(resampled);\n }\n }\n\n internal static int Gcd(int a, int b)\n {\n if (a <= 0 || b <= 0) {\n throw new ArgumentOutOfRangeException();\n }\n\n while (b > 1) {\n (a, b) = (b, a % b);\n }\n\n return a;\n }\n\n internal static (torch.Tensor, int) _get_sinc_resample_kernel(int orig_freq, int new_freq, int gcd, int lowpass_filter_width = 6, double rolloff = 0.99, ResamplingMethod resampling_method = ResamplingMethod.sinc_interpolation, double? beta = null, torch.Device device = null, torch.ScalarType? dtype = null)\n {\n orig_freq = orig_freq / gcd;\n new_freq = new_freq / gcd;\n\n if (lowpass_filter_width <= 0) {\n throw new ArgumentOutOfRangeException();\n }\n\n var kernels_list = new List<torch.Tensor>();\n double base_freq = Math.Min(orig_freq, new_freq);\n base_freq *= rolloff;\n\n var width = (int)Math.Ceiling(((double)lowpass_filter_width) * orig_freq / base_freq);\n var idx_dtype = dtype ?? torch.float64;\n var idx = torch.arange(-width, width + orig_freq, device: device, dtype: idx_dtype);\n\n for (int i = 0; i < new_freq; i++) {\n var t = (-i / new_freq + idx / orig_freq) * base_freq;\n t = t.clamp_(-lowpass_filter_width, lowpass_filter_width);\n\n torch.Tensor window;\n if (resampling_method == ResamplingMethod.sinc_interpolation) {\n window = torch.square(torch.cos(t * Math.PI / lowpass_filter_width / 2));\n } else {\n // kaiser_window\n if (!beta.HasValue) {\n beta = 14.769656459379492;\n }\n var beta_tensor = torch.tensor(beta.Value);\n window = torch.special.i0(beta_tensor * torch.sqrt(1 - torch.square(t / lowpass_filter_width))) / torch.special.i0(beta_tensor);\n }\n t *= Math.PI;\n // Tensor.to(Tensor) of TorchSharp desn't change dtype.\n var kernel = torch.where(t == 0, torch.tensor(1.0).to(t).type_as(t), torch.sin(t) / t);\n kernel.mul_(window);\n kernels_list.Add(kernel);\n }\n\n var scale = ((double)base_freq) / orig_freq;\n var kernels = torch.stack(kernels_list.ToArray()).view(new_freq, 1, -1).mul_(scale);\n if (dtype == null) {\n kernels = kernels.to(torch.float32);\n }\n return (kernels, width);\n }\n\n internal static torch.Tensor _apply_sinc_resample_kernel(torch.Tensor waveform, int orig_freq, int new_freq, int gcd, torch.Tensor kernel, int width)\n {\n if (!waveform.is_floating_point()) {\n throw new ArgumentException($\"Expected floating point type for waveform tensor, but received {waveform.dtype}.\");\n }\n\n orig_freq = orig_freq / gcd;\n new_freq = new_freq / gcd;\n\n // pack batch\n var shape = waveform.size();\n waveform = waveform.view(-1, shape[waveform.dim() - 1]);\n\n var num_wavs = waveform.shape[0];\n var length = waveform.shape[1];\n\n waveform = torch.nn.functional.pad(waveform, (width, width + orig_freq));\n var resampled = torch.nn.functional.conv1d(waveform.unsqueeze(1), kernel, stride: orig_freq);\n resampled = resampled.transpose(1, 2).reshape(num_wavs, -1);\n int target_length = (int)Math.Ceiling(((double)new_freq) * length / orig_freq);\n resampled = resampled[TensorIndex.Ellipsis, TensorIndex.Slice(0, target_length)];\n\n // unpack batch\n shape[shape.Length - 1] = resampled.shape[resampled.dim() - 1];\n resampled = resampled.view(shape);\n return resampled;\n }\n }\n }\n}"},{"alpha_fraction":0.5502307415008545,"alphanum_fraction":0.5512956976890564,"avg_line_length":39.826087951660156,"blob_id":"3db6efef8640e9c424e98f2384dd2cf400fe4428","content_id":"e6d3f120a3326d8e31c29798c867fef4f4e3138c","detected_licenses":["MIT"],"is_generated":false,"is_vendor":false,"language":"C#","length_bytes":2817,"license_type":"permissive","max_line_length":193,"num_lines":69,"path":"/src/TorchSharp/NN/PixelUnshuffle.cs","repo_name":"NiklasGustafsson/TorchSharp","src_encoding":"UTF-8","text":"// Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information.\nusing System;\nusing static TorchSharp.torch;\nusing static TorchSharp.PInvoke.NativeMethods;\n\nnamespace TorchSharp\n{\n using Modules;\n\n namespace Modules\n {\n /// <summary>\n /// This class is used to represent a dropout module.\n /// </summary>\n public sealed class PixelUnshuffle : torch.nn.Module<Tensor, Tensor>\n {\n internal PixelUnshuffle(IntPtr handle, IntPtr boxedHandle) : base(handle, boxedHandle) { }\n\n /// <summary>\n /// Forward pass.\n /// </summary>\n /// <param name=\"tensor\">Input tensor</param>\n /// <returns></returns>\n public override Tensor forward(Tensor tensor)\n {\n var res = THSNN_PixelUnshuffle_forward(handle, tensor.Handle);\n if (res == IntPtr.Zero) { torch.CheckForErrors(); }\n return new Tensor(res);\n }\n }\n }\n\n public static partial class torch\n {\n public static partial class nn\n {\n\n /// <summary>\n /// Reverses the PixelShuffle operation by rearranging elements in a tensor of shape (*, C, H * r, W * r) to a tensor of shape (*, C * r^2, H, W), where r is an downscale factor.\n /// </summary>\n /// <param name=\"downscaleFactor\">Factor to increase spatial resolution by</param>\n /// <returns></returns>\n public static PixelUnshuffle PixelUnshuffle(long downscaleFactor)\n {\n var handle = THSNN_PixelUnshuffle_ctor(downscaleFactor, out var boxedHandle);\n if (handle == IntPtr.Zero) { torch.CheckForErrors(); }\n return new PixelUnshuffle(handle, boxedHandle);\n }\n\n public static partial class functional\n {\n /// <summary>\n /// Reverses the PixelShuffle operation by rearranging elements in a tensor of shape (*, C * r^2, H, W) to a tensor of shape(*, C, H * r, W * r), where r is an downscale factor.\n /// This is useful for implementing efficient sub-pixel convolution with a stride of 1/r.\n /// </summary>\n /// <param name=\"x\">Input tensor</param>\n /// <param name=\"downscaleFactor\">Factor to increase spatial resolution by</param>\n /// <returns></returns>\n /// <returns></returns>\n public static Tensor pixel_unshuffle(Tensor x, long downscaleFactor)\n {\n using (var d = nn.PixelUnshuffle(downscaleFactor)) {\n return d.call(x);\n }\n }\n }\n }\n }\n}\n"},{"alpha_fraction":0.6845647096633911,"alphanum_fraction":0.7034650444984436,"avg_line_length":20.426380157470703,"blob_id":"2cc99c44c7be80d3c878641ec9ecbf45d26ef6aa","content_id":"b846557bc05ce1a8a6bb4a97b9a8a32c1f708331","detected_licenses":["MIT"],"is_generated":false,"is_vendor":false,"language":"C++","length_bytes":6984,"license_type":"permissive","max_line_length":130,"num_lines":326,"path":"/src/Native/LibTorchSharp/THSTorch.cpp","repo_name":"NiklasGustafsson/TorchSharp","src_encoding":"UTF-8","text":"// Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information.\n#include \"THSTorch.h\"\n\n#include \"torch/torch.h\"\n#include \"torch/cuda.h\"\n\nvoid THSTorch_manual_seed(const int64_t seed)\n{\n torch::manual_seed(seed);\n}\n\nGenerator THSGenerator_manual_seed(const int64_t seed)\n{\n auto gen = at::globalContext().defaultGenerator(at::DeviceType::CPU);\n gen.set_current_seed(seed);\n return new at::Generator(gen.getIntrusivePtr());\n}\n\nvoid THSCuda_manual_seed(const int64_t seed)\n{\n CATCH(torch::cuda::manual_seed(seed);)\n}\n\nvoid THSCuda_manual_seed_all(const int64_t seed)\n{\n CATCH(torch::cuda::manual_seed_all(seed);)\n}\n\nbool THSBackend_cublas_get_allow_tf32()\n{\n auto result = false;\n CATCH(result = at::globalContext().allowTF32CuBLAS(););\n return result;\n}\n\nvoid THSBackend_cublas_set_allow_tf32(const bool flag)\n{\n CATCH(at::globalContext().setAllowTF32CuBLAS(flag););\n}\n\nbool THSBackend_cudnn_get_allow_tf32()\n{\n auto result = false;\n CATCH(result = at::globalContext().allowTF32CuDNN(););\n return result;\n}\n\nvoid THSBackend_cudnn_set_allow_tf32(const bool flag)\n{\n CATCH(at::globalContext().setAllowTF32CuDNN(flag););\n}\n\nbool THSBackend_cuda_get_allow_fp16_reduced_precision_reduction()\n{\n auto result = false;\n CATCH(result = at::globalContext().allowFP16ReductionCuBLAS(););\n return result;\n}\n\nvoid THSBackend_cuda_set_allow_fp16_reduced_precision_reduction(const bool flag)\n{\n CATCH(at::globalContext().setAllowFP16ReductionCuBLAS(flag););\n}\n\nbool THSBackend_cuda_get_enable_flash_sdp()\n{\n auto result = false;\n CATCH(result = at::globalContext().userEnabledFlashSDP(););\n return result;\n}\n\nvoid THSBackend_cuda_set_enable_flash_sdp(const bool flag)\n{\n CATCH(at::globalContext().setSDPUseFlash(flag););\n}\n\nbool THSBackend_cuda_get_enable_math_sdp()\n{\n auto result = false;\n CATCH(result = at::globalContext().userEnabledMathSDP(););\n return result;\n}\n\nvoid THSBackend_cuda_set_enable_math_sdp(const bool flag)\n{\n CATCH(at::globalContext().setSDPUseMath(flag););\n}\n\nvoid THSGenerator_gen_manual_seed(const Generator generator, const int64_t seed)\n{\n generator->set_current_seed(seed);\n}\n\nGenerator THSGenerator_default_generator()\n{\n auto gen = at::globalContext().defaultGenerator(at::DeviceType::CPU);\n return new at::Generator(gen.getIntrusivePtr());\n}\n\nint64_t THSGenerator_initial_seed(const Generator gen)\n{\n return gen->current_seed();\n}\n\nTensor THSGenerator_get_rng_state(const Generator gen)\n{\n CATCH_TENSOR(gen->get_state());\n}\n\nvoid THSGenerator_set_rng_state(const Generator gen, const Tensor tensor)\n{\n gen->set_state(*tensor);\n}\n\n\nGenerator THSGenerator_new(uint64_t seed, int64_t device, int64_t index)\n{\n // TODO: Support creation of GPU RNGs. 'device' and 'index' are in the\n // function signature in preparation thereof.\n return new at::Generator(at::detail::createCPUGenerator(seed));\n}\n\nvoid THSGenerator_dispose(const Generator generator)\n{\n delete generator;\n}\n\nint THSTorchCuda_is_available()\n{\n return torch::cuda::is_available();\n}\n\nint THSTorchCuda_cudnn_is_available()\n{\n return torch::cuda::cudnn_is_available();\n}\n\nint THSTorchCuda_device_count()\n{\n return (int)torch::cuda::device_count();\n}\n\nvoid THSTorchCuda_synchronize(const int64_t device_index)\n{\n CATCH(torch::cuda::synchronize(device_index);)\n}\n\n\nconst char * THSTorch_get_and_reset_last_err()\n{\n char *tmp = torch_last_err;\n torch_last_err = nullptr;\n return tmp;\n}\n\nint THSTorch_get_num_threads()\n{\n CATCH_RETURN_RES(int, -1, res = torch::get_num_threads());\n}\n\nvoid THSTorch_set_num_threads(const int threads)\n{\n torch::set_num_threads(threads);\n}\n\nint THSTorch_get_num_interop_threads()\n{\n CATCH_RETURN_RES(int, -1, res = torch::get_num_interop_threads());\n}\n\nvoid THSTorch_set_num_interop_threads(const int threads)\n{\n torch::set_num_interop_threads(threads);\n}\n\nint THSTorch_can_cast(const int type1, const int type2)\n{\n CATCH_RETURN_RES(int, -1, res = (int)torch::can_cast((c10::ScalarType)type1, (c10::ScalarType)type2));\n}\n\nint THSTorch_promote_types(const int type1, const int type2)\n{\n CATCH_RETURN_RES(int, -1, res = (int)torch::promote_types((c10::ScalarType)type1, (c10::ScalarType)type2));\n}\n\n\nScalar THSTorch_int8_to_scalar(int8_t value)\n{\n return new torch::Scalar(value);\n}\n\nScalar THSTorch_uint8_to_scalar(uint8_t value)\n{\n return new torch::Scalar(value);\n}\n\nScalar THSTorch_int16_to_scalar(short value)\n{\n return new torch::Scalar(value);\n}\n\nScalar THSTorch_int32_to_scalar(int value)\n{\n return new torch::Scalar(value);\n}\n\nScalar THSTorch_int64_to_scalar(long value)\n{\n return new torch::Scalar(int64_t(value));\n}\n\nScalar THSTorch_float32_to_scalar(float value)\n{\n return new torch::Scalar(value);\n}\n\nScalar THSTorch_float64_to_scalar(double value)\n{\n return new torch::Scalar(value);\n}\n\nScalar THSTorch_float16_to_scalar(float value)\n{\n return new torch::Scalar((c10::Half)value);\n}\n\nScalar THSTorch_bfloat16_to_scalar(float value)\n{\n return new torch::Scalar((c10::BFloat16)value);\n}\n\nScalar THSTorch_bool_to_scalar(bool value)\n{\n return new torch::Scalar(value);\n}\n\nScalar THSTorch_complex32_to_scalar(float real, float imaginary)\n{\n return new torch::Scalar(c10::complex<float>(real, imaginary));\n}\n\nScalar THSTorch_complex64_to_scalar(double real, double imaginary)\n{\n return new torch::Scalar(c10::complex<double>(real, imaginary));\n}\n\nint8_t THSTorch_scalar_to_int8(Scalar value)\n{\n return value->toChar();\n}\n\nuint8_t THSTorch_scalar_to_uint8(Scalar value)\n{\n return value->toByte();\n}\n\nint16_t THSTorch_scalar_to_int16(Scalar value)\n{\n return value->toShort();\n}\n\nint32_t THSTorch_scalar_to_int32(Scalar value)\n{\n return value->toInt();\n}\n\nint64_t THSTorch_scalar_to_int64(Scalar value)\n{\n return value->toLong();\n}\n\nfloat THSTorch_scalar_to_float32(Scalar value)\n{\n return value->toFloat();\n}\n\ndouble THSTorch_scalar_to_float64(Scalar value)\n{\n return value->toDouble();\n}\n\nvoid THSTorch_scalar_to_float16(Scalar value, unsigned short *res)\n{\n *res = value->toHalf().x;\n}\n\nvoid THSTorch_scalar_to_complex32(Scalar value, float* (*allocator)(size_t length))\n{\n auto result = value->toComplexFloat();\n auto space = allocator(2);\n space[0] = result.real();\n space[1] = result.imag();\n}\n\nvoid THSTorch_scalar_to_complex64(Scalar value, double* (*allocator)(size_t length))\n{\n auto result = value->toComplexDouble();\n auto space = allocator(2);\n space[0] = result.real();\n space[1] = result.imag();\n}\n\nbool THSTorch_scalar_to_bool(Scalar value)\n{\n return value->toBool();\n}\n\nint8_t THSTorch_scalar_type(Scalar value)\n{\n return (int8_t)value->type();\n}\n\nvoid THSTorch_dispose_scalar(Scalar scalar)\n{\n delete scalar;\n}\n\ndouble THSSpecial_erf_scalar(const double x)\n{\n return erf(x);\n}\n\ndouble THSSpecial_erfc_scalar(const double x)\n{\n return erfc(x);\n}"},{"alpha_fraction":0.5176693797111511,"alphanum_fraction":0.5251898169517517,"avg_line_length":55.82987594604492,"blob_id":"b79b20c440ec1ec6fbeabd62d535f32071a5bfdb","content_id":"144dab863ca37abb4052b8dedf942dadd4bde27d","detected_licenses":["MIT"],"is_generated":false,"is_vendor":false,"language":"C#","length_bytes":13699,"license_type":"permissive","max_line_length":318,"num_lines":241,"path":"/src/TorchSharp/NN/Pooling/AvgPool2D.cs","repo_name":"NiklasGustafsson/TorchSharp","src_encoding":"UTF-8","text":"// Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information.\nusing System;\nusing static TorchSharp.torch;\nusing static TorchSharp.PInvoke.NativeMethods;\n\nnamespace TorchSharp\n{\n using Modules;\n\n namespace Modules\n {\n /// <summary>\n /// This class is used to represent a AvgPool2D module.\n /// </summary>\n public sealed class AvgPool2d : torch.nn.Module<Tensor, Tensor>\n {\n internal AvgPool2d(IntPtr handle, IntPtr boxedHandle) : base(handle, boxedHandle)\n {\n }\n\n public override Tensor forward(Tensor tensor)\n {\n var res = THSNN_AvgPool2d_forward(handle.DangerousGetHandle(), tensor.Handle);\n if (res == IntPtr.Zero) { torch.CheckForErrors(); }\n return new Tensor(res);\n }\n\n // Rather than spending cycles only to discover that this module has neither\n // parameters nor buffers, just shortcut the move completely.\n protected internal override nn.Module _to(Device device, ScalarType dtype) => this;\n protected internal override nn.Module _to(DeviceType deviceType, int deviceIndex = -1) => this;\n protected internal override nn.Module _to(ScalarType dtype) => this;\n }\n }\n\n public static partial class torch\n {\n public static partial class nn\n {\n /// <summary>\n /// Applies a 2D average pooling over an input signal composed of several input planes.\n /// </summary>\n /// <param name=\"kernel_size\">The size of the window</param>\n /// <param name=\"strides\">The stride of the window. Default value is kernel_size</param>\n /// <param name=\"padding\">implicit zero padding to be added on both sides</param>\n /// <param name=\"ceil_mode\">Whether to use ceil instead of floor to compute the output shape</param>\n /// <param name=\"count_include_pad\">Whether to include the zero-padding in the averaging calculation</param>\n /// <param name=\"divisor_override\">If specified, it will be used as divisor, otherwise size of the pooling region will be used</param>\n public static unsafe AvgPool2d AvgPool2d(long[] kernel_size, long[] strides = null, long[] padding = null, bool ceil_mode = false, bool count_include_pad = true, long? divisor_override = null)\n {\n fixed (long* pkernelSize = kernel_size, pstrides = strides, ppadding = padding) {\n var handle = THSNN_AvgPool2d_ctor((IntPtr)pkernelSize, kernel_size.Length, (IntPtr)pstrides, (strides == null ? 0 : strides.Length), (IntPtr)ppadding, (padding == null ? 0 : padding.Length), ceil_mode, count_include_pad, divisor_override.HasValue ? divisor_override.Value : 0, out var boxedHandle);\n if (handle == IntPtr.Zero) { torch.CheckForErrors(); }\n return new AvgPool2d(handle, boxedHandle);\n }\n }\n\n /// <summary>\n /// Applies a 2D average pooling over an input signal composed of several input planes.\n /// </summary>\n /// <param name=\"kernel_size\">The size of the window</param>\n /// <param name=\"stride\">The stride of the window.</param>\n /// <param name=\"padding\">implicit zero padding to be added on both sides</param>\n /// <param name=\"ceil_mode\">Whether to use ceil instead of floor to compute the output shape</param>\n /// <param name=\"count_include_pad\">Whether to include the zero-padding in the averaging calculation</param>\n /// <param name=\"divisor_override\">If specified, it will be used as divisor, otherwise size of the pooling region will be used</param>\n public static unsafe AvgPool2d AvgPool2d((long,long) kernel_size, (long,long)? stride = null, (long,long)? padding = null, bool ceil_mode = false, bool count_include_pad = true, long? divisor_override = null)\n {\n long svalue1 = (stride == null) ? kernel_size.Item1 : stride.Value.Item1;\n long svalue2 = (stride == null) ? kernel_size.Item2 : stride.Value.Item2;\n\n long pvalue1 = (padding == null) ? 0 : stride.Value.Item1;\n long pvalue2 = (padding == null) ? 0 : stride.Value.Item2;\n\n long* pkernelSize = stackalloc long[2] { kernel_size.Item1, kernel_size.Item2 };\n long* pstrides = stackalloc long[2] { svalue1, svalue2 };\n long* ppadding = stackalloc long[2] { pvalue1, pvalue2 };\n\n var handle = THSNN_AvgPool2d_ctor((IntPtr)pkernelSize, 2, (IntPtr)pstrides, 2, (IntPtr)ppadding, 2, ceil_mode, count_include_pad, divisor_override.HasValue ? divisor_override.Value : 0, out var boxedHandle);\n if (handle == IntPtr.Zero) { torch.CheckForErrors(); }\n return new AvgPool2d(handle, boxedHandle);\n }\n\n /// <summary>\n /// Applies a 2D average pooling over an input signal composed of several input planes.\n /// </summary>\n /// <param name=\"kernel_size\">The size of the window</param>\n /// <param name=\"stride\">The stride of the window.</param>\n /// <param name=\"padding\">implicit zero padding to be added on both sides</param>\n /// <param name=\"ceil_mode\">Whether to use ceil instead of floor to compute the output shape</param>\n /// <param name=\"count_include_pad\">Whether to include the zero-padding in the averaging calculation</param>\n /// <param name=\"divisor_override\">If specified, it will be used as divisor, otherwise size of the pooling region will be used</param>\n public static unsafe AvgPool2d AvgPool2d(long kernel_size, long? stride = null, long? padding = null, bool ceil_mode = false, bool count_include_pad = true, long? divisor_override = null)\n {\n long svalue = (stride == null) ? kernel_size : stride.Value;\n long pvalue = (padding == null) ? 0 : padding.Value;\n\n long* pkernelSize = stackalloc long[2] { kernel_size, kernel_size };\n long* pstrides = stackalloc long[2] { svalue, svalue };\n long* ppadding = stackalloc long[2] { pvalue, pvalue };\n\n var handle = THSNN_AvgPool2d_ctor((IntPtr)pkernelSize, 2, (IntPtr)pstrides, 2, (IntPtr)ppadding, 2, ceil_mode, count_include_pad, divisor_override.HasValue ? divisor_override.Value : 0, out var boxedHandle);\n if (handle == IntPtr.Zero) { torch.CheckForErrors(); }\n return new AvgPool2d(handle, boxedHandle);\n }\n\n public static partial class functional\n {\n /// <summary>\n /// Applies 2D average-pooling operation in kH × kW regions by step size sH * sW steps. The number of output features is equal to the number of input planes.\n /// </summary>\n /// <param name=\"input\">The input tensor.</param>\n /// <param name=\"kernelSizes\"></param>\n /// <param name=\"strides\"></param>\n /// <param name=\"paddings\"></param>\n /// <param name=\"ceil_mode\"></param>\n /// <param name=\"count_include_pad\"></param>\n /// <returns></returns>\n public static Tensor avg_pool2d(Tensor input, long[] kernelSizes,\n long[] strides = null,\n long[] paddings = null,\n bool ceil_mode = false,\n bool count_include_pad = true)\n {\n strides = (strides == null) ? new long[] { 1 } : strides;\n paddings = (paddings == null) ? new long[] { 0 } : paddings;\n unsafe {\n fixed (long* pkernelSize = kernelSizes, pstrides = strides, ppadding = paddings) {\n var res =\n THSTensor_avg_pool2d(input.Handle,\n (IntPtr)pkernelSize, kernelSizes.Length,\n (IntPtr)pstrides, strides.Length,\n (IntPtr)ppadding, paddings.Length,\n ceil_mode,\n count_include_pad);\n if (res == IntPtr.Zero) { torch.CheckForErrors(); }\n return new Tensor(res);\n }\n }\n }\n\n /// <summary>\n /// Applies 2D average-pooling operation in kH × kW regions by step size sH * sW steps. The number of output features is equal to the number of input planes.\n /// </summary>\n /// <param name=\"input\">The input tensor.</param>\n /// <param name=\"kernelSize\"></param>\n /// <param name=\"stride\"></param>\n /// <param name=\"padding\"></param>\n /// <param name=\"ceil_mode\"></param>\n /// <param name=\"count_include_pad\"></param>\n /// <returns></returns>\n public static unsafe Tensor avg_pool2d(Tensor input, long kernelSize,\n long? stride = null,\n long padding = 0,\n bool ceil_mode = false,\n bool count_include_pad = true)\n {\n long svalue = (stride == null) ? kernelSize : stride.Value;\n\n long* pkernelSize = stackalloc long[2] { kernelSize, kernelSize };\n long* pstrides = stackalloc long[2] { svalue, svalue };\n long* ppadding = stackalloc long[2] { padding, padding };\n\n var res =\n THSTensor_avg_pool2d(input.Handle,\n (IntPtr)pkernelSize, 2,\n (IntPtr)pstrides, 2,\n (IntPtr)ppadding, 2,\n ceil_mode,\n count_include_pad);\n if (res == IntPtr.Zero) { torch.CheckForErrors(); }\n return new Tensor(res);\n }\n\n /// <summary>\n /// Applies 2D average-pooling operation in kH × kW regions by step size sH * sW steps. The number of output features is equal to the number of input planes.\n /// </summary>\n /// <param name=\"input\">The input tensor.</param>\n /// <param name=\"kernelSize\"></param>\n /// <param name=\"stride\"></param>\n /// <param name=\"padding\"></param>\n /// <param name=\"ceil_mode\"></param>\n /// <param name=\"count_include_pad\"></param>\n /// <returns></returns>\n public static unsafe Tensor avg_pool2d(Tensor input, (long, long) kernelSize,\n (long, long)? stride = null,\n (long, long)? padding = null,\n bool ceil_mode = false,\n bool count_include_pad = true)\n {\n long svalue1 = (stride == null) ? kernelSize.Item1 : stride.Value.Item1;\n long svalue2 = (stride == null) ? kernelSize.Item2 : stride.Value.Item2;\n long pvalue1 = padding != null ? padding.Value.Item1 : 0;\n long pvalue2 = padding != null ? padding.Value.Item2 : 0;\n\n long* pstrides = stackalloc long[2] { svalue1, svalue2 };\n long* ppadding = stackalloc long[2] { pvalue1, pvalue2 };\n\n long* pkernelSize = stackalloc long[2] { kernelSize.Item1, kernelSize.Item2 };\n\n var res =\n THSTensor_avg_pool2d(input.Handle,\n (IntPtr)pkernelSize, 2,\n (IntPtr)pstrides, 2,\n (IntPtr)ppadding, 2,\n ceil_mode,\n count_include_pad);\n if (res == IntPtr.Zero) { torch.CheckForErrors(); }\n return new Tensor(res);\n }\n\n public static Tensor avg_pool2d_backward(Tensor input, Tensor originalInput,\n long[] kernelSizes,\n long[] strides = null,\n long[] paddings = null,\n bool ceil_mode = false,\n bool count_include_pad = true,\n long divisorOverride = 0)\n {\n strides = (strides == null) ? new long[] { 1 } : strides;\n paddings = (paddings == null) ? new long[] { 0 } : paddings;\n unsafe {\n fixed (long* pkernelSize = kernelSizes, pstrides = strides, ppadding = paddings) {\n var res =\n THSTensor_avg_pool2d_backward(input.Handle, originalInput.Handle,\n (IntPtr)pkernelSize, kernelSizes.Length,\n (IntPtr)pstrides, strides.Length,\n (IntPtr)ppadding, paddings.Length,\n ceil_mode,\n count_include_pad,\n divisorOverride);\n if (res == IntPtr.Zero) { torch.CheckForErrors(); }\n return new Tensor(res);\n }\n }\n }\n\n }\n }\n }\n}\n"},{"alpha_fraction":0.6172727942466736,"alphanum_fraction":0.6174814105033875,"avg_line_length":58.67219924926758,"blob_id":"914304d966c8214bb42104e1f8d5ab4712c95758","content_id":"e8fa5fbcab053d4cfa218e333a012d93663859d2","detected_licenses":["MIT"],"is_generated":false,"is_vendor":false,"language":"C#","length_bytes":14431,"license_type":"permissive","max_line_length":205,"num_lines":241,"path":"/src/TorchSharp/Autograd.cs","repo_name":"NiklasGustafsson/TorchSharp","src_encoding":"UTF-8","text":"// Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information.\nusing System;\nusing System.Linq;\nusing System.Collections.Generic;\nusing static TorchSharp.PInvoke.NativeMethods;\n\nnamespace TorchSharp\n{\n /// <summary>\n /// Helper class, relying on IDisposable to implement block-based scoping of autograd settings.\n /// </summary>\n internal class AutoGradMode : IDisposable\n {\n private readonly bool _isPrevGrad;\n\n public AutoGradMode(bool enabled)\n {\n _isPrevGrad = THSAutograd_isGradEnabled();\n THSAutograd_setGrad(enabled);\n }\n\n public void Dispose()\n {\n Dispose(true);\n GC.SuppressFinalize(this);\n }\n\n public void Dispose(bool disposing)\n {\n if (disposing)\n {\n THSAutograd_setGrad(_isPrevGrad);\n }\n }\n\n public static bool IsEnabled { get => THSAutograd_isGradEnabled(); }\n }\n\n /// <summary>\n /// Helper class, relying on IDisposable to implement block-based scoping of anomaly settings.\n /// </summary>\n public class AnomalyMode : IDisposable\n {\n private readonly bool _isPrevGrad;\n private readonly bool _shouldCheckNaN;\n\n public AnomalyMode(bool enabled, bool check_nan = true)\n {\n _isPrevGrad = THSAutograd_isAnomalyEnabled();\n _shouldCheckNaN = THSAutograd_shouldCheckNaN();\n THSAutograd_setAnomaly(enabled, check_nan);\n }\n\n public void Dispose()\n {\n Dispose(true);\n GC.SuppressFinalize(this);\n }\n\n protected virtual void Dispose(bool disposing)\n {\n if (disposing) {\n THSAutograd_setAnomaly(_isPrevGrad, _shouldCheckNaN);\n }\n }\n\n public static bool IsEnabled { get => THSAutograd_isAnomalyEnabled(); }\n\n public static bool ShouldCheckNaN { get => THSAutograd_shouldCheckNaN(); }\n }\n\n public static partial class torch\n {\n public static partial class autograd\n {\n /// <summary>\n /// Computes and returns the sum of gradients of outputs with respect to the inputs.\n /// </summary>\n /// <param name=\"outputs\">Outputs of the differentiated function.</param>\n /// <param name=\"inputs\">Inputs w.r.t. which the gradient will be returned (and not accumulated into .grad)..</param>\n /// <param name=\"grad_outputs\">\n /// The “vector” in the Jacobian-vector product. Usually gradients w.r.t. each output.\n /// Null values can be specified for scalar Tensors or ones that don’t require grad.\n /// If a null value would be acceptable for all grad_tensors, then this argument is optional.\n /// </param>\n /// <param name=\"retain_graph\">\n /// If false, the graph used to compute the grad will be freed.\n /// Note that in nearly all cases setting this option to true is not needed and often can be worked around in a much more efficient way. Defaults to the value of create_graph.\n /// </param>\n /// <param name=\"create_graph\">\n /// If true, graph of the derivative will be constructed, allowing to compute higher order derivative products.\n /// </param>\n /// <param name=\"allow_unused\">\n /// If false, specifying inputs that were not used when computing outputs (and therefore their grad is always zero) is an error.\n /// </param>\n /// <returns></returns>\n public static IList<Tensor> grad(IList<Tensor> outputs, IList<Tensor> inputs, IList<Tensor> grad_outputs = null, bool retain_graph = false, bool create_graph = false, bool allow_unused = false)\n {\n using var outs = new PinnedArray<IntPtr>();\n using var ins = new PinnedArray<IntPtr>();\n using var grads = new PinnedArray<IntPtr>();\n using var results = new PinnedArray<IntPtr>();\n\n IntPtr outsRef = outs.CreateArray(outputs.Select(p => p.Handle).ToArray());\n IntPtr insRef = ins.CreateArray(inputs.Select(p => p.Handle).ToArray());\n IntPtr gradsRef = grad_outputs == null ? IntPtr.Zero : grads.CreateArray(grad_outputs.Select(p => p.Handle).ToArray());\n long gradsLength = grad_outputs == null ? 0 : grads.Array.Length;\n\n THSAutograd_grad(outsRef, outs.Array.Length, insRef, ins.Array.Length, gradsRef, gradsLength, retain_graph, create_graph, allow_unused, results.CreateArray);\n CheckForErrors();\n return results.Array.Select(x => new Tensor(x)).ToList();\n }\n\n /// <summary>\n /// Computes the sum of gradients of given tensors with respect to graph leaves.\n /// </summary>\n /// <param name=\"tensors\">Tensors of which the derivative will be computed.</param>\n /// <param name=\"grad_tensors\">\n /// The “vector” in the Jacobian-vector product, usually gradients w.r.t. each element of corresponding tensors.\n /// Null values can be specified for scalar Tensors or ones that don’t require grad.\n /// If a null value would be acceptable for all grad_tensors, then this argument is optional.\n /// </param>\n /// <param name=\"retain_graph\">If false, the graph used to compute the grad will be freed.\n /// Note that in nearly all cases setting this option to true is not needed and often can be worked around in a much more efficient way.\n /// Defaults to the value of create_graph.</param>\n /// <param name=\"create_graph\">If true, graph of the derivative will be constructed, allowing to compute higher order derivative products. Defaults to false.</param>\n /// <param name=\"inputs\">\n /// Inputs w.r.t. which the gradient be will accumulated into .grad. All other Tensors will be ignored.\n /// If not provided, the gradient is accumulated into all the leaf Tensors that were used to compute the attr::tensors.\n /// </param>\n /// <remarks>\n /// The graph is differentiated using the chain rule. If any of tensors are non-scalar (i.e. their data has more than one element) and require gradient,\n /// then the Jacobian-vector product would be computed, in this case the function additionally requires specifying grad_tensors.\n ///\n /// It should be a sequence of matching length, that contains the “vector” in the Jacobian-vector product, usually the gradient of the differentiated\n /// function w.r.t. corresponding tensors (null is an acceptable value for all tensors that don’t need gradient tensors).\n ///\n /// This function accumulates gradients in the leaves - you might need to zero the .grad properties or set them to null before calling it.\n /// </remarks>\n public static void backward(IList<Tensor> tensors, IList<Tensor> grad_tensors = null, bool? retain_graph = null, bool create_graph = false, IList<Tensor> inputs = null)\n {\n bool rt = retain_graph ?? create_graph;\n\n using var ts = new PinnedArray<IntPtr>();\n using var gts = new PinnedArray<IntPtr>();\n using var ins = new PinnedArray<IntPtr>();\n IntPtr tensRef = ts.CreateArray(tensors.Select(p => p.Handle).ToArray());\n IntPtr gradsRef = grad_tensors == null ? IntPtr.Zero : gts.CreateArray(grad_tensors.Select(p => p.Handle).ToArray());\n IntPtr insRef = inputs == null ? IntPtr.Zero : ins.CreateArray(inputs.Select(p => p.Handle).ToArray());\n long insLength = inputs == null ? 0 : ins.Array.Length;\n long gradsLength = grad_tensors == null ? 0 : gts.Array.Length;\n\n THSAutograd_backward(tensRef, ts.Array.Length, gradsRef, gradsLength, rt, create_graph, insRef, insLength);\n CheckForErrors();\n }\n\n /// <summary>\n /// Computes the sum of gradients of given tensors with respect to graph leaves.\n /// </summary>\n /// <param name=\"tensor\">Tensor of which the derivative will be computed.</param>\n /// <param name=\"grad_tensors\">\n /// The “vector” in the Jacobian-vector product, usually gradients w.r.t. each element of corresponding tensors.\n /// Null values can be specified for scalar Tensors or ones that don’t require grad.\n /// If a null value would be acceptable for all grad_tensors, then this argument is optional.\n /// </param>\n /// <param name=\"retain_graph\">If false, the graph used to compute the grad will be freed.\n /// Note that in nearly all cases setting this option to true is not needed and often can be worked around in a much more efficient way.\n /// Defaults to the value of create_graph.</param>\n /// <param name=\"create_graph\">If true, graph of the derivative will be constructed, allowing to compute higher order derivative products. Defaults to false.</param>\n /// <param name=\"inputs\">\n /// Inputs w.r.t. which the gradient be will accumulated into .grad. All other Tensors will be ignored.\n /// If not provided, the gradient is accumulated into all the leaf Tensors that were used to compute the attr::tensors.\n /// </param>\n /// <remarks>\n /// The graph is differentiated using the chain rule. If any of tensors are non-scalar (i.e. their data has more than one element) and require gradient,\n /// then the Jacobian-vector product would be computed, in this case the function additionally requires specifying grad_tensors.\n ///\n /// It should be a sequence of matching length, that contains the “vector” in the Jacobian-vector product, usually the gradient of the differentiated\n /// function w.r.t. corresponding tensors (null is an acceptable value for all tensors that don’t need gradient tensors).\n ///\n /// This function accumulates gradients in the leaves - you might need to zero the .grad properties or set them to null before calling it.\n /// </remarks>\n public static void backward(Tensor tensor, IList<Tensor> grad_tensors = null, bool? retain_graph = null, bool create_graph = false, IList<Tensor> inputs = null)\n {\n backward(new[] { tensor }, grad_tensors, retain_graph, create_graph, inputs);\n }\n\n /// <summary>\n /// Computes the sum of gradients of given tensors with respect to graph leaves.\n /// </summary>\n /// <param name=\"tensor\">Tensor of which the derivative will be computed.</param>\n /// <param name=\"grad_tensor\">\n /// The “vector” in the Jacobian-vector product, usually gradients w.r.t. each element of corresponding tensors.\n /// Null values can be specified for scalar Tensors or ones that don’t require grad.\n /// If a null value would be acceptable for all grad_tensors, then this argument is optional.\n /// </param>\n /// <param name=\"retain_graph\">If false, the graph used to compute the grad will be freed.\n /// Note that in nearly all cases setting this option to true is not needed and often can be worked around in a much more efficient way.\n /// Defaults to the value of create_graph.</param>\n /// <param name=\"create_graph\">If true, graph of the derivative will be constructed, allowing to compute higher order derivative products. Defaults to false.</param>\n /// <param name=\"inputs\">\n /// Inputs w.r.t. which the gradient be will accumulated into .grad. All other Tensors will be ignored.\n /// If not provided, the gradient is accumulated into all the leaf Tensors that were used to compute the attr::tensors.\n /// </param>\n /// <remarks>\n /// The graph is differentiated using the chain rule. If any of tensors are non-scalar (i.e. their data has more than one element) and require gradient,\n /// then the Jacobian-vector product would be computed, in this case the function additionally requires specifying grad_tensors.\n ///\n /// It should be a sequence of matching length, that contains the “vector” in the Jacobian-vector product, usually the gradient of the differentiated\n /// function w.r.t. corresponding tensors (null is an acceptable value for all tensors that don’t need gradient tensors).\n ///\n /// This function accumulates gradients in the leaves - you might need to zero the .grad properties or set them to null before calling it.\n /// </remarks>\n public static void backward(Tensor tensor, Tensor grad_tensor, bool? retain_graph = null, bool create_graph = false, IList<Tensor> inputs = null)\n {\n backward(new[] { tensor }, new[] { grad_tensor }, retain_graph, create_graph, inputs);\n }\n\n /// <summary>\n /// Context-manager that enable anomaly detection for the autograd engine.\n /// </summary>\n /// <param name=\"check_nan\">Flag whether to raise an error when the backward generate “nan”</param>\n /// <returns></returns>\n public static AnomalyMode detect_anomaly(bool check_nan = true)\n {\n return new AnomalyMode(true, check_nan);\n }\n\n /// <summary>\n /// Context-manager that enable anomaly detection for the autograd engine.\n /// </summary>\n /// <param name=\"mode\">Flag whether to enable anomaly detection (true), or disable (false)</param>\n /// <param name=\"check_nan\">Flag whether to raise an error when the backward generate “nan”</param>\n /// <returns></returns>\n public static AnomalyMode set_detect_anomaly(bool mode, bool check_nan = true)\n {\n return new AnomalyMode(mode, check_nan);\n }\n }\n }\n}\n"},{"alpha_fraction":0.5682805180549622,"alphanum_fraction":0.5745536684989929,"avg_line_length":46.45801544189453,"blob_id":"081b922112cb27169ac09275c8dc5d83e506692f","content_id":"02ccab3116dc8da5d926e64b9c6e594cf89d7a19","detected_licenses":["MIT"],"is_generated":false,"is_vendor":false,"language":"C#","length_bytes":6217,"license_type":"permissive","max_line_length":224,"num_lines":131,"path":"/src/TorchSharp/Tensor/Factories/full.cs","repo_name":"NiklasGustafsson/TorchSharp","src_encoding":"UTF-8","text":"// Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information.\nusing System;\nusing static TorchSharp.PInvoke.NativeMethods;\n\n#nullable enable\nnamespace TorchSharp\n{\n public static partial class torch\n {\n /// <summary>\n /// Create a new tensor filled with a given value\n /// </summary>\n public static Tensor full(long[] size, Scalar value, ScalarType? dtype = null, Device? device = null, bool requires_grad = false, string[]? names = null)\n {\n return _full(size, value, dtype, device, requires_grad, names: names);\n }\n\n /// <summary>\n /// Create a new tensor filled with a given value\n /// </summary>\n public static Tensor full(ReadOnlySpan<long> size, Scalar value, ScalarType? dtype = null, Device? device = null, bool requires_grad = false, string[]? names = null)\n {\n return _full(size, value, dtype, device, requires_grad, names: names);\n }\n\n /// <summary>\n /// Create a new 1-D tensor filled with given value\n /// </summary>\n public static Tensor full(long size, Scalar value, ScalarType? dtype = null, Device? device = null, bool requires_grad = false, string[]? names = null)\n {\n return _full(stackalloc long[] { size }, value, dtype, device, requires_grad, names: names);\n }\n\n /// <summary>\n /// Create a new 2-D tensor filled with given value\n /// </summary>\n public static Tensor full(long rows, long columns, Scalar value, ScalarType? dtype = null, Device? device = null, bool requires_grad = false, string[]? names = null)\n {\n return _full(stackalloc long[] { rows, columns }, value, dtype, device, requires_grad, names: names);\n }\n\n /// <summary>\n /// Create a new 3-D tensor filled with given value\n /// </summary>\n public static Tensor full(long dim0, long dim1, long dim2, Scalar value, ScalarType? dtype = null, Device? device = null, bool requires_grad = false, string[]? names = null)\n {\n return _full(stackalloc long[] { dim0, dim1, dim2 }, value, dtype, device, requires_grad, names: names);\n }\n\n /// <summary>\n /// Create a new 4-D tensor filled with given value\n /// </summary>\n public static Tensor full(long dim0, long dim1, long dim2, long dim3, Scalar value, ScalarType? dtype = null, Device? device = null, bool requires_grad = false, string[]? names = null)\n {\n return _full(stackalloc long[] { dim0, dim1, dim2, dim3 }, value, dtype, device, requires_grad, names: names);\n }\n\n /// <summary>\n /// Create a new 1-D tensor filled with given value\n /// </summary>\n public static Tensor full(int size, Scalar value, ScalarType? dtype = null, Device? device = null, bool requires_grad = false, string[]? names = null)\n {\n return _full(stackalloc long[] { size }, value, dtype, device, requires_grad, names: names);\n }\n\n /// <summary>\n /// Create a new 2-D tensor filled with given value\n /// </summary>\n public static Tensor full(int rows, int columns, Scalar value, ScalarType? dtype = null, Device? device = null, bool requires_grad = false, string[]? names = null)\n {\n return _full(stackalloc long[] { rows, columns }, value, dtype, device, requires_grad, names: names);\n }\n\n /// <summary>\n /// Create a new 3-D tensor filled with given value\n /// </summary>\n public static Tensor full(int dim0, int dim1, int dim2, Scalar value, ScalarType? dtype = null, Device? device = null, bool requires_grad = false, string[]? names = null)\n {\n return _full(stackalloc long[] { dim0, dim1, dim2 }, value, dtype, device, requires_grad, names: names);\n }\n\n /// <summary>\n /// Create a new 4-D tensor filled with given value\n /// </summary>\n public static Tensor full(int dim0, int dim1, int dim2, int dim3, Scalar value, ScalarType? dtype = null, Device? device = null, bool requires_grad = false, string[]? names = null)\n {\n return _full(stackalloc long[] { dim0, dim1, dim2, dim3 }, value, dtype, device, requires_grad, names: names);\n }\n\n /// <summary>\n /// Returns a tensor with the same size as input filled with 'value.'\n /// </summary>\n public static Tensor full_like(Tensor input, Scalar value, ScalarType? dtype = null, Device? device = null, bool requires_grad = false, string[]? names = null) => input.full_like(value, dtype, device, requires_grad);\n\n /// <summary>\n /// Create a new tensor filled with a given value\n /// </summary>\n private static Tensor _full(ReadOnlySpan<long> size, Scalar value, ScalarType? dtype = null, Device? device = null, bool requires_grad = false, string[]? names = null)\n {\n device = InitializeDevice(device);\n if (!dtype.HasValue) {\n // Determine the element type dynamically.\n if (value.Type.IsIntegral()) {\n dtype = ScalarType.Int64;\n } else {\n dtype = get_default_dtype();\n }\n }\n\n unsafe {\n fixed (long* psizes = size) {\n var handle = THSTensor_full((IntPtr)psizes, size.Length, value.Handle, (sbyte)dtype, (int)device.type, device.index, requires_grad);\n if (handle == IntPtr.Zero) {\n GC.Collect();\n GC.WaitForPendingFinalizers();\n handle = THSTensor_full((IntPtr)psizes, size.Length, value.Handle, (sbyte)dtype, (int)device.type, device.index, requires_grad);\n }\n if (handle == IntPtr.Zero) { CheckForErrors(); }\n var result = new Tensor(handle);\n\n if (names != null && names.Length > 0) {\n\n result.rename_(names);\n }\n\n return result;\n }\n }\n }\n }\n}\n"},{"alpha_fraction":0.5659258961677551,"alphanum_fraction":0.5804938077926636,"avg_line_length":40.75257873535156,"blob_id":"8f6eab5981f3c29beda81e7bb79e56ce382304e4","content_id":"6fed5c29617f34645418b081c451ed8f325ef560","detected_licenses":["MIT"],"is_generated":false,"is_vendor":false,"language":"C#","length_bytes":4050,"license_type":"permissive","max_line_length":147,"num_lines":97,"path":"/src/TorchVision/Ops/FrozenBatchNorm2d.cs","repo_name":"NiklasGustafsson/TorchSharp","src_encoding":"UTF-8","text":"// Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information.\n\n// A number of implementation details in this file have been translated from the Python version of torchvision,\n// largely located in the files found in this folder:\n//\n// https://github.com/pytorch/vision/blob/a4f53308b2d0f1aa9191686e326f45c26053f686/torchvision/ops/misc.py\n//\n// The origin has the following copyright notice and license:\n//\n// https://github.com/pytorch/vision/blob/main/LICENSE\n//\n\nusing System;\nusing System.Collections.Generic;\nusing System.Runtime.CompilerServices;\nusing static TorchSharp.torch;\n\n#nullable enable\nnamespace TorchSharp\n{\n public static partial class torchvision\n {\n public static partial class ops\n {\n /// <summary>\n /// BatchNorm2d where the batch statistics and the affine parameters are fixed\n /// </summary>\n /// <param name=\"num_features\">Number of features C from an expected input of size (N, C, H, W)</param>\n /// <param name=\"eps\">A value added to the denominator for numerical stability.</param>\n /// <param name=\"device\">The target device for all buffers.</param>\n public static Modules.FrozenBatchNorm2d FrozenBatchNorm2d(int num_features, double eps = 1e-5, Device? device = null)\n {\n return new Modules.FrozenBatchNorm2d(num_features, eps, device);\n }\n }\n }\n\n namespace Modules\n {\n /// <summary>\n /// BatchNorm2d where the batch statistics and the affine parameters are fixed\n /// </summary>\n public class FrozenBatchNorm2d : nn.Module<Tensor, Tensor>\n {\n /// <summary>\n /// Constructor\n /// </summary>\n /// <param name=\"num_features\">Number of features C from an expected input of size (N, C, H, W)</param>\n /// <param name=\"eps\">A value added to the denominator for numerical stability.</param>\n /// <param name=\"device\">The target device for all buffers.</param>\n protected internal FrozenBatchNorm2d(int num_features, double eps = 1e-5, Device? device = null) : base(nameof(FrozenBatchNorm2d))\n {\n this.eps = eps;\n\n weight = torch.ones(num_features, device: device);\n bias = torch.zeros(num_features, device: device);\n running_mean = torch.zeros(num_features, device: device);\n running_var = torch.ones(num_features, device: device);\n\n register_buffer(nameof(weight), weight);\n register_buffer(nameof(bias), bias);\n register_buffer(nameof(running_mean), running_mean);\n register_buffer(nameof(running_var), running_var);\n }\n\n private double eps;\n\n public Tensor weight { get; protected set; }\n public Tensor bias { get; protected set; }\n public Tensor running_mean { get; protected set; }\n public Tensor running_var { get; protected set; }\n\n public override Tensor forward(Tensor x)\n {\n if (x.Dimensions != 4) throw new ArgumentException($\"Invalid number of dimensions for FrozenBatchNorm2d argument: {x.Dimensions}\");\n var w = weight.reshape(1, -1, 1, 1);\n var b = bias.reshape(1, -1, 1, 1);\n var rv = running_var.reshape(1, -1, 1, 1);\n var rm = running_mean.reshape(1, -1, 1, 1);\n var scale = w * (rv + eps).rsqrt();\n bias = b - rm * scale;\n return x * scale + bias;\n }\n\n protected override void Dispose(bool disposing)\n {\n if (disposing) {\n weight.Dispose();\n bias.Dispose();\n running_mean.Dispose();\n running_var.Dispose();\n }\n base.Dispose(disposing);\n }\n }\n }\n}\n"},{"alpha_fraction":0.43833625316619873,"alphanum_fraction":0.4447353184223175,"avg_line_length":26.733871459960938,"blob_id":"80d78df4e063d79aeff2e85b2b7d26b9c6eda1d7","content_id":"7b2b7d7b25264e571eb7f5048ce75cb861cb3bf0","detected_licenses":["MIT"],"is_generated":false,"is_vendor":false,"language":"C#","length_bytes":3438,"license_type":"permissive","max_line_length":130,"num_lines":124,"path":"/src/TorchSharp/Utils/FastShuffler.cs","repo_name":"NiklasGustafsson/TorchSharp","src_encoding":"UTF-8","text":"// Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information.\n\n//Algorithm from https://github.com/lemire/Code-used-on-Daniel-Lemire-s-blog/blob/master/2017/09/18_2/VisitInDisorder.java\nusing System;\nusing System.Collections;\nusing System.Collections.Generic;\n\nnamespace TorchSharp.Utils\n{\n class FastShuffler : IEnumerable<long>\n {\n private long size;\n private int? seed;\n\n public FastShuffler(long size, int? seed = null)\n {\n this.size = size;\n this.seed = seed;\n }\n\n private class ShuffleEnumerator : IEnumerator<long>\n {\n long maxrange;\n long prime;\n long index;\n long offset;\n long runningvalue;\n private Random rand;\n private int? seed;\n private long size;\n\n public ShuffleEnumerator(long size, int? seed = null)\n {\n this.seed = seed;\n this.size = size;\n Reset();\n }\n\n private const long MAX_COUNT = long.MaxValue;\n\n long SelectCoPrimeResev(long min, long target)\n {\n var count = 0L;\n var selected = 0L;\n for (var val = min; val < target; ++val) {\n if (Coprime(val, target)) {\n count += 1;\n if ((count == 1) || (NextLong(rand, count) < 1)) {\n selected = val;\n }\n }\n\n if (count == MAX_COUNT) return val;\n }\n\n return selected;\n }\n\n static bool Coprime(long u, long v) => Gcd(u, v) == 1;\n\n private static long Gcd(long a, long b)\n {\n while (a != 0 && b != 0)\n if (a > b) a %= b;\n else b %= a;\n\n return a | b;\n }\n\n private static long NextLong(Random r, long l)\n {\n var bytebuffer = new byte[8];\n r.NextBytes(bytebuffer);\n var t = 0L;\n foreach (var b in bytebuffer) {\n t <<= 8;\n t += b;\n }\n\n return t % l;\n }\n\n public bool MoveNext()\n {\n\n if (index >= maxrange) return false;\n runningvalue += prime;\n if (runningvalue >= maxrange) runningvalue -= maxrange;\n index++;\n return true;\n }\n\n public void Reset()\n {\n rand = seed is null ? new Random() : new Random(seed.Value);\n var min = size / 2;\n maxrange = size;\n prime = SelectCoPrimeResev(min, size);\n offset = NextLong(rand, size);\n index = 0;\n runningvalue = offset;\n }\n\n object IEnumerator.Current => Current;\n\n public long Current => runningvalue;\n\n public void Dispose()\n {\n\n }\n }\n\n public IEnumerator<long> GetEnumerator()\n {\n return new ShuffleEnumerator(size, seed);\n }\n\n IEnumerator IEnumerable.GetEnumerator()\n {\n return GetEnumerator();\n }\n }\n}"},{"alpha_fraction":0.4909479022026062,"alphanum_fraction":0.510158896446228,"avg_line_length":43.07316970825195,"blob_id":"21a52c29abcd4303ae1787aeb28e86d55f7ab74c","content_id":"921edff5a023bf3775802817b5d6e39128d889b8","detected_licenses":["MIT"],"is_generated":false,"is_vendor":false,"language":"C#","length_bytes":12649,"license_type":"permissive","max_line_length":133,"num_lines":287,"path":"/src/TorchVision/dsets/CelebA.cs","repo_name":"NiklasGustafsson/TorchSharp","src_encoding":"UTF-8","text":"// Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information.\nusing System;\nusing System.Collections.Generic;\nusing System.IO;\nusing System.Linq;\nusing static TorchSharp.torch;\nusing static TorchSharp.torch.nn;\nusing static TorchSharp.torch.utils.data;\nusing static TorchSharp.torchvision.datasets;\nusing static TorchSharp.torchvision.datasets.utils;\n\n// A number of implementation details in this file have been translated from the Python version of torchvision,\n// largely located in the files found in this folder:\n//\n// https://github.com/pytorch/vision/blob/b78d98bb152ffb9c0c0f5365f59f475c70b1784e/torchvision/datasets/celeba.py\n//\n// The origin has the following copyright notice and license:\n//\n// https://github.com/pytorch/vision/blob/main/LICENSE\n//\n\n#nullable enable\nnamespace TorchSharp\n{\n namespace Modules\n {\n internal sealed class CelebA : Dataset\n {\n private static readonly (string, string, string)[] file_list = new (string, string, string)[] {\n // File ID MD5 Hash Filename\n (\"0B7EVK8r0v71pZjFTYXZWM3FlRnM\", \"00d2c5bc6d35e252742224ab0c1e8fcb\", \"img_align_celeba.zip\"),\n // (\"0B7EVK8r0v71pbWNEUjJKdDQ3dGc\",\"b6cd7e93bc7a96c2dc33f819aa3ac651\", \"img_align_celeba_png.7z\"),\n // (\"0B7EVK8r0v71peklHb0pGdDl6R28\", \"b6cd7e93bc7a96c2dc33f819aa3ac651\", \"img_celeba.7z\"),\n (\"0B7EVK8r0v71pblRyaVFSWGxPY0U\", \"75e246fa4810816ffd6ee81facbd244c\", \"list_attr_celeba.txt\"),\n (\"1_ee_0u7vcNLOfNLegJRHmolfH5ICW-XS\", \"32bd1bd63d3c78cd57e08160ec5ed1e2\", \"identity_CelebA.txt\"),\n (\"0B7EVK8r0v71pbThiMVRxWXZ4dU0\", \"00566efa6fedff7a56946cd1c10f1c16\", \"list_bbox_celeba.txt\"),\n (\"0B7EVK8r0v71pd0FJY3Blby1HUTQ\", \"cc24ecafdb5b50baae59b03474781f8c\", \"list_landmarks_align_celeba.txt\"),\n // (\"0B7EVK8r0v71pTzJIdlJWdHczRlU\", \"063ee6ddb681f96bc9ca28c6febb9d1a\", \"list_landmarks_celeba.txt\"),\n (\"0B7EVK8r0v71pY0NSMzRuSXJEVkk\", \"d32c9cbf5e040fd4025c592c306e6668\", \"list_eval_partition.txt\"),\n };\n\n private const string base_folder = \"celeba\";\n private readonly string root;\n private readonly CelebADatasetSplit split;\n private readonly string[] target_type;\n private readonly IModule<Tensor, Tensor>? transform;\n private readonly IModule<Tensor, Tensor>? target_transform;\n private string[]? filename;\n private Tensor? identity;\n private Tensor? bbox;\n private Tensor? landmarks_align;\n private Tensor? attr;\n private string[]? attr_names;\n\n internal CelebA(\n string root,\n CelebADatasetSplit split,\n string[] target_type,\n IModule<Tensor, Tensor>? transform,\n IModule<Tensor, Tensor>? target_transform)\n {\n this.root = root;\n this.split = split;\n this.target_type = target_type;\n this.transform = transform;\n this.target_transform = target_transform;\n }\n\n public override long Count => this.attr is null ? 0 : this.attr.shape[0];\n\n protected override void Dispose(bool disposing)\n {\n if (disposing) {\n identity?.Dispose();\n bbox?.Dispose();\n landmarks_align?.Dispose();\n attr?.Dispose();\n }\n }\n\n internal void Load()\n {\n if (!this.CheckIntegrity()) {\n throw new InvalidDataException(\"Dataset not found or corrupted. You can use download=True to download it\");\n }\n\n var splits = this.LoadCsv(\"list_eval_partition.txt\");\n var identity = this.LoadCsv(\"identity_CelebA.txt\");\n var bbox = this.LoadCsv(\"list_bbox_celeba.txt\", header: 1);\n var landmarks_align = this.LoadCsv(\"list_landmarks_align_celeba.txt\", header: 1);\n var attr = this.LoadCsv(\"list_attr_celeba.txt\", header: 1);\n\n if (this.split == CelebADatasetSplit.All) {\n this.filename = splits.index;\n\n this.identity = identity.data;\n this.bbox = bbox.data;\n this.landmarks_align = landmarks_align.data;\n this.attr = attr.data;\n } else {\n var mask = (splits.data == (long)this.split).squeeze();\n\n var x = torch.squeeze(torch.nonzero(mask), dim: null);\n var y = new List<string>();\n for (int i = 0; i < x.shape[0]; i++) {\n y.Add(splits.index[x[i].item<long>()]);\n }\n this.filename = y.ToArray();\n\n this.identity = identity.data[mask];\n this.bbox = bbox.data[mask];\n this.landmarks_align = landmarks_align.data[mask];\n this.attr = attr.data[mask];\n }\n // map from {-1, 1} to {0, 1}\n this.attr = torch.div(this.attr + 1, 2, rounding_mode: RoundingMode.floor).to(this.attr.dtype);\n this.attr_names = attr.header;\n }\n\n private CSV LoadCsv(string filename, int? header = null)\n {\n IList<string[]> data = (\n File.ReadAllLines(Path.Combine(this.root, base_folder, filename))\n .Select(line => line.TrimStart().Split((char[]?)null, StringSplitOptions.RemoveEmptyEntries))).ToList();\n\n string[]? headers;\n if (header is not null) {\n headers = data[(int)header];\n data = data.Skip((int)header + 1).ToList();\n } else {\n headers = null;\n }\n\n var indices = data.Select(row => row[0]).ToArray();\n\n data = data.Select(row => row.AsSpan(1).ToArray()).ToList();\n var data_long = new long[data.Count, data[0].Length];\n for (int i = 0; i < data_long.GetLength(0); i++) {\n for (int j = 0; j < data_long.GetLength(1); j++) {\n if (data[i].Length != data_long.GetLength(1)) {\n throw new Exception();\n }\n long result = long.TryParse(data[i][j], out result) ? result : 0;\n data_long[i, j] = result;\n }\n }\n return new CSV(headers, indices, torch.tensor(data_long));\n }\n\n /// <summary>\n /// Get tensor according to index\n /// </summary>\n /// <param name=\"index\">Index for tensor</param>\n /// <returns>Tensors of index.</returns>\n public override Dictionary<string, Tensor> GetTensor(long index)\n {\n if (this.filename is null) throw new InvalidOperationException();\n Tensor X = torchvision.io.read_image(Path.Combine(this.root, base_folder, \"img_align_celeba\", this.filename[index]));\n\n var result = new Dictionary<string, Tensor>();\n foreach (var t in this.target_type) {\n if (t == \"attr\") {\n if (this.attr is null) throw new InvalidDataException();\n result[t] = this.attr[index, TensorIndex.Colon];\n } else if (t == \"identity\") {\n if (this.identity is null) throw new InvalidDataException();\n result[t] = this.identity[index, 0];\n } else if (t == \"bbox\") {\n if (this.bbox is null) throw new InvalidDataException();\n result[t] = this.bbox[index, TensorIndex.Colon];\n } else if (t == \"landmarks\") {\n if (this.landmarks_align is null) throw new InvalidDataException();\n result[t] = this.landmarks_align[index, TensorIndex.Colon];\n } else {\n throw new InvalidDataException($\"Target type \\\"{t}\\\" is not recognized.\");\n }\n }\n\n if (this.transform is not null) {\n X = this.transform.call(X);\n }\n result[\"input\"] = X;\n if (this.target_transform is not null) {\n string t = this.target_type[0];\n result[t] = this.target_transform.call(result[t]);\n }\n return result;\n }\n\n private bool CheckIntegrity()\n {\n foreach (var (_, md5, filename) in file_list) {\n var fpath = Path.Combine(this.root, base_folder, filename);\n var ext = Path.GetExtension(filename);\n // Allow original archive to be deleted (zip and 7z)\n // Only need the extracted images\n if (ext != \".zip\" && ext != \".7z\" && !torchvision.io.GDriveDownload.CheckIntegrity(fpath, md5)) {\n return false;\n }\n }\n\n // Should check a hash of the images\n return Directory.Exists(Path.Combine(this.root, base_folder, \"img_align_celeba\"));\n }\n\n public void download()\n {\n if (this.CheckIntegrity()) {\n Console.WriteLine(\"Files already downloaded and verified\");\n return;\n }\n\n foreach (var (file_id, md5, filename) in file_list) {\n download_file_from_google_drive(file_id, Path.Combine(this.root, base_folder), filename, md5);\n }\n\n extract_archive(Path.Combine(this.root, base_folder, \"img_align_celeba.zip\"));\n }\n\n private struct CSV\n {\n public string[]? header;\n public string[] index;\n public torch.Tensor data;\n public CSV(string[]? header, string[] index, torch.Tensor data)\n {\n this.header = header;\n this.index = index;\n this.data = data;\n }\n }\n }\n }\n\n public static partial class torchvision\n {\n public static partial class datasets\n {\n public enum CelebADatasetSplit\n {\n Train = 0,\n Valid = 1,\n Test = 2,\n All = -1,\n }\n\n /// <summary>\n /// `Large-scale CelebFaces Attributes (CelebA) Dataset http://mmlab.ie.cuhk.edu.hk/projects/CelebA.html Dataset.\n /// </summary>\n /// <param name=\"root\">Root directory where images are downloaded to.</param>\n /// <param name=\"split\">One of Train, Valid, Test, All.\n /// Accordingly dataset is selected.</param>\n /// <param name=\"target_type\">Type of target to use, ``attr``, ``identity``, ``bbox``,\n /// or ``landmarks``.</param>\n /// <param name=\"transform\">A function/transform that takes in an PIL image\n /// and returns a transformed version.</param>\n /// <param name=\"target_transform\">A function/transform that takes in the\n /// target and transforms it.</param>\n /// <param name=\"download\">If true, downloads the dataset from the internet and\n /// puts it in root directory. If dataset is already downloaded, it is not\n /// downloaded again.</param>\n public static Dataset CelebA(\n string root,\n CelebADatasetSplit split = CelebADatasetSplit.Train,\n string[]? target_type = null,\n IModule<Tensor, Tensor>? transform = null,\n IModule<Tensor, Tensor>? target_transform = null,\n bool download = false)\n {\n if (target_type == null) {\n target_type = new string[] { \"attr\" };\n }\n var dataset = new Modules.CelebA(\n root,\n split,\n target_type,\n transform,\n target_transform);\n if (download) {\n dataset.download();\n }\n dataset.Load();\n return dataset;\n }\n }\n }\n}\n"},{"alpha_fraction":0.5027042627334595,"alphanum_fraction":0.5125908851623535,"avg_line_length":45.72554397583008,"blob_id":"670c3a05857ce9a21925cccff8132d68de6820bb","content_id":"2a67102df5823af76f42a14074c4aa600fde98a5","detected_licenses":["MIT"],"is_generated":false,"is_vendor":false,"language":"C#","length_bytes":17195,"license_type":"permissive","max_line_length":220,"num_lines":368,"path":"/src/TorchSharp/Optimizers/ASGD.cs","repo_name":"NiklasGustafsson/TorchSharp","src_encoding":"UTF-8","text":"// Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information.\nusing System;\nusing System.IO;\nusing System.Collections.Generic;\nusing System.Linq;\nusing static TorchSharp.torch;\n\nnamespace TorchSharp\n{\n using Modules;\n\n public static partial class torch\n {\n public static partial class optim\n {\n\n /// <summary>\n /// Implements the Averaged Stochastic Gradient Descent.\n ///\n /// It has been proposed in Acceleration of stochastic approximation by averaging.\n /// https://dl.acm.org/citation.cfm?id=131098\n /// </summary>\n /// <param name=\"parameters\">Parameters to optimize. This optimizer requires the <b>named</b> parameters collection.</param>\n /// <param name=\"lr \">Learning rate</param>\n /// <param name=\"lambd\">Decay term (default: 1e-4)</param>\n /// <param name=\"alpha\">Power for eta update (default: 0.75)</param>\n /// <param name=\"t0\">Point at which to start averaging (default: 1e6)</param>\n /// <param name=\"weight_decay\">Weight decay (L2 penalty) (default: 0)</param>\n /// <param name=\"maximize\">Maximize the params based on the objective, instead of minimizing.</param>\n public static ASGD ASGD(IEnumerable<Parameter> parameters, double lr = 1e-3, double lambd = 1e-4, double alpha = 0.75, double t0 = 1e6, double weight_decay = 0, bool maximize = false)\n {\n return new Modules.ASGD(parameters, lr, lambd, alpha, t0, weight_decay, maximize);\n }\n\n /// <summary>\n /// Implements the Averaged Stochastic Gradient Descent.\n ///\n /// It has been proposed in Acceleration of stochastic approximation by averaging.\n /// https://dl.acm.org/citation.cfm?id=131098\n /// </summary>\n /// <param name=\"parameters\">Parameters to optimize. This optimizer requires the <b>named</b> parameters collection.</param>\n /// <param name=\"lr \">Learning rate</param>\n /// <param name=\"lambd\">Decay term (default: 1e-4)</param>\n /// <param name=\"alpha\">Power for eta update (default: 0.75)</param>\n /// <param name=\"t0\">Point at which to start averaging (default: 1e6)</param>\n /// <param name=\"weight_decay\">Weight decay (L2 penalty) (default: 0)</param>\n /// <param name=\"maximize\">Maximize the params based on the objective, instead of minimizing.</param>\n public static ASGD ASGD(IEnumerable<(string name, Parameter parameter)> parameters, double lr = 1e-3, double lambd = 1e-4, double alpha = 0.75, double t0 = 1e6, double weight_decay = 0, bool maximize = false)\n {\n return new Modules.ASGD(parameters.Select(np => np.parameter), lr, lambd, alpha, t0, weight_decay, maximize);\n }\n\n /// <summary>\n /// Implements the Averaged Stochastic Gradient Descent.\n ///\n /// It has been proposed in Acceleration of stochastic approximation by averaging.\n /// https://dl.acm.org/citation.cfm?id=131098\n /// </summary>\n /// <param name=\"parameters\">Parameters to optimize. This optimizer requires the <b>named</b> parameters collection.</param>\n /// <param name=\"lr \">Learning rate</param>\n /// <param name=\"lambd\">Decay term (default: 1e-4)</param>\n /// <param name=\"alpha\">Power for eta update (default: 0.75)</param>\n /// <param name=\"t0\">Point at which to start averaging (default: 1e6)</param>\n /// <param name=\"weight_decay\">Weight decay (L2 penalty) (default: 0)</param>\n /// <param name=\"maximize\">Maximize the params based on the objective, instead of minimizing.</param>\n public static ASGD ASGD(IEnumerable<ParamGroup<ASGD.Options>> parameters, double lr = 1e-3, double lambd = 1e-4, double alpha = 0.75, double t0 = 1e6, double weight_decay = 0, bool maximize = false)\n {\n return new Modules.ASGD(parameters, lr, lambd, alpha, t0, weight_decay, maximize);\n }\n }\n }\n\n namespace Modules\n {\n public class ASGD : OptimizerHelper\n {\n /// <summary>\n /// Implements ASGD algorithm (a variant of Adam based on infinity norm).\n ///\n /// It has been proposed in Adam: A Method for Stochastic Optimization.\n /// </summary>\n /// <param name=\"parameters\">Parameters to optimize. This optimizer requires the <b>named</b> parameters collection.</param>\n /// <param name=\"lr \">Learning rate</param>\n /// <param name=\"lambd\">Decay term (default: 1e-4)</param>\n /// <param name=\"alpha\">Power for eta update (default: 0.75)</param>\n /// <param name=\"t0\">Point at which to start averaging (default: 1e6)</param>\n /// <param name=\"weight_decay\">Weight decay (L2 penalty) (default: 0)</param>\n /// <param name=\"maximize\">Maximize the params based on the objective, instead of minimizing.</param>\n public ASGD(IEnumerable<Parameter> parameters, double lr = 0.01, double lambd = 1e-4, double alpha = 0.75, double t0 = 1e6, double weight_decay = 0, bool maximize = false)\n : this(new ParamGroup[] { new() { Parameters = parameters } }, lr, lambd, alpha, t0, weight_decay, maximize)\n {\n }\n\n /// <summary>\n /// Implements ASGD algorithm (a variant of Adam based on infinity norm).\n ///\n /// It has been proposed in Adam: A Method for Stochastic Optimization.\n /// </summary>\n /// <param name=\"parameters\">Parameters to optimize. This optimizer requires the <b>named</b> parameters collection.</param>\n /// <param name=\"lr \">Learning rate</param>\n /// <param name=\"lambd\">Decay term (default: 1e-4)</param>\n /// <param name=\"alpha\">Power for eta update (default: 0.75)</param>\n /// <param name=\"t0\">Point at which to start averaging (default: 1e6)</param>\n /// <param name=\"weight_decay\">Weight decay (L2 penalty) (default: 0)</param>\n /// <param name=\"maximize\">Maximize the params based on the objective, instead of minimizing.</param>\n /// <returns></returns>\n public ASGD(IEnumerable<ParamGroup<Options>> parameters, double lr = 0.01, double lambd = 1e-4, double alpha = 0.75, double t0 = 1e6, double weight_decay = 0, bool maximize = false)\n {\n if (lr < 0.0) throw new ArgumentException($\"Invalid learning rate: {lr}\");\n\n var options = new Options {\n LearningRate = lr,\n InitialLearningRate = lr,\n maximize = maximize,\n lambd = lambd,\n alpha = alpha,\n t0 = t0,\n weight_decay = weight_decay\n };\n\n _defaults = options;\n _parameter_groups = new List<Modules.ParamGroup>();\n\n foreach (var g in parameters) {\n add_param_group(g);\n }\n }\n\n /// <summary>\n /// Performs a single optimization step (parameter update).\n /// </summary>\n /// <param name=\"closure\">A closure that reevaluates the model and returns the loss. Optional for most optimizers.</param>\n /// <returns></returns>\n public override Tensor step(Func<Tensor> closure = null)\n {\n return _step<ParamGroup>(group => {\n\n var options = group.Options as Options;\n var maximize = options.maximize.Value;\n var lambd = options.lambd.Value;\n var alpha = options.alpha.Value;\n var weight_decay = options.weight_decay.Value;\n var t0 = options.t0.Value;\n var lr = options.LearningRate.Value;\n\n foreach (var param in group.Parameters) {\n\n var grad = param.grad();\n\n if (grad is null) continue;\n\n if (grad.is_sparse) throw new ArgumentException(\"ASGD does not support sparse gradients\");\n\n if (maximize) grad = -grad;\n\n var state = (State)_state[param.handle];\n\n state.step += 1;\n\n grad = (weight_decay != 0)\n ? grad.add(param, alpha: weight_decay)\n : grad.alias();\n\n param.mul_(1 - lambd * state.eta);\n param.add_(grad, alpha: -state.eta);\n\n if (state.mu != 1) {\n state.ax.add_(param.sub(state.ax).mul(state.mu));\n } else {\n state.ax.copy_(param);\n }\n\n state.eta = lr / Math.Pow((1 + lambd * lr * state.step), alpha);\n state.mu = 1 / Math.Max(1, state.step - t0);\n }\n }, closure);\n }\n\n protected override void Dispose(bool disposing)\n {\n base.Dispose(disposing);\n foreach (var kvp in _state) {\n ((State)kvp.Item2).Dispose();\n }\n _state.Clear();\n }\n\n public sealed class State : OptimizerState, IDisposable\n {\n public long step;\n public double eta;\n public double mu;\n public Tensor ax;\n\n public void Dispose()\n {\n Dispose(true);\n GC.SuppressFinalize(this);\n }\n\n private void Dispose(bool disposing)\n {\n if (disposing) {\n ax.Dispose();\n }\n }\n\n /// <summary>\n /// Move all the state to the indicated device.\n /// </summary>\n /// <param name=\"device\">The device to move all state to.</param>\n public override void to(Device device)\n {\n ax = ax.to(device);\n }\n\n /// <summary>\n /// Load the optimizer parameter state from a stream.\n /// </summary>\n /// <param name=\"reader\">A binary reader connected to a stream open for reading.</param>\n public override void LoadStateDict(BinaryReader reader)\n {\n step = reader.ReadInt64();\n eta = reader.ReadDouble();\n mu = reader.ReadDouble();\n ax.Load(reader);\n }\n\n /// <summary>\n /// Load optimizer parameter state from another optimizer.\n /// </summary>\n /// <param name=\"source\">An optimizer state record.</param>\n public override void LoadStateDict(OptimizerState source)\n {\n var st_state = source as State;\n step = st_state.step;\n eta = st_state.eta;\n mu = st_state.mu;\n ax.Dispose();\n ax = st_state.ax;\n }\n\n /// <summary>\n /// Save the optimizer parameter state to a stream.\n /// </summary>\n /// <param name=\"writer\">A binary writer connected to a stream open for writing.</param>\n public override void SaveStateDict(BinaryWriter writer)\n {\n writer.Write(step);\n writer.Write(eta);\n writer.Write(mu);\n ax.Save(writer);\n }\n\n /// <summary>\n /// Useful for tests, allows comparison of one state with another.\n /// </summary>\n /// <param name=\"other\">The other optimizer state</param>\n /// <returns></returns>\n public override bool ApproximatelyEquals(OptimizerState other)\n {\n var rhs = other as State;\n return (rhs is not null) && step == rhs.step && eta == rhs.eta && mu == rhs.mu && ax.allclose(rhs.ax);\n }\n }\n\n /// <summary>\n /// Add a param group to the Optimizer s param_groups.\n /// </summary>\n /// <param name=\"param_group\"></param>\n /// <remarks>This can be useful when fine tuning a pre-trained network as frozen layers can be made trainable and added to the Optimizer as training progresses.</remarks>\n public override void add_param_group(Modules.ParamGroup param_group)\n {\n var def = _defaults as Options;\n if (param_group.Options is null) {\n param_group.Options = new Options();\n }\n\n var opt = param_group.Options as Options;\n\n // Make sure all the options are set.\n if (!opt.maximize.HasValue) opt.maximize = def.maximize;\n if (!opt.LearningRate.HasValue) opt.LearningRate = def.LearningRate;\n if (!opt.lambd.HasValue) opt.lambd = def.lambd;\n if (!opt.alpha.HasValue) opt.alpha = def.alpha;\n if (!opt.weight_decay.HasValue) opt.weight_decay = def.weight_decay;\n if (!opt.t0.HasValue) opt.t0 = def.t0;\n\n opt.InitialLearningRate = opt.LearningRate.Value;\n\n _parameter_groups.Add(param_group);\n\n foreach (var p in param_group.Parameters) {\n var state = new State();\n _state[p.Handle] = state;\n state.step = 0;\n state.eta = param_group.LearningRate;\n state.mu = 1;\n state.ax = torch.zeros_like(p).DetachFromDisposeScope();\n }\n }\n\n public class Options : OptimizerOptions\n {\n public bool? maximize;\n public double? lambd;\n public double? alpha;\n public double? weight_decay;\n public double? t0;\n\n /// <summary>\n /// Load optimizer options (param-group hyperparameters) from another optimizer.\n /// </summary>\n /// <param name=\"source\">An optimizer options record.</param>\n public override void LoadStateDict(OptimizerOptions source)\n {\n base.LoadStateDict(source);\n var opts = source as Options;\n maximize = opts.maximize;\n lambd = opts.lambd;\n alpha = opts.alpha;\n weight_decay = opts.weight_decay;\n t0 = opts.t0;\n }\n\n /// <summary>\n /// Load the optimizer options (param-group hyperparameters) from a stream.\n /// </summary>\n /// <param name=\"reader\">A binary reader connected to a stream open for reading.</param>\n public override void LoadStateDict(BinaryReader reader)\n {\n base.LoadStateDict(reader);\n maximize = reader.ReadBoolean();\n lambd = reader.ReadDouble();\n alpha = reader.ReadDouble();\n weight_decay = reader.ReadDouble();\n t0 = reader.ReadDouble();\n }\n\n /// <summary>\n /// Save the optimizer options (param-group hyperparameters) to a stream.\n /// </summary>\n /// <param name=\"writer\">A binary writer connected to a stream open for writing.</param>\n public override void SaveStateDict(BinaryWriter writer)\n {\n base.SaveStateDict(writer);\n writer.Write(maximize.Value);\n writer.Write(lambd.Value);\n writer.Write(alpha.Value);\n writer.Write(weight_decay.Value);\n writer.Write(t0.Value);\n }\n }\n\n public class ParamGroup : ParamGroup<Options>\n {\n public ParamGroup() { }\n\n public ParamGroup(IEnumerable<Parameter> parameters, Options options) : base(parameters, options) { }\n\n public ParamGroup(IEnumerable<Parameter> parameters, double lr = 0.01, double lambd = 1e-4, double alpha = 0.75, double t0 = 1e6, double weight_decay = 0)\n : base(parameters, new ASGD.Options { LearningRate = lr, lambd = lambd, alpha = alpha, t0 = t0, weight_decay = weight_decay })\n {\n }\n }\n }\n }\n}\n"},{"alpha_fraction":0.7460804581642151,"alphanum_fraction":0.7654510140419006,"avg_line_length":59.9100341796875,"blob_id":"ae27bd45421292abb8f480de8627c8dd32173b40","content_id":"e9ce1eec771fd705803b46f724e0c73bdf832d7a","detected_licenses":["MIT"],"is_generated":false,"is_vendor":false,"language":"Markdown","length_bytes":17604,"license_type":"permissive","max_line_length":640,"num_lines":289,"path":"/DEVGUIDE.md","repo_name":"NiklasGustafsson/TorchSharp","src_encoding":"UTF-8","text":"\n# Building\n\n dotnet build /p:SkipNative=true\n dotnet build # for cuda support on Windows and Linux\n dotnet test\n dotnet pack\n\n## Windows\n\nRequirements:\n- Visual Studio\n- git\n- cmake (tested with 3.18)\n\n__NOTE:__ At this moment, VS versions 17.4.X will not build the native code library. Use 17.3.X until further notice. See: https://github.com/dotnet/TorchSharp/issues/858 for more information.\n\n\n## Linux\n\nRequirements:\n- requirements to run .NET Core 3.1\n- git\n- cmake (tested with 3.14)\n- clang 6.x +\n\nExample to fulfill the requirements in Ubuntu 16:\n```\nwget -O - https://apt.llvm.org/llvm-snapshot.gpg.key | sudo apt-key add -\nsudo apt-add-repository \"deb https://apt.llvm.org/xenial/ llvm-toolchain-xenial-6.0 main\"\nsudo apt-get -y update\nsudo apt-get -y install clang-6.0 git cmake libunwind8 curl libomp-dev\n```\n\nCommands:\n\n## Mac\n\nRequirements:\n- Clang/LLVM 12.0.0\n- git\n- .NET SDK 5.0.300\n- Cmake 3.20.3\n\nBuild with\n\n dotnet build /p:SkipNative=true\n\n## Packages\n\nAn ephemeral feed of packages from Azure DevOps CI is available for those \n\n* View link: https://dotnet.visualstudio.com/TorchSharp/_packaging?_a=feed&feed=SignedPackages\n* Nuget feed: https://dotnet.pkgs.visualstudio.com/TorchSharp/_packaging/SignedPackages/nuget/v3/index.json\n\nSome releases are pushed to nuget\n\n## Building the TorchSharp package\n\n dotnet build\n dotnet pack\n\nLocally built packages have names like this, names update every day. If repeatedly rebuilding them locally you may have to remove them\nfrom your local `.nuget` package cache.\n\n bin/packages/Debug/TorchSharp.0.3.0-local-Debug-20200520.nupkg\n bin/packages/Release/TorchSharp.0.3.0-local-Release-20200520.nupkg\n\nTo change the TorchSharp package version update this [file](https://github.com/dotnet/TorchSharp/blob/main/build/BranchInfo.props).\n\n## Doing releases of the TorchSharp package\n\nThe TorchSharp package is pushed to nuget.org via Azure DevOps CI release pipeline. Assuming you're not building or updating the LibTorch packages\n(`BuildLibTorchPackages` is `false` in [azure-pipelines.yml](azure-pipelines.yml)) this is pretty simple once you have the permissions:\n\n1. Update the version number in [./build/BranchInfo.props](./build/BranchInfo.props) and in the [Release Notes](./RELEASENOTES.md) file and then submit a PR. \n\n Updating the major or minor version number should only be done after a discussion with repo admins. The patch number should be incremented by one each release and set to zero after a change to the major or minor version.\n2. Integrate code to main and wait for CI to process\n3. Go to [releases](https://donsyme.visualstudio.com/TorchSharp/_release) and choose \"Create Release\" (top right)\n4. Under \"Artifacts-->Version\" choose the pipeline build corresponding to the thing you want to release. It should be a successful build on main\n5. Press \"Create\"\n\n6. Once the package has been successfully pushed and is available in the NuGet gallery, create a GitHub tag in the 'main' branch with the version as the name of the tag.\n\n# The libtorch packages\n\nThe libtorch packages are huge (~3GB compressed combined for CUDA Windows) and cause a\nlot of problems to make and deliver due to NuGet package size restrictions.\n\nThese problems include:\n\n1. A massive 2GB binary in the linux CUDA package and multiple 1.0GB binaries in Windows CUDA package\n\n2. Size limitations of about ~500MB on NuGet packages on the Azure DevOps CI system and about ~250MB on `nuget.org`\n\n4. Regular download/upload failures on these systems due to network interruptions for packages of this size\n\n5. 10GB VM image size restrictions for the containers userd to build these packages in the Azure DevOps CI system, we can easily run out of room.\n\n6. Complete libtorch-cpu packages can't be built using your local machine alone, since they won't contain the\n full range of native bits. Instead they are built using Azure Pipelines by combining builds\n\nFor this reason, we do the following\n\n1. The head, referenceable packages that deliver a functioning runtime are any of:\n\n libtorch-cpu\n libtorch-cuda-11.7-linux-x64\n libtorch-cuda-11.7-win-x64\n\n2. These packages are combo packages that reference multiple parts. The parts are **not** independently useful.\n Some parts deliver a single vast file via `primary` and `fragment` packages. A build task is then used to \"stitch\" these files back together\n to one file on the target machine with a SHA check. This is a hack but there is no other realistic way to deliver\n these vast files as packages (the alternative is to abandon packaging and require a manual\n install/detect/link of PyTorch CUDA on all downstream systems, whcih is extremely problematic\n for many practical reasons).\n\n For example, the CUDA package fragments are defined in [libtorch-cuda](src/Redist/libtorch-cuda-11.7/libtorch-cuda-11.7.proj). See more details later in this document.\n\n3. The `libtorch-*` packages are built in Azure DevOps CI\n [using this build pipeline](https://donsyme.visualstudio.com/TorchSharp/_build?definitionId=1&_a=summary) but only in main\n branch and only when `BuildLibTorchPackages` is set to true in [azure-pipelines.yml](azure-pipelines.yml) in the main branch.\n You must currently manually edit this and submit to main to get new `libtorch-*` packages\n built. Also increment `LibTorchPackageVersion` if necessary. Do a push to main and the packages will build. This process could be adjusted but at least gets us off the ground.\n\n4. After a successful build, the `libtorch-*` packages can be trialled using the package feed from CI (see above). When\n they are appropriate they can be pushed to nuget using\n [this manually invoked release pipeline](https://donsyme.visualstudio.com/TorchSharp/_release?_a=releases&view=mine&definitionId=1) in\n Azure DevOps CI (so they don't have to be manually downloaded and pushed to `nuget.org`)\n\n a. [Go to release pipeline](https://donsyme.visualstudio.com/TorchSharp/_release?_a=releases&view=mine&definitionId=1)\n\n b. Press 'New Release'\n\n c. Select the successful main CI build that includes the `libtorch` packages, create the release and wait for it to finish. You should\n see `Initialize job`, `Download artifact - dotnet.TorchSharp - packages`, `NuGet push`, `Finalize Job` succeeded.\n\n d. All packages should now be pushed to `nuget.org` and will appear after indexing.\n\n6. If updating libtorch packages, remember to delete all massive artifacts from Azure DevOps and reset this `BuildLibTorchPackages` in [azure-pipelines.yml](azure-pipelines.yml) in main branch.\n\n### Updating PyTorch version for new libtorch packages\n\nThis project grabs LibTorch and makes a C API wrapper for it, then calls these from C#. When updating to a newer\nversion of PyTorch then quite a lot of careful work needs to be done.\n\n0. Make sure you have plenty of disk space, e.g. 15GB\n\n0. Clean and reset to main\n\n git checkout main\n git clean -xfd .\n\n1. Familiarise yourself with download links. See https://pytorch.org/get-started/locally/ for download links.\n\n For example Linux, LibTorch 2.0.1 CPU download uses the link:\n\n https://download.pytorch.org/libtorch/cpu/libtorch-shared-with-deps-2.0.1%2Bcpu.zip\n\n Don't download anything yet, or manually. The downloads are acquired automatically in step 2.\n \n To update the version, update this in [Dependencies.props](build/Dependencies.props):\n\n <LibTorchVersion>2.0.1</LibTorchVersion>\n\n The libtorch version number is also referenced in source code, in the file 'src/TorchSharp/Torch.cs':\n\n ```C#\n const string libtorchPackageVersion = \"2.0.1.1\";\n ```\n\n2. Run these to test downloads and update SHA hashes for the various LibTorch downloads:\n\n dotnet build src\\Redist\\libtorch-cpu\\libtorch-cpu.proj /p:UpdateSHA=true /p:TargetOS=linux /p:Configuration=Release /t:Build /p:IncludeLibTorchCpuPackages=true\n dotnet build src\\Redist\\libtorch-cpu\\libtorch-cpu.proj /p:UpdateSHA=true /p:TargetOS=mac /p:Configuration=Release /t:Build /p:IncludeLibTorchCpuPackages=true\n dotnet build src\\Redist\\libtorch-cpu\\libtorch-cpu.proj /p:UpdateSHA=true /p:TargetOS=windows /p:Configuration=Release /t:Build /p:IncludeLibTorchCpuPackages=true \n dotnet build src\\Redist\\libtorch-cpu\\libtorch-cpu.proj /p:UpdateSHA=true /p:TargetOS=windows /p:Configuration=Debug /t:Build /p:IncludeLibTorchCpuPackages=true\n\n dotnet build src\\Redist\\libtorch-cuda-11.7\\libtorch-cuda-11.7.proj /p:UpdateSHA=true /p:TargetOS=linux /p:Configuration=Release /t:Build /p:IncludeLibTorchCudaPackages=true\n dotnet build src\\Redist\\libtorch-cuda-11.7\\libtorch-cuda-11.7.proj /p:UpdateSHA=true /p:TargetOS=windows /p:Configuration=Release /t:Build /p:IncludeLibTorchCudaPackages=true\n dotnet build src\\Redist\\libtorch-cuda-11.7\\libtorch-cuda-11.7.proj /p:UpdateSHA=true /p:TargetOS=windows /p:Configuration=Debug /t:Build /p:IncludeLibTorchCudaPackages=true\n\n Each of these will take a **very very long time** depending on your broadband connection. This can't currently be done in CI.\n\n If file names in the distribution have changed, or files have been removed, you will get errors saying that files cannot be found. That's okay and will be taken care of in the next step.\n\n3. At this point you must **very very carefully** update the `<File Include= ...` entries under src\\Redist projects for\n [libtorch-cpu](src/Redist/libtorch-cpu/libtorch-cpu.proj) and [libtorch-cuda](src/Redist/libtorch-cuda-11.7/libtorch-cuda-11.7.proj).\n\n This is the step in the upgrade process that takes the most effort and time. It requires extreme care.\n\n Check the contents of the unzip of the archive, e.g.\n\n bin\\obj\\x64.Debug\\libtorch-cpu\\libtorch-shared-with-deps-2.0.1\\libtorch\\lib\n\n You may also need to precisely refactor the CUDA binaries into multiple parts so each package ends up under ~300MB. The NuGet gallery does not allow packages larger than 250MB, so if files are\n 300MB, after compression, they are likely to be smaller than 250MB. However, you have to look out: if the compression is poor, then packages may end up larger. Note that it is 250 million\n bytes that is the limit, **not** 250*1024*1024. In other words, it is 250 MB, not 250 MiB. Note that Windows Explorer will show file sizes in KiB, not thousands of bytes. Use 'dir' from a CMD window to get the\n exact size in bytes for each file. For example -- the file `libtorch_cpu.so` shows up as 511,872 KB in Windows Explorer, but 524,156,144 bytes in CMD. The 2.4% difference can be significant.\n\n If the combined size of the files going into a part is smaller than 250MB, then everything is fine, and there is no need to split the part. It can be singular. If that is not the case, then the part should be fragmented into two or more parts that are linked together by their names.\n\n For example, the following snippet spreads the `torch_cuda_cu.dll` binary file into four fragments of 250 MB each. After compression, they will be even smaller.\n\n ```xml\n <File Include= \"libtorch\\lib\\torch_cuda_cu.dll\" PackageSuffix=\"part9-primary\" FileUnstitchIndex=\"0\" FileUnstitchStart=\"0\" FileUnstitchSize=\"250000000\" />\n <File Include= \"libtorch\\lib\\torch_cuda_cu.dll\" PackageSuffix=\"part9-fragment1\" FileUnstitchIndex=\"1\" FileUnstitchStart=\"250000000\" FileUnstitchSize=\"250000000\" />\n <File Include= \"libtorch\\lib\\torch_cuda_cu.dll\" PackageSuffix=\"part9-fragment2\" FileUnstitchIndex=\"2\" FileUnstitchStart=\"500000000\" FileUnstitchSize=\"250000000\" />\n <File Include= \"libtorch\\lib\\torch_cuda_cu.dll\" PackageSuffix=\"part9-fragment3\" FileUnstitchIndex=\"3\" FileUnstitchStart=\"750000000\" FileUnstitchSize=\"-1\" />\n ```\n\n They must all be called either 'primary,' which should be the first fragment, or 'fragmentN' where 'N' is the ordinal number of the fragment, starting with '1'. The current logic allows for as many as 10 non-primary fragments. If more are needed, the code in [FileRestitcher.cs](pkg/FileRestitcher/FileRestitcher/FileRestitcher.cs) and [RestitchPackage.targets](pkg/common/RestitchPackage.targets) needs to be updated. Note that the size of each fragment is expressed in bytes, and that fragment start must be\n the sum of the size of all previous fragments. A '-1' should be used for the last fragment (and only for the last fragment): it means that the fragment size will be based on how much there is still left of the file.\n\n Each part, whether singular or fragmented, should have its own .nupkgproj file in its own folder under pkg. The folder and file should have the same name as the part. If you need to add new fragments, it is straightforward to just copy an existing fragment folder and rename it as well as the project file to the new fragment. If you must fragment a previously singular part, it is best to rename the existing folder and file to '-fragment1' and then copy a '-primary' folder and rename with the right part name. This is because the primary .nupkgproj files look different from others. Specifically, they include different build targets:\n\n```\n<Content Include=\"..\\common\\NormalPackage.props\" Pack=\"true\" PackagePath=\"buildTransitive\\netstandard2.0\\$(MSBuildProjectName).props\" />\n<Content Include=\"..\\common\\NormalPackage.targets\" Pack=\"true\" PackagePath=\"buildTransitive\\netstandard2.0\\$(MSBuildProjectName).targets\" />\n```\nvs.\n```\n<Content Include=\"..\\common\\RestitchPackage.props\" Pack=\"true\" PackagePath=\"buildTransitive\\netstandard2.0\\$(MSBuildProjectName).props\" />\n<Content Include=\"..\\common\\RestitchPackage.targets\" Pack=\"true\" PackagePath=\"buildTransitive\\netstandard2.0\\$(MSBuildProjectName).targets\" />\n```\n\n It is the 'RestitchPackage.targets' that will trigger restitching packages on first build after a download.\n\n Because file sizes change from release to release, it may be necessary to add or remove fragments. When you add a fragment, you also need to add a corresponding project folder under the `pkg/` top-level folder. The process of doing so is copy-paste-rename of existing folders. The same goes for adding parts (whether fragmented or not): you should add a corresponding folder and project file. If you remove a fragment (or part), you should remove the corresponding folder, or CI will end up building empty packages.\n\n Once you have carefully edited the parts and the files that go into them, clean the build directory and re-issue the libtorch downloads commands until there are no errors.\n\n4. Add the SHA files:\n\n git add src\\Redist\\libtorch-cpu\\*.sha\n git add src\\Redist\\libtorch-cuda-11.7\\*.sha\n\n After this you may as well submit to CI just to see what happens, though keep going with the other steps below as well.\n\n5. Build the native and managed code without CUDA\n\n dotnet build /p:SkipCuda=true\n\n The first stage unzips the archives, then CMAKE is run.\n\n Unzipping the archives may take quite a while\n\n Note that things may have changed in the LibTorch header files, linking flags etc. There is a CMakeLists.txt that acquires\n the cmake information delievered in the LibTorch download. It can be subtle.\n\n If the vxcproj for the native code gets configured by cmake then you should now be able to start developing the C++ code in Visual Studio. In order to get the correct environment variables and PATH, start VS from the command line, not from the Start menu:\n\n devenv TorchSharp.sln\n\n e.g. the vcxproj is created here:\n\n bin\\obj\\x64.Debug\\Native\\LibTorchSharp\\LibTorchSharp.vcxproj\n\n6. Similarly build the native code with CUDA\n\n dotnet build\n\n7. You must also adjust the set of binaries referenced for tests, see various files under `tests` and `NativeAssemblyReference` in `TorchSharp\\Directory.Build.targets`.\n\n8. Run tests\n\n dotnet test -c Debug\n dotnet test -c Release\n\n9. Try building packages locally. The build (including CI) doesn't build `libtorch-*` packages by default, just the managed package. To\n get CI to build new `libtorch-*` packages update this version and set `BuildLibTorchPackages` in [azure-pipelines.yml](azure-pipelines.yml):\n\n <LibTorchPackageVersion>2.0.1.1</LibTorchPackageVersion>\n\n dotnet pack -c Release -v:n /p:SkipNative=true /p:SkipTests=true /p:IncludeTorchSharpPackage=true /p:IncludeLibTorchCpuPackages=true /p:IncludeLibTorchCudaPackages=true \n dotnet pack -c Release -v:n /p:SkipNative=true /p:SkipTests=true /p:TargetOS=linux /p:IncludeTorchSharpPackage=true /p:IncludeLibTorchCpuPackages=true /p:IncludeLibTorchCudaPackages=true \n\n Once these finish, the output can be found in `bin\\packages\\Release`. Look at the file sizes -- if anything is larger than 250,000,000 bytes, you need to go back to #3 above and redefine the package contents and fragmentation scheme. It maybe necessary to introduce new fragments.\n\n\t**Note:** The locally built TorchSharp packages will only contain binaries for the local platform, so they cannot be used with other platforms. Therefore, only the packages built in Azure Pipelines can be used across platforms.\n\n10. Submit to CI and debug problems.\n\n11. Remember to delete all massive artifacts from Azure DevOps and reset this `BuildLibTorchPackages` in in [azure-pipelines.yml](azure-pipelines.yml)\n\n\n## Building with Visual Studio\n\nIn order for builds to work properly using Visual Studio 2019 or 2022, you must start VS from the 'x64 Native Tools Command Prompt for VS 2022' (or 2019) in order for the full environment to be set up correctly. Starting VS from the desktop or taskbar will not work properly.\n"},{"alpha_fraction":0.5828996300697327,"alphanum_fraction":0.5828996300697327,"avg_line_length":29.56818199157715,"blob_id":"2b087b3d91125673cf2ede27866c749d34cf855a","content_id":"75fbb7fe1670679337ea08e3334aa33fcc755db2","detected_licenses":["MIT"],"is_generated":false,"is_vendor":false,"language":"C#","length_bytes":1345,"license_type":"permissive","max_line_length":130,"num_lines":44,"path":"/src/TorchVision/ConvertImageDType.cs","repo_name":"NiklasGustafsson/TorchSharp","src_encoding":"UTF-8","text":"// Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information.\n\nusing System;\nusing static TorchSharp.torch;\n\n\nnamespace TorchSharp\n{\n public static partial class torchvision\n {\n internal class ConvertImageDType : ITransform\n {\n internal ConvertImageDType(ScalarType dtype)\n {\n this.dtype = dtype;\n }\n\n public Tensor call(Tensor image)\n {\n return transforms.functional.convert_image_dtype(image, dtype);\n }\n\n private ScalarType dtype;\n }\n\n public static partial class transforms\n {\n /// <summary>\n /// Convert a tensor image to the given dtype and scale the values accordingly\n /// </summary>\n /// <param name=\"dtype\">Desired data type of the output</param>\n static public ITransform ConvertImageDtype(ScalarType dtype)\n {\n return new ConvertImageDType(dtype);\n }\n\n [Obsolete(\"Deprecating misspelled transform. Use 'ConvertImageDtype' instead.\", false)]\n static public ITransform ConvertImageDType(ScalarType dtype)\n {\n return new ConvertImageDType(dtype);\n }\n }\n }\n}\n"},{"alpha_fraction":0.6029554605484009,"alphanum_fraction":0.6115756034851074,"avg_line_length":55.10545349121094,"blob_id":"855079652f749440f763aacecbbbe169c7858425","content_id":"ca0beb8da07f1d446c03c6ab29da504e8fb52924","detected_licenses":["MIT"],"is_generated":false,"is_vendor":false,"language":"C#","length_bytes":15429,"license_type":"permissive","max_line_length":254,"num_lines":275,"path":"/src/TorchSharp/Tensor/Factories/tensor_Complex.cs","repo_name":"NiklasGustafsson/TorchSharp","src_encoding":"UTF-8","text":"// Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information.\nusing System;\nusing System.Collections.Generic;\nusing System.Diagnostics.Contracts;\nusing System.Linq;\nusing static TorchSharp.PInvoke.NativeMethods;\n\n#nullable enable\nnamespace TorchSharp\n{\n public static partial class torch\n {\n /// <summary>\n /// Create a scalar tensor from a single value\n /// </summary>\n public static Tensor tensor(System.Numerics.Complex scalar, ScalarType? dtype = null, Device? device = null, bool requires_grad = false)\n {\n device = InitializeDevice(device);\n var handle = THSTensor_newComplexFloat64Scalar(scalar.Real, scalar.Imaginary, (int)device.type, device.index, requires_grad);\n if (handle == IntPtr.Zero) { CheckForErrors(); }\n var tensor = new Tensor(handle);\n tensor = dtype.HasValue ? tensor.to(dtype.Value, device) : tensor.to(device);\n return tensor;\n }\n\n /// <summary>\n /// Create a tensor from an array of values, shaping it based on the shape passed in.\n /// </summary>\n /// <remarks>The Torch runtime does not take ownership of the data, so there is no device argument.</remarks>\n [Pure]\n public static Tensor tensor(IList<(float Real, float Imaginary)> rawArray, ReadOnlySpan<long> dimensions, ScalarType? dtype = null, Device? device = null, bool requires_grad = false, string[]? names = null)\n {\n return tensor(rawArray.ToArray(), dimensions, dtype, device, requires_grad, names: names);\n }\n\n /// <summary>\n /// Create a tensor from an array of values, shaping it based on the shape passed in.\n /// </summary>\n [Pure]\n public static Tensor tensor((float Real, float Imaginary)[] rawArray, ScalarType? dtype = null, Device? device = null, bool requires_grad = false, string[]? names = null)\n {\n var dataArray = new float[rawArray.Length * 2];\n for (var i = 0; i < rawArray.Length; i++) {\n dataArray[i * 2] = rawArray[i].Real;\n dataArray[i * 2 + 1] = rawArray[i].Imaginary;\n }\n\n return _tensor_generic(dataArray, stackalloc long[] { rawArray.LongLength }, (sbyte)ScalarType.ComplexFloat32, dtype, device, requires_grad, names: names);\n }\n\n /// <summary>\n /// Create a tensor from an array of values, shaping it based on the shape passed in.\n /// </summary>\n [Pure]\n public static Tensor tensor((float Real, float Imaginary)[] rawArray, ReadOnlySpan<long> dimensions, ScalarType? dtype = null, Device? device = null, bool requires_grad = false, string[]? names = null)\n {\n var dataArray = new float[rawArray.Length * 2];\n for (var i = 0; i < rawArray.Length; i++) {\n dataArray[i * 2] = rawArray[i].Real;\n dataArray[i * 2 + 1] = rawArray[i].Imaginary;\n }\n\n return _tensor_generic(dataArray, dimensions, (sbyte)ScalarType.ComplexFloat32, dtype, device, requires_grad, names: names);\n }\n\n /// <summary>\n /// Create a 1-D tensor from an array of values, shaping it based on the input array.\n /// </summary>\n /// <remarks>The Torch runtime does not take ownership of the data, so there is no device argument.</remarks>\n [Pure]\n public static Tensor tensor(IList<(float Real, float Imaginary)> rawArray, ScalarType? dtype = null, Device? device = null, bool requires_grad = false, string[]? names = null)\n {\n return tensor(rawArray, stackalloc long[] { (long)rawArray.Count }, dtype, device, requires_grad, names: names);\n }\n\n /// <summary>\n /// Create a tensor from an array of values, organizing it as a two-dimensional tensor.\n /// </summary>\n /// <remarks>\n /// The Torch runtime does not take ownership of the data, so there is no device argument.\n /// The input array must have rows * columns elements.\n /// </remarks>\n [Pure]\n public static Tensor tensor(IList<(float Real, float Imaginary)> rawArray, long rows, long columns, ScalarType? dtype = null, Device? device = null, bool requires_grad = false, string[]? names = null)\n {\n return tensor(rawArray, stackalloc long[] { rows, columns }, dtype, device, requires_grad, names: names);\n }\n\n /// <summary>\n /// Create a tensor from an array of values, organizing it as a three-dimensional tensor.\n /// </summary>\n /// <remarks>\n /// The Torch runtime does not take ownership of the data, so there is no device argument.\n /// The input array must have dim0*dim1*dim2 elements.\n /// </remarks>\n [Pure]\n public static Tensor tensor(IList<(float Real, float Imaginary)> rawArray, long dim0, long dim1, long dim2, ScalarType? dtype = null, Device? device = null, bool requires_grad = false, string[]? names = null)\n {\n return tensor(rawArray, stackalloc long[] { dim0, dim1, dim2 }, dtype, device, requires_grad, names: names);\n }\n\n /// <summary>\n /// Create a tensor from an array of values, organizing it as a four-dimensional tensor.\n /// </summary>\n /// <remarks>\n /// The Torch runtime does not take ownership of the data, so there is no device argument.\n /// The input array must have dim0*dim1*dim2*dim3 elements.\n /// </remarks>\n [Pure]\n public static Tensor tensor(IList<(float Real, float Imaginary)> rawArray, long dim0, long dim1, long dim2, long dim3, ScalarType? dtype = null, Device? device = null, bool requires_grad = false, string[]? names = null)\n {\n return tensor(rawArray, stackalloc long[] { dim0, dim1, dim2, dim3 }, dtype, device, requires_grad, names: names);\n }\n\n /// <summary>\n /// Create a two-dimensional tensor from a two-dimensional array of values.\n /// </summary>\n [Pure]\n public static Tensor tensor((float Real, float Imaginary)[,] rawArray, ScalarType? dtype = null, Device? device = null, bool requires_grad = false, string[]? names = null)\n {\n var dataArray = new float[rawArray.GetLongLength(0), rawArray.GetLongLength(1) * 2];\n for (var i = 0; i < rawArray.GetLongLength(0); i++) {\n for (var j = 0; j < rawArray.GetLongLength(1); j++) {\n dataArray[i, j * 2] = rawArray[i, j].Real;\n dataArray[i, j * 2 + 1] = rawArray[i, j].Imaginary;\n }\n }\n\n return _tensor_generic(dataArray, stackalloc long[] { rawArray.GetLongLength(0), rawArray.GetLongLength(1) }, (sbyte)ScalarType.ComplexFloat32, dtype, device, requires_grad, names: names);\n }\n\n /// <summary>\n /// Create a three-dimensional tensor from a three-dimensional array of values.\n /// </summary>\n [Pure]\n public static Tensor tensor((float Real, float Imaginary)[,,] rawArray, ScalarType? dtype = null, Device? device = null, bool requires_grad = false, string[]? names = null)\n {\n var dataArray = new float[rawArray.GetLongLength(0), rawArray.GetLongLength(1), rawArray.GetLongLength(2) * 2];\n for (var i = 0; i < rawArray.GetLongLength(0); i++) {\n for (var j = 0; j < rawArray.GetLongLength(1); j++) {\n for (var k = 0; k < rawArray.GetLongLength(2); k++) {\n dataArray[i, j, k * 2] = rawArray[i, j, k].Real;\n dataArray[i, j, k * 2 + 1] = rawArray[i, j, k].Imaginary;\n }\n }\n }\n\n return _tensor_generic(dataArray, stackalloc long[] { rawArray.GetLongLength(0), rawArray.GetLongLength(1), rawArray.GetLongLength(2) }, (sbyte)ScalarType.ComplexFloat32, dtype, device, requires_grad, names: names);\n }\n\n /// <summary>\n /// Create a four-dimensional tensor from a four-dimensional array of values.\n /// </summary>\n [Pure]\n public static Tensor tensor((float Real, float Imaginary)[,,,] rawArray, ScalarType? dtype = null, Device? device = null, bool requires_grad = false, string[]? names = null)\n {\n var dataArray = new float[rawArray.GetLongLength(0), rawArray.GetLongLength(1), rawArray.GetLongLength(2), rawArray.GetLongLength(3) * 2];\n for (var i = 0; i < rawArray.GetLongLength(0); i++) {\n for (var j = 0; j < rawArray.GetLongLength(1); j++) {\n for (var k = 0; k < rawArray.GetLongLength(2); k++) {\n for (var l = 0; l < rawArray.GetLongLength(3); l++) {\n dataArray[i, j, k, l * 2] = rawArray[i, j, k, l].Real;\n dataArray[i, j, k, l * 2 + 1] = rawArray[i, j, k, l].Imaginary;\n }\n }\n }\n }\n\n return _tensor_generic(dataArray, stackalloc long[] { rawArray.GetLongLength(0), rawArray.GetLongLength(1), rawArray.GetLongLength(2), rawArray.GetLongLength(3) }, (sbyte)ScalarType.ComplexFloat32, dtype, device, requires_grad, names: names);\n }\n\n /// <summary>\n /// Create a tensor from an array of values, shaping it based on the shape passed in.\n /// </summary>\n /// <remarks>The Torch runtime does not take ownership of the data, so there is no device argument.</remarks>\n [Pure]\n public static Tensor tensor(IList<System.Numerics.Complex> rawArray, ReadOnlySpan<long> dimensions, ScalarType? dtype = null, Device? device = null, bool requires_grad = false, string[]? names = null)\n {\n return tensor(rawArray.ToArray(), dimensions, dtype, device, requires_grad, names: names);\n }\n\n /// <summary>\n /// Create a tensor from an array of values, shaping it based on the shape passed in.\n /// </summary>\n [Pure]\n public static Tensor tensor(System.Numerics.Complex[] rawArray, ScalarType? dtype = null, Device? device = null, bool requires_grad = false, string[]? names = null)\n {\n return _tensor_generic(rawArray, stackalloc long[] { rawArray.LongLength }, (sbyte)ScalarType.ComplexFloat64, dtype, device, requires_grad, names: names);\n }\n\n /// <summary>\n /// Create a tensor from an array of values, shaping it based on the shape passed in.\n /// </summary>\n [Pure]\n public static Tensor tensor(System.Numerics.Complex[] rawArray, ReadOnlySpan<long> dimensions, ScalarType? dtype = null, Device? device = null, bool requires_grad = false, string[]? names = null)\n {\n return _tensor_generic(rawArray, dimensions, (sbyte)ScalarType.ComplexFloat64, dtype, device, requires_grad, names: names);\n }\n\n /// <summary>\n /// Create a 1-D tensor from an array of values, shaping it based on the input array.\n /// </summary>\n /// <remarks>The Torch runtime does not take ownership of the data, so there is no device argument.</remarks>\n [Pure]\n public static Tensor tensor(IList<System.Numerics.Complex> rawArray, ScalarType? dtype = null, Device? device = null, bool requires_grad = false, string[]? names = null)\n {\n return tensor(rawArray, stackalloc long[] { (long)rawArray.Count }, dtype, device, requires_grad, names: names);\n }\n\n /// <summary>\n /// Create a tensor from an array of values, organizing it as a two-dimensional tensor.\n /// </summary>\n /// <remarks>\n /// The Torch runtime does not take ownership of the data, so there is no device argument.\n /// The input array must have rows * columns elements.\n /// </remarks>\n [Pure]\n public static Tensor tensor(IList<System.Numerics.Complex> rawArray, long rows, long columns, ScalarType? dtype = null, Device? device = null, bool requires_grad = false, string[]? names = null)\n {\n return tensor(rawArray, stackalloc long[] { rows, columns }, dtype, device, requires_grad, names: names);\n }\n\n /// <summary>\n /// Create a tensor from an array of values, organizing it as a three-dimensional tensor.\n /// </summary>\n /// <remarks>\n /// The Torch runtime does not take ownership of the data, so there is no device argument.\n /// The input array must have dim0*dim1*dim2 elements.\n /// </remarks>\n [Pure]\n public static Tensor tensor(IList<System.Numerics.Complex> rawArray, long dim0, long dim1, long dim2, ScalarType? dtype = null, Device? device = null, bool requires_grad = false, string[]? names = null)\n {\n return tensor(rawArray, stackalloc long[] { dim0, dim1, dim2 }, dtype, device, requires_grad, names: names);\n }\n\n /// <summary>\n /// Create a tensor from an array of values, organizing it as a four-dimensional tensor.\n /// </summary>\n /// <remarks>\n /// The Torch runtime does not take ownership of the data, so there is no device argument.\n /// The input array must have dim0*dim1*dim2*dim3 elements.\n /// </remarks>\n public static Tensor tensor(IList<System.Numerics.Complex> rawArray, long dim0, long dim1, long dim2, long dim3, ScalarType? dtype = null, Device? device = null, bool requires_grad = false, string[]? names = null)\n {\n return tensor(rawArray, stackalloc long[] { dim0, dim1, dim2, dim3 }, dtype, device, requires_grad, names: names);\n }\n\n /// <summary>\n /// Create a two-dimensional tensor from a two-dimensional array of values.\n /// </summary>\n [Pure]\n public static Tensor tensor(System.Numerics.Complex[,] rawArray, ScalarType? dtype = null, Device? device = null, bool requires_grad = false, string[]? names = null)\n {\n return _tensor_generic(rawArray, stackalloc long[] { rawArray.GetLongLength(0), rawArray.GetLongLength(1) }, (sbyte)ScalarType.ComplexFloat64, dtype, device, requires_grad, names: names);\n }\n\n /// <summary>\n /// Create a three-dimensional tensor from a three-dimensional array of values.\n /// </summary>\n [Pure]\n public static Tensor tensor(System.Numerics.Complex[,,] rawArray, ScalarType? dtype = null, Device? device = null, bool requires_grad = false, string[]? names = null)\n {\n return _tensor_generic(rawArray, stackalloc long[] { rawArray.GetLongLength(0), rawArray.GetLongLength(1), rawArray.GetLongLength(2) }, (sbyte)ScalarType.ComplexFloat64, dtype, device, requires_grad, names: names);\n }\n\n /// <summary>\n /// Create a four-dimensional tensor from a four-dimensional array of values.\n /// </summary>\n [Pure]\n public static Tensor tensor(System.Numerics.Complex[,,,] rawArray, ScalarType? dtype = null, Device? device = null, bool requires_grad = false, string[]? names = null)\n {\n return _tensor_generic(rawArray, stackalloc long[] { rawArray.GetLongLength(0), rawArray.GetLongLength(1), rawArray.GetLongLength(2), rawArray.GetLongLength(3) }, (sbyte)ScalarType.ComplexFloat64, dtype, device, requires_grad, names: names);\n }\n }\n}\n"},{"alpha_fraction":0.5445655584335327,"alphanum_fraction":0.5601370334625244,"avg_line_length":27.168420791625977,"blob_id":"9d1758d9c9b3184f3ed8c0563d3114210bed1353","content_id":"f499fcb94f1eba7639ccd7d62baf9a6f9c67faf0","detected_licenses":["MIT"],"is_generated":false,"is_vendor":false,"language":"C++","length_bytes":16055,"license_type":"permissive","max_line_length":228,"num_lines":570,"path":"/src/Native/LibTorchSharp/THSJIT.cpp","repo_name":"NiklasGustafsson/TorchSharp","src_encoding":"UTF-8","text":"//// Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information.\n#include \"THSJIT.h\"\n\nJITModule THSJIT_load(const char* filename, int64_t device, int64_t index)\n{\n c10::DeviceType dev = c10::kCPU;\n if (device == 1)\n dev = c10::kCUDA;\n\n CATCH(\n auto res = torch::jit::load(filename, torch::Device(dev, index));\n auto copy = new torch::jit::Module(res);\n return new std::shared_ptr<torch::jit::Module>(copy);\n );\n\n return nullptr;\n}\n\nJITCompilationUnit THSJIT_compile(const char* script)\n{\n CATCH(\n auto res = torch::jit::compile(script);\n return new std::shared_ptr<torch::jit::CompilationUnit>(res);\n );\n\n return nullptr;\n}\n\nvoid THSJIT_save(JITModule module, const char* filename)\n{\n CATCH(\n (*module)->save(filename);\n );\n}\n\nint THSJIT_Module_is_training(JITModule module)\n{\n return (*module)->is_training();\n}\n\nvoid THSJIT_Module_train(JITModule module, bool on)\n{\n (*module)->train(on);\n}\n\nvoid THSJIT_Module_eval(JITModule module)\n{\n (*module)->eval();\n}\n\nvoid THSJIT_Module_to_device_dtype(JITModule module, int8_t dtype, int64_t device, int64_t index)\n{\n c10::DeviceType dev = c10::kCPU;\n if (device == 1)\n dev = c10::kCUDA;\n (*module)->to(torch::Device(dev, index), (at::ScalarType)dtype);\n}\n\nvoid THSJIT_Module_to_device(JITModule module, int64_t device, int64_t index)\n{\n c10::DeviceType dev = c10::kCPU;\n if (device == 1)\n dev = c10::kCUDA;\n (*module)->to(torch::Device(dev, index));\n}\n\nvoid THSJIT_Module_to_dtype(JITModule module, int8_t dtype)\n{\n (*module)->to((at::ScalarType)dtype);\n}\n\nvoid THSJIT_Module_modules(const JITModule module, JITModule* (*allocator)(size_t length))\n{\n auto modules = (*module)->modules();\n JITModule* result = allocator(modules.size());\n int i = 0;\n for (const auto& child : modules) {\n auto copy = new torch::jit::Module(child);\n result[i++] = new std::shared_ptr<torch::jit::Module>(copy);\n }\n}\n\nvoid THSJIT_Module_named_modules(const JITModule module,\n JITModule* (*allocator)(size_t length),\n const char** (*allocator2)(size_t length))\n{\n auto modules = (*module)->named_modules();\n JITModule* result = allocator(modules.size());\n const char** names = allocator2(modules.size());\n int i = 0;\n for (const auto& child : modules) {\n auto copy = new torch::jit::Module(child.value);\n result[i] = new std::shared_ptr<torch::jit::Module>(copy);\n names[i] = make_sharable_string(child.name);\n i++;\n }\n}\n\nvoid THSJIT_Module_named_children(const JITModule module,\n JITModule* (*allocator)(size_t length),\n const char** (*allocator2)(size_t length))\n{\n auto modules = (*module)->named_children();\n JITModule* result = allocator(modules.size());\n const char** names = allocator2(modules.size());\n int i = 0;\n for (const auto& child : modules) {\n auto copy = new torch::jit::Module(child.value);\n result[i] = new std::shared_ptr<torch::jit::Module>(copy);\n names[i] = make_sharable_string(child.name);\n i++;\n }\n}\n\nvoid THSJIT_Module_parameters(const JITModule module, Tensor* (*allocator)(size_t length))\n{\n auto parameters = (*module)->parameters();\n Tensor* result = allocator(parameters.size());\n int i = 0;\n for (const auto& child : parameters) {\n result[i++] = new torch::Tensor(child);\n }\n}\n\nvoid THSJIT_Module_named_parameters(const JITModule module,\n Tensor* (*allocator)(size_t length),\n const char** (*allocator2)(size_t length))\n{\n auto parameters = (*module)->named_parameters();\n Tensor* result = allocator(parameters.size());\n const char** names = allocator2(parameters.size());\n int i = 0;\n for (const auto& child : parameters) {\n result[i] = new torch::Tensor(child.value);\n names[i] = make_sharable_string(child.name);\n i++;\n }\n}\n\nvoid THSJIT_Module_named_buffers(const JITModule module,\n Tensor* (*allocator)(size_t length),\n const char** (*allocator2)(size_t length))\n{\n auto parameters = (*module)->named_buffers();\n Tensor* result = allocator(parameters.size());\n const char** names = allocator2(parameters.size());\n int i = 0;\n for (const auto& child : parameters) {\n result[i] = new torch::Tensor(child.value);\n names[i] = make_sharable_string(child.name);\n i++;\n }\n}\n\nJITMethod THSJIT_Module_get_method(const JITModule module, const char* name)\n{\n auto method = (*module)->get_method(name);\n auto copy = new torch::jit::Method(method);\n return new std::shared_ptr<torch::jit::Method>(copy);\n}\n\nTensorOrScalar* ReturnHelper(c10::IValue result, TensorOrScalar* (*allocator)(int32_t idx, size_t length), int8_t* typeCode, int32_t* idx)\n{\n // TypeCode:\n //\n // 0 -- Not supported\n // 1 -- Single tensor\n // 2 -- Tuple of tensors\n // 3 -- List of tensors\n // 4 -- Single scalar\n // 5 -- Scalar tuple\n // 6 -- List of scalars\n // 7 -- List of scalars and tensors\n // 8 -- None / null\n // 9 -- List of anything\n // 10 -- Tuple of anything\n\n if (result.isNone())\n {\n TensorOrScalar* output = allocator(idx[0]++, 1);\n output[0] = { 8, -1, (ptrdiff_t)0 };\n *typeCode = 8;\n return output;\n }\n\n if (result.isScalar())\n {\n TensorOrScalar* output = allocator(idx[0]++, 1);\n output[0] = { 0, -1, (ptrdiff_t)new torch::Scalar(result.toScalar()) };\n *typeCode = 4;\n return output;\n }\n\n if (result.isTensor()) {\n TensorOrScalar* output = allocator(idx[0]++, 1);\n output[0] = { 0, -1, (ptrdiff_t)ResultTensor(result.toTensor()) };\n *typeCode = 1;\n return output;\n }\n\n if (result.isTensorList()) {\n auto list = result.toTensorList();\n *typeCode = 3;\n TensorOrScalar* output = allocator(idx[0]++, list.size());\n for (size_t i = 0; i < list.size(); i++)\n output[i] = { 0, -1, (ptrdiff_t)ResultTensor(list[i]) };\n return output;\n }\n\n if (result.isList())\n {\n int foundTensor = 0;\n int foundScalar = 0;\n int foundNull = 0;\n int foundListOrTuple = 0;\n\n auto list = result.toList();\n TensorOrScalar* output = allocator(idx[0]++, list.size());\n\n for (int i = 0; i < list.size(); ++i)\n {\n output[i].Handle = -1;\n c10::IValue value = list[i];\n\n if (value.isTensor())\n {\n output[i] = { 0, -1, (ptrdiff_t)ResultTensor(value.toTensor()) };\n foundTensor += 1;\n continue;\n }\n if (value.isScalar())\n {\n output[i] = { 4, -1, (ptrdiff_t)new torch::Scalar(value.toScalar()) };\n foundScalar += 1;\n continue;\n }\n if (value.isNone())\n {\n output[i] = { 8, -1, (ptrdiff_t)0 };\n foundNull += 1;\n continue;\n }\n else {\n int8_t nestedTC = 0;\n int64_t arrIdx = idx[0];\n auto nested = ReturnHelper(value, allocator, &nestedTC, idx);\n foundListOrTuple += 1;\n output[i] = { nestedTC, arrIdx, (ptrdiff_t)nested };\n }\n }\n\n if (foundListOrTuple > 0) {\n *typeCode = 9;\n }\n else {\n *typeCode = 7;\n if (foundScalar == 0 && foundNull == 0)\n *typeCode = 3;\n if (foundTensor == 0 && foundNull == 0)\n *typeCode = 6;\n }\n return output;\n }\n\n if (result.isTuple()) {\n int foundTensor = 0;\n int foundScalar = 0;\n int foundNull = 0;\n int foundListOrTuple = 0;\n\n auto& list = result.toTuple()->elements();\n TensorOrScalar* output = allocator(idx[0]++, list.size());\n\n for (int i = 0; i < list.size(); ++i)\n {\n output[i].Handle = -1;\n c10::IValue value = list[i];\n\n if (value.isTensor())\n {\n output[i] = { 0, -1, (ptrdiff_t)ResultTensor(value.toTensor()) };\n foundTensor += 1;\n continue;\n }\n if (value.isScalar())\n {\n output[i] = { 4, -1, (ptrdiff_t)new torch::Scalar(value.toScalar()) };\n foundScalar += 1;\n continue;\n }\n if (value.isNone())\n {\n output[i] = { 8, -1, (ptrdiff_t)0 };\n foundNull += 1;\n continue;\n }\n else {\n int8_t nestedTC = 0;\n int64_t arrIdx = idx[0];\n auto nested = ReturnHelper(value, allocator, &nestedTC, idx);\n foundListOrTuple += 1;\n output[i] = { nestedTC, arrIdx, (ptrdiff_t)nested };\n }\n }\n\n *typeCode = 10;\n if (foundListOrTuple == 0) {\n if (foundScalar == 0 && foundNull == 0)\n *typeCode = 2;\n if (foundTensor == 0 && foundNull == 0)\n *typeCode = 5;\n }\n\n return output;\n }\n\n *typeCode = 0;\n return nullptr;\n}\n\nc10::impl::GenericList toScalarValueList(const TensorOrScalar* tensorPtrs, const int length)\n{\n auto list = c10::impl::GenericList(c10::ScalarTypeType::get());\n\n if (tensorPtrs != nullptr) {\n for (int i = 0; i < length; i++)\n {\n switch (tensorPtrs[i].TypeCode) {\n case 1:\n list.push_back(*(torch::Scalar*)(tensorPtrs[i].Handle));\n break;\n }\n }\n }\n\n return list;\n}\n\nc10::impl::GenericList toTensorValueList(const TensorOrScalar* tensorPtrs, const int length)\n{\n auto list = c10::impl::GenericList(c10::TensorType::get());\n\n if (tensorPtrs != nullptr) {\n for (int i = 0; i < length; i++)\n {\n switch (tensorPtrs[i].TypeCode) {\n case 0:\n list.push_back(*(torch::Tensor*)(tensorPtrs[i].Handle));\n break;\n }\n }\n }\n\n return list;\n}\n\nstd::vector<c10::IValue> toIValue(const TensorOrScalar* tensorPtrs, const int length)\n{\n // TypeCode:\n //\n // 0 -- Single tensor\n // 1 -- Single scalar\n // 2 -- Boolean\n // 3 -- Int32\n // 5 -- List of tensors\n // 6 -- List of scalars\n // 8 -- None / null\n\n std::vector<c10::IValue> tensors;\n\n if (tensorPtrs != nullptr) {\n for (int i = 0; i < length; i++)\n {\n switch (tensorPtrs[i].TypeCode) {\n case 0:\n tensors.push_back(*(torch::Tensor*)(tensorPtrs[i].Handle));\n break;\n case 1:\n tensors.push_back(*(torch::Scalar*)(tensorPtrs[i].Handle));\n break;\n case 2:\n tensors.push_back(tensorPtrs[i].Handle != 0);\n break;\n case 3:\n tensors.push_back((int)tensorPtrs[i].Handle);\n break;\n case 5:\n {\n auto ts = toTensorValueList(reinterpret_cast<const TensorOrScalar*>(tensorPtrs[i].Handle), (int)tensorPtrs[i].ArrayIndex);\n tensors.push_back(ts);\n break;\n }\n case 6:\n {\n auto ts = toScalarValueList(reinterpret_cast<const TensorOrScalar*>(tensorPtrs[i].Handle), (int)tensorPtrs[i].ArrayIndex);\n tensors.push_back(ts);\n break;\n }\n //case 4:\n // tensors.push_back(c10::IValue(tensorPtrs[i].Handle)); // Clang on MacOS doesn't like. Pass as Scalar from .NET.\n // break;\n case 8:\n tensors.push_back(c10::nullopt);\n break;\n }\n }\n }\n return tensors;\n}\n\nvoid THSJIT_Module_forward(const JITModule module, const TensorOrScalar* tensorPtrs, const int length, TensorOrScalar* (*allocator)(int32_t idx, size_t length), int8_t* typeCode, int32_t idx)\n{\n *typeCode = 0;\n\n CATCH(\n auto result = (*module)->forward(toIValue(tensorPtrs, length));\n ReturnHelper(result, allocator, typeCode, &idx);\n )\n}\n\nvoid THSJIT_Module_invoke(const JITModule module, const char* name, const TensorOrScalar* tensorPtrs, const int length, TensorOrScalar* (*allocator)(int32_t idx, size_t length), int8_t* typeCode, int32_t idx)\n{\n *typeCode = 0;\n\n CATCH(\n auto method = (*module)->get_method(name);\n auto result = method(toIValue(tensorPtrs, length));\n ReturnHelper(result, allocator, typeCode, &idx);\n )\n}\n\nvoid THSJIT_CompilationUnit_Invoke(const JITCompilationUnit module, const char* method, const TensorOrScalar* tensorPtrs, const int length, TensorOrScalar* (*allocator)(int32_t idx, size_t length), int8_t* typeCode, int32_t idx)\n{\n *typeCode = 0;\n\n CATCH(\n auto args = toIValue(tensorPtrs, length);\n auto func = (*module)->find_function(method);\n auto result = (*func)(args);\n ReturnHelper(result, allocator, typeCode, &idx);\n )\n}\n\nvoid THSJIT_Module_dispose(const JITModule module)\n{\n delete module;\n}\n\nconst char* THSJIT_Method_name(const JITMethod method)\n{\n return make_sharable_string((*method)->name());\n}\n\nint THSJIT_Method_num_inputs(const JITMethod method)\n{\n return (int)(*method)->num_inputs();\n}\n\nint THSJIT_Module_num_inputs(const JITModule module)\n{\n return (int)(*module)->get_method(\"forward\").num_inputs() - 1; // Don't count the 'self' argument.\n}\n\nint THSJIT_Module_num_outputs(const JITModule module)\n{\n return (int)(*module)->get_method(\"forward\").function().getSchema().returns().size();\n}\n\nJITFunction THSJIT_Method_function(const JITMethod method)\n{\n return new std::shared_ptr<torch::jit::Function>(&(*method)->function());\n}\n\nvoid THSJIT_Method_dispose(const JITMethod method)\n{\n delete method;\n}\n\n\n//-------------------------------------------------------------------------------------\n// JITFunction\n\nint THSJIT_Function_num_inputs(const JITFunction function)\n{\n return (int)(*function)->num_inputs();\n}\n\n// TODO other function operations\n\nvoid THSJIT_Function_dispose(const JITFunction function)\n{\n delete function;\n}\n\nvoid THSJIT_Type_dispose(const JITType type)\n{\n delete type;\n}\n\nvoid THSJIT_TensorType_dispose(const JITTensorType type)\n{\n delete type;\n}\n\nvoid THSJIT_CompilationUnit_dispose(const JITCompilationUnit module)\n{\n delete module;\n}\n\nvoid* THSJIT_Type_cast(const JITType type)\n{\n switch ((*type)->kind())\n {\n case c10::TypeKind::TensorType:\n return new std::shared_ptr<torch::jit::TensorType>((*type)->cast<c10::TensorType>());\n default:\n return nullptr;\n }\n}\n\nint8_t THSJIT_TensorType_dtype(const JITTensorType type)\n{\n auto scT = (*type)->scalarType();\n if (scT.has_value()) {\n return (int8_t)scT.value();\n }\n else {\n return -1;\n }\n}\n\nvoid THSJIT_TensorType_sizes(const JITTensorType type, int64_t* (*allocator)(int64_t length))\n{\n //CATCH(\n auto& t = *type;\n auto dim = t->dim();\n auto res = (*type)->sizes().concrete_sizes();\n if (res.has_value()) {\n const size_t sz = res.value().size();\n auto& vec = res.value();\n int64_t* result = allocator(sz);\n for (size_t i = 0; i < sz; i++)\n result[i] = vec[i];\n }\n //);\n}\n\nint8_t THSJIT_Type_kind(const JITType type)\n{\n switch ((*type)->kind())\n {\n case c10::TypeKind::TensorType:\n return (int8_t)TypeKind::TensorType;\n default:\n return -1;\n }\n}\n\nJITType THSJIT_Module_getInputType(JITModule module, int8_t index)\n{\n auto typ = (*module)->type();\n c10::TypeKind kind = typ->kind();\n auto& schema = typ->getMethod(\"forward\").getSchema();\n return new std::shared_ptr<c10::Type>(schema.arguments()[1 + index].type()->cast<c10::TensorType>());\n}\n\nvoid THSJIT_typeDispose(const JITType type)\n{\n delete type;\n}"},{"alpha_fraction":0.5610817670822144,"alphanum_fraction":0.5654336214065552,"avg_line_length":41.893333435058594,"blob_id":"294f19d6ce8e73ac9d7f3f89c6c3b83343b0db61","content_id":"269d5aeec9b37db8c643bdd8a0eb80e90278a1d7","detected_licenses":["MIT"],"is_generated":false,"is_vendor":false,"language":"C#","length_bytes":3217,"license_type":"permissive","max_line_length":130,"num_lines":75,"path":"/src/TorchSharp/NN/Pooling/AdaptiveMaxPool1D.cs","repo_name":"NiklasGustafsson/TorchSharp","src_encoding":"UTF-8","text":"// Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information.\nusing System;\nusing static TorchSharp.torch;\nusing static TorchSharp.PInvoke.NativeMethods;\n\nnamespace TorchSharp\n{\n using Modules;\n\n namespace Modules\n {\n /// <summary>\n /// This class is used to represent a AdaptiveMaxPool1D module.\n /// </summary>\n public sealed class AdaptiveMaxPool1d : torch.nn.Module<Tensor, Tensor>\n {\n internal AdaptiveMaxPool1d(IntPtr handle, IntPtr boxedHandle) : base(handle, boxedHandle)\n {\n }\n\n public override Tensor forward(Tensor tensor)\n {\n var res = THSNN_AdaptiveMaxPool1d_forward(handle.DangerousGetHandle(), tensor.Handle);\n if (res == IntPtr.Zero) { torch.CheckForErrors(); }\n return new Tensor(res);\n }\n\n // Rather than spending cycles only to discover that this module has neither\n // parameters nor buffers, just shortcut the move completely.\n protected internal override nn.Module _to(Device device, ScalarType dtype) => this;\n protected internal override nn.Module _to(DeviceType deviceType, int deviceIndex = -1) => this;\n protected internal override nn.Module _to(ScalarType dtype) => this;\n }\n }\n\n public static partial class torch\n {\n public static partial class nn\n {\n /// <summary>\n /// Applies a 1D adaptive max pooling over an input signal composed of several input planes.\n /// The output size is H, for any input size.The number of output features is equal to the number of input planes.\n /// </summary>\n /// <param name=\"outputSize\">The target output size H.</param>\n /// <returns></returns>\n public static AdaptiveMaxPool1d AdaptiveMaxPool1d(long outputSize)\n {\n unsafe {\n fixed (long* pkernelSize = new long[] { outputSize }) {\n var handle = THSNN_AdaptiveMaxPool1d_ctor((IntPtr)pkernelSize, 1, out var boxedHandle);\n if (handle == IntPtr.Zero) { torch.CheckForErrors(); }\n return new AdaptiveMaxPool1d(handle, boxedHandle);\n }\n }\n }\n\n public static partial class functional\n {\n /// <summary>\n /// Applies a 1D adaptive max pooling over an input signal composed of several input planes.\n /// The output size is H, for any input size.The number of output features is equal to the number of input planes.\n /// </summary>\n /// <param name=\"x\"></param>\n /// <param name=\"outputSize\">The target output size H.</param>\n /// <returns></returns>\n public static Tensor adaptive_max_pool1d(Tensor x, long outputSize)\n {\n using (var d = nn.AdaptiveMaxPool1d(outputSize)) {\n return d.call(x);\n }\n }\n }\n }\n }\n}\n"},{"alpha_fraction":0.5511892437934875,"alphanum_fraction":0.5511892437934875,"avg_line_length":27.47058868408203,"blob_id":"6f27f657ce50c7b10abdd1abf3c693a2ae03117e","content_id":"65cafa76d81cc8e1f60dd503b263689ad61b7156","detected_licenses":["MIT"],"is_generated":false,"is_vendor":false,"language":"C#","length_bytes":967,"license_type":"permissive","max_line_length":130,"num_lines":34,"path":"/src/TorchVision/Equalize.cs","repo_name":"NiklasGustafsson/TorchSharp","src_encoding":"UTF-8","text":"// Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information.\n\nusing static TorchSharp.torch;\n\nnamespace TorchSharp\n{\n public static partial class torchvision\n {\n internal class Equalize : ITransform\n {\n internal Equalize()\n {\n }\n\n public Tensor call(Tensor input)\n {\n return transforms.functional.equalize(input);\n }\n }\n\n public static partial class transforms\n {\n /// <summary>\n /// Equalize the histogram of an image by applying a non-linear mapping to the input\n /// in order to create a uniform distribution of grayscale values in the output.\n /// </summary>\n /// <returns></returns>\n static public ITransform Equalize()\n {\n return new Equalize();\n }\n }\n }\n}"},{"alpha_fraction":0.5818631052970886,"alphanum_fraction":0.5931545495986938,"avg_line_length":36.78666687011719,"blob_id":"1e4ceedc0f7af79c81dfd748726227cbb74e59fa","content_id":"1b7c04be6aee2408f8f9acddbd8b1e854c004237","detected_licenses":["MIT"],"is_generated":false,"is_vendor":false,"language":"C#","length_bytes":2834,"license_type":"permissive","max_line_length":147,"num_lines":75,"path":"/src/TorchAudio/Transforms/Resample.cs","repo_name":"NiklasGustafsson/TorchSharp","src_encoding":"UTF-8","text":"// Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information.\n\nusing static TorchSharp.torch;\nusing static TorchSharp.torchaudio;\n\n// A number of implementation details in this file have been translated from the Python version of torchaudio,\n// largely located in the files found in this folder:\n//\n// https://github.com/pytorch/audio/blob/bb77cbebb620a46fdc0dc7e6dae2253eef3f37e2/torchaudio/transforms/_transforms.py\n//\n// The origin has the following copyright notice and license:\n//\n// https://github.com/pytorch/audio/blob/main/LICENSE\n//\n\nnamespace TorchSharp.Transforms\n{\n public sealed class Resample : nn.Module<Tensor, Tensor>, ITransform\n {\n private readonly int orig_freq;\n private readonly int new_freq;\n private readonly int gcd;\n private readonly ResamplingMethod resampling_method;\n private readonly int lowpass_filter_width;\n private readonly double rolloff;\n private readonly double? beta;\n public readonly torch.Tensor kernel;\n private readonly int width;\n\n internal Resample(\n string name,\n int orig_freq = 16000,\n int new_freq = 16000,\n ResamplingMethod resampling_method = ResamplingMethod.sinc_interpolation,\n int lowpass_filter_width = 6,\n double rolloff = 0.99,\n double? beta = null,\n torch.Device device = null,\n torch.ScalarType? dtype = null) : base(name)\n {\n this.orig_freq = orig_freq;\n this.new_freq = new_freq;\n this.gcd = functional.Gcd(this.orig_freq, this.new_freq);\n this.resampling_method = resampling_method;\n this.lowpass_filter_width = lowpass_filter_width;\n this.rolloff = rolloff;\n this.beta = beta;\n\n if (this.orig_freq != this.new_freq) {\n (this.kernel, this.width) = functional._get_sinc_resample_kernel(\n this.orig_freq,\n this.new_freq,\n this.gcd,\n this.lowpass_filter_width,\n this.rolloff,\n this.resampling_method,\n beta,\n device: device,\n dtype: dtype);\n }\n }\n\n public override Tensor forward(Tensor waveform)\n {\n using (var d = torch.NewDisposeScope()) {\n\n if (this.orig_freq == this.new_freq) {\n return d.MoveToOuter(waveform.alias());\n }\n var resampled = functional._apply_sinc_resample_kernel(waveform, this.orig_freq, this.new_freq, this.gcd, this.kernel, this.width);\n return d.MoveToOuter(resampled);\n }\n }\n }\n}\n"},{"alpha_fraction":0.7683320641517639,"alphanum_fraction":0.7683320641517639,"avg_line_length":57.30434799194336,"blob_id":"a9364ee07f8e08f08ac001c315ec37eb79442958","content_id":"1449475a37a38f419d645e9e92b9f2ac7cbf81f7","detected_licenses":["MIT"],"is_generated":false,"is_vendor":false,"language":"Markdown","length_bytes":4023,"license_type":"permissive","max_line_length":554,"num_lines":69,"path":"/docfx/articles/saveload.md","repo_name":"NiklasGustafsson/TorchSharp","src_encoding":"UTF-8","text":"# Saving and Restoring Model Weights and Buffers\n\nThere are typically two kinds of state in a model -- parameters, which contain trained weights, and buffers, which contain data that is not trained, but still essential for the functioning of the model. Both should generally be saved and loaded when serializing models.\n\nWhen using PyTorch, the expected pattern to use when saving and later restoring models from disk or other permanent storage media, is to get the model's state and pickle that using the standard Python format, which is what torch.save() does.\n\n```Python\ntorch.save(model.state_dict(), 'model_weights.pth')\n```\n\nWhen restoring the model, you are expected to first create a model of the exact same structure as the original, with random weights, then restore the state from a unpickled object:\n\n```Python\nmodel = [...]\nmodel.load_state_dict(torch.load('model_weights.pth'))\n```\n\nThis presents a couple of problems for a .NET implementation. \n\nPython pickling is intimately coupled to Python and its runtime object model. It is a complex format that supports object graphs forming DAGs, faithfully maintaining all object state in the way necessary to restore the Python object later.\n\nIn order to share models between .NET applications, Python pickling is not at all necessary, and even for moving model state from Python to .NET, it is overkill. The state of a model is a simple dictionary where the keys are strings and the values are tensors.\n\nTherefore, TorchSharp, in its current form, implements its own very simple model serialization format, which allows models originating in either .NET or Python to be loaded using .NET, as long as the model was saved using the special format.\n\nThe MNIST and AdversarialExampleGeneration examples in this repo rely on saving and restoring model state -- the latter example relies on a pre-trained model from MNST.\n\n><br/>A future version of TorchSharp may include support for reading and writing Python pickle files directly.<br/><br/>\n\n## How to use the TorchSharp format\n\nIn C#, saving a model looks like this:\n\n```C#\nmodel.save(\"model_weights.dat\");\n```\n\nAnd loading it again is done by:\n\n```C#\nmodel = [...];\nmodel.load(\"model_weights.dat\");\n```\n\nFor efficient memory management, the model should be created on the CPU before loading weights, then moved to the target device. \n\n><br/>It is __critical__ that all submodules and buffers in a custom module or composed by a Sequential object have exactly the same name in the original and target models, since that is how persisted tensors are associated with the model into which they are loaded.<br/><br/>The CustomModule 'RegisterComponents' will automatically find all fields that are either modules or tensors, register the former as modules, and the latter as buffers. It registers all of these using the name of the field, just like the PyTorch Module base class does.<br/><br/>\n\n### Saving a TorchSharp format model in Python\n\nIf the model starts out in Python, there's a simple script that allows you to use code that is very similar to the Pytorch API to save models to the TorchSharp format. Rather than placing this trivial script in a Python package and publishing it, we choose to just refer you to the script file itself, [exportsd.py](../../src/Python/exportsd.py), which has all the necessary code.\n\n```Python\nf = open(\"model_weights.dat\", \"wb\")\nexportsd.save_state_dict(model.to(\"cpu\").state_dict(), f)\nf.close()\n```\n\n### Loading a TorchSharp format model in Python\n\nIf the model starts out in TorchSharp, there's also a simple script that allows you to load TorchSharp models in Python. All the necessary code can be found in [importsd.py](../../src/Python/importsd.py). And there is an example for using the script:\n\n```Python\nf = open(\"model_weights.dat\", \"rb\")\nmodel.load_state_dict(importsd.load_state_dict(f))\nf.close()\n```\n\nAlso, you can check [TestSaveSD.cs](../../test/TorchSharpTest/TestSaveSD.cs) and [pyimporttest.py](../../test/TorchSharpTest/pyimporttest.py) for more examples.\n"},{"alpha_fraction":0.5555388927459717,"alphanum_fraction":0.5616931915283203,"avg_line_length":42.828948974609375,"blob_id":"80811e6f5af309d9ece46b0526897c28d45cfbc1","content_id":"a64c2cf4e14e6a5efcf693bc379ab22a20581ce8","detected_licenses":["MIT"],"is_generated":false,"is_vendor":false,"language":"C#","length_bytes":6664,"license_type":"permissive","max_line_length":174,"num_lines":152,"path":"/src/TorchSharp/Distributions/Dirichlet.cs","repo_name":"NiklasGustafsson/TorchSharp","src_encoding":"UTF-8","text":"// Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information.\nusing System;\nusing System.Collections.Generic;\nusing System.Linq;\nusing static TorchSharp.torch;\n\nnamespace TorchSharp\n{\n using System.Reflection;\n using Modules;\n\n namespace Modules\n {\n /// <summary>\n /// A Dirichlet distribution parameterized by shape `concentration` and `rate`.\n /// </summary>\n public class Dirichlet : torch.distributions.ExponentialFamily\n {\n /// <summary>\n /// The mean of the distribution.\n /// </summary>\n public override Tensor mean => WrappedTensorDisposeScope(() => concentration / concentration.sum(-1, true));\n\n public override Tensor mode\n {\n get {\n using var _ = NewDisposeScope();\n var concentrationm1 = (concentration - 1).clamp(min: 0.0);\n var mode = concentrationm1 / concentrationm1.sum(-1, true);\n var mask = (concentration < 1).all(dim: -1);\n mode[mask] = torch.nn.functional.one_hot(mode[mask].argmax(dim: -1), concentrationm1.shape[concentrationm1.ndim-1]).to(mode);\n return mode.MoveToOuterDisposeScope();\n }\n }\n\n /// <summary>\n /// The variance of the distribution\n /// </summary>\n public override Tensor variance {\n get {\n using var _ = NewDisposeScope();\n var con0 = concentration.sum(-1, true);\n return (concentration * (con0 - concentration) / (con0.pow(2) * (con0 + 1))).MoveToOuterDisposeScope();\n }\n }\n\n /// <summary>\n /// Constructor\n /// </summary>\n /// <param name=\"concentration\">Shape parameter of the distribution (often referred to as 'α')</param>\n /// <param name=\"generator\">An optional random number generator object.</param>\n public Dirichlet(Tensor concentration, torch.Generator generator = null) : base(generator)\n {\n var cshape = concentration.shape;\n this.batch_shape = cshape.Take(cshape.Length - 1).ToArray();\n this.event_shape = new long[] { cshape[cshape.Length - 1] };\n this.concentration = concentration.alias().DetachFromDisposeScope();\n }\n\n internal Tensor concentration;\n\n /// <summary>\n /// Generates a sample_shape shaped reparameterized sample or sample_shape shaped batch of reparameterized samples\n /// if the distribution parameters are batched.\n /// </summary>\n /// <param name=\"sample_shape\">The sample shape.</param>\n public override Tensor rsample(params long[] sample_shape)\n {\n var shape = ExtendedShape(sample_shape);\n var con = concentration.expand(shape);\n return torch._sample_dirichlet(con, generator);\n }\n\n /// <summary>\n /// Returns the log of the probability density/mass function evaluated at `value`.\n /// </summary>\n /// <param name=\"value\"></param>\n public override Tensor log_prob(Tensor value)\n {\n return (concentration - 1).xlogy(value).sum(-1) + torch.lgamma(concentration.sum(-1)) - torch.lgamma(concentration).sum(-1);\n }\n\n /// <summary>\n /// Returns entropy of distribution, batched over batch_shape.\n /// </summary>\n /// <returns></returns>\n public override Tensor entropy()\n {\n var k = concentration.size(-1);\n var a0 = concentration.sum(-1);\n\n return torch.lgamma(concentration).sum(-1) - torch.lgamma(a0) - (k - a0) * torch.digamma(a0) - ((concentration - 1.0) * torch.digamma(concentration)).sum(-1);\n }\n\n /// <summary>\n /// Returns a new distribution instance (or populates an existing instance provided by a derived class) with batch dimensions expanded to\n /// `batch_shape`. This method calls `torch.Tensor.expand()` on the distribution's parameters. As such, this does not allocate new\n /// memory for the expanded distribution instance.\n /// </summary>\n /// <param name=\"batch_shape\">Tthe desired expanded size.</param>\n /// <param name=\"instance\">new instance provided by subclasses that need to override `.expand`.</param>\n /// <returns></returns>\n public override distributions.Distribution expand(Size batch_shape, distributions.Distribution instance = null)\n {\n if (instance != null && !(instance is Dirichlet))\n throw new ArgumentException(\"expand(): 'instance' must be a Dirichlet distribution\");\n\n var shape = new List<long>();\n shape.AddRange(batch_shape);\n shape.AddRange(event_shape);\n\n var c = concentration.expand(shape.ToArray());\n\n var newDistribution = ((instance == null) ? new Dirichlet(c, generator) : instance) as Dirichlet;\n\n newDistribution.batch_shape = batch_shape;\n if (newDistribution == instance) {\n newDistribution.concentration = c;\n }\n return newDistribution;\n }\n\n protected override IList<Tensor> NaturalParams => new Tensor[] { concentration - 1 };\n\n protected override Tensor MeanCarrierMeasure => new Tensor(IntPtr.Zero);\n\n protected override Tensor LogNormalizer(params Tensor[] parameters)\n {\n var x = parameters[0];\n\n return x.lgamma().sum(-1) - x.sum(-1).lgamma();\n }\n }\n\n }\n\n public static partial class torch\n {\n public static partial class distributions\n {\n /// <summary>\n /// Creates a Dirichlet distribution parameterized by shape `concentration` and `rate`.\n /// </summary>\n /// <param name=\"concentration\">Shape parameter of the distribution (often referred to as 'α')</param>\n /// <param name=\"generator\">An optional random number generator object.</param>\n public static Dirichlet Dirichlet(Tensor concentration, torch.Generator generator = null)\n {\n return new Dirichlet(concentration, generator);\n }\n }\n }\n}\n"},{"alpha_fraction":0.6019668579101562,"alphanum_fraction":0.6107660531997681,"avg_line_length":41.93333435058594,"blob_id":"34943468d063eab1bd7d1795d69cc6214ebe07be","content_id":"e1a05c2c1ff50a98a014898b866de336a8b34cb9","detected_licenses":["MIT"],"is_generated":false,"is_vendor":false,"language":"C#","length_bytes":1932,"license_type":"permissive","max_line_length":130,"num_lines":45,"path":"/src/Examples.Utils/Datasets.cs","repo_name":"NiklasGustafsson/TorchSharp","src_encoding":"UTF-8","text":"// Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information.\n\nusing System.Collections.Generic;\nusing System.IO;\nusing System.Linq;\n\nnamespace TorchText\n{\n /// <summary>\n /// This belongs in its own package, 'TorchText'.\n /// For now, it's useful to keep it with the examples that use it.\n /// </summary>\n public static class Datasets\n {\n /// <summary>\n /// WikiText2\n /// </summary>\n /// <param name=\"split\">One of 'train', 'valid', or 'test'</param>\n /// <param name=\"root\">The folder where the WikiText2 data set was downloaded and extracted.</param>\n /// <returns>An enumeration of lines from the text.</returns>\n /// <remarks>\n /// Download the data set at:\n /// https://s3.amazonaws.com/research.metamind.io/wikitext/wikitext-2-v1.zip\n /// </remarks>\n public static IEnumerable<string> WikiText2(string split, string root = \".data\")\n {\n var dataPath = Path.Combine(root, \"wikitext-2\", $\"wiki.{split}.tokens\");\n return File.ReadLines(dataPath).Select(line => line.Trim()).Where(line => line.Length > 0);\n }\n\n /// <summary>\n /// WikiText2\n /// </summary>\n /// <param name=\"root\">The folder where the WikiText2 data set was downloaded and extracted.</param>\n /// <returns>An enumeration of lines from the text for each of the data sets (training, validation, and test).</returns>\n /// <remarks>\n /// Download the data set at:\n /// https://s3.amazonaws.com/research.metamind.io/wikitext/wikitext-2-v1.zip\n /// </remarks>\n public static (IEnumerable<string>, IEnumerable<string>, IEnumerable<string>) WikiText2(string root = \".data\")\n {\n return (WikiText2(\"train\", root), WikiText2(\"valid\", root), WikiText2(\"test\", root));\n }\n }\n}\n"},{"alpha_fraction":0.56749027967453,"alphanum_fraction":0.5711507797241211,"avg_line_length":48.95428466796875,"blob_id":"c0b1df898de4c1b9016ebf756a5bcb14d4d8b619","content_id":"a98f3e46cd370e6c77a26ba8fcce35404eb25b14","detected_licenses":["MIT"],"is_generated":false,"is_vendor":false,"language":"C#","length_bytes":8742,"license_type":"permissive","max_line_length":282,"num_lines":175,"path":"/src/TorchSharp/NN/Recurrent/RNN.cs","repo_name":"NiklasGustafsson/TorchSharp","src_encoding":"UTF-8","text":"// Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information.\nusing System;\nusing static TorchSharp.torch;\nusing static TorchSharp.PInvoke.NativeMethods;\n\n#nullable enable\nnamespace TorchSharp\n{\n using Modules;\n\n namespace Modules\n {\n public sealed class RNN : torch.nn.Module<Tensor, Tensor?, (Tensor, Tensor)>\n {\n internal RNN(IntPtr handle, IntPtr boxedHandle, long hiddenSize, long numLayers, bool batchFirst, bool bidirectional) : base(handle, boxedHandle)\n {\n _hidden_size = hiddenSize;\n _num_layers = numLayers;\n _bidirectional = bidirectional;\n _batch_first = batchFirst;\n }\n\n private long _hidden_size;\n private long _num_layers;\n private bool _bidirectional;\n private bool _batch_first;\n\n /// <summary>\n /// Applies a multi-layer Elman RNN with \\tanhtanh or \\text{ReLU}ReLU non-linearity to an input sequence.\n /// </summary>\n /// <param name=\"input\">Tensor of shape (seq_len, batch, input_size) containing the features of the input sequence.</param>\n /// <param name=\"h0\">Tensor of shape (num_layers * num_directions, batch, hidden_size)containing the initial hidden state for each element in the batch.\n /// Defaults to 0 if not provided. If the RNN is bidirectional, num_directions should be 2, else it should be 1.</param>\n /// <returns></returns>\n public override (Tensor, Tensor) forward(Tensor input, Tensor? h0)\n {\n if (h0 is null) {\n var N = _batch_first ? input.shape[0] : input.shape[1];\n var D = _bidirectional ? 2 : 1;\n\n h0 = torch.zeros(D * _num_layers, N, _hidden_size, dtype: input.dtype, device: input.device);\n }\n\n var res = THSNN_RNN_forward(handle, input.Handle, h0.Handle, out IntPtr hN);\n if (res == IntPtr.Zero || hN == IntPtr.Zero) { torch.CheckForErrors(); }\n return (new Tensor(res), new Tensor(hN));\n }\n\n public new (Tensor, Tensor) call(Tensor input, Tensor? h0 = null) => base.call(input, h0);\n\n /// <summary>\n /// Applies a multi-layer Elman RNN with \\tanhtanh or \\text{ReLU}ReLU non-linearity to an input sequence.\n /// </summary>\n /// <param name=\"input\">PackedSequence containing the features of the input sequence.</param>\n /// <param name=\"h0\">Tensor of shape (num_layers * num_directions, batch, hidden_size)containing the initial hidden state for each element in the batch.\n /// Defaults to 0 if not provided. If the RNN is bidirectional, num_directions should be 2, else it should be 1.</param>\n /// <returns></returns>\n public (torch.nn.utils.rnn.PackedSequence, Tensor) call(torch.nn.utils.rnn.PackedSequence input, Tensor? h0 = null)\n {\n if (h0 is null) {\n var data = input.data;\n var batch_sizes = input.batch_sizes;\n var N = batch_sizes[0].item<long>();\n var D = _bidirectional ? 2 : 1;\n\n h0 = torch.zeros(D * _num_layers, N, _hidden_size, dtype: data.dtype, device: data.device);\n }\n\n var res = THSNN_RNN_forward_with_packed_input(handle, input.Handle, h0.Handle, out IntPtr hN);\n if (res.IsInvalid || hN == IntPtr.Zero) { torch.CheckForErrors(); }\n return (new torch.nn.utils.rnn.PackedSequence(res), new Tensor(hN));\n }\n\n public void flatten_parameters()\n {\n THSNN_RNN_flatten_parameters(handle);\n torch.CheckForErrors();\n }\n\n public Parameter? get_bias_ih(long idx)\n {\n var res = THSNN_RNN_bias_ih(handle, idx);\n if (res == IntPtr.Zero) { torch.CheckForErrors(); }\n return ((res == IntPtr.Zero) ? null : new Parameter(res));\n }\n\n public Parameter? get_bias_hh(long idx)\n {\n var res = THSNN_RNN_bias_hh(handle, idx);\n if (res == IntPtr.Zero) { torch.CheckForErrors(); }\n return ((res == IntPtr.Zero) ? null : new Parameter(res));\n }\n\n#if false // Disabled until we can figure out how to set the native code parameters.\n\n public void set_bias_ih(Tensor? value, long idx)\n {\n THSNN_RNN_set_bias_ih(handle, (value is null ? IntPtr.Zero : value.Handle), idx);\n torch.CheckForErrors();\n ConditionallyRegisterParameter($\"bias_ih_l{idx}\", value);\n }\n\n public void set_bias_hh(Tensor? value, long idx)\n {\n THSNN_RNN_set_bias_hh(handle, (value is null ? IntPtr.Zero : value.Handle), idx);\n torch.CheckForErrors();\n ConditionallyRegisterParameter($\"bias_hh_l{idx}\", value);\n }\n#endif\n\n public Parameter? get_weight_ih(long idx)\n {\n var res = THSNN_RNN_weight_ih(handle, idx);\n if (res == IntPtr.Zero) { torch.CheckForErrors(); }\n return (res == IntPtr.Zero) ? null : new Parameter(res);\n }\n\n public Parameter? get_weight_hh(long idx)\n {\n var res = THSNN_RNN_weight_hh(handle, idx);\n if (res == IntPtr.Zero) { torch.CheckForErrors(); }\n return (res == IntPtr.Zero) ? null : new Parameter(res);\n }\n\n#if false // Disabled until we can figure out how to set the native code parameters.\n public void set_weight_ih(Tensor? value, long idx)\n {\n THSNN_RNN_set_weight_ih(handle, (value is null ? IntPtr.Zero : value.Handle), idx);\n torch.CheckForErrors();\n ConditionallyRegisterParameter($\"weight_ih_l{idx}\", value);\n }\n\n public void set_weight_hh(Tensor? value, long idx)\n {\n THSNN_RNN_set_weight_hh(handle, (value is null ? IntPtr.Zero : value.Handle), idx);\n torch.CheckForErrors();\n ConditionallyRegisterParameter($\"weight_hh_l{idx}\", value);\n }\n#endif\n }\n }\n\n public static partial class torch\n {\n public static partial class nn\n {\n public enum NonLinearities\n {\n ReLU = 0,\n Tanh = 1\n }\n\n /// <summary>\n /// Creates an Elman RNN module with tanh or ReLU non-linearity.\n /// </summary>\n /// <param name=\"inputSize\">The number of expected features in the input x</param>\n /// <param name=\"hiddenSize\">The number of features in the hidden state h</param>\n /// <param name=\"numLayers\">Number of recurrent layers. Default: 1</param>\n /// <param name=\"nonLinearity\">The non-linearity to use. Can be either 'tanh' or 'relu'. Default: 'tanh'</param>\n /// <param name=\"bias\">If False, then the layer does not use bias weights b_ih and b_hh. Default: True</param>\n /// <param name=\"batchFirst\">if true, then the input and output tensors are provided as (batch, seq, feature). Default: False</param>\n /// <param name=\"dropout\">If non-zero, introduces a Dropout layer on the outputs of each RNN layer except the last layer, with dropout probability equal to dropout. Default: 0</param>\n /// <param name=\"bidirectional\">if true, becomes a bidirectional RNN. Default: False</param>\n /// <param name=\"device\">The desired device of the parameters and buffers in this module</param>\n /// <param name=\"dtype\">The desired floating point or complex dtype of the parameters and buffers in this module</param>\n /// <returns></returns>\n public static RNN RNN(long inputSize, long hiddenSize, long numLayers = 1, NonLinearities nonLinearity = nn.NonLinearities.Tanh, bool bias = true, bool batchFirst = false, double dropout = 0.0, bool bidirectional = false, Device? device = null, ScalarType? dtype = null)\n {\n var res = THSNN_RNN_ctor(inputSize, hiddenSize, numLayers, (long)nonLinearity, bias, batchFirst, dropout, bidirectional, out var boxedHandle);\n if (res == IntPtr.Zero) { torch.CheckForErrors(); }\n return new RNN(res, boxedHandle, hiddenSize, numLayers, batchFirst, bidirectional).MoveModule<RNN>(device, dtype);\n }\n }\n }\n}\n"},{"alpha_fraction":0.6434804797172546,"alphanum_fraction":0.6513090133666992,"avg_line_length":25.06354522705078,"blob_id":"1a6cf08481212f8c5c65005be864abb32d2fda25","content_id":"9fa77de5c5575bb5e9f3ac108ec1c31cf2ed4839","detected_licenses":["MIT"],"is_generated":false,"is_vendor":false,"language":"C++","length_bytes":7792,"license_type":"permissive","max_line_length":130,"num_lines":299,"path":"/src/Native/LibTorchSharp/Utils.h","repo_name":"NiklasGustafsson/TorchSharp","src_encoding":"UTF-8","text":"// Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information.\n#pragma once\n\n#include <string>\n\n#include \"torch/torch.h\"\n\nextern thread_local char *torch_last_err;\n\ntypedef torch::Tensor *Tensor;\ntypedef torch::Scalar *Scalar;\ntypedef torch::Generator* Generator;\ntypedef c10::Storage* Storage;\ntypedef torch::nn::utils::rnn::PackedSequence* PackedSequence;\n\ntypedef std::shared_ptr<torch::nn::Module> * NNModule;\ntypedef std::shared_ptr<torch::nn::AnyModule> * NNAnyModule;\ntypedef std::shared_ptr<torch::optim::Optimizer> * Optimizer;\ntypedef std::shared_ptr<torch::jit::CompilationUnit> * JITCompilationUnit;\ntypedef std::shared_ptr<torch::jit::Module>* JITModule;\ntypedef std::shared_ptr<torch::jit::Method>* JITMethod;\ntypedef std::shared_ptr<torch::jit::Function> * JITFunction;\ntypedef std::shared_ptr<c10::Type> * JITType;\ntypedef std::shared_ptr<c10::TensorType>* JITTensorType;\n\n//typedef std::shared_ptr<torch::jit::DimensionedTensorType>* JITDimensionedTensorType;\n\n#define THS_API TH_API\n\n#define CATCH(x) \\\n try { \\\n torch_last_err = 0; \\\n x \\\n } catch (const c10::Error e) { \\\n torch_last_err = strdup(e.what()); \\\n } catch (const std::runtime_error e) { \\\n torch_last_err = strdup(e.what()); \\\n }\n\n#define CATCH_RETURN_RES(ty, dflt, stmt) \\\n ty res = dflt; \\\n CATCH( \\\n stmt; \\\n ); \\\n return res;\n\n#define CATCH_RETURN(ty, dflt, expr) CATCH_RETURN_RES(ty, dflt, res = expr)\n#define CATCH_RETURN_NNModule(stmt) CATCH_RETURN_RES(NNModule, nullptr, stmt)\n#define CATCH_RETURN_Tensor(stmt) CATCH_RETURN_RES(Tensor, nullptr, stmt)\n\n// Return undefined tensors as nullptr to C#\ninline Tensor ResultTensor(const at::Tensor & res)\n{\n if (res.defined())\n return new torch::Tensor(res);\n else\n return nullptr;\n}\n\n#define CATCH_TENSOR(expr) \\\n at::Tensor res = at::Tensor(); \\\n CATCH( \\\n res = expr; \\\n ); \\\n return ResultTensor(res);\n\n#define CATCH_TENSORS_2(expr) \\\n at::Tensor fst = at::Tensor(); \\\n at::Tensor snd = at::Tensor(); \\\n CATCH( \\\n std::tie(fst,snd) = expr; \\\n ); \\\n res1 = ResultTensor(fst); \\\n res2 = ResultTensor(snd); \n\n#define CATCH_SCALAR(expr) \\\n at::Scalar res = at::Scalar(); \\\n CATCH( \\\n res = expr; \\\n ); \\\n return ResultTensor(res);\n\n\n// Utility method used to built sharable strings.\nconst char * make_sharable_string(const std::string str);\n\n// Method concerting arrays of tensor pointers into arrays of tensors.\ntemplate<class T>\nstd::vector<T> toTensors(torch::Tensor ** tensorPtrs, const int length)\n{\n std::vector<T> tensors;\n\n if (tensorPtrs != nullptr) {\n for (int i = 0; i < length; i++)\n {\n tensors.push_back(*tensorPtrs[i]);\n }\n }\n return tensors;\n}\n\n// Utilities for NN namespace.\n\ntemplate <typename T>\nTensor get_weight(const NNModule module)\n{\n CATCH_TENSOR((*module)->as<T>()->weight);\n}\n\ntemplate <typename T>\nvoid set_weight(const NNModule module, const Tensor weights)\n{\n CATCH(\n (*module)->as<T>()->weight = *weights;\n );\n}\n\ntemplate <typename T>\nTensor get_bias(const NNModule module)\n{\n CATCH_TENSOR((*module)->as<T>()->bias);\n}\n\ntemplate <typename T>\nvoid set_bias(const NNModule module, const Tensor bias)\n{\n CATCH(\n (*module)->as<T>()->bias = *bias;\n );\n}\n\ntemplate <typename T>\nTensor get_weight_ih(const NNModule module)\n{\n CATCH_TENSOR((*module)->as<T>()->weight_ih);\n}\n\ntemplate <typename T>\nTensor get_weight_hh(const NNModule module)\n{\n CATCH_TENSOR((*module)->as<T>()->weight_hh);\n}\n\ntemplate <typename T>\nvoid set_weight_ih(const NNModule module, const Tensor weights)\n{\n CATCH(\n (*module)->as<T>()->weight_ih = *weights;\n );\n}\n\ntemplate <typename T>\nvoid set_weight_hh(const NNModule module, const Tensor weights)\n{\n CATCH(\n (*module)->as<T>()->weight_hh = *weights;\n );\n}\n\ntemplate <typename T>\nTensor get_bias_ih(const NNModule module)\n{\n CATCH_TENSOR((*module)->as<T>()->bias_ih);\n}\n\ntemplate <typename T>\nTensor get_bias_hh(const NNModule module)\n{\n CATCH_TENSOR((*module)->as<T>()->bias_hh);\n}\n\ntemplate <typename T>\nvoid set_bias_ih(const NNModule module, const Tensor bias)\n{\n CATCH(\n (*module)->as<T>()->bias_ih = *bias;\n );\n}\n\ntemplate <typename T>\nvoid set_bias_hh(const NNModule module, const Tensor bias)\n{\n CATCH(\n (*module)->as<T>()->bias_hh = *bias;\n );\n}\n\n#define WIH_BASE 0\n#define WHH_BASE 1\n#define BIH_BASE 2\n#define BHH_BASE 3\n\ntemplate <typename T>\nTensor get_weight_ih(const NNModule module, const int64_t idx)\n{\n CATCH_TENSOR((*module)->as<T>()->all_weights()[WIH_BASE + idx * 4]);\n}\n\ntemplate <typename T>\nTensor get_weight_hh(const NNModule module, const int64_t idx)\n{\n CATCH_TENSOR((*module)->as<T>()->all_weights()[WHH_BASE + idx * 4]);\n}\n\ntemplate <typename T>\nvoid set_weight_ih(const NNModule module, const Tensor weights, const int64_t idx)\n{\n CATCH(\n (*module)->as<T>()->all_weights()[WIH_BASE + idx * 4] = *weights;\n );\n}\n\ntemplate <typename T>\nvoid set_weight_hh(const NNModule module, const Tensor weights, const int64_t idx)\n{\n CATCH(\n (*module)->as<T>()->all_weights()[WHH_BASE + idx * 4] = *weights;\n );\n}\n\ntemplate <typename T>\nTensor get_bias_ih(const NNModule module, const int64_t idx)\n{\n CATCH_TENSOR((*module)->as<T>()->all_weights()[BIH_BASE + idx * 4]);\n}\n\ntemplate <typename T>\nTensor get_bias_hh(const NNModule module, const int64_t idx)\n{\n CATCH_TENSOR((*module)->as<T>()->all_weights()[BHH_BASE + idx * 4]);\n}\n\ntemplate <typename T>\nvoid set_bias_ih(const NNModule module, const Tensor bias, const int64_t idx)\n{\n CATCH(\n (*module)->as<T>()->all_weights()[BIH_BASE + idx * 4] = *bias;\n );\n}\n\ntemplate <typename T>\nvoid set_bias_hh(const NNModule module, const Tensor bias, const int64_t idx)\n{\n CATCH(\n (*module)->as<T>()->all_weights()[BHH_BASE + idx * 4] = *bias;\n );\n}\n\ntemplate<typename TImpl>\nNNModule create_module(NNAnyModule* outAsAnyModule)\n{\n auto mod = std::make_shared<TImpl>();\n\n // Keep a boxed version of the module in case we add it to a Sequential later (the C++ templating means\n // a Module can only be boxed to AnyModule at the point its static type is known).\n if (outAsAnyModule != nullptr)\n {\n auto wrapped = std::make_shared<torch::nn::AnyModule>(torch::nn::ModuleHolder<TImpl>(*mod));\n *outAsAnyModule = new std::shared_ptr<torch::nn::AnyModule>(wrapped);\n }\n\n return new std::shared_ptr<torch::nn::Module>(mod);\n}\n\ntemplate<typename TImpl, typename TOptions>\nNNModule create_module(const TOptions& opts, NNAnyModule* outAsAnyModule)\n{\n auto mod = std::make_shared<TImpl>(opts);\n\n // Keep a boxed version of the module in case we add it to a Sequential later (the C++ templating means\n // a Module can only be boxed to AnyModule at the point its static type is known).\n if (outAsAnyModule != nullptr)\n {\n auto wrapped = std::make_shared<torch::nn::AnyModule>(torch::nn::ModuleHolder<TImpl>(*mod));\n *outAsAnyModule = new std::shared_ptr<torch::nn::AnyModule>(wrapped);\n }\n\n return new std::shared_ptr<torch::nn::Module>(mod);\n}\n\ninline\ntorch::nn::init::NonlinearityType get_nl_type(const int64_t nl)\n{\n switch (nl)\n {\n default:\n case 0: return torch::kLinear;\n case 1: return torch::kConv1D;\n case 2: return torch::kConv2D;\n case 3: return torch::kConv3D;\n case 4: return torch::kConvTranspose1D;\n case 5: return torch::kConvTranspose2D;\n case 6: return torch::kConvTranspose3D;\n case 7: return torch::kSigmoid;\n case 8: return torch::kTanh;\n case 9: return torch::kReLU;\n case 10: return torch::kLeakyReLU;\n }\n}"},{"alpha_fraction":0.5233196020126343,"alphanum_fraction":0.5345221757888794,"avg_line_length":38.0625,"blob_id":"0ca611ea1c431fc13899ff0bd97049216c2c92f6","content_id":"7c8c36e1af89a399d344fe423f7276f8cf3c819f","detected_licenses":["MIT"],"is_generated":false,"is_vendor":false,"language":"C#","length_bytes":4374,"license_type":"permissive","max_line_length":140,"num_lines":112,"path":"/src/TorchAudio/Transforms/MelScale.cs","repo_name":"NiklasGustafsson/TorchSharp","src_encoding":"UTF-8","text":"// Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information.\n\n// A number of implementation details in this file have been translated from the Python version of torchaudio,\n// largely located in the files found in this folder:\n//\n// https://github.com/pytorch/audio/blob/bb77cbebb620a46fdc0dc7e6dae2253eef3f37e2/torchaudio/transforms/_transforms.py\n//\n// The origin has the following copyright notice and license:\n//\n// https://github.com/pytorch/audio/blob/main/LICENSE\n//\n\nusing System;\nusing static TorchSharp.torch;\nusing static TorchSharp.torch.nn;\nusing static TorchSharp.torchaudio;\nusing F = TorchSharp.torchaudio.functional;\n\nnamespace TorchSharp\n{\n namespace Transforms\n {\n public class MelScale : Module<Tensor, Tensor>\n {\n public readonly long n_mels;\n public readonly long sample_rate;\n public readonly double f_max;\n public readonly double f_min;\n public readonly MelNorm norm;\n public readonly torchaudio.MelScale mel_scale;\n public readonly Tensor fb;\n\n protected override void Dispose(bool disposing)\n {\n if (disposing) {\n fb.Dispose();\n }\n base.Dispose(disposing);\n }\n\n internal MelScale(\n string name,\n long n_mels = 128,\n long sample_rate = 16000,\n double f_min = 0.0,\n double? f_max = null,\n long n_stft = 201,\n MelNorm norm = MelNorm.none,\n torchaudio.MelScale mel_scale = torchaudio.MelScale.htk) : base(name)\n {\n this.n_mels = n_mels;\n this.sample_rate = sample_rate;\n this.f_max = f_max ?? (sample_rate / 2);\n this.f_min = f_min;\n this.norm = norm;\n this.mel_scale = mel_scale;\n\n if (f_min > this.f_max) {\n throw new ArgumentOutOfRangeException($\"Require f_min: {f_min} < f_max: {this.f_max}\");\n }\n this.fb = F.melscale_fbanks(n_stft, this.f_min, this.f_max, this.n_mels, this.sample_rate, this.norm, this.mel_scale);\n this.register_buffer(\"fb\", fb);\n }\n\n /// <param name=\"specgram\">A spectrogram STFT of dimension (..., freq, time).</param>\n /// <returns>Mel frequency spectrogram of size (..., ``n_mels``, time).</returns>\n public override Tensor forward(Tensor specgram)\n {\n var mel_specgram = torch.matmul(specgram.transpose(-1, -2), this.fb).transpose(-1, -2);\n\n return mel_specgram;\n }\n }\n }\n\n public partial class torchaudio\n {\n public partial class transforms\n {\n /// <summary>\n /// Turn a normal STFT into a mel frequency STFT with triangular filter banks.\n /// </summary>\n /// <param name=\"n_mels\">Number of mel filterbanks.</param>\n /// <param name=\"sample_rate\">Sample rate of audio signal.</param>\n /// <param name=\"f_min\">Minimum frequency.</param>\n /// <param name=\"f_max\">Maximum frequency.</param>\n /// <param name=\"n_stft\">Number of bins in STFT. See ``n_fft`` in :class:`Spectrogram`.</param>\n /// <param name=\"norm\">If 'slaney', divide the triangular mel weights by the width of the mel band (area normalization).</param>\n /// <param name=\"mel_scale\">Scale to use: ``htk`` or ``slaney``.</param>\n /// <returns></returns>\n public static Transforms.MelScale MelScale(\n long n_mels = 128,\n long sample_rate = 16000,\n double f_min = 0.0,\n double? f_max = null,\n long n_stft = 201,\n MelNorm norm = MelNorm.none,\n torchaudio.MelScale mel_scale = torchaudio.MelScale.htk)\n {\n return new Transforms.MelScale(\n \"MelScale\",\n n_mels,\n sample_rate,\n f_min,\n f_max,\n n_stft,\n norm,\n mel_scale);\n }\n }\n }\n}"},{"alpha_fraction":0.5602758526802063,"alphanum_fraction":0.5664827823638916,"avg_line_length":50.78571319580078,"blob_id":"88660fcb39b3450174e2fe236fb65c7eff35f3e6","content_id":"862bf0b0282d9d6e7ea6160c6ccc0bd6e72ac444","detected_licenses":["MIT"],"is_generated":false,"is_vendor":false,"language":"C#","length_bytes":7250,"license_type":"permissive","max_line_length":137,"num_lines":140,"path":"/src/TorchSharp/NN/Pooling/AdaptiveAvgPool3D.cs","repo_name":"NiklasGustafsson/TorchSharp","src_encoding":"UTF-8","text":"// Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information.\nusing System;\nusing static TorchSharp.torch;\nusing static TorchSharp.PInvoke.NativeMethods;\n\nnamespace TorchSharp\n{\n using Modules;\n\n namespace Modules\n {\n /// <summary>\n /// This class is used to represent a AdaptiveAvgPool3D module.\n /// </summary>\n public sealed class AdaptiveAvgPool3d : torch.nn.Module<Tensor, Tensor>\n {\n internal AdaptiveAvgPool3d(IntPtr handle, IntPtr boxedHandle) : base(handle, boxedHandle)\n {\n }\n\n public override Tensor forward(Tensor tensor)\n {\n var res = THSNN_AdaptiveAvgPool3d_forward(handle.DangerousGetHandle(), tensor.Handle);\n if (res == IntPtr.Zero) { torch.CheckForErrors(); }\n return new Tensor(res);\n }\n\n // Rather than spending cycles only to discover that this module has neither\n // parameters nor buffers, just shortcut the move completely.\n protected internal override nn.Module _to(Device device, ScalarType dtype) => this;\n protected internal override nn.Module _to(DeviceType deviceType, int deviceIndex = -1) => this;\n protected internal override nn.Module _to(ScalarType dtype) => this;\n }\n }\n\n public static partial class torch\n {\n public static partial class nn\n {\n /// <summary>\n /// Applies a 3D adaptive average pooling over an input signal composed of several input planes.\n /// The output is of size D x H x W, for any input size.The number of output features is equal to the number of input planes.\n /// </summary>\n /// <param name=\"outputSize\">The target output size of the image of the form D x H x W.</param>\n /// <returns></returns>\n public static unsafe AdaptiveAvgPool3d AdaptiveAvgPool3d(long[] outputSize)\n {\n fixed (long* pkernelSize = outputSize) {\n var handle = THSNN_AdaptiveAvgPool3d_ctor((IntPtr)pkernelSize, outputSize.Length, out var boxedHandle);\n if (handle == IntPtr.Zero) { torch.CheckForErrors(); }\n return new AdaptiveAvgPool3d(handle, boxedHandle);\n }\n }\n\n /// <summary>\n /// Applies a 3D adaptive average pooling over an input signal composed of several input planes.\n /// The output is of size D x H x W, for any input size.The number of output features is equal to the number of input planes.\n /// </summary>\n /// <param name=\"outputSize\">The target output size (D,H,W) of the image of the form D x H x W.</param>\n /// <returns></returns>\n public static unsafe AdaptiveAvgPool3d AdaptiveAvgPool3d((long, long, long) outputSize)\n {\n long* pkernelSize = stackalloc long[3] { outputSize.Item1, outputSize.Item2, outputSize.Item3 };\n\n var handle = THSNN_AdaptiveAvgPool3d_ctor((IntPtr)pkernelSize, 3, out var boxedHandle);\n if (handle == IntPtr.Zero) { torch.CheckForErrors(); }\n return new AdaptiveAvgPool3d(handle, boxedHandle);\n }\n\n /// <summary>\n /// Applies a 3D adaptive average pooling over an input signal composed of several input planes.\n /// The output is of size D x H x W, for any input size.The number of output features is equal to the number of input planes.\n /// </summary>\n /// <param name=\"outputSize\">The target output size (D,H,W) of the image of the form H x W.</param>\n /// <returns></returns>\n public static unsafe AdaptiveAvgPool3d AdaptiveAvgPool3d(long outputSize)\n {\n long* pkernelSize = stackalloc long[3] { outputSize, outputSize, outputSize };\n var handle = THSNN_AdaptiveAvgPool3d_ctor((IntPtr)pkernelSize, 3, out var boxedHandle);\n if (handle == IntPtr.Zero) { torch.CheckForErrors(); }\n return new AdaptiveAvgPool3d(handle, boxedHandle);\n }\n\n public static partial class functional\n {\n /// <summary>\n /// Applies a 3D adaptive average pooling over an input signal composed of several input planes.\n /// </summary>\n /// <param name=\"input\">The input tensor.</param>\n /// <param name=\"output_size\"></param>\n /// <returns></returns>\n public static unsafe Tensor adaptive_avg_pool3d(Tensor input, long[] output_size)\n {\n fixed (long* poutputSize = output_size) {\n var res =\n THSTensor_adaptive_avg_pool3d(input.Handle, (IntPtr)poutputSize, output_size.Length);\n if (res == IntPtr.Zero) { torch.CheckForErrors(); }\n return new Tensor(res);\n }\n }\n\n /// <summary>\n /// Applies a 2D adaptive average pooling over an input signal composed of several input planes.\n /// </summary>\n /// <param name=\"input\">The input tensor.</param>\n /// <param name=\"output_size\"></param>\n /// <returns></returns>\n public static unsafe Tensor adaptive_avg_pool3d(Tensor input, (long, long, long) output_size)\n {\n long* poutputSize = stackalloc long[3] { output_size.Item1, output_size.Item2, output_size.Item3 };\n var res = THSTensor_adaptive_avg_pool3d(input.Handle, (IntPtr)poutputSize, 3);\n if (res == IntPtr.Zero) { torch.CheckForErrors(); }\n return new Tensor(res);\n }\n\n /// <summary>\n /// Applies a 2D adaptive average pooling over an input signal composed of several input planes.\n /// </summary>\n /// <param name=\"input\">The input tensor.</param>\n /// <param name=\"output_size\"></param>\n /// <returns></returns>\n public static unsafe Tensor adaptive_avg_pool3d(Tensor input, long output_size)\n {\n var os = new long[] { output_size, output_size, output_size };\n long* poutputSize = stackalloc long[3] { output_size, output_size, output_size };\n var res = THSTensor_adaptive_avg_pool3d(input.Handle, (IntPtr)poutputSize, 3);\n if (res == IntPtr.Zero) { torch.CheckForErrors(); }\n return new Tensor(res);\n }\n\n public static Tensor adaptive_avg_pool3d_backward(Tensor gradInput, Tensor gradOutput, Tensor originalInput)\n {\n var res = THSTensor_adaptive_avg_pool3d_backward_out(gradInput.Handle, gradOutput.Handle, originalInput.Handle);\n if (res == IntPtr.Zero) { torch.CheckForErrors(); }\n return new Tensor(res);\n }\n }\n }\n }\n}\n"},{"alpha_fraction":0.6364205479621887,"alphanum_fraction":0.6364205479621887,"avg_line_length":36.1860466003418,"blob_id":"6dc1bbc272e6c410ef62828c298181f82843ac30","content_id":"f1b162cbbfe5f8a00d860be63cce34fa2e184e6b","detected_licenses":["MIT"],"is_generated":false,"is_vendor":false,"language":"C#","length_bytes":1598,"license_type":"permissive","max_line_length":111,"num_lines":43,"path":"/test/TorchSharpTest/FactIgnoreOnPlattformAttribute.cs","repo_name":"NiklasGustafsson/TorchSharp","src_encoding":"UTF-8","text":"#nullable enable\nusing System.Linq;\nusing System.Runtime.InteropServices;\nusing Xunit;\n\nnamespace TorchSharp\n{\n /// <summary>\n /// To ignore an xUnit <see cref=\"FactAttribute\">Fact</see> on a given platform or architecture.\n /// </summary>\n public sealed class FactIgnoreOnPlatformAttribute : FactAttribute\n {\n public FactIgnoreOnPlatformAttribute(string skip, params string[] platforms)\n {\n if (platforms.Any(p => RuntimeInformation.IsOSPlatform(OSPlatform.Create(p.ToUpperInvariant())))) {\n Skip = skip;\n }\n }\n\n public FactIgnoreOnPlatformAttribute(params string[] platforms)\n {\n if (platforms.Any(p => RuntimeInformation.IsOSPlatform(OSPlatform.Create(p.ToUpperInvariant())))) {\n Skip = $\"based on platform {RuntimeInformation.OSDescription}\";\n }\n }\n\n public FactIgnoreOnPlatformAttribute(string skip, string platform, Architecture architecture)\n {\n if (RuntimeInformation.IsOSPlatform(OSPlatform.Create(platform.ToUpperInvariant()))\n && RuntimeInformation.ProcessArchitecture == architecture) {\n Skip = skip;\n }\n }\n\n public FactIgnoreOnPlatformAttribute(string platform, Architecture architecture)\n {\n if (RuntimeInformation.IsOSPlatform(OSPlatform.Create(platform.ToUpperInvariant()))\n && RuntimeInformation.ProcessArchitecture == architecture) {\n Skip = $\"based on platform {platform} {architecture}\";\n }\n }\n }\n}"},{"alpha_fraction":0.5757042169570923,"alphanum_fraction":0.5757042169570923,"avg_line_length":27.450000762939453,"blob_id":"63ce94dcd20f5f76c20d49c40909fa1c6a48528f","content_id":"e65a649f3cc4ce64f91ccc49fe64897386af0593","detected_licenses":["MIT"],"is_generated":false,"is_vendor":false,"language":"C#","length_bytes":568,"license_type":"permissive","max_line_length":130,"num_lines":20,"path":"/src/TorchSharp/JIT/Type/DynamicType .cs","repo_name":"NiklasGustafsson/TorchSharp","src_encoding":"UTF-8","text":"// Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information.\nusing System;\n\nnamespace TorchSharp\n{\n public static partial class torch\n {\n\n public static partial class jit\n {\n public sealed class DynamicType : Type\n {\n internal DynamicType(IntPtr handle) : base(handle, Type.TypeKind.AnyType)\n {\n this.handle = new HType(handle, true, Type.TypeKind.AnyType);\n }\n }\n }\n }\n}"},{"alpha_fraction":0.7560219168663025,"alphanum_fraction":0.7762938141822815,"avg_line_length":44.086021423339844,"blob_id":"345b2ce610fd0ee7a4ac2f09d04d910d97237f91","content_id":"9ab80e8280607b20ecc50566181f980edf4ffcc0","detected_licenses":["MIT"],"is_generated":false,"is_vendor":false,"language":"C","length_bytes":4193,"license_type":"permissive","max_line_length":130,"num_lines":93,"path":"/src/Native/LibTorchSharp/THSTorch.h","repo_name":"NiklasGustafsson/TorchSharp","src_encoding":"UTF-8","text":"// Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information.\n#pragma once\n\n#include \"../Stdafx.h\"\n\n#include \"Utils.h\"\n\n// API.\n\n// Sets manually the seed.\nEXPORT_API(void) THSTorch_manual_seed(const int64_t seed);\nEXPORT_API(void) THSCuda_manual_seed(const int64_t seed);\nEXPORT_API(void) THSCuda_manual_seed_all(const int64_t seed);\n\nEXPORT_API(Generator) THSGenerator_manual_seed(const int64_t seed);\nEXPORT_API(void) THSGenerator_gen_manual_seed(const Generator gen, const int64_t seed);\n\nEXPORT_API(Tensor) THSGenerator_get_rng_state(const Generator gen);\nEXPORT_API(void) THSGenerator_set_rng_state(const Generator gen, const Tensor tensor);\n\nEXPORT_API(Generator) THSGenerator_default_generator();\nEXPORT_API(Generator) THSGenerator_new(uint64_t seed, int64_t device, int64_t index);\nEXPORT_API(int64_t) THSGenerator_initial_seed(const Generator gen);\nEXPORT_API(void) THSGenerator_dispose(const Generator generator);\n\nEXPORT_API(int) THSTorchCuda_is_available();\nEXPORT_API(int) THSTorchCuda_cudnn_is_available();\nEXPORT_API(int) THSTorchCuda_device_count();\nEXPORT_API(void) THSTorchCuda_synchronize(const int64_t device);\n\nEXPORT_API(bool) THSBackend_cublas_get_allow_tf32();\nEXPORT_API(void) THSBackend_cublas_set_allow_tf32(const bool flag);\nEXPORT_API(bool) THSBackend_cudnn_get_allow_tf32();\nEXPORT_API(void) THSBackend_cudnn_set_allow_tf32(const bool flag);\n\nEXPORT_API(bool) THSBackend_cuda_get_allow_fp16_reduced_precision_reduction();\nEXPORT_API(void) THSBackend_cuda_set_allow_fp16_reduced_precision_reduction(const bool flag);\n\nEXPORT_API(bool) THSBackend_cuda_get_enable_flash_sdp();\nEXPORT_API(void) THSBackend_cuda_set_enable_flash_sdp(const bool flag);\nEXPORT_API(bool) THSBackend_cuda_get_enable_math_sdp();\nEXPORT_API(void) THSBackend_cuda_set_enable_math_sdp(const bool flag);\n\nEXPORT_API(int) THSTorch_get_num_threads();\nEXPORT_API(void) THSTorch_set_num_threads(const int threads);\n\nEXPORT_API(int) THSTorch_get_num_interop_threads();\nEXPORT_API(void) THSTorch_set_num_interop_threads(const int threads);\n\n// Returns the latest error. This is thread-local.\n\nEXPORT_API(const char *) THSTorch_get_and_reset_last_err();\n\nEXPORT_API(int) THSTorch_can_cast(const int type1, const int type2);\nEXPORT_API(int) THSTorch_promote_types(const int type1, const int type2);\n\nEXPORT_API(Scalar) THSTorch_int8_to_scalar(int8_t value);\nEXPORT_API(Scalar) THSTorch_uint8_to_scalar(uint8_t value);\nEXPORT_API(Scalar) THSTorch_int16_to_scalar(short value);\nEXPORT_API(Scalar) THSTorch_int32_to_scalar(int value);\nEXPORT_API(Scalar) THSTorch_int64_to_scalar(long value);\nEXPORT_API(Scalar) THSTorch_float32_to_scalar(float value);\nEXPORT_API(Scalar) THSTorch_float64_to_scalar(double value);\nEXPORT_API(Scalar) THSTorch_bool_to_scalar(bool value);\nEXPORT_API(Scalar) THSTorch_float16_to_scalar(float value);\nEXPORT_API(Scalar) THSTorch_bfloat16_to_scalar(float value);\n\nEXPORT_API(Scalar) THSTorch_complex32_to_scalar(float real, float imaginary);\nEXPORT_API(Scalar) THSTorch_complex64_to_scalar(double real, double imaginary);\n\nEXPORT_API(int8_t) THSTorch_scalar_to_int8(Scalar value);\nEXPORT_API(uint8_t) THSTorch_scalar_to_uint8(Scalar value);\nEXPORT_API(int16_t) THSTorch_scalar_to_int16(Scalar value);\nEXPORT_API(int32_t) THSTorch_scalar_to_int32(Scalar value);\nEXPORT_API(int64_t) THSTorch_scalar_to_int64(Scalar value);\nEXPORT_API(float) THSTorch_scalar_to_float32(Scalar value);\nEXPORT_API(double) THSTorch_scalar_to_float64(Scalar value);\nEXPORT_API(bool) THSTorch_scalar_to_bool(Scalar value);\n\nEXPORT_API(void) THSTorch_scalar_to_float16(Scalar value, unsigned short* res);\n\nEXPORT_API(void) THSTorch_scalar_to_complex32(Scalar value, float* (*allocator)(size_t length));\nEXPORT_API(void) THSTorch_scalar_to_complex64(Scalar value, double* (*allocator)(size_t length));\n\nEXPORT_API(int8_t) THSTorch_scalar_type(Scalar value);\n\n// Dispose the scalar.\nEXPORT_API(void) THSTorch_dispose_scalar(Scalar scalar);\n\n// Math functions not available in .NET standard libs\n\nEXPORT_API(double) THSSpecial_erf_scalar(const double x);\nEXPORT_API(double) THSSpecial_erfc_scalar(const double x);\n"},{"alpha_fraction":0.5712489485740662,"alphanum_fraction":0.5716680884361267,"avg_line_length":41.625,"blob_id":"444c170c743025b7e2dfcf77d8b912edcbb8dd59","content_id":"ff326db1228e1d32ffd3cf28bef4cb27291ad725","detected_licenses":["MIT"],"is_generated":false,"is_vendor":false,"language":"C#","length_bytes":2386,"license_type":"permissive","max_line_length":130,"num_lines":56,"path":"/src/TorchVision/ResizedCrop.cs","repo_name":"NiklasGustafsson/TorchSharp","src_encoding":"UTF-8","text":"// Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information.\n\nusing static TorchSharp.torch;\n\nnamespace TorchSharp\n{\n public static partial class torchvision\n {\n internal class ResizedCrop : ITransform\n {\n internal ResizedCrop(int top, int left, int height, int width, int newHeight, int newWidth)\n {\n cropper = transforms.Crop(top, left, height, width);\n resizer = transforms.Resize(newHeight, newWidth);\n }\n\n public Tensor call(Tensor input)\n {\n using var cr = cropper.call(input);\n return resizer.call(cr);\n }\n\n private ITransform cropper;\n private ITransform resizer;\n }\n\n public static partial class transforms\n {\n /// <summary>\n /// Crop the given image and resize it to desired size.\n /// </summary>\n /// <param name=\"top\">Vertical component of the top left corner of the crop box.</param>\n /// <param name=\"left\">Horizontal component of the top left corner of the crop box.</param>\n /// <param name=\"height\">Height of the crop box.</param>\n /// <param name=\"width\">Width of the crop box.</param>\n /// <param name=\"newHeight\">New height.</param>\n /// <param name=\"newWidth\">New width.</param>\n static public ITransform ResizedCrop(int top, int left, int height, int width, int newHeight, int newWidth)\n {\n return new ResizedCrop(top, left, height, width, newHeight, newWidth);\n }\n\n /// <summary>\n /// Crop the given image and resize it to desired (square) size.\n /// </summary>\n /// <param name=\"top\">Vertical component of the top left corner of the crop box.</param>\n /// <param name=\"left\">Horizontal component of the top left corner of the crop box.</param>\n /// <param name=\"size\">Height and width of the crop box.</param>\n /// <param name=\"newSize\">New height and width.</param>\n static public ITransform ResizedCrop(int top, int left, int size, int newSize)\n {\n return new ResizedCrop(top, left, size, size, newSize, -1);\n }\n }\n }\n}"},{"alpha_fraction":0.5909352898597717,"alphanum_fraction":0.5972728729248047,"avg_line_length":48.590476989746094,"blob_id":"0dd0d5fc172516968490d8dca5a9c578fff2f205","content_id":"1249b5ba570d01073a7a680c1c08e871e820b408","detected_licenses":["MIT"],"is_generated":false,"is_vendor":false,"language":"C#","length_bytes":5207,"license_type":"permissive","max_line_length":245,"num_lines":105,"path":"/src/TorchSharp/Optimizers/LBFGS.cs","repo_name":"NiklasGustafsson/TorchSharp","src_encoding":"UTF-8","text":"// Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information.\nusing System;\nusing System.Collections.Generic;\nusing System.Linq;\nusing static TorchSharp.torch;\nusing static TorchSharp.PInvoke.NativeMethods;\n\nnamespace TorchSharp\n{\n using Modules;\n\n public static partial class torch\n {\n public static partial class optim\n {\n /// <summary>\n /// Implements the L-BFGS algorithm, heavily inspired by `minFunc`\n ///\n /// https://www.cs.ubc.ca/~schmidtm/Software/minFunc.html\n /// </summary>\n /// <param name=\"parameters\">Parameters to optimize</param>\n /// <param name=\"lr\">Learning rate (default: 1e-2)</param>\n /// <param name=\"max_iter\">Maximal number of iterations per optimization step</param>\n /// <param name=\"max_eval\">Maximal number of function evaluations per optimization step</param>\n /// <param name=\"tolerange_grad\">Termination tolerance on first order optimality</param>\n /// <param name=\"tolerance_change\">Termination tolerance on function value/parameter changes</param>\n /// <param name=\"history_size\">Update history size</param>\n public static LBFGS LBFGS(IEnumerable<(string name, Parameter parameter)> parameters, double lr = 0.01, long max_iter = 20, long? max_eval = null, double tolerange_grad = 1e-5, double tolerance_change = 1e-9, long history_size = 100)\n {\n return LBFGS(parameters.Select(np => np.parameter), lr, max_iter, max_eval.Value, tolerange_grad, tolerance_change, history_size);\n }\n\n /// <summary>\n /// Implements the L-BFGS algorithm, heavily inspired by `minFunc`\n ///\n /// https://www.cs.ubc.ca/~schmidtm/Software/minFunc.html\n /// </summary>\n /// <param name=\"parameters\">Parameters to optimize</param>\n /// <param name=\"lr\">Learning rate (default: 1e-2)</param>\n /// <param name=\"max_iter\">Maximal number of iterations per optimization step</param>\n /// <param name=\"max_eval\">Maximal number of function evaluations per optimization step</param>\n /// <param name=\"tolerange_grad\">Termination tolerance on first order optimality</param>\n /// <param name=\"tolerance_change\">Termination tolerance on function value/parameter changes</param>\n /// <param name=\"history_size\">Update history size</param>\n public static LBFGS LBFGS(IEnumerable<Parameter> parameters, double lr = 0.01, long max_iter = 20, long? max_eval = null, double tolerange_grad = 1e-5, double tolerance_change = 1e-9, long history_size = 100)\n {\n if (!max_eval.HasValue) max_eval = 5 * max_iter / 4;\n\n using var parray = new PinnedArray<IntPtr>();\n IntPtr paramsRef = parray.CreateArray(parameters.Select(p => p.Handle).ToArray());\n\n var res = THSNN_LBFGS_ctor(paramsRef, parray.Array.Length, lr, max_iter, max_eval.Value, tolerange_grad, tolerance_change, history_size);\n if (res == IntPtr.Zero) { torch.CheckForErrors(); }\n return new LBFGS(res, lr);\n }\n }\n }\n\n namespace Modules\n {\n using static torch.optim;\n\n // LBGFS does not allow for param groups, so there's no need to use anything but the native implementation.\n public class LBFGS : Optimizer, ILearningRateController\n {\n /// <summary>\n /// Implements L-BFGS algorithm, heavily inspired by `minFunc`\n ///\n /// </summary>\n /// <param name=\"handle\"></param>\n /// <param name=\"lr\"></param>\n public LBFGS(IntPtr handle, double lr) : base(handle)\n {\n _rate = lr;\n InitialLearningRate = lr;\n _paramGroups = new ParamGroup[] { new ParamGroup { Parameters = this.parameters(), Options = new() { LearningRate = lr, InitialLearningRate = lr } } };\n }\n\n public double LearningRate {\n get { return _rate; }\n set { THSNN_LBFGS_set_lr(handle, value); torch.CheckForErrors(); _rate = value; }\n }\n\n public double InitialLearningRate { get; set; }\n\n public override IEnumerable<ILearningRateController> ParamGroups { get => _paramGroups; }\n\n\n /// <summary>\n /// Performs a single optimization step (parameter update).\n /// </summary>\n /// <param name=\"closure\">A closure that reevaluates the model and returns the loss. Optional for most optimizers.</param>\n /// <returns></returns>\n public override Tensor step(Func<Tensor> closure)\n {\n if (closure == null)\n throw new ArgumentNullException(\"'closure' must be non-null when using the LBFGS optimizer. See: https://pytorch.org/docs/1.9.0/optim.html\");\n return base.step(closure);\n }\n\n public double _rate;\n private ParamGroup[] _paramGroups;\n }\n }\n}\n"},{"alpha_fraction":0.5266386866569519,"alphanum_fraction":0.5404423475265503,"avg_line_length":40.85135269165039,"blob_id":"6fa6144d3f8ee081bcd47c76f9dba662e5bd9dc3","content_id":"e91971e5aa397067ea2b5f709543f7e2081e2f08","detected_licenses":["MIT"],"is_generated":false,"is_vendor":false,"language":"C#","length_bytes":12388,"license_type":"permissive","max_line_length":255,"num_lines":296,"path":"/src/Examples/SpeechCommands.cs","repo_name":"NiklasGustafsson/TorchSharp","src_encoding":"UTF-8","text":"// Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information.\nusing System;\nusing System.Collections.Generic;\nusing System.Diagnostics;\nusing System.IO;\nusing System.Linq;\nusing System.Runtime.InteropServices;\nusing static TorchSharp.torch;\n\nusing static TorchSharp.torch.nn;\nusing static TorchSharp.torch.nn.functional;\nusing static TorchSharp.torch.utils.data;\nusing static TorchSharp.torchaudio.datasets;\n\nnamespace TorchSharp.Examples\n{\n /// <summary>\n /// SpeechCommands model with convolusions.\n /// </summary>\n /// <remarks>\n /// Translated from Python implementation\n /// https://pytorch.org/tutorials/intermediate/speech_command_classification_with_torchaudio_tutorial.html\n /// </remarks>\n public class SpeechCommands\n {\n private static readonly string[] Labels = new string[] {\n \"bed\", \"bird\", \"backward\", \"cat\", \"dog\", \"down\", \"eight\",\n \"five\", \"follow\", \"forward\", \"four\", \"go\", \"happy\", \"house\",\n \"learn\", \"left\", \"marvin\", \"nine\", \"no\", \"off\", \"on\", \"one\",\n \"right\", \"seven\", \"six\", \"sheila\", \"stop\", \"three\", \"tree\",\n \"two\", \"up\", \"visual\", \"wow\", \"yes\", \"zero\"\n };\n\n private static int _epochs = 1;\n private static int _trainBatchSize = 64;\n private static int _testBatchSize = 128;\n private static int _sample_rate = 16000;\n private static int _new_sample_rate = 8000;\n\n private readonly static int _logInterval = 200;\n\n private static IDictionary<string, int> _labelToIndex;\n\n internal static void Main(string[] args)\n {\n var dataset = args.Length > 0 ? args[0] : \"speechcommands\";\n var datasetPath = Environment.GetFolderPath(Environment.SpecialFolder.LocalApplicationData);\n\n torchaudio.backend.utils.set_audio_backend(new WaveAudioBackend());\n torch.random.manual_seed(1);\n\n var cwd = Environment.CurrentDirectory;\n Console.WriteLine(datasetPath);\n\n var device = torch.device(torch.cuda.is_available() ? \"cuda\" : \"cpu\");\n\n Console.WriteLine($\"Running SpeechCommands on {device.type.ToString()}\");\n Console.WriteLine($\"Dataset: {dataset}\");\n\n if (device.type == DeviceType.CUDA) {\n _trainBatchSize *= 4;\n _testBatchSize *= 4;\n }\n\n var model = new M5(\"model\");\n model.to(device);\n\n var transform = torchaudio.transforms.Resample(_sample_rate, _new_sample_rate, device: device);\n\n _labelToIndex = Labels.Select((label, index) => (label, index)).ToDictionary(t => t.label, t => t.index);\n using (var train_data = SPEECHCOMMANDS(datasetPath, subset: \"training\", download: true))\n using (var test_data = SPEECHCOMMANDS(datasetPath, subset: \"testing\", download: true)) {\n TrainingLoop(\"speechcommands\", device, model, transform, train_data, test_data);\n }\n }\n\n private static BatchItem Collate(IEnumerable<SpeechCommandsDatasetItem> items, torch.Device device)\n {\n var audio_sequences = items.Select(item => item.waveform.t());\n var padded_audio = torch.nn.utils.rnn.pad_sequence(audio_sequences, batch_first: true, padding_value: 0.0);\n padded_audio = padded_audio.permute(0, 2, 1);\n var labels = items.Select(item => _labelToIndex[item.label]).ToArray();\n return new BatchItem {\n audio = padded_audio.to(device),\n label = torch.tensor(labels, dtype: torch.int64, device: device)\n };\n }\n\n internal static void TrainingLoop(string dataset, Device device, M5 model, nn.IModule<Tensor, Tensor> transform, Dataset<SpeechCommandsDatasetItem> train_data, Dataset<SpeechCommandsDatasetItem> test_data)\n {\n using (var train_loader = new DataLoader<SpeechCommandsDatasetItem, BatchItem>(\n train_data, _trainBatchSize, Collate, shuffle: true, device: device))\n using (var test_loader = new DataLoader<SpeechCommandsDatasetItem, BatchItem>(\n test_data, _testBatchSize, Collate, shuffle: false, device: device)) {\n if (device.type == DeviceType.CUDA) {\n _epochs *= 4;\n }\n\n var optimizer = optim.Adam(model.parameters(), lr: 0.01, weight_decay: 0.0001);\n var scheduler = optim.lr_scheduler.StepLR(optimizer, step_size: 20, gamma: 0.1);\n\n Stopwatch sw = new Stopwatch();\n sw.Start();\n\n for (var epoch = 1; epoch <= _epochs; epoch++) {\n Train(model, transform, optimizer, torch.nn.NLLLoss(reduction: torch.nn.Reduction.Mean), train_loader, epoch, train_data.Count);\n Test(model, transform, torch.nn.NLLLoss(reduction: torch.nn.Reduction.Sum), test_loader, test_data.Count);\n\n Console.WriteLine($\"End-of-epoch memory use: {GC.GetTotalMemory(false)}\");\n scheduler.step();\n }\n\n sw.Stop();\n Console.WriteLine($\"Elapsed time: {sw.Elapsed.TotalSeconds:F1} s.\");\n\n Console.WriteLine(\"Saving model to '{0}'\", dataset + \".model.bin\");\n model.save(dataset + \".model.bin\");\n }\n }\n\n private static void Train(\n M5 model,\n nn.IModule<Tensor, Tensor> transform,\n torch.optim.Optimizer optimizer,\n Loss<Tensor,Tensor,Tensor> criteria,\n DataLoader<SpeechCommandsDatasetItem, BatchItem> dataLoader,\n int epoch,\n long size)\n {\n int batchId = 1;\n long total = 0;\n\n Console.WriteLine($\"Epoch: {epoch}...\");\n\n using (var d = torch.NewDisposeScope()) {\n\n model.train();\n foreach (var batch in dataLoader) {\n var audio = transform.call(batch.audio);\n var target = batch.label;\n var output = model.call(batch.audio).squeeze();\n var loss = criteria.call(output, target);\n optimizer.zero_grad();\n loss.backward();\n optimizer.step();\n total += target.shape[0];\n\n if (batchId % _logInterval == 0 || total == size) {\n Console.WriteLine($\"\\rTrain: epoch {epoch} [{total} / {size}] Loss: {loss.ToSingle():F4}\");\n }\n\n batchId++;\n\n d.DisposeEverything();\n }\n }\n }\n\n private static void Test(\n M5 model,\n nn.IModule<Tensor, Tensor> transform,\n Loss<Tensor, Tensor, Tensor> criteria,\n DataLoader<SpeechCommandsDatasetItem, BatchItem> dataLoader,\n long size)\n {\n model.eval();\n\n double testLoss = 0;\n int correct = 0;\n\n using (var d = torch.NewDisposeScope()) {\n\n foreach (var batch in dataLoader) {\n var audio = transform.call(batch.audio);\n var target = batch.label;\n var output = model.call(batch.audio).squeeze();\n var loss = criteria.call(output, target);\n testLoss += loss.ToSingle();\n\n var pred = output.argmax(1);\n correct += pred.eq(batch.label).sum().ToInt32();\n\n d.DisposeEverything();\n }\n }\n\n Console.WriteLine($\"Size: {size}, Total: {size}\");\n\n Console.WriteLine($\"\\rTest set: Average loss {(testLoss / size):F4} | Accuracy {((double)correct / size):P2}\");\n }\n\n private class WaveAudioBackend : torchaudio.backend.AudioBackend\n {\n public override (torch.Tensor, int) load(string filepath, long frame_offset = 0, long num_frames = -1, bool normalize = true, bool channels_first = true, torchaudio.AudioFormat? format = null)\n {\n byte[] data = File.ReadAllBytes(filepath);\n // In many cases, the first 44 bytes are for RIFF header.\n short[] waveform = MemoryMarshal.Cast<byte, short>(data.AsSpan(11 * 4)).ToArray();\n return (torch.tensor(waveform).unsqueeze(0).to(torch.float32) / short.MaxValue, 16000);\n }\n\n public override void save(string filepath, torch.Tensor src, int sample_rate, bool channels_first = true, float? compression = null, torchaudio.AudioFormat? format = null, torchaudio.AudioEncoding? encoding = null, int? bits_per_sample = null)\n {\n throw new NotImplementedException();\n }\n\n public override torchaudio.AudioMetaData info(string filepath, torchaudio.AudioFormat? format = null)\n {\n throw new NotImplementedException();\n }\n }\n\n private class BatchItem\n {\n public torch.Tensor audio;\n public torch.Tensor label;\n }\n\n internal class M5 : Module<Tensor, Tensor>\n {\n private readonly Module<Tensor, Tensor> conv1;\n private readonly Module<Tensor, Tensor> bn1;\n private readonly Module<Tensor, Tensor> pool1;\n private readonly Module<Tensor, Tensor> conv2;\n private readonly Module<Tensor, Tensor> bn2;\n private readonly Module<Tensor, Tensor> pool2;\n private readonly Module<Tensor, Tensor> conv3;\n private readonly Module<Tensor, Tensor> bn3;\n private readonly Module<Tensor, Tensor> pool3;\n private readonly Module<Tensor, Tensor> conv4;\n private readonly Module<Tensor, Tensor> bn4;\n private readonly Module<Tensor, Tensor> pool4;\n private readonly Module<Tensor, Tensor> fc1;\n\n public M5(string name, int n_input = 1, int n_output = 35, int stride = 16, int n_channel = 32) : base(name)\n {\n conv1 = nn.Conv1d(n_input, n_channel, kernelSize: 80, stride: stride);\n bn1 = nn.BatchNorm1d(n_channel);\n pool1 = nn.MaxPool1d(4);\n conv2 = nn.Conv1d(n_channel, n_channel, kernelSize: 3);\n bn2 = nn.BatchNorm1d(n_channel);\n pool2 = nn.MaxPool1d(4);\n conv3 = nn.Conv1d(n_channel, 2 * n_channel, kernelSize: 3);\n bn3 = nn.BatchNorm1d(2 * n_channel);\n pool3 = nn.MaxPool1d(4);\n conv4 = nn.Conv1d(2 * n_channel, 2 * n_channel, kernelSize: 3);\n bn4 = nn.BatchNorm1d(2 * n_channel);\n pool4 = nn.MaxPool1d(4);\n fc1 = nn.Linear(2 * n_channel, n_output);\n RegisterComponents();\n }\n\n public override Tensor forward(Tensor input)\n {\n var x = input;\n x = conv1.call(x);\n x = relu(bn1.call(x));\n x = pool1.call(x);\n x = conv2.call(x);\n x = relu(bn2.call(x));\n x = pool2.call(x);\n x = conv3.call(x);\n x = relu(bn3.call(x));\n x = pool3.call(x);\n x = conv4.call(x);\n x = relu(bn4.call(x));\n x = pool4.call(x);\n x = avg_pool1d(x, x.shape[x.dim() - 1]);\n x = x.permute(0, 2, 1);\n x = fc1.call(x);\n return log_softmax(x, dim: 2);\n }\n\n protected override void Dispose(bool disposing)\n {\n if (disposing) {\n pool1.Dispose();\n pool2.Dispose();\n pool3.Dispose();\n pool4.Dispose();\n conv1.Dispose();\n conv2.Dispose();\n conv3.Dispose();\n conv4.Dispose();\n bn1.Dispose();\n bn2.Dispose();\n bn3.Dispose();\n bn4.Dispose();\n fc1.Dispose();\n ClearModules();\n }\n base.Dispose(disposing);\n }\n }\n }\n}\n"},{"alpha_fraction":0.632435142993927,"alphanum_fraction":0.6376579999923706,"avg_line_length":59.60685348510742,"blob_id":"8d3af93b72a7cab20184e4baeacf39d5606e6015","content_id":"ff6f4d6b17f64f9f4a56794aee36871b75d24fd1","detected_licenses":["MIT"],"is_generated":false,"is_vendor":false,"language":"C#","length_bytes":30084,"license_type":"permissive","max_line_length":232,"num_lines":496,"path":"/src/TorchSharp/Tensor/torch.BlasAndLapackOperations.cs","repo_name":"NiklasGustafsson/TorchSharp","src_encoding":"UTF-8","text":"// Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information.\n#nullable enable\n\nusing System;\nusing System.Collections.Generic;\nusing static TorchSharp.PInvoke.NativeMethods;\n\nnamespace TorchSharp\n{\n // https://pytorch.org/docs/stable/torch#blas-and-lapack-operations\n public static partial class torch\n {\n public enum LobpcgMethod\n {\n basic,\n ortho\n }\n\n // https://pytorch.org/docs/stable/generated/torch.addbmm\n /// <summary>\n /// Performs a batch matrix-matrix product of matrices stored in batch1 and batch2, with a reduced\n /// add step (all matrix multiplications get accumulated along the first dimension).\n /// input is added to the final result.\n /// </summary>\n /// <param name=\"input\">The input tensor.</param>\n /// <param name=\"batch1\">The first batch of matrices to be multiplied</param>\n /// <param name=\"batch2\">The second batch of matrices to be multiplied</param>\n /// <param name=\"beta\">Nultiplier for input (β)</param>\n /// <param name=\"alpha\">Multiplier for batch1 @ batch2 (α)</param>\n public static Tensor addbmm(Tensor input, Tensor batch1, Tensor batch2, float beta = 1, float alpha = 1)\n => input.addbmm(batch1, batch2, beta, alpha);\n\n /// <summary>\n /// Performs a batch matrix-matrix product of matrices stored in batch1 and batch2, with a reduced\n /// add step (all matrix multiplications get accumulated along the first dimension).\n /// input is added to the final result.\n /// In-place version of addbmm.\n /// </summary>\n /// <param name=\"input\">The input tensor.</param>\n /// <param name=\"batch1\">The first batch of matrices to be multiplied</param>\n /// <param name=\"batch2\">The second batch of matrices to be multiplied</param>\n /// <param name=\"beta\">Nultiplier for input (β)</param>\n /// <param name=\"alpha\">Multiplier for batch1 @ batch2 (α)</param>\n public static Tensor addbmm_(Tensor input, Tensor batch1, Tensor batch2, float beta = 1, float alpha = 1)\n => input.addbmm_(batch1, batch2, beta, alpha);\n\n // https://pytorch.org/docs/stable/generated/torch.addmm\n /// <summary>\n /// Performs a matrix multiplication of the matrices mat1 and mat2. The matrix input is added to the final result.\n /// </summary>\n /// <param name=\"input\">The input tensor.</param>\n /// <param name=\"mat1\">First matrix</param>\n /// <param name=\"mat2\">Second matrix</param>\n /// <param name=\"beta\">Input scale factor</param>\n /// <param name=\"alpha\">Matrix multiplication scale factor</param>\n public static Tensor addmm(Tensor input, Tensor mat1, Tensor mat2, float beta, float alpha) => input.addmm(mat1, mat2, beta, alpha);\n\n // https://pytorch.org/docs/stable/generated/torch.addmm\n /// <summary>\n /// Performs a matrix multiplication of the matrices mat1 and mat2. The matrix input is added to the final result.\n /// </summary>\n /// <param name=\"input\">The input tensor.</param>\n /// <param name=\"mat1\">First matrix</param>\n /// <param name=\"mat2\">Second matrix</param>\n /// <param name=\"beta\">Input scale factor</param>\n /// <param name=\"alpha\">Matrix multiplication scale factor</param>\n public static Tensor addmm_(Tensor input, Tensor mat1, Tensor mat2, float beta, float alpha) => input.addmm_(mat1, mat2, beta, alpha);\n\n // https://pytorch.org/docs/stable/generated/torch.addmv\n\n /// <summary>\n /// Performs a matrix multiplication of the matrices mat1 and mat2. The matrix input is added to the final result.\n /// </summary>\n /// <param name=\"input\">The input tensor.</param>\n /// <param name=\"mat1\">First matrix</param>\n /// <param name=\"mat2\">Second matrix</param>\n /// <param name=\"beta\">Input scale factor</param>\n /// <param name=\"alpha\">Matrix multiplication scale factor</param>\n public static Tensor addmv(Tensor input, Tensor mat1, Tensor mat2, float beta, float alpha) => input.addmv(mat1, mat2, beta, alpha);\n\n /// <summary>\n /// Performs a matrix multiplication of the matrices mat1 and mat2. The matrix input is added to the final result.\n /// </summary>\n /// <param name=\"input\">The input tensor.</param>\n /// <param name=\"mat1\">First matrix</param>\n /// <param name=\"mat2\">Second matrix</param>\n /// <param name=\"beta\">Input scale factor</param>\n /// <param name=\"alpha\">Matrix multiplication scale factor</param>\n public static Tensor addmv_(Tensor input, Tensor mat1, Tensor mat2, float beta, float alpha) => input.addmv_(mat1, mat2, beta, alpha);\n\n // https://pytorch.org/docs/stable/generated/torch.addr\n /// <summary>\n /// Performs the outer-product of vectors vec1 and vec2 and adds it to the input tensor.\n /// </summary>\n /// <returns></returns>\n public static Tensor addr(Tensor input, Tensor vec1, Tensor vec2, float beta = 1.0f, float alpha = 1.0f) => input.addr(vec1, vec2, beta, alpha);\n\n // https://pytorch.org/docs/stable/generated/torch.addr\n /// <summary>\n /// Performs the outer-product of vectors vec1 and vec2 and adds it to the input tensor.\n /// </summary>\n /// <param name=\"input\">The input tensor.</param>\n /// <param name=\"vec1\">The first vector of the outer product</param>\n /// <param name=\"vec2\">The second vector of the outer product</param>\n /// <param name=\"beta\">Input scale factor</param>\n /// <param name=\"alpha\">Outer-product scale factor</param>\n public static Tensor addr_(Tensor input, Tensor vec1, Tensor vec2, float beta = 1.0f, float alpha = 1.0f) => input.addr_(vec1, vec2, beta, alpha);\n\n // https://pytorch.org/docs/stable/generated/torch.baddbmm\n /// <summary>\n /// Performs a batch matrix-matrix product of matrices in batch1 and batch2. input is added to the final result.\n /// batch1 and batch2 must be 3-D tensors each containing the same number of matrices.\n /// </summary>\n /// <param name=\"input\">The tensor to be added</param>\n /// <param name=\"batch1\">The first batch of matrices to be multiplied</param>\n /// <param name=\"batch2\">The second batch of matrices to be multiplied</param>\n /// <param name=\"beta\">A multiplier for input</param>\n /// <param name=\"alpha\">A multiplier for batch1 @ batch2</param>\n public static Tensor baddbmm(Tensor input, Tensor batch1, Tensor batch2, float beta = 1, float alpha = 1) => input.baddbmm(batch1, batch2, beta, alpha);\n\n // https://pytorch.org/docs/stable/generated/torch.bmm\n /// <summary>\n /// Performs a batch matrix-matrix product of matrices stored in input and mat2.\n /// </summary>\n /// <param name=\"input\">The input tensor</param>\n /// <param name=\"batch2\">the second batch of matrices to be multiplied</param>\n public static Tensor bmm(Tensor input, Tensor batch2) => input.bmm(batch2);\n\n // https://pytorch.org/docs/stable/generated/torch.chain_matmul\n public static Tensor chain_matmul(params Tensor[] matrices) => torch.linalg.multi_dot(matrices);\n\n // https://pytorch.org/docs/stable/generated/torch.cholesky\n\n public static Tensor cholesky(Tensor input) => input.cholesky();\n\n // https://pytorch.org/docs/stable/generated/torch.cholesky_inverse\n /// <summary>\n /// Computes the inverse of a symmetric positive-definite matrix AA using its Cholesky factor uu : returns matrix inv\n /// </summary>\n /// <returns></returns>\n public static Tensor cholesky_inverse(Tensor input, bool upper = false)\n => input.cholesky_inverse(upper);\n\n // https://pytorch.org/docs/stable/generated/torch.cholesky_solve\n /// <summary>\n /// Solves a linear system of equations with a positive semidefinite matrix to be inverted given its Cholesky factor matrix u.\n /// </summary>\n /// <returns></returns>\n public static Tensor cholesky_solve(Tensor input, Tensor input2, bool upper = false)\n => input.cholesky_solve(input2, upper);\n\n // https://pytorch.org/docs/stable/generated/torch.dot\n /// <summary>\n /// Computes the dot product of two 1D tensors.\n /// </summary>\n public static Tensor dot(Tensor input, Tensor other) => input.dot(other);\n\n // https://pytorch.org/docs/stable/generated/torch.eig\n [Obsolete(\"Method removed in Pytorch. Please use the `torch.linalg.eig` function instead.\", true)]\n public static (Tensor eigenvalues, Tensor eigenvectors) eig(Tensor input, bool eigenvectors = false) => throw new NotImplementedException();\n\n // https://pytorch.org/docs/stable/generated/torch.geqrf\n /// <summary>\n /// This is a low-level function for calling LAPACK’s geqrf directly.\n /// This function returns a namedtuple (a, tau) as defined in LAPACK documentation for geqrf.\n /// </summary>\n /// <param name=\"input\">The input tensor.</param>\n /// <remarks>\n /// Computes a QR decomposition of input. Both Q and R matrices are stored in the same output tensor a.\n /// The elements of R are stored on and above the diagonal. Elementary reflectors (or Householder vectors)\n /// implicitly defining matrix Q are stored below the diagonal. The results of this function can be used\n /// together with torch.linalg.householder_product() to obtain the Q matrix or with torch.ormqr(), which\n /// uses an implicit representation of the Q matrix, for an efficient matrix-matrix multiplication.\n /// </remarks>\n public static (Tensor a, Tensor tau) geqrf(Tensor input) => input.geqrf();\n\n // https://pytorch.org/docs/stable/generated/torch.ger\n /// <summary>\n /// Outer product of input and vec2.\n /// </summary>\n /// <param name=\"input\">The input vector.</param>\n /// <param name=\"vec2\">1-D input vector.</param>\n /// <remarks>If input is a vector of size n and vec2 is a vector of size m, then out must be a matrix of size n×m.</remarks>\n public static Tensor ger(Tensor input, Tensor vec2) => input.ger(vec2);\n\n // https://pytorch.org/docs/stable/generated/torch.inner\n /// <summary>\n /// Computes the dot product for 1D tensors.\n /// For higher dimensions, sums the product of elements from input and other along their last dimension.\n /// </summary>\n public static Tensor inner(Tensor input, Tensor vec2) => input.inner(vec2);\n\n // https://pytorch.org/docs/stable/generated/torch.inverse\n /// <summary>\n /// Alias for torch.linalg.inv()\n /// </summary>\n public static Tensor inverse(Tensor input) => linalg.inv(input);\n\n // https://pytorch.org/docs/stable/generated/torch.det\n /// <summary>\n /// Computes the determinant of a square matrix.\n /// </summary>\n /// <param name=\"input\">The input tensor.</param>\n public static Tensor det(Tensor input) => input.det();\n\n // https://pytorch.org/docs/stable/generated/torch.logdet\n public static Tensor logdet(Tensor input) => input.logdet();\n\n // https://pytorch.org/docs/stable/generated/torch.slogdet\n public static (Tensor res, Tensor logabsdet) slogdet(Tensor A) => torch.linalg.slogdet(A);\n\n // https://pytorch.org/docs/stable/generated/torch.lstsq\n /// <summary>\n /// Computes the solution to the least squares and least norm problems for a full rank matrix A of size m×n and a matrix B of size m×k.\n /// </summary>\n /// <param name=\"A\">the m by n matrix AA</param>\n /// <param name=\"B\">the matrix BB</param>\n /// <returns></returns>\n public static (Tensor Solution, Tensor QR) lstsq(Tensor B, Tensor A)\n {\n var solution = THSTorch_lstsq(B.Handle, A.Handle, out var qr);\n if (solution == IntPtr.Zero || qr == IntPtr.Zero)\n CheckForErrors();\n return (new Tensor(solution), new Tensor(qr));\n }\n\n // https://pytorch.org/docs/stable/generated/torch.lu\n /// <summary>\n /// Computes the LU factorization of a matrix or batches of matrices A. Returns a tuple containing the LU factorization and pivots of A. Pivoting is done if pivot is set to true.\n /// </summary>\n /// <param name=\"A\">The tensor to factor of size (∗,m,n)</param>\n /// <param name=\"pivot\">Controls whether pivoting is done. Default: true</param>\n /// <param name=\"get_infos\">If set to True, returns an info IntTensor. Default: false</param>\n /// <returns></returns>\n public static (Tensor A_LU, Tensor? pivots, Tensor? infos) lu(Tensor A, bool pivot = true, bool get_infos = false)\n {\n var solution = THSTensor_lu(A.Handle, pivot, get_infos, out var infos, out var pivots);\n if (solution == IntPtr.Zero)\n torch.CheckForErrors();\n return (new Tensor(solution), pivots == IntPtr.Zero ? null : new Tensor(pivots), infos == IntPtr.Zero ? null : new Tensor(infos));\n }\n\n // https://pytorch.org/docs/stable/generated/torch.lu_solve\n /// <summary>\n /// Returns the LU solve of the linear system Ax = b using the partially pivoted LU factorization of A from torch.lu().\n /// </summary>\n /// <param name=\"b\">The RHS tensor of size (∗,m,k), where *∗ is zero or more batch dimensions.</param>\n /// <param name=\"LU_data\">The pivoted LU factorization of A from torch.lu() of size (∗,m,m), where *∗ is zero or more batch dimensions.</param>\n /// <param name=\"LU_pivots\">\n /// The pivots of the LU factorization from torch.lu() of size (∗,m), where *∗ is zero or more batch dimensions.\n /// The batch dimensions of LU_pivots must be equal to the batch dimensions of LU_data.</param>\n /// <returns></returns>\n public static Tensor lu_solve(Tensor b, Tensor LU_data, Tensor LU_pivots)\n {\n var solution = THSTensor_lu_solve(b.Handle, LU_data.Handle, LU_pivots.Handle);\n if (solution == IntPtr.Zero)\n CheckForErrors();\n return new Tensor(solution);\n }\n\n // https://pytorch.org/docs/stable/generated/torch.lu_unpack\n /// <summary>\n /// Unpacks the data and pivots from a LU factorization of a tensor into tensors L and U and a permutation tensor P.\n /// </summary>\n /// <param name=\"LU_data\">The packed LU factorization data</param>\n /// <param name=\"LU_pivots\">The packed LU factorization pivots</param>\n /// <param name=\"unpack_data\">A flag indicating if the data should be unpacked. If false, then the returned L and U are null. Default: true</param>\n /// <param name=\"unpack_pivots\">A flag indicating if the pivots should be unpacked into a permutation matrix P. If false, then the returned P is null. Default: true</param>\n /// <returns>A tuple of three tensors to use for the outputs (P, L, U)</returns>\n public static (Tensor P, Tensor? L, Tensor? U) lu_unpack(Tensor LU_data, Tensor LU_pivots, bool unpack_data = true, bool unpack_pivots = true)\n {\n var solution = THSTensor_lu_unpack(LU_data.Handle, LU_pivots.Handle, unpack_data, unpack_pivots, out var L, out var U);\n if (solution == IntPtr.Zero)\n CheckForErrors();\n return (new Tensor(solution), L == IntPtr.Zero ? null : new Tensor(L), U == IntPtr.Zero ? null : new Tensor(U));\n }\n\n // https://pytorch.org/docs/stable/generated/torch.matmul\n /// <summary>\n /// Matrix product of two tensors.\n /// </summary>\n /// <returns></returns>\n /// <remarks>\n /// The behavior depends on the dimensionality of the tensors as follows:\n /// 1. If both tensors are 1-dimensional, the dot product (scalar) is returned\n /// 2. If both arguments are 2-dimensional, the matrix-matrix product is returned.\n /// 3. If the first argument is 1-dimensional and the second argument is 2-dimensional, a 1 is prepended to its dimension for the purpose of the matrix multiply. After the matrix multiply, the prepended dimension is removed.\n /// 4. If the first argument is 2-dimensional and the second argument is 1-dimensional, the matrix-vector product is returned.\n /// 5. If both arguments are at least 1-dimensional and at least one argument is N-dimensional (where N > 2), then a batched matrix multiply is returned.\n /// </remarks>\n public static Tensor matmul(Tensor input, Tensor target) => input.matmul(target);\n\n // https://pytorch.org/docs/stable/generated/torch.matrix_power\n /// <summary>\n /// Computes the n-th power of a square matrix for an integer n.\n /// </summary>\n /// <param name=\"input\">The input square matrix.</param>\n /// <param name=\"n\">The exponent</param>\n /// <returns></returns>\n /// <remarks>Input tensor must be of shape (*, m, m) where * is zero or more batch dimensions.</remarks>\n public static Tensor matrix_power(Tensor input, int n) => input.matrix_power(n);\n\n // https://pytorch.org/docs/stable/generated/torch.matrix_rank\n [Obsolete(\"This function was deprecated since version 1.9 and is now removed. Please use the 'torch.linalg.matrix_rank' function instead.\", true)]\n public static Tensor matrix_rank(Tensor input, float? tol = null, bool symmetric = false) => throw new NotImplementedException();\n\n // https://pytorch.org/docs/stable/generated/torch.matrix_exp\n /// <summary>\n /// Computes the matrix exponential of a square matrix or of each square matrix in a batch.\n /// </summary>\n /// <param name=\"input\">The input tensor.</param>\n public static Tensor matrix_exp(Tensor input) => input.matrix_exp();\n\n // https://pytorch.org/docs/stable/generated/torch.mm\n /// <summary>\n /// Performs a matrix multiplication of the matrices input and mat2.\n /// </summary>\n /// <returns></returns>\n public static Tensor mm(Tensor input, Tensor target) => input.mm(target);\n\n // https://pytorch.org/docs/stable/generated/torch.mv\n /// <summary>\n /// Performs a matrix-vector product of the matrix input and the vector vec.\n /// </summary>\n /// <returns></returns>\n public static Tensor mv(Tensor input, Tensor target) => input.mv(target);\n\n // https://pytorch.org/docs/stable/generated/torch.orgqr\n /// <summary>\n /// Computes the first n columns of a product of Householder matrices.\n /// </summary>\n /// <param name=\"input\">tensor of shape (*, m, n) where * is zero or more batch dimensions.</param>\n /// <param name=\"tau\">tensor of shape (*, k) where * is zero or more batch dimensions.</param>\n public static Tensor orgqr(Tensor input, Tensor tau) => linalg.householder_product(input, tau);\n\n // https://pytorch.org/docs/stable/generated/torch.ormqr\n /// <summary>\n /// Computes the matrix-matrix multiplication of a product of Householder matrices with a general matrix.\n /// </summary>\n /// <param name=\"input\">Tensor of shape (*, mn, k) where * is zero or more batch dimensions and mn equals to m or n depending on the left.</param>\n /// <param name=\"tau\">Tensor of shape (*, min(mn, k)) where * is zero or more batch dimensions.</param>\n /// <param name=\"other\">Tensor of shape (*, m, n) where * is zero or more batch dimensions.</param>\n /// <param name=\"left\">Controls the order of multiplication.</param>\n /// <param name=\"transpose\">Controls whether the matrix Q is conjugate transposed or not.</param>\n public static Tensor ormqr(Tensor input, Tensor tau, Tensor other, bool left=true, bool transpose=false) => input.ormqr(tau, other, left, transpose); \n\n // https://pytorch.org/docs/stable/generated/torch.outer\n /// <summary>\n /// Outer product of input and vec2.\n /// </summary>\n /// <param name=\"input\">1-D input vector.</param>\n /// <param name=\"vec2\">1-D input vector.</param>\n /// <remarks>If input is a vector of size n and vec2 is a vector of size m, then out must be a matrix of size n×m.</remarks>\n public static Tensor outer(Tensor input, Tensor vec2) => input.outer(vec2);\n\n // https://pytorch.org/docs/stable/generated/torch.pinverse\n /// <summary>\n /// Computes the pseudoinverse (Moore-Penrose inverse) of a matrix.\n /// </summary>\n /// <param name=\"input\">Tensor of shape (*, m, n) where * is zero or more batch dimensions.</param>\n /// <param name=\"rcond\">The tolerance value to determine when is a singular value zero </param>\n /// <param name=\"hermitian\">Indicates whether A is Hermitian if complex or symmetric if real. </param>\n /// <remarks>Input should be tensor of shape (*, m, n) where * is zero or more batch dimensions.</remarks>\n public static Tensor pinverse(Tensor input, double rcond = 1e-15, bool hermitian = false) => input.pinverse(rcond, hermitian);\n\n // https://pytorch.org/docs/stable/generated/torch.qr\n [Obsolete(\"torch.qr() is deprecated in favor of torch.linalg.qr() and will be removed in a future PyTorch release.\", true)]\n public static Tensor qr(Tensor input, bool some = true) => throw new NotImplementedException();\n\n // https://pytorch.org/docs/stable/generated/torch.svd\n [Obsolete(\"torch.qr() is deprecated in favor of torch.linalg.svd() and will be removed in a future PyTorch release.\", true)]\n public static Tensor svd(Tensor input, bool some=true, bool compute_uv=true) => throw new NotImplementedException();\n\n // https://pytorch.org/docs/stable/generated/torch.svd_lowrank\n // NOTE TO SELF: there's no native method for this. PyTorch implements it in Python.\n [Obsolete(\"not implemented\", true)]\n public static Tensor svd_lowrank(Tensor A, int q=6, int niter=2,Tensor? M=null) => throw new NotImplementedException();\n\n // https://pytorch.org/docs/stable/generated/torch.pca_lowrank\n // NOTE TO SELF: there's no native method for this. PyTorch implements it in Python.\n [Obsolete(\"not implemented\", true)]\n public static Tensor pca_lowrank(Tensor A, int q=6, bool center=true, int niter=2) => throw new NotImplementedException();\n\n // https://pytorch.org/docs/stable/generated/torch.symeig\n [Obsolete(\"torch.symeig() is deprecated in favor of torch.linalg.eigh() and will be removed in a future PyTorch release\", true)]\n public static Tensor symeig(Tensor input, bool eigenvectors = false, bool upper = true) => throw new NotImplementedException();\n\n // https://pytorch.org/docs/stable/generated/torch.lobpcg\n [Obsolete(\"not implemented\", true)]\n static Tensor lobpcg(\n Tensor A,\n int k = 1,\n Tensor? B = null,\n Tensor? X = null,\n int? n = null,\n Tensor? iK = null,\n int? niter = null,\n float? tol=null,\n bool largest=true,\n LobpcgMethod method = LobpcgMethod.ortho,\n Action? tracker=null,\n IDictionary<string, string>? ortho_iparams=null,\n IDictionary<string, string>? ortho_fparams=null,\n IDictionary<string, string>? ortho_bparams=null)\n => throw new NotImplementedException();\n\n /// <summary>\n /// Computes the softmax function for the input tensor.\n /// </summary>\n /// <param name=\"input\">The input tensor</param>\n /// <param name=\"dim\">A dimension along which softmax will be computed.</param>\n /// <param name=\"dtype\">The desired data type of returned tensor.</param>\n public static Tensor softmax(Tensor input, int dim, ScalarType? dtype = null)\n => torch.special.softmax(input, dim, dtype);\n\n // https://pytorch.org/docs/stable/generated/torch.trapz\n /// <summary>\n /// Computes the trapezoidal rule along dim. By default the spacing between elements is assumed\n /// to be 1, but dx can be used to specify a different constant spacing, and x can be used to specify arbitrary spacing along dim.\n /// </summary>\n /// <param name=\"y\">Values to use when computing the trapezoidal rule.</param>\n /// <param name=\"x\">Defines spacing between values as specified above.</param>\n /// <param name=\"dim\">The dimension along which to compute the trapezoidal rule. The last (inner-most) dimension by default.</param>\n public static Tensor trapz(Tensor y, Tensor x, long dim = -1) => trapezoid(y, x, dim);\n\n /// <summary>\n /// Computes the trapezoidal rule along dim. By default the spacing between elements is assumed\n /// to be 1, but dx can be used to specify a different constant spacing, and x can be used to specify arbitrary spacing along dim.\n /// </summary>\n /// <param name=\"y\">Values to use when computing the trapezoidal rule.</param>\n /// <param name=\"dx\">Constant spacing between values.</param>\n /// <param name=\"dim\">The dimension along which to compute the trapezoidal rule. The last (inner-most) dimension by default.</param>\n public static Tensor trapz(Tensor y, double dx = 1, long dim = -1) => trapezoid(y, dx, dim);\n\n // https://pytorch.org/docs/stable/generated/torch.trapezoid\n /// <summary>\n /// Computes the trapezoidal rule along dim. By default the spacing between elements is assumed\n /// to be 1, but dx can be used to specify a different constant spacing, and x can be used to specify arbitrary spacing along dim.\n /// </summary>\n /// <param name=\"y\">Values to use when computing the trapezoidal rule.</param>\n /// <param name=\"x\">Defines spacing between values as specified above.</param>\n /// <param name=\"dim\">The dimension along which to compute the trapezoidal rule. The last (inner-most) dimension by default.</param>\n public static Tensor trapezoid(Tensor y, Tensor x, long dim = -1) => y.trapezoid(x, dim);\n\n /// <summary>\n /// Computes the trapezoidal rule along dim. By default the spacing between elements is assumed\n /// to be 1, but dx can be used to specify a different constant spacing, and x can be used to specify arbitrary spacing along dim.\n /// </summary>\n /// <param name=\"y\">Values to use when computing the trapezoidal rule.</param>\n /// <param name=\"dx\">Constant spacing between values.</param>\n /// <param name=\"dim\">The dimension along which to compute the trapezoidal rule. The last (inner-most) dimension by default.</param>\n public static Tensor trapezoid(Tensor y, double dx = 1, long dim = -1) => y.trapezoid(dx, dim);\n\n // https://pytorch.org/docs/stable/generated/torch.cumulative_trapezoid\n /// <summary>\n /// Cumulatively computes the trapezoidal rule along dim. By default the spacing between elements is assumed\n /// to be 1, but dx can be used to specify a different constant spacing, and x can be used to specify arbitrary spacing along dim.\n /// </summary>\n /// <param name=\"y\">Values to use when computing the trapezoidal rule.</param>\n /// <param name=\"x\">Defines spacing between values as specified above.</param>\n /// <param name=\"dim\">The dimension along which to compute the trapezoidal rule. The last (inner-most) dimension by default.</param>\n public static Tensor cumulative_trapezoid(Tensor y, Tensor x, long dim = -1) => y.cumulative_trapezoid(x, dim);\n\n /// <summary>\n /// Cumulatively computes the trapezoidal rule along dim. By default the spacing between elements is assumed\n /// to be 1, but dx can be used to specify a different constant spacing, and x can be used to specify arbitrary spacing along dim.\n /// </summary>\n /// <param name=\"y\">Values to use when computing the trapezoidal rule.</param>\n /// <param name=\"dx\">Constant spacing between values.</param>\n /// <param name=\"dim\">The dimension along which to compute the trapezoidal rule. The last (inner-most) dimension by default.</param>\n public static Tensor cumulative_trapezoid(Tensor y, double dx = 1, long dim = -1) => y.cumulative_trapezoid(dx, dim);\n\n // https://pytorch.org/docs/stable/generated/torch.triangular_solve\n [Obsolete(\"torch.triangular_solve() is deprecated in favor of torch.linalg.solve_triangular() and will be removed in a future PyTorch release.\", true)]\n static Tensor triangular_solve(\n Tensor b,\n Tensor A,\n bool upper = true,\n bool transpose = false,\n bool unitriangular = false)\n => throw new NotImplementedException();\n\n // https://pytorch.org/docs/stable/generated/torch.vdot\n /// <summary>\n /// Computes the dot product of two 1D tensors.\n /// </summary>\n /// <param name=\"input\"></param>\n /// <param name=\"target\"></param>\n /// <returns></returns>\n /// <remarks>\n /// The vdot(a, b) function handles complex numbers differently than dot(a, b).\n /// If the first argument is complex, the complex conjugate of the first argument is used for the calculation of the dot product.\n /// </remarks>\n public static Tensor vdot(Tensor input, Tensor target) => input.vdot(target);\n }\n}"},{"alpha_fraction":0.4919374883174896,"alphanum_fraction":0.4924336373806,"avg_line_length":32.59166717529297,"blob_id":"0ea4f8bf2e92f0d83ce8f138d3e05291366d4925","content_id":"3f9d27b8075e626bd97b322548a45b8fdf0e4e11","detected_licenses":["MIT"],"is_generated":false,"is_vendor":false,"language":"C#","length_bytes":4031,"license_type":"permissive","max_line_length":131,"num_lines":120,"path":"/src/TorchSharp/Generator.cs","repo_name":"NiklasGustafsson/TorchSharp","src_encoding":"UTF-8","text":"// Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information.\nusing System;\nusing static TorchSharp.PInvoke.NativeMethods;\n\n#nullable enable\n\nnamespace TorchSharp\n{\n public static partial class torch\n {\n /// <summary>\n /// Random Number Generator\n /// </summary>\n public class Generator : IDisposable\n {\n public IntPtr Handle { get; private set; }\n\n /// <summary>\n /// Gets the current device of the generator.\n /// </summary>\n public torch.Device device { get; private set; }\n\n /// <summary>\n /// Returns the Generator state as a torch.ByteTensor.\n /// </summary>\n /// <returns></returns>\n public Tensor get_state()\n {\n var res = THSGenerator_get_rng_state(Handle);\n if (res == IntPtr.Zero) { torch.CheckForErrors(); }\n return new Tensor(res);\n }\n\n /// <summary>\n /// Sets the Generator state.\n /// </summary>\n /// <param name=\"value\">The desired state.</param>\n public void set_state(Tensor value)\n {\n THSGenerator_set_rng_state(Handle, value is null ? IntPtr.Zero : value.Handle);\n torch.CheckForErrors();\n }\n\n public Generator manual_seed(long seed)\n {\n torch.TryInitializeDeviceType(DeviceType.CUDA);\n THSGenerator_gen_manual_seed(Handle, seed);\n return this;\n }\n\n /// <summary>\n /// Gets a non-deterministic random number from std::random_device or the current time and uses it to seed a Generator.\n /// </summary>\n /// <returns></returns>\n public long seed()\n {\n long seed = DateTime.UtcNow.Ticks;\n torch.TryInitializeDeviceType(DeviceType.CUDA);\n THSGenerator_gen_manual_seed(Handle, seed);\n return seed;\n }\n\n internal Generator(IntPtr nativeHandle)\n {\n Handle = nativeHandle;\n device = torch.CPU;\n }\n\n /// <summary>\n /// Constructor\n /// </summary>\n /// <param name=\"seed\">An initial seed to use with the generator.</param>\n /// <param name=\"device\">The desired device.</param>\n public Generator(ulong seed = 0, torch.Device? device = null) :\n this(THSGenerator_new(seed, (long)(device?.type ?? DeviceType.CPU), device?.index ?? -1))\n {\n this.device = device ?? torch.CPU;\n }\n\n public static Generator Default {\n get {\n return new Generator(THSGenerator_default_generator());\n }\n }\n\n /// <summary>\n /// Returns the initial seed for generating random numbers.\n /// </summary>\n /// <returns></returns>\n public long initial_seed()\n {\n return THSGenerator_initial_seed(Handle);\n }\n\n #region Dispose() support\n protected virtual void Dispose(bool disposing)\n {\n if (Handle != IntPtr.Zero) {\n var h = Handle;\n Handle = IntPtr.Zero;\n THSGenerator_dispose(h);\n }\n }\n\n ~Generator()\n {\n // Do not change this code. Put cleanup code in 'Dispose(bool disposing)' method\n Dispose(disposing: false);\n }\n\n public void Dispose()\n {\n // Do not change this code. Put cleanup code in 'Dispose(bool disposing)' method\n Dispose(disposing: true);\n GC.SuppressFinalize(this);\n }\n #endregion\n }\n }\n}\n"},{"alpha_fraction":0.5701754093170166,"alphanum_fraction":0.5742239952087402,"avg_line_length":34.28571319580078,"blob_id":"888583bb24b5c312f323f7359b4badabe3fcfce1","content_id":"cdf7f93bc10cab285c61c0b1a8b9376f0abcfdd0","detected_licenses":["MIT"],"is_generated":false,"is_vendor":false,"language":"C#","length_bytes":1482,"license_type":"permissive","max_line_length":130,"num_lines":42,"path":"/src/TorchVision/AdjustContrast.cs","repo_name":"NiklasGustafsson/TorchSharp","src_encoding":"UTF-8","text":"// Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information.\nusing System;\nusing static TorchSharp.torch;\n\nnamespace TorchSharp\n{\n public static partial class torchvision\n {\n internal class AdjustContrast : ITransform\n {\n internal AdjustContrast(double contrast_factor)\n {\n if (contrast_factor < 0.0)\n throw new ArgumentException($\"The sharpness factor ({contrast_factor}) must be non-negative.\");\n this.contrast_factor = contrast_factor;\n }\n\n public Tensor call(Tensor input)\n {\n return transforms.functional.adjust_contrast(input, contrast_factor);\n }\n\n private double contrast_factor;\n }\n\n public static partial class transforms\n {\n /// <summary>\n /// Adjust the contrast of the image. \n /// </summary>\n /// <param name=\"contrast_factor\">\n /// How much to adjust the contrast. Can be any non-negative number.\n /// 0 gives a solid gray image, 1 gives the original image while 2 increases the contrast by a factor of 2.\n /// </param>\n /// <returns></returns>\n static public ITransform AdjustContrast(double contrast_factor)\n {\n return new AdjustContrast(contrast_factor);\n }\n }\n }\n}\n"},{"alpha_fraction":0.738095223903656,"alphanum_fraction":0.7428571581840515,"avg_line_length":31.30769157409668,"blob_id":"bda0a3cf34e22556c9ab1b2f7229c8afe1773533","content_id":"f299c7b0e6fa2336c50f70420d1221eb65381048","detected_licenses":["MIT"],"is_generated":false,"is_vendor":false,"language":"C#","length_bytes":420,"license_type":"permissive","max_line_length":130,"num_lines":13,"path":"/src/TorchSharp/PInvoke/LibTorchSharp.crc32c.cs","repo_name":"NiklasGustafsson/TorchSharp","src_encoding":"UTF-8","text":"// Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information.\n#nullable enable\nusing System;\nusing System.Runtime.InteropServices;\n\nnamespace TorchSharp.PInvoke\n{\n internal static partial class NativeMethods\n {\n [DllImport(\"LibTorchSharp\")]\n internal static extern uint crc32c_append(uint crc, IntPtr value, ulong length);\n }\n}\n"},{"alpha_fraction":0.5171321034431458,"alphanum_fraction":0.5222156047821045,"avg_line_length":57.034481048583984,"blob_id":"8bd612088c2e48d10752d8fe514a557d09611e7b","content_id":"5dd5fe6e2fd128b473d4d4ad55c8146dbf760522","detected_licenses":["MIT"],"is_generated":false,"is_vendor":false,"language":"C#","length_bytes":15159,"license_type":"permissive","max_line_length":288,"num_lines":261,"path":"/src/TorchSharp/NN/Vision.cs","repo_name":"NiklasGustafsson/TorchSharp","src_encoding":"UTF-8","text":"// Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information.\nusing System;\nusing static TorchSharp.PInvoke.NativeMethods;\n\n#nullable enable\nnamespace TorchSharp\n{\n public static partial class torch\n {\n public enum UpsampleMode\n {\n Nearest = 0,\n Linear = 1,\n Bilinear = 2,\n Bicubic = 3,\n Trilinear = 4\n }\n\n public enum InterpolationMode\n {\n Nearest = 0,\n Linear = 1,\n Bilinear = 2,\n Bicubic = 3,\n Trilinear = 4,\n Area = 5\n }\n\n public enum GridSampleMode\n {\n Nearest = 0,\n Bilinear = 2,\n // PyTorch docs say this should be available,\n // but the libtorch native code does not support it.\n //Bicubic = 3\n }\n\n public enum GridSamplePaddingMode\n {\n Zeros = 0,\n Reflection = 1,\n Border = 2,\n }\n\n public static partial class nn\n {\n\n public static partial class functional\n {\n\n /// <summary>\n /// Pads tensor.\n /// </summary>\n /// <param name=\"input\">N-dimensional tensor</param>\n /// <param name=\"pad\">m-elements tuple, where m/2 ≤ input dimensions and m is even</param>\n /// <param name=\"mode\">'constant', 'reflect', 'replicate' or 'circular'. Default: 'constant'</param>\n /// <param name=\"value\">Fill value for 'constant' padding. Default: 0</param>\n /// <returns></returns>\n public static Tensor pad(Tensor input, long[] pad, PaddingModes mode = PaddingModes.Constant, double value = 0)\n {\n unsafe {\n fixed (long* psize = pad) {\n var res = THSNN_pad(input.Handle, (IntPtr)psize, pad.Length, (byte)mode, value);\n if (res == IntPtr.Zero) { torch.CheckForErrors(); }\n return new Tensor(res);\n }\n }\n }\n\n /// <summary>\n /// Pads tensor.\n /// </summary>\n /// <param name=\"input\">N-dimensional tensor</param>\n /// <param name=\"pad\">m-elements tuple, where m/2 ≤ input dimensions and m is even</param>\n /// <param name=\"mode\">'constant', 'reflect', 'replicate' or 'circular'. Default: 'constant'</param>\n /// <param name=\"value\">Fill value for 'constant' padding. Default: 0</param>\n /// <returns></returns>\n public static Tensor pad(Tensor input, ReadOnlySpan<long> pad, PaddingModes mode = PaddingModes.Constant, double value = 0)\n {\n unsafe {\n fixed (long* psize = pad) {\n var res = THSNN_pad(input.Handle, (IntPtr)psize, pad.Length, (byte)mode, value);\n if (res == IntPtr.Zero) { torch.CheckForErrors(); }\n return new Tensor(res);\n }\n }\n }\n\n /// <summary>\n /// Pads tensor.\n /// </summary>\n /// <param name=\"input\">N-dimensional tensor</param>\n /// <param name=\"pad\">m-elements tuple, where m/2 ≤ input dimensions and m is even</param>\n /// <param name=\"mode\">'constant', 'reflect', 'replicate' or 'circular'. Default: 'constant'</param>\n /// <param name=\"value\">Fill value for 'constant' padding. Default: 0</param>\n /// <returns></returns>\n public static Tensor pad(Tensor input, (long, long) pad, PaddingModes mode = PaddingModes.Constant, double value = 0)\n {\n unsafe {\n var correctedPad = stackalloc long[] { pad.Item1, pad.Item2 };\n\n var res = THSNN_pad(input.Handle, (IntPtr)correctedPad, 2, (byte)mode, value);\n if (res == IntPtr.Zero) { torch.CheckForErrors(); }\n return new Tensor(res);\n }\n }\n\n /// <summary>\n /// Pads tensor.\n /// </summary>\n /// <param name=\"input\">N-dimensional tensor</param>\n /// <param name=\"pad\">m-elements tuple, where m/2 ≤ input dimensions and m is even</param>\n /// <param name=\"mode\">'constant', 'reflect', 'replicate' or 'circular'. Default: 'constant'</param>\n /// <param name=\"value\">Fill value for 'constant' padding. Default: 0</param>\n /// <returns></returns>\n public static Tensor pad(Tensor input, (long, long, long, long) pad, PaddingModes mode = PaddingModes.Constant, double value = 0)\n {\n unsafe {\n var correctedPad = stackalloc long[] { pad.Item1, pad.Item2, pad.Item3, pad.Item4 };\n var res = THSNN_pad(input.Handle, (IntPtr)correctedPad, 4, (byte)mode, value);\n if (res == IntPtr.Zero) { torch.CheckForErrors(); }\n return new Tensor(res);\n }\n }\n\n /// <summary>\n /// Pads tensor.\n /// </summary>\n /// <param name=\"input\">N-dimensional tensor</param>\n /// <param name=\"pad\">A single padding size, used for all edges.</param>\n /// <param name=\"mode\">'constant', 'reflect', 'replicate' or 'circular'. Default: 'constant'</param>\n /// <param name=\"value\">Fill value for 'constant' padding. Default: 0</param>\n /// <returns></returns>\n public static Tensor pad(Tensor input, long pad, PaddingModes mode = PaddingModes.Constant, double value = 0)\n {\n int length = (int)input.ndim * 2;\n\n unsafe {\n var correctedPad = stackalloc long[length];\n for (var i = 0; i < length; i++) correctedPad[i] = pad;\n\n var res = THSNN_pad(input.Handle, (IntPtr)correctedPad, length, (byte)mode, value);\n if (res == IntPtr.Zero) { torch.CheckForErrors(); }\n return new Tensor(res);\n }\n }\n\n\n /// <summary>\n /// Given an input and a flow-field grid, computes the output using input values and pixel locations from grid.\n /// </summary>\n /// <param name=\"input\">Tensor of 4D or 5D</param>\n /// <param name=\"grid\">Flow-field tensor.</param>\n /// <param name=\"mode\">Interpolation mode to calculate output values 'bilinear' | 'nearest' | 'bicubic'.</param>\n /// <param name=\"padding_mode\">Padding mode for outside grid values 'zeros' | 'border' | 'reflection'. Default: 'zeros'</param>\n /// <param name=\"align_corners\">Geometrically, we consider the pixels of the input as squares rather than points.\n /// If set to true, the extrema (-1 and 1) are considered as referring to the center points of the input’s corner pixels.\n /// If set to false, they are instead considered as referring to the corner points of the input’s corner pixels, making the sampling more resolution agnostic.</param>\n /// <returns></returns>\n /// <remarks>\n /// Currently, only spatial (4-D) and volumetric (5-D) input are supported.\n /// Note: mode='bicubic' supports only 4-D input.\n /// </remarks>\n public static Tensor grid_sample(Tensor input, Tensor grid, GridSampleMode mode = GridSampleMode.Bilinear, GridSamplePaddingMode padding_mode = GridSamplePaddingMode.Zeros, bool? align_corners = null)\n {\n byte ac = (byte)((align_corners.HasValue) ? (align_corners.Value ? 1 : 2) : 0);\n var res = THSNN_grid_sample(input.Handle, grid.Handle, (byte)mode, (byte)padding_mode, ac);\n if (res == IntPtr.Zero) { torch.CheckForErrors(); }\n return new Tensor(res);\n }\n\n /// <summary>\n /// Generates a 2D or 3D flow field (sampling grid), given a batch of affine matrices theta.\n /// </summary>\n /// <param name=\"theta\">Input batch of affine matrices with shape (N, 2, 3 ) for 2D or (N, 3, 4 ) for 3D</param>\n /// <param name=\"size\">The target output image size</param>\n /// <param name=\"align_corners\">if true, consider -1 and 1 to refer to the centers of the corner pixels rather than the image corners.\n /// Refer to grid_sample() for a more complete description.</param>\n /// <returns></returns>\n public static Tensor affine_grid(Tensor theta, long[]? size = null, bool align_corners = false)\n {\n unsafe {\n fixed (long* psize = size) {\n var res = THSNN_affine_grid(theta.Handle, (IntPtr)psize, size is null ? 0 : size.Length, align_corners);\n if (res == IntPtr.Zero) { torch.CheckForErrors(); }\n return new Tensor(res);\n }\n }\n }\n\n /// <summary>\n ///\n /// </summary>\n /// <param name=\"x\">The input tensor</param>\n /// <param name=\"size\">Output spatial size</param>\n /// <param name=\"scale_factor\">Multiplier for spatial size. Has to match input size if it is a tuple.</param>\n /// <param name=\"mode\">The algorithm used for upsampling: 'nearest' | 'linear' | 'bilinear' | 'bicubic' | 'trilinear' | 'area'</param>\n /// <param name=\"align_corners\">Geometrically, we consider the pixels of the input and output as squares rather than points.\n /// If set to true, the input and output tensors are aligned by the center points of their corner pixels, preserving the values at the corner pixels.\n /// If set to false, the input and output tensors are aligned by the corner points of their corner pixels, and the interpolation uses edge value padding for out-of-boundary values, making this operation independent of input size when scale_factor is kept the same.</param>\n /// <param name=\"recompute_scale_factor\">\n /// Recompute the scale_factor for use in the interpolation calculation.\n /// When scale_factor is passed as a parameter, it is used to compute the output_size.\n /// If recompute_scale_factor is False or not specified, the passed-in scale_factor will be used in the interpolation computation.\n /// Otherwise, a new scale_factor will be computed based on the output and input sizes for use in the interpolation computation\n /// (i.e. the computation will be identical to if the computed output_size were passed-in explicitly).\n /// </param>\n /// <returns></returns>\n public static Tensor interpolate(Tensor x, long[]? size = null, double[]? scale_factor = null, InterpolationMode mode = InterpolationMode.Nearest, bool? align_corners = null, bool recompute_scale_factor = false)\n {\n unsafe {\n fixed (long* psize = size) {\n fixed (double* pSF = scale_factor) {\n byte ac = (byte)((align_corners.HasValue) ? (align_corners.Value ? 1 : 2) : 0);\n var res = THSNN_interpolate(x.Handle, (IntPtr)psize, size is null ? 0 : size.Length, (IntPtr)pSF, scale_factor is null ? 0 : scale_factor.Length, (byte)mode, ac, recompute_scale_factor);\n if (res == IntPtr.Zero) { torch.CheckForErrors(); }\n return new Tensor(res);\n }\n }\n }\n }\n\n /// <summary>\n /// Upsamples a given multi-channel 1D (temporal), 2D (spatial) or 3D (volumetric) data.\n /// The input data is assumed to be of the form minibatch x channels x[optional depth] x[optional height] x width.\n /// Hence, for spatial inputs, we expect a 4D Tensor and for volumetric inputs, we expect a 5D Tensor.\n /// </summary>\n /// <param name=\"x\">Input tensor</param>\n /// <param name=\"size\">Output spatial sizes</param>\n /// <param name=\"scale_factor\">Multiplier for spatial size. Has to match input size</param>\n /// <param name=\"align_corners\">If true, the corner pixels of the input and output tensors are aligned, and thus preserving the values at those pixels.\n /// This only has effect when mode is 'linear', 'bilinear', or 'trilinear'. Default: false</param>\n /// <returns></returns>\n public static Tensor upsample_bilinear(Tensor x, long[]? size = null, double[]? scale_factor = null, bool align_corners = false)\n {\n using (var d = torch.nn.Upsample(size, scale_factor, UpsampleMode.Bilinear, align_corners)) {\n return d.call(x);\n }\n }\n\n /// <summary>\n /// Upsamples a given multi-channel 1D (temporal), 2D (spatial) or 3D (volumetric) data.\n /// The input data is assumed to be of the form minibatch x channels x[optional depth] x[optional height] x width.\n /// Hence, for spatial inputs, we expect a 4D Tensor and for volumetric inputs, we expect a 5D Tensor.\n /// </summary>\n /// <param name=\"x\">Input tensor</param>\n /// <param name=\"size\">Output spatial sizes</param>\n /// <param name=\"scale_factor\">Multiplier for spatial size. Has to match input size</param>\n /// <param name=\"align_corners\">If true, the corner pixels of the input and output tensors are aligned, and thus preserving the values at those pixels.\n /// This only has effect when mode is 'linear', 'bilinear', or 'trilinear'. Default: false</param>\n /// <returns></returns>\n public static Tensor upsample_nearest(Tensor x, long[]? size = null, double[]? scale_factor = null, bool align_corners = false)\n {\n using (var d = torch.nn.Upsample(size, scale_factor, UpsampleMode.Nearest, align_corners)) {\n return d.call(x);\n }\n }\n }\n }\n }\n}\n"},{"alpha_fraction":0.6417730450630188,"alphanum_fraction":0.6452260613441467,"avg_line_length":76.83125305175781,"blob_id":"a24c9deda0e64edcddc858185877109356b6bc20","content_id":"bb2df53f325dab07cb4fcd887d8f431b6ca1a461","detected_licenses":["MIT"],"is_generated":false,"is_vendor":false,"language":"C#","length_bytes":12465,"license_type":"permissive","max_line_length":490,"num_lines":160,"path":"/src/TorchSharp/NN/EmbeddingBag.cs","repo_name":"NiklasGustafsson/TorchSharp","src_encoding":"UTF-8","text":"// Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information.\nusing System;\nusing static TorchSharp.torch;\nusing static TorchSharp.PInvoke.NativeMethods;\n\n#nullable enable\nnamespace TorchSharp\n{\n using Modules;\n\n public enum EmbeddingBagMode\n {\n Sum = 0,\n Mean = 1,\n Max = 2\n }\n\n namespace Modules\n {\n public sealed class EmbeddingBag : torch.nn.Module<Tensor, Tensor?, Tensor?, Tensor>, torch.nn.IModule<Tensor, Tensor, Tensor>, torch.nn.IModule<Tensor, Tensor?, Tensor?, Tensor>\n {\n internal EmbeddingBag(IntPtr handle, IntPtr boxedHandle) : base(handle, boxedHandle) { }\n\n /// <summary>\n /// Forward pass of EmbeddingBag.\n /// </summary>\n /// <param name=\"input\">Tensor containing bags of indices into the embedding matrix.</param>\n /// <param name=\"offsets\">Only used when input is 1D. offsets determines the starting index position of each bag (sequence) in input.</param>\n /// <param name=\"perSampleWeights\">a tensor of float / double weights, or None to indicate all weights should be taken to be 1.\n /// If specified, per_sample_weights must have exactly the same shape as input and is treated as having the same offsets, if those are not None.\n /// Only supported for mode='sum'.</param>\n /// <returns></returns>\n public override Tensor forward(Tensor input, Tensor? offsets, Tensor? perSampleWeights)\n { \n var res = THSNN_EmbeddingBag_forward(handle, input.Handle, (offsets is null) ? IntPtr.Zero : offsets.Handle, (perSampleWeights is null) ? IntPtr.Zero : perSampleWeights.Handle);\n if (res == IntPtr.Zero) { torch.CheckForErrors(); }\n return new Tensor(res);\n }\n\n public new Tensor call(Tensor input, Tensor? offsets, Tensor? perSampleWeights)\n {\n if (!input.IsIntegral()) throw new ArgumentException(\"Embedding input must be an integral tensor.\");\n if (!(offsets is null) && input.dtype != offsets.dtype) throw new ArgumentException(\"input and offsets must have the same element type.\");\n if (input.Dimensions == 1 && offsets is null) throw new ArgumentException(\"'offsets' must be non-null for a 1-D input.\");\n if (input.Dimensions == 2 && !(offsets is null)) throw new ArgumentException(\"'offsets' must be null for a 2-D input.\");\n\n if (input.Dimensions == 2 && input.dtype == ScalarType.Int32) throw new NotImplementedException(\"EmbeddingBag for 32-bit integers -- there's some issue in the native runtime that prevents this from working.\");\n\n return base.call(input, offsets, perSampleWeights);\n }\n\n /// <summary>\n /// Forward pass of EmbeddingBag.\n /// </summary>\n /// <param name=\"input\">Tensor containing bags of indices into the embedding matrix.</param>\n /// <param name=\"offsets\">Only used when input is 1D. offsets determines the starting index position of each bag (sequence) in input.</param>\n /// <returns></returns>\n public Tensor call(Tensor input, Tensor offsets)\n {\n if (!input.IsIntegral()) throw new ArgumentException(\"Embedding input must be an integral tensor.\");\n if (!(offsets is null) && input.dtype != offsets.dtype) throw new ArgumentException(\"input and offsets must have the same element type.\");\n if (input.Dimensions == 1 && offsets is null) throw new ArgumentException(\"'offsets' must be non-null for a 1-D input.\");\n if (input.Dimensions == 2 && !(offsets is null)) throw new ArgumentException(\"'offsets' must be null for a 2-D input.\");\n\n if (input.Dimensions == 2 && input.dtype == ScalarType.Int32) throw new NotImplementedException(\"EmbeddingBag for 32-bit integers -- there's some issue in the native runtime that prevents this from working.\");\n\n return base.call(input, offsets, null);\n }\n\n /// <summary>\n /// Forward pass of EmbeddingBag.\n /// </summary>\n /// <param name=\"input\">Tensor containing bags of indices into the embedding matrix.</param>\n /// <returns></returns>\n public Tensor call(Tensor input)\n {\n if (!input.IsIntegral()) throw new ArgumentException(\"Embedding input must be an integral tensor.\");\n if (input.Dimensions == 1) throw new ArgumentException(\"'offsets' must be non-null for a 1-D input.\");\n\n if (input.Dimensions == 2 && input.dtype == ScalarType.Int32) throw new NotImplementedException(\"EmbeddingBag for 32-bit integers -- there's some issue in the native runtime that prevents this from working.\");\n\n return base.call(input, null, null);\n }\n\n public Parameter? weight {\n get {\n var res = THSNN_EmbeddingBag_weight(handle);\n if (res == IntPtr.Zero) { torch.CheckForErrors(); }\n return (res == IntPtr.Zero) ? null : new Parameter(res);\n }\n set {\n THSNN_EmbeddingBag_set_weight(handle, value is null ? IntPtr.Zero : value.Handle);\n torch.CheckForErrors();\n ConditionallyRegisterParameter(\"weight\", value);\n }\n }\n }\n }\n\n public static partial class torch\n {\n public static partial class nn\n {\n /// <summary>\n /// A simple lookup table that stores embeddings of a fixed dictionary and size.\n /// This module is often used to store word embeddings and retrieve them using indices. The input to the module is a list of indices, and the output is the corresponding word embeddings.\n /// </summary>\n /// <param name=\"num_embeddings\">Size of the dictionary of embeddings, the vocabulary size.</param>\n /// <param name=\"embedding_dims\">The size of each embedding vector</param>\n /// <param name=\"max_norm\">If given, each embedding vector with norm larger than max_norm is renormalized to have norm max_norm.</param>\n /// <param name=\"norm_type\">The p of the p-norm to compute for the max_norm option. Default 2.</param>\n /// <param name=\"scale_grad_by_freq\">If given, this will scale gradients by the inverse of frequency of the words in the mini-batch. Default: false.</param>\n /// <param name=\"mode\">\"sum\", \"mean\" or \"max\". Specifies the way to reduce the bag.\n /// \"sum\" computes the weighted sum, taking per_sample_weights into consideration.\n /// \"mean\" computes the average of the values in the bag, \"max\" computes the max value over each bag. Default: \"mean\"</param>\n /// <param name=\"sparse\">If true, gradient w.r.t. weight matrix will be a sparse tensor. Default: false</param>\n /// <param name=\"include_last_offset\">If true, offsets has one additional element, where the last element is equivalent to the size of indices. This matches the CSR format.</param>\n /// <param name=\"padding_index\"> If specified, the entries at padding_idx do not contribute to the gradient; therefore, the embedding vector at padding_idx is not updated during training, i.e. it remains as a fixed “pad”. For a newly constructed EmbeddingBag, the embedding vector at padding_idx will default to all zeros, but can be updated to another value to be used as the padding vector. Note that the embedding vector at padding_idx is excluded from the reduction.</param>\n /// <param name=\"device\">The desired device of the parameters and buffers in this module</param>\n /// <param name=\"dtype\">The desired floating point or complex dtype of the parameters and buffers in this module</param>\n /// <returns></returns>\n /// <remarks>Keep in mind that only a limited number of optimizers support sparse gradients: currently it’s optim.SGD (CUDA and CPU), optim.SparseAdam (CUDA and CPU) and optim.Adagrad (CPU)</remarks>\n public static EmbeddingBag EmbeddingBag(long num_embeddings, long embedding_dims, double? max_norm = null, double norm_type = 2.0, bool scale_grad_by_freq = false, EmbeddingBagMode mode = EmbeddingBagMode.Mean, bool sparse = false, bool include_last_offset = false, long padding_index = -1, Device? device = null, ScalarType? dtype = null)\n {\n var res = THSNN_EmbeddingBag_ctor(num_embeddings, embedding_dims,\n max_norm.HasValue ? max_norm.Value : 0.0, max_norm.HasValue,\n norm_type, scale_grad_by_freq, (long)mode, sparse, include_last_offset, padding_index, out var boxedHandle);\n if (res == IntPtr.Zero) { torch.CheckForErrors(); }\n return new EmbeddingBag(res, boxedHandle).MoveModule<EmbeddingBag>(device, dtype);\n }\n\n /// <summary>\n /// A simple lookup table that stores embeddings of a fixed dictionary and size.\n /// This module is often used to store word embeddings and retrieve them using indices. The input to the module is a list of indices, and the output is the corresponding word embeddings.\n /// </summary>\n /// <param name=\"embeddings\">FloatTensor containing weights for the EmbeddingBag in two dimensions. First dimension is being passed to EmbeddingBag as num_embeddings, second as embedding_dim.</param>\n /// <param name=\"freeze\">If true (the default), the tensor does not get updated in the learning</param>\n /// <param name=\"max_norm\">If given, each embedding vector with norm larger than max_norm is renormalized to have norm max_norm.</param>\n /// <param name=\"norm_type\">The p of the p-norm to compute for the max_norm option. Default 2.</param>\n /// <param name=\"scale_grad_by_freq\">If given, this will scale gradients by the inverse of frequency of the words in the mini-batch. Default: false.</param>\n /// <param name=\"mode\"></param>\n /// <param name=\"sparse\">If true, gradient w.r.t. weight matrix will be a sparse tensor. Default: false</param>\n /// <param name=\"include_last_offset\">If true, offsets has one additional element, where the last element is equivalent to the size of indices. This matches the CSR format.</param>\n /// <param name=\"padding_index\"> If specified, the entries at padding_idx do not contribute to the gradient; therefore, the embedding vector at padding_idx is not updated during training, i.e. it remains as a fixed “pad”. For a newly constructed EmbeddingBag, the embedding vector at padding_idx will default to all zeros, but can be updated to another value to be used as the padding vector. Note that the embedding vector at padding_idx is excluded from the reduction.</param>\n /// <param name=\"device\">The desired device of the parameters and buffers in this module</param>\n /// <param name=\"dtype\">The desired floating point or complex dtype of the parameters and buffers in this module</param>\n /// <returns></returns>\n /// <remarks>Keep in mind that only a limited number of optimizers support sparse gradients: currently it’s optim.SGD (CUDA and CPU), optim.SparseAdam (CUDA and CPU) and optim.Adagrad (CPU)</remarks>\n public static EmbeddingBag EmbeddingBag_from_pretrained(Tensor embeddings, bool freeze = true, double? max_norm = null, double norm_type = 2.0, bool scale_grad_by_freq = false, EmbeddingBagMode mode = EmbeddingBagMode.Mean, bool sparse = false, bool include_last_offset = false, long padding_index = -1, Device? device = null, ScalarType? dtype = null)\n {\n var res = THSNN_EmbeddingBag_from_pretrained(embeddings.Handle, freeze,\n max_norm.HasValue ? max_norm.Value : 0.0, max_norm.HasValue,\n norm_type, scale_grad_by_freq, (long)mode, sparse, include_last_offset, padding_index, out var boxedHandle);\n if (res == IntPtr.Zero) { torch.CheckForErrors(); }\n return new EmbeddingBag(res, boxedHandle).MoveModule<EmbeddingBag>(device, dtype);\n\n }\n }\n }\n}\n"},{"alpha_fraction":0.5411838293075562,"alphanum_fraction":0.5430629253387451,"avg_line_length":38.41975402832031,"blob_id":"b401a64239981a2601ecfe56a567d19799a8c72e","content_id":"35470b03f72a3c9246800aab224e8b3a16798a8c","detected_licenses":["MIT"],"is_generated":false,"is_vendor":false,"language":"C#","length_bytes":3193,"license_type":"permissive","max_line_length":130,"num_lines":81,"path":"/src/TorchAudio/Backend.cs","repo_name":"NiklasGustafsson/TorchSharp","src_encoding":"UTF-8","text":"// Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information.\n\nnamespace TorchSharp\n{\n public static partial class torchaudio\n {\n /// <summary>\n /// Load audio data from a file.\n /// </summary>\n /// <param name=\"filepath\">The file path to load</param>\n /// <param name=\"frame_offset\">Number of frames to skip before reading data</param>\n /// <param name=\"num_frames\">Maximum number of frames to read.</param>\n /// <param name=\"normalize\">True to normalize the audio to [-1.0, 1.0]</param>\n /// <param name=\"channels_first\">The dimension of the returned tensor is [channel, time] when true</param>\n /// <param name=\"format\">The format of the audio file</param>\n /// <returns>A pair of waveform and sampling rate</returns>\n public static (torch.Tensor, int) load(\n string filepath,\n long frame_offset = 0,\n long num_frames = -1,\n bool normalize = true,\n bool channels_first = true,\n AudioFormat? format = null)\n {\n return backend.utils._backend.load(\n filepath,\n frame_offset,\n num_frames,\n normalize,\n channels_first,\n format);\n }\n\n /// <summary>\n /// Save audio data to a file.\n /// </summary>\n /// <param name=\"filepath\">The file path to save</param>\n /// <param name=\"src\">The waveform of audio</param>\n /// <param name=\"sample_rate\">The sampling rate of audio</param>\n /// <param name=\"channels_first\">The dimension of the returned tensor is [channel, time] when true</param>\n /// <param name=\"compression\">The compression factor</param>\n /// <param name=\"format\">The format of the audio file</param>\n /// <param name=\"encoding\">The audio encoding</param>\n /// <param name=\"bits_per_sample\">The number of bits per sample</param>\n public static void save(\n string filepath,\n torch.Tensor src,\n int sample_rate,\n bool channels_first = true,\n float? compression = null,\n AudioFormat? format = null,\n AudioEncoding? encoding = null,\n int? bits_per_sample = null)\n {\n backend.utils._backend.save(\n filepath,\n src,\n sample_rate,\n channels_first,\n compression,\n format,\n encoding,\n bits_per_sample);\n }\n\n /// <summary>\n /// Get the information of an audio file.\n /// </summary>\n /// <param name=\"filepath\">The file path of the audio file</param>\n /// <param name=\"format\">The format of the audio file</param>\n /// <returns>The information of the audio file</returns>\n public static AudioMetaData info(\n string filepath,\n AudioFormat? format = null)\n {\n return backend.utils._backend.info(\n filepath,\n format);\n }\n }\n}\n"},{"alpha_fraction":0.5016260147094727,"alphanum_fraction":0.5040650367736816,"avg_line_length":26.33333396911621,"blob_id":"5f658bfb6e1ecbbdad3cab413ed76b95377a5549","content_id":"2dcce34a2d33e02f0680a530b17e35c205d98488","detected_licenses":["MIT"],"is_generated":false,"is_vendor":false,"language":"C#","length_bytes":4920,"license_type":"permissive","max_line_length":103,"num_lines":180,"path":"/src/TorchSharp/Utils/OrderedDict.cs","repo_name":"NiklasGustafsson/TorchSharp","src_encoding":"UTF-8","text":"using System;\nusing System.Linq;\nusing System.Collections;\nusing System.Collections.Generic;\nusing System.Diagnostics.CodeAnalysis;\n\nnamespace TorchSharp.Utils\n{\n public class OrderedDict<TKey, TValue> : IDictionary<TKey, TValue>, IList<(TKey, TValue)>\n {\n /// <summary>\n /// Remove all items from the ParameterDict.\n /// </summary>\n public void clear()\n {\n _list.Clear();\n _dict.Clear();\n }\n\n /// <summary>\n /// Return an enumeration of the ParameterDict key/value pairs.\n /// </summary>\n /// <returns></returns>\n public IEnumerator<(TKey, TValue)> items() => _list.GetEnumerator();\n\n /// <summary>\n /// Return the ParameterDict keys.\n /// </summary>\n /// <returns></returns>\n public IEnumerable<TKey> keys() => _dict.Keys;\n\n /// <summary>\n /// Return the ParameterDict values.\n /// </summary>\n /// <returns></returns>\n public IEnumerable<TValue> values() => _dict.Values;\n\n public (TKey, TValue) this[int index] {\n get => _list[index];\n set {\n var key = value.Item1;\n _list[index] = value;\n _dict[key] = value.Item2;\n }\n }\n\n public bool IsReadOnly => false;\n\n public ICollection<TKey> Keys => _list.Select(kv => kv.Item1).ToList();\n\n public ICollection<TValue> Values => _list.Select(kv => kv.Item2).ToList();\n\n public int Count => _dict.Count;\n\n public TValue this[TKey key] {\n get => _dict[key];\n set {\n _dict[key] = value;\n var idx = _list.FindIndex(kv => kv.Item1.Equals(key));\n if (idx >= 0) {\n _list[idx] = (key, value);\n }\n else {\n _list.Add((key, value));\n }\n }\n }\n\n public void Add((TKey, TValue) item)\n {\n _dict.Add(item.Item1, item.Item2);\n _list.Add(item);\n }\n\n public void Add(TKey key, TValue value)\n {\n _dict.Add(key, value);\n _list.Add((key, value));\n }\n\n public void Add(KeyValuePair<TKey, TValue> item)\n {\n _dict.Add(item.Key, item.Value);\n _list.Add((item.Key, item.Value));\n }\n\n public bool Contains((TKey, TValue) item)\n {\n return _list.Contains(item);\n }\n\n public void CopyTo((TKey, TValue)[] array, int arrayIndex)\n {\n _list.CopyTo(array, arrayIndex);\n }\n\n public int IndexOf((TKey, TValue) item)\n {\n return _list.IndexOf(item);\n }\n\n public void Insert(int index, (TKey, TValue) item)\n {\n _dict.Add(item.Item1, item.Item2);\n _list.Insert(index, item);\n }\n\n public bool Remove((TKey, TValue) item)\n {\n _dict.Remove(item.Item1);\n return _list.Remove(item);\n }\n\n public void RemoveAt(int index)\n {\n if (index >= _list.Count) throw new IndexOutOfRangeException();\n var (n, p) = _list[index];\n _list.RemoveAt(index);\n _dict.Remove(n);\n }\n\n public bool ContainsKey(TKey key)\n {\n return _dict.ContainsKey(key);\n }\n\n public bool Remove(TKey key)\n {\n var value = _dict[key];\n var idx = _list.FindIndex(kv => kv.Item1.Equals(key));\n _list.RemoveAt(idx);\n return _dict.Remove(key);\n }\n\n public bool TryGetValue(TKey key, [MaybeNullWhen(false)] out TValue value)\n {\n return _dict.TryGetValue(key, out value);\n }\n\n public void Clear()\n {\n _dict.Clear();\n _list.Clear();\n }\n\n public bool Contains(KeyValuePair<TKey, TValue> item)\n {\n return _dict.ContainsKey(item.Key);\n }\n\n public void CopyTo(KeyValuePair<TKey, TValue>[] array, int arrayIndex)\n {\n throw new NotImplementedException();\n }\n\n public bool Remove(KeyValuePair<TKey, TValue> item)\n {\n return _dict.Remove(item.Key);\n }\n\n public IEnumerator<(TKey, TValue)> GetEnumerator()\n {\n return _list.GetEnumerator();\n }\n\n IEnumerator<KeyValuePair<TKey, TValue>> IEnumerable<KeyValuePair<TKey, TValue>>.GetEnumerator()\n {\n throw new NotImplementedException();\n }\n\n IEnumerator IEnumerable.GetEnumerator()\n {\n return ((IEnumerable<KeyValuePair<TKey, TValue>>)this).GetEnumerator();\n }\n\n private List<(TKey, TValue)> _list = new List<(TKey, TValue)>();\n private Dictionary<TKey, TValue> _dict = new Dictionary<TKey, TValue>();\n }\n\n}\n"},{"alpha_fraction":0.5328227281570435,"alphanum_fraction":0.5393872857093811,"avg_line_length":25.882352828979492,"blob_id":"c89bd7df0a7c79fa3ba153c9a2839c0038cb76f6","content_id":"1552dd0ff30fb7a65ed35f96daed0df9bebb9949","detected_licenses":["MIT"],"is_generated":false,"is_vendor":false,"language":"C#","length_bytes":914,"license_type":"permissive","max_line_length":139,"num_lines":34,"path":"/src/TorchVision/Invert.cs","repo_name":"NiklasGustafsson/TorchSharp","src_encoding":"UTF-8","text":"// Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information.\n\nusing static TorchSharp.torch;\n\n\nnamespace TorchSharp\n{\n public static partial class torchvision\n {\n internal class Invert : ITransform\n {\n internal Invert()\n {\n }\n\n public Tensor call(Tensor input)\n {\n return transforms.functional.invert(input);\n }\n }\n\n public static partial class transforms\n {\n /// <summary>\n /// Invert the image colors.\n /// </summary>\n /// <remarks>The code assumes that integer color values lie in the range [0,255], and floating point colors in [0,1[.</remarks>\n static public ITransform Invert()\n {\n return new Invert();\n }\n }\n }\n}\n"},{"alpha_fraction":0.5647902488708496,"alphanum_fraction":0.5658523440361023,"avg_line_length":44.37349319458008,"blob_id":"23e92a82c3dff027d7afcd2ac6b58afcd9498270","content_id":"4d70a476467d52ebb6e0475b1cbd8bd0597abff5","detected_licenses":["MIT"],"is_generated":false,"is_vendor":false,"language":"C#","length_bytes":3766,"license_type":"permissive","max_line_length":197,"num_lines":83,"path":"/src/TorchSharp/NN/Normalization/GroupNorm.cs","repo_name":"NiklasGustafsson/TorchSharp","src_encoding":"UTF-8","text":"// Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information.\nusing System;\nusing static TorchSharp.torch;\nusing static TorchSharp.PInvoke.NativeMethods;\n\n#nullable enable\nnamespace TorchSharp\n{\n using Modules;\n\n namespace Modules\n {\n\n /// <summary>\n /// This class is used to represent a GroupNorm module.\n /// </summary>\n public sealed class GroupNorm : torch.nn.Module<Tensor, Tensor>\n {\n internal GroupNorm(IntPtr handle, IntPtr boxedHandle) : base(handle, boxedHandle)\n {\n }\n\n public override Tensor forward(Tensor tensor)\n {\n if (tensor.Dimensions < 3) throw new ArgumentException($\"Invalid number of dimensions for GroupNorm argument: {tensor.Dimensions}\");\n var res = THSNN_GroupNorm_forward(handle.DangerousGetHandle(), tensor.Handle);\n if (res == IntPtr.Zero) { torch.CheckForErrors(); }\n return new Tensor(res);\n }\n\n public Parameter? bias {\n get {\n var res = THSNN_GroupNorm_bias(handle);\n if (res == IntPtr.Zero) { torch.CheckForErrors(); }\n return (res == IntPtr.Zero) ? null : new Parameter(res);\n }\n set {\n THSNN_GroupNorm_set_bias(handle, (value is null ? IntPtr.Zero : value.Handle));\n torch.CheckForErrors();\n ConditionallyRegisterParameter(\"bias\", value);\n }\n }\n\n public Parameter? weight {\n get {\n var res = THSNN_GroupNorm_weight(handle);\n if (res == IntPtr.Zero) { torch.CheckForErrors(); }\n return (res == IntPtr.Zero) ? null : new Parameter(res);\n }\n set {\n THSNN_GroupNorm_set_weight(handle, value is null ? IntPtr.Zero : value.Handle);\n torch.CheckForErrors();\n ConditionallyRegisterParameter(\"weight\", value);\n }\n }\n }\n }\n\n public static partial class torch\n {\n public static partial class nn\n {\n /// <summary>\n /// Applies Group Normalization over a mini-batch of inputs as described in the paper Group Normalization\n /// </summary>\n /// <param name=\"num_groups\">Number of groups to separate the channels into</param>\n /// <param name=\"num_channels\">Number of channels expected in input</param>\n /// <param name=\"eps\">A value added to the denominator for numerical stability.</param>\n /// <param name=\"affine\">A boolean value that when set to true, this module has learnable per-channel affine parameters initialized to ones (for weights) and zeros (for biases).</param>\n /// <param name=\"device\">The desired device of the parameters and buffers in this module</param>\n /// <param name=\"dtype\">The desired floating point or complex dtype of the parameters and buffers in this module</param>\n /// <returns></returns>\n public static GroupNorm GroupNorm(long num_groups, long num_channels, double eps = 1e-05, bool affine = true, Device? device = null, ScalarType? dtype = null)\n {\n unsafe {\n var handle = THSNN_GroupNorm_ctor(num_groups, num_channels, eps, affine, out var boxedHandle);\n if (handle == IntPtr.Zero) { torch.CheckForErrors(); }\n return new GroupNorm(handle, boxedHandle).MoveModule<GroupNorm>(device, dtype);\n }\n }\n }\n }\n}\n"},{"alpha_fraction":0.7562500238418579,"alphanum_fraction":0.7604166865348816,"avg_line_length":29,"blob_id":"b8432e578867293321999df53afcf2cf95228c71","content_id":"e66492e11d57fa8ddbfa2a83c34692add348eb6d","detected_licenses":["MIT"],"is_generated":false,"is_vendor":false,"language":"C","length_bytes":480,"license_type":"permissive","max_line_length":130,"num_lines":16,"path":"/src/Native/LibTorchSharp/THSStorage.h","repo_name":"NiklasGustafsson/TorchSharp","src_encoding":"UTF-8","text":"// Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information.\n#pragma once\n\n#include \"../Stdafx.h\"\n\n#include \"torch/torch.h\"\n\n#include \"Utils.h\"\n\nEXPORT_API(int64_t) THSTensor_storage_offset(const Tensor tensor);\n\nEXPORT_API(size_t) THSStorage_nbytes(const Tensor tensor);\n\nEXPORT_API(void) THSStorage_set_nbytes(const Tensor tensor, size_t nbytes);\n\nEXPORT_API(void*) THSStorage_data_ptr(const Tensor tensor);\n"},{"alpha_fraction":0.5736639499664307,"alphanum_fraction":0.5775156617164612,"avg_line_length":47.87058639526367,"blob_id":"1fc35aefb0dea60aeee88832873689c4f2df5020","content_id":"364727dbdf396b7500c7840590c8e5da795593e8","detected_licenses":["MIT"],"is_generated":false,"is_vendor":false,"language":"C#","length_bytes":4158,"license_type":"permissive","max_line_length":206,"num_lines":85,"path":"/src/TorchSharp/NN/TransformerEncoderLayer.cs","repo_name":"NiklasGustafsson/TorchSharp","src_encoding":"UTF-8","text":"// Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information.\nusing System;\nusing static TorchSharp.torch;\nusing static TorchSharp.PInvoke.NativeMethods;\n\nnamespace TorchSharp\n{\n using Modules;\n\n namespace Modules\n {\n public sealed class TransformerEncoderLayer : torch.nn.Module<Tensor, Tensor>, torch.nn.IModule<Tensor, Tensor, Tensor>, torch.nn.IModule<Tensor, Tensor, Tensor, Tensor>\n {\n internal TransformerEncoderLayer(IntPtr handle, IntPtr boxedHandle) : base(handle, boxedHandle) { }\n\n /// <summary>\n /// Pass the input through the encoder layer.\n /// </summary>\n /// <param name=\"src\">The sequence to the encoder (required).</param>\n /// <param name=\"src_mask\">The additive mask for the src sequence (optional).</param>\n /// <param name=\"src_key_padding_mask\">The ByteTensor mask for src keys per batch (optional).</param>\n /// <returns></returns>\n public Tensor call(Tensor src, Tensor src_mask, Tensor src_key_padding_mask)\n {\n var res = THSNN_TransformerEncoderLayer_forward(handle,\n src.Handle,\n src_mask?.Handle ?? IntPtr.Zero,\n src_key_padding_mask?.Handle ?? IntPtr.Zero);\n if (res == IntPtr.Zero) { torch.CheckForErrors(); }\n return new Tensor(res);\n }\n\n /// <summary>\n /// Pass the input through the encoder layer.\n /// </summary>\n /// <param name=\"src\">The sequence to the encoder (required).</param>\n /// <param name=\"src_mask\">The additive mask for the src sequence (optional).</param>\n public Tensor call(Tensor src, Tensor src_mask)\n {\n var res = THSNN_TransformerEncoderLayer_forward(handle,\n src.Handle,\n src_mask?.Handle ?? IntPtr.Zero,\n IntPtr.Zero);\n if (res == IntPtr.Zero) { torch.CheckForErrors(); }\n return new Tensor(res);\n }\n\n /// <summary>\n /// Pass the input through the encoder layer.\n /// </summary>\n /// <param name=\"src\">The sequence to the encoder (required).</param>\n public override Tensor forward(Tensor src)\n {\n var res = THSNN_TransformerEncoderLayer_forward(handle,\n src.Handle,\n IntPtr.Zero,\n IntPtr.Zero);\n if (res == IntPtr.Zero) { torch.CheckForErrors(); }\n return new Tensor(res);\n }\n }\n }\n\n public static partial class torch\n {\n public static partial class nn\n {\n /// <summary>\n /// TransformerEncoderLayer is made up of self-attn and feedforward network. This standard encoder layer is based on the paper “Attention Is All You Need”.\n /// </summary>\n /// <param name=\"d_model\">The number of expected features in the input (required).</param>\n /// <param name=\"nhead\">The number of heads in the multiheadattention models (required).</param>\n /// <param name=\"dim_feedforward\">The dimension of the feedforward network model (default=2048).</param>\n /// <param name=\"dropout\">The dropout value (default=0.1).</param>\n /// <param name=\"activation\">The activation function of intermediate layer, relu or gelu (default=relu).</param>\n /// <returns></returns>\n public static TransformerEncoderLayer TransformerEncoderLayer(long d_model = 512, long nhead = 8, long dim_feedforward = 2048, double dropout = 0.1, Activations activation = nn.Activations.ReLU)\n {\n var res = THSNN_TransformerEncoderLayer_ctor(d_model, nhead, dim_feedforward, dropout, (long)activation, out var boxedHandle);\n if (res == IntPtr.Zero) { torch.CheckForErrors(); }\n return new TransformerEncoderLayer(res, boxedHandle);\n }\n }\n }\n}\n"},{"alpha_fraction":0.5447816252708435,"alphanum_fraction":0.5481125116348267,"avg_line_length":36.52777862548828,"blob_id":"23e2c9f7d2fcc7112170bb1b63178d78965b2887","content_id":"de32e4dfb1d36409efb6d52a2fa27d5fbea94798","detected_licenses":["MIT"],"is_generated":false,"is_vendor":false,"language":"C#","length_bytes":2704,"license_type":"permissive","max_line_length":130,"num_lines":72,"path":"/src/TorchSharp/NN/Activation/Softshrink.cs","repo_name":"NiklasGustafsson/TorchSharp","src_encoding":"UTF-8","text":"// Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information.\nusing System;\nusing static TorchSharp.torch;\nusing static TorchSharp.PInvoke.NativeMethods;\n\nnamespace TorchSharp\n{\n using Modules;\n\n namespace Modules\n {\n /// <summary>\n /// This class is used to represent a Softshrink module.\n /// </summary>\n public sealed class Softshrink : torch.nn.Module<Tensor, Tensor>\n {\n internal Softshrink(IntPtr handle, IntPtr boxedHandle) : base(handle, boxedHandle) { }\n\n public override Tensor forward(Tensor tensor)\n {\n var res = THSNN_Softshrink_forward(handle, tensor.Handle);\n if (res == IntPtr.Zero) { torch.CheckForErrors(); }\n return new Tensor(res);\n }\n\n public override string GetName()\n {\n return typeof(Softshrink).Name;\n }\n\n // Rather than spending cycles only to discover that this module has neither\n // parameters nor buffers, just shortcut the move completely.\n protected internal override nn.Module _to(Device device, ScalarType dtype) => this;\n protected internal override nn.Module _to(DeviceType deviceType, int deviceIndex = -1) => this;\n protected internal override nn.Module _to(ScalarType dtype) => this;\n }\n }\n\n public static partial class torch\n {\n public static partial class nn\n {\n /// <summary>\n /// Softshrink\n /// </summary>\n /// <param name=\"lambda\"> the λ value for the Softshrink formulation. Default: 0.5</param>\n /// <returns></returns>\n public static Softshrink Softshrink(double lambda = 0.5)\n {\n var handle = THSNN_Softshrink_ctor(lambda, out var boxedHandle);\n if (handle == IntPtr.Zero) { torch.CheckForErrors(); }\n return new Softshrink(handle, boxedHandle);\n }\n\n public static partial class functional\n {\n /// <summary>\n /// Softshrink\n /// </summary>\n /// <param name=\"x\">The input tensor</param>\n /// <param name=\"lambda\">The λ value for the Softshrink formulation. Default: 0.5</param>\n /// <returns></returns>\n public static Tensor Softshrink(Tensor x, double lambda = 0.5)\n {\n using (var m = nn.Softshrink(lambda)) {\n return m.call(x);\n }\n }\n }\n }\n }\n}\n"},{"alpha_fraction":0.5315040946006775,"alphanum_fraction":0.5341463685035706,"avg_line_length":59.74074172973633,"blob_id":"b1742b617d40d5500d58e118bf5211f862a7abc9","content_id":"ab0b62cc5c3b9e1793a87c22b8feee0245871f8a","detected_licenses":["MIT"],"is_generated":false,"is_vendor":false,"language":"C#","length_bytes":4920,"license_type":"permissive","max_line_length":188,"num_lines":81,"path":"/src/TorchSharp/NN/Utils/RNNUtils.cs","repo_name":"NiklasGustafsson/TorchSharp","src_encoding":"UTF-8","text":"// Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information.\nusing System;\nusing System.Collections.Generic;\nusing System.Linq;\nusing static TorchSharp.PInvoke.NativeMethods;\n\nnamespace TorchSharp\n{\n public static partial class torch\n {\n public static partial class nn\n {\n public static partial class utils\n {\n public static partial class rnn\n {\n /// <summary>\n /// Pack a padded sequences.\n /// </summary>\n /// <param name=\"input\">The padded input tensor</param>\n /// <param name=\"lengths\">The lengths of sequences</param>\n /// <param name=\"batch_first\">If true, the size of input tensor is (batch, seq, *), otherwise (seq, batch, *)</param>\n /// <param name=\"enforce_sorted\">if true, checks that the input contains sequences sorted by length in a decreasing order.</param>\n /// <returns></returns>\n public static PackedSequence pack_padded_sequence(torch.Tensor input, torch.Tensor lengths, bool batch_first = false, bool enforce_sorted = true)\n {\n var res = THSNN_pack_padded_sequence(input.Handle, lengths.Handle, batch_first, enforce_sorted);\n if (res.IsInvalid) { torch.CheckForErrors(); }\n return new PackedSequence(res);\n }\n\n /// <summary>\n /// Pad a packed batch of variable length sequences.\n /// </summary>\n /// <param name=\"sequence\">A packed batch</param>\n /// <param name=\"batch_first\">If true, the size of output tensor is (batch, seq, *), otherwise (seq, batch, *)</param>\n /// <param name=\"padding_value\">The values of padding.</param>\n /// <param name=\"total_length\">If not null, the output tensor is padded to the length of total_length.</param>\n /// <returns>The padded tensor</returns>\n public static (torch.Tensor, torch.Tensor) pad_packed_sequence(PackedSequence sequence, bool batch_first = false, double padding_value = 0.0, long? total_length = null)\n {\n IntPtr res1, res2;\n long total_length_arg = total_length.HasValue ? total_length.Value : -1;\n THSNN_pad_packed_sequence(sequence.Handle, batch_first, padding_value, total_length_arg, out res1, out res2);\n if (res1 == IntPtr.Zero || res2 == IntPtr.Zero) { torch.CheckForErrors(); }\n return (new torch.Tensor(res1), new torch.Tensor(res2));\n }\n\n /// <summary>\n /// Pad a list of variable length sequences.\n /// </summary>\n /// <param name=\"sequences\">A list of variable length sequences</param>\n /// <param name=\"batch_first\">If true, the size of output tensor is (batch, seq, *), otherwise (seq, batch, *)</param>\n /// <param name=\"padding_value\">The values of padding.</param>\n /// <returns>The padded tensor</returns>\n public static torch.Tensor pad_sequence(IEnumerable<torch.Tensor> sequences, bool batch_first = false, double padding_value = 0.0)\n {\n var sequences_arg = sequences.Select(p => p.Handle).ToArray();\n var res = THSNN_pad_sequence(sequences_arg, sequences_arg.Length, batch_first, padding_value);\n if (res == IntPtr.Zero) { torch.CheckForErrors(); }\n return new torch.Tensor(res);\n }\n\n /// <summary>\n /// Pack a list of variable length sequences.\n /// </summary>\n /// <param name=\"sequences\">A list of variable length sequences</param>\n /// <param name=\"enforce_sorted\">if true, checks that the input contains sequences sorted by length in a decreasing order.</param>\n /// <returns>The packed batch of variable length sequences</returns>\n public static PackedSequence pack_sequence(IEnumerable<torch.Tensor> sequences, bool enforce_sorted = true)\n {\n var sequences_arg = sequences.Select(p => p.Handle).ToArray();\n var res = THSNN_pack_sequence(sequences_arg, sequences_arg.Length, enforce_sorted);\n if (res.IsInvalid) { torch.CheckForErrors(); }\n return new PackedSequence(res);\n }\n }\n }\n }\n }\n}\n"},{"alpha_fraction":0.5518966913223267,"alphanum_fraction":0.5646489262580872,"avg_line_length":40.0264892578125,"blob_id":"a4eb103ad3b3af3002bd96b8b37b82e27c4e759b","content_id":"c8b27c720c6e68d2110bc688f79f3674286a18fe","detected_licenses":["MIT"],"is_generated":false,"is_vendor":false,"language":"C#","length_bytes":6202,"license_type":"permissive","max_line_length":166,"num_lines":151,"path":"/src/Examples/AdversarialExampleGeneration.cs","repo_name":"NiklasGustafsson/TorchSharp","src_encoding":"UTF-8","text":"// Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information.\nusing System;\nusing System.IO;\nusing static TorchSharp.torch;\nusing static TorchSharp.torch.utils.data;\n\nnamespace TorchSharp.Examples\n{\n /// <summary>\n /// FGSM Attack\n ///\n /// Based on : https://pytorch.org/tutorials/beginner/fgsm_tutorial.html\n /// </summary>\n /// <remarks>\n /// There are at least two interesting data sets to use with this example:\n ///\n /// 1. The classic MNIST set of 60000 images of handwritten digits.\n ///\n /// It is available at: http://yann.lecun.com/exdb/mnist/\n ///\n /// 2. The 'fashion-mnist' data set, which has the exact same file names and format as MNIST, but is a harder\n /// data set to train on. It's just as large as MNIST, and has the same 60/10 split of training and test\n /// data.\n /// It is available at: https://github.com/zalandoresearch/fashion-mnist/tree/master/data/fashion\n ///\n /// The example is based on the PyTorch tutorial, but the results from attacking the model are very different from\n /// what the tutorial article notes, at least on the machine where it was developed. There is an order-of-magnitude lower\n /// drop-off in accuracy in this version. That said, when running the PyTorch tutorial on the same machine, the\n /// accuracy trajectories are the same between .NET and Python. If the base convulutational model is trained\n /// using Python, and then used for the FGSM attack in both .NET and Python, the drop-off trajectories are extremenly\n /// close.\n /// </remarks>\n public class AdversarialExampleGeneration\n {\n#if NET472_OR_GREATER\n private readonly static string _dataLocation = NSPath.Join(Environment.GetFolderPath(Environment.SpecialFolder.DesktopDirectory), \"..\", \"Downloads\", \"mnist\");\n#else\n private readonly static string _dataLocation = Path.Join(Environment.GetFolderPath(Environment.SpecialFolder.DesktopDirectory), \"..\", \"Downloads\", \"mnist\");\n#endif // NET472_OR_GREATER\n\n private static int _epochs = 4;\n private static int _trainBatchSize = 64;\n private static int _testBatchSize = 128;\n\n internal static void Main(string[] args)\n {\n var cwd = Environment.CurrentDirectory;\n\n var dataset = args.Length > 0 ? args[0] : \"mnist\";\n var datasetPath = Environment.GetFolderPath(Environment.SpecialFolder.LocalApplicationData);\n\n var _ = torch.random.manual_seed(1);\n\n //var device = torch.CPU;\n var device = torch.cuda.is_available() ? torch.CUDA : torch.CPU;\n Console.WriteLine($\"\\n Running AdversarialExampleGeneration on {device.type.ToString()}\\n\");\n Console.WriteLine($\"Dataset: {dataset}\");\n\n if (device.type == DeviceType.CUDA) {\n _trainBatchSize *= 4;\n _testBatchSize *= 4;\n _epochs *= 4;\n }\n\n MNIST.Model model = null;\n\n var normImage = torchvision.transforms.Normalize(new double[] {0.1307}, new double[] {0.3081});\n\n using (var test = torchvision.datasets.MNIST(datasetPath, false, true, normImage)) {\n\n var modelFile = dataset + \".model.bin\";\n\n if (!File.Exists(modelFile)) {\n // We need the model to be trained first, because we want to start with a trained model.\n Console.WriteLine(\n $\"\\n Running MNIST on {device.type.ToString()} in order to pre-train the model.\");\n\n model = new MNIST.Model(\"model\", device);\n\n using (var train = torchvision.datasets.MNIST(datasetPath, true, true, normImage)) {\n MNIST.TrainingLoop(dataset, (Device) device, model, train, test);\n }\n\n Console.WriteLine(\"Moving on to the Adversarial model.\\n\");\n\n } else {\n model = new MNIST.Model(\"model\", torch.CPU);\n model.load(modelFile);\n }\n\n model.to((Device) device);\n model.eval();\n }\n\n var epsilons = new double[] {0, 0.05, 0.1, 0.15, 0.20, 0.25, 0.30, 0.35, 0.40, 0.45, 0.50};\n\n foreach (var ε in epsilons) {\n using (var test = torchvision.datasets.MNIST(datasetPath, false, true, normImage)) {\n var attacked = Test(model, torch.nn.NLLLoss(), ε, device, test, test.Count);\n Console.WriteLine($\"Epsilon: {ε:F2}, accuracy: {attacked:P2}\");\n }\n }\n }\n\n private static Tensor Attack(Tensor image, double ε, Tensor data_grad)\n {\n using (var sign = data_grad.sign()) {\n var perturbed = (image + ε * sign).clamp(0.0, 1.0);\n return perturbed;\n }\n }\n\n private static double Test(\n MNIST.Model model,\n Loss<Tensor, Tensor, Tensor> criterion,\n double ε,\n Device device,\n Dataset dataset,\n long size)\n {\n int correct = 0;\n\n using (var d = torch.NewDisposeScope()) {\n using var dataLoader = new DataLoader(dataset, _testBatchSize, device: device, shuffle: true);\n foreach (var dat in dataLoader) {\n var data = dat[\"data\"];\n var label = dat[\"label\"];\n data.requires_grad = true;\n\n using (var output = model.call(data))\n using (var loss = criterion.call(output, label)) {\n\n model.zero_grad();\n loss.backward();\n\n var perturbed = Attack(data, ε, data.grad());\n\n using (var final = model.call(perturbed)) {\n\n correct += final.argmax(1).eq(label).sum().ToInt32();\n }\n }\n\n d.DisposeEverything();\n }\n }\n\n return (double)correct / size;\n }\n }\n}\n"},{"alpha_fraction":0.5358197093009949,"alphanum_fraction":0.5668997764587402,"avg_line_length":34.26301193237305,"blob_id":"e6d663937a299ec49c8cb316187faab3b3e86c92","content_id":"5cc6f832d31147c127b343056a1d48fd9a539ae6","detected_licenses":["MIT"],"is_generated":false,"is_vendor":false,"language":"C++","length_bytes":12870,"license_type":"permissive","max_line_length":181,"num_lines":365,"path":"/src/Native/LibTorchSharp/THSVision.cpp","repo_name":"NiklasGustafsson/TorchSharp","src_encoding":"UTF-8","text":"// Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information.\n#include \"THSVision.h\"\n\n#include <torch/nn/init.h>\n\n// The image processing code was ported from the Python implmentation found in:\n//\n// https://github.com/pytorch/vision/blob/main/torchvision/transforms/functional_tensor.py\n\nvoid _rgb_to_hsv(at::Tensor& img, at::Tensor& h, at::Tensor& saturation, at::Tensor& value)\n{\n auto RGB = img.unbind(-3);\n auto r = RGB[0];\n auto g = RGB[1];\n auto b = RGB[2];\n\n auto maxc = std::get<0>(img.max(-3));\n auto minc = std::get<0>(img.min(-3));\n\n auto eqc = maxc == minc;\n auto cr = maxc - minc;\n\n auto options = at::TensorOptions()\n .dtype(maxc.dtype())\n .device(maxc.device())\n .requires_grad(maxc.requires_grad());\n\n auto ones = torch::ones_like(maxc, options);\n\n auto s = cr / torch::where(eqc, ones, maxc);\n auto cr_divisor = torch::where(eqc, ones, cr);\n\n auto rc = (maxc - r) / cr_divisor;\n auto gc = (maxc - g) / cr_divisor;\n auto bc = (maxc - b) / cr_divisor;\n\n auto hr = (maxc == r) * (bc - gc);\n auto hg = ((maxc == g) & (maxc != r)) * (2.0 + rc - bc);\n auto hb = ((maxc != g) & (maxc != r)) * (4.0 + gc - rc);\n\n h = (hr + hg + hb);\n h = torch::fmod((h / 6.0 + 1.0), 1.0);\n\n saturation = s;\n value = maxc;\n}\n\nvoid _hsv_to_rgb(at::Tensor& h, at::Tensor& s, at::Tensor& v, at::Tensor& img)\n{\n auto h6 = h * 6.0f;\n auto i = torch::floor(h6);\n auto f = h6 - i;\n i = i.to(at::ScalarType::Int) % 6;\n\n auto p = torch::clamp((v * (1.0f - s)), 0.0, 1.0);\n auto q = torch::clamp((v * (1.0 - s * f)), 0.0, 1.0);\n auto t = torch::clamp((v * (1.0 - s * (1.0 - f))), 0.0, 1.0);\n\n auto iunsq = i.unsqueeze(-3);\n\n auto mask = iunsq == torch::arange(6, at::TensorOptions(i.device())).view({ -1, 1, 1 });\n\n auto a1 = torch::stack({ v, q, p, p, t, v }, -3);\n auto a2 = torch::stack({ t, v, v, q, p, p }, -3);\n auto a3 = torch::stack({ p, p, t, v, v, q }, -3);\n auto a4 = torch::stack({ a1, a2, a3 }, -4);\n\n img = torch::einsum(\"...ijk,...xijk ->...xjk\", { mask.to(h.dtype()), a4 });\n}\n\nTensor THSVision_AdjustHue(const Tensor i, const double hue_factor)\n{\n try {\n torch_last_err = 0;\n\n auto img = *i;\n\n auto orig_dtype = img.scalar_type();\n\n if (!torch::isFloatingType(orig_dtype)) {\n img = img.to(c10::ScalarType::Float) / 255.0;\n }\n\n at::Tensor h;\n at::Tensor s;\n at::Tensor v;\n\n _rgb_to_hsv(img, h, s, v);\n\n h = (h + hue_factor) % 1.0;\n\n at::Tensor img_hue_adj;\n\n _hsv_to_rgb(h, s, v, img_hue_adj);\n\n if (!torch::isFloatingType(orig_dtype)) {\n img_hue_adj = (img_hue_adj * 255.0).to(orig_dtype);\n }\n\n return ResultTensor(img_hue_adj);\n }\n catch (const c10::Error e) {\n torch_last_err = strdup(e.what());\n }\n catch (const std::runtime_error e) {\n torch_last_err = strdup(e.what());\n }\n\n return nullptr;\n}\n\nTensor THSVision_GenerateAffineGrid(Tensor theta, const int64_t w, const int64_t h, const int64_t ow, const int64_t oh)\n{\n try {\n torch_last_err = 0;\n\n auto d = 0.5;\n\n auto thetaOptions = at::TensorOptions().dtype(theta->dtype()).device(theta->device());\n auto thetaDevice = at::TensorOptions().device(theta->device());\n\n auto base_grid = torch::empty({ 1, oh, ow, 3 }, thetaOptions);\n\n auto x_grid = torch::linspace(-ow * 0.5 + d, ow * 0.5 + d - 1, ow, thetaDevice);\n base_grid.index({ at::indexing::Ellipsis, 0 }).copy_(x_grid);\n\n auto y_grid = torch::linspace(-oh * 0.5 + d, oh * 0.5 + d - 1, oh, thetaDevice).unsqueeze_(-1);\n base_grid.index({ at::indexing::Ellipsis, 1 }).copy_(y_grid);\n base_grid.index({ at::indexing::Ellipsis, 2 }).fill_(1);\n\n auto rescaled_theta = theta->transpose(1, 2) / torch::tensor({ 0.5f * w, 0.5f * h }, thetaOptions);\n auto output_grid = base_grid.view({ 1, oh * ow, 3 }).bmm(rescaled_theta);\n\n return ResultTensor(output_grid.view({ 1, oh, ow, 2 }));\n }\n catch (const c10::Error e) {\n torch_last_err = strdup(e.what());\n }\n catch (const std::runtime_error e) {\n torch_last_err = strdup(e.what());\n }\n\n return nullptr;\n}\n\nTensor THSVision_ApplyGridTransform(Tensor i, Tensor g, const int8_t m, const float* fill, const int64_t fill_length)\n{\n try {\n torch_last_err = 0;\n\n auto img = *i;\n auto grid = *g;\n\n auto imgOptions = at::TensorOptions().dtype(img.dtype()).device(img.device());\n\n if (img.size(0) > 1) {\n grid = grid.expand({ img.size(0), grid.size(1), grid.size(2), grid.size(3) });\n }\n\n if (fill != nullptr) {\n auto dummy = torch::ones({ img.size(0), 1, img.size(2), img.size(3) }, imgOptions);\n img = torch::cat({ img, dummy }, 1);\n }\n\n const torch::nn::functional::GridSampleFuncOptions::mode_t mode =\n (m == 0)\n ? (torch::nn::functional::GridSampleFuncOptions::mode_t)torch::kNearest\n : (torch::nn::functional::GridSampleFuncOptions::mode_t)torch::kBilinear;\n auto sampleOpts = torch::nn::functional::GridSampleFuncOptions().padding_mode(torch::kZeros).mode(mode);\n sampleOpts.align_corners(false); // Supress warning. Default=false for grid_sample since libtorch 1.3.0\n\n img = torch::nn::functional::grid_sample(img, grid, sampleOpts);\n\n\n if (fill != nullptr) {\n\n auto COLON = at::indexing::Slice();\n\n auto mask = img.index({ COLON, at::indexing::Slice(-1, c10::nullopt), COLON, COLON });\n img = img.index({ COLON, at::indexing::Slice(c10::nullopt, -1), COLON, COLON });\n mask = mask.expand_as(img);\n\n auto fill_img = torch::tensor(at::ArrayRef<float>(fill, fill_length), imgOptions).view({ 1, fill_length, 1, 1 }).expand_as(img);\n\n if (m == 0) {\n mask = mask < 0.5;\n img[mask] = fill_img[mask];\n }\n else {\n img = img * mask + (-mask + 1.0) * fill_img;\n }\n }\n\n return ResultTensor(img);\n }\n catch (const c10::Error e) {\n torch_last_err = strdup(e.what());\n }\n catch (const std::runtime_error e) {\n torch_last_err = strdup(e.what());\n }\n\n return nullptr;\n}\n\nTensor THSVision_ScaleChannel(Tensor ic)\n{\n try {\n torch_last_err = 0;\n auto img_chan = *ic;\n\n auto hist = img_chan.is_cuda()\n ? torch::histc(img_chan.to(at::ScalarType::Float), 256, 0, 255)\n : torch::bincount(img_chan.view(-1), {}, 256);\n\n auto nonzero_hist = hist.index({ hist != 0 });\n\n auto step = torch::div(nonzero_hist.index({ at::indexing::Slice(c10::nullopt, -1) }).sum(), 255, \"floor\");\n auto count = step.count_nonzero();\n\n if (count.item<int>() == 0)\n {\n return ResultTensor(img_chan);\n }\n\n auto lut = torch::div(torch::cumsum(hist, 0) + torch::div(step, 2, \"floor\"), step, \"floor\");\n\n auto padOptions = torch::nn::functional::PadFuncOptions({ 1, 0 });\n lut = torch::nn::functional::pad(lut, padOptions).index({ at::indexing::Slice(c10::nullopt, -1) }).clamp(0, 255);\n\n return ResultTensor(lut.index({ img_chan.to(c10::ScalarType::Long) }).to(c10::ScalarType::Byte));\n }\n catch (const c10::Error e) {\n torch_last_err = strdup(e.what());\n }\n catch (const std::runtime_error e) {\n torch_last_err = strdup(e.what());\n }\n\n return nullptr;\n}\n\nvoid THSVision_ComputeOutputSize(const float* matrix, const int64_t matrix_length, const int64_t w, const int64_t h, int32_t* first, int32_t* second)\n{\n try {\n torch_last_err = 0;\n\n auto pts = torch::tensor({ -0.5f * w, -0.5f * h, 1.0f, -0.5f * w, 0.5f * h, 1.0f, 0.5f * w, 0.5f * h, 1.0f, 0.5f * w, -0.5f * h, 1.0f }).reshape({ 4,3 });\n auto theta = torch::tensor(c10::ArrayRef<float>(matrix, matrix_length), c10::TensorOptions().dtype(c10::ScalarType::Float)).view({ 2, 3 });\n auto new_pts = torch::matmul(pts, theta.t());\n\n auto min_vals = std::get<0>(new_pts.min(0));\n auto max_vals = std::get<0>(new_pts.max(0));\n\n min_vals += torch::tensor({ w * 0.5f, h * 0.5f });\n max_vals += torch::tensor({ w * 0.5f, h * 0.5f });\n\n float tol = 1e-4;\n auto cmax = torch::ceil((max_vals / tol).trunc_() * tol);\n auto cmin = torch::floor((min_vals / tol).trunc_() * tol);\n\n auto size = cmax - cmin;\n\n *first = size[0].item<int>();\n *second = size[1].item<int>();\n }\n catch (const c10::Error e) {\n torch_last_err = strdup(e.what());\n }\n catch (const std::runtime_error e) {\n torch_last_err = strdup(e.what());\n }\n}\n\nTensor THSVision_PerspectiveGrid(const float* c, const int64_t c_length, const int64_t ow, const int64_t oh, const int8_t scalar_type, const int device_type, const int device_index)\n{\n try {\n torch_last_err = 0;\n\n auto fullOptions = at::TensorOptions()\n .dtype(at::ScalarType(scalar_type))\n .device(c10::Device((c10::DeviceType)device_type, (c10::DeviceIndex)device_index));\n auto devOptions = at::TensorOptions()\n .device(c10::Device((c10::DeviceType)device_type, (c10::DeviceIndex)device_index));\n\n auto coeffs = c10::ArrayRef<float>(c, c_length);\n\n auto theta1 = torch::tensor({ coeffs[0], coeffs[1], coeffs[2], coeffs[3], coeffs[4], coeffs[5] }, fullOptions).view({ 1, 2, 3 });\n auto theta2 = torch::tensor({ coeffs[6], coeffs[7], 1.0f, coeffs[6], coeffs[7], 1.0f }, fullOptions).view({ 1, 2, 3 });\n\n auto d = 0.5f;\n auto base_grid = torch::empty({ 1, oh, ow, 3 }, fullOptions);\n auto x_grid = torch::linspace(d, ow * 1.0 + d - 1.0, ow, devOptions);\n base_grid.index({ at::indexing::Ellipsis, 0 }).copy_(x_grid);\n\n auto y_grid = torch::linspace(d, oh * 1.0 + d - 1.0, oh, devOptions).unsqueeze_(-1);\n base_grid.index({ at::indexing::Ellipsis, 1 }).copy_(y_grid);\n base_grid.index({ at::indexing::Ellipsis, 2 }).fill_(1);\n\n auto rescaled_theta1 = theta1.transpose(1, 2) / torch::tensor({ 0.5f * ow, 0.5f * oh }, fullOptions);\n\n auto output_grid1 = base_grid.view({ 1, oh * ow, 3 }).bmm(rescaled_theta1);\n auto output_grid2 = base_grid.view({ 1, oh * ow, 3 }).bmm(theta2.transpose(1, 2));\n auto output_grid = output_grid1 / output_grid2 - 1.0f;\n\n return ResultTensor(output_grid.view({ 1, oh, ow, 2 }));\n }\n catch (const c10::Error e) {\n torch_last_err = strdup(e.what());\n }\n catch (const std::runtime_error e) {\n torch_last_err = strdup(e.what());\n }\n\n return nullptr;\n}\n\n\nvoid THSVision_BRGA_RGB(const uint8_t* inputBytes, uint8_t* redBytes, uint8_t* greenBytes, uint8_t* blueBytes, int64_t inputChannelCount, int64_t imageSize)\n{\n const int inputBlue = 0, inputGreen = 1, inputRed = 2;\n\n for (int64_t i = 0, j = 0; i < imageSize; i += 1, j += inputChannelCount) {\n redBytes[i] = inputBytes[inputRed + j];\n greenBytes[i] = inputBytes[inputGreen + j];\n blueBytes[i] = inputBytes[inputBlue + j];\n }\n}\n\nvoid THSVision_BRGA_RGBA(const uint8_t* inputBytes, uint8_t* redBytes, uint8_t* greenBytes, uint8_t* blueBytes, uint8_t* alphaBytes, int64_t inputChannelCount, int64_t imageSize)\n{\n const int inputBlue = 0, inputGreen = 1, inputRed = 2, inputAlpha = 3;\n\n bool inputHasAlpha = inputChannelCount == 4;\n\n for (int64_t i = 0, j = 0; i < imageSize; i += 1, j += inputChannelCount) {\n redBytes[i] = inputBytes[inputRed + j];\n greenBytes[i] = inputBytes[inputGreen + j];\n blueBytes[i] = inputBytes[inputBlue + j];\n alphaBytes[i] = inputHasAlpha ? inputBytes[inputAlpha + j] : 255;\n }\n}\n\nvoid THSVision_RGB_BRGA(const uint8_t* inputBytes, uint8_t* outBytes, int64_t inputChannelCount, int64_t imageSize)\n{\n bool isgrey = inputChannelCount == 1;\n bool inputHasAlpha = inputChannelCount == 4;\n\n const int inputRed = 0, inputGreen = imageSize, inputBlue = imageSize * 2, inputAlpha = imageSize * 3;\n const int outputBlue = 0, outputGreen = 1, outputRed = 2, outputAlpha = 3;\n\n for (int64_t i = 0, j = 0; i < imageSize; i += 1, j += 4) {\n auto redPixel = inputBytes[inputRed + i];\n outBytes[outputRed + j] = redPixel;\n if (!isgrey) {\n outBytes[outputGreen + j] = inputBytes[inputGreen + i];\n outBytes[outputBlue + j] = inputBytes[inputBlue + i];\n }\n else {\n outBytes[outputGreen + j] = redPixel;\n outBytes[outputBlue + j] = redPixel;\n }\n outBytes[outputAlpha + j] = inputHasAlpha ? inputBytes[inputBlue + i] : 255;\n }\n}"},{"alpha_fraction":0.6443196535110474,"alphanum_fraction":0.6474968194961548,"avg_line_length":66.30237579345703,"blob_id":"d0c1b51618030e4f76406b624d88476cecde6a57","content_id":"59afd1e9774c5a46a936ba5b99632ac6c6cd4b37","detected_licenses":["MIT"],"is_generated":false,"is_vendor":false,"language":"C#","length_bytes":31168,"license_type":"permissive","max_line_length":269,"num_lines":463,"path":"/src/TorchSharp/Tensor/torch.ComparisonOps.cs","repo_name":"NiklasGustafsson/TorchSharp","src_encoding":"UTF-8","text":"// Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information.\nusing System;\nusing System.Diagnostics.Contracts;\n\nnamespace TorchSharp\n{\n // https://pytorch.org/docs/stable/torch#comparison-ops\n public static partial class torch\n {\n // https://pytorch.org/docs/stable/generated/torch.allclose\n /// <summary>\n /// This function checks if all input and other lie within a certain distance from each other\n /// </summary>\n /// <param name=\"tensor\"></param>\n /// <param name=\"target\"></param>\n /// <param name=\"rtol\">Relative tolerance</param>\n /// <param name=\"atol\">Absolute tolerance</param>\n /// <param name=\"equal_nan\">If true, then two NaN s will be considered equal</param>\n [Pure]\n public static bool allclose(Tensor tensor, Tensor target, double rtol = 1e-05, double atol = 1e-08, bool equal_nan = false)\n => tensor.allclose(target, rtol, atol, equal_nan);\n\n // https://pytorch.org/docs/stable/generated/torch.argsort\n /// <summary>\n /// Returns the indices that sort a tensor along a given dimension in ascending order by value.\n /// </summary>\n [Pure]\n public static Tensor argsort(Tensor input, long dim = -1, bool descending = false)\n => input.argsort(dim, descending);\n\n // https://pytorch.org/docs/stable/generated/torch.eq\n /// <summary>\n /// Element-wise equal comparison\n /// </summary>\n /// <param name=\"left\">The left-hand operand.</param>\n /// <param name=\"right\">The right-hand operand.</param>\n [Pure]\n public static Tensor eq(Tensor left, Tensor right)\n => left.eq(right);\n\n // https://pytorch.org/docs/stable/generated/torch.equal\n [Pure]\n public static Tensor equal(Tensor tensor, Tensor target)\n => tensor.equal(target);\n\n // https://pytorch.org/docs/stable/generated/torch.ge\n /// <summary>\n /// Element-wise greater-than-or-equal comparison\n /// </summary>\n /// <param name=\"left\">The left-hand operand.</param>\n /// <param name=\"right\">The right-hand operand.</param>\n [Pure]\n public static Tensor ge(Tensor left, Tensor right)\n => left.ge(right);\n\n // https://pytorch.org/docs/stable/generated/torch.greater_equal\n [Pure]\n public static Tensor greater_equal(Tensor tensor, Tensor target)\n => tensor.greater_equal(target);\n\n // https://pytorch.org/docs/stable/generated/torch.gt\n /// <summary>\n /// Element-wise greater-than comparison\n /// </summary>\n /// <param name=\"left\">The left-hand operand.</param>\n /// <param name=\"right\">The right-hand operand.</param>\n [Pure]\n public static Tensor gt(Tensor left, Tensor right)\n => left.gt(right);\n\n // https://pytorch.org/docs/stable/generated/torch.greater\n [Pure]\n public static Tensor greater(Tensor tensor, Tensor target)\n => tensor.greater(target);\n\n // https://pytorch.org/docs/stable/generated/torch.isclose\n [Pure]\n public static Tensor isclose(Tensor tensor, Tensor other, double rtol = 1e-05, double atol = 1e-08, bool nanEqual = false)\n => tensor.isclose(other, rtol, atol, nanEqual);\n\n // https://pytorch.org/docs/stable/generated/torch.isfinite\n [Pure]\n public static Tensor isfinite(Tensor tensor)\n => tensor.isfinite();\n\n // https://pytorch.org/docs/stable/generated/torch.isin\n [Pure]\n public static Tensor isin(Tensor tensor, Tensor test_elements, bool assumeUnique = false, bool invert = false)\n => tensor.isin(test_elements, assumeUnique, invert);\n\n // https://pytorch.org/docs/stable/generated/torch.isinf\n [Pure]\n public static Tensor isinf(Tensor tensor)\n => tensor.isinf();\n\n // https://pytorch.org/docs/stable/generated/torch.isposinf\n [Pure]\n public static Tensor isposinf(Tensor tensor)\n => tensor.isposinf();\n\n // https://pytorch.org/docs/stable/generated/torch.isneginf\n [Pure]\n public static Tensor isneginf(Tensor tensor)\n => tensor.isneginf();\n\n // https://pytorch.org/docs/stable/generated/torch.isnan\n /// <summary>\n /// Returns a new tensor with boolean elements representing if each element of input is <value>NaN</value> or not.\n /// Complex values are considered <value>NaN</value> when either their real and/or imaginary part is <value>NaN</value>.\n /// </summary>\n /// <param name=\"input\">the input tensor</param>\n /// <returns>A boolean tensor that is <value>True</value> where input is <value>NaN</value> and <value>False</value> elsewhere</returns>\n [Pure]\n public static Tensor isnan(Tensor input)\n => input.isnan();\n\n // https://pytorch.org/docs/stable/generated/torch.isreal\n [Pure]\n public static Tensor isreal(Tensor tensor)\n => tensor.isreal();\n\n // https://pytorch.org/docs/stable/generated/torch.kthvalue\n /// <summary>\n /// Returns a named tuple (values, indices) where values is the k th smallest element of each row of the input tensor in the given dimension dim. And indices is the index location of each element found.\n /// If dim is not given, the last dimension of the input is chosen.\n /// </summary>\n /// <param name=\"input\">The input tensor.</param>\n /// <param name=\"k\">k for the k-th smallest element</param>\n /// <param name=\"dim\">The dimension to find the kth value along</param>\n /// <param name=\"keepdim\">Whether the output tensor has dim retained or not.</param>\n [Pure]\n public static (Tensor values, Tensor indices) kthvalue(Tensor input, long k, long? dim, bool keepdim = false)\n => input.kthvalue(k, dim, keepdim);\n\n // https://pytorch.org/docs/stable/generated/torch.le\n /// <summary>\n /// Element-wise less-than-or-equal comparison\n /// </summary>\n /// <param name=\"left\">The left-hand operand.</param>\n /// <param name=\"right\">The right-hand operand.</param>\n [Pure]\n public static Tensor le(Tensor left, Tensor right)\n => left.le(right);\n\n // https://pytorch.org/docs/stable/generated/torch.less_equal\n [Pure]\n public static Tensor less_equal(Tensor tensor, Tensor target)\n => tensor.less_equal(target);\n\n // https://pytorch.org/docs/stable/generated/torch.lt\n /// <summary>\n /// Element-wise less-than comparison\n /// </summary>\n /// <param name=\"left\">The left-hand operand.</param>\n /// <param name=\"right\">The right-hand operand.</param>\n [Pure]\n public static Tensor lt(Tensor left, Tensor right)\n => left.lt(right);\n\n // https://pytorch.org/docs/stable/generated/torch.less\n [Pure]\n public static Tensor less(Tensor tensor, Tensor target)\n => tensor.less(target);\n\n // https://pytorch.org/docs/stable/generated/torch.maximum\n /// <summary>\n /// Computes the element-wise maximum of input and other.\n /// </summary>\n /// <param name=\"input\">The first input tensor</param>\n /// <param name=\"other\">The second input tensor</param>\n [Pure]\n public static Tensor maximum(Tensor input, Tensor other)\n => input.maximum(other);\n\n // https://pytorch.org/docs/stable/generated/torch.minimum\n /// <summary>\n /// Computes the element-wise minimum of input and other.\n /// </summary>\n /// <param name=\"input\">The first input tensor</param>\n /// <param name=\"other\">The second input tensor</param>\n [Pure]\n public static Tensor minimum(Tensor input, Tensor other)\n => input.minimum(other);\n\n // https://pytorch.org/docs/stable/generated/torch.fmax\n /// <summary>\n /// Computes the element-wise maximum of input and other.\n ///\n /// This is like torch.maximum() except it handles NaNs differently: if exactly one of the two elements being compared is a NaN\n /// then the non-NaN element is taken as the maximum.\n /// Only if both elements are NaN is NaN propagated.\n /// </summary>\n /// <param name=\"input\">The first input tensor</param>\n /// <param name=\"other\">The second input tensor</param>\n [Pure]\n public static Tensor fmax(Tensor input, Tensor other)\n => input.fmax(other);\n\n // https://pytorch.org/docs/stable/generated/torch.fmin\n /// <summary>\n /// Computes the element-wise minimum of input and other.\n ///\n /// This is like torch.minimum() except it handles NaNs differently: if exactly one of the two elements being compared is a NaN\n /// then the non-NaN element is taken as the minimum.\n /// Only if both elements are NaN is NaN propagated.\n /// </summary>\n /// <param name=\"input\">The first input tensor</param>\n /// <param name=\"other\">The second input tensor</param>\n [Pure]\n public static Tensor fmin(Tensor input, Tensor other)\n => input.fmin(other);\n\n // https://pytorch.org/docs/stable/generated/torch.ne\n /// <summary>\n /// Element-wise not-equal comparison\n /// </summary>\n /// <param name=\"left\">The left-hand operand.</param>\n /// <param name=\"right\">The right-hand operand.</param>\n [Pure]\n public static Tensor ne(Tensor left, Tensor right)\n => left.ne(right);\n\n // https://pytorch.org/docs/stable/generated/torch.not_equal\n [Pure]\n public static Tensor not_equal(Tensor tensor, Tensor target)\n => tensor.not_equal(target);\n\n // https://pytorch.org/docs/stable/generated/torch.sort\n /// <summary>\n /// Sorts the elements of the input tensor along a given dimension in ascending order by value.\n /// </summary>\n /// <param name=\"input\">The input tensor</param>\n /// <param name=\"dim\">The dimension to sort along. If dim is not given, the last dimension of the input is chosen.</param>\n /// <param name=\"descending\">Controls the sorting order (ascending or descending)</param>\n /// <param name=\"stable\">Makes the sorting routine stable, which guarantees that the order of equivalent elements is preserved.</param>\n /// <returns>A named tuple of (values, indices) is returned, where the values are the sorted values and indices are the indices of the elements in the original input tensor.</returns>\n [Pure]\n public static (Tensor values, Tensor indices) sort(Tensor input, long dim = -1, bool descending = false, bool stable = false)\n => input.sort(dim, descending, stable);\n\n // https://pytorch.org/docs/stable/generated/torch.searchsorted.html\n /// <summary>\n /// Find the indices from the innermost dimension of sorted_sequence such that, if the corresponding values in values were inserted before the indices,\n /// when sorted, the order of the corresponding innermost dimension within sorted_sequence would be preserved.\n /// Return a new tensor with the same size as values.\n /// If right is false, then the left boundary of sorted_sequence is closed. \n /// </summary>\n /// <param name=\"sorted_sequence\">N-D or 1-D tensor, containing monotonically increasing sequence on the innermost dimension unless sorter is provided, in which case the sequence does not need to be sorted</param>\n /// <param name=\"values\">N-D tensor or a Scalar containing the search value(s).</param>\n /// <param name=\"out_int32\">Indicates the output data type. torch.int32 if true, torch.int64 otherwise. Default value is false, i.e. default output data type is torch.int64.</param>\n /// <param name=\"right\">Indicates the output data type. torch.int32 if true, torch.int64 otherwise. Default value is false, i.e. default output data type is torch.int64.</param>\n /// <param name=\"sorter\">If provided, a tensor matching the shape of the unsorted sorted_sequence containing a sequence of indices that sort it in the ascending order on the innermost dimension</param>\n public static Tensor searchsorted(Tensor sorted_sequence, Tensor values, bool out_int32 = false, bool right = false, Tensor sorter = null)\n {\n var res = PInvoke.NativeMethods.THSTensor_searchsorted_t(sorted_sequence.Handle, values.Handle, out_int32, right, sorter is null ? IntPtr.Zero : sorter.Handle);\n if (res == IntPtr.Zero) CheckForErrors();\n return new Tensor(res);\n }\n\n // https://pytorch.org/docs/stable/generated/torch.searchsorted.html\n /// <summary>\n /// Find the indices from the innermost dimension of sorted_sequence such that, if the corresponding values in values were inserted before the indices,\n /// when sorted, the order of the corresponding innermost dimension within sorted_sequence would be preserved.\n /// Return a new tensor with the same size as values.\n /// If right is false, then the left boundary of sorted_sequence is closed. \n /// </summary>\n /// <param name=\"sorted_sequence\">N-D or 1-D tensor, containing monotonically increasing sequence on the innermost dimension unless sorter is provided, in which case the sequence does not need to be sorted</param>\n /// <param name=\"values\">A Scalar containing the search value.</param>\n /// <param name=\"out_int32\">Indicates the output data type. torch.int32 if true, torch.int64 otherwise. Default value is false, i.e. default output data type is torch.int64.</param>\n /// <param name=\"right\">Indicates the output data type. torch.int32 if true, torch.int64 otherwise. Default value is false, i.e. default output data type is torch.int64.</param>\n /// <param name=\"sorter\">If provided, a tensor matching the shape of the unsorted sorted_sequence containing a sequence of indices that sort it in the ascending order on the innermost dimension</param>\n public static Tensor searchsorted(Tensor sorted_sequence, Scalar values, bool out_int32, bool right, Tensor sorter)\n {\n var res = PInvoke.NativeMethods.THSTensor_searchsorted_s(sorted_sequence.Handle, values.Handle, out_int32, right, sorter is null ? IntPtr.Zero : sorter.Handle);\n if (res == IntPtr.Zero) CheckForErrors();\n return new Tensor(res);\n }\n\n /// https://github.com/numpy/numpy/blob/v1.24.0/numpy/lib/histograms.py#L679\n /// <summary>\n /// Computes a histogram of the values in a tensor.\n /// bins can be an integer or a 1D tensor.\n /// If bins is an int, it specifies the number of equal-width bins. By default, the lower and upper range of the bins is determined by the minimum and maximum elements of the input tensor. The range argument can be provided to specify a range for the bins.\n /// If bins is a 1D tensor, it specifies the sequence of bin edges including the rightmost edge. It should contain at least 2 elements and its elements should be increasing.\n /// </summary>\n /// <param name=\"input\"> the input tensor. </param>\n /// <param name=\"bins\"> int or 1D Tensor. If int, defines the number of equal-width bins. If tensor, defines the sequence of bin edges including the rightmost edge. </param>\n /// <param name=\"range\"> Defines the range of the bins. </param>\n /// <param name=\"density\"> If False, the result will contain the count (or total weight) in each bin. If True, the result is the value of the probability density function over the bins, normalized such that the integral over the range of the bins is 1. </param>\n /// <returns></returns>\n public static (Tensor hist, Tensor bin_edges) histogram(Tensor input, HistogramBinSelector bins, (double min, double max)? range = null, bool density = false)\n => Utils.Histogram.histogram(input, bins, range, density);\n\n // https://pytorch.org/docs/stable/generated/torch.histogram.html\n /// <summary>\n /// Computes a histogram of the values in a tensor.\n /// bins can be an integer or a 1D tensor.\n /// If bins is an int, it specifies the number of equal-width bins. By default, the lower and upper range of the bins is determined by the minimum and maximum elements of the input tensor. The range argument can be provided to specify a range for the bins.\n /// If bins is a 1D tensor, it specifies the sequence of bin edges including the rightmost edge. It should contain at least 2 elements and its elements should be increasing.\n /// </summary>\n /// <param name=\"input\"> the input tensor. </param>\n /// <param name=\"bins\"> int or 1D Tensor. If int, defines the number of equal-width bins. If tensor, defines the sequence of bin edges including the rightmost edge. </param>\n /// <param name=\"weight\"> If provided, weight should have the same shape as input. Each value in input contributes its associated weight towards its bin’s result. </param>\n /// <param name=\"density\"> If False, the result will contain the count (or total weight) in each bin. If True, the result is the value of the probability density function over the bins, normalized such that the integral over the range of the bins is 1. </param>\n /// <returns></returns>\n public static (Tensor hist, Tensor bin_edges) histogram(Tensor input, Tensor bins, Tensor weight = null, bool density = false)\n {\n var res = PInvoke.NativeMethods.THSTensor_histogram_t(input.Handle, bins.Handle, weight is null ? IntPtr.Zero : weight.Handle, density, out var r_bin_edges);\n if (res == IntPtr.Zero) CheckForErrors();\n if (r_bin_edges == IntPtr.Zero) CheckForErrors();\n return (new Tensor(res), new Tensor(r_bin_edges));\n }\n\n // https://pytorch.org/docs/stable/generated/torch.histogram.html\n /// <summary>\n /// Computes a histogram of the values in a tensor.\n /// bins can be an integer or a 1D tensor.\n /// If bins is an int, it specifies the number of equal-width bins. By default, the lower and upper range of the bins is determined by the minimum and maximum elements of the input tensor. The range argument can be provided to specify a range for the bins.\n /// If bins is a 1D tensor, it specifies the sequence of bin edges including the rightmost edge. It should contain at least 2 elements and its elements should be increasing.\n /// </summary>\n /// <param name=\"input\"> the input tensor. </param>\n /// <param name=\"bins\"> int or 1D Tensor. If int, defines the number of equal-width bins. If tensor, defines the sequence of bin edges including the rightmost edge. </param>\n /// <param name=\"range\"> Defines the range of the bins. </param>\n /// <param name=\"weight\"> If provided, weight should have the same shape as input. Each value in input contributes its associated weight towards its bin’s result. </param>\n /// <param name=\"density\"> If False, the result will contain the count (or total weight) in each bin. If True, the result is the value of the probability density function over the bins, normalized such that the integral over the range of the bins is 1. </param>\n /// <returns></returns>\n public static (Tensor hist, Tensor bin_edges) histogram(Tensor input, long bins, (double min, double max)? range = null, Tensor weight = null, bool density = false)\n {\n double[] _range = Array.Empty<double>();\n if (range is not null)\n _range = new double[] { range.Value.min, range.Value.max };\n unsafe {\n fixed (double* prange = _range) {\n var res = _range == Array.Empty<double>()\n ? PInvoke.NativeMethods.THSTensor_histogram_i(input.Handle, bins, IntPtr.Zero, 0, weight is null ? IntPtr.Zero : weight.Handle, density, out var r_bin_edges)\n : PInvoke.NativeMethods.THSTensor_histogram_i(input.Handle, bins, (IntPtr)prange, _range.Length, weight is null ? IntPtr.Zero : weight.Handle, density, out r_bin_edges);\n if (res == IntPtr.Zero) CheckForErrors();\n if (r_bin_edges == IntPtr.Zero) CheckForErrors();\n return (new Tensor(res), new Tensor(r_bin_edges));\n }\n }\n }\n\n // https://pytorch.org/docs/stable/generated/torch.histogram.html\n /// <summary>\n /// Computes a histogram of the values in a tensor.\n /// bins can be an integer or a 1D tensor.\n /// If bins is an int, it specifies the number of equal-width bins. By default, the lower and upper range of the bins is determined by the minimum and maximum elements of the input tensor. The range argument can be provided to specify a range for the bins.\n /// If bins is a 1D tensor, it specifies the sequence of bin edges including the rightmost edge. It should contain at least 2 elements and its elements should be increasing.\n /// </summary>\n /// <param name=\"input\"> the input tensor. </param>\n /// <param name=\"bins\"> int or 1D Tensor. If int, defines the number of equal-width bins. If tensor, defines the sequence of bin edges including the rightmost edge. </param>\n /// <param name=\"out_tensor\"> the output tensor. (tuple, optional): The result tuple of two output tensors (hist, bin_edges). </param>\n /// <returns></returns>\n public static (Tensor hist, Tensor bin_edges) histogram(Tensor input, Tensor bins, out (Tensor hist, Tensor bin_edges) out_tensor)\n => histogram(input, bins, null, false, out out_tensor);\n\n // https://pytorch.org/docs/stable/generated/torch.histogram.html\n /// <summary>\n /// Computes a histogram of the values in a tensor.\n /// bins can be an integer or a 1D tensor.\n /// If bins is an int, it specifies the number of equal-width bins. By default, the lower and upper range of the bins is determined by the minimum and maximum elements of the input tensor. The range argument can be provided to specify a range for the bins.\n /// If bins is a 1D tensor, it specifies the sequence of bin edges including the rightmost edge. It should contain at least 2 elements and its elements should be increasing.\n /// </summary>\n /// <param name=\"input\"> the input tensor. </param>\n /// <param name=\"bins\"> int or 1D Tensor. If int, defines the number of equal-width bins. If tensor, defines the sequence of bin edges including the rightmost edge. </param>\n /// <param name=\"weight\"> If provided, weight should have the same shape as input. Each value in input contributes its associated weight towards its bin’s result. </param>\n /// <param name=\"density\"> If False, the result will contain the count (or total weight) in each bin. If True, the result is the value of the probability density function over the bins, normalized such that the integral over the range of the bins is 1. </param>\n /// <param name=\"out_tensor\"> the output tensor. (tuple, optional): The result tuple of two output tensors (hist, bin_edges). </param>\n /// <returns></returns>\n public static (Tensor hist, Tensor bin_edges) histogram(Tensor input, Tensor bins, Tensor weight, bool density, out (Tensor hist, Tensor bin_edges) out_tensor)\n {\n var res = PInvoke.NativeMethods.THSTensor_histogram_out_t(input.Handle, bins.Handle, weight is null ? IntPtr.Zero : weight.Handle, density, out var hist, out var bin_edges, out var r_bin_edges);\n if (res == IntPtr.Zero) CheckForErrors();\n if (hist == IntPtr.Zero) CheckForErrors();\n if (bin_edges == IntPtr.Zero) CheckForErrors();\n if (r_bin_edges == IntPtr.Zero) CheckForErrors();\n out_tensor = (new Tensor(hist), new Tensor(bin_edges));\n return (new Tensor(res), new Tensor(r_bin_edges));\n }\n\n // https://pytorch.org/docs/stable/generated/torch.histogram.html\n /// <summary>\n /// Computes a histogram of the values in a tensor.\n /// bins can be an integer or a 1D tensor.\n /// If bins is an int, it specifies the number of equal-width bins. By default, the lower and upper range of the bins is determined by the minimum and maximum elements of the input tensor. The range argument can be provided to specify a range for the bins.\n /// If bins is a 1D tensor, it specifies the sequence of bin edges including the rightmost edge. It should contain at least 2 elements and its elements should be increasing.\n /// </summary>\n /// <param name=\"input\"> the input tensor. </param>\n /// <param name=\"bins\"> int or 1D Tensor. If int, defines the number of equal-width bins. If tensor, defines the sequence of bin edges including the rightmost edge. </param>\n /// <param name=\"out_tensor\"> the output tensor. (tuple, optional): The result tuple of two output tensors (hist, bin_edges). </param>\n /// <returns></returns>\n public static (Tensor hist, Tensor bin_edges) histogram(Tensor input, long bins, out (Tensor hist, Tensor bin_edges) out_tensor)\n => histogram(input, bins, null, null, false, out out_tensor);\n\n // https://pytorch.org/docs/stable/generated/torch.histogram.html\n /// <summary>\n /// Computes a histogram of the values in a tensor.\n /// bins can be an integer or a 1D tensor.\n /// If bins is an int, it specifies the number of equal-width bins. By default, the lower and upper range of the bins is determined by the minimum and maximum elements of the input tensor. The range argument can be provided to specify a range for the bins.\n /// If bins is a 1D tensor, it specifies the sequence of bin edges including the rightmost edge. It should contain at least 2 elements and its elements should be increasing.\n /// </summary>\n /// <param name=\"input\"> the input tensor. </param>\n /// <param name=\"bins\"> int or 1D Tensor. If int, defines the number of equal-width bins. If tensor, defines the sequence of bin edges including the rightmost edge. </param>\n /// <param name=\"range\"> Defines the range of the bins. </param>\n /// <param name=\"out_tensor\"> the output tensor. (tuple, optional): The result tuple of two output tensors (hist, bin_edges). </param>\n /// <returns></returns>\n public static (Tensor hist, Tensor bin_edges) histogram(Tensor input, long bins, (double min, double max)? range, out (Tensor hist, Tensor bin_edges) out_tensor)\n => histogram(input, bins, range, null, false, out out_tensor);\n\n // https://pytorch.org/docs/stable/generated/torch.histogram.html\n /// <summary>\n /// Computes a histogram of the values in a tensor.\n /// bins can be an integer or a 1D tensor.\n /// If bins is an int, it specifies the number of equal-width bins. By default, the lower and upper range of the bins is determined by the minimum and maximum elements of the input tensor. The range argument can be provided to specify a range for the bins.\n /// If bins is a 1D tensor, it specifies the sequence of bin edges including the rightmost edge. It should contain at least 2 elements and its elements should be increasing.\n /// </summary>\n /// <param name=\"input\"> the input tensor. </param>\n /// <param name=\"bins\"> int or 1D Tensor. If int, defines the number of equal-width bins. If tensor, defines the sequence of bin edges including the rightmost edge. </param>\n /// <param name=\"range\"> Defines the range of the bins. </param>\n /// <param name=\"weight\"> If provided, weight should have the same shape as input. Each value in input contributes its associated weight towards its bin’s result. </param>\n /// <param name=\"density\"> If False, the result will contain the count (or total weight) in each bin. If True, the result is the value of the probability density function over the bins, normalized such that the integral over the range of the bins is 1. </param>\n /// <param name=\"out_tensor\"> the output tensor. (tuple, optional): The result tuple of two output tensors (hist, bin_edges). </param>\n /// <returns></returns>\n public static (Tensor hist, Tensor bin_edges) histogram(Tensor input, long bins, (double min, double max)? range, Tensor weight, bool density, out (Tensor hist, Tensor bin_edges) out_tensor)\n {\n double[] _range = Array.Empty<double>();\n if (range is not null)\n _range = new double[] { range.Value.min, range.Value.max };\n unsafe {\n fixed (double* prange = _range) {\n var res = _range == Array.Empty<double>()\n ? PInvoke.NativeMethods.THSTensor_histogram_out_i(input.Handle, bins, IntPtr.Zero, 0, weight is null ? IntPtr.Zero : weight.Handle, density, out var hist, out var bin_edges, out var r_bin_edges)\n : PInvoke.NativeMethods.THSTensor_histogram_out_i(input.Handle, bins, (IntPtr)prange, _range.Length, weight is null ? IntPtr.Zero : weight.Handle, density, out hist, out bin_edges, out r_bin_edges);\n if (res == IntPtr.Zero) CheckForErrors();\n if (hist == IntPtr.Zero) CheckForErrors();\n if (bin_edges == IntPtr.Zero) CheckForErrors();\n if (r_bin_edges == IntPtr.Zero) CheckForErrors();\n out_tensor = (new Tensor(hist), new Tensor(bin_edges));\n return (new Tensor(res), new Tensor(r_bin_edges));\n }\n }\n }\n\n // https://pytorch.org/docs/stable/generated/torch.topk\n /// <summary>\n /// Returns the k largest elements of the given input tensor along a given dimension.\n /// </summary>\n /// <param name=\"tensor\">The input tensor</param>\n /// <param name=\"k\">The 'k' in 'top-k'.</param>\n /// <param name=\"dim\">The dimension to sort along. If dim is not given, the last dimension of the input is chosen.</param>\n /// <param name=\"largest\">Controls whether to return largest or smallest elements</param>\n /// <param name=\"sorted\">Controls whether to return the elements in sorted order</param>\n [Pure]\n public static (Tensor values, Tensor indices) topk(Tensor tensor, int k, int dim = -1, bool largest = true, bool sorted = true)\n => tensor.topk(k, dim, largest, sorted);\n\n // https://pytorch.org/docs/stable/generated/torch.msort\n [Pure]\n public static Tensor msort(Tensor tensor)\n => tensor.msort();\n }\n}"},{"alpha_fraction":0.5837081670761108,"alphanum_fraction":0.5842078924179077,"avg_line_length":34.73214340209961,"blob_id":"c728f54ec0fb3877f8269e9da8af53a74c46ad81","content_id":"302627c388fb3bda0e1f24f42000811a79a76843","detected_licenses":["MIT"],"is_generated":false,"is_vendor":false,"language":"C#","length_bytes":2001,"license_type":"permissive","max_line_length":130,"num_lines":56,"path":"/src/TorchSharp/NN/Activation/LogSoftMax.cs","repo_name":"NiklasGustafsson/TorchSharp","src_encoding":"UTF-8","text":"// Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information.\nusing System;\nusing static TorchSharp.torch;\nusing static TorchSharp.PInvoke.NativeMethods;\n\nnamespace TorchSharp\n{\n using Modules;\n\n namespace Modules\n {\n /// <summary>\n /// This class is used to represent a log softmax module.\n /// </summary>\n public sealed class LogSoftmax : torch.nn.Module<Tensor, Tensor>\n {\n internal LogSoftmax(IntPtr handle, IntPtr boxedHandle) : base(handle, boxedHandle)\n {\n }\n\n public override Tensor forward(Tensor tensor)\n {\n var res = THSNN_LogSoftmax_forward(handle, tensor.Handle);\n if (res == IntPtr.Zero) { torch.CheckForErrors(); }\n return new Tensor(res);\n }\n\n // Rather than spending cycles only to discover that this module has neither\n // parameters nor buffers, just shortcut the move completely.\n protected internal override nn.Module _to(Device device, ScalarType dtype) => this;\n protected internal override nn.Module _to(DeviceType deviceType, int deviceIndex = -1) => this;\n protected internal override nn.Module _to(ScalarType dtype) => this;\n }\n }\n\n public static partial class torch\n {\n public static partial class nn\n {\n public static LogSoftmax LogSoftmax(long dim)\n {\n var handle = THSNN_LogSoftmax_ctor(dim, out var boxedHandle);\n if (handle == IntPtr.Zero) { torch.CheckForErrors(); }\n return new LogSoftmax(handle, boxedHandle);\n }\n\n public static partial class functional\n {\n public static Tensor log_softmax(Tensor x, long dim)\n {\n return torch.special.log_softmax(x, dim);\n }\n }\n }\n }\n}\n"},{"alpha_fraction":0.6044392585754395,"alphanum_fraction":0.6081775426864624,"avg_line_length":54.584415435791016,"blob_id":"4f9e7386da2dbe971c441f0bc68f11b548e2bad4","content_id":"6b8cfd62e6b86a6738a3683f091c86bd0928064d","detected_licenses":["MIT"],"is_generated":false,"is_vendor":false,"language":"C#","length_bytes":4284,"license_type":"permissive","max_line_length":206,"num_lines":77,"path":"/src/TorchSharp/NN/TransformerDecoderLayer.cs","repo_name":"NiklasGustafsson/TorchSharp","src_encoding":"UTF-8","text":"// Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information.\nusing System;\nusing static TorchSharp.torch;\nusing static TorchSharp.PInvoke.NativeMethods;\n\nnamespace TorchSharp\n{\n using Modules;\n\n namespace Modules\n {\n public sealed class TransformerDecoderLayer : torch.nn.Module<Tensor, Tensor, Tensor, Tensor, Tensor, Tensor, Tensor>, torch.nn.IModule<Tensor, Tensor, Tensor>\n {\n internal TransformerDecoderLayer(IntPtr handle, IntPtr boxedHandle) : base(handle, boxedHandle) { }\n\n /// <summary>\n /// Pass the inputs (and mask) through the decoder layer.\n /// </summary>\n /// <param name=\"tgt\">The sequence to the decoder layer (required).</param>\n /// <param name=\"memory\">The sequence from the last layer of the encoder (required).</param>\n /// <param name=\"tgt_mask\">The mask for the tgt sequence (optional).</param>\n /// <param name=\"memory_mask\">The mask for the memory sequence (optional).</param>\n /// <param name=\"tgt_key_padding_mask\">The mask for the tgt keys per batch (optional).</param>\n /// <param name=\"memory_key_padding_mask\">The mask for the memory keys per batch (optional).</param>\n /// <returns></returns>\n public override Tensor forward(Tensor tgt, Tensor memory, Tensor tgt_mask, Tensor memory_mask, Tensor tgt_key_padding_mask, Tensor memory_key_padding_mask)\n {\n var res = THSNN_TransformerDecoderLayer_forward(handle,\n tgt.Handle,\n memory.Handle,\n tgt_mask?.Handle ?? IntPtr.Zero,\n memory_mask?.Handle ?? IntPtr.Zero,\n tgt_key_padding_mask?.Handle ?? IntPtr.Zero,\n memory_key_padding_mask?.Handle ?? IntPtr.Zero);\n if (res == IntPtr.Zero) { torch.CheckForErrors(); }\n return new Tensor(res);\n }\n\n public new Tensor call(Tensor tgt, Tensor memory, Tensor tgt_mask, Tensor memory_mask = null, Tensor tgt_key_padding_mask = null, Tensor memory_key_padding_mask = null)\n {\n return base.call(tgt, memory, tgt_mask, memory_mask, tgt_key_padding_mask, memory_key_padding_mask);\n }\n\n /// <summary>\n /// Pass the inputs (and mask) through the decoder layer.\n /// </summary>\n /// <param name=\"tgt\">The sequence to the decoder layer (required).</param>\n /// <param name=\"memory\">The sequence from the last layer of the encoder (required).</param>\n public Tensor call(Tensor tgt, Tensor memory)\n {\n return base.call(tgt, memory, null, null, null, null);\n }\n }\n }\n\n public static partial class torch\n {\n public static partial class nn\n {\n /// <summary>\n /// TransformerDecoderLayer is made up of self-attn, multi-head-attn and feedforward network. This standard decoder layer is based on the paper “Attention Is All You Need”.\n /// </summary>\n /// <param name=\"d_model\">The number of expected features in the input (required).</param>\n /// <param name=\"nhead\">The number of heads in the multiheadattention models (required).</param>\n /// <param name=\"dim_feedforward\">The dimension of the feedforward network model (default=2048).</param>\n /// <param name=\"dropout\">The dropout value (default=0.1).</param>\n /// <param name=\"activation\">The activation function of intermediate layer, relu or gelu (default=relu).</param>\n /// <returns></returns>\n public static TransformerDecoderLayer TransformerDecoderLayer(long d_model = 512, long nhead = 8, long dim_feedforward = 2048, double dropout = 0.1, Activations activation = nn.Activations.ReLU)\n {\n var res = THSNN_TransformerDecoderLayer_ctor(d_model, nhead, dim_feedforward, dropout, (long)activation, out var boxedHandle);\n if (res == IntPtr.Zero) { torch.CheckForErrors(); }\n return new TransformerDecoderLayer(res, boxedHandle);\n }\n }\n }\n}\n"},{"alpha_fraction":0.5543248653411865,"alphanum_fraction":0.5543248653411865,"avg_line_length":36.939998626708984,"blob_id":"eaa31d5e10d4d83daea675f86f6e4f75e6f15f7e","content_id":"7cd5acc25193dcf0c4050bc89f3743c87e786671","detected_licenses":["MIT"],"is_generated":false,"is_vendor":false,"language":"C#","length_bytes":1896,"license_type":"permissive","max_line_length":130,"num_lines":50,"path":"/src/TorchVision/Erase.cs","repo_name":"NiklasGustafsson/TorchSharp","src_encoding":"UTF-8","text":"// Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information.\n\nusing static TorchSharp.torch;\n\n\nnamespace TorchSharp\n{\n public static partial class torchvision\n {\n internal class Erase : ITransform\n {\n internal Erase(int top, int left, int height, int width, Tensor value, bool inplace = false)\n {\n this.top = top;\n this.left = left;\n this.height = height;\n this.width = width;\n this.inplace = inplace;\n this.value = value;\n }\n\n public Tensor call(Tensor img)\n {\n return transforms.functional.erase(img, top, left, height, width, value, inplace);\n }\n\n private int top, left, height, width;\n private Tensor value;\n private bool inplace;\n }\n\n public static partial class transforms\n {\n /// <summary>\n /// Erase a region of an image tensor with given value. \n /// </summary>\n /// <param name=\"top\">Vertical component of the top left corner of the erased region.</param>\n /// <param name=\"left\">Horizontal component of the top left corner of the erased region.</param>\n /// <param name=\"height\">The height of the erased region.</param>\n /// <param name=\"width\">The width of the erased region.</param>\n /// <param name=\"value\">Erasing value.</param>\n /// <param name=\"inplace\">For in-place operations.</param>\n /// <returns></returns>\n static public ITransform Erase(int top, int left, int height, int width, Tensor value, bool inplace = false)\n {\n return new Erase(top, left, height, width, value, inplace);\n }\n }\n }\n}"},{"alpha_fraction":0.4808405339717865,"alphanum_fraction":0.48207664489746094,"avg_line_length":34.9555549621582,"blob_id":"3913362de13983495366efb8c1050080ea023826","content_id":"d99125ef8da7aa736c4f2826127a2d17fceab23a","detected_licenses":["MIT"],"is_generated":false,"is_vendor":false,"language":"C#","length_bytes":1618,"license_type":"permissive","max_line_length":130,"num_lines":45,"path":"/src/TorchAudio/NoBackend.cs","repo_name":"NiklasGustafsson/TorchSharp","src_encoding":"UTF-8","text":"// Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information.\nusing System;\n\nnamespace TorchSharp\n{\n public static partial class torchaudio\n {\n public static partial class backend\n {\n public class NoBackend : AudioBackend\n {\n public override (torch.Tensor, int) load(\n string filepath,\n long frame_offset = 0,\n long num_frames = -1,\n bool normalize = true,\n bool channels_first = true,\n AudioFormat? format = null)\n {\n throw new InvalidOperationException(\"No audio I/O backend is available.\");\n }\n\n public override void save(\n string filepath,\n torch.Tensor src,\n int sample_rate,\n bool channels_first = true,\n float? compression = null,\n AudioFormat? format = null,\n AudioEncoding? encoding = null,\n int? bits_per_sample = null)\n {\n throw new InvalidOperationException(\"No audio I/O backend is available.\");\n }\n\n public override AudioMetaData info(\n string filepath,\n AudioFormat? format = null)\n {\n throw new InvalidOperationException(\"No audio I/O backend is available.\");\n }\n }\n }\n }\n}\n"},{"alpha_fraction":0.45195272564888,"alphanum_fraction":0.46574169397354126,"avg_line_length":42.40520477294922,"blob_id":"2fb4839b32ed57d6b88412a95742aa3a1749b0c6","content_id":"1ae32898d1f0a52412218330ad8151bba7721a72","detected_licenses":["MIT"],"is_generated":false,"is_vendor":false,"language":"C#","length_bytes":11676,"license_type":"permissive","max_line_length":181,"num_lines":269,"path":"/src/TorchVision/models/MobileNetV2.cs","repo_name":"NiklasGustafsson/TorchSharp","src_encoding":"UTF-8","text":"// Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information.\n\n// A number of implementation details in this file have been translated from the Python version of torchvision,\n// largely located in the files found in this folder:\n//\n// https://github.com/pytorch/vision/blob/a4f53308b2d0f1aa9191686e326f45c26053f686/torchvision/models/mobilenetv2.py\n//\n// The origin has the following copyright notice and license:\n//\n// https://github.com/pytorch/vision/blob/main/LICENSE\n//\n\nusing System;\nusing System.Collections.Generic;\n\nusing static TorchSharp.torch;\nusing static TorchSharp.torchvision;\nusing static TorchSharp.torchvision.models._utils;\n\n#nullable enable\nnamespace TorchSharp\n{\n namespace Modules\n {\n /// <summary>\n /// MobileNet V2 main class\n /// </summary>\n public class MobileNetV2 : nn.Module<Tensor, Tensor>\n {\n private class InvertedResidual : nn.Module<Tensor, Tensor>\n {\n private readonly bool _is_cn;\n private readonly nn.Module<Tensor, Tensor> conv;\n private readonly long out_channels;\n private readonly long stride;\n private readonly bool use_res_connect;\n\n protected override void Dispose(bool disposing)\n {\n if (disposing) {\n conv.Dispose();\n }\n base.Dispose(disposing);\n }\n\n public InvertedResidual(\n string name,\n long inp,\n long oup,\n long stride,\n double expand_ratio,\n Func<long, nn.Module<Tensor, Tensor>>? norm_layer = null) : base(name)\n {\n this.stride = stride;\n if (stride != 1 && stride != 2) {\n throw new ArgumentOutOfRangeException($\"stride should be 1 or 2 insted of {stride}\");\n }\n\n if (norm_layer == null) {\n norm_layer = (features) => nn.BatchNorm2d(features);\n }\n\n var hidden_dim = (long)Math.Round(inp * expand_ratio);\n this.use_res_connect = this.stride == 1 && inp == oup;\n\n var layers = new List<nn.Module<Tensor, Tensor>>();\n if (expand_ratio != 1) {\n // pw\n layers.Add(\n ops.Conv2dNormActivation(\n inp,\n hidden_dim,\n kernel_size: 1,\n norm_layer: norm_layer,\n activation_layer: (inplace) => nn.ReLU6(inplace)));\n }\n layers.AddRange(new List<nn.Module<Tensor, Tensor>> {\n // dw\n ops.Conv2dNormActivation(\n hidden_dim,\n hidden_dim,\n stride: stride,\n groups: hidden_dim,\n norm_layer: norm_layer,\n activation_layer: (inplace) => nn.ReLU6(inplace)),\n // pw-linear\n nn.Conv2d(hidden_dim, oup, 1, 1, 0, bias: false),\n norm_layer(oup)\n });\n this.conv = nn.Sequential(layers);\n this.out_channels = oup;\n this._is_cn = stride > 1;\n RegisterComponents();\n }\n\n public override Tensor forward(Tensor x)\n {\n if (this.use_res_connect) {\n return x + this.conv.call(x);\n } else {\n return this.conv.call(x);\n }\n }\n }\n\n private readonly nn.Module<Tensor, Tensor> classifier;\n private readonly nn.Module<Tensor, Tensor> features;\n private readonly long last_channel;\n\n protected override void Dispose(bool disposing)\n {\n if (disposing) {\n classifier.Dispose();\n features.Dispose();\n }\n base.Dispose(disposing);\n }\n\n internal MobileNetV2(\n string name,\n long num_classes = 1000,\n double width_mult = 1.0,\n long[][]? inverted_residual_setting = null,\n long round_nearest = 8,\n Func<long, long, long, long, Func<long, nn.Module<Tensor, Tensor>>, nn.Module<Tensor, Tensor>>? block = null,\n Func<long, nn.Module<Tensor, Tensor>>? norm_layer = null,\n double dropout = 0.2) : base(name)\n {\n if (block == null) {\n block = (input_channel, output_channel, stride, t, norm_layer) => new InvertedResidual(\"InvertedResidual\", input_channel, output_channel, stride, t, norm_layer);\n }\n\n if (norm_layer == null) {\n norm_layer = (features) => nn.BatchNorm2d(features);\n }\n\n long input_channel = 32;\n long last_channel = 1280;\n\n if (inverted_residual_setting == null) {\n inverted_residual_setting = new long[][] {\n // t, c, n, s\n new long[] { 1, 16, 1, 1 },\n new long[] { 6, 24, 2, 2 },\n new long[] { 6, 32, 3, 2 },\n new long[] { 6, 64, 4, 2 },\n new long[] { 6, 96, 3, 1 },\n new long[] { 6, 160, 3, 2 },\n new long[] { 6, 320, 1, 1 }\n };\n }\n\n // only check the first element, assuming user knows t,c,n,s are required\n if (inverted_residual_setting.Length == 0 || inverted_residual_setting[0].Length != 4) {\n throw new ArgumentException($\"inverted_residual_setting should be non-empty or a 4-element list, got {inverted_residual_setting}\");\n }\n\n // building first layer\n input_channel = _make_divisible(input_channel * width_mult, round_nearest);\n this.last_channel = _make_divisible(last_channel * Math.Max(1.0, width_mult), round_nearest);\n var features = new List<nn.Module<Tensor, Tensor>> {\n ops.Conv2dNormActivation(3, input_channel, stride: 2, norm_layer: norm_layer, activation_layer: (inplace) => nn.ReLU6(inplace))\n };\n // building inverted residual blocks\n foreach (var x in inverted_residual_setting) {\n long t = x[0];\n long c = x[1];\n long n = x[2];\n long s = x[3];\n var output_channel = _make_divisible(c * width_mult, round_nearest);\n for (var i = 0; i < n; i++) {\n var stride = i == 0 ? s : 1;\n features.Add(block(input_channel, output_channel, stride, t, norm_layer));\n input_channel = output_channel;\n }\n }\n // building last several layers\n features.Add(\n ops.Conv2dNormActivation(\n input_channel,\n this.last_channel,\n kernel_size: 1,\n norm_layer: norm_layer,\n activation_layer: (inplace) => nn.ReLU6(inplace)));\n // make it nn.Sequential\n this.features = nn.Sequential(features);\n\n // building classifier\n this.classifier = nn.Sequential(\n nn.Dropout(p: dropout),\n nn.Linear(this.last_channel, num_classes));\n\n RegisterComponents();\n\n // weight initialization\n foreach (var (_, m) in this.named_modules()) {\n if (m is Modules.Conv2d) {\n var conv = (Modules.Conv2d)m;\n nn.init.kaiming_normal_(conv.weight, mode: nn.init.FanInOut.FanOut);\n if (conv.bias is not null) {\n nn.init.zeros_(conv.bias);\n }\n } else if (m is Modules.BatchNorm2d) {\n var norm = (Modules.BatchNorm2d)m;\n nn.init.ones_(norm.weight);\n nn.init.zeros_(norm.bias);\n } else if (m is Modules.GroupNorm) {\n var norm = (Modules.GroupNorm)m;\n nn.init.ones_(norm.weight);\n nn.init.zeros_(norm.bias);\n } else if (m is Modules.Linear) {\n var linear = (Modules.Linear)m;\n nn.init.normal_(linear.weight, 0, 0.01);\n nn.init.zeros_(linear.bias);\n }\n }\n }\n\n public override Tensor forward(Tensor x)\n {\n x = this.features.call(x);\n // Cannot use \"squeeze\" as batch-size can be 1\n x = nn.functional.adaptive_avg_pool2d(x, (1, 1));\n x = torch.flatten(x, 1);\n x = this.classifier.call(x);\n return x;\n }\n }\n }\n\n public static partial class torchvision\n {\n public static partial class models\n {\n /// <summary>\n /// MobileNetV2 architecture from the `MobileNetV2: Inverted Residuals and Linear\n /// Bottlenecks https://arxiv.org/abs/1801.04381 paper.\n /// </summary>\n /// <param name=\"num_classes\">Number of classes</param>\n /// <param name=\"width_mult\">Width multiplier - adjusts number of channels in each layer by this amount</param>\n /// <param name=\"inverted_residual_setting\">Network structure</param>\n /// <param name=\"round_nearest\">Round the number of channels in each layer to be a multiple of this number\n /// Set to 1 to turn off rounding</param>\n /// <param name=\"block\">Module specifying inverted residual building block for mobilenet</param>\n /// <param name=\"norm_layer\">Module specifying the normalization layer to use</param>\n /// <param name=\"dropout\">The droupout probability</param>\n /// <returns></returns>\n public static Modules.MobileNetV2 mobilenet_v2(\n long num_classes = 1000,\n double width_mult = 1.0,\n long[][]? inverted_residual_setting = null,\n long round_nearest = 8,\n Func<long, long, long, long, Func<long, nn.Module<Tensor, Tensor>>, nn.Module<Tensor, Tensor>>? block = null,\n Func<long, nn.Module<Tensor, Tensor>>? norm_layer = null,\n double dropout = 0.2)\n {\n return new Modules.MobileNetV2(\n \"MobileNetV2\",\n num_classes,\n width_mult,\n inverted_residual_setting,\n round_nearest,\n block,\n norm_layer,\n dropout);\n }\n }\n }\n}\n"},{"alpha_fraction":0.6534457206726074,"alphanum_fraction":0.6534457206726074,"avg_line_length":41.9361686706543,"blob_id":"e731a564d65bc7ce149081b7ac58d6e589b2509d","content_id":"0b97fd47ada3d048797cdc7dd5508b37858df055","detected_licenses":["MIT"],"is_generated":false,"is_vendor":false,"language":"C#","length_bytes":2017,"license_type":"permissive","max_line_length":130,"num_lines":47,"path":"/src/TorchSharp/Tensor/torch.LocallyDisablingGradientComputation.cs","repo_name":"NiklasGustafsson/TorchSharp","src_encoding":"UTF-8","text":"// Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information.\n#nullable enable\nusing System;\nusing System.Diagnostics.Contracts;\n\nnamespace TorchSharp\n{\n // https://pytorch.org/docs/stable/torch#locally-disabling-gradient-computation\n public static partial class torch\n {\n // https://pytorch.org/docs/stable/generated/torch.no_grad\n /// <summary>\n /// Context-manager that disables gradient calculation.\n /// </summary>\n /// <returns></returns>\n public static IDisposable no_grad() => new AutoGradMode(false);\n\n // https://pytorch.org/docs/stable/generated/torch.enable_grad\n /// <summary>\n /// Context-manager that enables gradient calculation.\n /// </summary>\n /// <returns></returns>\n public static IDisposable enable_grad(bool enabled = true) => new AutoGradMode(enabled);\n\n // https://pytorch.org/docs/stable/generated/torch.set_grad_enabled\n /// <summary>\n /// Context-manager that sets gradient calculation to on or off.\n /// </summary>\n /// <returns></returns>\n public static IDisposable set_grad_enabled(bool mode = true) => new AutoGradMode(mode);\n\n // https://pytorch.org/docs/stable/generated/torch.is_grad_enabled\n /// <summary>\n /// Returns true if grad mode is currently enabled.\n /// </summary>\n /// <returns></returns>\n [Pure]public static bool is_grad_enabled() => AutoGradMode.IsEnabled;\n\n // https://pytorch.org/docs/stable/generated/torch.inference_mode\n [Obsolete(\"not implemented\", true)]\n public static IDisposable inference_mode(bool mode = true) => throw new NotImplementedException();\n\n // https://pytorch.org/docs/stable/generated/torch.is_inference_mode_enabled\n [Obsolete(\"not implemented\", true)]\n [Pure]public static bool is_inference_mode_enabled() => throw new NotImplementedException();\n }\n}"},{"alpha_fraction":0.6219512224197388,"alphanum_fraction":0.6219512224197388,"avg_line_length":21.454545974731445,"blob_id":"80bc65f1f2e4ec01a30f0fdd78bc63b448f88e04","content_id":"f877827cb8faa19798a19dce0bf198099cf7f3d8","detected_licenses":["MIT"],"is_generated":false,"is_vendor":false,"language":"C#","length_bytes":248,"license_type":"permissive","max_line_length":131,"num_lines":11,"path":"/src/TorchSharp/Tensor/Enums/Reduce.cs","repo_name":"NiklasGustafsson/TorchSharp","src_encoding":"UTF-8","text":"// Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information.\nnamespace TorchSharp\n{\n public enum Reduce\n {\n prod,\n mean,\n amax,\n amin\n }\n}"},{"alpha_fraction":0.5455530285835266,"alphanum_fraction":0.5517278909683228,"avg_line_length":51.15254211425781,"blob_id":"c2f67612ef6869c1a0d67fb84f5ff6725a2bf673","content_id":"a6654ccc042cc19923f2a148fa4d09b4ffd1ec99","detected_licenses":["MIT"],"is_generated":false,"is_vendor":false,"language":"C#","length_bytes":9231,"license_type":"permissive","max_line_length":280,"num_lines":177,"path":"/src/TorchSharp/NN/Convolution/Conv1D.cs","repo_name":"NiklasGustafsson/TorchSharp","src_encoding":"UTF-8","text":"// Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information.\nusing System;\nusing static TorchSharp.torch;\nusing static TorchSharp.PInvoke.NativeMethods;\n\n#nullable enable\nnamespace TorchSharp\n{\n using Modules;\n\n public enum PaddingModes\n {\n Zeros = 0,\n Reflect = 1,\n Replicate = 2,\n Circular = 3,\n Constant = 4,\n }\n\n public enum Padding\n {\n Valid = 0,\n Same = 1\n }\n\n namespace Modules\n {\n public abstract class Convolution : torch.nn.Module<Tensor, Tensor>\n {\n protected Convolution(IntPtr handle, IntPtr boxedHandle, long input_channels) : base(handle, boxedHandle)\n {\n this.input_channels = input_channels;\n }\n\n protected bool ValidateShape(Tensor input, long dimensions)\n {\n var shape = input.shape;\n var ndim = shape.LongLength;\n\n return (ndim == dimensions+2) && (input.shape[1] == input_channels) || // Batched: N + C + dims\n (ndim == dimensions+1 && input.shape[0] == input_channels); // Unbathced: C + dims\n\n }\n\n protected long input_channels;\n }\n\n public sealed class Conv1d : Convolution\n {\n internal Conv1d(IntPtr handle, IntPtr boxedHandle, long input_channels) : base(handle, boxedHandle, input_channels) { }\n\n public override Tensor forward(Tensor input)\n {\n if (ValidateShape(input, 1)) {\n var res = THSNN_Conv1d_forward(handle, input.Handle);\n if (res == IntPtr.Zero) { torch.CheckForErrors(); }\n return new Tensor(res);\n }\n throw new ArgumentException($\"Expected 2D (unbatched) or 3D (batched) input with {input_channels} channels to Conv1d.\");\n }\n\n public Parameter? bias {\n get {\n var res = THSNN_Conv1d_bias(handle);\n if (res == IntPtr.Zero) { torch.CheckForErrors(); }\n return ((res == IntPtr.Zero) ? null : new Parameter(res));\n }\n set {\n THSNN_Conv1d_set_bias(handle, (value is null ? IntPtr.Zero : value.Handle));\n torch.CheckForErrors();\n ConditionallyRegisterParameter(\"bias\", value);\n }\n }\n public Parameter? weight {\n get {\n var res = THSNN_Conv1d_weight(handle);\n if (res == IntPtr.Zero) { torch.CheckForErrors(); }\n return (res == IntPtr.Zero) ? null : new Parameter(res);\n }\n set {\n THSNN_Conv1d_set_weight(handle, value is null ? IntPtr.Zero : value.Handle);\n torch.CheckForErrors();\n ConditionallyRegisterParameter(\"weight\", value);\n }\n }\n }\n }\n\n public static partial class torch\n {\n public static partial class nn\n {\n /// <summary>\n /// Applies a 1D convolution over an input signal composed of several input planes.\n /// </summary>\n /// <param name=\"inputChannel\">Number of channels in the input image</param>\n /// <param name=\"outputChannel\">Number of channels produced by the convolution</param>\n /// <param name=\"kernelSize\">Size of the convolving kernel</param>\n /// <param name=\"stride\">Stride of the convolution. Default: 1</param>\n /// <param name=\"padding\">Zero-padding added to both sides of the input. Default: 0</param>\n /// <param name=\"dilation\">Spacing between kernel elements. Default: 1</param>\n /// <param name=\"paddingMode\">'zeros', 'reflect', 'replicate' or 'circular'. Default: 'zeros'</param>\n /// <param name=\"groups\">Number of blocked connections from input channels to output channels. Default: 1</param>\n /// <param name=\"bias\">If true, adds a learnable bias to the output. Default: true</param>\n /// <param name=\"device\">The desired device of the parameters and buffers in this module</param>\n /// <param name=\"dtype\">The desired floating point or complex dtype of the parameters and buffers in this module</param>\n /// <returns>Tensor of shape (N,C_out,L_out)</returns>\n public static Conv1d Conv1d(long inputChannel, long outputChannel, long kernelSize, long stride = 1, long padding = 0, long dilation = 1, PaddingModes paddingMode = PaddingModes.Zeros, long groups = 1, bool bias = true, Device? device = null, ScalarType? dtype = null)\n {\n var res = THSNN_Conv1d_ctor(inputChannel, outputChannel, kernelSize, stride, padding, dilation, (long)paddingMode, groups, bias, out var boxedHandle);\n if (res == IntPtr.Zero) { torch.CheckForErrors(); }\n return new Conv1d(res, boxedHandle, inputChannel).MoveModule<Conv1d>(device, dtype);\n }\n\n /// <summary>\n /// Applies a 1D convolution over an input signal composed of several input planes.\n /// </summary>\n /// <param name=\"inputChannel\">Number of channels in the input image</param>\n /// <param name=\"outputChannel\">Number of channels produced by the convolution</param>\n /// <param name=\"kernelSize\">Size of the convolving kernel</param>\n /// <param name=\"stride\">Stride of the convolution. Default: 1</param>\n /// <param name=\"padding\">Zero-padding added to both sides of the input. padding=Valid is the same as no padding. padding=Same pads the input so the output has the shape as the input. </param>\n /// <param name=\"dilation\">Spacing between kernel elements. Default: 1</param>\n /// <param name=\"paddingMode\">'zeros', 'reflect', 'replicate' or 'circular'. Default: 'zeros'</param>\n /// <param name=\"groups\">Number of blocked connections from input channels to output channels. Default: 1</param>\n /// <param name=\"bias\">If true, adds a learnable bias to the output. Default: true</param>\n /// <param name=\"device\">The desired device of the parameters and buffers in this module</param>\n /// <param name=\"dtype\">The desired floating point or complex dtype of the parameters and buffers in this module</param>\n /// <returns>Tensor of shape (N,C_out,L_out)</returns>\n public static Conv1d Conv1d(long inputChannel, long outputChannel, long kernelSize, Padding padding, long stride = 1, long dilation = 1, PaddingModes paddingMode = PaddingModes.Zeros, long groups = 1, bool bias = true, Device? device = null, ScalarType? dtype = null)\n {\n var res = THSNN_Conv1d_ctor(inputChannel, outputChannel, kernelSize, stride, padding == Padding.Valid ? 0 : -1, dilation, (long)paddingMode, groups, bias, out var boxedHandle);\n if (res == IntPtr.Zero) { torch.CheckForErrors(); }\n return new Conv1d(res, boxedHandle, inputChannel).MoveModule<Conv1d>(device, dtype);\n }\n\n public static partial class functional\n {\n /// <summary>\n /// Applies a 1D convolution over an input signal composed of several input planes.\n /// </summary>\n /// <param name=\"input\">The input tensor.</param>\n /// <param name=\"weight\"></param>\n /// <param name=\"bias\"></param>\n /// <param name=\"stride\"></param>\n /// <param name=\"padding\"></param>\n /// <param name=\"dilation\"></param>\n /// <param name=\"groups\"></param>\n /// <returns></returns>\n public static Tensor conv1d(Tensor input, Tensor weight, Tensor? bias = null,\n long? stride = null,\n long? padding = null,\n long? dilation = null,\n long groups = 1)\n {\n var strides = new long[] { stride ?? 1 };\n var paddingArray = new long[] { padding ?? 0 };\n var dilationArray = new long[] { dilation ?? 1 };\n var biasHandle = (bias is null ? IntPtr.Zero : bias.Handle);\n unsafe {\n fixed (long* pstrides = strides, ppadding = paddingArray, pdilation = dilationArray) {\n var res =\n THSTensor_conv1d(input.Handle, weight.Handle, biasHandle,\n (IntPtr)pstrides, strides.Length,\n (IntPtr)ppadding, paddingArray.Length,\n (IntPtr)pdilation, dilationArray.Length,\n groups);\n if (res == IntPtr.Zero) { torch.CheckForErrors(); }\n return new Tensor(res);\n }\n }\n }\n\n }\n }\n }\n}\n"},{"alpha_fraction":0.5770045518875122,"alphanum_fraction":0.5954614281654358,"avg_line_length":52.30644989013672,"blob_id":"7fd5e7b256d5b553ee6fa3c2beca4c643a832206","content_id":"803aec93c03a124d335b955ff1f0af0ca2a37978","detected_licenses":["MIT"],"is_generated":false,"is_vendor":false,"language":"C#","length_bytes":3305,"license_type":"permissive","max_line_length":117,"num_lines":62,"path":"/pkg/FileRestitcher/FileRestitcher.Tests/UnitTest1.cs","repo_name":"NiklasGustafsson/TorchSharp","src_encoding":"UTF-8","text":"using System;\nusing System.IO;\nusing Xunit;\n\nnamespace FileRestitcherTests\n{\n public class Tests\n {\n //[Fact]\n public void TestRestitchingSmallFiles()\n {\n long size = 1024; // * 1024 * 1024; // enable this to test massive files, takes a long time\n var oneChunk = new byte[size];\n var rnd = new System.Random();\n for (int i = 0; i < 10; i++)\n oneChunk[i] = (byte)rnd.Next(0,255);\n var tmpDir = Path.GetDirectoryName(System.Reflection.Assembly.GetExecutingAssembly().Location) + @\"/tmp\";\n System.Console.WriteLine(\"tmpDir = {0}\", tmpDir);\n if (Directory.Exists(tmpDir))\n Directory.Delete(tmpDir, true);\n Directory.CreateDirectory(tmpDir);\n Directory.CreateDirectory(tmpDir + @\"/some-package-primary/runtimes\");\n Directory.CreateDirectory(tmpDir + @\"/some-package-fragment1/fragments\");\n Directory.CreateDirectory(tmpDir + @\"/some-package-fragment2/fragments\");\n Directory.CreateDirectory(tmpDir + @\"/some-package-fragment3/fragments\");\n File.WriteAllBytes(tmpDir + @\"/some-package-primary/runtimes/a.so\", oneChunk);\n File.WriteAllBytes(tmpDir + @\"/some-package-fragment1/fragments/a.so.fragment1\", oneChunk);\n File.WriteAllBytes(tmpDir + @\"/some-package-fragment2/fragments/a.so.fragment2\", oneChunk);\n File.WriteAllBytes(tmpDir + @\"/some-package-fragment3/fragments/a.so.fragment3\", oneChunk);\n System.Threading.Thread.Sleep(1000);\n using (var sha256Hash = System.Security.Cryptography.SHA256.Create()) {\n var expectedFile = tmpDir + @\"/a.so\";\n Console.WriteLine(\"Writing restored primary file at {0}\", expectedFile);\n var os = File.OpenWrite(expectedFile);\n os.Write(oneChunk, 0, oneChunk.Length);\n os.Write(oneChunk, 0, oneChunk.Length);\n os.Write(oneChunk, 0, oneChunk.Length);\n os.Write(oneChunk, 0, oneChunk.Length);\n os.Close();\n var os2 = File.OpenRead(expectedFile);\n byte[] bytes = sha256Hash.ComputeHash(os2);\n var builder = new System.Text.StringBuilder();\n for (int i = 0; i < bytes.Length; i++) {\n builder.Append(bytes[i].ToString(\"x2\"));\n }\n var sha = builder.ToString();\n File.WriteAllText(tmpDir + @\"/some-package-primary/runtimes/a.so.sha\", sha);\n os2.Close();\n }\n\n Assert.Equal(new FileInfo(tmpDir + @\"/some-package-primary/runtimes/a.so\").Length, 1* size);\n\n ConsoleApp2.Program.Restitch(tmpDir + @\"/some-package-primary\");\n Assert.True(File.Exists(tmpDir + @\"/some-package-primary/runtimes/a.so\"));\n Assert.Equal(new FileInfo(tmpDir + @\"/some-package-primary/runtimes/a.so\").Length, 4* size);\n Assert.False(File.Exists(tmpDir + @\"/some-package-fragment1/fragments/a.so.fragment1\"));\n Assert.False(File.Exists(tmpDir + @\"/some-package-fragment2/fragments/a.so.fragment2\"));\n Assert.False(File.Exists(tmpDir + @\"/some-package-fragment3/fragments/a.so.fragment3\"));\n }\n }\n\n}\n"},{"alpha_fraction":0.6319200992584229,"alphanum_fraction":0.6329389810562134,"avg_line_length":61.982173919677734,"blob_id":"77df93dca67f36d18fbc3b2036e9485d6820dad1","content_id":"43e52c8e66771e0664c9950b8b17a3324a3d5e9c","detected_licenses":["MIT"],"is_generated":false,"is_vendor":false,"language":"C#","length_bytes":35431,"license_type":"permissive","max_line_length":214,"num_lines":561,"path":"/src/TorchSharp/Tensor/torch.ReductionOps.cs","repo_name":"NiklasGustafsson/TorchSharp","src_encoding":"UTF-8","text":"// Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information.\n#nullable enable\nusing System;\nusing System.Collections.Generic;\nusing System.Diagnostics.Contracts;\nusing System.Linq;\n\nusing static TorchSharp.PInvoke.NativeMethods;\n\nnamespace TorchSharp\n{\n // https://pytorch.org/docs/stable/torch#reduction-ops\n public static partial class torch\n {\n // https://pytorch.org/docs/stable/generated/torch.argmax\n /// <summary>\n /// Returns the indices of the maximum value of all elements in the input tensor.\n /// </summary>\n [Pure]public static Tensor argmax(Tensor input)\n => input.argmax();\n\n // https://pytorch.org/docs/stable/generated/torch.argmax\n /// <summary>\n /// Returns the indices of the maximum value of all elements in the input tensor.\n /// </summary>\n [Pure]public static Tensor argmax(Tensor input, long dim, bool keepdim = false)\n => input.argmax(dim, keepdim);\n\n // https://pytorch.org/docs/stable/generated/torch.argmin\n /// <summary>\n /// Returns the indices of the minimum value of all elements in the input tensor.\n /// </summary>\n [Pure]public static Tensor argmin(Tensor input)\n => input.argmin();\n\n // https://pytorch.org/docs/stable/generated/torch.argmin\n /// <summary>\n /// Returns the indices of the minimum value of all elements in the input tensor.\n /// </summary>\n [Pure]public static Tensor argmin(Tensor input, long dim, bool keepdim = false)\n => input.argmin(dim, keepdim);\n\n // https://pytorch.org/docs/stable/generated/torch.amax\n /// <summary>\n /// Returns the maximum value of each slice of the input tensor in the given dimension(s) dim.\n /// </summary>\n /// <param name=\"input\">The input tensor</param>\n /// <param name=\"dims\">The dimension or dimensions to reduce.</param>\n /// <param name=\"keepdim\">Whether the output tensor has dim retained or not.</param>\n /// <param name=\"out\">The output tensor -- optional.</param>\n [Pure]public static Tensor amax(Tensor input, long[] dims, bool keepdim = false, Tensor? @out = null)\n => input.amax(dims, keepdim, @out);\n\n [Pure]public static Tensor amax(Tensor input, ReadOnlySpan<long> dims, bool keepdim = false, Tensor? @out = null)\n => input.amax(dims, keepdim, @out);\n\n [Pure]public static Tensor amax(Tensor input, long[] dims)\n => input.amax(dims);\n\n // https://pytorch.org/docs/stable/generated/torch.amin\n /// <summary>\n /// Returns the minimum value of each slice of the input tensor in the given dimension(s) dim.\n /// </summary>\n /// <param name=\"input\">The input tensor</param>\n /// <param name=\"dims\">The dimension or dimensions to reduce.</param>\n /// <param name=\"keepdim\">Whether the output tensor has dim retained or not.</param>\n /// <param name=\"out\">The output tensor -- optional.</param>\n [Pure]public static Tensor amin(Tensor input, long[] dims, bool keepdim = false, Tensor? @out = null)\n => input.amin(dims, keepdim, @out);\n\n // https://pytorch.org/docs/stable/generated/torch.amin\n /// <summary>\n /// Returns the minimum value of each slice of the input tensor in the given dimension(s) dim.\n /// </summary>\n /// <param name=\"input\">The input tensor</param>\n /// <param name=\"dims\">The dimension or dimensions to reduce.</param>\n /// <param name=\"keepdim\">Whether the output tensor has dim retained or not.</param>\n /// <param name=\"out\">The output tensor -- optional.</param>\n [Pure]public static Tensor amin(Tensor input, ReadOnlySpan<long> dims, bool keepdim = false, Tensor? @out = null)\n => input.amin(dims, keepdim, @out);\n\n // https://pytorch.org/docs/stable/generated/torch.aminmax\n [Pure]public static (Tensor min, Tensor max) aminmax(Tensor input, long? dim = null, bool keepdim = false)\n => input.aminmax(dim, keepdim);\n\n // https://pytorch.org/docs/stable/generated/torch.all\n /// <summary>\n /// Tests if all elements in input evaluate to true.\n /// <param name=\"input\">The input tensor</param>\n /// </summary>\n [Pure]public static Tensor all(Tensor input) => input.all();\n\n // https://pytorch.org/docs/stable/generated/torch.all\n /// <summary>\n /// Tests if all elements in input evaluate to true.\n /// <param name=\"input\">The input tensor</param>\n /// <param name=\"dim\">The dimension to reduce</param>\n /// <param name=\"keepdim\">Keep the dimension to reduce</param>\n /// </summary>\n [Pure]public static Tensor all(Tensor input, long dim, bool keepdim = false) => input.all(dim, keepdim);\n\n // https://pytorch.org/docs/stable/generated/torch.any\n /// <summary>\n /// Tests if all elements in input evaluate to true.\n /// <param name=\"input\">The input tensor</param>\n /// </summary>\n [Pure]public static Tensor any(Tensor input) => input.any();\n\n // https://pytorch.org/docs/stable/generated/torch.any\n /// <summary>\n /// Tests if any element in input evaluate to true.\n /// <param name=\"input\">The input tensor</param>\n /// <param name=\"dim\">The dimension to reduce</param>\n /// <param name=\"keepdim\">Keep the dimension to reduce</param>\n /// </summary>\n [Pure]public static Tensor any(Tensor input, long dim, bool keepdim = false) => input.any(dim, keepdim);\n\n // https://pytorch.org/docs/stable/generated/torch.max\n /// <summary>\n /// Returns the maximum value of all elements in the input tensor.\n /// </summary>\n /// <param name=\"input\">The input tensor.</param>\n [Pure]public static Tensor max(Tensor input) => input.max();\n\n /// <summary>\n /// Computes the element-wise maximum of input and other.\n /// </summary>\n /// <param name=\"input\">The input tensor.</param>\n /// <param name=\"other\">The second tensor.</param>\n [Pure] public static Tensor max(Tensor input, Tensor other) => torch.maximum(input, other);\n\n // https://pytorch.org/docs/stable/generated/torch.max\n /// <summary>\n /// Returns a named tuple (values, indexes) where values is the maximum value of each row of the input tensor in the given dimension dim.\n /// And indices is the index location of each maximum value found (argmax).\n /// </summary>\n /// <param name=\"input\">The input tensor</param>\n /// <param name=\"dim\">the dimension to reduce.</param>\n /// <param name=\"keepdim\">whether the output tensor has dim retained or not. Default: false.</param>\n /// <remarks>If keepdim is true, the output tensors are of the same size as input except in the dimension dim where they are of size 1.\n /// Otherwise, dim is squeezed(see torch.squeeze()), resulting in the output tensors having 1 fewer dimension than input.</remarks>\n [Pure]public static (Tensor values, Tensor indexes) max(Tensor input, long dim, bool keepdim = false) => input.max(dim, keepdim);\n\n // https://pytorch.org/docs/stable/generated/torch.min\n /// <summary>\n /// Returns the minimum value of all elements in the input tensor.\n /// </summary>\n /// <param name=\"input\">The input tensor.</param>\n [Pure]public static Tensor min(Tensor input) => input.min();\n\n /// <summary>\n /// Computes the element-wise minimum of input and other.\n /// </summary>\n /// <param name=\"input\">The input tensor.</param>\n /// <param name=\"other\">The second tensor.</param>\n [Pure] public static Tensor min(Tensor input, Tensor other) => torch.minimum(input, other);\n\n // https://pytorch.org/docs/stable/generated/torch.min\n /// <summary>\n /// Returns a named tuple (values, indexes) where values is the minimum value of each row of the input tensor in the given dimension dim.\n /// And indices is the index location of each minimum value found (argmin).\n /// </summary>\n /// <param name=\"input\">The input tensor</param>\n /// <param name=\"dim\">the dimension to reduce.</param>\n /// <param name=\"keepdim\">whether the output tensor has dim retained or not. Default: false.</param>\n /// <remarks>If keepdim is true, the output tensors are of the same size as input except in the dimension dim where they are of size 1.\n /// Otherwise, dim is squeezed(see torch.squeeze()), resulting in the output tensors having 1 fewer dimension than input.</remarks>\n [Pure]public static (Tensor values, Tensor indexes) min(Tensor input, long dim, bool keepdim = false) => input.min(dim, keepdim);\n\n // https://pytorch.org/docs/stable/generated/torch.dist\n /// <summary>\n /// Returns the p-norm of (input - other).\n /// The shapes of input and other must be broadcastable.\n /// </summary>\n /// <param name=\"input\">Left-hand side input tensor.</param>\n /// <param name=\"other\">Right-hand side input tensor</param>\n /// <param name=\"p\">The norm to be computed.</param>\n [Pure]public static Tensor dist(Tensor input, Tensor other, float p = 2.0f) => input.dist(other, p);\n\n // https://pytorch.org/docs/stable/generated/torch.logsumexp\n /// <summary>\n /// Returns the log of summed exponentials of each row of the input tensor in the given dimension dim.\n /// </summary>\n /// <param name=\"input\">The input tensor.</param>\n /// <param name=\"dim\">The dimension to do the operation over</param>\n /// <param name=\"keepdim\">Thether the output tensor has dim retained or not.</param>\n [Pure]public static Tensor logsumexp(Tensor input, long dim, bool keepdim = false)\n => input.logsumexp(dim, keepdim);\n\n // https://pytorch.org/docs/stable/generated/torch.mean\n /// <summary>\n /// Returns the mean value of all elements in the input tensor.\n /// </summary>\n [Pure]public static Tensor mean(Tensor input) => input.mean();\n\n // https://pytorch.org/docs/stable/generated/torch.\n /// <summary>\n /// Returns the mean value of each row of the input tensor in the given dimension dim. If dim is a list of dimensions, reduce over all of them.\n /// </summary>\n /// <param name=\"input\">The input tensor</param>\n /// <param name=\"dimensions\">The dimension or dimensions to reduce.</param>\n /// <param name=\"keepdim\">Whether the output tensor has dim retained or not.</param>\n /// <param name=\"type\">The desired data type of returned tensor. If specified, the input tensor is cast to dtype before the operation is performed. This is useful for preventing data type overflows.</param>\n /// <remarks>\n /// If keepdim is True, the output tensor is of the same size as input except in the dimension(s) dim where it is of size 1.\n /// Otherwise, dim is squeezed(see torch.squeeze()), resulting in the output tensor having 1 (or len(dim)) fewer dimension(s).\n /// </remarks>\n [Pure]public static Tensor mean(Tensor input, long[] dimensions, bool keepdim = false, ScalarType? type = null) => input.mean(dimensions, keepdim, type);\n\n // https://pytorch.org/docs/stable/generated/torch.nanmean\n [Pure]public static Tensor nanmean(Tensor input, int? dim = null, bool keepdim = false, ScalarType? dtype = null)\n => input.nanmean(dim, keepdim, dtype);\n\n // https://pytorch.org/docs/stable/generated/torch.median\n [Pure]public static Tensor median(Tensor input) => input.median();\n\n // https://pytorch.org/docs/stable/generated/torch.nanmedian\n [Pure]public static Tensor nanmedian(Tensor input)\n => input.nanmedian();\n\n // https://pytorch.org/docs/stable/generated/torch.mode\n [Pure]public static (Tensor values, Tensor indices) mode(Tensor input, long dim = -1L, bool keepdim = false)\n => input.mode(dim, keepdim);\n\n // https://pytorch.org/docs/stable/generated/torch.norm\n [Pure]public static Tensor norm(Tensor input, float p = 2.0f)\n => input.norm(p);\n\n [Pure]public static Tensor norm(Tensor input, int dimension, bool keepdim = false, float p = 2.0f)\n => input.norm(dimension, keepdim, p);\n\n // https://pytorch.org/docs/stable/generated/torch.nansum\n [Pure]public static Tensor nansum(Tensor input) => input.nansum();\n\n // https://pytorch.org/docs/stable/generated/torch.prod\n // TODO: torch.prod\n // static Tensor prod(Tensor input, ScalarType dtype = null) => input.prod(dtype);\n\n // https://pytorch.org/docs/stable/generated/torch.quantile\n [Pure]public static Tensor quantile(Tensor input, Tensor q, long dim = -1, bool keepdim = false)\n => input.quantile(q, dim, keepdim);\n\n // https://pytorch.org/docs/stable/generated/torch.nanquantile\n [Pure]public static Tensor nanquantile(Tensor input, Tensor q, long dim = -1, bool keepdim = false)\n => input.nanquantile(q, dim, keepdim);\n\n // https://pytorch.org/docs/stable/generated/torch.std\n /// <summary>\n /// Calculates the standard deviation of all elements in the tensor.\n /// </summary>\n /// <param name=\"input\">The input tensor.</param>\n /// <param name=\"unbiased\">Whether to use Bessel’s correction (δN=1).</param>\n [Pure]public static Tensor std(Tensor input, bool unbiased = true) => input.std(unbiased);\n\n /// <summary>Calculates the standard deviation of all elements in the tensor.</summary>\n /// <remarks>\n /// If <paramref name=\"unbiased\" /> is <value>true</value>, Bessel’s correction will be used.\n /// Otherwise, the sample deviation is calculated, without any correction.\n /// </remarks>\n /// <param name=\"input\">The input tensor.</param>\n /// <param name=\"dimensions\">The dimensions to reduce.</param>\n /// <param name=\"unbiased\">Whether to use Bessel’s correction (δN=1).</param>\n /// <param name=\"keepdim\">Whether the <see cref=\"Tensor\">output tensor</see> has dim retained or not.</param>\n /// <param name=\"type\"></param>\n /// <returns>The <see cref=\"Tensor\">output tensor</see>.</returns>\n [Pure]public static Tensor std(Tensor input, long[] dimensions, bool unbiased = true, bool keepdim = false, ScalarType? type = null)\n => input.std(dimensions, unbiased, keepdim, type);\n\n /// <summary>Calculates the standard deviation of all elements in the tensor.</summary>\n /// <remarks>\n /// If <paramref name=\"unbiased\" /> is <value>true</value>, Bessel’s correction will be used.\n /// Otherwise, the sample deviation is calculated, without any correction.\n /// </remarks>\n /// <param name=\"input\">The input tensor.</param>\n /// <param name=\"dimensions\">The dimensions to reduce.</param>\n /// <param name=\"unbiased\">Whether to use Bessel’s correction (δN=1).</param>\n /// <param name=\"keepdim\">Whether the <see cref=\"Tensor\">output tensor</see> has dim retained or not.</param>\n /// <param name=\"type\"></param>\n /// <returns>The <see cref=\"Tensor\">output tensor</see>.</returns>\n [Pure]public static Tensor std(Tensor input, long dimensions, bool unbiased = true, bool keepdim = false, ScalarType? type = null)\n => input.std(dimensions, unbiased, keepdim, type);\n\n /// <summary>Calculates the standard deviation of all elements in the tensor.</summary>\n /// <remarks>\n /// If <paramref name=\"unbiased\" /> is <value>true</value>, Bessel’s correction will be used.\n /// Otherwise, the sample deviation is calculated, without any correction.\n /// </remarks>\n /// <param name=\"input\">The input tensor.</param>\n /// <param name=\"dimensions\">The dimensions to reduce.</param>\n /// <param name=\"unbiased\">Whether to use Bessel’s correction (δN=1).</param>\n /// <param name=\"keepdim\">Whether the <see cref=\"Tensor\">output tensor</see> has dim retained or not.</param>\n /// <param name=\"type\"></param>\n /// <returns>The <see cref=\"Tensor\">output tensor</see>.</returns>\n [Pure]public static Tensor std(Tensor input, (long, long) dimensions, bool unbiased = true, bool keepdim = false, ScalarType? type = null)\n => input.std(dimensions, unbiased, keepdim, type);\n\n /// <summary>Calculates the standard deviation of all elements in the tensor.</summary>\n /// <remarks>\n /// If <paramref name=\"unbiased\" /> is <value>true</value>, Bessel’s correction will be used.\n /// Otherwise, the sample deviation is calculated, without any correction.\n /// </remarks>\n /// <param name=\"input\">The input tensor.</param>\n /// <param name=\"dimensions\">The dimensions to reduce.</param>\n /// <param name=\"unbiased\">Whether to use Bessel’s correction (δN=1).</param>\n /// <param name=\"keepdim\">Whether the <see cref=\"Tensor\">output tensor</see> has dim retained or not.</param>\n /// <param name=\"type\"></param>\n /// <returns>The <see cref=\"Tensor\">output tensor</see>.</returns>\n [Pure]public static Tensor std(Tensor input, (long, long, long) dimensions, bool unbiased = true, bool keepdim = false, ScalarType? type = null)\n => input.std(dimensions, unbiased, keepdim, type);\n\n // https://pytorch.org/docs/stable/generated/torch.std_mean\n /// <summary>Calculates the standard deviation and mean of all elements in the tensor.</summary>\n /// <remarks>\n /// If <paramref name=\"unbiased\" /> is <value>true</value>, Bessel’s correction will be used.\n /// Otherwise, the sample deviation is calculated, without any correction.\n /// </remarks>\n /// <param name=\"input\">The input tensor.</param>\n /// <param name=\"dimensions\">The dimensions to reduce.</param>\n /// <param name=\"unbiased\">Whether to use Bessel’s correction (δN=1).</param>\n /// <param name=\"keepdim\">Whether the <see cref=\"Tensor\">output tensor</see> has dim retained or not.</param>\n /// <param name=\"type\"></param>\n /// <returns>A <see cref=\"Tensor\">tensor</see> tuple of the standard deviation and the mean.</returns>\n [Pure]public static (Tensor std, Tensor mean) std_mean(Tensor input, long[] dimensions, bool unbiased = true, bool keepdim = false, ScalarType? type = null)\n => input.std_mean(dimensions, unbiased, keepdim, type);\n\n /// <summary>Calculates the standard deviation and mean of all elements in the tensor.</summary>\n /// <remarks>\n /// If <paramref name=\"unbiased\" /> is <value>true</value>, Bessel’s correction will be used.\n /// Otherwise, the sample deviation is calculated, without any correction.\n /// </remarks>\n /// <param name=\"input\">The input tensor.</param>\n /// <param name=\"dimensions\">The dimensions to reduce.</param>\n /// <param name=\"unbiased\">Whether to use Bessel’s correction (δN=1).</param>\n /// <param name=\"keepdim\">Whether the <see cref=\"Tensor\">output tensor</see> has dim retained or not.</param>\n /// <param name=\"type\"></param>\n /// <returns>A <see cref=\"Tensor\">tensor</see> tuple of the standard deviation and the mean.</returns>\n [Pure]public static (Tensor std, Tensor mean) std_mean(Tensor input, ReadOnlySpan<long> dimensions, bool unbiased = true, bool keepdim = false, ScalarType? type = null)\n => input.std_mean(dimensions, unbiased, keepdim, type);\n\n /// <summary>Calculates the standard deviation and mean of all elements in the tensor.</summary>\n /// <remarks>\n /// If <paramref name=\"unbiased\" /> is <value>true</value>, Bessel’s correction will be used.\n /// Otherwise, the sample deviation is calculated, without any correction.\n /// </remarks>\n /// <param name=\"input\">The input tensor.</param>\n /// <param name=\"dimensions\">The dimensions to reduce.</param>\n /// <param name=\"unbiased\">Whether to use Bessel’s correction (δN=1).</param>\n /// <param name=\"keepdim\">Whether the <see cref=\"Tensor\">output tensor</see> has dim retained or not.</param>\n /// <param name=\"type\"></param>\n /// <returns>A <see cref=\"Tensor\">tensor</see> tuple of the standard deviation and the mean.</returns>\n [Pure]public static (Tensor std, Tensor mean) std_mean(Tensor input, long dimensions, bool unbiased = true, bool keepdim = false, ScalarType? type = null)\n => input.std_mean(dimensions, unbiased, keepdim, type);\n\n /// <summary>Calculates the standard deviation and mean of all elements in the tensor.</summary>\n /// <remarks>\n /// If <paramref name=\"unbiased\" /> is <value>true</value>, Bessel’s correction will be used.\n /// Otherwise, the sample deviation is calculated, without any correction.\n /// </remarks>\n /// <param name=\"input\">The input tensor.</param>\n /// <param name=\"dimensions\">The dimensions to reduce.</param>\n /// <param name=\"unbiased\">Whether to use Bessel’s correction (δN=1).</param>\n /// <param name=\"keepdim\">Whether the <see cref=\"Tensor\">output tensor</see> has dim retained or not.</param>\n /// <param name=\"type\"></param>\n /// <returns>A <see cref=\"Tensor\">tensor</see> tuple of the standard deviation and the mean.</returns>\n [Pure]public static (Tensor std, Tensor mean) std_mean(Tensor input, (long, long) dimensions, bool unbiased = true, bool keepdim = false, ScalarType? type = null)\n => input.std_mean(dimensions, unbiased, keepdim, type);\n\n /// <summary>Calculates the standard deviation and mean of all elements in the tensor.</summary>\n /// <remarks>\n /// If <paramref name=\"unbiased\" /> is <value>true</value>, Bessel’s correction will be used.\n /// Otherwise, the sample deviation is calculated, without any correction.\n /// </remarks>\n /// <param name=\"input\">The input tensor.</param>\n /// <param name=\"dimensions\">The dimensions to reduce.</param>\n /// <param name=\"unbiased\">Whether to use Bessel’s correction (δN=1).</param>\n /// <param name=\"keepdim\">Whether the <see cref=\"Tensor\">output tensor</see> has dim retained or not.</param>\n /// <param name=\"type\"></param>\n /// <returns>A <see cref=\"Tensor\">tensor</see> tuple of the standard deviation and the mean.</returns>\n [Pure]public static (Tensor std, Tensor mean) std_mean(Tensor input, (long, long, long) dimensions, bool unbiased = true, bool keepdim = false, ScalarType? type = null)\n => input.std_mean(dimensions, unbiased, keepdim, type);\n\n // https://pytorch.org/docs/stable/generated/torch.sum\n /// <summary>\n /// Returns the sum of each row of the input tensor in the given dimensions.\n /// </summary>\n [Pure]public static Tensor sum(Tensor input, ScalarType? type = null) => input.sum(type);\n\n // https://pytorch.org/docs/stable/generated/torch.sum\n /// <summary>\n /// Returns the sum of each row of the input tensor in the given dimensions.\n /// </summary>\n [Pure]public static Tensor sum(Tensor input, ReadOnlySpan<long> dim, bool keepdim = false, ScalarType? type = null) => input.sum(dim, keepdim, type);\n\n // https://pytorch.org/docs/stable/generated/torch.sum\n /// <summary>\n /// Returns the sum of each row of the input tensor in the given dimension.\n /// </summary>\n [Pure]public static Tensor sum(Tensor input, long dim, bool keepdim = false, ScalarType? type = null) => input.sum(dim, keepdim, type);\n\n // https://pytorch.org/docs/stable/generated/torch.unique\n [Pure]static (Tensor output, Tensor? inverse_indices, Tensor? counts) unique(\n Tensor input, bool sorted = true, bool return_inverse = false, bool return_counts = false, int? dim = null)\n => input.unique(sorted, return_inverse, return_counts, dim);\n\n // https://pytorch.org/docs/stable/generated/torch.unique_consecutive\n [Pure]static (Tensor output, Tensor? inverse_indices, Tensor? counts) unique_consecutive(\n Tensor input, bool return_inverse = false, bool return_counts = false, int? dim = null)\n => input.unique_consecutive(return_inverse, return_counts, dim);\n\n // https://pytorch.org/docs/stable/generated/torch.var\n /// <summary>\n /// Calculates the variance of all elements in the tensor.\n /// </summary>\n /// <param name=\"input\">The input tensor.</param>\n /// <param name=\"unbiased\">Whether to use Bessel’s correction (δN=1).</param>\n [Pure]public static Tensor var(Tensor input, bool unbiased = true) => input.var(unbiased);\n\n /// <summary>Calculates the variance of all elements in the tensor.</summary>\n /// <remarks>\n /// If <paramref name=\"unbiased\" /> is <value>true</value>, Bessel’s correction will be used.\n /// Otherwise, the sample variance is calculated, without any correction.\n /// </remarks>\n /// <param name=\"input\">The input tensor.</param>\n /// <param name=\"dimensions\">The dimensions to reduce.</param>\n /// <param name=\"unbiased\">Whether to use Bessel’s correction (δN=1).</param>\n /// <param name=\"keepdim\">Whether the <see cref=\"Tensor\">output tensor</see> has dim retained or not.</param>\n /// <param name=\"type\"></param>\n /// <returns>The <see cref=\"Tensor\">output tensor</see>.</returns>\n [Pure]public static Tensor var(Tensor input, long[] dimensions, bool unbiased = true, bool keepdim = false, ScalarType? type = null)\n => input.var(dimensions, unbiased, keepdim, type);\n\n /// <summary>Calculates the variance of all elements in the tensor.</summary>\n /// <remarks>\n /// If <paramref name=\"unbiased\" /> is <value>true</value>, Bessel’s correction will be used.\n /// Otherwise, the sample variance is calculated, without any correction.\n /// </remarks>\n /// <param name=\"input\">The input tensor.</param>\n /// <param name=\"dimensions\">The dimensions to reduce.</param>\n /// <param name=\"unbiased\">Whether to use Bessel’s correction (δN=1).</param>\n /// <param name=\"keepdim\">Whether the <see cref=\"Tensor\">output tensor</see> has dim retained or not.</param>\n /// <param name=\"type\"></param>\n /// <returns>The <see cref=\"Tensor\">output tensor</see>.</returns>\n [Pure]public static Tensor var(Tensor input, long dimensions, bool unbiased = true, bool keepdim = false, ScalarType? type = null)\n => input.var(dimensions, unbiased, keepdim, type);\n\n /// <summary>Calculates the variance of all elements in the tensor.</summary>\n /// <remarks>\n /// If <paramref name=\"unbiased\" /> is <value>true</value>, Bessel’s correction will be used.\n /// Otherwise, the sample variance is calculated, without any correction.\n /// </remarks>\n /// <param name=\"input\">The input tensor.</param>\n /// <param name=\"dimensions\">The dimensions to reduce.</param>\n /// <param name=\"unbiased\">Whether to use Bessel’s correction (δN=1).</param>\n /// <param name=\"keepdim\">Whether the <see cref=\"Tensor\">output tensor</see> has dim retained or not.</param>\n /// <param name=\"type\"></param>\n /// <returns>The <see cref=\"Tensor\">output tensor</see>.</returns>\n [Pure]public static Tensor var(Tensor input, (long, long) dimensions, bool unbiased = true, bool keepdim = false, ScalarType? type = null)\n => input.var(dimensions, unbiased, keepdim, type);\n\n /// <summary>Calculates the variance of all elements in the tensor.</summary>\n /// <remarks>\n /// If <paramref name=\"unbiased\" /> is <value>true</value>, Bessel’s correction will be used.\n /// Otherwise, the sample variance is calculated, without any correction.\n /// </remarks>\n /// <param name=\"input\">The input tensor.</param>\n /// <param name=\"dimensions\">The dimensions to reduce.</param>\n /// <param name=\"unbiased\">Whether to use Bessel’s correction (δN=1).</param>\n /// <param name=\"keepdim\">Whether the <see cref=\"Tensor\">output tensor</see> has dim retained or not.</param>\n /// <param name=\"type\"></param>\n /// <returns>The <see cref=\"Tensor\">output tensor</see>.</returns>\n [Pure]public static Tensor var(Tensor input, (long, long, long) dimensions, bool unbiased = true, bool keepdim = false, ScalarType? type = null)\n => input.var(dimensions, unbiased, keepdim, type);\n\n // https://pytorch.org/docs/stable/generated/torch.var_mean\n /// <summary>\n /// Calculates the variance and mean of all elements in the tensor.\n /// </summary>\n /// <param name=\"input\">The input tensor.</param>\n /// <param name=\"unbiased\">Whether to use Bessel’s correction (δN=1).</param>\n [Pure]public static (Tensor @var, Tensor mean) var_mean(Tensor input, bool unbiased = true) => input.std_mean(unbiased);\n\n /// <summary>Calculates the variance and mean of all elements in the tensor.</summary>\n /// <remarks>\n /// If <paramref name=\"unbiased\" /> is <value>true</value>, Bessel’s correction will be used.\n /// Otherwise, the sample variance is calculated, without any correction.\n /// </remarks>\n /// <param name=\"input\">The input tensor.</param>\n /// <param name=\"dimensions\">The dimensions to reduce.</param>\n /// <param name=\"unbiased\">Whether to use Bessel’s correction (δN=1).</param>\n /// <param name=\"keepdim\">Whether the <see cref=\"Tensor\">output tensor</see> has dim retained or not.</param>\n /// <param name=\"type\"></param>\n /// <returns>A <see cref=\"Tensor\">tensor</see> tuple of the variance and the mean.</returns>\n [Pure]public static (Tensor @var, Tensor mean) var_mean(Tensor input, long[] dimensions, bool unbiased = true, bool keepdim = false, ScalarType? type = null)\n => input.var_mean(dimensions, unbiased, keepdim, type);\n\n /// <summary>Calculates the variance and mean of all elements in the tensor.</summary>\n /// <remarks>\n /// If <paramref name=\"unbiased\" /> is <value>true</value>, Bessel’s correction will be used.\n /// Otherwise, the sample variance is calculated, without any correction.\n /// </remarks>\n /// <param name=\"input\">The input tensor.</param>\n /// <param name=\"dimensions\">The dimensions to reduce.</param>\n /// <param name=\"unbiased\">Whether to use Bessel’s correction (δN=1).</param>\n /// <param name=\"keepdim\">Whether the <see cref=\"Tensor\">output tensor</see> has dim retained or not.</param>\n /// <param name=\"type\"></param>\n /// <returns>A <see cref=\"Tensor\">tensor</see> tuple of the variance and the mean.</returns>\n [Pure]public static (Tensor @var, Tensor mean) var_mean(Tensor input, ReadOnlySpan<long> dimensions, bool unbiased = true, bool keepdim = false, ScalarType? type = null)\n => input.var_mean(dimensions, unbiased, keepdim, type);\n\n /// <summary>Calculates the variance and mean of all elements in the tensor.</summary>\n /// <remarks>\n /// If <paramref name=\"unbiased\" /> is <value>true</value>, Bessel’s correction will be used.\n /// Otherwise, the sample variance is calculated, without any correction.\n /// </remarks>\n /// <param name=\"input\">The input tensor.</param>\n /// <param name=\"dimensions\">The dimensions to reduce.</param>\n /// <param name=\"unbiased\">Whether to use Bessel’s correction (δN=1).</param>\n /// <param name=\"keepdim\">Whether the <see cref=\"Tensor\">output tensor</see> has dim retained or not.</param>\n /// <param name=\"type\"></param>\n /// <returns>A <see cref=\"Tensor\">tensor</see> tuple of the variance and the mean.</returns>\n [Pure]public static (Tensor @var, Tensor mean) var_mean(Tensor input, long dimensions, bool unbiased = true, bool keepdim = false, ScalarType? type = null)\n => input.var_mean(dimensions, unbiased, keepdim, type);\n\n /// <summary>Calculates the variance and mean of all elements in the tensor.</summary>\n /// <remarks>\n /// If <paramref name=\"unbiased\" /> is <value>true</value>, Bessel’s correction will be used.\n /// Otherwise, the sample variance is calculated, without any correction.\n /// </remarks>\n /// <param name=\"input\">The input tensor.</param>\n /// <param name=\"dimensions\">The dimensions to reduce.</param>\n /// <param name=\"unbiased\">Whether to use Bessel’s correction (δN=1).</param>\n /// <param name=\"keepdim\">Whether the <see cref=\"Tensor\">output tensor</see> has dim retained or not.</param>\n /// <param name=\"type\"></param>\n /// <returns>A <see cref=\"Tensor\">tensor</see> tuple of the variance and the mean.</returns>\n [Pure]public static (Tensor @var, Tensor mean) var_mean(Tensor input, (long, long) dimensions, bool unbiased = true, bool keepdim = false, ScalarType? type = null)\n => input.var_mean(dimensions, unbiased, keepdim, type);\n\n /// <summary>Calculates the variance and mean of all elements in the tensor.</summary>\n /// <remarks>\n /// If <paramref name=\"unbiased\" /> is <value>true</value>, Bessel’s correction will be used.\n /// Otherwise, the sample variance is calculated, without any correction.\n /// </remarks>\n /// <param name=\"input\">The input tensor.</param>\n /// <param name=\"dimensions\">The dimensions to reduce.</param>\n /// <param name=\"unbiased\">Whether to use Bessel’s correction (δN=1).</param>\n /// <param name=\"keepdim\">Whether the <see cref=\"Tensor\">output tensor</see> has dim retained or not.</param>\n /// <param name=\"type\"></param>\n /// <returns>A <see cref=\"Tensor\">tensor</see> tuple of the variance and the mean.</returns>\n [Pure]public static (Tensor @var, Tensor mean) var_mean(Tensor input, (long, long, long) dimensions, bool unbiased = true, bool keepdim = false, ScalarType? type = null)\n => input.var_mean(dimensions, unbiased, keepdim, type);\n\n // https://pytorch.org/docs/stable/generated/torch.count_nonzero\n /// <summary>\n /// Counts the number of non-zero values in the tensor input along the given dim. If no dim is specified then all non-zeros in the tensor are counted.\n /// </summary>\n /// <param name=\"input\">The input tensor.</param>\n /// <param name=\"dims\">List of dims along which to count non-zeros.</param>\n [Pure]public static Tensor count_nonzero(Tensor input, long[]? dims = null)\n => input.count_nonzero(dims);\n }\n}"},{"alpha_fraction":0.6863289475440979,"alphanum_fraction":0.6969696879386902,"avg_line_length":37.59821319580078,"blob_id":"69748fc248494996ea127fc2769ab1f85cfedcd7","content_id":"3d3919ee3fe9da9ee4ae37d1980ab254339553ec","detected_licenses":["MIT"],"is_generated":false,"is_vendor":false,"language":"C#","length_bytes":4323,"license_type":"permissive","max_line_length":130,"num_lines":112,"path":"/src/TorchSharp/PInvoke/LibTorchSharp.THSTorch.cs","repo_name":"NiklasGustafsson/TorchSharp","src_encoding":"UTF-8","text":"// Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information.\n#nullable enable\nusing System;\nusing System.Runtime.InteropServices;\n\nnamespace TorchSharp.PInvoke\n{\n internal static partial class NativeMethods\n {\n [DllImport(\"LibTorchSharp\")]\n [return: MarshalAs(UnmanagedType.U1)]\n internal static extern bool THSTorch_scalar_to_bool(IntPtr handle);\n\n [DllImport(\"LibTorchSharp\")]\n internal static extern void THSTorch_dispose_scalar(IntPtr handle);\n\n [DllImport(\"LibTorchSharp\")]\n internal static extern byte THSTorch_scalar_type(IntPtr value);\n\n [DllImport(\"LibTorchSharp\")]\n internal static extern int THSTorch_can_cast(int type1, int type2);\n\n [DllImport(\"LibTorchSharp\")]\n internal static extern int THSTorch_promote_types(int type1, int type2);\n\n [DllImport(\"LibTorchSharp\")]\n internal static extern IntPtr THSTorch_uint8_to_scalar(byte value);\n\n [DllImport(\"LibTorchSharp\")]\n internal static extern IntPtr THSTorch_int8_to_scalar(sbyte value);\n\n [DllImport(\"LibTorchSharp\")]\n internal static extern IntPtr THSTorch_int16_to_scalar(short value);\n\n [DllImport(\"LibTorchSharp\")]\n internal static extern IntPtr THSTorch_int32_to_scalar(int value);\n\n [DllImport(\"LibTorchSharp\")]\n internal static extern IntPtr THSTorch_int64_to_scalar(long value);\n\n [DllImport(\"LibTorchSharp\")]\n internal static extern IntPtr THSTorch_float32_to_scalar(float value);\n\n [DllImport(\"LibTorchSharp\")]\n internal static extern IntPtr THSTorch_float64_to_scalar(double value);\n\n [DllImport(\"LibTorchSharp\")]\n internal static extern IntPtr THSTorch_complex32_to_scalar(float real, float imaginary);\n\n [DllImport(\"LibTorchSharp\")]\n internal static extern IntPtr THSTorch_complex64_to_scalar(double real, double imaginary);\n\n [DllImport(\"LibTorchSharp\")]\n internal static extern IntPtr THSTorch_bool_to_scalar([MarshalAs(UnmanagedType.U1)] bool value);\n\n [DllImport(\"LibTorchSharp\")]\n internal static extern IntPtr THSTorch_float16_to_scalar(float value);\n\n [DllImport(\"LibTorchSharp\")]\n internal static extern IntPtr THSTorch_bfloat16_to_scalar(float value);\n\n [DllImport(\"LibTorchSharp\")]\n internal static extern float THSTorch_scalar_to_float32(IntPtr handle);\n\n#if NET6_0_OR_GREATER\n [DllImport(\"LibTorchSharp\")]\n internal static extern void THSTorch_scalar_to_float16(IntPtr value, out Half res);\n#endif\n\n [DllImport(\"LibTorchSharp\")]\n internal static extern double THSTorch_scalar_to_float64(IntPtr handle);\n\n [DllImport(\"LibTorchSharp\")]\n internal static extern sbyte THSTorch_scalar_to_int8(IntPtr handle);\n\n [DllImport(\"LibTorchSharp\")]\n internal static extern byte THSTorch_scalar_to_uint8(IntPtr handle);\n\n [DllImport(\"LibTorchSharp\")]\n internal static extern short THSTorch_scalar_to_int16(IntPtr handle);\n\n [DllImport(\"LibTorchSharp\")]\n internal static extern int THSTorch_scalar_to_int32(IntPtr handle);\n\n [DllImport(\"LibTorchSharp\")]\n internal static extern long THSTorch_scalar_to_int64(IntPtr handle);\n\n [DllImport(\"LibTorchSharp\")]\n internal static extern void THSTorch_scalar_to_complex32(IntPtr handle, AllocatePinnedArray allocator);\n\n [DllImport(\"LibTorchSharp\")]\n internal static extern void THSTorch_scalar_to_complex64(IntPtr handle, AllocatePinnedArray allocator);\n\n [DllImport(\"LibTorchSharp\")]\n internal static extern IntPtr THSTorch_get_and_reset_last_err();\n\n [DllImport(\"LibTorchSharp\")]\n internal static extern IntPtr THSTorch_lstsq(IntPtr handle, IntPtr b, out IntPtr qr);\n\n [DllImport(\"LibTorchSharp\")]\n internal static extern int THSTorch_get_num_threads();\n\n [DllImport(\"LibTorchSharp\")]\n internal static extern void THSTorch_set_num_threads(int threads);\n\n [DllImport(\"LibTorchSharp\")]\n internal static extern int THSTorch_get_num_interop_threads();\n\n [DllImport(\"LibTorchSharp\")]\n internal static extern void THSTorch_set_num_interop_threads(int threads);\n }\n}\n"},{"alpha_fraction":0.47560974955558777,"alphanum_fraction":0.47560974955558777,"avg_line_length":25.62162208557129,"blob_id":"72a8fb9266b87cce7305ecce5bc759a205c33b4a","content_id":"43737a584f03ad472eee7db273bc1f9861c6ef9e","detected_licenses":["MIT"],"is_generated":false,"is_vendor":false,"language":"C#","length_bytes":984,"license_type":"permissive","max_line_length":130,"num_lines":37,"path":"/src/TorchAudio/AudioMetaData.cs","repo_name":"NiklasGustafsson/TorchSharp","src_encoding":"UTF-8","text":"// Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information.\nnamespace TorchSharp\n{\n public static partial class torchaudio\n {\n /// <summary>\n /// Meta-data of audio\n /// </summary>\n public struct AudioMetaData\n {\n /// <summary>\n /// Sample rate\n /// </summary>\n public int sample_rate;\n\n /// <summary>\n /// The number of frames\n /// </summary>\n public int num_frames;\n\n /// <summary>\n /// The number of channels\n /// </summary>\n public int num_channels;\n\n /// <summary>\n /// The number of bits per sample.\n /// </summary>\n public int bits_per_sample;\n\n /// <summary>\n /// Audio encoding\n /// </summary>\n public AudioEncoding encoding;\n }\n }\n}"},{"alpha_fraction":0.48172980546951294,"alphanum_fraction":0.547435462474823,"avg_line_length":38.77333450317383,"blob_id":"e4ca9d1cc963bab7f50db0c34c99cd85981d82cf","content_id":"e22ed515f95e40973ce5b9ce3a682f947151958a","detected_licenses":["MIT"],"is_generated":false,"is_vendor":false,"language":"C#","length_bytes":2983,"license_type":"permissive","max_line_length":131,"num_lines":75,"path":"/src/Examples/VGG.cs","repo_name":"NiklasGustafsson/TorchSharp","src_encoding":"UTF-8","text":"// Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information.\nusing System.Collections.Generic;\n\nusing static TorchSharp.torch;\nusing static TorchSharp.torch.nn;\n\nnamespace TorchSharp.Examples\n{\n /// <summary>\n /// Modified version of VGG to classify CIFAR10 32x32 images.\n /// </summary>\n /// <remarks>\n /// With an unaugmented CIFAR-10 data set, the author of this saw training converge\n /// at roughly 85% accuracy on the test set, after 50 epochs using VGG-16.\n /// </remarks>\n class VGG : Module<Tensor, Tensor>\n {\n // The code here is is loosely based on https://github.com/kuangliu/pytorch-cifar/blob/master/models/vgg.py\n // Licence and copypright notice at: https://github.com/kuangliu/pytorch-cifar/blob/master/LICENSE\n\n private readonly Dictionary<string, long[]> _channels = new Dictionary<string, long[]>() {\n { \"VGG11\", new long[] { 64, 0, 128, 0, 256, 256, 0, 512, 512, 0, 512, 512, 0 } },\n { \"VGG13\", new long[] { 64, 64, 0, 128, 128, 0, 256, 256, 0, 512, 512, 0, 512, 512, 0 } },\n { \"VGG16\", new long[] { 64, 64, 0, 128, 128, 0, 256, 256, 256, 0, 512, 512, 512, 0, 512, 512, 512, 0 } },\n { \"VGG19\", new long[] { 64, 64, 0, 128, 128, 0, 256, 256, 256, 256, 0, 512, 512, 512, 512, 0, 512, 512, 512, 512, 0 } }\n };\n\n private readonly Module<Tensor, Tensor> layers;\n\n public VGG(string name, int numClasses, Device device = null) : base(name)\n {\n var modules = new List<(string, Module<Tensor, Tensor>)>();\n\n var channels = _channels[name.ToUpper()];\n\n long in_channels = 3;\n\n for (var i = 0; i < channels.Length; i++) {\n\n if (channels[i] == 0) {\n modules.Add(($\"MaxPool2d-{i}a\", MaxPool2d(kernelSize: 2, stride: 2)));\n } else {\n modules.Add(($\"conv2d-{i}a\", Conv2d(in_channels, channels[i], kernelSize: 3, padding: 1)));\n modules.Add(($\"bnrm2d-{i}a\", BatchNorm2d(channels[i])));\n modules.Add(($\"relu-{i}b\", ReLU(inplace: true)));\n in_channels = channels[i];\n }\n }\n modules.Add((\"avgpool2d\", AvgPool2d(kernel_size: 1, stride: 1)));\n modules.Add((\"flatten\", Flatten()));\n modules.Add((\"linear\", Linear(512, numClasses)));\n\n layers = Sequential(modules);\n\n RegisterComponents();\n\n if (device != null && device.type == DeviceType.CUDA)\n this.to(device);\n }\n\n public override Tensor forward(Tensor input)\n {\n return layers.call(input);\n }\n\n protected override void Dispose(bool disposing)\n {\n if (disposing) {\n layers.Dispose();\n ClearModules();\n }\n base.Dispose(disposing);\n }\n }\n}\n"},{"alpha_fraction":0.5376311540603638,"alphanum_fraction":0.541280210018158,"avg_line_length":42.8466682434082,"blob_id":"a6501ccc6a48a91f6d49caa26f9a2c8c9db0e5d4","content_id":"2014f82bd8e38ed4e95909445226149e0ce2f470","detected_licenses":["MIT"],"is_generated":false,"is_vendor":false,"language":"C#","length_bytes":6577,"license_type":"permissive","max_line_length":149,"num_lines":150,"path":"/src/TorchSharp/Distributions/Geometric.cs","repo_name":"NiklasGustafsson/TorchSharp","src_encoding":"UTF-8","text":"// Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information.\nusing System;\nusing static TorchSharp.torch;\n\nnamespace TorchSharp\n{\n using Modules;\n\n namespace Modules\n {\n /// <summary>\n /// A Geometric distribution parameterized by probs,\n /// where probs is the probability of success of Bernoulli trials.\n ///\n /// It represents the probability that in k+1 Bernoulli trials, the\n /// first k trials failed, before seeing a success.\n /// </summary>\n public class Geometric : torch.distributions.Distribution\n {\n /// <summary>\n /// The mean of the distribution.\n /// </summary>\n public override Tensor mean => 1 / (probs - 1);\n\n /// <summary>\n /// The variance of the distribution\n /// </summary>\n public override Tensor variance => (1.0f / probs - 1.0f) / probs;\n\n /// <summary>\n /// Constructor\n /// </summary>\n /// <param name=\"probs\">The probability of sampling '1'. Must be in range (0, 1]</param>\n /// <param name=\"logits\">The log-odds of sampling '1'</param>\n /// <param name=\"generator\">An optional random number generator object.</param>\n public Geometric(Tensor probs = null, Tensor logits = null, torch.Generator generator = null) : base(generator) \n {\n this.batch_shape = probs is null ? logits.size() : probs.size();\n this._probs = probs?.alias().DetachFromDisposeScope();\n this._logits = logits?.alias().DetachFromDisposeScope();\n }\n\n /// <summary>\n /// Event probabilities\n /// </summary>\n public Tensor probs {\n get {\n return _probs ?? LogitsToProbs(_logits, true);\n }\n }\n\n /// <summary>\n /// Event log-odds\n /// </summary>\n public Tensor logits {\n get {\n return _logits ?? ProbsToLogits(_probs);\n }\n }\n\n private Tensor _probs;\n private Tensor _logits;\n\n /// <summary>\n /// Generates a sample_shape shaped reparameterized sample or sample_shape shaped batch of reparameterized samples\n /// if the distribution parameters are batched.\n /// </summary>\n /// <param name=\"sample_shape\">The sample shape.</param>\n public override Tensor rsample(params long[] sample_shape)\n {\n using var _ = torch.NewDisposeScope();\n var shape = ExtendedShape(sample_shape);\n var tiny = torch.finfo(probs.dtype).tiny;\n using (torch.no_grad()) {\n var u = probs.new_empty(shape).uniform_(tiny, 1, generator: generator);\n return (u.log() / (-probs).log1p()).floor().MoveToOuterDisposeScope();\n }\n }\n\n /// <summary>\n /// Returns the log of the probability density/mass function evaluated at `value`.\n /// </summary>\n /// <param name=\"value\"></param>\n public override Tensor log_prob(Tensor value)\n {\n using var _ = torch.NewDisposeScope();\n var bcast = torch.broadcast_tensors(value, probs);\n value = bcast[0];\n var p = bcast[1].clone();\n p[(p == 1) & (value == 0)] = torch.tensor(0);\n return (value * (-p).log1p() + probs.log()).MoveToOuterDisposeScope();\n }\n\n /// <summary>\n /// Returns entropy of distribution, batched over batch_shape.\n /// </summary>\n /// <returns></returns>\n public override Tensor entropy()\n {\n return torch.nn.functional.binary_cross_entropy_with_logits(logits, probs, reduction: nn.Reduction.None);\n }\n\n /// <summary>\n /// Returns a new distribution instance (or populates an existing instance provided by a derived class) with batch dimensions expanded to\n /// `batch_shape`. This method calls `torch.Tensor.expand()` on the distribution's parameters. As such, this does not allocate new\n /// memory for the expanded distribution instance.\n /// </summary>\n /// <param name=\"batch_shape\">Tthe desired expanded size.</param>\n /// <param name=\"instance\">new instance provided by subclasses that need to override `.expand`.</param>\n public override distributions.Distribution expand(Size batch_shape, distributions.Distribution instance = null)\n {\n if (instance != null && !(instance is Geometric))\n throw new ArgumentException(\"expand(): 'instance' must be a Geometric distribution\");\n\n var newDistribution = ((instance == null) ?\n new Geometric(probs: _probs?.expand(batch_shape), logits: logits?.expand(batch_shape), generator) :\n instance) as Geometric;\n\n newDistribution.batch_shape = batch_shape;\n if (newDistribution == instance) {\n newDistribution._probs = _probs?.expand(batch_shape);\n newDistribution._logits = _logits?.expand(batch_shape);\n }\n return newDistribution;\n }\n }\n\n }\n public static partial class torch\n {\n public static partial class distributions\n {\n /// <summary>\n /// Creates a Geometric distribution parameterized by probs,\n /// where probs is the probability of success of Bernoulli trials.\n ///\n /// It represents the probability that in k+1 Bernoulli trials, the\n /// first k trials failed, before seeing a success.\n /// </summary>\n /// <param name=\"probs\">The probability of sampling '1'. Must be in range (0, 1]</param>\n /// <param name=\"logits\">The log-odds of sampling '1'</param>\n /// <param name=\"generator\">An optional random number generator object.</param>\n /// <returns></returns>\n public static Geometric Geometric(Tensor probs = null, Tensor logits = null, torch.Generator generator = null)\n {\n return new Geometric(probs, logits, generator);\n }\n }\n }\n}\n"},{"alpha_fraction":0.49470704793930054,"alphanum_fraction":0.49643033742904663,"avg_line_length":32.024391174316406,"blob_id":"1e2cc235bdc03ee0abfbfea8f86d44d8c8662373","content_id":"64e4e3d560e15dde81dae4c2799768681b1ebd11","detected_licenses":["MIT"],"is_generated":false,"is_vendor":false,"language":"C#","length_bytes":8124,"license_type":"permissive","max_line_length":130,"num_lines":246,"path":"/src/TorchSharp/NN/ParameterDict.cs","repo_name":"NiklasGustafsson/TorchSharp","src_encoding":"UTF-8","text":"// Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information.\nusing System;\nusing System.Linq;\nusing System.Collections;\nusing System.Collections.Generic;\nusing System.Diagnostics.CodeAnalysis;\nusing static TorchSharp.torch.nn;\n\nnamespace TorchSharp\n{\n using Modules;\n\n namespace Modules\n {\n /// <summary>\n /// Holds parameters in a dictionary.\n /// \n /// ParameterDict can be indexed like a regular dictionary, but the parameters it\n /// contains are properly registered, and will be visible by all Module methods.\n ///\n /// ParameterDict is an ordered dictionary that respects the order of insertion, and\n /// in update(), the order of the merged OrderedDict or another ParameterDict (the argument to update()).\n /// </summary>\n public class ParameterDict : Module, IDictionary<string, Parameter>, IList<(string, Parameter)>\n {\n public ParameterDict() : base(nameof(ParameterDict))\n {\n }\n\n /// <summary>\n /// Remove all items from the ParameterDict.\n /// </summary>\n public void clear()\n {\n _list.Clear();\n _dict.Clear();\n }\n\n /// <summary>\n /// Return an enumeration of the ParameterDict key/value pairs.\n /// </summary>\n /// <returns></returns>\n public IEnumerator<(string, Parameter)> items() => _list.GetEnumerator();\n\n /// <summary>\n /// Return the ParameterDict keys.\n /// </summary>\n /// <returns></returns>\n public IEnumerable<string> keys() => _dict.Keys;\n\n protected override void RegisterComponents()\n {\n if (_registered) return;\n\n for (int i = 0; i < _list.Count; i++) {\n register_parameter($\"{_list[i].Item1}\", _list[i].Item2);\n }\n _registered = true;\n }\n\n private bool _registered = false;\n\n /// <summary>\n /// Return the ParameterDict values.\n /// </summary>\n /// <returns></returns>\n public IEnumerable<Parameter> values() => _dict.Values;\n\n public (string, Parameter) this[int index] {\n get => _list[index];\n set {\n var name = value.Item1;\n _list[index] = value;\n _dict[name] = value.Item2;\n }\n }\n\n public bool IsReadOnly => false;\n\n public ICollection<string> Keys => _list.Select(kv => kv.Item1).ToList();\n\n public ICollection<Parameter> Values => _list.Select(kv => kv.Item2).ToList();\n\n public int Count => _dict.Count;\n\n public Parameter this[string key] {\n get => _dict[key];\n set {\n _dict[key] = value;\n var idx = _list.FindIndex(kv => kv.Item1.Equals(key));\n _list[idx] = (key, value);\n }\n }\n\n public override bool has_parameter(string target)\n {\n return _dict.ContainsKey(target);\n }\n\n public override Parameter get_parameter(string target)\n {\n if (_dict.TryGetValue(target, out var result)) {\n return result;\n }\n return null;\n }\n\n public void Add((string, Parameter) item)\n {\n _dict.Add(item.Item1, item.Item2);\n _list.Add(item);\n }\n\n public void Add(string key, Parameter value)\n {\n _dict.Add(key, value);\n _list.Add((key, value));\n }\n\n public void Add(KeyValuePair<string, Parameter> item)\n {\n _dict.Add(item.Key, item.Value);\n _list.Add((item.Key, item.Value));\n }\n\n public bool Contains((string, Parameter) item)\n {\n return _list.Contains(item);\n }\n\n public void CopyTo((string, Parameter)[] array, int arrayIndex)\n {\n _list.CopyTo(array, arrayIndex);\n }\n\n public int IndexOf((string, Parameter) item)\n {\n return _list.IndexOf(item);\n }\n\n public void Insert(int index, (string, Parameter) item)\n {\n _dict.Add(item.Item1, item.Item2);\n _list.Insert(index, item);\n }\n\n public bool Remove((string, Parameter) item)\n {\n _dict.Remove(item.Item1);\n return _list.Remove(item);\n }\n\n public void RemoveAt(int index)\n {\n if (index >= _list.Count) throw new IndexOutOfRangeException();\n var (n,p) = _list[index];\n _list.RemoveAt(index);\n _dict.Remove(n);\n }\n\n public bool ContainsKey(string key)\n {\n return _dict.ContainsKey(key);\n }\n\n public bool Remove(string key)\n {\n var value = _dict[key];\n var idx = _list.FindIndex(kv => kv.Item1.Equals(key));\n _list.RemoveAt(idx);\n return _dict.Remove(key);\n }\n\n public bool TryGetValue(string key, [MaybeNullWhen(false)] out Parameter value)\n {\n return _dict.TryGetValue(key, out value);\n }\n\n public void Clear()\n {\n _dict.Clear();\n _list.Clear();\n }\n\n public bool Contains(KeyValuePair<string, Parameter> item)\n {\n return _dict.ContainsKey(item.Key);\n }\n\n public void CopyTo(KeyValuePair<string, Parameter>[] array, int arrayIndex)\n {\n throw new NotImplementedException();\n }\n\n public bool Remove(KeyValuePair<string, Parameter> item)\n {\n return _dict.Remove(item.Key);\n }\n\n public IEnumerator<(string, Parameter)> GetEnumerator()\n {\n return _list.GetEnumerator();\n }\n\n IEnumerator<KeyValuePair<string, Parameter>> IEnumerable<KeyValuePair<string, Parameter>>.GetEnumerator()\n {\n throw new NotImplementedException();\n }\n\n IEnumerator IEnumerable.GetEnumerator()\n {\n return ((IEnumerable<KeyValuePair<string, Parameter>>)this).GetEnumerator();\n }\n\n private List<(string, Parameter)> _list = new List<(string, Parameter)>();\n private Dictionary<string, Parameter> _dict = new Dictionary<string, Parameter>();\n }\n }\n\n public static partial class torch\n {\n public static partial class nn\n {\n\n /// <summary>\n /// Create a ParameterList instance from an array of parameter tensors.\n /// </summary>\n /// <param name=\"parameters\">A tuple of (name,parameter).</param>\n /// <remarks>\n /// ParameterDict can be indexed like a regular dictionary, but the parameters it\n /// contains are properly registered, and will be visible by all Module methods.\n ///\n /// ParameterDict is an ordered dictionary that respects the order of insertion, and\n /// in update(), the order of the merged OrderedDict or another ParameterDict (the argument to update()).\n /// </remarks>\n public static ParameterDict ParameterDict(params (string, Parameter)[] parameters)\n {\n var result = new ParameterDict();\n foreach (var (n, p) in parameters) {\n result.Add(n, p);\n }\n return result;\n }\n }\n }\n}\n"},{"alpha_fraction":0.5330060124397278,"alphanum_fraction":0.5351881980895996,"avg_line_length":30.620689392089844,"blob_id":"3afb855fa919ed78c584c133ba543d8fa16212b2","content_id":"472368314d627e59578360b268b42c2562506f29","detected_licenses":["MIT"],"is_generated":false,"is_vendor":false,"language":"C#","length_bytes":1833,"license_type":"permissive","max_line_length":130,"num_lines":58,"path":"/src/TorchVision/CenterCrop.cs","repo_name":"NiklasGustafsson/TorchSharp","src_encoding":"UTF-8","text":"// Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information.\n\nusing System;\nusing static TorchSharp.torch;\n\n\nnamespace TorchSharp\n{\n public static partial class torchvision\n {\n internal class CenterCrop : ITransform\n {\n internal CenterCrop(int height, int width)\n {\n if (height <= 0) \n throw new ArgumentOutOfRangeException(nameof(height), \"Height must be greater than 0.\");\n if (width <= 0) \n throw new ArgumentOutOfRangeException(nameof(width), \"Width must be greater than 0.\");\n\n this.height = height;\n this.width = width;\n }\n\n public Tensor call(Tensor input)\n {\n return transforms.functional.center_crop(input, height, width);\n }\n\n protected int height, width;\n\n public int Height => height;\n public int Width => width;\n\n }\n\n public static partial class transforms\n {\n /// <summary>\n /// Crop the center of the image.\n /// </summary>\n /// <param name=\"height\">Desired output height of the crop</param>\n /// <param name=\"width\">Desired output width of the crop</param>\n static public ITransform CenterCrop(int height, int width)\n {\n return new CenterCrop(height, width);\n }\n\n /// <summary>\n /// Crop the center of the image.\n /// </summary>\n /// <param name=\"size\">Desired output size of the crop</param>\n static public ITransform CenterCrop(int size)\n {\n return new CenterCrop(size, size);\n }\n }\n }\n}"},{"alpha_fraction":0.5069842338562012,"alphanum_fraction":0.5288323760032654,"avg_line_length":43.31745910644531,"blob_id":"9f8bef2aaee08a3c7eeeab404650c65c3cf2c48c","content_id":"475420505d090c32ce7b6ecc6d8141651b8ce422","detected_licenses":["MIT"],"is_generated":false,"is_vendor":false,"language":"C#","length_bytes":5584,"license_type":"permissive","max_line_length":167,"num_lines":126,"path":"/src/TorchVision/models/AlexNet.cs","repo_name":"NiklasGustafsson/TorchSharp","src_encoding":"UTF-8","text":"// Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information.\n\nusing static TorchSharp.torch;\nusing static TorchSharp.torch.nn;\n\n#nullable enable\nnamespace TorchSharp\n{\n public static partial class torchvision\n {\n public static partial class models\n {\n /// <summary>\n /// AlexNet\n /// </summary>\n /// <param name=\"num_classes\">The number of output classes.</param>\n /// <param name=\"dropout\">The dropout ratio.</param>\n /// <param name=\"weights_file\">The location of a file containing pre-trained weights for the model.</param>\n /// <param name=\"skipfc\">If true, the last linear layer of the classifier will not be loaded from the weights file.</param>\n /// <param name=\"device\">The device to locate the model on.</param>\n /// <remarks>\n /// Pre-trained weights may be retrieved by using Pytorch and saving the model state-dict\n /// using the exportsd.py script, then loading into the .NET instance:\n ///\n /// from torchvision import models\n /// import exportsd\n /// \n /// model = models.alexnet(pretrained=True)\n /// f = open(\"model_weights.dat\", \"wb\")\n /// exportsd.save_state_dict(model.state_dict(), f)\n /// f.close()\n ///\n /// See also: https://github.com/dotnet/TorchSharp/blob/main/docfx/articles/saveload.md\n ///\n /// In order for the weights to be loaded, the number of classes has to be the same as\n /// in the pre-trained model, which is 1000.\n ///\n /// It is also possible to skip loading the last linear layer and use it for transfer-learning\n /// with a different number of output classes. To do so, pass skipfc=true.\n /// as the skip list when loading.\n ///\n /// All pre-trained models expect input images normalized in the same way, i.e. mini-batches of 3-channel RGB\n /// images of shape (3 x H x W), where H and W are expected to be at least 224. The images have to be loaded\n /// in to a range of [0, 1] and then normalized using mean = [0.485, 0.456, 0.406] and std = [0.229, 0.224, 0.225].\n /// </remarks>\n public static Modules.AlexNet alexnet(int num_classes = 1000, float dropout = 0.5f, string? weights_file = null, bool skipfc = true, Device? device = null)\n {\n return new Modules.AlexNet(num_classes, dropout, weights_file, skipfc, device);\n }\n }\n }\n\n namespace Modules\n {\n // The code here is based on\n // https://github.com/pytorch/vision/blob/main/torchvision/models/alexnet.py\n // Licence and copypright notice at: https://github.com/pytorch/vision/blob/main/LICENSE\n\n public class AlexNet : Module<Tensor, Tensor>\n {\n private readonly Module<Tensor, Tensor> features;\n private readonly Module<Tensor, Tensor> avgpool;\n private readonly Module<Tensor, Tensor> classifier;\n\n protected override void Dispose(bool disposing)\n {\n if (disposing) {\n features.Dispose(); avgpool.Dispose();\n classifier.Dispose();\n }\n base.Dispose(disposing);\n }\n\n public AlexNet(int numClasses, float dropout = 0.5f, string? weights_file = null, bool skipfc = true, Device? device = null) : base(nameof(AlexNet))\n {\n features = Sequential(\n Conv2d(3, 64, kernelSize: 11, stride: 4, padding: 2),\n ReLU(inplace: true),\n MaxPool2d(kernelSize: 3, stride: 2),\n Conv2d(64, 192, kernelSize: 5, padding: 2),\n ReLU(inplace: true),\n MaxPool2d(kernelSize: 3, stride: 2),\n Conv2d(192, 384, kernelSize: 3, padding: 1),\n ReLU(inplace: true),\n Conv2d(384, 256, kernelSize: 3, padding: 1),\n ReLU(inplace: true),\n Conv2d(256, 256, kernelSize: 3, padding: 1),\n ReLU(inplace: true),\n MaxPool2d(kernelSize: 3, stride: 2)\n );\n\n avgpool = AdaptiveAvgPool2d(new long[] { 6, 6 });\n\n classifier = Sequential(\n Dropout(p: dropout),\n Linear(256 * 6 * 6, 4096),\n ReLU(inplace: true),\n Dropout(p: dropout),\n Linear(4096, 4096),\n ReLU(inplace: true),\n Linear(4096, numClasses)\n );\n\n RegisterComponents();\n\n if (!string.IsNullOrEmpty(weights_file)) {\n\n this.load(weights_file, skip: skipfc ? new[] { \"classifier.6.weight\", \"classifier.6.bias\" } : null);\n }\n\n if (device != null && device.type == DeviceType.CUDA)\n this.to(device);\n }\n\n public override Tensor forward(Tensor input)\n {\n using (var _ = NewDisposeScope()) {\n var f = features.call(input);\n var avg = avgpool.call(f);\n var x = avg.flatten(1);\n return classifier.call(x).MoveToOuterDisposeScope();\n }\n }\n }\n }\n}\n"},{"alpha_fraction":0.4583049416542053,"alphanum_fraction":0.48074302077293396,"avg_line_length":44.84635543823242,"blob_id":"6f2e1b423a0c2a869bdd0b406af734f9d6fb51d4","content_id":"9531bea4ccb1949a7c3c0247ac5992d3780a4f07","detected_licenses":["MIT"],"is_generated":false,"is_vendor":false,"language":"C#","length_bytes":17604,"license_type":"permissive","max_line_length":134,"num_lines":384,"path":"/src/TorchVision/models/MobileNetV3.cs","repo_name":"NiklasGustafsson/TorchSharp","src_encoding":"UTF-8","text":"// Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information.\n\n// A number of implementation details in this file have been translated from the Python version of torchvision,\n// largely located in the files found in this folder:\n//\n// https://github.com/pytorch/vision/blob/3c9ae0ac5dda3d881a6ce5004ce5756ae7de7bc4/torchvision/models/mobilenetv3.py\n//\n// The origin has the following copyright notice and license:\n//\n// https://github.com/pytorch/vision/blob/main/LICENSE\n//\n\nusing System.Collections.Generic;\nusing System;\nusing static TorchSharp.torch;\nusing static TorchSharp.torchvision.models._utils;\nusing static TorchSharp.torchvision.ops;\nusing TorchSharp.Modules;\n\n#nullable enable\nnamespace TorchSharp\n{\n namespace Modules\n {\n public class MobileNetV3 : nn.Module<Tensor, Tensor>\n {\n /// <summary>\n /// Stores information listed at Tables 1 and 2 of the MobileNetV3 paper\n /// </summary>\n internal class InvertedResidualConfig\n {\n public readonly long dilation;\n public readonly long expanded_channels;\n public readonly long input_channels;\n public readonly long kernel;\n public readonly long out_channels;\n public readonly long stride;\n public readonly bool use_hs;\n public readonly bool use_se;\n\n public InvertedResidualConfig(\n long input_channels,\n long kernel,\n long expanded_channels,\n long out_channels,\n bool use_se,\n string activation,\n long stride,\n long dilation,\n double width_mult)\n {\n this.input_channels = adjust_channels(input_channels, width_mult);\n this.kernel = kernel;\n this.expanded_channels = adjust_channels(expanded_channels, width_mult);\n this.out_channels = adjust_channels(out_channels, width_mult);\n this.use_se = use_se;\n this.use_hs = activation == \"HS\";\n this.stride = stride;\n this.dilation = dilation;\n }\n\n internal static long adjust_channels(long channels, double width_mult)\n {\n return _make_divisible(channels * width_mult, 8);\n }\n }\n\n /// <summary>\n /// Implemented as described at section 5 of MobileNetV3 paper\n /// </summary>\n private class InvertedResidual : nn.Module<Tensor, Tensor>\n {\n private readonly bool _is_cn;\n private readonly nn.Module<Tensor, Tensor> block;\n private readonly long out_channels;\n private readonly bool use_res_connect;\n\n protected override void Dispose(bool disposing)\n {\n if (disposing) {\n block.Dispose();\n }\n base.Dispose(disposing);\n }\n\n public InvertedResidual(\n string name,\n InvertedResidualConfig cnf,\n Func<long, nn.Module<Tensor, Tensor>> norm_layer,\n Func<long, long, nn.Module<Tensor, Tensor>>? se_layer = null) : base(name)\n {\n if (!(1 <= cnf.stride && cnf.stride <= 2)) {\n throw new ArgumentException(\"illegal stride value\");\n }\n\n this.use_res_connect = cnf.stride == 1 && cnf.input_channels == cnf.out_channels;\n\n var layers = new List<nn.Module<Tensor, Tensor>>();\n Func<bool, nn.Module<Tensor, Tensor>> activation_layer = (\n cnf.use_hs ? (inplace) => nn.Hardswish(inplace) : (inplace) => nn.ReLU(inplace));\n\n // expand\n if (cnf.expanded_channels != cnf.input_channels) {\n layers.Add(Conv2dNormActivation(\n cnf.input_channels,\n cnf.expanded_channels,\n kernel_size: 1,\n norm_layer: norm_layer,\n activation_layer: activation_layer));\n }\n\n // depthwise\n var stride = cnf.dilation > 1 ? 1 : cnf.stride;\n layers.Add(Conv2dNormActivation(\n cnf.expanded_channels,\n cnf.expanded_channels,\n kernel_size: cnf.kernel,\n stride: stride,\n dilation: cnf.dilation,\n groups: cnf.expanded_channels,\n norm_layer: norm_layer,\n activation_layer: activation_layer));\n if (cnf.use_se) {\n var squeeze_channels = _make_divisible(cnf.expanded_channels / 4, 8);\n if (se_layer != null) {\n layers.Add(se_layer(cnf.expanded_channels, squeeze_channels));\n } else {\n layers.Add(\n torchvision.ops.SqueezeExcitation(\n cnf.expanded_channels,\n squeeze_channels,\n activation: () => nn.ReLU6(),\n scale_activation: () => nn.Hardsigmoid()));\n }\n }\n\n // project\n layers.Add(\n Conv2dNormActivation(\n cnf.expanded_channels, cnf.out_channels, kernel_size: 1, norm_layer: norm_layer, activation_layer: null));\n\n this.block = nn.Sequential(layers);\n this.out_channels = cnf.out_channels;\n this._is_cn = cnf.stride > 1;\n\n RegisterComponents();\n }\n\n public override Tensor forward(Tensor input)\n {\n var result = this.block.call(input);\n if (this.use_res_connect) {\n result += input;\n }\n return result;\n }\n }\n\n protected override void Dispose(bool disposing)\n {\n if (disposing) {\n avgpool.Dispose();\n classifier.Dispose();\n features.Dispose();\n }\n base.Dispose(disposing);\n }\n private readonly nn.Module<Tensor, Tensor> avgpool;\n private readonly nn.Module<Tensor, Tensor> classifier;\n private readonly nn.Module<Tensor, Tensor> features;\n\n /// <summary>\n /// MobileNet V3 main class\n /// </summary>\n /// <param name=\"name\"></param>\n /// <param name=\"inverted_residual_setting\">Network structure</param>\n /// <param name=\"last_channel\">The number of channels on the penultimate layer</param>\n /// <param name=\"num_classes\">Number of classes</param>\n /// <param name=\"block\">Module specifying inverted residual building block for mobilenet</param>\n /// <param name=\"norm_layer\">Module specifying the normalization layer to use</param>\n /// <param name=\"dropout\">The droupout probability</param>\n /// <exception cref=\"ArgumentException\"></exception>\n internal MobileNetV3(\n string name,\n InvertedResidualConfig[] inverted_residual_setting,\n long last_channel,\n long num_classes = 1000,\n Func<InvertedResidualConfig, Func<long, nn.Module<Tensor, Tensor>>, nn.Module<Tensor, Tensor>>? block = null,\n Func<long, nn.Module<Tensor, Tensor>>? norm_layer = null,\n double dropout = 0.2) : base(name)\n {\n if (inverted_residual_setting == null || inverted_residual_setting.Length == 0) {\n throw new ArgumentException(\"The inverted_residual_setting should not be empty\");\n }\n\n if (block == null) {\n block = (cnf, norm_layer) => new InvertedResidual(\"InvertedResidual\", cnf, norm_layer);\n }\n\n if (norm_layer == null) {\n norm_layer = (features) => nn.BatchNorm2d(features, eps: 0.001, momentum: 0.01);\n }\n\n var layers = new List<nn.Module<Tensor, Tensor>>();\n\n // building first layer\n var firstconv_output_channels = inverted_residual_setting[0].input_channels;\n layers.Add(\n Conv2dNormActivation(\n 3,\n firstconv_output_channels,\n kernel_size: 3,\n stride: 2,\n norm_layer: norm_layer,\n activation_layer: (inplace) => nn.Hardswish(inplace)));\n\n // building inverted residual blocks\n foreach (var cnf in inverted_residual_setting) {\n layers.Add(block(cnf, norm_layer));\n }\n\n // building last several layers\n var lastconv_input_channels = inverted_residual_setting[inverted_residual_setting.Length - 1].out_channels;\n var lastconv_output_channels = 6 * lastconv_input_channels;\n layers.Add(\n Conv2dNormActivation(\n lastconv_input_channels,\n lastconv_output_channels,\n kernel_size: 1,\n norm_layer: norm_layer,\n activation_layer: (inplace) => nn.Hardswish(inplace)));\n\n this.features = nn.Sequential(layers);\n this.avgpool = nn.AdaptiveAvgPool2d(1);\n this.classifier = nn.Sequential(\n nn.Linear(lastconv_output_channels, last_channel),\n nn.Hardswish(inplace: true),\n nn.Dropout(p: dropout, inplace: true),\n nn.Linear(last_channel, num_classes));\n\n RegisterComponents();\n\n foreach (var (_, m) in this.named_modules()) {\n if (m is Modules.Conv2d) {\n var conv = (Modules.Conv2d)m;\n nn.init.kaiming_normal_(conv.weight, mode: nn.init.FanInOut.FanOut);\n if (conv.bias is not null) {\n nn.init.zeros_(conv.bias);\n }\n } else if (m is Modules.BatchNorm2d) {\n var norm = (Modules.BatchNorm2d)m;\n nn.init.ones_(norm.weight);\n nn.init.zeros_(norm.bias);\n } else if (m is Modules.GroupNorm) {\n var norm = (Modules.GroupNorm)m;\n nn.init.ones_(norm.weight);\n nn.init.zeros_(norm.bias);\n } else if (m is Modules.Linear) {\n var linear = (Modules.Linear)m;\n nn.init.normal_(linear.weight, 0, 0.01);\n nn.init.zeros_(linear.bias);\n }\n }\n }\n\n public override Tensor forward(Tensor x)\n {\n x = this.features.call(x);\n x = this.avgpool.call(x);\n x = torch.flatten(x, 1);\n x = this.classifier.call(x);\n return x;\n }\n }\n }\n\n public static partial class torchvision\n {\n public static partial class models\n {\n private static (MobileNetV3.InvertedResidualConfig[], long) _mobilenet_v3_conf(\n string arch,\n double width_mult = 1.0,\n bool reduced_tail = false,\n bool dilated = false)\n {\n long last_channel;\n MobileNetV3.InvertedResidualConfig[] inverted_residual_setting;\n var reduce_divider = reduced_tail ? 2 : 1;\n var dilation = dilated ? 2 : 1;\n MobileNetV3.InvertedResidualConfig bneck_conf(\n long input_channels,\n long kernel,\n long expanded_channels,\n long out_channels,\n bool use_se,\n string activation,\n long stride,\n long dilation) => new MobileNetV3.InvertedResidualConfig(\n input_channels,\n kernel,\n expanded_channels,\n out_channels,\n use_se,\n activation,\n stride,\n dilation,\n width_mult: width_mult);\n long adjust_channels(long channels) =>\n MobileNetV3.InvertedResidualConfig.adjust_channels(\n channels,\n width_mult: width_mult);\n if (arch == \"mobilenet_v3_large\") {\n inverted_residual_setting = new MobileNetV3.InvertedResidualConfig[] {\n bneck_conf(16, 3, 16, 16, false, \"RE\", 1, 1),\n bneck_conf(16, 3, 64, 24, false, \"RE\", 2, 1),\n bneck_conf(24, 3, 72, 24, false, \"RE\", 1, 1),\n bneck_conf(24, 5, 72, 40, true, \"RE\", 2, 1),\n bneck_conf(40, 5, 120, 40, true, \"RE\", 1, 1),\n bneck_conf(40, 5, 120, 40, true, \"RE\", 1, 1),\n bneck_conf(40, 3, 240, 80, false, \"HS\", 2, 1),\n bneck_conf(80, 3, 200, 80, false, \"HS\", 1, 1),\n bneck_conf(80, 3, 184, 80, false, \"HS\", 1, 1),\n bneck_conf(80, 3, 184, 80, false, \"HS\", 1, 1),\n bneck_conf(80, 3, 480, 112, true, \"HS\", 1, 1),\n bneck_conf(112, 3, 672, 112, true, \"HS\", 1, 1),\n bneck_conf(112, 5, 672, 160 / reduce_divider, true, \"HS\", 2, dilation),\n bneck_conf(160 / reduce_divider, 5, 960 / reduce_divider, 160 / reduce_divider, true, \"HS\", 1, dilation),\n bneck_conf(160 / reduce_divider, 5, 960 / reduce_divider, 160 / reduce_divider, true, \"HS\", 1, dilation)\n };\n last_channel = adjust_channels(1280 / reduce_divider);\n } else if (arch == \"mobilenet_v3_small\") {\n inverted_residual_setting = new MobileNetV3.InvertedResidualConfig[] {\n bneck_conf(16, 3, 16, 16, true, \"RE\", 2, 1),\n bneck_conf(16, 3, 72, 24, false, \"RE\", 2, 1),\n bneck_conf(24, 3, 88, 24, false, \"RE\", 1, 1),\n bneck_conf(24, 5, 96, 40, true, \"HS\", 2, 1),\n bneck_conf(40, 5, 240, 40, true, \"HS\", 1, 1),\n bneck_conf(40, 5, 240, 40, true, \"HS\", 1, 1),\n bneck_conf(40, 5, 120, 48, true, \"HS\", 1, 1),\n bneck_conf(48, 5, 144, 48, true, \"HS\", 1, 1),\n bneck_conf(48, 5, 288, 96 / reduce_divider, true, \"HS\", 2, dilation),\n bneck_conf(96 / reduce_divider, 5, 576 / reduce_divider, 96 / reduce_divider, true, \"HS\", 1, dilation),\n bneck_conf(96 / reduce_divider, 5, 576 / reduce_divider, 96 / reduce_divider, true, \"HS\", 1, dilation)\n };\n last_channel = adjust_channels(1024 / reduce_divider);\n } else {\n throw new ArgumentException($\"Unsupported model type {arch}\");\n }\n return (inverted_residual_setting, last_channel);\n }\n\n private static Modules.MobileNetV3 _mobilenet_v3(\n MobileNetV3.InvertedResidualConfig[] inverted_residual_setting,\n long last_channel)\n {\n var model = new MobileNetV3(\"MobileNetV3\", inverted_residual_setting, last_channel);\n return model;\n }\n\n /// <summary>\n /// Constructs a large MobileNetV3 architecture from\n /// `Searching for MobileNetV3 https://arxiv.org/abs/1905.02244`.\n /// </summary>\n /// <returns></returns>\n public static MobileNetV3 mobilenet_v3_large()\n {\n var (inverted_residual_setting, last_channel) = _mobilenet_v3_conf(\"mobilenet_v3_large\");\n return _mobilenet_v3(inverted_residual_setting, last_channel);\n }\n\n /// <summary>\n /// Constructs a small MobileNetV3 architecture from\n /// `Searching for MobileNetV3 https://arxiv.org/abs/1905.02244`.\n /// </summary>\n /// <returns></returns>\n public static MobileNetV3 mobilenet_v3_small()\n {\n var (inverted_residual_setting, last_channel) = _mobilenet_v3_conf(\"mobilenet_v3_small\");\n return _mobilenet_v3(inverted_residual_setting, last_channel);\n }\n }\n }\n}"},{"alpha_fraction":0.6567100882530212,"alphanum_fraction":0.6668975353240967,"avg_line_length":33.40584182739258,"blob_id":"85f8467351161cae1d780e06bcf55040963b751e","content_id":"6f9ca1742b0d924f4e3ecc8d6c65af862ca6ec8d","detected_licenses":["MIT"],"is_generated":false,"is_vendor":false,"language":"C++","length_bytes":44761,"license_type":"permissive","max_line_length":273,"num_lines":1301,"path":"/src/Native/LibTorchSharp/THSNN.cpp","repo_name":"NiklasGustafsson/TorchSharp","src_encoding":"UTF-8","text":"// Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information.\n#include \"THSNN.h\"\n\n#include <torch/nn/init.h>\n\n\nNNModule THSNN_Identity_ctor(NNAnyModule* outAsAnyModule)\n{\n CATCH_RETURN_NNModule(\n res = create_module<torch::nn::IdentityImpl>(outAsAnyModule);\n );\n}\n\nTensor THSNN_Identity_forward(const NNModule module, const Tensor tensor)\n{\n CATCH_TENSOR((*module)->as<torch::nn::Identity>()->forward(*tensor));\n}\n\nNNModule THSNN_Linear_ctor(const int64_t input_size, const int64_t output_size, const bool bias,\n NNAnyModule* outAsAnyModule)\n{\n CATCH_RETURN_NNModule(\n auto opts = torch::nn::LinearOptions(input_size, output_size).bias(bias);\n res = create_module<torch::nn::LinearImpl>(opts, outAsAnyModule);\n );\n}\n\nTensor THSNN_Linear_forward(const NNModule module, const Tensor tensor)\n{\n CATCH_TENSOR((*module)->as<torch::nn::Linear>()->forward(*tensor));\n}\n\nTensor THSNN_Linear_bias(const NNModule module)\n{\n return get_bias<torch::nn::Linear>(module);\n}\n\nvoid THSNN_Linear_set_bias(const NNModule module, const Tensor bias)\n{\n set_bias<torch::nn::Linear>(module, bias);\n}\n\nTensor THSNN_Linear_weight(const NNModule module)\n{\n return get_weight<torch::nn::Linear>(module);\n}\n\nvoid THSNN_Linear_set_weight(const NNModule module, const Tensor weight)\n{\n set_weight<torch::nn::Linear>(module, weight);\n}\n\nTensor THSNN_functional_linear(const Tensor input, const Tensor weights, const Tensor bias)\n{\n CATCH_TENSOR(bias == nullptr ?\n torch::nn::functional::linear(*input, *weights) :\n torch::nn::functional::linear(*input, *weights, *bias));\n}\n\nTensor THSNN_functional_bilinear(const Tensor input1, const Tensor input2, const Tensor weights, const Tensor bias)\n{\n CATCH_TENSOR(bias == nullptr ?\n torch::nn::functional::bilinear(*input1, *input2, *weights) :\n torch::nn::functional::bilinear(*input1, *input2, *weights, *bias));\n}\n\nNNModule THSNN_Bilinear_ctor(const int64_t input_size_1, const int64_t input_size_2, const int64_t output_size, const bool bias,\n NNAnyModule* outAsAnyModule)\n{\n CATCH_RETURN_NNModule(\n auto opts = torch::nn::BilinearOptions(input_size_1, input_size_2, output_size).bias(bias);\n res = create_module<torch::nn::BilinearImpl>(opts, outAsAnyModule);\n );\n}\n\nTensor THSNN_Bilinear_forward(const NNModule module, const Tensor x1, const Tensor x2)\n{\n CATCH_TENSOR((*module)->as<torch::nn::Bilinear>()->forward(*x1, *x2));\n}\n\nTensor THSNN_Bilinear_bias(const NNModule module)\n{\n return get_bias<torch::nn::Bilinear>(module);\n}\n\nvoid THSNN_Bilinear_set_bias(const NNModule module, const Tensor bias)\n{\n set_bias<torch::nn::Bilinear>(module, bias);\n}\n\nTensor THSNN_Bilinear_weight(const NNModule module)\n{\n return get_weight<torch::nn::Bilinear>(module);\n}\n\nvoid THSNN_Bilinear_set_weight(const NNModule module, const Tensor weight)\n{\n set_weight<torch::nn::Bilinear>(module, weight);\n}\n\nTensor THSNN_dropout(const Tensor input, const double p, bool training, bool inplace)\n{\n auto opts = torch::nn::functional::DropoutFuncOptions()\n .inplace(inplace)\n .training(training)\n .p(p);\n\n CATCH_TENSOR(torch::nn::functional::dropout(*input, opts));\n}\n\nTensor THSNN_dropout2d(const Tensor input, const double p, bool training, bool inplace)\n{\n auto opts = torch::nn::functional::Dropout2dFuncOptions()\n .inplace(inplace)\n .training(training)\n .p(p);\n\n CATCH_TENSOR(torch::nn::functional::dropout2d(*input, opts));\n}\n\nTensor THSNN_dropout3d(const Tensor input, const double p, bool training, bool inplace)\n{\n auto opts = torch::nn::functional::Dropout3dFuncOptions()\n .inplace(inplace)\n .training(training)\n .p(p);\n\n CATCH_TENSOR(torch::nn::functional::dropout3d(*input, opts));\n}\n\nTensor THSNN_alpha_dropout(const Tensor input, const double p, bool training, bool inplace)\n{\n auto opts = torch::nn::functional::AlphaDropoutFuncOptions()\n .inplace(inplace)\n .training(training)\n .p(p);\n\n CATCH_TENSOR(torch::nn::functional::alpha_dropout(*input, opts));\n}\n\nTensor THSNN_feature_alpha_dropout(const Tensor input, const double p, bool training, bool inplace)\n{\n auto opts = torch::nn::functional::FeatureAlphaDropoutFuncOptions()\n .inplace(inplace)\n .training(training)\n .p(p);\n\n CATCH_TENSOR(torch::nn::functional::feature_alpha_dropout(*input, opts));\n}\n\nNNModule THSNN_Dropout_ctor(double probability, bool inplace, NNAnyModule* outAsAnyModule)\n{\n CATCH_RETURN_NNModule(\n auto opts = torch::nn::DropoutOptions(probability).inplace(inplace);\n res = create_module<torch::nn::DropoutImpl>(opts, outAsAnyModule);\n );\n}\n\nTensor THSNN_Dropout_forward(const NNModule module, const Tensor tensor)\n{\n CATCH_TENSOR((*module)->as<torch::nn::Dropout>()->forward(*tensor));\n}\n\nNNModule THSNN_AlphaDropout_ctor(double probability, bool inplace, NNAnyModule* outAsAnyModule)\n{\n CATCH_RETURN_NNModule(\n auto opts = torch::nn::AlphaDropoutOptions(probability).inplace(inplace);\n res = create_module<torch::nn::AlphaDropoutImpl>(opts, outAsAnyModule);\n );\n}\n\nTensor THSNN_AlphaDropout_forward(const NNModule module, const Tensor tensor)\n{\n CATCH_TENSOR((*module)->as<torch::nn::AlphaDropout>()->forward(*tensor));\n}\n\nNNModule THSNN_Dropout1d_ctor(double probability, bool inplace, NNAnyModule* outAsAnyModule)\n{\n CATCH_RETURN_NNModule(\n // Creating a Dropout2d instance here is done on purpose. There's no torch::nn::Dropout1d\n auto opts = torch::nn::Dropout2dOptions(probability).inplace(inplace);\n res = create_module<torch::nn::Dropout2dImpl>(opts, outAsAnyModule);\n );\n}\n\nTensor THSNN_Dropout1d_forward(const NNModule module, const Tensor tensor)\n{\n auto drop1d = (*module)->as<torch::nn::Dropout2d>();\n CATCH_TENSOR(drop1d->options.inplace()\n ? drop1d->forward((*tensor).unsqueeze_(-1)).squeeze_(-1)\n : drop1d->forward((*tensor).unsqueeze(-1)).squeeze(-1));\n}\n\n\nNNModule THSNN_Dropout2d_ctor(double probability, bool inplace, NNAnyModule* outAsAnyModule)\n{\n CATCH_RETURN_NNModule(\n auto opts = torch::nn::Dropout2dOptions(probability).inplace(inplace);\n res = create_module<torch::nn::Dropout2dImpl>(opts, outAsAnyModule);\n );\n}\n\nTensor THSNN_Dropout2d_forward(const NNModule module, const Tensor tensor)\n{\n CATCH_TENSOR((*module)->as<torch::nn::Dropout2d>()->forward(*tensor));\n}\n\nNNModule THSNN_Dropout3d_ctor(double probability, bool inplace, NNAnyModule* outAsAnyModule)\n{\n CATCH_RETURN_NNModule(\n auto opts = torch::nn::Dropout3dOptions(probability).inplace(inplace);\n res = create_module<torch::nn::Dropout3dImpl>(opts, outAsAnyModule);\n );\n}\n\nTensor THSNN_Dropout3d_forward(const NNModule module, const Tensor tensor)\n{\n CATCH_TENSOR((*module)->as<torch::nn::Dropout3d>()->forward(*tensor));\n}\n\nNNModule THSNN_FeatureAlphaDropout_ctor(double probability, NNAnyModule* outAsAnyModule)\n{\n CATCH_RETURN_NNModule(\n auto opts = torch::nn::FeatureAlphaDropoutOptions(probability);\n res = create_module<torch::nn::FeatureAlphaDropoutImpl>(opts, outAsAnyModule);\n );\n}\n\nTensor THSNN_FeatureAlphaDropout_forward(const NNModule module, const Tensor tensor)\n{\n CATCH_TENSOR((*module)->as<torch::nn::FeatureAlphaDropout>()->forward(*tensor));\n}\n\nNNModule THSNN_PixelShuffle_ctor(const int64_t upscale_factor, NNAnyModule* outAsAnyModule)\n{\n CATCH_RETURN_NNModule(\n auto opts = torch::nn::PixelShuffleOptions(upscale_factor);\n res = create_module<torch::nn::PixelShuffleImpl>(opts, outAsAnyModule);\n );\n}\n\nTensor THSNN_PixelShuffle_forward(const NNModule module, const Tensor tensor)\n{\n CATCH_TENSOR((*module)->as<torch::nn::PixelShuffle>()->forward(*tensor));\n}\n\nNNModule THSNN_PixelUnshuffle_ctor(const int64_t downscale_factor, NNAnyModule* outAsAnyModule)\n{\n CATCH_RETURN_NNModule(\n auto opts = torch::nn::PixelUnshuffleOptions(downscale_factor);\n res = create_module<torch::nn::PixelUnshuffleImpl>(opts, outAsAnyModule);\n );\n}\n\nTensor THSNN_PixelUnshuffle_forward(const NNModule module, const Tensor tensor)\n{\n CATCH_TENSOR((*module)->as<torch::nn::PixelUnshuffle>()->forward(*tensor));\n}\n\ntemplate<typename T>\nvoid ApplyUpsampleMode(T& opts, const int8_t mode)\n{\n if (mode == 0)\n opts = opts.mode(torch::kNearest);\n if (mode == 1)\n opts = opts.mode(torch::kLinear);\n if (mode == 2)\n opts = opts.mode(torch::kBilinear);\n if (mode == 3)\n opts = opts.mode(torch::kBicubic);\n if (mode == 4)\n opts = opts.mode(torch::kTrilinear);\n}\n\ntemplate<typename T>\nvoid ApplyInterpolateMode(T& opts, const int8_t mode)\n{\n if (mode == 0)\n opts = opts.mode(torch::kNearest);\n if (mode == 1)\n opts = opts.mode(torch::kLinear);\n if (mode == 2)\n opts = opts.mode(torch::kBilinear);\n if (mode == 3)\n opts = opts.mode(torch::kBicubic);\n if (mode == 4)\n opts = opts.mode(torch::kTrilinear);\n if (mode == 5)\n opts = opts.mode(torch::kArea);\n}\n\nNNModule THSNN_Upsample_ctor(const int64_t* size, const int size_len, const double* scale_factor, const int scale_factor_len, const int8_t mode, const int8_t align_corners, NNAnyModule* outAsAnyModule)\n{\n auto opts = torch::nn::UpsampleOptions();\n // align_corners -- 0=None, 1=true, 2=false\n if (align_corners != 0)\n opts.align_corners(align_corners == 1);\n ApplyUpsampleMode(opts, mode);\n\n CATCH_RETURN_NNModule(\n if (size_len > 0) {\n std::vector<int64_t> sizes;\n for (int i = 0; i < size_len; ++i) {\n sizes.push_back(size[i]);\n }\n opts.size(sizes);\n }\n if (scale_factor_len > 0) {\n std::vector<double> scales;\n for (int i = 0; i < scale_factor_len; ++i) {\n scales.push_back(scale_factor[i]);\n }\n opts.scale_factor(scales);\n }\n res = create_module<torch::nn::UpsampleImpl>(opts, outAsAnyModule);\n );\n}\n\nTensor THSNN_Upsample_forward(const NNModule module, const Tensor tensor)\n{\n CATCH_TENSOR((*module)->as<torch::nn::Upsample>()->forward(*tensor));\n}\n\ntemplate<typename T>\nvoid ApplyPadMode(T& opts, const int64_t padding)\n{\n if (padding == 1)\n opts = opts.mode(torch::kReflect);\n if (padding == 2)\n opts = opts.mode(torch::kReplicate);\n if (padding == 3)\n opts = opts.mode(torch::kCircular);\n if (padding == 4)\n opts = opts.mode(torch::kConstant);\n}\n\ntemplate<typename T>\nvoid ApplyGridMode(T& opts, const int8_t mode)\n{\n if (mode == 0)\n opts = opts.mode(torch::kNearest);\n if (mode == 2)\n opts = opts.mode(torch::kBilinear);\n // The PyTorch docs say that bicubic should be supported, but the C++\n // mode type does not allow for it. I'm leaving this in for future use.\n //if (mode == 3)\n // opts = opts.mode(torch::kBicubic);\n}\n\ntemplate<typename T>\nvoid ApplyGridPadMode(T& opts, const int64_t padding)\n{\n if (padding == 0)\n opts = opts.padding_mode(torch::kZeros);\n if (padding == 1)\n opts = opts.padding_mode(torch::kReflection);\n if (padding == 2)\n opts = opts.padding_mode(torch::kBorder);\n}\n\nTensor THSNN_pad(const Tensor input, const int64_t* pad, const int pad_length, const int8_t mode, const double value)\n{\n std::vector<int64_t> padding;\n for (int i = 0; i < pad_length; ++i) {\n padding.push_back(pad[i]);\n }\n auto opts = torch::nn::functional::PadFuncOptions(padding).value(value);\n ApplyPadMode(opts, mode);\n\n CATCH_TENSOR(torch::nn::functional::pad(*input, opts));\n}\n\nTensor THSNN_grid_sample(const Tensor input, const Tensor grid, const int8_t mode, const int8_t padding_mode, const int8_t align_corners)\n{\n auto opts = torch::nn::functional::GridSampleFuncOptions();\n if (align_corners != 0)\n opts.align_corners(align_corners == 1);\n ApplyGridMode(opts, mode);\n ApplyGridPadMode(opts, padding_mode);\n CATCH_TENSOR(torch::nn::functional::grid_sample(*input, *grid, opts));\n}\n\nTensor THSNN_affine_grid(const Tensor theta, const int64_t* size, const int size_len, const bool align_corners)\n{\n CATCH_TENSOR(torch::nn::functional::affine_grid(*theta, at::ArrayRef<int64_t>(size, size_len), align_corners));\n}\n\n\nEXPORT_API(Tensor) THSNN_interpolate(const Tensor input, const int64_t* size, const int size_len, const double* scale_factor, const int scale_factor_len, const int8_t mode, const int8_t align_corners, const bool recompute_scale_factor, NNAnyModule* outAsAnyModule)\n{\n auto opts = torch::nn::functional::InterpolateFuncOptions().recompute_scale_factor(recompute_scale_factor);\n // align_corners -- 0=None, 1=true, 2=false\n if (align_corners != 0)\n opts.align_corners(align_corners == 1);\n ApplyInterpolateMode(opts, mode);\n\n if (size_len > 0) {\n std::vector<int64_t> sizes;\n for (int i = 0; i < size_len; ++i) {\n sizes.push_back(size[i]);\n }\n opts.size(sizes);\n }\n if (scale_factor_len > 0) {\n std::vector<double> scales;\n for (int i = 0; i < scale_factor_len; ++i) {\n scales.push_back(scale_factor[i]);\n }\n opts.scale_factor(scales);\n }\n\n CATCH_TENSOR(torch::nn::functional::interpolate(*input, opts));\n}\n\nNNModule THSNN_Embedding_ctor(const int64_t num_embeddings, const int64_t embedding_dims,\n const int64_t padding_idx, bool has_pi, const double max_norm, const bool has_mn, const double norm_type,\n const bool scale_grad_by_freq, const bool sparse,\n NNAnyModule* outAsAnyModule)\n{\n CATCH_RETURN_NNModule(\n auto opts = torch::nn::EmbeddingOptions(num_embeddings, embedding_dims)\n .norm_type(norm_type)\n .scale_grad_by_freq(scale_grad_by_freq)\n .sparse(sparse);\n\n if (has_pi)\n opts.padding_idx(padding_idx);\n if (has_mn)\n opts.max_norm(max_norm);\n\n res = create_module<torch::nn::EmbeddingImpl>(opts, outAsAnyModule);\n );\n}\n\nNNModule THSNN_Embedding_from_pretrained(const Tensor embeddings, const bool freeze,\n const int64_t padding_idx, bool has_pi, const double max_norm, const bool has_mn, const double norm_type,\n const bool scale_grad_by_freq, const bool sparse,\n NNAnyModule* outAsAnyModule)\n{\n CATCH_RETURN_NNModule(\n auto rows = embeddings->size(0);\n auto cols = embeddings->size(1);\n\n auto opts = torch::nn::EmbeddingOptions(rows, cols)\n .norm_type(norm_type)\n .scale_grad_by_freq(scale_grad_by_freq)\n .sparse(sparse);\n\n if (has_pi)\n opts.padding_idx(padding_idx);\n if (has_mn)\n opts.max_norm(max_norm);\n\n // Can't use the template function here -- custom logic.\n auto mod = std::make_shared<torch::nn::EmbeddingImpl>(opts);\n mod->weight = *embeddings;\n mod->weight.set_requires_grad(!freeze);\n\n // Keep a boxed version of the module in case we add it to a Sequential later (the C++ templating means\n // a Module can only be boxed to AnyModule at the point its static type is known).\n if (outAsAnyModule != nullptr)\n {\n auto wrapped = std::make_shared<torch::nn::AnyModule>(torch::nn::ModuleHolder<torch::nn::EmbeddingImpl>(*mod));\n *outAsAnyModule = new std::shared_ptr<torch::nn::AnyModule>(wrapped);\n }\n res = new std::shared_ptr<torch::nn::Module>(mod);\n );\n}\n\nTensor THSNN_Embedding_forward(const NNModule module, const Tensor tensor)\n{\n CATCH_TENSOR((*module)->as<torch::nn::Embedding>()->forward(*tensor));\n}\n\nTensor THSNN_Embedding_weight(const NNModule module)\n{\n return get_weight<torch::nn::Embedding>(module);\n}\n\nvoid THSNN_Embedding_set_weight(const NNModule module, const Tensor weights)\n{\n set_weight<torch::nn::Embedding>(module, weights);\n}\n\ntemplate<typename T>\nvoid ApplyEmbeddingBagMode(T& opts, const int64_t mode)\n{\n if (mode == 0)\n opts = opts.mode(torch::kSum);\n if (mode == 1)\n opts = opts.mode(torch::kMean);\n if (mode == 2)\n opts = opts.mode(torch::kMax);\n}\n\nNNModule THSNN_EmbeddingBag_ctor(const int64_t num_embeddings, const int64_t embedding_dims,\n const double max_norm, const bool has_mn, const double norm_type, const bool scale_grad_by_freq,\n const int64_t mode, const bool sparse, const bool include_last_offset, const int64_t padding_idx,\n NNAnyModule* outAsAnyModule)\n{\n CATCH_RETURN_NNModule(\n auto opts = torch::nn::EmbeddingBagOptions(num_embeddings, embedding_dims)\n .norm_type(norm_type)\n .scale_grad_by_freq(scale_grad_by_freq)\n .include_last_offset(include_last_offset)\n .sparse(sparse);\n\n ApplyEmbeddingBagMode(opts, mode);\n\n if (has_mn)\n opts = opts.max_norm(max_norm);\n if (padding_idx >= 0)\n opts = opts.padding_idx(padding_idx);\n\n res = create_module<torch::nn::EmbeddingBagImpl>(opts, outAsAnyModule);\n );\n}\n\nNNModule THSNN_EmbeddingBag_from_pretrained(const Tensor embeddings, const bool freeze,\n const double max_norm, const bool has_mn, const double norm_type, const bool scale_grad_by_freq,\n const int64_t mode, const bool sparse, const bool include_last_offset, const int64_t padding_idx,\n NNAnyModule* outAsAnyModule)\n{\n CATCH_RETURN_NNModule(\n auto rows = embeddings->size(0);\n auto cols = embeddings->size(1);\n\n auto opts = torch::nn::EmbeddingBagOptions(rows, cols)\n .norm_type(norm_type)\n .scale_grad_by_freq(scale_grad_by_freq)\n .include_last_offset(include_last_offset)\n .sparse(sparse);\n\n ApplyEmbeddingBagMode(opts, mode);\n\n if (has_mn)\n opts.max_norm(max_norm);\n if (padding_idx >= 0)\n opts = opts.padding_idx(padding_idx);\n\n // Can't use the template function here -- custom logic.\n auto mod = std::make_shared<torch::nn::EmbeddingBagImpl>(opts);\n mod->weight = *embeddings;\n mod->weight.set_requires_grad(!freeze);\n\n // Keep a boxed version of the module in case we add it to a Sequential later (the C++ templating means\n // a Module can only be boxed to AnyModule at the point its static type is known).\n if (outAsAnyModule != nullptr)\n {\n auto wrapped = std::make_shared<torch::nn::AnyModule>(torch::nn::ModuleHolder<torch::nn::EmbeddingBagImpl>(*mod));\n *outAsAnyModule = new std::shared_ptr<torch::nn::AnyModule>(wrapped);\n }\n res = new std::shared_ptr<torch::nn::Module>(mod);\n );\n}\n\nTensor THSNN_EmbeddingBag_forward(const NNModule module, const Tensor input, const Tensor offsets, const Tensor per_sample_weights)\n{\n if (offsets != nullptr && per_sample_weights != nullptr)\n {\n CATCH_TENSOR((*module)->as<torch::nn::EmbeddingBag>()->forward(*input, *offsets, *per_sample_weights));\n }\n else if (offsets == nullptr && per_sample_weights != nullptr)\n {\n CATCH_TENSOR((*module)->as<torch::nn::EmbeddingBag>()->forward(*input, {}, *per_sample_weights));\n }\n else if (offsets != nullptr && per_sample_weights == nullptr)\n {\n CATCH_TENSOR((*module)->as<torch::nn::EmbeddingBag>()->forward(*input, *offsets));\n }\n else\n {\n CATCH_TENSOR((*module)->as<torch::nn::EmbeddingBag>()->forward(*input));\n }\n}\n\nTensor THSNN_EmbeddingBag_weight(const NNModule module)\n{\n return get_weight<torch::nn::EmbeddingBag>(module);\n}\n\nvoid THSNN_EmbeddingBag_set_weight(const NNModule module, const Tensor weights)\n{\n set_weight<torch::nn::EmbeddingBag>(module, weights);\n}\n\ntemplate<typename T>\nvoid ApplyTransformerActivation(T& opts, const int64_t activation)\n{\n if (activation == 0)\n opts = opts.activation(torch::kReLU);\n if (activation == 1)\n opts = opts.activation(torch::kGELU);\n}\n\ntemplate<typename T>\nvoid ApplyRnnActivation(T& opts, const int64_t activation)\n{\n if (activation == 0)\n opts = opts.nonlinearity(torch::kReLU);\n if (activation == 1)\n opts = opts.nonlinearity(torch::kTanh);\n}\n\nNNModule THSNN_Transformer_ctor(const int64_t d_model, const int64_t nhead, const int64_t num_encoder_layers, const int64_t num_decoder_layers, const int64_t dim_feedforward, const double dropout, const int64_t activation, NNAnyModule* outAsAnyModule)\n{\n CATCH_RETURN_NNModule(\n auto opts = torch::nn::TransformerOptions(d_model, nhead)\n .num_encoder_layers(num_encoder_layers)\n .num_decoder_layers(num_decoder_layers)\n .dim_feedforward(dim_feedforward)\n .dropout(dropout);\n ApplyTransformerActivation(opts, activation);\n\n res = create_module<torch::nn::TransformerImpl>(opts, outAsAnyModule);\n );\n}\n\nTensor THSNN_Transformer_forward(const NNModule module, const Tensor src, const Tensor tgt, const Tensor src_mask, const Tensor tgt_mask, const Tensor memory_mask, const Tensor src_key_padding_mask, const Tensor tgt_key_padding_mask, const Tensor memory_key_padding_mask)\n{\n CATCH_TENSOR((*module)->as<torch::nn::Transformer>()->forward(\n *src,\n *tgt,\n (src_mask ? *src_mask : at::Tensor()),\n (tgt_mask ? *tgt_mask : at::Tensor()),\n (memory_mask ? *memory_mask : at::Tensor()),\n (src_key_padding_mask ? *src_key_padding_mask : at::Tensor()),\n (tgt_key_padding_mask ? *tgt_key_padding_mask : at::Tensor()),\n (memory_key_padding_mask ? *memory_key_padding_mask : at::Tensor()))\n );\n}\n\nNNModule THSNN_TransformerEncoderLayer_ctor(const int64_t d_model, const int64_t nhead, const int64_t dim_feedforward, const double dropout, const int64_t activation, NNAnyModule* outAsAnyModule)\n{\n CATCH_RETURN_NNModule(\n auto opts = torch::nn::TransformerEncoderLayerOptions(d_model, nhead)\n .dim_feedforward(dim_feedforward)\n .dropout(dropout);\n ApplyTransformerActivation(opts, activation);\n\n res = create_module<torch::nn::TransformerEncoderLayerImpl>(opts, outAsAnyModule);\n );\n}\n\nTensor THSNN_TransformerEncoderLayer_forward(const NNModule module, const Tensor src, const Tensor src_mask, const Tensor src_key_padding_mask)\n{\n CATCH_TENSOR((*module)->as<torch::nn::TransformerEncoderLayer>()->forward(\n *src,\n (src_mask ? *src_mask : at::Tensor()),\n (src_key_padding_mask ? *src_key_padding_mask : at::Tensor()))\n );\n}\n\nNNModule THSNN_TransformerDecoderLayer_ctor(const int64_t d_model, const int64_t nhead, const int64_t dim_feedforward, const double dropout, const int64_t activation, NNAnyModule* outAsAnyModule)\n{\n CATCH_RETURN_NNModule(\n auto opts = torch::nn::TransformerDecoderLayerOptions(d_model, nhead)\n .dim_feedforward(dim_feedforward)\n .dropout(dropout);\n ApplyTransformerActivation(opts, activation);\n\n res = create_module<torch::nn::TransformerDecoderLayerImpl>(opts, outAsAnyModule);\n );\n}\n\nTensor THSNN_TransformerDecoderLayer_forward(const NNModule module, const Tensor tgt, const Tensor memory, const Tensor tgt_mask, const Tensor memory_mask, const Tensor tgt_key_padding_mask, const Tensor memory_key_padding_mask)\n{\n CATCH_TENSOR((*module)->as<torch::nn::TransformerDecoderLayer>()->forward(\n *tgt,\n *memory,\n (tgt_mask ? *tgt_mask : at::Tensor()),\n (memory_mask ? *memory_mask : at::Tensor()),\n (tgt_key_padding_mask ? *tgt_key_padding_mask : at::Tensor()),\n (memory_key_padding_mask ? *memory_key_padding_mask : at::Tensor()))\n );\n}\n\nNNModule THSNN_MultiheadAttention_ctor(const int64_t embeded_dim, const int64_t num_heads, const double dropout, const bool bias, const bool add_bias_kv, const bool add_zero_attn, const int64_t kdim, const int64_t vdim, NNAnyModule* outAsAnyModule)\n{\n CATCH_RETURN_NNModule(\n auto opts = torch::nn::MultiheadAttentionOptions(embeded_dim, num_heads)\n .dropout(dropout)\n .bias(bias)\n .add_bias_kv(add_bias_kv)\n .add_zero_attn(add_zero_attn)\n .kdim(kdim)\n .vdim(vdim);\n\n res = create_module<torch::nn::MultiheadAttentionImpl>(opts, outAsAnyModule);\n );\n}\n\nvoid THSNN_MultiheadAttention_forward(const NNModule module, const Tensor query, const Tensor key, const Tensor value, const Tensor key_padding_mask, const bool need_weights, const Tensor attn_mask, Tensor& res1, Tensor& res2)\n{\n CATCH_TENSORS_2((*module)->as<torch::nn::MultiheadAttention>()->forward(\n *query,\n *key,\n *value,\n (key_padding_mask ? *key_padding_mask : at::Tensor()),\n need_weights,\n (attn_mask ? *attn_mask : at::Tensor()))\n );\n}\n\nNNModule THSNN_TransformerEncoder_ctor(const NNModule encoder_layer, const int64_t num_layers, NNAnyModule* outAsAnyModule)\n{\n CATCH_RETURN_NNModule(\n auto enc = (*encoder_layer)->as<torch::nn::TransformerEncoderLayer>();\n auto opts = torch::nn::TransformerEncoderOptions(torch::nn::TransformerEncoderLayer(*enc), num_layers);\n\n res = create_module<torch::nn::TransformerEncoderImpl>(opts, outAsAnyModule);\n );\n}\n\nTensor THSNN_TransformerEncoder_forward(const NNModule module, const Tensor src, const Tensor src_mask, const Tensor src_key_padding_mask)\n{\n CATCH_TENSOR((*module)->as<torch::nn::TransformerEncoder>()->forward(\n *src,\n (src_mask ? *src_mask : at::Tensor()),\n (src_key_padding_mask ? *src_key_padding_mask : at::Tensor()))\n );\n}\n\nNNModule THSNN_TransformerDecoder_ctor(const NNModule decoder_layer, const int64_t num_layers, NNAnyModule* outAsAnyModule)\n{\n CATCH_RETURN_NNModule(\n auto dec = (*decoder_layer)->as<torch::nn::TransformerDecoderLayer>();\n auto opts = torch::nn::TransformerDecoderOptions(torch::nn::TransformerDecoderLayer(*dec), num_layers);\n\n res = create_module<torch::nn::TransformerDecoderImpl>(opts, outAsAnyModule);\n );\n}\n\nTensor THSNN_TransformerDecoder_forward(const NNModule module, const Tensor tgt, const Tensor memory, const Tensor tgt_mask, const Tensor memory_mask, const Tensor tgt_key_padding_mask, const Tensor memory_key_padding_mask)\n{\n CATCH_TENSOR((*module)->as<torch::nn::TransformerDecoder>()->forward(\n *tgt,\n *memory,\n (tgt_mask ? *tgt_mask : at::Tensor()),\n (memory_mask ? *memory_mask : at::Tensor()),\n (tgt_key_padding_mask ? *tgt_key_padding_mask : at::Tensor()),\n (memory_key_padding_mask ? *memory_key_padding_mask : at::Tensor()))\n );\n}\n\nNNModule THSNN_Flatten_ctor(const int64_t start_dim, const int64_t end_dim, NNAnyModule* outAsAnyModule)\n{\n CATCH_RETURN_NNModule(\n auto opts = torch::nn::FlattenOptions()\n .start_dim(start_dim)\n .end_dim(end_dim);\n\n res = create_module<torch::nn::FlattenImpl>(opts, outAsAnyModule);\n );\n}\nTensor THSNN_Flatten_forward(const NNModule module, const Tensor tensor)\n{\n CATCH_TENSOR((*module)->as<torch::nn::Flatten>()->forward(*tensor));\n}\n\nNNModule THSNN_Unflatten_ctor(const int64_t dim, const int64_t* shape, const int64_t shape_len, NNAnyModule* outAsAnyModule)\n{\n CATCH_RETURN_NNModule(\n std::vector<int64_t> sizes;\n for (int64_t i = 0; i < shape_len; ++i)\n {\n sizes.push_back(shape[i]);\n }\n auto opts = torch::nn::UnflattenOptions(dim, sizes);\n res = create_module<torch::nn::UnflattenImpl>(opts, outAsAnyModule);\n );\n}\n\nTensor THSNN_Unflatten_forward(const NNModule module, const Tensor tensor)\n{\n CATCH_TENSOR((*module)->as<torch::nn::Unflatten>()->forward(*tensor));\n}\n\nNNModule THSNN_CosineSimilarity_ctor(const int64_t dim, double eps, NNAnyModule* outAsAnyModule)\n{\n CATCH_RETURN_NNModule(\n auto opts = torch::nn::CosineSimilarityOptions()\n .dim(dim)\n .eps(eps);\n\n res = create_module<torch::nn::CosineSimilarityImpl>(opts, outAsAnyModule);\n );\n\n}\n\nTensor THSNN_CosineSimilarity_forward(const NNModule module, const Tensor input1, const Tensor input2)\n{\n CATCH_TENSOR((*module)->as<torch::nn::CosineSimilarity>()->forward(*input1, *input2));\n}\n\nNNModule THSNN_PairwiseDistance_ctor(double p, double eps, bool keep_dim, NNAnyModule* outAsAnyModule)\n{\n CATCH_RETURN_NNModule(\n auto opts = torch::nn::PairwiseDistanceOptions()\n .p(p)\n .eps(eps)\n .keepdim(keep_dim);\n\n res = create_module<torch::nn::PairwiseDistanceImpl>(opts, outAsAnyModule);\n );\n\n}\n\nTensor THSNN_PairwiseDistance_forward(const NNModule module, const Tensor input1, const Tensor input2)\n{\n CATCH_TENSOR((*module)->as<torch::nn::PairwiseDistance>()->forward(*input1, *input2));\n}\n\nNNModule THSNN_RNN_ctor(const int64_t input_size, const int64_t hidden_size, const int64_t num_layers, const int64_t nonlinearity, const bool bias, const bool batchFirst, const double dropout, const bool bidirectional, NNAnyModule* outAsAnyModule)\n{\n CATCH_RETURN_NNModule(\n auto opts = torch::nn::RNNOptions(input_size, hidden_size)\n .num_layers(num_layers)\n .bias(bias)\n .batch_first(batchFirst)\n .dropout(dropout)\n .bidirectional(bidirectional);\n\n ApplyRnnActivation(opts, nonlinearity);\n\n res = create_module<torch::nn::RNNImpl>(opts, outAsAnyModule);\n );\n}\n\nTensor THSNN_RNN_forward(const NNModule module, const Tensor input1, const Tensor input2, Tensor* h_n)\n{\n Tensor output = nullptr;\n *h_n = nullptr;\n CATCH(\n auto result = (*module)->as<torch::nn::RNN>()->forward(*input1, (input2 ? *input2 : at::Tensor()));\n output = new torch::Tensor(std::get<0>(result));\n *h_n = new torch::Tensor(std::get<1>(result));\n );\n return output;\n}\n\nPackedSequence THSNN_RNN_forward_with_packed_input(const NNModule module, const PackedSequence input1, const Tensor input2, Tensor* h_n)\n{\n PackedSequence output = nullptr;\n *h_n = nullptr;\n CATCH(\n auto result = (*module)->as<torch::nn::RNN>()->forward_with_packed_input(*input1, (input2 ? *input2 : at::Tensor()));\n output = new torch::nn::utils::rnn::PackedSequence(std::get<0>(result));\n *h_n = new torch::Tensor(std::get<1>(result));\n );\n return output;\n}\n\nvoid THSNN_RNN_flatten_parameters(const NNModule module)\n{\n CATCH(\n (*module)->as<torch::nn::RNN>()->flatten_parameters();\n );\n}\n\nTensor THSNN_RNN_bias_ih(const NNModule module, const int64_t idx)\n{\n return get_bias_ih<torch::nn::RNN>(module, idx);\n}\n\nvoid THSNN_RNN_set_bias_ih(const NNModule module, const Tensor bias, const int64_t idx)\n{\n set_bias_ih<torch::nn::RNN>(module, bias, idx);\n}\n\nTensor THSNN_RNN_weight_ih(const NNModule module, const int64_t idx)\n{\n return get_weight_ih<torch::nn::RNN>(module, idx);\n}\n\nvoid THSNN_RNN_set_weight_ih(const NNModule module, const Tensor weight, const int64_t idx)\n{\n set_weight_ih<torch::nn::RNN>(module, weight, idx);\n}\n\nTensor THSNN_RNN_bias_hh(const NNModule module, const int64_t idx)\n{\n return get_bias_hh<torch::nn::RNN>(module, idx);\n}\n\nvoid THSNN_RNN_set_bias_hh(const NNModule module, const Tensor bias, const int64_t idx)\n{\n set_bias_hh<torch::nn::RNN>(module, bias, idx);\n}\n\nTensor THSNN_RNN_weight_hh(const NNModule module, const int64_t idx)\n{\n return get_weight_hh<torch::nn::RNN>(module, idx);\n}\n\nvoid THSNN_RNN_set_weight_hh(const NNModule module, const Tensor weight, const int64_t idx)\n{\n set_weight_hh<torch::nn::RNN>(module, weight, idx);\n}\n\n\nNNModule THSNN_GRU_ctor(const int64_t input_size, const int64_t hidden_size, const int64_t num_layers, const bool bias, const bool batchFirst, const double dropout, const bool bidirectional, NNAnyModule* outAsAnyModule)\n{\n CATCH_RETURN_NNModule(\n auto opts = torch::nn::GRUOptions(input_size, hidden_size)\n .num_layers(num_layers)\n .bias(bias)\n .batch_first(batchFirst)\n .dropout(dropout)\n .bidirectional(bidirectional);\n\n res = create_module<torch::nn::GRUImpl>(opts, outAsAnyModule);\n );\n}\n\nTensor THSNN_GRU_forward(const NNModule module, const Tensor input1, const Tensor input2, Tensor* h_n)\n{\n Tensor output = nullptr;\n *h_n = nullptr;\n CATCH(\n auto result = (*module)->as<torch::nn::GRU>()->forward(*input1, (input2 ? *input2 : at::Tensor()));\n output = new torch::Tensor(std::get<0>(result));\n *h_n = new torch::Tensor(std::get<1>(result));\n );\n return output;\n}\n\nPackedSequence THSNN_GRU_forward_with_packed_input(const NNModule module, const PackedSequence input1, const Tensor input2, Tensor* h_n)\n{\n PackedSequence output = nullptr;\n *h_n = nullptr;\n CATCH(\n auto result = (*module)->as<torch::nn::GRU>()->forward_with_packed_input(*input1, (input2 ? *input2 : at::Tensor()));\n output = new torch::nn::utils::rnn::PackedSequence(std::get<0>(result));\n *h_n = new torch::Tensor(std::get<1>(result));\n );\n return output;\n}\n\nvoid THSNN_GRU_flatten_parameters(const NNModule module)\n{\n CATCH(\n (*module)->as<torch::nn::GRU>()->flatten_parameters();\n );\n}\n\nNNModule THSNN_LSTM_ctor(const int64_t input_size, const int64_t hidden_size, const int64_t num_layers, const bool bias, const bool batchFirst, const double dropout, const bool bidirectional, NNAnyModule* outAsAnyModule)\n{\n CATCH_RETURN_NNModule(\n auto opts = torch::nn::LSTMOptions(input_size, hidden_size)\n .num_layers(num_layers)\n .bias(bias)\n .batch_first(batchFirst)\n .dropout(dropout)\n .bidirectional(bidirectional);\n\n res = create_module<torch::nn::LSTMImpl>(opts, outAsAnyModule);\n );\n}\n\nTensor THSNN_LSTM_forward(const NNModule module, const Tensor input1, const Tensor h0, const Tensor c0, Tensor* h_n, Tensor* c_n)\n{\n const std::tuple<at::Tensor, at::Tensor>& second_arg = (h0 == nullptr || c0 == nullptr) ? std::make_tuple(at::Tensor(), at::Tensor()) : std::make_tuple(*h0, *c0);\n\n Tensor output = nullptr;\n *h_n = nullptr;\n *c_n = nullptr;\n CATCH(\n auto result = (*module)->as<torch::nn::LSTM>()->forward(*input1, second_arg);\n output = new torch::Tensor(std::get<0>(result));\n *h_n = new torch::Tensor(std::get<0>(std::get<1>(result)));\n *c_n = new torch::Tensor(std::get<1>(std::get<1>(result)));\n );\n return output;\n}\n\nPackedSequence THSNN_LSTM_forward_with_packed_input(const NNModule module, const PackedSequence input1, const Tensor h0, const Tensor c0, Tensor* h_n, Tensor* c_n)\n{\n const std::tuple<at::Tensor, at::Tensor>& second_arg = (h0 == nullptr || c0 == nullptr) ? std::make_tuple(at::Tensor(), at::Tensor()) : std::make_tuple(*h0, *c0);\n\n PackedSequence output = nullptr;\n *h_n = nullptr;\n *c_n = nullptr;\n CATCH(\n auto result = (*module)->as<torch::nn::LSTM>()->forward_with_packed_input(*input1, second_arg);\n output = new torch::nn::utils::rnn::PackedSequence(std::get<0>(result));\n *h_n = new torch::Tensor(std::get<0>(std::get<1>(result)));\n *c_n = new torch::Tensor(std::get<1>(std::get<1>(result)));\n );\n return output;\n}\n\nvoid THSNN_LSTM_flatten_parameters(const NNModule module)\n{\n CATCH(\n (*module)->as<torch::nn::LSTM>()->flatten_parameters();\n );\n}\n\nNNModule THSNN_RNNCell_ctor(const int64_t input_size, const int64_t hidden_size, const int64_t nonlinearity, const bool bias, NNAnyModule* outAsAnyModule)\n{\n CATCH_RETURN_NNModule(\n auto opts = torch::nn::RNNCellOptions(input_size, hidden_size)\n .bias(bias);\n\n ApplyRnnActivation(opts, nonlinearity);\n\n res = create_module<torch::nn::RNNCellImpl>(opts, outAsAnyModule);\n );\n}\n\nTensor THSNN_RNNCell_forward(const NNModule module, const Tensor input1, const Tensor h0)\n{\n CATCH_TENSOR((*module)->as<torch::nn::RNNCell>()->forward(*input1, (h0 ? *h0 : at::Tensor())));\n}\n\nTensor THSNN_RNNCell_bias_ih(const NNModule module)\n{\n return get_bias_ih<torch::nn::RNNCell>(module);\n}\n\nvoid THSNN_RNNCell_set_bias_ih(const NNModule module, const Tensor bias)\n{\n set_bias_ih<torch::nn::RNNCell>(module, bias);\n}\n\nTensor THSNN_RNNCell_weight_ih(const NNModule module)\n{\n return get_weight_ih<torch::nn::RNNCell>(module);\n}\n\nvoid THSNN_RNNCell_set_weight_ih(const NNModule module, const Tensor weight)\n{\n set_weight_ih<torch::nn::RNNCell>(module, weight);\n}\n\nTensor THSNN_RNNCell_bias_hh(const NNModule module)\n{\n return get_bias_hh<torch::nn::RNNCell>(module);\n}\n\nvoid THSNN_RNNCell_set_bias_hh(const NNModule module, const Tensor bias)\n{\n set_bias_hh<torch::nn::RNNCell>(module, bias);\n}\n\nTensor THSNN_RNNCell_weight_hh(const NNModule module)\n{\n return get_weight_hh<torch::nn::RNNCell>(module);\n}\n\nvoid THSNN_RNNCell_set_weight_hh(const NNModule module, const Tensor weight)\n{\n set_weight_hh<torch::nn::RNNCell>(module, weight);\n}\n\nNNModule THSNN_GRUCell_ctor(const int64_t input_size, const int64_t hidden_size, const bool bias, NNAnyModule* outAsAnyModule)\n{\n CATCH_RETURN_NNModule(\n auto opts = torch::nn::GRUCellOptions(input_size, hidden_size)\n .bias(bias);\n\n res = create_module<torch::nn::GRUCellImpl>(opts, outAsAnyModule);\n );\n}\n\nTensor THSNN_GRUCell_forward(const NNModule module, const Tensor input1, const Tensor h0)\n{\n CATCH_TENSOR((*module)->as<torch::nn::GRUCell>()->forward(*input1, (h0 ? *h0 : at::Tensor())));\n}\n\nTensor THSNN_GRUCell_bias_ih(const NNModule module)\n{\n return get_bias_ih<torch::nn::GRUCell>(module);\n}\n\nvoid THSNN_GRUCell_set_bias_ih(const NNModule module, const Tensor bias)\n{\n set_bias_ih<torch::nn::GRUCell>(module, bias);\n}\n\nTensor THSNN_GRUCell_weight_ih(const NNModule module)\n{\n return get_weight_ih<torch::nn::GRUCell>(module);\n}\n\nvoid THSNN_GRUCell_set_weight_ih(const NNModule module, const Tensor weight)\n{\n set_weight_ih<torch::nn::GRUCell>(module, weight);\n}\n\nTensor THSNN_GRUCell_bias_hh(const NNModule module)\n{\n return get_bias_hh<torch::nn::GRUCell>(module);\n}\n\nvoid THSNN_GRUCell_set_bias_hh(const NNModule module, const Tensor bias)\n{\n set_bias_hh<torch::nn::GRUCell>(module, bias);\n}\n\nTensor THSNN_GRUCell_weight_hh(const NNModule module)\n{\n return get_weight_hh<torch::nn::GRUCell>(module);\n}\n\nvoid THSNN_GRUCell_set_weight_hh(const NNModule module, const Tensor weight)\n{\n set_weight_hh<torch::nn::GRUCell>(module, weight);\n}\n\nNNModule THSNN_LSTMCell_ctor(const int64_t input_size, const int64_t hidden_size, const bool bias, NNAnyModule* outAsAnyModule)\n{\n CATCH_RETURN_NNModule(\n auto opts = torch::nn::LSTMCellOptions(input_size, hidden_size)\n .bias(bias);\n\n res = create_module<torch::nn::LSTMCellImpl>(opts, outAsAnyModule);\n );\n}\n\nTensor THSNN_LSTMCell_forward(const NNModule module, const Tensor input1, const Tensor h0, const Tensor c0, Tensor* c_n)\n{\n const std::tuple<at::Tensor, at::Tensor>& second_arg = (h0 == nullptr || c0 == nullptr) ? std::make_tuple(at::Tensor(), at::Tensor()) : std::make_tuple(*h0, *c0);\n\n Tensor output;\n CATCH(\n auto result = (*module)->as<torch::nn::LSTMCell>()->forward(*input1, second_arg);\n output = new torch::Tensor(std::get<0>(result));\n *c_n = new torch::Tensor(std::get<1>(result));\n );\n\n return output;\n}\n\nTensor THSNN_LSTMCell_bias_ih(const NNModule module)\n{\n return get_bias_ih<torch::nn::LSTMCell>(module);\n}\n\nvoid THSNN_LSTMCell_set_bias_ih(const NNModule module, const Tensor bias)\n{\n set_bias_ih<torch::nn::LSTMCell>(module, bias);\n}\n\nTensor THSNN_LSTMCell_weight_ih(const NNModule module)\n{\n return get_weight_ih<torch::nn::LSTMCell>(module);\n}\n\nvoid THSNN_LSTMCell_set_weight_ih(const NNModule module, const Tensor weight)\n{\n set_weight_ih<torch::nn::LSTMCell>(module, weight);\n}\n\nTensor THSNN_LSTMCell_bias_hh(const NNModule module)\n{\n return get_bias_hh<torch::nn::LSTMCell>(module);\n}\n\nvoid THSNN_LSTMCell_set_bias_hh(const NNModule module, const Tensor bias)\n{\n set_bias_hh<torch::nn::LSTMCell>(module, bias);\n}\n\nTensor THSNN_LSTMCell_weight_hh(const NNModule module)\n{\n return get_weight_hh<torch::nn::LSTMCell>(module);\n}\n\nvoid THSNN_LSTMCell_set_weight_hh(const NNModule module, const Tensor weight)\n{\n set_weight_hh<torch::nn::LSTMCell>(module, weight);\n}\n\n\nNNModule THSNN_Sequential_ctor( /* NNAnyModule *submodules, const int length */ )\n{\n //std::vector<torch::nn::NamedAnyModule> modules;\n //for (int i = 0; i < length; i++)\n //{\n //\tmodules.push_back(*(*submodules[i])->as<torch::nn::NamedAnyModule>());\n //}\n\n auto mod = std::make_shared<torch::nn::SequentialImpl>( /* std::begin(modules), std::end(modules) */ );\n return new std::shared_ptr<torch::nn::Module>(mod);\n}\n\nvoid THSNN_Sequential_push_back(const NNModule module, const char *name, const NNAnyModule submodule)\n{\n CATCH (\n (*module)->as<torch::nn::Sequential>()->push_back(name, *(*submodule));\n )\n}\n\nTensor THSNN_Sequential_forward(const NNModule module, const Tensor tensor)\n{\n CATCH_TENSOR((*module)->as<torch::nn::Sequential>()->forward(*tensor));\n}\n\nTensor THSNN_one_hot(const Tensor self, const int64_t num_classes)\n{\n CATCH_RETURN_Tensor(\n res = ResultTensor(torch::nn::functional::one_hot(*self, num_classes));\n )\n}\n\nTensor THSNN_PackedSequence_data(PackedSequence sequence)\n{\n CATCH_TENSOR(sequence->data());\n}\n\nTensor THSNN_PackedSequence_batch_sizes(PackedSequence sequence)\n{\n CATCH_TENSOR(sequence->batch_sizes());\n}\n\nTensor THSNN_PackedSequence_sorted_indices(PackedSequence sequence)\n{\n CATCH_TENSOR(sequence->sorted_indices());\n}\n\nTensor THSNN_PackedSequence_unsorted_indices(PackedSequence sequence)\n{\n CATCH_TENSOR(sequence->unsorted_indices());\n}\n\nvoid THSNN_PackedSequence_dispose(PackedSequence sequence)\n{\n delete sequence;\n}\n\nPackedSequence THSNN_pack_padded_sequence(Tensor input, Tensor lengths, bool batch_first, bool enforce_sorted)\n{\n CATCH_RETURN(\n torch::nn::utils::rnn::PackedSequence*,\n nullptr,\n new torch::nn::utils::rnn::PackedSequence(\n torch::nn::utils::rnn::pack_padded_sequence(\n *input, *lengths, batch_first, enforce_sorted)));\n}\n\nvoid THSNN_pad_packed_sequence(PackedSequence sequence, bool batch_first, double padding_value, int64_t total_length, Tensor* res1_, Tensor* res2_)\n{\n Tensor& res1 = *res1_;\n Tensor& res2 = *res2_;\n CATCH_TENSORS_2(\n torch::nn::utils::rnn::pad_packed_sequence(\n *sequence, batch_first, padding_value,\n total_length == -1 ? torch::nullopt : c10::optional<int64_t>(total_length)));\n}\n\nTensor THSNN_pad_sequence(const Tensor* sequences, const int sequences_len, bool batch_first, double padding_value)\n{\n CATCH_TENSOR(\n torch::nn::utils::rnn::pad_sequence(\n toTensors<at::Tensor>((torch::Tensor**)sequences, sequences_len),\n batch_first, padding_value));\n}\n\nPackedSequence THSNN_pack_sequence(const Tensor* sequences, int sequences_len, bool enforce_sorted)\n{\n CATCH_RETURN(\n torch::nn::utils::rnn::PackedSequence*,\n nullptr,\n new torch::nn::utils::rnn::PackedSequence(\n torch::nn::utils::rnn::pack_sequence(\n toTensors<at::Tensor>((torch::Tensor**)sequences, sequences_len),\n enforce_sorted)));\n}\n\nTensor THSNN_fold(const Tensor input, const int64_t out1, const int64_t out2, const int64_t kernel1, const int64_t kernel2, const int64_t stride1, const int64_t stride2, const int64_t pad1, const int64_t pad2, const int64_t dil1, const int64_t dil2)\n{\n auto opts =\n torch::nn::functional::FoldFuncOptions({ out1, out2 }, {kernel1, kernel2})\n .dilation({dil1, dil2})\n .padding({pad1, pad2})\n .stride({ stride1, stride2 });\n\n CATCH_TENSOR(torch::nn::functional::fold(*input, opts));\n}\n\n\n\nTensor THSNN_unfold(const Tensor input, const int64_t kernel1, const int64_t kernel2, const int64_t stride1, const int64_t stride2, const int64_t pad1, const int64_t pad2, const int64_t dil1, const int64_t dil2)\n{\n auto opts =\n torch::nn::functional::UnfoldFuncOptions({ kernel1, kernel2 })\n .dilation({ dil1, dil2 })\n .padding({ pad1, pad2 })\n .stride({ stride1, stride2 });\n\n CATCH_TENSOR(torch::nn::functional::unfold(*input, opts));\n}\n\nTensor THSNN_scaled_dot_product_attention(const Tensor query, const Tensor key, const Tensor value, const Tensor attention_mask, double p, bool casual)\n{\n auto mask = attention_mask == nullptr ? c10::nullopt : c10::optional<at::Tensor>(*attention_mask);\n\n CATCH_TENSOR(torch::scaled_dot_product_attention(*query, *key, *value, mask, p, casual));\n}"},{"alpha_fraction":0.5144138932228088,"alphanum_fraction":0.5206185579299927,"avg_line_length":47.957942962646484,"blob_id":"3e2a4ebee17073f79e61ecc403dfa0f8ce7379d6","content_id":"7b9a8cd2a8344acc097dc7239bc8ccdd77ae19a0","detected_licenses":["MIT"],"is_generated":false,"is_vendor":false,"language":"C#","length_bytes":10476,"license_type":"permissive","max_line_length":218,"num_lines":214,"path":"/src/TorchVision/Ops/Misc.cs","repo_name":"NiklasGustafsson/TorchSharp","src_encoding":"UTF-8","text":"// Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information.\n\n// A number of implementation details in this file have been translated from the Python version of torchvision,\n// largely located in the files found in this folder:\n//\n// https://github.com/pytorch/vision/blob/a4f53308b2d0f1aa9191686e326f45c26053f686/torchvision/ops/misc.py\n//\n// The origin has the following copyright notice and license:\n//\n// https://github.com/pytorch/vision/blob/main/LICENSE\n//\n\nusing System;\nusing System.Collections.Generic;\nusing System.Runtime.CompilerServices;\nusing static TorchSharp.torch;\n\n#nullable enable\nnamespace TorchSharp\n{\n public static partial class torchvision\n {\n public static partial class ops\n {\n private static nn.Module<Tensor, Tensor> ConvNormActivation(\n long in_channels,\n long out_channels,\n long kernel_size = 3,\n long stride = 1,\n long? padding = null,\n long groups = 1,\n Func<long, nn.Module<Tensor, Tensor>>? norm_layer = null,\n Func<bool, nn.Module<Tensor, Tensor>>? activation_layer = null,\n long dilation = 1,\n bool inplace = true,\n bool? bias = null,\n int rank = 2)\n {\n if (padding == null) {\n padding = (kernel_size - 1) / 2 * dilation;\n }\n\n if (bias == null) {\n bias = norm_layer == null;\n }\n\n var layers = new List<nn.Module<Tensor, Tensor>>();\n if (rank == 2) {\n layers.Add(\n nn.Conv2d(\n in_channels,\n out_channels,\n kernelSize: kernel_size,\n stride: stride,\n padding: padding.Value,\n dilation: dilation,\n groups: groups,\n bias: bias.Value));\n } else if (rank == 3) {\n layers.Add(\n nn.Conv3d(\n in_channels,\n out_channels,\n kernelSize: kernel_size,\n stride: stride,\n padding: padding.Value,\n dilation: dilation,\n groups: groups,\n bias: bias.Value));\n } else {\n throw new ArgumentOutOfRangeException(\"rank must be 2 or 3.\");\n }\n\n if (norm_layer != null) {\n layers.Add(norm_layer(out_channels));\n }\n\n if (activation_layer != null) {\n layers.Add(activation_layer(inplace));\n }\n return nn.Sequential(layers);\n }\n\n /// <summary>\n /// Configurable block used for Convolution2d-Normalization-Activation blocks.\n /// </summary>\n /// <param name=\"in_channels\">Number of channels in the input image</param>\n /// <param name=\"out_channels\">Number of channels produced by the Convolution-Normalization-Activation block</param>\n /// <param name=\"kernel_size\">Size of the convolving kernel.</param>\n /// <param name=\"stride\">Stride of the convolution.</param>\n /// <param name=\"padding\">Padding added to all four sides of the input. Default: null, in which case it will calculated as ``padding = (kernel_size - 1) // 2 * dilation``</param>\n /// <param name=\"groups\">Number of blocked connections from input channels to output channels.</param>\n /// <param name=\"norm_layer\">Norm layer that will be stacked on top of the convolution layer. If ``null`` this layer wont be used.</param>\n /// <param name=\"activation_layer\">Activation function which will be stacked on top of the normalization layer (if not null), otherwise on top of the conv layer. If ``null`` this layer wont be used.</param>\n /// <param name=\"dilation\">Spacing between kernel elements.</param>\n /// <param name=\"inplace\">Parameter for the activation layer, which can optionally do the operation in-place.</param>\n /// <param name=\"bias\">Whether to use bias in the convolution layer. By default, biases are included if ``norm_layer is null``.</param>\n public static nn.Module<Tensor, Tensor> Conv2dNormActivation(\n long in_channels,\n long out_channels,\n long kernel_size = 3,\n long stride = 1,\n long? padding = null,\n long groups = 1,\n Func<long, nn.Module<Tensor, Tensor>>? norm_layer = null,\n Func<bool, nn.Module<Tensor, Tensor>>? activation_layer = null,\n long dilation = 1,\n bool inplace = true,\n bool? bias = null)\n {\n return ConvNormActivation(\n in_channels,\n out_channels,\n kernel_size,\n stride,\n padding,\n groups,\n norm_layer,\n activation_layer,\n dilation,\n inplace,\n bias,\n rank: 2);\n }\n\n /// <summary>\n /// Configurable block used for Convolution3d-Normalization-Activation blocks.\n /// </summary>\n /// <param name=\"in_channels\">Number of channels in the input image</param>\n /// <param name=\"out_channels\">Number of channels produced by the Convolution-Normalization-Activation block</param>\n /// <param name=\"kernel_size\">Size of the convolving kernel.</param>\n /// <param name=\"stride\">Stride of the convolution.</param>\n /// <param name=\"padding\">Padding added to all four sides of the input. Default: null, in which case it will calculated as ``padding = (kernel_size - 1) // 2 * dilation``</param>\n /// <param name=\"groups\">Number of blocked connections from input channels to output channels.</param>\n /// <param name=\"norm_layer\">Norm layer that will be stacked on top of the convolution layer. If ``null`` this layer wont be used.</param>\n /// <param name=\"activation_layer\">Activation function which will be stacked on top of the normalization layer (if not null), otherwise on top of the conv layer. If ``null`` this layer wont be used.</param>\n /// <param name=\"dilation\">Spacing between kernel elements.</param>\n /// <param name=\"inplace\">Parameter for the activation layer, which can optionally do the operation in-place.</param>\n /// <param name=\"bias\">Whether to use bias in the convolution layer. By default, biases are included if ``norm_layer is null``.</param>\n public static nn.Module<Tensor, Tensor> Conv3dNormActivation(\n long in_channels,\n long out_channels,\n long kernel_size = 3,\n long stride = 1,\n long? padding = null,\n long groups = 1,\n Func<long, nn.Module<Tensor, Tensor>>? norm_layer = null,\n Func<bool, nn.Module<Tensor, Tensor>>? activation_layer = null,\n long dilation = 1,\n bool inplace = true,\n bool? bias = null)\n {\n return ConvNormActivation(\n in_channels,\n out_channels,\n kernel_size,\n stride,\n padding,\n groups,\n norm_layer,\n activation_layer,\n dilation,\n inplace,\n bias,\n rank: 3);\n }\n\n\n /// <summary>\n /// This block implements the multi-layer perceptron (MLP) module.\n /// </summary>\n /// <param name=\"in_channels\">Number of channels in the input image</param>\n /// <param name=\"hidden_channels\">List of the hidden channel dimensions</param>\n /// <param name=\"norm_layer\">Norm layer that will be stacked on top of the linear layer. If null, will be ignored</param>\n /// <param name=\"activation_layer\">Activation function which will be stacked on top of the normalization layer. Defaults to ReLU.</param>\n /// <param name=\"inplace\">Parameter for the activation layer, which can optionally do the operation in-place.</param>\n /// <param name=\"bias\">Whether to use bias in the linear and dropout layers.</param>\n /// <param name=\"dropout\">The probability for the dropout layer.</param>\n /// <returns></returns>\n public static Modules.Sequential MLP(\n long in_channels,\n IList<int> hidden_channels,\n Func<int, nn.Module<Tensor, Tensor>>? norm_layer = null,\n Func<nn.Module<Tensor, Tensor>>? activation_layer = null,\n bool inplace = false,\n bool bias = true,\n double dropout = 0.0\n )\n {\n if (activation_layer == null) {\n activation_layer = () => nn.ReLU(inplace);\n }\n\n var layers = new List<nn.Module<Tensor, Tensor>>();\n var in_dim = in_channels;\n for (int i = 0; i < hidden_channels.Count-1; i++) {\n var hidden_dim = hidden_channels[i];\n layers.Add(nn.Linear(in_dim, hidden_dim, hasBias: bias));\n if (norm_layer != null) {\n layers.Add(norm_layer(hidden_dim));\n }\n layers.Add(activation_layer());\n layers.Add(nn.Dropout(dropout, inplace));\n in_dim = hidden_dim;\n }\n\n layers.Add(nn.Linear(in_dim, hidden_channels[hidden_channels.Count-1], hasBias: bias));\n layers.Add(nn.Dropout(dropout, inplace));\n\n return nn.Sequential(layers);\n }\n }\n }\n}"},{"alpha_fraction":0.49920737743377686,"alphanum_fraction":0.5010274648666382,"avg_line_length":42.67179489135742,"blob_id":"a078e914274425b9efeaab77754b2f5737725b3f","content_id":"59b98e91b1d01a95029e2e8072c63105b6d259c0","detected_licenses":["MIT"],"is_generated":false,"is_vendor":false,"language":"C#","length_bytes":34066,"license_type":"permissive","max_line_length":178,"num_lines":780,"path":"/src/TorchSharp/Special.cs","repo_name":"NiklasGustafsson/TorchSharp","src_encoding":"UTF-8","text":"// Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information.\nusing System;\nusing static TorchSharp.PInvoke.NativeMethods;\n\nnamespace TorchSharp\n{\n public static partial class torch\n {\n public static class special\n {\n /// <summary>\n /// Airy function.\n /// </summary>\n /// <param name=\"input\">Input tensor</param>\n /// <param name=\"out\">Optional output tensor, will be modified if present.</param>\n /// <returns></returns>\n public static Tensor airy_ai(Tensor input, Tensor @out = null)\n {\n var res = @out is null ?\n THSSpecial_airy_ai(input.Handle) :\n THSSpecial_airy_ai_out(input.Handle, @out.Handle);\n if (res == IntPtr.Zero)\n torch.CheckForErrors();\n return new Tensor(res);\n }\n\n /// <summary>\n /// Bessel function of the first kind of order 0.\n /// </summary>\n /// <param name=\"input\">The input tensor</param>\n /// <param name=\"out\">An optional output tensor, which will be modified if present.</param>\n public static Tensor bessel_j0(Tensor input, Tensor @out = null)\n {\n var res = @out is null ?\n THSSpecial_bessel_j0(input.Handle) :\n THSSpecial_bessel_j0_out(input.Handle, @out.Handle);\n if (res == IntPtr.Zero)\n torch.CheckForErrors();\n return new Tensor(res);\n }\n\n /// <summary>\n /// Bessel function of the first kind of order 1.\n /// </summary>\n /// <param name=\"input\">The input tensor</param>\n /// <param name=\"out\">An optional output tensor, which will be modified if present.</param>\n public static Tensor bessel_j1(Tensor input, Tensor @out = null)\n {\n var res = @out is null ?\n THSSpecial_bessel_j1(input.Handle) :\n THSSpecial_bessel_j1_out(input.Handle, @out.Handle);\n if (res == IntPtr.Zero)\n torch.CheckForErrors();\n return new Tensor(res);\n }\n\n /// <summary>\n /// Bessel function of the second kind of order 1.\n /// </summary>\n /// <param name=\"input\">The input tensor</param>\n /// <param name=\"out\">An optional output tensor, which will be modified if present.</param>\n public static Tensor bessel_y0(Tensor input, Tensor @out = null)\n {\n var res = @out is null ?\n THSSpecial_bessel_y0(input.Handle) :\n THSSpecial_bessel_y0_out(input.Handle, @out.Handle);\n if (res == IntPtr.Zero)\n torch.CheckForErrors();\n return new Tensor(res);\n }\n\n /// <summary>\n /// Bessel function of the second kind of order 1.\n /// </summary>\n /// <param name=\"input\">The input tensor</param>\n /// <param name=\"out\">An optional output tensor, which will be modified if present.</param>\n public static Tensor bessel_y1(Tensor input, Tensor @out = null)\n {\n var res = @out is null ?\n THSSpecial_bessel_y1(input.Handle) :\n THSSpecial_bessel_y1_out(input.Handle, @out.Handle);\n if (res == IntPtr.Zero)\n torch.CheckForErrors();\n return new Tensor(res);\n }\n\n /// <summary>\n /// Modified Bessel function of the first kind of order 0.\n /// </summary>\n /// <param name=\"input\">The input tensor</param>\n /// <param name=\"out\">An optional output tensor, which will be modified if present.</param>\n public static Tensor modified_bessel_i0(Tensor input, Tensor @out = null)\n {\n var res = @out is null ?\n THSSpecial_modified_bessel_i0(input.Handle) :\n THSSpecial_modified_bessel_i0_out(input.Handle, @out.Handle);\n if (res == IntPtr.Zero)\n torch.CheckForErrors();\n return new Tensor(res);\n }\n\n /// <summary>\n /// Modified Bessel function of the first kind of order 1.\n /// </summary>\n /// <param name=\"input\">The input tensor</param>\n /// <param name=\"out\">An optional output tensor, which will be modified if present.</param>\n public static Tensor modified_bessel_i1(Tensor input, Tensor @out = null)\n {\n var res = @out is null ?\n THSSpecial_modified_bessel_i1(input.Handle) :\n THSSpecial_modified_bessel_i1_out(input.Handle, @out.Handle);\n if (res == IntPtr.Zero)\n torch.CheckForErrors();\n return new Tensor(res);\n }\n\n /// <summary>\n /// Modified Bessel function of the second kind of order 1.\n /// </summary>\n /// <param name=\"input\">The input tensor</param>\n /// <param name=\"out\">An optional output tensor, which will be modified if present.</param>\n public static Tensor modified_bessel_k0(Tensor input, Tensor @out = null)\n {\n var res = @out is null ?\n THSSpecial_modified_bessel_k0(input.Handle) :\n THSSpecial_modified_bessel_k0_out(input.Handle, @out.Handle);\n if (res == IntPtr.Zero)\n torch.CheckForErrors();\n return new Tensor(res);\n }\n\n /// <summary>\n /// Modified Bessel function of the second kind of order 1.\n /// </summary>\n /// <param name=\"input\">The input tensor</param>\n /// <param name=\"out\">An optional output tensor, which will be modified if present.</param>\n public static Tensor modified_bessel_k1(Tensor input, Tensor @out = null)\n {\n var res = @out is null ?\n THSSpecial_modified_bessel_k1(input.Handle) :\n THSSpecial_modified_bessel_k1_out(input.Handle, @out.Handle);\n if (res == IntPtr.Zero)\n torch.CheckForErrors();\n return new Tensor(res);\n }\n\n /// <summary>\n /// Scaled modified Bessel function of the second kind of order 0\n /// </summary>\n /// <param name=\"input\">The input tensor</param>\n /// <param name=\"out\">An optional output tensor, which will be modified if present.</param>\n public static Tensor scaled_modified_bessel_k0(Tensor input, Tensor @out = null)\n {\n var res = @out is null ?\n THSSpecial_scaled_modified_bessel_k0(input.Handle) :\n THSSpecial_scaled_modified_bessel_k0_out(input.Handle, @out.Handle);\n if (res == IntPtr.Zero)\n torch.CheckForErrors();\n return new Tensor(res);\n }\n\n /// <summary>\n /// Scaled modified Bessel function of the second kind of order 1\n /// </summary>\n /// <param name=\"input\">The input tensor</param>\n /// <param name=\"out\">An optional output tensor, which will be modified if present.</param>\n public static Tensor scaled_modified_bessel_k1(Tensor input, Tensor @out = null)\n {\n var res = @out is null ?\n THSSpecial_scaled_modified_bessel_k1(input.Handle) :\n THSSpecial_scaled_modified_bessel_k1_out(input.Handle, @out.Handle);\n if (res == IntPtr.Zero)\n torch.CheckForErrors();\n return new Tensor(res);\n }\n\n /// <summary>\n /// Bessel function of the first kind of order 0.\n /// </summary>\n /// <param name=\"input\">The input tensor</param>\n /// <param name=\"out\">An optional output tensor, which will be modified if present.</param>\n public static Tensor spherical_bessel_j0(Tensor input, Tensor @out = null)\n {\n var res = @out is null ?\n THSSpecial_spherical_bessel_j0(input.Handle) :\n THSSpecial_spherical_bessel_j0_out(input.Handle, @out.Handle);\n if (res == IntPtr.Zero)\n torch.CheckForErrors();\n return new Tensor(res);\n }\n\n /// <summary>\n /// Computes the Chebyshev polynomial of the first kind.\n ///\n /// See: https://en.wikipedia.org/wiki/Chebyshev_polynomials\n /// </summary>\n /// <param name=\"x\">The input tensor.</param>\n /// <param name=\"n\">n</param>\n /// <param name=\"out\">An optional output tensor.</param>\n public static Tensor chebyshev_polynomial_t(Tensor x, Tensor n, Tensor @out = null)\n {\n var res = @out is null ?\n THSSpecial_chebyshev_polynomial_t(x.Handle, n.Handle) :\n THSSpecial_chebyshev_polynomial_t_out(x.Handle, n.Handle, @out.Handle);\n if (res == IntPtr.Zero)\n torch.CheckForErrors();\n return new Tensor(res);\n }\n\n\n /// <summary>\n /// Computes the Chebyshev polynomial of the second kind.\n ///\n /// See: https://en.wikipedia.org/wiki/Chebyshev_polynomials\n /// </summary>\n /// <param name=\"x\">The input tensor.</param>\n /// <param name=\"n\">n</param>\n /// <param name=\"out\">An optional output tensor.</param>\n public static Tensor chebyshev_polynomial_u(Tensor x, Tensor n, Tensor @out = null)\n {\n var res = @out is null ?\n THSSpecial_chebyshev_polynomial_u(x.Handle, n.Handle) :\n THSSpecial_chebyshev_polynomial_u_out(x.Handle, n.Handle, @out.Handle);\n if (res == IntPtr.Zero)\n torch.CheckForErrors();\n return new Tensor(res);\n }\n\n /// <summary>\n /// Computes the Chebyshev polynomial of the third kind.\n ///\n /// See: https://en.wikipedia.org/wiki/Chebyshev_polynomials\n /// </summary>\n /// <param name=\"x\">The input tensor.</param>\n /// <param name=\"n\">n</param>\n /// <param name=\"out\">An optional output tensor.</param>\n public static Tensor chebyshev_polynomial_v(Tensor x, Tensor n, Tensor @out = null)\n {\n var res = @out is null ?\n THSSpecial_chebyshev_polynomial_v(x.Handle, n.Handle) :\n THSSpecial_chebyshev_polynomial_v_out(x.Handle, n.Handle, @out.Handle);\n if (res == IntPtr.Zero)\n torch.CheckForErrors();\n return new Tensor(res);\n }\n\n /// <summary>\n /// Computes the Chebyshev polynomial of the fourth kind.\n ///\n /// See: https://en.wikipedia.org/wiki/Chebyshev_polynomials\n /// </summary>\n /// <param name=\"x\">The input tensor.</param>\n /// <param name=\"n\">n</param>\n /// <param name=\"out\">An optional output tensor.</param>\n public static Tensor chebyshev_polynomial_w(Tensor x, Tensor n, Tensor @out = null)\n {\n var res = @out is null ?\n THSSpecial_chebyshev_polynomial_w(x.Handle, n.Handle) :\n THSSpecial_chebyshev_polynomial_w_out(x.Handle, n.Handle, @out.Handle);\n if (res == IntPtr.Zero)\n torch.CheckForErrors();\n return new Tensor(res);\n }\n\n /// <summary>\n /// Computes the Chebyshev polynomial of the first kind.\n ///\n /// See: https://en.wikipedia.org/wiki/Chebyshev_polynomials\n /// </summary>\n /// <param name=\"x\">The input tensor.</param>\n /// <param name=\"n\">n</param>\n /// <param name=\"out\">An optional output tensor.</param>\n public static Tensor shifted_chebyshev_polynomial_t(Tensor x, Tensor n, Tensor @out = null)\n {\n var res = @out is null ?\n THSSpecial_shifted_chebyshev_polynomial_t(x.Handle, n.Handle) :\n THSSpecial_shifted_chebyshev_polynomial_t_out(x.Handle, n.Handle, @out.Handle);\n if (res == IntPtr.Zero)\n torch.CheckForErrors();\n return new Tensor(res);\n }\n\n\n /// <summary>\n /// Computes the Chebyshev polynomial of the second kind.\n ///\n /// See: https://en.wikipedia.org/wiki/Chebyshev_polynomials\n /// </summary>\n /// <param name=\"x\">The input tensor.</param>\n /// <param name=\"n\">n</param>\n /// <param name=\"out\">An optional output tensor.</param>\n public static Tensor shifted_chebyshev_polynomial_u(Tensor x, Tensor n, Tensor @out = null)\n {\n var res = @out is null ?\n THSSpecial_shifted_chebyshev_polynomial_u(x.Handle, n.Handle) :\n THSSpecial_shifted_chebyshev_polynomial_u_out(x.Handle, n.Handle, @out.Handle);\n if (res == IntPtr.Zero)\n torch.CheckForErrors();\n return new Tensor(res);\n }\n\n /// <summary>\n /// Computes the Chebyshev polynomial of the third kind.\n ///\n /// See: https://en.wikipedia.org/wiki/Chebyshev_polynomials\n /// </summary>\n /// <param name=\"x\">The input tensor.</param>\n /// <param name=\"n\">n</param>\n /// <param name=\"out\">An optional output tensor.</param>\n public static Tensor shifted_chebyshev_polynomial_v(Tensor x, Tensor n, Tensor @out = null)\n {\n var res = @out is null ?\n THSSpecial_shifted_chebyshev_polynomial_v(x.Handle, n.Handle) :\n THSSpecial_shifted_chebyshev_polynomial_v_out(x.Handle, n.Handle, @out.Handle);\n if (res == IntPtr.Zero)\n torch.CheckForErrors();\n return new Tensor(res);\n }\n\n /// <summary>\n /// Computes the Chebyshev polynomial of the fourth kind.\n ///\n /// See: https://en.wikipedia.org/wiki/Chebyshev_polynomials\n /// </summary>\n /// <param name=\"x\">The input tensor.</param>\n /// <param name=\"n\">n</param>\n /// <param name=\"out\">An optional output tensor.</param>\n public static Tensor shifted_chebyshev_polynomial_w(Tensor x, Tensor n, Tensor @out = null)\n {\n var res = @out is null ?\n THSSpecial_shifted_chebyshev_polynomial_w(x.Handle, n.Handle) :\n THSSpecial_shifted_chebyshev_polynomial_w_out(x.Handle, n.Handle, @out.Handle);\n if (res == IntPtr.Zero)\n torch.CheckForErrors();\n return new Tensor(res);\n }\n\n /// <summary>\n /// Probabilist's Hermite polynomials.\n ///\n /// See: https://en.wikipedia.org/wiki/Hermite_polynomials\n /// </summary>\n /// <param name=\"x\">The input tensor.</param>\n /// <param name=\"n\">n</param>\n /// <param name=\"out\">An optional output tensor.</param>\n public static Tensor hermite_polynomial_h(Tensor x, Tensor n, Tensor @out = null)\n {\n var res = @out is null ?\n THSSpecial_hermite_polynomial_h(x.Handle, n.Handle) :\n THSSpecial_hermite_polynomial_h_out(x.Handle, n.Handle, @out.Handle);\n if (res == IntPtr.Zero)\n torch.CheckForErrors();\n return new Tensor(res);\n }\n\n /// <summary>\n /// Physicists's Hermite polynomials.\n ///\n /// See: https://en.wikipedia.org/wiki/Hermite_polynomials\n /// </summary>\n /// <param name=\"x\">The input tensor.</param>\n /// <param name=\"n\">n</param>\n /// <param name=\"out\">An optional output tensor.</param>\n public static Tensor hermite_polynomial_he(Tensor x, Tensor n, Tensor @out = null)\n {\n var res = @out is null ?\n THSSpecial_hermite_polynomial_he(x.Handle, n.Handle) :\n THSSpecial_hermite_polynomial_he_out(x.Handle, n.Handle, @out.Handle);\n if (res == IntPtr.Zero)\n torch.CheckForErrors();\n return new Tensor(res);\n }\n\n /// <summary>\n /// Laguerre polynomials\n /// \n /// See: https://en.wikipedia.org/wiki/Laguerre_polynomials\n /// </summary>\n /// <param name=\"x\">The input tensor.</param>\n /// <param name=\"n\">n</param>\n /// <param name=\"out\">An optional output tensor.</param>\n /// <returns></returns>\n public static Tensor laguerre_polynomial_l(Tensor x, Tensor n, Tensor @out = null)\n {\n var res = @out is null ?\n THSSpecial_laguerre_polynomial_l(x.Handle, n.Handle) :\n THSSpecial_laguerre_polynomial_l_out(x.Handle, n.Handle, @out.Handle);\n if (res == IntPtr.Zero)\n torch.CheckForErrors();\n return new Tensor(res);\n }\n\n /// <summary>\n /// Legendre polynomials\n /// \n /// https://en.wikipedia.org/wiki/Legendre_polynomials\n /// </summary>\n /// <param name=\"x\">The input tensor.</param>\n /// <param name=\"n\">n</param>\n /// <param name=\"out\">An optional output tensor.</param>\n public static Tensor legendre_polynomial_p(Tensor x, Tensor n, Tensor @out = null)\n {\n var res = @out is null ?\n THSSpecial_legendre_polynomial_p(x.Handle, n.Handle) :\n THSSpecial_legendre_polynomial_p_out(x.Handle, n.Handle, @out.Handle);\n if (res == IntPtr.Zero)\n torch.CheckForErrors();\n return new Tensor(res);\n }\n\n /// <summary>\n /// Computes the entropy on input, elementwise.\n /// </summary>\n /// <param name=\"input\">The input tensor</param>\n /// <returns></returns>\n public static Tensor entr(Tensor input)\n {\n var res = THSSpecial_entr(input.Handle);\n if (res == IntPtr.Zero)\n torch.CheckForErrors();\n return new Tensor(res);\n }\n\n /// <summary>\n /// Computes the error function of input.\n /// </summary>\n /// <param name=\"input\">The input tensor</param>\n /// <returns></returns>\n public static Tensor erf(Tensor input)\n {\n var res = THSSpecial_erf(input.Handle);\n if (res == IntPtr.Zero)\n torch.CheckForErrors();\n return new Tensor(res);\n }\n\n /// <summary>\n /// Computes the complementary error function of input.\n /// </summary>\n /// <param name=\"input\">The input tensor</param>\n /// <returns></returns>\n public static Tensor erfc(Tensor input)\n {\n var res = THSSpecial_erfc(input.Handle);\n if (res == IntPtr.Zero)\n torch.CheckForErrors();\n return new Tensor(res);\n }\n\n /// <summary>\n /// Computes the scaled complementary error function of input.\n /// </summary>\n /// <param name=\"input\">The input tensor</param>\n /// <returns></returns>\n public static Tensor erfcx(Tensor input)\n {\n var res = THSSpecial_erfc(input.Handle);\n if (res == IntPtr.Zero)\n torch.CheckForErrors();\n return new Tensor(res);\n }\n\n /// <summary>\n /// Computes the inverse error function of input.\n /// </summary>\n /// <param name=\"input\">The input tensor</param>\n /// <returns></returns>\n public static Tensor erfinv(Tensor input)\n {\n var res = THSSpecial_erfinv(input.Handle);\n if (res == IntPtr.Zero)\n torch.CheckForErrors();\n return new Tensor(res);\n }\n\n /// <summary>\n /// Computes the expit (also known as the logistic sigmoid function) of the elements of input.\n /// </summary>\n /// <param name=\"input\">The input tensor</param>\n /// <returns></returns>\n public static Tensor expit(Tensor input)\n {\n var res = THSSpecial_expit(input.Handle);\n if (res == IntPtr.Zero)\n torch.CheckForErrors();\n return new Tensor(res);\n }\n\n /// <summary>\n /// Computes the exponential of the elements minus 1 of input.\n /// </summary>\n /// <param name=\"input\">The input tensor</param>\n /// <returns></returns>\n public static Tensor expm1(Tensor input)\n {\n var res = THSSpecial_expm1(input.Handle);\n if (res == IntPtr.Zero)\n torch.CheckForErrors();\n return new Tensor(res);\n }\n\n /// <summary>\n /// Computes the base two exponential function of input.\n /// </summary>\n /// <param name=\"input\">The input tensor</param>\n /// <returns></returns>\n public static Tensor exp2(Tensor input)\n {\n var res = THSSpecial_exp2(input.Handle);\n if (res == IntPtr.Zero)\n torch.CheckForErrors();\n return new Tensor(res);\n }\n\n /// <summary>\n /// Computes the natural logarithm of the absolute value of the gamma function on input.\n /// </summary>\n /// <param name=\"input\">The input tensor</param>\n /// <returns></returns>\n public static Tensor gammaln(Tensor input)\n {\n var res = THSSpecial_gammaln(input.Handle);\n if (res == IntPtr.Zero)\n torch.CheckForErrors();\n return new Tensor(res);\n }\n\n /// <summary>\n /// Computes the regularized lower incomplete gamma function.\n /// </summary>\n /// <param name=\"input\">The first non-negative input tensor</param>\n /// <param name=\"other\">The second non-negative input tensor</param>\n /// <returns></returns>\n public static Tensor gammainc(Tensor input, Tensor other)\n {\n var res = THSSpecial_gammainc(input.Handle, other.Handle);\n if (res == IntPtr.Zero)\n torch.CheckForErrors();\n return new Tensor(res);\n }\n\n /// <summary>\n /// Computes the regularized upper incomplete gamma function.\n /// </summary>\n /// <param name=\"input\">The first non-negative input tensor</param>\n /// <param name=\"other\">The second non-negative input tensor</param>\n /// <returns></returns>\n public static Tensor gammaincc(Tensor input, Tensor other)\n {\n var res = THSSpecial_gammaincc(input.Handle, other.Handle);\n if (res == IntPtr.Zero)\n torch.CheckForErrors();\n return new Tensor(res);\n }\n\n /// <summary>\n /// Computes the n-th derivative of the digamma function on input.\n /// </summary>\n /// <param name=\"n\">The order of the polygamma function</param>\n /// <param name=\"input\">The second non-negative input tensor</param>\n /// <returns></returns>\n public static Tensor polygamma(long n, Tensor input)\n {\n var res = THSSpecial_polygamma(n, input.Handle);\n if (res == IntPtr.Zero)\n torch.CheckForErrors();\n return new Tensor(res);\n }\n\n /// <summary>\n /// Computes the multivariate log-gamma function with dimension pp element-wise.\n /// </summary>\n /// <param name=\"input\">The tensor to compute the multivariate log-gamma function</param>\n /// <param name=\"p\">The number of dimensions</param>\n /// <returns></returns>\n public static Tensor multigammaln(Tensor input, long p)\n {\n var res = THSSpecial_multigammaln(input.Handle, p);\n if (res == IntPtr.Zero)\n torch.CheckForErrors();\n return new Tensor(res);\n }\n\n /// <summary>\n /// Computes the logarithmic derivative of the gamma function on input.\n /// </summary>\n /// <param name=\"input\">The second non-negative input tensor</param>\n /// <returns></returns>\n public static Tensor digamma(Tensor input)\n {\n var res = THSSpecial_digamma(input.Handle);\n if (res == IntPtr.Zero)\n torch.CheckForErrors();\n return new Tensor(res);\n }\n\n /// <summary>\n /// Computes the logarithmic derivative of the gamma function on input.\n /// </summary>\n /// <param name=\"input\">The second non-negative input tensor</param>\n public static Tensor psi(Tensor input) => digamma(input);\n\n /// <summary>\n /// Computes the zeroth order modified Bessel function of the first kind for each element of input.\n /// </summary>\n /// <param name=\"input\">The input tensor</param>\n /// <returns></returns>\n public static Tensor i0(Tensor input)\n {\n var res = THSSpecial_i0(input.Handle);\n if (res == IntPtr.Zero)\n torch.CheckForErrors();\n return new Tensor(res);\n }\n\n /// <summary>\n /// Computes the exponentially scaled zeroth order modified Bessel function of the first kind (as defined below) for each element of input.\n /// </summary>\n /// <param name=\"input\">The input tensor</param>\n /// <returns></returns>\n public static Tensor i0e(Tensor input)\n {\n var res = THSSpecial_i0e(input.Handle);\n if (res == IntPtr.Zero)\n torch.CheckForErrors();\n return new Tensor(res);\n }\n\n /// <summary>\n /// Computes the first order modified Bessel function of the first kind for each element of input.\n /// </summary>\n /// <param name=\"input\">The input tensor</param>\n /// <returns></returns>\n public static Tensor i1(Tensor input)\n {\n var res = THSSpecial_i1(input.Handle);\n if (res == IntPtr.Zero)\n torch.CheckForErrors();\n return new Tensor(res);\n }\n\n /// <summary>\n /// Computes the exponentially scaled first order modified Bessel function of the first kind (as defined below) for each element of input.\n /// </summary>\n /// <param name=\"input\">The input tensor</param>\n /// <returns></returns>\n public static Tensor i1e(Tensor input)\n {\n var res = THSSpecial_i1e(input.Handle);\n if (res == IntPtr.Zero)\n torch.CheckForErrors();\n return new Tensor(res);\n }\n\n /// <summary>\n /// Returns a new tensor with the logit of the elements of input.\n /// </summary>\n /// <param name=\"input\">The input tensor</param>\n /// <returns></returns>\n public static Tensor logit(Tensor input)\n {\n var res = THSSpecial_logit(input.Handle);\n if (res == IntPtr.Zero)\n torch.CheckForErrors();\n return new Tensor(res);\n }\n\n /// <summary>\n /// Returns a new tensor with the logit of the elements of input.\n /// </summary>\n /// <param name=\"input\">The input tensor</param>\n /// <param name=\"dim\">A dimension along which log_softmax will be computed.</param>\n /// <param name=\"dtype\">The desired data type of returned tensor.</param>\n /// <returns></returns>\n public static Tensor log_softmax(Tensor input, long dim, ScalarType? dtype = null)\n {\n var dt = dtype.HasValue ? dtype.Value : input.dtype;\n var res = THSSpecial_log_softmax(input.Handle, dim, (sbyte)dt);\n if (res == IntPtr.Zero)\n torch.CheckForErrors();\n return new Tensor(res);\n }\n\n /// <summary>\n /// Computes the area under the standard Gaussian probability density function, integrated from minus infinity to input, elementwise.\n /// </summary>\n /// <param name=\"input\">The input tensor</param>\n /// <returns></returns>\n public static Tensor ndtr(Tensor input)\n {\n var res = THSSpecial_ndtr(input.Handle);\n if (res == IntPtr.Zero)\n torch.CheckForErrors();\n return new Tensor(res);\n }\n\n /// <summary>\n /// Computes the argument, x, for which the area under the Gaussian probability density function (integrated from minus infinity to x) is equal to input, elementwise.\n /// </summary>\n /// <param name=\"input\">The input tensor</param>\n /// <returns></returns>\n public static Tensor ndtri(Tensor input)\n {\n var res = THSSpecial_ndtri(input.Handle);\n if (res == IntPtr.Zero)\n torch.CheckForErrors();\n return new Tensor(res);\n }\n\n /// <summary>\n /// Computes the normalized sinc of input.\n /// </summary>\n /// <param name=\"input\">The input tensor</param>\n /// <returns></returns>\n public static Tensor sinc(Tensor input)\n {\n var res = THSSpecial_sinc(input.Handle);\n if (res == IntPtr.Zero)\n torch.CheckForErrors();\n return new Tensor(res);\n }\n\n /// <summary>\n /// Alias for torch.special.expit()\n /// </summary>\n /// <returns></returns>\n public static Tensor sigmoid(Tensor input) => input.sigmoid();\n\n /// <summary>\n /// Alias for torch.special.expit()\n /// </summary>\n /// <returns></returns>\n public static Tensor sigmoid_(Tensor input) => input.sigmoid_();\n\n /// <summary>\n /// Computes the softmax function for the input tensor.\n /// </summary>\n /// <param name=\"input\">The input tensor</param>\n /// <param name=\"dim\">A dimension along which softmax will be computed.</param>\n /// <param name=\"dtype\">The desired data type of returned tensor.</param>\n /// <returns></returns>\n public static Tensor softmax(Tensor input, long dim, ScalarType? dtype = null)\n {\n var dt = dtype.HasValue ? dtype.Value : input.dtype;\n var res = THSSpecial_softmax(input.Handle, dim, (sbyte)dt);\n if (res == IntPtr.Zero)\n torch.CheckForErrors();\n return new Tensor(res);\n }\n\n /// <summary>\n /// Computes input * log1p(other). Similar to SciPy’s scipy.special.xlog1py.\n /// </summary>\n /// <param name=\"input\">The input tensor</param>\n /// <param name=\"other\"></param>\n /// <returns></returns>\n public static Tensor xlog1py(Tensor input, Tensor other)\n {\n var res = THSSpecial_xlog1py(input.Handle, other.Handle);\n if (res == IntPtr.Zero)\n torch.CheckForErrors();\n return new Tensor(res);\n }\n\n /// <summary>\n /// Computes the Hurwitz zeta function, elementwise.\n /// </summary>\n /// <param name=\"x\">The input tensor corresponding to x</param>\n /// <param name=\"q\">The input tensor corresponding to q</param>\n /// <remarks>The Riemann zeta function corresponds to the case when q = 1.</remarks>\n public static Tensor zeta(Tensor x, Tensor q)\n {\n var res = THSSpecial_zeta(x.Handle, q.Handle);\n if (res == IntPtr.Zero)\n torch.CheckForErrors();\n return new Tensor(res);\n }\n }\n }\n}\n"},{"alpha_fraction":0.5192856192588806,"alphanum_fraction":0.5412377715110779,"avg_line_length":40.78238296508789,"blob_id":"2109429199f404fab762abf1343a6286414df5ea","content_id":"821b521c2f99c9bc3862dac13c50f42ba722d558","detected_licenses":["MIT"],"is_generated":false,"is_vendor":false,"language":"C#","length_bytes":8063,"license_type":"permissive","max_line_length":213,"num_lines":193,"path":"/test/TorchSharpTest/TestDataLoader.cs","repo_name":"NiklasGustafsson/TorchSharp","src_encoding":"UTF-8","text":"// Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information.\n\nusing System.Collections.Generic;\nusing Xunit;\n\n\nnamespace TorchSharp\n{\n [Collection(\"Sequential\")]\n public class TestDataLoader\n {\n private class TestDataset : torch.utils.data.Dataset\n {\n public override long Count { get; } = 10;\n public override Dictionary<string, torch.Tensor> GetTensor(long index)\n {\n return new() {{\"data\", torch.tensor(1)}, {\"label\", torch.tensor(13)}, {\"index\", torch.tensor(index)}};\n }\n }\n\n [Fact]\n public void DatasetTest()\n {\n using var dataset = new TestDataset();\n var d = dataset.GetTensor(0);\n Assert.True(d.ContainsKey(\"data\"));\n Assert.True(d.ContainsKey(\"index\"));\n Assert.True(d.ContainsKey(\"label\"));\n\n Assert.Equal(d[\"data\"], torch.tensor(1));\n Assert.Equal(d[\"label\"], torch.tensor(13));\n Assert.Equal(d[\"index\"], torch.tensor(0L));\n }\n\n [Fact]\n public void TensorDatasetTest()\n {\n var x = torch.randn(4, 12);\n var y = torch.randn(4, 16);\n using var dataset = torch.utils.data.TensorDataset(x, y);\n Assert.Equal(2, dataset.Count);\n\n var d = dataset.GetTensor(0);\n\n Assert.Equal(2, d.Count);\n Assert.Equal(x[0], d[0]);\n Assert.Equal(y[0], d[1]);\n }\n\n // Cannot assert index because ConcurrentBag append tensors randomly\n [Fact]\n public void DataLoaderTest1()\n {\n using var dataset = new TestDataset();\n using var dataloader = new torch.utils.data.DataLoader(dataset, 2, false, torch.CPU);\n var iterator = dataloader.GetEnumerator();\n Assert.True(iterator.MoveNext());\n Assert.Equal(iterator.Current[\"data\"], torch.tensor(rawArray: new[]{1L, 1L}, dimensions: new[]{2L}, dtype: torch.ScalarType.Int32));\n Assert.Equal(iterator.Current[\"label\"], torch.tensor(rawArray: new[]{13L, 13L}, dimensions: new[]{2L}, dtype: torch.ScalarType.Int32));\n Assert.Equal(iterator.Current[\"index\"].ToString(TensorStringStyle.Julia), torch.tensor(rawArray: new[]{0L, 1L}, dimensions: new[]{2L}, dtype: torch.ScalarType.Int64).ToString(TensorStringStyle.Julia));\n iterator.Dispose();\n }\n\n [Fact]\n public void DataLoaderTest2()\n {\n using var dataset = new TestDataset();\n using var dataloader = new torch.utils.data.DataLoader(dataset, 2, false, torch.CPU);\n long idx = 0;\n foreach (var x in dataloader) {\n Assert.Equal(x[\"data\"], torch.tensor(new[]{1, 1}, new[]{2L}));\n Assert.Equal(x[\"index\"], torch.tensor(new[]{idx++, idx++}, new[]{2L}));\n }\n }\n\n [Fact]\n public void DataLoaderTest3()\n {\n using var dataset = new TestDataset();\n {\n using var dataloader = new torch.utils.data.DataLoader(dataset, 3, false, torch.CPU);\n Assert.Equal(4, dataloader.Count);\n }\n {\n using var dataloader = new torch.utils.data.DataLoader(dataset, 3, false, torch.CPU, drop_last: true);\n Assert.Equal(3, dataloader.Count);\n }\n {\n using var dataloader = new torch.utils.data.DataLoader(dataset, 2, false, torch.CPU, drop_last: true);\n Assert.Equal(5, dataloader.Count);\n }\n }\n\n private const int stressBatchSize = 32;\n\n private class LargeTestDataset : torch.utils.data.Dataset\n {\n public override long Count { get; } = 2*stressBatchSize;\n public override Dictionary<string, torch.Tensor> GetTensor(long index)\n {\n return new() { { \"data\", torch.rand(3, 512, 512) }, { \"label\", torch.tensor(16) }, { \"index\", torch.tensor(index) } };\n }\n }\n\n [Fact]\n public void BigDataLoaderTest3()\n {\n using var dataset = new LargeTestDataset();\n using var dataloader = new torch.utils.data.DataLoader(dataset, stressBatchSize, false, torch.CPU);\n var iter = dataloader.GetEnumerator();\n iter.MoveNext();\n var x = iter.Current;\n Assert.Equal(new long[] { stressBatchSize, 3, 512, 512 }, x[\"data\"].shape);\n iter.MoveNext();\n x = iter.Current;\n Assert.Equal(new long[] { stressBatchSize, 3, 512, 512 }, x[\"data\"].shape);\n Assert.False(iter.MoveNext());\n iter.Dispose();\n }\n\n [Fact]\n public void MultiThreadDataLoaderTest1()\n {\n using var dataset = new TestDataset();\n using var dataloader = new torch.utils.data.DataLoader(dataset, 4, false, torch.CPU, num_worker: 2);\n var iter = dataloader.GetEnumerator();\n iter.MoveNext();\n var x = iter.Current;\n Assert.Equal(x[\"data\"], torch.tensor(new[]{1, 1, 1, 1}, new[]{4L}));\n Assert.Equal(x[\"index\"], torch.tensor(new[]{0L, 1, 2, 3}, new[]{4L}));\n iter.MoveNext();\n x = iter.Current;\n Assert.Equal(x[\"data\"], torch.tensor(new[]{1, 1, 1, 1}, new[]{4L}));\n Assert.Equal(x[\"index\"], torch.tensor(new[]{4L, 5, 6, 7}, new[]{4L}));\n iter.MoveNext();\n x = iter.Current;\n Assert.Equal(x[\"data\"], torch.tensor(new[]{1, 1}, new[]{2L}));\n Assert.Equal(x[\"index\"], torch.tensor(new[]{8L, 9}, new[]{2L}));\n iter.Dispose();\n }\n\n [Fact]\n public void MultiThreadDataLoaderTest2()\n {\n using var dataset = new TestDataset();\n using var dataloader = new torch.utils.data.DataLoader(dataset, 5, false, torch.CPU, num_worker: 2);\n var iter = dataloader.GetEnumerator();\n iter.MoveNext();\n var x = iter.Current;\n Assert.Equal(x[\"data\"], torch.tensor(new[]{1, 1, 1, 1, 1}, new[]{5L}));\n Assert.Equal(x[\"index\"], torch.tensor(new[]{0L, 1, 2, 3, 4}, new[]{5L}));\n iter.MoveNext();\n x = iter.Current;\n Assert.Equal(x[\"data\"], torch.tensor(new[]{1, 1, 1, 1, 1}, new[]{5L}));\n Assert.Equal(x[\"index\"], torch.tensor(new[]{5L, 6, 7, 8, 9}, new[]{5L}));\n Assert.False(iter.MoveNext());\n iter.Dispose();\n }\n\n [Fact]\n public void MultiThreadDataLoaderTest3()\n {\n using var dataset = new TestDataset();\n using var dataloader = new torch.utils.data.DataLoader(dataset, 5, false, torch.CPU, num_worker: 11);\n var iter = dataloader.GetEnumerator();\n iter.MoveNext();\n var x = iter.Current;\n Assert.Equal(x[\"data\"], torch.tensor(new[]{1, 1, 1, 1, 1}, new[]{5L}));\n Assert.Equal(x[\"index\"], torch.tensor(new[]{0L, 1, 2, 3, 4}, new[]{5L}));\n iter.MoveNext();\n x = iter.Current;\n Assert.Equal(x[\"data\"], torch.tensor(new[]{1, 1, 1, 1, 1}, new[]{5L}));\n Assert.Equal(x[\"index\"], torch.tensor(new[]{5L, 6, 7, 8, 9}, new[]{5L}));\n Assert.False(iter.MoveNext());\n iter.Dispose();\n }\n\n [Fact]\n public void CustomSeedTest()\n {\n using var dataset = new TestDataset();\n using var dataloader = new torch.utils.data.DataLoader(dataset, 2, true, seed: 1);\n using var dataloader2 = new torch.utils.data.DataLoader(dataset, 2, true, seed: 1);\n var iterator = dataloader.GetEnumerator();\n var iterator2 = dataloader2.GetEnumerator();\n iterator.MoveNext();\n iterator2.MoveNext();\n Assert.Equal(iterator.Current, iterator2.Current);\n iterator.Dispose();\n iterator2.Dispose();\n }\n }\n}"},{"alpha_fraction":0.5452775359153748,"alphanum_fraction":0.5530672073364258,"avg_line_length":34.41379165649414,"blob_id":"e99e77e99bf1d20ce47d658288da2bcb2f12f8c1","content_id":"8837d1ce2f1c8b05fee686c02b56ade6f2ffa528","detected_licenses":["MIT"],"is_generated":false,"is_vendor":false,"language":"C#","length_bytes":1027,"license_type":"permissive","max_line_length":130,"num_lines":29,"path":"/test/TorchSharpTest/TestTorchHub.cs","repo_name":"NiklasGustafsson/TorchSharp","src_encoding":"UTF-8","text":"// Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information.\n\nusing System.Reflection;\nusing Xunit;\n\n#nullable enable\nnamespace TorchSharp\n{\n public class TestTorchHub\n {\n [Fact]\n public void TestProgressBar()\n {\n var type = typeof(torch.hub);\n foreach (var method in type.GetMethods(BindingFlags.NonPublic | BindingFlags.Static)) {\n if (method.Name == \"_create_progress_bar\") {\n var result = (IProgressBar?)method.Invoke(null, new object[] { false });\n Assert.NotNull(result);\n Assert.Equal(\"TorchSharp.ConsoleProgressBar\", result.GetType().FullName);\n Assert.Equal(0, result.Value);\n Assert.Null(result.Maximum);\n result.Value = 100;\n result.Maximum = 50;\n Assert.Equal(50, result.Value);\n }\n }\n }\n }\n}\n"},{"alpha_fraction":0.5553908944129944,"alphanum_fraction":0.5569749474525452,"avg_line_length":46.73170852661133,"blob_id":"42411a79c6e947feaf3d27d062f3225fb9fa9b1d","content_id":"7ae4007cd043ceb54e26361084293fdb9a35f4d5","detected_licenses":["MIT"],"is_generated":false,"is_vendor":false,"language":"C#","length_bytes":19594,"license_type":"permissive","max_line_length":211,"num_lines":410,"path":"/src/TorchSharp/NN/Sequential.cs","repo_name":"NiklasGustafsson/TorchSharp","src_encoding":"UTF-8","text":"// Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information.\nusing System;\nusing System.Collections.Generic;\nusing System.Diagnostics;\nusing static TorchSharp.PInvoke.NativeMethods;\n\nusing static TorchSharp.torch;\n\nnamespace TorchSharp\n{\n using System.Runtime.CompilerServices;\n using Modules;\n using TorchSharp.PInvoke;\n\n namespace Modules\n {\n /// <summary>\n /// This class is used to represent a Sequential module.\n /// </summary>\n public class Sequential : torch.nn.Module<Tensor, Tensor>\n {\n public Sequential append(string name, torch.nn.IModule<Tensor, Tensor> module)\n {\n Add(name, module);\n return this;\n }\n\n internal void Add(string name, torch.nn.IModule<Tensor, Tensor> sm)\n {\n var submodule = (torch.nn.Module)sm;\n\n Debug.Assert(!handle.IsInvalid);\n Debug.Assert(!submodule.handle.IsInvalid);\n\n // Keep the sub-module alive for at least as long as the Sequential object is alive.\n _modules.Add(sm);\n _names.Add(name);\n }\n\n public Sequential append(torch.nn.IModule<Tensor, Tensor> module)\n {\n var name = _modules.Count.ToString();\n Add(name, module);\n return this;\n }\n\n internal void Add(torch.nn.IModule<Tensor, Tensor> module)\n {\n var name = _modules.Count.ToString();\n Add(name, module);\n }\n\n public override IEnumerable<(string name, Parameter parameter)> named_parameters(bool recurse = true)\n {\n if (!recurse) yield break;\n\n var seen = new HashSet<IntPtr>();\n seen.Add(IntPtr.Zero); // Ignore invalid parameters\n\n for (var i = 0; i < _names.Count; i++) {\n foreach (var (n, p) in ((torch.nn.Module)_modules[i]).named_parameters(true)) {\n if (seen.Contains(p.handle)) continue;\n seen.Add(p.handle);\n yield return ($\"{_names[i]}.{n}\", p);\n }\n }\n }\n\n public override IEnumerable<(string name, Tensor buffer)> named_buffers(bool recurse = true)\n {\n if (!recurse) yield break;\n\n var seen = new HashSet<IntPtr>();\n seen.Add(IntPtr.Zero); // Ignore invalid buffers\n\n for (var i = 0; i < _names.Count; i++) {\n foreach (var (n, p) in ((torch.nn.Module)_modules[i]).named_buffers(true)) {\n if (seen.Contains(p.handle)) continue;\n seen.Add(p.handle);\n yield return ($\"{_names[i]}.{n}\", p);\n }\n }\n }\n\n public override IEnumerable<(string name, torch.nn.Module module)> named_children()\n {\n for (var i = 0; i < _names.Count; i++) {\n yield return ($\"{_names[i]}\", ((torch.nn.Module)_modules[i]));\n }\n }\n\n public override IEnumerable<(string name, torch.nn.Module module)> named_modules()\n {\n for (var i = 0; i < _names.Count; i++) {\n yield return ($\"{_names[i]}\", ((torch.nn.Module)_modules[i]));\n }\n\n for (var i = 0; i < _names.Count; i++) {\n var sm = (torch.nn.Module)_modules[i];\n var name = _names[i];\n foreach (var (n, p) in sm.named_modules()) {\n yield return ($\"{name}.{n}\", p);\n }\n }\n\n }\n internal Sequential(IntPtr handle) : base(handle, IntPtr.Zero)\n {\n }\n\n /// <summary>\n /// Constructor, intended for derived modules.\n /// </summary>\n /// <param name=\"modules\">An ordered list of the contained modules.</param>\n protected Sequential(params (string name, torch.nn.Module<Tensor, Tensor> submodule)[] modules) : base(IntPtr.Zero, IntPtr.Zero)\n {\n var handle = THSNN_Sequential_ctor();\n if (handle == IntPtr.Zero) { torch.CheckForErrors(); }\n\n this.handle = new HType(handle, true);\n\n foreach (var module in modules) Add(module.name, module.submodule);\n }\n\n /// <summary>\n /// Constructor, intended for derived modules.\n /// </summary>\n /// <param name=\"modules\">An ordered list of the contained modules.</param>\n protected Sequential(params torch.nn.Module<Tensor, Tensor>[] modules) : base(IntPtr.Zero, IntPtr.Zero)\n {\n var handle = THSNN_Sequential_ctor();\n if (handle == IntPtr.Zero) { torch.CheckForErrors(); }\n\n this.handle = new HType(handle, true);\n\n foreach (var m in modules) Add(m);\n }\n\n public override Tensor forward(Tensor tensor)\n {\n // If there are no modules, just return a fresh handle to the input.\n if (_modules.Count == 0) return tensor.alias();\n\n // The loop-based logic below only works for n > 1, so here's another special case.\n if (_modules.Count == 1) return _modules[0].call(tensor);\n\n // Note: we have not been able to detect any significant performance difference between\n // implementing forward() in native or managed code.\n\n // Using an for loop helps debugging, since we can know the ordinal of the submodule.\n\n var t0 = _modules[0].call(tensor);\n\n for (var idx = 1; idx < _modules.Count - 1; idx++) {\n var t1 = _modules[idx].call(t0);\n t0.Dispose();\n t0 = t1;\n }\n\n var result = _modules[_modules.Count - 1].call(t0);\n t0.Dispose();\n\n return result;\n }\n\n public override nn.Module apply(Action<nn.Module> fn)\n {\n // More efficient than asking C++ for the children. We already have the list, after all.\n foreach (var m in _modules) ((torch.nn.Module)m).apply(fn);\n fn(this);\n return this;\n\n }\n\n /// <summary>\n /// The number of modules in the Sequential collection.\n /// </summary>\n public int Count => _modules.Count;\n\n /// <summary>\n /// Module indexer.\n /// </summary>\n [IndexerName(\"SequentialItems\")]\n public nn.IModule<Tensor,Tensor> this[int index] {\n get {\n return _modules[index];\n }\n }\n\n /// <summary>\n /// Module indexer.\n /// </summary>\n [IndexerName(\"SequentialItems\")]\n public Sequential this[(int? start, int? end) index] {\n get {\n var start = index.start.HasValue ? index.start.Value : 0;\n var end = index.end.HasValue ? index.end.Value : _modules.Count;\n\n return Slice(start, end);\n }\n }\n\n private Sequential Slice(int start, int end)\n {\n if (start < 0 || start > _modules.Count) throw new IndexOutOfRangeException($\"{start} is not a valid index.\");\n if (end < 0 || end > _modules.Count) throw new IndexOutOfRangeException($\"{end} is not a valid index.\");\n\n var stop = Math.Min(_modules.Count, end);\n\n var result = new Sequential(Array.Empty<torch.nn.Module<Tensor, Tensor>>());\n\n for (var i = start; i < stop; i++) {\n result.Add(_names[i], _modules[i]);\n }\n return result;\n }\n\n#if !NETSTANDARD2_0_OR_GREATER\n /// <summary>\n /// Module indexer.\n /// </summary>\n [IndexerName(\"SequentialItems\")]\n public Sequential this[System.Range index] {\n get {\n var start = index.Start.IsFromEnd ? _modules.Count - index.Start.Value : index.Start.Value;\n var end = index.End.IsFromEnd ? _modules.Count - index.End.Value : index.End.Value;\n\n return this[(start, end)];\n }\n }\n#endif\n protected override void Dispose(bool disposing)\n {\n if (disposing) {\n foreach (var m in _modules) { ((torch.nn.Module)m).Dispose(); }\n }\n base.Dispose(disposing);\n }\n\n /// <summary>\n /// Sets the module in training mode.\n /// </summary>\n /// <remarks>\n /// This has any effect only on certain modules.See documentations of particular modules for details of their behaviors in training/evaluation mode, if they are affected, e.g.Dropout, BatchNorm, etc.\n /// </remarks>\n public override void train(bool on = true)\n {\n foreach (var m in _modules) { ((torch.nn.Module)m).train(on); }\n }\n\n /// <summary>\n /// Sets the module in evaluation mode.\n /// </summary>\n /// <remarks>\n /// This has any effect only on certain modules.See documentations of particular modules for details of their behaviors in training/evaluation mode, if they are affected, e.g.Dropout, BatchNorm, etc.\n /// </remarks>\n public override void eval()\n {\n foreach (var m in _modules) { ((torch.nn.Module)m).eval(); }\n }\n\n protected internal override nn.Module _to(ScalarType dtype)\n {\n foreach (var m in _modules) { ((torch.nn.Module)m)._to(dtype); }\n return this;\n }\n\n protected internal override nn.Module _to(Device device, ScalarType dtype)\n {\n foreach (var m in _modules) { ((torch.nn.Module)m)._to(device, dtype); }\n return this;\n }\n\n protected internal override nn.Module _to(DeviceType deviceType, int deviceIndex = -1)\n {\n foreach (var m in _modules) { ((torch.nn.Module)m)._to(deviceType, deviceIndex); }\n return this;\n }\n\n // Currently, Sequential is implemented entirely in managed code, but if we go back to\n // using the native forward() implementation, this collection is still necessary.\n // The module handles are held in the native runtime, which calls back into managed code,\n // the .NET module instances need to stay alive, and keeping a list of them will do that.\n\n private List<torch.nn.IModule<Tensor, Tensor>> _modules = new List<nn.IModule<Tensor, Tensor>>();\n private List<string> _names = new List<string>();\n }\n }\n\n public static partial class torch\n {\n public static partial class nn\n {\n /// <summary>\n /// Get empty sequential\n /// </summary>\n /// <returns></returns>\n public static Sequential Sequential()\n {\n var handle = THSNN_Sequential_ctor();\n if (handle == IntPtr.Zero) { torch.CheckForErrors(); }\n\n return new Sequential(handle);\n }\n\n /// <summary>\n /// A sequential container. Modules will be added to it in the order they are passed in the constructor.\n /// Alternatively, an OrderedDict of modules can be passed in. The forward() method of Sequential accepts any input and forwards it to the first module it contains.\n /// It then “chains” outputs to inputs sequentially for each subsequent module, finally returning the output of the last module.\n /// The value a Sequential provides over manually calling a sequence of modules is that it allows treating the whole container as a single module,\n /// such that performing a transformation on the Sequential applies to each of the modules it stores (which are each a registered submodule of the Sequential).\n /// </summary>\n /// <param name=\"modules\">An ordered list of the contained modules.</param>\n /// <returns></returns>\n /// <remarks>Sequential will take ownership of the modules and dispose of them when disposed.</remarks>\n public static Sequential Sequential(params (string name, torch.nn.Module<Tensor, Tensor> submodule)[] modules)\n {\n var res = Sequential();\n foreach (var module in modules)\n res.Add(module.name, module.submodule);\n return res;\n }\n\n /// <summary>\n /// A sequential container. Modules will be added to it in the order they are passed in the constructor.\n /// It then “chains” outputs to inputs sequentially for each subsequent module, finally returning the output of the last module.\n /// The value a Sequential provides over manually calling a sequence of modules is that it allows treating the whole container as a single module,\n /// such that performing a transformation on the Sequential applies to each of the modules it stores (which are each a registered submodule of the Sequential).\n /// </summary>\n /// <param name=\"modules\">An ordered list of the contained modules.</param>\n /// <returns></returns>\n /// <remarks>Sequential will take ownership of the modules and dispose of them when disposed.</remarks>\n public static Sequential Sequential(params torch.nn.Module<Tensor, Tensor>[] modules)\n {\n var res = Sequential();\n foreach (var m in modules)\n res.Add(m);\n return res;\n }\n\n /// <summary>\n /// A sequential container. Modules will be added to it in the order they are passed in the constructor.\n /// Alternatively, an OrderedDict of modules can be passed in. The forward() method of Sequential accepts any input and forwards it to the first module it contains.\n /// It then “chains” outputs to inputs sequentially for each subsequent module, finally returning the output of the last module.\n /// The value a Sequential provides over manually calling a sequence of modules is that it allows treating the whole container as a single module,\n /// such that performing a transformation on the Sequential applies to each of the modules it stores (which are each a registered submodule of the Sequential).\n /// </summary>\n /// <param name=\"modules\">An ordered list of the contained modules.</param>\n /// <remarks>Sequential will take ownership of the modules and dispose of them when disposed.</remarks>\n public static Sequential Sequential(params System.Tuple<string, torch.nn.Module<Tensor, Tensor>>[] modules)\n {\n var res = Sequential();\n foreach (var module in modules)\n res.Add(module.Item1, module.Item2);\n return res;\n }\n\n /// <summary>\n /// A sequential container. Modules will be added to it in the order they are passed in the constructor.\n /// Alternatively, an OrderedDict of modules can be passed in. The forward() method of Sequential accepts any input and forwards it to the first module it contains.\n /// It then “chains” outputs to inputs sequentially for each subsequent module, finally returning the output of the last module.\n /// The value a Sequential provides over manually calling a sequence of modules is that it allows treating the whole container as a single module,\n /// such that performing a transformation on the Sequential applies to each of the modules it stores (which are each a registered submodule of the Sequential).\n /// </summary>\n /// <param name=\"modules\">An ordered list of the contained modules.</param>\n /// <remarks>Sequential will take ownership of the modules and dispose of them when disposed.</remarks>\n public static Sequential Sequential(IEnumerable<(string name, torch.nn.Module<Tensor, Tensor> submodule)> modules)\n {\n var res = Sequential();\n foreach (var module in modules)\n res.Add(module.name, module.submodule);\n return res;\n }\n\n /// <summary>\n /// A sequential container. Modules will be added to it in the order they are passed in the constructor.\n /// Alternatively, an OrderedDict of modules can be passed in. The forward() method of Sequential accepts any input and forwards it to the first module it contains.\n /// It then “chains” outputs to inputs sequentially for each subsequent module, finally returning the output of the last module.\n /// The value a Sequential provides over manually calling a sequence of modules is that it allows treating the whole container as a single module,\n /// such that performing a transformation on the Sequential applies to each of the modules it stores (which are each a registered submodule of the Sequential).\n /// </summary>\n /// <param name=\"modules\">An ordered list of the contained modules.</param>\n /// <remarks>Sequential will take ownership of the modules and dispose of them when disposed.</remarks>\n public static Sequential Sequential(IEnumerable<System.Tuple<string, torch.nn.Module<Tensor, Tensor>>> modules)\n {\n var res = Sequential();\n foreach (var module in modules)\n res.Add(module.Item1, module.Item2);\n return res;\n }\n\n /// <summary>\n /// A sequential container. Modules will be added to it in the order they are passed in the constructor.\n /// It then “chains” outputs to inputs sequentially for each subsequent module, finally returning the output of the last module.\n /// The value a Sequential provides over manually calling a sequence of modules is that it allows treating the whole container as a single module,\n /// such that performing a transformation on the Sequential applies to each of the modules it stores (which are each a registered submodule of the Sequential).\n /// </summary>\n /// <param name=\"modules\">An ordered list of the contained modules.</param>\n /// <returns></returns>\n /// <remarks>Sequential will take ownership of the modules and dispose of them when disposed.</remarks>\n public static Sequential Sequential(IEnumerable<torch.nn.Module<Tensor, Tensor>> modules)\n {\n var res = Sequential();\n foreach (var module in modules)\n res.Add(module);\n return res;\n }\n }\n }\n}\n"},{"alpha_fraction":0.5300552248954773,"alphanum_fraction":0.5334318280220032,"avg_line_length":54.96653366088867,"blob_id":"3ade0ce040dae85a22bc86a23884a84178453193","content_id":"06df3cb78167fb8a19f24752d7c0ebef0ed3b88f","detected_licenses":["MIT"],"is_generated":false,"is_vendor":false,"language":"C#","length_bytes":28431,"license_type":"permissive","max_line_length":214,"num_lines":508,"path":"/src/TorchSharp/FFT.cs","repo_name":"NiklasGustafsson/TorchSharp","src_encoding":"UTF-8","text":"// Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information.\nusing System;\nusing static TorchSharp.PInvoke.NativeMethods;\n\nnamespace TorchSharp\n{\n // This file contains the mathematical operators on Tensor\n public enum FFTNormType\n {\n Backward = 0,\n Forward = 1,\n Ortho = 2\n }\n\n public static partial class torch\n {\n public static partial class fft\n {\n /// <summary>\n /// Computes the one dimensional discrete Fourier transform of input.\n /// </summary>\n /// <param name=\"input\">The input tensor</param>\n /// <param name=\"n\">Signal length. If given, the input will either be zero-padded or trimmed to this length before computing the FFT.</param>\n /// <param name=\"dim\">The dimension along which to take the one dimensional FFT.</param>\n /// <param name=\"norm\">Normalization mode.</param>\n /// <returns></returns>\n /// <remarks>The name was changed because it would conflict with its surrounding scope. That's not legal in .NET.</remarks>\n public static Tensor fft_(Tensor input, long n = -1, long dim = -1, FFTNormType norm = FFTNormType.Backward)\n {\n var res = THSTensor_fft(input.Handle, n, dim, (sbyte)norm);\n if (res == IntPtr.Zero) { torch.CheckForErrors(); }\n return new Tensor(res);\n }\n\n /// <summary>\n /// Computes the one dimensional inverse discrete Fourier transform of input.\n /// </summary>\n /// <param name=\"input\">The input tensor</param>\n /// <param name=\"n\">Signal length. If given, the input will either be zero-padded or trimmed to this length before computing the IFFT.</param>\n /// <param name=\"dim\">The dimension along which to take the one dimensional IFFT.</param>\n /// <param name=\"norm\">Normalization mode.</param>\n /// <returns></returns>\n public static Tensor ifft(Tensor input, long n = -1, long dim = -1, FFTNormType norm = FFTNormType.Backward)\n {\n var res = THSTensor_ifft(input.Handle, n, dim, (sbyte)norm);\n if (res == IntPtr.Zero) { torch.CheckForErrors(); }\n return new Tensor(res);\n }\n\n /// <summary>\n /// Computes the two-dimensional discrete Fourier transform of input.\n /// </summary>\n /// <param name=\"input\">The input tensor</param>\n /// <param name=\"s\">\n /// Signal size in the transformed dimensions.\n /// If given, each dimension dim[i] will either be zero-padded or trimmed to the length s[i] before computing the FFT.\n /// If a length -1 is specified, no padding is done in that dimension.\n /// </param>\n /// <param name=\"dim\">Dimensions to be transformed</param>\n /// <param name=\"norm\">Normalization mode.</param>\n /// <returns></returns>\n public static Tensor fft2(Tensor input, long[] s = null, long[] dim = null, FFTNormType norm = FFTNormType.Backward)\n {\n if (input.Dimensions < 2) throw new ArgumentException(\"fft2() input should be at least 2D\");\n if (dim == null) dim = new long[] { -2, -1 };\n unsafe {\n fixed (long* ps = s, pDim = dim) {\n var res = THSTensor_fft2(input.Handle, (IntPtr)ps, (IntPtr)pDim, (sbyte)norm);\n if (res == IntPtr.Zero) { torch.CheckForErrors(); }\n return new Tensor(res);\n }\n }\n }\n\n /// <summary>\n /// Computes the two-dimensional inverse discrete Fourier transform of input.\n /// </summary>\n /// <param name=\"input\">The input tensor</param>\n /// <param name=\"s\">\n /// Signal size in the transformed dimensions.\n /// If given, each dimension dim[i] will either be zero-padded or trimmed to the length s[i] before computing the IFFT.\n /// If a length -1 is specified, no padding is done in that dimension.\n /// </param>\n /// <param name=\"dim\">Dimensions to be transformed</param>\n /// <param name=\"norm\">Normalization mode.</param>\n public static Tensor ifft2(Tensor input, long[] s = null, long[] dim = null, FFTNormType norm = FFTNormType.Backward)\n {\n if (input.Dimensions < 2) throw new ArgumentException(\"ifft2() input should be at least 2D\");\n if (dim == null) dim = new long[] { -2, -1 };\n unsafe {\n fixed (long* ps = s, pDim = dim) {\n var res = THSTensor_ifft2(input.Handle, (IntPtr)ps, (IntPtr)pDim, (sbyte)norm);\n if (res == IntPtr.Zero) { torch.CheckForErrors(); }\n return new Tensor(res);\n }\n }\n }\n\n /// <summary>\n /// Computes the N-dimensional discrete Fourier transform of input.\n /// </summary>\n /// <param name=\"input\">The input tensor</param>\n /// <param name=\"s\">\n /// Signal size in the transformed dimensions.\n /// If given, each dimension dim[i] will either be zero-padded or trimmed to the length s[i] before computing the IFFT.\n /// If a length -1 is specified, no padding is done in that dimension.\n /// </param>\n /// <param name=\"dim\">Dimensions to be transformed</param>\n /// <param name=\"norm\">Normalization mode.</param>\n /// <returns></returns>\n public static Tensor fftn(Tensor input, long[] s = null, long[] dim = null, FFTNormType norm = FFTNormType.Backward)\n {\n var slen = (s == null) ? 0 : s.Length;\n var dlen = (dim == null) ? 0 : dim.Length;\n unsafe {\n fixed (long* ps = s, pDim = dim) {\n var res = THSTensor_fftn(input.Handle, (IntPtr)ps, slen, (IntPtr)pDim, dlen, (sbyte)norm);\n if (res == IntPtr.Zero) { torch.CheckForErrors(); }\n return new Tensor(res);\n }\n }\n }\n\n /// <summary>\n /// Computes the N-dimensional inverse discrete Fourier transform of input.\n /// </summary>\n /// <param name=\"input\">The input tensor</param>\n /// <param name=\"s\">\n /// Signal size in the transformed dimensions.\n /// If given, each dimension dim[i] will either be zero-padded or trimmed to the length s[i] before computing the IFFT.\n /// If a length -1 is specified, no padding is done in that dimension.\n /// </param>\n /// <param name=\"dim\">Dimensions to be transformed</param>\n /// <param name=\"norm\">Normalization mode.</param>\n /// <returns></returns>\n public static Tensor ifftn(Tensor input, long[] s = null, long[] dim = null, FFTNormType norm = FFTNormType.Backward)\n {\n var slen = (s == null) ? 0 : s.Length;\n var dlen = (dim == null) ? 0 : dim.Length;\n unsafe {\n fixed (long* ps = s, pDim = dim) {\n var res = THSTensor_ifftn(input.Handle, (IntPtr)ps, slen, (IntPtr)pDim, dlen, (sbyte)norm);\n if (res == IntPtr.Zero) { torch.CheckForErrors(); }\n return new Tensor(res);\n }\n }\n }\n\n /// <summary>\n /// Computes the one dimensional inverse Fourier transform of real-valued input.\n /// </summary>\n /// <param name=\"input\">The input tensor</param>\n /// <param name=\"n\">Signal length. If given, the input will either be zero-padded or trimmed to this length before computing the FFT.</param>\n /// <param name=\"dim\">The dimension along which to take the one dimensional FFT.</param>\n /// <param name=\"norm\">Normalization mode.</param>\n public static Tensor irfft(Tensor input, long n = -1, long dim = -1, FFTNormType norm = FFTNormType.Backward)\n {\n var res = THSTensor_irfft(input.Handle, n, dim, (sbyte)norm);\n if (res == IntPtr.Zero) { torch.CheckForErrors(); }\n return new Tensor(res);\n }\n\n /// <summary>\n /// Computes the one dimensional Fourier transform of real-valued input.\n /// </summary>\n /// <param name=\"input\">The input tensor</param>\n /// <param name=\"n\">Signal length. If given, the input will either be zero-padded or trimmed to this length before computing the FFT.</param>\n /// <param name=\"dim\">The dimension along which to take the one dimensional FFT.</param>\n /// <param name=\"norm\">Normalization mode.</param>\n /// <returns></returns>\n public static Tensor rfft(Tensor input, long n = -1, long dim = -1, FFTNormType norm = FFTNormType.Backward)\n {\n var res = THSTensor_rfft(input.Handle, n, dim, (sbyte)norm);\n if (res == IntPtr.Zero) { torch.CheckForErrors(); }\n return new Tensor(res);\n }\n\n /// <summary>\n /// Computes the two-dimensional discrete Fourier transform of real-vaued input.\n /// </summary>\n /// <param name=\"input\">The input tensor</param>\n /// <param name=\"s\">\n /// Signal size in the transformed dimensions.\n /// If given, each dimension dim[i] will either be zero-padded or trimmed to the length s[i] before computing the FFT.\n /// If a length -1 is specified, no padding is done in that dimension.\n /// </param>\n /// <param name=\"dim\">Dimensions to be transformed</param>\n /// <param name=\"norm\">Normalization mode.</param>\n public static Tensor rfft2(Tensor input, long[] s = null, long[] dim = null, FFTNormType norm = FFTNormType.Backward)\n {\n if (input.Dimensions < 2) throw new ArgumentException(\"rfft2() input should be at least 2D\");\n if (dim == null) dim = new long[] { -2, -1 };\n unsafe {\n fixed (long* ps = s, pDim = dim) {\n var res = THSTensor_rfft2(input.Handle, (IntPtr)ps, (IntPtr)pDim, (sbyte)norm);\n if (res == IntPtr.Zero) { torch.CheckForErrors(); }\n return new Tensor(res);\n }\n }\n }\n\n /// <summary>\n /// Computes the two-dimensional inverse discrete Fourier transform of real-valued input.\n /// </summary>\n /// <param name=\"input\">The input tensor</param>\n /// <param name=\"s\">\n /// Signal size in the transformed dimensions.\n /// If given, each dimension dim[i] will either be zero-padded or trimmed to the length s[i] before computing the IFFT.\n /// If a length -1 is specified, no padding is done in that dimension.\n /// </param>\n /// <param name=\"dim\">Dimensions to be transformed</param>\n /// <param name=\"norm\">Normalization mode.</param>\n public static Tensor irfft2(Tensor input, long[] s = null, long[] dim = null, FFTNormType norm = FFTNormType.Backward)\n {\n if (input.Dimensions < 2) throw new ArgumentException(\"irfft2() input should be at least 2D\");\n if (dim == null) dim = new long[] { -2, -1 };\n unsafe {\n fixed (long* ps = s, pDim = dim) {\n var res = THSTensor_irfft2(input.Handle, (IntPtr)ps, (IntPtr)pDim, (sbyte)norm);\n if (res == IntPtr.Zero) { torch.CheckForErrors(); }\n return new Tensor(res);\n }\n }\n }\n\n /// <summary>\n /// Computes the N-dimensional discrete Fourier transform of real-valued input.\n /// </summary>\n /// <param name=\"input\">The input tensor</param>\n /// <param name=\"s\">\n /// Signal size in the transformed dimensions.\n /// If given, each dimension dim[i] will either be zero-padded or trimmed to the length s[i] before computing the IFFT.\n /// If a length -1 is specified, no padding is done in that dimension.\n /// </param>\n /// <param name=\"dim\">Dimensions to be transformed</param>\n /// <param name=\"norm\">Normalization mode.</param>\n public static Tensor rfftn(Tensor input, long[] s = null, long[] dim = null, FFTNormType norm = FFTNormType.Backward)\n {\n var slen = (s == null) ? 0 : s.Length;\n var dlen = (dim == null) ? 0 : dim.Length;\n unsafe {\n fixed (long* ps = s, pDim = dim) {\n var res = THSTensor_rfftn(input.Handle, (IntPtr)ps, slen, (IntPtr)pDim, dlen, (sbyte)norm);\n if (res == IntPtr.Zero) { torch.CheckForErrors(); }\n return new Tensor(res);\n }\n }\n }\n\n /// <summary>\n /// Computes the N-dimensional inverse discrete Fourier transform of real-valued input.\n /// </summary>\n /// <param name=\"input\">The input tensor</param>\n /// <param name=\"s\">\n /// Signal size in the transformed dimensions.\n /// If given, each dimension dim[i] will either be zero-padded or trimmed to the length s[i] before computing the IFFT.\n /// If a length -1 is specified, no padding is done in that dimension.\n /// </param>\n /// <param name=\"dim\">Dimensions to be transformed</param>\n /// <param name=\"norm\">Normalization mode.</param>\n public static Tensor irfftn(Tensor input, long[] s = null, long[] dim = null, FFTNormType norm = FFTNormType.Backward)\n {\n var slen = (s == null) ? 0 : s.Length;\n var dlen = (dim == null) ? 0 : dim.Length;\n unsafe {\n fixed (long* ps = s, pDim = dim) {\n var res = THSTensor_irfftn(input.Handle, (IntPtr)ps, slen, (IntPtr)pDim, dlen, (sbyte)norm);\n if (res == IntPtr.Zero) { torch.CheckForErrors(); }\n return new Tensor(res);\n }\n }\n }\n\n /// <summary>\n /// Computes the one dimensional discrete Fourier transform of a Hermitian symmetric input signal.\n /// </summary>\n /// <param name=\"input\">The input tensor representing a half-Hermitian signal</param>\n /// <param name=\"n\">\n /// Output signal length. This determines the length of the real output.\n /// If given, the input will either be zero-padded or trimmed to this length before computing the Hermitian FFT.</param>\n /// <param name=\"dim\">The dimension along which to take the one dimensional Hermitian FFT.</param>\n /// <param name=\"norm\">Normalization mode.</param>\n /// <returns></returns>\n public static Tensor hfft(Tensor input, long n = -1, long dim = -1, FFTNormType norm = FFTNormType.Backward)\n {\n var res = THSTensor_hfft(input.Handle, n, dim, (sbyte)norm);\n if (res == IntPtr.Zero) { torch.CheckForErrors(); }\n return new Tensor(res);\n }\n\n /// <summary>\n /// Computes the inverse of hfft().\n /// </summary>\n /// <param name=\"input\">The input tensor representing a half-Hermitian signal</param>\n /// <param name=\"n\">\n /// Output signal length. This determines the length of the real output.\n /// If given, the input will either be zero-padded or trimmed to this length before computing the Hermitian FFT.</param>\n /// <param name=\"dim\">The dimension along which to take the one dimensional Hermitian FFT.</param>\n /// <param name=\"norm\">Normalization mode.</param>\n public static Tensor ihfft(Tensor input, long n = -1, long dim = -1, FFTNormType norm = FFTNormType.Backward)\n {\n var res = THSTensor_ihfft(input.Handle, n, dim, (sbyte)norm);\n if (res == IntPtr.Zero) { torch.CheckForErrors(); }\n return new Tensor(res);\n }\n\n /// <summary>\n /// Reorders n-dimensional FFT data, as provided by fftn(), to have negative frequency terms first.\n /// This performs a periodic shift of n-dimensional data such that the origin(0, ..., 0) is moved to the center of the tensor.Specifically, to input.shape[dim] // 2 in each selected dimension.\n /// </summary>\n /// <param name=\"input\">The tensor in FFT order</param>\n /// <param name=\"dim\">The dimensions to rearrange. Only dimensions specified here will be rearranged, any other dimensions will be left in their original order. Default: All dimensions of input.</param>\n /// <returns></returns>\n public static Tensor fftshift(Tensor input, long[] dim = null)\n {\n var dlen = (dim == null) ? 0 : dim.Length;\n unsafe {\n fixed (long* pDim = dim) {\n var res = THSTensor_fftshift(input.Handle, (IntPtr)pDim, dlen);\n if (res == IntPtr.Zero) { torch.CheckForErrors(); }\n return new Tensor(res);\n }\n }\n }\n\n /// <summary>\n /// Inverse of fftshift().\n /// </summary>\n /// <param name=\"input\">The tensor in FFT order</param>\n /// <param name=\"dim\">The dimensions to rearrange. Only dimensions specified here will be rearranged, any other dimensions will be left in their original order. Default: All dimensions of input.</param>\n public static Tensor ifftshift(Tensor input, long[] dim = null)\n {\n var dlen = (dim == null) ? 0 : dim.Length;\n unsafe {\n fixed (long* pDim = dim) {\n var res = THSTensor_ifftshift(input.Handle, (IntPtr)pDim, dlen);\n if (res == IntPtr.Zero) { torch.CheckForErrors(); }\n return new Tensor(res);\n }\n }\n }\n\n /// <summary>\n /// Computes the discrete Fourier Transform sample frequencies for a signal of size n.\n /// </summary>\n /// <param name=\"n\">The FFT length</param>\n /// <param name=\"d\">The sampling length scale. </param>\n /// <param name=\"dtype\">The desired data type of the returned tensor</param>\n /// <param name=\"device\">the desired device of the returned tensor</param>\n /// <param name=\"requires_grad\">If autograd should record operations on the returned tensor.</param>\n public static Tensor fftfreq(long n, double d = 1.0, torch.ScalarType? dtype = null, torch.Device device = null, bool requires_grad = false)\n {\n device = torch.InitializeDevice(device);\n if (!dtype.HasValue) {\n // Determine the element type dynamically.\n dtype = get_default_dtype();\n }\n\n var handle = THSTensor_fftfreq(n, d, (sbyte)dtype, (int)device.type, device.index, requires_grad);\n if (handle == IntPtr.Zero) {\n GC.Collect();\n GC.WaitForPendingFinalizers();\n handle = THSTensor_fftfreq(n, d, (sbyte)dtype, (int)device.type, device.index, requires_grad);\n }\n if (handle == IntPtr.Zero) { torch.CheckForErrors(); }\n return new Tensor(handle);\n }\n\n /// <summary>\n /// Computes the sample frequencies for rfft() with a signal of size n.\n /// </summary>\n /// <param name=\"n\">The FFT length</param>\n /// <param name=\"d\">The sampling length scale. </param>\n /// <param name=\"dtype\">The desired data type of the returned tensor</param>\n /// <param name=\"device\">the desired device of the returned tensor</param>\n /// <param name=\"requires_grad\">If autograd should record operations on the returned tensor.</param>\n public static Tensor rfftfreq(long n, double d = 1.0, torch.ScalarType? dtype = null, torch.Device device = null, bool requires_grad = false)\n {\n device = torch.InitializeDevice(device);\n if (!dtype.HasValue) {\n // Determine the element type dynamically.\n dtype = get_default_dtype();\n }\n\n var handle = THSTensor_rfftfreq(n, d, (sbyte)dtype, (int)device.type, device.index, requires_grad);\n if (handle == IntPtr.Zero) {\n GC.Collect();\n GC.WaitForPendingFinalizers();\n handle = THSTensor_rfftfreq(n, d, (sbyte)dtype, (int)device.type, device.index, requires_grad);\n }\n if (handle == IntPtr.Zero) { torch.CheckForErrors(); }\n return new Tensor(handle);\n }\n\n /// <summary>\n /// Computes the 2-dimensional discrete Fourier transform of a Hermitian symmetric input signal.\n ///\n /// Equivalent to hfftn() but only transforms the last two dimensions by default.\n /// input is interpreted as a one-sided Hermitian signal in the time domain.\n /// By the Hermitian property, the Fourier transform will be real-valued.\n /// </summary>\n /// <param name=\"input\">The input tensor</param>\n /// <param name=\"s\">\n /// Signal size in the transformed dimensions.\n /// If given, each dimension dim[i] will either be zero-padded or trimmed to the length s[i] before computing the IFFT.\n /// If a length -1 is specified, no padding is done in that dimension.\n /// </param>\n /// <param name=\"dim\">Dimensions to be transformed</param>\n /// <param name=\"norm\">Normalization mode.</param>\n public static Tensor hfft2(Tensor input, long[] s = null, long[] dim = null, FFTNormType norm = FFTNormType.Backward)\n {\n if (input.Dimensions < 2) throw new ArgumentException(\"hfft2() input should be at least 2D\");\n if (dim == null) dim = new long[] { -2, -1 };\n unsafe {\n fixed (long* ps = s, pDim = dim) {\n var res = THSTensor_hfft2(input.Handle, (IntPtr)ps, (IntPtr)pDim, (sbyte)norm);\n if (res == IntPtr.Zero) { torch.CheckForErrors(); }\n return new Tensor(res);\n }\n }\n }\n\n /// <summary>\n /// Computes the 2-dimensional inverse discrete Fourier transform of a Hermitian symmetric input signal.\n ///\n /// Equivalent to hfftn() but only transforms the last two dimensions by default.\n /// input is interpreted as a one-sided Hermitian signal in the time domain.\n /// By the Hermitian property, the Fourier transform will be real-valued.\n /// </summary>\n /// <param name=\"input\">The input tensor</param>\n /// <param name=\"s\">\n /// Signal size in the transformed dimensions.\n /// If given, each dimension dim[i] will either be zero-padded or trimmed to the length s[i] before computing the IFFT.\n /// If a length -1 is specified, no padding is done in that dimension.\n /// </param>\n /// <param name=\"dim\">Dimensions to be transformed</param>\n /// <param name=\"norm\">Normalization mode.</param>\n public static Tensor ihfft2(Tensor input, long[] s = null, long[] dim = null, FFTNormType norm = FFTNormType.Backward)\n {\n if (input.Dimensions < 2) throw new ArgumentException(\"ihfft2() input should be at least 2D\");\n if (dim == null) dim = new long[] { -2, -1 };\n unsafe {\n fixed (long* ps = s, pDim = dim) {\n var res = THSTensor_ihfft2(input.Handle, (IntPtr)ps, (IntPtr)pDim, (sbyte)norm);\n if (res == IntPtr.Zero) { torch.CheckForErrors(); }\n return new Tensor(res);\n }\n }\n }\n\n /// <summary>\n /// Computes the n-dimensional discrete Fourier transform of a Herimitian symmetric input signal.\n ///\n /// input is interpreted as a one-sided Hermitian signal in the time domain.\n /// By the Hermitian property, the Fourier transform will be real-valued.\n /// </summary>\n /// <param name=\"input\">The input tensor</param>\n /// <param name=\"s\">\n /// Signal size in the transformed dimensions.\n /// If given, each dimension dim[i] will either be zero-padded or trimmed to the length s[i] before computing the IFFT.\n /// If a length -1 is specified, no padding is done in that dimension.\n /// </param>\n /// <param name=\"dim\">Dimensions to be transformed</param>\n /// <param name=\"norm\">Normalization mode.</param>\n public static Tensor hfftn(Tensor input, long[] s = null, long[] dim = null, FFTNormType norm = FFTNormType.Backward)\n {\n if (input.Dimensions < 2) throw new ArgumentException(\"hfftn() input should be at least 2D\");\n var slen = (s == null) ? 0 : s.Length;\n var dlen = (dim == null) ? 0 : dim.Length;\n unsafe {\n fixed (long* ps = s, pDim = dim) {\n var res = THSTensor_hfftn(input.Handle, (IntPtr)ps, slen, (IntPtr)pDim, dlen, (sbyte)norm);\n if (res == IntPtr.Zero) { torch.CheckForErrors(); }\n return new Tensor(res);\n }\n }\n }\n\n /// <summary>\n /// Computes the n-dimensional inverse discrete Fourier transform of a Herimitian symmetric input signal.\n ///\n /// input is interpreted as a one-sided Hermitian signal in the time domain.\n /// By the Hermitian property, the Fourier transform will be real-valued.\n /// </summary>\n /// <param name=\"input\">The input tensor</param>\n /// <param name=\"s\">\n /// Signal size in the transformed dimensions.\n /// If given, each dimension dim[i] will either be zero-padded or trimmed to the length s[i] before computing the IFFT.\n /// If a length -1 is specified, no padding is done in that dimension.\n /// </param>\n /// <param name=\"dim\">Dimensions to be transformed</param>\n /// <param name=\"norm\">Normalization mode.</param>\n public static Tensor ihfftn(Tensor input, long[] s = null, long[] dim = null, FFTNormType norm = FFTNormType.Backward)\n {\n if (input.Dimensions < 2) throw new ArgumentException(\"ihfftn() input should be at least 2D\");\n var slen = (s == null) ? 0 : s.Length;\n var dlen = (dim == null) ? 0 : dim.Length;\n unsafe {\n fixed (long* ps = s, pDim = dim) {\n var res = THSTensor_ihfftn(input.Handle, (IntPtr)ps, slen, (IntPtr)pDim, dlen, (sbyte)norm);\n if (res == IntPtr.Zero) { torch.CheckForErrors(); }\n return new Tensor(res);\n }\n }\n }\n }\n }\n}\n"},{"alpha_fraction":0.6702355742454529,"alphanum_fraction":0.6761242151260376,"avg_line_length":38.74468231201172,"blob_id":"df97e2c877d61ee2e0d34fbf4747b4506e8ec7a7","content_id":"07b3e5f52d9a5fac6e3c5d1f55bce2067b1f518b","detected_licenses":["MIT"],"is_generated":false,"is_vendor":false,"language":"C#","length_bytes":1868,"license_type":"permissive","max_line_length":149,"num_lines":47,"path":"/src/TorchSharp/PInvoke/LibTorchSharp.THSAutograd.cs","repo_name":"NiklasGustafsson/TorchSharp","src_encoding":"UTF-8","text":"// Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information.\n#nullable enable\nusing System;\nusing System.Runtime.InteropServices;\n\nnamespace TorchSharp.PInvoke\n{\n internal static partial class NativeMethods\n {\n [DllImport(\"LibTorchSharp\")]\n [return: MarshalAs(UnmanagedType.U1)]\n internal static extern bool THSAutograd_isGradEnabled();\n\n [DllImport(\"LibTorchSharp\")]\n internal static extern void THSAutograd_setGrad([MarshalAs(UnmanagedType.U1)] bool enabled);\n\n [DllImport(\"LibTorchSharp\")]\n [return: MarshalAs(UnmanagedType.U1)]\n internal static extern bool THSAutograd_isAnomalyEnabled();\n\n [DllImport(\"LibTorchSharp\")]\n [return: MarshalAs(UnmanagedType.U1)]\n internal static extern bool THSAutograd_shouldCheckNaN();\n\n [DllImport(\"LibTorchSharp\")]\n internal static extern void THSAutograd_setAnomaly([MarshalAs(UnmanagedType.U1)] bool enabled, [MarshalAs(UnmanagedType.U1)] bool check_nan);\n\n [DllImport(\"LibTorchSharp\")]\n internal static extern void THSAutograd_grad(\n IntPtr outputs, long oLength,\n IntPtr inputs, long iLength,\n IntPtr grad_outs, long gLength,\n [MarshalAs(UnmanagedType.U1)] bool retain_graph,\n [MarshalAs(UnmanagedType.U1)] bool create_graph,\n [MarshalAs(UnmanagedType.U1)] bool allow_unused,\n AllocatePinnedArray allocator);\n\n [DllImport(\"LibTorchSharp\")]\n internal static extern void THSAutograd_backward(\n IntPtr tensors, long tLength,\n IntPtr grad_tensors, long gtLength,\n [MarshalAs(UnmanagedType.U1)] bool retain_graph,\n [MarshalAs(UnmanagedType.U1)] bool create_graph,\n IntPtr inputs, long iLength);\n\n }\n}\n"},{"alpha_fraction":0.5888888835906982,"alphanum_fraction":0.5888888835906982,"avg_line_length":25.52941131591797,"blob_id":"f6e08bcdaea1d2a53fb46a7fcb82c8051d9e5d75","content_id":"b40c2060b32b7c454e9663ddb5c62baaecef3de8","detected_licenses":["MIT"],"is_generated":false,"is_vendor":false,"language":"C#","length_bytes":452,"license_type":"permissive","max_line_length":131,"num_lines":17,"path":"/src/TorchSharp/Tensor/Enums/layout.cs","repo_name":"NiklasGustafsson/TorchSharp","src_encoding":"UTF-8","text":"// Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information.\nnamespace TorchSharp\n{\n // https://pytorch.org/docs/stable/tensor_attributes.html#torch.layout\n public enum layout\n {\n /// <summary>\n /// dense Tensors\n /// </summary>\n strided,\n\n /// <summary>\n /// sparse COO Tensors\n /// </summary>\n sparse_coo\n }\n}"},{"alpha_fraction":0.6787330508232117,"alphanum_fraction":0.6854012608528137,"avg_line_length":49.60240936279297,"blob_id":"a2b316f3ffc736cfddc5c80b03967f2d718421b5","content_id":"42745a786e9aa4a8a519e12c229a41bf66ffd78e","detected_licenses":["MIT"],"is_generated":false,"is_vendor":false,"language":"C#","length_bytes":4199,"license_type":"permissive","max_line_length":144,"num_lines":83,"path":"/src/TorchSharp/Tensor/torch.Utilities.cs","repo_name":"NiklasGustafsson/TorchSharp","src_encoding":"UTF-8","text":"// Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information.\n#nullable enable\nusing System;\nusing System.Diagnostics.Contracts;\nusing static TorchSharp.PInvoke.NativeMethods;\n\nnamespace TorchSharp\n{\n // https://pytorch.org/docs/stable/torch#utilities\n public static partial class torch\n {\n // https://pytorch.org/docs/stable/generated/torch.compiled_with_cxx11_abi\n [Pure, Obsolete(\"not implemented\", true)]\n public static bool compiled_with_cxx11_abi() => throw new NotImplementedException();\n\n // https://pytorch.org/docs/stable/generated/torch.result_type\n public static ScalarType result_type(Tensor tensor1, Tensor tensor2)\n {\n var res = THSTensor_result_type(tensor1.Handle, tensor2.Handle);\n if (res == -1) CheckForErrors();\n return (ScalarType)res;\n }\n\n // https://pytorch.org/docs/stable/generated/torch.can_cast\n public static bool can_cast(ScalarType from, ScalarType to)\n {\n var res = THSTorch_can_cast((int)from, (int)to);\n if (res == -1) CheckForErrors();\n return res != 0;\n }\n\n // https://pytorch.org/docs/stable/generated/torch.promote_types\n public static ScalarType promote_types(ScalarType type1, ScalarType type2)\n {\n var res = THSTorch_promote_types((int)type1, (int)type2);\n if (res == -1) CheckForErrors();\n return (ScalarType)res;\n }\n\n // https://pytorch.org/docs/stable/generated/torch.use_deterministic_algorithms\n [Obsolete(\"not implemented\", true)]\n public static IDisposable use_deterministic_algorithms(bool mode = true, bool warn_only = false) => throw new NotImplementedException();\n\n // https://pytorch.org/docs/stable/generated/torch.are_deterministic_algorithms_enabled\n [Pure, Obsolete(\"not implemented\", true)]\n public static bool are_deterministic_algorithms_enabled() => throw new NotImplementedException();\n\n // https://pytorch.org/docs/stable/generated/torch.is_deterministic_algorithms_warn_only_enabled\n [Pure, Obsolete(\"not implemented\", true)]\n public static bool is_deterministic_algorithms_warn_only_enabled() => throw new NotImplementedException();\n\n // https://pytorch.org/docs/stable/generated/torch.set_deterministic_debug_mode\n [Obsolete(\"not implemented\", true)]\n public static IDisposable set_deterministic_debug_mode(DebugMode mode) => throw new NotImplementedException();\n\n // https://pytorch.org/docs/stable/generated/torch.get_deterministic_debug_mode\n [Pure, Obsolete(\"not implemented\", true)]\n public static DebugMode get_deterministic_debug_mode() => throw new NotImplementedException();\n\n // https://pytorch.org/docs/stable/generated/torch.set_float32_matmul_precision\n [Obsolete(\"not implemented\", true)]\n public static IDisposable set_float32_matmul_precision(Float32MatmulPrecision precision) => throw new NotImplementedException();\n\n // https://pytorch.org/docs/stable/generated/torch.get_float32_matmul_precision\n [Pure, Obsolete(\"not implemented\", true)]\n public static Float32MatmulPrecision get_float32_matmul_precision() => throw new NotImplementedException();\n\n // https://pytorch.org/docs/stable/generated/torch.set_warn_always\n [Obsolete(\"not implemented\", true)]\n public static IDisposable set_warn_always(bool mode = true) => throw new NotImplementedException();\n\n // https://pytorch.org/docs/stable/generated/torch.is_warn_always_enabled\n [Pure, Obsolete(\"not implemented\", true)]\n public static bool is_warn_always_enabled() => throw new NotImplementedException();\n\n // https://pytorch.org/docs/stable/generated/torch._assert\n [Obsolete(\"not implemented\", true)]\n public static void _assert(bool condition, string message) => throw new NotImplementedException();\n\n [Obsolete(\"not implemented\", true)]\n public static void _assert(Func<bool> condition, string message) => throw new NotImplementedException();\n }\n}"},{"alpha_fraction":0.45644113421440125,"alphanum_fraction":0.4817243814468384,"avg_line_length":36.910335540771484,"blob_id":"fbc4c51d0749eed58be5e937508ccc7d38825fdd","content_id":"3ece4ce4af0a5b8d288c075b145522f194d1a6d5","detected_licenses":["MIT"],"is_generated":false,"is_vendor":false,"language":"C#","length_bytes":96823,"license_type":"permissive","max_line_length":130,"num_lines":2554,"path":"/test/TorchSharpTest/TestLoadSave.cs","repo_name":"NiklasGustafsson/TorchSharp","src_encoding":"UTF-8","text":"// Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information.\nusing System;\nusing System.IO;\nusing System.Linq;\nusing TorchSharp.Modules;\nusing static TorchSharp.torch.nn;\nusing Xunit;\nusing System.Collections.Generic;\n\n#nullable enable\n\nnamespace TorchSharp\n{\n [Collection(\"Sequential\")]\n public class TestLoadSave\n {\n private static Random rand = new Random();\n\n static string RandomFileName(string extension)\n {\n return $\"_STR{rand.Next()}.{extension}\";\n }\n\n [Fact]\n public void TestSaveLoadLinear1()\n {\n var location = RandomFileName(\"ts\");\n\n if (File.Exists(location)) File.Delete(location);\n\n try {\n var linear = Linear(100, 10, true);\n var params0 = linear.parameters();\n linear.save(location);\n\n var loadedLinear = Linear(100, 10, true);\n loadedLinear.load(location);\n\n var params1 = loadedLinear.parameters();\n Assert.Equal(params0, params1);\n\n loadedLinear = Linear(100, 10, true);\n loadedLinear.load(location, skip: new[] { \"weight\" });\n var params2 = loadedLinear.parameters();\n\n Assert.NotEqual(params0.First(), params2.First());\n Assert.Equal(params0.Skip(1).First(), params2.Skip(1).First());\n } finally {\n if (File.Exists(location)) File.Delete(location);\n }\n }\n\n [Fact]\n public void TestSaveLoadLinear2()\n {\n var location = RandomFileName(\"ts\");\n\n if (File.Exists(location)) File.Delete(location);\n try {\n var linear = Linear(100, 10, true);\n var params0 = linear.parameters();\n linear.save(location, skip: new[] { \"weight\" });\n\n var loadedLinear = Linear(100, 10, true);\n Assert.Throws<ArgumentException>(() => loadedLinear.load(location, strict: true));\n loadedLinear.load(location, strict: false);\n var params2 = loadedLinear.parameters();\n\n Assert.NotEqual(params0.First(), params2.First());\n Assert.Equal(params0.Skip(1).First(), params2.Skip(1).First());\n } finally {\n if (File.Exists(location)) File.Delete(location);\n }\n }\n\n [Fact]\n public void TestSaveLoadLinear3()\n {\n var location = RandomFileName(\"ts\");\n if (File.Exists(location)) File.Delete(location);\n\n try {\n using var linear = Linear(100, 10, true);\n var params0 = linear.parameters();\n linear.save(location);\n\n var loadedLinear = Linear(10, 10, true); // Mismatched shape, shouldn't matter when skipped.\n Assert.Throws<ArgumentException>(() => loadedLinear.load(location));\n\n loadedLinear.load(location, skip: new[] { \"weight\" });\n var params2 = loadedLinear.parameters();\n\n Assert.NotEqual(params0.First(), params2.First());\n Assert.Equal(params0.Skip(1).First(), params2.Skip(1).First());\n } finally {\n if (File.Exists(location)) File.Delete(location);\n }\n }\n\n [Fact]\n public void TestSaveLoadConv2D()\n {\n var location = RandomFileName(\"ts\");\n if (File.Exists(location)) File.Delete(location);\n try {\n using var conv = Conv2d(100, 10, 5);\n var params0 = conv.parameters();\n conv.save(location);\n using var loaded = Conv2d(100, 10, 5);\n loaded.load(location);\n var params1 = loaded.parameters();\n\n Assert.Equal(params0, params1);\n } finally {\n if (File.Exists(location)) File.Delete(location);\n }\n }\n\n [Fact]\n public void TestSaveLoadConv2D_sd()\n {\n using var conv = Conv2d(100, 10, 5);\n var params0 = conv.parameters();\n\n var sd = conv.state_dict();\n\n using var loaded = Conv2d(100, 10, 5);\n Assert.NotEqual(params0, loaded.parameters());\n\n loaded.load_state_dict(sd);\n Assert.Equal(params0, loaded.parameters());\n }\n\n [Fact]\n public void TestSaveLoadConv2DStream()\n {\n var location = RandomFileName(\"ts\");\n if (File.Exists(location)) File.Delete(location);\n try {\n using var conv = Conv2d(100, 10, 5);\n var params0 = conv.parameters();\n\n using (var stream = System.IO.File.OpenWrite(location)) {\n conv.save(stream);\n }\n\n using var loaded = Conv2d(100, 10, 5);\n\n using (var stream = System.IO.File.OpenRead(location)) {\n loaded.load(stream);\n }\n var params1 = loaded.parameters();\n\n Assert.Equal(params0, params1);\n\n } finally {\n if (File.Exists(location)) File.Delete(location);\n }\n }\n\n [Fact]\n public void TestSaveLoadSequential()\n {\n var location = RandomFileName(\"ts\");\n if (File.Exists(location)) File.Delete(location);\n try {\n using var conv = Sequential(Conv2d(100, 10, 5), Linear(100, 10, true));\n var params0 = conv.parameters();\n conv.save(location);\n using var loaded = Sequential(Conv2d(100, 10, 5), Linear(100, 10, true));\n loaded.load(location);\n var params1 = loaded.parameters();\n\n Assert.Equal(params0, params1);\n } finally {\n if (File.Exists(location)) File.Delete(location);\n }\n }\n\n [Fact]\n public void TestSaveLoadSequentialStream()\n {\n var location = RandomFileName(\"ts\");\n if (File.Exists(location)) File.Delete(location);\n try {\n using var conv = Sequential(Conv2d(100, 10, 5), Linear(100, 10, true));\n var params0 = conv.parameters();\n using (var stream = System.IO.File.OpenWrite(location)) {\n conv.save(stream);\n }\n using var loaded = Sequential(Conv2d(100, 10, 5), Linear(100, 10, true));\n using (var stream = System.IO.File.OpenRead(location)) {\n loaded.load(stream);\n }\n var params1 = loaded.parameters();\n\n Assert.Equal(params0, params1);\n } finally {\n if (File.Exists(location)) File.Delete(location);\n }\n }\n\n [Fact]\n public void TestSaveLoadSequential_sd()\n {\n using var conv = Sequential(Conv2d(100, 10, 5), Linear(100, 10, true));\n var params0 = conv.parameters();\n\n var sd = conv.state_dict();\n\n using var loaded = Sequential(Conv2d(100, 10, 5), Linear(100, 10, true));\n Assert.NotEqual(params0, loaded.parameters());\n\n loaded.load_state_dict(sd);\n Assert.Equal(params0, loaded.parameters());\n }\n\n [Fact]\n public void TestSaveLoadSequential_error1()\n {\n using var conv = Sequential(Conv2d(100, 10, 5), Linear(100, 10, true));\n var params0 = conv.parameters();\n\n var sd = conv.state_dict();\n sd.Remove(\"0.bias\");\n\n using var loaded = Sequential(Conv2d(100, 10, 5), Linear(100, 10, true));\n Assert.NotEqual(params0, loaded.parameters());\n\n Assert.Throws<InvalidOperationException>(() => loaded.load_state_dict(sd));\n }\n\n [Fact]\n public void TestSaveLoadSequential_error2()\n {\n using var conv = Sequential(Conv2d(100, 10, 5), Linear(100, 10, true));\n var params0 = conv.parameters();\n\n var sd = conv.state_dict();\n var t = sd[\"0.bias\"];\n\n sd.Add(\"2.bias\", t);\n\n using var loaded = Sequential(Conv2d(100, 10, 5), Linear(100, 10, true));\n Assert.NotEqual(params0, loaded.parameters());\n\n Assert.Throws<InvalidOperationException>(() => loaded.load_state_dict(sd));\n }\n\n [Fact]\n public void TestSaveLoadSequential_lax()\n {\n using var conv = Sequential(Conv2d(100, 10, 5), Linear(100, 10, true));\n var params0 = conv.parameters();\n\n var sd = conv.state_dict();\n var t = sd[\"0.bias\"];\n sd.Remove(\"0.bias\");\n\n sd.Add(\"2.bias\", t);\n\n using var loaded = Sequential(Conv2d(100, 10, 5), Linear(100, 10, true));\n Assert.NotEqual(params0, loaded.parameters());\n\n var (m, u) = loaded.load_state_dict(sd, false);\n Assert.NotEqual(params0, loaded.parameters());\n\n Assert.NotEmpty(m);\n Assert.NotEmpty(u);\n }\n\n [Fact]\n public void TestSaveLoadCustomWithParameters()\n {\n var location = RandomFileName(\"ts\");\n if (File.Exists(location)) File.Delete(location);\n\n try {\n using var original = new TestModule1();\n Assert.True(original.has_parameter(\"test\"));\n\n var params0 = original.parameters();\n Assert.True(params0.ToArray().ToArray()[0].requires_grad);\n original.save(location);\n\n using var loaded = new TestModule1();\n Assert.True(loaded.has_parameter(\"test\"));\n\n var params1 = loaded.parameters();\n Assert.True(params1.ToArray()[0].requires_grad);\n Assert.NotEqual(params0.ToArray(), params1);\n\n loaded.load(location);\n var params2 = loaded.parameters();\n Assert.True(params2.ToArray()[0].requires_grad);\n Assert.Equal(params0, params2);\n } finally {\n if (File.Exists(location)) File.Delete(location);\n }\n }\n\n private class TestModule1 : Module<torch.Tensor, torch.Tensor>\n {\n public TestModule1() : base(\"TestModule1\")\n {\n RegisterComponents();\n }\n\n public override torch.Tensor forward(torch.Tensor input)\n {\n throw new NotImplementedException();\n }\n\n private Parameter test = Parameter(torch.randn(new long[] { 2, 2 }));\n }\n\n\n [Fact]\n public void TestSaveLoadError_1()\n {\n var location = RandomFileName(\"ts\");\n if (File.Exists(location)) File.Delete(location);\n\n try {\n var linear = Linear(100, 10, true);\n var params0 = linear.parameters();\n linear.save(location);\n var loaded = Conv2d(100, 10, 5);\n Assert.Throws<ArgumentException>(() => loaded.load(location));\n } finally {\n if (File.Exists(location)) File.Delete(location);\n }\n }\n\n [Fact]\n public void TestSaveLoadError_2()\n {\n var location = \"TestSaveLoadError_2.ts\";\n\n // Submodule count mismatch\n if (File.Exists(location)) File.Delete(location);\n try {\n var linear = Sequential((\"linear1\", Linear(100, 10, true)));\n var params0 = linear.parameters();\n linear.save(location);\n var loaded = Sequential((\"linear1\", Linear(100, 10, true)), (\"conv1\", Conv2d(100, 10, 5)));\n Assert.Throws<ArgumentException>(() => loaded.load(location));\n // Shouldn't get an error for this:\n loaded.load(location, strict: false);\n } finally {\n if (File.Exists(location)) File.Delete(location);\n }\n }\n\n [Fact]\n public void TestSaveLoadError_3()\n {\n var location = RandomFileName(\"ts\");\n // Submodule name mismatch\n if (File.Exists(location)) File.Delete(location);\n try {\n var linear = Sequential((\"linear1\", Linear(100, 10, true)));\n var params0 = linear.parameters();\n linear.save(location);\n var loaded = Sequential((\"linear2\", Linear(100, 10, true)));\n Assert.Throws<ArgumentException>(() => loaded.load(location));\n File.Delete(location);\n } finally {\n if (File.Exists(location)) File.Delete(location);\n }\n }\n\n [Fact]\n public void TestSaveLoadSequence()\n {\n if (File.Exists(\".model-list.txt\")) File.Delete(\".model-list.txt\");\n\n var lin1 = Linear(100, 10, true);\n var lin2 = Linear(10, 5, true);\n var seq = Sequential((\"lin1\", lin1), (\"lin2\", lin2));\n var params0 = seq.parameters();\n seq.save(\".model-list.txt\");\n\n var lin3 = Linear(100, 10, true);\n var lin4 = Linear(10, 5, true);\n var seq1 = Sequential((\"lin1\", lin3), (\"lin2\", lin4));\n seq1.load(\".model-list.txt\");\n File.Delete(\"model-list.txt\");\n\n var params1 = seq1.parameters();\n Assert.Equal(params0, params1);\n }\n\n [Fact]\n public void TestSaveLoadGruOnCPU()\n {\n var location = RandomFileName(\"ts\");\n // Works on CPU\n if (File.Exists(location)) File.Delete(location);\n try {\n var gru = GRU(2, 2, 2);\n var params0 = gru.parameters();\n gru.save(location);\n\n var loadedGru = GRU(2, 2, 2);\n loadedGru.load(location);\n var params1 = loadedGru.parameters();\n File.Delete(location);\n Assert.Equal(params0, params1);\n } finally {\n if (File.Exists(location)) File.Delete(location);\n }\n }\n\n [Fact]\n public void TestSaveLoadGruOnCUDA()\n {\n var location = RandomFileName(\"ts\");\n if (torch.cuda.is_available()) {\n\n // Fails on CUDA\n if (File.Exists(location)) File.Delete(location);\n try {\n var gru = GRU(2, 2, 2);\n gru.to(DeviceType.CUDA);\n var params0 = gru.parameters().ToArray();\n Assert.Equal(DeviceType.CUDA, params0[0].device_type);\n\n gru.save(location);\n\n // Make sure the model is still on the GPU when we come back.\n\n params0 = gru.parameters().ToArray();\n Assert.Equal(DeviceType.CUDA, params0[0].device_type);\n\n var loadedGru = GRU(2, 2, 2);\n loadedGru.to(DeviceType.CUDA);\n loadedGru.load(location);\n var params1 = loadedGru.parameters().ToArray();\n\n Assert.Equal(DeviceType.CUDA, params1[0].device_type);\n\n File.Delete(location);\n Assert.Equal(params0, params1);\n } finally {\n if (File.Exists(location)) File.Delete(location);\n }\n\n }\n }\n\n [Fact]\n public void TestSummaryWriterLogDir()\n {\n var w1 = torch.utils.tensorboard.SummaryWriter(\"runs/123\");\n Assert.Equal(\"runs/123\", w1.LogDir);\n if (Directory.Exists(w1.LogDir)) {\n Directory.Delete(w1.LogDir, recursive: true);\n }\n\n var w2 = torch.utils.tensorboard.SummaryWriter(\"garbage\", createRunName: true);\n Assert.NotEqual(\"garbage\", w2.LogDir);\n Assert.StartsWith(\"garbage\", w2.LogDir);\n if (Directory.Exists(w2.LogDir)) {\n Directory.Delete(w2.LogDir, recursive: true);\n }\n }\n\n [Fact]\n public void TestTensorBoardScalar()\n {\n var writer = torch.utils.tensorboard.SummaryWriter();\n Assert.StartsWith(\"runs\", writer.LogDir);\n\n for (var i = 0; i < 100; i++) {\n writer.add_scalar(\"a/b\", MathF.Sin(i * MathF.PI / 8), i);\n }\n\n // Comment this out to look at the output data in tensorboard\n if (Directory.Exists(writer.LogDir)) {\n Directory.Delete(writer.LogDir, recursive: true);\n }\n }\n\n [Fact]\n public void TestTensorBoardScalars1()\n {\n var writer = torch.utils.tensorboard.SummaryWriter();\n for (var i = 0; i < 100; i++) {\n float f = i;\n writer.add_scalars(\"run_14h\", new Dictionary<string, float> {\n { \"sin\",i* MathF.Sin(f / 5) },\n { \"cos\", i* MathF.Cos(f / 5) },\n { \"tan\", MathF.Tan(f / 5) }\n }, i);\n }\n\n // Comment this out to look at the output data in tensorboard\n if (Directory.Exists(writer.LogDir)) {\n Directory.Delete(writer.LogDir, recursive: true);\n }\n }\n\n [Fact]\n public void TestTensorBoardScalars2()\n {\n var writer = torch.utils.tensorboard.SummaryWriter();\n for (var i = 0; i < 100; i++) {\n float f = i;\n writer.add_scalars(\"run_14h\", new[] {\n (\"sin\",i* MathF.Sin(f / 5)),\n (\"cos\", i* MathF.Cos(f / 5)),\n (\"tan\", MathF.Tan(f / 5))\n }, i);\n }\n\n // Comment this out to look at the output data in tensorboard\n if (Directory.Exists(writer.LogDir)) {\n Directory.Delete(writer.LogDir, recursive: true);\n }\n }\n\n [Fact]\n public void TestSaveRprop()\n {\n var l1 = Linear(10, 10, true);\n var l2 = Linear(10, 10, true);\n var seq = Sequential(l1, l2);\n\n var optim = torch.optim.Rprop(seq.parameters());\n\n var sd = optim.state_dict();\n Assert.Multiple(\n () => Assert.Equal(4, sd.State.Count),\n () => Assert.Single(sd.Options)\n );\n\n var optim2 = torch.optim.Rprop(seq.parameters(), 0.5, 0.01, 1.25, 1e-8, 35);\n optim2.load_state_dict(sd);\n\n var sd2 = optim2.state_dict();\n\n Assert.Multiple(\n () => Assert.Equal(4, sd2.State.Count),\n () => Assert.Single(sd2.Options)\n );\n\n for (int i = 0; i < sd.Options.Count; i++) {\n var opt = sd.Options[i] as Modules.Rprop.Options;\n var opt2 = sd2.Options[i] as Modules.Rprop.Options;\n Assert.Multiple(\n () => Assert.Equal(opt!.InitialLearningRate, opt2!.InitialLearningRate),\n () => Assert.Equal(opt!.LearningRate, opt2!.LearningRate),\n () => Assert.Equal(opt!.etaminus, opt2!.etaminus),\n () => Assert.Equal(opt!.etaplus, opt2!.etaplus),\n () => Assert.Equal(opt!.min_step, opt2!.min_step),\n () => Assert.Equal(opt!.max_step, opt2!.max_step)\n );\n }\n for (int i = 0; i < sd.State.Count; i++) {\n var opt = sd.State[i] as Modules.Rprop.State;\n var opt2 = sd2.State[i] as Modules.Rprop.State;\n Assert.True(opt!.ApproximatelyEquals(opt2));\n }\n }\n\n [Fact]\n public void TestSaveRpropFile()\n {\n var location = RandomFileName(\"ts\");\n\n if (File.Exists(location)) File.Delete(location);\n var l1 = Linear(10, 10, true);\n var l2 = Linear(10, 10, true);\n var seq = Sequential(l1, l2);\n\n var optim = torch.optim.Rprop(seq.parameters());\n\n var sd = optim.state_dict();\n Assert.Multiple(\n () => Assert.Equal(4, sd.State.Count),\n () => Assert.Single(sd.Options)\n );\n\n var optim2 = torch.optim.Rprop(seq.parameters(), 0.5, 0.01, 1.25, 1e-8, 35);\n\n try {\n optim.save_state_dict(location);\n optim2.load_state_dict(location);\n\n var sd2 = optim2.state_dict();\n\n Assert.Multiple(\n () => Assert.Equal(4, sd2.State.Count),\n () => Assert.Single(sd2.Options)\n );\n\n for (int i = 0; i < sd.Options.Count; i++) {\n var opt = sd.Options[i] as Modules.Rprop.Options;\n var opt2 = sd2.Options[i] as Modules.Rprop.Options;\n Assert.Multiple(\n () => Assert.Equal(opt!.InitialLearningRate, opt2!.InitialLearningRate),\n () => Assert.Equal(opt!.LearningRate, opt2!.LearningRate),\n () => Assert.Equal(opt!.etaminus, opt2!.etaminus),\n () => Assert.Equal(opt!.etaplus, opt2!.etaplus),\n () => Assert.Equal(opt!.min_step, opt2!.min_step),\n () => Assert.Equal(opt!.max_step, opt2!.max_step)\n );\n }\n for (int i = 0; i < sd.State.Count; i++) {\n var opt = sd.State[i] as Modules.Rprop.State;\n var opt2 = sd2.State[i] as Modules.Rprop.State;\n Assert.True(opt!.ApproximatelyEquals(opt2));\n }\n } finally {\n File.Delete(location);\n }\n }\n\n [Fact]\n public void TestSaveSGD()\n {\n var l1 = Linear(10, 10, true);\n var l2 = Linear(10, 10, true);\n var seq = Sequential(l1, l2);\n\n var optim = torch.optim.SGD(seq.parameters(), 0.01);\n\n var sd = optim.state_dict();\n Assert.Multiple(\n () => Assert.Equal(4, sd.State.Count),\n () => Assert.Single(sd.Options)\n );\n\n var optim2 = torch.optim.SGD(seq.parameters(), 0.5, 0.01, 1.25, 1e-8);\n optim2.load_state_dict(sd);\n\n var sd2 = optim2.state_dict();\n\n Assert.Multiple(\n () => Assert.Equal(4, sd2.State.Count),\n () => Assert.Single(sd2.Options)\n );\n\n for (int i = 0; i < sd.Options.Count; i++) {\n var opt = sd.Options[i] as Modules.SGD.Options;\n var opt2 = sd2.Options[i] as Modules.SGD.Options;\n Assert.Multiple(\n () => Assert.Equal(opt!.InitialLearningRate, opt2!.InitialLearningRate),\n () => Assert.Equal(opt!.LearningRate, opt2!.LearningRate),\n () => Assert.Equal(opt!.momentum, opt2!.momentum),\n () => Assert.Equal(opt!.dampening, opt2!.dampening),\n () => Assert.Equal(opt!.weight_decay, opt2!.weight_decay)\n );\n }\n for (int i = 0; i < sd.State.Count; i++) {\n var opt = sd.State[i] as Modules.SGD.State;\n var opt2 = sd2.State[i] as Modules.SGD.State;\n Assert.True(opt!.ApproximatelyEquals(opt2));\n }\n }\n\n [Fact]\n public void TestSaveSGDFile()\n {\n var location = RandomFileName(\"ts\");\n\n if (File.Exists(location)) File.Delete(location);\n var l1 = Linear(10, 10, true);\n var l2 = Linear(10, 10, true);\n var seq = Sequential(l1, l2);\n\n var optim = torch.optim.SGD(seq.parameters(), 0.01);\n\n var sd = optim.state_dict();\n Assert.Multiple(\n () => Assert.Equal(4, sd.State.Count),\n () => Assert.Single(sd.Options)\n );\n\n var optim2 = torch.optim.SGD(seq.parameters(), 0.5, 0.01, 1.25, 1e-8);\n\n try {\n optim.save_state_dict(location);\n optim2.load_state_dict(location);\n\n var sd2 = optim2.state_dict();\n\n Assert.Multiple(\n () => Assert.Equal(4, sd2.State.Count),\n () => Assert.Single(sd2.Options)\n );\n\n for (int i = 0; i < sd.Options.Count; i++) {\n var opt = sd.Options[i] as Modules.SGD.Options;\n var opt2 = sd2.Options[i] as Modules.SGD.Options;\n Assert.Multiple(\n () => Assert.Equal(opt!.InitialLearningRate, opt2!.InitialLearningRate),\n () => Assert.Equal(opt!.LearningRate, opt2!.LearningRate),\n () => Assert.Equal(opt!.momentum, opt2!.momentum),\n () => Assert.Equal(opt!.dampening, opt2!.dampening),\n () => Assert.Equal(opt!.weight_decay, opt2!.weight_decay)\n );\n }\n for (int i = 0; i < sd.State.Count; i++) {\n var opt = sd.State[i] as Modules.SGD.State;\n var opt2 = sd2.State[i] as Modules.SGD.State;\n Assert.True(opt!.ApproximatelyEquals(opt2));\n }\n } finally {\n File.Delete(location);\n }\n }\n\n [Fact]\n public void TestSaveSGDFile2()\n {\n var location = RandomFileName(\"ts\");\n\n if (File.Exists(location)) File.Delete(location);\n var l1 = Linear(10, 10, true);\n var l2 = Linear(10, 10, true);\n var seq = Sequential(l1, l2);\n\n var optim = torch.optim.SGD(seq.parameters(), 0.01);\n\n var sd = optim.state_dict();\n Assert.Multiple(\n () => Assert.Equal(4, sd.State.Count),\n () => Assert.Single(sd.Options)\n );\n\n var optim2 = torch.optim.SGD(seq.parameters(), 0.5, 0.01, 1.25, 1e-8);\n\n // Force the SGD momentum_buffer to be created.\n\n using var x = torch.randn(new long[] { 64, 10 });\n using var y = torch.randn(new long[] { 64, 10 });\n\n var loss = torch.nn.MSELoss(Reduction.Sum);\n\n using var eval = seq.call(x);\n var output = loss.call(eval, y);\n\n var l = output.ToSingle();\n\n optim2.zero_grad();\n\n output.backward();\n\n optim2.step();\n\n try {\n optim.save_state_dict(location);\n optim2.load_state_dict(location);\n\n var sd2 = optim2.state_dict();\n\n Assert.Multiple(\n () => Assert.Equal(4, sd2.State.Count),\n () => Assert.Single(sd2.Options)\n );\n\n for (int i = 0; i < sd.Options.Count; i++) {\n var opt = sd.Options[i] as Modules.SGD.Options;\n var opt2 = sd2.Options[i] as Modules.SGD.Options;\n Assert.Multiple(\n () => Assert.Equal(opt!.InitialLearningRate, opt2!.InitialLearningRate),\n () => Assert.Equal(opt!.LearningRate, opt2!.LearningRate),\n () => Assert.Equal(opt!.momentum, opt2!.momentum),\n () => Assert.Equal(opt!.dampening, opt2!.dampening),\n () => Assert.Equal(opt!.weight_decay, opt2!.weight_decay)\n );\n }\n for (int i = 0; i < sd.State.Count; i++) {\n var opt = sd.State[i] as Modules.SGD.State;\n var opt2 = sd2.State[i] as Modules.SGD.State;\n Assert.True(opt!.ApproximatelyEquals(opt2));\n }\n } finally {\n File.Delete(location);\n }\n }\n\n [Fact]\n public void TestSaveSGDFile3()\n {\n var location = RandomFileName(\"ts\");\n\n if (File.Exists(location)) File.Delete(location);\n var l1 = Linear(10, 10, true);\n var l2 = Linear(10, 10, true);\n var seq = Sequential(l1, l2);\n\n var optim = torch.optim.SGD(seq.parameters(), 0.01, 0.025);\n\n var sd = optim.state_dict();\n Assert.Multiple(\n () => Assert.Equal(4, sd.State.Count),\n () => Assert.Single(sd.Options)\n );\n\n var optim2 = torch.optim.SGD(seq.parameters(), 0.5, 0.01, 1.25, 1e-8);\n\n // Force the SGD momentum_buffer to be created.\n\n using var x = torch.randn(new long[] { 64, 10 });\n using var y = torch.randn(new long[] { 64, 10 });\n\n var loss = torch.nn.MSELoss(Reduction.Sum);\n\n using var eval = seq.call(x);\n var output = loss.call(eval, y);\n\n var l = output.ToSingle();\n\n optim.zero_grad();\n\n output.backward();\n\n optim.step();\n\n try {\n optim.save_state_dict(location);\n optim2.load_state_dict(location);\n\n var sd2 = optim2.state_dict();\n\n Assert.Multiple(\n () => Assert.Equal(4, sd2.State.Count),\n () => Assert.Single(sd2.Options)\n );\n\n for (int i = 0; i < sd.Options.Count; i++) {\n var opt = sd.Options[i] as Modules.SGD.Options;\n var opt2 = sd2.Options[i] as Modules.SGD.Options;\n Assert.Multiple(\n () => Assert.Equal(opt!.InitialLearningRate, opt2!.InitialLearningRate),\n () => Assert.Equal(opt!.LearningRate, opt2!.LearningRate),\n () => Assert.Equal(opt!.momentum, opt2!.momentum),\n () => Assert.Equal(opt!.dampening, opt2!.dampening),\n () => Assert.Equal(opt!.weight_decay, opt2!.weight_decay)\n );\n }\n for (int i = 0; i < sd.State.Count; i++) {\n var opt = sd.State[i] as Modules.SGD.State;\n var opt2 = sd2.State[i] as Modules.SGD.State;\n Assert.True(opt!.ApproximatelyEquals(opt2));\n }\n } finally {\n File.Delete(location);\n }\n }\n\n [Fact]\n public void TestSaveASGD()\n {\n var l1 = Linear(10, 10, true);\n var l2 = Linear(10, 10, true);\n var seq = Sequential(l1, l2);\n\n var optim = torch.optim.ASGD(seq.parameters());\n\n var sd = optim.state_dict();\n Assert.Multiple(\n () => Assert.Equal(4, sd.State.Count),\n () => Assert.Single(sd.Options)\n );\n\n var optim2 = torch.optim.ASGD(seq.parameters(), 0.5, 0.01, 1.25, 1e-8, 0.25);\n optim2.load_state_dict(sd);\n\n var sd2 = optim2.state_dict();\n\n Assert.Multiple(\n () => Assert.Equal(4, sd2.State.Count),\n () => Assert.Single(sd2.Options)\n );\n\n for (int i = 0; i < sd.Options.Count; i++) {\n var opt = sd.Options[i] as Modules.ASGD.Options;\n var opt2 = sd2.Options[i] as Modules.ASGD.Options;\n Assert.Multiple(\n () => Assert.Equal(opt!.InitialLearningRate, opt2!.InitialLearningRate),\n () => Assert.Equal(opt!.LearningRate, opt2!.LearningRate),\n () => Assert.Equal(opt!.lambd, opt2!.lambd),\n () => Assert.Equal(opt!.alpha, opt2!.alpha),\n () => Assert.Equal(opt!.t0, opt2!.t0),\n () => Assert.Equal(opt!.weight_decay, opt2!.weight_decay)\n );\n }\n for (int i = 0; i < sd.State.Count; i++) {\n var opt = sd.State[i] as Modules.ASGD.State;\n var opt2 = sd2.State[i] as Modules.ASGD.State;\n Assert.True(opt!.ApproximatelyEquals(opt2));\n }\n }\n\n [Fact]\n public void TestSaveASGDFile()\n {\n var location = RandomFileName(\"ts\");\n if (File.Exists(location)) File.Delete(location);\n\n var l1 = Linear(10, 10, true);\n var l2 = Linear(10, 10, true);\n var seq = Sequential(l1, l2);\n\n var optim = torch.optim.ASGD(seq.parameters());\n\n var sd = optim.state_dict();\n Assert.Multiple(\n () => Assert.Equal(4, sd.State.Count),\n () => Assert.Single(sd.Options)\n );\n\n var optim2 = torch.optim.ASGD(seq.parameters(), 0.5, 0.01, 1.25, 1e-8, 0.25);\n\n try {\n optim.save_state_dict(location);\n optim2.load_state_dict(location);\n\n var sd2 = optim2.state_dict();\n\n Assert.Multiple(\n () => Assert.Equal(4, sd2.State.Count),\n () => Assert.Single(sd2.Options)\n );\n\n for (int i = 0; i < sd.Options.Count; i++) {\n var opt = sd.Options[i] as Modules.ASGD.Options;\n var opt2 = sd2.Options[i] as Modules.ASGD.Options;\n Assert.Multiple(\n () => Assert.Equal(opt!.InitialLearningRate, opt2!.InitialLearningRate),\n () => Assert.Equal(opt!.LearningRate, opt2!.LearningRate),\n () => Assert.Equal(opt!.lambd, opt2!.lambd),\n () => Assert.Equal(opt!.alpha, opt2!.alpha),\n () => Assert.Equal(opt!.t0, opt2!.t0),\n () => Assert.Equal(opt!.weight_decay, opt2!.weight_decay)\n );\n }\n for (int i = 0; i < sd.State.Count; i++) {\n var opt = sd.State[i] as Modules.ASGD.State;\n var opt2 = sd2.State[i] as Modules.ASGD.State;\n Assert.True(opt!.ApproximatelyEquals(opt2));\n }\n } finally {\n File.Delete(location);\n }\n }\n\n [Fact]\n public void TestSaveRMSProp()\n {\n var l1 = Linear(10, 10, true);\n var l2 = Linear(10, 10, true);\n var seq = Sequential(l1, l2);\n\n var optim = torch.optim.RMSProp(seq.parameters());\n\n var sd = optim.state_dict();\n Assert.Multiple(\n () => Assert.Equal(4, sd.State.Count),\n () => Assert.Single(sd.Options)\n );\n\n var optim2 = torch.optim.RMSProp(seq.parameters(), 0.5, 0.01, 1.25, 1e-8, 0.25);\n optim2.load_state_dict(sd);\n\n var sd2 = optim2.state_dict();\n\n Assert.Multiple(\n () => Assert.Equal(4, sd2.State.Count),\n () => Assert.Single(sd2.Options)\n );\n\n for (int i = 0; i < sd.Options.Count; i++) {\n var opt = sd.Options[i] as Modules.RMSProp.Options;\n var opt2 = sd2.Options[i] as Modules.RMSProp.Options;\n Assert.Multiple(\n () => Assert.Equal(opt!.InitialLearningRate, opt2!.InitialLearningRate),\n () => Assert.Equal(opt!.LearningRate, opt2!.LearningRate),\n () => Assert.Equal(opt!.momentum, opt2!.momentum),\n () => Assert.Equal(opt!.alpha, opt2!.alpha),\n () => Assert.Equal(opt!.eps, opt2!.eps),\n () => Assert.Equal(opt!.weight_decay, opt2!.weight_decay)\n );\n }\n for (int i = 0; i < sd.State.Count; i++) {\n var opt = sd.State[i] as Modules.RMSProp.State;\n var opt2 = sd2.State[i] as Modules.RMSProp.State;\n Assert.True(opt!.ApproximatelyEquals(opt2));\n }\n }\n\n [Fact]\n public void TestSaveRMSPropFile()\n {\n var location = RandomFileName(\"ts\");\n if (File.Exists(location)) File.Delete(location);\n\n var l1 = Linear(10, 10, true);\n var l2 = Linear(10, 10, true);\n var seq = Sequential(l1, l2);\n\n var optim = torch.optim.RMSProp(seq.parameters());\n\n var sd = optim.state_dict();\n Assert.Multiple(\n () => Assert.Equal(4, sd.State.Count),\n () => Assert.Single(sd.Options)\n );\n\n var optim2 = torch.optim.RMSProp(seq.parameters(), 0.5, 0.01, 1.25, 1e-8, 0.25);\n\n try {\n optim.save_state_dict(location);\n optim2.load_state_dict(location);\n\n var sd2 = optim2.state_dict();\n\n Assert.Multiple(\n () => Assert.Equal(4, sd2.State.Count),\n () => Assert.Single(sd2.Options)\n );\n\n for (int i = 0; i < sd.Options.Count; i++) {\n var opt = sd.Options[i] as Modules.RMSProp.Options;\n var opt2 = sd2.Options[i] as Modules.RMSProp.Options;\n Assert.Multiple(\n () => Assert.Equal(opt!.InitialLearningRate, opt2!.InitialLearningRate),\n () => Assert.Equal(opt!.LearningRate, opt2!.LearningRate),\n () => Assert.Equal(opt!.momentum, opt2!.momentum),\n () => Assert.Equal(opt!.alpha, opt2!.alpha),\n () => Assert.Equal(opt!.eps, opt2!.eps),\n () => Assert.Equal(opt!.weight_decay, opt2!.weight_decay)\n );\n }\n for (int i = 0; i < sd.State.Count; i++) {\n var opt = sd.State[i] as Modules.RMSProp.State;\n var opt2 = sd2.State[i] as Modules.RMSProp.State;\n Assert.True(opt!.ApproximatelyEquals(opt2));\n }\n } finally {\n File.Delete(location);\n }\n }\n\n [Fact]\n public void TestSaveRAdam()\n {\n var l1 = Linear(10, 10, true);\n var l2 = Linear(10, 10, true);\n var seq = Sequential(l1, l2);\n\n var optim = torch.optim.RAdam(seq.parameters());\n\n var sd = optim.state_dict();\n Assert.Multiple(\n () => Assert.Equal(4, sd.State.Count),\n () => Assert.Single(sd.Options)\n );\n\n var optim2 = torch.optim.RAdam(seq.parameters(), 0.5, 0.01, 0.75, 1e-8, 0.25);\n optim2.load_state_dict(sd);\n\n var sd2 = optim2.state_dict();\n\n Assert.Multiple(\n () => Assert.Equal(4, sd2.State.Count),\n () => Assert.Single(sd2.Options)\n );\n\n for (int i = 0; i < sd.Options.Count; i++) {\n var opt = sd.Options[i] as Modules.RAdam.Options;\n var opt2 = sd2.Options[i] as Modules.RAdam.Options;\n Assert.Multiple(\n () => Assert.Equal(opt!.InitialLearningRate, opt2!.InitialLearningRate),\n () => Assert.Equal(opt!.LearningRate, opt2!.LearningRate),\n () => Assert.Equal(opt!.beta1, opt2!.beta1),\n () => Assert.Equal(opt!.beta2, opt2!.beta2),\n () => Assert.Equal(opt!.eps, opt2!.eps),\n () => Assert.Equal(opt!.weight_decay, opt2!.weight_decay)\n );\n }\n for (int i = 0; i < sd.State.Count; i++) {\n var opt = sd.State[i] as Modules.RAdam.State;\n var opt2 = sd2.State[i] as Modules.RAdam.State;\n Assert.True(opt!.ApproximatelyEquals(opt2));\n }\n }\n\n [Fact]\n public void TestSaveRAdamFile()\n {\n var location = RandomFileName(\"ts\");\n if (File.Exists(location)) File.Delete(location);\n\n var l1 = Linear(10, 10, true);\n var l2 = Linear(10, 10, true);\n var seq = Sequential(l1, l2);\n\n var optim = torch.optim.RAdam(seq.parameters());\n\n var sd = optim.state_dict();\n Assert.Multiple(\n () => Assert.Equal(4, sd.State.Count),\n () => Assert.Single(sd.Options)\n );\n\n var optim2 = torch.optim.RAdam(seq.parameters(), 0.5, 0.01, 0.75, 1e-8, 0.25);\n\n try {\n optim.save_state_dict(location);\n optim2.load_state_dict(location);\n\n var sd2 = optim2.state_dict();\n\n Assert.Multiple(\n () => Assert.Equal(4, sd2.State.Count),\n () => Assert.Single(sd2.Options)\n );\n\n for (int i = 0; i < sd.Options.Count; i++) {\n var opt = sd.Options[i] as Modules.RAdam.Options;\n var opt2 = sd2.Options[i] as Modules.RAdam.Options;\n Assert.Multiple(\n () => Assert.Equal(opt!.InitialLearningRate, opt2!.InitialLearningRate),\n () => Assert.Equal(opt!.LearningRate, opt2!.LearningRate),\n () => Assert.Equal(opt!.beta1, opt2!.beta1),\n () => Assert.Equal(opt!.beta2, opt2!.beta2),\n () => Assert.Equal(opt!.eps, opt2!.eps),\n () => Assert.Equal(opt!.weight_decay, opt2!.weight_decay)\n );\n }\n for (int i = 0; i < sd.State.Count; i++) {\n var opt = sd.State[i] as Modules.RAdam.State;\n var opt2 = sd2.State[i] as Modules.RAdam.State;\n Assert.True(opt!.ApproximatelyEquals(opt2));\n }\n } finally {\n File.Delete(location);\n }\n }\n\n [Fact]\n public void TestSaveNAdam()\n {\n var l1 = Linear(10, 10, true);\n var l2 = Linear(10, 10, true);\n var seq = Sequential(l1, l2);\n\n var optim = torch.optim.NAdam(seq.parameters());\n\n var sd = optim.state_dict();\n Assert.Multiple(\n () => Assert.Equal(4, sd.State.Count),\n () => Assert.Single(sd.Options)\n );\n\n var optim2 = torch.optim.NAdam(seq.parameters(), 0.5, 0.01, 0.75, 1e-8, 0.25);\n optim2.load_state_dict(sd);\n\n var sd2 = optim2.state_dict();\n\n Assert.Multiple(\n () => Assert.Equal(4, sd2.State.Count),\n () => Assert.Single(sd2.Options)\n );\n\n for (int i = 0; i < sd.Options.Count; i++) {\n var opt = sd.Options[i] as Modules.NAdam.Options;\n var opt2 = sd2.Options[i] as Modules.NAdam.Options;\n Assert.Multiple(\n () => Assert.Equal(opt!.InitialLearningRate, opt2!.InitialLearningRate),\n () => Assert.Equal(opt!.LearningRate, opt2!.LearningRate),\n () => Assert.Equal(opt!.beta1, opt2!.beta1),\n () => Assert.Equal(opt!.beta2, opt2!.beta2),\n () => Assert.Equal(opt!.eps, opt2!.eps),\n () => Assert.Equal(opt!.weight_decay, opt2!.weight_decay),\n () => Assert.Equal(opt!.momentum_decay, opt2!.momentum_decay)\n );\n }\n for (int i = 0; i < sd.State.Count; i++) {\n var opt = sd.State[i] as Modules.NAdam.State;\n var opt2 = sd2.State[i] as Modules.NAdam.State;\n Assert.True(opt!.ApproximatelyEquals(opt2));\n }\n }\n\n [Fact]\n public void TestSaveNAdamFile()\n {\n var location = RandomFileName(\"ts\");\n if (File.Exists(location)) File.Delete(location);\n\n var l1 = Linear(10, 10, true);\n var l2 = Linear(10, 10, true);\n var seq = Sequential(l1, l2);\n\n var optim = torch.optim.NAdam(seq.parameters());\n\n var sd = optim.state_dict();\n Assert.Multiple(\n () => Assert.Equal(4, sd.State.Count),\n () => Assert.Single(sd.Options)\n );\n\n var optim2 = torch.optim.NAdam(seq.parameters(), 0.5, 0.01, 0.75, 1e-8, 0.25);\n\n try {\n optim.save_state_dict(location);\n optim2.load_state_dict(location);\n\n var sd2 = optim2.state_dict();\n\n Assert.Multiple(\n () => Assert.Equal(4, sd2.State.Count),\n () => Assert.Single(sd2.Options)\n );\n\n for (int i = 0; i < sd.Options.Count; i++) {\n var opt = sd.Options[i] as Modules.NAdam.Options;\n var opt2 = sd2.Options[i] as Modules.NAdam.Options;\n Assert.Multiple(\n () => Assert.Equal(opt!.InitialLearningRate, opt2!.InitialLearningRate),\n () => Assert.Equal(opt!.LearningRate, opt2!.LearningRate),\n () => Assert.Equal(opt!.beta1, opt2!.beta1),\n () => Assert.Equal(opt!.beta2, opt2!.beta2),\n () => Assert.Equal(opt!.eps, opt2!.eps),\n () => Assert.Equal(opt!.weight_decay, opt2!.weight_decay),\n () => Assert.Equal(opt!.momentum_decay, opt2!.momentum_decay)\n );\n }\n for (int i = 0; i < sd.State.Count; i++) {\n var opt = sd.State[i] as Modules.NAdam.State;\n var opt2 = sd2.State[i] as Modules.NAdam.State;\n Assert.True(opt!.ApproximatelyEquals(opt2));\n }\n } finally {\n File.Delete(location);\n }\n }\n\n [Fact]\n public void TestSaveAdam()\n {\n var l1 = Linear(10, 10, true);\n var l2 = Linear(10, 10, true);\n var seq = Sequential(l1, l2);\n\n var optim = torch.optim.Adam(seq.parameters());\n\n var sd = optim.state_dict();\n Assert.Multiple(\n () => Assert.Equal(4, sd.State.Count),\n () => Assert.Single(sd.Options)\n );\n\n var optim2 = torch.optim.Adam(seq.parameters(), 0.5, 0.01, 0.75, 1e-8, 0.25);\n optim2.load_state_dict(sd);\n\n var sd2 = optim2.state_dict();\n\n Assert.Multiple(\n () => Assert.Equal(4, sd2.State.Count),\n () => Assert.Single(sd2.Options)\n );\n\n for (int i = 0; i < sd.Options.Count; i++) {\n var opt = sd.Options[i] as Modules.Adam.Options;\n var opt2 = sd2.Options[i] as Modules.Adam.Options;\n Assert.Multiple(\n () => Assert.Equal(opt!.InitialLearningRate, opt2!.InitialLearningRate),\n () => Assert.Equal(opt!.LearningRate, opt2!.LearningRate),\n () => Assert.Equal(opt!.beta1, opt2!.beta1),\n () => Assert.Equal(opt!.beta2, opt2!.beta2),\n () => Assert.Equal(opt!.eps, opt2!.eps),\n () => Assert.Equal(opt!.weight_decay, opt2!.weight_decay),\n () => Assert.Equal(opt!.amsgrad, opt2!.amsgrad),\n () => Assert.Equal(opt!.maximize, opt2!.maximize)\n );\n }\n for (int i = 0; i < sd.State.Count; i++) {\n var opt = sd.State[i] as Modules.Adam.State;\n var opt2 = sd2.State[i] as Modules.Adam.State;\n Assert.True(opt!.ApproximatelyEquals(opt2));\n }\n }\n\n [Fact]\n public void TestSaveAdamFile()\n {\n var location = RandomFileName(\"ts\");\n if (File.Exists(location)) File.Delete(location);\n\n var l1 = Linear(10, 10, true);\n var l2 = Linear(10, 10, true);\n var seq = Sequential(l1, l2);\n\n var optim = torch.optim.Adam(seq.parameters());\n\n var sd = optim.state_dict();\n Assert.Multiple(\n () => Assert.Equal(4, sd.State.Count),\n () => Assert.Single(sd.Options)\n );\n\n var optim2 = torch.optim.Adam(seq.parameters(), 0.5, 0.01, 0.75, 1e-8, 0.25);\n\n try {\n optim.save_state_dict(location);\n optim2.load_state_dict(location);\n\n var sd2 = optim2.state_dict();\n\n Assert.Multiple(\n () => Assert.Equal(4, sd2.State.Count),\n () => Assert.Single(sd2.Options)\n );\n\n for (int i = 0; i < sd.Options.Count; i++) {\n var opt = sd.Options[i] as Modules.Adam.Options;\n var opt2 = sd2.Options[i] as Modules.Adam.Options;\n Assert.Multiple(\n () => Assert.Equal(opt!.InitialLearningRate, opt2!.InitialLearningRate),\n () => Assert.Equal(opt!.LearningRate, opt2!.LearningRate),\n () => Assert.Equal(opt!.beta1, opt2!.beta1),\n () => Assert.Equal(opt!.beta2, opt2!.beta2),\n () => Assert.Equal(opt!.eps, opt2!.eps),\n () => Assert.Equal(opt!.weight_decay, opt2!.weight_decay),\n () => Assert.Equal(opt!.amsgrad, opt2!.amsgrad),\n () => Assert.Equal(opt!.maximize, opt2!.maximize)\n );\n }\n for (int i = 0; i < sd.State.Count; i++) {\n var opt = sd.State[i] as Modules.Adam.State;\n var opt2 = sd2.State[i] as Modules.Adam.State;\n Assert.True(opt!.ApproximatelyEquals(opt2));\n }\n } finally {\n File.Delete(location);\n }\n }\n\n [Fact]\n public void TestSaveAdamFile2()\n {\n var location = RandomFileName(\"ts\");\n if (File.Exists(location)) File.Delete(location);\n\n var l1 = Linear(10, 10, true);\n var l2 = Linear(10, 10, true);\n var seq = Sequential(l1, l2);\n\n var optim = torch.optim.Adam(seq.parameters());\n\n var sd = optim.state_dict();\n Assert.Multiple(\n () => Assert.Equal(4, sd.State.Count),\n () => Assert.Single(sd.Options)\n );\n\n var optim2 = torch.optim.Adam(seq.parameters(), 0.5, 0.01, 0.75, 1e-8, 0.25, true);\n\n try {\n optim.save_state_dict(location);\n optim2.load_state_dict(location);\n\n var sd2 = optim2.state_dict();\n\n Assert.Multiple(\n () => Assert.Equal(4, sd2.State.Count),\n () => Assert.Single(sd2.Options)\n );\n\n for (int i = 0; i < sd.Options.Count; i++) {\n var opt = sd.Options[i] as Modules.Adam.Options;\n var opt2 = sd2.Options[i] as Modules.Adam.Options;\n Assert.Multiple(\n () => Assert.Equal(opt!.InitialLearningRate, opt2!.InitialLearningRate),\n () => Assert.Equal(opt!.LearningRate, opt2!.LearningRate),\n () => Assert.Equal(opt!.beta1, opt2!.beta1),\n () => Assert.Equal(opt!.beta2, opt2!.beta2),\n () => Assert.Equal(opt!.eps, opt2!.eps),\n () => Assert.Equal(opt!.weight_decay, opt2!.weight_decay),\n () => Assert.Equal(opt!.amsgrad, opt2!.amsgrad),\n () => Assert.Equal(opt!.maximize, opt2!.maximize)\n );\n }\n for (int i = 0; i < sd.State.Count; i++) {\n var opt = sd.State[i] as Modules.Adam.State;\n var opt2 = sd2.State[i] as Modules.Adam.State;\n Assert.True(opt!.ApproximatelyEquals(opt2));\n }\n } finally {\n File.Delete(location);\n }\n }\n\n [Fact]\n public void TestSaveAdamFile3()\n {\n var location = RandomFileName(\"ts\");\n if (File.Exists(location)) File.Delete(location);\n\n var l1 = Linear(10, 10, true);\n var l2 = Linear(10, 10, true);\n var seq = Sequential(l1, l2);\n\n var optim = torch.optim.Adam(seq.parameters(), amsgrad: true);\n\n var sd = optim.state_dict();\n Assert.Multiple(\n () => Assert.Equal(4, sd.State.Count),\n () => Assert.Single(sd.Options)\n );\n\n var optim2 = torch.optim.Adam(seq.parameters(), 0.5, 0.01, 0.75, 1e-8, 0.25);\n\n try {\n optim.save_state_dict(location);\n optim2.load_state_dict(location);\n\n var sd2 = optim2.state_dict();\n\n Assert.Multiple(\n () => Assert.Equal(4, sd2.State.Count),\n () => Assert.Single(sd2.Options)\n );\n\n for (int i = 0; i < sd.Options.Count; i++) {\n var opt = sd.Options[i] as Modules.Adam.Options;\n var opt2 = sd2.Options[i] as Modules.Adam.Options;\n Assert.Multiple(\n () => Assert.Equal(opt!.InitialLearningRate, opt2!.InitialLearningRate),\n () => Assert.Equal(opt!.LearningRate, opt2!.LearningRate),\n () => Assert.Equal(opt!.beta1, opt2!.beta1),\n () => Assert.Equal(opt!.beta2, opt2!.beta2),\n () => Assert.Equal(opt!.eps, opt2!.eps),\n () => Assert.Equal(opt!.weight_decay, opt2!.weight_decay),\n () => Assert.Equal(opt!.amsgrad, opt2!.amsgrad),\n () => Assert.Equal(opt!.maximize, opt2!.maximize)\n );\n }\n for (int i = 0; i < sd.State.Count; i++) {\n var opt = sd.State[i] as Modules.Adam.State;\n var opt2 = sd2.State[i] as Modules.Adam.State;\n Assert.True(opt!.ApproximatelyEquals(opt2));\n }\n } finally {\n File.Delete(location);\n }\n }\n\n [Fact]\n public void TestSaveAdamW()\n {\n var l1 = Linear(10, 10, true);\n var l2 = Linear(10, 10, true);\n var seq = Sequential(l1, l2);\n\n var optim = torch.optim.AdamW(seq.parameters());\n\n var sd = optim.state_dict();\n Assert.Multiple(\n () => Assert.Equal(4, sd.State.Count),\n () => Assert.Single(sd.Options)\n );\n\n var optim2 = torch.optim.AdamW(seq.parameters(), 0.5, 0.01, 0.75, 1e-8, 0.25);\n optim2.load_state_dict(sd);\n\n var sd2 = optim2.state_dict();\n\n Assert.Multiple(\n () => Assert.Equal(4, sd2.State.Count),\n () => Assert.Single(sd2.Options)\n );\n\n for (int i = 0; i < sd.Options.Count; i++) {\n var opt = sd.Options[i] as Modules.AdamW.Options;\n var opt2 = sd2.Options[i] as Modules.AdamW.Options;\n Assert.Multiple(\n () => Assert.Equal(opt!.InitialLearningRate, opt2!.InitialLearningRate),\n () => Assert.Equal(opt!.LearningRate, opt2!.LearningRate),\n () => Assert.Equal(opt!.beta1, opt2!.beta1),\n () => Assert.Equal(opt!.beta2, opt2!.beta2),\n () => Assert.Equal(opt!.eps, opt2!.eps),\n () => Assert.Equal(opt!.weight_decay, opt2!.weight_decay),\n () => Assert.Equal(opt!.amsgrad, opt2!.amsgrad),\n () => Assert.Equal(opt!.maximize, opt2!.maximize)\n );\n }\n for (int i = 0; i < sd.State.Count; i++) {\n var opt = sd.State[i] as Modules.AdamW.State;\n var opt2 = sd2.State[i] as Modules.AdamW.State;\n Assert.True(opt!.ApproximatelyEquals(opt2));\n }\n }\n\n [Fact]\n public void TestSaveAdamWFile()\n {\n var location = RandomFileName(\"ts\");\n if (File.Exists(location)) File.Delete(location);\n\n var l1 = Linear(10, 10, true);\n var l2 = Linear(10, 10, true);\n var seq = Sequential(l1, l2);\n\n var optim = torch.optim.AdamW(seq.parameters());\n\n var sd = optim.state_dict();\n Assert.Multiple(\n () => Assert.Equal(4, sd.State.Count),\n () => Assert.Single(sd.Options)\n );\n\n var optim2 = torch.optim.AdamW(seq.parameters(), 0.5, 0.01, 0.75, 1e-8, 0.25);\n\n try {\n optim.save_state_dict(location);\n optim2.load_state_dict(location);\n\n var sd2 = optim2.state_dict();\n\n Assert.Multiple(\n () => Assert.Equal(4, sd2.State.Count),\n () => Assert.Single(sd2.Options)\n );\n\n for (int i = 0; i < sd.Options.Count; i++) {\n var opt = sd.Options[i] as Modules.AdamW.Options;\n var opt2 = sd2.Options[i] as Modules.AdamW.Options;\n Assert.Multiple(\n () => Assert.Equal(opt!.InitialLearningRate, opt2!.InitialLearningRate),\n () => Assert.Equal(opt!.LearningRate, opt2!.LearningRate),\n () => Assert.Equal(opt!.beta1, opt2!.beta1),\n () => Assert.Equal(opt!.beta2, opt2!.beta2),\n () => Assert.Equal(opt!.eps, opt2!.eps),\n () => Assert.Equal(opt!.weight_decay, opt2!.weight_decay),\n () => Assert.Equal(opt!.amsgrad, opt2!.amsgrad),\n () => Assert.Equal(opt!.maximize, opt2!.maximize)\n );\n }\n for (int i = 0; i < sd.State.Count; i++) {\n var opt = sd.State[i] as Modules.AdamW.State;\n var opt2 = sd2.State[i] as Modules.AdamW.State;\n Assert.True(opt!.ApproximatelyEquals(opt2));\n }\n } finally {\n File.Delete(location);\n }\n }\n\n [Fact]\n public void TestSaveAdamWFile2()\n {\n var location = RandomFileName(\"ts\");\n if (File.Exists(location)) File.Delete(location);\n\n var l1 = Linear(10, 10, true);\n var l2 = Linear(10, 10, true);\n var seq = Sequential(l1, l2);\n\n var optim = torch.optim.AdamW(seq.parameters());\n\n var sd = optim.state_dict();\n Assert.Multiple(\n () => Assert.Equal(4, sd.State.Count),\n () => Assert.Single(sd.Options)\n );\n\n var optim2 = torch.optim.AdamW(seq.parameters(), 0.5, 0.01, 0.75, 1e-8, 0.25, true);\n\n try {\n optim.save_state_dict(location);\n optim2.load_state_dict(location);\n\n var sd2 = optim2.state_dict();\n\n Assert.Multiple(\n () => Assert.Equal(4, sd2.State.Count),\n () => Assert.Single(sd2.Options)\n );\n\n for (int i = 0; i < sd.Options.Count; i++) {\n var opt = sd.Options[i] as Modules.AdamW.Options;\n var opt2 = sd2.Options[i] as Modules.AdamW.Options;\n Assert.Multiple(\n () => Assert.Equal(opt!.InitialLearningRate, opt2!.InitialLearningRate),\n () => Assert.Equal(opt!.LearningRate, opt2!.LearningRate),\n () => Assert.Equal(opt!.beta1, opt2!.beta1),\n () => Assert.Equal(opt!.beta2, opt2!.beta2),\n () => Assert.Equal(opt!.eps, opt2!.eps),\n () => Assert.Equal(opt!.weight_decay, opt2!.weight_decay),\n () => Assert.Equal(opt!.amsgrad, opt2!.amsgrad),\n () => Assert.Equal(opt!.maximize, opt2!.maximize)\n );\n }\n for (int i = 0; i < sd.State.Count; i++) {\n var opt = sd.State[i] as Modules.AdamW.State;\n var opt2 = sd2.State[i] as Modules.AdamW.State;\n Assert.True(opt!.ApproximatelyEquals(opt2));\n }\n } finally {\n File.Delete(location);\n }\n }\n\n [Fact]\n public void TestSaveAdamWFile3()\n {\n var location = RandomFileName(\"ts\");\n if (File.Exists(location)) File.Delete(location);\n\n var l1 = Linear(10, 10, true);\n var l2 = Linear(10, 10, true);\n var seq = Sequential(l1, l2);\n\n var optim = torch.optim.AdamW(seq.parameters(), amsgrad: true);\n\n var sd = optim.state_dict();\n Assert.Multiple(\n () => Assert.Equal(4, sd.State.Count),\n () => Assert.Single(sd.Options)\n );\n\n var optim2 = torch.optim.AdamW(seq.parameters(), 0.5, 0.01, 0.75, 1e-8, 0.25);\n\n try {\n optim.save_state_dict(location);\n optim2.load_state_dict(location);\n\n var sd2 = optim2.state_dict();\n\n Assert.Multiple(\n () => Assert.Equal(4, sd2.State.Count),\n () => Assert.Single(sd2.Options)\n );\n\n for (int i = 0; i < sd.Options.Count; i++) {\n var opt = sd.Options[i] as Modules.AdamW.Options;\n var opt2 = sd2.Options[i] as Modules.AdamW.Options;\n Assert.Multiple(\n () => Assert.Equal(opt!.InitialLearningRate, opt2!.InitialLearningRate),\n () => Assert.Equal(opt!.LearningRate, opt2!.LearningRate),\n () => Assert.Equal(opt!.beta1, opt2!.beta1),\n () => Assert.Equal(opt!.beta2, opt2!.beta2),\n () => Assert.Equal(opt!.eps, opt2!.eps),\n () => Assert.Equal(opt!.weight_decay, opt2!.weight_decay),\n () => Assert.Equal(opt!.amsgrad, opt2!.amsgrad),\n () => Assert.Equal(opt!.maximize, opt2!.maximize)\n );\n }\n for (int i = 0; i < sd.State.Count; i++) {\n var opt = sd.State[i] as Modules.AdamW.State;\n var opt2 = sd2.State[i] as Modules.AdamW.State;\n Assert.True(opt!.ApproximatelyEquals(opt2));\n }\n } finally {\n File.Delete(location);\n }\n }\n\n [Fact]\n public void TestSaveAdamax()\n {\n var l1 = Linear(10, 10, true);\n var l2 = Linear(10, 10, true);\n var seq = Sequential(l1, l2);\n\n var optim = torch.optim.Adamax(seq.parameters());\n\n var sd = optim.state_dict();\n Assert.Multiple(\n () => Assert.Equal(4, sd.State.Count),\n () => Assert.Single(sd.Options)\n );\n\n var optim2 = torch.optim.Adamax(seq.parameters(), 0.5, 0.01, 0.75, 1e-8, 0.25);\n optim2.load_state_dict(sd);\n\n var sd2 = optim2.state_dict();\n\n Assert.Multiple(\n () => Assert.Equal(4, sd2.State.Count),\n () => Assert.Single(sd2.Options)\n );\n\n for (int i = 0; i < sd.Options.Count; i++) {\n var opt = sd.Options[i] as Modules.Adamax.Options;\n var opt2 = sd2.Options[i] as Modules.Adamax.Options;\n Assert.Multiple(\n () => Assert.Equal(opt!.InitialLearningRate, opt2!.InitialLearningRate),\n () => Assert.Equal(opt!.LearningRate, opt2!.LearningRate),\n () => Assert.Equal(opt!.beta1, opt2!.beta1),\n () => Assert.Equal(opt!.beta2, opt2!.beta2),\n () => Assert.Equal(opt!.eps, opt2!.eps),\n () => Assert.Equal(opt!.weight_decay, opt2!.weight_decay)\n );\n }\n for (int i = 0; i < sd.State.Count; i++) {\n var opt = sd.State[i] as Modules.Adamax.State;\n var opt2 = sd2.State[i] as Modules.Adamax.State;\n Assert.True(opt!.ApproximatelyEquals(opt2));\n }\n }\n\n [Fact]\n public void TestSaveAdamaxFile()\n {\n var location = RandomFileName(\"ts\");\n if (File.Exists(location)) File.Delete(location);\n\n var l1 = Linear(10, 10, true);\n var l2 = Linear(10, 10, true);\n var seq = Sequential(l1, l2);\n\n var optim = torch.optim.Adamax(seq.parameters());\n\n var sd = optim.state_dict();\n Assert.Multiple(\n () => Assert.Equal(4, sd.State.Count),\n () => Assert.Single(sd.Options)\n );\n\n var optim2 = torch.optim.Adamax(seq.parameters(), 0.5, 0.01, 0.75, 1e-8, 0.25);\n\n try {\n optim.save_state_dict(location);\n optim2.load_state_dict(location);\n\n var sd2 = optim2.state_dict();\n\n Assert.Multiple(\n () => Assert.Equal(4, sd2.State.Count),\n () => Assert.Single(sd2.Options)\n );\n\n for (int i = 0; i < sd.Options.Count; i++) {\n var opt = sd.Options[i] as Modules.Adamax.Options;\n var opt2 = sd2.Options[i] as Modules.Adamax.Options;\n Assert.Multiple(\n () => Assert.Equal(opt!.InitialLearningRate, opt2!.InitialLearningRate),\n () => Assert.Equal(opt!.LearningRate, opt2!.LearningRate),\n () => Assert.Equal(opt!.beta1, opt2!.beta1),\n () => Assert.Equal(opt!.beta2, opt2!.beta2),\n () => Assert.Equal(opt!.eps, opt2!.eps),\n () => Assert.Equal(opt!.weight_decay, opt2!.weight_decay)\n );\n }\n for (int i = 0; i < sd.State.Count; i++) {\n var opt = sd.State[i] as Modules.Adamax.State;\n var opt2 = sd2.State[i] as Modules.Adamax.State;\n Assert.True(opt!.ApproximatelyEquals(opt2));\n }\n } finally {\n File.Delete(location);\n }\n }\n\n [Fact]\n public void TestSaveAdagrad()\n {\n var l1 = Linear(10, 10, true);\n var l2 = Linear(10, 10, true);\n var seq = Sequential(l1, l2);\n\n var optim = torch.optim.Adagrad(seq.parameters());\n\n var sd = optim.state_dict();\n Assert.Multiple(\n () => Assert.Equal(4, sd.State.Count),\n () => Assert.Single(sd.Options)\n );\n\n var optim2 = torch.optim.Adagrad(seq.parameters(), 0.5, 0.01, 0.75, 1e-8, 0.25);\n optim2.load_state_dict(sd);\n\n var sd2 = optim2.state_dict();\n\n Assert.Multiple(\n () => Assert.Equal(4, sd2.State.Count),\n () => Assert.Single(sd2.Options)\n );\n\n for (int i = 0; i < sd.Options.Count; i++) {\n var opt = sd.Options[i] as Modules.Adagrad.Options;\n var opt2 = sd2.Options[i] as Modules.Adagrad.Options;\n Assert.Multiple(\n () => Assert.Equal(opt!.InitialLearningRate, opt2!.InitialLearningRate),\n () => Assert.Equal(opt!.LearningRate, opt2!.LearningRate),\n () => Assert.Equal(opt!.lr_decay, opt2!.lr_decay),\n () => Assert.Equal(opt!.initial_accumulator_value, opt2!.initial_accumulator_value),\n () => Assert.Equal(opt!.eps, opt2!.eps),\n () => Assert.Equal(opt!.weight_decay, opt2!.weight_decay)\n );\n }\n for (int i = 0; i < sd.State.Count; i++) {\n var opt = sd.State[i] as Modules.Adagrad.State;\n var opt2 = sd2.State[i] as Modules.Adagrad.State;\n Assert.True(opt!.ApproximatelyEquals(opt2));\n }\n }\n\n [Fact(Skip = \"Often takes a very long time. No idea why.\")]\n public void TestSaveAdagradFile()\n {\n var location = RandomFileName(\"ts\");\n if (File.Exists(location)) File.Delete(location);\n\n var l1 = Linear(10, 10, true);\n var l2 = Linear(10, 10, true);\n var seq = Sequential(l1, l2);\n\n var optim = torch.optim.Adagrad(seq.parameters());\n\n var sd = optim.state_dict();\n Assert.Multiple(\n () => Assert.Equal(4, sd.State.Count),\n () => Assert.Single(sd.Options)\n );\n\n var optim2 = torch.optim.Adagrad(seq.parameters(), 0.5, 0.01, 0.75, 1e-8, 0.25);\n\n try {\n optim.save_state_dict(location);\n optim2.load_state_dict(location);\n\n var sd2 = optim2.state_dict();\n\n Assert.Multiple(\n () => Assert.Equal(4, sd2.State.Count),\n () => Assert.Single(sd2.Options)\n );\n\n for (int i = 0; i < sd.Options.Count; i++) {\n var opt = sd.Options[i] as Modules.Adagrad.Options;\n var opt2 = sd2.Options[i] as Modules.Adagrad.Options;\n Assert.Multiple(\n () => Assert.Equal(opt!.InitialLearningRate, opt2!.InitialLearningRate),\n () => Assert.Equal(opt!.LearningRate, opt2!.LearningRate),\n () => Assert.Equal(opt!.lr_decay, opt2!.lr_decay),\n () => Assert.Equal(opt!.initial_accumulator_value, opt2!.initial_accumulator_value),\n () => Assert.Equal(opt!.eps, opt2!.eps),\n () => Assert.Equal(opt!.weight_decay, opt2!.weight_decay)\n );\n }\n for (int i = 0; i < sd.State.Count; i++) {\n var opt = sd.State[i] as Modules.Adagrad.State;\n var opt2 = sd2.State[i] as Modules.Adagrad.State;\n Assert.True(opt!.ApproximatelyEquals(opt2));\n }\n } finally {\n File.Delete(location);\n }\n }\n\n [Fact]\n public void TestSaveAdadelta()\n {\n var l1 = Linear(10, 10, true);\n var l2 = Linear(10, 10, true);\n var seq = Sequential(l1, l2);\n\n var optim = torch.optim.Adadelta(seq.parameters());\n\n var sd = optim.state_dict();\n Assert.Multiple(\n () => Assert.Equal(4, sd.State.Count),\n () => Assert.Single(sd.Options)\n );\n\n var optim2 = torch.optim.Adadelta(seq.parameters(), 0.5, 0.01, 1e-8, 0.25);\n optim2.load_state_dict(sd);\n\n var sd2 = optim2.state_dict();\n\n Assert.Multiple(\n () => Assert.Equal(4, sd2.State.Count),\n () => Assert.Single(sd2.Options)\n );\n\n for (int i = 0; i < sd.Options.Count; i++) {\n var opt = sd.Options[i] as Modules.Adadelta.Options;\n var opt2 = sd2.Options[i] as Modules.Adadelta.Options;\n Assert.Multiple(\n () => Assert.Equal(opt!.InitialLearningRate, opt2!.InitialLearningRate),\n () => Assert.Equal(opt!.LearningRate, opt2!.LearningRate),\n () => Assert.Equal(opt!.rho, opt2!.rho),\n () => Assert.Equal(opt!.eps, opt2!.eps),\n () => Assert.Equal(opt!.weight_decay, opt2!.weight_decay)\n );\n }\n for (int i = 0; i < sd.State.Count; i++) {\n var opt = sd.State[i] as Modules.Adadelta.State;\n var opt2 = sd2.State[i] as Modules.Adadelta.State;\n Assert.True(opt!.ApproximatelyEquals(opt2));\n }\n }\n\n [Fact]\n public void TestSaveAdadeltaFile()\n {\n var location = RandomFileName(\"ts\");\n if (File.Exists(location)) File.Delete(location);\n\n var l1 = Linear(10, 10, true);\n var l2 = Linear(10, 10, true);\n var seq = Sequential(l1, l2);\n\n var optim = torch.optim.Adadelta(seq.parameters());\n\n var sd = optim.state_dict();\n Assert.Multiple(\n () => Assert.Equal(4, sd.State.Count),\n () => Assert.Single(sd.Options)\n );\n\n var optim2 = torch.optim.Adadelta(seq.parameters(), 0.5, 0.01, 1e-8, 0.25);\n\n try {\n optim.save_state_dict(location);\n optim2.load_state_dict(location);\n\n var sd2 = optim2.state_dict();\n\n Assert.Multiple(\n () => Assert.Equal(4, sd2.State.Count),\n () => Assert.Single(sd2.Options)\n );\n\n for (int i = 0; i < sd.Options.Count; i++) {\n var opt = sd.Options[i] as Modules.Adadelta.Options;\n var opt2 = sd2.Options[i] as Modules.Adadelta.Options;\n Assert.Multiple(\n () => Assert.Equal(opt!.InitialLearningRate, opt2!.InitialLearningRate),\n () => Assert.Equal(opt!.LearningRate, opt2!.LearningRate),\n () => Assert.Equal(opt!.rho, opt2!.rho),\n () => Assert.Equal(opt!.eps, opt2!.eps),\n () => Assert.Equal(opt!.weight_decay, opt2!.weight_decay)\n );\n }\n for (int i = 0; i < sd.State.Count; i++) {\n var opt = sd.State[i] as Modules.Adadelta.State;\n var opt2 = sd2.State[i] as Modules.Adadelta.State;\n Assert.True(opt!.ApproximatelyEquals(opt2));\n }\n } finally {\n File.Delete(location);\n }\n }\n\n [Fact]\n public void TestMisMatchedFile()\n {\n\n var location = RandomFileName(\"ts\");\n if (File.Exists(location)) File.Delete(location);\n\n var l1 = Linear(10, 10, true);\n var l2 = Linear(10, 10, true);\n var seq = Sequential(l1, l2);\n\n var optim1 = torch.optim.SGD(seq.parameters(), 0.05);\n\n try {\n optim1.save_state_dict(location);\n Assert.Throws<InvalidDataException>(() => torch.optim.ASGD(seq.parameters()).load_state_dict(location));\n Assert.Throws<InvalidDataException>(() => torch.optim.Rprop(seq.parameters()).load_state_dict(location));\n Assert.Throws<InvalidDataException>(() => torch.optim.RMSProp(seq.parameters()).load_state_dict(location));\n Assert.Throws<InvalidDataException>(() => torch.optim.RAdam(seq.parameters()).load_state_dict(location));\n Assert.Throws<InvalidDataException>(() => torch.optim.NAdam(seq.parameters()).load_state_dict(location));\n Assert.Throws<InvalidDataException>(() => torch.optim.Adam(seq.parameters()).load_state_dict(location));\n Assert.Throws<InvalidDataException>(() => torch.optim.AdamW(seq.parameters()).load_state_dict(location));\n Assert.Throws<InvalidDataException>(() => torch.optim.Adamax(seq.parameters()).load_state_dict(location));\n Assert.Throws<InvalidDataException>(() => torch.optim.Adagrad(seq.parameters()).load_state_dict(location));\n Assert.Throws<InvalidDataException>(() => torch.optim.Adadelta(seq.parameters()).load_state_dict(location));\n } finally {\n File.Delete(location);\n }\n }\n\n [Fact]\n public void TestLoadingSGDStateFromPython()\n {\n var lin = torch.nn.Linear(10, 10);\n\n double learning_rate = 0.00004f;\n var optimizer = torch.optim.SGD(lin.parameters(), learning_rate);\n\n optimizer.load_state_dict(\"sgd1.dat\");\n\n var sd = optimizer.state_dict();\n\n Assert.Single(sd.Options);\n Assert.Equal(2, sd.State.Count);\n\n foreach (var opts in sd.Options) {\n var options = opts as Modules.SGD.Options;\n Assert.Equal(0.1, options!.momentum);\n Assert.NotEqual(learning_rate, options!.LearningRate);\n }\n\n foreach (var st in sd.State) {\n var state = st as Modules.SGD.State;\n Assert.NotNull(state!.momentum_buffer);\n }\n }\n\n [Fact]\n public void TestLoadingASGDStateFromPython()\n {\n var lin = torch.nn.Linear(10, 10);\n\n double learning_rate = 0.004f;\n var optimizer = torch.optim.ASGD(lin.parameters(), learning_rate);\n\n optimizer.load_state_dict(\"asgd1.dat\");\n\n var sd = optimizer.state_dict();\n\n Assert.Single(sd.Options);\n Assert.Equal(2, sd.State.Count);\n\n foreach (var opts in sd.Options) {\n var options = opts as Modules.ASGD.Options;\n Assert.Equal(0.65, options!.alpha);\n Assert.Equal(1e-3, options!.lambd);\n Assert.Equal(1e5, options!.t0);\n Assert.NotEqual(learning_rate, options!.LearningRate);\n }\n\n foreach (var st in sd.State) {\n var state = st as Modules.ASGD.State;\n Assert.Equal(1, state!.step);\n Assert.NotNull(state!.ax);\n }\n }\n\n [Fact]\n public void TestLoadingRMSpropStateFromPython()\n {\n var lin1 = torch.nn.Linear(10, 10);\n var lin2 = torch.nn.Linear(10, 10);\n\n var seq = Sequential((\"lin1\", lin1), (\"lin2\", lin2));\n\n var pgs = new RMSProp.ParamGroup[] {\n new () { Parameters = lin1.parameters(), Options = new() { LearningRate = 0.00005 } },\n new (lin2.parameters(), lr: 0.00003, centered: false, momentum: 0.25)\n };\n\n double learning_rate = 0.00004f;\n var optimizer = torch.optim.RMSProp(pgs, learning_rate);\n\n optimizer.load_state_dict(\"rmsprop1.dat\");\n\n var sd = optimizer.state_dict();\n\n Assert.Equal(2, sd.Options.Count);\n Assert.Equal(4, sd.State.Count);\n\n var options = sd.Options[0] as Modules.RMSProp.Options;\n Assert.Multiple(\n () => Assert.Equal(0.1, options!.momentum),\n () => Assert.Equal(0.001, options!.LearningRate),\n () => Assert.False(options!.centered)\n );\n\n options = sd.Options[1] as Modules.RMSProp.Options;\n Assert.Multiple(\n () => Assert.Equal(0, options!.momentum),\n () => Assert.Equal(0.01, options!.LearningRate),\n () => Assert.True(options!.centered)\n );\n\n var state = sd.State[0] as Modules.RMSProp.State;\n Assert.Multiple(\n () => Assert.Equal(1, state!.step),\n () => Assert.NotNull(state!.square_avg),\n () => Assert.NotNull(state!.momentum_buffer),\n () => Assert.Null(state!.grad_avg)\n );\n\n state = sd.State[1] as Modules.RMSProp.State;\n Assert.Multiple(\n () => Assert.Equal(1, state!.step),\n () => Assert.NotNull(state!.square_avg),\n () => Assert.NotNull(state!.momentum_buffer),\n () => Assert.Null(state!.grad_avg)\n );\n\n state = sd.State[2] as Modules.RMSProp.State;\n Assert.Multiple(\n () => Assert.Equal(1, state!.step),\n () => Assert.NotNull(state!.square_avg),\n () => Assert.Null(state!.momentum_buffer),\n () => Assert.NotNull(state!.grad_avg)\n );\n\n state = sd.State[3] as Modules.RMSProp.State;\n Assert.Multiple(\n () => Assert.Equal(1, state!.step),\n () => Assert.NotNull(state!.square_avg),\n () => Assert.Null(state!.momentum_buffer),\n () => Assert.NotNull(state!.grad_avg)\n );\n }\n\n [Fact]\n public void TestLoadingRpropStateFromPython()\n {\n var lin1 = torch.nn.Linear(10, 10);\n var lin2 = torch.nn.Linear(10, 10);\n\n var seq = Sequential((\"lin1\", lin1), (\"lin2\", lin2));\n\n var pgs = new Rprop.ParamGroup[] {\n new () { Parameters = lin1.parameters(), Options = new() { LearningRate = 0.00005 } },\n new (lin2.parameters(), lr: 0.00003, maximize: false)\n };\n\n double learning_rate = 0.00004f;\n var optimizer = torch.optim.Rprop(pgs, learning_rate);\n\n optimizer.load_state_dict(\"rprop1.dat\");\n\n var sd = optimizer.state_dict();\n\n Assert.Equal(2, sd.Options.Count);\n Assert.Equal(4, sd.State.Count);\n\n var options = sd.Options[0] as Modules.Rprop.Options;\n Assert.Multiple(\n () => Assert.Equal(0.35, options!.etaminus),\n () => Assert.Equal(1.5, options!.etaplus),\n () => Assert.Equal(1e-5, options!.min_step),\n () => Assert.Equal(5, options!.max_step),\n () => Assert.Equal(0.001, options!.LearningRate),\n () => Assert.False(options!.maximize)\n );\n\n options = sd.Options[1] as Modules.Rprop.Options;\n Assert.Multiple(\n () => Assert.Equal(0.45, options!.etaminus),\n () => Assert.Equal(1.5, options!.etaplus),\n () => Assert.Equal(1e-5, options!.min_step),\n () => Assert.Equal(5, options!.max_step),\n () => Assert.Equal(0.01, options!.LearningRate),\n () => Assert.True(options!.maximize)\n );\n\n foreach (var st in sd.State) {\n var state = sd.State[0] as Modules.Rprop.State;\n Assert.Multiple(\n () => Assert.Equal(1, state!.step),\n () => Assert.NotNull(state!.prev),\n () => Assert.NotNull(state!.step_size)\n );\n }\n }\n\n [Fact]\n public void TestLoadingAdamStateFromPython()\n {\n var lin1 = torch.nn.Linear(10, 10);\n var lin2 = torch.nn.Linear(10, 10);\n\n var seq = Sequential((\"lin1\", lin1), (\"lin2\", lin2));\n\n var pgs = new Adam.ParamGroup[] {\n new () { Parameters = lin1.parameters(), Options = new() { LearningRate = 0.00005 } },\n new (lin2.parameters(), lr: 0.00003, amsgrad: false, beta1: 0.25)\n };\n\n double learning_rate = 0.00004f;\n var optimizer = torch.optim.Adam(pgs, learning_rate);\n\n optimizer.load_state_dict(\"adam1.dat\");\n\n var sd = optimizer.state_dict();\n\n Assert.Equal(2, sd.Options.Count);\n Assert.Equal(4, sd.State.Count);\n\n var options = sd.Options[0] as Modules.Adam.Options;\n Assert.Multiple(\n () => Assert.Equal(0.8, options!.beta1),\n () => Assert.Equal(0.9, options!.beta2),\n () => Assert.Equal(0.001, options!.LearningRate),\n () => Assert.False(options!.amsgrad)\n );\n\n options = sd.Options[1] as Modules.Adam.Options;\n Assert.Multiple(\n () => Assert.Equal(0.7, options!.beta1),\n () => Assert.Equal(0.79, options!.beta2),\n () => Assert.Equal(0.01, options!.LearningRate),\n () => Assert.True(options!.amsgrad)\n );\n\n var state = sd.State[0] as Modules.Adam.State;\n Assert.Multiple(\n () => Assert.Equal(1, state!.step),\n () => Assert.NotNull(state!.exp_avg),\n () => Assert.NotNull(state!.exp_avg_sq),\n () => Assert.Null(state!.max_exp_avg_sq)\n );\n\n state = sd.State[1] as Modules.Adam.State;\n Assert.Multiple(\n () => Assert.Equal(1, state!.step),\n () => Assert.NotNull(state!.exp_avg),\n () => Assert.NotNull(state!.exp_avg_sq),\n () => Assert.Null(state!.max_exp_avg_sq)\n );\n\n state = sd.State[2] as Modules.Adam.State;\n Assert.Multiple(\n () => Assert.Equal(1, state!.step),\n () => Assert.NotNull(state!.exp_avg),\n () => Assert.NotNull(state!.exp_avg_sq),\n () => Assert.NotNull(state!.max_exp_avg_sq)\n );\n\n state = sd.State[3] as Modules.Adam.State;\n Assert.Multiple(\n () => Assert.Equal(1, state!.step),\n () => Assert.NotNull(state!.exp_avg),\n () => Assert.NotNull(state!.exp_avg_sq),\n () => Assert.NotNull(state!.max_exp_avg_sq)\n );\n }\n\n [Fact]\n public void TestLoadingAdamWStateFromPython()\n {\n var lin1 = torch.nn.Linear(10, 10);\n var lin2 = torch.nn.Linear(10, 10);\n\n var seq = Sequential((\"lin1\", lin1), (\"lin2\", lin2));\n\n var pgs = new AdamW.ParamGroup[] {\n new () { Parameters = lin1.parameters(), Options = new() { LearningRate = 0.00005 } },\n new (lin2.parameters(), lr: 0.00003, amsgrad: false, beta1: 0.25)\n };\n\n double learning_rate = 0.00004f;\n var optimizer = torch.optim.AdamW(pgs, learning_rate);\n\n optimizer.load_state_dict(\"adamw1.dat\");\n\n var sd = optimizer.state_dict();\n\n Assert.Equal(2, sd.Options.Count);\n Assert.Equal(4, sd.State.Count);\n\n var options = sd.Options[0] as Modules.AdamW.Options;\n Assert.Multiple(\n () => Assert.Equal(0.8, options!.beta1),\n () => Assert.Equal(0.9, options!.beta2),\n () => Assert.Equal(0.001, options!.LearningRate),\n () => Assert.False(options!.amsgrad)\n );\n\n options = sd.Options[1] as Modules.AdamW.Options;\n Assert.Multiple(\n () => Assert.Equal(0.7, options!.beta1),\n () => Assert.Equal(0.79, options!.beta2),\n () => Assert.Equal(0.01, options!.LearningRate),\n () => Assert.True(options!.amsgrad)\n );\n\n var state = sd.State[0] as Modules.AdamW.State;\n Assert.Multiple(\n () => Assert.Equal(1, state!.step),\n () => Assert.NotNull(state!.exp_avg),\n () => Assert.NotNull(state!.exp_avg_sq),\n () => Assert.Null(state!.max_exp_avg_sq)\n );\n\n state = sd.State[1] as Modules.AdamW.State;\n Assert.Multiple(\n () => Assert.Equal(1, state!.step),\n () => Assert.NotNull(state!.exp_avg),\n () => Assert.NotNull(state!.exp_avg_sq),\n () => Assert.Null(state!.max_exp_avg_sq)\n );\n\n state = sd.State[2] as Modules.AdamW.State;\n Assert.Multiple(\n () => Assert.Equal(1, state!.step),\n () => Assert.NotNull(state!.exp_avg),\n () => Assert.NotNull(state!.exp_avg_sq),\n () => Assert.NotNull(state!.max_exp_avg_sq)\n );\n\n state = sd.State[3] as Modules.AdamW.State;\n Assert.Multiple(\n () => Assert.Equal(1, state!.step),\n () => Assert.NotNull(state!.exp_avg),\n () => Assert.NotNull(state!.exp_avg_sq),\n () => Assert.NotNull(state!.max_exp_avg_sq)\n );\n }\n\n [Fact]\n public void TestLoadingNAdamStateFromPython()\n {\n var lin1 = torch.nn.Linear(10, 10);\n var lin2 = torch.nn.Linear(10, 10);\n\n var seq = Sequential((\"lin1\", lin1), (\"lin2\", lin2));\n\n var pgs = new NAdam.ParamGroup[] {\n new () { Parameters = lin1.parameters(), Options = new() { LearningRate = 0.00005 } },\n new (lin2.parameters(), lr: 0.00003, beta1: 0.25, weight_decay: 0.1)\n };\n\n double learning_rate = 0.00004f;\n var optimizer = torch.optim.NAdam(pgs, learning_rate);\n\n optimizer.load_state_dict(\"nadam1.dat\");\n\n var sd = optimizer.state_dict();\n\n Assert.Equal(2, sd.Options.Count);\n Assert.Equal(4, sd.State.Count);\n\n var options = sd.Options[0] as Modules.NAdam.Options;\n Assert.Multiple(\n () => Assert.Equal(0.8, options!.beta1),\n () => Assert.Equal(0.9, options!.beta2),\n () => Assert.Equal(0.001, options!.LearningRate),\n () => Assert.Equal(0, options!.weight_decay)\n );\n\n options = sd.Options[1] as Modules.NAdam.Options;\n Assert.Multiple(\n () => Assert.Equal(0.7, options!.beta1),\n () => Assert.Equal(0.79, options!.beta2),\n () => Assert.Equal(0.01, options!.LearningRate),\n () => Assert.Equal(0.3, options!.weight_decay)\n );\n\n foreach (var st in sd.State) {\n var state = st as Modules.NAdam.State;\n Assert.Multiple(\n () => Assert.Equal(1, state!.step),\n () => Assert.NotNull(state!.exp_avg),\n () => Assert.NotNull(state!.exp_avg_sq)\n );\n }\n }\n\n [Fact]\n public void TestLoadingRAdamStateFromPython()\n {\n var lin1 = torch.nn.Linear(10, 10);\n var lin2 = torch.nn.Linear(10, 10);\n\n var seq = Sequential((\"lin1\", lin1), (\"lin2\", lin2));\n\n var pgs = new RAdam.ParamGroup[] {\n new () { Parameters = lin1.parameters(), Options = new() { LearningRate = 0.00005 } },\n new (lin2.parameters(), lr: 0.00003, beta1: 0.25)\n };\n\n double learning_rate = 0.00004f;\n var optimizer = torch.optim.RAdam(pgs, learning_rate);\n\n optimizer.load_state_dict(\"radam1.dat\");\n\n var sd = optimizer.state_dict();\n\n var options = sd.Options[0] as Modules.RAdam.Options;\n Assert.Multiple(\n () => Assert.Equal(0.8, options!.beta1),\n () => Assert.Equal(0.9, options!.beta2),\n () => Assert.Equal(0.001, options!.LearningRate),\n () => Assert.Equal(0, options!.weight_decay)\n );\n\n options = sd.Options[1] as Modules.RAdam.Options;\n Assert.Multiple(\n () => Assert.Equal(0.7, options!.beta1),\n () => Assert.Equal(0.79, options!.beta2),\n () => Assert.Equal(0.01, options!.LearningRate),\n () => Assert.Equal(0.3, options!.weight_decay)\n );\n\n foreach (var st in sd.State) {\n var state = st as Modules.RAdam.State;\n Assert.Multiple(\n () => Assert.Equal(1, state!.step),\n () => Assert.NotNull(state!.exp_avg),\n () => Assert.NotNull(state!.exp_avg_sq)\n );\n }\n }\n\n [Fact]\n public void TestLoadingAdadeltaStateFromPython()\n {\n var lin1 = torch.nn.Linear(10, 10);\n var lin2 = torch.nn.Linear(10, 10);\n\n var seq = Sequential((\"lin1\", lin1), (\"lin2\", lin2));\n\n var pgs = new Adadelta.ParamGroup[] {\n new () { Parameters = lin1.parameters(), Options = new() { LearningRate = 0.00005 } },\n new (lin2.parameters(), lr: 0.00003, maximize: false, rho: 0.25)\n };\n\n double learning_rate = 0.00004f;\n var optimizer = torch.optim.Adadelta(pgs, learning_rate);\n\n optimizer.load_state_dict(\"adadelta1.dat\");\n\n var sd = optimizer.state_dict();\n\n Assert.Equal(2, sd.Options.Count);\n Assert.Equal(4, sd.State.Count);\n\n var options = sd.Options[0] as Modules.Adadelta.Options;\n Assert.Multiple(\n () => Assert.Equal(0.85, options!.rho),\n () => Assert.Equal(0.3, options!.weight_decay),\n () => Assert.Equal(0.001, options!.LearningRate),\n () => Assert.False(options!.maximize)\n );\n\n options = sd.Options[1] as Modules.Adadelta.Options;\n Assert.Multiple(\n () => Assert.Equal(0.79, options!.rho),\n () => Assert.Equal(0.3, options!.weight_decay),\n () => Assert.Equal(0.01, options!.LearningRate),\n () => Assert.True(options!.maximize)\n );\n\n foreach (var st in sd.State) {\n var state = st as Modules.Adadelta.State;\n Assert.Multiple(\n () => Assert.Equal(1, state!.step),\n () => Assert.NotNull(state!.square_avg),\n () => Assert.NotNull(state!.acc_delta)\n );\n }\n }\n\n [Fact]\n public void TestLoadingAdagradStateFromPython()\n {\n var lin = torch.nn.Linear(10, 10);\n\n double learning_rate = 0.00004f;\n var optimizer = torch.optim.Adagrad(lin.parameters(), learning_rate);\n\n optimizer.load_state_dict(\"adagrad1.dat\");\n\n var sd = optimizer.state_dict();\n\n Assert.Single(sd.Options);\n Assert.Equal(2, sd.State.Count);\n\n foreach (var opts in sd.Options) {\n var options = opts as Modules.Adagrad.Options;\n Assert.Equal(0.85, options!.lr_decay);\n Assert.Equal(0.3, options!.weight_decay);\n Assert.NotEqual(learning_rate, options!.LearningRate);\n }\n\n foreach (var st in sd.State) {\n var state = st as Modules.Adagrad.State;\n Assert.Equal(1, state!.step);\n Assert.NotNull(state!.sum);\n }\n }\n\n [Fact]\n public void TestLoadingAdamaxStateFromPython()\n {\n var lin1 = torch.nn.Linear(10, 10);\n var lin2 = torch.nn.Linear(10, 10);\n\n var seq = Sequential((\"lin1\", lin1), (\"lin2\", lin2));\n\n var pgs = new Adamax.ParamGroup[] {\n new () { Parameters = lin1.parameters(), Options = new() { LearningRate = 0.00005 } },\n new (lin2.parameters(), lr: 0.00003, weight_decay: 0.25, beta1: 0.25)\n };\n\n double learning_rate = 0.00004f;\n var optimizer = torch.optim.Adamax(pgs, learning_rate);\n\n optimizer.load_state_dict(\"adamax1.dat\");\n\n var sd = optimizer.state_dict();\n\n Assert.Equal(2, sd.Options.Count);\n Assert.Equal(4, sd.State.Count);\n\n var options = sd.Options[0] as Modules.Adamax.Options;\n Assert.Multiple(\n () => Assert.Equal(0.8, options!.beta1),\n () => Assert.Equal(0.9, options!.beta2),\n () => Assert.Equal(0.001, options!.LearningRate),\n () => Assert.Equal(0, options!.weight_decay)\n );\n\n options = sd.Options[1] as Modules.Adamax.Options;\n Assert.Multiple(\n () => Assert.Equal(0.7, options!.beta1),\n () => Assert.Equal(0.79, options!.beta2),\n () => Assert.Equal(0.01, options!.LearningRate),\n () => Assert.Equal(0.3, options!.weight_decay)\n );\n\n var state = sd.State[0] as Modules.Adamax.State;\n Assert.Multiple(\n () => Assert.Equal(1, state!.step),\n () => Assert.NotNull(state!.exp_avg),\n () => Assert.NotNull(state!.exp_inf)\n );\n\n state = sd.State[1] as Modules.Adamax.State;\n Assert.Multiple(\n () => Assert.Equal(1, state!.step),\n () => Assert.NotNull(state!.exp_avg),\n () => Assert.NotNull(state!.exp_avg),\n () => Assert.NotNull(state!.exp_inf)\n );\n\n state = sd.State[2] as Modules.Adamax.State;\n Assert.Multiple(\n () => Assert.Equal(1, state!.step),\n () => Assert.NotNull(state!.exp_avg),\n () => Assert.NotNull(state!.exp_avg),\n () => Assert.NotNull(state!.exp_inf)\n );\n\n state = sd.State[3] as Modules.Adamax.State;\n Assert.Multiple(\n () => Assert.Equal(1, state!.step),\n () => Assert.NotNull(state!.exp_avg),\n () => Assert.NotNull(state!.exp_avg),\n () => Assert.NotNull(state!.exp_inf)\n );\n }\n }\n}\n"},{"alpha_fraction":0.5175543427467346,"alphanum_fraction":0.5232505798339844,"avg_line_length":42.73690414428711,"blob_id":"6d2895a61f8f3431c5674d3f44ef5a1981061133","content_id":"0846ff4a3e4ee130a597d55fa8fa944e891ee370","detected_licenses":["MIT"],"is_generated":false,"is_vendor":false,"language":"C#","length_bytes":37569,"license_type":"permissive","max_line_length":175,"num_lines":859,"path":"/src/TorchAudio/Modules/Tacotron2.cs","repo_name":"NiklasGustafsson/TorchSharp","src_encoding":"UTF-8","text":"// Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information.\n\n// A number of implementation details in this file have been translated from the Python version of torchaudio,\n// largely located in the files found in this folder:\n//\n// https://github.com/pytorch/audio/blob/e502df0106403f7666f89fee09715256ea2e0df3/torchaudio/models/tacotron2.py\n//\n// The origin has the following copyright notice and license:\n//\n// https://github.com/pytorch/audio/blob/main/LICENSE\n//\n\n// *****************************************************************************\n// Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved.\n//\n// Redistribution and use in source and binary forms, with or without\n// modification, are permitted provided that the following conditions are met:\n// * Redistributions of source code must retain the above copyright\n// notice, this list of conditions and the following disclaimer.\n// * Redistributions in binary form must reproduce the above copyright\n// notice, this list of conditions and the following disclaimer in the\n// documentation and/or other materials provided with the distribution.\n// * Neither the name of the NVIDIA CORPORATION nor the\n// names of its contributors may be used to endorse or promote products\n// derived from this software without specific prior written permission.\n//\n// THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS \"AS IS\" AND\n// ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED\n// WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE\n// DISCLAIMED. IN NO EVENT SHALL NVIDIA CORPORATION BE LIABLE FOR ANY\n// DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES\n// (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;\n// LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND\n// ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT\n// (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS\n// SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.\n//\n// *****************************************************************************\n\nusing System;\nusing System.Collections.Generic;\nusing System.Diagnostics;\nusing static TorchSharp.torch;\n\nusing static TorchSharp.torch.nn;\nusing F = TorchSharp.torch.nn.functional;\n\n#nullable enable\nnamespace TorchSharp.Modules\n{\n /// <summary>\n /// Tacotron2 model based on the implementation from\n /// Nvidia https://github.com/NVIDIA/DeepLearningExamples/.\n /// </summary>\n public class Tacotron2 : nn.Module<Tensor,Tensor,Tensor,Tensor, (Tensor, Tensor, Tensor, Tensor)>\n {\n public readonly bool mask_padding;\n public readonly int n_mels;\n public readonly int n_frames_per_step;\n\n private readonly Modules.Embedding embedding;\n private readonly Encoder encoder;\n private readonly Decoder decoder;\n private readonly Postnet postnet;\n\n protected override void Dispose(bool disposing)\n {\n if (disposing) {\n embedding.Dispose();\n encoder.Dispose();\n decoder.Dispose();\n postnet.Dispose();\n }\n base.Dispose(disposing);\n }\n\n internal Tacotron2(\n string name,\n bool mask_padding = false,\n int n_mels = 80,\n int n_symbol = 148,\n int n_frames_per_step = 1,\n int symbol_embedding_dim = 512,\n int encoder_embedding_dim = 512,\n int encoder_n_convolution = 3,\n int encoder_kernel_size = 5,\n int decoder_rnn_dim = 1024,\n int decoder_max_step = 2000,\n double decoder_dropout = 0.1,\n bool decoder_early_stopping = true,\n int attention_rnn_dim = 1024,\n int attention_hidden_dim = 128,\n int attention_location_n_filter = 32,\n int attention_location_kernel_size = 31,\n double attention_dropout = 0.1,\n int prenet_dim = 256,\n int postnet_n_convolution = 5,\n int postnet_kernel_size = 5,\n int postnet_embedding_dim = 512,\n double gate_threshold = 0.5) : base(name)\n {\n this.mask_padding = mask_padding;\n this.n_mels = n_mels;\n this.n_frames_per_step = n_frames_per_step;\n this.embedding = nn.Embedding(n_symbol, symbol_embedding_dim);\n torch.nn.init.xavier_uniform_(this.embedding.weight);\n this.encoder = new Encoder(\"encoder\", encoder_embedding_dim, encoder_n_convolution, encoder_kernel_size);\n this.decoder = new Decoder(\n \"decoder\",\n n_mels,\n n_frames_per_step,\n encoder_embedding_dim,\n decoder_rnn_dim,\n decoder_max_step,\n decoder_dropout,\n decoder_early_stopping,\n attention_rnn_dim,\n attention_hidden_dim,\n attention_location_n_filter,\n attention_location_kernel_size,\n attention_dropout,\n prenet_dim,\n gate_threshold);\n this.postnet = new Postnet(\"postnet\", n_mels, postnet_embedding_dim, postnet_kernel_size, postnet_n_convolution);\n RegisterComponents();\n }\n\n public override (Tensor, Tensor, Tensor, Tensor) forward(\n Tensor tokens,\n Tensor token_lengths,\n Tensor mel_specgram,\n Tensor mel_specgram_lengths)\n {\n var embedded_inputs = this.embedding.call(tokens).transpose(1, 2);\n\n var encoder_outputs = this.encoder.call(embedded_inputs, token_lengths);\n var (x, gate_outputs, alignments) = this.decoder.call(\n encoder_outputs, mel_specgram, memory_lengths: token_lengths\n );\n mel_specgram = x;\n\n var mel_specgram_postnet = this.postnet.call(mel_specgram);\n mel_specgram_postnet = mel_specgram + mel_specgram_postnet;\n\n if (this.mask_padding) {\n var mask = _get_mask_from_lengths(mel_specgram_lengths);\n mask = mask.expand(this.n_mels, mask.size(0), mask.size(1));\n mask = mask.permute(1, 0, 2);\n\n mel_specgram = mel_specgram.masked_fill(mask, 0.0);\n mel_specgram_postnet = mel_specgram_postnet.masked_fill(mask, 0.0);\n gate_outputs = gate_outputs.masked_fill(mask[TensorIndex.Colon, 0, TensorIndex.Colon], 1e3);\n }\n\n return (mel_specgram, mel_specgram_postnet, gate_outputs, alignments);\n }\n\n public (Tensor, Tensor, Tensor) infer(\n Tensor tokens, Tensor? lengths = null)\n {\n var n_batch = tokens.shape[0];\n var max_length = tokens.shape[1];\n if (lengths is null) {\n lengths = torch.tensor(new long[] { max_length }).expand(n_batch).to(tokens.device).to(tokens.dtype);\n }\n\n if (lengths is null) {\n throw new ArgumentNullException();\n }\n\n var embedded_inputs = this.embedding.call(tokens).transpose(1, 2);\n var encoder_outputs = this.encoder.call(embedded_inputs, lengths);\n var (mel_specgram, mel_specgram_lengths, _, alignments) = this.decoder.infer(encoder_outputs, lengths);\n\n var mel_outputs_postnet = this.postnet.call(mel_specgram);\n mel_outputs_postnet = mel_specgram + mel_outputs_postnet;\n\n alignments = alignments.unfold(1, n_batch, n_batch).transpose(0, 2);\n\n return (mel_outputs_postnet, mel_specgram_lengths, alignments);\n }\n\n private static Modules.Linear _get_linear_layer(long in_dim, long out_dim, bool bias = true, init.NonlinearityType w_init_gain = init.NonlinearityType.Linear)\n {\n var linear = torch.nn.Linear(in_dim, out_dim, hasBias: bias);\n torch.nn.init.xavier_uniform_(linear.weight, gain: torch.nn.init.calculate_gain(w_init_gain));\n return linear;\n }\n\n private static Modules.Conv1d _get_conv1d_layer(\n int in_channels,\n int out_channels,\n int kernel_size = 1,\n int stride = 1,\n long? padding = null,\n int dilation = 1,\n bool bias = true,\n init.NonlinearityType w_init_gain = init.NonlinearityType.Linear)\n {\n if (padding is null) {\n if (kernel_size % 2 != 1) {\n throw new ArgumentException(\"kernel_size must be odd\");\n }\n padding = dilation * (kernel_size - 1) / 2;\n }\n\n var conv1d = torch.nn.Conv1d(\n in_channels,\n out_channels,\n kernelSize: kernel_size,\n stride: stride,\n padding: padding.Value,\n dilation: dilation,\n bias: bias);\n\n torch.nn.init.xavier_uniform_(conv1d.weight, gain: torch.nn.init.calculate_gain(w_init_gain));\n\n return conv1d;\n }\n\n private static Tensor _get_mask_from_lengths(Tensor lengths)\n {\n var max_len = torch.max(lengths).item<int>();\n var ids = torch.arange(0, max_len, device: lengths.device, dtype: lengths.dtype);\n var mask = (ids < lengths.unsqueeze(1)).to(torch.uint8);\n mask = torch.le(mask, 0);\n return mask;\n }\n\n private class LocationLayer : nn.Module<Tensor, Tensor>\n {\n private readonly Modules.Conv1d location_conv;\n private readonly Modules.Linear location_dense;\n\n protected override void Dispose(bool disposing)\n {\n if (disposing) {\n location_conv.Dispose();\n location_dense.Dispose();\n }\n base.Dispose(disposing);\n }\n\n public LocationLayer(\n string name,\n int attention_n_filter,\n int attention_kernel_size,\n int attention_hidden_dim) : base(name)\n {\n var padding = (attention_kernel_size - 1) / 2;\n this.location_conv = _get_conv1d_layer(\n 2,\n attention_n_filter,\n kernel_size: attention_kernel_size,\n padding: padding,\n bias: false,\n stride: 1,\n dilation: 1);\n this.location_dense = _get_linear_layer(\n attention_n_filter,\n attention_hidden_dim,\n bias: false,\n w_init_gain: init.NonlinearityType.Tanh);\n RegisterComponents();\n }\n\n public override Tensor forward(Tensor attention_weights_cat)\n {\n // (n_batch, attention_n_filter, text_lengths.max())\n var processed_attention = this.location_conv.call(attention_weights_cat);\n processed_attention = processed_attention.transpose(1, 2);\n // (n_batch, text_lengths.max(), attention_hidden_dim)\n processed_attention = this.location_dense.call(processed_attention);\n return processed_attention;\n }\n }\n\n private class Attention : nn.Module<Tensor,Tensor,Tensor,Tensor,Tensor, (Tensor, Tensor)>\n {\n private readonly LocationLayer location_layer;\n public readonly Modules.Linear memory_layer;\n private readonly Modules.Linear query_layer;\n public readonly float score_mask_value;\n private readonly Modules.Linear v;\n\n protected override void Dispose(bool disposing)\n {\n if (disposing) {\n location_layer.Dispose();\n memory_layer.Dispose();\n query_layer.Dispose();\n v.Dispose();\n }\n base.Dispose(disposing);\n }\n\n public Attention(\n string name,\n int attention_rnn_dim,\n int encoder_embedding_dim,\n int attention_hidden_dim,\n int attention_location_n_filter,\n int attention_location_kernel_size) : base(name)\n {\n this.query_layer = _get_linear_layer(attention_rnn_dim, attention_hidden_dim, bias: false, w_init_gain: init.NonlinearityType.Tanh);\n this.memory_layer = _get_linear_layer(\n encoder_embedding_dim, attention_hidden_dim, bias: false, w_init_gain: init.NonlinearityType.Tanh);\n this.v = _get_linear_layer(attention_hidden_dim, 1, bias: false);\n this.location_layer = new LocationLayer(\n \"location_layer\",\n attention_location_n_filter,\n attention_location_kernel_size,\n attention_hidden_dim);\n this.score_mask_value = float.NegativeInfinity;\n RegisterComponents();\n }\n\n private Tensor _get_alignment_energies(Tensor query, Tensor processed_memory, Tensor attention_weights_cat)\n {\n var processed_query = this.query_layer.call(query.unsqueeze(1));\n var processed_attention_weights = this.location_layer.call(attention_weights_cat);\n var energies = this.v.call(torch.tanh(processed_query + processed_attention_weights + processed_memory));\n\n var alignment = energies.squeeze(2);\n return alignment;\n }\n\n public override (Tensor, Tensor) forward(\n Tensor attention_hidden_state,\n Tensor memory,\n Tensor processed_memory,\n Tensor attention_weights_cat,\n Tensor mask)\n {\n var alignment = this._get_alignment_energies(attention_hidden_state, processed_memory, attention_weights_cat);\n\n alignment = alignment.masked_fill(mask, this.score_mask_value);\n\n var attention_weights = F.softmax(alignment, dim: 1);\n var attention_context = torch.bmm(attention_weights.unsqueeze(1), memory);\n attention_context = attention_context.squeeze(1);\n\n return (attention_context, attention_weights);\n }\n }\n\n public class Prenet : nn.Module<Tensor, Tensor>\n {\n private readonly Modules.ModuleList<Module<Tensor, Tensor>> layers;\n\n protected override void Dispose(bool disposing)\n {\n if (disposing) {\n layers.Dispose();\n }\n base.Dispose(disposing);\n }\n\n public Prenet(string name, int in_dim, long[] out_sizes) : base(name)\n {\n this.layers = nn.ModuleList<Module<Tensor, Tensor>>();\n long prev_size = in_dim;\n for (int i = 0; i < out_sizes.Length; i++) {\n long in_size = prev_size;\n long out_size = out_sizes[i];\n this.layers.Add(\n _get_linear_layer(in_size, out_size, bias: false));\n prev_size = out_size;\n }\n RegisterComponents();\n }\n\n public override Tensor forward(Tensor x)\n {\n foreach (var linear in this.layers) {\n x = F.dropout(F.relu(linear.call(x)), p: 0.5, training: true);\n }\n return x;\n }\n }\n\n private class Postnet : nn.Module<Tensor, Tensor>\n {\n private readonly Modules.ModuleList<Module<Tensor, Tensor>> convolutions;\n public readonly int n_convs;\n\n protected override void Dispose(bool disposing)\n {\n if (disposing) {\n convolutions.Dispose();\n }\n\n base.Dispose(disposing);\n }\n\n public Postnet(\n string name,\n int n_mels,\n int postnet_embedding_dim,\n int postnet_kernel_size,\n int postnet_n_convolution) : base(name)\n {\n this.convolutions = nn.ModuleList<Module<Tensor, Tensor>>();\n\n for (int i = 0; i < postnet_n_convolution; i++) {\n var in_channels = i == 0 ? n_mels : postnet_embedding_dim;\n var out_channels = i == postnet_n_convolution - 1 ? n_mels : postnet_embedding_dim;\n var init_gain = i == postnet_n_convolution - 1 ? init.NonlinearityType.Linear : init.NonlinearityType.Tanh;\n var num_features = i == postnet_n_convolution - 1 ? n_mels : postnet_embedding_dim;\n this.convolutions.append(\n nn.Sequential(\n _get_conv1d_layer(\n in_channels,\n out_channels,\n kernel_size: postnet_kernel_size,\n stride: 1,\n padding: (postnet_kernel_size - 1) / 2,\n dilation: 1,\n w_init_gain: init_gain),\n nn.BatchNorm1d(num_features)));\n }\n\n this.n_convs = this.convolutions.Count;\n RegisterComponents();\n }\n\n public override Tensor forward(Tensor x)\n {\n for (int i = 0; i < this.convolutions.Count; i++) {\n var conv = this.convolutions[i];\n if (i < this.n_convs - 1) {\n x = F.dropout(torch.tanh(conv.call(x)), 0.5, training: this.training);\n } else {\n x = F.dropout(conv.call(x), 0.5, training: this.training);\n }\n }\n return x;\n }\n }\n\n private class Encoder : nn.Module<Tensor, Tensor, Tensor>\n {\n private readonly Modules.ModuleList<Module<Tensor, Tensor>> convolutions;\n private readonly Modules.LSTM lstm;\n\n protected override void Dispose(bool disposing)\n {\n if (disposing) {\n convolutions.Dispose();\n lstm.Dispose();\n }\n base.Dispose(disposing);\n }\n\n public Encoder(\n string name,\n int encoder_embedding_dim,\n int encoder_n_convolution,\n int encoder_kernel_size) : base(name)\n {\n this.convolutions = nn.ModuleList<Module<Tensor, Tensor>>();\n for (int i = 0; i < encoder_n_convolution; i++) {\n var conv_layer = nn.Sequential(\n _get_conv1d_layer(\n encoder_embedding_dim,\n encoder_embedding_dim,\n kernel_size: encoder_kernel_size,\n stride: 1,\n padding: (encoder_kernel_size - 1) / 2,\n dilation: 1,\n w_init_gain: init.NonlinearityType.ReLU\n ),\n nn.BatchNorm1d(encoder_embedding_dim)\n );\n this.convolutions.append(conv_layer);\n }\n\n this.lstm = nn.LSTM(\n encoder_embedding_dim,\n encoder_embedding_dim / 2,\n 1,\n batchFirst: true,\n bidirectional: true\n );\n this.lstm.flatten_parameters();\n RegisterComponents();\n }\n\n public override Tensor forward(Tensor x, Tensor input_lengths)\n {\n foreach (var conv in this.convolutions) {\n x = F.dropout(F.relu(conv.call(x)), 0.5, training: this.training);\n }\n\n x = x.transpose(1, 2);\n\n input_lengths = input_lengths.cpu();\n var packed_x = nn.utils.rnn.pack_padded_sequence(x, input_lengths, batch_first: true);\n\n var (packed_outputs, _, _) = this.lstm.call(packed_x);\n var (outputs, _) = nn.utils.rnn.pad_packed_sequence(packed_outputs, batch_first: true);\n\n return outputs;\n }\n }\n\n private class Decoder : nn.Module<Tensor, Tensor, Tensor, (Tensor, Tensor, Tensor)>\n {\n public readonly double attention_dropout;\n private readonly Attention attention_layer;\n private readonly Modules.LSTMCell attention_rnn;\n public readonly long attention_rnn_dim;\n public readonly double decoder_dropout;\n public readonly bool decoder_early_stopping;\n public readonly long decoder_max_step;\n private readonly Modules.LSTMCell decoder_rnn;\n public readonly long decoder_rnn_dim;\n public readonly long encoder_embedding_dim;\n private readonly Modules.Linear gate_layer;\n public readonly double gate_threshold;\n private readonly Modules.Linear linear_projection;\n public readonly int n_frames_per_step;\n public readonly int n_mels;\n public readonly Prenet prenet;\n public readonly long prenet_dim;\n\n protected override void Dispose(bool disposing)\n {\n if (disposing) {\n attention_layer.Dispose(); attention_rnn.Dispose();\n decoder_rnn.Dispose();\n gate_layer.Dispose();\n linear_projection.Dispose();\n prenet.Dispose();\n }\n base.Dispose(disposing);\n }\n\n public Decoder(\n string name,\n int n_mels,\n int n_frames_per_step,\n int encoder_embedding_dim,\n int decoder_rnn_dim,\n int decoder_max_step,\n double decoder_dropout,\n bool decoder_early_stopping,\n int attention_rnn_dim,\n int attention_hidden_dim,\n int attention_location_n_filter,\n int attention_location_kernel_size,\n double attention_dropout,\n long prenet_dim,\n double gate_threshold) : base(name)\n {\n this.n_mels = n_mels;\n this.n_frames_per_step = n_frames_per_step;\n this.encoder_embedding_dim = encoder_embedding_dim;\n this.attention_rnn_dim = attention_rnn_dim;\n this.decoder_rnn_dim = decoder_rnn_dim;\n this.prenet_dim = prenet_dim;\n this.decoder_max_step = decoder_max_step;\n this.gate_threshold = gate_threshold;\n this.attention_dropout = attention_dropout;\n this.decoder_dropout = decoder_dropout;\n this.decoder_early_stopping = decoder_early_stopping;\n\n this.prenet = new Prenet(\"prenet\", n_mels * n_frames_per_step, new[] {\n prenet_dim,\n prenet_dim\n });\n\n this.attention_rnn = nn.LSTMCell(prenet_dim + encoder_embedding_dim, attention_rnn_dim);\n\n this.attention_layer = new Attention(\n \"attention\",\n attention_rnn_dim,\n encoder_embedding_dim,\n attention_hidden_dim,\n attention_location_n_filter,\n attention_location_kernel_size);\n\n this.decoder_rnn = nn.LSTMCell(attention_rnn_dim + encoder_embedding_dim, decoder_rnn_dim, true);\n\n this.linear_projection = _get_linear_layer(decoder_rnn_dim + encoder_embedding_dim, n_mels * n_frames_per_step);\n\n this.gate_layer = _get_linear_layer(\n decoder_rnn_dim + encoder_embedding_dim, 1, bias: true, w_init_gain: init.NonlinearityType.Sigmoid);\n RegisterComponents();\n }\n\n private Tensor _get_initial_frame(Tensor memory)\n {\n var n_batch = memory.size(0);\n var dtype = memory.dtype;\n var device = memory.device;\n var decoder_input = torch.zeros(n_batch, this.n_mels * this.n_frames_per_step, dtype: dtype, device: device);\n return decoder_input;\n }\n\n private (Tensor, Tensor, Tensor, Tensor, Tensor, Tensor, Tensor, Tensor) _initialize_decoder_states(Tensor memory)\n {\n var n_batch = memory.size(0);\n var max_time = memory.size(1);\n var dtype = memory.dtype;\n var device = memory.device;\n\n var attention_hidden = torch.zeros(n_batch, this.attention_rnn_dim, dtype: dtype, device: device);\n var attention_cell = torch.zeros(n_batch, this.attention_rnn_dim, dtype: dtype, device: device);\n\n var decoder_hidden = torch.zeros(n_batch, this.decoder_rnn_dim, dtype: dtype, device: device);\n var decoder_cell = torch.zeros(n_batch, this.decoder_rnn_dim, dtype: dtype, device: device);\n\n var attention_weights = torch.zeros(n_batch, max_time, dtype: dtype, device: device);\n var attention_weights_cum = torch.zeros(n_batch, max_time, dtype: dtype, device: device);\n var attention_context = torch.zeros(n_batch, this.encoder_embedding_dim, dtype: dtype, device: device);\n\n var processed_memory = this.attention_layer.memory_layer.call(memory);\n\n return (\n attention_hidden,\n attention_cell,\n decoder_hidden,\n decoder_cell,\n attention_weights,\n attention_weights_cum,\n attention_context,\n processed_memory);\n }\n\n private Tensor _parse_decoder_inputs(Tensor decoder_inputs)\n {\n // (n_batch, n_mels, mel_specgram_lengths.max()) -> (n_batch, mel_specgram_lengths.max(), n_mels)\n decoder_inputs = decoder_inputs.transpose(1, 2);\n decoder_inputs = decoder_inputs.view(\n decoder_inputs.size(0),\n decoder_inputs.size(1) / this.n_frames_per_step,\n -1);\n // (n_batch, mel_specgram_lengths.max(), n_mels) -> (mel_specgram_lengths.max(), n_batch, n_mels)\n decoder_inputs = decoder_inputs.transpose(0, 1);\n return decoder_inputs;\n }\n\n private (Tensor, Tensor, Tensor) _parse_decoder_outputs(Tensor mel_specgram, Tensor gate_outputs, Tensor alignments)\n {\n // (mel_specgram_lengths.max(), n_batch, text_lengths.max())\n // -> (n_batch, mel_specgram_lengths.max(), text_lengths.max())\n alignments = alignments.transpose(0, 1).contiguous();\n // (mel_specgram_lengths.max(), n_batch) -> (n_batch, mel_specgram_lengths.max())\n gate_outputs = gate_outputs.transpose(0, 1).contiguous();\n // (mel_specgram_lengths.max(), n_batch, n_mels) -> (n_batch, mel_specgram_lengths.max(), n_mels)\n mel_specgram = mel_specgram.transpose(0, 1).contiguous();\n // decouple frames per step\n var shape = new long[] { mel_specgram.shape[0], -1, this.n_mels };\n mel_specgram = mel_specgram.view(shape);\n // (n_batch, mel_specgram_lengths.max(), n_mels) -> (n_batch, n_mels, T_out)\n mel_specgram = mel_specgram.transpose(1, 2);\n\n return (mel_specgram, gate_outputs, alignments);\n }\n\n public (Tensor, Tensor, Tensor, Tensor, Tensor, Tensor, Tensor, Tensor, Tensor) decode(\n Tensor decoder_input,\n Tensor attention_hidden,\n Tensor attention_cell,\n Tensor decoder_hidden,\n Tensor decoder_cell,\n Tensor attention_weights,\n Tensor attention_weights_cum,\n Tensor attention_context,\n Tensor memory,\n Tensor processed_memory,\n Tensor mask)\n {\n var cell_input = torch.cat(new[] { decoder_input, attention_context }, -1);\n\n (attention_hidden, attention_cell) = this.attention_rnn.call(cell_input, (attention_hidden, attention_cell));\n attention_hidden = F.dropout(attention_hidden, this.attention_dropout, training: this.training);\n\n var attention_weights_cat = torch.cat(new[] { attention_weights.unsqueeze(1), attention_weights_cum.unsqueeze(1) }, dim: 1);\n (attention_context, attention_weights) = this.attention_layer.call(\n attention_hidden, memory, processed_memory, attention_weights_cat, mask);\n\n attention_weights_cum += attention_weights;\n decoder_input = torch.cat(new[] { attention_hidden, attention_context }, -1);\n\n (decoder_hidden, decoder_cell) = this.decoder_rnn.call(decoder_input, (decoder_hidden, decoder_cell));\n decoder_hidden = F.dropout(decoder_hidden, this.decoder_dropout, training: this.training);\n\n var decoder_hidden_attention_context = torch.cat(new[] { decoder_hidden, attention_context }, dim: 1);\n var decoder_output = this.linear_projection.call(decoder_hidden_attention_context);\n\n var gate_prediction = this.gate_layer.call(decoder_hidden_attention_context);\n\n return (\n decoder_output,\n gate_prediction,\n attention_hidden,\n attention_cell,\n decoder_hidden,\n decoder_cell,\n attention_weights,\n attention_weights_cum,\n attention_context);\n }\n\n // Decoder forward pass for training.\n public override (Tensor, Tensor, Tensor) forward(Tensor memory, Tensor mel_specgram_truth, Tensor memory_lengths)\n {\n var decoder_input = this._get_initial_frame(memory).unsqueeze(0);\n var decoder_inputs = this._parse_decoder_inputs(mel_specgram_truth);\n decoder_inputs = torch.cat(new[] { decoder_input, decoder_inputs }, dim: 0);\n decoder_inputs = this.prenet.call(decoder_inputs);\n\n var mask = _get_mask_from_lengths(memory_lengths);\n var (\n attention_hidden,\n attention_cell,\n decoder_hidden,\n decoder_cell,\n attention_weights,\n attention_weights_cum,\n attention_context,\n processed_memory\n ) = this._initialize_decoder_states(memory);\n\n var mel_output_list = new List<Tensor>();\n var gate_output_list = new List<Tensor>();\n var alignment_list = new List<Tensor>();\n while (mel_output_list.Count < decoder_inputs.size(0) - 1) {\n decoder_input = decoder_inputs[mel_output_list.Count];\n Tensor mel_output, gate_output;\n (\n mel_output,\n gate_output,\n attention_hidden,\n attention_cell,\n decoder_hidden,\n decoder_cell,\n attention_weights,\n attention_weights_cum,\n attention_context\n ) = this.decode(\n decoder_input,\n attention_hidden,\n attention_cell,\n decoder_hidden,\n decoder_cell,\n attention_weights,\n attention_weights_cum,\n attention_context,\n memory,\n processed_memory,\n mask);\n\n mel_output_list.Add(mel_output.squeeze(1));\n gate_output_list.Add(gate_output.squeeze(1));\n alignment_list.Add(attention_weights);\n }\n\n var (mel_specgram, gate_outputs, alignments) = this._parse_decoder_outputs(\n torch.stack(mel_output_list), torch.stack(gate_output_list), torch.stack(alignment_list));\n\n return (mel_specgram, gate_outputs, alignments);\n }\n\n public new (Tensor, Tensor, Tensor) call(Tensor memory, Tensor mel_specgram_truth, Tensor memory_lengths) => base.call(memory, mel_specgram_truth, memory_lengths);\n\n private Tensor _get_go_frame(Tensor memory)\n {\n var n_batch = memory.size(0);\n var dtype = memory.dtype;\n var device = memory.device;\n var decoder_input = torch.zeros(n_batch, this.n_mels * this.n_frames_per_step, dtype: dtype, device: device);\n return decoder_input;\n }\n\n public (Tensor, Tensor, Tensor, Tensor) infer(Tensor memory, Tensor memory_lengths)\n {\n var batch_size = memory.size(0);\n var device = memory.device;\n\n var decoder_input = this._get_go_frame(memory);\n\n var mask = _get_mask_from_lengths(memory_lengths);\n var (\n attention_hidden,\n attention_cell,\n decoder_hidden,\n decoder_cell,\n attention_weights,\n attention_weights_cum,\n attention_context,\n processed_memory\n ) = this._initialize_decoder_states(memory);\n\n var mel_specgram_lengths = torch.zeros(new[] {\n batch_size\n }, dtype: torch.int32, device: device);\n var finished = torch.zeros(new[] {\n batch_size\n }, dtype: torch.@bool, device: device);\n var mel_specgram_list = new List<Tensor>();\n var gate_output_list = new List<Tensor>();\n var alignment_list = new List<Tensor>();\n for (long i = 0; i < this.decoder_max_step; i++) {\n decoder_input = this.prenet.call(decoder_input);\n Tensor mel_specgram, gate_output;\n (\n mel_specgram,\n gate_output,\n attention_hidden,\n attention_cell,\n decoder_hidden,\n decoder_cell,\n attention_weights,\n attention_weights_cum,\n attention_context\n ) = this.decode(\n decoder_input,\n attention_hidden,\n attention_cell,\n decoder_hidden,\n decoder_cell,\n attention_weights,\n attention_weights_cum,\n attention_context,\n memory,\n processed_memory,\n mask);\n\n mel_specgram_list.Add(mel_specgram.unsqueeze(0));\n gate_output_list.Add(gate_output.transpose(0, 1));\n alignment_list.Add(attention_weights);\n mel_specgram_lengths[~finished] += 1;\n\n finished |= torch.sigmoid(gate_output.squeeze(1)) > this.gate_threshold;\n if (this.decoder_early_stopping && torch.all(finished).item<bool>()) {\n break;\n }\n\n decoder_input = mel_specgram;\n }\n\n if (mel_specgram_list.Count == this.decoder_max_step) {\n Debug.WriteLine(\"Reached max decoder steps. The generated spectrogram might not cover the whole transcript.\");\n }\n\n var mel_specgrams = torch.cat(mel_specgram_list, dim: 0);\n var gate_outputs = torch.cat(gate_output_list, dim: 0);\n var alignments = torch.cat(alignment_list, dim: 0);\n\n (mel_specgrams, gate_outputs, alignments) = this._parse_decoder_outputs(mel_specgrams, gate_outputs, alignments);\n\n return (mel_specgrams, mel_specgram_lengths, gate_outputs, alignments);\n }\n }\n }\n}"},{"alpha_fraction":0.579365074634552,"alphanum_fraction":0.5833333134651184,"avg_line_length":35,"blob_id":"11a9d48ac1f5fb8f3a83c4a3f934f208a7e39eb8","content_id":"e5a5fd1861a598f17dfc79a2886f102c839249a3","detected_licenses":["MIT"],"is_generated":false,"is_vendor":false,"language":"C#","length_bytes":1512,"license_type":"permissive","max_line_length":130,"num_lines":42,"path":"/src/TorchVision/AdjustBrightness.cs","repo_name":"NiklasGustafsson/TorchSharp","src_encoding":"UTF-8","text":"// Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information.\nusing System;\nusing static TorchSharp.torch;\n\nnamespace TorchSharp\n{\n public static partial class torchvision\n {\n internal class AdjustBrightness : ITransform\n {\n internal AdjustBrightness(double brightness_factor)\n {\n if (brightness_factor < 0.0)\n throw new ArgumentException($\"The sharpness factor ({brightness_factor}) must be non-negative.\");\n this.brightness_factor = brightness_factor;\n }\n\n public Tensor call(Tensor input)\n {\n return transforms.functional.adjust_brightness(input, brightness_factor);\n }\n\n private double brightness_factor;\n }\n\n public static partial class transforms\n {\n /// <summary>\n /// Adjust the sharpness of the image. \n /// </summary>\n /// <param name=\"brightness_factor\">\n /// How much to adjust the brightness. Can be any non negative number.\n /// 0 gives a black image, 1 gives the original image while 2 increases the brightness by a factor of 2.\n /// </param>\n /// <returns></returns>\n static public ITransform AdjustBrightness(double brightness_factor)\n {\n return new AdjustBrightness(brightness_factor);\n }\n }\n }\n}\n"},{"alpha_fraction":0.5237137675285339,"alphanum_fraction":0.530632734298706,"avg_line_length":47.039772033691406,"blob_id":"4f0c2c66c872fe6be47a04d8211558d1127104ca","content_id":"8ca7dae45dba7040fa26059a1c73b4ef4696277e","detected_licenses":["MIT"],"is_generated":false,"is_vendor":false,"language":"C#","length_bytes":16911,"license_type":"permissive","max_line_length":223,"num_lines":352,"path":"/src/TorchSharp/Optimizers/Adagrad.cs","repo_name":"NiklasGustafsson/TorchSharp","src_encoding":"UTF-8","text":"// Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information.\nusing System;\nusing System.Collections.Generic;\nusing System.Linq;\nusing static TorchSharp.torch;\n\nnamespace TorchSharp\n{\n using System.IO;\n using Modules;\n\n public static partial class torch\n {\n public static partial class optim\n {\n\n /// <summary>\n /// Implements the Adagrad algorithm.\n ///\n /// It has been proposed in Adaptive Subgradient Methods for Online Learning and Stochastic Optimization.\n /// http://jmlr.org/papers/v12/duchi11a.html\n /// </summary>\n /// <param name=\"parameters\">Parameters to optimize</param>\n /// <param name=\"lr\">learning rate (default: 1e-2)</param>\n /// <param name=\"lr_decay\">learning rate decay (default: 0)</param>\n /// <param name=\"weight_decay\">weight decay (L2 penalty) (default: 0)</param>\n /// <param name=\"initial_accumulator_value\"></param>\n /// <param name=\"eps\">Term added to the denominator to improve numerical stability (default: 1e-10)</param>\n /// <returns></returns>\n public static Adagrad Adagrad(IEnumerable<Parameter> parameters, double lr = 1e-2, double lr_decay = 0, double weight_decay = 0, double initial_accumulator_value = 0, double eps = 1e-10)\n {\n return new Adagrad(parameters, lr, lr_decay, weight_decay, initial_accumulator_value, eps);\n }\n\n /// <summary>\n /// Implements the Adagrad algorithm.\n ///\n /// It has been proposed in Adaptive Subgradient Methods for Online Learning and Stochastic Optimization.\n /// http://jmlr.org/papers/v12/duchi11a.html\n /// </summary>\n /// <param name=\"parameters\">Parameters to optimize</param>\n /// <param name=\"lr\">learning rate (default: 1e-2)</param>\n /// <param name=\"lr_decay\">learning rate decay (default: 0)</param>\n /// <param name=\"weight_decay\">weight decay (L2 penalty) (default: 0)</param>\n /// <param name=\"initial_accumulator_value\"></param>\n /// <param name=\"eps\">Term added to the denominator to improve numerical stability (default: 1e-10)</param>\n /// <returns></returns>\n public static Adagrad Adagrad(IEnumerable<(string name, Parameter parameter)> parameters, double lr = 1e-2, double lr_decay = 0, double weight_decay = 0, double initial_accumulator_value = 0, double eps = 1e-10)\n {\n return new Adagrad(parameters.Select(np => np.parameter), lr, lr_decay, weight_decay, initial_accumulator_value, eps);\n }\n\n /// <summary>\n /// Implements the Adagrad algorithm.\n ///\n /// It has been proposed in Adaptive Subgradient Methods for Online Learning and Stochastic Optimization.\n /// http://jmlr.org/papers/v12/duchi11a.html\n /// </summary>\n /// <param name=\"parameters\">Parameters to optimize</param>\n /// <param name=\"lr\">learning rate (default: 1e-2)</param>\n /// <param name=\"lr_decay\">learning rate decay (default: 0)</param>\n /// <param name=\"weight_decay\">weight decay (L2 penalty) (default: 0)</param>\n /// <param name=\"initial_accumulator_value\"></param>\n /// <param name=\"eps\">Term added to the denominator to improve numerical stability (default: 1e-10)</param>\n /// <returns></returns>\n public static Adagrad Adagrad(IEnumerable<Adagrad.ParamGroup> parameters, double lr = 1e-2, double lr_decay = 0, double weight_decay = 0, double initial_accumulator_value = 0, double eps = 1e-10)\n {\n return new Adagrad(parameters, lr, lr_decay, weight_decay, initial_accumulator_value, eps);\n }\n }\n }\n\n namespace Modules\n {\n public class Adagrad : OptimizerHelper\n {\n /// <summary>\n /// Implements Adagrad algorithm.\n ///\n /// It has been proposed in Adaptive Subgradient Methods for Online Learning and Stochastic Optimization.\n /// </summary>\n /// <param name=\"parameters\">Parameters to optimize</param>\n /// <param name=\"lr\">learning rate (default: 1e-2)</param>\n /// <param name=\"lr_decay\">learning rate decay (default: 0)</param>\n /// <param name=\"weight_decay\">weight decay (L2 penalty) (default: 0)</param>\n /// <param name=\"initial_accumulator_value\"></param>\n /// <param name=\"eps\">Term added to the denominator to improve numerical stability (default: 1e-10)</param>\n /// <returns></returns>\n public Adagrad(IEnumerable<Parameter> parameters, double lr = 1e-2, double lr_decay = 0, double weight_decay = 0, double initial_accumulator_value = 0, double eps = 1e-10)\n : this(new ParamGroup[] { new() { Parameters = parameters } }, lr, lr_decay, weight_decay, initial_accumulator_value, eps)\n {\n }\n /// <summary>\n /// Implements Adagrad algorithm.\n ///\n /// It has been proposed in Adaptive Subgradient Methods for Online Learning and Stochastic Optimization.\n /// </summary>\n /// <param name=\"parameters\">Parameters to optimize</param>\n /// <param name=\"lr\">learning rate (default: 1e-2)</param>\n /// <param name=\"lr_decay\">learning rate decay (default: 0)</param>\n /// <param name=\"weight_decay\">weight decay (L2 penalty) (default: 0)</param>\n /// <param name=\"initial_accumulator_value\"></param>\n /// <param name=\"eps\">Term added to the denominator to improve numerical stability (default: 1e-10)</param>\n /// <returns></returns>\n public Adagrad(IEnumerable<ParamGroup> parameters, double lr = 1e-2, double lr_decay = 0, double weight_decay = 0, double initial_accumulator_value = 0, double eps = 1e-10)\n {\n if (lr < 0) throw new ArgumentException($\"Invalid learning rate: {lr}\");\n if (eps < 0) throw new ArgumentException($\"Invalid ε: {eps}\");\n if (lr_decay < 0.0) throw new ArgumentException($\"Invalid lr_decay value: {lr_decay}\");\n if (weight_decay < 0.0) throw new ArgumentException($\"Invalid weight_decay value: {weight_decay}\");\n if (initial_accumulator_value < 0) throw new ArgumentException($\"Invalid initial_accumulator_value: {initial_accumulator_value}\");\n\n var options = new Options {\n LearningRate = lr,\n InitialLearningRate = lr,\n lr_decay = lr_decay,\n eps = eps,\n initial_accumulator_value = initial_accumulator_value,\n weight_decay = weight_decay\n };\n\n _defaults = options;\n _parameter_groups = new List<Modules.ParamGroup>();\n\n foreach (var g in parameters) {\n add_param_group(g);\n }\n }\n\n /// <summary>\n /// Performs a single optimization step (parameter update).\n /// </summary>\n /// <param name=\"closure\">A closure that reevaluates the model and returns the loss. Optional for most optimizers.</param>\n /// <returns></returns>\n public override Tensor step(Func<Tensor> closure = null)\n {\n return _step<ParamGroup>(group => {\n\n var options = group.Options as Options;\n var lr_decay = options.lr_decay.Value;\n var weight_decay = options.weight_decay.Value;\n var eps = options.eps.Value;\n var initial_accumulator_value = options.initial_accumulator_value.Value;\n var lr = options.LearningRate.Value;\n\n foreach (var param in group.Parameters) {\n\n var state = (State)_state[param.handle];\n\n var grad = param.grad();\n\n if (grad is null) continue;\n\n state.step += 1;\n\n if (weight_decay != 0) {\n grad = grad.add(param, alpha: weight_decay);\n }\n\n var clr = lr / (1 + (state.step - 1) * lr_decay);\n\n if (grad.is_sparse)\n throw new NotImplementedException(\"Adagrad optimization over sparse parameters\");\n if (torch.is_complex(grad))\n throw new NotImplementedException(\"Adagrad optimization over complex parameters\");\n\n state.sum.addcmul_(grad, grad, value: 1);\n var std = state.sum.sqrt().add_(eps);\n param.addcdiv_(grad, std, value: -clr);\n }\n\n\n }, closure);\n }\n\n protected override void Dispose(bool disposing)\n {\n base.Dispose(disposing);\n foreach (var kvp in _state) {\n ((State)kvp.Item2).Dispose();\n }\n }\n\n public sealed class State : OptimizerState, IDisposable\n {\n public long step;\n public Tensor sum;\n\n public void Dispose()\n {\n Dispose(true);\n GC.SuppressFinalize(this);\n }\n\n private void Dispose(bool disposing)\n {\n if (disposing) {\n sum.Dispose();\n }\n }\n\n /// <summary>\n /// Move all the state to the indicated device.\n /// </summary>\n /// <param name=\"device\">The device to move all state to.</param>\n public override void to(Device device)\n {\n sum = sum.to(device);\n }\n\n /// <summary>\n /// Load the optimizer parameter state from a stream.\n /// </summary>\n /// <param name=\"reader\">A binary reader connected to a stream open for reading.</param>\n public override void LoadStateDict(BinaryReader reader)\n {\n step = reader.ReadInt64();\n sum.Load(reader);\n }\n\n /// <summary>\n /// Load optimizer parameter state from another optimizer.\n /// </summary>\n /// <param name=\"source\">An optimizer state record.</param>\n public override void LoadStateDict(OptimizerState source)\n {\n var st_state = source as State;\n sum.Dispose();\n step = st_state.step;\n sum = st_state.sum;\n }\n\n /// <summary>\n /// Save the optimizer parameter state to a stream.\n /// </summary>\n /// <param name=\"writer\">A binary writer connected to a stream open for writing.</param>\n public override void SaveStateDict(BinaryWriter writer)\n {\n writer.Write(step);\n sum.Save(writer);\n }\n\n /// <summary>\n /// Useful for tests, allows comparison of one state with another.\n /// </summary>\n /// <param name=\"other\">The other optimizer state</param>\n /// <returns></returns>\n public override bool ApproximatelyEquals(OptimizerState other)\n {\n var rhs = other as State;\n return (rhs is not null) && step == rhs.step && sum.allclose(rhs.sum);\n }\n }\n\n /// <summary>\n /// Add a param group to the Optimizer s param_groups.\n /// </summary>\n /// <param name=\"param_group\"></param>\n /// <remarks>This can be useful when fine tuning a pre-trained network as frozen layers can be made trainable and added to the Optimizer as training progresses.</remarks>\n public override void add_param_group(Modules.ParamGroup param_group)\n {\n var def = _defaults as Options;\n if (param_group.Options is null) {\n param_group.Options = new Options();\n }\n\n var opt = param_group.Options as Options;\n\n // Make sure all the options are set.\n if (!opt.LearningRate.HasValue) opt.LearningRate = def.LearningRate;\n if (!opt.lr_decay.HasValue) opt.lr_decay = def.lr_decay;\n if (!opt.eps.HasValue) opt.eps = def.eps;\n if (!opt.weight_decay.HasValue) opt.weight_decay = def.weight_decay;\n if (!opt.initial_accumulator_value.HasValue) opt.initial_accumulator_value = def.initial_accumulator_value;\n\n opt.InitialLearningRate = opt.LearningRate.Value;\n\n _parameter_groups.Add(param_group);\n\n foreach (var p in param_group.Parameters) {\n var state = new State();\n _state[p.Handle] = state;\n state.step = 0;\n var init_value = torch.is_complex(p.dtype)\n ? (Scalar)new System.Numerics.Complex((param_group.Options as Options).initial_accumulator_value.Value, (param_group.Options as Options).initial_accumulator_value.Value)\n : (Scalar)(param_group.Options as Options).initial_accumulator_value.Value;\n state.sum = torch.full_like(p, init_value).DetachFromDisposeScope();\n }\n }\n\n public class Options : OptimizerOptions\n {\n public double? lr_decay;\n public double? initial_accumulator_value;\n public double? eps;\n public double? weight_decay;\n\n /// <summary>\n /// Load optimizer options (param-group hyperparameters) from another optimizer.\n /// </summary>\n /// <param name=\"source\">An optimizer options record.</param>\n public override void LoadStateDict(OptimizerOptions source)\n {\n base.LoadStateDict(source);\n var opts = source as Options;\n lr_decay = opts.lr_decay;\n initial_accumulator_value = opts.initial_accumulator_value;\n eps = opts.eps;\n weight_decay = opts.weight_decay;\n }\n\n /// <summary>\n /// Load the optimizer options (param-group hyperparameters) from a stream.\n /// </summary>\n /// <param name=\"reader\">A binary reader connected to a stream open for reading.</param>\n public override void LoadStateDict(BinaryReader reader)\n {\n base.LoadStateDict(reader);\n lr_decay = reader.ReadDouble();\n initial_accumulator_value = reader.ReadDouble();\n eps = reader.ReadDouble();\n weight_decay = reader.ReadDouble();\n }\n\n /// <summary>\n /// Save the optimizer options (param-group hyperparameters) to a stream.\n /// </summary>\n /// <param name=\"writer\">A binary writer connected to a stream open for writing.</param>\n public override void SaveStateDict(BinaryWriter writer)\n {\n base.SaveStateDict(writer);\n writer.Write(lr_decay.Value);\n writer.Write(initial_accumulator_value.Value);\n writer.Write(eps.Value);\n writer.Write(weight_decay.Value);\n }\n }\n\n public class ParamGroup : ParamGroup<Options>\n {\n public ParamGroup() { }\n\n public ParamGroup(IEnumerable<Parameter> parameters, Options options) : base(parameters, options) { }\n\n public ParamGroup(IEnumerable<Parameter> parameters, double lr = 0.01, double lr_decay = 0, double weight_decay = 0, double initial_accumulator_value = 0, double eps = 1e-10)\n : base(parameters, new Adagrad.Options { LearningRate = lr, lr_decay = lr_decay, initial_accumulator_value = initial_accumulator_value, weight_decay = weight_decay, eps = eps })\n {\n }\n }\n }\n }\n}\n"},{"alpha_fraction":0.5762327313423157,"alphanum_fraction":0.5782958269119263,"avg_line_length":48.96907043457031,"blob_id":"b0cbb8c9cc2b7315394332c0df7b6a68de0ac4a8","content_id":"004be40ba6ad7b53bc9a3a985b6981790b454455","detected_licenses":["MIT"],"is_generated":false,"is_vendor":false,"language":"C#","length_bytes":4847,"license_type":"permissive","max_line_length":209,"num_lines":97,"path":"/src/TorchSharp/NN/Normalization/LayerNorm.cs","repo_name":"NiklasGustafsson/TorchSharp","src_encoding":"UTF-8","text":"// Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information.\nusing System;\nusing static TorchSharp.torch;\nusing static TorchSharp.PInvoke.NativeMethods;\n\n#nullable enable\nnamespace TorchSharp\n{\n using Modules;\n\n namespace Modules\n {\n\n /// <summary>\n /// This class is used to represent a LayerNorm module.\n /// </summary>\n public sealed class LayerNorm : torch.nn.Module<Tensor, Tensor>\n {\n internal LayerNorm(IntPtr handle, IntPtr boxedHandle) : base(handle, boxedHandle)\n {\n }\n\n public override Tensor forward(Tensor tensor)\n {\n var res = THSNN_LayerNorm_forward(handle.DangerousGetHandle(), tensor.Handle);\n if (res == IntPtr.Zero) { torch.CheckForErrors(); }\n return new Tensor(res);\n }\n\n public Parameter? bias {\n get {\n var res = THSNN_LayerNorm_bias(handle);\n if (res == IntPtr.Zero) { torch.CheckForErrors(); }\n return (res == IntPtr.Zero) ? null : new Parameter(res);\n }\n set {\n THSNN_LayerNorm_set_bias(handle, (value is null ? IntPtr.Zero : value.Handle));\n torch.CheckForErrors();\n ConditionallyRegisterParameter(\"bias\", value);\n }\n }\n\n public Parameter? weight {\n get {\n var res = THSNN_LayerNorm_weight(handle);\n if (res == IntPtr.Zero) { torch.CheckForErrors(); }\n return (res == IntPtr.Zero) ? null : new Parameter(res);\n }\n set {\n THSNN_LayerNorm_set_weight(handle, value is null ? IntPtr.Zero : value.Handle);\n torch.CheckForErrors();\n ConditionallyRegisterParameter(\"weight\", value);\n }\n }\n }\n }\n\n public static partial class torch\n {\n public static partial class nn\n {\n /// <summary>\n /// Applies Layer Normalization over a mini-batch of inputs as described in the paper Layer Normalization\n /// </summary>\n /// <param name=\"normalized_shape\">Input shape from an expected input.</param>\n /// <param name=\"eps\">A value added to the denominator for numerical stability. Default: 1e-5</param>\n /// <param name=\"elementwise_affine\">a boolean value that when set to true, this module has learnable per-element affine parameters initialized to ones (for weights) and zeros (for biases).</param>\n /// <param name=\"device\">The desired device of the parameters and buffers in this module</param>\n /// <param name=\"dtype\">The desired floating point or complex dtype of the parameters and buffers in this module</param>\n /// <returns></returns>\n public static LayerNorm LayerNorm(long[] normalized_shape, double eps = 1e-05, bool elementwise_affine = true, Device? device = null, ScalarType? dtype = null)\n {\n unsafe {\n fixed (long* pNormShape = normalized_shape) {\n var handle = THSNN_LayerNorm_ctor((IntPtr)pNormShape, normalized_shape.Length, eps, elementwise_affine, out var boxedHandle);\n if (handle == IntPtr.Zero) { torch.CheckForErrors(); }\n return new LayerNorm(handle, boxedHandle).MoveModule<LayerNorm>(device, dtype);\n }\n }\n }\n\n /// <summary>\n /// Applies Layer Normalization over a mini-batch of inputs as described in the paper Layer Normalization\n /// </summary>\n /// <param name=\"normalized_shape\">Input shape from an expected input.</param>\n /// <param name=\"eps\">A value added to the denominator for numerical stability. Default: 1e-5</param>\n /// <param name=\"elementwise_affine\">a boolean value that when set to true, this module has learnable per-element affine parameters initialized to ones (for weights) and zeros (for biases).</param>\n /// <param name=\"device\">The desired device of the parameters and buffers in this module</param>\n /// <param name=\"dtype\">The desired floating point or complex dtype of the parameters and buffers in this module</param>\n /// <returns></returns>\n public static LayerNorm LayerNorm(long normalized_shape, double eps = 1e-05, bool elementwise_affine = true, Device? device = null, ScalarType? dtype = null)\n {\n return LayerNorm(new[] { normalized_shape }, eps, elementwise_affine, device, dtype);\n }\n }\n }\n}\n"},{"alpha_fraction":0.5151121616363525,"alphanum_fraction":0.5373632311820984,"avg_line_length":38.36496353149414,"blob_id":"7d640ab4d770079a8ea2a56fc241555559cbefb9","content_id":"64c4053d7332262504fdcc6514a71bcfd59c90e6","detected_licenses":["MIT"],"is_generated":false,"is_vendor":false,"language":"C#","length_bytes":5393,"license_type":"permissive","max_line_length":130,"num_lines":137,"path":"/test/TorchSharpTest/TestNNUtils.cs","repo_name":"NiklasGustafsson/TorchSharp","src_encoding":"UTF-8","text":"// Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information.\n\nusing System.Linq;\nusing Xunit;\n\nnamespace TorchSharp\n{\n public class TestNNUtils\n {\n (torch.Tensor[], torch.Tensor) make_test()\n {\n var sequences = new torch.Tensor[]\n {\n torch.tensor(new long[] { 1, 2, 3, 4 }),\n torch.tensor(new long[] { 5, 6 }),\n torch.tensor(new long[] { 7, 8 }),\n };\n var sequences_len = torch.tensor(sequences.Select(x => x.shape[0]).ToArray());\n return (sequences, sequences_len);\n }\n\n [Fact]\n public void TestPadSequence()\n {\n var (sequences, sequences_len) = make_test();\n var padded_sequences = torch.nn.utils.rnn.pad_sequence(sequences);\n Assert.Equal(2, padded_sequences.ndim);\n Assert.Equal(new long[] { 4, 3 }, padded_sequences.shape);\n Assert.True(padded_sequences.sum().item<long>() == 36);\n\n var packed_sequence = torch.nn.utils.rnn.pack_padded_sequence(padded_sequences, sequences_len);\n var (inverted_sequences, inverted_sequences_len) = torch.nn.utils.rnn.pad_packed_sequence(packed_sequence);\n packed_sequence.Dispose();\n Assert.True(torch.max(torch.square(inverted_sequences - padded_sequences)).item<long>() == 0);\n }\n\n [Fact]\n public void TestPackSequence()\n {\n var (sequences, sequences_len) = make_test();\n var padded_sequences = torch.nn.utils.rnn.pad_sequence(sequences);\n Assert.Equal(2, padded_sequences.ndim);\n Assert.Equal(new long[] { 4, 3 }, padded_sequences.shape);\n Assert.True(padded_sequences.sum().item<long>() == 36);\n\n var packed_sequence = torch.nn.utils.rnn.pack_sequence(sequences);\n var (inverted_sequences, inverted_sequences_len) = torch.nn.utils.rnn.pad_packed_sequence(packed_sequence);\n packed_sequence.Dispose();\n Assert.True(torch.max(torch.square(inverted_sequences - padded_sequences)).item<long>() == 0);\n }\n\n [Fact]\n public void TestAutoGradGrad()\n {\n using var _ = torch.NewDisposeScope();\n var x1 = torch.rand(1, requires_grad: true);\n var x2 = torch.rand(1, requires_grad: true);\n\n var y = x1.pow(2) + 5 * x2;\n\n var grad = torch.autograd.grad(new[] { y }, new[] { x1, x2 }, new[] { torch.ones_like(y) });\n Assert.Equal(x1.shape, grad[0].shape);\n Assert.Equal(x2.shape, grad[1].shape);\n Assert.Equal(2.0f * x1.item<float>(), grad[0].item<float>());\n Assert.Equal(5.0f, grad[1].item<float>());\n }\n\n [Fact]\n public void TestAutoGradBackward1()\n {\n using var _ = torch.NewDisposeScope();\n var x1 = torch.rand(1, requires_grad: true);\n var x2 = torch.rand(1, requires_grad: true);\n\n var y = x1.pow(2) + 5 * x2;\n\n torch.autograd.backward(new[] { y }, new[] { torch.ones_like(y) });\n Assert.Equal(x1.shape, x1.grad().shape);\n Assert.Equal(x2.shape, x2.grad().shape);\n Assert.Equal(2.0f*x1.item<float>(), x1.grad().item<float>());\n Assert.Equal(5.0f, x2.grad().item<float>());\n }\n\n [Fact]\n public void TestAutoGradBackward2()\n {\n using var _ = torch.NewDisposeScope();\n var x1 = torch.rand(1, requires_grad: true);\n var x2 = torch.rand(1, requires_grad: true);\n\n var y = x1.pow(2) + 5 * x2;\n\n y.backward(new[] { torch.ones_like(y) });\n Assert.Equal(x1.shape, x1.grad().shape);\n Assert.Equal(x2.shape, x2.grad().shape);\n Assert.Equal(2.0f * x1.item<float>(), x1.grad().item<float>());\n Assert.Equal(5.0f, x2.grad().item<float>());\n }\n\n [Fact]\n public void TestAutoGradAnomaly()\n {\n Assert.False(AnomalyMode.IsEnabled);\n\n using (var ad = torch.autograd.detect_anomaly(false)) {\n Assert.True(AnomalyMode.IsEnabled);\n Assert.False(AnomalyMode.ShouldCheckNaN);\n }\n\n Assert.False(AnomalyMode.IsEnabled);\n\n using (var ad = torch.autograd.set_detect_anomaly(false)) {\n Assert.False(AnomalyMode.IsEnabled);\n Assert.True(AnomalyMode.ShouldCheckNaN);\n }\n\n Assert.False(AnomalyMode.IsEnabled);\n }\n\n [Fact]\n public void TestSizeSlice()\n {\n var shape = Enumerable.Range(0, 10).Select(i => (long)i).ToArray();\n var sz = new torch.Size(shape);\n Assert.Multiple(\n () => Assert.Equal(new long[] { 0, 1, 2, 3, 4, 5 }, sz.Slice(0, 6).Shape),\n () => Assert.Equal(new long[] { 2, 3, 4, 5 }, sz.Slice(2, 6).Shape),\n () => Assert.Equal(new long[] { 0, 1, 2, 3, 4, 5 }, sz.Slice(0, -4).Shape),\n () => Assert.Equal(new long[] { 2, 3, 4, 5 }, sz.Slice(2, -4).Shape),\n () => Assert.Equal(new long[] { 8, 9 }, sz.Slice(-2, sz.Length).Shape),\n () => Assert.True(sz.Slice(0, 0).IsEmpty),\n () => Assert.True(sz.Slice(-2, 0).IsEmpty)\n );\n\n }\n }\n}\n"},{"alpha_fraction":0.5609756112098694,"alphanum_fraction":0.5711751580238342,"avg_line_length":50.2613639831543,"blob_id":"002ccb71dd275858ec6733dfaeda39fe13dc0ca2","content_id":"c3ad686e871b310689fec0d07a65661ca199dc37","detected_licenses":["MIT"],"is_generated":false,"is_vendor":false,"language":"C#","length_bytes":4518,"license_type":"permissive","max_line_length":147,"num_lines":88,"path":"/src/TorchVision/ColorJitter.cs","repo_name":"NiklasGustafsson/TorchSharp","src_encoding":"UTF-8","text":"// Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information.\nusing System;\nusing static TorchSharp.torch;\n\n\nnamespace TorchSharp\n{\n public static partial class torchvision\n {\n internal class ColorJitter : ITransform\n {\n internal ColorJitter((float, float) brightness, (float, float) contrast, (float, float) saturation, (float, float) hue)\n {\n this.brightness = brightness;\n this.contrast = contrast;\n this.saturation = saturation;\n this.hue = hue;\n }\n\n public Tensor call(Tensor image)\n {\n var randoms = torch.rand(4, ScalarType.Float32).data<float>().ToArray();\n var b = Adjust(randoms[0], brightness.Item1, brightness.Item2);\n var c = Adjust(randoms[1], contrast.Item1, contrast.Item2);\n var s = Adjust(randoms[2], saturation.Item1, saturation.Item2);\n var h = Adjust(randoms[3], hue.Item1, hue.Item2);\n\n var transform = torchvision.transforms.Compose(\n transforms.AdjustBrightness(b),\n transforms.AdjustContrast(c),\n transforms.AdjustSaturation(s),\n transforms.AdjustHue(h)\n );\n return transform.call(image);\n }\n\n internal static float Adjust(float input, float min, float max)\n {\n return input * (max - min) + min;\n }\n\n private (float, float) brightness;\n private (float, float) contrast;\n private (float, float) saturation;\n private (float, float) hue;\n }\n\n public static partial class transforms\n {\n /// <summary>\n /// Randomly change the brightness, contrast, saturation and hue of an image.\n /// The image is expected to have […, 3, H, W] shape, where … means an arbitrary number of leading dimensions.\n /// </summary>\n /// <param name=\"brightness\">How much to jitter brightness, [min,max]. Should be non-negative.</param>\n /// <param name=\"contrast\">How much to jitter contrast, [min,max]. Should be non-negative.</param>\n /// <param name=\"saturation\">How much to jitter saturation, [min,max]. Should be non-negative. </param>\n /// <param name=\"hue\">How much to jitter hue. Should lie between -0.5 and 0.5, with min less than max.</param>\n /// <returns></returns>\n /// <remarks>The image will not be cropped outside its boundaries.</remarks>\n static public ITransform ColorJitter((float, float) brightness, (float, float) contrast, (float, float) saturation, (float, float) hue)\n {\n return new ColorJitter(brightness, contrast, saturation, hue);\n }\n\n /// <summary>\n /// Randomly change the brightness, contrast, saturation and hue of an image.\n /// The image is expected to have […, 3, H, W] shape, where … means an arbitrary number of leading dimensions.\n /// </summary>\n /// <param name=\"brightness\">How much to jitter brightness. Should be non-negative.\n /// The brightness_factor used is chosen uniformly from [max(0, 1 - brightness), 1 + brightness]</param>\n /// <param name=\"contrast\">How much to jitter contrast. Should be non-negative.\n /// The contrast_factor used is chosen uniformly from [max(0, 1 - contrast), 1 + contrast]</param>\n /// <param name=\"saturation\">How much to jitter saturation. Should be non-negative.\n /// The saturation_factor used is chosen uniformly from [max(0, 1 - saturation), 1 + saturation] </param>\n /// <param name=\"hue\">How much to jitter hue. Should be between 0 and 0.5.</param>\n /// <returns></returns>\n /// <remarks>The image will not be cropped outside its boundaries.</remarks>\n static public ITransform ColorJitter(float brightness = 0, float contrast = 0, float saturation = 0, float hue = 0)\n {\n return new ColorJitter(\n (MathF.Max(0, 1 - brightness), 1 + brightness),\n (MathF.Max(0, 1 - contrast), 1 + contrast),\n (MathF.Max(0, 1 - saturation), 1 + saturation),\n (-hue, hue));\n }\n }\n }\n}"},{"alpha_fraction":0.5708770155906677,"alphanum_fraction":0.5821841955184937,"avg_line_length":39.741573333740234,"blob_id":"d2dc199c046010d734648cf22145691ef218629a","content_id":"cb615bc2baf82e38575c4adf76cdb00027e00743","detected_licenses":["MIT"],"is_generated":false,"is_vendor":false,"language":"C#","length_bytes":3634,"license_type":"permissive","max_line_length":162,"num_lines":89,"path":"/src/TorchVision/Ops/StochasticDepth.cs","repo_name":"NiklasGustafsson/TorchSharp","src_encoding":"UTF-8","text":"// Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information.\n\n// A number of implementation details in this file have been translated from the Python version of torchvision,\n// largely located in the files found in this folder:\n//\n// https://github.com/pytorch/vision/blob/a4f53308b2d0f1aa9191686e326f45c26053f686/torchvision/ops/stochastic_depth.py\n//\n// The origin has the following copyright notice and license:\n//\n// https://github.com/pytorch/vision/blob/main/LICENSE\n//\n\nusing System;\nusing System.Collections.Generic;\nusing System.Runtime.CompilerServices;\nusing static TorchSharp.torch;\n\n#nullable enable\nnamespace TorchSharp\n{\n public static partial class torchvision\n {\n\n public static partial class ops\n {\n public static StochasticDepth StochasticDepth(double p, StochasticDepth.Mode mode) => new StochasticDepth(p, mode);\n\n /// <summary>\n /// Implements the Stochastic Depth from “Deep Networks with Stochastic Depth” used for randomly dropping residual branches of residual architectures.\n /// </summary>\n /// <param name=\"input\">The input tensor</param>\n /// <param name=\"p\">Probability of the input to be zeroed.</param>\n /// <param name=\"mode\">\"batch\" or \"row\". \"batch\" randomly zeroes the entire input, \"row\" zeroes randomly selected rows from the batch.</param>\n /// <param name=\"training\">Apply stochastic depth if is True</param>\n public static Tensor stochastic_depth(Tensor input, double p, StochasticDepth.Mode mode, bool training = true)\n {\n if (p < 0 || p > 1) throw new ArgumentOutOfRangeException(nameof(p));\n if (!training || p == 0.0) return input.alias();\n\n var survival_rate = 1 - p;\n var size = new List<long>();\n\n if (mode == torchvision.StochasticDepth.Mode.Row) {\n size.Add(input.shape[0]);\n for (var i = 0; i < input.ndim-1; i++)\n size.Add(1);\n } else {\n for (var i = 0; i < input.ndim; i++)\n size.Add(1);\n }\n\n var noise = torch.empty(size.ToArray(), dtype: input.dtype, device: input.device);\n noise.bernoulli_(survival_rate);\n\n if (survival_rate > 0) {\n noise.div_(survival_rate);\n }\n return input * noise;\n }\n }\n\n /// <summary>\n /// Implements the Stochastic Depth from “Deep Networks with Stochastic Depth” used for randomly dropping residual branches of residual architectures.\n /// </summary>\n public class StochasticDepth : nn.Module<Tensor, Tensor>\n {\n /// <summary>\n /// Constructor\n /// </summary>\n /// <param name=\"p\">Probability of the input to be zeroed.</param>\n /// <param name=\"mode\">\"batch\" or \"row\". \"batch\" randomly zeroes the entire input, \"row\" zeroes randomly selected rows from the batch.</param>\n public StochasticDepth(double p, Mode mode) : base(nameof(StochasticDepth))\n {\n this.p = p;\n this.mode = mode;\n }\n\n public override Tensor forward(Tensor input)\n {\n return ops.stochastic_depth(input, p, mode, training);\n }\n\n public enum Mode { Batch, Row }\n\n private double p;\n private Mode mode;\n }\n }\n}\n"},{"alpha_fraction":0.6013584136962891,"alphanum_fraction":0.607105553150177,"avg_line_length":38.875,"blob_id":"720dcaf68f2dd5bec595d0e6dc03cfb5cc21a407","content_id":"5fc5f07b7f0a6ee8841f1ef8e2b36e397977d9f3","detected_licenses":["MIT"],"is_generated":false,"is_vendor":false,"language":"C#","length_bytes":1914,"license_type":"permissive","max_line_length":190,"num_lines":48,"path":"/src/TorchSharp/NN/Normalization/LocalResponseNorm.cs","repo_name":"NiklasGustafsson/TorchSharp","src_encoding":"UTF-8","text":"// Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information.\nusing System;\nusing static TorchSharp.torch;\nusing static TorchSharp.PInvoke.NativeMethods;\n\nnamespace TorchSharp\n{\n using Modules;\n\n namespace Modules\n {\n /// <summary>\n /// This class is used to represent a LocalResponseNorm module.\n /// </summary>\n public sealed class LocalResponseNorm : torch.nn.Module<Tensor, Tensor>\n {\n internal LocalResponseNorm(IntPtr handle, IntPtr boxedHandle) : base(handle, boxedHandle)\n {\n }\n\n public override Tensor forward(Tensor tensor)\n {\n if (tensor.Dimensions < 3) throw new ArgumentException($\"Invalid number of dimensions for LocalResponseNorm argument: {tensor.Dimensions}\");\n var res = THSNN_LocalResponseNorm_forward(handle.DangerousGetHandle(), tensor.Handle);\n if (res == IntPtr.Zero) { torch.CheckForErrors(); }\n return new Tensor(res);\n }\n }\n }\n\n public static partial class torch\n {\n public static partial class nn\n {\n /// <summary>\n /// Applies local response normalization over an input signal composed of several input planes, where channels occupy the second dimension. Applies normalization across channels.\n /// </summary>\n public static LocalResponseNorm LocalResponseNorm(long size, double alpha = 0.0001, double beta = 0.75, double k = 1.0)\n {\n unsafe {\n var handle = THSNN_LocalResponseNorm_ctor(size, alpha, beta, k, out var boxedHandle);\n if (handle == IntPtr.Zero) { torch.CheckForErrors(); }\n return new LocalResponseNorm(handle, boxedHandle);\n }\n }\n }\n }\n}\n"},{"alpha_fraction":0.46465590596199036,"alphanum_fraction":0.4722936749458313,"avg_line_length":49.71936798095703,"blob_id":"9b41dd1801824c2ea66a632dca5ebaf98db50a06","content_id":"c988ec3c00dd027f11d698bce25b17caaf3c659c","detected_licenses":["MIT"],"is_generated":false,"is_vendor":false,"language":"C#","length_bytes":12831,"license_type":"permissive","max_line_length":147,"num_lines":253,"path":"/src/TorchVision/IO/GDriveDownload.cs","repo_name":"NiklasGustafsson/TorchSharp","src_encoding":"UTF-8","text":"// Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information.\nusing System;\nusing System.Collections.Generic;\nusing System.IO;\nusing System.Net;\nusing System.Net.Http;\nusing System.Security.Cryptography;\nusing System.Text;\nusing System.Text.RegularExpressions;\nusing System.Threading;\nusing System.Threading.Tasks;\nusing System.Web;\n\n// A number of implementation details in this file have been translated from the Python version of torchvision,\n// largely located in the files found in this folder:\n//\n// https://github.com/pytorch/vision/blob/b78d98bb152ffb9c0c0f5365f59f475c70b1784e/torchvision/datasets/utils.py\n// The origin has the following copyright notice and license:\n//\n// https://github.com/pytorch/vision/blob/main/LICENSE\n//\n\n#nullable enable\nnamespace TorchSharp\n{\n public static partial class torchvision\n {\n public static partial class io\n {\n public class GDriveDownload\n {\n private static async Task SaveResponseContentAsync(\n byte[] head,\n Stream content,\n string destination,\n long? length = null,\n CancellationToken cancellationToken = default)\n {\n using (var progress_bar = torch.hub.CreateProgressBar(false))\n using (var fh = File.OpenWrite(destination)) {\n progress_bar.Maximum = length;\n await fh.WriteAsync(head, 0, head.Length, cancellationToken);\n progress_bar.Value = head.Length;\n byte[] buffer = new byte[64 * 1024];\n while (true) {\n int ret = await content.ReadAsync(buffer, 0, buffer.Length, cancellationToken);\n if (ret == 0) break;\n await fh.WriteAsync(buffer, 0, ret, cancellationToken);\n progress_bar.Value += ret;\n }\n }\n }\n\n private static string CalculateMD5(string fpath)\n {\n MD5 md5 = MD5.Create();\n byte[] hash;\n using (var stream = File.OpenRead(fpath)) {\n hash = md5.ComputeHash(stream);\n }\n return BitConverter.ToString(hash).Replace(\"-\", \"\").ToLowerInvariant();\n }\n\n private static bool CheckMD5(string fpath, string md5)\n {\n return md5 == CalculateMD5(fpath);\n }\n\n /// <summary>\n /// Check if the file exists and verify its content with MD5.\n /// </summary>\n /// <param name=\"fpath\">A path to the file</param>\n /// <param name=\"md5\">MD5 checksum</param>\n /// <returns>True if the file exists and MD5 of its content matches</returns>\n public static bool CheckIntegrity(string fpath, string? md5 = null)\n {\n if (!File.Exists(fpath)) {\n return false;\n }\n if (md5 is null) {\n return true;\n }\n return CheckMD5(fpath, md5);\n }\n\n private static async Task<(string, byte[], Stream)> ExtractGdriveApiResponseAsync(\n HttpResponseMessage response,\n int chunk_size = 32 * 1024,\n CancellationToken cancellationToken = default)\n {\n var stream = await response.Content.ReadAsStreamAsync();\n var buf = new byte[chunk_size];\n int pos = 0;\n while (pos < chunk_size) {\n int readLen = await stream.ReadAsync(buf, pos, chunk_size - pos, cancellationToken);\n if (readLen == 0) break;\n pos += readLen;\n }\n buf = buf.AsSpan(0, pos).ToArray();\n\n string api_response = string.Empty;\n string first_chunk = Encoding.ASCII.GetString(buf);\n string pattern = \"<title>Google Drive - (?<api_response>.+?)</title>\";\n var match = Regex.Match(first_chunk, pattern);\n if (match.Success) {\n api_response = match.Groups[\"api_response\"].Value;\n }\n return (api_response, buf, stream);\n }\n\n /// <summary>\n /// Download a Google Drive file and place it in root.\n /// </summary>\n /// <param name=\"file_id\">id of file to be downloaded</param>\n /// <param name=\"root\">Directory to place downloaded file in</param>\n /// <param name=\"filename\">Name to save the file under. If null, use the id of the file.</param>\n /// <param name=\"md5\">MD5 checksum of the download. If null, do not check</param>\n public static void DownloadFileFromGoogleDrive(\n string file_id, string root, string? filename = null, string? md5 = null)\n {\n try {\n Task.Run(async () => {\n await DownloadFileFromGoogleDriveAsync(file_id, root, filename, md5);\n }).Wait();\n } catch (AggregateException ex) {\n throw ex.InnerException ?? ex;\n }\n }\n\n /// <summary>\n /// Download a Google Drive file and place it in root.\n /// </summary>\n /// <param name=\"file_id\">id of file to be downloaded</param>\n /// <param name=\"root\">Directory to place downloaded file in</param>\n /// <param name=\"filename\">Name to save the file under. If null, use the id of the file.</param>\n /// <param name=\"md5\">MD5 checksum of the download. If null, do not check</param>\n /// <param name=\"cancellationToken\">A cancellation token</param>\n public static async Task DownloadFileFromGoogleDriveAsync(\n string file_id, string root, string? filename = null, string? md5 = null,\n CancellationToken cancellationToken = default)\n {\n // Based on https://stackoverflow.com/questions/38511444/python-download-files-from-google-drive-using-url\n if (filename is null) {\n filename = file_id;\n }\n var fpath = Path.Combine(root, filename);\n\n Directory.CreateDirectory(root);\n\n if (CheckIntegrity(fpath, md5)) {\n if (md5 is null) {\n Console.WriteLine($\"Using downloaded file: {fpath}\");\n } else {\n Console.WriteLine($\"Using downloaded and verified file: {fpath}\");\n }\n return;\n }\n\n CookieContainer cookies = new CookieContainer();\n HttpClientHandler handler = new HttpClientHandler();\n handler.CookieContainer = cookies;\n\n using (var httpClient = new HttpClient(handler)) {\n string url = \"https://drive.google.com/uc\";\n\n var queryString = HttpUtility.ParseQueryString(string.Empty);\n queryString.Add(\"id\", file_id);\n queryString.Add(\"export\", \"download\");\n string query_url = url + \"?\" + queryString.ToString();\n HttpResponseMessage? response = null;\n\n try {\n response = await httpClient.GetAsync(query_url, HttpCompletionOption.ResponseHeadersRead, cancellationToken);\n response.EnsureSuccessStatusCode();\n var responseCookies = cookies.GetCookies(new Uri(url));\n string token = string.Empty;\n foreach (var obj in responseCookies) {\n Cookie? kv = obj as Cookie;\n if (kv != null && kv.Name.StartsWith(\"download_warning\")) {\n token = kv.Value;\n }\n }\n string api_response = string.Empty;\n byte[]? head = null;\n Stream? stream = null;\n if (string.IsNullOrEmpty(token)) {\n (api_response, head, stream) = await ExtractGdriveApiResponseAsync(response, cancellationToken: cancellationToken);\n token = api_response == \"Virus scan warning\" ? \"t\" : string.Empty;\n }\n\n if (!string.IsNullOrEmpty(token)) {\n queryString.Add(\"confirm\", token);\n query_url = url + \"?\" + queryString.ToString();\n response.Dispose();\n response = null;\n response = await httpClient.GetAsync(query_url, HttpCompletionOption.ResponseHeadersRead, cancellationToken);\n (api_response, head, stream) = await ExtractGdriveApiResponseAsync(response);\n }\n\n if (head == null || stream == null) {\n // unreachable\n throw new InvalidDataException();\n }\n\n if (api_response == \"Quota exceeded\") {\n throw new InvalidDataException(\n $\"The daily quota of the file {filename} is exceeded and it \" +\n \"can't be downloaded. This is a limitation of Google Drive \" +\n \"and can only be overcome by trying again later.\");\n }\n\n await SaveResponseContentAsync(head, stream, fpath, cancellationToken: cancellationToken);\n } finally {\n if (response != null) {\n response.Dispose();\n }\n }\n\n // In case we deal with an unhandled GDrive API response, the file should be smaller than 10kB and contain only text\n if (new FileInfo(fpath).Length < 10 * 1024) {\n string text = ReadAllTextAsync(fpath);\n // Regular expression to detect HTML. Copied from https://stackoverflow.com/a/70585604\n if (Regex.Match(text, @\"</?\\s*[a-z-][^>]*\\s*>|(&(?:[\\w\\d]+|#\\d+|#x[a-f\\d]+);)\").Success) {\n Console.WriteLine(\n $\"We detected some HTML elements in the downloaded file. \" +\n $\"This most likely means that the download triggered an unhandled API response by GDrive. \" +\n $\"Please report this to torchvision at https://github.com/pytorch/vision/issues including \" +\n $\"the response:\\n\\n{text}\"\n );\n }\n }\n\n if (md5 is not null && !CheckMD5(fpath, md5)) {\n throw new InvalidDataException(\n $\"The MD5 checksum of the download file {fpath} does not match the one on record.\" +\n $\"Please delete the file and try again. \" +\n $\"If the issue persists, please report this to torchvision at https://github.com/pytorch/vision/issues.\");\n }\n }\n }\n\n private static string ReadAllTextAsync(string path, CancellationToken cancellationToken = default)\n {\n var memory = new MemoryStream();\n using (var stream = File.OpenRead(path)) {\n stream.CopyTo(memory);\n }\n return Encoding.ASCII.GetString(memory.GetBuffer());\n }\n }\n }\n }\n}"},{"alpha_fraction":0.5197963118553162,"alphanum_fraction":0.5339757204055786,"avg_line_length":49.45088195800781,"blob_id":"d799ee0c7d732ad819dec3b25bd95d810ae842d2","content_id":"541684bf997745f2121bf8be5e44161dcb8d380d","detected_licenses":["MIT"],"is_generated":false,"is_vendor":false,"language":"C#","length_bytes":20029,"license_type":"permissive","max_line_length":238,"num_lines":397,"path":"/src/TorchSharp/Optimizers/NAdam.cs","repo_name":"NiklasGustafsson/TorchSharp","src_encoding":"UTF-8","text":"// Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information.\nusing System;\nusing System.Collections.Generic;\nusing System.Linq;\nusing static TorchSharp.torch;\n\nnamespace TorchSharp\n{\n using System.IO;\n using Modules;\n\n public static partial class torch\n {\n public static partial class optim\n {\n\n /// <summary>\n /// Implements the NAdam algorithm.\n ///\n /// For further details regarding the algorithm we refer to Incorporating Nesterov Momentum into Adam.\n /// https://openreview.net/forum?id=OM0jvwB8jIp57ZJjtNEZ\n /// </summary>\n /// <param name=\"named_parameters\">Parameters to optimize. This optimizer requires the <b>named</b> parameters collection.</param>\n /// <param name=\"lr \">Learning rate</param>\n /// <param name=\"beta1\">Coefficient used for computing running averages of gradient and its square (default: 0.9)</param>\n /// <param name=\"beta2\">Coefficient used for computing running averages of gradient and its square (default: 0.999)</param>\n /// <param name=\"eps\">Term added to the denominator to improve numerical stability, i.e. avoid division-by-zero (default: 1e-8)</param>\n /// <param name=\"weight_decay\">Weight decay (L2 penalty) (default: 0)</param>\n /// <param name=\"momentum_decay\">Momentum decay</param>\n public static NAdam NAdam(IEnumerable<Parameter> named_parameters, double lr = 0.002, double beta1 = 0.9, double beta2 = 0.999, double eps = 1e-8, double weight_decay = 0, double momentum_decay = 4e-3)\n {\n return new NAdam(named_parameters, lr, beta1, beta2, eps, weight_decay, momentum_decay);\n }\n\n /// <summary>\n /// Implements the NAdam algorithm.\n ///\n /// For further details regarding the algorithm we refer to Incorporating Nesterov Momentum into Adam.\n /// https://openreview.net/forum?id=OM0jvwB8jIp57ZJjtNEZ\n /// </summary>\n /// <param name=\"named_parameters\">Parameters to optimize. This optimizer requires the <b>named</b> parameters collection.</param>\n /// <param name=\"lr \">Learning rate</param>\n /// <param name=\"beta1\">Coefficient used for computing running averages of gradient and its square (default: 0.9)</param>\n /// <param name=\"beta2\">Coefficient used for computing running averages of gradient and its square (default: 0.999)</param>\n /// <param name=\"eps\">Term added to the denominator to improve numerical stability, i.e. avoid division-by-zero (default: 1e-8)</param>\n /// <param name=\"weight_decay\">Weight decay (L2 penalty) (default: 0)</param>\n /// <param name=\"momentum_decay\">Momentum decay</param>\n public static NAdam NAdam(IEnumerable<(string name, Parameter parameter)> named_parameters, double lr = 0.002, double beta1 = 0.9, double beta2 = 0.999, double eps = 1e-8, double weight_decay = 0, double momentum_decay = 4e-3)\n {\n return new NAdam(named_parameters.Select(np => np.parameter), lr, beta1, beta2, eps, weight_decay, momentum_decay);\n }\n\n /// <summary>\n /// Implements the NAdam algorithm.\n ///\n /// For further details regarding the algorithm we refer to Incorporating Nesterov Momentum into Adam.\n /// https://openreview.net/forum?id=OM0jvwB8jIp57ZJjtNEZ\n /// </summary>\n /// <param name=\"parameters\">Parameters to optimize. This optimizer requires the <b>named</b> parameters collection.</param>\n /// <param name=\"lr \">Learning rate</param>\n /// <param name=\"beta1\">Coefficient used for computing running averages of gradient and its square (default: 0.9)</param>\n /// <param name=\"beta2\">Coefficient used for computing running averages of gradient and its square (default: 0.999)</param>\n /// <param name=\"eps\">Term added to the denominator to improve numerical stability, i.e. avoid division-by-zero (default: 1e-8)</param>\n /// <param name=\"weight_decay\">Weight decay (L2 penalty) (default: 0)</param>\n /// <param name=\"momentum_decay\">Momentum decay</param>\n public static NAdam NAdam(IEnumerable<NAdam.ParamGroup> parameters, double lr = 0.002, double beta1 = 0.9, double beta2 = 0.999, double eps = 1e-8, double weight_decay = 0, double momentum_decay = 4e-3)\n {\n return new NAdam(parameters, lr, beta1, beta2, eps, weight_decay, momentum_decay);\n }\n }\n }\n\n namespace Modules\n {\n using static torch.optim;\n\n public class NAdam : OptimizerHelper, IBetas\n {\n /// <summary>\n /// Implements NAdam algorithm.\n ///\n /// For further details regarding the algorithm we refer to Incorporating Nesterov Momentum into Adam.\n /// https://openreview.net/forum?id=OM0jvwB8jIp57ZJjtNEZ\n /// </summary>\n /// <param name=\"parameters\">Parameters to optimize. This optimizer requires the <b>named</b> parameters collection.</param>\n /// <param name=\"lr \">Learning rate</param>\n /// <param name=\"beta1\">Coefficient used for computing running averages of gradient and its square (default: 0.9)</param>\n /// <param name=\"beta2\">Coefficient used for computing running averages of gradient and its square (default: 0.999)</param>\n /// <param name=\"eps\">Term added to the denominator to improve numerical stability, i.e. avoid division-by-zero (default: 1e-8)</param>\n /// <param name=\"weight_decay\">Weight decay (L2 penalty) (default: 0)</param>\n /// <param name=\"momentum_decay\">Momentum decay</param>\n public NAdam(IEnumerable<Parameter> parameters, double lr, double beta1 = 0.9, double beta2 = 0.999, double eps = 1e-8, double weight_decay = 0, double momentum_decay = 4e-3)\n : this(new ParamGroup[] { new ParamGroup { Parameters = parameters } }, lr, beta1, beta2, eps, weight_decay, momentum_decay)\n {\n }\n\n /// <summary>\n /// Implements NAdam algorithm.\n ///\n /// For further details regarding the algorithm we refer to Incorporating Nesterov Momentum into Adam.\n /// https://openreview.net/forum?id=OM0jvwB8jIp57ZJjtNEZ\n /// </summary>\n /// <param name=\"parameters\">Parameters to optimize. This optimizer requires the <b>named</b> parameters collection.</param>\n /// <param name=\"lr \">Learning rate</param>\n /// <param name=\"beta1\">Coefficient used for computing running averages of gradient and its square (default: 0.9)</param>\n /// <param name=\"beta2\">Coefficient used for computing running averages of gradient and its square (default: 0.999)</param>\n /// <param name=\"eps\">Term added to the denominator to improve numerical stability, i.e. avoid division-by-zero (default: 1e-8)</param>\n /// <param name=\"weight_decay\">Weight decay (L2 penalty) (default: 0)</param>\n /// <param name=\"momentum_decay\">Momentum decay</param>\n /// <returns></returns>\n public NAdam(IEnumerable<ParamGroup> parameters, double lr = 0.002, double beta1 = 0.9, double beta2 = 0.999, double eps = 1e-8, double weight_decay = 0, double momentum_decay = 4e-3)\n {\n if (lr < 0.0) throw new ArgumentException($\"Invalid learning rate: {lr}\");\n if (beta1 < 0.0 || beta1 > 1.0) throw new ArgumentException($\"Invalid beta1 value: {beta1}\");\n if (beta2 < 0.0 || beta2 > 1.0) throw new ArgumentException($\"Invalid beta2 value: {beta2}\");\n if (eps < 0.0) throw new ArgumentException($\"Invalid eps value: {eps}\");\n if (weight_decay < 0.0) throw new ArgumentException($\"Invalid weight_decay value: {weight_decay}\");\n if (momentum_decay < 0.0) throw new ArgumentException($\"Invalid momentum_decay value: {momentum_decay}\");\n\n var options = new Options {\n LearningRate = lr,\n InitialLearningRate = lr,\n beta1 = beta1,\n beta2 = beta2,\n eps = eps,\n weight_decay = weight_decay,\n momentum_decay = momentum_decay\n };\n\n _defaults = options;\n _parameter_groups = new List<Modules.ParamGroup>();\n\n foreach (var g in parameters) {\n add_param_group(g);\n }\n }\n\n /// <summary>\n /// Performs a single optimization step (parameter update).\n /// </summary>\n /// <param name=\"closure\">A closure that reevaluates the model and returns the loss. Optional for most optimizers.</param>\n /// <returns></returns>\n public override Tensor step(Func<Tensor> closure = null)\n {\n return _step<ParamGroup>(group => {\n\n var options = group.Options as Options;\n var beta1 = options.beta1.Value;\n var beta2 = options.beta2.Value;\n var eps = options.eps.Value;\n var weight_decay = options.weight_decay.Value;\n var momentum_decay = options.momentum_decay.Value;\n var lr = options.LearningRate.Value;\n\n foreach (var param in group.Parameters) {\n\n var grad = param.grad();\n\n if (grad is null) continue;\n\n var state = (State)_state[param.handle];\n\n state.step += 1;\n\n var exp_avg = state.exp_avg;\n var exp_avg_sq = state.exp_avg_sq;\n\n var bias_correction2 = 1 - Math.Pow(beta2, state.step);\n\n grad = (weight_decay != 0)\n ? grad.add(param, alpha: weight_decay)\n : grad.alias();\n\n var mu = beta1 * (1.0 - 0.5 * Math.Pow(0.96, state.step * momentum_decay));\n var mu_next = beta1 * (1.0 - 0.5 * Math.Pow(0.96, (state.step + 1) * momentum_decay));\n\n var mu_product = state.mu_product * mu;\n var mu_product_next = mu_product * mu * mu_next;\n\n exp_avg.mul_(beta1).add_(grad, alpha: 1 - beta1);\n exp_avg_sq.mul_(beta2).addcmul_(grad, grad, value: 1 - beta2);\n\n var denom = exp_avg_sq.div(bias_correction2).sqrt_().add_(eps);\n\n param.addcdiv_(grad, denom, value: -lr * (1 - mu) / (1 - mu_product));\n param.addcdiv_(exp_avg, denom, value: -lr * mu_next / (1 - mu_product_next));\n\n state.mu_product = mu_product;\n }\n }, closure);\n }\n\n protected override void Dispose(bool disposing)\n {\n base.Dispose(disposing);\n foreach (var kvp in _state) {\n ((State)kvp.Item2).Dispose();\n }\n }\n\n public sealed class State : OptimizerState, IDisposable\n {\n public long step;\n public double mu_product;\n public Tensor exp_avg;\n public Tensor exp_avg_sq;\n\n public void Dispose()\n {\n Dispose(true);\n GC.SuppressFinalize(this);\n }\n\n private void Dispose(bool disposing)\n {\n if (disposing) {\n exp_avg.Dispose();\n exp_avg_sq.Dispose();\n }\n }\n\n /// <summary>\n /// Move all the state to the indicated device.\n /// </summary>\n /// <param name=\"device\">The device to move all state to.</param>\n public override void to(Device device)\n {\n exp_avg = exp_avg.to(device);\n exp_avg_sq = exp_avg_sq.to(device);\n }\n\n /// <summary>\n /// Load the optimizer parameter state from a stream.\n /// </summary>\n /// <param name=\"reader\">A binary reader connected to a stream open for reading.</param>\n public override void LoadStateDict(BinaryReader reader)\n {\n step = reader.ReadInt64();\n mu_product = reader.ReadDouble();\n exp_avg.Load(reader);\n exp_avg_sq.Load(reader);\n }\n\n /// <summary>\n /// Save the optimizer parameter state to a stream.\n /// </summary>\n /// <param name=\"writer\">A binary writer connected to a stream open for writing.</param>\n public override void SaveStateDict(BinaryWriter writer)\n {\n writer.Write(step);\n writer.Write(mu_product);\n exp_avg.Save(writer);\n exp_avg_sq.Save(writer);\n }\n\n /// <summary>\n /// Load optimizer parameter state from another optimizer.\n /// </summary>\n /// <param name=\"source\">An optimizer state record.</param>\n public override void LoadStateDict(OptimizerState source)\n {\n var st_state = source as State;\n exp_avg.Dispose();\n exp_avg_sq.Dispose();\n\n step = st_state.step;\n mu_product = st_state.mu_product;\n exp_avg = st_state.exp_avg;\n exp_avg_sq = st_state.exp_avg_sq;\n }\n\n /// <summary>\n /// Useful for tests, allows comparison of one state with another.\n /// </summary>\n /// <param name=\"other\">The other optimizer state</param>\n /// <returns></returns>\n public override bool ApproximatelyEquals(OptimizerState other)\n {\n var rhs = other as State;\n return (rhs is not null) && step == rhs.step &&\n mu_product == rhs.mu_product &&\n exp_avg.allclose(rhs.exp_avg) &&\n exp_avg_sq.allclose(rhs.exp_avg_sq);\n }\n }\n\n /// <summary>\n /// Add a param group to the Optimizer s param_groups.\n /// </summary>\n /// <param name=\"param_group\"></param>\n /// <remarks>This can be useful when fine tuning a pre-trained network as frozen layers can be made trainable and added to the Optimizer as training progresses.</remarks>\n public override void add_param_group(Modules.ParamGroup param_group)\n {\n var def = _defaults as Options;\n if (param_group.Options is null) {\n param_group.Options = new Options();\n }\n\n var opt = param_group.Options as Options;\n\n // Make sure all the options are set.\n if (!opt.LearningRate.HasValue) opt.LearningRate = def.LearningRate;\n if (!opt.beta1.HasValue) opt.beta1 = def.beta1;\n if (!opt.beta2.HasValue) opt.beta2 = def.beta2;\n if (!opt.eps.HasValue) opt.eps = def.eps;\n if (!opt.weight_decay.HasValue) opt.weight_decay = def.weight_decay;\n if (!opt.momentum_decay.HasValue) opt.momentum_decay = def.momentum_decay;\n\n opt.InitialLearningRate = opt.LearningRate.Value;\n\n _parameter_groups.Add(param_group);\n\n foreach (var p in param_group.Parameters) {\n var state = new State();\n _state[p.Handle] = state;\n state.step = 0;\n state.exp_avg = torch.zeros_like(p).DetachFromDisposeScope();\n state.exp_avg_sq = torch.zeros_like(p).DetachFromDisposeScope();\n }\n }\n\n public class Options : OptimizerOptions\n {\n public double? beta1;\n public double? beta2;\n public double? eps;\n public double? weight_decay;\n public double? momentum_decay;\n\n /// <summary>\n /// Load optimizer options (param-group hyperparameters) from another optimizer.\n /// </summary>\n /// <param name=\"source\">An optimizer options record.</param>\n public override void LoadStateDict(OptimizerOptions source)\n {\n base.LoadStateDict(source);\n var opts = source as Options;\n beta1 = opts.beta1;\n beta2 = opts.beta2;\n eps = opts.eps;\n weight_decay = opts.weight_decay;\n momentum_decay = opts.momentum_decay;\n }\n\n /// <summary>\n /// Load the optimizer options (param-group hyperparameters) from a stream.\n /// </summary>\n /// <param name=\"reader\">A binary reader connected to a stream open for reading.</param>\n public override void LoadStateDict(BinaryReader reader)\n {\n base.LoadStateDict(reader);\n beta1 = reader.ReadDouble();\n beta2 = reader.ReadDouble();\n eps = reader.ReadDouble();\n weight_decay = reader.ReadDouble();\n momentum_decay = reader.ReadDouble();\n }\n\n /// <summary>\n /// Save the optimizer options (param-group hyperparameters) to a stream.\n /// </summary>\n /// <param name=\"writer\">A binary writer connected to a stream open for writing.</param>\n public override void SaveStateDict(BinaryWriter writer)\n {\n base.SaveStateDict(writer);\n writer.Write(beta1.Value);\n writer.Write(beta2.Value);\n writer.Write(eps.Value);\n writer.Write(weight_decay.Value);\n writer.Write(momentum_decay.Value);\n }\n }\n\n public class ParamGroup : ParamGroup<Options>, IBetas\n {\n public ParamGroup() { }\n\n public ParamGroup(IEnumerable<Parameter> parameters, Options options) : base(parameters, options) { }\n\n public ParamGroup(IEnumerable<Parameter> parameters, double lr = 1.0, double beta1 = 0.9, double beta2 = 0.999, double eps = 1e-8, double weight_decay = 0, double momentum_decay = 4e-3)\n : base(parameters, new NAdam.Options { LearningRate = lr, beta1 = beta1, beta2 = beta2, eps = eps, weight_decay = weight_decay, momentum_decay = momentum_decay })\n {\n }\n\n public (double, double) Betas {\n get => (Options.beta1.Value, Options.beta2.Value);\n set { Options.beta1 = value.Item1; Options.beta2 = value.Item2; }\n }\n }\n\n public (double, double) Betas {\n get => ((_defaults as Options).beta1.Value, (_defaults as Options).beta2.Value);\n set { (_defaults as Options).beta1 = value.Item1; (_defaults as Options).beta2 = value.Item2; }\n }\n }\n }\n}\n"},{"alpha_fraction":0.6769911646842957,"alphanum_fraction":0.6769911646842957,"avg_line_length":24.22222137451172,"blob_id":"7f2afcfc3435ec6731a16bbdcfd92c00bcc5ed1f","content_id":"98c0714e78779e6aea9452903ce9301eea460dbe","detected_licenses":["MIT"],"is_generated":false,"is_vendor":false,"language":"C#","length_bytes":228,"license_type":"permissive","max_line_length":131,"num_lines":9,"path":"/src/TorchSharp/Tensor/Enums/LobpcgMethod.cs","repo_name":"NiklasGustafsson/TorchSharp","src_encoding":"UTF-8","text":"// Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information.\nnamespace TorchSharp\n{\n public enum LobpcgMethod\n {\n basic,\n ortho\n }\n}"},{"alpha_fraction":0.5157126784324646,"alphanum_fraction":0.5157126784324646,"avg_line_length":30.839284896850586,"blob_id":"2fb046f455d1def83b4608595d48b6d8403ddfb6","content_id":"19bd3b84be81d74db9486c0a32e72f6aa1ad36a9","detected_licenses":["MIT"],"is_generated":false,"is_vendor":false,"language":"C#","length_bytes":1782,"license_type":"permissive","max_line_length":130,"num_lines":56,"path":"/src/TorchVision/RandomOrder.cs","repo_name":"NiklasGustafsson/TorchSharp","src_encoding":"UTF-8","text":"// Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information.\nusing System;\nusing System.Collections.Generic;\nusing System.Linq;\nusing static TorchSharp.torch;\n\nnamespace TorchSharp\n{\n public static partial class torchvision\n {\n internal class RandomOrder : IDisposable, ITransform\n {\n public RandomOrder(ITransform[] transforms)\n {\n this.transforms = transforms;\n }\n\n public void Dispose()\n {\n foreach (var t in transforms) {\n if (t is IDisposable) {\n ((IDisposable)t).Dispose();\n }\n }\n }\n\n public Tensor call(Tensor input)\n {\n var rng = new Random();\n foreach (var t in transforms.OrderBy(t => rng.NextDouble())) {\n input = t.call(input);\n }\n return input;\n }\n\n private IList<ITransform> transforms;\n }\n\n public static partial class transforms\n {\n /// <summary>\n /// Apply a list of transformations in a random order.\n /// </summary>\n /// <param name=\"transforms\">A list of transforms to apply.</param>\n /// <remarks>\n /// This transform uses the .NET Random API, not the Torch RNG.\n /// Each invocation of 'forward()' will randomize the order in which transforms\n /// are applied.\n /// </remarks>\n static public ITransform RandomOrder(params ITransform[] transforms)\n {\n return new RandomOrder(transforms);\n }\n }\n }\n}"}],"string":"[\n {\n \"alpha_fraction\": 0.579688310623169,\n \"alphanum_fraction\": 0.5816993713378906,\n \"avg_line_length\": 33.894737243652344,\n \"blob_id\": \"dc6642da8bac695da6d9561b1107e4a76d007c7f\",\n \"content_id\": \"f820cae575d520b0faeea69bc01e5142146730c7\",\n \"detected_licenses\": [\n \"MIT\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"C#\",\n \"length_bytes\": 1989,\n \"license_type\": \"permissive\",\n \"max_line_length\": 130,\n \"num_lines\": 57,\n \"path\": \"/src/TorchSharp/NN/Shuffle/ChannelShuffle.cs\",\n \"repo_name\": \"NiklasGustafsson/TorchSharp\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"// Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information.\\n\\nusing static TorchSharp.torch;\\n\\nnamespace TorchSharp\\n{\\n using Modules;\\n\\n namespace Modules\\n {\\n /// <summary>\\n /// This class is used to represent a ChannelShuffle module.\\n /// </summary>\\n public sealed class ChannelShuffle : torch.nn.Module<Tensor, Tensor>\\n {\\n internal ChannelShuffle(long groups) : base(nameof(ChannelShuffle))\\n {\\n this.groups = groups;\\n }\\n private long groups;\\n\\n public override Tensor forward(Tensor tensor)\\n {\\n return tensor.channel_shuffle(groups);\\n }\\n\\n public override string GetName()\\n {\\n return typeof(ChannelShuffle).Name;\\n }\\n\\n // Rather than spending cycles only to discover that this module has neither\\n // parameters nor buffers, just shortcut the move completely.\\n protected internal override nn.Module _to(Device device, ScalarType dtype) => this;\\n protected internal override nn.Module _to(DeviceType deviceType, int deviceIndex = -1) => this;\\n protected internal override nn.Module _to(ScalarType dtype) => this;\\n }\\n }\\n\\n public static partial class torch\\n {\\n public static partial class nn\\n {\\n /// <summary>\\n /// Divide the channels in a tensor into g groups and rearrange them.\\n ///\\n /// See: https://pytorch.org/docs/1.10/generated/torch.nn.ChannelShuffle.html#channelshuffle\\n /// </summary>\\n /// <param name=\\\"groups\\\">The number of groups to divide channels in.</param>\\n /// <returns></returns>\\n public static ChannelShuffle ChannelShuffle(long groups)\\n {\\n return new ChannelShuffle(groups);\\n }\\n }\\n }\\n}\\n\"\n },\n {\n \"alpha_fraction\": 0.5412596464157104,\n \"alphanum_fraction\": 0.552996039390564,\n \"avg_line_length\": 46.63194274902344,\n \"blob_id\": \"b16606fd0d515332e8eb5053d611fcce6f4268d7\",\n \"content_id\": \"bec75b98b969df154d60131758079f60b216e267\",\n \"detected_licenses\": [\n \"MIT\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"C#\",\n \"length_bytes\": 13718,\n \"license_type\": \"permissive\",\n \"max_line_length\": 159,\n \"num_lines\": 288,\n \"path\": \"/src/TorchSharp/Utils/Histogram.cs\",\n \"repo_name\": \"NiklasGustafsson/TorchSharp\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"// Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information.\\n\\nusing System;\\nusing System.Collections.Generic;\\nusing System.Linq;\\nusing static TorchSharp.torch;\\n\\nnamespace TorchSharp.Utils\\n{\\n // https://github.com/numpy/numpy/blob/v1.24.0/numpy/lib/histograms.py\\n internal static class Histogram\\n {\\n public static (Tensor hist, Tensor bin_edges) histogram(Tensor input, HistogramBinSelector bins, (double min, double max)? range, bool density = false)\\n {\\n input = RavelAndCheckWeights(input.cpu());\\n (Tensor bin_edges, (double, double, int) uniform_bins) = GetBinEdges(input, bins, range);\\n ScalarType ntype = ScalarType.Int32;\\n int block = 65536;\\n\\n\\n (double first_edge, double last_edge, int n_equal_bins) = uniform_bins;\\n Tensor n = zeros(n_equal_bins, ntype);\\n Tensor norm = n_equal_bins / subtract(last_edge, first_edge);\\n\\n for (int i = 0; i < input.shape[0]; i += block) {\\n Tensor tmp_a = input[TensorIndex.Slice(i, i + block)];\\n Tensor keep = (tmp_a >= first_edge);\\n keep &= (tmp_a <= last_edge);\\n if (keep.sum().item<long>() != tmp_a.numel())\\n tmp_a = tmp_a.masked_select(keep);\\n\\n tmp_a = tmp_a.to_type(bin_edges.dtype);\\n Tensor f_indices = subtract(tmp_a, first_edge) * norm;\\n Tensor indices = f_indices.to_type(ScalarType.Int64);\\n indices[indices == n_equal_bins] -= 1;\\n\\n Tensor decrement = tmp_a < bin_edges[indices];\\n indices[decrement] -= 1;\\n Tensor increment = ((tmp_a >= bin_edges[indices + 1]) & (indices != n_equal_bins - 1));\\n indices[increment] += 1;\\n n += bincount(indices, minlength: n_equal_bins).to_type(ntype);\\n }\\n\\n if (density) {\\n Tensor db = diff(bin_edges).to_type(ScalarType.Float32);\\n return (n / db / n.sum(), bin_edges);\\n }\\n return (n, bin_edges);\\n }\\n\\n /// <summary>\\n /// Computes the bins used internally by `histogram`.\\n /// </summary>\\n /// <param name=\\\"a\\\"> Ravelled data array </param>\\n /// <param name=\\\"bins\\\"> Forwarded arguments from `histogram`. </param>\\n /// <param name=\\\"range\\\"> Ravelled weights array, or None </param>\\n /// <returns></returns>\\n private static (Tensor, (double, double, int)) GetBinEdges(Tensor a, HistogramBinSelector bins, (double min, double max)? range)\\n {\\n (double first_edge, double last_edge) = GetOuterEdges(a, range);\\n if (range is not null) {\\n Tensor keep = (a >= first_edge);\\n keep &= (a <= last_edge);\\n if (keep.sum().item<long>() != a.numel())\\n a = a.masked_select(keep);\\n }\\n\\n int n_equal_bins;\\n if (a.numel() == 0)\\n n_equal_bins = 1;\\n else {\\n if (a.dtype != ScalarType.Float64)\\n a = a.to_type(ScalarType.Float64);\\n Tensor width = histBinSelectors[bins](a, range);\\n if ((width > 0).item<bool>())\\n n_equal_bins = ceil(subtract(last_edge, first_edge) / width).to_type(ScalarType.Int32).item<int>();\\n else\\n n_equal_bins = 1;\\n }\\n\\n Tensor bin_edges = linspace(first_edge, last_edge, n_equal_bins + 1, ScalarType.Float64, requires_grad: true);\\n return (bin_edges, (first_edge, last_edge, n_equal_bins));\\n }\\n\\n /// <summary>\\n /// Determine the outer bin edges to use, from either the data or the range argument\\n /// </summary>\\n /// <param name=\\\"a\\\"></param>\\n /// <param name=\\\"range\\\"></param>\\n /// <returns></returns>\\n /// <exception cref=\\\"ArgumentException\\\"></exception>\\n private static (double, double) GetOuterEdges(Tensor a, (double min, double max)? range)\\n {\\n double first_edge, last_edge;\\n if (range is not null) {\\n (first_edge, last_edge) = range.Value;\\n if (first_edge > last_edge)\\n throw new ArgumentException(\\\"max must be larger than min in range parameter.\\\");\\n if (double.IsInfinity(first_edge) || double.IsNaN(first_edge) || double.IsInfinity(last_edge) || double.IsNaN(last_edge))\\n throw new ArgumentException($\\\"supplied range of [{first_edge}, {last_edge}] is not finite\\\");\\n } else if (a.numel() == 0) {\\n (first_edge, last_edge) = (0, 1);\\n } else {\\n (first_edge, last_edge) = (a.min().to_type(ScalarType.Float64).item<double>(), a.max().to_type(ScalarType.Float64).item<double>());\\n if (double.IsInfinity(first_edge) || double.IsNaN(first_edge) || double.IsInfinity(last_edge) || double.IsNaN(last_edge))\\n throw new ArgumentException($\\\"autodetected range of [{first_edge}, {last_edge}] is not finite\\\");\\n }\\n\\n if (first_edge == last_edge)\\n (first_edge, last_edge) = (first_edge - 0.5, last_edge + 0.5);\\n return (first_edge, last_edge);\\n }\\n\\n /// <summary>\\n /// Check a and weights have matching shapes, and ravel both\\n ///\\n /// https://github.com/numpy/numpy/blob/v1.24.0/numpy/lib/histograms.py#L283\\n /// </summary>\\n /// <param name=\\\"input\\\"></param>\\n /// <returns></returns>\\n private static Tensor RavelAndCheckWeights(Tensor input)\\n {\\n if (input.dtype == ScalarType.Bool)\\n input = input.to_type(ScalarType.Int8);\\n input = input.ravel();\\n\\n return input;\\n }\\n\\n #region hist_bin\\n private static Dictionary<HistogramBinSelector, Func<Tensor, (double min, double max)?, Tensor>> histBinSelectors\\n = new Dictionary<HistogramBinSelector, Func<Tensor, (double min, double max)?, Tensor>>()\\n {\\n { HistogramBinSelector.Stone, HistBinStone },\\n { HistogramBinSelector.Doane, HistBinDoane },\\n { HistogramBinSelector.Rice, HistBinRice },\\n { HistogramBinSelector.Scott, HistBinScott },\\n { HistogramBinSelector.Sqrt, HistBinSqrt },\\n { HistogramBinSelector.Sturges, HistBinSturges },\\n };\\n\\n /// <summary>\\n /// Square root histogram bin estimator.\\n ///\\n /// Bin width is inversely proportional to the data size. Used by many\\n /// programs for its simplicity.\\n ///\\n /// https://github.com/numpy/numpy/blob/v1.24.0/numpy/lib/histograms.py#L32\\n /// </summary>\\n /// <param name=\\\"x\\\"> Input data that is to be histogrammed, trimmed to range. May not be empty. </param>\\n /// <param name=\\\"_\\\"></param>\\n /// <returns> An estimate of the optimal bin width for the given data. </returns>\\n private static Tensor HistBinSqrt(Tensor x, (double min, double max)? _)\\n => Ptp(x) / sqrt(x.numel());\\n\\n /// <summary>\\n /// Sturges histogram bin estimator.\\n ///\\n /// A very simplistic estimator based on the assumption of normality of\\n /// the data.This estimator has poor performance for non-normal data,\\n /// which becomes especially obvious for large data sets.The estimate\\n /// depends only on size of the data.\\n ///\\n /// https://github.com/numpy/numpy/blob/v1.24.0/numpy/lib/histograms.py#L53\\n /// </summary>\\n /// <param name=\\\"x\\\"> Input data that is to be histogrammed, trimmed to range. May not be empty. </param>\\n /// <param name=\\\"_\\\"></param>\\n /// <returns> An estimate of the optimal bin width for the given data. </returns>\\n private static Tensor HistBinSturges(Tensor x, (double min, double max)? _)\\n => Ptp(x) / (log2(x.numel()) + 1);\\n\\n /// <summary>\\n /// Rice histogram bin estimator.\\n ///\\n /// Another simple estimator with no normality assumption. It has better\\n /// performance for large data than Sturges, but tends to overestimate\\n /// the number of bins. The number of bins is proportional to the cube\\n /// root of data size (asymptotically optimal). The estimate depends\\n /// only on size of the data.\\n ///\\n /// https://github.com/numpy/numpy/blob/v1.24.0/numpy/lib/histograms.py#L76\\n /// </summary>\\n /// <param name=\\\"x\\\"> Input data that is to be histogrammed, trimmed to range. May not be empty. </param>\\n /// <param name=\\\"_\\\"></param>\\n /// <returns> An estimate of the optimal bin width for the given data. </returns>\\n private static Tensor HistBinRice(Tensor x, (double min, double max)? _)\\n => Ptp(x) / (2 * pow(x.numel(), 1.0 / 3.0));\\n\\n /// <summary>\\n /// Scott histogram bin estimator.\\n ///\\n /// The binwidth is proportional to the standard deviation of the data\\n /// and inversely proportional to the cube root of data size\\n /// (asymptotically optimal).\\n ///\\n /// https://github.com/numpy/numpy/blob/v1.24.0/numpy/lib/histograms.py#L100\\n /// </summary>\\n /// <param name=\\\"x\\\"> Input data that is to be histogrammed, trimmed to range. May not be empty. </param>\\n /// <param name=\\\"_\\\"></param>\\n /// <returns> An estimate of the optimal bin width for the given data. </returns>\\n private static Tensor HistBinScott(Tensor x, (double min, double max)? _)\\n => Math.Pow(24.0 * Math.Pow(Math.PI, 0.5) / x.numel(), 1.0 / 3.0) * std(x, false);\\n\\n /// <summary>\\n /// Histogram bin estimator based on minimizing the estimated integrated squared error (ISE).\\n ///\\n /// The number of bins is chosen by minimizing the estimated ISE against the unknown true distribution.\\n /// The ISE is estimated using cross-validation and can be regarded as a generalization of Scott's rule.\\n /// https://en.wikipedia.org/wiki/Histogram#Scott.27s_normal_reference_rule\\n /// \\n /// This paper by Stone appears to be the origination of this rule.\\n /// http://digitalassets.lib.berkeley.edu/sdtr/ucb/text/34.pdf\\n ///\\n /// https://github.com/numpy/numpy/blob/v1.24.0/numpy/lib/histograms.py#L122\\n /// </summary>\\n /// <param name=\\\"x\\\"> Input data that is to be histogrammed, trimmed to range. May not be empty. </param>\\n /// <param name=\\\"range\\\"> The lower and upper range of the bins. </param>\\n /// <returns> An estimate of the optimal bin width for the given data. </returns>\\n private static Tensor HistBinStone(Tensor x, (double min, double max)? range)\\n {\\n long n = x.numel();\\n Tensor ptp_x = Ptp(x);\\n if (n <= 1 || (ptp_x == 0).item<bool>())\\n return 0;\\n\\n double Jhat(int nbins)\\n {\\n Tensor hh = ptp_x / nbins;\\n Tensor pk = torch.histogram(x, bins: nbins, range: range).hist / n;\\n return ((2 - (n + 1) * pk.dot(pk)) / hh).to_type(ScalarType.Float64).item<double>();\\n }\\n\\n int nbinsUpperBound = Math.Max(100, Convert.ToInt32(Math.Sqrt(n)));\\n int nbins = 0;\\n double jhatTemp = double.PositiveInfinity;\\n foreach (int item in Enumerable.Range(1, nbinsUpperBound + 1)) {\\n double jhat = Jhat(item);\\n if (jhat < jhatTemp) {\\n jhatTemp = jhat;\\n nbins = item;\\n }\\n }\\n return ptp_x / nbins;\\n }\\n\\n /// <summary>\\n /// Doane's histogram bin estimator.\\n ///\\n /// Improved version of Sturges' formula which works better for\\n /// non-normal data. See\\n /// stats.stackexchange.com/questions/55134/doanes-formula-for-histogram-binning\\n ///\\n /// https://github.com/numpy/numpy/blob/v1.24.0/numpy/lib/histograms.py#L164\\n /// </summary>\\n /// <param name=\\\"x\\\"> Input data that is to be histogrammed, trimmed to range. May not be empty. </param>\\n /// <param name=\\\"_\\\"></param>\\n /// <returns> An estimate of the optimal bin width for the given data. </returns>\\n private static Tensor HistBinDoane(Tensor x, (double min, double max)? _)\\n {\\n long size = x.numel();\\n if (size > 2) {\\n Tensor sg1 = sqrt(6.0 * (size - 2) / ((size + 1.0) * (size + 3)));\\n Tensor sigma = x.std();\\n if ((sigma > 0.0).item<bool>()) {\\n Tensor temp = x - x.mean();\\n temp = true_divide(temp, sigma);\\n temp = float_power(temp, 3);\\n Tensor g1 = temp.mean();\\n return Ptp(x) / (1.0 + log2(size) + log2(1.0 + absolute(g1) / sg1));\\n }\\n }\\n return 0.0;\\n }\\n #endregion\\n\\n /// <summary>\\n /// This implementation avoids the problem of signed integer arrays having a\\n /// peak-to-peak value that cannot be represented with the array's data type.\\n /// This function returns an value for signed integer arrays.\\n ///\\n /// https://github.com/numpy/numpy/blob/v1.24.0/numpy/lib/histograms.py#L22\\n /// </summary>\\n /// <param name=\\\"input\\\"></param>\\n /// <returns></returns>\\n private static Tensor Ptp(Tensor input)\\n => subtract(input.max(), input.min());\\n }\\n}\\n\"\n },\n {\n \"alpha_fraction\": 0.5765171647071838,\n \"alphanum_fraction\": 0.581134557723999,\n \"avg_line_length\": 47.90322494506836,\n \"blob_id\": \"3cf7aa6ce150e34123514fd8f9e6fc47a418fc21\",\n \"content_id\": \"002d9beb2a1dfb6de8138779dfb5b9f20b942529\",\n \"detected_licenses\": [\n \"MIT\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"C#\",\n \"length_bytes\": 1516,\n \"license_type\": \"permissive\",\n \"max_line_length\": 152,\n \"num_lines\": 31,\n \"path\": \"/src/TorchSharp/NN/OneHot.cs\",\n \"repo_name\": \"NiklasGustafsson/TorchSharp\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"// Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information.\\nusing System;\\nusing static TorchSharp.PInvoke.NativeMethods;\\n\\nnamespace TorchSharp\\n{\\n public static partial class torch\\n {\\n public static partial class nn\\n {\\n public static partial class functional\\n {\\n /// <summary>\\n /// Takes LongTensor with index values of shape (*) and returns a tensor of shape (*, num_classes) that have zeros\\n /// everywhere except where the index of last dimension matches the corresponding value of the input tensor, in which case it will be 1.\\n /// </summary>\\n /// <param name=\\\"x\\\">Category values of any shape</param>\\n /// <param name=\\\"num_classes\\\">Total number of classes.\\n /// If set to -1, the number of classes will be inferred as one greater than the largest class value in the input tensor</param>\\n /// <returns></returns>\\n public static Tensor one_hot(Tensor x, long num_classes = -1)\\n {\\n if (x.dtype != ScalarType.Int64) throw new ArgumentException(\\\"OneHot input tensor must have elements of type Int64\\\");\\n var res = THSNN_one_hot(x.Handle, num_classes);\\n if (res == IntPtr.Zero) { torch.CheckForErrors(); }\\n return new Tensor(res);\\n }\\n }\\n }\\n }\\n}\\n\"\n },\n {\n \"alpha_fraction\": 0.6079510450363159,\n \"alphanum_fraction\": 0.6106014251708984,\n \"avg_line_length\": 48.04999923706055,\n \"blob_id\": \"9d19f3a2be7c947ca2161f9d3db47750d5605e5e\",\n \"content_id\": \"e85cf9adedb3907930819c446b109a444e143eb8\",\n \"detected_licenses\": [\n \"MIT\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"C#\",\n \"length_bytes\": 4905,\n \"license_type\": \"permissive\",\n \"max_line_length\": 220,\n \"num_lines\": 100,\n \"path\": \"/src/TorchSharp/Distributions/Weibull.cs\",\n \"repo_name\": \"NiklasGustafsson/TorchSharp\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"// Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information.\\n\\nusing static TorchSharp.torch;\\n\\nnamespace TorchSharp\\n{\\n using Modules;\\n using static torch.distributions;\\n\\n namespace Modules\\n {\\n /// <summary>\\n /// Samples from a two-parameter Weibull distribution.\\n /// </summary>\\n public class Weibull : TransformedDistribution\\n {\\n internal Weibull(Tensor scale, Tensor concentration, Tensor concentration_reciprocal, Distribution base_distribution, torch.distributions.transforms.Transform[] transforms, torch.Generator generator = null) :\\n base(base_distribution, transforms, generator)\\n {\\n this.scale = scale;\\n this.concentration = concentration;\\n this.concentration_reciprocal = concentration_reciprocal;\\n }\\n\\n private Tensor scale;\\n private Tensor concentration;\\n private Tensor concentration_reciprocal;\\n\\n public override Tensor mean =>\\n WrappedTensorDisposeScope(() => scale * torch.exp(torch.lgamma(1 + concentration_reciprocal)));\\n\\n public override Tensor mode =>\\n WrappedTensorDisposeScope(() => scale * ((concentration - 1) / concentration).pow(concentration_reciprocal));\\n\\n public override Tensor variance =>\\n WrappedTensorDisposeScope(() =>\\n scale.pow(2) * (torch.exp(torch.lgamma(1 + 2 * concentration_reciprocal)) - torch.exp(2 * torch.lgamma(1 + concentration_reciprocal)))\\n );\\n\\n /// <summary>\\n /// Returns entropy of distribution, batched over batch_shape.\\n /// </summary>\\n public override Tensor entropy() =>\\n torch.WrappedTensorDisposeScope(() => euler_constant * (1 - concentration_reciprocal) + torch.log(scale * concentration_reciprocal) + 1);\\n\\n /// <summary>\\n /// Returns a new distribution instance (or populates an existing instance provided by a derived class) with batch dimensions expanded to\\n /// `batch_shape`. This method calls `torch.Tensor.expand()` on the distribution's parameters. As such, this does not allocate new\\n /// memory for the expanded distribution instance.\\n /// </summary>\\n /// <param name=\\\"batch_shape\\\">Tthe desired expanded size.</param>\\n /// <param name=\\\"instance\\\">new instance provided by subclasses that need to override `.expand`.</param>\\n public override Distribution expand(Size batch_shape, Distribution instance = null)\\n {\\n var cExp = concentration.expand(batch_shape);\\n var cExpR = cExp.reciprocal();\\n var nScale = scale.expand(batch_shape);\\n\\n var transforms = new torch.distributions.transforms.Transform[] {\\n new distributions.transforms.PowerTransform(cExpR),\\n new torch.distributions.transforms.AffineTransform(0, nScale)\\n };\\n\\n var newDistribution = ((instance == null)\\n ? new Weibull(nScale, cExp, cExpR, base_distribution.expand(batch_shape), transforms, generator)\\n : instance) as Weibull;\\n return newDistribution;\\n }\\n }\\n }\\n\\n\\n public static partial class torch\\n {\\n public static partial class distributions\\n {\\n /// <summary>\\n /// Samples from a two-parameter Weibull distribution.\\n /// </summary>\\n /// <param name=\\\"concentration\\\">Concentration parameter of distribution (k/shape).</param>\\n /// <param name=\\\"scale\\\">Scale parameter of the distribution.</param>\\n /// <param name=\\\"generator\\\">An optional random number generator object.</param>\\n /// <returns></returns>\\n public static Weibull Weibull(Tensor scale, Tensor concentration, torch.Generator generator = null)\\n {\\n var locScale = torch.broadcast_tensors(scale, concentration);\\n scale = locScale[0].alias().DetachFromDisposeScope();\\n concentration = locScale[1].alias().DetachFromDisposeScope();\\n var concentration_reciprocal = concentration.reciprocal();\\n\\n var base_dist = Exponential(torch.ones_like(scale), generator);\\n var transforms = new torch.distributions.transforms.Transform[] {\\n new torch.distributions.transforms.PowerTransform(exponent: concentration_reciprocal),\\n new torch.distributions.transforms.AffineTransform(0, -torch.ones_like(scale))\\n };\\n return new Weibull(scale, concentration, concentration_reciprocal, base_dist, transforms, generator);\\n }\\n }\\n }\\n}\\n\"\n },\n {\n \"alpha_fraction\": 0.6000000238418579,\n \"alphanum_fraction\": 0.6000000238418579,\n \"avg_line_length\": 25.733333587646484,\n \"blob_id\": \"bc2955eff95ae662a7f6769ab474ff413d4b8e34\",\n \"content_id\": \"4721c34b3aadbfdfcbdceeedd5298a6c31e4e754\",\n \"detected_licenses\": [\n \"MIT\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"C#\",\n \"length_bytes\": 400,\n \"license_type\": \"permissive\",\n \"max_line_length\": 130,\n \"num_lines\": 15,\n \"path\": \"/src/TorchAudio/ResamplingMethod.cs\",\n \"repo_name\": \"NiklasGustafsson/TorchSharp\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"// Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information.\\nnamespace TorchSharp\\n{\\n public static partial class torchaudio\\n {\\n /// <summary>\\n /// Resampling method\\n /// </summary>\\n public enum ResamplingMethod\\n {\\n sinc_interpolation,\\n kaiser_window\\n }\\n }\\n}\"\n },\n {\n \"alpha_fraction\": 0.5194174647331238,\n \"alphanum_fraction\": 0.5206310749053955,\n \"avg_line_length\": 23.264705657958984,\n \"blob_id\": \"bd8a178e9a2fcf833c1093dbec009388d3ebc380\",\n \"content_id\": \"98994fb2aafd882a05f66014cea8f79416d250aa\",\n \"detected_licenses\": [\n \"MIT\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"C#\",\n \"length_bytes\": 824,\n \"license_type\": \"permissive\",\n \"max_line_length\": 130,\n \"num_lines\": 34,\n \"path\": \"/src/TorchVision/HorizontalFlip.cs\",\n \"repo_name\": \"NiklasGustafsson/TorchSharp\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"// Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information.\\n\\nusing static TorchSharp.torch;\\n\\n\\nnamespace TorchSharp\\n{\\n public static partial class torchvision\\n {\\n internal class HorizontalFlip : ITransform\\n {\\n internal HorizontalFlip()\\n {\\n }\\n\\n public Tensor call(Tensor input)\\n {\\n return input.flip(-1);\\n }\\n }\\n\\n public static partial class transforms\\n {\\n /// <summary>\\n /// Flip the image horizontally.\\n /// </summary>\\n /// <returns></returns>\\n static public ITransform HorizontalFlip()\\n {\\n return new HorizontalFlip();\\n }\\n }\\n }\\n}\"\n },\n {\n \"alpha_fraction\": 0.6583850979804993,\n \"alphanum_fraction\": 0.6677018404006958,\n \"avg_line_length\": 31.299999237060547,\n \"blob_id\": \"b184bd7fa55def0a6837624b95307b75d9dd940c\",\n \"content_id\": \"61e8fba631ab37509f5bf4c89cdadf9a788d4172\",\n \"detected_licenses\": [\n \"MIT\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"C#\",\n \"length_bytes\": 322,\n \"license_type\": \"permissive\",\n \"max_line_length\": 130,\n \"num_lines\": 10,\n \"path\": \"/src/TorchSharp/Tensor/Enums/compute_mode.cs\",\n \"repo_name\": \"NiklasGustafsson/TorchSharp\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"// Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information.\\nnamespace TorchSharp\\n{\\n public enum compute_mode\\n {\\n use_mm_for_euclid_dist_if_necessary = 0,\\n use_mm_for_euclid_dist = 1,\\n donot_use_mm_for_euclid_dist = 2\\n }\\n}\"\n },\n {\n \"alpha_fraction\": 0.5763434767723083,\n \"alphanum_fraction\": 0.5806084871292114,\n \"avg_line_length\": 45.893333435058594,\n \"blob_id\": \"4a159ecade26f7eccda61e2064220689fdcc26e4\",\n \"content_id\": \"424da18d5c7789a634d0d03b41255d4a45fedc58\",\n \"detected_licenses\": [\n \"MIT\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"C#\",\n \"length_bytes\": 3517,\n \"license_type\": \"permissive\",\n \"max_line_length\": 139,\n \"num_lines\": 75,\n \"path\": \"/src/TorchSharp/NN/Pooling/LPPool1d.cs\",\n \"repo_name\": \"NiklasGustafsson/TorchSharp\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"// Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information.\\nusing System;\\nusing static TorchSharp.torch;\\nusing static TorchSharp.PInvoke.NativeMethods;\\n\\nnamespace TorchSharp\\n{\\n using Modules;\\n\\n namespace Modules\\n {\\n /// <summary>\\n /// This class is used to represent a LPPool1D module.\\n /// </summary>\\n public sealed class LPPool1d : torch.nn.Module<Tensor, Tensor>\\n {\\n internal LPPool1d(IntPtr handle, IntPtr boxedHandle) : base(handle, boxedHandle)\\n {\\n }\\n\\n public override Tensor forward(Tensor tensor)\\n {\\n var res = THSNN_LPPool1d_forward(handle.DangerousGetHandle(), tensor.Handle);\\n if (res == IntPtr.Zero) { torch.CheckForErrors(); }\\n return new Tensor(res);\\n }\\n\\n // Rather than spending cycles only to discover that this module has neither\\n // parameters nor buffers, just shortcut the move completely.\\n protected internal override nn.Module _to(Device device, ScalarType dtype) => this;\\n protected internal override nn.Module _to(DeviceType deviceType, int deviceIndex = -1) => this;\\n protected internal override nn.Module _to(ScalarType dtype) => this;\\n }\\n }\\n\\n public static partial class torch\\n {\\n public static partial class nn\\n {\\n /// <summary>\\n /// Applies a 1D power-average pooling over an input signal composed of several input planes.\\n /// </summary>\\n /// <param name=\\\"norm_type\\\">The LP norm (exponent)</param>\\n /// <param name=\\\"kernelSize\\\">The size of the window</param>\\n /// <param name=\\\"stride\\\">The stride of the window. Default value is kernel_size</param>\\n /// <param name=\\\"ceil_mode\\\">Use ceil instead of floor to compute the output shape</param>\\n /// <returns></returns>\\n public static LPPool1d LPPool1d(double norm_type, long kernelSize, long? stride = null, bool ceil_mode = false)\\n {\\n return stride.HasValue ?\\n LPPool1d(norm_type, new long[] { kernelSize }, new long[] { stride.Value }, ceil_mode) :\\n LPPool1d(norm_type, new long[] { kernelSize }, null);\\n }\\n\\n /// <summary>\\n /// Applies a 1D power-average pooling over an input signal composed of several input planes.\\n /// </summary>\\n /// <param name=\\\"norm_type\\\">The LP norm (exponent)</param>\\n /// <param name=\\\"kernelSize\\\">The size of the window</param>\\n /// <param name=\\\"strides\\\">The stride of the window. Default value is kernel_size</param>\\n /// <param name=\\\"ceil_mode\\\">Use ceil instead of floor to compute the output shape</param>\\n /// <returns></returns>\\n private static LPPool1d LPPool1d(double norm_type, long[] kernelSize, long[] strides = null, bool ceil_mode = false)\\n {\\n unsafe {\\n fixed (long* pkernelSize = kernelSize, pstrides = strides) {\\n var handle = THSNN_LPPool1d_ctor(norm_type, (IntPtr)pkernelSize, (IntPtr)pstrides, ceil_mode, out var boxedHandle);\\n if (handle == IntPtr.Zero) { torch.CheckForErrors(); }\\n return new LPPool1d(handle, boxedHandle);\\n }\\n }\\n }\\n }\\n }\\n}\\n\"\n },\n {\n \"alpha_fraction\": 0.4348173439502716,\n \"alphanum_fraction\": 0.438812792301178,\n \"avg_line_length\": 39.80745315551758,\n \"blob_id\": \"4bd0bc2cf22bbd2f81ce53bbeb99bc8b0f53daa6\",\n \"content_id\": \"0c4aa26f57b0487737df613c52f34494c554a763\",\n \"detected_licenses\": [\n \"MIT\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"C#\",\n \"length_bytes\": 26280,\n \"license_type\": \"permissive\",\n \"max_line_length\": 198,\n \"num_lines\": 644,\n \"path\": \"/src/TorchSharp/Distributions/Constraints.cs\",\n \"repo_name\": \"NiklasGustafsson/TorchSharp\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"// Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information.\\nusing System;\\nusing System.Collections.Generic;\\nusing System.Linq;\\n\\nnamespace TorchSharp\\n{\\n\\n public static partial class torch\\n {\\n public static partial class distributions\\n {\\n public static partial class constraints\\n {\\n /// <summary>\\n /// Abstract base class for constraints.\\n /// \\n /// A constraint object represents a region over which a variable is valid, e.g. within which a variable can be optimized.\\n /// </summary>\\n /// <remarks>\\n /// // It's not ideal to use a '_' first in a public .NET type name, but that's what Pytorch does for all contraint types.\\n /// </remarks>\\n public abstract class Constraint\\n {\\n /// <summary>\\n /// Constructor\\n /// </summary>\\n /// <param name=\\\"is_discrete\\\">Whether constrained space is discrete.</param>\\n /// <param name=\\\"event_dim\\\">\\n /// Number of rightmost dimensions that together define an event.\\n /// The check() method will remove this many dimensions when computing validity.\\n /// </param>\\n protected Constraint(bool is_discrete, int event_dim)\\n {\\n this.is_discrete = is_discrete;\\n this.event_dim = event_dim;\\n }\\n\\n /// <summary>\\n /// Constructor\\n /// </summary>\\n protected Constraint() : this(false, 0)\\n {\\n }\\n\\n /// <summary>\\n /// Returns a byte tensor of sample_shape + batch_shape indicating whether each event in value satisfies this constraint.\\n /// </summary>\\n /// <param name=\\\"value\\\"></param>\\n /// <returns></returns>\\n public abstract Tensor check(Tensor value);\\n\\n /// <summary>\\n /// Whether constrained space is discrete.\\n /// </summary>\\n public virtual bool is_discrete { get; protected set; }\\n\\n /// <summary>\\n /// Number of rightmost dimensions that together define an event.\\n /// The check() method will remove this many dimensions when computing validity.\\n /// </summary>\\n public virtual int event_dim { get; protected set; }\\n\\n public override string ToString()\\n {\\n return this.GetType().Name + \\\"()\\\";\\n }\\n }\\n\\n /// <summary>\\n /// Placeholder for variables whose support depends on other variables.\\n /// These variables obey no simple coordinate-wise constraints.\\n /// </summary>\\n public class _Dependent : Constraint\\n {\\n public _Dependent(bool is_discrete = false, int event_dim = 0) : base(is_discrete, event_dim) { }\\n\\n public override Tensor check(Tensor value)\\n {\\n throw new ArgumentException(\\\"Cannot determine validity of dependent constraint\\\");\\n }\\n }\\n\\n /// <summary>\\n /// Wraps a constraint by aggregating over ``reinterpreted_batch_ndims``-many\\n /// dims in :meth:`check`, so that an event is valid only if all its\\n /// independent entries are valid.\\n /// </summary>\\n public class _IndependentConstraint : Constraint\\n {\\n public _IndependentConstraint(Constraint base_constraint, int reinterpreted_batch_ndims)\\n {\\n\\n }\\n\\n public override Tensor check(Tensor value)\\n {\\n throw new NotImplementedException();\\n }\\n\\n public override string ToString()\\n {\\n return this.GetType().Name + \\\"()\\\";\\n }\\n\\n public Constraint base_constraint { get; private set; }\\n\\n /// <summary>\\n /// Whether constrained space is discrete.\\n /// </summary>\\n public override bool is_discrete { get => base_constraint.is_discrete; }\\n\\n /// <summary>\\n /// Number of rightmost dimensions that together define an event.\\n /// The check() method will remove this many dimensions when computing validity.\\n /// </summary>\\n public override int event_dim { get => base_constraint.event_dim; }\\n\\n }\\n\\n /// <summary>\\n /// Constrain to the two values {0, 1}.\\n /// </summary>\\n public class _Boolean : Constraint\\n {\\n public _Boolean() : base(true, 0) { }\\n\\n public override Tensor check(Tensor value) => (value == 0) | (value == 1);\\n }\\n\\n /// <summary>\\n /// Constrain to one-hot vectors.\\n /// </summary>\\n public class _OneHot : Constraint\\n {\\n public _OneHot() : base(true, 1) { }\\n\\n public override Tensor check(Tensor value)\\n {\\n var is_boolean = (value == 0) | (value == 1);\\n var is_normalized = value.sum(-1).eq(1);\\n return is_boolean.all(-1) & is_normalized;\\n }\\n }\\n\\n /// <summary>\\n /// Constrain to an integer interval [lower_bound, upper_bound].\\n /// </summary>\\n public class _IntegerInterval : Constraint\\n {\\n public _IntegerInterval(long lower_bound, long upper_bound) : base(true, 0)\\n {\\n this.lower_bound = lower_bound;\\n this.upper_bound = upper_bound;\\n }\\n\\n public long lower_bound { get; private set; }\\n\\n public long upper_bound { get; private set; }\\n\\n public override Tensor check(Tensor value)\\n {\\n return (value % 1 == 0) & (lower_bound <= value) & (value <= upper_bound);\\n }\\n\\n public override string ToString()\\n {\\n return $\\\"{GetType().Name}(lower_bound={lower_bound},upper_bound={upper_bound})\\\";\\n }\\n }\\n\\n /// <summary>\\n /// Constrain to an integer interval [-inf, upper_bound].\\n /// </summary>\\n public class _IntegerLessThan : Constraint\\n {\\n public _IntegerLessThan(long upper_bound) : base(true, 0)\\n {\\n this.upper_bound = upper_bound;\\n }\\n\\n public long upper_bound { get; private set; }\\n\\n public override Tensor check(Tensor value)\\n {\\n return (value % 1 == 0) & (value <= upper_bound);\\n }\\n\\n public override string ToString()\\n {\\n return $\\\"{GetType().Name}(upper_bound={upper_bound})\\\";\\n }\\n }\\n\\n /// <summary>\\n /// Constrain to an integer interval [lower_bound, inf].\\n /// </summary>\\n public class _IntegerGreaterThan : Constraint\\n {\\n public _IntegerGreaterThan(long lower_bound) : base(true, 0)\\n {\\n this.lower_bound = lower_bound;\\n }\\n\\n public long lower_bound { get; private set; }\\n\\n public override Tensor check(Tensor value)\\n {\\n return (value % 1 == 0) & (lower_bound <= value);\\n }\\n\\n public override string ToString()\\n {\\n return $\\\"{GetType().Name}(lower_bound={lower_bound})\\\";\\n }\\n }\\n\\n /// <summary>\\n /// Trivially constrain to the extended real numbers [-inf, inf].\\n /// </summary>\\n public class _Real : Constraint\\n {\\n public _Real() : base(false, 0) { }\\n\\n public override Tensor check(Tensor value) => value.eq(value); // False only for NaN.\\n }\\n\\n /// <summary>\\n /// Constrain to an interval [lower_bound, upper_bound].\\n /// </summary>\\n public class _Interval : Constraint\\n {\\n public _Interval(double lower_bound, double upper_bound) : base(true, 0)\\n {\\n this.lower_bound = lower_bound;\\n this.upper_bound = upper_bound;\\n }\\n\\n public double lower_bound { get; private set; }\\n\\n public double upper_bound { get; private set; }\\n\\n public override Tensor check(Tensor value)\\n {\\n return (lower_bound <= value) & (value <= upper_bound);\\n }\\n\\n public override string ToString()\\n {\\n return $\\\"{GetType().Name}(lower_bound={lower_bound},upper_bound={upper_bound})\\\";\\n }\\n }\\n\\n /// <summary>\\n /// Constrain to an interval [lower_bound, upper_bound).\\n /// </summary>\\n public class _HalfOpenInterval : Constraint\\n {\\n public _HalfOpenInterval(double lower_bound, double upper_bound) : base(true, 0)\\n {\\n this.lower_bound = lower_bound;\\n this.upper_bound = upper_bound;\\n }\\n\\n public double lower_bound { get; private set; }\\n\\n public double upper_bound { get; private set; }\\n\\n public override Tensor check(Tensor value)\\n {\\n return (lower_bound <= value) & (value < upper_bound);\\n }\\n\\n public override string ToString()\\n {\\n return $\\\"{GetType().Name}(lower_bound={lower_bound},upper_bound={upper_bound})\\\";\\n }\\n }\\n\\n /// <summary>\\n /// Constrain to an interval (lower_bound, inf].\\n /// </summary>\\n public class _GreaterThan : Constraint\\n {\\n public _GreaterThan(double lower_bound) : base(true, 0)\\n {\\n this.lower_bound = lower_bound;\\n }\\n\\n public double lower_bound { get; private set; }\\n\\n public override Tensor check(Tensor value)\\n {\\n return (lower_bound < value);\\n }\\n\\n public override string ToString()\\n {\\n return $\\\"{GetType().Name}(lower_bound={lower_bound})\\\";\\n }\\n }\\n\\n /// <summary>\\n /// Constrain to an interval [lower_bound, inf].\\n /// </summary>\\n public class _GreaterThanEq : Constraint\\n {\\n public _GreaterThanEq(double lower_bound) : base(true, 0)\\n {\\n this.lower_bound = lower_bound;\\n }\\n\\n public double lower_bound { get; private set; }\\n\\n public override Tensor check(Tensor value)\\n {\\n return (lower_bound <= value);\\n }\\n\\n public override string ToString()\\n {\\n return $\\\"{GetType().Name}(lower_bound={lower_bound})\\\";\\n }\\n }\\n\\n /// <summary>\\n /// Constrain to an integer interval [-inf, upper_bound).\\n /// </summary>\\n public class _LessThan : Constraint\\n {\\n public _LessThan(double upper_bound) : base(true, 0)\\n {\\n this.upper_bound = upper_bound;\\n }\\n\\n public double upper_bound { get; private set; }\\n\\n public override Tensor check(Tensor value)\\n {\\n return (value < upper_bound);\\n }\\n\\n public override string ToString()\\n {\\n return $\\\"{GetType().Name}(upper_bound={upper_bound})\\\";\\n }\\n }\\n\\n /// <summary>\\n /// Constrain to the unit simplex in the innermost (rightmost) dimension.\\n /// Specifically: `x >= 0` and `x.sum(-1) == 1`.\\n /// </summary>\\n public class _Simplex : Constraint\\n {\\n public _Simplex() : base(false, 1) { }\\n\\n public override Tensor check(Tensor value)\\n {\\n var is_positive = value >= 0;\\n return is_positive.all(dim: -1) & ((value.sum(-1) - 1).abs() < 1e-6);\\n }\\n }\\n\\n /// <summary>\\n /// Constrain to nonnegative integer values summing to at most an upper bound.\\n /// </summary>\\n public class _Multinomial : Constraint\\n {\\n public _Multinomial(long upper_bound) : base(true, 1)\\n {\\n this.upper_bound = upper_bound;\\n }\\n\\n public long upper_bound { get; private set; }\\n\\n public override Tensor check(Tensor x)\\n {\\n return (x >= 0).all(dim: -1) & (x.sum(dim: -1) <= upper_bound);\\n }\\n\\n public override string ToString()\\n {\\n return $\\\"{GetType().Name}(upper_bound={upper_bound})\\\";\\n }\\n }\\n\\n /// <summary>\\n /// Constrain to lower-triangular square matrices.\\n /// </summary>\\n public class _LowerTriangular : Constraint\\n {\\n public _LowerTriangular() : base(false, 2) { }\\n\\n public override Tensor check(Tensor value)\\n {\\n var tril = value.tril();\\n var newshape = new long[value.shape.Length - 1];\\n var i = 0;\\n for (; i < value.shape.Length - 2; i++) newshape[i] = value.shape[i];\\n newshape[i] = -1;\\n return (tril == value).view(newshape).min(-1).values;\\n }\\n }\\n\\n /// <summary>\\n /// Constrain to lower-triangular square matrices with positive diagonals.\\n /// </summary>\\n public class _LowerCholesky : Constraint\\n {\\n public _LowerCholesky() : base(false, 2) { }\\n\\n public override Tensor check(Tensor value)\\n {\\n var value_tril = value.tril();\\n var newshape = new long[value.shape.Length - 1];\\n var i = 0;\\n for (; i < value.shape.Length - 2; i++) newshape[i] = value.shape[i];\\n newshape[i] = -1;\\n\\n var lower_triangular = (value_tril == value).view(newshape).min(-1).values;\\n var positive_diagonal = (value.diagonal(dim1: -2, dim2: -1) > 0).min(-1).values;\\n return lower_triangular & positive_diagonal;\\n }\\n }\\n\\n /// <summary>\\n /// Constrain to lower-triangular square matrices with positive diagonals and each\\n /// row vector being of unit length.\\n /// </summary>\\n public class _CorrCholesky : Constraint\\n {\\n public _CorrCholesky() : base(false, 2) { }\\n\\n public override Tensor check(Tensor value)\\n {\\n var tol = torch.finfo(value.dtype).eps * value.size(-1) * 10; // 10 is an adjustable fudge factor\\n var row_norm = torch.linalg.norm(value.detach(), dims: new[] { -1L });\\n var unit_row_norm = (row_norm - 1.0).abs().le(tol).all(dim: -1);\\n return lc.check(value) & unit_row_norm;\\n }\\n\\n private _LowerCholesky lc = new _LowerCholesky();\\n }\\n\\n /// <summary>\\n /// Constrain to square matrices.\\n /// </summary>\\n public class _Square : Constraint\\n {\\n public _Square() : base(false, 2) { }\\n\\n public override Tensor check(Tensor value)\\n {\\n var newshape = new long[value.shape.Length - 2];\\n var i = 0;\\n for (; i < value.shape.Length - 2; i++) newshape[i] = value.shape[i];\\n\\n return torch.full(\\n size: newshape,\\n value: (value.shape[value.shape.Length - 2] == value.shape[value.shape.Length - 1]),\\n dtype: torch.@bool,\\n device: value.device);\\n }\\n }\\n\\n /// <summary>\\n /// Constrain to symmetric square matrices.\\n /// </summary>\\n public class _Symmetric : _Square\\n {\\n\\n public override Tensor check(Tensor value)\\n {\\n var square_check = base.check(value);\\n if (!square_check.all().item<bool>())\\n return square_check;\\n\\n return value.isclose(value.mT, atol: 1e-6).all(-2).all(-1);\\n }\\n }\\n\\n /// <summary>\\n /// Constrain to positive-semidefinite matrices.\\n /// </summary>\\n public class _PositiveSemiDefinite : _Symmetric\\n {\\n public override Tensor check(Tensor value)\\n {\\n var sym_check = base.check(value);\\n if (!sym_check.all().item<bool>())\\n return sym_check;\\n return torch.linalg.eigvalsh(value).ge(0).all(-1);\\n }\\n }\\n\\n /// <summary>\\n /// Constrain to positive-definite matrices.\\n /// </summary>\\n public class _PositiveDefinite : _Symmetric\\n {\\n public override Tensor check(Tensor value)\\n {\\n var sym_check = base.check(value);\\n if (!sym_check.all().item<bool>())\\n return sym_check;\\n return torch.linalg.cholesky_ex(value).info.eq(0);\\n }\\n }\\n\\n /// <summary>\\n /// Constraint functor that applies a sequence of constraints cseq at the submatrices at dimension dim,\\n /// each of size lengths[dim], in a way compatible with torch.cat().\\n /// </summary>\\n public class _Cat : Constraint\\n {\\n public _Cat(IList<Constraint> cseq, long dim = 0, IList<long> lengths = null)\\n {\\n this.cseq = cseq;\\n if (lengths == null) {\\n lengths = Enumerable.Repeat(1L, cseq.Count).ToList();\\n }\\n this.lengths = lengths;\\n this.dim = dim;\\n }\\n\\n public override Tensor check(Tensor value)\\n {\\n var checks = new List<Tensor>();\\n long start = 0;\\n for (int i = 0; i < cseq.Count; i++) {\\n var length = lengths[i];\\n var constr = cseq[i];\\n var v = value.narrow(dim, start, length);\\n checks.Add(constr.check(v));\\n start = start + length;\\n }\\n return torch.cat(checks, dim);\\n }\\n\\n public override bool is_discrete { get => cseq.Any(c => c.is_discrete); }\\n\\n public override int event_dim { get => cseq.Select(c => c.event_dim).Max(); }\\n\\n private IList<Constraint> cseq;\\n private IList<long> lengths;\\n private long dim;\\n }\\n /// <summary>\\n /// Constraint functor that applies a sequence of constraints cseq at the submatrices at dimension dim,\\n /// each of size lengths[dim], in a way compatible with torch.cat().\\n /// </summary>\\n public class _Stack : Constraint\\n {\\n public _Stack(IList<Constraint> cseq, int dim = 0)\\n {\\n this.cseq = cseq;\\n this.dim = dim;\\n }\\n\\n public override Tensor check(Tensor value)\\n {\\n var vs = Enumerable.Range(0, (int)value.size(dim)).Select(i => value.select(dim, i)).ToList();\\n return torch.stack(Enumerable.Range(0, vs.Count).Select(i => cseq[i].check(vs[i])));\\n }\\n\\n public override bool is_discrete { get => cseq.Any(c => c.is_discrete); }\\n\\n public override int event_dim {\\n get {\\n var dim = cseq.Select(c => c.event_dim).Max();\\n\\n if (this.dim + dim < 0)\\n dim += 1;\\n\\n return dim;\\n }\\n }\\n\\n private IList<Constraint> cseq;\\n private int dim;\\n }\\n\\n\\n // Public interface\\n\\n public static _Dependent dependent = new _Dependent();\\n\\n public static _IndependentConstraint independent(Constraint base_constraint, int reinterpreted_batch_ndims) => new _IndependentConstraint(base_constraint, reinterpreted_batch_ndims);\\n\\n public static _Boolean boolean = new _Boolean();\\n\\n public static _OneHot one_hot = new _OneHot();\\n\\n public static _IntegerGreaterThan nonnegative_integer = new _IntegerGreaterThan(0);\\n\\n public static _IntegerGreaterThan positive_integer = new _IntegerGreaterThan(1);\\n\\n public static _IntegerInterval integer_interval(long lb, long ub) => new _IntegerInterval(lb, ub);\\n\\n public static _Real real = new _Real();\\n\\n public static _IndependentConstraint real_vector = independent(real, 1);\\n\\n public static _GreaterThan positive = new _GreaterThan(0.0);\\n\\n public static _GreaterThanEq nonnegative = new _GreaterThanEq(0.0);\\n\\n public static _GreaterThan greater_than(double lower_bound) => new _GreaterThan(lower_bound);\\n\\n public static _GreaterThanEq greater_than_eq(double lower_bound) => new _GreaterThanEq(lower_bound);\\n\\n public static _LessThan less_than(double upper_bound) => new _LessThan(upper_bound);\\n\\n public static _Multinomial multinomial(long upper_bound) => new _Multinomial(upper_bound);\\n\\n public static _Interval unit_interval = new _Interval(0.0, 1.0);\\n\\n public static _Interval interval(double lower_bound, double upper_bound) => new _Interval(lower_bound, upper_bound);\\n\\n public static _HalfOpenInterval half_open_interval(double lower_bound, double upper_bound) => new _HalfOpenInterval(lower_bound, upper_bound);\\n\\n public static _Simplex simplex = new _Simplex();\\n\\n public static _LowerTriangular lower_triangular = new _LowerTriangular();\\n\\n public static _LowerCholesky lower_cholesky = new _LowerCholesky();\\n\\n public static _CorrCholesky corr_cholesky = new _CorrCholesky();\\n\\n public static _Square square = new _Square();\\n\\n public static _Symmetric symmetric = new _Symmetric();\\n\\n public static _PositiveSemiDefinite positive_semidefinite = new _PositiveSemiDefinite();\\n\\n public static _PositiveDefinite positive_definite = new _PositiveDefinite();\\n\\n public static _Cat cat(IList<Constraint> cseq, long dim = 0, IList<long> lengths = null) => new _Cat(cseq, dim, lengths);\\n\\n public static _Stack stack(IList<Constraint> cseq, int dim = 0) => new _Stack(cseq, dim);\\n }\\n }\\n }\\n}\\n\"\n },\n {\n \"alpha_fraction\": 0.724047064781189,\n \"alphanum_fraction\": 0.729131281375885,\n \"avg_line_length\": 55.49357604980469,\n \"blob_id\": \"5fb8e36a159d1e848460f94553714ec1910c1d35\",\n \"content_id\": \"efeaec1b39afa5b447abb4589b9a19077394b658\",\n \"detected_licenses\": [\n \"MIT\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"C#\",\n \"length_bytes\": 74741,\n \"license_type\": \"permissive\",\n \"max_line_length\": 420,\n \"num_lines\": 1323,\n \"path\": \"/src/TorchSharp/PInvoke/LibTorchSharp.THSNN.cs\",\n \"repo_name\": \"NiklasGustafsson/TorchSharp\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"// Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information.\\n#nullable enable\\nusing System;\\nusing System.Runtime.InteropServices;\\n\\nnamespace TorchSharp.PInvoke\\n{\\n#pragma warning disable CA2101\\n internal static partial class NativeMethods\\n {\\n [DllImport(\\\"LibTorchSharp\\\", CharSet = CharSet.Ansi, BestFitMapping = false, ThrowOnUnmappableChar = true)]\\n internal static extern void THSNN_Module_save(\\n torch.nn.Module.HType handle,\\n [MarshalAs(UnmanagedType.LPStr)] string location);\\n\\n [DllImport(\\\"LibTorchSharp\\\", CharSet = CharSet.Ansi, BestFitMapping = false, ThrowOnUnmappableChar = true)]\\n [return: MarshalAs(UnmanagedType.LPStr)]\\n internal static extern string THSNN_Module_name(torch.nn.Module.HType module);\\n\\n [DllImport(\\\"LibTorchSharp\\\", CharSet = CharSet.Ansi, BestFitMapping = false, ThrowOnUnmappableChar = true)]\\n internal static extern IntPtr THSNN_custom_module(\\n [MarshalAs(UnmanagedType.LPStr)] string name,\\n ForwardFunctionC forward,\\n out IntPtr pBoxedModule);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern torch.nn.utils.rnn.PackedSequence.HType THSNN_RNN_forward_with_packed_input(torch.nn.Module.HType module, torch.nn.utils.rnn.PackedSequence.HType input, IntPtr h_0, out IntPtr h_n);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern torch.nn.utils.rnn.PackedSequence.HType THSNN_pack_padded_sequence(IntPtr input, IntPtr lengths, [MarshalAs(UnmanagedType.U1)] bool batch_first, [MarshalAs(UnmanagedType.U1)] bool enforce_sorted);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern torch.nn.utils.rnn.PackedSequence.HType THSNN_pack_sequence(IntPtr[] sequences, int sequences_len, [MarshalAs(UnmanagedType.U1)] bool enforce_sorted);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern torch.nn.utils.rnn.PackedSequence.HType THSNN_LSTM_forward_with_packed_input(torch.nn.Module.HType module, torch.nn.utils.rnn.PackedSequence.HType input, IntPtr h_0, IntPtr c_0, out IntPtr h_n, out IntPtr c_n);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern torch.nn.utils.rnn.PackedSequence.HType THSNN_GRU_forward_with_packed_input(torch.nn.Module.HType module, torch.nn.utils.rnn.PackedSequence.HType input, IntPtr h_0, out IntPtr h_n);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n // align_corners -- 0=None, 1=true, 2=false\\n internal static extern IntPtr THSNN_Upsample_ctor(IntPtr size, int size_length, IntPtr scale_factor, int scale_factor_length, byte mode, byte align_corners, out IntPtr pBoxedModule);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n // align_corners -- 0=None, 1=true, 2=false\\n internal static extern IntPtr THSNN_interpolate(IntPtr input, IntPtr size, int size_len, IntPtr scale_factor, int scale_factor_len, byte mode, byte align_corners, [MarshalAs(UnmanagedType.U1)] bool recompute_scale_factor);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n // align_corners -- 0=None, 1=true, 2=false\\n internal static extern IntPtr THSNN_grid_sample(IntPtr input, IntPtr grid, byte mode, byte padding_mode, byte align_corners);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern IntPtr THSNN_AlphaDropout_forward(torch.nn.Module.HType module, IntPtr tensor);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern IntPtr THSNN_AlphaDropout_ctor(double p, [MarshalAs(UnmanagedType.U1)] bool inplace, out IntPtr pBoxedModule);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern IntPtr THSNN_alpha_dropout(IntPtr input, double p, [MarshalAs(UnmanagedType.U1)] bool training, [MarshalAs(UnmanagedType.U1)] bool inplace);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern IntPtr THSNN_Bilinear_forward(torch.nn.Module.HType module, IntPtr input1, IntPtr input2);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern IntPtr THSNN_Bilinear_bias(torch.nn.Module.HType module);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern void THSNN_Bilinear_set_bias(torch.nn.Module.HType module, IntPtr tensor);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern IntPtr THSNN_Bilinear_weight(torch.nn.Module.HType module);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern void THSNN_Bilinear_set_weight(torch.nn.Module.HType module, IntPtr tensor);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern IntPtr THSNN_Bilinear_ctor(long in1_features, long in2_features, long output_size, [MarshalAs(UnmanagedType.U1)] bool bias, out IntPtr pBoxedModule);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern IntPtr THSNN_functional_bilinear(IntPtr input1, IntPtr input2, IntPtr weights, IntPtr bias);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern IntPtr THSNN_CosineSimilarity_ctor(long dim, double eps, out IntPtr pBoxedModule);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern IntPtr THSNN_CosineSimilarity_forward(torch.nn.Module.HType module, IntPtr input1, IntPtr input2);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern IntPtr THSNN_dropout(IntPtr input, double p, [MarshalAs(UnmanagedType.U1)] bool training, [MarshalAs(UnmanagedType.U1)] bool inplace);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern IntPtr THSNN_dropout2d(IntPtr input, double p, [MarshalAs(UnmanagedType.U1)] bool training, [MarshalAs(UnmanagedType.U1)] bool inplace);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern IntPtr THSNN_Dropout_ctor(double p, [MarshalAs(UnmanagedType.U1)] bool inplace, out IntPtr pBoxedModule);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern IntPtr THSNN_Dropout_forward(torch.nn.Module.HType module, IntPtr tensor);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern IntPtr THSNN_Dropout1d_forward(torch.nn.Module.HType module, IntPtr tensor);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern IntPtr THSNN_Dropout1d_ctor(double p, [MarshalAs(UnmanagedType.U1)] bool inplace, out IntPtr pBoxedModule);\\n\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern IntPtr THSNN_Dropout2d_forward(torch.nn.Module.HType module, IntPtr tensor);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern IntPtr THSNN_Dropout2d_ctor(double p, [MarshalAs(UnmanagedType.U1)] bool inplace, out IntPtr pBoxedModule);\\n\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern IntPtr THSNN_Embedding_forward(torch.nn.Module.HType module, IntPtr tensor);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern IntPtr THSNN_Embedding_weight(torch.nn.Module.HType module);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern void THSNN_Embedding_set_weight(torch.nn.Module.HType module, IntPtr tensor);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern IntPtr THSNN_Embedding_ctor(long num_embeddings, long embedding_dims, long padding_idx, [MarshalAs(UnmanagedType.U1)] bool hasPI, double max_norm, [MarshalAs(UnmanagedType.U1)] bool hasMN, double norm_type, [MarshalAs(UnmanagedType.U1)] bool scale_grad_by_freq, [MarshalAs(UnmanagedType.U1)] bool sparse, out IntPtr pBoxedModule);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern IntPtr THSNN_Embedding_from_pretrained(IntPtr embeddings, [MarshalAs(UnmanagedType.U1)] bool freeze, long padding_idx, [MarshalAs(UnmanagedType.U1)] bool hasPI, double max_norm, [MarshalAs(UnmanagedType.U1)] bool hasMN, double norm_type, [MarshalAs(UnmanagedType.U1)] bool scale_grad_by_freq, [MarshalAs(UnmanagedType.U1)] bool sparse, out IntPtr pBoxedModule);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern IntPtr THSNN_Dropout3d_forward(torch.nn.Module.HType module, IntPtr tensor);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern IntPtr THSNN_Dropout3d_ctor(double p, [MarshalAs(UnmanagedType.U1)] bool inplace, out IntPtr pBoxedModule);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern IntPtr THSNN_dropout3d(IntPtr input, double p, [MarshalAs(UnmanagedType.U1)] bool training, [MarshalAs(UnmanagedType.U1)] bool inplace);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern IntPtr THSNN_EmbeddingBag_forward(torch.nn.Module.HType module, IntPtr tensor, IntPtr offsets, IntPtr per_sample_weights);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern IntPtr THSNN_EmbeddingBag_weight(torch.nn.Module.HType module);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern void THSNN_EmbeddingBag_set_weight(torch.nn.Module.HType module, IntPtr tensor);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern IntPtr THSNN_EmbeddingBag_ctor(long num_embeddings, long embedding_dims, double max_norm, [MarshalAs(UnmanagedType.U1)] bool hasMN, double norm_type, [MarshalAs(UnmanagedType.U1)] bool scale_grad_by_freq, long mode, [MarshalAs(UnmanagedType.U1)] bool sparse, [MarshalAs(UnmanagedType.U1)] bool include_last_offset, long padding_idx, out IntPtr pBoxedModule);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern IntPtr THSNN_EmbeddingBag_from_pretrained(IntPtr embeddings, [MarshalAs(UnmanagedType.U1)] bool freeze, double max_norm, [MarshalAs(UnmanagedType.U1)] bool hasMN, double norm_type, [MarshalAs(UnmanagedType.U1)] bool scale_grad_by_freq, long mode, [MarshalAs(UnmanagedType.U1)] bool sparse, [MarshalAs(UnmanagedType.U1)] bool include_last_offset, long padding_idx, out IntPtr pBoxedModule);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern IntPtr THSNN_FeatureAlphaDropout_forward(torch.nn.Module.HType module, IntPtr tensor);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern IntPtr THSNN_FeatureAlphaDropout_ctor(double p, out IntPtr pBoxedModule);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern IntPtr THSNN_feature_alpha_dropout(IntPtr input, double p, [MarshalAs(UnmanagedType.U1)] bool training, [MarshalAs(UnmanagedType.U1)] bool inplace);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern IntPtr THSNN_fold(IntPtr input, long out1, long out2, long kernel1, long kernel2, long stride1, long stride, long pad1, long pad2, long dil1, long dil2);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern IntPtr THSNN_unfold(IntPtr input, long kernel1, long kernel2, long stride1, long stride, long pad1, long pad2, long dil1, long dil2);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern IntPtr THSNN_cross_entropy(IntPtr srct, IntPtr trgt, IntPtr wgt, long ignore_index, [MarshalAs(UnmanagedType.U1)] bool hasII, long reduction, double smooting);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern IntPtr THSNN_binary_cross_entropy(IntPtr srct, IntPtr trgt, IntPtr wgt, long reduction);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern IntPtr THSNN_binary_cross_entropy_with_logits(IntPtr srct, IntPtr trgt, IntPtr wgt, long reduction, IntPtr posWeights);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern IntPtr THSNN_cosine_embedding_loss(IntPtr input1, IntPtr input2, IntPtr trgt, double margin, long reduction);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern IntPtr THSNN_ctc_loss(IntPtr log_probs, IntPtr targets, IntPtr input_lengths, IntPtr target_lengths, long blank, [MarshalAs(UnmanagedType.U1)] bool zero_infinity, long reduction);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern IntPtr THSNN_hinge_embedding_loss(IntPtr input, IntPtr trgt, double margin, long reduction);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern IntPtr THSNN_huber_loss(IntPtr input, IntPtr trgt, double delta, long reduction);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern IntPtr THSNN_margin_ranking_loss(IntPtr input1, IntPtr input2, IntPtr target, double margin, long reduction);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern IntPtr THSNN_multilabel_margin_loss(IntPtr input, IntPtr target, long reduction);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern IntPtr THSNN_multilabel_soft_margin_loss(IntPtr input, IntPtr target, IntPtr weight, long reduction);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern IntPtr THSNN_multi_margin_loss(IntPtr input, IntPtr target, long p, double margin, IntPtr weight, long reduction);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern IntPtr THSNN_mse_loss(IntPtr srct, IntPtr trgt, long reduction);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern IntPtr THSNN_l1_loss(IntPtr srct, IntPtr trgt, long reduction);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern IntPtr THSNN_nll_loss(IntPtr srct, IntPtr trgt, IntPtr wgt, long reduction);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern IntPtr THSNN_poisson_loss(IntPtr srct, IntPtr trgt, [MarshalAs(UnmanagedType.U1)] bool logInput, [MarshalAs(UnmanagedType.U1)] bool full, float eps, long reduction);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern IntPtr THSNN_kl_div_loss(IntPtr input, IntPtr target, long reduction, [MarshalAs(UnmanagedType.U1)] bool logTarget);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern IntPtr THSNN_smooth_l1_loss(IntPtr srct, IntPtr trgt, long reduction, double beta);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern IntPtr THSNN_soft_margin_loss(IntPtr srct, IntPtr trgt, long reduction);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern IntPtr THSNN_triplet_margin_loss(IntPtr anchor, IntPtr positive, IntPtr negative, double margin, long p, double eps, [MarshalAs(UnmanagedType.U1)] bool swap, long reduction);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern IntPtr THSNN_triplet_margin_with_distance_loss(IntPtr anchor, IntPtr positive, IntPtr negative, DistanceFunctionNative? distance_function, double margin, [MarshalAs(UnmanagedType.U1)] bool swap, long reduction);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern void THSNN_Optimizer_dispose(torch.optim.Optimizer.HType handle);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern void THSNN_Optimizer_zero_grad(torch.optim.Optimizer.HType module);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern IntPtr THSNN_Optimizer_step(torch.optim.Optimizer.HType module, torch.optim.Optimizer.LossClosure closure);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern void THSNN_Optimizer_getParameters(torch.optim.Optimizer.HType module, AllocatePinnedArray allocator);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern void THSNN_Module_dispose(torch.nn.Module.HType handle);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern void THSNN_Module_to_device_dtype(torch.nn.Module.HType module, sbyte dtype, long deviceType, long deviceIndex);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern void THSNN_Module_to_device(torch.nn.Module.HType module, long deviceType, long deviceIndex);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern void THSNN_Module_to_dtype(torch.nn.Module.HType module, sbyte dtype);\\n\\n [DllImport(\\\"LibTorchSharp\\\", CharSet = CharSet.Ansi, BestFitMapping = false, ThrowOnUnmappableChar = true)]\\n internal static extern IntPtr THSNN_Module_load([MarshalAs(UnmanagedType.LPStr)] string location);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern void THSNN_Module_train(torch.nn.Module.HType module, [MarshalAs(UnmanagedType.U1)] bool on);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern void THSNN_Module_eval(torch.nn.Module.HType module);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n [return: MarshalAs(UnmanagedType.U1)]\\n internal static extern bool THSNN_Module_is_training(torch.nn.Module.HType module);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern void THSNN_Module_zero_grad(torch.nn.Module.HType module);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern void THSNN_Module_get_named_parameters(torch.nn.Module.HType module, AllocatePinnedArray allocator1, AllocatePinnedArray allocator2);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern void THSNN_Module_get_named_buffers(torch.nn.Module.HType module, AllocatePinnedArray allocator1, AllocatePinnedArray allocator2);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern void THSNN_Module_get_parameters(torch.nn.Module.HType module, AllocatePinnedArray allocator, [MarshalAs(UnmanagedType.U1)] bool recurse);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern void THSNN_AnyModule_dispose(torch.nn.BoxedModule.HType handle);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern IntPtr THSNN_RNN_forward(torch.nn.Module.HType module, IntPtr input, IntPtr h_0, out IntPtr h_n);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern void THSNN_RNN_flatten_parameters(torch.nn.Module.HType module);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern IntPtr THSNN_RNN_bias_ih(torch.nn.Module.HType module, long idx);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern IntPtr THSNN_RNN_bias_hh(torch.nn.Module.HType module, long idx);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern void THSNN_RNN_set_bias_ih(torch.nn.Module.HType module, IntPtr tensor, long idx);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern void THSNN_RNN_set_bias_hh(torch.nn.Module.HType module, IntPtr tensor, long idx);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern IntPtr THSNN_RNN_weight_ih(torch.nn.Module.HType module, long idx);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern IntPtr THSNN_RNN_weight_hh(torch.nn.Module.HType module, long idx);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern void THSNN_RNN_set_weight_ih(torch.nn.Module.HType module, IntPtr tensor, long idx);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern void THSNN_RNN_set_weight_hh(torch.nn.Module.HType module, IntPtr tensor, long idx);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern IntPtr THSNN_RNN_ctor(long input_size, long hidden_size, long num_layers, long nonlinearity, [MarshalAs(UnmanagedType.U1)] bool bias, [MarshalAs(UnmanagedType.U1)] bool batchFirst, double dropout, [MarshalAs(UnmanagedType.U1)] bool bidirectional, out IntPtr pBoxedModule);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern IntPtr THSNN_RNNCell_forward(torch.nn.Module.HType module, IntPtr input, IntPtr h_0);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern IntPtr THSNN_RNNCell_bias_ih(torch.nn.Module.HType module);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern void THSNN_RNNCell_set_bias_ih(torch.nn.Module.HType module, IntPtr tensor);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern IntPtr THSNN_RNNCell_bias_hh(torch.nn.Module.HType module);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern void THSNN_RNNCell_set_bias_hh(torch.nn.Module.HType module, IntPtr tensor);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern IntPtr THSNN_RNNCell_weight_ih(torch.nn.Module.HType module);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern void THSNN_RNNCell_set_weight_ih(torch.nn.Module.HType module, IntPtr tensor);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern IntPtr THSNN_RNNCell_weight_hh(torch.nn.Module.HType module);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern void THSNN_RNNCell_set_weight_hh(torch.nn.Module.HType module, IntPtr tensor);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern IntPtr THSNN_RNNCell_ctor(long input_size, long hidden_size, long nonlinearity, [MarshalAs(UnmanagedType.U1)] bool bias, out IntPtr pBoxedModule);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern IntPtr THSNN_Linear_forward(torch.nn.Module.HType module, IntPtr tensor);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern IntPtr THSNN_Linear_bias(torch.nn.Module.HType module);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern void THSNN_Linear_set_bias(torch.nn.Module.HType module, IntPtr tensor);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern IntPtr THSNN_Linear_weight(torch.nn.Module.HType module);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern void THSNN_Linear_set_weight(torch.nn.Module.HType module, IntPtr tensor);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern IntPtr THSNN_Linear_ctor(long input_size, long output_size, [MarshalAs(UnmanagedType.U1)] bool bias, out IntPtr pBoxedModule);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern IntPtr THSNN_functional_linear(IntPtr input, IntPtr weights, IntPtr bias);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern IntPtr THSNN_Flatten_forward(torch.nn.Module.HType module, IntPtr tensor);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern IntPtr THSNN_Flatten_ctor(long startDim, long endDim, out IntPtr pBoxedModule);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern IntPtr THSNN_Identity_forward(torch.nn.Module.HType module, IntPtr tensor);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern IntPtr THSNN_Identity_ctor(out IntPtr pBoxedModule);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern IntPtr THSNN_LSTMCell_forward(torch.nn.Module.HType module, IntPtr input, IntPtr h_0, IntPtr c_0, out IntPtr c_n);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern IntPtr THSNN_LSTMCell_bias_ih(torch.nn.Module.HType module);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern void THSNN_LSTMCell_set_bias_ih(torch.nn.Module.HType module, IntPtr tensor);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern IntPtr THSNN_LSTMCell_bias_hh(torch.nn.Module.HType module);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern void THSNN_LSTMCell_set_bias_hh(torch.nn.Module.HType module, IntPtr tensor);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern IntPtr THSNN_LSTMCell_weight_ih(torch.nn.Module.HType module);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern void THSNN_LSTMCell_set_weight_ih(torch.nn.Module.HType module, IntPtr tensor);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern IntPtr THSNN_LSTMCell_weight_hh(torch.nn.Module.HType module);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern void THSNN_LSTMCell_set_weight_hh(torch.nn.Module.HType module, IntPtr tensor);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern IntPtr THSNN_LSTMCell_ctor(long input_size, long hidden_size, [MarshalAs(UnmanagedType.U1)] bool bias, out IntPtr pBoxedModule);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern void THSNN_PackedSequence_dispose(IntPtr handle);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern IntPtr THSNN_PackedSequence_data(torch.nn.utils.rnn.PackedSequence.HType handle);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern IntPtr THSNN_PackedSequence_batch_sizes(torch.nn.utils.rnn.PackedSequence.HType handle);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern IntPtr THSNN_PackedSequence_sorted_indices(torch.nn.utils.rnn.PackedSequence.HType handle);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern IntPtr THSNN_PackedSequence_unsorted_indices(torch.nn.utils.rnn.PackedSequence.HType handle);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern void THSNN_pad_packed_sequence(torch.nn.utils.rnn.PackedSequence.HType sequence, [MarshalAs(UnmanagedType.U1)] bool batch_first, double padding_value, long total_length, out IntPtr res1, out IntPtr res2);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern IntPtr THSNN_pad_sequence(IntPtr[] sequences, int sequences_len, [MarshalAs(UnmanagedType.U1)] bool batch_first, double padding_value);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern void THSNN_MultiheadAttention_forward(torch.nn.Module.HType module, IntPtr query, IntPtr key, IntPtr value, IntPtr key_padding_mask, [MarshalAs(UnmanagedType.U1)] bool need_weights, IntPtr attn_mask, out IntPtr res1, out IntPtr res2);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern IntPtr THSNN_MultiheadAttention_ctor(long embeded_dim, long num_heads, double dropout, [MarshalAs(UnmanagedType.U1)] bool bias, [MarshalAs(UnmanagedType.U1)] bool add_bias_kv, [MarshalAs(UnmanagedType.U1)] bool add_zero_attn, long kdim, long vdim, out IntPtr pBoxedModule);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern IntPtr THSNN_one_hot(IntPtr self, long num_classes);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern IntPtr THSNN_PairwiseDistance_forward(torch.nn.Module.HType module, IntPtr input1, IntPtr input2);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern IntPtr THSNN_PairwiseDistance_ctor(double p, double eps, [MarshalAs(UnmanagedType.U1)] bool keep_dim, out IntPtr pBoxedModule);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern IntPtr THSNN_PixelUnshuffle_forward(torch.nn.Module.HType module, IntPtr tensor);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern IntPtr THSNN_PixelUnshuffle_ctor(long downscaleFactor, out IntPtr pBoxedModule);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern IntPtr THSNN_PixelShuffle_forward(torch.nn.Module.HType module, IntPtr tensor);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern IntPtr THSNN_PixelShuffle_ctor(long upscaleFactor, out IntPtr pBoxedModule);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern IntPtr THSNN_GRUCell_forward(torch.nn.Module.HType module, IntPtr input, IntPtr h_0);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern IntPtr THSNN_GRUCell_bias_ih(torch.nn.Module.HType module);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern void THSNN_GRUCell_set_bias_ih(torch.nn.Module.HType module, IntPtr tensor);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern IntPtr THSNN_GRUCell_bias_hh(torch.nn.Module.HType module);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern void THSNN_GRUCell_set_bias_hh(torch.nn.Module.HType module, IntPtr tensor);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern IntPtr THSNN_GRUCell_weight_ih(torch.nn.Module.HType module);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern void THSNN_GRUCell_set_weight_ih(torch.nn.Module.HType module, IntPtr tensor);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern IntPtr THSNN_GRUCell_weight_hh(torch.nn.Module.HType module);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern void THSNN_GRUCell_set_weight_hh(torch.nn.Module.HType module, IntPtr tensor);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern IntPtr THSNN_GRUCell_ctor(long input_size, long hidden_size, [MarshalAs(UnmanagedType.U1)] bool bias, out IntPtr pBoxedModule);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern IntPtr THSNN_LSTM_forward(torch.nn.Module.HType module, IntPtr input, IntPtr h_0, IntPtr c_0, out IntPtr h_n, out IntPtr c_n);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern void THSNN_LSTM_flatten_parameters(torch.nn.Module.HType module);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern IntPtr THSNN_LSTM_ctor(long input_size, long hidden_size, long num_layers, [MarshalAs(UnmanagedType.U1)] bool bias, [MarshalAs(UnmanagedType.U1)] bool batchFirst, double dropout, [MarshalAs(UnmanagedType.U1)] bool bidirectional, out IntPtr pBoxedModule);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern void THSNN_GRU_flatten_parameters(torch.nn.Module.HType module);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern IntPtr THSNN_GRU_ctor(long input_size, long hidden_size, long num_layers, [MarshalAs(UnmanagedType.U1)] bool bias, [MarshalAs(UnmanagedType.U1)] bool batchFirst, double dropout, [MarshalAs(UnmanagedType.U1)] bool bidirectional, out IntPtr pBoxedModule);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern IntPtr THSNN_GRU_forward(torch.nn.Module.HType module, IntPtr input, IntPtr h_0, out IntPtr h_n);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern IntPtr THSNN_LBFGS_ctor(IntPtr parameters, int len, double learningRate, long max_iter, long max_eval, double tolerange_grad, double tolerance_change, long history_size);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern void THSNN_LBFGS_set_lr(torch.optim.Optimizer.HType optimizer, double lr);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern IntPtr THSNN_Sequential_ctor();\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern IntPtr THSNN_Transformer_forward(torch.nn.Module.HType module, IntPtr src, IntPtr tgt, IntPtr src_mask, IntPtr tgt_mask, IntPtr memory_mask, IntPtr src_key_padding_mask, IntPtr tgt_key_padding_mask, IntPtr memory_key_padding_mask);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern IntPtr THSNN_Transformer_ctor(long d_model, long nhead, long num_encoder_layers, long num_decoder_layers, long dim_feedforward, double dropout, long activation, out IntPtr pBoxedModule);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern IntPtr THSNN_TransformerDecoder_forward(torch.nn.Module.HType module, IntPtr tgt, IntPtr memory, IntPtr tgt_mask, IntPtr memory_mask, IntPtr tgt_key_padding_mask, IntPtr memory_key_padding_mask);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern IntPtr THSNN_TransformerDecoder_ctor(torch.nn.Module.HType decoder_layer, long num_layers, out IntPtr pBoxedModule);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern IntPtr THSNN_TransformerDecoderLayer_forward(torch.nn.Module.HType module, IntPtr tgt, IntPtr memory, IntPtr tgt_mask, IntPtr memory_mask, IntPtr tgt_key_padding_mask, IntPtr memory_key_padding_mask);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern IntPtr THSNN_TransformerDecoderLayer_ctor(long d_model, long nhead, long dim_feedforward, double dropout, long activation, out IntPtr pBoxedModule);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern IntPtr THSNN_TransformerEncoderLayer_forward(torch.nn.Module.HType module, IntPtr src, IntPtr src_mask, IntPtr src_key_padding_mask);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern IntPtr THSNN_TransformerEncoderLayer_ctor(long d_model, long nhead, long dim_feedforward, double dropout, long activation, out IntPtr pBoxedModule);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern IntPtr THSNN_Upsample_forward(torch.nn.Module.HType module, IntPtr tensor);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern IntPtr THSNN_TransformerEncoder_ctor(torch.nn.Module.HType encoder_layer, long num_layers, out IntPtr pBoxedModule);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern IntPtr THSNN_TransformerEncoder_forward(torch.nn.Module.HType module, IntPtr src, IntPtr src_mask, IntPtr src_key_padding_mask);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern IntPtr THSNN_pad(IntPtr input, IntPtr pad, int pad_length, byte mode, double value);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern IntPtr THSNN_affine_grid(IntPtr theta, IntPtr size, int size_len, [MarshalAs(UnmanagedType.U1)] bool align_corners);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern IntPtr THSNN_CELU_forward(torch.nn.Module.HType module, IntPtr tensor);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern IntPtr THSNN_CELU_ctor(double alpha, [MarshalAs(UnmanagedType.U1)] bool inplace, out IntPtr pBoxedModule);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern IntPtr THSNN_LeakyReLU_forward(torch.nn.Module.HType module, IntPtr tensor);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern IntPtr THSNN_LeakyReLU_ctor(double negative_slope, [MarshalAs(UnmanagedType.U1)] bool inplace, out IntPtr pBoxedModule);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern IntPtr THSNN_LogSoftmax_forward(torch.nn.Module.HType handle, IntPtr tensor);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern IntPtr THSNN_LogSoftmax_ctor(long dim, out IntPtr pBoxedModule);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern IntPtr THSNN_InstanceNorm3d_forward(IntPtr module, IntPtr tensor);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern IntPtr THSNN_InstanceNorm3d_bias(torch.nn.Module.HType module);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern void THSNN_InstanceNorm3d_set_bias(torch.nn.Module.HType module, IntPtr bias);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern IntPtr THSNN_InstanceNorm3d_weight(torch.nn.Module.HType module);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern void THSNN_InstanceNorm3d_set_weight(torch.nn.Module.HType module, IntPtr weight);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern void THSNN_InstanceNorm3d_reset_stats(torch.nn.Module.HType module);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern IntPtr THSNN_InstanceNorm3d_get_mean(torch.nn.Module.HType module);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern IntPtr THSNN_InstanceNorm3d_get_var(torch.nn.Module.HType module);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern IntPtr THSNN_InstanceNorm3d_get_batches(torch.nn.Module.HType module);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern void THSNN_InstanceNorm3d_set_mean(torch.nn.Module.HType module, IntPtr weight);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern void THSNN_InstanceNorm3d_set_var(torch.nn.Module.HType module, IntPtr weight);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern IntPtr THSNN_InstanceNorm3d_ctor(long features, double eps, double momentum, [MarshalAs(UnmanagedType.U1)] bool affine, [MarshalAs(UnmanagedType.U1)] bool track_running_stats, out IntPtr pBoxedModule);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern IntPtr THSNN_LayerNorm_forward(IntPtr module, IntPtr tensor);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern IntPtr THSNN_LayerNorm_bias(torch.nn.Module.HType module);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern void THSNN_LayerNorm_set_bias(torch.nn.Module.HType module, IntPtr bias);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern IntPtr THSNN_LayerNorm_weight(torch.nn.Module.HType module);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern void THSNN_LayerNorm_set_weight(torch.nn.Module.HType module, IntPtr weight);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern IntPtr THSNN_LayerNorm_ctor(IntPtr norm_shape, long norm_shape_len, double eps, [MarshalAs(UnmanagedType.U1)] bool elementwise_affine, out IntPtr pBoxedModule);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern IntPtr THSNN_InstanceNorm2d_forward(IntPtr module, IntPtr tensor);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern IntPtr THSNN_InstanceNorm2d_bias(torch.nn.Module.HType module);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern void THSNN_InstanceNorm2d_set_bias(torch.nn.Module.HType module, IntPtr bias);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern IntPtr THSNN_InstanceNorm2d_weight(torch.nn.Module.HType module);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern void THSNN_InstanceNorm2d_set_weight(torch.nn.Module.HType module, IntPtr weight);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern void THSNN_InstanceNorm2d_reset_stats(torch.nn.Module.HType module);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern IntPtr THSNN_InstanceNorm2d_get_mean(torch.nn.Module.HType module);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern IntPtr THSNN_InstanceNorm2d_get_var(torch.nn.Module.HType module);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern IntPtr THSNN_InstanceNorm2d_get_batches(torch.nn.Module.HType module);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern void THSNN_InstanceNorm2d_set_mean(torch.nn.Module.HType module, IntPtr weight);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern void THSNN_InstanceNorm2d_set_var(torch.nn.Module.HType module, IntPtr weight);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern IntPtr THSNN_InstanceNorm2d_ctor(long features, double eps, double momentum, [MarshalAs(UnmanagedType.U1)] bool affine, [MarshalAs(UnmanagedType.U1)] bool track_running_stats, out IntPtr pBoxedModule);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern IntPtr THSNN_InstanceNorm1d_forward(IntPtr module, IntPtr tensor);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern IntPtr THSNN_InstanceNorm1d_bias(torch.nn.Module.HType module);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern void THSNN_InstanceNorm1d_set_bias(torch.nn.Module.HType module, IntPtr bias);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern IntPtr THSNN_InstanceNorm1d_weight(torch.nn.Module.HType module);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern void THSNN_InstanceNorm1d_set_weight(torch.nn.Module.HType module, IntPtr weight);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern void THSNN_InstanceNorm1d_reset_stats(torch.nn.Module.HType module);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern IntPtr THSNN_InstanceNorm1d_get_mean(torch.nn.Module.HType module);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern IntPtr THSNN_InstanceNorm1d_get_var(torch.nn.Module.HType module);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern IntPtr THSNN_InstanceNorm1d_get_batches(torch.nn.Module.HType module);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern void THSNN_InstanceNorm1d_set_mean(torch.nn.Module.HType module, IntPtr weight);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern void THSNN_InstanceNorm1d_set_var(torch.nn.Module.HType module, IntPtr weight);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern IntPtr THSNN_InstanceNorm1d_ctor(long features, double eps, double momentum, [MarshalAs(UnmanagedType.U1)] bool affine, [MarshalAs(UnmanagedType.U1)] bool track_running_stats, out IntPtr pBoxedModule);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern IntPtr THSNN_Conv1d_forward(torch.nn.Module.HType module, IntPtr tensor);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern IntPtr THSNN_Conv1d_bias(torch.nn.Module.HType module);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern void THSNN_Conv1d_set_bias(torch.nn.Module.HType module, IntPtr tensor);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern IntPtr THSNN_Conv1d_weight(torch.nn.Module.HType module);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern void THSNN_Conv1d_set_weight(torch.nn.Module.HType module, IntPtr tensor);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern IntPtr THSNN_Conv1d_ctor(long inputChannel, long outputChannel, long kernelSize, long stride, long padding, long dilation, long paddingMode, long groups, [MarshalAs(UnmanagedType.U1)] bool bias, out IntPtr pBoxedModule);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern IntPtr THSNN_Conv2d_forward(torch.nn.Module.HType module, IntPtr tensor);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern IntPtr THSNN_Conv2d_bias(torch.nn.Module.HType module);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern void THSNN_Conv2d_set_bias(torch.nn.Module.HType module, IntPtr tensor);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern IntPtr THSNN_Conv2d_weight(torch.nn.Module.HType module);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern void THSNN_Conv2d_set_weight(torch.nn.Module.HType module, IntPtr tensor);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern IntPtr THSNN_Conv2d_ctor(long inputChannel, long outputChannel, long kernelSize, long stride, long padding, long dilation, long paddingMode, long groups, [MarshalAs(UnmanagedType.U1)] bool bias, out IntPtr pBoxedModule);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern IntPtr THSNN_Conv2d_ctor_1(long inputChannel, long outputChannel, long kernelSizeX, long kernelSizeY, long strideX, long strideY, long paddingX, long paddingY, long dilationX, long dilationY, long paddingMode, long groups, [MarshalAs(UnmanagedType.U1)] bool bias, out IntPtr pBoxedModule);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern IntPtr THSNN_Conv3d_forward(torch.nn.Module.HType module, IntPtr tensor);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern IntPtr THSNN_Conv3d_bias(torch.nn.Module.HType module);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern void THSNN_Conv3d_set_bias(torch.nn.Module.HType module, IntPtr tensor);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern IntPtr THSNN_Conv3d_weight(torch.nn.Module.HType module);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern void THSNN_Conv3d_set_weight(torch.nn.Module.HType module, IntPtr tensor);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern IntPtr THSNN_Conv3d_ctor(long inputChannel, long outputChannel, long kernelSize, long stride, long padding, long dilation, long paddingMode, long groups, [MarshalAs(UnmanagedType.U1)] bool bias, out IntPtr pBoxedModule);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern IntPtr THSNN_Conv3d_ctor_1(long inputChannel, long outputChannel, long kernelSizeX, long kernelSizeY, long kernelSizeZ, long strideX, long strideY, long strideZ, long paddingX, long paddingY, long paddingZ, long dilationX, long dilationY, long dilationZ, long paddingMode, long groups, [MarshalAs(UnmanagedType.U1)] bool bias, out IntPtr pBoxedModule);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern IntPtr THSNN_ConvTranspose1d_forward(torch.nn.Module.HType module, IntPtr tensor);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern IntPtr THSNN_ConvTranspose1d_bias(torch.nn.Module.HType module);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern void THSNN_ConvTranspose1d_set_bias(torch.nn.Module.HType module, IntPtr tensor);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern IntPtr THSNN_ConvTranspose1d_weight(torch.nn.Module.HType module);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern void THSNN_ConvTranspose1d_set_weight(torch.nn.Module.HType module, IntPtr tensor);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern IntPtr THSNN_ConvTranspose1d_ctor(long inputChannel, long outputChannel, long kernelSize, long stride, long padding, long outputPadding, long dilation, long paddingMode, long groups, [MarshalAs(UnmanagedType.U1)] bool bias, out IntPtr pBoxedModule);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern IntPtr THSNN_ConvTranspose3d_forward(torch.nn.Module.HType module, IntPtr tensor);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern IntPtr THSNN_ConvTranspose3d_bias(torch.nn.Module.HType module);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern void THSNN_ConvTranspose3d_set_bias(torch.nn.Module.HType module, IntPtr tensor);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern IntPtr THSNN_ConvTranspose3d_weight(torch.nn.Module.HType module);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern void THSNN_ConvTranspose3d_set_weight(torch.nn.Module.HType module, IntPtr tensor);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern IntPtr THSNN_ConvTranspose3d_ctor(long inputChannel, long outputChannel, long kernelSize, long stride, long padding, long outputPadding, long dilation, long paddingMode, long groups, [MarshalAs(UnmanagedType.U1)] bool bias, out IntPtr pBoxedModule);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern IntPtr THSNN_BatchNorm1d_forward(IntPtr module, IntPtr tensor);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern IntPtr THSNN_BatchNorm1d_bias(torch.nn.Module.HType module);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern void THSNN_BatchNorm1d_set_bias(torch.nn.Module.HType module, IntPtr bias);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern IntPtr THSNN_BatchNorm1d_weight(torch.nn.Module.HType module);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern void THSNN_BatchNorm1d_set_weight(torch.nn.Module.HType module, IntPtr weight);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern void THSNN_BatchNorm1d_reset_stats(torch.nn.Module.HType module);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern IntPtr THSNN_BatchNorm1d_get_mean(torch.nn.Module.HType module);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern IntPtr THSNN_BatchNorm1d_get_var(torch.nn.Module.HType module);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern IntPtr THSNN_BatchNorm1d_get_batches(torch.nn.Module.HType module);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern void THSNN_BatchNorm1d_set_mean(torch.nn.Module.HType module, IntPtr weight);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern void THSNN_BatchNorm1d_set_var(torch.nn.Module.HType module, IntPtr weight);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern IntPtr THSNN_BatchNorm1d_ctor(long features, double eps, double momentum, [MarshalAs(UnmanagedType.U1)] bool affine, [MarshalAs(UnmanagedType.U1)] bool track_running_stats, out IntPtr pBoxedModule);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern IntPtr THSNN_GroupNorm_forward(IntPtr module, IntPtr tensor);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern IntPtr THSNN_GroupNorm_bias(torch.nn.Module.HType module);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern void THSNN_GroupNorm_set_bias(torch.nn.Module.HType module, IntPtr bias);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern IntPtr THSNN_GroupNorm_weight(torch.nn.Module.HType module);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern void THSNN_GroupNorm_set_weight(torch.nn.Module.HType module, IntPtr weight);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern IntPtr THSNN_GroupNorm_ctor(long num_groups, long num_channels, double eps, [MarshalAs(UnmanagedType.U1)] bool affine, out IntPtr pBoxedModule);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern IntPtr THSNN_Unflatten_forward(torch.nn.Module.HType module, IntPtr tensor);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern IntPtr THSNN_Unflatten_ctor(long dim, IntPtr shape, long shape_len, out IntPtr pBoxedModule);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern IntPtr THSNN_batch_norm(IntPtr input, IntPtr running_mean, IntPtr running_var, IntPtr weight, IntPtr bias, [MarshalAs(UnmanagedType.U1)] bool training, double momentum, double eps);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern IntPtr THSNN_group_norm(IntPtr input, long num_groups, IntPtr weight, IntPtr bias, double eps);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern IntPtr THSNN_instance_norm(IntPtr input, IntPtr running_mean, IntPtr running_var, IntPtr weight, IntPtr bias, [MarshalAs(UnmanagedType.U1)] bool use_input_stats, double momentum, double eps);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern unsafe IntPtr THSNN_layer_norm(IntPtr input, long* normalized_shape, long normalized_shape_len, IntPtr weight, IntPtr bias, double eps);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern IntPtr THSNN_local_response_norm(IntPtr input, long size, double alpha, double beta, double k);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern IntPtr THSNN_ConvTranspose2d_forward(torch.nn.Module.HType module, IntPtr tensor);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern IntPtr THSNN_ConvTranspose2d_bias(torch.nn.Module.HType module);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern void THSNN_ConvTranspose2d_set_bias(torch.nn.Module.HType module, IntPtr tensor);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern IntPtr THSNN_ConvTranspose2d_weight(torch.nn.Module.HType module);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern void THSNN_ConvTranspose2d_set_weight(torch.nn.Module.HType module, IntPtr tensor);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern IntPtr THSNN_ConvTranspose2d_ctor(long inputChannel, long outputChannel, long kernelSize, long stride, long padding, long outputPadding, long dilation, long paddingMode, long groups, [MarshalAs(UnmanagedType.U1)] bool bias, out IntPtr pBoxedModule);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern IntPtr THSNN_BatchNorm2d_forward(IntPtr module, IntPtr tensor);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern IntPtr THSNN_BatchNorm2d_bias(torch.nn.Module.HType module);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern void THSNN_BatchNorm2d_set_bias(torch.nn.Module.HType module, IntPtr bias);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern IntPtr THSNN_BatchNorm2d_weight(torch.nn.Module.HType module);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern void THSNN_BatchNorm2d_set_weight(torch.nn.Module.HType module, IntPtr weight);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern void THSNN_BatchNorm2d_reset_stats(torch.nn.Module.HType module);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern IntPtr THSNN_BatchNorm2d_get_mean(torch.nn.Module.HType module);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern IntPtr THSNN_BatchNorm2d_get_var(torch.nn.Module.HType module);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern IntPtr THSNN_BatchNorm2d_get_batches(torch.nn.Module.HType module);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern void THSNN_BatchNorm2d_set_mean(torch.nn.Module.HType module, IntPtr weight);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern void THSNN_BatchNorm2d_set_var(torch.nn.Module.HType module, IntPtr weight);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern IntPtr THSNN_BatchNorm2d_ctor(long features, double eps, double momentum, [MarshalAs(UnmanagedType.U1)] bool affine, [MarshalAs(UnmanagedType.U1)] bool track_running_stats, out IntPtr pBoxedModule);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern IntPtr THSNN_BatchNorm3d_forward(IntPtr module, IntPtr tensor);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern IntPtr THSNN_BatchNorm3d_bias(torch.nn.Module.HType module);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern void THSNN_BatchNorm3d_set_bias(torch.nn.Module.HType module, IntPtr bias);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern IntPtr THSNN_BatchNorm3d_weight(torch.nn.Module.HType module);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern void THSNN_BatchNorm3d_set_weight(torch.nn.Module.HType module, IntPtr weight);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern void THSNN_BatchNorm3d_reset_stats(torch.nn.Module.HType module);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern IntPtr THSNN_BatchNorm3d_get_mean(torch.nn.Module.HType module);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern IntPtr THSNN_BatchNorm3d_get_var(torch.nn.Module.HType module);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern IntPtr THSNN_BatchNorm3d_get_batches(torch.nn.Module.HType module);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern void THSNN_BatchNorm3d_set_mean(torch.nn.Module.HType module, IntPtr weight);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern void THSNN_BatchNorm3d_set_var(torch.nn.Module.HType module, IntPtr weight);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern IntPtr THSNN_BatchNorm3d_ctor(long features, double eps, double momentum, [MarshalAs(UnmanagedType.U1)] bool affine, [MarshalAs(UnmanagedType.U1)] bool track_running_stats, out IntPtr pBoxedModule);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern IntPtr THSNN_MaxPool1d_forward(torch.nn.Module.HType module, IntPtr tensor);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern IntPtr THSNN_MaxPool1d_forward_with_indices(torch.nn.Module.HType module, IntPtr tensor, out IntPtr indices);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern IntPtr THSNN_MaxPool1d_ctor(IntPtr pkernelSize, IntPtr pStrides, IntPtr pPadding, IntPtr pDilation, [MarshalAs(UnmanagedType.U1)] bool ceilMode, out IntPtr pBoxedModule);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern IntPtr THSNN_MaxUnpool3d_forward(torch.nn.Module.HType module, IntPtr tensor, IntPtr indices, IntPtr outSize, int outputSizeLength);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern IntPtr THSNN_MaxUnpool3d_ctor(IntPtr pkernelSize, int kernelSizeLength, IntPtr pstrides, int stridesLength, IntPtr pPadding, int paddingLength, out IntPtr pBoxedModule);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern IntPtr THSNN_ELU_forward(torch.nn.Module.HType module, IntPtr tensor);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern IntPtr THSNN_ELU_ctor(double alpha, [MarshalAs(UnmanagedType.U1)] bool inplace, out IntPtr pBoxedModule);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern IntPtr THSNN_GELU_forward(torch.nn.Module.HType module, IntPtr tensor);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern IntPtr THSNN_GELU_ctor(out IntPtr pBoxedModule);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern IntPtr THSNN_GLU_forward(torch.nn.Module.HType module, IntPtr tensor);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern IntPtr THSNN_GLU_ctor(long dim, out IntPtr pBoxedModule);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern IntPtr THSNN_Hardshrink_forward(torch.nn.Module.HType module, IntPtr tensor);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern IntPtr THSNN_Hardshrink_ctor(double lambd, out IntPtr pBoxedModule);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern IntPtr THSNN_Hardtanh_forward(torch.nn.Module.HType module, IntPtr tensor);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern IntPtr THSNN_Hardtanh_ctor(double min_val, double max_val, [MarshalAs(UnmanagedType.U1)] bool inplace, out IntPtr pBoxedModule);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern IntPtr THSNN_Mish_forward(torch.nn.Module.HType module, IntPtr tensor);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern IntPtr THSNN_Mish_ctor(out IntPtr pBoxedModule);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern IntPtr THSNN_PReLU_forward(torch.nn.Module.HType module, IntPtr tensor);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern IntPtr THSNN_PReLU_ctor(long nparams, double init, out IntPtr pBoxedModule);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern IntPtr THSNN_PReLU_weight(torch.nn.Module.HType module);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern void THSNN_PReLU_set_weight(torch.nn.Module.HType module, IntPtr tensor);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern IntPtr THSNN_ReLU_forward(torch.nn.Module.HType module, IntPtr tensor);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern IntPtr THSNN_ReLU_ctor([MarshalAs(UnmanagedType.U1)] bool inplace, out IntPtr pBoxedModule);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern IntPtr THSNN_ReLU6_forward(torch.nn.Module.HType module, IntPtr tensor);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern IntPtr THSNN_ReLU6_ctor([MarshalAs(UnmanagedType.U1)] bool inplace, out IntPtr pBoxedModule);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern IntPtr THSNN_RReLU_forward(torch.nn.Module.HType module, IntPtr tensor);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern IntPtr THSNN_RReLU_ctor(double lower, double upper, [MarshalAs(UnmanagedType.U1)] bool inplace, out IntPtr pBoxedModule);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern IntPtr THSNN_scaled_dot_product_attention(IntPtr query, IntPtr key, IntPtr value, IntPtr attention_mask, double p, [MarshalAs(UnmanagedType.U1)] bool casual);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern IntPtr THSNN_SELU_forward(torch.nn.Module.HType module, IntPtr tensor);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern IntPtr THSNN_SELU_ctor([MarshalAs(UnmanagedType.U1)] bool inplace, out IntPtr pBoxedModule);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern IntPtr THSNN_Sigmoid_forward(torch.nn.Module.HType module, IntPtr tensor);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern IntPtr THSNN_Sigmoid_ctor(out IntPtr pBoxedModule);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern IntPtr THSNN_SiLU_forward(torch.nn.Module.HType module, IntPtr tensor);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern IntPtr THSNN_SiLU_ctor(out IntPtr pBoxedModule);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern IntPtr THSNN_Softmax_forward(torch.nn.Module.HType module, IntPtr tensor);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern IntPtr THSNN_Softmax_ctor(long dim, out IntPtr pBoxedModule);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern IntPtr THSNN_Softmax2d_forward(torch.nn.Module.HType module, IntPtr tensor);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern IntPtr THSNN_Softmax2d_ctor(out IntPtr pBoxedModule);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern IntPtr THSNN_Softmin_forward(torch.nn.Module.HType module, IntPtr tensor);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern IntPtr THSNN_Softmin_ctor(long dim, out IntPtr pBoxedModule);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern IntPtr THSNN_Softplus_forward(torch.nn.Module.HType module, IntPtr tensor);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern IntPtr THSNN_Softplus_ctor(double beta, double threshold, out IntPtr pBoxedModule);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern IntPtr THSNN_Softshrink_forward(torch.nn.Module.HType module, IntPtr tensor);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern IntPtr THSNN_Softshrink_ctor(double lambd, out IntPtr pBoxedModule);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern IntPtr THSNN_Softsign_forward(torch.nn.Module.HType module, IntPtr tensor);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern IntPtr THSNN_Softsign_ctor(out IntPtr pBoxedModule);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern IntPtr THSNN_Tanh_forward(torch.nn.Module.HType module, IntPtr tensor);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern IntPtr THSNN_Tanh_ctor(out IntPtr pBoxedModule);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern IntPtr THSNN_Tanhshrink_forward(torch.nn.Module.HType module, IntPtr tensor);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern IntPtr THSNN_Tanhshrink_ctor(out IntPtr pBoxedModule);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern IntPtr THSNN_Threshold_forward(torch.nn.Module.HType module, IntPtr tensor);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern IntPtr THSNN_Threshold_ctor(double threshold, double value, [MarshalAs(UnmanagedType.U1)] bool inplace, out IntPtr pBoxedModule);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern IntPtr THSNN_LocalResponseNorm_forward(IntPtr module, IntPtr tensor);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern IntPtr THSNN_LocalResponseNorm_ctor(long size, double alpha, double beta, double k, out IntPtr pBoxedModule);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern IntPtr THSNN_ConstantPad1d_forward(torch.nn.Module.HType module, IntPtr tensor);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern IntPtr THSNN_ConstantPad1d_ctor(double value, long padding, out IntPtr pBoxedModule);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern IntPtr THSNN_ConstantPad1d_ctor_tuple(double value, long padding_left, long padding_right, out IntPtr pBoxedModule);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern IntPtr THSNN_ConstantPad2d_forward(torch.nn.Module.HType module, IntPtr tensor);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern IntPtr THSNN_ConstantPad2d_ctor(double value, long padding, out IntPtr pBoxedModule);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern IntPtr THSNN_ConstantPad2d_ctor_tuple(double value, long padding_left, long padding_right, long padding_top, long padding_bottom, out IntPtr pBoxedModule);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern IntPtr THSNN_ConstantPad3d_forward(torch.nn.Module.HType module, IntPtr tensor);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern IntPtr THSNN_ConstantPad3d_ctor(double value, long padding, out IntPtr pBoxedModule);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern IntPtr THSNN_ConstantPad3d_ctor_tuple(double value, long padding_left, long padding_right, long padding_top, long padding_bottom, long padding_front, long padding_back, out IntPtr pBoxedModule);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern IntPtr THSNN_ReflectionPad1d_forward(torch.nn.Module.HType module, IntPtr tensor);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern IntPtr THSNN_ReflectionPad1d_ctor(long padding, out IntPtr pBoxedModule);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern IntPtr THSNN_ReflectionPad1d_ctor_tuple(long padding_left, long padding_right, out IntPtr pBoxedModule);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern IntPtr THSNN_ReflectionPad2d_forward(torch.nn.Module.HType module, IntPtr tensor);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern IntPtr THSNN_ReflectionPad2d_ctor(long padding, out IntPtr pBoxedModule);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern IntPtr THSNN_ReflectionPad2d_ctor_tuple(long padding_left, long padding_right, long padding_top, long padding_bottom, out IntPtr pBoxedModule);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern IntPtr THSNN_ReflectionPad3d_forward(torch.nn.Module.HType module, IntPtr tensor);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern IntPtr THSNN_ReflectionPad3d_ctor(long padding, out IntPtr pBoxedModule);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern IntPtr THSNN_ReflectionPad3d_ctor_tuple(long padding_left, long padding_right, long padding_top, long padding_bottom, long padding_front, long padding_back, out IntPtr pBoxedModule);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern IntPtr THSNN_ReplicationPad1d_forward(torch.nn.Module.HType module, IntPtr tensor);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern IntPtr THSNN_ReplicationPad1d_ctor(long padding, out IntPtr pBoxedModule);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern IntPtr THSNN_ReplicationPad1d_ctor_tuple(long padding_left, long padding_right, out IntPtr pBoxedModule);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern IntPtr THSNN_ReplicationPad2d_forward(torch.nn.Module.HType module, IntPtr tensor);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern IntPtr THSNN_ReplicationPad2d_ctor(long padding, out IntPtr pBoxedModule);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern IntPtr THSNN_ReplicationPad2d_ctor_tuple(long padding_left, long padding_right, long padding_top, long padding_bottom, out IntPtr pBoxedModule);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern IntPtr THSNN_ReplicationPad3d_forward(torch.nn.Module.HType module, IntPtr tensor);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern IntPtr THSNN_ReplicationPad3d_ctor(long padding, out IntPtr pBoxedModule);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern IntPtr THSNN_ReplicationPad3d_ctor_tuple(long padding_left, long padding_right, long padding_top, long padding_bottom, long padding_front, long padding_back, out IntPtr pBoxedModule);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern IntPtr THSNN_ZeroPad2d_forward(torch.nn.Module.HType module, IntPtr tensor);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern IntPtr THSNN_ZeroPad2d_ctor(long padding, out IntPtr pBoxedModule);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern IntPtr THSNN_ZeroPad2d_ctor_tuple(long padding_left, long padding_right, long padding_top, long padding_bottom, out IntPtr pBoxedModule);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern IntPtr THSNN_AdaptiveAvgPool1d_forward(IntPtr module, IntPtr tensor);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern IntPtr THSNN_AdaptiveAvgPool1d_ctor(IntPtr psizes, int length, out IntPtr pBoxedModule);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern IntPtr THSNN_AdaptiveAvgPool2d_forward(IntPtr module, IntPtr tensor);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern IntPtr THSNN_AdaptiveAvgPool2d_ctor(IntPtr psizes, int length, out IntPtr pBoxedModule);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern IntPtr THSNN_AdaptiveAvgPool3d_forward(IntPtr module, IntPtr tensor);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern IntPtr THSNN_AdaptiveAvgPool3d_ctor(IntPtr psizes, int length, out IntPtr pBoxedModule);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern IntPtr THSNN_AdaptiveMaxPool1d_forward(IntPtr module, IntPtr tensor);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern IntPtr THSNN_AdaptiveMaxPool1d_ctor(IntPtr psizes, int length, out IntPtr pBoxedModule);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern IntPtr THSNN_AdaptiveMaxPool2d_forward(IntPtr module, IntPtr tensor);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern IntPtr THSNN_AdaptiveMaxPool2d_ctor(IntPtr psizes, int length, out IntPtr pBoxedModule);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern IntPtr THSNN_AdaptiveMaxPool3d_forward(IntPtr module, IntPtr tensor);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern IntPtr THSNN_AdaptiveMaxPool3d_ctor(IntPtr psizes, int length, out IntPtr pBoxedModule);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern IntPtr THSNN_AvgPool1d_forward(IntPtr module, IntPtr tensor);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern IntPtr THSNN_AvgPool1d_ctor(IntPtr pkernelSize, IntPtr pstrides, IntPtr ppadding, [MarshalAs(UnmanagedType.U1)] bool ceil_mode, [MarshalAs(UnmanagedType.U1)] bool count_include_pad, long divisor_override, out IntPtr pBoxedModule);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern IntPtr THSNN_AvgPool2d_forward(IntPtr module, IntPtr tensor);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern IntPtr THSNN_AvgPool2d_ctor(IntPtr pkernelSize, int kernelSizeLength, IntPtr pstrides, int stridesLength, IntPtr ppadding, int paddingLength, [MarshalAs(UnmanagedType.U1)] bool ceil_mode, [MarshalAs(UnmanagedType.U1)] bool count_include_pad, long divisor_override, out IntPtr pBoxedModule);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern IntPtr THSNN_AvgPool3d_forward(IntPtr module, IntPtr tensor);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern IntPtr THSNN_AvgPool3d_ctor(IntPtr pkernelSize, int kernelSizeLength, IntPtr pstrides, int stridesLength, IntPtr ppadding, int paddingLength, [MarshalAs(UnmanagedType.U1)] bool ceil_mode, [MarshalAs(UnmanagedType.U1)] bool count_include_pad, long divisor_override, out IntPtr pBoxedModule);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern IntPtr THSNN_FractionalMaxPool2d_forward(torch.nn.Module.HType module, IntPtr tensor);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern IntPtr THSNN_FractionalMaxPool2d_forward_with_indices(torch.nn.Module.HType module, IntPtr tensor, out IntPtr indices);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern IntPtr THSNN_FractionalMaxPool2d_ctor(IntPtr pkernelSize, int kernelSizeLength, IntPtr pOutputSize, int sizeLength, IntPtr pOutputRatio, int ratioLength, out IntPtr pBoxedModule);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern IntPtr THSNN_FractionalMaxPool3d_forward(torch.nn.Module.HType module, IntPtr tensor);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern IntPtr THSNN_FractionalMaxPool3d_forward_with_indices(torch.nn.Module.HType module, IntPtr tensor, out IntPtr indices);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern IntPtr THSNN_FractionalMaxPool3d_ctor(IntPtr pkernelSize, int kernelSizeLength, IntPtr pOutputSize, int sizeLength, IntPtr pOutputRatio, int ratioLength, out IntPtr pBoxedModule);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern IntPtr THSNN_LPPool1d_forward(IntPtr module, IntPtr tensor);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern IntPtr THSNN_LPPool1d_ctor(double norm_type, IntPtr pkernelSize, IntPtr pstrides, [MarshalAs(UnmanagedType.U1)] bool ceil_mode, out IntPtr pBoxedModule);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern IntPtr THSNN_LPPool2d_forward(IntPtr module, IntPtr tensor);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern IntPtr THSNN_LPPool2d_ctor(double norm_type, IntPtr pkernelSize, int kernelSizeLength, IntPtr pstrides, int stridesLength, [MarshalAs(UnmanagedType.U1)] bool ceil_mode, out IntPtr pBoxedModule);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern IntPtr THSNN_MaxPool2d_forward(torch.nn.Module.HType module, IntPtr tensor);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern IntPtr THSNN_MaxPool2d_forward_with_indices(torch.nn.Module.HType module, IntPtr tensor, out IntPtr indices);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern IntPtr THSNN_MaxPool2d_ctor(IntPtr pkernelSize, int kernelSizeLength, IntPtr pstrides, int stridesLength, IntPtr pPadding, int paddingLength, IntPtr pDilation, int dilationLength, [MarshalAs(UnmanagedType.U1)] bool ceilMode, out IntPtr pBoxedModule);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern IntPtr THSNN_MaxPool3d_forward(torch.nn.Module.HType module, IntPtr tensor);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern IntPtr THSNN_MaxPool3d_forward_with_indices(torch.nn.Module.HType module, IntPtr tensor, out IntPtr indices);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern IntPtr THSNN_MaxPool3d_ctor(IntPtr pkernelSize, int kernelSizeLength, IntPtr pstrides, int stridesLength, IntPtr pPadding, int paddingLength, IntPtr pDilation, int dilationLength, [MarshalAs(UnmanagedType.U1)] bool ceilMode, out IntPtr pBoxedModule);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern IntPtr THSNN_MaxUnpool1d_forward(torch.nn.Module.HType module, IntPtr tensor, IntPtr indices, IntPtr outSize);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern IntPtr THSNN_MaxUnpool1d_ctor(IntPtr pkernelSize, IntPtr pStrides, IntPtr pPadding, out IntPtr pBoxedModule);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern IntPtr THSNN_MaxUnpool2d_forward(torch.nn.Module.HType module, IntPtr tensor, IntPtr indices, IntPtr outSize, int outputSizeLength);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern IntPtr THSNN_MaxUnpool2d_ctor(IntPtr pkernelSize, int kernelSizeLength, IntPtr pstrides, int stridesLength, IntPtr pPadding, int paddingLength, out IntPtr pBoxedModule);\\n }\\n#pragma warning restore CA2101\\n}\\n\"\n },\n {\n \"alpha_fraction\": 0.491913378238678,\n \"alphanum_fraction\": 0.49916040897369385,\n \"avg_line_length\": 49.28888702392578,\n \"blob_id\": \"d3e50c633a00b5643035cb061f8c7e95fe041587\",\n \"content_id\": \"623f332b3a15e94c5923257d1117b1fd569e56d2\",\n \"detected_licenses\": [\n \"MIT\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"C#\",\n \"length_bytes\": 11315,\n \"license_type\": \"permissive\",\n \"max_line_length\": 129,\n \"num_lines\": 225,\n \"path\": \"/src/TorchVision/Utils.cs\",\n \"repo_name\": \"NiklasGustafsson/TorchSharp\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"using System;\\nusing System.Collections.Generic;\\nusing System.IO;\\nusing static TorchSharp.torch;\\nusing static TorchSharp.torchvision.io;\\n\\nnamespace TorchSharp\\n{\\n public static partial class torchvision\\n {\\n public static class utils\\n {\\n /// <summary>\\n /// Make a grid of images.\\n /// </summary>\\n /// <param name=\\\"tensors\\\">A list of images all of the same size.</param>\\n /// <param name=\\\"nrow\\\">Number of images displayed in each row of the grid.\\n /// The final grid size is (B / nrow, nrow). Default: 8.</param>\\n /// <param name=\\\"padding\\\">Amount of padding. Default: 2.</param>\\n /// <param name=\\\"normalize\\\">If true, shift the image to the range (0, 1),\\n /// by the min and max values specified by value_range. Default: false.</param>\\n /// <param name=\\\"value_range\\\">Tuple (min, max) where min and max are numbers,\\n /// then these numbers are used to normalize the image. By default, min and max\\n /// are computed from the tensor.</param>\\n /// <param name=\\\"scale_each\\\">If true, scale each image in the batch of\\n /// images separately rather than the (min, max) over all images. Default: false.</param>\\n /// <param name=\\\"pad_value\\\">Value for the padded pixels. Default: 0.</param>\\n /// <returns>The tensor containing grid of images.</returns>\\n public static Tensor make_grid(\\n IEnumerable<Tensor> tensors,\\n long nrow = 8,\\n int padding = 2,\\n bool normalize = false,\\n (double low, double high)? value_range = null,\\n bool scale_each = false,\\n double pad_value = 0.0f)\\n {\\n return make_grid(torch.stack(tensors, dim: 0), nrow, padding, normalize, value_range, scale_each, pad_value);\\n }\\n\\n /// <summary>\\n /// Make a grid of images.\\n /// </summary>\\n /// <param name=\\\"tensor\\\">4D mini-batch Tensor of shape (B x C x H x W).</param>\\n /// <param name=\\\"nrow\\\">Number of images displayed in each row of the grid.\\n /// The final grid size is (B / nrow, nrow). Default: 8.</param>\\n /// <param name=\\\"padding\\\">Amount of padding. Default: 2.</param>\\n /// <param name=\\\"normalize\\\">If true, shift the image to the range (0, 1),\\n /// by the min and max values specified by value_range. Default: false.</param>\\n /// <param name=\\\"value_range\\\">Tuple (min, max) where min and max are numbers,\\n /// then these numbers are used to normalize the image. By default, min and max\\n /// are computed from the tensor.</param>\\n /// <param name=\\\"scale_each\\\">If true, scale each image in the batch of\\n /// images separately rather than the (min, max) over all images. Default: false.</param>\\n /// <param name=\\\"pad_value\\\">Value for the padded pixels. Default: 0.</param>\\n /// <returns>The tensor containing grid of images.</returns>\\n public static Tensor make_grid(\\n Tensor tensor,\\n long nrow = 8,\\n int padding = 2,\\n bool normalize = false,\\n (double low, double high)? value_range = null,\\n bool scale_each = false,\\n double pad_value = 0.0f)\\n {\\n using var _ = torch.NewDisposeScope();\\n\\n if (tensor.Dimensions == 2) // Single image H x W\\n {\\n tensor = tensor.unsqueeze(0);\\n }\\n if (tensor.Dimensions == 3) // Single image\\n {\\n if (tensor.size(0) == 1) // Convert single channel to 3 channel\\n {\\n tensor = torch.cat(new[] { tensor, tensor, tensor }, 0);\\n }\\n tensor = tensor.unsqueeze(0);\\n }\\n if (tensor.Dimensions == 4 && tensor.size(1) == 1) // Single channel images\\n {\\n tensor = torch.cat(new[] { tensor, tensor, tensor }, 1);\\n }\\n\\n if (normalize == true)\\n {\\n tensor = tensor.clone(); // avoid modifying tensor in-place\\n void norm_ip(Tensor img, double low, double high)\\n {\\n img.clamp_(min: low, max: high);\\n img.sub_(low).div_(Math.Max(high - low, 1e-5));\\n }\\n\\n void norm_range(Tensor t, (double low, double high)? range)\\n {\\n if (range.HasValue)\\n {\\n var (low, high) = value_range.Value;\\n norm_ip(t, low, high);\\n }\\n else\\n {\\n norm_ip(t, t.min().ToSingle(), t.max().ToSingle());\\n }\\n }\\n\\n if (scale_each == true)\\n {\\n for (long i = 0; i < tensor.size(0); ++i)\\n {\\n norm_range(tensor[i], value_range);\\n }\\n }\\n else\\n {\\n norm_range(tensor, value_range);\\n }\\n }\\n\\n if (tensor.size(0) == 1) {\\n tensor = tensor.squeeze(0);\\n return tensor.MoveToOuterDisposeScope();\\n }\\n\\n var nmaps = tensor.size(0);\\n var xmaps = Math.Min(nrow, nmaps);\\n var ymaps = (long)Math.Ceiling((double)nmaps / xmaps);\\n var width = tensor.size(3) + padding;\\n var height = tensor.size(2) + padding;\\n var num_channels = tensor.size(1);\\n\\n var grid = tensor.new_full(new[] { num_channels, height * ymaps + padding, width * xmaps + padding }, pad_value);\\n var k = 0L;\\n for (long y = 0; y < ymaps; ++y)\\n {\\n for (long x = 0; x < xmaps; ++x)\\n {\\n if (k > nmaps) break;\\n grid.narrow(1, y * height, height - padding).narrow(\\n 2, x * width + padding, width - padding\\n ).copy_(tensor[k]);\\n ++k;\\n }\\n }\\n\\n return grid.MoveToOuterDisposeScope();\\n }\\n\\n /// <summary>\\n /// Save a given Tensor into an image file.\\n /// </summary>\\n /// <param name=\\\"tensor\\\">Image to be saved. If given a mini-batch tensor,\\n /// saves the tensor as a grid of images by calling make_grid.</param>\\n /// <param name=\\\"filename\\\">A file name</param>\\n /// <param name=\\\"format\\\">The format to use is not determined from the\\n /// filename extension this parameter should always be used.</param>\\n /// <param name=\\\"nrow\\\">Number of images displayed in each row of the grid.\\n /// The final grid size is (B / nrow, nrow). Default: 8.</param>\\n /// <param name=\\\"padding\\\">Amount of padding. Default: 2.</param>\\n /// <param name=\\\"normalize\\\">If true, shift the image to the range (0, 1),\\n /// by the min and max values specified by value_range. Default: false.</param>\\n /// <param name=\\\"value_range\\\">Tuple (min, max) where min and max are numbers,\\n /// then these numbers are used to normalize the image. By default, min and max\\n /// are computed from the tensor.</param>\\n /// <param name=\\\"scale_each\\\">If true, scale each image in the batch of\\n /// images separately rather than the (min, max) over all images. Default: false.</param>\\n /// <param name=\\\"pad_value\\\">Value for the padded pixels. Default: 0.</param>\\n /// <param name=\\\"imager\\\">Imager to use instead of DefaultImager. Default: null</param>\\n public static void save_image(\\n Tensor tensor,\\n string filename,\\n ImageFormat format,\\n long nrow = 8,\\n int padding = 2,\\n bool normalize = false,\\n (double low, double high)? value_range = null,\\n bool scale_each = false,\\n double pad_value = 0.0f,\\n Imager imager = null)\\n {\\n using var filestream = new FileStream(filename, FileMode.OpenOrCreate);\\n save_image(tensor, filestream, format, nrow, padding, normalize, value_range, scale_each, pad_value, imager);\\n }\\n\\n /// <summary>\\n /// Save a given Tensor into an image file.\\n /// </summary>\\n /// <param name=\\\"tensor\\\">Image to be saved. If given a mini-batch tensor,\\n /// saves the tensor as a grid of images by calling make_grid.</param>\\n /// <param name=\\\"filestream\\\">A file stream</param>\\n /// <param name=\\\"format\\\">The format to use is not determined from the\\n /// filename extension this parameter should always be used.</param>\\n /// <param name=\\\"nrow\\\">Number of images displayed in each row of the grid.\\n /// The final grid size is (B / nrow, nrow). Default: 8.</param>\\n /// <param name=\\\"padding\\\">Amount of padding. Default: 2.</param>\\n /// <param name=\\\"normalize\\\">If true, shift the image to the range (0, 1),\\n /// by the min and max values specified by value_range. Default: false.</param>\\n /// <param name=\\\"value_range\\\">Tuple (min, max) where min and max are numbers,\\n /// then these numbers are used to normalize the image. By default, min and max\\n /// are computed from the tensor.</param>\\n /// <param name=\\\"scale_each\\\">If true, scale each image in the batch of\\n /// images separately rather than the (min, max) over all images. Default: false.</param>\\n /// <param name=\\\"pad_value\\\">Value for the padded pixels. Default: 0.</param>\\n /// <param name=\\\"imager\\\">Imager to use instead of DefaultImager. Default: null</param>\\n public static void save_image(\\n Tensor tensor,\\n Stream filestream,\\n ImageFormat format,\\n long nrow = 8,\\n int padding = 2,\\n bool normalize = false,\\n (double low, double high)? value_range = null,\\n bool scale_each = false,\\n double pad_value = 0.0f,\\n Imager imager = null)\\n {\\n using var _ = torch.NewDisposeScope();\\n var grid = make_grid(tensor, nrow, padding, normalize, value_range, scale_each, pad_value);\\n // Add 0.5 after unnormalizing to [0, 255] to round to nearest integer\\n var narr = grid.mul(255).add_(0.5).clamp_(0, 255).to(uint8, CPU);\\n (imager ?? DefaultImager).EncodeImage(narr, format, filestream);\\n }\\n }\\n }\\n}\\n\"\n },\n {\n \"alpha_fraction\": 0.496349036693573,\n \"alphanum_fraction\": 0.5023730993270874,\n \"avg_line_length\": 42.832000732421875,\n \"blob_id\": \"10bb8eca4461847d3a2357c21461267e0113cea1\",\n \"content_id\": \"7c03c8e411b33a04a7dc195b0180693b13eae3f8\",\n \"detected_licenses\": [\n \"MIT\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"C#\",\n \"length_bytes\": 5478,\n \"license_type\": \"permissive\",\n \"max_line_length\": 149,\n \"num_lines\": 125,\n \"path\": \"/src/TorchVision/dsets/Utils.cs\",\n \"repo_name\": \"NiklasGustafsson/TorchSharp\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"// Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information.\\nusing ICSharpCode.SharpZipLib.GZip;\\nusing ICSharpCode.SharpZipLib.Tar;\\nusing System;\\nusing System.Collections.Generic;\\nusing System.IO;\\nusing System.IO.Compression;\\nusing System.Text;\\n\\n// A number of implementation details in this file have been translated from the Python version of torchvision,\\n// largely located in the files found in this folder:\\n//\\n// https://github.com/pytorch/vision/blob/b78d98bb152ffb9c0c0f5365f59f475c70b1784e/torchvision/datasets/utils.py\\n// The origin has the following copyright notice and license:\\n//\\n// https://github.com/pytorch/vision/blob/main/LICENSE\\n//\\n\\n#nullable enable\\nnamespace TorchSharp\\n{\\n public static partial class torchvision\\n {\\n public static partial class datasets\\n {\\n public static partial class utils\\n {\\n /// <summary>\\n /// Download a Google Drive file and place it in root.\\n /// </summary>\\n /// <param name=\\\"file_id\\\">id of file to be downloaded</param>\\n /// <param name=\\\"root\\\">Directory to place downloaded file in</param>\\n /// <param name=\\\"filename\\\">Name to save the file under. If null, use the id of the file.</param>\\n /// <param name=\\\"md5\\\">MD5 checksum of the download. If null, do not check</param>\\n public static void download_file_from_google_drive(\\n string file_id, string root, string? filename = null, string? md5 = null)\\n {\\n io.GDriveDownload.DownloadFileFromGoogleDrive(file_id, root, filename, md5);\\n }\\n\\n private static void ExtractTar(string from_path, string to_path)\\n {\\n using (var fileStream = File.OpenRead(from_path)) {\\n using (var inputStream = new GZipInputStream(fileStream)) {\\n using (TarArchive tarArchive = TarArchive.CreateInputTarArchive(inputStream, Encoding.UTF8)) {\\n tarArchive.ExtractContents(to_path);\\n }\\n }\\n }\\n }\\n\\n private static void ExtractZip(string from_path, string to_path)\\n {\\n ZipFile.ExtractToDirectory(from_path, to_path);\\n }\\n\\n /// <summary>\\n /// Extract an archive.\\n ///\\n /// The archive type and a possible compression is automatically detected from the file name.If the file is compressed\\n /// but not an archive the call is dispatched to :func:`decompress`.\\n /// </summary>\\n /// <param name=\\\"from_path\\\">Path to the file to be extracted.</param>\\n /// <param name=\\\"to_path\\\"> Path to the directory the file will be extracted to.If omitted, the directory of the file is used.</param>\\n /// <param name=\\\"remove_finished\\\">If ``True``, remove the file after the extraction.</param>\\n /// <returns>Path to the directory the file was extracted to.</returns>\\n /// <exception cref=\\\"InvalidDataException\\\"></exception>\\n /// <exception cref=\\\"ArgumentException\\\"></exception>\\n public static string extract_archive(string from_path, string? to_path = null, bool remove_finished = false)\\n {\\n if (to_path is null) {\\n to_path = Path.GetDirectoryName(from_path);\\n if (to_path is null) {\\n throw new InvalidDataException();\\n }\\n }\\n\\n if (Path.GetExtension(from_path) == \\\".zip\\\") {\\n ExtractZip(from_path, to_path);\\n } else if (from_path.EndsWith(\\\".tar.gz\\\")) {\\n ExtractTar(from_path, to_path);\\n } else {\\n throw new ArgumentException();\\n }\\n\\n if (remove_finished) {\\n File.Delete(from_path);\\n }\\n\\n return to_path;\\n }\\n\\n private static string IterableToStr(IList<string> iterable)\\n {\\n return \\\"'\\\" + string.Join(\\\"', '\\\", iterable) + \\\"'\\\";\\n }\\n\\n internal static string VerifyStrArg(\\n string value,\\n string? arg = null,\\n IList<string>? valid_values = null,\\n string? custom_msg = null)\\n {\\n if (valid_values is null) {\\n return value;\\n }\\n\\n if (!valid_values.Contains(value)) {\\n string msg;\\n if (custom_msg is not null) {\\n msg = custom_msg;\\n } else {\\n msg = string.Format(\\n \\\"Unknown value '{0}' for argument {1}. Valid values are {2}.\\\",\\n value, arg, IterableToStr(valid_values));\\n }\\n throw new ArgumentException(msg);\\n }\\n\\n return value;\\n }\\n }\\n }\\n }\\n}\"\n },\n {\n \"alpha_fraction\": 0.5809458494186401,\n \"alphanum_fraction\": 0.5897189974784851,\n \"avg_line_length\": 59.79166793823242,\n \"blob_id\": \"a66883da7330e945b023281ed7b3e1c752613883\",\n \"content_id\": \"7fbfa371a5e5f8ca1274cd63e3c802e94ec6eb6a\",\n \"detected_licenses\": [\n \"MIT\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"C#\",\n \"length_bytes\": 7295,\n \"license_type\": \"permissive\",\n \"max_line_length\": 195,\n \"num_lines\": 120,\n \"path\": \"/src/TorchSharp/NN/Pooling/FractionalMaxPool2d.cs\",\n \"repo_name\": \"NiklasGustafsson/TorchSharp\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"// Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information.\\nusing System;\\nusing static TorchSharp.torch;\\nusing static TorchSharp.PInvoke.NativeMethods;\\n\\nnamespace TorchSharp\\n{\\n using Modules;\\n\\n namespace Modules\\n {\\n /// <summary>\\n /// This class is used to represent a FractionalMaxPool2D module.\\n /// </summary>\\n public sealed class FractionalMaxPool2d : torch.nn.Module<Tensor, Tensor>\\n {\\n internal FractionalMaxPool2d(IntPtr handle, IntPtr boxedHandle) : base(handle, boxedHandle)\\n {\\n }\\n\\n public override Tensor forward(Tensor tensor)\\n {\\n var res = THSNN_FractionalMaxPool2d_forward(handle, tensor.Handle);\\n if (res == IntPtr.Zero) { torch.CheckForErrors(); }\\n return new Tensor(res);\\n }\\n\\n public (Tensor Values, Tensor Indices) forward_with_indices(Tensor tensor)\\n {\\n var res = THSNN_FractionalMaxPool2d_forward_with_indices(handle, tensor.Handle, out var indices);\\n if (res == IntPtr.Zero || indices == IntPtr.Zero) { torch.CheckForErrors(); }\\n return (new Tensor(res), new Tensor(indices));\\n }\\n\\n // Rather than spending cycles only to discover that this module has neither\\n // parameters nor buffers, just shortcut the move completely.\\n protected internal override nn.Module _to(Device device, ScalarType dtype) => this;\\n protected internal override nn.Module _to(DeviceType deviceType, int deviceIndex = -1) => this;\\n protected internal override nn.Module _to(ScalarType dtype) => this;\\n }\\n }\\n\\n public static partial class torch\\n {\\n public static partial class nn\\n {\\n /// <summary>\\n /// Applies a 2D fractional max pooling over an input signal composed of several input planes.\\n ///\\n /// Fractional MaxPooling is described in detail in the paper Fractional MaxPooling by Ben Graham,\\n /// see: https://arxiv.org/abs/1412.6071\\n /// </summary>\\n /// <param name=\\\"kernel_size\\\">The size of the sliding window, must be > 0.</param>\\n /// <param name=\\\"output_size\\\">The target output size of the image of the form oH x oW. Can be a tuple (oH, oW) or a single number oH for a square image oH x oH</param>\\n /// <param name=\\\"output_ratio\\\">If one wants to have an output size as a ratio of the input size, this option can be given. This has to be a number or tuple in the range (0, 1)</param>\\n /// <returns></returns>\\n public static FractionalMaxPool2d FractionalMaxPool2d(long kernel_size, long? output_size = null, double? output_ratio = null)\\n {\\n var pSize = output_size.HasValue ? new long[] { output_size.Value, output_size.Value } : null;\\n var pRatio = output_ratio.HasValue ? new double[] { output_ratio.Value, output_ratio.Value } : null;\\n return FractionalMaxPool2d(new long[] { kernel_size, kernel_size }, pSize, pRatio);\\n }\\n\\n /// <summary>\\n /// Applies a 2D fractional max pooling over an input signal composed of several input planes.\\n ///\\n /// Fractional MaxPooling is described in detail in the paper Fractional MaxPooling by Ben Graham,\\n /// see: https://arxiv.org/abs/1412.6071\\n /// </summary>\\n /// <param name=\\\"kernel_size\\\">The size of the sliding window, must be > 0.</param>\\n /// <param name=\\\"output_size\\\">The target output size of the image of the form oH x oW. Can be a tuple (oH, oW) or a single number oH for a square image oH x oH</param>\\n /// <param name=\\\"output_ratio\\\">If one wants to have an output size as a ratio of the input size, this option can be given. This has to be a number or tuple in the range (0, 1)</param>\\n /// <returns></returns>\\n public static FractionalMaxPool2d FractionalMaxPool2d((long, long) kernel_size, (long, long)? output_size = null, (double, double)? output_ratio = null)\\n {\\n var pSize = output_size.HasValue ? new long[] { output_size.Value.Item1, output_size.Value.Item2 } : null;\\n var pRatio = output_ratio.HasValue ? new double[] { output_ratio.Value.Item1, output_ratio.Value.Item2 } : null;\\n return FractionalMaxPool2d(new long[] { kernel_size.Item1, kernel_size.Item2 }, pSize, pRatio);\\n }\\n\\n /// <summary>\\n /// Applies a 2D fractional max pooling over an input signal composed of several input planes.\\n ///\\n /// Fractional MaxPooling is described in detail in the paper Fractional MaxPooling by Ben Graham,\\n /// see: https://arxiv.org/abs/1412.6071\\n /// </summary>\\n /// <param name=\\\"kernel_size\\\">The size of the sliding window, must be > 0.</param>\\n /// <param name=\\\"output_size\\\">The target output size of the image of the form oH x oW. Can be a tuple (oH, oW) or a single number oH for a square image oH x oH</param>\\n /// <param name=\\\"output_ratio\\\">If one wants to have an output size as a ratio of the input size, this option can be given. This has to be a number or tuple in the range (0, 1)</param>\\n /// <returns></returns>\\n public static FractionalMaxPool2d FractionalMaxPool2d(long[] kernel_size, long[] output_size = null, double[] output_ratio = null)\\n {\\n if (kernel_size == null || kernel_size.Length != 2)\\n throw new ArgumentException(\\\"Kernel size must contain two elements.\\\");\\n if (output_size != null && output_size.Length != 2)\\n throw new ArgumentException(\\\"output_size must contain two elements.\\\");\\n if (output_ratio != null && output_ratio.Length != 2)\\n throw new ArgumentException(\\\"output_ratio must contain two elements.\\\");\\n if (output_size == null && output_ratio == null)\\n throw new ArgumentNullException(\\\"Only one of output_size and output_ratio may be specified.\\\");\\n if (output_size != null && output_ratio != null)\\n throw new ArgumentNullException(\\\"FractionalMaxPool2d requires specifying either an output size, or a pooling ratio.\\\");\\n\\n unsafe {\\n fixed (long* pkernelSize = kernel_size, pSize = output_size) {\\n fixed (double* pRatio = output_ratio) {\\n var handle = THSNN_FractionalMaxPool2d_ctor(\\n (IntPtr)pkernelSize, kernel_size.Length,\\n (IntPtr)pSize, (output_size == null ? 0 : output_size.Length),\\n (IntPtr)pRatio, (output_ratio == null ? 0 : output_ratio.Length),\\n out var boxedHandle);\\n if (handle == IntPtr.Zero) { torch.CheckForErrors(); }\\n return new FractionalMaxPool2d(handle, boxedHandle);\\n }\\n }\\n }\\n }\\n }\\n }\\n}\\n\"\n },\n {\n \"alpha_fraction\": 0.5725051760673523,\n \"alphanum_fraction\": 0.5847193598747253,\n \"avg_line_length\": 45.93902587890625,\n \"blob_id\": \"d9db89f2063cd64749a113e672ead87206fd3d3a\",\n \"content_id\": \"80d32bf23060b4ae8743d26cc965a1787525f40c\",\n \"detected_licenses\": [\n \"MIT\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"C#\",\n \"length_bytes\": 3848,\n \"license_type\": \"permissive\",\n \"max_line_length\": 183,\n \"num_lines\": 82,\n \"path\": \"/src/TorchVision/RandomResizedCrop.cs\",\n \"repo_name\": \"NiklasGustafsson/TorchSharp\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"// Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information.\\nusing System;\\nusing static TorchSharp.torch;\\n\\nnamespace TorchSharp\\n{\\n public static partial class torchvision\\n {\\n internal class RandomResizedCrop : ITransform\\n {\\n public RandomResizedCrop(int height, int width, double scaleMin, double scaleMax, double ratioMin, double ratioMax)\\n {\\n this.height = height;\\n this.width = width;\\n this.scaleMax = scaleMax;\\n this.scaleMin = scaleMin;\\n this.ratioMax = ratioMax;\\n this.ratioMin = ratioMin;\\n }\\n\\n public Tensor call(Tensor input)\\n {\\n var hoffset = input.Dimensions - 2;\\n var iHeight = input.shape[hoffset];\\n var iWidth = input.shape[hoffset + 1];\\n\\n var scale = scaleMax - scaleMin;\\n\\n var random = new Random();\\n\\n // First, figure out how high and wide the crop should be.\\n\\n var randomScale = Math.Min(random.NextDouble() * scale + scaleMin, 1); // Preserves the aspect ratio.\\n\\n var h = iHeight * randomScale;\\n var w = iWidth * randomScale;\\n\\n // Then, place the top and left corner at a random location,\\n // so that the crop doesn't go outside the image boundaries.\\n\\n var top = (int)Math.Floor((iHeight - h) * random.NextDouble());\\n var left = (int)Math.Floor((iWidth - w) * random.NextDouble());\\n\\n return new ResizedCrop(top, left, (int)Math.Floor(h), (int)Math.Floor(w), height, width).call(input);\\n }\\n\\n private int height, width;\\n private double scaleMin, scaleMax;\\n private double ratioMin, ratioMax;\\n }\\n\\n public static partial class transforms\\n {\\n /// <summary>\\n /// Crop a random portion of image and resize it to a given size. \\n /// </summary>\\n /// <param name=\\\"height\\\">Expected output height of the crop</param>\\n /// <param name=\\\"width\\\">Expected output width of the crop</param>\\n /// <param name=\\\"scaleMin\\\">Lower bounds for the random area of the crop</param>\\n /// <param name=\\\"scaleMax\\\">Upper bounds for the random area of the crop</param>\\n /// <param name=\\\"ratioMin\\\">Lower bounds for the random aspect ratio of the crop</param>\\n /// <param name=\\\"ratioMax\\\">Upper bounds for the random aspect ratio of the crop</param>\\n static public ITransform RandomResizedCrop(int height, int width, double scaleMin = 0.08, double scaleMax = 1.0, double ratioMin = 0.75, double ratioMax = 1.3333333333333)\\n {\\n return new RandomResizedCrop(height, width, scaleMin, scaleMax, ratioMin, ratioMax);\\n }\\n\\n /// <summary>\\n /// Crop a random portion of image and resize it to a given size. \\n /// </summary>\\n /// <param name=\\\"size\\\">Expected output size of the crop</param>\\n /// <param name=\\\"scaleMin\\\">Lower for the random area of the crop</param>\\n /// <param name=\\\"scaleMax\\\">Upper bounds for the random area of the crop</param>\\n /// <param name=\\\"ratioMin\\\">Lower bounds for the random aspect ratio of the crop</param>\\n /// <param name=\\\"ratioMax\\\">Upper bounds for the random aspect ratio of the crop</param>\\n static public ITransform RandomResizedCrop(int size, double scaleMin = 0.08, double scaleMax = 1.0, double ratioMin = 0.75, double ratioMax = 1.3333333333333)\\n {\\n return RandomResizedCrop(size, size, scaleMin, scaleMax, ratioMin, ratioMax);\\n }\\n }\\n }\\n}\"\n },\n {\n \"alpha_fraction\": 0.4970722198486328,\n \"alphanum_fraction\": 0.5018364191055298,\n \"avg_line_length\": 52.17745590209961,\n \"blob_id\": \"94b67c2591044c58c00a6aa130ae2fc19478deea\",\n \"content_id\": \"1048af5610d30f9b0a72b5b913eb5688dc96ece7\",\n \"detected_licenses\": [\n \"MIT\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"C#\",\n \"length_bytes\": 47675,\n \"license_type\": \"permissive\",\n \"max_line_length\": 253,\n \"num_lines\": 896,\n \"path\": \"/src/TorchSharp/JIT/ScriptModule.cs\",\n \"repo_name\": \"NiklasGustafsson/TorchSharp\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"// Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information.\\nusing System;\\nusing System.Collections.Generic;\\nusing System.Diagnostics;\\nusing System.Linq;\\nusing System.Runtime.InteropServices;\\nusing TorchSharp.PInvoke;\\nusing static TorchSharp.PInvoke.NativeMethods;\\n\\nnamespace TorchSharp\\n{\\n public static partial class torch\\n {\\n public static partial class jit\\n {\\n /// <summary>\\n /// This class represents a TorchScript module.\\n /// </summary>\\n public class ScriptModule : nn.Module\\n {\\n internal ScriptModule(IntPtr handle) : base(new HType(handle, true, THSJIT_Module_dispose), null)\\n {\\n }\\n\\n protected override (string name, TorchSharp.Modules.Parameter parameter)[] _named_parameters()\\n {\\n using var pa = new PinnedArray<IntPtr>();\\n using var sa = new PinnedArray<IntPtr>();\\n THSJIT_Module_named_parameters(handle, pa.CreateArray, sa.CreateArray);\\n CheckForErrors();\\n var ptrArray = pa.Array;\\n var strArray = sa.Array;\\n\\n return ptrArray.Select((x, i) => (Marshal.PtrToStringAnsi(strArray[i]), new TorchSharp.Modules.Parameter(x))).ToArray();\\n }\\n\\n protected override (string name, Tensor buffer)[] _named_buffers()\\n {\\n using var pa = new PinnedArray<IntPtr>();\\n using var sa = new PinnedArray<IntPtr>();\\n THSJIT_Module_named_buffers(handle, pa.CreateArray, sa.CreateArray);\\n CheckForErrors();\\n var ptrArray = pa.Array;\\n var strArray = sa.Array;\\n\\n return ptrArray.Select((x, i) => (Marshal.PtrToStringAnsi(strArray[i]), new Tensor(x))).ToArray();\\n }\\n\\n /// <summary>\\n /// Returns an enumerable of all modules in the network, yielding both the name of the module as well as the module itself.\\n /// </summary>\\n /// <returns>(string, Module) – Tuple of name and module</returns>\\n public override IEnumerable<(string name, nn.Module module)> named_modules()\\n {\\n using var pa = new PinnedArray<IntPtr>();\\n using var sa = new PinnedArray<IntPtr>();\\n THSJIT_Module_named_modules(handle, pa.CreateArray, sa.CreateArray);\\n CheckForErrors();\\n var ptrArray = pa.Array;\\n var strArray = sa.Array;\\n\\n return ptrArray.Select((x, i) => (Marshal.PtrToStringAnsi(strArray[i]), new ScriptModule(x) as nn.Module)).Where(m => !string.IsNullOrEmpty(m.Item1));\\n }\\n\\n /// <summary>\\n /// Returns an enumerable of immediate children modules, yielding both the name of the module as well as the module itself.\\n /// </summary>\\n /// <returns>(string, Module) – Tuple containing a name and child module</returns>\\n public override IEnumerable<(string name, nn.Module module)> named_children()\\n {\\n using var pa = new PinnedArray<IntPtr>();\\n using var sa = new PinnedArray<IntPtr>();\\n THSJIT_Module_named_children(handle, pa.CreateArray, sa.CreateArray);\\n CheckForErrors();\\n var ptrArray = pa.Array;\\n var strArray = sa.Array;\\n\\n return ptrArray.Select((x, i) => (Marshal.PtrToStringAnsi(strArray[i]), new ScriptModule(x) as nn.Module));\\n }\\n\\n public int GetNumberOfInputs()\\n {\\n return THSJIT_Module_num_inputs(handle);\\n }\\n\\n public int GetNumberOfOutputs()\\n {\\n return THSJIT_Module_num_outputs(handle);\\n }\\n\\n /// <summary>\\n /// Sets the module in evaluation mode.\\n /// </summary>\\n /// <remarks>\\n /// Any script module that was created using torch.jit.trace() will be unaffected. The behavior of such\\n /// modules will be captured when traced.\\n /// </remarks>\\n public override void train(bool on = true)\\n {\\n THSJIT_Module_train(handle, on);\\n CheckForErrors();\\n }\\n\\n /// <summary>\\n /// Sets the module in evaluation mode.\\n /// </summary>\\n /// <remarks>\\n /// Any script module that was created using torch.jit.trace() will be unaffected. The behavior of such\\n /// modules will be captured when traced.\\n /// </remarks>\\n public override void eval()\\n {\\n THSJIT_Module_eval(handle);\\n CheckForErrors();\\n }\\n\\n /// <summary>\\n /// Check whether the module is set to training or evaluation mode.\\n /// </summary>\\n public override bool training {\\n get {\\n var res = THSJIT_Module_is_training(handle);\\n CheckForErrors();\\n return res;\\n }\\n }\\n\\n protected internal override nn.Module _to(Device device, ScalarType dtype)\\n {\\n if (device.type != DeviceType.CUDA) { device = new Device(device.type, -1); };\\n\\n if (device.type == DeviceType.CUDA && !torch.cuda.is_available()) throw new InvalidOperationException(\\\"CUDA is not available.\\\");\\n\\n\\n InitializeDeviceType(device.type);\\n THSJIT_Module_to_device_dtype(handle, (sbyte)dtype, (int)device.type, device.index);\\n CheckForErrors();\\n\\n _toEpilog(device, dtype);\\n\\n return this;\\n }\\n\\n /// <summary>\\n /// Moves the parameters and buffers.\\n /// </summary>\\n /// <param name=\\\"deviceType\\\">The device type, e.g. 'CPU' or 'CUDA'.</param>\\n /// <param name=\\\"deviceIndex\\\">The optional device index.</param>\\n /// <returns></returns>\\n protected internal override nn.Module _to(DeviceType deviceType, int deviceIndex = -1)\\n {\\n if (deviceType != DeviceType.CUDA) deviceIndex = -1;\\n\\n if (deviceType == DeviceType.CUDA && !torch.cuda.is_available()) throw new InvalidOperationException(\\\"CUDA is not available.\\\");\\n\\n if (deviceType != _deviceType || deviceIndex != _deviceIndex) {\\n\\n InitializeDeviceType(deviceType);\\n THSJIT_Module_to_device(handle, (int)deviceType, deviceIndex);\\n CheckForErrors();\\n\\n _toEpilog(deviceType, deviceIndex);\\n }\\n\\n Debug.Assert(_deviceType == DeviceType.CUDA || _deviceIndex == -1);\\n\\n return this;\\n }\\n\\n private DeviceType _deviceType = DeviceType.CPU;\\n private int _deviceIndex = -1;\\n\\n /// <summary>\\n /// Convert the parameters and buffers.\\n /// </summary>\\n /// <returns></returns>\\n protected internal override nn.Module _to(ScalarType dtype)\\n {\\n THSJIT_Module_to_dtype(handle, (sbyte)dtype);\\n CheckForErrors();\\n\\n _toEpilog(dtype);\\n\\n return this;\\n }\\n\\n#if false // These functions \\\"work,\\\" but the native code doesn't seem to find any interesting information.\\n\\n public Type GetInputType(int index)\\n {\\n var type = new Type(THSJIT_Module_getInputType(handle, index), Type.TypeKind.AnyType);\\n\\n return GetType(type);\\n }\\n\\n public Type GetOutputType(int index)\\n {\\n var type = new Type(THSJIT_getOutputType(handle, index), Type.TypeKind.AnyType);\\n\\n return GetType(type);\\n }\\n\\n private Type GetType(Type type)\\n {\\n switch (type.Kind) {\\n case Type.TypeKind.TensorType:\\n var dynamic = type.AsTensorType();\\n type.Dispose();\\n return dynamic;\\n //case Type.TypeKind.DimensionedTensorType:\\n // var tensor = type.AsTensorType();\\n // type.Dispose();\\n // return tensor;\\n default:\\n return type;\\n }\\n }\\n#endif\\n\\n /// <summary>\\n /// Invoke the 'forward' function of the script with any number of arguments.\\n /// </summary>\\n /// <param name=\\\"objs\\\"></param>\\n /// <returns></returns>\\n /// <remarks>\\n /// Only certain types can currently be passed:\\n /// 1. Tensor\\n /// 2. Scalar\\n /// 3. int/long\\n /// 4. double/float\\n /// 5. bool\\n ///\\n /// Only certain types can currently be returned:\\n /// 1. Tensor / Scalar\\n /// 2. Tuple of Tensor / Scalar\\n /// 3. Array (Python list) of Tensor / Scalar\\n ///\\n /// For returned types, if the number of values returned in a tuple is greaterh than 5, it is returned as an array, instead.\\n /// If a tuple contains both tensors and scalars, it is returned as an object[].\\n /// </remarks>\\n /// <exception cref=\\\"NotImplementedException\\\"></exception>\\n public object call(params object[] objs)\\n {\\n TensorOrScalar[] ptrArray = null;\\n sbyte typeCode = 0;\\n\\n using (var parray = new IndexedPinnedArrays<TensorOrScalar>()) {\\n\\n var tRefsHandle = DetermineArgumentTypeRefs(objs, out var count, parray);\\n\\n var allocated = parray.Count;\\n\\n THSJIT_Module_forward(handle, tRefsHandle, count, parray.CreateArray, out typeCode, allocated);\\n torch.CheckForErrors();\\n ptrArray = parray[allocated];\\n\\n return ProcessReturnValue(name, parray, ptrArray, typeCode);\\n }\\n }\\n\\n /// <summary>\\n /// Invoke a function from the script module.\\n /// </summary>\\n /// <param name=\\\"name\\\">The name of the function.</param>\\n /// <param name=\\\"objs\\\">Function arguments.</param>\\n /// <remarks>\\n /// Only certain types can currently be passed:\\n /// 1. Tensor\\n /// 2. Scalar\\n /// 3. int/long\\n /// 4. double/float\\n /// 5. bool\\n /// 6. 'null' object\\n /// \\n /// Only certain types can currently be returned:\\n /// 1. Tensor / Scalar\\n /// 2. Tuple of Tensor / Scalar\\n /// 3. Array (Python list) of Tensor / Scalar\\n /// 4. null object\\n ///\\n /// For returned types, if the number of values returned in a tuple is greaterh than 5, it is returned as an array, instead.\\n /// If a tuple contains both tensors and scalars, it is returned as an object[].\\n /// </remarks>\\n public object invoke(string name, params object[] objs)\\n {\\n if (string.IsNullOrEmpty(name)) throw new ArgumentNullException(\\\"method name\\\");\\n\\n TensorOrScalar[] ptrArray = null;\\n sbyte typeCode = 0;\\n\\n using (var parray = new IndexedPinnedArrays<TensorOrScalar>()) {\\n\\n var tRefsHandle = DetermineArgumentTypeRefs(objs, out var count, parray);\\n\\n var allocated = parray.Count;\\n\\n THSJIT_Module_invoke(handle, name, tRefsHandle, count, parray.CreateArray, out typeCode, allocated);\\n torch.CheckForErrors();\\n ptrArray = parray[allocated];\\n\\n return ProcessReturnValue(name, parray, ptrArray, typeCode);\\n }\\n }\\n\\n internal static IntPtr DetermineArgumentTypeRefs(object[] objs, out int count, IndexedPinnedArrays<TensorOrScalar> pinned)\\n {\\n count = objs.Length;\\n var tensorRefs = new TensorOrScalar[count];\\n var result = pinned.CreateArray(pinned.Count, tensorRefs);\\n\\n for (var idx = 0; idx < objs.Length; idx++) {\\n switch (objs[idx]) {\\n case Tensor t:\\n tensorRefs[idx].Handle = t.Handle;\\n tensorRefs[idx].TypeCode = 0;\\n break;\\n case Scalar s:\\n tensorRefs[idx].Handle = s.Handle;\\n tensorRefs[idx].TypeCode = 1;\\n break;\\n case float f:\\n tensorRefs[idx].Handle = ((Scalar)f).Handle;\\n tensorRefs[idx].TypeCode = 1;\\n break;\\n case double d:\\n tensorRefs[idx].Handle = ((Scalar)d).Handle;\\n tensorRefs[idx].TypeCode = 1;\\n break;\\n case bool i:\\n tensorRefs[idx].Handle = (IntPtr)(i ? 1L : 0L);\\n tensorRefs[idx].TypeCode = 2;\\n break;\\n case int i:\\n tensorRefs[idx].Handle = (IntPtr)i;\\n tensorRefs[idx].TypeCode = 3;\\n break;\\n case long l:\\n tensorRefs[idx].Handle = ((Scalar)l).Handle;\\n tensorRefs[idx].TypeCode = 1;\\n // The MacOS version of Clang doesn't like the use of int64_t, so pass as a Scalar instance, instead.\\n //tensorRefs[idx].Handle = (IntPtr)l;\\n //tensorRefs[idx].TypeCode = 4;\\n break;\\n case Tensor[] tensors: {\\n tensorRefs[idx].Handle = DetermineArgumentTypeRefs(tensors, out var _, pinned);\\n tensorRefs[idx].TypeCode = 5;\\n tensorRefs[idx].ArrayIndex = tensors.Length;\\n }\\n break;\\n default:\\n if (objs[idx] is null) {\\n tensorRefs[idx].Handle = IntPtr.Zero;\\n tensorRefs[idx].TypeCode = 8;\\n } else {\\n throw new NotImplementedException($\\\"Passing arguments of type {objs[idx].GetType().Name} to TorchScript.\\\");\\n }\\n break;\\n }\\n }\\n\\n return result;\\n }\\n\\n internal static object ProcessReturnValue(string name, IndexedPinnedArrays<TensorOrScalar> pinned, TensorOrScalar[] ptrArray, long typeCode)\\n {\\n switch (typeCode) {\\n default:\\n // Nothing.\\n throw new NotImplementedException($\\\"ScriptModule.{name}() returning something else than a tensor, a tuple of tensors, or list of tensors. TypeCode: {typeCode}\\\");\\n case 1:\\n // Tensor\\n return new Tensor(ptrArray[0].Handle);\\n case 2:\\n // Tuple\\n switch (ptrArray.Length) {\\n case 1:\\n return new Tensor(ptrArray[0].Handle);\\n case 2:\\n return (new Tensor(ptrArray[0].Handle), new Tensor(ptrArray[1].Handle));\\n case 3:\\n return (new Tensor(ptrArray[0].Handle), new Tensor(ptrArray[1].Handle), new Tensor(ptrArray[2].Handle));\\n case 4:\\n return (new Tensor(ptrArray[0].Handle), new Tensor(ptrArray[1].Handle), new Tensor(ptrArray[2].Handle), new Tensor(ptrArray[3].Handle));\\n case 5:\\n return (new Tensor(ptrArray[0].Handle), new Tensor(ptrArray[1].Handle), new Tensor(ptrArray[2].Handle), new Tensor(ptrArray[3].Handle), new Tensor(ptrArray[4].Handle));\\n default: {\\n // Too long a tuple, return as a list, instead.\\n var result = new Tensor[ptrArray.Length];\\n for (var i = 0; i < ptrArray.Length; i++) {\\n result[i] = new Tensor(ptrArray[i].Handle);\\n }\\n return result;\\n }\\n }\\n case 3: {\\n // List of tensors\\n var result = new Tensor[ptrArray.Length];\\n for (var i = 0; i < ptrArray.Length; i++) {\\n result[i] = new Tensor(ptrArray[i].Handle);\\n }\\n return result;\\n }\\n case 4:\\n // Scalar\\n return new Scalar(ptrArray[0].Handle);\\n case 5:\\n // Scalar tuple\\n switch (ptrArray.Length) {\\n case 1:\\n return new Scalar(ptrArray[0].Handle);\\n case 2:\\n return (new Scalar(ptrArray[0].Handle), new Scalar(ptrArray[1].Handle));\\n case 3:\\n return (new Scalar(ptrArray[0].Handle), new Scalar(ptrArray[1].Handle), new Scalar(ptrArray[2].Handle));\\n case 4:\\n return (new Scalar(ptrArray[0].Handle), new Scalar(ptrArray[1].Handle), new Scalar(ptrArray[2].Handle), new Scalar(ptrArray[3].Handle));\\n case 5:\\n return (new Scalar(ptrArray[0].Handle), new Scalar(ptrArray[1].Handle), new Scalar(ptrArray[2].Handle), new Scalar(ptrArray[3].Handle), new Scalar(ptrArray[4].Handle));\\n default: {\\n // Too long a tuple, return as a list, instead.\\n var result = new Scalar[ptrArray.Length];\\n for (var i = 0; i < ptrArray.Length; i++) {\\n result[i] = new Scalar(ptrArray[i].Handle);\\n }\\n return result;\\n }\\n }\\n case 6: {\\n // List of scalars\\n var result = new Scalar[ptrArray.Length];\\n for (var i = 0; i < ptrArray.Length; i++) {\\n result[i] = new Scalar(ptrArray[i].Handle);\\n }\\n return result;\\n }\\n case 7: {\\n // List of scalars and tensors\\n var result = new object[ptrArray.Length];\\n for (var i = 0; i < ptrArray.Length; i++) {\\n switch(ptrArray[i].TypeCode) {\\n case 0:\\n result[i] = new Tensor(ptrArray[i].Handle);\\n break;\\n case 8:\\n result[i] = null;\\n break;\\n case 4:\\n result[i] = new Scalar(ptrArray[i].Handle);\\n break;\\n default:\\n throw new NotImplementedException($\\\"ScriptModule.{name}() returning something else than a tensor/scalar, a tuple of tensors/scalars, or list of tensors/scalars.\\\");\\n }\\n }\\n return result;\\n }\\n case 8:\\n // The value 'null' of any reference type\\n return null;\\n\\n case 10: {\\n switch (ptrArray.Length) {\\n case 1:\\n return ProcessReturnValue(name, pinned, ptrArray[0]);\\n case 2:\\n return (ProcessReturnValue(name, pinned, ptrArray[0]),\\n ProcessReturnValue(name, pinned, ptrArray[1]));\\n case 3:\\n return (ProcessReturnValue(name, pinned, ptrArray[0]),\\n ProcessReturnValue(name, pinned, ptrArray[1]),\\n ProcessReturnValue(name, pinned, ptrArray[2]));\\n case 4:\\n return (ProcessReturnValue(name, pinned, ptrArray[0]),\\n ProcessReturnValue(name, pinned, ptrArray[1]),\\n ProcessReturnValue(name, pinned, ptrArray[2]),\\n ProcessReturnValue(name, pinned, ptrArray[3]));\\n case 5:\\n return (ProcessReturnValue(name, pinned, ptrArray[0]),\\n ProcessReturnValue(name, pinned, ptrArray[1]),\\n ProcessReturnValue(name, pinned, ptrArray[2]),\\n ProcessReturnValue(name, pinned, ptrArray[3]),\\n ProcessReturnValue(name, pinned, ptrArray[4]));\\n default: {\\n // Too long a tuple, return as a list, instead.\\n var result = new Scalar[ptrArray.Length];\\n for (var i = 0; i < ptrArray.Length; i++) {\\n result[i] = new Scalar(ptrArray[i].Handle);\\n }\\n return result;\\n }\\n }\\n }\\n case 9: {\\n // List of anything\\n var result = new object[ptrArray.Length];\\n for (var i = 0; i < ptrArray.Length; i++) {\\n result[i] = ProcessReturnValue(name, pinned, ptrArray[i]);\\n }\\n return result;\\n }\\n }\\n }\\n\\n internal static object ProcessReturnValue(string name, IndexedPinnedArrays<TensorOrScalar> pinned, TensorOrScalar value)\\n {\\n switch (value.TypeCode) {\\n case 0:\\n return new Tensor(value.Handle);\\n case 4:\\n return new Scalar(value.Handle);\\n case 8:\\n return null;\\n default:\\n return ProcessReturnValue(name, pinned, pinned[(int)value.ArrayIndex], value.TypeCode);\\n }\\n\\n throw new NotImplementedException($\\\"ScriptModule.{name}() returning something else than a tensor/scalar, a tuple of tensors/scalars, or list of tensors/scalars.\\\");\\n }\\n\\n /// <summary>\\n /// Invoke a function from the script module.\\n /// </summary>\\n /// <typeparam name=\\\"TResult\\\">The return type of the TorchScript function.</typeparam>\\n /// <param name=\\\"name\\\">The name of the function.</param>\\n /// <param name=\\\"inputs\\\">Function arguments.</param>\\n /// <remarks>\\n /// Only certain types can currently be passed:\\n /// 1. Tensor\\n /// 2. Scalar\\n /// 3. int/long\\n /// 4. double/float\\n /// 5. bool\\n ///\\n /// Only certain types can currently be returned:\\n /// 1. Tensor / Scalar\\n /// 2. Tuple of Tensor / Scalar\\n /// 3. Array (Python list) of Tensor / Scalar\\n ///\\n /// For returned types, if the number of values returned in a tuple is greaterh than 5, it is returned as an array, instead.\\n /// If a tuple contains both tensors and scalars, it is returned as an object[].\\n /// </remarks>\\n public TResult invoke<TResult>(string name, params object[] inputs) => (TResult)invoke(name, inputs);\\n\\n /// <summary>\\n /// Invoke a function from the script module.\\n /// </summary>\\n /// <typeparam name=\\\"T\\\">The type of all function arguments.</typeparam>\\n /// <typeparam name=\\\"TResult\\\">The return type of the TorchScript function.</typeparam>\\n /// <param name=\\\"name\\\">The name of the function.</param>\\n /// <param name=\\\"inputs\\\">Function arguments.</param>\\n /// <remarks>\\n /// Only certain types can currently be passed:\\n /// 1. Tensor\\n /// 2. Scalar\\n /// 3. int/long\\n /// 4. double/float\\n /// 5. bool\\n ///\\n /// Only certain types can currently be returned:\\n /// 1. Tensor / Scalar\\n /// 2. Tuple of Tensor / Scalar\\n /// 3. Array (Python list) of Tensor / Scalar\\n ///\\n /// For returned types, if the number of values returned in a tuple is greaterh than 5, it is returned as an array, instead.\\n /// If a tuple contains both tensors and scalars, it is returned as an object[].\\n /// </remarks>\\n public TResult invoke<T, TResult>(string name, params T[] inputs) => (TResult)invoke(name, inputs);\\n }\\n\\n /// <summary>\\n /// A script module taking any number of tensors as input\\n /// </summary>\\n /// <typeparam name=\\\"TResult\\\">The return type of the module.</typeparam>\\n public class ScriptModule<TResult> : ScriptModule, torch.nn.IModule<Tensor[], TResult>\\n {\\n internal ScriptModule(IntPtr handle) : base(handle) { }\\n\\n /// <summary>\\n /// Invoke the 'forward' function of the script with one tensor as its argument\\n /// </summary>\\n /// <remarks>\\n /// Only certain types can currently be passed:\\n /// 1. Tensor\\n /// 2. Scalar\\n /// 3. int/long\\n /// 4. double/float\\n /// 5. bool\\n ///\\n /// Only certain types can currently be returned:\\n /// 1. Tensor / Scalar\\n /// 2. Tuple of Tensor / Scalar\\n /// 3. Array (Python list) of Tensor / Scalar\\n ///\\n /// For returned types, if the number of values returned in a tuple is greaterh than 5, it is returned as an array, instead.\\n /// If a tuple contains both tensors and scalars, it is returned as an object[].\\n /// </remarks>\\n public TResult call(params Tensor[] tensor)\\n {\\n return (TResult)base.call(tensor);\\n }\\n }\\n\\n /// <summary>\\n /// A script module taking a single argument.\\n /// </summary>\\n /// <typeparam name=\\\"T\\\">The argument type.</typeparam>\\n /// <typeparam name=\\\"TResult\\\">The return type of the module.</typeparam>\\n public class ScriptModule<T, TResult> : ScriptModule, torch.nn.IModule<T, TResult>\\n {\\n internal ScriptModule(IntPtr handle) : base(handle) { }\\n\\n /// <summary>\\n /// Invoke the 'forward' function of the script with one tensor as its argument\\n /// </summary>\\n /// <remarks>\\n /// Only certain types can currently be passed:\\n /// 1. Tensor\\n /// 2. Scalar\\n /// 3. int/long\\n /// 4. double/float\\n /// 5. bool\\n ///\\n /// Only certain types can currently be returned:\\n /// 1. Tensor / Scalar\\n /// 2. Tuple of Tensor / Scalar\\n /// 3. Array (Python list) of Tensor / Scalar\\n ///\\n /// For returned types, if the number of values returned in a tuple is greaterh than 5, it is returned as an array, instead.\\n /// If a tuple contains both tensors and scalars, it is returned as an object[].\\n /// </remarks>\\n public TResult call(T tensor)\\n {\\n return (TResult)base.call(tensor);\\n }\\n }\\n\\n /// <summary>\\n /// A script module taking two arguments.\\n /// </summary>\\n /// <typeparam name=\\\"T1\\\">The first argument type.</typeparam>\\n /// <typeparam name=\\\"T2\\\">The second argument type.</typeparam>\\n /// <typeparam name=\\\"TResult\\\">The return type of the module.</typeparam>\\n public class ScriptModule<T1, T2, TResult> : ScriptModule, torch.nn.IModule<T1, T2, TResult>\\n {\\n internal ScriptModule(IntPtr handle) : base(handle) { }\\n\\n /// <summary>\\n /// Invoke the 'forward' function of the script with one tensor as its argument\\n /// </summary>\\n /// <remarks>\\n /// Only certain types can currently be passed:\\n /// 1. Tensor\\n /// 2. Scalar\\n /// 3. int/long\\n /// 4. double/float\\n /// 5. bool\\n ///\\n /// Only certain types can currently be returned:\\n /// 1. Tensor / Scalar\\n /// 2. Tuple of Tensor / Scalar\\n /// 3. Array (Python list) of Tensor / Scalar\\n ///\\n /// For returned types, if the number of values returned in a tuple is greaterh than 5, it is returned as an array, instead.\\n /// If a tuple contains both tensors and scalars, it is returned as an object[].\\n /// </remarks>\\n public TResult call(T1 input1, T2 input2)\\n {\\n return (TResult)base.call(input1, input2);\\n }\\n }\\n\\n /// <summary>\\n /// Load a ScriptModule or ScriptFunction previously saved with torch.jit.save\\n /// </summary>\\n /// <param name=\\\"filename\\\">The file name of the module.</param>\\n /// <param name=\\\"device_type\\\">The device type, e.g. 'CPU' or 'CUDA'.</param>\\n /// <param name=\\\"device_index\\\">The optional device index.</param>\\n /// <returns>A ScriptModule instance, whether the script originated as a module or function.</returns>\\n /// <remarks>\\n /// All previously saved modules, no matter their device, are first loaded onto CPU, and then are moved to the devices they were saved from.If this fails (e.g.because the run time system doesn’t have certain devices), an exception is raised.\\n /// </remarks>\\n /// <exception cref=\\\"System.IO.FileNotFoundException\\\">Raised if the file is not found.</exception>\\n public static ScriptModule load(string filename, DeviceType device_type = DeviceType.CPU, long device_index = -1)\\n {\\n return new ScriptModule(_load(filename, device_type, device_index));\\n }\\n\\n /// <summary>\\n /// Load a ScriptModule or ScriptFunction previously saved with torch.jit.save\\n /// </summary>\\n /// <param name=\\\"filename\\\">The file name of the module.</param>\\n /// <param name=\\\"map_location\\\">The device type where the script module should be loaded.</param>\\n /// <returns>A ScriptModule instance, whether the script originated as a module or function.</returns>\\n /// <remarks>\\n /// All previously saved modules, no matter their device, are first loaded onto CPU, and then are moved to the devices they were saved from.If this fails (e.g.because the run time system doesn’t have certain devices), an exception is raised.\\n /// </remarks>\\n public static ScriptModule load(string filename, Device map_location)\\n {\\n return new ScriptModule(_load(filename, map_location.type, map_location.index));\\n }\\n\\n /// <summary>\\n /// Load a ScriptModule or ScriptFunction previously saved with torch.jit.save\\n /// </summary>\\n /// <param name=\\\"filename\\\">The file name of the module.</param>\\n /// <param name=\\\"map_location\\\">The device type where the script module should be loaded.</param>\\n /// <returns>A ScriptModule instance, whether the script originated as a module or function.</returns>\\n /// <remarks>\\n /// All previously saved modules, no matter their device, are first loaded onto CPU, and then are moved to the devices they were saved from.If this fails (e.g.because the run time system doesn’t have certain devices), an exception is raised.\\n /// </remarks>\\n public static ScriptModule load(string filename, string map_location)\\n {\\n return load(filename, new Device(map_location));\\n }\\n\\n /// <summary>\\n /// Load a ScriptModule or ScriptFunction previously saved with torch.jit.save\\n /// </summary>\\n /// <typeparam name=\\\"TResult\\\">The return type of the module.</typeparam>\\n /// <param name=\\\"filename\\\">The file name of the module.</param>\\n /// <param name=\\\"device_type\\\">The device type, e.g. 'CPU' or 'CUDA'.</param>\\n /// <param name=\\\"device_index\\\">The optional device index.</param>\\n /// <returns>A ScriptModule instance, whether the script originated as a module or function.</returns>\\n /// <remarks>\\n /// All previously saved modules, no matter their device, are first loaded onto CPU, and then are moved to the devices they were saved from.If this fails (e.g.because the run time system doesn’t have certain devices), an exception is raised.\\n /// </remarks>\\n /// <exception cref=\\\"System.IO.FileNotFoundException\\\">Raised if the file is not found.</exception>\\n public static ScriptModule<TResult> load<TResult>(string filename, DeviceType device_type = DeviceType.CPU, long device_index = -1)\\n {\\n return new ScriptModule<TResult>(_load(filename, device_type, device_index));\\n }\\n\\n /// <summary>\\n /// Load a ScriptModule or ScriptFunction previously saved with torch.jit.save\\n /// </summary>\\n /// <typeparam name=\\\"TResult\\\">The return type of the module.</typeparam>\\n /// <param name=\\\"filename\\\">The file name of the module.</param>\\n /// <param name=\\\"map_location\\\">The device type where the script module should be loaded.</param>\\n /// <returns>A ScriptModule instance, whether the script originated as a module or function.</returns>\\n /// <remarks>\\n /// All previously saved modules, no matter their device, are first loaded onto CPU, and then are moved to the devices they were saved from.If this fails (e.g.because the run time system doesn’t have certain devices), an exception is raised.\\n /// </remarks>\\n public static ScriptModule<TResult> load<TResult>(string filename, Device map_location)\\n {\\n return new ScriptModule<TResult>(_load(filename, map_location.type, map_location.index));\\n }\\n\\n /// <summary>\\n /// Load a ScriptModule or ScriptFunction previously saved with torch.jit.save\\n /// </summary>\\n /// <typeparam name=\\\"TResult\\\">The return type of the module.</typeparam>\\n /// <param name=\\\"filename\\\">The file name of the module.</param>\\n /// <param name=\\\"map_location\\\">The device type where the script module should be loaded.</param>\\n /// <returns>A ScriptModule instance, whether the script originated as a module or function.</returns>\\n /// <remarks>\\n /// All previously saved modules, no matter their device, are first loaded onto CPU, and then are moved to the devices they were saved from.If this fails (e.g.because the run time system doesn’t have certain devices), an exception is raised.\\n /// </remarks>\\n public static ScriptModule<TResult> load<TResult>(string filename, string map_location)\\n {\\n return load<TResult>(filename, new Device(map_location));\\n }\\n\\n /// <summary>\\n /// Load a ScriptModule or ScriptFunction previously saved with torch.jit.save\\n /// </summary>\\n /// <typeparam name=\\\"T1\\\">The argument type.</typeparam>\\n /// <typeparam name=\\\"TResult\\\">The return type of the module.</typeparam>\\n /// <param name=\\\"filename\\\">The file name of the module.</param>\\n /// <param name=\\\"device_type\\\">The device type, e.g. 'CPU' or 'CUDA'.</param>\\n /// <param name=\\\"device_index\\\">The optional device index.</param>\\n /// <returns>A ScriptModule instance, whether the script originated as a module or function.</returns>\\n /// <remarks>\\n /// All previously saved modules, no matter their device, are first loaded onto CPU, and then are moved to the devices they were saved from.If this fails (e.g.because the run time system doesn’t have certain devices), an exception is raised.\\n /// </remarks>\\n /// <exception cref=\\\"System.IO.FileNotFoundException\\\">Raised if the file is not found.</exception>\\n public static ScriptModule<T1, TResult> load<T1, TResult>(string filename, DeviceType device_type = DeviceType.CPU, long device_index = -1)\\n {\\n return new ScriptModule<T1, TResult>(_load(filename, device_type, device_index));\\n }\\n\\n /// <summary>\\n /// Load a ScriptModule or ScriptFunction previously saved with torch.jit.save\\n /// </summary>\\n /// <typeparam name=\\\"T1\\\">The argument type.</typeparam>\\n /// <typeparam name=\\\"TResult\\\">The return type of the module.</typeparam>\\n /// <param name=\\\"filename\\\">The file name of the module.</param>\\n /// <param name=\\\"map_location\\\">The device type where the script module should be loaded.</param>\\n /// <returns>A ScriptModule instance, whether the script originated as a module or function.</returns>\\n /// <remarks>\\n /// All previously saved modules, no matter their device, are first loaded onto CPU, and then are moved to the devices they were saved from.If this fails (e.g.because the run time system doesn’t have certain devices), an exception is raised.\\n /// </remarks>\\n public static ScriptModule<T1, TResult> load<T1, TResult>(string filename, Device map_location)\\n {\\n return new ScriptModule<T1, TResult>(_load(filename, map_location.type, map_location.index));\\n }\\n\\n /// <summary>\\n /// Load a ScriptModule or ScriptFunction previously saved with torch.jit.save\\n /// </summary>\\n /// <typeparam name=\\\"T1\\\">The argument type.</typeparam>\\n /// <typeparam name=\\\"TResult\\\">The return type of the module.</typeparam>\\n /// <param name=\\\"filename\\\">The file name of the module.</param>\\n /// <param name=\\\"map_location\\\">The device type where the script module should be loaded.</param>\\n /// <returns>A ScriptModule instance, whether the script originated as a module or function.</returns>\\n /// <remarks>\\n /// All previously saved modules, no matter their device, are first loaded onto CPU, and then are moved to the devices they were saved from.If this fails (e.g.because the run time system doesn’t have certain devices), an exception is raised.\\n /// </remarks>\\n public static ScriptModule<T1, TResult> load<T1, TResult>(string filename, string map_location)\\n {\\n return load<T1, TResult>(filename, new Device(map_location));\\n }\\n\\n /// <summary>\\n /// Load a ScriptModule or ScriptFunction previously saved with torch.jit.save\\n /// </summary>\\n /// <typeparam name=\\\"T1\\\">The first argument type.</typeparam>\\n /// <typeparam name=\\\"T2\\\">The second argument type.</typeparam>\\n /// <typeparam name=\\\"TResult\\\">The return type of the module.</typeparam>\\n /// <param name=\\\"filename\\\">The file name of the module.</param>\\n /// <param name=\\\"device_type\\\">The device type, e.g. 'CPU' or 'CUDA'.</param>\\n /// <param name=\\\"device_index\\\">The optional device index.</param>\\n /// <returns>A ScriptModule instance, whether the script originated as a module or function.</returns>\\n /// <remarks>\\n /// All previously saved modules, no matter their device, are first loaded onto CPU, and then are moved to the devices they were saved from.If this fails (e.g.because the run time system doesn’t have certain devices), an exception is raised.\\n /// </remarks>\\n /// <exception cref=\\\"System.IO.FileNotFoundException\\\">Raised if the file is not found.</exception>\\n public static ScriptModule<T1, T2, TResult> load<T1, T2, TResult>(string filename, DeviceType device_type = DeviceType.CPU, long device_index = -1)\\n {\\n return new ScriptModule<T1, T2, TResult>(_load(filename, device_type, device_index));\\n }\\n\\n /// <summary>\\n /// Load a ScriptModule or ScriptFunction previously saved with torch.jit.save\\n /// </summary>\\n /// <typeparam name=\\\"T1\\\">The first argument type.</typeparam>\\n /// <typeparam name=\\\"T2\\\">The second argument type.</typeparam>\\n /// <typeparam name=\\\"TResult\\\">The return type of the module.</typeparam>\\n /// <param name=\\\"filename\\\">The file name of the module.</param>\\n /// <param name=\\\"map_location\\\">The device type where the script module should be loaded.</param>\\n /// <returns>A ScriptModule instance, whether the script originated as a module or function.</returns>\\n /// <remarks>\\n /// All previously saved modules, no matter their device, are first loaded onto CPU, and then are moved to the devices they were saved from.If this fails (e.g.because the run time system doesn’t have certain devices), an exception is raised.\\n /// </remarks>\\n public static ScriptModule<T1, T2, TResult> load<T1, T2, TResult>(string filename, Device map_location)\\n {\\n return new ScriptModule<T1, T2, TResult>(_load(filename, map_location.type, map_location.index));\\n }\\n\\n /// <summary>\\n /// Load a ScriptModule or ScriptFunction previously saved with torch.jit.save\\n /// </summary>\\n /// <typeparam name=\\\"T1\\\">The first argument type.</typeparam>\\n /// <typeparam name=\\\"T2\\\">The second argument type.</typeparam>\\n /// <typeparam name=\\\"TResult\\\">The return type of the module.</typeparam>\\n /// <param name=\\\"filename\\\">The file name of the module.</param>\\n /// <param name=\\\"map_location\\\">The device type where the script module should be loaded.</param>\\n /// <returns>A ScriptModule instance, whether the script originated as a module or function.</returns>\\n /// <remarks>\\n /// All previously saved modules, no matter their device, are first loaded onto CPU, and then are moved to the devices they were saved from.If this fails (e.g.because the run time system doesn’t have certain devices), an exception is raised.\\n /// </remarks>\\n public static ScriptModule<T1, T2, TResult> load<T1, T2, TResult>(string filename, string map_location)\\n {\\n return load<T1, T2, TResult>(filename, new Device(map_location));\\n }\\n\\n private static IntPtr _load(string filename, DeviceType device_type, long device_index)\\n {\\n if (!System.IO.File.Exists(filename))\\n throw new System.IO.FileNotFoundException(filename);\\n\\n if (device_type != DeviceType.CUDA) device_index = -1;\\n\\n if (device_type == DeviceType.CUDA && !torch.cuda.is_available()) throw new InvalidOperationException(\\\"CUDA is not available.\\\");\\n\\n var result = THSJIT_load(filename, (long)device_type, device_index);\\n if (result == IntPtr.Zero)\\n CheckForErrors();\\n return result;\\n }\\n\\n /// <summary>\\n /// Save an offline version of a previously loaded script module.\\n ///\\n /// The saved module serializes all of the methods, submodules, parameters, and attributes of this module.\\n /// It can be loaded into the C++ API using torch::jit::load(filename) or into the .NET API with torch.jit.load().\\n /// </summary>\\n /// <param name=\\\"module\\\">The script module to save.</param>\\n /// <param name=\\\"filename\\\">The file name of the module.</param>\\n public static void save(ScriptModule module, string filename)\\n {\\n THSJIT_save(module.handle, filename);\\n CheckForErrors();\\n }\\n\\n }\\n }\\n}\\n\"\n },\n {\n \"alpha_fraction\": 0.55967777967453,\n \"alphanum_fraction\": 0.5726097226142883,\n \"avg_line_length\": 40.377193450927734,\n \"blob_id\": \"87f01e76eaebda0068ea5300fd0613d0935a1f76\",\n \"content_id\": \"b1736b10af745cdac1e6e3a18f9ca34e2826a1f1\",\n \"detected_licenses\": [\n \"MIT\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"C#\",\n \"length_bytes\": 4717,\n \"license_type\": \"permissive\",\n \"max_line_length\": 130,\n \"num_lines\": 114,\n \"path\": \"/src/Examples/Tensorboard.cs\",\n \"repo_name\": \"NiklasGustafsson/TorchSharp\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"// Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information.\\nusing System;\\nusing System.IO;\\nusing System.Linq;\\nusing System.Net.Http;\\nusing System.Threading.Tasks;\\nusing SixLabors.ImageSharp;\\nusing SixLabors.ImageSharp.PixelFormats;\\nusing SkiaSharp;\\nusing static TorchSharp.torch;\\n\\nnamespace TorchSharp.Examples\\n{\\n public static class Tensorboard\\n {\\n private static string imagePath = \\\"TensorboardExample/Images\\\";\\n internal static async Task Main(string[] args)\\n {\\n await DownloadExampleData(\\\"https://agirls.aottercdn.com/media/0d532b3f-0196-466a-96e1-7c6db56d0142.gif\\\");\\n var device = cuda.is_available() ? CUDA : CPU;\\n var writer = utils.tensorboard.SummaryWriter(\\\"tensorboard\\\");\\n Console.WriteLine($\\\"Running Tensorboard on {device.type}\\\");\\n AddText(writer);\\n AddImageUsePath(writer);\\n AddImageUseTensor(writer, device);\\n AddVideo(writer, device);\\n AddHistogram(writer, device);\\n }\\n\\n private static void AddHistogram(Modules.SummaryWriter writer, Device device)\\n {\\n for (int i = 0; i < 10; i++) {\\n Tensor x = randn(1000, device: device);\\n writer.add_histogram(\\\"AddHistogram\\\", x + i, i);\\n }\\n }\\n\\n private static void AddText(Modules.SummaryWriter writer)\\n {\\n writer.add_text(\\\"AddText\\\", \\\"step_1\\\", 1);\\n writer.add_text(\\\"AddText\\\", \\\"step_2\\\", 2);\\n writer.add_text(\\\"AddText\\\", \\\"step_3\\\", 3);\\n writer.add_text(\\\"AddText\\\", \\\"step_4\\\", 4);\\n writer.add_text(\\\"AddText\\\", \\\"step_5\\\", 5);\\n }\\n\\n private static void AddImageUsePath(Modules.SummaryWriter writer)\\n {\\n var imagesPath = Directory.GetFiles(imagePath);\\n for (var i = 0; i < imagesPath.Length; i++) {\\n writer.add_img(\\\"AddImageUsePath\\\", imagesPath[i], i);\\n }\\n }\\n\\n private static void AddImageUseTensor(Modules.SummaryWriter writer, Device device)\\n {\\n var images = Directory.GetFiles(imagePath).Select(item => SKBitmap.Decode(item)).ToArray();\\n using var d = NewDisposeScope();\\n for (var i = 0; i < images.Length; i++) {\\n var tensor = SKBitmapToTensor(images[i], device);\\n writer.add_img(\\\"AddImageUseTensor\\\", tensor, i, dataformats: \\\"CHW\\\");\\n images[i].Dispose();\\n }\\n }\\n\\n private static void AddVideo(Modules.SummaryWriter writer, Device device)\\n {\\n var images = Directory.GetFiles(imagePath).Select(item => SKBitmap.Decode(item)).ToArray();\\n using var d = NewDisposeScope();\\n var tensor = stack(images.Select(item => SKBitmapToTensor(item, device)).ToArray());\\n tensor = stack(new Tensor[] { tensor, tensor, tensor, tensor, tensor, tensor, tensor, tensor, tensor, tensor });\\n foreach (var image in images)\\n image.Dispose();\\n writer.add_video(\\\"AddVideo\\\", tensor, 1, 10);\\n writer.add_video(\\\"AddVideo\\\", tensor, 2, 10);\\n writer.add_video(\\\"AddVideo\\\", tensor, 3, 10);\\n }\\n\\n private static Tensor SKBitmapToTensor(SKBitmap skBitmap, Device device)\\n {\\n var width = skBitmap.Width;\\n var height = skBitmap.Height;\\n var hwcData = new byte[4, height, width];\\n for (var y = 0; y < height; y++) {\\n for (var x = 0; x < width; x++) {\\n var color = skBitmap.GetPixel(x, y);\\n hwcData[0, y, x] = color.Red;\\n hwcData[1, y, x] = color.Green;\\n hwcData[2, y, x] = color.Blue;\\n hwcData[3, y, x] = color.Alpha;\\n }\\n }\\n\\n return tensor(hwcData, ScalarType.Byte, device);\\n }\\n\\n private static async Task DownloadExampleData(string url)\\n {\\n if (Directory.Exists(imagePath))\\n Directory.Delete(imagePath, true);\\n Directory.CreateDirectory(imagePath);\\n using var client = new HttpClient();\\n\\n using var message = await client.GetAsync(url);\\n using var stream = await message.Content.ReadAsStreamAsync();\\n using var animatedImage = Image.Load<Rgba32>(stream);\\n for (var i = 0; i < animatedImage.Frames.Count; i++) {\\n var pngFilename = Path.Combine(imagePath, $\\\"frame_{i}.png\\\");\\n using var pngImage = animatedImage.Frames.CloneFrame(i);\\n pngImage.SaveAsPng(pngFilename);\\n }\\n }\\n }\\n}\\n\"\n },\n {\n \"alpha_fraction\": 0.5763546824455261,\n \"alphanum_fraction\": 0.5927750468254089,\n \"avg_line_length\": 40.305084228515625,\n \"blob_id\": \"f589f3f33a08169ee87b3b43bdb2c224fec76be1\",\n \"content_id\": \"62f44b6b8c5fe2b58bdaaa9951036199b893bbb8\",\n \"detected_licenses\": [\n \"MIT\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"C#\",\n \"length_bytes\": 2436,\n \"license_type\": \"permissive\",\n \"max_line_length\": 130,\n \"num_lines\": 59,\n \"path\": \"/src/TorchVision/models/Utils.cs\",\n \"repo_name\": \"NiklasGustafsson/TorchSharp\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"// Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information.\\n\\n// A number of implementation details in this file have been translated from the Python version of torchvision,\\n// largely located in the files found in this folder:\\n//\\n// https://github.com/pytorch/vision/blob/6ebbdfe8a6b47e1f6d6164b0c86ac48839281602/torchvision/models/_utils.py\\n//\\n// The origin has the following copyright notice and license:\\n//\\n// https://github.com/pytorch/vision/blob/main/LICENSE\\n//\\n\\n// Copyright 2018 The TensorFlow Authors. All Rights Reserved.\\n//\\n// Licensed under the Apache License, Version 2.0 (the \\\"License\\\");\\n// you may not use this file except in compliance with the License.\\n// You may obtain a copy of the License at\\n//\\n// http://www.apache.org/licenses/LICENSE-2.0\\n//\\n// Unless required by applicable law or agreed to in writing, software\\n// distributed under the License is distributed on an \\\"AS IS\\\" BASIS,\\n// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\\n// See the License for the specific language governing permissions and\\n// limitations under the License.\\n// ==============================================================================\\n\\nusing System;\\n\\nnamespace TorchSharp\\n{\\n public static partial class torchvision\\n {\\n public static partial class models\\n {\\n internal static partial class _utils\\n {\\n /// <summary>\\n /// This function is taken from the original tf repo.\\n /// It ensures that all layers have a channel number that is divisible by 8\\n /// It can be seen here:\\n /// https://github.com/tensorflow/models/blob/master/research/slim/nets/mobilenet/mobilenet.py\\n /// </summary>\\n internal static long _make_divisible(double v, long divisor, long? min_value = null)\\n {\\n if (!min_value.HasValue) {\\n min_value = divisor;\\n }\\n var new_v = Math.Max(min_value.Value, (long)(v + divisor / 2.0) / divisor * divisor);\\n // Make sure that round down does not go down by more than 10%.\\n if (new_v < 0.9 * v) {\\n new_v += divisor;\\n }\\n return new_v;\\n }\\n }\\n }\\n }\\n}\"\n },\n {\n \"alpha_fraction\": 0.54841548204422,\n \"alphanum_fraction\": 0.54841548204422,\n \"avg_line_length\": 28.153846740722656,\n \"blob_id\": \"670f70b04e3caf4d518614f697194eb1f7520d8c\",\n \"content_id\": \"ce02d56de466c8efce0f67475e7045395cb20e87\",\n \"detected_licenses\": [\n \"MIT\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"C#\",\n \"length_bytes\": 1136,\n \"license_type\": \"permissive\",\n \"max_line_length\": 130,\n \"num_lines\": 39,\n \"path\": \"/src/TorchVision/Posterize.cs\",\n \"repo_name\": \"NiklasGustafsson/TorchSharp\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"// Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information.\\n\\nusing static TorchSharp.torch;\\n\\n\\nnamespace TorchSharp\\n{\\n public static partial class torchvision\\n {\\n internal class Posterize : ITransform\\n {\\n internal Posterize(int bits)\\n {\\n this.bits = bits;\\n }\\n\\n public Tensor call(Tensor input)\\n {\\n return transforms.functional.posterize(input, bits);\\n }\\n\\n protected int bits;\\n }\\n\\n public static partial class transforms\\n {\\n /// <summary>\\n /// Posterize the image by reducing the number of bits for each color channel. \\n /// </summary>\\n /// <param name=\\\"bits\\\">Number of high-order bits to keep for each channel.</param>\\n /// <returns></returns>\\n /// <remarks>The tensor must be an integer tensor.</remarks>\\n static public ITransform Posterize(int bits)\\n {\\n return new Posterize(bits);\\n }\\n }\\n }\\n}\"\n },\n {\n \"alpha_fraction\": 0.5193594694137573,\n \"alphanum_fraction\": 0.533819317817688,\n \"avg_line_length\": 36.693695068359375,\n \"blob_id\": \"fdc8fb26eccd93cd0063ec5697cb4028303e58ee\",\n \"content_id\": \"f17af4df5bbb6deed748b9657109ccff7622c534\",\n \"detected_licenses\": [\n \"MIT\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"C#\",\n \"length_bytes\": 8368,\n \"license_type\": \"permissive\",\n \"max_line_length\": 150,\n \"num_lines\": 222,\n \"path\": \"/src/Examples/ImageTransforms.cs\",\n \"repo_name\": \"NiklasGustafsson/TorchSharp\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"// Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information.\\nusing System;\\nusing System.Collections.Generic;\\nusing System.Linq;\\nusing System.IO;\\nusing SkiaSharp;\\nusing static TorchSharp.torch;\\nusing System.Runtime.InteropServices;\\n\\nnamespace TorchSharp.Examples\\n{\\n public class ImageTransforms\\n {\\n internal static void Main(string[] args)\\n {\\n var images = new string[] {\\n //\\n // Find some PNG (or JPEG, etc.) files, download them, and then put their file paths here.\\n //\\n };\\n\\n const string outputPathPrefix = /* Add the very first part of your repo path here. */ @\\\"\\\\TorchSharp\\\\output-\\\";\\n var tensors = LoadImages(images, 4, 3, 256, 256);\\n\\n var first = tensors[0];\\n\\n int n = 0;\\n\\n // First, use the transform version.\\n\\n var transform = torchvision.transforms.Compose(\\n torchvision.transforms.ConvertImageDtype(ScalarType.Float32),\\n //torchvision.transforms.ColorJitter(.5f, .5f, .5f, .25f),\\n torchvision.transforms.ConvertImageDtype(ScalarType.Byte),\\n torchvision.transforms.Resize(256, 256)\\n );\\n\\n var second = transform.call(first);\\n\\n for (; n < second.shape[0]; n++) {\\n\\n var image = second[n]; // CxHxW\\n var channels = image.shape[0];\\n\\n using (var stream = File.OpenWrite(outputPathPrefix + n + \\\".png\\\")) {\\n var bitmap = GetBitmapFromBytes(image.data<byte>().ToArray(), 256, 256, channels == 1 ? SKColorType.Gray8 : SKColorType.Bgra8888);\\n bitmap.Encode(stream, SKEncodedImageFormat.Png, 100);\\n }\\n }\\n\\n // Then the functional API version.\\n\\n second = torchvision.transforms.functional.convert_image_dtype(first);\\n // Have to do this to make sure that everything's in the right format before saving.\\n second = torchvision.transforms.functional.convert_image_dtype(second, dtype: ScalarType.Byte);\\n second = torchvision.transforms.functional.equalize(second);\\n second = torchvision.transforms.functional.resize(second, 256, 256);\\n\\n for (n = 0; n < second.shape[0]; n++) {\\n\\n var image = second[n]; // CxHxW\\n var channels = image.shape[0];\\n\\n using (var stream = File.OpenWrite(outputPathPrefix + (n + first.shape[0]) + \\\".png\\\")) {\\n var bitmap = GetBitmapFromBytes(image.data<byte>().ToArray(), 256, 256, channels == 1 ? SKColorType.Gray8 : SKColorType.Bgra8888);\\n bitmap.Encode(stream, SKEncodedImageFormat.Png, 100);\\n }\\n }\\n }\\n\\n private static List<Tensor> LoadImages(IList<string> images, int batchSize, int channels, int height, int width)\\n {\\n List<Tensor> tensors = new List<Tensor>();\\n\\n var imgSize = channels * height * width;\\n bool shuffle = false;\\n\\n Random rnd = new Random();\\n var indices = !shuffle ?\\n Enumerable.Range(0, images.Count).ToArray() :\\n Enumerable.Range(0, images.Count).OrderBy(c => rnd.Next()).ToArray();\\n\\n\\n // Go through the data and create tensors\\n for (var i = 0; i < images.Count;) {\\n\\n var take = Math.Min(batchSize, Math.Max(0, images.Count - i));\\n\\n if (take < 1) break;\\n\\n var dataTensor = torch.zeros(new long[] { take, imgSize }, ScalarType.Byte);\\n\\n // Take\\n for (var j = 0; j < take; j++) {\\n var idx = indices[i++];\\n var lblStart = idx * (1 + imgSize);\\n var imgStart = lblStart + 1;\\n\\n using (var stream = new SKManagedStream(File.OpenRead(images[idx])))\\n using (var bitmap = SKBitmap.Decode(stream)) {\\n using (var inputTensor = torch.tensor(GetBytesWithoutAlpha(bitmap))) {\\n\\n Tensor finalized = inputTensor;\\n\\n var nz = inputTensor.count_nonzero().item<long>();\\n\\n if (bitmap.Width != width || bitmap.Height != height) {\\n var t = inputTensor.reshape(1, channels, bitmap.Height, bitmap.Width);\\n finalized = torchvision.transforms.functional.resize(t, height, width).reshape(imgSize);\\n }\\n\\n dataTensor.index_put_(finalized, TensorIndex.Single(j));\\n }\\n }\\n }\\n\\n tensors.Add(dataTensor.reshape(take, channels, height, width));\\n dataTensor.Dispose();\\n }\\n\\n return tensors;\\n }\\n\\n private static byte[] GetBytesWithoutAlpha(SKBitmap bitmap)\\n {\\n var height = bitmap.Height;\\n var width = bitmap.Width;\\n\\n var inputBytes = bitmap.Bytes;\\n\\n if (bitmap.ColorType == SKColorType.Gray8)\\n return inputBytes;\\n\\n if (bitmap.BytesPerPixel != 4 && bitmap.BytesPerPixel != 1)\\n throw new ArgumentException(\\\"Conversion only supports grayscale and ARGB\\\");\\n\\n var channelLength = height * width;\\n\\n var channelCount = 3;\\n\\n int inputBlue = 0, inputGreen = 0, inputRed = 0;\\n int outputRed = 0, outputGreen = channelLength, outputBlue = channelLength * 2;\\n\\n switch (bitmap.ColorType) {\\n case SKColorType.Bgra8888:\\n inputBlue = 0;\\n inputGreen = 1;\\n inputRed = 2;\\n break;\\n\\n default:\\n throw new NotImplementedException($\\\"Conversion from {bitmap.ColorType} to bytes\\\");\\n }\\n var outBytes = new byte[channelCount * channelLength];\\n\\n for (int i = 0, j = 0; i < channelLength; i += 1, j += 4) {\\n outBytes[outputRed + i] = inputBytes[inputRed + j];\\n outBytes[outputGreen + i] = inputBytes[inputGreen + j];\\n outBytes[outputBlue + i] = inputBytes[inputBlue + j];\\n }\\n\\n return outBytes;\\n }\\n\\n private static SKBitmap GetBitmapFromBytes(byte[] inputBytes, int height, int width, SKColorType colorType)\\n {\\n var result = new SKBitmap();\\n\\n var channelLength = height * width;\\n\\n var channelCount = 0;\\n\\n int inputRed = 0, inputGreen = channelLength, inputBlue = channelLength * 2;\\n int outputBlue = 0, outputGreen = 0, outputRed = 0, outputAlpha = 0;\\n\\n switch (colorType) {\\n case SKColorType.Bgra8888:\\n outputBlue = 0;\\n outputGreen = 1;\\n outputRed = 2;\\n outputAlpha = 3;\\n channelCount = 3;\\n break;\\n\\n case SKColorType.Gray8:\\n channelCount = 1;\\n break;\\n\\n default:\\n throw new NotImplementedException($\\\"Conversion from {colorType} to bytes\\\");\\n }\\n\\n byte[] outBytes = null;\\n\\n if (channelCount == 1) {\\n\\n // Greyscale\\n\\n outBytes = inputBytes;\\n } else {\\n\\n outBytes = new byte[(channelCount + 1) * channelLength];\\n\\n for (int i = 0, j = 0; i < channelLength; i += 1, j += 4) {\\n outBytes[outputRed + j] = inputBytes[inputRed + i];\\n outBytes[outputGreen + j] = inputBytes[inputGreen + i];\\n outBytes[outputBlue + j] = inputBytes[inputBlue + i];\\n outBytes[outputAlpha + j] = 255;\\n }\\n }\\n\\n // pin the managed array so that the GC doesn't move it\\n var gcHandle = GCHandle.Alloc(outBytes, GCHandleType.Pinned);\\n\\n // install the pixels with the color type of the pixel data\\n var info = new SKImageInfo(width, height, colorType, SKAlphaType.Unpremul);\\n result.InstallPixels(info, gcHandle.AddrOfPinnedObject(), info.RowBytes, delegate { gcHandle.Free(); }, null);\\n\\n return result;\\n }\\n }\\n}\\n\"\n },\n {\n \"alpha_fraction\": 0.5183297395706177,\n \"alphanum_fraction\": 0.5279356837272644,\n \"avg_line_length\": 57.85769271850586,\n \"blob_id\": \"85c14395ea3ad306cb5bbc3dfe854281d3db3e27\",\n \"content_id\": \"5c7e8a1214722835e2c164138283b1dddaed584d\",\n \"detected_licenses\": [\n \"MIT\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"C#\",\n \"length_bytes\": 15303,\n \"license_type\": \"permissive\",\n \"max_line_length\": 302,\n \"num_lines\": 260,\n \"path\": \"/src/TorchSharp/NN/Pooling/MaxPool2D.cs\",\n \"repo_name\": \"NiklasGustafsson/TorchSharp\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"// Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information.\\nusing System;\\nusing System.Linq;\\nusing static TorchSharp.torch;\\nusing static TorchSharp.PInvoke.NativeMethods;\\n\\nnamespace TorchSharp\\n{\\n using Modules;\\n\\n namespace Modules\\n {\\n /// <summary>\\n /// This class is used to represent a MaxPool2D module.\\n /// </summary>\\n public sealed class MaxPool2d : torch.nn.Module<Tensor, Tensor>\\n {\\n internal MaxPool2d(IntPtr handle, IntPtr boxedHandle) : base(handle, boxedHandle)\\n {\\n }\\n\\n public override Tensor forward(Tensor tensor)\\n {\\n var res = THSNN_MaxPool2d_forward(handle, tensor.Handle);\\n if (res == IntPtr.Zero) { torch.CheckForErrors(); }\\n return new Tensor(res);\\n }\\n public (Tensor Values, Tensor Indices) forward_with_indices(Tensor tensor)\\n {\\n var res = THSNN_MaxPool2d_forward_with_indices(handle, tensor.Handle, out var indices);\\n if (res == IntPtr.Zero || indices == IntPtr.Zero) { torch.CheckForErrors(); }\\n return (new Tensor(res), new Tensor(indices));\\n }\\n\\n // Rather than spending cycles only to discover that this module has neither\\n // parameters nor buffers, just shortcut the move completely.\\n protected internal override nn.Module _to(Device device, ScalarType dtype) => this;\\n protected internal override nn.Module _to(DeviceType deviceType, int deviceIndex = -1) => this;\\n protected internal override nn.Module _to(ScalarType dtype) => this;\\n }\\n }\\n\\n public static partial class torch\\n {\\n public static partial class nn\\n {\\n /// <summary>\\n /// Applies a 2D max pooling over an input signal composed of several input planes.\\n /// </summary>\\n /// <param name=\\\"kernelSize\\\">The size of the sliding window, must be > 0.</param>\\n /// <param name=\\\"stride\\\">The stride of the sliding window, must be > 0. Default value is kernel_size.</param>\\n /// <param name=\\\"padding\\\">Implicit negative infinity padding to be added on both sides, must be >= 0 and less than or equal to kernel_size / 2</param>\\n /// <param name=\\\"dilation\\\">The stride between elements within a sliding window, must be > 0.</param>\\n /// <param name=\\\"ceilMode\\\">If true, will use ceil instead of floor to compute the output shape. This ensures that every element in the input tensor is covered by a sliding window.</param>\\n /// <returns></returns>\\n public static unsafe MaxPool2d MaxPool2d(long kernelSize, long? stride = null, long? padding = null, long? dilation = null, bool ceilMode = false)\\n {\\n long svalue = stride.HasValue ? stride.Value : kernelSize;\\n long pvalue = padding.HasValue ? padding.Value : 0;\\n long dvalue = dilation.HasValue ? dilation.Value : 1;\\n\\n long* pStride = stackalloc long[2] { svalue, svalue };\\n long* pPadding = stackalloc long[2] { pvalue, pvalue };\\n long* pDilation = stackalloc long[2] { dvalue, dvalue };\\n\\n long* pkernelSize = stackalloc long[2] { kernelSize, kernelSize };\\n\\n var handle = THSNN_MaxPool2d_ctor((IntPtr)pkernelSize, 2, (IntPtr)pStride, 2, (IntPtr)pPadding, 2, (IntPtr)pDilation, 2, ceilMode, out var boxedHandle);\\n if (handle == IntPtr.Zero) { torch.CheckForErrors(); }\\n return new MaxPool2d(handle, boxedHandle);\\n }\\n\\n /// <summary>\\n /// Applies a 2D max pooling over an input signal composed of several input planes.\\n /// </summary>\\n /// <param name=\\\"kernelSize\\\">The size of the sliding window, must be > 0.</param>\\n /// <param name=\\\"stride\\\">The stride of the sliding window, must be > 0. Default value is kernel_size.</param>\\n /// <param name=\\\"padding\\\">Implicit negative infinity padding to be added on both sides, must be >= 0 and less than or equal to kernel_size / 2</param>\\n /// <param name=\\\"dilation\\\">The stride between elements within a sliding window, must be > 0.</param>\\n /// <param name=\\\"ceilMode\\\">If true, will use ceil instead of floor to compute the output shape. This ensures that every element in the input tensor is covered by a sliding window.</param>\\n /// <returns></returns>\\n public static unsafe MaxPool2d MaxPool2d((long, long) kernelSize, (long, long)? stride = null, (long, long)? padding = null, (long, long)? dilation = null, bool ceilMode = false)\\n {\\n long svalue1 = stride != null ? stride.Value.Item1 : kernelSize.Item1;\\n long svalue2 = stride != null ? stride.Value.Item2 : kernelSize.Item2;\\n long pvalue1 = padding != null ? padding.Value.Item1 : 0;\\n long pvalue2 = padding != null ? padding.Value.Item2 : 0;\\n long dvalue1 = dilation != null ? dilation.Value.Item1 : 1;\\n long dvalue2 = dilation != null ? dilation.Value.Item2 : 1;\\n\\n long* pStride = stackalloc long[2] { svalue1, svalue2 };\\n long* pPadding = stackalloc long[2] { pvalue1, pvalue2 };\\n long* pDilation = stackalloc long[2] { dvalue1, dvalue2 };\\n\\n long* pkernelSize = stackalloc long[2] { kernelSize.Item1, kernelSize.Item2 };\\n\\n var handle = THSNN_MaxPool2d_ctor((IntPtr)pkernelSize, 2, (IntPtr)pStride, 2, (IntPtr)pPadding, 2, (IntPtr)pDilation, 2, ceilMode, out var boxedHandle);\\n if (handle == IntPtr.Zero) { torch.CheckForErrors(); }\\n return new MaxPool2d(handle, boxedHandle);\\n }\\n\\n /// <summary>\\n /// Applies a 2D max pooling over an input signal composed of several input planes.\\n /// </summary>\\n /// <param name=\\\"kernelSize\\\">The size of the sliding window, must be > 0.</param>\\n /// <param name=\\\"strides\\\">The stride of the sliding window, must be > 0. Default value is kernel_size.</param>\\n /// <param name=\\\"padding\\\">Implicit negative infinity padding to be added on both sides, must be >= 0 and less than or equal to kernel_size / 2</param>\\n /// <param name=\\\"dilation\\\">The stride between elements within a sliding window, must be > 0.</param>\\n /// <param name=\\\"ceilMode\\\">If true, will use ceil instead of floor to compute the output shape. This ensures that every element in the input tensor is covered by a sliding window.</param>\\n /// <returns></returns>\\n public static unsafe MaxPool2d MaxPool2d(long[] kernelSize, long[] strides = null, long[] padding = null, long[] dilation = null, bool ceilMode = false)\\n {\\n fixed (long* pkernelSize = kernelSize, pstrides = strides, pPadding = padding, pDilation = dilation) {\\n var handle = THSNN_MaxPool2d_ctor((IntPtr)pkernelSize, kernelSize.Length, (IntPtr)pstrides, (strides == null ? 0 : strides.Length), (IntPtr)pPadding, (padding == null ? 0 : padding.Length), (IntPtr)pDilation, (dilation == null ? 0 : dilation.Length), ceilMode, out var boxedHandle);\\n if (handle == IntPtr.Zero) { torch.CheckForErrors(); }\\n return new MaxPool2d(handle, boxedHandle);\\n }\\n }\\n\\n public static partial class functional\\n {\\n /// <summary>\\n /// Applies a 2D max pooling over an input signal composed of several input planes.\\n /// </summary>\\n /// <param name=\\\"input\\\">The input tensor.</param>\\n /// <param name=\\\"kernelSize\\\"></param>\\n /// <param name=\\\"strides\\\"></param>\\n /// <param name=\\\"padding\\\"></param>\\n /// <param name=\\\"dilation\\\"></param>\\n /// <param name=\\\"ceil_mode\\\"></param>\\n /// <returns></returns>\\n public static Tensor max_pool2d(Tensor input, long[] kernelSize, long[] strides = null,\\n long[] padding = null, long[] dilation = null, bool ceil_mode = false)\\n {\\n strides = strides ?? kernelSize.Select(x => 1L).ToArray();\\n padding = padding ?? kernelSize.Select(x => 0L).ToArray();\\n dilation = dilation ?? kernelSize.Select(x => 1L).ToArray();\\n unsafe {\\n fixed (long* pkernelSize = kernelSize, pstrides = strides, ppadding = padding, pdilation = dilation) {\\n var res =\\n THSTensor_max_pool2d(input.Handle,\\n (IntPtr)pkernelSize, kernelSize.Length,\\n (IntPtr)pstrides, strides.Length,\\n (IntPtr)ppadding, padding.Length,\\n (IntPtr)pdilation, dilation.Length,\\n ceil_mode);\\n if (res == IntPtr.Zero) { torch.CheckForErrors(); }\\n return new Tensor(res);\\n }\\n }\\n }\\n\\n /// <summary>\\n /// Applies a 2D max pooling over an input signal composed of several input planes.\\n /// </summary>\\n /// <param name=\\\"input\\\">The input tensor.</param>\\n /// <param name=\\\"kernelSize\\\"></param>\\n /// <param name=\\\"stride\\\"></param>\\n /// <param name=\\\"padding\\\"></param>\\n /// <param name=\\\"dilation\\\"></param>\\n /// <param name=\\\"ceil_mode\\\"></param>\\n /// <returns></returns>\\n public static unsafe Tensor max_pool2d(Tensor input, long kernelSize, long? stride = null,\\n long? padding = null, long? dilation = null, bool ceil_mode = false)\\n {\\n long svalue = stride.HasValue ? stride.Value : kernelSize;\\n long pvalue = padding.HasValue ? padding.Value : 0;\\n long dvalue = dilation.HasValue ? dilation.Value : 1;\\n\\n long* pStride = stackalloc long[2] { svalue, svalue };\\n long* pPadding = stackalloc long[2] { pvalue, pvalue };\\n long* pDilation = stackalloc long[2] { dvalue, dvalue };\\n\\n long* pkernelSize = stackalloc long[2] { kernelSize, kernelSize };\\n\\n var res = THSTensor_max_pool2d(input.Handle,\\n (IntPtr)pkernelSize, 2,\\n (IntPtr)pStride, 2,\\n (IntPtr)pPadding, 2,\\n (IntPtr)pDilation, 2,\\n ceil_mode);\\n if (res == IntPtr.Zero) { torch.CheckForErrors(); }\\n return new Tensor(res);\\n }\\n\\n /// <summary>\\n /// Applies a 2D max pooling over an input signal composed of several input planes.\\n /// </summary>\\n /// <param name=\\\"input\\\">The input tensor.</param>\\n /// <param name=\\\"kernelSize\\\"></param>\\n /// <param name=\\\"stride\\\"></param>\\n /// <param name=\\\"padding\\\"></param>\\n /// <param name=\\\"dilation\\\"></param>\\n /// <param name=\\\"ceil_mode\\\"></param>\\n /// <returns></returns>\\n public static unsafe Tensor max_pool2d(Tensor input, (long, long) kernelSize, (long, long)? stride = null,\\n (long, long)? padding = null, (long, long)? dilation = null, bool ceil_mode = false)\\n {\\n long svalue1 = stride != null ? stride.Value.Item1 : kernelSize.Item1;\\n long svalue2 = stride != null ? stride.Value.Item2 : kernelSize.Item2;\\n long pvalue1 = padding != null ? padding.Value.Item1 : 0;\\n long pvalue2 = padding != null ? padding.Value.Item2 : 0;\\n long dvalue1 = dilation != null ? dilation.Value.Item1 : 1;\\n long dvalue2 = dilation != null ? dilation.Value.Item2 : 1;\\n\\n long* pStride = stackalloc long[2] { svalue1, svalue2 };\\n long* pPadding = stackalloc long[2] { pvalue1, pvalue2 };\\n long* pDilation = stackalloc long[2] { dvalue1, dvalue2 };\\n\\n long* pkernelSize = stackalloc long[2] { kernelSize.Item1, kernelSize.Item2 };\\n\\n var res = THSTensor_max_pool2d(input.Handle,\\n (IntPtr)pkernelSize, 2,\\n (IntPtr)pStride, 2,\\n (IntPtr)pPadding, 2,\\n (IntPtr)pDilation, 2,\\n ceil_mode);\\n if (res == IntPtr.Zero) { torch.CheckForErrors(); }\\n return new Tensor(res);\\n }\\n\\n /// <summary>\\n /// Applies a 2D max pooling over an input signal composed of several input planes.\\n /// </summary>\\n /// <param name=\\\"input\\\">The input tensor.</param>\\n /// <param name=\\\"kernelSize\\\"></param>\\n /// <param name=\\\"strides\\\"></param>\\n /// <param name=\\\"padding\\\"></param>\\n /// <param name=\\\"dilation\\\"></param>\\n /// <param name=\\\"ceil_mode\\\"></param>\\n /// <returns></returns>\\n public static (Tensor output, Tensor indices) max_pool2d_with_indices(Tensor input, long[] kernelSize, long[] strides = null,\\n long[] padding = null, long[] dilation = null, bool ceil_mode = false)\\n {\\n strides = strides ?? kernelSize.Select(x => 1L).ToArray();\\n padding = padding ?? kernelSize.Select(x => 0L).ToArray();\\n dilation = dilation ?? kernelSize.Select(x => 1L).ToArray();\\n IntPtr[] ptrArray;\\n\\n using (var pa = new PinnedArray<IntPtr>()) {\\n unsafe {\\n fixed (long* pkernelSize = kernelSize, pstrides = strides, ppadding = padding, pdilation = dilation) {\\n THSTensor_max_pool2d_with_indices(input.Handle,\\n pa.CreateArray,\\n (IntPtr)pkernelSize, kernelSize.Length,\\n (IntPtr)pstrides, strides.Length,\\n (IntPtr)ppadding, padding.Length,\\n (IntPtr)pdilation, dilation.Length,\\n ceil_mode);\\n torch.CheckForErrors();\\n }\\n }\\n ptrArray = pa.Array;\\n }\\n return (new Tensor(ptrArray[0]), new Tensor(ptrArray[1]));\\n }\\n }\\n }\\n }\\n}\\n\"\n },\n {\n \"alpha_fraction\": 0.5946969985961914,\n \"alphanum_fraction\": 0.6032196879386902,\n \"avg_line_length\": 31,\n \"blob_id\": \"481df313a8e1a18d61b74be79e92d01038fd6dfd\",\n \"content_id\": \"d1a36278c533c5b7a345edf081810f4dd452d261\",\n \"detected_licenses\": [\n \"MIT\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"C#\",\n \"length_bytes\": 1056,\n \"license_type\": \"permissive\",\n \"max_line_length\": 121,\n \"num_lines\": 33,\n \"path\": \"/src/Examples/IOReadWrite.cs\",\n \"repo_name\": \"NiklasGustafsson/TorchSharp\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"using System;\\n\\nnamespace TorchSharp.Examples\\n{\\n class IOReadWrite\\n {\\n internal static void Main(string[] args)\\n {\\n torchvision.io.DefaultImager = new torchvision.io.SkiaImager();\\n\\n var filename = args[0];\\n\\n Console.WriteLine($\\\"Reading file {filename}\\\");\\n\\n var img = torchvision.io.read_image(filename, torchvision.io.ImageReadMode.GRAY);\\n\\n Console.WriteLine($\\\"Image has {img.shape[0]} colour channels with dimensions {img.shape[1]}x{img.shape[2]}\\\");\\n\\n var transformed = torchvision.transforms.Compose(\\n //torchvision.transforms.Invert(),\\n torchvision.transforms.HorizontalFlip(),\\n //torchvision.transforms.CenterCrop(256),\\n torchvision.transforms.Rotate(50)\\n ).call(img);\\n\\n var out_file = \\\"image_transformed.jpg\\\";\\n\\n torchvision.io.write_image(transformed, out_file, torchvision.ImageFormat.Jpeg);\\n\\n Console.WriteLine($\\\"Wrote file {out_file}\\\");\\n }\\n }\\n}\\n\"\n },\n {\n \"alpha_fraction\": 0.704081654548645,\n \"alphanum_fraction\": 0.7052153944969177,\n \"avg_line_length\": 43.150001525878906,\n \"blob_id\": \"2b651afb27fc39cfe7debf93ea0a8419cff5871c\",\n \"content_id\": \"205f94c422580cef49be3aded7cd57907a2efdfc\",\n \"detected_licenses\": [\n \"MIT\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"C#\",\n \"length_bytes\": 882,\n \"license_type\": \"permissive\",\n \"max_line_length\": 114,\n \"num_lines\": 20,\n \"path\": \"/src/TorchSharp/Utils/ObjectReferenceEqualityComparer.cs\",\n \"repo_name\": \"NiklasGustafsson/TorchSharp\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"using System;\\nusing System.Collections.Generic;\\nusing System.Runtime.CompilerServices;\\n\\nnamespace TorchSharp.Utils\\n{\\n /// <summary>\\n /// A generic object comparerer that would only use object's reference,\\n /// ignoring any <see cref=\\\"IEquatable{T}\\\"/> or <see cref=\\\"object.Equals(object)\\\"/> overrides.\\n /// This should be replaced with the official ReferenceEqualityComparer as soon as the TorchSharp uses .NET 6.\\n /// </summary>\\n public class ReferenceEqualityComparer<T> : EqualityComparer<T>\\n where T : class\\n {\\n private static IEqualityComparer<T> _defaultComparer;\\n public new static IEqualityComparer<T> Default => _defaultComparer ??= new ReferenceEqualityComparer<T>();\\n public override bool Equals(T x, T y) => ReferenceEquals(x, y);\\n public override int GetHashCode(T obj) => RuntimeHelpers.GetHashCode(obj);\\n }\\n}\"\n },\n {\n \"alpha_fraction\": 0.4921130836009979,\n \"alphanum_fraction\": 0.4954659640789032,\n \"avg_line_length\": 56.056522369384766,\n \"blob_id\": \"565ff5396364bfd0c12ec0ae8e94db9cbca72915\",\n \"content_id\": \"149eca23c273cdf24ebbd4489e359a8d8ab41408\",\n \"detected_licenses\": [\n \"MIT\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"C#\",\n \"length_bytes\": 13129,\n \"license_type\": \"permissive\",\n \"max_line_length\": 203,\n \"num_lines\": 230,\n \"path\": \"/src/TorchSharp/NN/Upsample.cs\",\n \"repo_name\": \"NiklasGustafsson/TorchSharp\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"// Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information.\\nusing System;\\nusing static TorchSharp.torch;\\nusing static TorchSharp.PInvoke.NativeMethods;\\n\\n#nullable enable\\nnamespace TorchSharp\\n{\\n using Modules;\\n\\n public static partial class torch\\n {\\n public static partial class nn\\n {\\n /// <summary>\\n /// Upsamples a given multi-channel 1D (temporal), 2D (spatial) or 3D (volumetric) data.\\n /// The input data is assumed to be of the form minibatch x channels x[optional depth] x[optional height] x width.\\n /// Hence, for spatial inputs, we expect a 4D Tensor and for volumetric inputs, we expect a 5D Tensor.\\n /// </summary>\\n /// <param name=\\\"size\\\">Output spatial sizes</param>\\n /// <param name=\\\"scale_factor\\\">Multiplier for spatial size. Has to match input size</param>\\n /// <param name=\\\"mode\\\">The upsampling algorithm: one of 'nearest', 'linear', 'bilinear', 'bicubic' and 'trilinear'. Default: 'nearest'</param>\\n /// <param name=\\\"alignCorners\\\">If true, the corner pixels of the input and output tensors are aligned, and thus preserving the values at those pixels.\\n /// This only has effect when mode is 'linear', 'bilinear', or 'trilinear'. Default: false</param>\\n /// <returns></returns>\\n public static Upsample Upsample(long[]? size = null, double[]? scale_factor = null, UpsampleMode mode = UpsampleMode.Nearest, bool? alignCorners = null)\\n {\\n unsafe {\\n fixed (long* psize = size) {\\n fixed (double* pSF = scale_factor) {\\n byte ac = (byte)((alignCorners.HasValue) ? (alignCorners.Value ? 1 : 2) : 0);\\n var res = THSNN_Upsample_ctor((IntPtr)psize, size is null ? 0 : size.Length, (IntPtr)pSF, scale_factor is null ? 0 : scale_factor.Length, (byte)mode, ac, out var boxedHandle);\\n if (res == IntPtr.Zero) { torch.CheckForErrors(); }\\n return new Upsample(res, boxedHandle);\\n }\\n }\\n }\\n }\\n\\n public static partial class functional\\n {\\n /// <summary>\\n /// Upsamples a given multi-channel 1D (temporal), 2D (spatial) or 3D (volumetric) data.\\n /// The input data is assumed to be of the form minibatch x channels x[optional depth] x[optional height] x width.\\n /// Hence, for spatial inputs, we expect a 4D Tensor and for volumetric inputs, we expect a 5D Tensor.\\n /// </summary>\\n /// <param name=\\\"x\\\">Input tensor</param>\\n /// <param name=\\\"size\\\">Output spatial sizes</param>\\n /// <param name=\\\"scale_factor\\\">Multiplier for spatial size. Has to match input size</param>\\n /// <param name=\\\"mode\\\">The upsampling algorithm: one of 'nearest', 'linear', 'bilinear', 'bicubic' and 'trilinear'. Default: 'nearest'</param>\\n /// <param name=\\\"alignCorners\\\">If true, the corner pixels of the input and output tensors are aligned, and thus preserving the values at those pixels.\\n /// This only has effect when mode is 'linear', 'bilinear', or 'trilinear'. Default: false</param>\\n /// <returns></returns>\\n public static Tensor upsample(Tensor x, long[]? size = null, double[]? scale_factor = null, UpsampleMode mode = UpsampleMode.Nearest, bool alignCorners = false)\\n {\\n using (var d = nn.Upsample(size, scale_factor, mode, alignCorners)) {\\n return d.call(x);\\n }\\n }\\n\\n\\n /// <summary>\\n /// Upsamples the input, using nearest neighbours’ pixel values.\\n /// </summary>\\n /// <param name=\\\"input\\\">The input tensor.</param>\\n /// <param name=\\\"outputSize\\\"></param>\\n /// <param name=\\\"scaleFactor\\\"></param>\\n /// <returns></returns>\\n public static Tensor upsample_nearest1d(Tensor input, long? outputSize, double? scaleFactor)\\n {\\n var outputSizes = outputSize.HasValue ? new long[] { outputSize.Value } : null;\\n var outputSizesLength = outputSize.HasValue ? 1 : 0;\\n var scaleFactors = scaleFactor.HasValue ? new double[] { scaleFactor.Value } : null;\\n var scaleFactorsLength = scaleFactor.HasValue ? 1 : 0;\\n unsafe {\\n fixed (long* poutputSizes = outputSizes) {\\n fixed (double* pscaleFactors = scaleFactors) {\\n var res =\\n THSTensor_upsample_nearest1d(input.Handle,\\n (IntPtr)poutputSizes, outputSizesLength,\\n (IntPtr)pscaleFactors, scaleFactorsLength);\\n if (res == IntPtr.Zero) { torch.CheckForErrors(); }\\n return new Tensor(res);\\n }\\n }\\n }\\n }\\n\\n public static Tensor upsample_nearest1d_backward(Tensor grad_output, long? outputSize, long inputSize, double? scaleFactor)\\n {\\n var outputSizes = outputSize.HasValue ? new long[] { outputSize.Value } : null;\\n var outputSizesLength = outputSize.HasValue ? 1 : 0;\\n var inputSizes = new long[] { inputSize };\\n var scaleFactors = scaleFactor.HasValue ? new double[] { scaleFactor.Value } : null;\\n var scaleFactorsLength = scaleFactor.HasValue ? 1 : 0;\\n unsafe {\\n fixed (long* poutputSizes = outputSizes, pinputSizes = inputSizes) {\\n fixed (double* pscaleFactors = scaleFactors) {\\n var res =\\n THSTensor_upsample_nearest1d_backward(grad_output.Handle,\\n (IntPtr)poutputSizes, outputSizesLength,\\n (IntPtr)pinputSizes, inputSizes.Length,\\n (IntPtr)pscaleFactors, scaleFactorsLength);\\n if (res == IntPtr.Zero) { torch.CheckForErrors(); }\\n return new Tensor(res);\\n }\\n }\\n }\\n }\\n\\n /// <summary>\\n /// Upsamples the input, using nearest neighbours’ pixel values.\\n /// </summary>\\n /// <param name=\\\"input\\\">The input tensor.</param>\\n /// <param name=\\\"outputSizes\\\"></param>\\n /// <param name=\\\"scaleFactors\\\"></param>\\n /// <returns></returns>\\n public static Tensor upsample_nearest2d(Tensor input, long[]? outputSizes = null, double[]? scaleFactors = null)\\n {\\n var outputSizesLength = outputSizes == null ? 0 : outputSizes.Length;\\n var scaleFactorsLength = scaleFactors == null ? 0 : scaleFactors.Length;\\n unsafe {\\n fixed (long* poutputSizes = outputSizes) {\\n fixed (double* pscaleFactors = scaleFactors) {\\n var res =\\n THSTensor_upsample_nearest2d(input.Handle,\\n (IntPtr)poutputSizes, outputSizesLength,\\n (IntPtr)pscaleFactors, scaleFactorsLength);\\n if (res == IntPtr.Zero) { torch.CheckForErrors(); }\\n return new Tensor(res);\\n }\\n }\\n }\\n }\\n\\n public static Tensor upsample_nearest2d_backward(Tensor grad_output, long[] inputSizes, long[]? outputSizes = null, double[]? scaleFactors = null)\\n {\\n var outputSizesLength = outputSizes == null ? 0 : outputSizes.Length;\\n var scaleFactorsLength = scaleFactors == null ? 0 : scaleFactors.Length;\\n unsafe {\\n fixed (long* poutputSizes = outputSizes, pinputSizes = inputSizes) {\\n fixed (double* pscaleFactors = scaleFactors) {\\n var res =\\n THSTensor_upsample_nearest2d_backward(grad_output.Handle,\\n (IntPtr)poutputSizes, outputSizesLength,\\n (IntPtr)pinputSizes, inputSizes.Length,\\n (IntPtr)pscaleFactors, scaleFactorsLength);\\n if (res == IntPtr.Zero) { torch.CheckForErrors(); }\\n return new Tensor(res);\\n }\\n }\\n }\\n }\\n\\n public static Tensor upsample_nearest3d_backward(Tensor grad_output, long[] inputSizes, long[]? outputSizes = null, double[]? scaleFactors = null)\\n {\\n var outputSizesLength = outputSizes == null ? 0 : outputSizes.Length;\\n var scaleFactorsLength = scaleFactors == null ? 0 : scaleFactors.Length;\\n unsafe {\\n fixed (long* poutputSizes = outputSizes, pinputSizes = inputSizes) {\\n fixed (double* pscaleFactors = scaleFactors) {\\n var res =\\n THSTensor_upsample_nearest3d_backward(grad_output.Handle,\\n (IntPtr)poutputSizes, outputSizesLength,\\n (IntPtr)pinputSizes, inputSizes.Length,\\n (IntPtr)pscaleFactors, scaleFactorsLength);\\n if (res == IntPtr.Zero) { torch.CheckForErrors(); }\\n return new Tensor(res);\\n }\\n }\\n }\\n }\\n\\n /// <summary>\\n /// Upsamples the input, using nearest neighbours’ pixel values.\\n /// </summary>\\n /// <param name=\\\"input\\\">The input tensor.</param>\\n /// <param name=\\\"outputSizes\\\"></param>\\n /// <param name=\\\"scaleFactors\\\"></param>\\n /// <returns></returns>\\n public static Tensor upsample_nearest3d(Tensor input, long[]? outputSizes = null, double[]? scaleFactors = null)\\n {\\n var outputSizesLength = outputSizes == null ? 0 : outputSizes.Length;\\n var scaleFactorsLength = scaleFactors == null ? 0 : scaleFactors.Length;\\n unsafe {\\n fixed (long* poutputSizes = outputSizes) {\\n fixed (double* pscaleFactors = scaleFactors) {\\n var res =\\n THSTensor_upsample_nearest3d(input.Handle,\\n (IntPtr)poutputSizes, outputSizesLength,\\n (IntPtr)pscaleFactors, scaleFactorsLength);\\n if (res == IntPtr.Zero) { torch.CheckForErrors(); }\\n return new Tensor(res);\\n }\\n }\\n }\\n }\\n }\\n }\\n }\\n\\n namespace Modules\\n {\\n /// <summary>\\n /// This class is used to represent an Upsample module.\\n /// </summary>\\n public sealed class Upsample : torch.nn.Module<Tensor, Tensor>\\n {\\n internal Upsample(IntPtr handle, IntPtr boxedHandle) : base(handle, boxedHandle) { }\\n\\n /// <summary>\\n /// Forward pass.\\n /// </summary>\\n /// <param name=\\\"tensor\\\">Input tensor</param>\\n /// <returns></returns>\\n public override Tensor forward(Tensor tensor)\\n {\\n var res = THSNN_Upsample_forward(handle, tensor.Handle);\\n if (res == IntPtr.Zero) { torch.CheckForErrors(); }\\n return new Tensor(res);\\n }\\n\\n // Rather than spending cycles only to discover that this module has neither\\n // parameters nor buffers, just shortcut the move completely.\\n protected internal override nn.Module _to(Device device, ScalarType dtype) => this;\\n protected internal override nn.Module _to(DeviceType deviceType, int deviceIndex = -1) => this;\\n protected internal override nn.Module _to(ScalarType dtype) => this;\\n }\\n }\\n}\\n\"\n },\n {\n \"alpha_fraction\": 0.5745007395744324,\n \"alphanum_fraction\": 0.5794931054115295,\n \"avg_line_length\": 40.33333206176758,\n \"blob_id\": \"366a75813ff0d0bc2a4dfd151942758407b0368a\",\n \"content_id\": \"5583c61cfcd6319981d3a7a9755e4b396e79b82b\",\n \"detected_licenses\": [\n \"MIT\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"C#\",\n \"length_bytes\": 2604,\n \"license_type\": \"permissive\",\n \"max_line_length\": 149,\n \"num_lines\": 63,\n \"path\": \"/src/TorchSharp/Distributions/Chi2.cs\",\n \"repo_name\": \"NiklasGustafsson/TorchSharp\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"// Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information.\\nusing System;\\nusing static TorchSharp.torch;\\n\\nnamespace TorchSharp\\n{\\n using Modules;\\n\\n namespace Modules\\n {\\n /// <summary>\\n /// A Gamma distribution parameterized by a single shape parameter.\\n /// </summary>\\n public class Chi2 : Gamma\\n {\\n /// <summary>\\n /// Constructor\\n /// </summary>\\n /// <param name=\\\"df\\\">Shape parameter of the distribution</param>\\n /// <param name=\\\"generator\\\">An optional random number generator object.</param>\\n public Chi2(Tensor df, torch.Generator generator = null) : base(df * 0.5, torch.tensor(0.5), generator)\\n {\\n }\\n\\n /// <summary>\\n /// Returns a new distribution instance (or populates an existing instance provided by a derived class) with batch dimensions expanded to\\n /// `batch_shape`. This method calls `torch.Tensor.expand()` on the distribution's parameters. As such, this does not allocate new\\n /// memory for the expanded distribution instance.\\n /// </summary>\\n /// <param name=\\\"batch_shape\\\">Tthe desired expanded size.</param>\\n /// <param name=\\\"instance\\\">new instance provided by subclasses that need to override `.expand`.</param>\\n public override distributions.Distribution expand(Size batch_shape, distributions.Distribution instance = null)\\n {\\n if (instance != null && !(instance is Chi2))\\n throw new ArgumentException(\\\"expand(): 'instance' must be a Chi2 distribution\\\");\\n\\n if (instance == null) {\\n instance = new Chi2(concentration, generator);\\n }\\n return base.expand(batch_shape, instance);\\n }\\n\\n public Tensor df => concentration * 2;\\n }\\n }\\n\\n public static partial class torch\\n {\\n public static partial class distributions\\n {\\n /// <summary>\\n /// Creates a Gamma distribution parameterized by a single shape parameter.\\n /// </summary>\\n /// <param name=\\\"df\\\">Shape parameter of the distribution</param>\\n /// <param name=\\\"generator\\\">An optional random number generator object.</param>\\n /// <returns></returns>\\n public static Chi2 Chi2(Tensor df, torch.Generator generator = null)\\n {\\n return new Chi2(df, generator);\\n }\\n }\\n }\\n}\\n\"\n },\n {\n \"alpha_fraction\": 0.5546642541885376,\n \"alphanum_fraction\": 0.5611974596977234,\n \"avg_line_length\": 45.5076904296875,\n \"blob_id\": \"096e94f3413948a711906417763e1d15e96fd175\",\n \"content_id\": \"7fee61dfa820cbcbe83cd258abc8508f962aad6d\",\n \"detected_licenses\": [\n \"MIT\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"C#\",\n \"length_bytes\": 12092,\n \"license_type\": \"permissive\",\n \"max_line_length\": 167,\n \"num_lines\": 260,\n \"path\": \"/src/TorchVision/dsets/MNIST.cs\",\n \"repo_name\": \"NiklasGustafsson/TorchSharp\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"// Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information.\\n\\nusing System.Collections.Generic;\\nusing System.IO;\\nusing System.Net;\\nusing System.Net.Http;\\nusing TorchSharp.Utils;\\nusing static TorchSharp.torch;\\nusing static TorchSharp.torch.utils.data;\\n\\nnamespace TorchSharp\\n{\\n public static partial class torchvision\\n {\\n public static partial class datasets\\n {\\n /// <summary>\\n /// MNIST Dataset\\n /// http://yann.lecun.com/exdb/mnist/\\n /// </summary>\\n /// <param name=\\\"root\\\">Root directory of dataset where the MNIST .gz data files exist.</param>\\n /// <param name=\\\"train\\\">If true, creates dataset from the 'train' files, otherwise from the 't10k' files.</param>\\n /// <param name=\\\"download\\\">\\n /// If true, downloads the dataset from the internet and puts it in root directory.\\n /// If the dataset is already downloaded, it is not downloaded again.\\n /// </param>\\n /// <param name=\\\"target_transform\\\">A function/transform that takes in the target and transforms it.</param>\\n /// <returns>An iterable dataset.</returns>\\n public static Dataset MNIST(string root, bool train, bool download = false, torchvision.ITransform target_transform = null)\\n {\\n return new Modules.MNIST(root, train, download, target_transform);\\n }\\n\\n /// <summary>\\n /// Fashion-MNIST Dataset\\n /// https://github.com/zalandoresearch/fashion-mnist/raw/master/data/fashion/\\n /// </summary>\\n /// <param name=\\\"root\\\">Root directory of dataset where the MNIST .gz data files exist.</param>\\n /// <param name=\\\"train\\\">If true, creates dataset from the 'train' files, otherwise from the 't10k' files.</param>\\n /// <param name=\\\"download\\\">\\n /// If true, downloads the dataset from the internet and puts it in root directory.\\n /// If the dataset is already downloaded, it is not downloaded again.\\n /// </param>\\n /// <param name=\\\"target_transform\\\">A function/transform that takes in the target and transforms it.</param>\\n /// <returns>An iterable dataset.</returns>\\n public static Dataset FashionMNIST(string root, bool train, bool download = false, torchvision.ITransform target_transform = null)\\n {\\n return new Modules.FashionMNIST(root, train, download, target_transform);\\n }\\n\\n /// <summary>\\n /// Kuzushiji-MNIST Dataset (https://github.com/rois-codh/kmnist)\\n /// </summary>\\n /// <param name=\\\"root\\\">Root directory of dataset where the KMNIST .gz data files exist.</param>\\n /// <param name=\\\"train\\\">If true, creates dataset from the 'train' files, otherwise from the 't10k' files.</param>\\n /// <param name=\\\"download\\\">\\n /// If true, downloads the dataset from the internet and puts it in root directory.\\n /// If the dataset is already downloaded, it is not downloaded again.\\n /// </param>\\n /// <param name=\\\"target_transform\\\">A function/transform that takes in the target and transforms it.</param>\\n /// <returns>An iterable dataset.</returns>\\n public static Dataset KMNIST(string root, bool train, bool download = false, torchvision.ITransform target_transform = null)\\n {\\n return new Modules.KMNIST(root, train, download, target_transform);\\n }\\n }\\n }\\n\\n namespace Modules\\n {\\n /// <summary>\\n /// Data reader utility for datasets that follow the MNIST data set's layout:\\n ///\\n /// A number of single-channel (grayscale) images are laid out in a flat file with four 32-bit integers at the head.\\n /// The format is documented at the bottom of the page at: http://yann.lecun.com/exdb/mnist/\\n /// </summary>\\n internal class MNIST : DatasetHelper\\n {\\n /// <summary>\\n /// Constructor\\n /// </summary>\\n /// <param name=\\\"root\\\">Path to the folder containing the image files.</param>\\n /// <param name=\\\"train\\\">The file name prefix, either 'train' or 't10k' (the latter being the test data set).</param>\\n /// <param name=\\\"download\\\"></param>\\n /// <param name=\\\"transform\\\">Transform for input MNIST image</param>\\n public MNIST(string root, bool train, bool download = false, torchvision.ITransform transform = null) :\\n this(root, \\\"mnist\\\", train ? \\\"train\\\" : \\\"t10k\\\", \\\"http://yann.lecun.com/exdb/mnist/\\\", download, transform)\\n {\\n }\\n\\n protected MNIST(string root, string datasetName, string prefix, string baseUrl, bool download, torchvision.ITransform transform)\\n {\\n if (download) Download(root, baseUrl, datasetName);\\n\\n this.transform = transform;\\n\\n#if NETSTANDARD2_0_OR_GREATER\\n var datasetPath = NSPath.Join(root, datasetName, \\\"test_data\\\");\\n#else\\n var datasetPath = Path.Join(root, datasetName, \\\"test_data\\\");\\n#endif // NETSTANDARD2_0_OR_GREATER\\n\\n var dataPath = Path.Combine(datasetPath, prefix + \\\"-images-idx3-ubyte\\\");\\n var labelPath = Path.Combine(datasetPath, prefix + \\\"-labels-idx1-ubyte\\\");\\n\\n var count = -1;\\n var height = 0;\\n var width = 0;\\n\\n byte[] dataBytes = null;\\n byte[] labelBytes = null;\\n\\n using (var file = File.Open(dataPath, FileMode.Open, FileAccess.Read, FileShare.Read))\\n using (var rdr = new System.IO.BinaryReader(file)) {\\n\\n var reader = new BigEndianReader(rdr);\\n var x = reader.ReadInt32(); // Magic number\\n count = reader.ReadInt32();\\n\\n height = reader.ReadInt32();\\n width = reader.ReadInt32();\\n\\n // Read all the data into memory.\\n dataBytes = reader.ReadBytes(height * width * count);\\n }\\n\\n using (var file = File.Open(labelPath, FileMode.Open, FileAccess.Read, FileShare.Read))\\n using (var rdr = new System.IO.BinaryReader(file)) {\\n\\n var reader = new BigEndianReader(rdr);\\n var x = reader.ReadInt32(); // Magic number\\n var lblcnt = reader.ReadInt32();\\n\\n if (lblcnt != count) throw new InvalidDataException(\\\"Image data and label counts are different.\\\");\\n\\n // Read all the data into memory.\\n labelBytes = reader.ReadBytes(lblcnt);\\n }\\n\\n var imgSize = height * width;\\n\\n // Go through the data and create tensors\\n\\n // A previous version of this relied on LINQ expressions, but it was about 20% slower.\\n\\n for (var i = 0; i < count; i++) {\\n var imgStart = i * imgSize;\\n var floats = new float[imgSize];\\n for (int j = 0; j < imgSize; j++)\\n {\\n floats[j] = dataBytes[j+imgStart] / 256.0f;\\n }\\n data.Add(tensor(floats, new long[] { width, height }));\\n\\n labels.Add(tensor(labelBytes[i], int64));\\n }\\n }\\n\\n private void Download(string root, string baseUrl, string dataset)\\n {\\n#if NETSTANDARD2_0_OR_GREATER\\n var datasetPath = NSPath.Join(root, dataset);\\n#else\\n var datasetPath = Path.Join(root, dataset);\\n#endif // NETSTANDARD2_0_OR_GREATER\\n\\n var sourceDir = datasetPath;\\n var targetDir = Path.Combine(datasetPath, \\\"test_data\\\");\\n\\n if (!Directory.Exists(sourceDir)) {\\n Directory.CreateDirectory(sourceDir);\\n }\\n\\n DownloadFile(\\\"train-images-idx3-ubyte.gz\\\", sourceDir, baseUrl);\\n DownloadFile(\\\"train-labels-idx1-ubyte.gz\\\", sourceDir, baseUrl);\\n DownloadFile(\\\"t10k-images-idx3-ubyte.gz\\\", sourceDir, baseUrl);\\n DownloadFile(\\\"t10k-labels-idx1-ubyte.gz\\\", sourceDir, baseUrl);\\n\\n if (!Directory.Exists(targetDir)) {\\n Directory.CreateDirectory(targetDir);\\n }\\n\\n DecompressFile(\\\"train-images-idx3-ubyte\\\", sourceDir, targetDir);\\n DecompressFile(\\\"train-labels-idx1-ubyte\\\", sourceDir, targetDir);\\n DecompressFile(\\\"t10k-images-idx3-ubyte\\\", sourceDir, targetDir);\\n DecompressFile(\\\"t10k-labels-idx1-ubyte\\\", sourceDir, targetDir);\\n }\\n\\n private static void DecompressFile(string file, string sourceDir, string targetDir)\\n {\\n var filePath = Path.Combine(targetDir, file);\\n if (!File.Exists(filePath))\\n Utils.Decompress.DecompressGZipFile(Path.Combine(sourceDir, file + \\\".gz\\\"), targetDir);\\n }\\n\\n private torchvision.ITransform transform;\\n\\n /// <summary>\\n /// Size of dataset\\n /// </summary>\\n public override long Count => data.Count;\\n\\n private List<Tensor> data = new();\\n private List<Tensor> labels = new();\\n\\n protected override void Dispose(bool disposing)\\n {\\n if (disposing) {\\n data.ForEach(d => d.Dispose());\\n labels.ForEach(d => d.Dispose());\\n }\\n }\\n\\n /// <summary>\\n /// Get tensor according to index\\n /// </summary>\\n /// <param name=\\\"index\\\">Index for tensor</param>\\n /// <returns>Tensors of index. DataLoader will catenate these tensors as batchSize * 784 for image, batchSize * 1 for label</returns>\\n public override Dictionary<string, Tensor> GetTensor(long index)\\n {\\n var rdic = new Dictionary<string, Tensor>();\\n if (transform is not null)\\n rdic.Add(\\\"data\\\", transform.call(data[(int)index].unsqueeze(0).unsqueeze(0)).squeeze(0));\\n else rdic.Add(\\\"data\\\", data[(int)index].unsqueeze(0));\\n rdic.Add(\\\"label\\\", labels[(int)index]);\\n return rdic;\\n }\\n }\\n\\n internal class FashionMNIST : MNIST\\n {\\n /// <summary>\\n /// Constructor\\n /// </summary>\\n /// <param name=\\\"root\\\">Path to the folder containing the image files.</param>\\n /// <param name=\\\"train\\\">The file name prefix, either 'train' or 't10k' (the latter being the test data set).</param>\\n /// <param name=\\\"download\\\"></param>\\n /// <param name=\\\"transform\\\">Transform for input MNIST image</param>\\n public FashionMNIST(string root, bool train, bool download = false, torchvision.ITransform transform = null) :\\n base(root, \\\"fashion-mnist\\\", train ? \\\"train\\\" : \\\"t10k\\\", \\\"https://github.com/zalandoresearch/fashion-mnist/raw/master/data/fashion/\\\", download, transform)\\n {\\n }\\n }\\n\\n internal class KMNIST : MNIST\\n {\\n /// <summary>\\n /// Constructor\\n /// </summary>\\n /// <param name=\\\"root\\\">Path to the folder containing the image files.</param>\\n /// <param name=\\\"train\\\">The file name prefix, either 'train' or 't10k' (the latter being the test data set).</param>\\n /// <param name=\\\"download\\\"></param>\\n /// <param name=\\\"transform\\\">Transform for input MNIST image</param>\\n public KMNIST(string root, bool train, bool download = false, torchvision.ITransform transform = null) :\\n base(root, \\\"kmnist\\\", train ? \\\"train\\\" : \\\"t10k\\\", \\\"http://codh.rois.ac.jp/kmnist/dataset/kmnist/\\\", download, transform)\\n {\\n }\\n }\\n }\\n}\\n\"\n },\n {\n \"alpha_fraction\": 0.5542635917663574,\n \"alphanum_fraction\": 0.5542635917663574,\n \"avg_line_length\": 26.157894134521484,\n \"blob_id\": \"1aa4f25d7ed5d9ff66feedd490d1e7d6fbfaf2c7\",\n \"content_id\": \"9a523a102a41071d43c94050dbb2e9c60837828e\",\n \"detected_licenses\": [\n \"MIT\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"C#\",\n \"length_bytes\": 516,\n \"license_type\": \"permissive\",\n \"max_line_length\": 130,\n \"num_lines\": 19,\n \"path\": \"/src/TorchAudio/FeatureExtractorNormMode.cs\",\n \"repo_name\": \"NiklasGustafsson/TorchSharp\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"// Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information.\\n\\nnamespace TorchSharp\\n{\\n public static partial class torchaudio\\n {\\n public static partial class models\\n {\\n /// <summary>\\n /// Normalization mode of feature extractor\\n /// </summary>\\n public enum FeatureExtractorNormMode\\n {\\n group_norm,\\n layer_norm\\n }\\n }\\n }\\n}\\n\"\n },\n {\n \"alpha_fraction\": 0.6343148946762085,\n \"alphanum_fraction\": 0.6364350318908691,\n \"avg_line_length\": 55.599998474121094,\n \"blob_id\": \"6e6ca0ed900c2a5f4d70b21cc6f980e1a7c86470\",\n \"content_id\": \"6892d2b69bf68953cf74b7660ea45ab4f0c1e645\",\n \"detected_licenses\": [\n \"MIT\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"C#\",\n \"length_bytes\": 12738,\n \"license_type\": \"permissive\",\n \"max_line_length\": 217,\n \"num_lines\": 225,\n \"path\": \"/src/TorchSharp/Tensor/torch.cs\",\n \"repo_name\": \"NiklasGustafsson/TorchSharp\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"// Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information.\\n#nullable enable\\nusing System;\\nusing System.Collections.Generic;\\nusing System.Linq;\\nusing static TorchSharp.PInvoke.NativeMethods;\\n\\nnamespace TorchSharp\\n{\\n // This file contains methods that are manipulating Tensors, and are not also defined on the Tensor class.\\n public static partial class torch\\n {\\n /// <summary>\\n /// Computes the Cholesky decomposition of a symmetric positive-definite matrix 'input' or for batches of symmetric positive-definite matrices.\\n /// </summary>\\n /// <param name=\\\"input\\\">The input matrix</param>\\n /// <param name=\\\"upper\\\">If upper is true, the returned matrix U is upper-triangular. If upper is false, the returned matrix L is lower-triangular</param>\\n /// <returns></returns>\\n public static Tensor cholesky(Tensor input, bool upper) => input.cholesky(upper);\\n\\n /// <summary>\\n /// Returns the matrix norm or vector norm of a given tensor.\\n /// </summary>\\n /// <param name=\\\"input\\\">The input tensor.</param>\\n public static Tensor norm(Tensor input) => input.norm();\\n\\n /// <summary>\\n /// Concatenates the given sequence of tensors along the given axis (dimension).\\n /// </summary>\\n /// <param name=\\\"tensors\\\">A sequence of tensors of the same type. Non-empty tensors provided must have the same shape, except in the cat dimension.</param>\\n /// <param name=\\\"axis\\\">The dimension over which the tensors are concatenated</param>\\n /// <returns></returns>\\n /// <remarks> All tensors must either have the same shape (except in the concatenating dimension) or be empty.</remarks>\\n public static Tensor concatenate(IList<Tensor> tensors, long axis = 0) => torch.cat(tensors, axis);\\n\\n /// <summary>\\n /// Returns a tensor with all the dimensions of input of size 1 removed. When dim is given, a squeeze operation is done only in the given dimension.\\n /// </summary>\\n /// <param name=\\\"input\\\">The input tensor</param>\\n /// <param name=\\\"dim\\\">If given, the input will be squeezed only in this dimension</param>\\n /// <returns></returns>\\n public static Tensor squeeze(Tensor input, long? dim = null) => input.squeeze(dim);\\n\\n /// <summary>\\n /// Modifies (in-place) a tensor with all the dimensions of input of size 1 removed. When dim is given, a squeeze operation is done only in the given dimension.\\n /// </summary>\\n /// <param name=\\\"input\\\">The input tensor</param>\\n /// <param name=\\\"dim\\\">If given, the input will be squeezed only in this dimension</param>\\n /// <returns></returns>\\n public static Tensor squeeze_(Tensor input, long? dim = null) => input.squeeze_(dim);\\n\\n /// <summary>\\n /// Creates a new tensor by horizontally stacking the input tensors.\\n /// </summary>\\n /// <param name=\\\"tensors\\\">A list of input tensors.</param>\\n /// <returns></returns>\\n /// <remarks>Equivalent to torch.hstack(tensors), except each zero or one dimensional tensor t in tensors is first reshaped into a (t.numel(), 1) column before being stacked horizontally.</remarks>\\n public static Tensor column_stack(IList<Tensor> tensors)\\n {\\n using var parray = new PinnedArray<IntPtr>();\\n IntPtr tensorsRef = parray.CreateArray(tensors.Select(p => p.Handle).ToArray());\\n\\n var res = THSTensor_column_stack(tensorsRef, parray.Array.Length);\\n if (res == IntPtr.Zero) { CheckForErrors(); }\\n return new Tensor(res);\\n }\\n\\n /// <summary>\\n /// Creates a new tensor by horizontally stacking the input tensors.\\n /// </summary>\\n /// <param name=\\\"tensors\\\">A list of input tensors.</param>\\n /// <returns></returns>\\n /// <remarks>Equivalent to torch.hstack(tensors), except each zero or one dimensional tensor t in tensors is first reshaped into a (t.numel(), 1) column before being stacked horizontally.</remarks>\\n public static Tensor column_stack(params Tensor[] tensors) => column_stack((IList<Tensor>)tensors);\\n\\n /// <summary>\\n /// Stack tensors in sequence vertically (row wise).\\n /// </summary>\\n /// <param name=\\\"tensors\\\"></param>\\n /// <returns></returns>\\n public static Tensor row_stack(IList<Tensor> tensors)\\n {\\n using var parray = new PinnedArray<IntPtr>();\\n IntPtr tensorsRef = parray.CreateArray(tensors.Select(p => p.Handle).ToArray());\\n\\n var res = THSTensor_row_stack(tensorsRef, parray.Array.Length);\\n if (res == IntPtr.Zero) { CheckForErrors(); }\\n return new Tensor(res);\\n }\\n\\n /// <summary>\\n /// Stack tensors in sequence vertically (row wise).\\n /// </summary>\\n /// <param name=\\\"tensors\\\"></param>\\n /// <returns></returns>\\n public static Tensor row_stack(params Tensor[] tensors) => row_stack((IList<Tensor>)tensors);\\n\\n /// <summary>\\n /// Removes a tensor dimension.\\n /// </summary>\\n /// <param name=\\\"tensor\\\">The input tensor</param>\\n /// <param name=\\\"dim\\\">The dimension to remove.</param>\\n /// <returns>An array of all slices along a given dimension, already without it.</returns>\\n public static Tensor[] unbind(Tensor tensor, int dim = 0) => tensor.unbind(dim);\\n\\n /// <summary>\\n /// Adds all values from the tensor other into input at the indices specified in the index tensor in a similar fashion as scatter_().\\n /// For each value in src, it is added to an index in self which is specified by its index in src for dimension != dim and by the\\n /// corresponding value in index for dimension = dim.\\n /// </summary>\\n public static Tensor scatter_add_(Tensor input, long dim, Tensor index, Tensor src) => input.scatter_add_(dim, index, src);\\n\\n public static Tensor clamp_max(Tensor input, Scalar max) => input.clamp_max(max);\\n\\n public static Tensor clamp_max_(Tensor input, Scalar max) => input.clamp_max(max);\\n\\n public static Tensor clamp_min(Tensor input, Scalar min) => input.clamp_min(min);\\n\\n public static Tensor clamp_min_(Tensor input, Scalar min) => input.clamp_min(min);\\n\\n /// <summary>\\n /// Expands the dimension dim of the self tensor over multiple dimensions of sizes given by sizes.\\n /// </summary>\\n /// <param name=\\\"input\\\">The input tensor</param>\\n /// <param name=\\\"dim\\\">Dimension to unflatten.</param>\\n /// <param name=\\\"sizes\\\">New shape of the unflattened dimension.</param>\\n public static Tensor unflatten(Tensor input, long dim, params long[] sizes) => input.unflatten(dim, sizes);\\n\\n /// <summary>\\n /// Expands the dimension dim of the self tensor over multiple dimensions of sizes given by sizes.\\n /// </summary>\\n /// <param name=\\\"input\\\">The input tensor</param>\\n /// <param name=\\\"dim\\\">Dimension to unflatten.</param>\\n /// <param name=\\\"sizes\\\">New shape of the unflattened dimension.</param>\\n public static Tensor unflatten(Tensor input, long dim, Size sizes) => input.unflatten(dim, sizes.Shape);\\n\\n public static Tensor _standard_gamma(Tensor input, Generator? generator = null)\\n {\\n var res = THSTensor_standard_gamma_(input.Handle, generator is null ? IntPtr.Zero : generator.Handle);\\n if (res == IntPtr.Zero) { CheckForErrors(); }\\n return new Tensor(res);\\n }\\n\\n public static Tensor _sample_dirichlet(Tensor input, Generator? generator = null)\\n {\\n var res = THSTensor_sample_dirichlet_(input.Handle, generator is null ? IntPtr.Zero : generator.Handle);\\n if (res == IntPtr.Zero) { CheckForErrors(); }\\n return new Tensor(res);\\n }\\n\\n /// <summary>\\n /// Fills the elements of the input tensor with value value by selecting the indices in the order given in index.\\n ///\\n /// For example, if dim == 0, index[i] == j, and alpha=-1, then the ith row of source is subtracted from the jth row of the input tensor.\\n /// The dimth dimension of source must have the same size as the length of index(which must be a vector), and all other dimensions must match self, or an error will be raised.\\n /// </summary>\\n /// <param name=\\\"input\\\">The input tensor</param>\\n /// <param name=\\\"dim\\\">Dimension along which to index</param>\\n /// <param name=\\\"index\\\">Indices of source to select from, should have dtype either torch.int64 or torch.int32</param>\\n /// <param name=\\\"value\\\">The scalar multiplier for source</param>\\n /// <returns></returns>\\n public static Tensor index_fill(Tensor input, long dim, Tensor index, Scalar value) => input.index_fill(dim, index, value);\\n\\n /// <summary>\\n /// Fills, in place, the elements of the input tensor with value value by selecting the indices in the order given in index.\\n ///\\n /// For example, if dim == 0, index[i] == j, and alpha=-1, then the ith row of source is subtracted from the jth row of the input tensor.\\n /// The dimth dimension of source must have the same size as the length of index(which must be a vector), and all other dimensions must match self, or an error will be raised.\\n /// </summary>\\n /// <param name=\\\"input\\\">The input tensor</param>\\n /// <param name=\\\"dim\\\">Dimension along which to index</param>\\n /// <param name=\\\"index\\\">Indices of source to select from, should have dtype either torch.int64 or torch.int32</param>\\n /// <param name=\\\"value\\\">The scalar multiplier for source</param>\\n /// <returns></returns>\\n public static Tensor index_fill_(Tensor input, long dim, Tensor index, Scalar value) => input.index_fill_(dim, index, value);\\n\\n /// <summary>\\n /// Returns true if the input is a conjugated tensor, i.e. its conjugate bit is set to True.\\n /// </summary>\\n /// <param name=\\\"input\\\">The input tensor.</param>\\n public static bool is_conj(Tensor input) => input.is_conj();\\n\\n /// <summary>\\n /// Calculates the standard deviation and mean of all elements in the tensor.\\n /// </summary>\\n /// <param name=\\\"input\\\">The input tensor.</param>\\n /// <param name=\\\"unbiased\\\">Whether to use Bessel’s correction (δN=1).</param>\\n public static (Tensor std, Tensor mean) std_mean(Tensor input, bool unbiased = true) => input.std_mean(unbiased);\\n\\n /// <summary>\\n /// Mutates the tensor to be filled with random values taken from a uniform distribution in [0, 1).\\n /// </summary>\\n public static Tensor rand_out(Tensor input, params long[] sizes) => input.randn_out(sizes);\\n\\n /// <summary>\\n /// Mutates the tensor to be filled with random values taken from a normal distribution with mean 0 and variance 1.\\n /// </summary>\\n public static Tensor randint_out(Tensor input, long high, long[] sizes) => input.randint_out(high, sizes);\\n\\n /// <summary>\\n /// Returns a tensor with the same size as input that is filled with random numbers from a normal distribution with mean 0 and variance 1.\\n /// </summary>\\n public static Tensor randn_like(Tensor input, ScalarType? dtype = null, Device? device = null, bool requires_grad = false) => input.randn_like(dtype, device, requires_grad);\\n\\n /// <summary>\\n /// Returns a tensor with the same shape as Tensor input filled with random integers generated uniformly in the range [low,high).\\n /// </summary>\\n public static Tensor randint_like(Tensor input, long low, long high, ScalarType? dtype = null, Device? device = null, bool requires_grad = false) => input.randint_like(low, high, dtype, device, requires_grad);\\n\\n /// <summary>\\n /// Mutates the tensor to be a 1-D tensor of size [n] with a random permutation of [0, n).\\n /// </summary>\\n [Obsolete(\\\"This doesn't exist in PyTorch.\\\")]\\n public static Tensor randperm_out(Tensor input, long n) => torch.randperm(n, input);\\n\\n /// <summary>\\n /// Draws a binomial distribution given a trial count and probabilities.\\n /// </summary>\\n /// <param name=\\\"count\\\">Trial count</param>\\n /// <param name=\\\"probs\\\">Probability vector</param>\\n /// <param name=\\\"generator\\\">Optional random number generator</param>\\n /// <returns></returns>\\n public static Tensor binomial(Tensor count, Tensor probs, Generator? generator = null) => count.binomial(probs, generator);\\n }\\n}\\n\"\n },\n {\n \"alpha_fraction\": 0.5608370900154114,\n \"alphanum_fraction\": 0.5614349842071533,\n \"avg_line_length\": 40.8125,\n \"blob_id\": \"e2ed7a9ea7ec2984d9efcdd2ee0d190ae0e0e42a\",\n \"content_id\": \"d90f2f6359bc49f69b2cf51e30730d1bdad56c0e\",\n \"detected_licenses\": [\n \"MIT\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"C#\",\n \"length_bytes\": 3345,\n \"license_type\": \"permissive\",\n \"max_line_length\": 171,\n \"num_lines\": 80,\n \"path\": \"/src/TorchSharp/NN/TransformerEncoder.cs\",\n \"repo_name\": \"NiklasGustafsson/TorchSharp\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"// Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information.\\nusing System;\\nusing static TorchSharp.torch;\\nusing static TorchSharp.PInvoke.NativeMethods;\\n\\nnamespace TorchSharp\\n{\\n using Modules;\\n\\n namespace Modules\\n {\\n public sealed class TransformerEncoder : torch.nn.Module<Tensor, Tensor ,Tensor, Tensor>, torch.nn.IModule<Tensor,Tensor, Tensor>, torch.nn.IModule<Tensor, Tensor>\\n {\\n public enum Activations\\n {\\n ReLU = 0,\\n GELU = 1\\n }\\n\\n internal TransformerEncoder(IntPtr handle, IntPtr boxedHandle) : base(handle, boxedHandle) { }\\n\\n /// <summary>\\n /// Pass the input through the encoder layers in turn.\\n /// </summary>\\n /// <param name=\\\"src\\\">The sequence to the encoder (required).</param>\\n /// <param name=\\\"src_mask\\\">The additive mask for the src sequence (optional).</param>\\n /// <param name=\\\"src_key_padding_mask\\\">The ByteTensor mask for src keys per batch (optional).</param>\\n /// <returns></returns>\\n public override Tensor forward(Tensor src, Tensor src_mask, Tensor src_key_padding_mask)\\n {\\n var res = THSNN_TransformerEncoder_forward(handle,\\n src.Handle,\\n src_mask?.Handle ?? IntPtr.Zero,\\n src_key_padding_mask?.Handle ?? IntPtr.Zero);\\n if (res == IntPtr.Zero) { torch.CheckForErrors(); }\\n return new Tensor(res);\\n }\\n\\n /// <summary>\\n /// Pass the input through the encoder layers in turn.\\n /// </summary>\\n /// <param name=\\\"src\\\">The sequence to the encoder (required).</param>\\n /// <param name=\\\"src_mask\\\">The additive mask for the src sequence (optional).</param>\\n /// <returns></returns>\\n public Tensor call(Tensor src, Tensor src_mask)\\n {\\n return base.call(src, src_mask, null);\\n }\\n\\n /// <summary>\\n /// Pass the input through the encoder layers in turn.\\n /// </summary>\\n /// <param name=\\\"src\\\">The sequence to the encoder (required).</param>\\n /// <returns></returns>\\n public Tensor call(Tensor src)\\n {\\n return base.call(src, null, null);\\n }\\n }\\n }\\n\\n public static partial class torch\\n {\\n public static partial class nn\\n {\\n /// <summary>\\n /// TransformerEncoder is a stack of N encoder layers\\n /// </summary>\\n /// <param name=\\\"encoder_layer\\\">An instance of the TransformerEncoderLayer class (required).</param>\\n /// <param name=\\\"num_layers\\\">The number of sub-encoder-layers in the encoder (required).</param>\\n /// <returns></returns>\\n public static TransformerEncoder TransformerEncoder(TransformerEncoderLayer encoder_layer, long num_layers)\\n {\\n var res = THSNN_TransformerEncoder_ctor(encoder_layer.handle, num_layers, out var boxedHandle);\\n if (res == IntPtr.Zero) { torch.CheckForErrors(); }\\n return new TransformerEncoder(res, boxedHandle);\\n }\\n }\\n }\\n}\\n\"\n },\n {\n \"alpha_fraction\": 0.6534584760665894,\n \"alphanum_fraction\": 0.6840459108352661,\n \"avg_line_length\": 31.325841903686523,\n \"blob_id\": \"9cbb83476ee65e5f26719fcafe6eb4f7281c7c01\",\n \"content_id\": \"13c1c9f0bc4e2c880fb576fd8f89292f2aefddde\",\n \"detected_licenses\": [\n \"MIT\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"C\",\n \"length_bytes\": 2877,\n \"license_type\": \"permissive\",\n \"max_line_length\": 148,\n \"num_lines\": 89,\n \"path\": \"/src/Native/LibTorchSharp/crc32c.h\",\n \"repo_name\": \"NiklasGustafsson/TorchSharp\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"/* Part of CRC-32C library: https://crc32c.machinezoo.com/ */\\n/*\\n Copyright (c) 2013 - 2014, 2016 Mark Adler, Robert Vazan, Max Vysokikh\\n\\n This software is provided 'as-is', without any express or implied\\n warranty. In no event will the author be held liable for any damages\\n arising from the use of this software.\\n\\n Permission is granted to anyone to use this software for any purpose,\\n including commercial applications, and to alter it and redistribute it\\n freely, subject to the following restrictions:\\n\\n 1. The origin of this software must not be misrepresented; you must not\\n claim that you wrote the original software. If you use this software\\n in a product, an acknowledgment in the product documentation would be\\n appreciated but is not required.\\n 2. Altered source versions must be plainly marked as such, and must not be\\n misrepresented as being the original software.\\n 3. This notice may not be removed or altered from any source distribution.\\n*/\\n\\n#ifndef CRC32C_H\\n#define CRC32C_H\\n\\n#if defined(__GNUC__) || defined(__GNUG__)\\n#define CRC32C_GCC\\n#elif defined(_MSC_VER)\\n#define CRC32C_MSC\\n#endif\\n\\n// ALTERED SOURCE VERSION\\n//\\n// Per #2 in the copyright notice above:\\n//\\n// The definition of CRC32C_API has been altered from the original.\\n\\n#ifndef _WIN32\\n#define CRC32C_API __attribute__((visibility(\\\"default\\\")))\\n#else\\n#define CRC32C_API __declspec(dllexport)\\n#endif\\n\\n#include <stdint.h>\\n#include <stddef.h>\\n\\n#ifdef __cplusplus\\nextern \\\"C\\\" {\\n#endif\\n\\n/*\\n Computes CRC-32C (Castagnoli) checksum. Uses Intel's CRC32 instruction if it is available.\\n Otherwise it uses a very fast software fallback.\\n*/\\nCRC32C_API uint32_t crc32c_append(\\n uint32_t crc, /* Initial CRC value. Typically it's 0. */\\n /* You can supply non-trivial initial value here. */\\n /* Initial value can be used to chain CRC from multiple buffers. */\\n const uint8_t *input, /* Data to be put through the CRC algorithm. */\\n size_t length); /* Length of the data in the input buffer. */\\n\\n\\n/*\\n\\tSoftware fallback version of CRC-32C (Castagnoli) checksum.\\n*/\\nCRC32C_API uint32_t crc32c_append_sw(uint32_t crc, const uint8_t *input, size_t length);\\n\\n/*\\n\\tHardware version of CRC-32C (Castagnoli) checksum. Will fail, if CPU does not support related instructions. Use a crc32c_append version instead of.\\n*/\\nCRC32C_API uint32_t crc32c_append_hw(uint32_t crc, const uint8_t *input, size_t length);\\n\\n/*\\n\\tChecks is hardware version of CRC-32C is available.\\n*/\\nCRC32C_API int crc32c_hw_available();\\n\\n#ifndef __cplusplus\\n/*\\n Initializes the CRC-32C library. Should be called only by C users to enable hardware version for crc32c_append.\\n*/\\nCRC32C_API void crc32c_init();\\n#endif\\n\\n\\n#ifdef __cplusplus\\n}\\n#endif\\n\\n#endif\\n\"\n },\n {\n \"alpha_fraction\": 0.5698798894882202,\n \"alphanum_fraction\": 0.581599771976471,\n \"avg_line_length\": 39.630950927734375,\n \"blob_id\": \"7e19d66eea3dd039f830ee66fcacff989ab8ee3f\",\n \"content_id\": \"239083d4b556f3841809663cb4a4e42f8634b7dd\",\n \"detected_licenses\": [\n \"MIT\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"C#\",\n \"length_bytes\": 3413,\n \"license_type\": \"permissive\",\n \"max_line_length\": 176,\n \"num_lines\": 84,\n \"path\": \"/src/TorchSharp/Distributions/GumbEL.cs\",\n \"repo_name\": \"NiklasGustafsson/TorchSharp\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"// Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information.\\n\\nusing static TorchSharp.torch;\\n\\nnamespace TorchSharp\\n{\\n using Modules;\\n using static torch.distributions;\\n\\n namespace Modules\\n {\\n /// <summary>\\n /// Samples from a Gumbel Distribution.\\n /// </summary>\\n public class Gumbel : TransformedDistribution\\n {\\n internal Gumbel(Tensor loc, Tensor scale, Distribution base_distribution, torch.distributions.transforms.Transform[] transforms, torch.Generator generator = null) :\\n base(base_distribution, transforms, generator)\\n {\\n var locScale = torch.broadcast_tensors(loc, scale);\\n this.batch_shape = loc.size();\\n this.loc = locScale[0].DetachFromDisposeScope();\\n this.scale = locScale[1].DetachFromDisposeScope();\\n }\\n\\n private Tensor loc;\\n private Tensor scale;\\n\\n public override Tensor mean => WrappedTensorDisposeScope(() => loc + scale * euler_constant);\\n\\n public override Tensor mode => loc;\\n\\n public override Tensor variance => stddev.pow(2);\\n\\n public override Tensor stddev => pioversqrtsix * scale;\\n\\n public override Tensor log_prob(Tensor value)\\n {\\n using var _ = NewDisposeScope(); \\n var y = (loc - value) / scale;\\n return ((y - y.exp()) - scale.log()).MoveToOuterDisposeScope();\\n }\\n\\n /// <summary>\\n /// Returns entropy of distribution, batched over batch_shape.\\n /// </summary>\\n public override Tensor entropy() => WrappedTensorDisposeScope(() => scale.log() + (1 + euler_constant));\\n\\n private readonly double pioversqrtsix = 1.282549830161864095544036359671; // Math.PI / Math.Sqrt(6);\\n }\\n }\\n\\n\\n public static partial class torch\\n {\\n public static partial class distributions\\n {\\n /// <summary>\\n /// Samples from a Gumbel Distribution.\\n /// </summary>\\n /// <param name=\\\"loc\\\">Location parameter of the distribution.</param>\\n /// <param name=\\\"scale\\\">Scale parameter of the distribution.</param>\\n /// <param name=\\\"generator\\\">An optional random number generator object.</param>\\n /// <returns></returns>\\n public static Gumbel Gumbel(Tensor loc, Tensor scale, torch.Generator generator = null)\\n {\\n var locScale = torch.broadcast_tensors(loc, scale);\\n loc = locScale[0];\\n scale = locScale[1];\\n\\n var finfo = torch.finfo(loc.dtype);\\n\\n var base_dist = Uniform(torch.full_like(loc, finfo.tiny), torch.full_like(loc, 1 - finfo.eps), generator);\\n var transforms = new torch.distributions.transforms.Transform[] {\\n new torch.distributions.transforms.LogTransform(),\\n new torch.distributions.transforms.AffineTransform(0, -torch.ones_like(scale)),\\n new torch.distributions.transforms.LogTransform(),\\n new torch.distributions.transforms.AffineTransform(loc, -scale)\\n };\\n return new Gumbel(loc, scale, base_dist, transforms, generator);\\n }\\n }\\n }\\n}\\n\"\n },\n {\n \"alpha_fraction\": 0.6957463622093201,\n \"alphanum_fraction\": 0.6976171731948853,\n \"avg_line_length\": 29.772727966308594,\n \"blob_id\": \"4825d0f5dfdf435f8da130f9b7a11cc77fc4dba1\",\n \"content_id\": \"21b2e14a9806745bc5254ce4635234e5065ad18b\",\n \"detected_licenses\": [\n \"MIT\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"C++\",\n \"length_bytes\": 10156,\n \"license_type\": \"permissive\",\n \"max_line_length\": 130,\n \"num_lines\": 330,\n \"path\": \"/src/Native/LibTorchSharp/THSActivation.cpp\",\n \"repo_name\": \"NiklasGustafsson/TorchSharp\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"// Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information.\\n#include \\\"THSNN.h\\\"\\n\\n#include <torch/nn/init.h>\\n\\nNNModule THSNN_CELU_ctor(const double alpha, const bool inplace, NNAnyModule* outAsAnyModule)\\n{\\n CATCH_RETURN_NNModule(\\n auto opts = torch::nn::CELUOptions().alpha(alpha).inplace(inplace);\\n res = create_module<torch::nn::CELUImpl>(opts, outAsAnyModule);\\n );\\n}\\n\\nTensor THSNN_CELU_forward(const NNModule module, const Tensor tensor)\\n{\\n CATCH_TENSOR((*module)->as<torch::nn::CELU>()->forward(*tensor));\\n}\\n\\nNNModule THSNN_ELU_ctor(const double alpha, const bool inplace, NNAnyModule* outAsAnyModule)\\n{\\n CATCH_RETURN_NNModule(\\n auto opts = torch::nn::ELUOptions().alpha(alpha).inplace(inplace);\\n res = create_module<torch::nn::ELUImpl>(opts, outAsAnyModule);\\n );\\n}\\n\\nTensor THSNN_ELU_forward(const NNModule module, const Tensor tensor)\\n{\\n CATCH_TENSOR((*module)->as<torch::nn::ELU>()->forward(*tensor));\\n}\\n\\nNNModule THSNN_GELU_ctor(NNAnyModule* outAsAnyModule)\\n{\\n CATCH_RETURN_NNModule(\\n res = create_module<torch::nn::GELUImpl>(outAsAnyModule);\\n );\\n}\\n\\nTensor THSNN_GELU_forward(const NNModule module, const Tensor tensor)\\n{\\n CATCH_TENSOR((*module)->as<torch::nn::GELU>()->forward(*tensor));\\n}\\n\\nNNModule THSNN_GLU_ctor(const int64_t dim, NNAnyModule* outAsAnyModule)\\n{\\n CATCH_RETURN_NNModule(\\n auto opts = torch::nn::GLUOptions().dim(dim);\\n res = create_module<torch::nn::GLUImpl>(opts, outAsAnyModule);\\n );\\n}\\n\\nTensor THSNN_GLU_forward(const NNModule module, const Tensor tensor)\\n{\\n CATCH_TENSOR((*module)->as<torch::nn::GLU>()->forward(*tensor));\\n}\\n\\nNNModule THSNN_Hardshrink_ctor(const double lambda, NNAnyModule* outAsAnyModule)\\n{\\n CATCH_RETURN_NNModule(\\n auto opts = torch::nn::HardshrinkOptions(lambda);\\n res = create_module<torch::nn::HardshrinkImpl>(opts, outAsAnyModule);\\n );\\n}\\n\\nTensor THSNN_Hardshrink_forward(const NNModule module, const Tensor tensor)\\n{\\n CATCH_TENSOR((*module)->as<torch::nn::Hardshrink>()->forward(*tensor));\\n}\\n\\nNNModule THSNN_Hardtanh_ctor(const double min_val, const double max_val, const bool inplace, NNAnyModule* outAsAnyModule)\\n{\\n CATCH_RETURN_NNModule(\\n auto opts = torch::nn::HardtanhOptions()\\n .min_val(min_val)\\n .max_val(max_val)\\n .inplace(inplace);\\n res = create_module<torch::nn::HardtanhImpl>(opts, outAsAnyModule);\\n );\\n}\\n\\nTensor THSNN_Hardtanh_forward(const NNModule module, const Tensor tensor)\\n{\\n CATCH_TENSOR((*module)->as<torch::nn::Hardtanh>()->forward(*tensor));\\n}\\n\\n\\nNNModule THSNN_LeakyReLU_ctor(const double negative_sloope, const bool inplace, NNAnyModule* outAsAnyModule)\\n{\\n CATCH_RETURN_NNModule(\\n auto opts = torch::nn::LeakyReLUOptions().negative_slope(negative_sloope).inplace(inplace);\\n res = create_module<torch::nn::LeakyReLUImpl>(opts, outAsAnyModule);\\n );\\n}\\n\\nTensor THSNN_LeakyReLU_forward(const NNModule module, const Tensor tensor)\\n{\\n CATCH_TENSOR((*module)->as<torch::nn::LeakyReLU>()->forward(*tensor));\\n}\\n\\nNNModule THSNN_LogSoftmax_ctor(int64_t dim, NNAnyModule* outAsAnyModule)\\n{\\n CATCH_RETURN_NNModule(\\n auto opts = torch::nn::LogSoftmaxOptions(dim);\\n res = create_module<torch::nn::LogSoftmaxImpl>(opts, outAsAnyModule);\\n );\\n}\\n\\nTensor THSNN_LogSoftmax_forward(const NNModule module, const Tensor tensor)\\n{\\n CATCH_TENSOR((*module)->as<torch::nn::LogSoftmax>()->forward(*tensor));\\n}\\n\\nNNModule THSNN_Mish_ctor(NNAnyModule* outAsAnyModule)\\n{\\n CATCH_RETURN_NNModule(\\n res = create_module<torch::nn::MishImpl>(outAsAnyModule);\\n );\\n}\\n\\nTensor THSNN_Mish_forward(const NNModule module, const Tensor tensor)\\n{\\n CATCH_TENSOR((*module)->as<torch::nn::Mish>()->forward(*tensor));\\n}\\n\\nNNModule THSNN_PReLU_ctor(const int64_t nparams, const double init, NNAnyModule* outAsAnyModule)\\n{\\n CATCH_RETURN_NNModule(\\n auto opts = torch::nn::PReLUOptions().num_parameters(nparams).init(init);\\n res = create_module<torch::nn::PReLUImpl>(opts, outAsAnyModule);\\n );\\n}\\n\\nTensor THSNN_PReLU_forward(const NNModule module, const Tensor tensor)\\n{\\n CATCH_TENSOR((*module)->as<torch::nn::PReLU>()->forward(*tensor));\\n}\\n\\nTensor THSNN_PReLU_weight(const NNModule module)\\n{\\n return get_weight<torch::nn::PReLU>(module);\\n}\\n\\nvoid THSNN_PReLU_set_weight(const NNModule module, const Tensor weight)\\n{\\n set_weight<torch::nn::PReLU>(module, weight);\\n}\\n\\nNNModule THSNN_ReLU_ctor(bool inplace, NNAnyModule* outAsAnyModule)\\n{\\n CATCH_RETURN_NNModule(\\n auto opts = torch::nn::ReLUOptions(inplace);\\n res = create_module<torch::nn::ReLUImpl>(opts, outAsAnyModule);\\n );\\n}\\n\\nTensor THSNN_ReLU_forward(const NNModule module, const Tensor tensor)\\n{\\n CATCH_TENSOR((*module)->as<torch::nn::ReLU>()->forward(*tensor));\\n}\\n\\nNNModule THSNN_RReLU_ctor(const double lower, const double upper, const bool inplace, NNAnyModule* outAsAnyModule)\\n{\\n CATCH_RETURN_NNModule(\\n auto opts = torch::nn::RReLUOptions().lower(lower).upper(upper).inplace(inplace);\\n res = create_module<torch::nn::RReLUImpl>(opts, outAsAnyModule);\\n );\\n}\\n\\nTensor THSNN_RReLU_forward(const NNModule module, const Tensor tensor)\\n{\\n CATCH_TENSOR((*module)->as<torch::nn::RReLU>()->forward(*tensor));\\n}\\n\\nNNModule THSNN_ReLU6_ctor(bool inplace, NNAnyModule* outAsAnyModule)\\n{\\n CATCH_RETURN_NNModule(\\n auto opts = torch::nn::ReLU6Options(inplace);\\n res = create_module<torch::nn::ReLU6Impl>(opts, outAsAnyModule);\\n );\\n}\\n\\nTensor THSNN_ReLU6_forward(const NNModule module, const Tensor tensor)\\n{\\n CATCH_TENSOR((*module)->as<torch::nn::ReLU6>()->forward(*tensor));\\n}\\n\\nNNModule THSNN_SELU_ctor(bool inplace, NNAnyModule* outAsAnyModule)\\n{\\n CATCH_RETURN_NNModule(\\n auto opts = torch::nn::SELUOptions(inplace);\\n res = create_module<torch::nn::SELUImpl>(opts, outAsAnyModule);\\n );\\n}\\n\\nTensor THSNN_SELU_forward(const NNModule module, const Tensor tensor)\\n{\\n CATCH_TENSOR((*module)->as<torch::nn::SELU>()->forward(*tensor));\\n}\\n\\nNNModule THSNN_Sigmoid_ctor(NNAnyModule* outAsAnyModule)\\n{\\n CATCH_RETURN_NNModule(\\n res = create_module<torch::nn::SigmoidImpl>(outAsAnyModule);\\n );\\n}\\n\\nTensor THSNN_Sigmoid_forward(const NNModule module, const Tensor tensor)\\n{\\n CATCH_TENSOR((*module)->as<torch::nn::Sigmoid>()->forward(*tensor));\\n}\\n\\nNNModule THSNN_SiLU_ctor(NNAnyModule* outAsAnyModule)\\n{\\n CATCH_RETURN_NNModule(\\n res = create_module<torch::nn::SiLUImpl>(outAsAnyModule);\\n );\\n}\\n\\nTensor THSNN_SiLU_forward(const NNModule module, const Tensor tensor)\\n{\\n CATCH_TENSOR((*module)->as<torch::nn::SiLU>()->forward(*tensor));\\n}\\n\\nNNModule THSNN_Softmax2d_ctor(NNAnyModule* outAsAnyModule)\\n{\\n CATCH_RETURN_NNModule(\\n res = create_module<torch::nn::Softmax2dImpl>(outAsAnyModule);\\n );\\n}\\n\\nTensor THSNN_Softmax2d_forward(const NNModule module, const Tensor tensor)\\n{\\n CATCH_TENSOR((*module)->as<torch::nn::Softmax2d>()->forward(*tensor));\\n}\\n\\nNNModule THSNN_Softmax_ctor(const int64_t dim, NNAnyModule* outAsAnyModule)\\n{\\n CATCH_RETURN_NNModule(\\n auto opts = torch::nn::SoftmaxOptions(dim);\\n res = create_module<torch::nn::SoftmaxImpl>(opts, outAsAnyModule);\\n );\\n}\\n\\nTensor THSNN_Softmax_forward(const NNModule module, const Tensor tensor)\\n{\\n CATCH_TENSOR((*module)->as<torch::nn::Softmax>()->forward(*tensor));\\n}\\n\\nNNModule THSNN_Softmin_ctor(const int64_t dim, NNAnyModule* outAsAnyModule)\\n{\\n CATCH_RETURN_NNModule(\\n auto opts = torch::nn::SoftminOptions(dim);\\n res = create_module<torch::nn::SoftminImpl>(opts, outAsAnyModule);\\n );\\n}\\n\\nTensor THSNN_Softmin_forward(const NNModule module, const Tensor tensor)\\n{\\n CATCH_TENSOR((*module)->as<torch::nn::Softmin>()->forward(*tensor));\\n}\\n\\nNNModule THSNN_Softplus_ctor(const double beta, const double threshold, NNAnyModule* outAsAnyModule)\\n{\\n CATCH_RETURN_NNModule(\\n auto opts = torch::nn::SoftplusOptions().beta(beta).threshold(threshold);\\n res = create_module<torch::nn::SoftplusImpl>(opts, outAsAnyModule);\\n );\\n}\\n\\nTensor THSNN_Softplus_forward(const NNModule module, const Tensor tensor) {\\n CATCH_TENSOR((*module)->as<torch::nn::Softplus>()->forward(*tensor));\\n}\\n\\nNNModule THSNN_Softshrink_ctor(const double lambda, NNAnyModule* outAsAnyModule)\\n{\\n CATCH_RETURN_NNModule(\\n auto opts = torch::nn::SoftshrinkOptions().lambda(lambda);\\n res = create_module<torch::nn::SoftshrinkImpl>(opts, outAsAnyModule);\\n );\\n}\\n\\nTensor THSNN_Softshrink_forward(const NNModule module, const Tensor tensor) {\\n CATCH_TENSOR((*module)->as<torch::nn::Softshrink>()->forward(*tensor));\\n}\\n\\nNNModule THSNN_Softsign_ctor(NNAnyModule* outAsAnyModule)\\n{\\n CATCH_RETURN_NNModule(\\n res = create_module<torch::nn::SoftsignImpl>(outAsAnyModule);\\n );\\n}\\n\\nTensor THSNN_Softsign_forward(const NNModule module, const Tensor tensor) {\\n CATCH_TENSOR((*module)->as<torch::nn::Softsign>()->forward(*tensor));\\n}\\n\\nNNModule THSNN_Tanh_ctor(NNAnyModule* outAsAnyModule)\\n{\\n CATCH_RETURN_NNModule(\\n res = create_module<torch::nn::TanhImpl>(outAsAnyModule);\\n );\\n}\\n\\nTensor THSNN_Tanh_forward(const NNModule module, const Tensor tensor)\\n{\\n CATCH_TENSOR((*module)->as<torch::nn::Tanh>()->forward(*tensor));\\n}\\n\\nNNModule THSNN_Tanhshrink_ctor(NNAnyModule* outAsAnyModule)\\n{\\n CATCH_RETURN_NNModule(\\n res = create_module<torch::nn::TanhshrinkImpl>(outAsAnyModule);\\n );\\n}\\n\\nTensor THSNN_Tanhshrink_forward(const NNModule module, const Tensor tensor) {\\n CATCH_TENSOR((*module)->as<torch::nn::Tanhshrink>()->forward(*tensor));\\n}\\n\\nNNModule THSNN_Threshold_ctor(const double threshold, const double value, const bool inplace, NNAnyModule* outAsAnyModule)\\n{\\n CATCH_RETURN_NNModule(\\n auto opts = torch::nn::ThresholdOptions(threshold, value).inplace(inplace);\\n res = create_module<torch::nn::ThresholdImpl>(opts, outAsAnyModule);\\n );\\n}\\n\\nTensor THSNN_Threshold_forward(const NNModule module, const Tensor tensor) {\\n CATCH_TENSOR((*module)->as<torch::nn::Threshold>()->forward(*tensor));\\n}\\n\\n\"\n },\n {\n \"alpha_fraction\": 0.5740630626678467,\n \"alphanum_fraction\": 0.5778073072433472,\n \"avg_line_length\": 44.57735061645508,\n \"blob_id\": \"e61166188c99a32362d9f7d6581df88ee1dfb7b5\",\n \"content_id\": \"8b5ffd362e99fda7d1576e00dd70be89f3070adb\",\n \"detected_licenses\": [\n \"MIT\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"C#\",\n \"length_bytes\": 28577,\n \"license_type\": \"permissive\",\n \"max_line_length\": 207,\n \"num_lines\": 627,\n \"path\": \"/src/TorchSharp/Tensor/TensorExtensionMethods.cs\",\n \"repo_name\": \"NiklasGustafsson/TorchSharp\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"// Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information.\\n#nullable enable\\nusing System;\\nusing System.Collections.Generic;\\nusing System.Globalization;\\nusing System.Linq;\\nusing static TorchSharp.Utils.LEB128Codec;\\n\\nnamespace TorchSharp\\n{\\n using static torch;\\n\\n public enum TensorStringStyle\\n {\\n Metadata, // Print only shape, dtype, and device\\n Julia, // Print tensor in the style of Julia\\n Numpy, // Print tensor in the style of NumPy\\n CSharp, // Print tensor in the style of CSharp\\n Default, // Pick up the style to use from torch.TensorStringStyle\\n }\\n\\n public static partial class torch\\n {\\n /// <summary>\\n /// The default string formatting style used by ToString(), print(), and str()\\n /// </summary>\\n public static TensorStringStyle TensorStringStyle {\\n get {\\n return _style;\\n }\\n set {\\n\\n if (value == TensorStringStyle.Default)\\n throw new ArgumentException(\\\"The style cannot be set to 'Default'\\\");\\n _style = value;\\n }\\n }\\n\\n /// <summary>\\n /// Set options for printing.\\n /// </summary>\\n /// <param name=\\\"precision\\\">Number of digits of precision for floating point output.</param>\\n /// <param name=\\\"linewidth\\\">The number of characters per line for the purpose of inserting line breaks (default = 100).</param>\\n /// <param name=\\\"newLine\\\">The string to use to represent new-lines. Starts out as 'Environment.NewLine'</param>\\n /// <param name=\\\"sci_mode\\\">Enable scientific notation.</param>\\n /// <param name=\\\"maxRows\\\">The maximum number of rows prrinted.</param>\\n /// <param name=\\\"maxColumns\\\">The maximum number of columns prrinted.</param>\\n public static void set_printoptions(\\n int precision,\\n int? linewidth = null,\\n string? newLine = null,\\n bool sci_mode = false,\\n int? maxRows = null,\\n int? maxColumns = null)\\n {\\n torch.floatFormat = sci_mode ? $\\\"E{precision}\\\" : $\\\"F{precision}\\\";\\n if (newLine is not null)\\n torch.newLine = newLine;\\n if (linewidth.HasValue)\\n torch.lineWidth = linewidth.Value;\\n if (maxRows.HasValue)\\n torch.maxRows = maxRows.Value;\\n if (maxColumns.HasValue)\\n torch.maxColumns = maxColumns.Value;\\n }\\n\\n /// <summary>\\n /// Set options for printing.\\n /// </summary>\\n /// <param name=\\\"style\\\">The default string formatting style used by ToString(), print(), and str()</param>\\n /// <param name=\\\"floatFormat\\\">\\n /// The format string to use for floating point values.\\n /// See: https://learn.microsoft.com/en-us/dotnet/standard/base-types/standard-numeric-format-strings\\n /// </param>\\n /// <param name=\\\"linewidth\\\">The number of characters per line for the purpose of inserting line breaks (default = 100).</param>\\n /// <param name=\\\"newLine\\\">The string to use to represent new-lines. Starts out as 'Environment.NewLine'</param>\\n /// <param name=\\\"maxRows\\\">The maximum number of rows prrinted.</param>\\n /// <param name=\\\"maxColumns\\\">The maximum number of columns prrinted.</param>\\n public static void set_printoptions(\\n TensorStringStyle? style = null,\\n string? floatFormat = null,\\n int? linewidth = null,\\n string? newLine = null,\\n int? maxRows = null,\\n int? maxColumns = null)\\n {\\n if (style.HasValue)\\n torch._style = style.Value;\\n if (floatFormat is not null)\\n torch.floatFormat = floatFormat;\\n if (newLine is not null)\\n torch.newLine = newLine;\\n if (linewidth.HasValue)\\n torch.lineWidth = linewidth.Value;\\n if (maxRows.HasValue)\\n torch.maxRows = maxRows.Value;\\n if (maxColumns.HasValue)\\n torch.maxColumns = maxColumns.Value;\\n }\\n\\n public const TensorStringStyle julia = TensorStringStyle.Julia;\\n public const TensorStringStyle numpy = TensorStringStyle.Numpy;\\n public const TensorStringStyle csharp = TensorStringStyle.CSharp;\\n\\n private static TensorStringStyle _style = TensorStringStyle.Julia;\\n\\n internal static string floatFormat = \\\"g5\\\";\\n internal static string newLine = Environment.NewLine;\\n internal static int lineWidth = 100;\\n internal static int maxRows = 6;\\n internal static int maxColumns = 6;\\n }\\n\\n /// <summary>\\n /// A few extensions to the Tensor type.\\n /// </summary>\\n public static class TensorExtensionMethods\\n {\\n /// <summary>\\n /// Convert to a parameter.\\n /// </summary>\\n /// <param name=\\\"tensor\\\">A tensor.</param>\\n /// <returns></returns>\\n public static Modules.Parameter AsParameter(this Tensor tensor)\\n {\\n return new Modules.Parameter(tensor);\\n }\\n\\n /// <summary>\\n /// Get a string representation of the tensor.\\n /// </summary>\\n /// <param name=\\\"tensor\\\">The input tensor.</param>\\n /// <param name=\\\"fltFormat\\\">\\n /// The format string to use for floating point values.\\n /// See: https://learn.microsoft.com/en-us/dotnet/standard/base-types/standard-numeric-format-strings\\n /// </param>\\n /// <param name=\\\"width\\\">The width of each line of the output string.</param>\\n /// <param name=\\\"newLine\\\">The newline string to use, defaults to system default.</param>\\n /// <param name=\\\"cultureInfo\\\">The culture info to be used when formatting the numbers.</param>\\n /// <param name=\\\"style\\\">\\n /// The style to use -- either 'default,' 'metadata,' 'julia,' or 'numpy'\\n /// </param>\\n /// <remarks>\\n /// This method does exactly the same as ToString(bool, string, int), but is shorter,\\n /// looks more like Python 'str' and doesn't require a style argument in order\\n /// to discriminate.\\n ///\\n /// Primarily intended for use in interactive notebooks.\\n /// </remarks>\\n public static string str(this Tensor tensor, string? fltFormat = null, int? width = null, string? newLine = \\\"\\\\n\\\", CultureInfo? cultureInfo = null, TensorStringStyle style = TensorStringStyle.Default)\\n {\\n return tensor.ToString(style, fltFormat, width, cultureInfo, newLine);\\n }\\n\\n /// <summary>\\n /// Get a Julia-style string representation of the tensor.\\n /// </summary>\\n /// <param name=\\\"tensor\\\">The input tensor.</param>\\n /// <param name=\\\"fltFormat\\\">\\n /// The format string to use for floating point values.\\n /// See: https://learn.microsoft.com/en-us/dotnet/standard/base-types/standard-numeric-format-strings\\n /// </param>\\n /// <param name=\\\"width\\\">The width of each line of the output string.</param>\\n /// <param name=\\\"newLine\\\">The newline string to use, defaults to system default.</param>\\n /// <param name=\\\"cultureInfo\\\">The culture info to be used when formatting the numbers.</param>\\n /// <returns></returns>\\n /// <remarks>\\n /// This method does exactly the same as str(TensorStringStyle.Julia, ...) but is shorter,\\n /// looks more like Python 'str' and doesn't require a style argument in order\\n /// to discriminate.\\n ///\\n /// Primarily intended for use in interactive notebooks.\\n /// </remarks>\\n public static string jlstr(this Tensor tensor, string? fltFormat = null, int? width = null, string? newLine = \\\"\\\\n\\\", CultureInfo? cultureInfo = null)\\n {\\n return tensor.ToString(TensorStringStyle.Julia, fltFormat, width, cultureInfo, newLine);\\n }\\n\\n /// <summary>\\n /// Get a metadata string representation of the tensor.\\n /// Creating metadata string will ignore fltFormat, etc. so this method will not accept them as parameter.\\n /// </summary>\\n /// <param name=\\\"tensor\\\">The input tensor.</param>\\n /// <returns></returns>\\n /// <remarks>\\n /// This method does exactly the same as str(TensorStringStyle.Metadata, ...) but is shorter,\\n /// looks more like Python 'str' and doesn't require a style argument in order\\n /// to discriminate.\\n ///\\n /// Primarily intended for use in interactive notebooks.\\n /// </remarks>\\n public static string metastr(this Tensor tensor)\\n {\\n return tensor.ToString(TensorStringStyle.Metadata);\\n }\\n\\n /// <summary>\\n /// Get a numpy-style string representation of the tensor.\\n /// </summary>\\n /// <param name=\\\"tensor\\\">The input tensor.</param>\\n /// <param name=\\\"fltFormat\\\">\\n /// The format string to use for floating point values.\\n /// See: https://learn.microsoft.com/en-us/dotnet/standard/base-types/standard-numeric-format-strings\\n /// </param>\\n /// <param name=\\\"width\\\">The width of each line of the output string.</param>\\n /// <param name=\\\"newLine\\\">The newline string to use, defaults to system default.</param>\\n /// <param name=\\\"cultureInfo\\\">The culture info to be used when formatting the numbers.</param>\\n /// <returns></returns>\\n /// <remarks>\\n /// This method does exactly the same as str(TensorStringStyle.Numpy, ...) but is shorter,\\n /// looks more like Python 'str()' and doesn't require a style argument in order\\n /// to discriminate.\\n ///\\n /// Primarily intended for use in interactive notebooks.\\n /// </remarks>\\n public static string npstr(this Tensor tensor, string? fltFormat = \\\"g5\\\", int? width = 100, string? newLine = \\\"\\\\n\\\", CultureInfo? cultureInfo = null)\\n {\\n return tensor.ToString(TensorStringStyle.Numpy, fltFormat, width, cultureInfo, newLine);\\n }\\n\\n /// <summary>\\n /// Get a C#-style string representation of the tensor.\\n /// </summary>\\n /// <param name=\\\"tensor\\\">The input tensor.</param>\\n /// <param name=\\\"fltFormat\\\">\\n /// The format string to use for floating point values.\\n /// See: https://learn.microsoft.com/en-us/dotnet/standard/base-types/standard-numeric-format-strings\\n /// </param>\\n /// <param name=\\\"width\\\">The width of each line of the output string.</param>\\n /// <param name=\\\"newLine\\\">The newline string to use, defaults to system default.</param>\\n /// <param name=\\\"cultureInfo\\\">The culture info to be used when formatting the numbers.</param>\\n /// <returns></returns>\\n /// <remarks>\\n /// This method does exactly the same as str(TensorStringStyle.CSharp, ...) but is shorter,\\n /// looks more like Python 'str()' and doesn't require a style argument in order\\n /// to discriminate.\\n ///\\n /// Primarily intended for use in interactive notebooks.\\n /// </remarks>\\n public static string cstr(this Tensor tensor, string? fltFormat = \\\"g5\\\", int? width = 100, string? newLine = \\\"\\\\n\\\", CultureInfo? cultureInfo = null)\\n {\\n return tensor.ToString(TensorStringStyle.CSharp, fltFormat, width, cultureInfo, newLine);\\n }\\n\\n /// <summary>\\n /// Uses Console.WriteLine to print a tensor expression on stdout. This is intended for\\n /// interactive notebook use, primarily.\\n /// </summary>\\n /// <param name=\\\"t\\\">The input tensor.</param>\\n /// <param name=\\\"fltFormat\\\">\\n /// The format string to use for floating point values.\\n /// See: https://learn.microsoft.com/en-us/dotnet/standard/base-types/standard-numeric-format-strings\\n /// </param>\\n /// <param name=\\\"width\\\">The width of each line of the output string.</param>\\n /// <param name=\\\"newLine\\\">The newline string to use, defaults to system default.</param>\\n /// <param name=\\\"cultureInfo\\\">The culture info to be used when formatting the numbers.</param>\\n /// <param name=\\\"style\\\">\\n /// The style to use -- either 'default,' 'metadata,' 'julia,' or 'numpy'\\n /// </param>\\n /// <returns></returns>\\n public static Tensor print(this Tensor t, string? fltFormat = \\\"g5\\\", int? width = 100, string? newLine = \\\"\\\", CultureInfo? cultureInfo = null, TensorStringStyle style = TensorStringStyle.Default)\\n {\\n Console.WriteLine(t.ToString(style, fltFormat, width, cultureInfo, newLine));\\n return t;\\n }\\n\\n /// <summary>\\n /// Indicates whether the element type of a given tensor is integral.\\n /// </summary>\\n /// <param name=\\\"tensor\\\">The input tensor.</param>\\n /// <returns></returns>\\n internal static bool IsIntegral(this Tensor tensor)\\n {\\n return IsIntegral(tensor.dtype);\\n }\\n\\n public static ReadOnlySpan<int> IntShape(this Tensor tensor)\\n {\\n var shape = tensor.shape;\\n var int_shape = new int[shape.Length];\\n for (var i = 0; i < shape.Length; ++i) int_shape[i] = (int)shape[i];\\n return int_shape;\\n }\\n\\n /// <summary>\\n /// Indicates whether a given element type is integral.\\n /// </summary>\\n /// <param name=\\\"type\\\">The input type.</param>\\n /// <returns></returns>\\n internal static bool IsIntegral(this ScalarType type)\\n {\\n switch (type) {\\n case ScalarType.Byte:\\n case ScalarType.Int8:\\n case ScalarType.Int16:\\n case ScalarType.Int32:\\n case ScalarType.Int64:\\n case ScalarType.Bool:\\n return true;\\n default:\\n return false;\\n }\\n }\\n\\n /// <summary>\\n /// Indicates whether a given element type is real.\\n /// </summary>\\n /// <param name=\\\"type\\\">The input type.</param>\\n /// <returns></returns>\\n /// <remarks>\\n /// Complex numbers are not real, and thus will return 'false'\\n /// </remarks>\\n internal static bool IsFloatingPoint(this ScalarType type)\\n {\\n switch (type) {\\n case ScalarType.BFloat16:\\n case ScalarType.Float16:\\n case ScalarType.Float32:\\n case ScalarType.Float64:\\n return true;\\n default:\\n return false;\\n }\\n }\\n\\n /// <summary>\\n /// Indicates whether a given element type is complex.\\n /// </summary>\\n /// <param name=\\\"type\\\">The input type.</param>\\n /// <returns></returns>\\n /// <remarks>\\n /// Complex numbers are not real, and thus will return 'false'\\n /// </remarks>\\n internal static bool IsComplex(this ScalarType type)\\n {\\n switch (type) {\\n case ScalarType.ComplexFloat32:\\n case ScalarType.ComplexFloat64:\\n return true;\\n default:\\n return false;\\n }\\n }\\n\\n /// <summary>\\n /// Save the tensor in a .NET-specific format.\\n /// </summary>\\n /// <param name=\\\"tensor\\\"></param>\\n /// <param name=\\\"writer\\\"></param>\\n public static void Save(this Tensor tensor, System.IO.BinaryWriter writer)\\n {\\n bool copied = false;\\n\\n if (tensor.device_type != DeviceType.CPU) {\\n tensor = tensor.to(CPU);\\n copied = true;\\n }\\n\\n // First, write the type\\n writer.Encode((int)tensor.dtype); // 4 bytes\\n // Then, the shape.\\n writer.Encode(tensor.shape.Length); // 4 bytes\\n foreach (var s in tensor.shape) writer.Encode(s); // n * 8 bytes\\n // Then, the data\\n#if NETSTANDARD2_0_OR_GREATER\\n // TODO: NETSTANDARD2_0_OR_GREATER Try to optimize to avoid the allocation\\n writer.Write(tensor.bytes.ToArray()); // ElementSize * NumberOfElements\\n#else\\n writer.Write(tensor.bytes); // ElementSize * NumberOfElements\\n#endif // NETSTANDARD2_0_OR_GREATER\\n\\n if (copied) tensor.Dispose();\\n }\\n\\n /// <summary>\\n /// Load the tensor using a .NET-specific format.\\n /// </summary>\\n /// <param name=\\\"tensor\\\">The tensor into which to load serialized data.</param>\\n /// <param name=\\\"reader\\\">A BinaryReader instance</param>\\n /// <param name=\\\"skip\\\">If true, the data will be read from the stream, but not copied to the target tensor.</param>\\n /// <remarks>\\n /// Using a skip list only prevents tensors in the target module from being modified, it\\n /// does not alter the logic related to checking for matching tensor element types.\\n /// </remarks>\\n public static void Load(this Tensor tensor, System.IO.BinaryReader reader, bool skip = false)\\n {\\n // First, read the type\\n var type = (ScalarType)reader.Decode();\\n\\n if (type != tensor.dtype)\\n throw new ArgumentException(\\\"Mismatched tensor data types while loading. Make sure that the model you are loading into is exactly the same as the origin.\\\");\\n\\n // Then, the shape\\n var shLen = reader.Decode();\\n long[] loadedShape = new long[shLen];\\n\\n long totalSize = 1;\\n for (int i = 0; i < shLen; ++i) {\\n loadedShape[i] = reader.Decode();\\n totalSize *= loadedShape[i];\\n }\\n\\n if (!skip && !loadedShape.SequenceEqual(tensor.shape))\\n // We only care about this if the bytes will be written to the tensor.\\n throw new ArgumentException(\\\"Mismatched tensor shape while loading. Make sure that the model you are loading into is exactly the same as the origin.\\\");\\n\\n //\\n // TODO: Fix this so that you can read large tensors. Right now, they are limited to 2GB\\n //\\n if (totalSize > int.MaxValue)\\n throw new NotImplementedException(\\\"Loading tensors larger than 2GB\\\");\\n\\n // This needs to be done even if the tensor is skipped, since we have to advance the input stream.\\n var bytes = reader.ReadBytes((int)(totalSize * tensor.ElementSize));\\n\\n if (!skip) {\\n var device = tensor.device;\\n if (device.type != DeviceType.CPU) tensor.to(CPU);\\n tensor.bytes = bytes;\\n tensor.to(device);\\n }\\n }\\n\\n public static void Load(ref Tensor tensor, System.IO.BinaryReader reader, bool skip = false)\\n {\\n // First, read the type\\n var type = (ScalarType)reader.Decode();\\n\\n // Then, the shape\\n var shLen = reader.Decode();\\n long[] loadedShape = new long[shLen];\\n\\n long totalSize = 1;\\n for (int i = 0; i < shLen; ++i) {\\n loadedShape[i] = reader.Decode();\\n totalSize *= loadedShape[i];\\n }\\n\\n //\\n // TODO: Fix this so that you can read large tensors. Right now, they are limited to 2GB\\n //\\n if (totalSize > int.MaxValue)\\n throw new NotImplementedException(\\\"Loading tensors larger than 2GB\\\");\\n\\n if (tensor is null) {\\n // If the tensor doesn't exist, initialize by zeros unless\\n // it's going to be loaded from the stream.\\n tensor = skip\\n ? torch.zeros(loadedShape, dtype: type)\\n : torch.empty(loadedShape, dtype: type);\\n } else if (!skip && !loadedShape.SequenceEqual(tensor.shape)) {\\n // We only care about this if the bytes will be written to the tensor.\\n throw new ArgumentException(\\\"Mismatched tensor shape while loading. Make sure that the model you are loading into is exactly the same as the origin.\\\");\\n }\\n\\n // This needs to be done even if the tensor is skipped, since we have to advance the input stream.\\n var bytes = reader.ReadBytes((int)(totalSize * tensor.ElementSize));\\n\\n if (!skip) {\\n var device = tensor.device;\\n if (device.type != DeviceType.CPU) tensor.to(CPU);\\n tensor.bytes = bytes;\\n tensor.to(device);\\n }\\n }\\n\\n /// <summary>\\n /// Creating a tensor form an array of data.\\n /// </summary>\\n /// <typeparam name=\\\"T\\\">The .NET element type.</typeparam>\\n /// <param name=\\\"rawArray\\\">Input data.</param>\\n /// <param name=\\\"dimensions\\\">The shape of the tensor that is created.</param>\\n /// <param name=\\\"doCopy\\\">Perform a copy rather than using the array as backing storage for the tensor.</param>\\n /// <param name=\\\"requires_grad\\\">If true, the tensor must track its gradients.</param>\\n /// <returns></returns>\\n public static Tensor ToTensor<T>(this T[] rawArray, long[] dimensions, bool doCopy = false, bool requires_grad = false)\\n {\\n var array = doCopy ? (T[])rawArray.Clone() : rawArray;\\n\\n return true switch {\\n true when typeof(T) == typeof(byte) => tensor((array as byte[])!, dimensions,\\n requires_grad: requires_grad),\\n true when typeof(T) == typeof(sbyte) => tensor((array as sbyte[])!, dimensions,\\n requires_grad: requires_grad),\\n true when typeof(T) == typeof(short) => tensor((array as short[])!, dimensions,\\n requires_grad: requires_grad),\\n true when typeof(T) == typeof(int) => tensor((array as int[])!, dimensions,\\n requires_grad: requires_grad),\\n true when typeof(T) == typeof(long) => tensor((array as long[])!, dimensions,\\n requires_grad: requires_grad),\\n true when typeof(T) == typeof(double) => tensor((array as double[])!, dimensions,\\n requires_grad: requires_grad),\\n true when typeof(T) == typeof(float) => tensor((array as float[])!, dimensions,\\n requires_grad: requires_grad),\\n true when typeof(T) == typeof(bool) => tensor((array as bool[])!, dimensions,\\n requires_grad: requires_grad),\\n _ => throw new NotImplementedException($\\\"Creating tensor of type {typeof(T)} is not supported.\\\")\\n };\\n }\\n\\n /// <summary>\\n /// Creating a tensor from a scalar value.\\n /// </summary>\\n /// <typeparam name=\\\"T\\\">The .NET element type.</typeparam>\\n /// <param name=\\\"scalar\\\">Scalar input value</param>\\n /// <param name=\\\"device\\\">The device to place the tensor on.</param>\\n /// <param name=\\\"requires_grad\\\">If true, the tensor must track its gradients.</param>\\n /// <returns></returns>\\n public static Tensor ToTensor<T>(this T scalar, Device? device = null, bool requires_grad = false) where T : struct\\n {\\n if (requires_grad && typeof(T) != typeof(float) && typeof(T) != typeof(double)) {\\n throw new ArgumentException(nameof(requires_grad), \\\"Only floating point types support gradients.\\\");\\n }\\n\\n if (typeof(T) == typeof(byte))\\n return tensor((byte)(object)scalar, uint8, device, requires_grad);\\n if (typeof(T) == typeof(sbyte))\\n return tensor((sbyte)(object)scalar, int8, device, requires_grad);\\n if (typeof(T) == typeof(short))\\n return tensor((short)(object)scalar, int16, device, requires_grad);\\n if (typeof(T) == typeof(int))\\n return tensor((int)(object)scalar, int32, device, requires_grad);\\n if (typeof(T) == typeof(long))\\n return tensor((long)(object)scalar, int64, device, requires_grad);\\n if (typeof(T) == typeof(float))\\n return tensor((float)(object)scalar, float32, device, requires_grad);\\n if (typeof(T) == typeof(double))\\n return tensor((double)(object)scalar, float64, device, requires_grad);\\n throw new NotImplementedException($\\\"Creating tensor of type {typeof(T)} is not supported.\\\");\\n }\\n\\n /// <summary>\\n /// Explicitly convert a singleton tensor to a .NET scalar value.\\n /// </summary>\\n /// <param name=\\\"value\\\">The input tensor</param>\\n public static (float Real, float Imaginary) ToComplexFloat32(this Tensor value) => value.ToScalar().ToComplexFloat32();\\n\\n /// <summary>\\n /// Explicitly convert a singleton tensor to a .NET scalar value.\\n /// </summary>\\n /// <param name=\\\"value\\\">The input tensor</param>\\n public static System.Numerics.Complex ToComplexFloat64(this Tensor value) => value.ToScalar().ToComplexFloat64();\\n\\n#if NET6_0_OR_GREATER\\n /// <summary>\\n /// Explicitly convert a singleton tensor to a .NET scalar value.\\n /// </summary>\\n /// <param name=\\\"value\\\">The input tensor</param>\\n public static Half ToHalf(this Tensor value) => value.ToScalar().ToHalf();\\n#endif\\n\\n /// <summary>\\n /// Explicitly convert a singleton tensor to a .NET scalar value.\\n /// </summary>\\n /// <param name=\\\"value\\\">The input tensor</param>\\n public static float ToSingle(this Tensor value) => value.ToScalar().ToSingle();\\n\\n /// <summary>\\n /// Explicitly convert a singleton tensor to a .NET scalar value.\\n /// </summary>\\n /// <param name=\\\"value\\\">The input tensor</param>\\n public static double ToDouble(this Tensor value) => value.ToScalar().ToDouble();\\n\\n /// <summary>\\n /// Explicitly convert a singleton tensor to a .NET scalar value.\\n /// </summary>\\n /// <param name=\\\"value\\\">The input tensor</param>\\n public static sbyte ToSByte(this Tensor value) => value.ToScalar().ToSByte();\\n\\n /// <summary>\\n /// Explicitly convert a singleton tensor to a .NET scalar value.\\n /// </summary>\\n /// <param name=\\\"value\\\">The input tensor</param>\\n public static byte ToByte(this Tensor value) => value.ToScalar().ToByte();\\n\\n /// <summary>\\n /// Explicitly convert a singleton tensor to a .NET scalar value.\\n /// </summary>\\n /// <param name=\\\"value\\\">The input tensor</param>\\n public static short ToInt16(this Tensor value) => value.ToScalar().ToInt16();\\n\\n /// <summary>\\n /// Explicitly convert a singleton tensor to a .NET scalar value.\\n /// </summary>\\n /// <param name=\\\"value\\\">The input tensor</param>\\n public static int ToInt32(this Tensor value) => value.ToScalar().ToInt32();\\n\\n /// <summary>\\n /// Explicitly convert a singleton tensor to a .NET scalar value.\\n /// </summary>\\n /// <param name=\\\"value\\\">The input tensor</param>\\n public static long ToInt64(this Tensor value) => value.ToScalar().ToInt64();\\n\\n /// <summary>\\n /// Explicitly convert a singleton tensor to a .NET scalar value.\\n /// </summary>\\n /// <param name=\\\"value\\\">The input tensor</param>\\n public static bool ToBoolean(this Tensor value) => value.ToScalar().ToBoolean();\\n\\n\\n /// <summary>\\n /// Explicitly convert a singleton tensor to a .NET scalar value.\\n /// </summary>\\n /// <param name=\\\"value\\\">The input tensor</param>\\n public static (float Real, float Imaginary) ToComplex32(this Tensor value) => value.ToScalar().ToComplexFloat32();\\n\\n /// <summary>\\n /// Explicitly convert a singleton tensor to a .NET scalar value.\\n /// </summary>\\n /// <param name=\\\"value\\\">The input tensor</param>\\n public static System.Numerics.Complex ToComplex64(this Tensor value) => value.ToScalar().ToComplexFloat64();\\n\\n /// <summary>\\n /// Multiply the dimensions of a tensor shape to provide a complete size.\\n /// </summary>\\n /// <param name=\\\"shape\\\">The shape.</param>\\n /// <returns></returns>\\n public static long TotalSize(this IEnumerable<long> shape)\\n {\\n long result = 1;\\n foreach (var sz in shape) {\\n result *= sz;\\n }\\n return result;\\n }\\n }\\n}\\n\"\n },\n {\n \"alpha_fraction\": 0.4054102599620819,\n \"alphanum_fraction\": 0.4857393801212311,\n \"avg_line_length\": 39.48113250732422,\n \"blob_id\": \"26245a406d58251ceaf07e9edad9824a2a57d6d6\",\n \"content_id\": \"cefbf0ac06f85965f8f7f3067307dad50e91e6fe\",\n \"detected_licenses\": [\n \"MIT\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"C#\",\n \"length_bytes\": 55783,\n \"license_type\": \"permissive\",\n \"max_line_length\": 158,\n \"num_lines\": 1378,\n \"path\": \"/test/TorchSharpTest/TestDistributions.cs\",\n \"repo_name\": \"NiklasGustafsson/TorchSharp\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"// Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information.\\n\\nusing static TorchSharp.torch;\\nusing static TorchSharp.torch.distributions;\\n\\nusing Xunit;\\nusing System;\\n\\n#nullable enable\\n\\nnamespace TorchSharp\\n{\\n [Collection(\\\"Sequential\\\")]\\n public class TestDistributions\\n {\\n [Fact]\\n public void TestUniform()\\n {\\n var dist = Uniform(torch.tensor(new[] { 0.5, 0.25, 0.15 }), torch.tensor(3.5));\\n Assert.True(torch.tensor(new[] { 2, 1.875, 1.825, }).allclose(dist.mean));\\n Assert.True(torch.tensor(new[] { 0.75, 0.88020833, 0.93520833 }).allclose(dist.variance));\\n Assert.True(torch.tensor(new[] { 1.0986123, 1.178655, 1.2089603 }).allclose(dist.entropy(), rtol: 1e-3, atol: 1e-4));\\n Assert.True(torch.tensor(new[] { 0.16666667, 0.2307692308, 0.2537313433 }).allclose(dist.cdf(torch.ones(3))));\\n Assert.True(torch.tensor(new[] { 3.5000, 3.5000, 3.5000 }).allclose(dist.icdf(torch.ones(3))));\\n Assert.True(torch.tensor(new[] { -1.0986123, -1.178655, -1.2089603 }).allclose(dist.log_prob(torch.ones(3))));\\n {\\n var sample = dist.sample();\\n\\n Assert.Equal(new long[] { 3 }, sample.shape);\\n Assert.All<double>(sample.data<double>().ToArray(), d => Assert.True(d >= 0 && d < 3.5));\\n }\\n {\\n var sample = dist.sample(2, 3);\\n\\n Assert.Equal(new long[] { 2, 3, 3 }, sample.shape);\\n Assert.All<double>(sample.data<double>().ToArray(), d => Assert.True(d >= 0 && d < 3.5));\\n }\\n {\\n var sample = dist.expand(new long[] { 3, 3 }).sample(2, 3);\\n\\n Assert.Equal(new long[] { 2, 3, 3, 3 }, sample.shape);\\n Assert.All<double>(sample.data<double>().ToArray(), d => Assert.True(d >= 0 && d < 3.5));\\n }\\n }\\n\\n [Fact]\\n public void TestUniformGen()\\n {\\n var gen = new Generator(4711);\\n\\n var dist = Uniform(torch.tensor(new[] { 0.5, 0.25, 0.15 }), torch.tensor(3.5), gen);\\n Assert.True(torch.tensor(new[] { 2, 1.875, 1.825, }).allclose(dist.mean));\\n Assert.True(torch.tensor(new[] { 0.75, 0.88020833, 0.93520833 }).allclose(dist.variance));\\n Assert.True(torch.tensor(new[] { 1.0986123, 1.178655, 1.2089603 }).allclose(dist.entropy(), rtol: 1e-3, atol: 1e-4));\\n Assert.True(torch.tensor(new[] { 0.16666667, 0.2307692308, 0.2537313433 }).allclose(dist.cdf(torch.ones(3))));\\n Assert.True(torch.tensor(new[] { 3.5000, 3.5000, 3.5000 }).allclose(dist.icdf(torch.ones(3))));\\n Assert.True(torch.tensor(new[] { -1.0986123, -1.178655, -1.2089603 }).allclose(dist.log_prob(torch.ones(3))));\\n {\\n var sample = dist.sample();\\n\\n Assert.Equal(new long[] { 3 }, sample.shape);\\n Assert.All<double>(sample.data<double>().ToArray(), d => Assert.True(d >= 0 && d < 3.5));\\n }\\n {\\n var sample = dist.sample(2, 3);\\n\\n Assert.Equal(new long[] { 2, 3, 3 }, sample.shape);\\n Assert.All<double>(sample.data<double>().ToArray(), d => Assert.True(d >= 0 && d < 3.5));\\n }\\n {\\n var sample = dist.expand(new long[] { 3, 3 }).sample(2, 3);\\n\\n Assert.Equal(new long[] { 2, 3, 3, 3 }, sample.shape);\\n Assert.All<double>(sample.data<double>().ToArray(), d => Assert.True(d >= 0 && d < 3.5));\\n }\\n }\\n\\n [Fact]\\n public void TestNormal()\\n {\\n var dist = Normal(torch.tensor(new[] { 0.5000, 0.2500, 0.1500 }), torch.tensor(new[] { 0.15, 0.05, 0.25 }));\\n Assert.True(torch.tensor(new[] { 0.5000, 0.2500, 0.1500 }).allclose(dist.mean));\\n Assert.True(torch.tensor(new[] { 0.0225, 0.0025, 0.0625 }).allclose(dist.variance));\\n Assert.True(torch.tensor(new[] { 0.5000, 0.2500, 0.1500 }).allclose(dist.mode));\\n\\n Assert.True(torch.tensor(new[] { -4.5774, -110.4232, -26.9126 }).allclose(dist.log_prob(torch.arange(3))));\\n Assert.True(torch.tensor(new[] { -0.478181, -1.576794, 0.032644 }).allclose(dist.entropy(), rtol: 1e-3, atol: 1e-4));\\n\\n {\\n var sample = dist.sample();\\n\\n Assert.Equal(new long[] { 3 }, sample.shape);\\n }\\n {\\n var sample = dist.sample(2, 3);\\n\\n Assert.Equal(new long[] { 2, 3, 3 }, sample.shape);\\n }\\n {\\n var sample = dist.expand(new long[] { 3, 3 }).sample(2, 3);\\n\\n Assert.Equal(new long[] { 2, 3, 3, 3 }, sample.shape);\\n }\\n }\\n\\n [Fact]\\n public void TestNormalGen()\\n {\\n var gen = new Generator(4711);\\n\\n var dist = Normal(torch.tensor(new[] { 0.5000, 0.2500, 0.1500 }), torch.tensor(new[] { 0.15, 0.05, 0.25 }), gen);\\n Assert.True(torch.tensor(new[] { 0.5000, 0.2500, 0.1500 }).allclose(dist.mean));\\n Assert.True(torch.tensor(new[] { 0.0225, 0.0025, 0.0625 }).allclose(dist.variance));\\n Assert.True(torch.tensor(new[] { 0.5000, 0.2500, 0.1500 }).allclose(dist.mode));\\n\\n Assert.True(torch.tensor(new[] { -4.5774, -110.4232, -26.9126 }).allclose(dist.log_prob(torch.arange(3))));\\n Assert.True(torch.tensor(new[] { -0.478181, -1.576794, 0.032644 }).allclose(dist.entropy()));\\n\\n {\\n var sample = dist.sample();\\n\\n Assert.Equal(new long[] { 3 }, sample.shape);\\n }\\n {\\n var sample = dist.sample(2, 3);\\n\\n Assert.Equal(new long[] { 2, 3, 3 }, sample.shape);\\n }\\n {\\n var sample = dist.expand(new long[] { 3, 3 }).sample(2, 3);\\n\\n Assert.Equal(new long[] { 2, 3, 3, 3 }, sample.shape);\\n }\\n }\\n\\n [Fact]\\n public void TestHalfNormal()\\n {\\n var dist = HalfNormal(torch.tensor(new[] { 0.5000, 0.2500, 0.1500 }));\\n Assert.True(torch.tensor(new[] { 0.39894228, 0.19947114, 0.11968268 }).allclose(dist.mean));\\n Assert.True(torch.tensor(new[] { 0.0, 0.0, 0.0 }).allclose(dist.mode));\\n Assert.True(torch.tensor(new[] { 0.090845057, 0.022711264, 0.0081760551 }).allclose(dist.variance));\\n\\n Assert.True(torch.tensor(new[] { 0.9544997, 0.9999366, 0.99999999 }).allclose(dist.cdf(torch.ones(3))));\\n Assert.True(torch.tensor(new[] { 0.032644172, -0.66050301, -1.1713286 }).allclose(dist.entropy()));\\n Assert.True(torch.tensor(new[] { -1.5326442, -6.839497, -20.550894 }).allclose(dist.log_prob(torch.ones(3))));\\n {\\n var sample = dist.sample();\\n\\n Assert.Equal(new long[] { 3 }, sample.shape);\\n }\\n {\\n var sample = dist.sample(2, 3);\\n\\n Assert.Equal(new long[] { 2, 3, 3 }, sample.shape);\\n }\\n {\\n var sample = dist.expand(new long[] { 3, 3 }).sample(2, 3);\\n\\n Assert.Equal(new long[] { 2, 3, 3, 3 }, sample.shape);\\n }\\n }\\n\\n [Fact]\\n public void TestHalfNormalGen()\\n {\\n var dist = HalfNormal(torch.tensor(new[] { 0.5000, 0.2500, 0.1500 }), new Generator(4711));\\n Assert.True(torch.tensor(new[] { 0.39894228, 0.19947114, 0.11968268 }).allclose(dist.mean));\\n Assert.True(torch.tensor(new[] { 0.0, 0.0, 0.0 }).allclose(dist.mode));\\n Assert.True(torch.tensor(new[] { 0.090845057, 0.022711264, 0.0081760551 }).allclose(dist.variance));\\n\\n Assert.True(torch.tensor(new[] { 0.9544997, 0.9999366, 0.99999999 }).allclose(dist.cdf(torch.ones(3))));\\n Assert.True(torch.tensor(new[] { 0.032644172, -0.66050301, -1.1713286 }).allclose(dist.entropy()));\\n Assert.True(torch.tensor(new[] { -1.5326442, -6.839497, -20.550894 }).allclose(dist.log_prob(torch.ones(3))));\\n {\\n var sample = dist.sample();\\n\\n Assert.Equal(new long[] { 3 }, sample.shape);\\n }\\n {\\n var sample = dist.sample(2, 3);\\n\\n Assert.Equal(new long[] { 2, 3, 3 }, sample.shape);\\n }\\n {\\n var sample = dist.expand(new long[] { 3, 3 }).sample(2, 3);\\n\\n Assert.Equal(new long[] { 2, 3, 3, 3 }, sample.shape);\\n }\\n }\\n\\n [Fact]\\n public void TestHalfCauchy()\\n {\\n var dist = HalfCauchy(torch.tensor(new[] { 0.5, 0.25, 0.15 }));\\n Assert.True(torch.tensor(new[] { 0.0, 0.0, 0.0 }).allclose(dist.mode));\\n Assert.True(torch.tensor(new[] { 0.70483276, 0.84404174, 0.90521372 }).allclose(dist.cdf(torch.ones(3))));\\n Assert.True(torch.tensor(new[] { -11438666, -5719333, -3431599.8 }).allclose(dist.icdf(torch.ones(3))));\\n Assert.True(torch.tensor(new[] { 1.1447299, 0.45158271, -0.059242918 }).allclose(dist.entropy()));\\n Assert.True(torch.tensor(new[] { -1.3678734, -1.8985017, -2.3709533 }).allclose(dist.log_prob(torch.ones(3))));\\n {\\n var sample = dist.sample();\\n\\n Assert.Equal(new long[] { 3 }, sample.shape);\\n }\\n {\\n var sample = dist.sample(2, 3);\\n\\n Assert.Equal(new long[] { 2, 3, 3 }, sample.shape);\\n }\\n {\\n var sample = dist.expand(new long[] { 3, 3 }).sample(2, 3);\\n\\n Assert.Equal(new long[] { 2, 3, 3, 3 }, sample.shape);\\n }\\n }\\n\\n [Fact]\\n public void TestHalfCauchyGen()\\n {\\n var dist = HalfCauchy(torch.tensor(new[] { 0.5, 0.25, 0.15 }), new Generator(4711));\\n Assert.True(torch.tensor(new[] { 0.0, 0.0, 0.0 }).allclose(dist.mode));\\n Assert.True(torch.tensor(new[] { 0.70483276, 0.84404174, 0.90521372 }).allclose(dist.cdf(torch.ones(3))));\\n Assert.True(torch.tensor(new[] { -11438666, -5719333, -3431599.8 }).allclose(dist.icdf(torch.ones(3))));\\n Assert.True(torch.tensor(new[] { 1.1447299, 0.45158271, -0.059242918 }).allclose(dist.entropy()));\\n Assert.True(torch.tensor(new[] { -1.3678734, -1.8985017, -2.3709533 }).allclose(dist.log_prob(torch.ones(3))));\\n {\\n var sample = dist.sample();\\n\\n Assert.Equal(new long[] { 3 }, sample.shape);\\n }\\n {\\n var sample = dist.sample(2, 3);\\n\\n Assert.Equal(new long[] { 2, 3, 3 }, sample.shape);\\n }\\n {\\n var sample = dist.expand(new long[] { 3, 3 }).sample(2, 3);\\n\\n Assert.Equal(new long[] { 2, 3, 3, 3 }, sample.shape);\\n }\\n }\\n\\n [Fact]\\n public void TestPareto()\\n {\\n var dist = Pareto(torch.tensor(new[] { 0.5, 0.25, 0.15 }), torch.tensor(1.0));\\n Assert.True(torch.tensor(new[] { 0.5, 0.25, 0.15, }).allclose(dist.mode));\\n Assert.True(torch.tensor(new[] { 0.5000, 0.7500, 0.8500 }).allclose(dist.cdf(torch.ones(3))));\\n Assert.True(torch.tensor(new[] { 1.3068528, 0.61370564, 0.10288002 }).allclose(dist.entropy()));\\n Assert.True(torch.tensor(new[] { -0.69314718, -1.3862944, -1.89712 }).allclose(dist.log_prob(torch.ones(3))));\\n {\\n var sample = dist.sample();\\n\\n Assert.Equal(new long[] { 3 }, sample.shape);\\n }\\n {\\n var sample = dist.sample(2, 3);\\n\\n Assert.Equal(new long[] { 2, 3, 3 }, sample.shape);\\n }\\n {\\n var sample = dist.expand(new long[] { 3, 3 }).sample(2, 3);\\n\\n Assert.Equal(new long[] { 2, 3, 3, 3 }, sample.shape);\\n }\\n }\\n\\n [Fact]\\n public void TestParetoGen()\\n {\\n var dist = Pareto(torch.tensor(new[] { 0.5, 0.25, 0.15 }), torch.tensor(1.0), new Generator(4711));\\n Assert.True(torch.tensor(new[] { 0.5, 0.25, 0.15, }).allclose(dist.mode));\\n Assert.True(torch.tensor(new[] { 0.5000, 0.7500, 0.8500 }).allclose(dist.cdf(torch.ones(3))));\\n Assert.True(torch.tensor(new[] { 1.3068528, 0.61370564, 0.10288002 }).allclose(dist.entropy()));\\n Assert.True(torch.tensor(new[] { -0.69314718, -1.3862944, -1.89712 }).allclose(dist.log_prob(torch.ones(3))));\\n {\\n var sample = dist.sample();\\n\\n Assert.Equal(new long[] { 3 }, sample.shape);\\n }\\n {\\n var sample = dist.sample(2, 3);\\n\\n Assert.Equal(new long[] { 2, 3, 3 }, sample.shape);\\n }\\n {\\n var sample = dist.expand(new long[] { 3, 3 }).sample(2, 3);\\n\\n Assert.Equal(new long[] { 2, 3, 3, 3 }, sample.shape);\\n }\\n }\\n\\n [Fact]\\n public void TestLogNormal()\\n {\\n var dist = LogNormal(torch.tensor(new[] { 0.5, 0.25, 0.15 }), torch.tensor(0.5));\\n Assert.True(torch.tensor(new[] { 1.868246, 1.4549914, 1.3165307 }).allclose(dist.mean));\\n Assert.True(torch.tensor(new[] { 1.2840254, 1, 0.90483742 }).allclose(dist.mode));\\n Assert.True(torch.tensor(new[] { 0.99134611, 0.60128181, 0.49228791 }).allclose(dist.variance));\\n Assert.True(torch.tensor(new[] { 1.2257914, 0.97579135, 0.87579135 }).allclose(dist.entropy()));\\n Assert.True(torch.tensor(new[] { 0.15865525, 0.308537538, 0.38208857 }).allclose(dist.cdf(torch.ones(3))));\\n Assert.True(torch.tensor(new[] { double.PositiveInfinity, double.PositiveInfinity, double.PositiveInfinity }).allclose(dist.icdf(torch.ones(3))));\\n Assert.True(torch.tensor(new[] { -0.72579135, -0.35079135, -0.27079135 }).allclose(dist.log_prob(torch.ones(3))));\\n {\\n var sample = dist.sample();\\n\\n Assert.Equal(new long[] { 3 }, sample.shape);\\n }\\n {\\n var sample = dist.sample(2, 3);\\n\\n Assert.Equal(new long[] { 2, 3, 3 }, sample.shape);\\n }\\n {\\n var sample = dist.expand(new long[] { 3, 3 }).sample(2, 3);\\n\\n Assert.Equal(new long[] { 2, 3, 3, 3 }, sample.shape);\\n }\\n }\\n\\n [Fact]\\n public void TestLogNormalGen()\\n {\\n var dist = LogNormal(torch.tensor(new[] { 0.5, 0.25, 0.15 }), torch.tensor(0.5), new Generator(4711));\\n Assert.True(torch.tensor(new[] { 1.868246, 1.4549914, 1.3165307 }).allclose(dist.mean));\\n Assert.True(torch.tensor(new[] { 1.2840254, 1, 0.90483742 }).allclose(dist.mode));\\n Assert.True(torch.tensor(new[] { 0.99134611, 0.60128181, 0.49228791 }).allclose(dist.variance));\\n Assert.True(torch.tensor(new[] { 1.2257914, 0.97579135, 0.87579135 }).allclose(dist.entropy()));\\n Assert.True(torch.tensor(new[] { 0.15865525, 0.308537538, 0.38208857 }).allclose(dist.cdf(torch.ones(3))));\\n Assert.True(torch.tensor(new[] { double.PositiveInfinity, double.PositiveInfinity, double.PositiveInfinity }).allclose(dist.icdf(torch.ones(3))));\\n Assert.True(torch.tensor(new[] { -0.72579135, -0.35079135, -0.27079135 }).allclose(dist.log_prob(torch.ones(3))));\\n {\\n var sample = dist.sample();\\n\\n Assert.Equal(new long[] { 3 }, sample.shape);\\n }\\n {\\n var sample = dist.sample(2, 3);\\n\\n Assert.Equal(new long[] { 2, 3, 3 }, sample.shape);\\n }\\n {\\n var sample = dist.expand(new long[] { 3, 3 }).sample(2, 3);\\n\\n Assert.Equal(new long[] { 2, 3, 3, 3 }, sample.shape);\\n }\\n }\\n\\n [Fact]\\n public void TestGumbel()\\n {\\n var dist = Gumbel(torch.tensor(new[] { 0.5, 0.25, 0.15 }), torch.tensor(3.5));\\n Assert.True(torch.tensor(new[] { 2.5202548, 2.2702548, 2.1702548 }).allclose(dist.mean));\\n Assert.True(torch.tensor(new[] { 0.5, 0.25, 0.15 }).allclose(dist.mode));\\n Assert.True(torch.tensor(new[] { 20.150442, 20.150442, 20.150442 }).allclose(dist.variance));\\n Assert.True(torch.tensor(new[] { 2.8299786, 2.8299786, 2.8299786 }).allclose(dist.entropy()));\\n Assert.True(torch.tensor(new[] { 0.42026160, 0.44614211, 0.456400959 }).allclose(dist.cdf(torch.ones(3))));\\n Assert.True(torch.tensor(new[] { -2.26249801, -2.274166429, -2.28000367 }).allclose(dist.log_prob(torch.ones(3))));\\n {\\n var sample = dist.sample();\\n\\n Assert.Equal(new long[] { 3 }, sample.shape);\\n }\\n {\\n var sample = dist.sample(2, 3);\\n\\n Assert.Equal(new long[] { 2, 3, 3 }, sample.shape);\\n }\\n {\\n var sample = dist.expand(new long[] { 3, 3 }).sample(2, 3);\\n\\n Assert.Equal(new long[] { 2, 3, 3, 3 }, sample.shape);\\n }\\n }\\n\\n [Fact]\\n public void TestGumbelGen()\\n {\\n var dist = Gumbel(torch.tensor(new[] { 0.5, 0.25, 0.15 }), torch.tensor(3.5), new Generator(4711));\\n Assert.True(torch.tensor(new[] { 2.5202548, 2.2702548, 2.1702548 }).allclose(dist.mean));\\n Assert.True(torch.tensor(new[] { 0.5, 0.25, 0.15 }).allclose(dist.mode));\\n Assert.True(torch.tensor(new[] { 20.150442, 20.150442, 20.150442 }).allclose(dist.variance));\\n Assert.True(torch.tensor(new[] { 2.8299786, 2.8299786, 2.8299786 }).allclose(dist.entropy()));\\n Assert.True(torch.tensor(new[] { 0.42026160, 0.44614211, 0.456400959 }).allclose(dist.cdf(torch.ones(3))));\\n Assert.True(torch.tensor(new[] { -2.26249801, -2.274166429, -2.28000367 }).allclose(dist.log_prob(torch.ones(3))));\\n {\\n var sample = dist.sample();\\n\\n Assert.Equal(new long[] { 3 }, sample.shape);\\n }\\n {\\n var sample = dist.sample(2, 3);\\n\\n Assert.Equal(new long[] { 2, 3, 3 }, sample.shape);\\n }\\n {\\n var sample = dist.expand(new long[] { 3, 3 }).sample(2, 3);\\n\\n Assert.Equal(new long[] { 2, 3, 3, 3 }, sample.shape);\\n }\\n }\\n\\n [Fact]\\n public void TestLaplace()\\n {\\n var dist = Laplace(torch.tensor(new[] { 0.5, 0.25, 0.15 }), torch.tensor(0.5));\\n Assert.True(torch.tensor(new[] { 0.5, 0.25, 0.15 }).allclose(dist.mean));\\n Assert.True(torch.tensor(new[] { 0.5, 0.25, 0.15 }).allclose(dist.mode));\\n Assert.True(torch.tensor(new[] { 0.5, 0.5, 0.5 }).allclose(dist.variance));\\n Assert.True(torch.tensor(new[] { 1.0, 1.0, 1.0 }).allclose(dist.entropy()));\\n Assert.True(torch.tensor(new[] { 0.81606028, 0.88843492, 0.90865824 }).allclose(dist.cdf(torch.ones(3))));\\n Assert.True(torch.tensor(new[] { double.PositiveInfinity, double.PositiveInfinity, double.PositiveInfinity }).allclose(dist.icdf(torch.ones(3))));\\n Assert.True(torch.tensor(new[] { -1, -1.5, -1.7 }).allclose(dist.log_prob(torch.ones(3))));\\n {\\n var sample = dist.sample();\\n\\n Assert.Equal(new long[] { 3 }, sample.shape);\\n }\\n {\\n var sample = dist.sample(2, 3);\\n\\n Assert.Equal(new long[] { 2, 3, 3 }, sample.shape);\\n }\\n {\\n var sample = dist.expand(new long[] { 3, 3 }).sample(2, 3);\\n\\n Assert.Equal(new long[] { 2, 3, 3, 3 }, sample.shape);\\n }\\n }\\n\\n [Fact]\\n public void TestLaplaceGen()\\n {\\n var gen = new Generator(4711);\\n\\n var dist = Laplace(torch.tensor(new[] { 0.5, 0.25, 0.15 }), torch.tensor(0.5), gen);\\n Assert.True(torch.tensor(new[] { 0.5, 0.25, 0.15 }).allclose(dist.mean));\\n Assert.True(torch.tensor(new[] { 0.5, 0.25, 0.15 }).allclose(dist.mode));\\n Assert.True(torch.tensor(new[] { 0.5, 0.5, 0.5 }).allclose(dist.variance));\\n Assert.True(torch.tensor(new[] { 1.0, 1.0, 1.0 }).allclose(dist.entropy()));\\n Assert.True(torch.tensor(new[] { 0.81606028, 0.88843492, 0.90865824 }).allclose(dist.cdf(torch.ones(3))));\\n Assert.True(torch.tensor(new[] { double.PositiveInfinity, double.PositiveInfinity, double.PositiveInfinity }).allclose(dist.icdf(torch.ones(3))));\\n Assert.True(torch.tensor(new[] { -1, -1.5, -1.7 }).allclose(dist.log_prob(torch.ones(3))));\\n {\\n var sample = dist.sample();\\n\\n Assert.Equal(new long[] { 3 }, sample.shape);\\n }\\n {\\n var sample = dist.sample(2, 3);\\n\\n Assert.Equal(new long[] { 2, 3, 3 }, sample.shape);\\n }\\n {\\n var sample = dist.expand(new long[] { 3, 3 }).sample(2, 3);\\n\\n Assert.Equal(new long[] { 2, 3, 3, 3 }, sample.shape);\\n }\\n }\\n\\n [Fact]\\n public void TestPoisson()\\n {\\n var dist = Poisson(torch.tensor(new[] { 0.5000, 0.2500, 0.1500 }));\\n Assert.True(torch.tensor(new[] { 0.5000, 0.2500, 0.1500 }).allclose(dist.mean));\\n Assert.True(torch.tensor(new[] { 0.5000, 0.2500, 0.1500 }).allclose(dist.variance));\\n Assert.True(torch.tensor(new[] { 0.0, 0.0, 0.0 }).allclose(dist.mode));\\n\\n var log_prob = dist.log_prob(torch.arange(3));\\n\\n Assert.True(torch.tensor(new[] { -0.5000, -1.6363, -4.6374 }).allclose(log_prob));\\n\\n {\\n var sample = dist.sample();\\n\\n Assert.Equal(new long[] { 3 }, sample.shape);\\n }\\n {\\n var sample = dist.sample(2, 3);\\n\\n Assert.Equal(new long[] { 2, 3, 3 }, sample.shape);\\n }\\n {\\n var sample = dist.expand(new long[] { 3, 3 }).sample(2, 3);\\n\\n Assert.Equal(new long[] { 2, 3, 3, 3 }, sample.shape);\\n }\\n }\\n\\n [Fact]\\n public void TestPoissonGen()\\n {\\n var gen = new Generator(4711);\\n var dist = Poisson(torch.tensor(new[] { 0.5000, 0.2500, 0.1500 }), gen);\\n Assert.True(torch.tensor(new[] { 0.5000, 0.2500, 0.1500 }).allclose(dist.mean));\\n Assert.True(torch.tensor(new[] { 0.5000, 0.2500, 0.1500 }).allclose(dist.variance));\\n Assert.True(torch.tensor(new[] { 0.0, 0.0, 0.0 }).allclose(dist.mode));\\n\\n var log_prob = dist.log_prob(torch.arange(3));\\n\\n Assert.True(torch.tensor(new[] { -0.5000, -1.6363, -4.6374 }).allclose(log_prob));\\n\\n {\\n var sample = dist.sample();\\n\\n Assert.Equal(new long[] { 3 }, sample.shape);\\n }\\n {\\n var sample = dist.sample(2, 3);\\n\\n Assert.Equal(new long[] { 2, 3, 3 }, sample.shape);\\n }\\n {\\n var sample = dist.expand(new long[] { 3, 3 }).sample(2, 3);\\n\\n Assert.Equal(new long[] { 2, 3, 3, 3 }, sample.shape);\\n }\\n }\\n\\n [Fact]\\n public void TestBernoulli()\\n {\\n var dist = Bernoulli(torch.tensor(new[] { 0.5, 0.25, 0.15 }));\\n Assert.True(torch.tensor(new[] { 0.5000, 0.2500, 0.1500 }).allclose(dist.mean));\\n Assert.True(torch.tensor(new[] { 0.2500, 0.1875, 0.1275 }).allclose(dist.variance, rtol: 1e-3, atol: 1e-4));\\n Assert.True(torch.tensor(new[] { -0.69314718, -1.38629436, -1.897119984 }).allclose(dist.log_prob(torch.ones(3).to(torch.float64))));\\n Assert.True(torch.tensor(new[] { 0.693147181, 0.5623351446, 0.4227090878 }).allclose(dist.entropy()));\\n\\n {\\n var sample = dist.sample();\\n\\n Assert.Single(sample.shape);\\n Assert.All<double>(sample.data<double>().ToArray(), d => Assert.True(d == 0 || d == 1));\\n }\\n {\\n var sample = dist.sample(2, 3);\\n\\n Assert.Equal(new long[] { 2, 3, 3 }, sample.shape);\\n Assert.All<double>(sample.data<double>().ToArray(), d => Assert.True(d == 0 || d == 1));\\n }\\n {\\n var sample = dist.expand(new long[] { 3, 3 }).sample(2, 3);\\n\\n Assert.Equal(new long[] { 2, 3, 3, 3 }, sample.shape);\\n Assert.All<double>(sample.data<double>().ToArray(), d => Assert.True(d == 0 || d == 1));\\n }\\n }\\n\\n [Fact]\\n public void TestBernoulliGen()\\n {\\n var gen = new Generator(4711);\\n var dist = Bernoulli(torch.tensor(new[] { 0.5, 0.25, 0.15 }), generator: gen);\\n Assert.True(torch.tensor(new[] { 0.5000, 0.2500, 0.1500 }).allclose(dist.mean));\\n Assert.True(torch.tensor(new[] { 0.2500, 0.1875, 0.1275 }).allclose(dist.variance, rtol: 1e-3, atol: 1e-4));\\n Assert.True(torch.tensor(new[] { -0.69314718, -1.38629436, -1.897119984 }).allclose(dist.log_prob(torch.ones(3).to(torch.float64))));\\n Assert.True(torch.tensor(new[] { 0.693147181, 0.5623351446, 0.4227090878 }).allclose(dist.entropy()));\\n\\n {\\n var sample = dist.sample();\\n\\n Assert.Single(sample.shape);\\n Assert.All<double>(sample.data<double>().ToArray(), d => Assert.True(d == 0 || d == 1));\\n }\\n {\\n var sample = dist.sample(2, 3);\\n\\n Assert.Equal(new long[] { 2, 3, 3 }, sample.shape);\\n Assert.All<double>(sample.data<double>().ToArray(), d => Assert.True(d == 0 || d == 1));\\n }\\n {\\n var sample = dist.expand(new long[] { 3, 3 }).sample(2, 3);\\n\\n Assert.Equal(new long[] { 2, 3, 3, 3 }, sample.shape);\\n Assert.All<double>(sample.data<double>().ToArray(), d => Assert.True(d == 0 || d == 1));\\n }\\n }\\n\\n [Fact]\\n public void TestLogitRelaxedBernoulli()\\n {\\n var temp = torch.rand(3, dtype: ScalarType.Float64);\\n\\n var dist = LogitRelaxedBernoulli(temp, torch.rand(3, dtype: ScalarType.Float64));\\n {\\n var sample = dist.sample();\\n\\n Assert.Single(sample.shape);\\n }\\n {\\n var sample = dist.sample(2, 3);\\n\\n Assert.Equal(new long[] { 2, 3, 3 }, sample.shape);\\n }\\n {\\n var sample = dist.expand(new long[] { 3, 3 }).sample(2, 3);\\n\\n Assert.Equal(new long[] { 2, 3, 3, 3 }, sample.shape);\\n }\\n }\\n\\n [Fact]\\n public void TestLogitRelaxedBernoulliGen()\\n {\\n var gen = new Generator(4711);\\n var temp = torch.rand(3, dtype: ScalarType.Float64);\\n\\n var dist = LogitRelaxedBernoulli(temp, torch.rand(3, dtype: ScalarType.Float64), generator: gen);\\n {\\n var sample = dist.sample();\\n\\n Assert.Single(sample.shape);\\n }\\n {\\n var sample = dist.sample(2, 3);\\n\\n Assert.Equal(new long[] { 2, 3, 3 }, sample.shape);\\n }\\n {\\n var sample = dist.expand(new long[] { 3, 3 }).sample(2, 3);\\n\\n Assert.Equal(new long[] { 2, 3, 3, 3 }, sample.shape);\\n }\\n }\\n\\n [Fact]\\n public void TestRelaxedBernoulli()\\n {\\n var temp = torch.rand(3, dtype: ScalarType.Float64);\\n\\n var dist = RelaxedBernoulli(temp, torch.rand(3, dtype: ScalarType.Float64));\\n {\\n var sample = dist.sample();\\n\\n Assert.Single(sample.shape);\\n }\\n {\\n var sample = dist.sample(2, 3);\\n\\n Assert.Equal(new long[] { 2, 3, 3 }, sample.shape);\\n }\\n {\\n var sample = dist.expand(new long[] { 3, 3 }).sample(2, 3);\\n\\n Assert.Equal(new long[] { 2, 3, 3, 3 }, sample.shape);\\n }\\n }\\n\\n [Fact]\\n public void TestRelaxedBernoulliGen()\\n {\\n var gen = new Generator(4711);\\n var temp = torch.rand(3, dtype: ScalarType.Float64);\\n\\n var dist = RelaxedBernoulli(temp, torch.rand(3, dtype: ScalarType.Float64), generator: gen);\\n {\\n var sample = dist.sample();\\n\\n Assert.Single(sample.shape);\\n }\\n {\\n var sample = dist.sample(2, 3);\\n\\n Assert.Equal(new long[] { 2, 3, 3 }, sample.shape);\\n }\\n {\\n var sample = dist.expand(new long[] { 3, 3 }).sample(2, 3);\\n\\n Assert.Equal(new long[] { 2, 3, 3, 3 }, sample.shape);\\n }\\n }\\n\\n [Fact]\\n public void TestBeta()\\n {\\n var dist = Beta(torch.tensor(new[] { 0.5, 0.25, 0.15 }), torch.tensor(0.15f));\\n Assert.True(torch.tensor(new[] { 0.769230762, 0.62499999, 0.5000 }).allclose(dist.mean));\\n Assert.True(torch.tensor(new[] { 1.0, 1.0, 1.0 }).allclose(dist.mode));\\n Assert.True(torch.tensor(new[] { 0.10758472, 0.16741072, 0.192307691 }).allclose(dist.variance));\\n Assert.True(torch.tensor(new[] { -3.0250008, -2.72106057, -3.4215678 }).allclose(dist.entropy()));\\n {\\n var sample = dist.sample();\\n\\n Assert.Equal(new long[] { 3 }, sample.shape);\\n }\\n {\\n var sample = dist.sample(2, 3);\\n\\n Assert.Equal(new long[] { 2, 3, 3 }, sample.shape);\\n }\\n {\\n var sample = dist.expand(new long[] { 3, 3, 3 }).sample(2, 3);\\n\\n Assert.Equal(new long[] { 2, 3, 3, 3, 3 }, sample.shape);\\n }\\n }\\n\\n [Fact]\\n public void TestBetaGen()\\n {\\n var gen = new Generator(4711);\\n var dist = Beta(torch.rand(3, 3) * 0.5f, torch.tensor(0.5f), gen);\\n {\\n var sample = dist.sample();\\n var entropy = dist.entropy();\\n\\n Assert.Equal(new long[] { 3, 3 }, sample.shape);\\n Assert.Equal(new long[] { 3, 3 }, entropy.shape);\\n }\\n {\\n var sample = dist.sample(2, 3);\\n\\n Assert.Equal(new long[] { 2, 3, 3, 3 }, sample.shape);\\n }\\n {\\n var sample = dist.expand(new long[] { 3, 3, 3 }).sample(2, 3);\\n\\n Assert.Equal(new long[] { 2, 3, 3, 3, 3 }, sample.shape);\\n }\\n }\\n\\n [Fact]\\n public void TestBinomial()\\n {\\n var dist = Binomial(torch.tensor(100), torch.tensor(new[] { 0.25, 0.25, 0.15 }));\\n Assert.True(torch.tensor(new[] { 25.0, 25.0, 15.0 }).allclose(dist.mean));\\n Assert.True(torch.tensor(new[] { 18.7500, 18.7500, 12.7500 }).allclose(dist.variance));\\n Assert.True(torch.tensor(new[] { 25.0, 25.0, 15.0 }).allclose(dist.mode));\\n\\n var log_prob = dist.log_prob(torch.arange(3));\\n\\n Assert.True(torch.tensor(new[] { -28.7682, -25.2616, -11.2140 }).allclose(log_prob));\\n\\n {\\n var sample = dist.sample();\\n\\n Assert.Single(sample.shape);\\n Assert.All<double>(sample.data<double>().ToArray(), d => Assert.True(d >= 0 && d <= 100));\\n }\\n {\\n var sample = dist.sample(2, 3);\\n\\n Assert.Equal(new long[] { 2, 3, 3 }, sample.shape);\\n Assert.All<double>(sample.data<double>().ToArray(), d => Assert.True(d >= 0 && d <= 100));\\n }\\n {\\n var sample = dist.expand(new long[] { 3, 3 }).sample(2, 3);\\n\\n Assert.Equal(new long[] { 2, 3, 3, 3 }, sample.shape);\\n Assert.All<double>(sample.data<double>().ToArray(), d => Assert.True(d >= 0 && d <= 100));\\n }\\n }\\n\\n [Fact]\\n public void TestBinomialGen()\\n {\\n var gen = new Generator(4711);\\n var dist = Binomial(torch.tensor(100), torch.tensor(new[] { 0.25, 0.25, 0.15 }), generator: gen);\\n Assert.True(torch.tensor(new[] { 25.0, 25.0, 15.0 }).allclose(dist.mean));\\n Assert.True(torch.tensor(new[] { 18.7500, 18.7500, 12.7500 }).allclose(dist.variance));\\n Assert.True(torch.tensor(new[] { 25.0, 25.0, 15.0 }).allclose(dist.mode));\\n\\n var log_prob = dist.log_prob(torch.arange(3));\\n\\n Assert.True(torch.tensor(new[] { -28.7682, -25.2616, -11.2140 }).allclose(log_prob));\\n\\n {\\n var sample = dist.sample();\\n\\n Assert.Single(sample.shape);\\n Assert.All<double>(sample.data<double>().ToArray(), d => Assert.True(d >= 0 && d <= 100));\\n }\\n {\\n var sample = dist.sample(2, 3);\\n\\n Assert.Equal(new long[] { 2, 3, 3 }, sample.shape);\\n Assert.All<double>(sample.data<double>().ToArray(), d => Assert.True(d >= 0 && d <= 100));\\n }\\n {\\n var sample = dist.expand(new long[] { 3, 3 }).sample(2, 3);\\n\\n Assert.Equal(new long[] { 2, 3, 3, 3 }, sample.shape);\\n Assert.All<double>(sample.data<double>().ToArray(), d => Assert.True(d >= 0 && d <= 100));\\n }\\n }\\n\\n [Fact]\\n public void TestNegativeBinomial()\\n {\\n var dist = NegativeBinomial(torch.tensor(100), torch.tensor(new[] { 0.25, 0.25, 0.15 }));\\n Assert.True(torch.tensor(new[] { 33.3333, 33.3333, 17.6471 }).allclose(dist.mean));\\n Assert.True(torch.tensor(new[] { 44.4444, 44.4444, 20.7612 }).allclose(dist.variance));\\n Assert.True(torch.tensor(new[] { 33.0, 33.0, 17.0 }).allclose(dist.mode));\\n\\n var log_prob = dist.log_prob(torch.arange(3));\\n\\n Assert.True(torch.tensor(new[] { -28.7682, -25.5493, -11.5190 }).allclose(log_prob));\\n\\n {\\n var sample = dist.sample();\\n\\n Assert.Single(sample.shape);\\n Assert.All<double>(sample.data<double>().ToArray(), d => Assert.True(d >= 0 && d <= 100));\\n }\\n {\\n var sample = dist.sample(2, 3);\\n\\n Assert.Equal(new long[] { 2, 3, 3 }, sample.shape);\\n Assert.All<double>(sample.data<double>().ToArray(), d => Assert.True(d >= 0 && d <= 100));\\n }\\n {\\n var sample = dist.expand(new long[] { 3, 3 }).sample(2, 3);\\n\\n Assert.Equal(new long[] { 2, 3, 3, 3 }, sample.shape);\\n Assert.All<double>(sample.data<double>().ToArray(), d => Assert.True(d >= 0 && d <= 100));\\n }\\n }\\n\\n [Fact]\\n public void TestCategorical()\\n {\\n var gen = new Generator(4711);\\n var categories = 7;\\n var dist = Categorical(torch.rand(3, categories, dtype: ScalarType.Float64));\\n {\\n var sample = dist.sample();\\n\\n Assert.Single(sample.shape);\\n Assert.Equal(3, sample.shape[0]);\\n Assert.All<long>(sample.data<long>().ToArray(), l => Assert.True(l >= 0 && l < categories));\\n }\\n {\\n var sample = dist.sample(2, 3);\\n\\n Assert.Equal(new long[] { 2, 3, 3 }, sample.shape);\\n Assert.All<long>(sample.data<long>().ToArray(), l => Assert.True(l >= 0 && l < categories));\\n }\\n }\\n\\n\\n [Fact]\\n public void TestCategoricalGen()\\n {\\n var gen = new Generator(4711);\\n var categories = 7;\\n var dist = Categorical(torch.rand(3, categories, dtype: ScalarType.Float64), generator: gen);\\n {\\n var sample = dist.sample();\\n\\n Assert.Single(sample.shape);\\n Assert.Equal(3, sample.shape[0]);\\n Assert.All<long>(sample.data<long>().ToArray(), l => Assert.True(l >= 0 && l < categories));\\n }\\n {\\n var sample = dist.sample(2, 3);\\n\\n Assert.Equal(new long[] { 2, 3, 3 }, sample.shape);\\n Assert.All<long>(sample.data<long>().ToArray(), l => Assert.True(l >= 0 && l < categories));\\n }\\n }\\n\\n [Fact]\\n public void TestOneHotCategorical()\\n {\\n var categories = 7;\\n var dist = OneHotCategorical(torch.rand(3, categories, dtype: ScalarType.Float64));\\n {\\n var sample = dist.sample();\\n\\n Assert.Equal(2, sample.ndim);\\n Assert.Equal(3, sample.shape[0]);\\n Assert.Equal(categories, sample.shape[1]);\\n }\\n {\\n var sample = dist.sample(2, 3);\\n\\n Assert.Equal(4, sample.ndim);\\n Assert.Equal(new long[] { 2, 3, 3, 7 }, sample.shape);\\n }\\n }\\n\\n\\n [Fact]\\n public void TestOneHotCategoricalGen()\\n {\\n var gen = new Generator(4711);\\n var categories = 7;\\n var dist = OneHotCategorical(torch.rand(3, categories, dtype: ScalarType.Float64, generator: gen));\\n {\\n var sample = dist.sample();\\n\\n Assert.Equal(2, sample.ndim);\\n Assert.Equal(3, sample.shape[0]);\\n Assert.Equal(categories, sample.shape[1]);\\n }\\n {\\n var sample = dist.sample(2, 3);\\n\\n Assert.Equal(4, sample.ndim);\\n Assert.Equal(new long[] { 2, 3, 3, 7 }, sample.shape);\\n }\\n }\\n\\n [Fact]\\n public void TestCauchy()\\n {\\n var dist = Cauchy(torch.tensor(new[] { 0.5, 0.25, 0.15 }), torch.tensor(new[] { 0.05, 0.10, 0.15 }));\\n\\n Assert.True(torch.tensor(new[] { 0.5000, 0.2500, 0.1500 }).allclose(dist.mode));\\n Assert.True(torch.tensor(new[] { -2.7641, -2.8896, -2.7475 }).allclose(dist.log_prob(torch.ones(3))));\\n Assert.True(torch.tensor(new[] { -0.46470802658, 0.22843915398, 0.633904262 }).allclose(dist.entropy()));\\n\\n {\\n var sample = dist.sample();\\n\\n Assert.Equal(new long[] { 3 }, sample.shape);\\n }\\n {\\n var sample = dist.sample(2, 3);\\n\\n Assert.Equal(new long[] { 2, 3, 3 }, sample.shape);\\n }\\n {\\n var sample = dist.expand(new long[] { 3, 3, 3 }).sample(2, 3);\\n\\n Assert.Equal(new long[] { 2, 3, 3, 3, 3 }, sample.shape);\\n }\\n }\\n\\n [Fact]\\n public void TestCauchyGen()\\n {\\n var gen = new Generator(4711);\\n var dist = Cauchy(torch.tensor(new[] { 0.5, 0.25, 0.15 }), torch.tensor(new[] { 0.05, 0.10, 0.15 }), gen);\\n Assert.True(torch.tensor(new[] { 0.5000, 0.2500, 0.1500 }).allclose(dist.mode));\\n Assert.True(torch.tensor(new[] { -2.7641, -2.8896, -2.7475 }).allclose(dist.log_prob(torch.ones(3))));\\n Assert.True(torch.tensor(new[] { -0.46470802658, 0.22843915398, 0.633904262 }).allclose(dist.entropy()));\\n\\n {\\n var sample = dist.sample();\\n\\n Assert.Equal(new long[] { 3 }, sample.shape);\\n }\\n {\\n var sample = dist.sample(2, 3);\\n\\n Assert.Equal(new long[] { 2, 3, 3 }, sample.shape);\\n }\\n {\\n var sample = dist.expand(new long[] { 3, 3, 3 }).sample(2, 3);\\n\\n Assert.Equal(new long[] { 2, 3, 3, 3, 3 }, sample.shape);\\n }\\n }\\n\\n [Fact]\\n public void TestChi2()\\n {\\n var dist = Chi2(torch.tensor(new[] { 0.5, 0.25, 0.15 }));\\n Assert.True(torch.tensor(new[] { 0.5, 0.25, 0.15 }).allclose(dist.mean));\\n Assert.True(torch.tensor(new[] { 1, 0.5, 0.3 }).allclose(dist.variance));\\n Assert.True(torch.tensor(new[] { -1.9613093, -2.6060618, -3.1034275, }).allclose(dist.log_prob(torch.ones(3))));\\n Assert.True(torch.tensor(new[] { -0.9394204, -4.502366, -9.439412 }).allclose(dist.entropy()));\\n {\\n var sample = dist.sample();\\n\\n Assert.Equal(new long[] { 3 }, sample.shape);\\n }\\n {\\n var sample = dist.sample(2, 3);\\n\\n Assert.Equal(new long[] { 2, 3, 3 }, sample.shape);\\n }\\n {\\n var sample = dist.expand(new long[] { 3, 3, 3 }).sample(2, 3);\\n\\n Assert.Equal(new long[] { 2, 3, 3, 3, 3 }, sample.shape);\\n }\\n }\\n\\n [Fact]\\n public void TestChi2Gen()\\n {\\n var gen = new Generator(4711);\\n var dist = Chi2(torch.tensor(new[] { 0.5, 0.25, 0.15 }), gen);\\n Assert.True(torch.tensor(new[] { 0.5, 0.25, 0.15 }).allclose(dist.mean));\\n Assert.True(torch.tensor(new[] { 1, 0.5, 0.3 }).allclose(dist.variance));\\n Assert.True(torch.tensor(new[] { -1.9613093, -2.6060618, -3.1034275, }).allclose(dist.log_prob(torch.ones(3))));\\n Assert.True(torch.tensor(new[] { -0.9394204, -4.502366, -9.439412 }).allclose(dist.entropy()));\\n {\\n var sample = dist.sample();\\n\\n Assert.Equal(new long[] { 3 }, sample.shape);\\n }\\n {\\n var sample = dist.sample(2, 3);\\n\\n Assert.Equal(new long[] { 2, 3, 3 }, sample.shape);\\n }\\n {\\n var sample = dist.expand(new long[] { 3, 3, 3 }).sample(2, 3);\\n\\n Assert.Equal(new long[] { 2, 3, 3, 3, 3 }, sample.shape);\\n }\\n }\\n\\n [Fact]\\n public void TestDirichlet()\\n {\\n var dist = Dirichlet(torch.tensor(new[] { 0.5, 0.25, 0.15 }));\\n Assert.True(torch.tensor(new[] { 0.55555556, 0.27777778, 0.16666667, }).allclose(dist.mean));\\n Assert.True(torch.tensor(new[] { 0.12995452, 0.10558804, 0.073099415 }).allclose(dist.variance));\\n Assert.True(torch.tensor(-4.9130).allclose(dist.entropy()));\\n Assert.True(torch.tensor(-3.621825).allclose(dist.log_prob(torch.ones(3))));\\n {\\n var sample = dist.sample();\\n\\n Assert.Equal(new long[] { 3 }, sample.shape);\\n }\\n {\\n var sample = dist.sample(2, 3);\\n\\n Assert.Equal(new long[] { 2, 3, 3 }, sample.shape);\\n }\\n {\\n var sample = dist.expand(new long[] { 3, 3, 3 }).sample(2, 3);\\n\\n Assert.Equal(new long[] { 2, 3, 3, 3, 3, 3 }, sample.shape);\\n }\\n }\\n\\n [Fact]\\n public void TestDirichletGen()\\n {\\n var gen = new Generator(4711);\\n var dist = Dirichlet(torch.tensor(new[] { 0.5, 0.25, 0.15 }), gen);\\n Assert.True(torch.tensor(new[] { 0.55555556, 0.27777778, 0.16666667, }).allclose(dist.mean));\\n Assert.True(torch.tensor(new[] { 0.12995452, 0.10558804, 0.073099415 }).allclose(dist.variance));\\n Assert.True(torch.tensor(-4.9130).allclose(dist.entropy()));\\n Assert.True(torch.tensor(-3.621825).allclose(dist.log_prob(torch.ones(3))));\\n {\\n var sample = dist.sample();\\n\\n Assert.Equal(new long[] { 3 }, sample.shape);\\n }\\n {\\n var sample = dist.sample(2, 3);\\n\\n Assert.Equal(new long[] { 2, 3, 3 }, sample.shape);\\n }\\n {\\n var sample = dist.expand(new long[] { 3, 3, 3 }).sample(2, 3);\\n\\n Assert.Equal(new long[] { 2, 3, 3, 3, 3, 3 }, sample.shape);\\n }\\n }\\n\\n [Fact]\\n public void TestExponential()\\n {\\n var dist = Exponential(torch.tensor(new[] { 0.5, 0.25, 0.15 }));\\n Assert.True(torch.tensor(new[] { 2, 4, 6.6666667, }).allclose(dist.mean));\\n Assert.True(torch.tensor(new[] { 0.0, 0.0, 0.0 }).allclose(dist.mode));\\n Assert.True(torch.tensor(new[] { 0.25, 0.0625, 0.0225 }).allclose(dist.variance));\\n Assert.True(torch.tensor(new[] { 1.6931472, 2.3862944, 2.89712 }).allclose(dist.entropy()));\\n Assert.True(torch.tensor(new[] { -1.1931472, -1.6362944, -2.04712 }).allclose(dist.log_prob(torch.ones(3))));\\n {\\n var sample = dist.sample();\\n\\n Assert.Equal(new long[] { 3 }, sample.shape);\\n }\\n {\\n var sample = dist.sample(2, 3);\\n\\n Assert.Equal(new long[] { 2, 3, 3 }, sample.shape);\\n }\\n {\\n var sample = dist.expand(new long[] { 3, 3, 3 }).sample(2, 3);\\n\\n Assert.Equal(new long[] { 2, 3, 3, 3, 3 }, sample.shape);\\n }\\n }\\n\\n [Fact]\\n public void TestExponentialGen()\\n {\\n var gen = new Generator(4711);\\n var dist = Exponential(torch.tensor(new[] { 0.5, 0.25, 0.15 }), gen);\\n Assert.True(torch.tensor(new[] { 2, 4, 6.6666667, }).allclose(dist.mean));\\n Assert.True(torch.tensor(new[] { 0.0, 0.0, 0.0 }).allclose(dist.mode));\\n Assert.True(torch.tensor(new[] { 0.25, 0.0625, 0.0225 }).allclose(dist.variance));\\n Assert.True(torch.tensor(new[] { 1.6931472, 2.3862944, 2.89712 }).allclose(dist.entropy()));\\n Assert.True(torch.tensor(new[] { -1.1931472, -1.6362944, -2.04712 }).allclose(dist.log_prob(torch.ones(3))));\\n {\\n var sample = dist.sample();\\n\\n Assert.Equal(new long[] { 3 }, sample.shape);\\n }\\n {\\n var sample = dist.sample(2, 3);\\n\\n Assert.Equal(new long[] { 2, 3, 3 }, sample.shape);\\n }\\n {\\n var sample = dist.expand(new long[] { 3, 3, 3 }).sample(2, 3);\\n\\n Assert.Equal(new long[] { 2, 3, 3, 3, 3 }, sample.shape);\\n }\\n }\\n\\n [Fact]\\n public void TestFisherSnedecor()\\n {\\n var dist = FisherSnedecor(torch.tensor(new[] { 0.5, 0.25, 0.15 }), torch.tensor(2.0));\\n Assert.True(torch.tensor(new[] { -2.0117974, -2.4718776, -2.8622819 }).allclose(dist.log_prob(torch.ones(3))));\\n Assert.Throws<NotImplementedException>(() => dist.entropy());\\n Assert.Throws<NotImplementedException>(() => dist.cdf(torch.ones(3)));\\n {\\n var sample = dist.sample();\\n\\n Assert.Equal(new long[] { 3 }, sample.shape);\\n }\\n {\\n var sample = dist.sample(2, 3);\\n\\n Assert.Equal(new long[] { 2, 3, 3 }, sample.shape);\\n }\\n {\\n var sample = dist.expand(new long[] { 3, 3, 3 }).sample(2, 3);\\n\\n Assert.Equal(new long[] { 2, 3, 3, 3, 3 }, sample.shape);\\n }\\n }\\n\\n [Fact]\\n public void TestFisherSnedecorGen()\\n {\\n var gen = new Generator(4711);\\n var dist = FisherSnedecor(torch.tensor(new[] { 0.5, 0.25, 0.15 }), torch.tensor(2.0), gen);\\n Assert.True(torch.tensor(new[] { -2.0117974, -2.4718776, -2.8622819 }).allclose(dist.log_prob(torch.ones(3))));\\n Assert.Throws<NotImplementedException>(() => dist.entropy());\\n Assert.Throws<NotImplementedException>(() => dist.cdf(torch.ones(3)));\\n {\\n var sample = dist.sample();\\n\\n Assert.Equal(new long[] { 3 }, sample.shape);\\n }\\n {\\n var sample = dist.sample(2, 3);\\n\\n Assert.Equal(new long[] { 2, 3, 3 }, sample.shape);\\n }\\n {\\n var sample = dist.expand(new long[] { 3, 3, 3 }).sample(2, 3);\\n\\n Assert.Equal(new long[] { 2, 3, 3, 3, 3 }, sample.shape);\\n }\\n }\\n\\n [Fact]\\n public void TestGamma()\\n {\\n var dist = Gamma(torch.tensor(new[] { 0.5, 0.25, 0.15 }), torch.tensor(0.5f));\\n Assert.True(torch.tensor(new[] { 1, 0.5, 0.3 }).allclose(dist.mean));\\n Assert.True(torch.tensor(new[] { 2, 1, 0.6 }).allclose(dist.variance));\\n Assert.True(torch.tensor(new[] { -1.4189385, -1.9613093, -2.4317859 }).allclose(dist.log_prob(torch.ones(3))));\\n Assert.True(torch.tensor(new[] { 0.7837571, -0.9394204, -3.296883, }).allclose(dist.entropy()));\\n {\\n var sample = dist.sample();\\n var entropy = dist.entropy();\\n\\n Assert.Equal(new long[] { 3 }, sample.shape);\\n }\\n {\\n var sample = dist.sample(2, 3);\\n\\n Assert.Equal(new long[] { 2, 3, 3 }, sample.shape);\\n }\\n {\\n var sample = dist.expand(new long[] { 3, 3, 3 }).sample(2, 3);\\n\\n Assert.Equal(new long[] { 2, 3, 3, 3, 3 }, sample.shape);\\n }\\n }\\n\\n [Fact]\\n public void TestGammaGen()\\n {\\n var gen = new Generator(4711);\\n var dist = Gamma(torch.tensor(new[] { 0.5, 0.25, 0.15 }), torch.tensor(0.5f), gen);\\n Assert.True(torch.tensor(new[] { 1, 0.5, 0.3 }).allclose(dist.mean));\\n Assert.True(torch.tensor(new[] { 2, 1, 0.6 }).allclose(dist.variance));\\n Assert.True(torch.tensor(new[] { -1.4189385, -1.9613093, -2.4317859 }).allclose(dist.log_prob(torch.ones(3))));\\n Assert.True(torch.tensor(new[] { 0.7837571, -0.9394204, -3.296883, }).allclose(dist.entropy()));\\n {\\n var sample = dist.sample();\\n var entropy = dist.entropy();\\n\\n Assert.Equal(new long[] { 3 }, sample.shape);\\n }\\n {\\n var sample = dist.sample(2, 3);\\n\\n Assert.Equal(new long[] { 2, 3, 3 }, sample.shape);\\n }\\n {\\n var sample = dist.expand(new long[] { 3, 3, 3 }).sample(2, 3);\\n\\n Assert.Equal(new long[] { 2, 3, 3, 3, 3 }, sample.shape);\\n }\\n }\\n\\n [Fact]\\n public void TestGeometric()\\n {\\n var dist = Geometric(torch.rand(3, 3));\\n {\\n var sample = dist.sample();\\n var entropy = dist.entropy();\\n\\n Assert.Equal(new long[] { 3, 3 }, sample.shape);\\n Assert.Equal(new long[] { 3, 3 }, entropy.shape);\\n }\\n {\\n var sample = dist.sample(2, 3);\\n\\n Assert.Equal(new long[] { 2, 3, 3, 3 }, sample.shape);\\n }\\n {\\n var sample = dist.expand(new long[] { 3, 3, 3 }).sample(2, 3);\\n\\n Assert.Equal(new long[] { 2, 3, 3, 3, 3 }, sample.shape);\\n }\\n }\\n\\n [Fact]\\n public void TestGeometricGen()\\n {\\n var gen = new Generator(4711);\\n var dist = Geometric(torch.rand(3, 3), generator: gen);\\n {\\n var sample = dist.sample();\\n var entropy = dist.entropy();\\n\\n Assert.Equal(new long[] { 3, 3 }, sample.shape);\\n Assert.Equal(new long[] { 3, 3 }, entropy.shape);\\n }\\n {\\n var sample = dist.sample(2, 3);\\n\\n Assert.Equal(new long[] { 2, 3, 3, 3 }, sample.shape);\\n }\\n {\\n var sample = dist.expand(new long[] { 3, 3, 3 }).sample(2, 3);\\n\\n Assert.Equal(new long[] { 2, 3, 3, 3, 3 }, sample.shape);\\n }\\n }\\n\\n [Fact]\\n public void TestMultinomial()\\n {\\n var categories = 17;\\n var dist = Multinomial(100, torch.rand(3, categories));\\n {\\n var sample = dist.sample();\\n\\n Assert.Equal(new long[] { 3, categories }, sample.shape);\\n Assert.All<float>(sample.data<float>().ToArray(), d => Assert.True(d >= 0 && d <= 100));\\n }\\n {\\n var sample = dist.sample(2, 3);\\n\\n Assert.Equal(new long[] { 2, 3, 3, categories }, sample.shape);\\n Assert.All<float>(sample.data<float>().ToArray(), d => Assert.True(d >= 0 && d <= 100));\\n }\\n {\\n var sample = dist.expand(new long[] { 3, 3 }).sample(2, 3);\\n\\n Assert.Equal(new long[] { 2, 3, 3, 3, categories }, sample.shape);\\n Assert.All<float>(sample.data<float>().ToArray(), d => Assert.True(d >= 0 && d <= 100));\\n }\\n }\\n\\n [Fact]\\n public void TestMultinomialGen()\\n {\\n var gen = new Generator(4711);\\n var categories = 17;\\n var dist = Multinomial(100, torch.rand(3, categories), generator: gen);\\n {\\n var sample = dist.sample();\\n\\n Assert.Equal(new long[] { 3, categories }, sample.shape);\\n Assert.All<float>(sample.data<float>().ToArray(), d => Assert.True(d >= 0 && d <= 100));\\n }\\n {\\n var sample = dist.sample(2, 3);\\n\\n Assert.Equal(new long[] { 2, 3, 3, categories }, sample.shape);\\n Assert.All<float>(sample.data<float>().ToArray(), d => Assert.True(d >= 0 && d <= 100));\\n }\\n {\\n var sample = dist.expand(new long[] { 3, 3 }).sample(2, 3);\\n\\n Assert.Equal(new long[] { 2, 3, 3, 3, categories }, sample.shape);\\n Assert.All<float>(sample.data<float>().ToArray(), d => Assert.True(d >= 0 && d <= 100));\\n }\\n }\\n\\n [Fact]\\n public void TestMultivariateNormal()\\n {\\n var bs = 2;\\n var dist = torch.distributions.MultivariateNormal(torch.zeros(bs), precision_matrix: torch.eye(bs));\\n {\\n var sample = dist.sample(2, 2);\\n\\n Assert.Equal(new long[] { bs, 2, 2 }, sample.shape);\\n }\\n {\\n torch.manual_seed(0);\\n var sample = dist.sample(2, 3);\\n\\n Assert.Equal(new long[] { bs, 3, 2 }, sample.shape);\\n }\\n {\\n var sample = dist.expand(new long[] { 3, 3 }).sample(2, 3);\\n\\n Assert.Equal(new long[] { bs, 3, 3, 3, 2 }, sample.shape);\\n }\\n }\\n\\n [Fact]\\n public void TestWeibull()\\n {\\n var dist = Weibull(torch.ones(3, 3), torch.ones(3, 3));\\n {\\n var sample = dist.sample();\\n\\n Assert.Equal(new long[] { 3, 3 }, sample.shape);\\n }\\n {\\n var sample = dist.sample(2, 3);\\n\\n Assert.Equal(new long[] { 2, 3, 3, 3 }, sample.shape);\\n }\\n {\\n var sample = dist.expand(new long[] { 3, 3, 3 }).sample(2, 3);\\n\\n Assert.Equal(new long[] { 2, 3, 3, 3, 3 }, sample.shape);\\n }\\n }\\n\\n [Fact]\\n public void TestWeibullGen()\\n {\\n var gen = new Generator(4711);\\n var dist = Weibull(torch.ones(3, 3), torch.ones(3, 3), generator: gen);\\n {\\n var sample = dist.sample();\\n\\n Assert.Equal(new long[] { 3, 3 }, sample.shape);\\n }\\n {\\n var sample = dist.sample(2, 3);\\n\\n Assert.Equal(new long[] { 2, 3, 3, 3 }, sample.shape);\\n }\\n {\\n var sample = dist.expand(new long[] { 3, 3, 3 }).sample(2, 3);\\n\\n Assert.Equal(new long[] { 2, 3, 3, 3, 3 }, sample.shape);\\n }\\n }\\n }\\n}\\n\"\n },\n {\n \"alpha_fraction\": 0.7812061905860901,\n \"alphanum_fraction\": 0.7812061905860901,\n \"avg_line_length\": 36.47368240356445,\n \"blob_id\": \"1d5d928688d07546f66d56d8f0e15b2d14ab5320\",\n \"content_id\": \"e7dda7beb5c021da730a640bd8c3580a0c5074d5\",\n \"detected_licenses\": [\n \"MIT\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Markdown\",\n \"length_bytes\": 713,\n \"license_type\": \"permissive\",\n \"max_line_length\": 116,\n \"num_lines\": 19,\n \"path\": \"/docfx/index.md\",\n \"repo_name\": \"NiklasGustafsson/TorchSharp\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"\\nTorchSharp are .NET bindings to the Torch library published here:\\n\\nhttps://pytorch.org/get-started/locally/\\n\\nThis surfaces the C++ library as a strongly-typed .NET API.\\n\\n## Getting Started\\n\\nCheck the [GitHub project page](https://github.com/dotnet/TorchSharp) for TorchSharp.\\n\\nThen, start by reading up on [creating your own modules](articles/modules.md).\\n\\n## Tips and Tricks\\n\\n[Some thoughts](articles/memory.md) on how to manage memory well in your TorchSharp training code.\\n\\nAn intruction on how to [share model](articles/saveload.md) weights between applications, whether in Python or .NET.\\n\\nLoading existing TorchScript files is now supported and described in [Loading TorchScript](articles/torchscript.md).\\n\"\n },\n {\n \"alpha_fraction\": 0.74205482006073,\n \"alphanum_fraction\": 0.7609589099884033,\n \"avg_line_length\": 52.67647171020508,\n \"blob_id\": \"bd6dd7245bf74e90d73aa89a9ae3d6cbf78d0e65\",\n \"content_id\": \"c3a836465d1ce860eff386f0e101e59bac8ae4dc\",\n \"detected_licenses\": [\n \"MIT\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Markdown\",\n \"length_bytes\": 7300,\n \"license_type\": \"permissive\",\n \"max_line_length\": 391,\n \"num_lines\": 136,\n \"path\": \"/README.md\",\n \"repo_name\": \"NiklasGustafsson/TorchSharp\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"[](https://gitter.im/dotnet/TorchSharp?utm_source=badge&utm_medium=badge&utm_campaign=pr-badge)\\n<br/>\\n[](https://dotnet.visualstudio.com/TorchSharp/_build/latest?definitionId=174&branchName=main)\\n<br/>\\n[](https://www.nuget.org/packages/TorchSharp)<br/>\\n[](https://www.nuget.org/packages/TorchAudio)<br/>\\n[](https://www.nuget.org/packages/TorchVision)<br/>\\n[](https://www.nuget.org/packages/TorchSharp-cpu)\\n[](https://www.nuget.org/packages/TorchSharp-cuda-windows)\\n[](https://www.nuget.org/packages/TorchSharp-cuda-linux)<br/>\\n<br/>\\nPlease check the [Release Notes](RELEASENOTES.md) file for news on what's been updated in each new release.\\n\\n__TorchSharp is now in the .NET Foundation!__\\n\\nIf you are using TorchSharp from NuGet, you should be using a version >= 0.98.3 of TorchSharp, and >= 1.12.0 of the libtorch-xxx redistributable packages. We recommend using one of the 'bundled' packages: TorchSharp-cpu, TorchSharp-cuda-windows, or TorchSharp-cuda-linux. They will pull in the right libtorch backends.\\n\\n__TorchSharp examples has their own home!__\\n\\nHead over to the [TorchSharp Examples Repo](https://github.com/dotnet/TorchSharpExamples) for convenient access to existing and upcoming examples.\\n\\n__IMPORTANT NOTES:__\\n\\nWhen targeting __.NET FX__ on Windows, the project configuration must be set to 'x64' rather than 'Any CPU' for anything that depends on TorchSharp.\\n\\nAs we build up to a v1.0 release, we will continue to make breaking changes, but only when we consider it necessary for usability. Similarity to the PyTorch experience is a primary design tenet, and we will continue on that path.\\n\\n# TorchSharp\\n\\nTorchSharp is a .NET library that provides access to the library that powers PyTorch. It is part of the .NET Foundation.\\n\\nThe focus is to bind the API surfaced by libtorch with a particular focus on tensors. The design intent is to stay as close as possible to the Pytorch experience, while still taking advantage of the benefits of the .NET static type system where it makes sense. For example: method overloading is relied on when Pytorch defines multiple valid types for a particular parameter.\\n\\nThe technology is a \\\"wrapper library\\\": no more, no less. [DiffSharp](https://github.com/DiffSharp/DiffSharp/) uses this\\nrepository extensively and has been a major factor in iterating support.\\n\\nThings that you can try:\\n\\n```csharp\\nusing TorchSharp;\\nusing static TorchSharp.torch.nn;\\n\\nvar lin1 = Linear(1000, 100);\\nvar lin2 = Linear(100, 10);\\nvar seq = Sequential((\\\"lin1\\\", lin1), (\\\"relu1\\\", ReLU()), (\\\"drop1\\\", Dropout(0.1)), (\\\"lin2\\\", lin2));\\n\\nvar x = torch.randn(64, 1000);\\nvar y = torch.randn(64, 10);\\n\\nvar optimizer = torch.optim.Adam(seq.parameters());\\n\\nfor (int i = 0; i < 10; i++) {\\n var eval = seq.forward(x);\\n var output = functional.mse_loss(eval, y, Reduction.Sum);\\n\\n optimizer.zero_grad();\\n\\n output.backward();\\n\\n optimizer.step();\\n}\\n```\\n\\n## A Few Things to Know\\n\\nWhile the intent has been to stay close to the Pytorch experience, there are some peculiarities to take note of:\\n\\n1. We have disregarded .NET naming conventions in favor of Python where it impacts the experience. We know this will feel wrong to some, but after a lot of deliberation, we decided to follow the lead of the SciSharp community and embrace naming similarity with Python over .NET tradition. We believe this will make it easier to take Python-based examples and snippets and apply them in .NET.\\n\\n2. In order to make a constructor call look more the Pytorch code, each class has a factory method with the same name. Because we cannot have a method and a class with the same name in a scope, we moved the class declarations to a nested scope 'Modules.'\\n\\n For example:\\n\\n ```csharp\\n\\n Module conv1 = Conv1d(...);\\n\\n ```\\n creates an instance of `Modules.Conv1d`, which has 'torch.Module' as its base class.\\n\\n3. C# uses ':' when passing a named parameter, while F# and Python uses '=', and Pytorch functions have enough parameters to encourage passing them by name. This means that you cannot simply copy a lot of code into C#.\\n\\n4. There are a number of APIs where Pytorch encodes what are effectively enum types as strings. We have chosen to use proper .NET enumeration types in most cases.\\n\\n5. The type `torch.device` is `torch.Device` in TorchSharp. We felt that using all-lowercase for a class type was one step too far. The device object constructors, which is what you use most of the time, are still called `device()`\\n\\n\\n# Memory management\\n\\nSee [docfx/articles/memory.md](docfx/articles/memory.md).\\n\\n# Download\\n\\nTorchSharp is distributed via the NuGet gallery: https://www.nuget.org/packages/TorchSharp/\\n\\nTo use TorchSharp, you also need one of the LibTorch backend packages: https://www.nuget.org/packages?q=libtorch, specifically one of\\n\\n* `libtorch-cpu-linux-x64` (CPU, Linux)\\n\\n* `libtorch-cpu-win-x64` (CPU, Windows)\\n\\n* `libtorch-cpu-osx-x64` (CPU, OSX)\\n\\n* `libtorch-cpu` (CPU, references all three, larger download but simpler)\\n\\n* `libtorch-cuda-11.7-linux-x64` (CPU/CUDA 11.3, Linux)\\n\\n > NOTE: Due to the presence of very large native binaries, using the `libtorch-cuda-11.7-linux-x64` package requires\\n > .NET 6, e.g. .NET SDK version `6.0.100-preview.5.21302.13` or greater.\\n\\n* `libtorch-cuda-11.7-win-x64` (CPU/CUDA 11.3, Windows)\\n\\nAlternatively you can access the libtorch native binaries via direct reference to existing local native\\nbinaries of LibTorch installed through other means (for example, by installing [PyTorch](https://pytorch.org/) using a Python package manager). You will have to add an explicit load of the relevant native library, for example:\\n\\n```csharp\\n using System.Runtime.InteropServices;\\n NativeLibrary.Load(\\\"/home/gunes/anaconda3/lib/python3.8/site-packages/torch/lib/libtorch.so\\\")\\n```\\n\\n# Code of Conduct\\nThis project has adopted the code of conduct defined by the Contributor Covenant to clarify expected behavior in our community.\\nFor more information see the [.NET Foundation Code of Conduct](https://dotnetfoundation.org/code-of-conduct).\\n\\n# Developing and Contributing\\n\\nSee [DEVGUIDE.md](DEVGUIDE.md) and [CONTRIBUTING.md](CONTRIBUTING.md).\\n\\n<a href=\\\"https://github.com/dotnet/TorchSharp/graphs/contributors\\\">\\n <img src=\\\"https://contrib.rocks/image?repo=dotnet/TorchSharp\\\" />\\n</a>\\n\\n# Uses\\n\\n[DiffSharp](https://github.com/DiffSharp/DiffSharp/) also uses this\\nrepository extensively and has been a major factor in iterating support.\\n\"\n },\n {\n \"alpha_fraction\": 0.5457543134689331,\n \"alphanum_fraction\": 0.5507007241249084,\n \"avg_line_length\": 42.32143020629883,\n \"blob_id\": \"df2dae985ee1ee03da16d487c4936028ebb33d70\",\n \"content_id\": \"27ec1bb44bf57748de582b67a49c3cdbaf34da34\",\n \"detected_licenses\": [\n \"MIT\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"C#\",\n \"length_bytes\": 3639,\n \"license_type\": \"permissive\",\n \"max_line_length\": 182,\n \"num_lines\": 84,\n \"path\": \"/src/TorchVision/Normalize.cs\",\n \"repo_name\": \"NiklasGustafsson/TorchSharp\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"// Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information.\\nusing System;\\nusing static TorchSharp.torch;\\n\\n#nullable enable\\nnamespace TorchSharp\\n{\\n public static partial class torchvision\\n {\\n internal class Normalize : ITransform, IDisposable\\n {\\n internal Normalize(double[] means, double[] stdevs, ScalarType dtype = ScalarType.Float32, torch.Device? device = null)\\n {\\n if (means is null) throw new ArgumentNullException(nameof(means));\\n if (stdevs is null) throw new ArgumentNullException(nameof(stdevs));\\n if (means.Length != stdevs.Length)\\n throw new ArgumentException($\\\"{nameof(means)} and {nameof(stdevs)} must be the same length in call to Normalize\\\");\\n if (means.Length != 1 && means.Length != 3)\\n throw new ArgumentException($\\\"Since they correspond to the number of channels in an image, {nameof(means)} and {nameof(stdevs)} must both be either 1 or 3 long\\\");\\n\\n this.means = means.ToTensor(new long[] { 1, means.Length, 1, 1 }); // Assumes NxCxHxW\\n this.stdevs = stdevs.ToTensor(new long[] { 1, stdevs.Length, 1, 1 }); // Assumes NxCxHxW\\n\\n if (dtype != ScalarType.Float64) {\\n this.means = this.means.to_type(dtype);\\n this.stdevs = this.stdevs.to_type(dtype);\\n }\\n\\n if (device != null && device.type != DeviceType.CPU) {\\n this.means = this.means.to(device);\\n this.stdevs = this.stdevs.to(device);\\n }\\n }\\n\\n public Tensor call(Tensor input)\\n {\\n if (means.size(1) != input.size(1)) throw new ArgumentException(\\\"The number of channels is not equal to the number of means and standard deviations\\\");\\n return (input - means) / stdevs;\\n }\\n\\n private Tensor means;\\n private Tensor stdevs;\\n bool disposedValue;\\n\\n protected virtual void Dispose(bool disposing)\\n {\\n if (!disposedValue) {\\n means?.Dispose();\\n stdevs?.Dispose();\\n disposedValue = true;\\n }\\n }\\n\\n ~Normalize()\\n {\\n // Do not change this code. Put cleanup code in 'Dispose(bool disposing)' method\\n Dispose(disposing: false);\\n }\\n\\n public void Dispose()\\n {\\n // Do not change this code. Put cleanup code in 'Dispose(bool disposing)' method\\n Dispose(disposing: true);\\n GC.SuppressFinalize(this);\\n }\\n }\\n\\n public static partial class transforms\\n {\\n /// <summary>\\n /// Normalize a float tensor image with mean and standard deviation.\\n /// </summary>\\n /// <param name=\\\"means\\\">Sequence of means for each channel.</param>\\n /// <param name=\\\"stdevs\\\">Sequence of standard deviations for each channel.</param>\\n /// <param name=\\\"dtype\\\">Bool to make this operation inplace.</param>\\n /// <param name=\\\"device\\\">The device to place the output tensor on.</param>\\n /// <returns></returns>\\n static public ITransform Normalize(double[] means, double[] stdevs, ScalarType dtype = ScalarType.Float32, torch.Device? device = null)\\n {\\n return new Normalize(means, stdevs, dtype, device);\\n }\\n }\\n }\\n}\\n\"\n },\n {\n \"alpha_fraction\": 0.5594344735145569,\n \"alphanum_fraction\": 0.5599448680877686,\n \"avg_line_length\": 59.84782791137695,\n \"blob_id\": \"90ea4b1df94f430accb62facc997c1932b9d509e\",\n \"content_id\": \"691d24b7f5479080f88309fa748f78bde6080d3d\",\n \"detected_licenses\": [\n \"MIT\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"C#\",\n \"length_bytes\": 19593,\n \"license_type\": \"permissive\",\n \"max_line_length\": 172,\n \"num_lines\": 322,\n \"path\": \"/src/TorchVision/IO/Image.cs\",\n \"repo_name\": \"NiklasGustafsson/TorchSharp\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"// Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information.\\nusing System.IO;\\nusing System.Threading;\\nusing System.Threading.Tasks;\\n\\nusing static TorchSharp.torch;\\n\\nnamespace TorchSharp\\n{\\n public static partial class torchvision\\n {\\n public static partial class io\\n {\\n /// <summary>\\n /// <cref>Imager</cref> to be used when a <cref>torchvision.io</cref> image method's <c>imager</c> is unspecified.\\n /// </summary>\\n public static Imager DefaultImager { get; set; } = new NotImplementedImager();\\n\\n /// <summary>\\n /// Abstract class providing a generic way to decode and encode images as <cref>Tensor</cref>s.\\n /// Used by <cref>torchvision.io</cref> image methods.\\n /// </summary>\\n public abstract class Imager\\n {\\n /// <summary>\\n /// Decode the contents of an image file and returns the result as a <cref>Tensor</cref>.\\n /// </summary>\\n /// <param name=\\\"stream\\\">Stream to read from.</param>\\n /// <param name=\\\"mode\\\">Image read mode.</param>\\n /// <remarks>\\n /// The image format is detected from image file contents.\\n /// </remarks>\\n /// <returns>\\n /// <cref>Tensor</cref> with <c>shape = [color_channels, image_height, image_width]</c>.\\n /// </returns>\\n public abstract Tensor DecodeImage(Stream stream, ImageReadMode mode = ImageReadMode.UNCHANGED);\\n\\n /// <summary>\\n /// Decode the contents of a byte array and returns the result as a <cref>Tensor</cref>.\\n /// </summary>\\n /// <param name=\\\"data\\\">byte array to decode from.</param>\\n /// <param name=\\\"mode\\\">Image read mode.</param>\\n /// <remarks>\\n /// The image format is detected from image file contents.\\n /// </remarks>\\n /// <returns>\\n /// <cref>Tensor</cref> with <c>shape = [color_channels, image_height, image_width]</c>.\\n /// </returns>\\n public abstract Tensor DecodeImage(byte[] data, ImageReadMode mode = ImageReadMode.UNCHANGED);\\n\\n /// <summary>\\n /// Asynchronously decode the contents of an image file and returns the result as a <cref>Tensor</cref>.\\n /// </summary>\\n /// <param name=\\\"stream\\\">Stream to read from.</param>\\n /// <param name=\\\"mode\\\">Image read mode.</param>\\n /// <param name=\\\"cancellationToken\\\">Cancellation token.</param>\\n /// <remarks>\\n /// The image format is detected from image file contents.\\n /// </remarks>\\n /// <returns>\\n /// <cref>Tensor</cref> with <c>shape = [color_channels, image_height, image_width]</c>.\\n /// </returns>\\n public virtual Task<Tensor> DecodeImageAsync(Stream stream, ImageReadMode mode = ImageReadMode.UNCHANGED, CancellationToken cancellationToken = default)\\n {\\n // Default implementation -- just do things synchronously.\\n var t = DecodeImage(stream, mode);\\n return Task<Tensor>.FromResult(t);\\n }\\n\\n /// <summary>\\n /// Encodes a <cref>Tensor</cref> with <c>shape = [color_channels, image_height, image_width]</c> into a stream.\\n /// </summary>\\n /// <param name=\\\"image\\\"><cref>Tensor</cref> with <c>shape = [color_channels, image_height, image_width]</c>.</param>\\n /// <param name=\\\"format\\\">Image format.</param>\\n /// <param name=\\\"stream\\\">Stream to write to.</param>\\n public abstract void EncodeImage(Tensor image, ImageFormat format, Stream stream);\\n\\n /// <summary>\\n /// Encodes a <cref>Tensor</cref> with <c>shape = [color_channels, image_height, image_width]</c> into a stream.\\n /// </summary>\\n /// <param name=\\\"image\\\"><cref>Tensor</cref> with <c>shape = [color_channels, image_height, image_width]</c>.</param>\\n /// <param name=\\\"format\\\">Image format.</param>\\n /// <returns>The encoded image as a byte array.</returns>\\n public abstract byte[] EncodeImage(Tensor image, ImageFormat format);\\n\\n /// <summary>\\n /// Asynchronously encodes a <cref>Tensor</cref> with <c>shape = [color_channels, image_height, image_width]</c> into a stream.\\n /// </summary>\\n /// <param name=\\\"image\\\"><cref>Tensor</cref> with <c>shape = [color_channels, image_height, image_width]</c>.</param>\\n /// <param name=\\\"format\\\">Image format.</param>\\n /// <param name=\\\"stream\\\">Stream to write to.</param>\\n /// <param name=\\\"cancellationToken\\\">Cancellation token.</param>\\n public virtual Task EncodeImageAsync(Tensor image, ImageFormat format, Stream stream, CancellationToken cancellationToken = default)\\n {\\n // Default implementation -- just do things synchronously.\\n EncodeImage(image, format, stream);\\n return Task.CompletedTask;\\n }\\n }\\n\\n /// <summary>\\n /// Support for various modes while reading images. Affects the returned <cref>Tensor</cref>'s <c>color_channels</c>.\\n /// </summary>\\n public enum ImageReadMode\\n {\\n /// <summary>\\n /// Read as is. Returned <cref>Tensor</cref>'s color_channels depend on the <cref>ImageFormat</cref>.\\n /// </summary>\\n UNCHANGED,\\n /// <summary>\\n /// Read as grayscale. Return <cref>Tensor</cref> with <c>color_channels = 1 </c>.\\n /// </summary>\\n GRAY,\\n /// <summary>\\n /// Read as grayscale with transparency. Return <cref>Tensor</cref> with <c>color_channels = 2 </c>.\\n /// </summary>\\n GRAY_ALPHA,\\n /// <summary>\\n /// Read as RGB. Return <cref>Tensor</cref> with <c>color_channels = 3 </c>.\\n /// </summary>\\n RGB,\\n /// <summary>\\n /// Read as RGB with transparency. Return <cref>Tensor</cref> with <c>color_channels = 4 </c>.\\n /// </summary>\\n RGB_ALPHA\\n }\\n\\n /// <summary>\\n /// Reads an image file and returns the result as a <cref>Tensor</cref>.\\n /// </summary>\\n /// <param name=\\\"filename\\\">Path to the image.</param>\\n /// <param name=\\\"mode\\\">Image read mode.</param>\\n /// <param name=\\\"imager\\\"><cref>Imager</cref> to be use. Will use <cref>DefaultImager</cref> if null.</param>\\n /// <returns>\\n /// <cref>Tensor</cref> with <c>shape = [color_channels, image_height, image_width]</c> and <c>dtype = uint8</c>.\\n /// </returns>\\n public static Tensor read_image(string filename, ImageReadMode mode = ImageReadMode.UNCHANGED, Imager imager = null)\\n {\\n using (FileStream stream = File.Open(filename, FileMode.Open))\\n return (imager ?? DefaultImager).DecodeImage(stream, mode);\\n }\\n\\n /// <summary>\\n /// Reads an image file and returns the result as a <cref>Tensor</cref>.\\n /// </summary>\\n /// <param name=\\\"stream\\\">Stream to read from.</param>\\n /// <param name=\\\"mode\\\">Image read mode.</param>\\n /// <param name=\\\"imager\\\"><cref>Imager</cref> to be use. Will use <cref>DefaultImager</cref> if null.</param>\\n /// <returns>\\n /// <cref>Tensor</cref> with <c>shape = [color_channels, image_height, image_width]</c> and <c>dtype = uint8</c>.\\n /// </returns>\\n public static Tensor read_image(Stream stream, ImageReadMode mode = ImageReadMode.UNCHANGED, Imager imager = null)\\n {\\n return (imager ?? DefaultImager).DecodeImage(stream, mode);\\n }\\n\\n /// <summary>\\n /// Asynchronously reads an image file and returns the result as a <cref>Tensor</cref>.\\n /// </summary>\\n /// <param name=\\\"filename\\\">Path to the image.</param>\\n /// <param name=\\\"mode\\\">Read mode.</param>\\n /// <param name=\\\"imager\\\"><cref>Imager</cref> to be use. Will use <cref>DefaultImager</cref> if null.</param>\\n /// <returns>\\n /// A task that represents the asynchronous read operation.\\n /// The value of the TResult parameter is a <cref>Tensor</cref> with <c>shape = [color_channels, image_height, image_width]</c> and <c>dtype = uint8</c>.\\n /// </returns>\\n public static async Task<Tensor> read_image_async(string filename, ImageReadMode mode = ImageReadMode.UNCHANGED, Imager imager = null)\\n {\\n\\n using (FileStream stream = File.Open(filename, FileMode.Open))\\n return await (imager ?? DefaultImager).DecodeImageAsync(stream, mode);\\n }\\n\\n /// <summary>\\n /// Asynchronously reads an image file and returns the result as a <cref>Tensor</cref>.\\n /// </summary>\\n /// <param name=\\\"stream\\\">Stream to read from.</param>\\n /// <param name=\\\"mode\\\">Read mode.</param>\\n /// <param name=\\\"imager\\\"><cref>Imager</cref> to be use. Will use <cref>DefaultImager</cref> if null.</param>\\n /// <returns>\\n /// A task that represents the asynchronous read operation.\\n /// The value of the TResult parameter is a <cref>Tensor</cref> with <c>shape = [color_channels, image_height, image_width]</c> and <c>dtype = uint8</c>.\\n /// </returns>\\n public static async Task<Tensor> read_image_async(Stream stream, ImageReadMode mode = ImageReadMode.UNCHANGED, Imager imager = null)\\n {\\n return await (imager ?? DefaultImager).DecodeImageAsync(stream, mode);\\n }\\n\\n /// <summary>\\n /// Write a image <cref>Tensor</cref> with <c>shape = [color_channels, image_height, image_width]</c> into a file.\\n /// </summary>\\n /// <param name=\\\"image\\\"><cref>Tensor</cref> with <c>shape = [color_channels, image_height, image_width]</c>.</param>\\n /// <param name=\\\"filename\\\">Path to the file.</param>\\n /// <param name=\\\"format\\\">Image format.</param>\\n /// <param name=\\\"imager\\\"><cref>Imager</cref> to be use. Will use <cref>DefaultImager</cref> if null.</param>\\n public static void write_image(Tensor image, string filename, ImageFormat format, Imager imager = null)\\n {\\n if (image.dtype != ScalarType.Byte) throw new System.ArgumentException(\\\"Image tensors must be 'byte' tensors when passed to 'write_image()'.\\\");\\n using (FileStream stream = File.Open(filename, FileMode.OpenOrCreate))\\n (imager ?? DefaultImager).EncodeImage(image, format, stream);\\n }\\n\\n /// <summary>\\n /// Write a PNG image <cref>Tensor</cref> with <c>shape = [color_channels, image_height, image_width]</c> into a file.\\n /// </summary>\\n /// <param name=\\\"image\\\"><cref>Tensor</cref> with <c>shape = [color_channels, image_height, image_width]</c>.</param>\\n /// <param name=\\\"filename\\\">Path to the file.</param>\\n /// <param name=\\\"imager\\\"><cref>Imager</cref> to be use. Will use <cref>DefaultImager</cref> if null.</param>\\n public static void write_png(Tensor image, string filename, Imager imager = null)\\n {\\n write_image(image, filename, ImageFormat.Png, imager);\\n }\\n\\n /// <summary>\\n /// Write a JPEG image <cref>Tensor</cref> with <c>shape = [color_channels, image_height, image_width]</c> into a file.\\n /// </summary>\\n /// <param name=\\\"image\\\"><cref>Tensor</cref> with <c>shape = [color_channels, image_height, image_width]</c>.</param>\\n /// <param name=\\\"filename\\\">Path to the file.</param>\\n /// <param name=\\\"imager\\\"><cref>Imager</cref> to be use. Will use <cref>DefaultImager</cref> if null.</param>\\n public static void write_jpeg(Tensor image, string filename, Imager imager = null)\\n {\\n write_image(image, filename, ImageFormat.Jpeg, imager);\\n }\\n\\n /// <summary>\\n /// Write a image <cref>Tensor</cref> with <c>shape = [color_channels, image_height, image_width]</c> into a file.\\n /// </summary>\\n /// <param name=\\\"image\\\"><cref>Tensor</cref> with <c>shape = [color_channels, image_height, image_width]</c>.</param>\\n /// <param name=\\\"stream\\\">Stream to write to.</param>\\n /// <param name=\\\"format\\\">Image format.</param>\\n /// <param name=\\\"imager\\\"><cref>Imager</cref> to be use. Will use <cref>DefaultImager</cref> if null.</param>\\n public static void write_image(Tensor image, Stream stream, ImageFormat format, Imager imager = null)\\n {\\n if (image.dtype != ScalarType.Byte) throw new System.ArgumentException(\\\"Image tensors must be 'byte' tensors when passed to 'write_image()'.\\\");\\n (imager ?? DefaultImager).EncodeImage(image, format, stream);\\n }\\n\\n /// <summary>\\n /// Write a PNG image <cref>Tensor</cref> with <c>shape = [color_channels, image_height, image_width]</c> into a file.\\n /// </summary>\\n /// <param name=\\\"image\\\"><cref>Tensor</cref> with <c>shape = [color_channels, image_height, image_width]</c>.</param>\\n /// <param name=\\\"stream\\\">Stream to write to.</param>\\n /// <param name=\\\"imager\\\"><cref>Imager</cref> to be use. Will use <cref>DefaultImager</cref> if null.</param>\\n public static void write_png(Tensor image, Stream stream, Imager imager = null)\\n {\\n write_image(image, stream, ImageFormat.Png, imager);\\n }\\n\\n /// <summary>\\n /// Write a JPEG image <cref>Tensor</cref> with <c>shape = [color_channels, image_height, image_width]</c> into a file.\\n /// </summary>\\n /// <param name=\\\"image\\\"><cref>Tensor</cref> with <c>shape = [color_channels, image_height, image_width]</c>.</param>\\n /// <param name=\\\"stream\\\">Stream to write to.</param>\\n /// <param name=\\\"imager\\\"><cref>Imager</cref> to be use. Will use <cref>DefaultImager</cref> if null.</param>\\n public static void write_jpeg(Tensor image, Stream stream, Imager imager = null)\\n {\\n write_image(image, stream, ImageFormat.Jpeg, imager);\\n }\\n\\n /// <summary>\\n /// Asynchronously write a image <cref>Tensor</cref> with <c>shape = [color_channels, image_height, image_width]</c> into a file.\\n /// </summary>\\n /// <param name=\\\"image\\\"><cref>Tensor</cref> with <c>shape = [color_channels, image_height, image_width]</c>.</param>\\n /// <param name=\\\"filename\\\">Path to the file.</param>\\n /// <param name=\\\"format\\\">Image format.</param>\\n /// <param name=\\\"imager\\\"><cref>Imager</cref> to be use. Will use <cref>DefaultImager</cref> if null.</param>\\n public static async Task write_image_async(Tensor image, string filename, ImageFormat format, Imager imager = null)\\n {\\n if (image.dtype != ScalarType.Byte) throw new System.ArgumentException(\\\"Image tensors must be 'byte' tensors when passed to 'write_image()'.\\\");\\n using (FileStream stream = File.Open(filename, FileMode.OpenOrCreate))\\n await (imager ?? DefaultImager).EncodeImageAsync(image, format, stream);\\n }\\n\\n /// <summary>\\n /// Asynchronously write a image <cref>Tensor</cref> with <c>shape = [color_channels, image_height, image_width]</c> into a file.\\n /// </summary>\\n /// <param name=\\\"image\\\"><cref>Tensor</cref> with <c>shape = [color_channels, image_height, image_width]</c>.</param>\\n /// <param name=\\\"stream\\\">Stream to write to.</param>\\n /// <param name=\\\"format\\\">Image format.</param>\\n /// <param name=\\\"cancellationToken\\\">Cancellation token</param>\\n /// <param name=\\\"imager\\\"><cref>Imager</cref> to be use. Will use <cref>DefaultImager</cref> if null.</param>\\n public static async Task write_image_async(Tensor image, Stream stream, ImageFormat format, CancellationToken cancellationToken = default, Imager imager = null)\\n {\\n if (image.dtype != ScalarType.Byte) throw new System.ArgumentException(\\\"Image tensors must be 'byte' tensors when passed to 'write_image()'.\\\");\\n await (imager ?? DefaultImager).EncodeImageAsync(image, format, stream);\\n }\\n\\n /// <summary>\\n /// Encodes a <cref>Tensor</cref> with <c>shape = [color_channels, image_height, image_width]</c>\\n /// into a image <cref>Tensor</cref> buffer.\\n /// </summary>\\n /// <param name=\\\"image\\\"><cref>Tensor</cref> with <c>shape = [color_channels, image_height, image_width]</c>.</param>\\n /// <param name=\\\"format\\\">Image format.</param>\\n /// <param name=\\\"imager\\\"><cref>Imager</cref> to be use. Will use <cref>DefaultImager</cref> if null.</param>\\n /// <returns>A one dimensional <c>uint8</c> <cref>Tensor</cref> that contains the raw bytes of <c>image</c> encoded in the provided format.</returns>\\n public static Tensor encode_image(Tensor image, ImageFormat format, Imager imager = null)\\n {\\n if (image.dtype != ScalarType.Byte) throw new System.ArgumentException(\\\"Image tensors must be 'byte' tensors when passed to 'encode_image()'.\\\");\\n using (var stream = new MemoryStream()) {\\n (imager ?? DefaultImager).EncodeImage(image, format, stream);\\n return stream.ToArray();\\n }\\n }\\n\\n /// <summary>\\n /// Decodes an image <cref>Tensor</cref> buffer into a <cref>Tensor</cref> with <c>shape = [color_channels, image_height, image_width]</c>.\\n /// </summary>\\n /// <param name=\\\"image\\\">A one dimensional <c>uint8</c> <cref>Tensor</cref> that contains the raw bytes of an image.</param>\\n /// <param name=\\\"mode\\\">Decode mode.</param>\\n /// <param name=\\\"imager\\\"><cref>Imager</cref> to be use. Will use <cref>DefaultImager</cref> if null.</param>\\n /// <returns><cref>Tensor</cref> with <c>shape = [color_channels, image_height, image_width]</c>.</returns>\\n public static Tensor decode_image(Tensor image, ImageReadMode mode = ImageReadMode.UNCHANGED, Imager imager = null)\\n {\\n if (image.dtype != ScalarType.Byte) throw new System.ArgumentException(\\\"Image tensors must be 'byte' tensors when passed to 'decode_image()'.\\\");\\n using (var stream = new MemoryStream()) {\\n (imager ?? DefaultImager).DecodeImage(stream, mode);\\n return stream.ToArray();\\n }\\n }\\n }\\n }\\n}\\n\"\n },\n {\n \"alpha_fraction\": 0.4548049867153168,\n \"alphanum_fraction\": 0.48809829354286194,\n \"avg_line_length\": 49.892784118652344,\n \"blob_id\": \"b25763e3c30de41cce907d6b5fd4985aaec5b796\",\n \"content_id\": \"ae9099ad8c9b1c4f8bacbc0f8a73022c5833ed48\",\n \"detected_licenses\": [\n \"MIT\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"C#\",\n \"length_bytes\": 50791,\n \"license_type\": \"permissive\",\n \"max_line_length\": 160,\n \"num_lines\": 998,\n \"path\": \"/test/TorchSharpTest/PointwiseTensorMath.cs\",\n \"repo_name\": \"NiklasGustafsson/TorchSharp\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"// Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information.\\nusing System;\\nusing System.Linq;\\nusing Xunit;\\nusing static TorchSharp.torch;\\n\\n#nullable enable\\n\\nnamespace TorchSharp\\n{\\n [Collection(\\\"Sequential\\\")]\\n public class PointwiseTensorMath\\n {\\n [Fact]\\n [TestOf(nameof(Tensor))]\\n public void TestArithmeticOperatorsFloat16()\\n {\\n // Float16 arange_cuda not available on cuda in LibTorch 1.8.0\\n // Float16 arange_cpu not available on cuda in LibTorch 1.8.0\\n foreach (var device in new Device[] { torch.CPU, torch.CUDA }) {\\n if (device.type != DeviceType.CUDA || torch.cuda.is_available()) {\\n var c1 = torch.ones(new long[] { 10, 10 }, float16, device: device);\\n var c2 = torch.ones(new long[] { 10, 10 }, float16, device: device);\\n var c3 = torch.ones(new long[] { 10, 10 }, float16, device: device);\\n#if NET6_0_OR_GREATER\\n Func<Tensor, long, long, Half> getFunc = (tt, i, j) => tt[i, j].ToHalf();\\n\\n // scalar-tensor operators\\n TestOneTensor(c1, c2, getFunc, getFunc, a => a + (Half)0.5, a => (Half)((double)a + 0.5f));\\n TestOneTensor(c1, c2, getFunc, getFunc, a => (Half)0.5 + a, a => (Half)(0.5 + (double)a));\\n TestOneTensor(c1, c2, getFunc, getFunc, a => a - (Half)0.5, a => (Half)((double)a - 0.5));\\n TestOneTensor(c1, c2, getFunc, getFunc, a => a * (Half)0.5, a => (Half)((double)a * 0.5f));\\n TestOneTensor(c1, c2, getFunc, getFunc, a => (Half)0.5 * a, a => (Half)(0.5f * (double)a));\\n TestOneTensor(c1, c2, getFunc, getFunc, a => a / (Half)0.5, a => (Half)((double)a / 0.5f));\\n\\n TestOneTensor(c1, c2, getFunc, getFunc, a => a.add((Half)0.5), a => (Half)((double)a + 0.5f));\\n TestOneTensor(c1, c2, getFunc, getFunc, a => a.sub((Half)0.5), a => (Half)((double)a - 0.5f));\\n TestOneTensor(c1, c2, getFunc, getFunc, a => a.mul((Half)0.5), a => (Half)((double)a * 0.5f));\\n TestOneTensor(c1, c2, getFunc, getFunc, a => a.div((Half)0.5), a => (Half)((double)a / 0.5f));\\n\\n TestOneTensorInPlace(c1, c2, getFunc, a => a.add_((Half)0.5), a => (Half)((double)a + 0.5f));\\n TestOneTensorInPlace(c1, c2, getFunc, a => a.sub_((Half)0.5), a => (Half)((double)a - 0.5f));\\n TestOneTensorInPlace(c1, c2, getFunc, a => a.mul_((Half)0.5), a => (Half)((double)a * 0.5f));\\n TestOneTensorInPlace(c1, c2, getFunc, a => a.div_((Half)0.5), a => (Half)((double)a / 0.5f));\\n\\n // tensor-tensor operators\\n TestTwoTensor(c1, c2, c3, getFunc, getFunc, (a, b) => a + b, (a, b) => (Half)((double)a + (double)b));\\n TestTwoTensor(c1, c2, c3, getFunc, getFunc, (a, b) => a - b, (a, b) => (Half)((double)a - (double)b));\\n TestTwoTensor(c1, c2, c3, getFunc, getFunc, (a, b) => a * b, (a, b) => (Half)((double)a * (double)b));\\n TestTwoTensor(c1, c2, c3, getFunc, getFunc, (a, b) => a / b, (a, b) => (Half)((double)a / (double)b));\\n\\n TestTwoTensor(c1, c2, c3, getFunc, getFunc, (a, b) => a.add(b), (a, b) => (Half)((double)a + (double)b));\\n TestTwoTensor(c1, c2, c3, getFunc, getFunc, (a, b) => a.sub(b), (a, b) => (Half)((double)a - (double)b));\\n TestTwoTensor(c1, c2, c3, getFunc, getFunc, (a, b) => a.mul(b), (a, b) => (Half)((double)a * (double)b));\\n TestTwoTensor(c1, c2, c3, getFunc, getFunc, (a, b) => a.div(b), (a, b) => (Half)((double)a / (double)b));\\n\\n TestTwoTensorInPlace(c1, c2, c3, getFunc, (a, b) => a.add_(b), (a, b) => (Half)((double)a + (double)b));\\n TestTwoTensorInPlace(c1, c2, c3, getFunc, (a, b) => a.sub_(b), (a, b) => (Half)((double)a - (double)b));\\n TestTwoTensorInPlace(c1, c2, c3, getFunc, (a, b) => a.mul_(b), (a, b) => (Half)((double)a * (double)b));\\n TestTwoTensorInPlace(c1, c2, c3, getFunc, (a, b) => a.div_(b), (a, b) => (Half)((double)a / (double)b));\\n#else\\n Func<Tensor, long, long, float> getFunc = (tt, i, j) => tt[i, j].ToSingle();\\n\\n // scalar-tensor operators\\n TestOneTensor(c1, c2, getFunc, getFunc, a => a + 0.5f, a => a + 0.5f);\\n TestOneTensor(c1, c2, getFunc, getFunc, a => 0.5f + a, a => 0.5f + a);\\n TestOneTensor(c1, c2, getFunc, getFunc, a => a - 0.5f, a => a - 0.5f);\\n TestOneTensor(c1, c2, getFunc, getFunc, a => a * 0.5f, a => a * 0.5f);\\n TestOneTensor(c1, c2, getFunc, getFunc, a => 0.5f * a, a => 0.5f * a);\\n TestOneTensor(c1, c2, getFunc, getFunc, a => a / 0.5f, a => a / 0.5f);\\n\\n TestOneTensor(c1, c2, getFunc, getFunc, a => a.add(0.5f), a => a + 0.5f);\\n TestOneTensor(c1, c2, getFunc, getFunc, a => a.sub(0.5f), a => a - 0.5f);\\n TestOneTensor(c1, c2, getFunc, getFunc, a => a.mul(0.5f), a => a * 0.5f);\\n TestOneTensor(c1, c2, getFunc, getFunc, a => a.div(0.5f), a => a / 0.5f);\\n\\n TestOneTensorInPlace(c1, c2, getFunc, a => a.add_(0.5f), a => a + 0.5f);\\n TestOneTensorInPlace(c1, c2, getFunc, a => a.sub_(0.5f), a => a - 0.5f);\\n TestOneTensorInPlace(c1, c2, getFunc, a => a.mul_(0.5f), a => a * 0.5f);\\n TestOneTensorInPlace(c1, c2, getFunc, a => a.div_(0.5f), a => a / 0.5f);\\n\\n // tensor-tensor operators\\n TestTwoTensor(c1, c2, c3, getFunc, getFunc, (a, b) => a + b, (a, b) => a + b);\\n TestTwoTensor(c1, c2, c3, getFunc, getFunc, (a, b) => a - b, (a, b) => a - b);\\n TestTwoTensor(c1, c2, c3, getFunc, getFunc, (a, b) => a * b, (a, b) => a * b);\\n TestTwoTensor(c1, c2, c3, getFunc, getFunc, (a, b) => a / b, (a, b) => a / b);\\n\\n TestTwoTensor(c1, c2, c3, getFunc, getFunc, (a, b) => a.add(b), (a, b) => a + b);\\n TestTwoTensor(c1, c2, c3, getFunc, getFunc, (a, b) => a.sub(b), (a, b) => a - b);\\n TestTwoTensor(c1, c2, c3, getFunc, getFunc, (a, b) => a.mul(b), (a, b) => a * b);\\n TestTwoTensor(c1, c2, c3, getFunc, getFunc, (a, b) => a.div(b), (a, b) => a / b);\\n\\n TestTwoTensorInPlace(c1, c2, c3, getFunc, (a, b) => a.add_(b), (a, b) => a + b);\\n TestTwoTensorInPlace(c1, c2, c3, getFunc, (a, b) => a.sub_(b), (a, b) => a - b);\\n TestTwoTensorInPlace(c1, c2, c3, getFunc, (a, b) => a.mul_(b), (a, b) => a * b);\\n TestTwoTensorInPlace(c1, c2, c3, getFunc, (a, b) => a.div_(b), (a, b) => a / b);\\n#endif\\n }\\n }\\n }\\n\\n#if false\\n [Fact]\\n [TestOf(nameof(Tensor))]\\n public void TestArithmeticOperatorsBFloat16()\\n {\\n // BFloat16 arange_cuda not available on cuda in LibTorch 1.8.0\\n // BFloat16 arange_cpu not available on cuda in LibTorch 1.8.0\\n foreach (var device in new Device[] { torch.CPU, torch.CUDA }) {\\n if (device.type != DeviceType.CUDA || torch.cuda.is_available()) {\\n var c1 = torch.ones(new long[] { 10, 10 }, bfloat16, device: device);\\n var c2 = torch.ones(new long[] { 10, 10 }, bfloat16, device: device);\\n var c3 = torch.ones(new long[] { 10, 10 }, bfloat16, device: device);\\n Func<Tensor, long, long, float> getFunc = (tt, i, j) => tt[i, j].ToSingle();\\n // scalar-tensor operators\\n TestOneTensor<float, float>(c1, c2, getFunc, getFunc, a => a + 0.5f, a => a + 0.5f);\\n TestOneTensor<float, float>(c1, c2, getFunc, getFunc, a => 0.5f + a, a => 0.5f + a);\\n TestOneTensor<float, float>(c1, c2, getFunc, getFunc, a => a - 0.5f, a => a - 0.5f);\\n TestOneTensor<float, float>(c1, c2, getFunc, getFunc, a => a * 0.5f, a => a * 0.5f);\\n TestOneTensor<float, float>(c1, c2, getFunc, getFunc, a => 0.5f * a, a => 0.5f * a);\\n TestOneTensor<float, float>(c1, c2, getFunc, getFunc, a => a / 0.5f, a => a / 0.5f);\\n\\n TestOneTensor<float, float>(c1, c2, getFunc, getFunc, a => a.add(0.5f), a => a + 0.5f);\\n TestOneTensor<float, float>(c1, c2, getFunc, getFunc, a => a.sub(0.5f), a => a - 0.5f);\\n TestOneTensor<float, float>(c1, c2, getFunc, getFunc, a => a.mul(0.5f), a => a * 0.5f);\\n TestOneTensor<float, float>(c1, c2, getFunc, getFunc, a => a.div(0.5f), a => a / 0.5f);\\n\\n TestOneTensorInPlace<float>(c1, c2, getFunc, a => a.add_(0.5f), a => a + 0.5f);\\n TestOneTensorInPlace<float>(c1, c2, getFunc, a => a.sub_(0.5f), a => a - 0.5f);\\n TestOneTensorInPlace<float>(c1, c2, getFunc, a => a.mul_(0.5f), a => a * 0.5f);\\n TestOneTensorInPlace<float>(c1, c2, getFunc, a => a.div_(0.5f), a => a / 0.5f);\\n\\n // tensor-tensor operators\\n TestTwoTensor<float, float>(c1, c2, c3, getFunc, getFunc, (a, b) => a + b, (a, b) => a + b);\\n TestTwoTensor<float, float>(c1, c2, c3, getFunc, getFunc, (a, b) => a - b, (a, b) => a - b);\\n TestTwoTensor<float, float>(c1, c2, c3, getFunc, getFunc, (a, b) => a * b, (a, b) => a * b);\\n TestTwoTensor<float, float>(c1, c2, c3, getFunc, getFunc, (a, b) => a / b, (a, b) => a / b);\\n\\n TestTwoTensor<float, float>(c1, c2, c3, getFunc, getFunc, (a, b) => a.add(b), (a, b) => a + b);\\n TestTwoTensor<float, float>(c1, c2, c3, getFunc, getFunc, (a, b) => a.sub(b), (a, b) => a - b);\\n TestTwoTensor<float, float>(c1, c2, c3, getFunc, getFunc, (a, b) => a.mul(b), (a, b) => a * b);\\n TestTwoTensor<float, float>(c1, c2, c3, getFunc, getFunc, (a, b) => a.div(b), (a, b) => a / b);\\n\\n TestTwoTensorInPlace<float>(c1, c2, c3, getFunc, (a, b) => a.add_(b), (a, b) => a + b);\\n TestTwoTensorInPlace<float>(c1, c2, c3, getFunc, (a, b) => a.sub_(b), (a, b) => a - b);\\n TestTwoTensorInPlace<float>(c1, c2, c3, getFunc, (a, b) => a.mul_(b), (a, b) => a * b);\\n TestTwoTensorInPlace<float>(c1, c2, c3, getFunc, (a, b) => a.div_(b), (a, b) => a / b);\\n }\\n }\\n }\\n#endif\\n\\n [Fact]\\n [TestOf(nameof(Tensor))]\\n public void TestArithmeticOperatorsFloat32()\\n {\\n foreach (var device in new Device[] { torch.CPU, torch.CUDA }) {\\n if (device.type != DeviceType.CUDA || torch.cuda.is_available()) {\\n var c1 = torch.arange(0, 10, float32, device: device).expand(new long[] { 10, 10 });\\n var c2 = torch.arange(10, 0, -1, float32, device: device).expand(new long[] { 10, 10 });\\n var c3 = torch.ones(new long[] { 10, 10 }, float32, device: device);\\n Func<Tensor, long, long, float> getFunc = (tt, i, j) => tt[i, j].ToSingle();\\n // scalar-tensor operators\\n TestOneTensor<float, float>(c1, c2, getFunc, getFunc, a => a + 0.5f, a => a + 0.5f);\\n TestOneTensor<float, float>(c1, c2, getFunc, getFunc, a => 0.5f + a, a => 0.5f + a);\\n TestOneTensor<float, float>(c1, c2, getFunc, getFunc, a => a - 0.5f, a => a - 0.5f);\\n TestOneTensor<float, float>(c1, c2, getFunc, getFunc, a => a * 0.5f, a => a * 0.5f);\\n TestOneTensor<float, float>(c1, c2, getFunc, getFunc, a => 0.5f * a, a => 0.5f * a);\\n TestOneTensor<float, float>(c1, c2, getFunc, getFunc, a => a / 0.5f, a => a / 0.5f);\\n\\n TestOneTensor<float, float>(c1, c2, getFunc, getFunc, a => a.add(0.5f), a => a + 0.5f);\\n TestOneTensor<float, float>(c1, c2, getFunc, getFunc, a => a.sub(0.5f), a => a - 0.5f);\\n TestOneTensor<float, float>(c1, c2, getFunc, getFunc, a => a.mul(0.5f), a => a * 0.5f);\\n TestOneTensor<float, float>(c1, c2, getFunc, getFunc, a => a.div(0.5f), a => a / 0.5f);\\n\\n TestOneTensorInPlace<float>(c1, c2, getFunc, a => a.add_(0.5f), a => a + 0.5f);\\n TestOneTensorInPlace<float>(c1, c2, getFunc, a => a.sub_(0.5f), a => a - 0.5f);\\n TestOneTensorInPlace<float>(c1, c2, getFunc, a => a.mul_(0.5f), a => a * 0.5f);\\n TestOneTensorInPlace<float>(c1, c2, getFunc, a => a.div_(0.5f), a => a / 0.5f);\\n\\n // tensor-tensor operators\\n TestTwoTensor<float, float>(c1, c2, c3, getFunc, getFunc, (a, b) => a + b, (a, b) => a + b);\\n TestTwoTensor<float, float>(c1, c2, c3, getFunc, getFunc, (a, b) => a - b, (a, b) => a - b);\\n TestTwoTensor<float, float>(c1, c2, c3, getFunc, getFunc, (a, b) => a * b, (a, b) => a * b);\\n TestTwoTensor<float, float>(c1, c2, c3, getFunc, getFunc, (a, b) => a / b, (a, b) => a / b);\\n\\n TestTwoTensor<float, float>(c1, c2, c3, getFunc, getFunc, (a, b) => a.add(b), (a, b) => a + b);\\n TestTwoTensor<float, float>(c1, c2, c3, getFunc, getFunc, (a, b) => a.sub(b), (a, b) => a - b);\\n TestTwoTensor<float, float>(c1, c2, c3, getFunc, getFunc, (a, b) => a.mul(b), (a, b) => a * b);\\n TestTwoTensor<float, float>(c1, c2, c3, getFunc, getFunc, (a, b) => a.div(b), (a, b) => a / b);\\n\\n TestTwoTensorInPlace<float>(c1, c2, c3, getFunc, (a, b) => a.add_(b), (a, b) => a + b);\\n TestTwoTensorInPlace<float>(c1, c2, c3, getFunc, (a, b) => a.sub_(b), (a, b) => a - b);\\n TestTwoTensorInPlace<float>(c1, c2, c3, getFunc, (a, b) => a.mul_(b), (a, b) => a * b);\\n TestTwoTensorInPlace<float>(c1, c2, c3, getFunc, (a, b) => a.div_(b), (a, b) => a / b);\\n }\\n }\\n }\\n\\n [Fact]\\n [TestOf(nameof(Tensor))]\\n public void TestArithmeticOperatorsFloat64()\\n {\\n foreach (var device in new Device[] { torch.CPU, torch.CUDA }) {\\n if (device.type != DeviceType.CUDA || torch.cuda.is_available()) {\\n var c1 = torch.arange(0, 10, float64, device: device).expand(new long[] { 10, 10 });\\n var c2 = torch.arange(10, 0, -1, float64, device: device).expand(new long[] { 10, 10 });\\n var c3 = torch.ones(new long[] { 10, 10 }, float64, device: device);\\n Func<Tensor, long, long, double> getFunc = (tt, i, j) => tt[i, j].ToDouble();\\n // scalar-tensor operators\\n TestOneTensor<double, double>(c1, c2, getFunc, getFunc, a => a + 0.5, a => a + 0.5);\\n TestOneTensor<double, double>(c1, c2, getFunc, getFunc, a => 0.5 + a, a => 0.5 + a);\\n TestOneTensor<double, double>(c1, c2, getFunc, getFunc, a => a - 0.5, a => a - 0.5);\\n TestOneTensor<double, double>(c1, c2, getFunc, getFunc, a => a * 0.5, a => a * 0.5);\\n TestOneTensor<double, double>(c1, c2, getFunc, getFunc, a => 0.5 * a, a => 0.5 * a);\\n TestOneTensor<double, double>(c1, c2, getFunc, getFunc, a => a / 0.5, a => a / 0.5);\\n\\n TestOneTensor<double, double>(c1, c2, getFunc, getFunc, a => a.add(0.5), a => a + 0.5);\\n TestOneTensor<double, double>(c1, c2, getFunc, getFunc, a => a.sub(0.5), a => a - 0.5);\\n TestOneTensor<double, double>(c1, c2, getFunc, getFunc, a => a.mul(0.5), a => a * 0.5);\\n TestOneTensor<double, double>(c1, c2, getFunc, getFunc, a => a.div(0.5), a => a / 0.5);\\n\\n TestOneTensorInPlace<double>(c1, c2, getFunc, a => a.add_(0.5), a => a + 0.5);\\n TestOneTensorInPlace<double>(c1, c2, getFunc, a => a.sub_(0.5), a => a - 0.5);\\n TestOneTensorInPlace<double>(c1, c2, getFunc, a => a.mul_(0.5), a => a * 0.5);\\n TestOneTensorInPlace<double>(c1, c2, getFunc, a => a.div_(0.5), a => a / 0.5);\\n\\n // tensor-tensor operators\\n TestTwoTensor<double, double>(c1, c2, c3, getFunc, getFunc, (a, b) => a + b, (a, b) => a + b);\\n TestTwoTensor<double, double>(c1, c2, c3, getFunc, getFunc, (a, b) => a - b, (a, b) => a - b);\\n TestTwoTensor<double, double>(c1, c2, c3, getFunc, getFunc, (a, b) => a * b, (a, b) => a * b);\\n TestTwoTensor<double, double>(c1, c2, c3, getFunc, getFunc, (a, b) => a / b, (a, b) => a / b);\\n\\n TestTwoTensor<double, double>(c1, c2, c3, getFunc, getFunc, (a, b) => a.add(b), (a, b) => a + b);\\n TestTwoTensor<double, double>(c1, c2, c3, getFunc, getFunc, (a, b) => a.sub(b), (a, b) => a - b);\\n TestTwoTensor<double, double>(c1, c2, c3, getFunc, getFunc, (a, b) => a.mul(b), (a, b) => a * b);\\n TestTwoTensor<double, double>(c1, c2, c3, getFunc, getFunc, (a, b) => a.div(b), (a, b) => a / b);\\n\\n TestTwoTensorInPlace<double>(c1, c2, c3, getFunc, (a, b) => a.add_(b), (a, b) => a + b);\\n TestTwoTensorInPlace<double>(c1, c2, c3, getFunc, (a, b) => a.sub_(b), (a, b) => a - b);\\n TestTwoTensorInPlace<double>(c1, c2, c3, getFunc, (a, b) => a.mul_(b), (a, b) => a * b);\\n TestTwoTensorInPlace<double>(c1, c2, c3, getFunc, (a, b) => a.div_(b), (a, b) => a / b);\\n }\\n }\\n }\\n\\n [Fact]\\n [TestOf(nameof(Tensor))]\\n public void TestArithmeticOperatorsComplexFloat64()\\n {\\n foreach (var device in new Device[] { torch.CPU, torch.CUDA }) {\\n if (device.type != DeviceType.CUDA || torch.cuda.is_available()) {\\n var c1 = torch.arange(0, 10, complex128, device: device).expand(new long[] { 10, 10 });\\n var c2 = torch.arange(10, 0, -1, complex128, device: device).expand(new long[] { 10, 10 });\\n var c3 = torch.ones(new long[] { 10, 10 }, complex128, device: device);\\n Func<Tensor, long, long, System.Numerics.Complex> getFunc = (tt, i, j) => tt[i, j].ToComplexFloat64();\\n // scalar-tensor operators\\n TestOneTensor<System.Numerics.Complex, System.Numerics.Complex>(c1, c2, getFunc, getFunc, a => a + 0.5, a => a + 0.5);\\n TestOneTensor<System.Numerics.Complex, System.Numerics.Complex>(c1, c2, getFunc, getFunc, a => 0.5 + a, a => 0.5 + a);\\n TestOneTensor<System.Numerics.Complex, System.Numerics.Complex>(c1, c2, getFunc, getFunc, a => a - 0.5, a => a - 0.5);\\n TestOneTensor<System.Numerics.Complex, System.Numerics.Complex>(c1, c2, getFunc, getFunc, a => a * 0.5, a => a * 0.5);\\n TestOneTensor<System.Numerics.Complex, System.Numerics.Complex>(c1, c2, getFunc, getFunc, a => 0.5 * a, a => 0.5 * a);\\n TestOneTensor<System.Numerics.Complex, System.Numerics.Complex>(c1, c2, getFunc, getFunc, a => a / 0.5, a => a / 0.5);\\n\\n TestOneTensor<System.Numerics.Complex, System.Numerics.Complex>(c1, c2, getFunc, getFunc, a => a.add(0.5), a => a + 0.5);\\n TestOneTensor<System.Numerics.Complex, System.Numerics.Complex>(c1, c2, getFunc, getFunc, a => a.sub(0.5), a => a - 0.5);\\n TestOneTensor<System.Numerics.Complex, System.Numerics.Complex>(c1, c2, getFunc, getFunc, a => a.mul(0.5), a => a * 0.5);\\n TestOneTensor<System.Numerics.Complex, System.Numerics.Complex>(c1, c2, getFunc, getFunc, a => a.div(0.5), a => a / 0.5);\\n\\n TestOneTensorInPlace<System.Numerics.Complex>(c1, c2, getFunc, a => a.add_(0.5), a => a + 0.5);\\n TestOneTensorInPlace<System.Numerics.Complex>(c1, c2, getFunc, a => a.sub_(0.5), a => a - 0.5);\\n TestOneTensorInPlace<System.Numerics.Complex>(c1, c2, getFunc, a => a.mul_(0.5), a => a * 0.5);\\n TestOneTensorInPlace<System.Numerics.Complex>(c1, c2, getFunc, a => a.div_(0.5), a => a / 0.5);\\n\\n // tensor-tensor operators\\n TestTwoTensor<System.Numerics.Complex, System.Numerics.Complex>(c1, c2, c3, getFunc, getFunc, (a, b) => a + b, (a, b) => a + b);\\n TestTwoTensor<System.Numerics.Complex, System.Numerics.Complex>(c1, c2, c3, getFunc, getFunc, (a, b) => a - b, (a, b) => a - b);\\n TestTwoTensor<System.Numerics.Complex, System.Numerics.Complex>(c1, c2, c3, getFunc, getFunc, (a, b) => a * b, (a, b) => a * b);\\n // Rounding errors make this test volatile\\n //TestTwoTensor<System.Numerics.Complex, System.Numerics.Complex>(c1, c2, c3, getFunc, getFunc, (a, b) => a / b, (a, b) => a / b);\\n\\n TestTwoTensor<System.Numerics.Complex, System.Numerics.Complex>(c1, c2, c3, getFunc, getFunc, (a, b) => a.add(b), (a, b) => a + b);\\n TestTwoTensor<System.Numerics.Complex, System.Numerics.Complex>(c1, c2, c3, getFunc, getFunc, (a, b) => a.sub(b), (a, b) => a - b);\\n TestTwoTensor<System.Numerics.Complex, System.Numerics.Complex>(c1, c2, c3, getFunc, getFunc, (a, b) => a.mul(b), (a, b) => a * b);\\n // Rounding errors make this test volatile\\n //TestTwoTensor<System.Numerics.Complex, System.Numerics.Complex>(c1, c2, c3, getFunc, getFunc, (a, b) => a.div(b), (a, b) => a / b);\\n\\n TestTwoTensorInPlace<System.Numerics.Complex>(c1, c2, c3, getFunc, (a, b) => a.add_(b), (a, b) => a + b);\\n TestTwoTensorInPlace<System.Numerics.Complex>(c1, c2, c3, getFunc, (a, b) => a.sub_(b), (a, b) => a - b);\\n TestTwoTensorInPlace<System.Numerics.Complex>(c1, c2, c3, getFunc, (a, b) => a.mul_(b), (a, b) => a * b);\\n // Rounding errors make this test volatile\\n //TestTwoTensorInPlace<System.Numerics.Complex>(c1, c2, c3, getFunc, (a, b) => a.div_(b), (a, b) => a / b);\\n }\\n }\\n }\\n\\n [Fact]\\n [TestOf(nameof(Tensor))]\\n public void TestComparisonOperatorsFloat32()\\n {\\n foreach (var device in new Device[] { torch.CPU, torch.CUDA }) {\\n if (device.type != DeviceType.CUDA || torch.cuda.is_available()) {\\n var c1 = torch.arange(0, 10, float32, device: device).expand(new long[] { 10, 10 });\\n var c2 = torch.arange(10, 0, -1, float32, device: device).expand(new long[] { 10, 10 });\\n var c3 = torch.ones(new long[] { 10, 10 }, float32, device: device);\\n Func<Tensor, long, long, float> getFunc = (tt, i, j) => tt[i, j].ToSingle();\\n Func<Tensor, long, long, bool> getFuncBool = (tt, i, j) => tt[i, j].ToBoolean();\\n // scalar-tensor operators\\n TestOneTensor<float, bool>(c1, c2, getFunc, getFuncBool, a => a == 5.0f, a => a == 5.0f);\\n TestOneTensor<float, bool>(c1, c2, getFunc, getFuncBool, a => a != 5.0f, a => a != 5.0f);\\n TestOneTensorInPlace<float>(c1, c2, getFunc, a => a.eq_(5.0f), a => a == 5.0f ? 1.0f : 0.0f);\\n TestOneTensorInPlace<float>(c1, c2, getFunc, a => a.ne_(5.0f), a => a != 5.0f ? 1.0f : 0.0f);\\n\\n TestOneTensor<float, bool>(c1, c2, getFunc, getFuncBool, a => a < 5.0f, a => a < 5.0f);\\n TestOneTensor<float, bool>(c1, c2, getFunc, getFuncBool, a => 5.0f < a, a => 5.0f < a);\\n TestOneTensor<float, bool>(c1, c2, getFunc, getFuncBool, a => a <= 5.0f, a => a <= 5.0f);\\n TestOneTensor<float, bool>(c1, c2, getFunc, getFuncBool, a => 5.0f <= a, a => 5.0f <= a);\\n TestOneTensor<float, bool>(c1, c2, getFunc, getFuncBool, a => a > 5.0f, a => a > 5.0f);\\n TestOneTensor<float, bool>(c1, c2, getFunc, getFuncBool, a => 5.0f > a, a => 5.0f > a);\\n TestOneTensor<float, bool>(c1, c2, getFunc, getFuncBool, a => a >= 5.0f, a => a >= 5.0f);\\n TestOneTensor<float, bool>(c1, c2, getFunc, getFuncBool, a => 5.0f >= a, a => 5.0f >= a);\\n\\n TestOneTensorInPlace<float>(c1, c2, getFunc, a => a.lt_(5.0f), a => a < 5.0f ? 1.0f : 0.0f);\\n TestOneTensorInPlace<float>(c1, c2, getFunc, a => a.le_(5.0f), a => a <= 5.0f ? 1.0f : 0.0f);\\n TestOneTensorInPlace<float>(c1, c2, getFunc, a => a.gt_(5.0f), a => a > 5.0f ? 1.0f : 0.0f);\\n TestOneTensorInPlace<float>(c1, c2, getFunc, a => a.ge_(5.0f), a => a >= 5.0f ? 1.0f : 0.0f);\\n\\n TestOneTensor<float, float>(c1, c2, getFunc, getFunc, a => a % 5.0f, a => a % 5.0f);\\n TestOneTensorInPlace<float>(c1, c2, getFunc, a => a.remainder_(5.0f), a => a % 5.0f);\\n\\n // tensor-tensor operators\\n TestTwoTensor<float, bool>(c1, c2, c3, getFunc, getFuncBool, (a, b) => a == b, (a, b) => a == b);\\n TestTwoTensor<float, bool>(c1, c2, c3, getFunc, getFuncBool, (a, b) => a != b, (a, b) => a != b);\\n TestTwoTensorInPlace<float>(c1, c2, c3, getFunc, (a, b) => a.eq_(b), (a, b) => a == b ? 1.0f : 0.0f);\\n TestTwoTensorInPlace<float>(c1, c2, c3, getFunc, (a, b) => a.ne_(b), (a, b) => a != b ? 1.0f : 0.0f);\\n\\n TestTwoTensor<float, bool>(c1, c2, c3, getFunc, getFuncBool, (a, b) => a < b, (a, b) => a < b);\\n TestTwoTensor<float, bool>(c1, c2, c3, getFunc, getFuncBool, (a, b) => a <= b, (a, b) => a <= b);\\n TestTwoTensor<float, bool>(c1, c2, c3, getFunc, getFuncBool, (a, b) => a > b, (a, b) => a > b);\\n TestTwoTensor<float, bool>(c1, c2, c3, getFunc, getFuncBool, (a, b) => a >= b, (a, b) => a >= b);\\n\\n TestTwoTensorInPlace<float>(c1, c2, c3, getFunc, (a, b) => a.lt_(b), (a, b) => a < b ? 1.0f : 0.0f);\\n TestTwoTensorInPlace<float>(c1, c2, c3, getFunc, (a, b) => a.le_(b), (a, b) => a <= b ? 1.0f : 0.0f);\\n TestTwoTensorInPlace<float>(c1, c2, c3, getFunc, (a, b) => a.gt_(b), (a, b) => a > b ? 1.0f : 0.0f);\\n TestTwoTensorInPlace<float>(c1, c2, c3, getFunc, (a, b) => a.ge_(b), (a, b) => a >= b ? 1.0f : 0.0f);\\n\\n TestTwoTensor<float, float>(c1, c2, c3, getFunc, getFunc, (a, b) => a % b, (a, b) => a % b);\\n TestTwoTensorInPlace<float>(c1, c2, c3, getFunc, (a, b) => a.remainder_(b), (a, b) => a % b);\\n }\\n }\\n }\\n\\n private void TestOneTensor<Tin, Tout>(\\n Tensor c1,\\n Tensor c2,\\n Func<Tensor, long, long, Tin> getFuncIn,\\n Func<Tensor, long, long, Tout> getFuncOut,\\n Func<Tensor, Tensor> tensorFunc,\\n Func<Tin, Tout> scalarFunc)\\n {\\n var x = c1 * c2;\\n var y = tensorFunc(x);\\n\\n for (int i = 0; i < 10; i++) {\\n for (int j = 0; j < 10; j++) {\\n var xv = getFuncIn(x, i, j);\\n var yv = getFuncOut(y, i, j);\\n Assert.Equal<Tout>(yv, scalarFunc(xv));\\n }\\n }\\n }\\n\\n private void TestOneTensorInPlace<Tin>(\\n Tensor c1,\\n Tensor c2,\\n Func<Tensor, long, long, Tin> getFuncIn,\\n Func<Tensor, Tensor> tensorFunc,\\n Func<Tin, Tin> scalarFunc)\\n {\\n\\n var x = c1 * c2;\\n var xClone = x.clone();\\n var y = tensorFunc(x);\\n\\n for (int i = 0; i < 10; i++) {\\n for (int j = 0; j < 10; j++) {\\n var xClonev = getFuncIn(xClone, i, j);\\n var xv = getFuncIn(x, i, j);\\n var yv = getFuncIn(y, i, j);\\n Assert.Equal(yv, scalarFunc(xClonev));\\n Assert.Equal(yv, xv);\\n }\\n }\\n }\\n\\n private void TestTwoTensor<Tin, Tout>(\\n Tensor c1,\\n Tensor c2,\\n Tensor c3,\\n Func<Tensor, long, long, Tin> getFuncIn,\\n Func<Tensor, long, long, Tout> getFuncOut,\\n Func<Tensor, Tensor, Tensor> tensorFunc,\\n Func<Tin, Tin, Tout> scalarFunc)\\n {\\n\\n var x = c1 * c3;\\n var y = c2 * c3;\\n\\n var z = tensorFunc(x, y);\\n\\n for (int i = 0; i < 10; i++) {\\n for (int j = 0; j < 10; j++) {\\n var xv = getFuncIn(x, i, j);\\n var yv = getFuncIn(y, i, j);\\n var zv = getFuncOut(z, i, j);\\n Assert.Equal(zv, scalarFunc(xv, yv));\\n }\\n }\\n }\\n\\n private void TestTwoTensorInPlace<Tin>(\\n Tensor c1,\\n Tensor c2,\\n Tensor c3,\\n Func<Tensor, long, long, Tin> getFuncIn,\\n Func<Tensor, Tensor, Tensor> tensorFunc,\\n Func<Tin, Tin, Tin> scalarFunc) where Tin : unmanaged\\n {\\n\\n var x = c1 * c3;\\n var xClone = x.clone();\\n var y = c2 * c3;\\n\\n var z = tensorFunc(x, y);\\n\\n if (x.device_type == DeviceType.CPU) {\\n var xData = x.data<Tin>();\\n var yData = y.data<Tin>();\\n var zData = z.data<Tin>();\\n\\n Assert.True(xData == zData);\\n }\\n\\n for (int i = 0; i < 10; i++) {\\n for (int j = 0; j < 10; j++) {\\n var xClonev = getFuncIn(xClone, i, j);\\n var xv = getFuncIn(x, i, j);\\n var yv = getFuncIn(y, i, j);\\n var zv = getFuncIn(z, i, j);\\n Assert.Equal(zv, scalarFunc(xClonev, yv));\\n Assert.Equal(zv, xv);\\n }\\n }\\n }\\n\\n [Fact]\\n [TestOf(nameof(Tensor.eq))]\\n [TestOf(nameof(Tensor.ne))]\\n [TestOf(nameof(Tensor.lt))]\\n [TestOf(nameof(Tensor.gt))]\\n [TestOf(nameof(Tensor.le))]\\n public void TestComparison()\\n {\\n var A = torch.tensor(new float[] { 1.2f, 3.4f, 1.4f, 3.3f }).reshape(2, 2);\\n var B = torch.tensor(new float[] { 1.3f, 3.3f });\\n Assert.Equal(new bool[] { false, false, false, true }, A.eq(B).data<bool>().ToArray());\\n Assert.Equal(new bool[] { false, false, false, true }, torch.eq(A, B).data<bool>().ToArray());\\n Assert.Equal(new bool[] { true, true, true, false }, A.ne(B).data<bool>().ToArray());\\n Assert.Equal(new bool[] { true, true, true, false }, torch.ne(A, B).data<bool>().ToArray());\\n Assert.Equal(new bool[] { true, false, false, false }, A.lt(B).data<bool>().ToArray());\\n Assert.Equal(new bool[] { true, false, false, false }, torch.lt(A, B).data<bool>().ToArray());\\n Assert.Equal(new bool[] { true, false, false, true }, A.le(B).data<bool>().ToArray());\\n Assert.Equal(new bool[] { true, false, false, true }, torch.le(A, B).data<bool>().ToArray());\\n Assert.Equal(new bool[] { false, true, true, false }, A.gt(B).data<bool>().ToArray());\\n Assert.Equal(new bool[] { false, true, true, false }, torch.gt(A, B).data<bool>().ToArray());\\n Assert.Equal(new bool[] { false, true, true, true }, A.ge(B).data<bool>().ToArray());\\n Assert.Equal(new bool[] { false, true, true, true }, torch.ge(A, B).data<bool>().ToArray());\\n }\\n\\n [Fact]\\n [TestOf(nameof(Tensor.frexp))]\\n public void TestFrexp()\\n {\\n var x = torch.arange(9, float32);\\n var r = x.frexp();\\n\\n Assert.Equal(new float[] { 0.0000f, 0.5000f, 0.5000f, 0.7500f, 0.5000f, 0.6250f, 0.7500f, 0.8750f, 0.5000f }, r.Mantissa.data<float>().ToArray());\\n Assert.Equal(new int[] { 0, 1, 2, 2, 3, 3, 3, 3, 4 }, r.Exponent.data<int>().ToArray());\\n }\\n\\n [Fact]\\n [TestOf(nameof(Tensor.deg2rad))]\\n public void Deg2RadTest()\\n {\\n var data = new float[] { 1.0f, 2.0f, 3.0f };\\n var expected = data.Select(angl => (angl * MathF.PI) / 180.0f).ToArray();\\n var res = torch.tensor(data).deg2rad();\\n Assert.True(res.allclose(torch.tensor(expected)));\\n }\\n\\n [Fact]\\n [TestOf(nameof(Tensor.clamp))]\\n public void ClampTest1()\\n {\\n var data = torch.rand(3, 3, 3) * 10;\\n var cl = data.clamp(1, 5);\\n\\n Assert.All(cl.data<float>().ToArray(), d => Assert.True(d >= 1.0f && d <= 5.0f));\\n }\\n\\n [Fact]\\n [TestOf(nameof(Tensor.clamp))]\\n public void ClampTest2()\\n {\\n var data = torch.rand(3, 3, 3) * 10;\\n var cl = data.clamp(torch.ones(3, 3, 3), torch.ones(3, 3, 3) * 5);\\n\\n Assert.All(cl.data<float>().ToArray(), d => Assert.True(d >= 1.0f && d <= 5.0f));\\n }\\n\\n [Fact]\\n [TestOf(nameof(Tensor.clamp))]\\n public void ClampTest3()\\n {\\n var data = torch.rand(3, 3, 3) * 10;\\n var cl = torch.clamp(data, 1, 5);\\n\\n Assert.All(cl.data<float>().ToArray(), d => Assert.True(d >= 1.0f && d <= 5.0f));\\n }\\n\\n [Fact]\\n [TestOf(nameof(Tensor.clamp))]\\n public void ClampTest4()\\n {\\n var data = torch.rand(3, 3, 3) * 10;\\n var cl = torch.clamp(data, torch.ones(3, 3, 3), torch.ones(3, 3, 3) * 5);\\n\\n Assert.All(cl.data<float>().ToArray(), d => Assert.True(d >= 1.0f && d <= 5.0f));\\n }\\n\\n [Fact]\\n [TestOf(nameof(Tensor.rad2deg))]\\n public void Rad2DegTest()\\n {\\n var data = new float[] { 1.0f, 2.0f, 3.0f };\\n var expected = data.Select(angl => (angl * 180.0f) / MathF.PI).ToArray();\\n var res = torch.tensor(data).rad2deg();\\n Assert.True(res.allclose(torch.tensor(expected)));\\n }\\n\\n [Fact]\\n [TestOf(nameof(Tensor.abs))]\\n public void AbsTest()\\n {\\n var data = torch.arange(-10.0f, 10.0f, 1.0f);\\n var expected = data.data<float>().ToArray().Select(MathF.Abs).ToArray();\\n var res = data.abs();\\n Assert.True(res.allclose(torch.tensor(expected)));\\n }\\n\\n [Fact]\\n [TestOf(nameof(Tensor.abs))]\\n public void AbsTestC32()\\n {\\n var data = torch.rand(new long[] { 25 }, complex64);\\n var expected = data.data<(float R, float I)>().ToArray().Select(c => MathF.Sqrt(c.R * c.R + c.I * c.I)).ToArray();\\n var res = data.abs();\\n Assert.True(res.allclose(torch.tensor(expected)));\\n }\\n\\n [Fact]\\n [TestOf(nameof(Tensor.abs))]\\n public void AbsTestC64()\\n {\\n var data = torch.rand(new long[] { 25 }, complex128);\\n var expected = data.data<System.Numerics.Complex>().ToArray().Select(c => Math.Sqrt(c.Real * c.Real + c.Imaginary * c.Imaginary)).ToArray<double>();\\n var res = data.abs();\\n Assert.True(res.allclose(torch.tensor(expected, float64)));\\n }\\n\\n [Fact]\\n [TestOf(nameof(Tensor.angle))]\\n public void AngleTestC32()\\n {\\n var data = torch.randn(new long[] { 25 }, complex64);\\n var expected = data.data<(float R, float I)>().ToArray().Select(c => {\\n var x = c.R;\\n var y = c.I;\\n return (x > 0 || y != 0) ? 2 * MathF.Atan(y / (MathF.Sqrt(x * x + y * y) + x)) : (x < 0 && y == 0) ? MathF.PI : 0;\\n }).ToArray();\\n var res = data.angle();\\n Assert.True(res.allclose(torch.tensor(expected), rtol: 1e-03, atol: 1e-05));\\n }\\n\\n [Fact]\\n [TestOf(nameof(Tensor.angle))]\\n public void AngleTestC64()\\n {\\n var data = torch.randn(new long[] { 25 }, complex128);\\n var expected = data.data<System.Numerics.Complex>().ToArray().Select(c => {\\n var x = c.Real;\\n var y = c.Imaginary;\\n return (x > 0 || y != 0) ? 2 * Math.Atan(y / (Math.Sqrt(x * x + y * y) + x)) : (x < 0 && y == 0) ? Math.PI : 0;\\n }).ToArray<double>();\\n var res = data.angle();\\n Assert.True(res.allclose(torch.tensor(expected, float64), rtol: 1e-03, atol: 1e-05));\\n }\\n\\n [Fact]\\n [TestOf(nameof(Tensor.sqrt))]\\n public void SqrtTest()\\n {\\n var data = new float[] { 1.0f, 2.0f, 3.0f };\\n var expected = data.Select(MathF.Sqrt).ToArray();\\n var res = torch.tensor(data).sqrt();\\n Assert.True(res.allclose(torch.tensor(expected)));\\n }\\n\\n [Fact]\\n [TestOf(nameof(Tensor.sin))]\\n public void SinTest()\\n {\\n var data = new float[] { 1.0f, 2.0f, 3.0f };\\n var expected = data.Select(MathF.Sin).ToArray();\\n var res = torch.tensor(data).sin();\\n Assert.True(res.allclose(torch.tensor(expected)));\\n res = torch.sin(torch.tensor(data));\\n Assert.True(res.allclose(torch.tensor(expected)));\\n }\\n\\n [Fact]\\n [TestOf(nameof(Tensor.cos))]\\n public void CosTest()\\n {\\n var data = new float[] { 1.0f, 2.0f, 3.0f };\\n var expected = data.Select(MathF.Cos).ToArray();\\n var res = torch.tensor(data).cos();\\n Assert.True(res.allclose(torch.tensor(expected)));\\n res = torch.cos(torch.tensor(data));\\n Assert.True(res.allclose(torch.tensor(expected)));\\n }\\n\\n [Fact]\\n [TestOf(nameof(Tensor.i0))]\\n public void I0Test()\\n {\\n var data = torch.arange(0, 5, 1, float32);\\n var expected = new float[] { 0.99999994f, 1.266066f, 2.27958512f, 4.88079262f, 11.3019209f };\\n var res = data.i0();\\n Assert.True(res.allclose(torch.tensor(expected)));\\n }\\n\\n [Fact]\\n [TestOf(nameof(Tensor.hypot))]\\n public void HypotTest()\\n {\\n var a = new float[] { 1.0f, 2.0f, 3.0f };\\n var b = new float[] { 1.0f, 2.0f, 3.0f };\\n var expected = a.Select(x => MathF.Sqrt(2.0f) * x).ToArray();\\n var res = torch.tensor(a).hypot(torch.tensor(b));\\n Assert.True(res.allclose(torch.tensor(expected)));\\n }\\n\\n [Fact]\\n [TestOf(nameof(Tensor.tan))]\\n public void TanTest()\\n {\\n var data = new float[] { 1.0f, 2.0f, 3.0f };\\n var expected = data.Select(MathF.Tan).ToArray();\\n var res = torch.tensor(data).tan();\\n Assert.True(res.allclose(torch.tensor(expected)));\\n }\\n\\n [Fact]\\n [TestOf(nameof(Tensor.sinh))]\\n public void SinhTest()\\n {\\n var data = new float[] { 1.0f, 2.0f, 3.0f };\\n var expected = data.Select(MathF.Sinh).ToArray();\\n var res = torch.tensor(data).sinh();\\n Assert.True(res.allclose(torch.tensor(expected)));\\n }\\n\\n [Fact]\\n [TestOf(nameof(Tensor.cosh))]\\n public void CoshTest()\\n {\\n var data = new float[] { 1.0f, 2.0f, 3.0f };\\n var expected = data.Select(MathF.Cosh).ToArray();\\n var res = torch.tensor(data).cosh();\\n var tmp = res.data<Single>();\\n Assert.True(res.allclose(torch.tensor(expected)));\\n }\\n\\n [Fact]\\n [TestOf(nameof(Tensor.tanh))]\\n public void TanhTest()\\n {\\n var data = new float[] { 1.0f, 2.0f, 3.0f };\\n var expected = data.Select(MathF.Tanh).ToArray();\\n var res = torch.tensor(data).tanh();\\n Assert.True(res.allclose(torch.tensor(expected)));\\n }\\n\\n [Fact]\\n [TestOf(nameof(Tensor.asinh))]\\n public void ArcSinhTest()\\n {\\n var data = new float[] { -0.1f, 0.0f, 0.1f };\\n var expected = data.Select(MathF.Asinh).ToArray();\\n var res = torch.tensor(data).asinh();\\n Assert.True(res.allclose(torch.tensor(expected)));\\n }\\n\\n [Fact]\\n [TestOf(nameof(Tensor.acosh))]\\n public void ArcCoshTest()\\n {\\n var data = new float[] { 1.0f, 2.0f, 3.0f };\\n var expected = data.Select(MathF.Acosh).ToArray();\\n var res = torch.tensor(data).acosh();\\n Assert.True(res.allclose(torch.tensor(expected)));\\n }\\n\\n [Fact]\\n [TestOf(nameof(Tensor.atanh))]\\n public void ArcTanhTest()\\n {\\n var data = new float[] { -0.1f, 0.0f, 0.1f };\\n var expected = data.Select(MathF.Atanh).ToArray();\\n var res = torch.tensor(data).atanh();\\n Assert.True(res.allclose(torch.tensor(expected)));\\n }\\n\\n [Fact]\\n [TestOf(nameof(Tensor.asin))]\\n public void AsinTest()\\n {\\n var data = new float[] { 1.0f, 0.2f, -0.1f };\\n var expected = data.Select(MathF.Asin).ToArray();\\n {\\n var res = torch.tensor(data).asin();\\n Assert.True(res.allclose(torch.tensor(expected)));\\n }\\n {\\n var res = torch.tensor(data).arcsin();\\n Assert.True(res.allclose(torch.tensor(expected)));\\n }\\n }\\n\\n [Fact]\\n [TestOf(nameof(Tensor.acos))]\\n public void AcosTest()\\n {\\n var data = new float[] { 1.0f, 0.2f, -0.1f };\\n var expected = data.Select(MathF.Acos).ToArray();\\n {\\n var res = torch.tensor(data).acos();\\n Assert.True(res.allclose(torch.tensor(expected)));\\n }\\n {\\n var res = torch.tensor(data).arccos();\\n Assert.True(res.allclose(torch.tensor(expected)));\\n }\\n }\\n\\n [Fact]\\n [TestOf(nameof(Tensor.atan))]\\n public void AtanTest()\\n {\\n var data = new float[] { 1.0f, 0.2f, -0.1f };\\n var expected = data.Select(MathF.Atan).ToArray();\\n {\\n var res = torch.tensor(data).atan();\\n Assert.True(res.allclose(torch.tensor(expected)));\\n }\\n {\\n var res = torch.tensor(data).arctan();\\n Assert.True(res.allclose(torch.tensor(expected)));\\n }\\n }\\n\\n [Fact]\\n [TestOf(nameof(Tensor.log))]\\n public void LogTest()\\n {\\n var data = new float[] { 1.0f, 2.0f, 3.0f };\\n var expected = data.Select(x => MathF.Log(x)).ToArray();\\n var res = torch.tensor(data).log();\\n Assert.True(res.allclose(torch.tensor(expected)));\\n }\\n\\n [Fact]\\n [TestOf(nameof(Tensor.log10))]\\n public void Log10Test()\\n {\\n var data = new float[] { 1.0f, 2.0f, 3.0f };\\n var expected = data.Select(MathF.Log10).ToArray();\\n var res = torch.tensor(data).log10();\\n Assert.True(res.allclose(torch.tensor(expected)));\\n }\\n\\n [Fact]\\n [TestOf(nameof(Tensor.log2))]\\n public void Log2Test()\\n {\\n var data = new float[] { 1.0f, 2.0f, 32.0f };\\n var expected = data.Select(MathF.Log2).ToArray();\\n var res = torch.tensor(data).log2();\\n Assert.True(res.allclose(torch.tensor(expected)));\\n }\\n\\n [Fact]\\n [TestOf(nameof(Tensor.logaddexp))]\\n public void LogAddExpTest()\\n {\\n var x = new float[] { 1.0f, 2.0f, 3.0f };\\n var y = new float[] { 4.0f, 5.0f, 6.0f };\\n var expected = new float[x.Length];\\n for (int i = 0; i < x.Length; i++) {\\n expected[i] = MathF.Log(MathF.Exp(x[i]) + MathF.Exp(y[i]));\\n }\\n var res = torch.tensor(x).logaddexp(torch.tensor(y));\\n Assert.True(res.allclose(torch.tensor(expected)));\\n }\\n\\n [Fact]\\n [TestOf(nameof(Tensor.logaddexp2))]\\n public void LogAddExp2Test()\\n {\\n var x = new float[] { 1.0f, 2.0f, 3.0f };\\n var y = new float[] { 4.0f, 5.0f, 6.0f };\\n var expected = new float[x.Length];\\n for (int i = 0; i < x.Length; i++) {\\n expected[i] = MathF.Log(MathF.Pow(2.0f, x[i]) + MathF.Pow(2.0f, y[i]), 2.0f);\\n }\\n var res = torch.tensor(x).logaddexp2(torch.tensor(y));\\n Assert.True(res.allclose(torch.tensor(expected)));\\n }\\n\\n [Fact]\\n [TestOf(nameof(Tensor.reciprocal))]\\n public void ReciprocalTest()\\n {\\n var x = torch.ones(new long[] { 10, 10 });\\n x.fill_(4.0f);\\n var y = x.reciprocal();\\n\\n Assert.All(x.data<float>().ToArray(), a => Assert.Equal(4.0f, a));\\n Assert.All(y.data<float>().ToArray(), a => Assert.Equal(0.25f, a));\\n\\n x.reciprocal_();\\n Assert.All(x.data<float>().ToArray(), a => Assert.Equal(0.25f, a));\\n }\\n\\n [Fact]\\n [TestOf(nameof(Tensor.exp2))]\\n public void Exp2Test()\\n {\\n var x = new float[] { 1.0f, 2.0f, 3.0f };\\n var expected = new float[] { 2.0f, 4.0f, 8.0f };\\n var res = torch.tensor(x).exp2();\\n Assert.True(res.allclose(torch.tensor(expected)));\\n }\\n\\n [Fact]\\n [TestOf(nameof(Tensor.floor))]\\n public void FloorTest()\\n {\\n var data = new float[] { 1.1f, 2.0f, 3.1f };\\n var expected = data.Select(MathF.Floor).ToArray();\\n var input = torch.tensor(data);\\n var res = input.floor();\\n Assert.True(res.allclose(torch.tensor(expected)));\\n\\n input.floor_();\\n Assert.True(input.allclose(torch.tensor(expected)));\\n }\\n\\n [Fact]\\n [TestOf(nameof(Tensor.floor_divide))]\\n public void FloorDivideTest()\\n {\\n var data = new float[] { 1.1f, 2.0f, 3.1f };\\n var expected = data.Select(d => MathF.Floor(d / 2)).ToArray();\\n var input = torch.tensor(data);\\n var res = input.floor_divide(2.0f);\\n Assert.True(res.allclose(torch.tensor(expected)));\\n\\n input.floor_divide_(2.0f);\\n Assert.True(input.allclose(torch.tensor(expected)));\\n }\\n\\n [Fact]\\n [TestOf(nameof(Tensor.trunc))]\\n public void TruncTest()\\n {\\n var input = torch.randn(new long[] { 25 });\\n var expected = input.data<float>().ToArray().Select(MathF.Truncate).ToArray();\\n var res = input.trunc();\\n Assert.True(res.allclose(torch.tensor(expected)));\\n\\n input.trunc_();\\n Assert.True(input.allclose(torch.tensor(expected)));\\n }\\n\\n [Fact]\\n [TestOf(nameof(Tensor.ceil))]\\n public void CeilTest()\\n {\\n var data = new float[] { 1.1f, 2.0f, 3.1f };\\n var expected = data.Select(MathF.Ceiling).ToArray();\\n var input = torch.tensor(data);\\n var res = input.ceil();\\n Assert.True(res.allclose(torch.tensor(expected)));\\n\\n input.ceil_();\\n Assert.True(res.allclose(torch.tensor(expected)));\\n }\\n\\n [Fact]\\n [TestOf(nameof(Tensor.round))]\\n public void RoundTest()\\n {\\n var rnd = new Random();\\n var data = Enumerable.Range(1, 100).Select(i => (float)rnd.NextDouble() * 10000).ToArray();\\n\\n {\\n var expected = data.Select(x => MathF.Round(x)).ToArray();\\n var input = torch.tensor(data);\\n var res = input.round();\\n Assert.True(res.allclose(torch.tensor(expected)));\\n\\n input.round_();\\n Assert.True(input.allclose(torch.tensor(expected)));\\n }\\n {\\n var expected = data.Select(x => MathF.Round(x * 10.0f) / 10.0f).ToArray();\\n var input = torch.tensor(data);\\n var res = input.round(1);\\n Assert.True(res.allclose(torch.tensor(expected)));\\n\\n input.round_(1);\\n Assert.True(input.allclose(torch.tensor(expected)));\\n }\\n {\\n var expected = data.Select(x => MathF.Round(x * 100.0f) / 100.0f).ToArray();\\n var input = torch.tensor(data);\\n var res = input.round(2);\\n Assert.True(res.allclose(torch.tensor(expected)));\\n\\n input.round_(2);\\n Assert.True(input.allclose(torch.tensor(expected)));\\n }\\n {\\n var expected = data.Select(x => MathF.Round(x * 0.1f) / 0.1f).ToArray();\\n var input = torch.tensor(data);\\n var res = input.round(-1);\\n Assert.True(res.allclose(torch.tensor(expected)));\\n\\n input.round_(-1);\\n Assert.True(input.allclose(torch.tensor(expected)));\\n }\\n {\\n var expected = data.Select(x => MathF.Round(x * 0.01f) / 0.01f).ToArray();\\n var input = torch.tensor(data);\\n var res = input.round(-2);\\n Assert.True(res.allclose(torch.tensor(expected)));\\n\\n input.round_(-2);\\n Assert.True(input.allclose(torch.tensor(expected)));\\n }\\n }\\n\\n [Fact]\\n [TestOf(nameof(torch.round))]\\n [TestOf(nameof(torch.round_))]\\n public void RoundTestWithDecimals()\\n {\\n const long n = 7L;\\n var i = eye(n); // identity matrix\\n var a = rand(new[] { n, n });\\n var b = linalg.inv(a);\\n\\n // check non-inline version\\n var r0 = round(matmul(a, b), 2L);\\n var r1 = round(matmul(b, a), 3L);\\n Assert.True(i.allclose(r0), \\\"round() failed\\\");\\n Assert.True(i.allclose(r1), \\\"round() failed\\\");\\n\\n // check inline version\\n var r0_ = matmul(a, b).round_(2L);\\n var r1_ = matmul(b, a).round_(3L);\\n Assert.True(i.allclose(r0_), \\\"round_() failed\\\");\\n Assert.True(i.allclose(r1_), \\\"round_() failed\\\");\\n }\\n }\\n}\\n\"\n },\n {\n \"alpha_fraction\": 0.5193738341331482,\n \"alphanum_fraction\": 0.5306881666183472,\n \"avg_line_length\": 49.8110237121582,\n \"blob_id\": \"4d83b250d6733947c9a6ddd787b1753e2497ca0a\",\n \"content_id\": \"2b20d3bdc9a4cd9f21de535f0487945e3c4ad8f8\",\n \"detected_licenses\": [\n \"MIT\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"C#\",\n \"length_bytes\": 6452,\n \"license_type\": \"permissive\",\n \"max_line_length\": 130,\n \"num_lines\": 127,\n \"path\": \"/src/TorchAudio/Models.cs\",\n \"repo_name\": \"NiklasGustafsson/TorchSharp\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"// Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information.\\n\\nnamespace TorchSharp\\n{\\n public static partial class torchaudio\\n {\\n public static partial class models\\n {\\n /// <summary>\\n /// Tacotron2 model based on the implementation from\\n /// Nvidia https://github.com/NVIDIA/DeepLearningExamples/.\\n /// </summary>\\n /// <param name=\\\"mask_padding\\\">Use mask padding</param>\\n /// <param name=\\\"n_mels\\\">Number of mel bins</param>\\n /// <param name=\\\"n_symbol\\\">Number of symbols for the input text</param>\\n /// <param name=\\\"n_frames_per_step\\\">Number of frames processed per step, only 1 is supported</param>\\n /// <param name=\\\"symbol_embedding_dim\\\">Input embedding dimension</param>\\n /// <param name=\\\"encoder_n_convolution\\\">Number of encoder convolutions</param>\\n /// <param name=\\\"encoder_kernel_size\\\">Encoder kernel size</param>\\n /// <param name=\\\"encoder_embedding_dim\\\">Encoder embedding dimension</param>\\n /// <param name=\\\"decoder_rnn_dim\\\">Number of units in decoder LSTM</param>\\n /// <param name=\\\"decoder_max_step\\\">Maximum number of output mel spectrograms</param>\\n /// <param name=\\\"decoder_dropout\\\">Dropout probability for decoder LSTM</param>\\n /// <param name=\\\"decoder_early_stopping\\\">Continue decoding after all samples are finished</param>\\n /// <param name=\\\"attention_rnn_dim\\\">Number of units in attention LSTM</param>\\n /// <param name=\\\"attention_hidden_dim\\\">Dimension of attention hidden representation</param>\\n /// <param name=\\\"attention_location_n_filter\\\">Number of filters for attention model</param>\\n /// <param name=\\\"attention_location_kernel_size\\\">Kernel size for attention model</param>\\n /// <param name=\\\"attention_dropout\\\">Dropout probability for attention LSTM</param>\\n /// <param name=\\\"prenet_dim\\\">Number of ReLU units in prenet layers</param>\\n /// <param name=\\\"postnet_n_convolution\\\">Number of postnet convolutions</param>\\n /// <param name=\\\"postnet_kernel_size\\\">Postnet kernel size</param>\\n /// <param name=\\\"postnet_embedding_dim\\\">Postnet embedding dimension</param>\\n /// <param name=\\\"gate_threshold\\\">Probability threshold for stop token</param>\\n /// <returns>Tacotron2 model</returns>\\n public static Modules.Tacotron2 Tacotron2(\\n bool mask_padding = false,\\n int n_mels = 80,\\n int n_symbol = 148,\\n int n_frames_per_step = 1,\\n int symbol_embedding_dim = 512,\\n int encoder_embedding_dim = 512,\\n int encoder_n_convolution = 3,\\n int encoder_kernel_size = 5,\\n int decoder_rnn_dim = 1024,\\n int decoder_max_step = 2000,\\n double decoder_dropout = 0.1,\\n bool decoder_early_stopping = true,\\n int attention_rnn_dim = 1024,\\n int attention_hidden_dim = 128,\\n int attention_location_n_filter = 32,\\n int attention_location_kernel_size = 31,\\n double attention_dropout = 0.1,\\n int prenet_dim = 256,\\n int postnet_n_convolution = 5,\\n int postnet_kernel_size = 5,\\n int postnet_embedding_dim = 512,\\n double gate_threshold = 0.5)\\n {\\n return new Modules.Tacotron2(\\n \\\"tacotron2\\\",\\n mask_padding,\\n n_mels,\\n n_symbol,\\n n_frames_per_step,\\n symbol_embedding_dim,\\n encoder_embedding_dim,\\n encoder_n_convolution,\\n encoder_kernel_size,\\n decoder_rnn_dim,\\n decoder_max_step,\\n decoder_dropout,\\n decoder_early_stopping,\\n attention_rnn_dim,\\n attention_hidden_dim,\\n attention_location_n_filter,\\n attention_location_kernel_size,\\n attention_dropout,\\n prenet_dim,\\n postnet_n_convolution,\\n postnet_kernel_size,\\n postnet_embedding_dim,\\n gate_threshold);\\n }\\n\\n /// <summary>\\n /// WaveRNN model based on the implementation from `fatchord https://github.com/fatchord/WaveRNN`.\\n /// </summary>\\n /// <param name=\\\"upsample_scales\\\">The list of upsample scales.</param>\\n /// <param name=\\\"n_classes\\\">The number of output classes.</param>\\n /// <param name=\\\"hop_length\\\">The number of samples between the starts of consecutive frames.</param>\\n /// <param name=\\\"n_res_block\\\">The number of ResBlock in stack.</param>\\n /// <param name=\\\"n_rnn\\\">The dimension of RNN layer.</param>\\n /// <param name=\\\"n_fc\\\">The dimension of fully connected layer.</param>\\n /// <param name=\\\"kernel_size\\\">The number of kernel size in the first Conv1d layer.</param>\\n /// <param name=\\\"n_freq\\\">The number of bins in a spectrogram.</param>\\n /// <param name=\\\"n_hidden\\\">The number of hidden dimensions of resblock.</param>\\n /// <param name=\\\"n_output\\\">The number of output dimensions of melresnet.</param>\\n /// <returns>The WaveRNN model</returns>\\n public static Modules.WaveRNN WaveRNN(\\n long[] upsample_scales,\\n int n_classes,\\n int hop_length,\\n int n_res_block = 10,\\n int n_rnn = 512,\\n int n_fc = 512,\\n int kernel_size = 5,\\n int n_freq = 128,\\n int n_hidden = 128,\\n int n_output = 128)\\n {\\n return new Modules.WaveRNN(\\n \\\"wavernn\\\",\\n upsample_scales,\\n n_classes,\\n hop_length,\\n n_res_block,\\n n_rnn,\\n n_fc,\\n kernel_size,\\n n_freq,\\n n_hidden,\\n n_output);\\n }\\n }\\n }\\n}\"\n },\n {\n \"alpha_fraction\": 0.39836984872817993,\n \"alphanum_fraction\": 0.39836984872817993,\n \"avg_line_length\": 39.06122589111328,\n \"blob_id\": \"0be7c010607895aad1bfdf3de67a7b2ebe4e1275\",\n \"content_id\": \"78b53e7ff1a0b7a271913b0ce427f5313c9e9e72\",\n \"detected_licenses\": [\n \"MIT\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"C#\",\n \"length_bytes\": 3926,\n \"license_type\": \"permissive\",\n \"max_line_length\": 130,\n \"num_lines\": 98,\n \"path\": \"/src/TorchSharp/NN/Utils/PackedSequence.cs\",\n \"repo_name\": \"NiklasGustafsson/TorchSharp\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"// Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information.\\nusing System;\\nusing System.Runtime.InteropServices;\\nusing static TorchSharp.PInvoke.NativeMethods;\\n\\nnamespace TorchSharp\\n{\\n public static partial class torch\\n {\\n public static partial class nn\\n {\\n public static partial class utils\\n {\\n public static partial class rnn\\n {\\n /// <summary>\\n /// A packed batch of variable length sequences.\\n /// </summary>\\n public sealed class PackedSequence : IDisposable\\n {\\n /// <summary>\\n /// Class wrapping PyTorch's packedsequence object reference.\\n /// </summary>\\n internal sealed class HType : SafeHandle\\n {\\n public HType(IntPtr preexistingHandle, bool ownsHandle)\\n : base(IntPtr.Zero, ownsHandle)\\n {\\n SetHandle(preexistingHandle);\\n }\\n\\n public override bool IsInvalid => handle == IntPtr.Zero;\\n\\n // This is just for marshalling\\n internal HType() : base(IntPtr.Zero, true)\\n {\\n }\\n\\n protected override bool ReleaseHandle()\\n {\\n THSNN_PackedSequence_dispose(handle);\\n return true;\\n }\\n }\\n\\n /// <summary>\\n /// The packed sequences\\n /// </summary>\\n public readonly Tensor data;\\n\\n /// <summary>\\n /// Batch size at each sequence step\\n /// </summary>\\n public readonly Tensor batch_sizes;\\n\\n /// <summary>\\n /// The sorted indices\\n /// </summary>\\n public readonly Tensor sorted_indices;\\n\\n /// <summary>\\n /// The original indices\\n /// </summary>\\n public readonly Tensor unsorted_indices;\\n private HType handle;\\n\\n internal PackedSequence(HType handle)\\n {\\n this.handle = handle;\\n this.data = new Tensor(THSNN_PackedSequence_data(handle));\\n this.batch_sizes = new Tensor(THSNN_PackedSequence_batch_sizes(handle));\\n this.sorted_indices = new Tensor(THSNN_PackedSequence_sorted_indices(handle));\\n this.unsorted_indices = new Tensor(THSNN_PackedSequence_unsorted_indices(handle));\\n }\\n\\n internal HType Handle => handle;\\n\\n /// <summary>\\n /// Releases the storage.\\n /// </summary>\\n public void Dispose()\\n {\\n this.data.Dispose();\\n this.batch_sizes.Dispose();\\n this.sorted_indices.Dispose();\\n this.unsorted_indices.Dispose();\\n\\n if (handle != null && !handle.IsInvalid) {\\n handle.Dispose();\\n handle.SetHandleAsInvalid();\\n }\\n }\\n}\\n }\\n }\\n }\\n }\\n}\\n\"\n },\n {\n \"alpha_fraction\": 0.561834990978241,\n \"alphanum_fraction\": 0.5684632658958435,\n \"avg_line_length\": 44.14173126220703,\n \"blob_id\": \"562d61ae5d3ff7fb0c91269fd317266b13f0cc03\",\n \"content_id\": \"b90f26a1e15b94512467c9056c85c8abe4d0f72f\",\n \"detected_licenses\": [\n \"MIT\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"C#\",\n \"length_bytes\": 5733,\n \"license_type\": \"permissive\",\n \"max_line_length\": 203,\n \"num_lines\": 127,\n \"path\": \"/src/TorchSharp/Tensor/Factories/ones.cs\",\n \"repo_name\": \"NiklasGustafsson/TorchSharp\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"// Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information.\\nusing System;\\nusing static TorchSharp.PInvoke.NativeMethods;\\n\\n#nullable enable\\nnamespace TorchSharp\\n{\\n public static partial class torch\\n {\\n /// <summary>\\n /// Create a new tensor filled with ones\\n /// </summary>\\n public static Tensor ones(long[] size, ScalarType? dtype = null, Device? device = null, bool requires_grad = false, string[]? names = null)\\n {\\n return _ones(size, dtype, device, requires_grad, names: names);\\n }\\n\\n /// <summary>\\n /// Create a new tensor filled with ones\\n /// </summary>\\n public static Tensor ones(ReadOnlySpan<long> size, ScalarType? dtype = null, Device? device = null, bool requires_grad = false, string[]? names = null)\\n {\\n return _ones(size, dtype, device, requires_grad, names: names);\\n }\\n\\n /// <summary>\\n /// Create a new 1-D tensor filled with ones\\n /// </summary>\\n public static Tensor ones(long size, ScalarType? dtype = null, Device? device = null, bool requires_grad = false, string[]? names = null)\\n {\\n return _ones(stackalloc long[] { size }, dtype, device, requires_grad, names: names);\\n }\\n\\n /// <summary>\\n /// Create a new 2-D tensor filled with ones\\n /// </summary>\\n public static Tensor ones(long rows, long columns, ScalarType? dtype = null, Device? device = null, bool requires_grad = false, string[]? names = null)\\n {\\n return _ones(stackalloc long[] { rows, columns }, dtype, device, requires_grad, names: names);\\n }\\n\\n /// <summary>\\n /// Create a new 3-D tensor filled with ones\\n /// </summary>\\n public static Tensor ones(long dim0, long dim1, long dim2, ScalarType? dtype = null, Device? device = null, bool requires_grad = false, string[]? names = null)\\n {\\n return _ones(stackalloc long[] { dim0, dim1, dim2 }, dtype, device, requires_grad, names: names);\\n }\\n\\n /// <summary>\\n /// Create a new 4-D tensor filled with ones\\n /// </summary>\\n public static Tensor ones(long dim0, long dim1, long dim2, long dim3, ScalarType? dtype = null, Device? device = null, bool requires_grad = false, string[]? names = null)\\n {\\n return _ones(stackalloc long[] { dim0, dim1, dim2, dim3 }, dtype, device, requires_grad, names: names);\\n }\\n\\n /// <summary>\\n /// Create a new 1-D tensor filled with ones\\n /// </summary>\\n public static Tensor ones(int size, ScalarType? dtype = null, Device? device = null, bool requires_grad = false, string[]? names = null)\\n {\\n return _ones(stackalloc long[] { size }, dtype, device, requires_grad, names: names);\\n }\\n\\n /// <summary>\\n /// Create a new 2-D tensor filled with ones\\n /// </summary>\\n public static Tensor ones(int rows, int columns, ScalarType? dtype = null, Device? device = null, bool requires_grad = false, string[]? names = null)\\n {\\n return _ones(stackalloc long[] { rows, columns }, dtype, device, requires_grad, names: names);\\n }\\n\\n /// <summary>\\n /// Create a new 3-D tensor filled with ones\\n /// </summary>\\n public static Tensor ones(int dim0, int dim1, int dim2, ScalarType? dtype = null, Device? device = null, bool requires_grad = false, string[]? names = null)\\n {\\n return _ones(stackalloc long[] { dim0, dim1, dim2 }, dtype, device, requires_grad, names: names);\\n }\\n\\n /// <summary>\\n /// Create a new 4-D tensor filled with ones\\n /// </summary>\\n public static Tensor ones(int dim0, int dim1, int dim2, int dim3, ScalarType? dtype = null, Device? device = null, bool requires_grad = false, string[]? names = null)\\n {\\n return _ones(stackalloc long[] { dim0, dim1, dim2, dim3 }, dtype, device, requires_grad, names: names);\\n }\\n\\n /// <summary>\\n /// Returns a tensor filled with the scalar value 1, with the same size as input.\\n /// </summary>\\n public static Tensor ones_like(Tensor input, ScalarType? dtype = null, Device? device = null, bool requires_grad = false, string[]? names = null) => input.ones_like(dtype, device, requires_grad);\\n\\n /// <summary>\\n /// Create a new tensor filled with ones\\n /// </summary>\\n private static Tensor _ones(ReadOnlySpan<long> size, ScalarType? dtype = null, Device? device = null, bool requires_grad = false, string[]? names = null)\\n {\\n device = InitializeDevice(device);\\n if (!dtype.HasValue) {\\n // Determine the element type dynamically.\\n dtype = get_default_dtype();\\n }\\n\\n unsafe {\\n fixed (long* psizes = size) {\\n var handle = THSTensor_ones((IntPtr)psizes, size.Length, (sbyte)dtype, (int)device.type, device.index, requires_grad);\\n if (handle == IntPtr.Zero) {\\n GC.Collect();\\n GC.WaitForPendingFinalizers();\\n handle = THSTensor_ones((IntPtr)psizes, size.Length, (sbyte)dtype, (int)device.type, device.index, requires_grad);\\n }\\n if (handle == IntPtr.Zero) { CheckForErrors(); }\\n var result = new Tensor(handle);\\n\\n if (names != null && names.Length > 0) {\\n\\n result.rename_(names);\\n }\\n\\n return result;\\n }\\n }\\n }\\n }\\n}\\n\"\n },\n {\n \"alpha_fraction\": 0.599662184715271,\n \"alphanum_fraction\": 0.599662184715271,\n \"avg_line_length\": 27.238094329833984,\n \"blob_id\": \"fcc80b30bbf53709bc8b739fa5319eda719136f2\",\n \"content_id\": \"420ca40773ed2e53a23dd86c60bb728fb3928fce\",\n \"detected_licenses\": [\n \"MIT\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"C#\",\n \"length_bytes\": 592,\n \"license_type\": \"permissive\",\n \"max_line_length\": 130,\n \"num_lines\": 21,\n \"path\": \"/src/TorchSharp/IProgressBar.cs\",\n \"repo_name\": \"NiklasGustafsson/TorchSharp\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"// Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information.\\nusing System;\\n\\nnamespace TorchSharp\\n{\\n /// <summary>\\n /// Interface to implement progress bar.\\n /// </summary>\\n public interface IProgressBar : IDisposable\\n {\\n /// <summary>\\n /// The current position of the progress bar\\n /// </summary>\\n public long Value { set; get; }\\n\\n /// <summary>\\n /// The maximum position of the progress bar\\n /// </summary>\\n public long? Maximum { set; get; }\\n }\\n}\"\n },\n {\n \"alpha_fraction\": 0.49848297238349915,\n \"alphanum_fraction\": 0.5171367526054382,\n \"avg_line_length\": 38.90583038330078,\n \"blob_id\": \"cb09ea6ed9f1a211bd810f74f26166342dde4d38\",\n \"content_id\": \"4d1665c7e8b0d01c5e848dda5f925293d0639a25\",\n \"detected_licenses\": [\n \"MIT\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"C#\",\n \"length_bytes\": 8899,\n \"license_type\": \"permissive\",\n \"max_line_length\": 192,\n \"num_lines\": 223,\n \"path\": \"/src/Examples/ResNet.cs\",\n \"repo_name\": \"NiklasGustafsson/TorchSharp\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"// Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information.\\nusing System;\\nusing System.Collections.Generic;\\n\\nusing static TorchSharp.torch;\\nusing static TorchSharp.torch.nn;\\n\\nnamespace TorchSharp.Examples\\n{\\n /// <summary>\\n /// Modified version of ResNet to classify CIFAR10 32x32 images.\\n /// </summary>\\n class ResNet : Module<Tensor, Tensor>\\n {\\n // The code here is is loosely based on https://github.com/kuangliu/pytorch-cifar/blob/master/models/resnet.py\\n // Licence and copypright notice at: https://github.com/kuangliu/pytorch-cifar/blob/master/LICENSE\\n\\n private readonly Module<Tensor, Tensor> layers;\\n private int in_planes = 64;\\n\\n public static ResNet ResNet18(int numClasses, Device device = null)\\n {\\n return new ResNet(\\n \\\"ResNet18\\\",\\n (name, in_planes, planes, stride) => new BasicBlock(name, in_planes, planes, stride),\\n BasicBlock.expansion, new int[] { 2, 2, 2, 2 },\\n 10,\\n device); \\n }\\n\\n public static ResNet ResNet34(int numClasses, Device device = null)\\n {\\n return new ResNet(\\n \\\"ResNet34\\\",\\n (name, in_planes, planes, stride) => new BasicBlock(name, in_planes, planes, stride),\\n BasicBlock.expansion, new int[] { 3, 4, 6, 3 },\\n 10,\\n device);\\n }\\n\\n public static ResNet ResNet50(int numClasses, Device device = null)\\n {\\n return new ResNet(\\n \\\"ResNet50\\\",\\n (name, in_planes, planes, stride) => new Bottleneck(name, in_planes, planes, stride),\\n Bottleneck.expansion, new int[] { 3, 4, 6, 3 },\\n 10,\\n device);\\n }\\n\\n public static ResNet ResNet101(int numClasses, Device device = null)\\n {\\n return new ResNet(\\n \\\"ResNet101\\\",\\n (name, in_planes, planes, stride) => new Bottleneck(name, in_planes, planes, stride),\\n Bottleneck.expansion, new int[] { 3, 4, 23, 3 },\\n 10,\\n device);\\n }\\n\\n public static ResNet ResNet152(int numClasses, Device device = null)\\n {\\n return new ResNet(\\n \\\"ResNet152\\\",\\n (name, in_planes, planes, stride) => new Bottleneck(name, in_planes, planes, stride),\\n Bottleneck.expansion, new int[] { 3, 4, 36, 3 },\\n 10,\\n device);\\n }\\n\\n public ResNet(string name, Func<string, int,int,int,Module<Tensor, Tensor>> block, int expansion, IList<int> num_blocks, int numClasses, Device device = null) : base(name)\\n {\\n var modules = new List<(string, Module<Tensor, Tensor>)>();\\n\\n modules.Add(($\\\"conv2d-first\\\", Conv2d(3, 64, kernelSize: 3, stride: 1, padding: 1, bias: false)));\\n modules.Add(($\\\"bnrm2d-first\\\", BatchNorm2d(64)));\\n modules.Add(($\\\"relu-first\\\", ReLU(inplace:true)));\\n MakeLayer(modules, block, expansion, 64, num_blocks[0], 1);\\n MakeLayer(modules, block, expansion, 128, num_blocks[1], 2);\\n MakeLayer(modules, block, expansion, 256, num_blocks[2], 2);\\n MakeLayer(modules, block, expansion, 512, num_blocks[3], 2);\\n modules.Add((\\\"avgpool\\\", AvgPool2d(new long[] { 4, 4 })));\\n modules.Add((\\\"flatten\\\", Flatten()));\\n modules.Add(($\\\"linear\\\", Linear(512 * expansion, numClasses)));\\n\\n layers = Sequential(modules);\\n\\n RegisterComponents();\\n\\n if (device != null && device.type == DeviceType.CUDA)\\n this.to(device);\\n }\\n\\n private void MakeLayer(List<(string, Module<Tensor, Tensor>)> modules, Func<string, int, int, int, Module<Tensor, Tensor>> block, int expansion, int planes, int num_blocks, int stride)\\n {\\n var strides = new List<int>();\\n strides.Add(stride);\\n for (var i = 0; i < num_blocks-1; i++) { strides.Add(1); }\\n\\n for (var i = 0; i < strides.Count; i++) {\\n var s = strides[i];\\n modules.Add(($\\\"blck-{planes}-{i}\\\", block($\\\"blck-{planes}-{i}\\\", in_planes, planes, s)));\\n in_planes = planes * expansion;\\n }\\n }\\n\\n public override Tensor forward(Tensor input)\\n {\\n return layers.forward(input);\\n }\\n\\n protected override void Dispose(bool disposing)\\n {\\n if (disposing) {\\n layers.Dispose();\\n ClearModules();\\n }\\n base.Dispose(disposing);\\n }\\n\\n class BasicBlock : Module<Tensor, Tensor>\\n {\\n public BasicBlock (string name, int in_planes, int planes, int stride) : base(name)\\n {\\n var modules = new List<(string, Module<Tensor, Tensor>)>();\\n\\n modules.Add(($\\\"{name}-conv2d-1\\\", Conv2d(in_planes, planes, kernelSize: 3, stride: stride, padding: 1, bias: false)));\\n modules.Add(($\\\"{name}-bnrm2d-1\\\", BatchNorm2d(planes)));\\n modules.Add(($\\\"{name}-relu-1\\\", ReLU(inplace: true)));\\n modules.Add(($\\\"{name}-conv2d-2\\\", Conv2d(planes, planes, kernelSize: 3, stride: 1, padding: 1, bias: false)));\\n modules.Add(($\\\"{name}-bnrm2d-2\\\", BatchNorm2d(planes)));\\n\\n layers = Sequential(modules);\\n\\n if (stride != 1 || in_planes != expansion*planes) {\\n shortcut = Sequential(\\n ($\\\"{name}-conv2d-3\\\", Conv2d(in_planes, expansion * planes, kernelSize: 1, stride: stride, bias: false)),\\n ($\\\"{name}-bnrm2d-3\\\", BatchNorm2d(expansion * planes)));\\n }\\n else {\\n shortcut = Sequential();\\n }\\n\\n modules.Add(($\\\"{name}-relu-2\\\", ReLU(inplace: true)));\\n\\n RegisterComponents();\\n }\\n\\n public override Tensor forward(Tensor t)\\n {\\n var x = layers.forward(t);\\n var y = shortcut.forward(t);\\n return x.add_(y).relu_();\\n }\\n\\n protected override void Dispose(bool disposing)\\n {\\n if (disposing) {\\n layers.Dispose();\\n shortcut.Dispose();\\n ClearModules();\\n }\\n base.Dispose(disposing);\\n }\\n\\n public static int expansion = 1;\\n\\n private readonly Module<Tensor, Tensor> layers;\\n private readonly Module<Tensor, Tensor> shortcut;\\n }\\n\\n class Bottleneck : Module<Tensor, Tensor>\\n {\\n public Bottleneck(string name, int in_planes, int planes, int stride) : base(name)\\n {\\n var modules = new List<(string, Module<Tensor, Tensor>)>();\\n\\n modules.Add(($\\\"{name}-conv2d-1\\\", Conv2d(in_planes, planes, kernelSize: 1, bias: false)));\\n modules.Add(($\\\"{name}-bnrm2d-1\\\", BatchNorm2d(planes)));\\n modules.Add(($\\\"{name}relu-1\\\", ReLU(inplace:true)));\\n modules.Add(($\\\"{name}-conv2d-2\\\", Conv2d(planes, planes, kernelSize: 3, stride: stride, padding: 1, bias: false)));\\n modules.Add(($\\\"{name}-bnrm2d-2\\\", BatchNorm2d(planes)));\\n modules.Add(($\\\"{name}relu-2\\\", ReLU(inplace: true)));\\n modules.Add(($\\\"{name}-conv2d-3\\\", Conv2d(planes, expansion * planes, kernelSize: 1, bias: false)));\\n modules.Add(($\\\"{name}-bnrm2d-3\\\", BatchNorm2d(expansion * planes)));\\n\\n layers = Sequential(modules);\\n\\n if (stride != 1 || in_planes != expansion * planes) {\\n shortcut = Sequential(\\n ($\\\"{name}-conv2d-4\\\", Conv2d(in_planes, expansion * planes, kernelSize: 1, stride: stride, bias: false)),\\n ($\\\"{name}-bnrm2d-4\\\", BatchNorm2d(expansion * planes)));\\n } else {\\n shortcut = Sequential();\\n }\\n\\n RegisterComponents();\\n }\\n\\n public override Tensor forward(Tensor t)\\n {\\n var x = layers.forward(t);\\n using var y = shortcut.forward(t);\\n return x.add_(y).relu_();\\n }\\n\\n protected override void Dispose(bool disposing)\\n {\\n if (disposing) {\\n layers.Dispose();\\n shortcut.Dispose();\\n ClearModules();\\n }\\n base.Dispose(disposing);\\n }\\n\\n public static int expansion = 4;\\n\\n private readonly Module<Tensor, Tensor> layers;\\n private readonly Module<Tensor, Tensor> shortcut;\\n }\\n }\\n}\\n\"\n },\n {\n \"alpha_fraction\": 0.5612648129463196,\n \"alphanum_fraction\": 0.5626673698425293,\n \"avg_line_length\": 43.061798095703125,\n \"blob_id\": \"793488beb749a87efdf464eda231b99e0f2a3715\",\n \"content_id\": \"81bbd64ea1b585fba2f6a98dc4f72abd36500960\",\n \"detected_licenses\": [\n \"MIT\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"C#\",\n \"length_bytes\": 7843,\n \"license_type\": \"permissive\",\n \"max_line_length\": 192,\n \"num_lines\": 178,\n \"path\": \"/src/TorchSharp/Distributions/TransformedDistribution.cs\",\n \"repo_name\": \"NiklasGustafsson/TorchSharp\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"// Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information.\\nusing System;\\nusing System.Collections.Generic;\\nusing static TorchSharp.torch;\\n\\nnamespace TorchSharp\\n{\\n using System.Linq;\\n using static torch.distributions;\\n\\n namespace Modules\\n {\\n /// <summary>\\n /// Extension of the Distribution class, which applies a sequence of Transforms to a base distribution.\\n /// </summary>\\n public class TransformedDistribution : Distribution\\n {\\n protected Distribution base_distribution;\\n private torch.distributions.transforms.Transform[] transforms;\\n private torch.distributions.transforms.Transform[] reverse_transforms;\\n\\n public TransformedDistribution(torch.Generator generator = null) : base(generator)\\n {\\n }\\n\\n public TransformedDistribution(Distribution base_distribution, torch.distributions.transforms.Transform transform, torch.Generator generator = null) :\\n this(base_distribution, new torch.distributions.transforms.Transform[] { transform}, generator)\\n {\\n _init(base_distribution, transforms);\\n }\\n\\n public TransformedDistribution(Distribution base_distribution, IEnumerable<torch.distributions.transforms.Transform> transforms, torch.Generator generator = null) : base(generator)\\n {\\n _init(base_distribution, transforms.ToArray());\\n }\\n\\n protected void _init(Distribution base_distribution, torch.distributions.transforms.Transform[] transforms)\\n {\\n this.transforms = transforms;\\n this.reverse_transforms = transforms.Reverse().ToArray();\\n\\n var base_shape = base_distribution.batch_shape.Concat(base_distribution.event_shape).ToArray();\\n var base_event_dim = base_distribution.event_shape.Length;\\n var transform = new distributions.transforms.ComposeTransform(transforms.ToArray());\\n var domain_event_dim = transform.domain.event_dim;\\n var shape = transform.forward_shape(base_shape);\\n var expanded_base_shape = transform.inverse_shape(shape);\\n\\n if (base_shape != expanded_base_shape) {\\n var base_batch_shape = expanded_base_shape.Take(expanded_base_shape.Length - base_event_dim).ToArray();\\n base_distribution = base_distribution.expand(base_batch_shape);\\n }\\n\\n var reinterpreted_batch_ndims = domain_event_dim - base_event_dim;\\n if (reinterpreted_batch_ndims > 0) {\\n //base_distribution = new distributions.Inde\\n }\\n\\n this.base_distribution = base_distribution;\\n\\n var event_dim = transform.codomain.event_dim;\\n event_dim += (base_event_dim - domain_event_dim > 0) ? base_event_dim - domain_event_dim : 0;\\n\\n var cut = shape.Length - event_dim;\\n this.batch_shape = shape.Take(cut).ToArray();\\n this.event_shape = shape.Skip(cut).ToArray();\\n }\\n\\n public override Tensor mean => new Tensor(IntPtr.Zero);\\n\\n public override Tensor mode => base.mode;\\n\\n public override Tensor variance => new Tensor(IntPtr.Zero);\\n\\n public override Tensor stddev => base.stddev;\\n\\n public override Tensor cdf(Tensor value)\\n {\\n using var scope = torch.NewDisposeScope();\\n foreach (var transform in reverse_transforms) {\\n value = transform._inverse(value);\\n }\\n value = base_distribution.cdf(value);\\n value = monotonize_cdf(value);\\n return value.MoveToOuterDisposeScope();\\n }\\n\\n public override Tensor entropy()\\n {\\n throw new NotImplementedException();\\n }\\n\\n public override distributions.Distribution expand(Size batch_shape, distributions.Distribution instance = null)\\n {\\n if (instance != null && !(instance is TransformedDistribution))\\n throw new ArgumentException(\\\"expand(): 'instance' must be a TransformedDistribution distribution\\\");\\n\\n var shape = batch_shape.Concat(event_shape).ToArray();\\n foreach (var t in reverse_transforms) {\\n shape = t.inverse_shape(shape);\\n }\\n int baseLen = shape.Length - base_distribution.event_shape.Length;\\n var base_batch_shape = new long[baseLen];\\n for (var i = 0; i < baseLen; i++) base_batch_shape[i] = shape[i];\\n\\n var newDistribution = ((instance == null) ?\\n new TransformedDistribution(base_distribution.expand(base_batch_shape), transforms) :\\n instance) as TransformedDistribution;\\n\\n newDistribution.batch_shape = batch_shape;\\n newDistribution.event_shape = event_shape;\\n if (newDistribution == instance) {\\n newDistribution.transforms = transforms;\\n newDistribution.base_distribution = base_distribution.expand(base_batch_shape);\\n }\\n return newDistribution;\\n }\\n\\n public override Tensor icdf(Tensor value)\\n {\\n using var scope = torch.NewDisposeScope();\\n value = monotonize_cdf(value);\\n value = base_distribution.icdf(value);\\n foreach (var transform in transforms) {\\n value = transform.forward(value);\\n }\\n return value.MoveToOuterDisposeScope();\\n }\\n\\n public override Tensor log_prob(Tensor value)\\n {\\n using var scope = torch.NewDisposeScope();\\n var event_dim = event_shape.Length;\\n Tensor lp = 0.0;\\n var y = value;\\n foreach (var t in reverse_transforms) {\\n var x = t._inverse(y);\\n event_dim += t.domain.event_dim - t.codomain.event_dim;\\n lp = lp - distributions.transforms.Transform._sum_rightmost(t.log_abs_det_jacobian(x, y), event_dim - t.domain.event_dim);\\n y = x;\\n }\\n lp = lp + distributions.transforms.Transform._sum_rightmost(base_distribution.log_prob(y), event_dim - base_distribution.event_shape.Length);\\n return lp.MoveToOuterDisposeScope();\\n }\\n\\n public override Tensor rsample(params long[] sample_shape)\\n {\\n using var scope = torch.NewDisposeScope();\\n using var _ = torch.no_grad();\\n var x = base_distribution.rsample(sample_shape);\\n foreach (var t in transforms) {\\n x = t.forward(x);\\n }\\n return x.MoveToOuterDisposeScope();\\n }\\n\\n public override Tensor sample(params long[] sample_shape)\\n {\\n using var scope = torch.NewDisposeScope();\\n using var _ = torch.no_grad();\\n var x = base_distribution.sample(sample_shape);\\n foreach (var t in transforms) {\\n x = t.forward(x);\\n }\\n return x.MoveToOuterDisposeScope();\\n }\\n\\n private Tensor monotonize_cdf(Tensor value)\\n {\\n Tensor sign = 1;\\n foreach (var transform in transforms) {\\n sign = sign * transform.sign;\\n }\\n return sign * (value - 0.5) + 0.5;\\n }\\n }\\n }\\n}\\n\"\n },\n {\n \"alpha_fraction\": 0.6270833611488342,\n \"alphanum_fraction\": 0.6445833444595337,\n \"avg_line_length\": 36.5,\n \"blob_id\": \"c6ace6e0fdff07f6c0e450e010f7467b4a309bd7\",\n \"content_id\": \"8fe98587a98489ae5cc72f45e12cb85fbdc5b5c2\",\n \"detected_licenses\": [\n \"MIT\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"C#\",\n \"length_bytes\": 2400,\n \"license_type\": \"permissive\",\n \"max_line_length\": 130,\n \"num_lines\": 64,\n \"path\": \"/src/TorchSharp/Utils/CRC32C.cs\",\n \"repo_name\": \"NiklasGustafsson/TorchSharp\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"// Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information.\\n\\n// Note: the native implementation of CRC32C has the following copyright / license notice:\\n\\n/* Part of CRC-32C library: https://crc32c.machinezoo.com/ */\\n/*\\n Copyright (c) 2013 - 2014, 2016 Mark Adler, Robert Vazan, Max Vysokikh\\n\\n This software is provided 'as-is', without any express or implied\\n warranty. In no event will the author be held liable for any damages\\n arising from the use of this software.\\n\\n Permission is granted to anyone to use this software for any purpose,\\n including commercial applications, and to alter it and redistribute it\\n freely, subject to the following restrictions:\\n\\n 1. The origin of this software must not be misrepresented; you must not\\n claim that you wrote the original software. If you use this software\\n in a product, an acknowledgment in the product documentation would be\\n appreciated but is not required.\\n 2. Altered source versions must be plainly marked as such, and must not be\\n misrepresented as being the original software.\\n 3. This notice may not be removed or altered from any source distribution.\\n*/\\n\\nusing System;\\nusing static TorchSharp.PInvoke.NativeMethods;\\n\\nnamespace TorchSharp.Utils\\n{\\n public static class CRC32C\\n {\\n /// <summary>\\n /// Compule the CRC32C value for a byte array.\\n /// </summary>\\n /// <param name=\\\"data\\\">A byte array.</param>\\n public static unsafe uint process(byte[] data)\\n {\\n fixed (byte* ptr = data) {\\n return crc32c_append(0, (IntPtr)ptr, (ulong)data.Length);\\n }\\n }\\n\\n /// <summary>\\n /// Compule the CRC32C value for a 32-bit integer.\\n /// </summary>\\n /// <param name=\\\"data\\\">A byte array.</param>\\n public static unsafe uint process(int data)\\n {\\n fixed (int* ptr = stackalloc int[] { data })\\n return crc32c_append(0, (IntPtr)ptr, (ulong)sizeof(int));\\n }\\n\\n /// <summary>\\n /// Compule the CRC32C value for a 64-bit integer.\\n /// </summary>\\n /// <param name=\\\"data\\\">A byte array.</param>\\n public static unsafe uint process(long data)\\n {\\n fixed (long* ptr = stackalloc long[] { data })\\n return crc32c_append(0, (IntPtr)ptr, (ulong)sizeof(long));\\n }\\n }\\n}\\n\"\n },\n {\n \"alpha_fraction\": 0.6931216716766357,\n \"alphanum_fraction\": 0.7037037014961243,\n \"avg_line_length\": 45.20000076293945,\n \"blob_id\": \"c7738b495a150866afc3bafdec1371851f4c8c78\",\n \"content_id\": \"8920a141a85c73f0e136562f31f67a1af34654ff\",\n \"detected_licenses\": [\n \"MIT\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"C#\",\n \"length_bytes\": 2079,\n \"license_type\": \"permissive\",\n \"max_line_length\": 136,\n \"num_lines\": 45,\n \"path\": \"/src/TorchSharp/PInvoke/LibTorchSharp.THSCuda.cs\",\n \"repo_name\": \"NiklasGustafsson/TorchSharp\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"// Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information.\\n#nullable enable\\nusing System.Runtime.InteropServices;\\n\\nnamespace TorchSharp.PInvoke\\n{\\n internal static partial class NativeMethods\\n {\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern void THSCuda_manual_seed(long seed);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern void THSCuda_manual_seed_all(long seed);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n [return: MarshalAs(UnmanagedType.U1)]\\n internal static extern bool THSBackend_cublas_get_allow_tf32();\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern void THSBackend_cublas_set_allow_tf32([MarshalAs(UnmanagedType.U1)] bool flag);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n [return: MarshalAs(UnmanagedType.U1)]\\n internal static extern bool THSBackend_cudnn_get_allow_tf32();\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern void THSBackend_cudnn_set_allow_tf32([MarshalAs(UnmanagedType.U1)] bool flag);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n [return: MarshalAs(UnmanagedType.U1)]\\n internal static extern bool THSBackend_cuda_get_allow_fp16_reduced_precision_reduction();\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern void THSBackend_cuda_set_allow_fp16_reduced_precision_reduction([MarshalAs(UnmanagedType.U1)] bool flag);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n [return: MarshalAs(UnmanagedType.U1)]\\n internal static extern bool THSBackend_cuda_get_enable_flash_sdp();\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern void THSBackend_cuda_set_enable_flash_sdp([MarshalAs(UnmanagedType.U1)] bool flag);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n [return: MarshalAs(UnmanagedType.U1)]\\n internal static extern bool THSBackend_cuda_get_enable_math_sdp();\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern void THSBackend_cuda_set_enable_math_sdp([MarshalAs(UnmanagedType.U1)] bool flag);\\n }\\n}\\n\"\n },\n {\n \"alpha_fraction\": 0.5758683681488037,\n \"alphanum_fraction\": 0.5865560173988342,\n \"avg_line_length\": 42.35975646972656,\n \"blob_id\": \"0eecd3028b7ecbf00ae2c2a87a216d079875db58\",\n \"content_id\": \"52f27cd50948f6a9dfe42df734eb1f871f61184a\",\n \"detected_licenses\": [\n \"MIT\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"C#\",\n \"length_bytes\": 7111,\n \"license_type\": \"permissive\",\n \"max_line_length\": 173,\n \"num_lines\": 164,\n \"path\": \"/src/Examples.Utils/ImagerSharp.cs\",\n \"repo_name\": \"NiklasGustafsson/TorchSharp\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"using System;\\nusing System.IO;\\nusing System.Runtime.CompilerServices;\\nusing System.Threading;\\nusing System.Threading.Tasks;\\nusing SixLabors.ImageSharp;\\nusing SixLabors.ImageSharp.Formats;\\nusing SixLabors.ImageSharp.Formats.Jpeg;\\nusing SixLabors.ImageSharp.Formats.Png;\\nusing SixLabors.ImageSharp.PixelFormats;\\nusing static TorchSharp.torch;\\nusing static TorchSharp.torchvision;\\n\\nnamespace TorchSharp.Examples.Utils\\n{\\n /// <summary>\\n /// <cref>Imager</cref> implemented using ImageSharp.\\n /// </summary>\\n public sealed class ImagerSharp : io.Imager\\n {\\n private Tensor ToTensor<TPixel>(Stream stream) where TPixel : unmanaged, IPixel<TPixel>\\n {\\n var image = Image.Load<TPixel>(stream);\\n var channels = Unsafe.SizeOf<TPixel>();\\n byte[] imageBytes = new byte[image.Height * image.Width * channels];\\n image.CopyPixelDataTo(imageBytes);\\n return tensor(imageBytes, new long[] { image.Height, image.Width, channels }).permute(2, 0, 1);\\n }\\n\\n private async Task<Tensor> ToTensorAsync<TPixel>(Stream stream, CancellationToken token) where TPixel : unmanaged, IPixel<TPixel>\\n {\\n var image = await Image.LoadAsync<TPixel>(stream, token);\\n var channels = Unsafe.SizeOf<TPixel>();\\n byte[] imageBytes = new byte[image.Height * image.Width * channels];\\n image.CopyPixelDataTo(imageBytes);\\n return tensor(imageBytes, new long[] { image.Height, image.Width, channels }).permute(2, 0, 1);\\n }\\n\\n private void FromTensor<TPixel>(Tensor t, ImageFormat format, Stream stream) where TPixel : unmanaged, IPixel<TPixel>\\n {\\n var shape = t.shape;\\n var tt = t.reshape(new long[] { shape[0] * shape[1] * shape[2] });\\n var image = Image.LoadPixelData<TPixel>(tt.data<byte>().ToArray(), (int)shape[1], (int)shape[0]);\\n IImageEncoder encoder = format switch {\\n ImageFormat.Png => new PngEncoder(),\\n ImageFormat.Jpeg => new JpegEncoder(),\\n _ => throw new ArgumentException(\\\"Cannot encode to Unknown format\\\"),\\n };\\n encoder.Encode(image, stream);\\n }\\n\\n private Task FromTensorAsync<TPixel>(Tensor t, ImageFormat format, Stream stream, CancellationToken token) where TPixel : unmanaged, IPixel<TPixel>\\n {\\n var shape = t.shape;\\n var tt = t.reshape(new long[] { shape[0] * shape[1] * shape[2] });\\n var image = Image.LoadPixelData<TPixel>(tt.data<byte>().ToArray(), (int)shape[1], (int)shape[0]);\\n IImageEncoder encoder = format switch {\\n ImageFormat.Png => new PngEncoder(),\\n ImageFormat.Jpeg => new JpegEncoder(),\\n _ => throw new ArgumentException(\\\"Cannot encode to Unknown format\\\"),\\n };\\n return encoder.EncodeAsync(image, stream, token);\\n }\\n\\n public override async Task<Tensor> DecodeImageAsync(Stream stream, io.ImageReadMode mode = io.ImageReadMode.UNCHANGED, CancellationToken cancellationToken = default)\\n {\\n switch (mode) {\\n case io.ImageReadMode.UNCHANGED:\\n var format = Image.DetectFormat(stream);\\n if (format is PngFormat) {\\n return await ToTensorAsync<Rgba32>(stream, cancellationToken);\\n } else {\\n return await ToTensorAsync<Rgb24>(stream, cancellationToken);\\n }\\n case io.ImageReadMode.RGB_ALPHA:\\n return await ToTensorAsync<Rgba32>(stream, cancellationToken);\\n case io.ImageReadMode.RGB:\\n return await ToTensorAsync<Rgb24>(stream, cancellationToken);\\n case io.ImageReadMode.GRAY_ALPHA:\\n return await ToTensorAsync<La16>(stream, cancellationToken);\\n case io.ImageReadMode.GRAY:\\n return await ToTensorAsync<L8>(stream, cancellationToken);\\n default: throw new NotImplementedException();\\n }\\n }\\n\\n public override Tensor DecodeImage(Stream stream, io.ImageReadMode mode = io.ImageReadMode.UNCHANGED)\\n {\\n switch (mode) {\\n case io.ImageReadMode.UNCHANGED:\\n var format = Image.DetectFormat(stream);\\n if (format is PngFormat) {\\n return ToTensor<Rgba32>(stream);\\n } else {\\n return ToTensor<Rgb24>(stream);\\n }\\n case io.ImageReadMode.RGB_ALPHA:\\n return ToTensor<Rgba32>(stream);\\n case io.ImageReadMode.RGB:\\n return ToTensor<Rgb24>(stream);\\n case io.ImageReadMode.GRAY_ALPHA:\\n return ToTensor<La16>(stream);\\n case io.ImageReadMode.GRAY:\\n return ToTensor<L8>(stream);\\n default: throw new NotImplementedException();\\n }\\n }\\n\\n public override void EncodeImage(Tensor image, ImageFormat format, Stream stream)\\n {\\n Tensor permuted = image.permute(1, 2, 0);\\n switch (image.shape[0]) {\\n case 1:\\n FromTensor<L8>(permuted, format, stream);\\n break;\\n case 2:\\n FromTensor<L16>(permuted, format, stream);\\n break;\\n case 3:\\n FromTensor<Rgb24>(permuted, format, stream);\\n break;\\n case 4:\\n FromTensor<Rgba32>(permuted, format, stream);\\n break;\\n default:\\n throw new ArgumentException(\\\"image tensor must have a colour channel of 1,2,3 or 4\\\");\\n }\\n }\\n\\n public override async Task EncodeImageAsync(Tensor image, ImageFormat format, Stream stream, CancellationToken cancellationToken = default)\\n {\\n Tensor permuted = image.permute(1, 2, 0);\\n switch (image.shape[0]) {\\n case 1:\\n await FromTensorAsync<L8>(permuted, format, stream, cancellationToken);\\n break;\\n case 2:\\n await FromTensorAsync<L16>(permuted, format, stream, cancellationToken);\\n break;\\n case 3:\\n await FromTensorAsync<Rgb24>(permuted, format, stream, cancellationToken);\\n break;\\n case 4:\\n await FromTensorAsync<Rgba32>(permuted, format, stream, cancellationToken);\\n break;\\n default:\\n throw new ArgumentException(\\\"image tensor must have a colour channel of 1,2,3 or 4\\\");\\n }\\n }\\n\\n public override Tensor DecodeImage(byte[] data, io.ImageReadMode mode = io.ImageReadMode.UNCHANGED)\\n {\\n using var memStream = new MemoryStream(data);\\n return DecodeImage(memStream, mode);\\n }\\n\\n public override byte[] EncodeImage(Tensor image, ImageFormat format)\\n {\\n using var memStream = new MemoryStream();\\n EncodeImage(image, format, memStream);\\n return memStream.ToArray();\\n }\\n }\\n}\\n\"\n },\n {\n \"alpha_fraction\": 0.5566746592521667,\n \"alphanum_fraction\": 0.560511589050293,\n \"avg_line_length\": 54.353981018066406,\n \"blob_id\": \"f15ed733fb1b751c47802f7efa809a9641ca7479\",\n \"content_id\": \"9cd0b3f3f2f148d25f55647e70cfaaf846a6a63a\",\n \"detected_licenses\": [\n \"MIT\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"C#\",\n \"length_bytes\": 6255,\n \"license_type\": \"permissive\",\n \"max_line_length\": 222,\n \"num_lines\": 113,\n \"path\": \"/src/TorchSharp/NN/Pooling/AvgPool1D.cs\",\n \"repo_name\": \"NiklasGustafsson/TorchSharp\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"// Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information.\\nusing System;\\nusing static TorchSharp.torch;\\nusing static TorchSharp.PInvoke.NativeMethods;\\n\\nnamespace TorchSharp\\n{\\n using Modules;\\n\\n namespace Modules\\n {\\n /// <summary>\\n /// This class is used to represent a AvgPool1D module.\\n /// </summary>\\n public sealed class AvgPool1d : torch.nn.Module<Tensor, Tensor>\\n {\\n internal AvgPool1d(IntPtr handle, IntPtr boxedHandle) : base(handle, boxedHandle)\\n {\\n }\\n\\n public override Tensor forward(Tensor tensor)\\n {\\n var res = THSNN_AvgPool1d_forward(handle.DangerousGetHandle(), tensor.Handle);\\n if (res == IntPtr.Zero) { torch.CheckForErrors(); }\\n return new Tensor(res);\\n }\\n\\n // Rather than spending cycles only to discover that this module has neither\\n // parameters nor buffers, just shortcut the move completely.\\n protected internal override nn.Module _to(Device device, ScalarType dtype) => this;\\n protected internal override nn.Module _to(DeviceType deviceType, int deviceIndex = -1) => this;\\n protected internal override nn.Module _to(ScalarType dtype) => this;\\n }\\n }\\n\\n public static partial class torch\\n {\\n public static partial class nn\\n {\\n /// <summary>\\n /// Applies a 1D average pooling over an input signal composed of several input planes.\\n /// </summary>\\n /// <param name=\\\"kernelSize\\\">The size of the window</param>\\n /// <param name=\\\"stride\\\">The stride of the window. Default value is kernel_size</param>\\n /// <param name=\\\"padding\\\">implicit zero padding to be added on both sides</param>\\n /// <param name=\\\"ceil_mode\\\">Whether to use ceil instead of floor to compute the output shape</param>\\n /// <param name=\\\"count_include_pad\\\">Whether to include the zero-padding in the averaging calculation</param>\\n /// <param name=\\\"divisor_override\\\">If specified, it will be used as divisor, otherwise size of the pooling region will be used</param>\\n public static AvgPool1d AvgPool1d(long kernelSize, long? stride = null, long padding = 0, bool ceil_mode = false, bool count_include_pad = true, long? divisor_override = null)\\n {\\n return stride.HasValue ?\\n AvgPool1d(new long[] { kernelSize }, new long[] { stride.Value }, new long[] { padding }, ceil_mode, count_include_pad, divisor_override.HasValue ? divisor_override.Value : 0) :\\n AvgPool1d(new long[] { kernelSize }, null, new long[] { padding }, ceil_mode, count_include_pad, divisor_override.HasValue ? divisor_override.Value : 0);\\n }\\n\\n /// <summary>\\n /// Applies a 1D average pooling over an input signal composed of several input planes.\\n /// </summary>\\n /// <param name=\\\"kernelSize\\\">The size of the window</param>\\n /// <param name=\\\"strides\\\">The stride of the window. Default value is kernel_size</param>\\n /// <param name=\\\"padding\\\">implicit zero padding to be added on both sides</param>\\n /// <param name=\\\"ceil_mode\\\">Whether to use ceil instead of floor to compute the output shape</param>\\n /// <param name=\\\"count_include_pad\\\">Whether to include the zero-padding in the averaging calculation</param>\\n /// <param name=\\\"divisor_override\\\">If specified, it will be used as divisor, otherwise size of the pooling region will be used</param>\\n private static AvgPool1d AvgPool1d(long[] kernelSize, long[] strides = null, long[] padding = null, bool ceil_mode = false, bool count_include_pad = true, long? divisor_override = null)\\n {\\n unsafe {\\n fixed (long* pkernelSize = kernelSize, pstrides = strides, ppadding = padding) {\\n var handle = THSNN_AvgPool1d_ctor((IntPtr)pkernelSize, (IntPtr)pstrides, (IntPtr)ppadding, ceil_mode, count_include_pad, divisor_override.HasValue ? divisor_override.Value : 0, out var boxedHandle);\\n if (handle == IntPtr.Zero) { torch.CheckForErrors(); }\\n return new AvgPool1d(handle, boxedHandle);\\n }\\n }\\n }\\n\\n public static partial class functional\\n {\\n\\n /// <summary>\\n /// Applies a 1D average pooling over an input signal composed of several input planes.\\n /// </summary>\\n /// <param name=\\\"input\\\">The input tensor.</param>\\n /// <param name=\\\"kernelSize\\\"></param>\\n /// <param name=\\\"stride\\\"></param>\\n /// <param name=\\\"padding\\\"></param>\\n /// <param name=\\\"ceil_mode\\\"></param>\\n /// <param name=\\\"count_include_pad\\\"></param>\\n /// <returns></returns>\\n public static Tensor avg_pool1d(Tensor input, long kernelSize, long? stride = null,\\n long? padding = null, bool ceil_mode = false, bool count_include_pad = true)\\n {\\n var kernelSizes = new long[] { kernelSize };\\n var strides = new long[] { stride ?? 1 };\\n var paddings = new long[] { padding ?? 0 };\\n unsafe {\\n fixed (long* pkernelSize = kernelSizes, pstrides = strides, ppadding = paddings) {\\n var res =\\n THSTensor_avg_pool1d(input.Handle,\\n (IntPtr)pkernelSize, kernelSizes.Length,\\n (IntPtr)pstrides, strides.Length,\\n (IntPtr)ppadding, paddings.Length,\\n ceil_mode,\\n count_include_pad);\\n if (res == IntPtr.Zero) { torch.CheckForErrors(); }\\n return new Tensor(res);\\n }\\n }\\n }\\n\\n }\\n }\\n }\\n}\\n\"\n },\n {\n \"alpha_fraction\": 0.6544735431671143,\n \"alphanum_fraction\": 0.6544735431671143,\n \"avg_line_length\": 50.89743423461914,\n \"blob_id\": \"bab664a71c62dfa9cd73cf13f34c722d060a5823\",\n \"content_id\": \"0a10586af87a562da326077d5cbafd01eb6af399\",\n \"detected_licenses\": [\n \"MIT\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"C#\",\n \"length_bytes\": 2025,\n \"license_type\": \"permissive\",\n \"max_line_length\": 131,\n \"num_lines\": 39,\n \"path\": \"/src/TorchSharp/Tensor/Tensor.Operators.cs\",\n \"repo_name\": \"NiklasGustafsson/TorchSharp\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"// Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information.\\n#nullable enable\\n\\nnamespace TorchSharp\\n{\\n public static partial class torch\\n {\\n // this file contains operator overloads\\n public partial class Tensor\\n {\\n public static Tensor operator +(Tensor left, Tensor right) => left.add(right);\\n public static Tensor operator +(Tensor left, Scalar right) => left.add(right);\\n public static Tensor operator +(Scalar left, Tensor right) => right.add(left);\\n\\n public static Tensor operator *(Tensor left, Tensor right) => left.mul(right);\\n public static Tensor operator *(Tensor left, Scalar right) => left.mul(right);\\n public static Tensor operator *(Scalar left, Tensor right) => right.mul(left);\\n\\n public static Tensor operator -(Tensor left, Tensor right) => left.sub(right);\\n public static Tensor operator -(Tensor left, Scalar right) => left.sub(right);\\n public static Tensor operator -(Scalar left, Tensor right) => right.negative().add(left);\\n\\n public static Tensor operator /(Tensor left, Tensor right) => left.div(right);\\n public static Tensor operator /(Tensor left, Scalar right) => left.div(right);\\n public static Tensor operator /(Scalar left, Tensor right) => right.reciprocal().mul(left);\\n\\n public static Tensor operator %(Tensor left, Tensor right) => left.remainder(right);\\n public static Tensor operator %(Tensor left, Scalar right) => left.remainder(right);\\n\\n public static Tensor operator &(Tensor left, Tensor right) => left.bitwise_and(right);\\n\\n public static Tensor operator |(Tensor left, Tensor right) => left.bitwise_or(right);\\n\\n public static Tensor operator ^(Tensor left, Tensor right) => left.bitwise_xor(right);\\n\\n public static Tensor operator ~(Tensor left) => left.bitwise_not();\\n }\\n }\\n}\"\n },\n {\n \"alpha_fraction\": 0.4836462438106537,\n \"alphanum_fraction\": 0.4851812422275543,\n \"avg_line_length\": 32.60714340209961,\n \"blob_id\": \"579ae51cd0b580b53c12195bdc22c84052693b2d\",\n \"content_id\": \"89402fd412033e55831b12a85ef5b84ca569f634\",\n \"detected_licenses\": [\n \"MIT\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"C#\",\n \"length_bytes\": 8469,\n \"license_type\": \"permissive\",\n \"max_line_length\": 130,\n \"num_lines\": 252,\n \"path\": \"/src/TorchSharp/NN/ModuleDict.cs\",\n \"repo_name\": \"NiklasGustafsson/TorchSharp\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"// Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information.\\nusing System;\\nusing System.Linq;\\nusing System.Collections;\\nusing System.Collections.Generic;\\nusing System.Diagnostics.CodeAnalysis;\\nusing static TorchSharp.torch.nn;\\n\\nnamespace TorchSharp\\n{\\n using Modules;\\n\\n namespace Modules\\n {\\n /// <summary>\\n /// Holds parameters in a dictionary.\\n /// \\n /// ModuleDict can be indexed like a regular dictionary, but the modules it\\n /// contains are properly registered, and will be visible by all Module methods.\\n ///\\n /// ModuleDict is an ordered dictionary that respects the order of insertion, and\\n /// in update(), the order of the merged OrderedDict or another ModuleDict (the argument to update()).\\n /// </summary>\\n public class ModuleDict<T> : Module, IDictionary<string, T>, IList<(string, T)> where T : Module\\n {\\n public ModuleDict() : base(nameof(ModuleDict))\\n {\\n }\\n\\n /// <summary>\\n /// Remove all items from the ParameterDict.\\n /// </summary>\\n public void clear()\\n {\\n _list.Clear();\\n _dict.Clear();\\n }\\n\\n /// <summary>\\n /// Return an enumeration of the ParameterDict key/value pairs.\\n /// </summary>\\n /// <returns></returns>\\n public IEnumerator<(string, T)> items() => _list.GetEnumerator();\\n\\n /// <summary>\\n /// Return the ParameterDict keys.\\n /// </summary>\\n /// <returns></returns>\\n public IEnumerable<string> keys() => _dict.Keys;\\n\\n protected override void RegisterComponents()\\n {\\n if (_registered) return;\\n\\n for (int i = 0; i < _list.Count; i++) {\\n register_module($\\\"{_list[i].Item1}\\\", _list[i].Item2);\\n }\\n _registered = true;\\n }\\n\\n private bool _registered = false;\\n\\n /// <summary>\\n /// Return the ParameterDict values.\\n /// </summary>\\n /// <returns></returns>\\n public IEnumerable<T> values() => _dict.Values;\\n\\n public (string, T) this[int index] {\\n get => _list[index];\\n set {\\n var name = value.Item1;\\n _list[index] = value;\\n _dict[name] = value.Item2;\\n }\\n }\\n\\n public bool IsReadOnly => false;\\n\\n public ICollection<string> Keys => _list.Select(kv => kv.Item1).ToList();\\n\\n public ICollection<T> Values => _list.Select(kv => kv.Item2).ToList();\\n\\n public int Count => _dict.Count;\\n\\n public T this[string key] {\\n get => _dict[key];\\n set {\\n _dict[key] = value;\\n var idx = _list.FindIndex(kv => kv.Item1.Equals(key));\\n _list[idx] = (key, value);\\n }\\n }\\n\\n public void Add((string, T) item)\\n {\\n _dict.Add(item.Item1, item.Item2);\\n _list.Add(item);\\n }\\n\\n public void Add(string key, T value)\\n {\\n _dict.Add(key, value);\\n _list.Add((key, value));\\n }\\n\\n public void Add(KeyValuePair<string, T> item)\\n {\\n _dict.Add(item.Key, item.Value);\\n _list.Add((item.Key, item.Value));\\n }\\n\\n public bool Contains((string, T) item)\\n {\\n return _list.Contains(item);\\n }\\n\\n public void CopyTo((string, T)[] array, int arrayIndex)\\n {\\n _list.CopyTo(array, arrayIndex);\\n }\\n\\n public int IndexOf((string, T) item)\\n {\\n return _list.IndexOf(item);\\n }\\n\\n public void Insert(int index, (string, T) item)\\n {\\n _dict.Add(item.Item1, item.Item2);\\n _list.Insert(index, item);\\n }\\n\\n public bool Remove((string, T) item)\\n {\\n _dict.Remove(item.Item1);\\n return _list.Remove(item);\\n }\\n\\n public void RemoveAt(int index)\\n {\\n if (index >= _list.Count) throw new IndexOutOfRangeException();\\n var (n, p) = _list[index];\\n _list.RemoveAt(index);\\n _dict.Remove(n);\\n }\\n\\n public bool ContainsKey(string key)\\n {\\n return _dict.ContainsKey(key);\\n }\\n\\n public bool Remove(string key)\\n {\\n var value = _dict[key];\\n return _dict.Remove(key) && _list.Remove((key, value));\\n }\\n\\n public bool TryGetValue(string key, [MaybeNullWhen(false)] out T value)\\n {\\n return _dict.TryGetValue(key, out value);\\n }\\n\\n public void Clear()\\n {\\n _dict.Clear();\\n _list.Clear();\\n }\\n\\n public bool Contains(KeyValuePair<string, T> item)\\n {\\n return _dict.ContainsKey(item.Key);\\n }\\n\\n public void CopyTo(KeyValuePair<string, T>[] array, int arrayIndex)\\n {\\n throw new NotImplementedException();\\n }\\n\\n public bool Remove(KeyValuePair<string, T> item)\\n {\\n return _dict.Remove(item.Key);\\n }\\n\\n public IEnumerator<(string, T)> GetEnumerator()\\n {\\n return _list.GetEnumerator();\\n }\\n\\n IEnumerator<KeyValuePair<string, T>> IEnumerable<KeyValuePair<string, T>>.GetEnumerator()\\n {\\n throw new NotImplementedException();\\n }\\n\\n IEnumerator IEnumerable.GetEnumerator()\\n {\\n return ((IEnumerable<KeyValuePair<string, T>>)this).GetEnumerator();\\n }\\n\\n private List<(string, T)> _list = new List<(string, T)>();\\n private Dictionary<string, T> _dict = new Dictionary<string, T>();\\n };\\n }\\n\\n public static partial class torch\\n {\\n public static partial class nn\\n {\\n /// <summary>\\n /// Create a ModuleDict instance from an array of modules.\\n /// </summary>\\n /// <param name=\\\"modules\\\">A list of (name,module) tuples.</param>\\n /// <returns></returns>\\n /// <remarks>\\n /// ModuleDict can be indexed like a regular dictionary, but the modules it\\n /// contains are properly registered, and will be visible by all Module methods.\\n ///\\n /// ModuleDict is an ordered dictionary that respects the order of insertion, and\\n /// in update(), the order of the merged OrderedDict or another ModuleDict (the argument to update()).\\n /// </remarks>\\n public static ModuleDict<Module> ModuleDict(params (string, Module)[] modules)\\n {\\n var result = new ModuleDict<Module>();\\n foreach (var (n, m) in modules) {\\n result.Add((n, m));\\n }\\n return result;\\n }\\n\\n /// <summary>\\n /// Create a ModuleDict instance from an array of modules.\\n /// </summary>\\n /// <param name=\\\"modules\\\">A list of (name,module) tuples.</param>\\n /// <returns></returns>\\n /// <remarks>\\n /// ModuleDict can be indexed like a regular dictionary, but the modules it\\n /// contains are properly registered, and will be visible by all Module methods.\\n ///\\n /// ModuleDict is an ordered dictionary that respects the order of insertion, and\\n /// in update(), the order of the merged OrderedDict or another ModuleDict (the argument to update()).\\n /// </remarks>\\n public static ModuleDict<T> ModuleDict<T>(params (string, T)[] modules) where T: Module\\n {\\n var result = new ModuleDict<T>();\\n foreach (var (n, m) in modules) {\\n result.Add((n, m));\\n }\\n return result;\\n }\\n }\\n }\\n}\\n\"\n },\n {\n \"alpha_fraction\": 0.6134146451950073,\n \"alphanum_fraction\": 0.6134146451950073,\n \"avg_line_length\": 24.625,\n \"blob_id\": \"c05a07833f4663be083ba35f5c8756dfa8ca6f67\",\n \"content_id\": \"be28d12e6aad6f430c96dee92fda18308ee7a872\",\n \"detected_licenses\": [\n \"MIT\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"C#\",\n \"length_bytes\": 1640,\n \"license_type\": \"permissive\",\n \"max_line_length\": 101,\n \"num_lines\": 64,\n \"path\": \"/src/TorchSharp/Utils/NotebookFormattingSupport.cs\",\n \"repo_name\": \"NiklasGustafsson/TorchSharp\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"using System;\\nusing System.Collections.Generic;\\nusing System.IO;\\nusing System.Linq;\\nusing System.Text;\\nusing System.Threading.Tasks;\\n\\nnamespace TorchSharp.Utils\\n{\\n /// <summary>\\n /// Class used to identify the formatting logic for Tensors when using .NET Interactive.\\n /// </summary>\\n [AttributeUsage(AttributeTargets.Class)]\\n public class TypeFormatterSourceAttribute : Attribute\\n {\\n public TypeFormatterSourceAttribute(Type formatterSourceType)\\n {\\n FormatterSourceType = formatterSourceType;\\n }\\n\\n public Type FormatterSourceType { get; }\\n }\\n\\n internal class TypeFormatterSource\\n {\\n public IEnumerable<object> CreateTypeFormatters()\\n {\\n yield return new TensorPlainTextFormatter();\\n yield return new TensorHTMLFormatter();\\n }\\n }\\n\\n internal class TensorPlainTextFormatter\\n {\\n public string MimeType => \\\"text/plain\\\";\\n\\n public bool Format(object instance, TextWriter writer)\\n {\\n if (instance is not torch.Tensor result) {\\n return false;\\n }\\n\\n writer.Write(result.ToString(TensorStringStyle.Default));\\n\\n return true;\\n }\\n }\\n\\n internal class TensorHTMLFormatter\\n {\\n public string MimeType => \\\"text/html\\\";\\n\\n public bool Format(object instance, TextWriter writer)\\n {\\n if (instance is not torch.Tensor result) {\\n return false;\\n }\\n\\n writer.Write(\\\"<div><pre>\\\" + result.ToString(TensorStringStyle.Default) + \\\"</pre></div>\\\");\\n\\n return true;\\n }\\n }\\n}\\n\"\n },\n {\n \"alpha_fraction\": 0.5134429335594177,\n \"alphanum_fraction\": 0.5295370817184448,\n \"avg_line_length\": 46.39130401611328,\n \"blob_id\": \"b5ddd342adbfc5abd98ed8cd7a4b39eb5cb96a61\",\n \"content_id\": \"2179b0fde9fae04ceea1217c4bc76129dd45ce7b\",\n \"detected_licenses\": [\n \"MIT\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"C#\",\n \"length_bytes\": 64309,\n \"license_type\": \"permissive\",\n \"max_line_length\": 162,\n \"num_lines\": 1357,\n \"path\": \"/src/TorchAudio/Modules/Wav2Vec2Components.cs\",\n \"repo_name\": \"NiklasGustafsson/TorchSharp\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"// Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information.\\n\\n// A number of implementation details in this file have been translated from the Python version of torchaudio,\\n// largely located in the files found in this folder:\\n//\\n// https://github.com/pytorch/audio/blob/76fca37ac8941b72a509a6e58d623632efe04543/torchaudio/models/wav2vec2/components.py\\n//\\n// The origin has the following copyright notice and license:\\n//\\n// https://github.com/pytorch/audio/blob/main/LICENSE\\n//\\n\\nusing System;\\nusing System.Collections.Generic;\\nusing System.Data;\\nusing System.Linq;\\n\\nusing static TorchSharp.torch;\\nusing static TorchSharp.torch.nn;\\nusing static TorchSharp.torchaudio.models;\\n\\n#nullable enable\\nnamespace TorchSharp.Modules\\n{\\n public partial class Wav2Vec2Model : nn.Module<Tensor, Tensor?, (Tensor, Tensor?)>\\n {\\n /// <summary>\\n /// Layer norm with transpose\\n /// </summary>\\n private class LayerNorm : Module<Tensor, Tensor>\\n {\\n public readonly long[] normalized_shape;\\n public readonly Parameter weight;\\n public readonly Parameter bias;\\n public readonly double eps;\\n\\n protected override void Dispose(bool disposing)\\n {\\n if (disposing) {\\n weight.Dispose();\\n bias.Dispose();\\n }\\n base.Dispose(disposing);\\n }\\n\\n public LayerNorm(\\n string name,\\n long[] normalized_shape,\\n double eps = 1e-05,\\n bool elementwise_affine = true) : base(name)\\n {\\n this.normalized_shape = normalized_shape;\\n this.weight = torch.nn.Parameter(torch.ones(normalized_shape));\\n this.bias = torch.nn.Parameter(torch.zeros(normalized_shape));\\n this.eps = eps;\\n RegisterComponents();\\n }\\n\\n public override Tensor forward(Tensor input)\\n {\\n var x = input.transpose(-2, -1);\\n x = nn.functional.layer_norm(x, this.normalized_shape, this.weight, this.bias, this.eps);\\n x = x.transpose(-2, -1);\\n return x;\\n }\\n }\\n\\n /// <summary>\\n /// Convolution unit of FeatureExtractor\\n /// </summary>\\n private class ConvLayerBlock : Module<Tensor, Tensor?, (Tensor, Tensor?)>\\n {\\n public readonly Module<Tensor, Tensor> conv;\\n public readonly long kernel_size;\\n public readonly Module<Tensor, Tensor>? layer_norm;\\n public readonly long stride;\\n\\n protected override void Dispose(bool disposing)\\n {\\n if (disposing) {\\n conv.Dispose();\\n }\\n base.Dispose(disposing);\\n }\\n\\n public ConvLayerBlock(\\n string name,\\n long in_channels,\\n long out_channels,\\n long kernel_size,\\n long stride,\\n bool bias,\\n Module<Tensor, Tensor>? layer_norm) : base(name)\\n {\\n this.kernel_size = kernel_size;\\n this.stride = stride;\\n this.layer_norm = layer_norm;\\n this.conv = nn.Conv1d(\\n inputChannel: in_channels,\\n outputChannel: out_channels,\\n kernelSize: kernel_size,\\n stride: stride,\\n bias: bias);\\n RegisterComponents();\\n }\\n\\n /// <param name=\\\"x\\\">Shape: ``[batch, in_channels, in_frame]``</param>\\n /// <param name=\\\"length\\\">Shape ``[batch, ]``</param>\\n /// <returns>\\n /// Shape ``[batch, out_channels, out_frames]``.\\n /// Shape ``[batch, ]``.\\n /// </returns>\\n public override (Tensor, Tensor?) forward(\\n Tensor x,\\n Tensor? length)\\n {\\n x = this.conv.call(x);\\n if (this.layer_norm != null) {\\n x = this.layer_norm.call(x);\\n }\\n x = nn.functional.gelu(x);\\n\\n if (length is not null) {\\n length = torch.div(length - this.kernel_size, this.stride, rounding_mode: RoundingMode.floor) + 1;\\n // When input length is 0, the resulting length can be negative. So fix it here.\\n length = torch.maximum(torch.zeros_like(length), length);\\n }\\n return (x, length);\\n }\\n }\\n\\n /// <summary>\\n /// Extract features from audio\\n /// </summary>\\n internal class FeatureExtractor : Module<Tensor, Tensor?, (Tensor, Tensor?)>\\n {\\n public readonly ModuleList<Module<Tensor, Tensor?, (Tensor, Tensor?)>> conv_layers;\\n\\n /// <param name=\\\"name\\\"></param>\\n /// <param name=\\\"conv_layers\\\">convolution layers</param>\\n public FeatureExtractor(\\n string name,\\n ModuleList<Module<Tensor, Tensor?, (Tensor, Tensor?)>> conv_layers) : base(name)\\n {\\n this.conv_layers = conv_layers;\\n RegisterComponents();\\n }\\n\\n /// <param name=\\\"x\\\">Input Tensor representing a batch of audio, shape: ``[batch, time]``.</param>\\n /// <param name=\\\"length\\\">Valid length of each input sample. shape: ``[batch, ]``.</param>\\n /// <returns>\\n /// The resulting feature, shape: ``[batch, frame, feature]``\\n /// Valid length of each output sample. shape: ``[batch, ]``.\\n /// </returns>\\n /// <exception cref=\\\"ArgumentException\\\"></exception>\\n public override (Tensor, Tensor?) forward(Tensor x, Tensor? length)\\n {\\n if (x.ndim != 2) {\\n throw new ArgumentException(\\\"Expected the input Tensor to be 2D (batch, time), but received {list(x.shape)}\\\");\\n }\\n\\n x = x.unsqueeze(1); // (batch, channel==1, frame)\\n foreach (var layer in this.conv_layers) {\\n var conv_layer = (ConvLayerBlock)layer;\\n (x, length) = conv_layer.call(x, length); // (batch, feature, frame)\\n }\\n x = x.transpose(1, 2); // (batch, frame, feature)\\n return (x, length);\\n }\\n }\\n\\n /// <summary>\\n /// Layer that connects FeatureExtractor and Encoder\\n /// </summary>\\n private class FeatureProjection : Module<Tensor, Tensor>\\n {\\n public readonly Module<Tensor, Tensor> dropout;\\n public readonly Module<Tensor, Tensor> layer_norm;\\n public readonly Module<Tensor, Tensor> projection;\\n\\n protected override void Dispose(bool disposing)\\n {\\n if (disposing) {\\n dropout.Dispose();\\n layer_norm.Dispose();\\n projection.Dispose();\\n }\\n base.Dispose(disposing);\\n }\\n\\n /// <summary>\\n /// Projects features to encoder dimension.\\n /// </summary>\\n /// <param name=\\\"name\\\"></param>\\n /// <param name=\\\"in_features\\\">Input feature dim.</param>\\n /// <param name=\\\"out_features\\\">Output feature dim.</param>\\n /// <param name=\\\"dropout\\\">Dropout probability.</param>\\n public FeatureProjection(\\n string name,\\n long in_features,\\n long out_features,\\n double dropout) : base(name)\\n {\\n this.layer_norm = nn.LayerNorm(new long[] { in_features });\\n this.projection = nn.Linear(\\n in_features,\\n out_features);\\n this.dropout = nn.Dropout(dropout);\\n RegisterComponents();\\n }\\n\\n /// <param name=\\\"x\\\">Feature Tensor. shape: ``[batch, frame, in_feature]``</param>\\n /// <returns>Projected features. ``[batch, frame, out_feature]``.</returns>\\n public override Tensor forward(Tensor x)\\n {\\n x = this.layer_norm.call(x);\\n x = this.projection.call(x);\\n x = this.dropout.call(x);\\n return x;\\n }\\n }\\n\\n /// <summary>\\n /// Positional embedding which is placed at the beginning of Transformer.\\n /// </summary>\\n internal class ConvolutionalPositionalEmbedding : Module<Tensor, Tensor>\\n {\\n public readonly Module<Tensor, Tensor> conv;\\n public readonly long embed_dim;\\n public readonly long num_remove;\\n\\n protected override void Dispose(bool disposing)\\n {\\n if (disposing) {\\n conv.Dispose();\\n }\\n base.Dispose(disposing);\\n }\\n\\n /// <param name=\\\"name\\\"></param>\\n /// <param name=\\\"embed_dim\\\">Feature dimension of the input Tensor.</param>\\n /// <param name=\\\"kernel_size\\\">The number of frames to be use.</param>\\n /// <param name=\\\"groups\\\">The number of groups in feature dimensions.</param>\\n public ConvolutionalPositionalEmbedding(\\n string name,\\n long embed_dim,\\n long kernel_size,\\n long groups) : base(name)\\n {\\n this.embed_dim = embed_dim;\\n // TODO: Replace when nn.utils.weight_norm() is supported.\\n // https://github.com/dotnet/TorchSharp/issues/357\\n // this.conv = nn.Conv1d(inputChannel: embed_dim, outputChannel: embed_dim, kernelSize: kernel_size, padding: kernel_size / 2, groups: groups);\\n // this.conv = nn.utils.weight_norm(this.conv, name: \\\"weight\\\", dim: 2);\\n this.conv = new WeightNormConv1d(\\n \\\"WeightNormConv1d\\\",\\n in_channels: embed_dim,\\n out_channels: embed_dim,\\n kernel_size: kernel_size,\\n padding: kernel_size / 2,\\n groups: groups);\\n this.num_remove = kernel_size % 2 == 0 ? 1 : 0;\\n RegisterComponents();\\n }\\n\\n /// <param name=\\\"x\\\">shape ``[batch, frame, feature]``.</param>\\n /// <returns>The resulting feature. Shape ``[batch, frame, feature]``.</returns>\\n public override Tensor forward(Tensor x)\\n {\\n x = x.transpose(-2, -1);\\n x = this.conv.call(x);\\n if (this.num_remove > 0) {\\n x = x[TensorIndex.Ellipsis, TensorIndex.Slice(null, -this.num_remove)];\\n }\\n x = torch.nn.functional.gelu(x);\\n x = x.transpose(-2, -1);\\n return x;\\n }\\n\\n private class WeightNormConv1d : Module<Tensor, Tensor>\\n {\\n private readonly Parameter weight_g;\\n private readonly Parameter weight_v;\\n private readonly Parameter bias;\\n private readonly long padding;\\n private readonly long groups;\\n\\n protected override void Dispose(bool disposing)\\n {\\n if (disposing) {\\n weight_g.Dispose();\\n weight_v.Dispose();\\n bias.Dispose();\\n }\\n base.Dispose(disposing);\\n }\\n\\n public WeightNormConv1d(string name, long in_channels, long out_channels, long kernel_size, long padding, long groups) : base(name)\\n {\\n this.weight_g = Parameter(\\n torch.empty(\\n new long[] { 1, in_channels / groups, kernel_size },\\n dtype: torch.float32));\\n this.weight_v = Parameter(\\n torch.empty(\\n new long[] { out_channels, in_channels / groups, kernel_size },\\n dtype: torch.float32));\\n this.bias = Parameter(torch.empty(out_channels, dtype: torch.float32));\\n this.padding = padding;\\n this.groups = groups;\\n this.RegisterComponents();\\n this.reset_parameters();\\n }\\n\\n public void reset_parameters()\\n {\\n var weight = torch.empty_like(this.weight_v);\\n init.kaiming_uniform_(weight, Math.Sqrt(5));\\n using (torch.no_grad()) {\\n this.weight_g.set_(torch.sqrt(torch.square(weight).sum(dim: new long[] { 0, 1 }, keepdim: true)));\\n this.weight_v.set_(weight / this.weight_g);\\n }\\n var fan_in = this.weight_v.size(1);\\n var bound = 1.0 / Math.Sqrt(fan_in);\\n init.uniform_(this.bias, -bound, bound);\\n }\\n\\n public override Tensor forward(Tensor input)\\n {\\n var weight_v_norm = torch.linalg.norm(weight_v, dims: new long[] { 0, 1 }, keepdim: true);\\n var weight = torch.mul(weight_v / weight_v_norm, weight_g);\\n return nn.functional.conv1d(input, weight, bias, padding: this.padding, groups: this.groups);\\n }\\n }\\n }\\n\\n /// <summary>\\n /// Multihead Self Attention module\\n /// </summary>\\n private class SelfAttention : Module<Tensor, Tensor?, Tensor>\\n {\\n public readonly Module<Tensor, Tensor> dropout;\\n public readonly long embed_dim;\\n public readonly long head_dim;\\n public readonly Module<Tensor, Tensor> k_proj;\\n public readonly long num_heads;\\n public readonly Module<Tensor, Tensor> out_proj;\\n public readonly Module<Tensor, Tensor> q_proj;\\n public readonly double scaling;\\n public readonly Module<Tensor, Tensor> v_proj;\\n\\n protected override void Dispose(bool disposing)\\n {\\n if (disposing) {\\n dropout.Dispose();\\n k_proj.Dispose();\\n out_proj.Dispose();\\n q_proj.Dispose();\\n v_proj.Dispose();\\n }\\n base.Dispose(disposing);\\n }\\n\\n /// <param name=\\\"name\\\"></param>\\n /// <param name=\\\"embed_dim\\\">Total dimension of the model.</param>\\n /// <param name=\\\"num_heads\\\">The number of heads.</param>\\n /// <param name=\\\"dropout\\\">Dropout probabiliry on attn_output_weights. Default: ``0.0``</param>\\n /// <exception cref=\\\"ArgumentException\\\"></exception>\\n public SelfAttention(\\n string name,\\n long embed_dim,\\n long num_heads,\\n double dropout = 0.0) : base(name)\\n {\\n var head_dim = embed_dim / num_heads;\\n if (head_dim * num_heads != embed_dim) {\\n throw new ArgumentException($\\\"`embed_dim ({embed_dim})` is not divisible by `num_heads ({num_heads})`\\\");\\n }\\n this.embed_dim = embed_dim;\\n this.num_heads = num_heads;\\n this.dropout = torch.nn.Dropout(dropout);\\n this.head_dim = head_dim;\\n\\n this.scaling = Math.Pow(this.head_dim, -0.5);\\n\\n this.k_proj = nn.Linear(embed_dim, embed_dim, hasBias: true);\\n this.v_proj = nn.Linear(embed_dim, embed_dim, hasBias: true);\\n this.q_proj = nn.Linear(embed_dim, embed_dim, hasBias: true);\\n this.out_proj = nn.Linear(embed_dim, embed_dim, hasBias: true);\\n RegisterComponents();\\n }\\n\\n /// <param name=\\\"x\\\">shape: ``[batch_size, sequence_length, embed_dim]``.</param>\\n /// <param name=\\\"attention_mask\\\">shape: ``[batch_size, 1, sequence_length, sequence_length]``</param>\\n /// <returns>The resulting tensor. shape: ``[batch, sequence_length, embed_dim]``</returns>\\n /// <exception cref=\\\"ArgumentException\\\"></exception>\\n public override Tensor forward(Tensor x, Tensor? attention_mask)\\n {\\n if (x.ndim != 3 || x.shape[2] != this.embed_dim) {\\n throw new ArgumentException(\\\"The expected input shape is (batch, sequence, embed_dim=={self.embed_dim}). Found {x.shape}.\\\");\\n }\\n var batch_size = x.size(0);\\n var length = x.size(1);\\n var embed_dim = x.size(2);\\n if (attention_mask is not null) {\\n var shape_ = new long[] { batch_size, 1, length, length };\\n if (attention_mask.dim() != shape_.Length ||\\n attention_mask.size(0) != shape_[0] ||\\n attention_mask.size(1) != shape_[1] ||\\n attention_mask.size(2) != shape_[2] ||\\n attention_mask.size(3) != shape_[3]) {\\n throw new ArgumentException($\\\"The expected attention mask shape is {shape_}. Found {attention_mask.size()}.\\\");\\n }\\n }\\n\\n var shape = new long[] { batch_size, length, this.num_heads, this.head_dim };\\n var q = this.q_proj.call(x).view(shape).transpose(2, 1); // B, nH, L, Hd\\n var k = this.k_proj.call(x).view(shape).permute(0, 2, 3, 1); // B, nH, Hd, L\\n var v = this.v_proj.call(x).view(shape).transpose(2, 1); // B, nH, L, Hd\\n\\n var weights = this.scaling * torch.matmul(q, k); // B, nH, L, L\\n if (attention_mask is not null) {\\n weights += attention_mask;\\n }\\n\\n weights = torch.nn.functional.softmax(weights, dim: -1);\\n weights = this.dropout.call(weights);\\n\\n var output = torch.matmul(weights, v); // B, nH, L, Hd\\n output = output.transpose(2, 1).reshape(batch_size, length, embed_dim);\\n\\n output = this.out_proj.call(output);\\n return output;\\n }\\n\\n public new Tensor call(Tensor x, Tensor? attention_mask = null)\\n {\\n return base.call(x, attention_mask);\\n }\\n }\\n\\n /// <summary>\\n /// Layer that follows attention layer in encoder layer.\\n /// </summary>\\n private class FeedForward : Module<Tensor, Tensor>\\n {\\n public readonly Module<Tensor, Tensor> intermediate_dense;\\n public readonly Module<Tensor, Tensor> intermediate_dropout;\\n public readonly Module<Tensor, Tensor> output_dense;\\n public readonly Module<Tensor, Tensor> output_dropout;\\n\\n protected override void Dispose(bool disposing)\\n {\\n if (disposing) {\\n intermediate_dense.Dispose();\\n intermediate_dropout.Dispose();\\n output_dense.Dispose();\\n output_dropout.Dispose();\\n }\\n base.Dispose(disposing);\\n }\\n\\n public FeedForward(\\n string name,\\n long io_features,\\n long intermediate_features,\\n double intermediate_dropout,\\n double output_dropout) : base(name)\\n {\\n this.intermediate_dense = nn.Linear(io_features, intermediate_features);\\n this.intermediate_dropout = nn.Dropout(intermediate_dropout);\\n this.output_dense = nn.Linear(intermediate_features, io_features);\\n this.output_dropout = nn.Dropout(output_dropout);\\n RegisterComponents();\\n }\\n\\n /// <param name=\\\"x\\\">shape: `(batch, sequence_length, io_features)`</param>\\n /// <returns>shape: `(batch, sequence_length, io_features)`</returns>\\n public override Tensor forward(Tensor x)\\n {\\n x = this.intermediate_dense.call(x);\\n x = torch.nn.functional.gelu(x);\\n x = this.intermediate_dropout.call(x);\\n\\n x = this.output_dense.call(x);\\n x = this.output_dropout.call(x);\\n return x;\\n }\\n }\\n\\n /// <summary>\\n /// A layer unit in encoder. Combines multihead self attention and feed forward.\\n /// </summary>\\n private class EncoderLayer : Module<Tensor, Tensor?, Tensor>\\n {\\n public readonly SelfAttention attention;\\n public readonly Module<Tensor, Tensor> dropout;\\n public readonly Module<Tensor, Tensor> feed_forward;\\n public readonly Module<Tensor, Tensor> final_layer_norm;\\n public readonly Module<Tensor, Tensor> layer_norm;\\n public bool layer_norm_first;\\n\\n protected override void Dispose(bool disposing)\\n {\\n if (disposing) {\\n dropout.Dispose();\\n feed_forward.Dispose();\\n final_layer_norm.Dispose();\\n layer_norm.Dispose();\\n }\\n base.Dispose(disposing);\\n }\\n\\n public EncoderLayer(\\n string name,\\n SelfAttention attention,\\n double dropout,\\n bool layer_norm_first,\\n Module<Tensor, Tensor> feed_forward) : base(name)\\n {\\n this.attention = attention;\\n this.dropout = nn.Dropout(dropout);\\n this.layer_norm = nn.LayerNorm(new long[] { attention.embed_dim });\\n this.layer_norm_first = layer_norm_first;\\n this.feed_forward = feed_forward;\\n this.final_layer_norm = nn.LayerNorm(new long[] { attention.embed_dim });\\n RegisterComponents();\\n }\\n\\n /// <param name=\\\"x\\\">shape: `(batch, sequence_length, embed_dim)`</param>\\n /// <param name=\\\"attention_mask\\\">shape: `(batch, 1, sequence_length, sequence_length)`</param>\\n /// <returns></returns>\\n public override Tensor forward(\\n Tensor x,\\n Tensor? attention_mask = null)\\n {\\n var residual = x;\\n\\n if (this.layer_norm_first) {\\n x = this.layer_norm.call(x);\\n }\\n\\n x = this.attention.call(x, attention_mask);\\n x = this.dropout.call(x);\\n x = residual + x;\\n\\n if (this.layer_norm_first) {\\n x = x + this.feed_forward.call(this.final_layer_norm.call(x));\\n } else {\\n x = this.layer_norm.call(x);\\n x = this.final_layer_norm.call(x + this.feed_forward.call(x));\\n }\\n return x;\\n }\\n }\\n\\n internal class Transformer : Module<Tensor, Tensor?, Tensor>\\n {\\n public readonly Module<Tensor, Tensor> dropout;\\n public readonly double layer_drop;\\n public readonly Module<Tensor, Tensor> layer_norm;\\n public readonly bool layer_norm_first;\\n public readonly ModuleList<Module<Tensor, Tensor?, Tensor>> layers;\\n\\n public ConvolutionalPositionalEmbedding pos_conv_embed;\\n\\n protected override void Dispose(bool disposing)\\n {\\n if (disposing) {\\n dropout.Dispose();\\n layer_norm.Dispose();\\n }\\n base.Dispose(disposing);\\n }\\n\\n public Transformer(\\n string name,\\n ConvolutionalPositionalEmbedding pos_conv_embed,\\n double dropout,\\n ModuleList<Module<Tensor, Tensor?, Tensor>> layers,\\n bool layer_norm_first,\\n double layer_drop) : base(name)\\n {\\n this.pos_conv_embed = pos_conv_embed;\\n this.layer_norm = nn.LayerNorm(new long[] { pos_conv_embed.embed_dim });\\n this.layer_norm_first = layer_norm_first;\\n this.layer_drop = layer_drop;\\n this.dropout = nn.Dropout(dropout);\\n this.layers = layers;\\n RegisterComponents();\\n }\\n\\n public Tensor _preprocess(Tensor x)\\n {\\n x = x + this.pos_conv_embed.call(x);\\n\\n if (this.layer_norm_first) {\\n x = this.layer_norm.call(x);\\n }\\n\\n x = this.dropout.call(x);\\n return x;\\n }\\n\\n public override Tensor forward(\\n Tensor x,\\n Tensor? attention_mask = null)\\n {\\n x = this._preprocess(x);\\n foreach (var layer in this.layers) {\\n if (!(this.training && torch.rand(1).item<float>() <= this.layer_drop)) {\\n x = ((nn.Module<Tensor, Tensor?, Tensor>)layer).call(x, attention_mask);\\n }\\n }\\n\\n if (!this.layer_norm_first) {\\n x = this.layer_norm.call(x);\\n }\\n\\n return x;\\n }\\n\\n public new Tensor call(Tensor x, Tensor? attention_mask = null) => base.call(x, attention_mask);\\n\\n public Tensor[] get_intermediate_outputs(\\n Tensor x,\\n Tensor? attention_mask = null,\\n int? num_layers = null)\\n {\\n if (num_layers != null) {\\n if (!(0 < num_layers && num_layers <= this.layers.Count)) {\\n throw new ArgumentException($\\\"`num_layers` must be between [1, {this.layers.Count}]\\\");\\n }\\n }\\n var ret = new List<Tensor>();\\n x = this._preprocess(x);\\n foreach (var layer in this.layers) {\\n x = ((nn.Module<Tensor, Tensor?, Tensor>)layer).call(x, attention_mask);\\n ret.Add(x);\\n if (num_layers != null && ret.Count >= num_layers) {\\n return ret.ToArray();\\n }\\n }\\n return ret.ToArray();\\n }\\n }\\n\\n internal class Encoder : Module<Tensor, Tensor?, Tensor>\\n {\\n public readonly Module<Tensor, Tensor> feature_projection;\\n public readonly Transformer transformer;\\n\\n public Encoder(\\n string name,\\n Module<Tensor, Tensor> feature_projection,\\n Transformer transformer) : base(name)\\n {\\n this.feature_projection = feature_projection;\\n this.transformer = transformer;\\n RegisterComponents();\\n }\\n\\n public (Tensor, Tensor?) _preprocess(\\n Tensor features,\\n Tensor? lengths = null)\\n {\\n var x = this.feature_projection.call(features);\\n\\n Tensor? mask = null;\\n if (lengths is not null) {\\n var batch_size = x.size(0);\\n var max_len = x.size(1);\\n // create mask for padded elements and zero-out them\\n mask = torch.arange(max_len, device: lengths.device).expand(batch_size, max_len) >= lengths[TensorIndex.Colon, TensorIndex.None];\\n x[mask] = 0.0;\\n // extend the mask to attention shape and set weight\\n mask = -10000.0 * mask[TensorIndex.Colon, TensorIndex.None, TensorIndex.None, TensorIndex.Colon].to(type: features.dtype);\\n mask = mask.expand(batch_size, 1, max_len, max_len);\\n }\\n return (x, mask);\\n }\\n\\n public override Tensor forward(\\n Tensor features,\\n Tensor? lengths = null)\\n {\\n var (x, mask) = this._preprocess(features, lengths);\\n x = this.transformer.call(x, attention_mask: mask);\\n return x;\\n }\\n\\n public Tensor[] extract_features(\\n Tensor features,\\n Tensor? lengths = null,\\n int? num_layers = null)\\n {\\n var (x, masks) = this._preprocess(features, lengths);\\n return this.transformer.get_intermediate_outputs(x, attention_mask: masks, num_layers: num_layers);\\n }\\n }\\n\\n /// <summary>\\n /// See Also:\\n /// * Original implementation\\n /// https:///github.com/pytorch/fairseq/blob/425c36eafff535fe7337f8bdd5ace22ebacc78cb/fairseq/models/wav2vec/wav2vec2.py#L666-L733\\n /// * \\\"extractor_mode\\\"\\n /// - Def and base:\\n /// https:///github.com/pytorch/fairseq/blob/425c36eafff535fe7337f8bdd5ace22ebacc78cb/fairseq/models/wav2vec/wav2vec2.py#L38-L45\\n /// - Large:\\n /// https:///github.com/pytorch/fairseq/blob/425c36eafff535fe7337f8bdd5ace22ebacc78cb/examples/wav2vec/config/pretraining/wav2vec2_large_librivox.yaml#L52\\n /// * \\\"conv_feature_layers\\\"\\n /// - Def, base and large:\\n /// https:///github.com/pytorch/fairseq/blob/425c36eafff535fe7337f8bdd5ace22ebacc78cb/fairseq/models/wav2vec/wav2vec2.py#L94-L100\\n /// * \\\"conv_bias\\\"\\n /// - Def and base:\\n /// https:///github.com/pytorch/fairseq/blob/425c36eafff535fe7337f8bdd5ace22ebacc78cb/fairseq/models/wav2vec/wav2vec2.py#L101-L103\\n /// - Large:\\n /// https://github.com/pytorch/fairseq/blob/425c36eafff535fe7337f8bdd5ace22ebacc78cb/examples/wav2vec/config/pretraining/wav2vec2_large_librivox.yaml#L61\\n /// If \\\"group_norm\\\", then a single normalization is applied\\n /// </summary>\\n /// <param name=\\\"norm_mode\\\">Either \\\"group_norm\\\" or \\\"layer_norm\\\".\\n /// If \\\"group_norm\\\", then a single normalization is applied\\n /// in the first convolution block. Otherwise, all the convolution\\n /// blocks will have layer normalization.\\n /// This option corresponds to \\\"extractor_mode\\\" from fairseq.\\n /// Expected values are \\\"group_norm\\\" for Base arch, and\\n /// \\\"layer_norm\\\" for Large arch.</param>\\n /// <param name=\\\"shapes\\\">Configuration of convolution layers. List of convolution configuration,\\n /// i.e. ``[(output_channel, kernel_size, stride), ...]``\\n /// This option corresponds to \\\"conv_feature_layers\\\" from fairseq.\\n /// Expected values are\\n /// ``[(512, 10, 5)] + [(512, 3, 2)] * 4 + [(512, 2, 2)] * 2``\\n /// for all the architectures.</param>\\n /// <param name=\\\"bias\\\">Whether to include bias term to each convolution operation.\\n /// This option corresponds to \\\"conv_bias\\\" from fairseq.\\n /// Expected values are False for Base arch, and True for Large arch.</param>\\n /// <returns></returns>\\n /// <exception cref=\\\"ArgumentException\\\"></exception>\\n internal static FeatureExtractor _get_feature_extractor(FeatureExtractorNormMode norm_mode, long[][] shapes, bool bias)\\n {\\n var blocks = ModuleList<Module<Tensor, Tensor?, (Tensor, Tensor?)>> ();\\n long in_channels = 1;\\n for (int i = 0; i < shapes.Length; i++) {\\n var shape = shapes[i];\\n var out_channels = shape[0];\\n var kernel_size = shape[1];\\n var stride = shape[2];\\n Module<Tensor, Tensor>? normalization = null;\\n if (norm_mode == FeatureExtractorNormMode.group_norm && i == 0) {\\n normalization = nn.GroupNorm(\\n num_groups: out_channels,\\n num_channels: out_channels,\\n affine: true);\\n } else if (norm_mode == FeatureExtractorNormMode.layer_norm) {\\n normalization = new Wav2Vec2Model.LayerNorm(\\n \\\"LayerNorm\\\",\\n normalized_shape: new long[] { out_channels },\\n elementwise_affine: true);\\n }\\n blocks.Add(\\n new ConvLayerBlock(\\n \\\"ConvlayerBlock\\\",\\n in_channels: in_channels,\\n out_channels: out_channels,\\n kernel_size: kernel_size,\\n stride: stride,\\n bias: bias,\\n layer_norm: normalization));\\n in_channels = out_channels;\\n }\\n return new FeatureExtractor(\\\"FeatureExtractor\\\", blocks);\\n }\\n\\n /// <summary>\\n /// See Also:\\n /// * \\\"encoder_embed_dim\\\"\\n /// - Def and base\\n /// https://github.com/pytorch/fairseq/blob/425c36eafff535fe7337f8bdd5ace22ebacc78cb/fairseq/models/wav2vec/wav2vec2.py#L49-L51\\n /// - Large\\n /// https://github.com/pytorch/fairseq/blob/425c36eafff535fe7337f8bdd5ace22ebacc78cb/examples/wav2vec/config/pretraining/wav2vec2_large_librivox.yaml#L64\\n /// * \\\"dropout_input\\\"\\n /// - Def, base and large\\n /// https://github.com/pytorch/fairseq/blob/425c36eafff535fe7337f8bdd5ace22ebacc78cb/fairseq/models/wav2vec/wav2vec2.py#L75-L78\\n /// * \\\"conv_pos\\\"\\n /// - Def, base and large\\n /// NOTE: The description is wrong.\\n /// https://github.com/pytorch/fairseq/blob/425c36eafff535fe7337f8bdd5ace22ebacc78cb/fairseq/models/wav2vec/wav2vec2.py#L204-L207\\n /// - Usage\\n /// https://github.com/pytorch/fairseq/blob/425c36eafff535fe7337f8bdd5ace22ebacc78cb/fairseq/models/wav2vec/wav2vec2.py#L756\\n /// * \\\"conv_pos_groups\\\"\\n /// - Def, base and large\\n /// https://github.com/pytorch/fairseq/blob/425c36eafff535fe7337f8bdd5ace22ebacc78cb/fairseq/models/wav2vec/wav2vec2.py#L208-L211\\n /// * \\\"encoder_layers\\\"\\n /// - Def and base\\n /// https://github.com/pytorch/fairseq/blob/425c36eafff535fe7337f8bdd5ace22ebacc78cb/fairseq/models/wav2vec/wav2vec2.py#L46-L48\\n /// - Large\\n /// https://github.com/pytorch/fairseq/blob/425c36eafff535fe7337f8bdd5ace22ebacc78cb/examples/wav2vec/config/pretraining/wav2vec2_large_librivox.yaml#L63\\n /// * \\\"encoder_attention_heads\\\"\\n /// - Def and base\\n /// https://github.com/pytorch/fairseq/blob/425c36eafff535fe7337f8bdd5ace22ebacc78cb/fairseq/models/wav2vec/wav2vec2.py#L55-L57\\n /// - Large\\n /// https://github.com/pytorch/fairseq/blob/425c36eafff535fe7337f8bdd5ace22ebacc78cb/examples/wav2vec/config/pretraining/wav2vec2_large_librivox.yaml#L66\\n /// * \\\"attention_dropout\\\"\\n /// - Def and base\\n /// https://github.com/pytorch/fairseq/blob/425c36eafff535fe7337f8bdd5ace22ebacc78cb/fairseq/models/wav2vec/wav2vec2.py#L66-L68\\n /// - Large\\n /// https://github.com/pytorch/fairseq/blob/425c36eafff535fe7337f8bdd5ace22ebacc78cb/examples/wav2vec/config/pretraining/wav2vec2_large_librivox.yaml#L60\\n /// * \\\"encoder_ffn_embed_dim\\\"\\n /// - Def and base\\n /// https://github.com/pytorch/fairseq/blob/425c36eafff535fe7337f8bdd5ace22ebacc78cb/fairseq/models/wav2vec/wav2vec2.py#L52-L54\\n /// - Large\\n /// https://github.com/pytorch/fairseq/blob/425c36eafff535fe7337f8bdd5ace22ebacc78cb/examples/wav2vec/config/pretraining/wav2vec2_large_librivox.yaml#L65\\n /// * \\\"activation_dropout\\\"\\n /// - Def\\n /// https://github.com/pytorch/fairseq/blob/425c36eafff535fe7337f8bdd5ace22ebacc78cb/fairseq/models/wav2vec/wav2vec2.py#L69-L71\\n /// - Base\\n /// https://github.com/pytorch/fairseq/blob/425c36eafff535fe7337f8bdd5ace22ebacc78cb/examples/wav2vec/config/finetuning/base_960h.yaml#L55\\n /// - Large\\n /// https://github.com/pytorch/fairseq/blob/425c36eafff535fe7337f8bdd5ace22ebacc78cb/examples/wav2vec/config/finetuning/vox_960h.yaml#L55\\n /// * \\\"dropout\\\"\\n /// - Def and base\\n /// https://github.com/pytorch/fairseq/blob/425c36eafff535fe7337f8bdd5ace22ebacc78cb/fairseq/models/wav2vec/wav2vec2.py#L63-L65\\n /// - Large\\n /// https://github.com/pytorch/fairseq/blob/425c36eafff535fe7337f8bdd5ace22ebacc78cb/examples/wav2vec/config/pretraining/wav2vec2_large_librivox.yaml#L59\\n /// * \\\"layer_norm_first\\\"\\n /// - Def and base\\n /// https://github.com/pytorch/fairseq/blob/425c36eafff535fe7337f8bdd5ace22ebacc78cb/fairseq/models/wav2vec/wav2vec2.py#L91-L93\\n /// - Large\\n /// https://github.com/pytorch/fairseq/blob/425c36eafff535fe7337f8bdd5ace22ebacc78cb/examples/wav2vec/config/pretraining/wav2vec2_large_librivox.yaml#L53\\n /// * \\\"layerdrop\\\"\\n /// - Def\\n /// https://github.com/pytorch/fairseq/blob/425c36eafff535fe7337f8bdd5ace22ebacc78cb/fairseq/models/wav2vec/wav2vec2.py#L72-L74\\n /// - Base\\n /// https://github.com/pytorch/fairseq/blob/425c36eafff535fe7337f8bdd5ace22ebacc78cb/examples/wav2vec/config/finetuning/base_960h.yaml#L54\\n /// - Large\\n /// https://github.com/pytorch/fairseq/blob/425c36eafff535fe7337f8bdd5ace22ebacc78cb/examples/wav2vec/config/finetuning/vox_960h.yaml#L54\\n /// </summary>\\n /// <param name=\\\"in_features\\\">in_features (int): The number of input features.</param>\\n /// <param name=\\\"embed_dim\\\">The dimension of embedding.\\n /// This option corresponds to \\\"encoder_embed_dim\\\" from fairseq.\\n /// Expected values are 768 for Base arch, and 1024 for Large arch.</param>\\n /// <param name=\\\"dropout_input\\\">The dropout probability applied after the input feature is projected\\n /// to ``embed_dim``.\\n /// This option corresponds to \\\"dropout_input\\\" from fairseq.\\n /// Expected values are 0.1 for both Base and Large arch.</param>\\n /// <param name=\\\"pos_conv_kernel\\\">The kernel size of convolutional positional embeddings.\\n /// This option corresponds to \\\"conv_pos\\\" from fairseq.\\n /// Expected values are 128 for both Base and Large arch.</param>\\n /// <param name=\\\"pos_conv_groups\\\">The number of groups of convolutional positional embeddings.\\n /// This option corresponds to \\\"conv_pos_groups\\\" from fairseq.\\n /// Expected values are 16 for both Base and Large arch.</param>\\n /// <param name=\\\"num_layers\\\">The number of self attention layers in transformer block.\\n /// This option corresponds to \\\"encoder_layers\\\" from fairseq.\\n /// Expected values are 12 for Base and 24 for Large arch.</param>\\n /// <param name=\\\"num_heads\\\">The number of heads in self attention layers.\\n /// This option corresponds to \\\"encoder_attention_heads\\\" from fairseq.\\n /// Expected values are 12 for Base and 16 for Large arch.</param>\\n /// <param name=\\\"attention_dropout\\\">The dropout probability applied after softmax in self-attention layer.\\n /// This option corresponds to \\\"attention_dropout\\\" from fairseq.\\n /// Expected values are 0.1 for Base and 0.0 for Large arch.</param>\\n /// <param name=\\\"ff_interm_features\\\">The dimension of hidden features in feed forward layer.\\n /// This option corresponds to \\\"encoder_ffn_embed_dim\\\" from fairseq.\\n /// Expected values are 3072 for Base and 4096 for Large arch.</param>\\n /// <param name=\\\"ff_interm_dropout\\\">The dropout probability applied in feedforward layer.\\n /// This option correspinds to \\\"activation_dropout\\\" from fairseq.\\n /// Expected values are 0.1 for both Base and Large arch.</param>\\n /// <param name=\\\"dropout\\\">The dropout probability applied at the end of feed forward layer.\\n /// This option corresponds to \\\"dropout\\\" from fairseq.\\n /// Expected values are 0.1 for Base and 0.0 for Large arch.</param>\\n /// <param name=\\\"layer_norm_first\\\">Control the order of layer norm in transformer layer and each encoder layer.\\n /// If True, in transformer layer, layer norm is applied before features are fed\\n /// to encoder layers. In encoder layer, two layer norms are applied before and after\\n /// self attention.\\n /// If False, in transformer layer, layer norm is applied after features are fed\\n /// to encoder layers. In encoder layer, two layer norms are applied after self\\n /// attention, before and after feed forward.\\n /// This option corresponds to \\\"layer_norm_first\\\" from fairseq.\\n /// Expected values are False for Base and True for Large arch.</param>\\n /// <param name=\\\"layer_drop\\\">Probability to drop each encoder layer during training.\\n /// This option corresponds to \\\"layerdrop\\\" from fairseq.\\n /// Expected values are 0.1 for both Base and Large arch.</param>\\n /// <returns></returns>\\n internal static Encoder _get_encoder(\\n long in_features,\\n long embed_dim,\\n double dropout_input,\\n long pos_conv_kernel,\\n long pos_conv_groups,\\n long num_layers,\\n long num_heads,\\n double attention_dropout,\\n long ff_interm_features,\\n double ff_interm_dropout,\\n double dropout,\\n bool layer_norm_first,\\n double layer_drop)\\n {\\n var feature_projection = new FeatureProjection(\\\"featureprojection\\\", in_features, embed_dim, dropout_input);\\n var pos_conv = new ConvolutionalPositionalEmbedding(\\\"convolutionalpositionalembedding\\\", embed_dim, pos_conv_kernel, pos_conv_groups);\\n\\n // Original impl\\n // https://github.com/pytorch/fairseq/blob/425c36eafff535fe7337f8bdd5ace22ebacc78cb/fairseq/models/wav2vec/wav2vec2.py#L768-L782\\n var encoder_layers = ModuleList<Module<Tensor, Tensor?, Tensor>>();\\n for (long i = 0; i < num_layers; i++) {\\n var attention = new SelfAttention(\\n \\\"SelfAttention\\\",\\n embed_dim: embed_dim,\\n num_heads: num_heads,\\n dropout: attention_dropout);\\n var feed_forward = new FeedForward(\\n \\\"FeedForward\\\",\\n io_features: embed_dim,\\n intermediate_features: ff_interm_features,\\n intermediate_dropout: ff_interm_dropout,\\n output_dropout: dropout);\\n encoder_layers.append(\\n new EncoderLayer(\\n \\\"EncoderLayer\\\",\\n attention: attention,\\n dropout: dropout,\\n layer_norm_first: layer_norm_first,\\n feed_forward: feed_forward));\\n }\\n var transformer = new Transformer(\\n \\\"Transformer\\\",\\n pos_conv_embed: pos_conv,\\n dropout: dropout,\\n layers: encoder_layers,\\n layer_norm_first: !layer_norm_first,\\n layer_drop: layer_drop);\\n return new Encoder(\\\"Encoder\\\", feature_projection, transformer);\\n }\\n\\n /// <summary>\\n /// Computes random mask spans for a given shape.\\n /// </summary>\\n /// <param name=\\\"shape\\\">The shape for which to compute masks.\\n /// The first element is batch size and second is the number of frames.</param>\\n /// <param name=\\\"padding_mask\\\">The padding mask of the same dimension as shape,\\n /// which will prevent masking padded elements.</param>\\n /// <param name=\\\"mask_prob\\\">Probability for each token to be chosen as start of the span to be masked.\\n /// This will be multiplied by number of timesteps divided by length of mask span to mask\\n /// approximately this percentage of all elements. However due to overlaps, the actual number\\n /// will be smaller (unless no_overlap is True).</param>\\n /// <param name=\\\"mask_length\\\"></param>\\n /// <param name=\\\"mask_type\\\">How to compute mask lengths. Options: [``static``, ``uniform``, ``normal``, ``poisson``].\\n /// ``static``: Fixed size\\n /// ``uniform``: Sample from uniform distribution [mask_other, mask_length*2]\\n /// ``normal``: Sample from normal distribution with mean ``mask_length`` and stdev ``mask_other``.\\n /// ``poisson``: Sample from possion distribution with lambda = ``mask_length``.</param>\\n /// <param name=\\\"mask_other\\\"></param>\\n /// <param name=\\\"min_masks\\\">Minimum number of masked spans.</param>\\n /// <param name=\\\"no_overlap\\\">If false, will switch to an alternative recursive algorithm\\n /// that prevents spans from overlapping.</param>\\n /// <param name=\\\"min_space\\\">How many frames to keep unmasked between spans (Only used if no_overlap is true).</param>\\n /// <returns>The mask indices of dimension `[batch, frame]`.</returns>\\n /// <exception cref=\\\"Exception\\\"></exception>\\n private static Tensor _compute_mask_indices(\\n long[] shape,\\n Tensor? padding_mask,\\n double mask_prob,\\n long mask_length,\\n string mask_type = \\\"static\\\",\\n double mask_other = 0.0,\\n long min_masks = 0,\\n bool no_overlap = false,\\n long min_space = 0)\\n {\\n long min_len;\\n var batch_size = shape[0];\\n var frame = shape[1];\\n var mask = torch.full(new long[] { batch_size, frame }, false);\\n // add a random number for probabilistic rounding\\n var all_num_mask = (long)(mask_prob * frame / mask_length + torch.rand(1));\\n\\n all_num_mask = Math.Max(min_masks, all_num_mask);\\n\\n var mask_idcs = new List<Tensor>();\\n for (long i = 0; i < batch_size; i++) {\\n Tensor mask_idc;\\n Tensor lengths;\\n long num_mask;\\n long sz;\\n\\n if (padding_mask is not null) {\\n sz = frame - padding_mask[i].@long().sum().item<long>();\\n // add a random number for probabilistic rounding\\n num_mask = (long)((mask_prob * sz / mask_length) + torch.rand(1));\\n num_mask = Math.Max(min_masks, num_mask);\\n } else {\\n sz = frame;\\n num_mask = all_num_mask;\\n }\\n\\n if (mask_type == \\\"static\\\") {\\n lengths = torch.full(new long[] { num_mask }, mask_length);\\n } else if (mask_type == \\\"uniform\\\") {\\n lengths = torch.randint((long)mask_other, mask_length * 2 + 1, size: new long[] { num_mask });\\n } else if (mask_type == \\\"normal\\\") {\\n lengths = torch.normal(mask_length, mask_other, size: new long[] { num_mask });\\n lengths = torch.maximum(torch.ones(1), torch.round(lengths)).@int();\\n } else if (mask_type == \\\"poisson\\\") {\\n // torch.poisson() of PyTorch doesn't accept the argument `size'.\\n throw new Exception($\\\"unsupported mask selection: {mask_type}\\\");\\n // lengths = torch.poisson(mask_length, size: new long[] { num_mask });\\n // lengths = torch.round(lengths).@int();\\n } else {\\n throw new Exception($\\\"unknown mask selection: {mask_type}\\\");\\n }\\n\\n if (lengths.sum().item<int>() == 0) {\\n lengths[0] = Math.Min(mask_length, sz - 1);\\n }\\n\\n if (no_overlap) {\\n var mask_idc_data = new List<long>();\\n\\n List<(long, long)> arrange(long s, long e, long length, long keep_length)\\n {\\n long span_start = torch.randint(s, e - length, dtype: torch.int64, size: new long[] { 1 }).item<long>();\\n for (long i = 0; i < length; i++) {\\n mask_idc_data.Add(span_start + i);\\n }\\n var new_parts = new List<(long, long)>();\\n if (span_start - s - min_space >= keep_length) {\\n new_parts.Add((s, span_start - min_space + 1));\\n }\\n if (e - span_start - keep_length - min_space > keep_length) {\\n new_parts.Add((span_start + length + min_space, e));\\n }\\n return new_parts;\\n }\\n\\n var parts = new List<(long, long)> { (0, sz) };\\n var min_length = torch.min(lengths).item<long>();\\n foreach (var length in lengths.data<long>().OrderByDescending(x => x)) {\\n var lens = torch.tensor((\\n from p in parts\\n select p.Item2 - p.Item1).ToArray(), dtype: torch.int64);\\n lens[lens < length + min_space] = 0;\\n long l_sum = lens.sum().item<long>();\\n if (l_sum == 0) {\\n break;\\n }\\n var probs = lens / l_sum;\\n var c = (int)torch.distributions.Categorical(probs).sample().item<long>();\\n var (s, e) = parts[c];\\n parts.RemoveAt(c);\\n parts.AddRange(arrange(s, e, length, min_length));\\n }\\n mask_idc = torch.tensor(mask_idc_data.ToArray());\\n } else {\\n min_len = torch.min(lengths).item<long>();\\n if (sz - min_len <= num_mask) {\\n min_len = sz - num_mask - 1;\\n }\\n\\n mask_idc = torch.multinomial(torch.ones(new long[] { sz - min_len }), num_samples: num_mask, replacement: false);\\n\\n mask_idc = torch.tensor((\\n from j in Enumerable.Range(0, (int)mask_idc.size(0))\\n from offset in Enumerable.Range(0, (int)lengths[j].item<long>())\\n select (mask_idc[j].item<long>() + offset)).ToArray());\\n }\\n\\n mask_idcs.Add(mask_idc[mask_idc < sz].unique().Item1);\\n }\\n\\n min_len = (from m in mask_idcs select m.size(0)).Min();\\n for (int i = 0; i < mask_idcs.Count; i++) {\\n var mask_idc = mask_idcs[i];\\n if (mask_idc.size(0) > min_len) {\\n mask_idc = torch.index_select(\\n mask_idc,\\n 0,\\n torch.multinomial(\\n torch.ones(new long[] { mask_idc.shape[0] }),\\n num_samples: min_len,\\n replacement: false));\\n }\\n mask[i, mask_idc] = true;\\n }\\n\\n return mask;\\n }\\n\\n /// <summary>\\n /// Generate the padding mask given the padded input and the lengths Tensors.\\n /// </summary>\\n /// <param name=\\\"input\\\">The padded Tensor of dimension `[batch, max_len, frequency]`.</param>\\n /// <param name=\\\"lengths\\\">The lengths Tensor of dimension `[batch,]`.</param>\\n /// <returns>The padding mask.</returns>\\n internal static Tensor _get_padding_mask(Tensor input, Tensor lengths)\\n {\\n var batch_size = input.size(0);\\n var max_len = input.size(1);\\n var mask = torch.arange(max_len, device: lengths.device).expand(batch_size, max_len) >= lengths[TensorIndex.Colon, TensorIndex.None];\\n return mask;\\n }\\n\\n /// <summary>\\n /// Generate the masks for masked prediction.\\n /// </summary>\\n internal class MaskGenerator : Module<Tensor, Tensor?, (Tensor, Tensor?)>\\n {\\n public readonly long mask_channel_length;\\n public readonly long mask_channel_min_space;\\n public readonly double mask_channel_other;\\n public readonly double mask_channel_prob;\\n public readonly string mask_channel_selection;\\n public readonly Parameter mask_embedding;\\n public readonly long mask_length;\\n public readonly long mask_min_space;\\n public readonly double mask_other;\\n public readonly double mask_prob;\\n public readonly string mask_selection;\\n public readonly bool no_mask_channel_overlap;\\n public readonly bool no_mask_overlap;\\n\\n protected override void Dispose(bool disposing)\\n {\\n if (disposing) {\\n mask_embedding.Dispose();\\n }\\n base.Dispose(disposing);\\n }\\n\\n /// <param name=\\\"name\\\"></param>\\n /// <param name=\\\"encoder_embed_dim\\\">The dimension of the transformer embedding output.</param>\\n /// <param name=\\\"mask_prob\\\">Probability for each token to be chosen as start of the span to be masked.\\n /// This will be multiplied by number of timesteps divided by length of mask span to mask\\n /// approximately this percentage of all elements. However due to overlaps, the actual number\\n /// will be smaller (unless no_overlap is True).</param>\\n /// <param name=\\\"mask_selection\\\">How to choose the mask length.\\n /// Options: [``static``, ``uniform``, ``normal``, ``poisson``].</param>\\n /// <param name=\\\"mask_other\\\">Secondary mask argument (used for more complex distributions).</param>\\n /// <param name=\\\"mask_length\\\">The lengths of the mask.</param>\\n /// <param name=\\\"no_mask_overlap\\\">Whether to allow masks to overlap.</param>\\n /// <param name=\\\"mask_min_space\\\">Minimum space between spans (if no overlap is enabled).</param>\\n /// <param name=\\\"mask_channel_prob\\\">The probability of replacing a feature with 0.</param>\\n /// <param name=\\\"mask_channel_selection\\\">How to choose the mask length for channel masking.\\n /// Options: [``static``, ``uniform``, ``normal``, ``poisson``].</param>\\n /// <param name=\\\"mask_channel_other\\\">Secondary mask argument for channel masking(used for more complex distributions).</param>\\n /// <param name=\\\"mask_channel_length\\\">Minimum space between spans (if no overlap is enabled) for channel masking.</param>\\n /// <param name=\\\"no_mask_channel_overlap\\\">Whether to allow channel masks to overlap.</param>\\n /// <param name=\\\"mask_channel_min_space\\\">Minimum space between spans for channel masking(if no overlap is enabled).</param>\\n public MaskGenerator(\\n string name,\\n long encoder_embed_dim,\\n double mask_prob,\\n string mask_selection,\\n double mask_other,\\n long mask_length,\\n bool no_mask_overlap,\\n long mask_min_space,\\n double mask_channel_prob,\\n string mask_channel_selection,\\n double mask_channel_other,\\n long mask_channel_length,\\n bool no_mask_channel_overlap,\\n long mask_channel_min_space) : base(name)\\n {\\n this.mask_prob = mask_prob;\\n this.mask_selection = mask_selection;\\n this.mask_other = mask_other;\\n this.mask_length = mask_length;\\n this.no_mask_overlap = no_mask_overlap;\\n this.mask_min_space = mask_min_space;\\n this.mask_channel_prob = mask_channel_prob;\\n this.mask_channel_selection = mask_channel_selection;\\n this.mask_channel_other = mask_channel_other;\\n this.mask_channel_length = mask_channel_length;\\n this.no_mask_channel_overlap = no_mask_channel_overlap;\\n this.mask_channel_min_space = mask_channel_min_space;\\n this.mask_embedding = Parameter(torch.empty(encoder_embed_dim, dtype: torch.float32));\\n torch.nn.init.uniform_(this.mask_embedding);\\n RegisterComponents();\\n }\\n\\n /// <param name=\\\"x\\\">The encoded representations after feature extraction module.</param>\\n /// <param name=\\\"padding_mask\\\">The padding mask of the same dimension as shape,\\n /// which will prevent masking padded elements.</param>\\n /// <returns>\\n /// The feature representations after masking.\\n /// The generated mask indices.\\n /// </returns>\\n public override (Tensor, Tensor?) forward(Tensor x, Tensor? padding_mask)\\n {\\n Tensor? mask_indices;\\n var B = x.size(0);\\n var T = x.size(1);\\n var C = x.size(2);\\n if (this.mask_prob > 0) {\\n mask_indices = _compute_mask_indices(\\n new long[] { B, T },\\n padding_mask,\\n this.mask_prob,\\n this.mask_length,\\n this.mask_selection,\\n this.mask_other,\\n min_masks: 2,\\n no_overlap: this.no_mask_overlap,\\n min_space: this.mask_min_space);\\n mask_indices = mask_indices.to(x.device);\\n x[mask_indices] = this.mask_embedding;\\n } else {\\n mask_indices = null;\\n }\\n\\n if (this.mask_channel_prob > 0) {\\n var mask_channel_indices = _compute_mask_indices(\\n new long[] { B, C },\\n null,\\n this.mask_channel_prob,\\n this.mask_channel_length,\\n this.mask_channel_selection,\\n this.mask_channel_other,\\n no_overlap: this.no_mask_channel_overlap,\\n min_space: this.mask_channel_min_space);\\n mask_channel_indices = mask_channel_indices.to(x.device).unsqueeze(1).expand(-1, T, -1);\\n x[mask_channel_indices] = 0;\\n }\\n\\n return (x, mask_indices);\\n }\\n }\\n\\n /// <summary>\\n /// Compute the logits of the embeddings.\\n /// </summary>\\n /// <param name=\\\"proj_x\\\">The projected masked representations of dimension `[batch, frame, final_dim]`.</param>\\n /// <param name=\\\"target\\\">The target Tensor of dimension `[batch, frame, final_dim]`.</param>\\n /// <param name=\\\"label_embeddings\\\">The trainable embeddings of target of dimension `[num_class, final_dim]`.</param>\\n /// <returns>The logits of the inputs.</returns>\\n private static Tensor _compute_logits(\\n Tensor proj_x,\\n Tensor target,\\n Tensor label_embeddings)\\n {\\n var logit_temp = 0.1;\\n var pos = torch.index_select(label_embeddings, 0, target.@long());\\n var negs = label_embeddings.unsqueeze(1).expand(-1, proj_x.size(0), -1);\\n var neg_is_pos = (pos == negs).all(-1);\\n pos = pos.unsqueeze(0);\\n var targets = torch.cat(new Tensor[] { pos, negs }, dim: 0);\\n\\n var logits = torch.nn.functional.cosine_similarity(proj_x.@float(), targets.@float(), dim: -1).type_as(proj_x);\\n logits /= logit_temp;\\n if (neg_is_pos.any().item<bool>()) {\\n logits[1][neg_is_pos] = double.NegativeInfinity;\\n }\\n logits = logits.transpose(0, 1); // (num_x, num_cls+1)\\n return logits;\\n }\\n\\n /// <summary>\\n /// Generate the logits of masked and unmasked inputs.\\n /// </summary>\\n internal class LogitGenerator : Module<Tensor, Tensor, Tensor, Tensor, (Tensor?, Tensor?)>\\n {\\n public readonly Module<Tensor, Tensor> final_proj;\\n public readonly Tensor label_embeddings;\\n public readonly bool skip_masked;\\n public readonly bool skip_nomask;\\n\\n protected override void Dispose(bool disposing)\\n {\\n if (disposing) {\\n final_proj.Dispose();\\n label_embeddings.Dispose();\\n }\\n base.Dispose(disposing);\\n }\\n\\n /// <param name=\\\"name\\\"></param>\\n /// <param name=\\\"encoder_embed_dim\\\">The dimension of the transformer embedding output.</param>\\n /// <param name=\\\"num_classes\\\">The number of classes in the labels.</param>\\n /// <param name=\\\"final_dim\\\">Project final representations and targets to `final_dim`.</param>\\n /// <param name=\\\"skip_masked\\\">If true, skip computing losses over masked frames.</param>\\n /// <param name=\\\"skip_nomask\\\">If true, skip computing losses over unmasked frames.</param>\\n public LogitGenerator(\\n string name,\\n long encoder_embed_dim,\\n long num_classes,\\n long final_dim,\\n bool skip_masked,\\n bool skip_nomask) : base(name)\\n {\\n this.label_embeddings = Parameter(torch.empty(new long[] { num_classes, final_dim }, dtype: torch.float32));\\n torch.nn.init.uniform_(this.label_embeddings);\\n this.final_proj = torch.nn.Linear(encoder_embed_dim, final_dim);\\n this.skip_masked = skip_masked;\\n this.skip_nomask = skip_nomask;\\n RegisterComponents();\\n }\\n\\n /// <param name=\\\"x\\\">The feature representation of the last transformer layer.</param>\\n /// <param name=\\\"label\\\"> The label Tensor of dimension `[batch, frame]`.</param>\\n /// <param name=\\\"mask_m\\\">The masked indices of dimension `[batch, frame]`.</param>\\n /// <param name=\\\"mask_u\\\">The unmasked indices of dimension `[batch, frame]`.</param>\\n /// <returns>\\n /// The logits of masked frames. Tensor of dimension `[masked_frame, final_dim]`.\\n /// The logits of unmasked frames. Tensor of dimension `[unmasked_frame, final_dim]`.\\n /// </returns>\\n public override (Tensor?, Tensor?) forward(Tensor x, Tensor label, Tensor mask_m, Tensor mask_u)\\n {\\n Tensor? logit_u;\\n Tensor? logit_m;\\n var proj_x = this.final_proj.call(x);\\n if (this.skip_masked) {\\n logit_m = null;\\n } else {\\n var proj_x_m = proj_x[mask_m];\\n var label_m = label[mask_m];\\n logit_m = _compute_logits(proj_x_m, label_m, this.label_embeddings);\\n }\\n\\n if (this.skip_nomask) {\\n logit_u = null;\\n } else {\\n var proj_x_u = proj_x[mask_u];\\n var label_u = label[mask_u];\\n logit_u = _compute_logits(proj_x_u, label_u, this.label_embeddings);\\n }\\n return (logit_m, logit_u);\\n }\\n }\\n\\n#if true\\n // TODO: https://github.com/dotnet/TorchSharp/issues/606\\n internal static class GradMultiply\\n {\\n public static Tensor apply(Tensor x, double scale)\\n {\\n return x;\\n }\\n }\\n#else\\n internal class GradMultiply : torch.autograd.Function\\n {\\n private double scale;\\n\\n public static Tensor forward(GradMultiply ctx, Tensor x, double scale)\\n {\\n ctx.scale = scale;\\n var res = x.@new(x);\\n return res;\\n }\\n\\n public static (Tensor, object?) backward(GradMultiply ctx, Tensor grad)\\n {\\n return (grad * ctx.scale, null);\\n }\\n }\\n#endif\\n }\\n}\"\n },\n {\n \"alpha_fraction\": 0.6117035150527954,\n \"alphanum_fraction\": 0.6660597920417786,\n \"avg_line_length\": 21.10344886779785,\n \"blob_id\": \"cc7561fb06511df8e8424892f4cf099489dd1554\",\n \"content_id\": \"57ef8430be0966ac83124c30dadc4fec66375eee\",\n \"detected_licenses\": [\n \"MIT\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 3845,\n \"license_type\": \"permissive\",\n \"max_line_length\": 129,\n \"num_lines\": 174,\n \"path\": \"/test/TorchSharpTest/optim.py\",\n \"repo_name\": \"NiklasGustafsson/TorchSharp\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"# Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information.\\n#\\n# This file is used to regenerate the optimizer state_dict test files,\\n# where PyTorch optimizer state is transferred to TorchSharp.\\n#\\n# Execute this from the command line in the $/tests/TorchSharpTest directory.\\n\\n\\nimport torch\\nimport shutil\\n\\nshutil.copy2('../../src/Python/exportsd.py', 'exportsd.py')\\n\\nimport exportsd\\n\\nlin1 = torch.nn.Linear(10,10)\\nlin2 = torch.nn.Linear(10,10)\\n\\ninput = torch.rand(4,10)\\n\\n# SGD\\n\\noptim = torch.optim.SGD(lin1.parameters(), lr=0.001, momentum=0.1)\\n\\noutput = lin1(input).sum()\\noutput.backward()\\n\\noptim.step()\\n\\nf = open(\\\"sgd1.dat\\\", \\\"wb\\\")\\nexportsd.save_sgd(optim, f)\\nf.close()\\n\\n# ASGD\\n\\noptim = torch.optim.ASGD(lin1.parameters(), lr=0.001, alpha=0.65, t0=1e5, lambd=1e-3)\\n\\noutput = lin1(input).sum()\\noutput.backward()\\n\\noptim.step()\\n\\nf = open(\\\"asgd1.dat\\\", \\\"wb\\\")\\nexportsd.save_asgd(optim, f)\\nf.close()\\n\\n\\n# RMSprop\\n\\nseq = torch.nn.Sequential(lin1, lin2)\\n\\n\\noptim = torch.optim.RMSprop(lin1.parameters(), lr=0.001, momentum=0.1)\\noptim.add_param_group({'params': lin2.parameters(), 'lr': 0.01, 'momentum' : 0, 'centered': True})\\n\\noutput = seq(input).sum()\\noutput.backward()\\n\\noptim.step()\\n\\nf = open(\\\"rmsprop1.dat\\\", \\\"wb\\\")\\nexportsd.save_rmsprop(optim, f)\\nf.close()\\n\\n# Rprop\\n\\noptim = torch.optim.Rprop(lin1.parameters(), lr=0.001, etas=(0.35, 1.5), step_sizes=(1e-5, 5))\\noptim.add_param_group({'params': lin2.parameters(), 'lr': 0.01, 'etas': (0.45, 1.5), 'step_sizes': (1e-5, 5), 'maximize': True})\\n\\noutput = seq(input).sum()\\noutput.backward()\\n\\noptim.step()\\n\\nf = open(\\\"rprop1.dat\\\", \\\"wb\\\")\\nexportsd.save_rprop(optim, f)\\nf.close()\\n\\n# Adam\\n\\noptim = torch.optim.Adam(lin1.parameters(), lr=0.001, betas=(0.8, 0.9))\\noptim.add_param_group({'params': lin2.parameters(), 'lr': 0.01, 'betas' : (0.7, 0.79), 'amsgrad': True})\\n\\noutput = seq(input).sum()\\noutput.backward()\\n\\noptim.step()\\n\\nf = open(\\\"adam1.dat\\\", \\\"wb\\\")\\nexportsd.save_adam(optim, f)\\nf.close()\\n\\n# AdamW\\n\\noptim = torch.optim.AdamW(lin1.parameters(), lr=0.001, betas=(0.8, 0.9))\\noptim.add_param_group({'params': lin2.parameters(), 'lr': 0.01, 'betas' : (0.7, 0.79), 'amsgrad': True})\\n\\noutput = seq(input).sum()\\noutput.backward()\\n\\noptim.step()\\n\\nf = open(\\\"adamw1.dat\\\", \\\"wb\\\")\\nexportsd.save_adamw(optim, f)\\nf.close()\\n\\n# NAdam\\n\\noptim = torch.optim.NAdam(lin1.parameters(), lr=0.001, betas=(0.8, 0.9))\\noptim.add_param_group({'params': lin2.parameters(), 'lr': 0.01, 'betas' : (0.7, 0.79), 'weight_decay': 0.3})\\n\\noutput = seq(input).sum()\\noutput.backward()\\n\\noptim.step()\\n\\nf = open(\\\"nadam1.dat\\\", \\\"wb\\\")\\nexportsd.save_nadam(optim, f)\\nf.close()\\n\\n# RAdam\\n\\noptim = torch.optim.RAdam(lin1.parameters(), lr=0.001, betas=(0.8, 0.9))\\noptim.add_param_group({'params': lin2.parameters(), 'lr': 0.01, 'betas' : (0.7, 0.79), 'weight_decay': 0.3})\\n\\noutput = seq(input).sum()\\noutput.backward()\\n\\noptim.step()\\n\\nf = open(\\\"radam1.dat\\\", \\\"wb\\\")\\nexportsd.save_radam(optim, f)\\nf.close()\\n\\n# Adamax\\n\\noptim = torch.optim.Adamax(lin1.parameters(), lr=0.001, betas=(0.8, 0.9))\\noptim.add_param_group({'params': lin2.parameters(), 'lr': 0.01, 'betas' : (0.7, 0.79), 'weight_decay' : 0.3})\\n\\noutput = seq(input).sum()\\noutput.backward()\\n\\noptim.step()\\n\\nf = open(\\\"adamax1.dat\\\", \\\"wb\\\")\\nexportsd.save_adamax(optim, f)\\nf.close()\\n\\n# Adadelta\\n\\noptim = torch.optim.Adadelta(lin1.parameters(), lr=0.001, rho=0.85, weight_decay=0.3)\\noptim.add_param_group({'params': lin2.parameters(), 'lr': 0.01, 'rho' : 0.79, 'maximize': True})\\n\\noutput = seq(input).sum()\\noutput.backward()\\n\\noptim.step()\\n\\nf = open(\\\"adadelta1.dat\\\", \\\"wb\\\")\\nexportsd.save_adadelta(optim, f)\\nf.close()\\n\\n# Adagrad\\n\\noptim = torch.optim.Adagrad(lin1.parameters(), lr=0.001, lr_decay=0.85, weight_decay=0.3)\\n\\noutput = seq(input).sum()\\noutput.backward()\\n\\noptim.step()\\n\\nf = open(\\\"adagrad1.dat\\\", \\\"wb\\\")\\nexportsd.save_adagrad(optim, f)\\nf.close()\"\n },\n {\n \"alpha_fraction\": 0.7681818008422852,\n \"alphanum_fraction\": 0.7863636612892151,\n \"avg_line_length\": 72.33333587646484,\n \"blob_id\": \"d8a18130a01f5db76922392f758dd42fc0b5056c\",\n \"content_id\": \"6500791e3099af4c4e45047d05d70a671e80441f\",\n \"detected_licenses\": [\n \"MIT\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"C#\",\n \"length_bytes\": 222,\n \"license_type\": \"permissive\",\n \"max_line_length\": 178,\n \"num_lines\": 3,\n \"path\": \"/src/TorchSharp/GlobalSuppressions.cs\",\n \"repo_name\": \"NiklasGustafsson/TorchSharp\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"using System.Diagnostics.CodeAnalysis;\\n\\n[assembly: SuppressMessage(\\\"Style\\\", \\\"IDE1006:Naming Styles\\\", Justification = \\\"Solution uses Python naming conventions\\\", Scope = \\\"namespaceanddescendants\\\", Target = \\\"TorchSharp\\\")]\\n\"\n },\n {\n \"alpha_fraction\": 0.7155542373657227,\n \"alphanum_fraction\": 0.7226277589797974,\n \"avg_line_length\": 42.28338623046875,\n \"blob_id\": \"35b02df0b5dcd71af9f531394a7fc55f7061c1c5\",\n \"content_id\": \"9a098be990cd9ddfec55a656feed222c93ee1ecb\",\n \"detected_licenses\": [\n \"MIT\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Markdown\",\n \"length_bytes\": 13325,\n \"license_type\": \"permissive\",\n \"max_line_length\": 477,\n \"num_lines\": 307,\n \"path\": \"/docfx/articles/memory.md\",\n \"repo_name\": \"NiklasGustafsson/TorchSharp\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"# Memory Management\\n\\nThree approaches are available for memory management. Technique 1 is the default and simplest way to program. It is the recommended starting point.\\n\\n- If having trouble with CPU memory you may have to resort to technique 2 or 3.\\n\\n- If having trouble with GPU memory you may have to resort to technique 2 or 3.\\n\\nIn both cases, you may want to experiment with using a smaller batch size -- temporary tensor values produced by computation on the training data is the main memory problem, and they are in most cases proportional to the batch size. A smaller batch size means more batches, which will take longer, but can often speed up training convergence.\\n\\nNote DiffSharp (which uses TorchSharp) relies on techniques 1.\\n\\n> Most of the examples included will use technique #1, doing frequent explicit calls to GC.Collect() in the training code -- if not after each batch in the training loop, at least after each epoch.\\n\\n## Technique 1. Automatic disposal via Garbage Collection\\n\\nIn this technique all tensors (CPU and GPU) are implicitly disposed via .NET finalizers. Just allocate new tensors to your heart's content, and let GC take care of them. It will only work for small models that do not require a lot of memory. If you do use this approach, you may want to place a call to `GC.Collect()` after each mini-batch of data. It is generally not sufficient to do it at the end of each epoch.\\n\\n👍 Simple\\n\\n👎 The .NET GC doesn't know about the memory pressure from CPU tensors, so failure may happen if large tensors can't be allocated\\n\\n👎 The .NET GC doesn't know about GPU resources.\\n\\n👎 Native operations that allocate temporaries, whether on CPU or GPU, may fail -- the GC scheme implemented by TorchSharp only works when the allocation is initiated by .NET code.\\n\\n\\n## Technique 2. Explicit disposal via 'using var x = ...' (C#) or 'use x = ...' (F#)\\n\\nThis technique is more cumbersome, but will result in better performance and have a higher memory ceiling. For many non-trivial models, it is more or less required in order to train on a GPU.\\n\\n👍 Specific lifetime management of all resources.\\n\\n👎 Cumbersome, requiring lots of using statements in your code.\\n\\n👎 You must know when to dispose.\\n\\n👎 Temporaries are not covered by this approach, so to maximize the benefit, you may have to store all temporaries to variables and dispose.\\n\\n__Note__: Even with this approach, it is a good idea to place a call to `GC.Collect()` after each mini-batch of data. There may be temporaries that were overlooked, or inconvenient to pull out, or ones where the lifetime was unclear; calling `GC.Collect()` will catch them.\\n\\n\\n### Returning Fresh References\\n\\nIt is important to understand that all TorchSharp \\\"tensors\\\" (type Tensor) are actually \\\"tensor aliases\\\", referring to a C++ tensor. When a C++ tensor is created and returned to .NET as a tensor alias, and the reference count on the C++ tensor is incremented. When you call `Dispose()` on the TorchSharp tensor alias (that is, type Tensor), it is decremented. If the tensor alias is finalized instead, the decrement happens implicitly.\\n\\nTo enable this technique, all operations that return one or more TorchSharp `Tensor`s should return \\\"fresh\\\" Tensor aliases (though that doesn't always mean freshly copied C++ tensors). This is true even for in-place, destructive operations like `add_()`, which overwrites the underlying native tensor with data, but still returns a fresh tensor alias to that same tensor. \\n\\nThus, when you write methods and functions that take and produce type Tensor, for example in the `forward()` method of a model, you should always make sure to return a fresh alias. Most of the time, this happens automatically, because the last action of your code will normally be to call another tensor function, which itself will be returning a fresh alias, but there are cases when it's not, especially when returning input tensors or tensors stored in some lookaside table.\\n\\nFor example, consider a function that returns its input if its one-dimensional, otherwise it returns a reshaped version:\\n\\n```C#\\nTensor flatten(Tensor input) {\\n if (input.shape.Length == 1)\\n return input.alias();\\n else\\n return input.reshape(input.numel()); \\n}\\n```\\n\\nThe `alias()` function avoids doing a clone of the tensor, but still returns a fresh tensor. I you simply return `input`, the caller won't know whether both input and output should be disposed, so the protocol is to always return a fresh tensor.\\n\\n### Disposing Tensor\\n\\nIn order to manage native storage, in particular GPU storage, it is necessary to do some explicit memory management for all temporaries, especially ones that are involved in a model's computation chain.\\n\\nHere are the simple guidance rules:\\n\\n1. Create a variable for each computed Tensor.\\n\\n2. Use the `using` (C#) or `use` (F#) syntax to declare the variable.\\n\\n3. Don't call Dispose on the input of a function. Let the caller handle its lifetime.\\n\\nFor example, consider this expression from the 'TextClassification' example:\\n\\n```C#\\ntotal_acc += (predicted_labels.argmax(1) == labels).sum().to(torch.CPU).item<long>();\\n```\\n\\nThere are lots of hidden temporaries in this relatively innocuous expression. In this particular case, it's involved in figuring out whether a prediction was accurate or not, so it's not going to be super-impactful on memory (the tensors are small), but it's still illustrative. A version where all temporaries are pulled out looks like this:\\n\\n```C#\\nusing var am = predicted_labels.argmax(1);\\nusing var eq = am == labels;\\nusing var sum = eq.sum();\\nusing var moved = sum.to(torch.CPU);\\n\\ntotal_acc += moved.item<long>();\\n```\\n\\nThe most essential places to do explicit memory management is in any function that might be involved with data preparation or the model computation, since the tensors are big and repeatedly used.\\n\\nSome additional examples, in F# this time:\\n\\n```fsharp\\n\\nlet myTensorFunction0(input: Tensor) =\\n input.alias()\\n\\nlet myTensorFunction1() =\\n if today then \\n table[4].alias() \\n else\\n table[5].alias() \\n\\nlet myTensorFunction2(input: Tensor) =\\n input.add(tensor(1))\\n\\nlet myTensorFunction3(input: Tensor) =\\n use tmp = input.add(tensor(1))\\n tmp.add(tensor(1))\\n\\nlet myTensorFunction4(input: Tensor) =\\n use tmp1 = input.add(tensor(1))\\n use tmp2 = input.add(tensor(1))\\n tmp2.add(tensor(1))\\n\\nlet myTensorFunction5(go: bool, input: Tensor) =\\n if go then\\n use tmp1 = input.add(tensor(1))\\n use tmp2 = input.add(tensor(1))\\n tmp2.add(tensor(1))\\n else\\n input.alias()\\n \\nlet myTensorFunction5(go: bool, input: Tensor) =\\n if go then \\n use tmp1 = input.add_(tensor(1)) // NOTE: even for in-place mutations\\n use tmp2 = input.add_(tensor(1)) // NOTE: even for in-place mutations\\n tmp2.add(tensor(1))\\n else\\n input.alias()\\n```\\n\\n### Use 'Sequential' when possible.\\n\\nRather than passing tensor arguments between neural network layers inside a custom module's `forward()`, you should rely on the 'Sequential' layer collection, which will be efficient at memory management.\\n\\nIt may not be ideal when first experimenting with a model and trying to debug it, but once you are done with that and move on to a full training data set, it is advisable.\\n\\n## Technique 3: torch.NewDisposeScope()\\n\\nThis approach, which was added in TorchSharp 0.95.4, makes it easier to dispose of tensors, without the non-determinism of technique 1, or the many temporaries of technique 2. It has most of the advantages of technique 2, and the code elegance of technique 1.\\n\\n👍 Specific lifetime management of all resources, but in groups.\\n\\n👍 Temporaries __are__ covered by this approach.\\n\\n👎 You don't have fine-grained control over when each tensor is reclaimed. All tensors created while a scope is in effect are disposed at once.\\n\\n👎 It's a new code pattern, not widely used with other libraries.\\n\\nLet's look at an example, similar to the earlier, technique 2 example:\\n\\n```C#\\nusing (var d = torch.NewDisposeScope()) {\\n\\n total_acc += (predicted_labels.argmax(1) == labels).sum().cpu().item<long>();\\n\\n ...\\n}\\n```\\n\\nWhat happens here, is that all tensors that are created while `d` is alive will be disposed when `d` is disposed. This includes temporaries, so you don't have to do anything special to get them to be disposed. (There's a problem with this simplistic example, which will be discussed later.)\\n\\nIn F#, it would look like:\\n\\n```F#\\nuse d = torch.NewDisposeScope()\\n\\ntotal_acc <- total_acc + (predicted_labels.argmax(1) == labels).sum().cpu().item<long>()\\n```\\n\\nIf you need to dispose some tensors before the scope is disposed, you can use `DisposeEverything()`, or `DisposeEverythingBut(...)` if you want to exclude a few tensors from disposal. These can be useful when tensor lifetimes aren't cleanly nested in dynamic scopes. \\n\\n__NOTE: It is absolutely essential for the proper functioning of dynamic dispose scopes that the scope is created with a 'using' statemen (C#) or 'use' expression (F#).__\\n\\nIt's important to note that these scopes are dynamic -- if any functions are called, the tensors inside them are also registered and disposed, unless there's a nested scope within those functions.\\n\\nIt is advisable to place a dispose scope around your training and test code, and in any library code that can be called from contexts that do not have dispose scopes. \\n\\nThat said, you should use dispose scope very carefully: having _too few_ scope raises the pressure on native memory, which is particularly bad for GPUs. Having too _many_ scopes, managing too few temporaries, will add runtime overhead to computations. For example, it may be better to put a scope outside an inner loop that contains multiple computations than to place it inside the loop. There is no single best answer.\\n\\n\\n### Passing Tensors Out of Scopes\\n\\nAny tensor that needs to survive the dynamic dispose scope must be either removed from management completely, or promoted to a nesting (outer) scope.\\n\\nFor example, if a tensor variable `tensor` is overwritten in a scope, there are two problems:\\n\\n1. The tensor held in `tensor` will be overwritten. Since it's not created within the scope, the scope will not dispose of it. A nesting scope must be added to managed the lifetime of all tensors kept in `tensor`:\\n\\n```C#\\nusing (var d0 = torch.NewDisposeScope()) {\\n\\n var tensor = torch.zeros(...)\\n\\n for ( ... ) {\\n ...\\n using (var d1 = torch.NewDisposeScope()) {\\n\\n var x = ...;\\n tensor += x.log();\\n ...\\n }\\n }\\n}\\n```\\n\\n2. The new tensor that is placed in `tensor` will be disposed when the scope is exited. Since the static scope of `tensor` is not the same as the dynamic scope where it is created, there's a problem.\\n\\nThis is probably not what you intended, so the tensor needs to be either detached, or moved to an outer scope (if one exists).\\n\\nFor example:\\n\\n```C#\\nusing (var d0 = torch.NewDisposeScope()) {\\n\\n var tensor = torch.zeros(...)\\n\\n for ( ... ) {\\n ...\\n using (var d1 = torch.NewDisposeScope()) {\\n\\n var x = ...;\\n tensor = (tensor + x.log()).MoveToOuterDisposeScope();\\n ...\\n }\\n }\\n}\\n```\\n\\nSometimes, less is more -- a simple solution is to have fewer nested scopes:\\n\\n```C#\\nusing (var d0 = torch.NewDisposeScope()) {\\n\\n var tensor = torch.zeros(...)\\n\\n for ( ... ) {\\n ...\\n var x = ...;\\n tensor += x.log();\\n ...\\n }\\n}\\n```\\n\\nbut sometimes, you still have to move the tensor out, for example when you return a tensor from a method:\\n\\n```C#\\npublic Tensor foo() {\\n using (var d0 = torch.NewDisposeScope()) {\\n\\n var tensor = torch.zeros(...)\\n\\n foreach ( ... ) {\\n ...\\n var x = ...;\\n tensor += x.log();\\n ...\\n }\\n\\n return tensor.MoveToOuterDisposeScope();\\n }\\n}\\n```\\n\\nThese examples show how to move a tensor up one level in the stack of scopes. To completely remove a tensor from scoped management, use `DetatchFromDisposeScope()` instead of `MoveToOuterDisposeScope()`.\\n\\nEven with this technique, it is a good practice to use `Sequential` when possible.\\n<br/>\\n\\n### torch.WrappedTensorDisposeScope\\n\\nA conveniece method was added in 0.97.3 -- it is useful for wrapping a complex expression with multiple temporaries without having to set up a scope explicitly.\\n\\nIt is defined as:\\n\\n```C#\\npublic static Tensor WrappedTensorDisposeScope(Func<Tensor> expr)\\n{\\n using var scope = torch.NewDisposeScope();\\n var result = expr();\\n return result.MoveToOuterDisposeScope();\\n}\\n```\\n\\nThis is particularly useful in one-line functions and properties, such as in this example from the the Pareto distribution class:\\n\\n```C#\\npublic override Tensor entropy() => torch.WrappedTensorDisposeScope(() => ((scale / alpha).log() + (1 + alpha.reciprocal())));\\n\\n```\\n\\n\\n## Links and resources\\n\\nThese articles might give you ides about techniques to use to analyse memory. The code is in python but generally will translate across:\\n\\n* https://gitmemory.com/issue/pytorch/pytorch/31252/565550016\\n\\n* https://discuss.pytorch.org/t/how-to-debug-causes-of-gpu-memory-leaks/6741\\n\\n* https://discuss.pytorch.org/t/i-run-out-of-memory-after-a-certain-amount-of-batches-when-training-a-resnet18/1911\\n\\n\"\n },\n {\n \"alpha_fraction\": 0.6080678701400757,\n \"alphanum_fraction\": 0.609610915184021,\n \"avg_line_length\": 54.32926940917969,\n \"blob_id\": \"e166e8f8c404dccfb4a7cd146081a4ccdfa1e653\",\n \"content_id\": \"cc6dcf0222b4c7f35f7e8d60fb40fed18a17b287\",\n \"detected_licenses\": [\n \"MIT\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"C#\",\n \"length_bytes\": 9075,\n \"license_type\": \"permissive\",\n \"max_line_length\": 269,\n \"num_lines\": 164,\n \"path\": \"/src/TorchSharp/Tensor/torch.SpectralOps.cs\",\n \"repo_name\": \"NiklasGustafsson/TorchSharp\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"// Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information.\\n#nullable enable\\n\\nusing System;\\nusing static TorchSharp.PInvoke.NativeMethods;\\n\\nnamespace TorchSharp\\n{\\n // https://pytorch.org/docs/stable/torch#spectral-ops\\n public static partial class torch\\n {\\n // https://pytorch.org/docs/stable/generated/torch.stft\\n /// <summary>\\n /// Returns a tensor containing the result of Short-time Fourier transform (STFT).\\n /// </summary>\\n /// <param name=\\\"input\\\">The input tensor</param>\\n /// <param name=\\\"n_fft\\\">The size of Fourier transform</param>\\n /// <param name=\\\"hop_length\\\">The hop length</param>\\n /// <param name=\\\"win_length\\\">The window length</param>\\n /// <param name=\\\"window\\\">The window function</param>\\n /// <param name=\\\"center\\\">Whether the t-th frame is centered around t * hop_window, or not.</param>\\n /// <param name=\\\"pad_mode\\\">The padding mode used when center is true.</param>\\n /// <param name=\\\"normalized\\\">Whether the output is normalized, or not.</param>\\n /// <param name=\\\"onesided\\\">Whether the output is onesided or not.</param>\\n /// <param name=\\\"return_complex\\\">Whether a complex tensor is returned, or not.</param>\\n /// <returns>A tensor containing the result of Short-time Fourier transform (STFT).</returns>\\n public static Tensor stft(Tensor input, long n_fft, long hop_length = -1, long win_length = -1, Tensor? window = null, bool center = true, PaddingModes pad_mode = PaddingModes.Reflect, bool normalized = false, bool? onesided = null, bool? return_complex = null)\\n => input.stft(n_fft, hop_length, win_length, window, center, pad_mode, normalized, onesided, return_complex);\\n\\n // https://pytorch.org/docs/stable/generated/torch.istft\\n /// <summary>\\n /// Returns a tensor containing the result of Inverse Short-time Fourier transform.\\n /// </summary>\\n /// <param name=\\\"input\\\">The input tensor</param>\\n /// <param name=\\\"n_fft\\\">The size of Fourier transform</param>\\n /// <param name=\\\"hop_length\\\">The hop length</param>\\n /// <param name=\\\"win_length\\\">The window length</param>\\n /// <param name=\\\"window\\\">The window function</param>\\n /// <param name=\\\"center\\\">Whether the t-th frame is centered around t * hop_window, or not.</param>\\n /// <param name=\\\"normalized\\\">Whether the output is normalized, or not.</param>\\n /// <param name=\\\"onesided\\\">Whether the output is onesided or not.</param>\\n /// <param name=\\\"length\\\">The length of the output tensor.</param>\\n /// <param name=\\\"return_complex\\\">Whether a complex tensor is returned, or not.</param>\\n /// <returns>A tensor containing the result of Inverse Short-time Fourier transform</returns>\\n public static Tensor istft(Tensor input, long n_fft, long hop_length = -1, long win_length = -1, Tensor? window = null, bool center = true, bool normalized = false, bool? onesided = null, long length = -1, bool return_complex = false)\\n => input.istft(n_fft, hop_length, win_length, window, center, normalized, onesided, length, return_complex);\\n\\n // https://pytorch.org/docs/stable/generated/torch.bartlett_window\\n /// <summary>\\n /// Bartlett window function.\\n /// </summary>\\n public static Tensor bartlett_window(long len, bool periodic = true, ScalarType? dtype = null, Device? device = null, bool requires_grad = false)\\n {\\n device = InitializeDevice(device);\\n if (!dtype.HasValue) {\\n // Determine the element type dynamically.\\n dtype = get_default_dtype();\\n }\\n if (!dtype.Value.IsFloatingPoint()) throw new ArgumentException(\\\"Only floating point types are supported.\\\");\\n\\n var handle = THSTensor_bartlett_window(len, periodic, (sbyte)dtype, (int)device.type, device.index, requires_grad);\\n if (handle == IntPtr.Zero) {\\n GC.Collect();\\n GC.WaitForPendingFinalizers();\\n handle = THSTensor_bartlett_window(len, periodic, (sbyte)dtype, (int)device.type, device.index, requires_grad);\\n }\\n if (handle == IntPtr.Zero) { CheckForErrors(); }\\n return new Tensor(handle);\\n }\\n\\n // https://pytorch.org/docs/stable/generated/torch.blackman_window\\n /// <summary>\\n /// Blackman window function.\\n /// </summary>\\n public static Tensor blackman_window(long len, bool periodic = true, ScalarType? dtype = null, Device? device = null, bool requires_grad = false)\\n {\\n device = InitializeDevice(device);\\n if (!dtype.HasValue) {\\n // Determine the element type dynamically.\\n dtype = get_default_dtype();\\n }\\n if (!dtype.Value.IsFloatingPoint()) throw new ArgumentException(\\\"Only floating point types are supported.\\\");\\n\\n var handle = THSTensor_blackman_window(len, periodic, (sbyte)dtype, (int)device.type, device.index, requires_grad);\\n if (handle == IntPtr.Zero) {\\n GC.Collect();\\n GC.WaitForPendingFinalizers();\\n handle = THSTensor_blackman_window(len, periodic, (sbyte)dtype, (int)device.type, device.index, requires_grad);\\n }\\n if (handle == IntPtr.Zero) { CheckForErrors(); }\\n return new Tensor(handle);\\n }\\n\\n // https://pytorch.org/docs/stable/generated/torch.hamming_window\\n\\n /// <summary>\\n /// Hamming window function.\\n /// </summary>\\n public static Tensor hamming_window(long len, bool periodic = true, float alpha = 0.54f, float beta = 0.46f, ScalarType? dtype = null, Device? device = null, bool requires_grad = false)\\n {\\n device = InitializeDevice(device);\\n if (!dtype.HasValue) {\\n // Determine the element type dynamically.\\n dtype = get_default_dtype();\\n }\\n if (!dtype.Value.IsFloatingPoint()) throw new ArgumentException(\\\"Only floating point types are supported.\\\");\\n\\n var handle = THSTensor_hamming_window(len, periodic, alpha, beta, (sbyte)dtype, (int)device.type, device.index, requires_grad);\\n if (handle == IntPtr.Zero) {\\n GC.Collect();\\n GC.WaitForPendingFinalizers();\\n handle = THSTensor_hamming_window(len, periodic, alpha, beta, (sbyte)dtype, (int)device.type, device.index, requires_grad);\\n }\\n if (handle == IntPtr.Zero) { CheckForErrors(); }\\n return new Tensor(handle);\\n }\\n\\n // https://pytorch.org/docs/stable/generated/torch.hann_window\\n /// <summary>\\n /// Hann window function.\\n /// </summary>\\n public static Tensor hann_window(long len, bool periodic = true, ScalarType? dtype = null, Device? device = null, bool requires_grad = false)\\n {\\n device = InitializeDevice(device);\\n if (!dtype.HasValue) {\\n // Determine the element type dynamically.\\n dtype = get_default_dtype();\\n }\\n if (!dtype.Value.IsFloatingPoint()) throw new ArgumentException(\\\"Only floating point types are supported.\\\");\\n\\n var handle = THSTensor_hann_window(len, periodic, (sbyte)dtype, (int)device.type, device.index, requires_grad);\\n if (handle == IntPtr.Zero) {\\n GC.Collect();\\n GC.WaitForPendingFinalizers();\\n handle = THSTensor_hann_window(len, periodic, (sbyte)dtype, (int)device.type, device.index, requires_grad);\\n }\\n if (handle == IntPtr.Zero) { CheckForErrors(); }\\n return new Tensor(handle);\\n }\\n\\n // https://pytorch.org/docs/stable/generated/torch.kaiser_window\\n /// <summary>\\n /// Computes the Kaiser window with window length window_length and shape parameter beta.\\n /// </summary>\\n public static Tensor kaiser_window(long len, bool periodic = true, float beta = 12.0f, ScalarType? dtype = null, Device? device = null, bool requires_grad = false)\\n {\\n device = InitializeDevice(device);\\n if (!dtype.HasValue) {\\n // Determine the element type dynamically.\\n dtype = get_default_dtype();\\n }\\n if (!dtype.Value.IsFloatingPoint()) throw new ArgumentException(\\\"Only floating point types are supported.\\\");\\n\\n var handle = THSTensor_kaiser_window(len, periodic, beta, (sbyte)dtype, (int)device.type, device.index, requires_grad);\\n if (handle == IntPtr.Zero) {\\n GC.Collect();\\n GC.WaitForPendingFinalizers();\\n handle = THSTensor_kaiser_window(len, periodic, beta, (sbyte)dtype, (int)device.type, device.index, requires_grad);\\n }\\n if (handle == IntPtr.Zero) { CheckForErrors(); }\\n return new Tensor(handle);\\n }\\n }\\n}\"\n },\n {\n \"alpha_fraction\": 0.5896599888801575,\n \"alphanum_fraction\": 0.5929203629493713,\n \"avg_line_length\": 38.75925827026367,\n \"blob_id\": \"29a41668d7b390a540937f7fa77d4b27757b8e24\",\n \"content_id\": \"b1568938c865170a111f7908180e744d23425752\",\n \"detected_licenses\": [\n \"MIT\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"C#\",\n \"length_bytes\": 2147,\n \"license_type\": \"permissive\",\n \"max_line_length\": 130,\n \"num_lines\": 54,\n \"path\": \"/src/TorchSharp/NN/Flatten.cs\",\n \"repo_name\": \"NiklasGustafsson/TorchSharp\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"// Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information.\\nusing System;\\nusing static TorchSharp.torch;\\nusing static TorchSharp.PInvoke.NativeMethods;\\n\\nnamespace TorchSharp\\n{\\n using Modules;\\n\\n namespace Modules\\n {\\n /// <summary>\\n /// This class is used to represent a dropout module for 2d/3d convolutational layers.\\n /// </summary>\\n public sealed class Flatten : torch.nn.Module<Tensor, Tensor>\\n {\\n internal Flatten(IntPtr handle, IntPtr boxedHandle) : base(handle, boxedHandle)\\n {\\n }\\n\\n public override Tensor forward(Tensor tensor)\\n {\\n var res = THSNN_Flatten_forward(handle, tensor.Handle);\\n if (res == IntPtr.Zero) { torch.CheckForErrors(); }\\n return new Tensor(res);\\n }\\n\\n // Rather than spending cycles only to discover that this module has neither\\n // parameters nor buffers, just shortcut the move completely.\\n protected internal override nn.Module _to(Device device, ScalarType dtype) => this;\\n protected internal override nn.Module _to(DeviceType deviceType, int deviceIndex = -1) => this;\\n protected internal override nn.Module _to(ScalarType dtype) => this;\\n }\\n }\\n\\n public static partial class torch\\n {\\n public static partial class nn\\n {\\n /// <summary>\\n /// Flattens a contiguous range of dims into a tensor. For use with Sequential.\\n /// </summary>\\n /// <param name=\\\"startDim\\\">First dim to flatten (default = 1).</param>\\n /// <param name=\\\"endDim\\\">Last dim to flatten (default = -1).</param>\\n /// <returns></returns>\\n public static Flatten Flatten(long startDim = 1, long endDim = -1)\\n {\\n var handle = THSNN_Flatten_ctor(startDim, endDim, out var boxedHandle);\\n if (handle == IntPtr.Zero) { torch.CheckForErrors(); }\\n return new Flatten(handle, boxedHandle);\\n }\\n }\\n }\\n}\\n\"\n },\n {\n \"alpha_fraction\": 0.5165091156959534,\n \"alphanum_fraction\": 0.5413853526115417,\n \"avg_line_length\": 54.17155838012695,\n \"blob_id\": \"553112166e2dcd4df08b2fc824f7472fb9204057\",\n \"content_id\": \"8438e36c8cc9e5574f89b36a813188dd96c25e96\",\n \"detected_licenses\": [\n \"MIT\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"C#\",\n \"length_bytes\": 24441,\n \"license_type\": \"permissive\",\n \"max_line_length\": 164,\n \"num_lines\": 443,\n \"path\": \"/src/TorchVision/models/VGG.cs\",\n \"repo_name\": \"NiklasGustafsson/TorchSharp\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"// Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information.\\n\\nusing System.Collections.Generic;\\nusing static TorchSharp.torch;\\nusing static TorchSharp.torch.nn;\\n\\n#nullable enable\\nnamespace TorchSharp\\n{\\n public static partial class torchvision\\n {\\n public static partial class models\\n {\\n /// <summary>\\n /// VGG-11 without batch-norm layers\\n /// </summary>\\n /// <param name=\\\"num_classes\\\">The number of output classes.</param>\\n /// <param name=\\\"dropout\\\">The dropout ratio.</param>\\n /// <param name=\\\"weights_file\\\">The location of a file containing pre-trained weights for the model.</param>\\n /// <param name=\\\"skipfc\\\">If true, the last linear layer of the classifier will not be loaded from the weights file.</param>\\n /// <param name=\\\"device\\\">The device to locate the model on.</param>\\n /// <remarks>\\n /// Pre-trained weights may be retrieved by using Pytorch and saving the model state-dict\\n /// using the exportsd.py script, then loading into the .NET instance:\\n ///\\n /// from torchvision import models\\n /// import exportsd\\n /// \\n /// model = models.vgg11(pretrained=True)\\n /// f = open(\\\"model_weights.dat\\\", \\\"wb\\\")\\n /// exportsd.save_state_dict(model.state_dict(), f)\\n /// f.close()\\n ///\\n /// See also: https://github.com/dotnet/TorchSharp/blob/main/docfx/articles/saveload.md\\n ///\\n /// In order for the weights to be loaded, the number of classes has to be the same as\\n /// in the pre-trained model, which is 1000.\\n ///\\n /// It is also possible to skip loading the last linear layer and use it for transfer-learning\\n /// with a different number of output classes. To do so, pass skipfc=true.\\n /// as the skip list when loading.\\n ///\\n /// All pre-trained models expect input images normalized in the same way, i.e. mini-batches of 3-channel RGB\\n /// images of shape (3 x H x W), where H and W are expected to be at least 224. The images have to be loaded\\n /// in to a range of [0, 1] and then normalized using mean = [0.485, 0.456, 0.406] and std = [0.229, 0.224, 0.225].\\n /// </remarks>\\n public static Modules.VGG vgg11(int num_classes = 1000, float dropout = 0.5f, string? weights_file = null, bool skipfc = true, Device? device = null)\\n {\\n return new Modules.VGG(\\\"VGG11\\\", num_classes, false, dropout, weights_file, skipfc, device);\\n }\\n\\n /// <summary>\\n /// VGG-11 with batch-norm layers\\n /// </summary>\\n /// <param name=\\\"num_classes\\\">The number of output classes.</param>\\n /// <param name=\\\"dropout\\\">The dropout ratio.</param>\\n /// <param name=\\\"weights_file\\\">The location of a file containing pre-trained weights for the model.</param>\\n /// <param name=\\\"skipfc\\\">If true, the last linear layer of the classifier will not be loaded from the weights file.</param>\\n /// <param name=\\\"device\\\">The device to locate the model on.</param>\\n /// <remarks>\\n /// Pre-trained weights may be retrieved by using Pytorch and saving the model state-dict\\n /// using the exportsd.py script, then loading into the .NET instance:\\n ///\\n /// from torchvision import models\\n /// import exportsd\\n /// \\n /// model = models.vgg11_bn(pretrained=True)\\n /// f = open(\\\"model_weights.dat\\\", \\\"wb\\\")\\n /// exportsd.save_state_dict(model.state_dict(), f)\\n /// f.close()\\n ///\\n /// See also: https://github.com/dotnet/TorchSharp/blob/main/docfx/articles/saveload.md\\n ///\\n /// In order for the weights to be loaded, the number of classes has to be the same as\\n /// in the pre-trained model, which is 1000.\\n ///\\n /// It is also possible to skip loading the last linear layer and use it for transfer-learning\\n /// with a different number of output classes. To do so, pass skipfc=true.\\n /// as the skip list when loading.\\n ///\\n /// All pre-trained models expect input images normalized in the same way, i.e. mini-batches of 3-channel RGB\\n /// images of shape (3 x H x W), where H and W are expected to be at least 224. The images have to be loaded\\n /// in to a range of [0, 1] and then normalized using mean = [0.485, 0.456, 0.406] and std = [0.229, 0.224, 0.225].\\n /// </remarks>\\n public static Modules.VGG vgg11_bn(int num_classes = 1000, float dropout = 0.5f, string? weights_file = null, bool skipfc = true, Device? device = null)\\n {\\n return new Modules.VGG(\\\"VGG11\\\", num_classes, true, dropout, weights_file, skipfc, device);\\n }\\n\\n /// <summary>\\n /// VGG-13 without batch-norm layers\\n /// </summary>\\n /// <param name=\\\"num_classes\\\">The number of output classes.</param>\\n /// <param name=\\\"dropout\\\">The dropout ratio.</param>\\n /// <param name=\\\"weights_file\\\">The location of a file containing pre-trained weights for the model.</param>\\n /// <param name=\\\"skipfc\\\">If true, the last linear layer of the classifier will not be loaded from the weights file.</param>\\n /// <param name=\\\"device\\\">The device to locate the model on.</param>\\n /// <remarks>\\n /// Pre-trained weights may be retrieved by using Pytorch and saving the model state-dict\\n /// using the exportsd.py script, then loading into the .NET instance:\\n ///\\n /// from torchvision import models\\n /// import exportsd\\n /// \\n /// model = models.vgg13(pretrained=True)\\n /// f = open(\\\"model_weights.dat\\\", \\\"wb\\\")\\n /// exportsd.save_state_dict(model.state_dict(), f)\\n /// f.close()\\n ///\\n /// See also: https://github.com/dotnet/TorchSharp/blob/main/docfx/articles/saveload.md\\n ///\\n /// In order for the weights to be loaded, the number of classes has to be the same as\\n /// in the pre-trained model, which is 1000.\\n ///\\n /// It is also possible to skip loading the last linear layer and use it for transfer-learning\\n /// with a different number of output classes. To do so, pass skipfc=true.\\n /// as the skip list when loading.\\n ///\\n /// All pre-trained models expect input images normalized in the same way, i.e. mini-batches of 3-channel RGB\\n /// images of shape (3 x H x W), where H and W are expected to be at least 224. The images have to be loaded\\n /// in to a range of [0, 1] and then normalized using mean = [0.485, 0.456, 0.406] and std = [0.229, 0.224, 0.225].\\n /// </remarks>\\n public static Modules.VGG vgg13(int num_classes = 1000, float dropout = 0.5f, string? weights_file = null, bool skipfc = true, Device? device = null)\\n {\\n return new Modules.VGG(\\\"VGG13\\\", num_classes, false, dropout, weights_file, skipfc, device);\\n }\\n\\n /// <summary>\\n /// VGG-13 with batch-norm layers\\n /// </summary>\\n /// <param name=\\\"num_classes\\\">The number of output classes.</param>\\n /// <param name=\\\"dropout\\\">The dropout ratio.</param>\\n /// <param name=\\\"weights_file\\\">The location of a file containing pre-trained weights for the model.</param>\\n /// <param name=\\\"skipfc\\\">If true, the last linear layer of the classifier will not be loaded from the weights file.</param>\\n /// <param name=\\\"device\\\">The device to locate the model on.</param>\\n /// <remarks>\\n /// Pre-trained weights may be retrieved by using Pytorch and saving the model state-dict\\n /// using the exportsd.py script, then loading into the .NET instance:\\n ///\\n /// from torchvision import models\\n /// import exportsd\\n /// \\n /// model = models.vgg13_bn(pretrained=True)\\n /// f = open(\\\"model_weights.dat\\\", \\\"wb\\\")\\n /// exportsd.save_state_dict(model.state_dict(), f)\\n /// f.close()\\n ///\\n /// See also: https://github.com/dotnet/TorchSharp/blob/main/docfx/articles/saveload.md\\n ///\\n /// In order for the weights to be loaded, the number of classes has to be the same as\\n /// in the pre-trained model, which is 1000.\\n ///\\n /// It is also possible to skip loading the last linear layer and use it for transfer-learning\\n /// with a different number of output classes. To do so, pass skipfc=true.\\n /// as the skip list when loading.\\n ///\\n /// All pre-trained models expect input images normalized in the same way, i.e. mini-batches of 3-channel RGB\\n /// images of shape (3 x H x W), where H and W are expected to be at least 224. The images have to be loaded\\n /// in to a range of [0, 1] and then normalized using mean = [0.485, 0.456, 0.406] and std = [0.229, 0.224, 0.225].\\n /// </remarks>\\n public static Modules.VGG vgg13_bn(int num_classes = 1000, float dropout = 0.5f, string? weights_file = null, bool skipfc = true, Device? device = null)\\n {\\n return new Modules.VGG(\\\"VGG13\\\", num_classes, true, dropout, weights_file, skipfc, device);\\n }\\n\\n /// <summary>\\n /// VGG-16 without batch-norm layers\\n /// </summary>\\n /// <param name=\\\"num_classes\\\">The number of output classes.</param>\\n /// <param name=\\\"dropout\\\">The dropout ratio.</param>\\n /// <param name=\\\"weights_file\\\">The location of a file containing pre-trained weights for the model.</param>\\n /// <param name=\\\"skipfc\\\">If true, the last linear layer of the classifier will not be loaded from the weights file.</param>\\n /// <param name=\\\"device\\\">The device to locate the model on.</param>\\n /// <remarks>\\n /// Pre-trained weights may be retrieved by using Pytorch and saving the model state-dict\\n /// using the exportsd.py script, then loading into the .NET instance:\\n ///\\n /// from torchvision import models\\n /// import exportsd\\n /// \\n /// model = models.vgg16(pretrained=True)\\n /// f = open(\\\"model_weights.dat\\\", \\\"wb\\\")\\n /// exportsd.save_state_dict(model.state_dict(), f)\\n /// f.close()\\n ///\\n /// See also: https://github.com/dotnet/TorchSharp/blob/main/docfx/articles/saveload.md\\n ///\\n /// In order for the weights to be loaded, the number of classes has to be the same as\\n /// in the pre-trained model, which is 1000.\\n ///\\n /// It is also possible to skip loading the last linear layer and use it for transfer-learning\\n /// with a different number of output classes. To do so, pass skipfc=true.\\n /// as the skip list when loading.\\n ///\\n /// All pre-trained models expect input images normalized in the same way, i.e. mini-batches of 3-channel RGB\\n /// images of shape (3 x H x W), where H and W are expected to be at least 224. The images have to be loaded\\n /// in to a range of [0, 1] and then normalized using mean = [0.485, 0.456, 0.406] and std = [0.229, 0.224, 0.225].\\n /// </remarks>\\n public static Modules.VGG vgg16(int num_classes = 1000, float dropout = 0.5f, string? weights_file = null, bool skipfc = true, Device? device = null)\\n {\\n return new Modules.VGG(\\\"VGG16\\\", num_classes, false, dropout, weights_file, skipfc, device);\\n }\\n\\n /// <summary>\\n /// VGG-16 with batch-norm layers\\n /// </summary>\\n /// <param name=\\\"num_classes\\\">The number of output classes.</param>\\n /// <param name=\\\"dropout\\\">The dropout ratio.</param>\\n /// <param name=\\\"weights_file\\\">The location of a file containing pre-trained weights for the model.</param>\\n /// <param name=\\\"skipfc\\\">If true, the last linear layer of the classifier will not be loaded from the weights file.</param>\\n /// <param name=\\\"device\\\">The device to locate the model on.</param>\\n /// <remarks>\\n /// Pre-trained weights may be retrieved by using Pytorch and saving the model state-dict\\n /// using the exportsd.py script, then loading into the .NET instance:\\n ///\\n /// from torchvision import models\\n /// import exportsd\\n /// \\n /// model = models.vgg16_bn(pretrained=True)\\n /// f = open(\\\"model_weights.dat\\\", \\\"wb\\\")\\n /// exportsd.save_state_dict(model.state_dict(), f)\\n /// f.close()\\n ///\\n /// See also: https://github.com/dotnet/TorchSharp/blob/main/docfx/articles/saveload.md\\n ///\\n /// In order for the weights to be loaded, the number of classes has to be the same as\\n /// in the pre-trained model, which is 1000.\\n ///\\n /// It is also possible to skip loading the last linear layer and use it for transfer-learning\\n /// with a different number of output classes. To do so, pass skipfc=true.\\n /// as the skip list when loading.\\n ///\\n /// All pre-trained models expect input images normalized in the same way, i.e. mini-batches of 3-channel RGB\\n /// images of shape (3 x H x W), where H and W are expected to be at least 224. The images have to be loaded\\n /// in to a range of [0, 1] and then normalized using mean = [0.485, 0.456, 0.406] and std = [0.229, 0.224, 0.225].\\n /// </remarks>\\n public static Modules.VGG vgg16_bn(int num_classes = 1000, float dropout = 0.5f, string? weights_file = null, bool skipfc = true, Device? device = null)\\n {\\n return new Modules.VGG(\\\"VGG16\\\", num_classes, true, dropout, weights_file, skipfc, device);\\n }\\n\\n /// <summary>\\n /// VGG-19 without batch-norm layers\\n /// </summary>\\n /// <param name=\\\"num_classes\\\">The number of output classes.</param>\\n /// <param name=\\\"dropout\\\">The dropout ratio.</param>\\n /// <param name=\\\"weights_file\\\">The location of a file containing pre-trained weights for the model.</param>\\n /// <param name=\\\"skipfc\\\">If true, the last linear layer of the classifier will not be loaded from the weights file.</param>\\n /// <param name=\\\"device\\\">The device to locate the model on.</param>\\n /// <remarks>\\n /// Pre-trained weights may be retrieved by using Pytorch and saving the model state-dict\\n /// using the exportsd.py script, then loading into the .NET instance:\\n ///\\n /// from torchvision import models\\n /// import exportsd\\n /// \\n /// model = models.vgg19(pretrained=True)\\n /// f = open(\\\"model_weights.dat\\\", \\\"wb\\\")\\n /// exportsd.save_state_dict(model.state_dict(), f)\\n /// f.close()\\n ///\\n /// See also: https://github.com/dotnet/TorchSharp/blob/main/docfx/articles/saveload.md\\n ///\\n /// In order for the weights to be loaded, the number of classes has to be the same as\\n /// in the pre-trained model, which is 1000.\\n ///\\n /// It is also possible to skip loading the last linear layer and use it for transfer-learning\\n /// with a different number of output classes. To do so, pass skipfc=true.\\n /// as the skip list when loading.\\n ///\\n /// All pre-trained models expect input images normalized in the same way, i.e. mini-batches of 3-channel RGB\\n /// images of shape (3 x H x W), where H and W are expected to be at least 224. The images have to be loaded\\n /// in to a range of [0, 1] and then normalized using mean = [0.485, 0.456, 0.406] and std = [0.229, 0.224, 0.225].\\n /// </remarks>\\n public static Modules.VGG vgg19(int num_classes = 1000, float dropout = 0.5f, string? weights_file = null, bool skipfc = true, Device? device = null)\\n {\\n return new Modules.VGG(\\\"VGG19\\\", num_classes, false, dropout, weights_file, skipfc, device);\\n }\\n\\n /// <summary>\\n /// VGG-19 with batch-norm layers\\n /// </summary>\\n /// <param name=\\\"num_classes\\\">The number of output classes.</param>\\n /// <param name=\\\"dropout\\\">The dropout ratio.</param>\\n /// <param name=\\\"weights_file\\\">The location of a file containing pre-trained weights for the model.</param>\\n /// <param name=\\\"skipfc\\\">If true, the last linear layer of the classifier will not be loaded from the weights file.</param>\\n /// <param name=\\\"device\\\">The device to locate the model on.</param>\\n /// <remarks>\\n /// Pre-trained weights may be retrieved by using Pytorch and saving the model state-dict\\n /// using the exportsd.py script, then loading into the .NET instance:\\n ///\\n /// from torchvision import models\\n /// import exportsd\\n /// \\n /// model = models.vgg19_bn(pretrained=True)\\n /// f = open(\\\"model_weights.dat\\\", \\\"wb\\\")\\n /// exportsd.save_state_dict(model.state_dict(), f)\\n /// f.close()\\n ///\\n /// See also: https://github.com/dotnet/TorchSharp/blob/main/docfx/articles/saveload.md\\n ///\\n /// In order for the weights to be loaded, the number of classes has to be the same as\\n /// in the pre-trained model, which is 1000.\\n ///\\n /// It is also possible to skip loading the last linear layer and use it for transfer-learning\\n /// with a different number of output classes. To do so, pass skipfc=true.\\n /// as the skip list when loading.\\n ///\\n /// All pre-trained models expect input images normalized in the same way, i.e. mini-batches of 3-channel RGB\\n /// images of shape (3 x H x W), where H and W are expected to be at least 224. The images have to be loaded\\n /// in to a range of [0, 1] and then normalized using mean = [0.485, 0.456, 0.406] and std = [0.229, 0.224, 0.225].\\n /// </remarks>\\n public static Modules.VGG vgg19_bn(int num_classes = 1000, float dropout = 0.5f, string? weights_file = null, bool skipfc = true, Device? device = null)\\n {\\n return new Modules.VGG(\\\"VGG19\\\", num_classes, true, dropout, weights_file, skipfc, device);\\n }\\n }\\n }\\n\\n namespace Modules\\n {\\n public class VGG : Module<Tensor, Tensor>\\n {\\n // The code here is based on\\n // https://github.com/pytorch/vision/blob/main/torchvision/models/vgg.py\\n // Licence and copypright notice at: https://github.com/pytorch/vision/blob/main/LICENSE\\n\\n private readonly Dictionary<string, long[]> _channels = new Dictionary<string, long[]>() {\\n { \\\"VGG11\\\", new long[] { 64, 0, 128, 0, 256, 256, 0, 512, 512, 0, 512, 512, 0 } },\\n { \\\"VGG13\\\", new long[] { 64, 64, 0, 128, 128, 0, 256, 256, 0, 512, 512, 0, 512, 512, 0 } },\\n { \\\"VGG16\\\", new long[] { 64, 64, 0, 128, 128, 0, 256, 256, 256, 0, 512, 512, 512, 0, 512, 512, 512, 0 } },\\n { \\\"VGG19\\\", new long[] { 64, 64, 0, 128, 128, 0, 256, 256, 256, 256, 0, 512, 512, 512, 512, 0, 512, 512, 512, 512, 0 } }\\n };\\n\\n private readonly Module<Tensor, Tensor> features;\\n private readonly Module<Tensor, Tensor> avgpool;\\n private readonly Module<Tensor, Tensor> classifier;\\n\\n protected override void Dispose(bool disposing)\\n {\\n if (disposing) {\\n features.Dispose();\\n avgpool.Dispose();\\n classifier.Dispose();\\n }\\n base.Dispose(disposing);\\n }\\n\\n public VGG(string name,\\n int numClasses,\\n bool batch_norm,\\n float dropout = 0.5f,\\n string? weights_file = null,\\n bool skipfc = true,\\n Device? device = null) : base(name)\\n {\\n var layers = new List<Module<Tensor, Tensor>>();\\n\\n var channels = _channels[name];\\n\\n long in_channels = 3;\\n\\n for (var i = 0; i < channels.Length; i++) {\\n\\n if (channels[i] == 0) {\\n layers.Add(MaxPool2d(kernelSize: 2, stride: 2));\\n } else {\\n layers.Add(Conv2d(in_channels, channels[i], kernelSize: 3, padding: 1));\\n if (batch_norm) {\\n layers.Add(BatchNorm2d(channels[i]));\\n }\\n layers.Add(ReLU(inplace: true));\\n in_channels = channels[i];\\n }\\n }\\n\\n features = Sequential(layers);\\n\\n avgpool = AdaptiveAvgPool2d(new[] { 7L, 7L });\\n\\n classifier = Sequential(\\n Linear(512 * 7 * 7, 4096),\\n ReLU(true),\\n Dropout(p: dropout),\\n Linear(4096, 4096),\\n ReLU(true),\\n Dropout(p: dropout),\\n Linear(4096, numClasses)\\n );\\n\\n RegisterComponents();\\n\\n if (string.IsNullOrEmpty(weights_file)) {\\n\\n foreach (var (_, m) in named_modules()) {\\n switch (m) {\\n // This test must come before the Tensor test\\n case TorchSharp.Modules.Conv2d conv:\\n torch.nn.init.kaiming_normal_(conv.weight, mode: init.FanInOut.FanOut, nonlinearity: init.NonlinearityType.ReLU);\\n if (conv.bias is not null && !conv.bias.IsInvalid) {\\n torch.nn.init.constant_(conv.bias, 0);\\n }\\n break;\\n case TorchSharp.Modules.BatchNorm2d bn:\\n torch.nn.init.constant_(bn.weight, 1);\\n torch.nn.init.constant_(bn.bias, 0);\\n break;\\n case TorchSharp.Modules.Linear ln:\\n torch.nn.init.normal_(ln.weight, 0, 0.01);\\n torch.nn.init.constant_(ln.bias, 0);\\n break;\\n }\\n }\\n } else {\\n\\n foreach (var (_, m) in named_modules()) {\\n switch (m) {\\n // This test must come before the Tensor test\\n case TorchSharp.Modules.Linear ln:\\n torch.nn.init.normal_(ln.weight, 0, 0.01);\\n torch.nn.init.constant_(ln.bias, 0);\\n break;\\n }\\n }\\n\\n this.load(weights_file, skip: skipfc ? new[] { \\\"classifier.6.weight\\\", \\\"classifier.6.bias\\\" } : null);\\n }\\n\\n if (device != null && device.type == DeviceType.CUDA)\\n this.to(device);\\n }\\n\\n public override Tensor forward(Tensor input)\\n {\\n using (var _ = NewDisposeScope()) {\\n input = features.call(input);\\n input = avgpool.call(input).flatten(1);\\n return classifier.call(input).MoveToOuterDisposeScope();\\n }\\n }\\n }\\n }\\n}\\n\"\n },\n {\n \"alpha_fraction\": 0.547451913356781,\n \"alphanum_fraction\": 0.5544705390930176,\n \"avg_line_length\": 32.783504486083984,\n \"blob_id\": \"6595721bc86c2997112ce217b9845db0462d83f4\",\n \"content_id\": \"22f3b7787810dcd309a8e5f8651df967216c1476\",\n \"detected_licenses\": [\n \"MIT\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"C#\",\n \"length_bytes\": 3277,\n \"license_type\": \"permissive\",\n \"max_line_length\": 130,\n \"num_lines\": 97,\n \"path\": \"/src/TorchAudio/Transforms/InverseSpectrogram.cs\",\n \"repo_name\": \"NiklasGustafsson/TorchSharp\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"// Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information.\\n\\nusing static TorchSharp.torch;\\nusing static TorchSharp.torchaudio;\\n\\n// A number of implementation details in this file have been translated from the Python version of torchaudio,\\n// largely located in the files found in this folder:\\n//\\n// https://github.com/pytorch/audio/blob/bb77cbebb620a46fdc0dc7e6dae2253eef3f37e2/torchaudio/transforms/_transforms.py\\n//\\n// The origin has the following copyright notice and license:\\n//\\n// https://github.com/pytorch/audio/blob/main/LICENSE\\n//\\n\\nnamespace TorchSharp.Transforms\\n{\\n public sealed class InverseSpectrogram : nn.Module<Tensor, Tensor>, nn.IModule<Tensor, long?, Tensor>, ITransform\\n {\\n private readonly long n_fft;\\n private readonly long win_length;\\n private readonly long hop_length;\\n private readonly long pad;\\n private readonly Tensor window;\\n private readonly bool normalized;\\n private readonly bool center;\\n private readonly PaddingModes pad_mode;\\n private readonly bool onesided;\\n\\n protected override void Dispose(bool disposing)\\n {\\n if (disposing) {\\n window.Dispose();\\n }\\n base.Dispose(disposing);\\n }\\n\\n internal InverseSpectrogram(\\n string name,\\n long n_fft = 400,\\n long? win_length = null,\\n long? hop_length = null,\\n long pad = 0,\\n WindowFunction window_fn = null,\\n Tensor window = null,\\n bool normalized = false,\\n bool center = true,\\n PaddingModes pad_mode = PaddingModes.Reflect,\\n bool onesided = true) : base(name)\\n {\\n this.n_fft = n_fft;\\n if (win_length.HasValue) {\\n this.win_length = win_length.Value;\\n } else {\\n this.win_length = n_fft;\\n }\\n if (hop_length.HasValue) {\\n this.hop_length = hop_length.Value;\\n } else {\\n this.hop_length = this.win_length / 2;\\n }\\n this.pad = pad;\\n if (window is not null) {\\n this.window = window;\\n } else if (window_fn != null) {\\n this.window = window_fn(this.win_length);\\n } else {\\n this.window = torch.hann_window(this.win_length);\\n }\\n this.normalized = normalized;\\n this.center = center;\\n this.pad_mode = pad_mode;\\n this.onesided = onesided;\\n }\\n\\n public override Tensor forward(Tensor input)\\n {\\n return call(input, null);\\n }\\n\\n public Tensor call(Tensor input, long? length = null)\\n {\\n return torchaudio.functional.inverse_spectrogram(\\n spectrogram: input,\\n length: length,\\n pad: pad,\\n window: window,\\n n_fft: n_fft,\\n hop_length: hop_length,\\n win_length: win_length,\\n normalized: normalized,\\n center: center,\\n pad_mode: pad_mode,\\n onesided: onesided);\\n }\\n }\\n}\\n\"\n },\n {\n \"alpha_fraction\": 0.6636179089546204,\n \"alphanum_fraction\": 0.670223593711853,\n \"avg_line_length\": 28.81818199157715,\n \"blob_id\": \"796079dae25df75cd8aed6e0bffbbbe2d0bcaea5\",\n \"content_id\": \"cdbf754f77235dd5cbac07721310b946ef084160\",\n \"detected_licenses\": [\n \"MIT\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"C++\",\n \"length_bytes\": 1968,\n \"license_type\": \"permissive\",\n \"max_line_length\": 130,\n \"num_lines\": 66,\n \"path\": \"/src/Native/LibTorchSharp/THSAutograd.cpp\",\n \"repo_name\": \"NiklasGustafsson/TorchSharp\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"// Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information.\\n#include \\\"THSAutograd.h\\\"\\n\\n#include \\\"torch/torch.h\\\"\\n\\nbool THSAutograd_isGradEnabled()\\n{\\n bool result = torch::autograd::GradMode::is_enabled();\\n return result;\\n}\\n\\nvoid THSAutograd_setGrad(bool enabled)\\n{\\n torch::autograd::GradMode::set_enabled(enabled);\\n}\\n\\nbool THSAutograd_isAnomalyEnabled()\\n{\\n bool result = torch::autograd::AnomalyMode::is_enabled();\\n return result;\\n}\\n\\nbool THSAutograd_shouldCheckNaN()\\n{\\n return torch::autograd::AnomalyMode::should_check_nan();\\n}\\n\\nvoid THSAutograd_setAnomaly(bool enabled, bool check_nan)\\n{\\n torch::autograd::AnomalyMode::set_enabled(enabled, check_nan);\\n}\\n\\nvoid THSAutograd_grad(\\n Tensor* outputs, const int64_t oLength,\\n Tensor* inputs, const int64_t iLength,\\n Tensor* grad_outs, const int64_t gLength,\\n bool retain_graph, bool create_graph, bool allow_unused,\\n Tensor* (*allocator)(size_t length))\\n{\\n auto res = torch::autograd::grad(\\n toTensors<at::Tensor>((torch::Tensor**)outputs, oLength),\\n toTensors<at::Tensor>((torch::Tensor**)inputs, iLength),\\n toTensors<at::Tensor>((torch::Tensor**)grad_outs, gLength),\\n retain_graph, create_graph, allow_unused);\\n\\n const size_t sz = res.size();\\n\\n Tensor* result = allocator(sz);\\n for (size_t i = 0; i < sz; i++)\\n result[i] = ResultTensor(res[i]);\\n}\\n\\nvoid THSAutograd_backward(\\n Tensor* tensors, const int64_t tLength,\\n Tensor* grad_tensors, const int64_t gtLength,\\n bool retain_graph, bool create_graph,\\n Tensor* inputs, const int64_t iLength)\\n{\\n CATCH(\\n torch::autograd::backward(\\n toTensors<at::Tensor>((torch::Tensor**)tensors, tLength),\\n toTensors<at::Tensor>((torch::Tensor**)grad_tensors, gtLength),\\n retain_graph, create_graph,\\n toTensors<at::Tensor>((torch::Tensor**)inputs, iLength));\\n );\\n}\\n\"\n },\n {\n \"alpha_fraction\": 0.5957894921302795,\n \"alphanum_fraction\": 0.6085965037345886,\n \"avg_line_length\": 45.721309661865234,\n \"blob_id\": \"d386e7b82851400e625ed9b4c6d6ed646a6da182\",\n \"content_id\": \"7c6e121c8d5878ad1d0b32741069e278d9c47b44\",\n \"detected_licenses\": [\n \"MIT\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"C#\",\n \"length_bytes\": 5700,\n \"license_type\": \"permissive\",\n \"max_line_length\": 130,\n \"num_lines\": 122,\n \"path\": \"/src/TorchAudio/Modules/HuBERTPretrainModel.cs\",\n \"repo_name\": \"NiklasGustafsson/TorchSharp\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"// Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information.\\n\\n// A number of implementation details in this file have been translated from the Python version of torchaudio,\\n// largely located in the files found in this folder:\\n//\\n// https://github.com/pytorch/audio/blob/76fca37ac8941b72a509a6e58d623632efe04543/torchaudio/models/wav2vec2/model.py\\n//\\n// The origin has the following copyright notice and license:\\n//\\n// https://github.com/pytorch/audio/blob/main/LICENSE\\n//\\n\\nusing System;\\nusing System.IO;\\n\\nusing static TorchSharp.torch;\\n\\n#nullable enable\\nnamespace TorchSharp.Modules\\n{\\n /// <summary>\\n /// HuBERT pre-train model for training from scratch.\\n /// \\n /// Note:\\n /// To build the model, please use one of the factory functions in\\n /// `[hubert_pretrain_base, hubert_pretrain_large, hubert_pretrain_xlarge]`.\\n /// </summary>\\n public class HuBERTPretrainModel : nn.Module<Tensor, Tensor, Tensor?, (Tensor?, Tensor?, Tensor)>\\n {\\n private readonly Wav2Vec2Model wav2vec2;\\n private readonly Wav2Vec2Model.MaskGenerator mask_generator;\\n private readonly Wav2Vec2Model.LogitGenerator logit_generator;\\n private readonly double? feature_grad_mult;\\n\\n /// <param name=\\\"name\\\"></param>\\n /// <param name=\\\"wav2vec2\\\"></param>\\n /// <param name=\\\"mask_generator\\\">Mask generator that generates the mask for masked prediction during the training.</param>\\n /// <param name=\\\"logit_generator\\\">Logit generator that predicts the logits of the masked and unmasked inputs.</param>\\n /// <param name=\\\"feature_grad_mult\\\">The factor to scale the convolutional feature extraction layer gradients by.\\n /// If ``None``, the gradients of feature extraction layers are not affected.\\n /// The scale factor will not affect the forward pass.</param>\\n internal HuBERTPretrainModel(\\n string name,\\n Wav2Vec2Model wav2vec2,\\n Wav2Vec2Model.MaskGenerator mask_generator,\\n Wav2Vec2Model.LogitGenerator logit_generator,\\n double? feature_grad_mult) : base(name)\\n {\\n this.wav2vec2 = wav2vec2;\\n this.mask_generator = mask_generator;\\n this.logit_generator = logit_generator;\\n if (feature_grad_mult != null && !(0.0 < feature_grad_mult.Value && feature_grad_mult.Value < 1.0)) {\\n throw new ArgumentException(\\n $\\\"The value of `feature_grad_mult` must be ``null``or between (0, 1). Found {feature_grad_mult}\\\");\\n }\\n this.feature_grad_mult = feature_grad_mult;\\n RegisterComponents();\\n }\\n\\n /// <summary>\\n /// Compute the sequence of probability distribution over labels.\\n /// </summary>\\n /// <param name=\\\"waveforms\\\">Audio tensor of dimension `[batch, frames]`.</param>\\n /// <param name=\\\"labels\\\">Label for pre-training. A Tensor of dimension `[batch, frames]`.</param>\\n /// <param name=\\\"audio_lengths\\\">Indicates the valid length of each audio in the batch.\\n /// Shape: `[batch, ]`.\\n /// When the ``waveforms`` contains audios with different durations,\\n /// by providing ``lengths`` argument, the model will compute\\n /// the corresponding valid output lengths and apply proper mask in\\n /// transformer attention layer.\\n /// If ``None``, it is assumed that all the audio in ``waveforms``\\n /// have valid length. Default: ``None``.</param>\\n /// <returns>\\n /// The masked sequences of probability distribution (in logit).\\n /// Shape: `(masked_frames, num labels)`.\\n /// The unmasked sequence of probability distribution (in logit).\\n /// Shape: `(unmasked_frames, num labels)`.\\n /// Tensor\\n /// The feature mean value for additional penalty loss.\\n /// Shape: `(1,)`.\\n /// </returns>\\n public override (Tensor?, Tensor?, Tensor) forward(\\n Tensor waveforms,\\n Tensor labels,\\n Tensor? audio_lengths = null)\\n {\\n Tensor mask_u;\\n Tensor mask_m;\\n Tensor? padding_mask;\\n var (x, lengths) = this.wav2vec2.feature_extractor.call(waveforms, audio_lengths);\\n if (this.feature_grad_mult != null && this.feature_grad_mult < 1.0) {\\n x = Wav2Vec2Model.GradMultiply.apply(x, this.feature_grad_mult.Value);\\n }\\n var features_pen = x.@float().pow(2).mean();\\n if (lengths is not null) {\\n padding_mask = Wav2Vec2Model._get_padding_mask(x, lengths);\\n } else {\\n padding_mask = null;\\n }\\n Tensor? attention_mask;\\n Tensor? mask;\\n (x, attention_mask) = this.wav2vec2.encoder._preprocess(x, lengths);\\n (x, mask) = this.mask_generator.call(x, padding_mask);\\n if (mask is null) throw new InvalidDataException();\\n x = this.wav2vec2.encoder.transformer.call(x, attention_mask: attention_mask);\\n if (x.shape[1] != labels.shape[1]) {\\n throw new ArgumentException(\\\"The length of label must match that of HuBERT model output\\\");\\n }\\n if (padding_mask is not null) {\\n mask_m = torch.logical_and(~padding_mask, mask);\\n mask_u = torch.logical_and(~padding_mask, ~mask_m);\\n } else {\\n mask_m = mask;\\n mask_u = ~mask_m;\\n }\\n\\n var (logit_m, logit_u) = this.logit_generator.call(x, labels, mask_m, mask_u);\\n\\n return (logit_m, logit_u, features_pen);\\n }\\n }\\n}\\n\"\n },\n {\n \"alpha_fraction\": 0.4402332305908203,\n \"alphanum_fraction\": 0.44081631302833557,\n \"avg_line_length\": 27.58333396911621,\n \"blob_id\": \"7383742948b59e6c5e0d57a20b1c823f6d705dee\",\n \"content_id\": \"7aea914661c75f697c833ad23ca851a3bedd4169\",\n \"detected_licenses\": [\n \"MIT\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"C#\",\n \"length_bytes\": 1715,\n \"license_type\": \"permissive\",\n \"max_line_length\": 130,\n \"num_lines\": 60,\n \"path\": \"/src/Examples.Utils/TorchText.Data.Utils.cs\",\n \"repo_name\": \"NiklasGustafsson/TorchSharp\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"// Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information.\\nusing System;\\nusing System.Collections.Generic;\\nusing System.Text.RegularExpressions;\\n\\nnamespace TorchText.Data\\n{\\n public static partial class Utils\\n {\\n public static Func<string, IEnumerable<string>> get_tokenizer(string name)\\n {\\n if (name == \\\"basic_english\\\") return BasicEnglish;\\n throw new NotImplementedException($\\\"The '{name}' text tokenizer is not implemented.\\\");\\n }\\n\\n private static string[] _patterns = new string []{\\n \\\"\\\\'\\\",\\n \\\"\\\\\\\"\\\",\\n \\\"\\\\\\\\.\\\",\\n \\\"<br \\\\\\\\/>\\\",\\n \\\",\\\",\\n \\\"\\\\\\\\(\\\",\\n \\\"\\\\\\\\)\\\",\\n \\\"\\\\\\\\!\\\",\\n \\\"\\\\\\\\?\\\",\\n \\\"\\\\\\\\;\\\",\\n \\\"\\\\\\\\:\\\",\\n \\\"\\\\\\\\\\\\\\\\\\\",\\n \\\"\\\\\\\\s+\\\",\\n };\\n private static string[] _replacements = new string[] {\\n \\\" \\\\\\\\' \\\",\\n \\\"\\\",\\n \\\" . \\\",\\n \\\" \\\",\\n \\\" , \\\",\\n \\\" ( \\\",\\n \\\" ) \\\",\\n \\\" ! \\\",\\n \\\" ? \\\",\\n \\\" \\\",\\n \\\" \\\",\\n \\\" \\\",\\n \\\" \\\"\\n };\\n\\n private static IEnumerable<string> BasicEnglish(string input)\\n {\\n if (_patterns.Length != _replacements.Length)\\n throw new InvalidProgramException(\\\"internal error: patterns and replacements are not the same length\\\");\\n\\n input = input.Trim().ToLowerInvariant();\\n\\n for (var i = 0; i < _patterns.Length; ++i) {\\n input = Regex.Replace(input, _patterns[i], _replacements[i]);\\n }\\n return input.Split(' ');\\n }\\n }\\n}\\n\"\n },\n {\n \"alpha_fraction\": 0.5373831987380981,\n \"alphanum_fraction\": 0.5373831987380981,\n \"avg_line_length\": 24.969696044921875,\n \"blob_id\": \"669691ad9b79009d24fedf6e67fee84e5781ad94\",\n \"content_id\": \"acc0c7aad07b133735bb6d9591c3a7befad9f193\",\n \"detected_licenses\": [\n \"MIT\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"C#\",\n \"length_bytes\": 856,\n \"license_type\": \"permissive\",\n \"max_line_length\": 130,\n \"num_lines\": 33,\n \"path\": \"/src/TorchVision/AutoContrast.cs\",\n \"repo_name\": \"NiklasGustafsson/TorchSharp\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"// Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information.\\n\\nusing static TorchSharp.torch;\\n\\nnamespace TorchSharp\\n{\\n public static partial class torchvision\\n {\\n internal class AutoContrast : ITransform\\n {\\n internal AutoContrast()\\n {\\n }\\n\\n public Tensor call(Tensor input)\\n {\\n return transforms.functional.autocontrast(input);\\n }\\n }\\n\\n public static partial class transforms\\n {\\n /// <summary>\\n /// Autocontrast the pixels of the given image\\n /// </summary>\\n /// <returns></returns>\\n static public ITransform AutoContrast()\\n {\\n return new AutoContrast();\\n }\\n }\\n }\\n}\"\n },\n {\n \"alpha_fraction\": 0.5645561218261719,\n \"alphanum_fraction\": 0.5662819743156433,\n \"avg_line_length\": 46.0609130859375,\n \"blob_id\": \"c5696468d92846d4e1465cca823e193540044f54\",\n \"content_id\": \"b65b5327065ef565f5fda16241d6cb8168c3d3b3\",\n \"detected_licenses\": [\n \"MIT\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"C#\",\n \"length_bytes\": 9271,\n \"license_type\": \"permissive\",\n \"max_line_length\": 166,\n \"num_lines\": 197,\n \"path\": \"/src/TorchSharp/Distributions/ExpRelaxedCategorical.cs\",\n \"repo_name\": \"NiklasGustafsson/TorchSharp\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"// Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information.\\nusing System;\\nusing static TorchSharp.torch;\\n\\nnamespace TorchSharp\\n{\\n using Modules;\\n using static torch.distributions;\\n\\n namespace Modules\\n {\\n /// <summary>\\n /// Creates a ExpRelaxedCategorical parameterized by `temperature`, and either `probs` or `logits` (but not both).\\n /// Returns the log of a point in the simplex.\\n /// </summary>\\n public class ExpRelaxedCategorical : Distribution\\n {\\n /// <summary>\\n /// Constructor\\n /// </summary>\\n /// <param name=\\\"temperature\\\">Relaxation temperature</param>\\n /// <param name=\\\"probs\\\">the probability of sampling `1`</param>\\n /// <param name=\\\"logits\\\">the log-odds of sampling `1`</param>\\n /// <param name=\\\"generator\\\"></param>\\n internal ExpRelaxedCategorical(Tensor temperature, Tensor probs = null, Tensor logits = null, torch.Generator generator = null) :\\n base(generator)\\n {\\n this._categorical = Categorical(probs, logits, generator);\\n this._probs = probs?.alias().DetachFromDisposeScope();\\n this._logits = logits?.alias().DetachFromDisposeScope();\\n this._temperature = temperature.alias().DetachFromDisposeScope();\\n base._init(_categorical.batch_shape, _categorical.event_shape);\\n }\\n\\n private Tensor _probs;\\n private Tensor _logits;\\n private Tensor _temperature;\\n private Categorical _categorical;\\n\\n /// <summary>\\n /// The probability of sampling 1\\n /// </summary>\\n public Tensor probs {\\n get {\\n return _categorical.probs;\\n }\\n }\\n\\n /// <summary>\\n /// The log-odds of sampling 1\\n /// </summary>\\n public Tensor logits {\\n get {\\n return _categorical.logits;\\n }\\n }\\n\\n public Tensor temperature {\\n get {\\n return _temperature;\\n }\\n }\\n\\n public override Tensor mean => new Tensor(IntPtr.Zero);\\n\\n public override Tensor mode => base.mode;\\n\\n public override Tensor variance => new Tensor(IntPtr.Zero);\\n\\n public override Tensor stddev => base.stddev;\\n\\n /// <summary>\\n /// Returns a new distribution instance (or populates an existing instance provided by a derived class) with batch dimensions expanded to\\n /// `batch_shape`. This method calls `torch.Tensor.expand()` on the distribution's parameters. As such, this does not allocate new\\n /// memory for the expanded distribution instance.\\n /// </summary>\\n /// <param name=\\\"batch_shape\\\">Tthe desired expanded size.</param>\\n /// <param name=\\\"instance\\\">new instance provided by subclasses that need to override `.expand`.</param>\\n /// <returns></returns>\\n public override distributions.Distribution expand(Size batch_shape, distributions.Distribution instance = null)\\n {\\n if (instance != null && !(instance is ExpRelaxedCategorical))\\n throw new ArgumentException(\\\"expand(): 'instance' must be a ExpRelaxedCategorical distribution\\\");\\n\\n var newDistribution = ((instance == null) ? new ExpRelaxedCategorical(temperature, _probs, _logits, generator) : instance) as ExpRelaxedCategorical;\\n\\n newDistribution.batch_shape = batch_shape;\\n if (newDistribution == instance) {\\n newDistribution._temperature = _temperature;\\n newDistribution._categorical = _categorical.expand(batch_shape) as Categorical;\\n }\\n return newDistribution;\\n }\\n\\n /// <summary>\\n /// The shape of the input parameter.\\n /// </summary>\\n public long[] param_shape {\\n get {\\n return _categorical.param_shape;\\n }\\n }\\n\\n public override Tensor sample(params long[] sample_shape)\\n {\\n return rsample(sample_shape);\\n }\\n\\n public override Tensor rsample(params long[] sample_shape)\\n {\\n using var _ = torch.NewDisposeScope();\\n var shape = ExtendedShape(sample_shape);\\n var uniforms = ClampProbs(torch.rand(shape, dtype: _logits.dtype, device: _logits.device));\\n var gumbels = -((-(uniforms.log())).log());\\n var scores = (_logits + gumbels) / _temperature;\\n return (scores - scores.logsumexp(dim: -1, keepdim: true)).MoveToOuterDisposeScope();\\n }\\n\\n public override Tensor log_prob(Tensor value)\\n {\\n using var _ = torch.NewDisposeScope();\\n float K = _categorical.num_events;\\n var logitsValue = broadcast_tensors(_logits, value);\\n var logits = logitsValue[0];\\n value = logitsValue[1];\\n var log_scale = (torch.full_like(_temperature, K).lgamma() - _temperature.log().mul(-(K - 1)));\\n var score = logits - value.mul(_temperature);\\n score = (score - score.logsumexp(dim: -1, keepdim: true)).sum(-1);\\n return (score + log_scale).MoveToOuterDisposeScope();\\n }\\n\\n public override Tensor entropy()\\n {\\n throw new NotImplementedException();\\n }\\n }\\n\\n }\\n public static partial class torch\\n {\\n public static partial class distributions\\n {\\n /// <summary>\\n /// Creates a ExpRelaxedCategorical parameterized by `temperature`, and either `probs` or `logits` (but not both).\\n /// Returns the log of a point in the simplex.\\n /// </summary>\\n /// <param name=\\\"temperature\\\">Relaxation temperature</param>\\n /// <param name=\\\"probs\\\">The probability of sampling '1'</param>\\n /// <param name=\\\"logits\\\">The log-odds of sampling '1'</param>\\n /// <param name=\\\"generator\\\">An optional random number generator object.</param>\\n /// <returns></returns>\\n public static ExpRelaxedCategorical ExpRelaxedCategorical(Tensor temperature, Tensor probs = null, Tensor logits = null, torch.Generator generator = null)\\n {\\n return new ExpRelaxedCategorical(temperature, probs, logits, generator);\\n }\\n\\n /// <summary>\\n /// Creates a ExpRelaxedCategorical parameterized by `temperature`, and either `probs` or `logits` (but not both).\\n /// Returns the log of a point in the simplex.\\n /// </summary>\\n /// <param name=\\\"temperature\\\">Relaxation temperature</param>\\n /// <param name=\\\"probs\\\">The probability of sampling '1'</param>\\n /// <param name=\\\"logits\\\">The log-odds of sampling '1'</param>\\n /// <param name=\\\"generator\\\">An optional random number generator object.</param>\\n /// <returns></returns>\\n public static ExpRelaxedCategorical ExpRelaxedCategorical(Tensor temperature, float? probs, float? logits, torch.Generator generator = null)\\n {\\n if (probs.HasValue && !logits.HasValue)\\n return new ExpRelaxedCategorical(temperature, torch.tensor(probs.Value), null, generator);\\n else if (!probs.HasValue && logits.HasValue)\\n return new ExpRelaxedCategorical(temperature, null, torch.tensor(logits.Value), generator);\\n else\\n throw new ArgumentException(\\\"One and only one of 'probs' and logits should be provided.\\\");\\n }\\n\\n\\n /// <summary>\\n /// Creates a ExpRelaxedCategorical parameterized by `temperature`, and either `probs` or `logits` (but not both).\\n /// Returns the log of a point in the simplex.\\n /// </summary>\\n /// <param name=\\\"temperature\\\">Relaxation temperature</param>\\n /// <param name=\\\"probs\\\">The probability of sampling '1'</param>\\n /// <param name=\\\"logits\\\">The log-odds of sampling '1'</param>\\n /// <param name=\\\"generator\\\">An optional random number generator object.</param>\\n /// <returns></returns>\\n public static ExpRelaxedCategorical ExpRelaxedCategorical(Tensor temperature, double? probs, double? logits, torch.Generator generator = null)\\n {\\n if (probs.HasValue && !logits.HasValue)\\n return new ExpRelaxedCategorical(temperature, torch.tensor(probs.Value), null, generator);\\n else if (!probs.HasValue && logits.HasValue)\\n return new ExpRelaxedCategorical(temperature, null, torch.tensor(logits.Value), generator);\\n else\\n throw new ArgumentException(\\\"One and only one of 'probs' and 'logits' should be non-null\\\");\\n }\\n }\\n }\\n}\\n\"\n },\n {\n \"alpha_fraction\": 0.602291464805603,\n \"alphanum_fraction\": 0.6483959555625916,\n \"avg_line_length\": 52.48039245605469,\n \"blob_id\": \"b74ed739802619c547cbc7ae7f5cb380579009db\",\n \"content_id\": \"81ea280a6b5d60bd68977faa810e4130ff63029b\",\n \"detected_licenses\": [\n \"MIT\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"C++\",\n \"length_bytes\": 10910,\n \"license_type\": \"permissive\",\n \"max_line_length\": 199,\n \"num_lines\": 204,\n \"path\": \"/src/Native/LibTorchSharp/THSFFT.cpp\",\n \"repo_name\": \"NiklasGustafsson/TorchSharp\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"// Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information.\\n#include \\\"THSTensor.h\\\"\\n\\n#include <iostream>\\n#include <fstream>\\n\\nTensor THSTensor_fft(const Tensor tensor, const int64_t n, const int64_t dim, int8_t norm)\\n{\\n auto nArg = (n == -1 ? c10::optional<int64_t>() : c10::optional<int64_t>(n));\\n auto normArg = (norm == 0) ? \\\"backward\\\" : (norm == 1) ? \\\"forward\\\" : \\\"ortho\\\";\\n CATCH_TENSOR(torch::fft::fft(*tensor, nArg, dim, normArg));\\n}\\n\\nTensor THSTensor_ifft(const Tensor tensor, const int64_t n, const int64_t dim, int8_t norm)\\n{\\n auto nArg = (n == -1 ? c10::optional<int64_t>() : c10::optional<int64_t>(n));\\n auto normArg = (norm == 0) ? \\\"backward\\\" : (norm == 1) ? \\\"forward\\\" : \\\"ortho\\\";\\n CATCH_TENSOR(torch::fft::ifft(*tensor, nArg, dim, normArg));\\n}\\n\\nTensor THSTensor_fft2(const Tensor tensor, const int64_t* s, const int64_t* dim, int8_t norm)\\n{\\n auto normArg = (norm == 0) ? \\\"backward\\\" : (norm == 1) ? \\\"forward\\\" : \\\"ortho\\\";\\n auto sArg = (s == nullptr) ? c10::nullopt : c10::optional<c10::IntArrayRef>(c10::IntArrayRef(s, 2));\\n auto dArg = (dim == nullptr) ? c10::IntArrayRef({-2, -1}) : c10::IntArrayRef(dim, 2);\\n\\n CATCH_TENSOR(torch::fft::fft2(*tensor, sArg, dArg, normArg));\\n}\\n\\nTensor THSTensor_ifft2(const Tensor tensor, const int64_t* s, const int64_t* dim, int8_t norm)\\n{\\n auto normArg = (norm == 0) ? \\\"backward\\\" : (norm == 1) ? \\\"forward\\\" : \\\"ortho\\\";\\n auto sArg = (s == nullptr) ? c10::nullopt : c10::optional<c10::IntArrayRef>(c10::IntArrayRef(s, 2));\\n auto dArg = (dim == nullptr) ? c10::IntArrayRef({ -2, -1 }) : c10::IntArrayRef(dim, 2);\\n\\n CATCH_TENSOR(torch::fft::ifft2(*tensor, sArg, dArg, normArg));\\n}\\n\\nTensor THSTensor_fftn(const Tensor tensor, const int64_t* s, const int s_length, const int64_t* dim, const int dim_length, int8_t norm)\\n{\\n auto normArg = (norm == 0) ? \\\"backward\\\" : (norm == 1) ? \\\"forward\\\" : \\\"ortho\\\";\\n auto sArg = (s == nullptr) ? c10::nullopt : c10::optional<c10::IntArrayRef>(c10::IntArrayRef(s, s_length));\\n auto dArg = (dim == nullptr) ? c10::nullopt : c10::optional<c10::IntArrayRef>(c10::IntArrayRef(dim, dim_length));\\n\\n CATCH_TENSOR(torch::fft::fftn(*tensor, sArg, dArg, normArg));\\n}\\n\\nTensor THSTensor_ifftn(const Tensor tensor, const int64_t* s, const int s_length, const int64_t* dim, const int dim_length, int8_t norm)\\n{\\n auto normArg = (norm == 0) ? \\\"backward\\\" : (norm == 1) ? \\\"forward\\\" : \\\"ortho\\\";\\n auto sArg = (s == nullptr) ? c10::nullopt : c10::optional<c10::IntArrayRef>(c10::IntArrayRef(s, s_length));\\n auto dArg = (dim == nullptr) ? c10::nullopt : c10::optional<c10::IntArrayRef>(c10::IntArrayRef(dim, dim_length));\\n\\n CATCH_TENSOR(torch::fft::ifftn(*tensor, sArg, dArg, normArg));\\n}\\n\\nTensor THSTensor_hfft(const Tensor tensor, const int64_t n, const int64_t dim, int8_t norm)\\n{\\n auto nArg = (n == -1 ? c10::optional<int64_t>() : c10::optional<int64_t>(n));\\n auto normArg = (norm == 0) ? \\\"backward\\\" : (norm == 1) ? \\\"forward\\\" : \\\"ortho\\\";\\n CATCH_TENSOR(torch::fft::hfft(*tensor, nArg, dim, normArg));\\n}\\n\\nTensor THSTensor_hfft2(const Tensor tensor, const int64_t* s, const int64_t* dim, int8_t norm)\\n{\\n auto sArg = (s == nullptr) ? c10::nullopt : c10::optional<c10::IntArrayRef>(c10::IntArrayRef(s, 2));\\n auto normArg = (norm == 0) ? \\\"backward\\\" : (norm == 1) ? \\\"forward\\\" : \\\"ortho\\\";\\n auto dArg = (dim == nullptr) ? c10::IntArrayRef({ -2, -1 }) : c10::IntArrayRef(dim, 2);\\n CATCH_TENSOR(torch::fft::hfft2(*tensor, sArg, dArg, normArg));\\n}\\n\\nTensor THSTensor_hfftn(const Tensor tensor, const int64_t* s, const int s_length, const int64_t* dim, const int dim_length, int8_t norm)\\n{\\n auto normArg = (norm == 0) ? \\\"backward\\\" : (norm == 1) ? \\\"forward\\\" : \\\"ortho\\\";\\n auto sArg = (s == nullptr) ? c10::nullopt : c10::optional<c10::IntArrayRef>(c10::IntArrayRef(s, s_length));\\n c10::IntArrayRef dArg = (dim == nullptr) ? c10::IntArrayRef({-2, -1}) : c10::IntArrayRef(dim, dim_length);\\n\\n CATCH_TENSOR(torch::fft::hfftn(*tensor, sArg, dArg, normArg));\\n}\\n\\nTensor THSTensor_ihfft(const Tensor tensor, const int64_t n, const int64_t dim, int8_t norm)\\n{\\n auto nArg = (n == -1 ? c10::optional<int64_t>() : c10::optional<int64_t>(n));\\n auto normArg = (norm == 0) ? \\\"backward\\\" : (norm == 1) ? \\\"forward\\\" : \\\"ortho\\\";\\n CATCH_TENSOR(torch::fft::ihfft(*tensor, nArg, dim, normArg));\\n}\\n\\nTensor THSTensor_ihfft2(const Tensor tensor, const int64_t* s, const int64_t* dim, int8_t norm)\\n{\\n auto sArg = (s == nullptr) ? c10::nullopt : c10::optional<c10::IntArrayRef>(c10::IntArrayRef(s, 2));\\n auto normArg = (norm == 0) ? \\\"backward\\\" : (norm == 1) ? \\\"forward\\\" : \\\"ortho\\\";\\n auto dArg = (dim == nullptr) ? c10::IntArrayRef({ -2, -1 }) : c10::IntArrayRef(dim, 2);\\n CATCH_TENSOR(torch::fft::ihfft2(*tensor, sArg, dArg, normArg));\\n}\\n\\nTensor THSTensor_ihfftn(const Tensor tensor, const int64_t* s, const int s_length, const int64_t* dim, const int dim_length, int8_t norm)\\n{\\n auto normArg = (norm == 0) ? \\\"backward\\\" : (norm == 1) ? \\\"forward\\\" : \\\"ortho\\\";\\n auto sArg = (s == nullptr) ? c10::nullopt : c10::optional<c10::IntArrayRef>(c10::IntArrayRef(s, s_length));\\n c10::IntArrayRef dArg = (dim == nullptr) ? c10::IntArrayRef({ -2, -1 }) : c10::IntArrayRef(dim, dim_length);\\n\\n CATCH_TENSOR(torch::fft::ihfftn(*tensor, sArg, dArg, normArg));\\n}\\n\\nTensor THSTensor_rfft(const Tensor tensor, const int64_t n, const int64_t dim, int8_t norm)\\n{\\n auto nArg = (n == -1 ? c10::optional<int64_t>() : c10::optional<int64_t>(n));\\n auto normArg = (norm == 0) ? \\\"backward\\\" : (norm == 1) ? \\\"forward\\\" : \\\"ortho\\\";\\n CATCH_TENSOR(torch::fft::rfft(*tensor, nArg, dim, normArg));\\n}\\n\\nTensor THSTensor_irfft(const Tensor tensor, const int64_t n, const int64_t dim, int8_t norm)\\n{\\n auto nArg = (n == -1 ? c10::optional<int64_t>() : c10::optional<int64_t>(n));\\n auto normArg = (norm == 0) ? \\\"backward\\\" : (norm == 1) ? \\\"forward\\\" : \\\"ortho\\\";\\n CATCH_TENSOR(torch::fft::irfft(*tensor, nArg, dim, normArg));\\n}\\n\\nTensor THSTensor_rfft2(const Tensor tensor, const int64_t* s, const int64_t* dim, int8_t norm)\\n{\\n auto normArg = (norm == 0) ? \\\"backward\\\" : (norm == 1) ? \\\"forward\\\" : \\\"ortho\\\";\\n auto sArg = (s == nullptr) ? c10::nullopt : c10::optional<c10::IntArrayRef>(c10::IntArrayRef(s, 2));\\n auto dArg = (dim == nullptr) ? c10::IntArrayRef({ -2, -1 }) : c10::IntArrayRef(dim, 2);\\n\\n CATCH_TENSOR(torch::fft::rfft2(*tensor, sArg, dArg, normArg));\\n}\\n\\nTensor THSTensor_irfft2(const Tensor tensor, const int64_t* s, const int64_t* dim, int8_t norm)\\n{\\n auto normArg = (norm == 0) ? \\\"backward\\\" : (norm == 1) ? \\\"forward\\\" : \\\"ortho\\\";\\n auto sArg = (s == nullptr) ? c10::nullopt : c10::optional<c10::IntArrayRef>(c10::IntArrayRef(s, 2));\\n auto dArg = (dim == nullptr) ? c10::IntArrayRef({ -2, -1 }) : c10::IntArrayRef(dim, 2);\\n\\n CATCH_TENSOR(torch::fft::irfft2(*tensor, sArg, dArg, normArg));\\n}\\n\\nTensor THSTensor_rfftn(const Tensor tensor, const int64_t* s, const int s_length, const int64_t* dim, const int dim_length, int8_t norm)\\n{\\n auto normArg = (norm == 0) ? \\\"backward\\\" : (norm == 1) ? \\\"forward\\\" : \\\"ortho\\\";\\n auto sArg = (s == nullptr) ? c10::nullopt : c10::optional<c10::IntArrayRef>(c10::IntArrayRef(s, s_length));\\n auto dArg = (dim == nullptr) ? c10::nullopt : c10::optional<c10::IntArrayRef>(c10::IntArrayRef(dim, dim_length));\\n\\n CATCH_TENSOR(torch::fft::rfftn(*tensor, sArg, dArg, normArg));\\n}\\n\\nTensor THSTensor_irfftn(const Tensor tensor, const int64_t* s, const int s_length, const int64_t* dim, const int dim_length, int8_t norm)\\n{\\n auto normArg = (norm == 0) ? \\\"backward\\\" : (norm == 1) ? \\\"forward\\\" : \\\"ortho\\\";\\n auto sArg = (s == nullptr) ? c10::nullopt : c10::optional<c10::IntArrayRef>(c10::IntArrayRef(s, s_length));\\n auto dArg = (dim == nullptr) ? c10::nullopt : c10::optional<c10::IntArrayRef>(c10::IntArrayRef(dim, dim_length));\\n\\n CATCH_TENSOR(torch::fft::irfftn(*tensor, sArg, dArg, normArg));\\n}\\n\\nTensor THSTensor_fftfreq(const int64_t n, const double d, const int8_t scalar_type, const int device_type, const int device_index, const bool requires_grad)\\n{\\n auto options = at::TensorOptions()\\n .dtype(at::ScalarType(scalar_type))\\n .device(c10::Device((c10::DeviceType)device_type, (c10::DeviceIndex)device_index))\\n .requires_grad(requires_grad);\\n\\n CATCH_TENSOR(d == 0.0 ? torch::fft::fftfreq(n, options) : torch::fft::fftfreq(n, d, options));\\n}\\n\\nTensor THSTensor_rfftfreq(const int64_t n, const double d, const int8_t scalar_type, const int device_type, const int device_index, const bool requires_grad)\\n{\\n auto options = at::TensorOptions()\\n .dtype(at::ScalarType(scalar_type))\\n .device(c10::Device((c10::DeviceType)device_type, (c10::DeviceIndex)device_index))\\n .requires_grad(requires_grad);\\n\\n CATCH_TENSOR(d == 0.0 ? torch::fft::rfftfreq(n, options) : torch::fft::rfftfreq(n, d, options));\\n}\\n\\nTensor THSTensor_fftshift(const Tensor tensor, const int64_t* dim, const int dim_length)\\n{\\n auto dArg = (dim == nullptr) ? c10::nullopt : c10::optional<c10::IntArrayRef>(c10::IntArrayRef(dim, dim_length));\\n CATCH_TENSOR(torch::fft::fftshift(*tensor, dArg));\\n}\\n\\nTensor THSTensor_ifftshift(const Tensor tensor, const int64_t* dim, const int dim_length)\\n{\\n auto dArg = (dim == nullptr) ? c10::nullopt : c10::optional<c10::IntArrayRef>(c10::IntArrayRef(dim, dim_length));\\n CATCH_TENSOR(torch::fft::ifftshift(*tensor, dArg));\\n}\\n\\nTensor THSTensor_stft(const Tensor x, int64_t n_fft, int64_t hop_length, int64_t win_length, const Tensor window, bool normalized, int64_t onesided, bool return_complex)\\n{\\n auto _hop_length = hop_length == -1 ? c10::optional<int64_t>() : c10::optional<int64_t>(hop_length);\\n auto _win_length = win_length == -1 ? c10::optional<int64_t>() : c10::optional<int64_t>(win_length);\\n auto _window = window == nullptr ? c10::optional<at::Tensor>() : *window;\\n auto _onesided = (onesided == -1) ? c10::optional<bool>() : c10::optional<bool>((bool)onesided);\\n CATCH_TENSOR(x->stft(n_fft, _hop_length, _win_length, _window, normalized, _onesided, return_complex));\\n}\\n\\nTensor THSTensor_istft(const Tensor x, int64_t n_fft, int64_t hop_length, int64_t win_length, const Tensor window, bool center, bool normalized, int64_t onesided, int64_t length, bool return_complex)\\n{\\n auto _hop_length = hop_length == -1 ? c10::optional<int64_t>() : c10::optional<int64_t>(hop_length);\\n auto _win_length = win_length == -1 ? c10::optional<int64_t>() : c10::optional<int64_t>(win_length);\\n auto _window = window == nullptr ? c10::optional<at::Tensor>() : *window;\\n auto _length = length == -1 ? c10::optional<int64_t>() : c10::optional<int64_t>(length);\\n auto _onesided = (onesided == -1) ? c10::optional<bool>() : c10::optional<bool>((bool)onesided);\\n CATCH_TENSOR(x->istft(n_fft, _hop_length, _win_length, _window, center, normalized, _onesided, _length, return_complex));\\n}\\n\"\n },\n {\n \"alpha_fraction\": 0.7039856910705566,\n \"alphanum_fraction\": 0.7039856910705566,\n \"avg_line_length\": 39.599998474121094,\n \"blob_id\": \"1045f3329b8230c45e5f4936aa26ff3b0ddc8a5e\",\n \"content_id\": \"7da11e592d6ca8f9c5fec779197148b41af99306\",\n \"detected_licenses\": [\n \"MIT\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"C#\",\n \"length_bytes\": 2233,\n \"license_type\": \"permissive\",\n \"max_line_length\": 130,\n \"num_lines\": 55,\n \"path\": \"/src/TorchSharp/PInvoke/LibTorchSharp.THSInit.cs\",\n \"repo_name\": \"NiklasGustafsson/TorchSharp\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"// Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information.\\n#nullable enable\\nusing System;\\nusing System.Runtime.InteropServices;\\n\\nnamespace TorchSharp.PInvoke\\n{\\n internal static partial class NativeMethods\\n {\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern double THSInit_calculate_gain(long nonlinearity, double param);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern IntPtr THSInit_constant_(IntPtr tensor, IntPtr value);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern IntPtr THSInit_dirac_(IntPtr tensor);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern IntPtr THSInit_eye_(IntPtr matrix);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern IntPtr THSInit_normal_(IntPtr tensor, double mean, double std);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern IntPtr THSInit_trunc_normal_(IntPtr tensor, double mean, double std, double a, double b);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern IntPtr THSInit_ones_(IntPtr tensor);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern IntPtr THSInit_orthogonal_(IntPtr tensor, double gain);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern IntPtr THSInit_sparse_(IntPtr tensor, double sparsity, double std);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern IntPtr THSInit_uniform_(IntPtr tensor, double low, double high);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern IntPtr THSInit_kaiming_normal_(IntPtr tensor, double a, long mode, long nonlinearity);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern IntPtr THSInit_kaiming_uniform_(IntPtr tensor, double a, long mode, long nonlinearity);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern IntPtr THSInit_xavier_normal_(IntPtr tensor, double gain);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern IntPtr THSInit_xavier_uniform_(IntPtr tensor, double gain);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern IntPtr THSInit_zeros_(IntPtr tensor);\\n }\\n}\\n\"\n },\n {\n \"alpha_fraction\": 0.554710328578949,\n \"alphanum_fraction\": 0.555295467376709,\n \"avg_line_length\": 36.173912048339844,\n \"blob_id\": \"3ce4d056f7608640886c8e1afe99afd95a6e7647\",\n \"content_id\": \"c47b0b35fc3ca4628201b966d83f0c306bf21e66\",\n \"detected_licenses\": [\n \"MIT\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"C#\",\n \"length_bytes\": 1713,\n \"license_type\": \"permissive\",\n \"max_line_length\": 130,\n \"num_lines\": 46,\n \"path\": \"/src/TorchVision/Crop.cs\",\n \"repo_name\": \"NiklasGustafsson/TorchSharp\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"// Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information.\\n\\nusing static TorchSharp.torch;\\n\\n\\nnamespace TorchSharp\\n{\\n public static partial class torchvision\\n {\\n internal class Crop : ITransform\\n {\\n internal Crop(int top, int left, int height, int width)\\n {\\n this.top = top;\\n this.left = left;\\n this.height = height;\\n this.width = width;\\n }\\n\\n public Tensor call(Tensor input)\\n {\\n return input.crop(top, left, height, width);\\n }\\n\\n private int top, left, height, width;\\n }\\n\\n public static partial class transforms\\n {\\n /// <summary>\\n /// Crop an image at specified location and output size. The image is expected to have […, H, W] shape,\\n /// where … means an arbitrary number of leading dimensions. If image size is smaller than output size along any edge,\\n /// image is padded with 0 and then cropped.\\n /// </summary>\\n /// <param name=\\\"top\\\">Vertical component of the top left corner of the crop box.</param>\\n /// <param name=\\\"left\\\">Horizontal component of the top left corner of the crop box.</param>\\n /// <param name=\\\"height\\\">The height of the crop box.</param>\\n /// <param name=\\\"width\\\">The width of the crop box.</param>\\n /// <returns></returns>\\n static public ITransform Crop(int top, int left, int height, int width)\\n {\\n return new Crop(top, left, height, width);\\n }\\n }\\n }\\n}\"\n },\n {\n \"alpha_fraction\": 0.6487455368041992,\n \"alphanum_fraction\": 0.6612903475761414,\n \"avg_line_length\": 31.823530197143555,\n \"blob_id\": \"56ade3653a565ae0cda25064731e85b74b2fc77e\",\n \"content_id\": \"439644b2d3aecf5beb28cc37af4b9276ce2b7186\",\n \"detected_licenses\": [\n \"MIT\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"C#\",\n \"length_bytes\": 558,\n \"license_type\": \"permissive\",\n \"max_line_length\": 142,\n \"num_lines\": 17,\n \"path\": \"/src/TorchSharp/Utils/StringEncoder.cs\",\n \"repo_name\": \"NiklasGustafsson/TorchSharp\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"using System.Text;\\n\\nnamespace TorchSharp.Utils\\n{\\n internal static class StringEncoder\\n {\\n private static readonly Encoding s_utfEncoding = new UTF8Encoding(encoderShouldEmitUTF8Identifier: false, throwOnInvalidBytes: false);\\n\\n internal static byte[] GetNullTerminatedUTF8ByteArray(string input)\\n {\\n var bytes = new byte[s_utfEncoding.GetMaxByteCount(input.Length)+1];\\n var len = s_utfEncoding.GetBytes(input, 0, input.Length, bytes, 0);\\n bytes[len] = 0;\\n return bytes;\\n }\\n }\\n}\\n\"\n },\n {\n \"alpha_fraction\": 0.6319724917411804,\n \"alphanum_fraction\": 0.6337670087814331,\n \"avg_line_length\": 70.13829803466797,\n \"blob_id\": \"68016c0a4035315d91b09dc574b1695ed80691b3\",\n \"content_id\": \"da1f0fbe9d5dd559f31a4bcab37212b0884c470e\",\n \"detected_licenses\": [\n \"MIT\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"C#\",\n \"length_bytes\": 6691,\n \"license_type\": \"permissive\",\n \"max_line_length\": 278,\n \"num_lines\": 94,\n \"path\": \"/src/TorchSharp/NN/Embedding.cs\",\n \"repo_name\": \"NiklasGustafsson/TorchSharp\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"// Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information.\\nusing System;\\nusing static TorchSharp.torch;\\nusing static TorchSharp.PInvoke.NativeMethods;\\n\\n#nullable enable\\nnamespace TorchSharp\\n{\\n using Modules;\\n\\n namespace Modules\\n {\\n public sealed class Embedding : torch.nn.Module<Tensor, Tensor>\\n {\\n internal Embedding(IntPtr handle, IntPtr boxedHandle) : base(handle, boxedHandle) { }\\n\\n public override Tensor forward(Tensor input)\\n {\\n var res = THSNN_Embedding_forward(handle, input.Handle);\\n if (res == IntPtr.Zero) { torch.CheckForErrors(); }\\n return new Tensor(res);\\n }\\n\\n public Parameter? weight {\\n get {\\n var res = THSNN_Embedding_weight(handle);\\n if (res == IntPtr.Zero) { torch.CheckForErrors(); }\\n return (res == IntPtr.Zero) ? null : new Parameter(res);\\n }\\n set {\\n THSNN_Embedding_set_weight(handle, value is null ? IntPtr.Zero : value.Handle);\\n torch.CheckForErrors();\\n ConditionallyRegisterParameter(\\\"weight\\\", value);\\n }\\n }\\n }\\n }\\n\\n public static partial class torch\\n {\\n public static partial class nn\\n {\\n /// <summary>\\n /// A simple lookup table that stores embeddings of a fixed dictionary and size.\\n /// This module is often used to store word embeddings and retrieve them using indices. The input to the module is a list of indices, and the output is the corresponding word embeddings.\\n /// </summary>\\n /// <param name=\\\"num_embeddings\\\">Size of the dictionary of embeddings, the vocabulary size.</param>\\n /// <param name=\\\"embedding_dims\\\">The size of each embedding vector</param>\\n /// <param name=\\\"padding_idx\\\">If given, pads the output with the embedding vector at padding_idx (initialized to zeros) whenever it encounters the index.</param>\\n /// <param name=\\\"max_norm\\\">If given, each embedding vector with norm larger than max_norm is renormalized to have norm max_norm.</param>\\n /// <param name=\\\"norm_type\\\">The p of the p-norm to compute for the max_norm option. Default 2.</param>\\n /// <param name=\\\"scale_grad_by_freq\\\">If given, this will scale gradients by the inverse of frequency of the words in the mini-batch. Default: false.</param>\\n /// <param name=\\\"sparse\\\">If true, gradient w.r.t. weight matrix will be a sparse tensor. Default: false</param>\\n /// <param name=\\\"device\\\">The desired device of the parameters and buffers in this module</param>\\n /// <param name=\\\"dtype\\\">The desired floating point or complex dtype of the parameters and buffers in this module</param>\\n /// <returns></returns>\\n /// <remarks>Keep in mind that only a limited number of optimizers support sparse gradients: currently it’s optim.SGD (CUDA and CPU), optim.SparseAdam (CUDA and CPU) and optim.Adagrad (CPU)</remarks>\\n public static Embedding Embedding(long num_embeddings, long embedding_dims, long? padding_idx = null, double? max_norm = null, double norm_type = 2.0, bool scale_grad_by_freq = false, bool sparse = false, Device? device = null, ScalarType? dtype = null)\\n {\\n var res = THSNN_Embedding_ctor(num_embeddings, embedding_dims,\\n padding_idx.HasValue ? padding_idx.Value : -1, padding_idx.HasValue,\\n max_norm.HasValue ? max_norm.Value : 0.0, max_norm.HasValue,\\n norm_type, scale_grad_by_freq, sparse, out var boxedHandle);\\n if (res == IntPtr.Zero) { torch.CheckForErrors(); }\\n return new Embedding(res, boxedHandle).MoveModule<Embedding>(device,dtype);\\n }\\n\\n /// <summary>\\n /// A simple lookup table that stores embeddings of a fixed dictionary and size.\\n /// This module is often used to store word embeddings and retrieve them using indices. The input to the module is a list of indices, and the output is the corresponding word embeddings.\\n /// </summary>\\n /// <param name=\\\"embeddings\\\">FloatTensor containing weights for the Embedding in two dimensions. First dimension is being passed to Embedding as num_embeddings, second as embedding_dim.</param>\\n /// <param name=\\\"freeze\\\">If true (the default), the tensor does not get updated in the learning</param>\\n /// <param name=\\\"padding_idx\\\">If given, pads the output with the embedding vector at padding_idx (initialized to zeros) whenever it encounters the index.</param>\\n /// <param name=\\\"max_norm\\\">If given, each embedding vector with norm larger than max_norm is renormalized to have norm max_norm.</param>\\n /// <param name=\\\"norm_type\\\">The p of the p-norm to compute for the max_norm option. Default 2.</param>\\n /// <param name=\\\"scale_grad_by_freq\\\">If given, this will scale gradients by the inverse of frequency of the words in the mini-batch. Default: false.</param>\\n /// <param name=\\\"sparse\\\">If true, gradient w.r.t. weight matrix will be a sparse tensor. Default: false</param>\\n /// <param name=\\\"device\\\">The desired device of the parameters and buffers in this module</param>\\n /// <param name=\\\"dtype\\\">The desired floating point or complex dtype of the parameters and buffers in this module</param>\\n /// <returns></returns>\\n /// <remarks>Keep in mind that only a limited number of optimizers support sparse gradients: currently it’s optim.SGD (CUDA and CPU), optim.SparseAdam (CUDA and CPU) and optim.Adagrad (CPU)</remarks>\\n public static Embedding Embedding_from_pretrained(Tensor embeddings, bool freeze = true, long? padding_idx = null, double? max_norm = null, double norm_type = 2.0, bool scale_grad_by_freq = false, bool sparse = false, Device? device = null, ScalarType? dtype = null)\\n {\\n var res = THSNN_Embedding_from_pretrained(embeddings.Handle, freeze,\\n padding_idx.HasValue ? padding_idx.Value : -1, padding_idx.HasValue,\\n max_norm.HasValue ? max_norm.Value : 0.0, max_norm.HasValue,\\n norm_type, scale_grad_by_freq, sparse, out var boxedHandle);\\n if (res == IntPtr.Zero) { torch.CheckForErrors(); }\\n return new Embedding(res, boxedHandle).MoveModule<Embedding>(device, dtype);\\n }\\n }\\n }\\n}\\n\"\n },\n {\n \"alpha_fraction\": 0.6640316247940063,\n \"alphanum_fraction\": 0.6719367504119873,\n \"avg_line_length\": 24.399999618530273,\n \"blob_id\": \"04276fdee0b36ce05fce7204bd9d1a61119d31f4\",\n \"content_id\": \"be870ca426990dfd73094f126f864fda17c29dd6\",\n \"detected_licenses\": [\n \"MIT\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"C#\",\n \"length_bytes\": 255,\n \"license_type\": \"permissive\",\n \"max_line_length\": 131,\n \"num_lines\": 10,\n \"path\": \"/src/TorchSharp/Tensor/Enums/Float32MatmulPrecision.cs\",\n \"repo_name\": \"NiklasGustafsson/TorchSharp\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"// Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information.\\nnamespace TorchSharp\\n{\\n public enum Float32MatmulPrecision\\n {\\n highest,\\n high,\\n medium\\n }\\n}\"\n },\n {\n \"alpha_fraction\": 0.5307591557502747,\n \"alphanum_fraction\": 0.5314136147499084,\n \"avg_line_length\": 28.97058868408203,\n \"blob_id\": \"560a98336d4759021059ba0bfd441f1060bfb4c3\",\n \"content_id\": \"a266a128e706ee0419ef69201b94b1d21f0c7f04\",\n \"detected_licenses\": [\n \"MIT\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"C#\",\n \"length_bytes\": 3056,\n \"license_type\": \"permissive\",\n \"max_line_length\": 130,\n \"num_lines\": 102,\n \"path\": \"/src/TorchSharp/Utils/IndexedPinnedArrays.cs\",\n \"repo_name\": \"NiklasGustafsson/TorchSharp\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"// Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information.\\nusing System;\\nusing System.Collections.Generic;\\nusing System.Reflection;\\nusing System.Runtime.InteropServices;\\n\\nnamespace TorchSharp\\n{\\n /// <summary>\\n /// Allocator of T[] that pins the memory and handles unpinning.\\n /// Modified version of PinnedArray, this class allows multiple arrays to be handled\\n /// and unpinned together. Each array is identified by an integer index.\\n /// </summary>\\n /// <typeparam name=\\\"T\\\"></typeparam>\\n internal sealed class IndexedPinnedArrays<T> : IDisposable where T : struct\\n {\\n private List<GCHandle> handles = new List<GCHandle>();\\n private List<T[]> arrays = new List<T[]>();\\n\\n public int Count {\\n get { return arrays.Count; }\\n }\\n\\n public T[] this[int idx] {\\n get {\\n return arrays[idx];\\n }\\n set {\\n ExtendHandlesList(idx);\\n arrays[idx] = value;\\n }\\n }\\n\\n public IntPtr CreateArray(int index, int length)\\n {\\n this[index] = new T[length];\\n\\n // try... finally trick to be sure that the code isn't interrupted by asynchronous exceptions\\n try {\\n } finally {\\n handles[index] = GCHandle.Alloc(this[index], GCHandleType.Pinned);\\n }\\n\\n return handles[index].AddrOfPinnedObject();\\n }\\n\\n public IntPtr CreateArray(int index, IntPtr length)\\n {\\n return CreateArray(index, (int)length);\\n }\\n\\n public IntPtr CreateArray(int index, T[] array)\\n {\\n this[index] = array;\\n\\n // try... finally trick to be sure that the code isn't interrupted by asynchronous exceptions\\n try {\\n } finally {\\n handles[index] = GCHandle.Alloc(array, GCHandleType.Pinned);\\n }\\n\\n return handles[index].AddrOfPinnedObject();\\n }\\n\\n public void Dispose()\\n {\\n foreach (var array in arrays)\\n foreach (var val in array) {\\n (val as IDisposable)?.Dispose();\\n }\\n FreeHandles();\\n }\\n\\n ~IndexedPinnedArrays()\\n {\\n FreeHandles();\\n }\\n\\n private void ExtendHandlesList(int idx)\\n {\\n if (idx >= handles.Count) {\\n var extras = idx - handles.Count + 1;\\n for (var i = 0; i < extras; i++) {\\n handles.Add(default(GCHandle));\\n arrays.Add(default(T[]));\\n }\\n }\\n }\\n\\n private void FreeHandles()\\n {\\n foreach (var handle in handles) {\\n if (handle.IsAllocated) {\\n handle.Free();\\n }\\n }\\n }\\n }\\n\\n [UnmanagedFunctionPointer(CallingConvention.Cdecl)]\\n public delegate IntPtr AllocateIndexedPinnedArray(int id, IntPtr length);\\n}\"\n },\n {\n \"alpha_fraction\": 0.4888888895511627,\n \"alphanum_fraction\": 0.48956915736198425,\n \"avg_line_length\": 30.056337356567383,\n \"blob_id\": \"db7b424d1e7580d60db94e406e447594f521151d\",\n \"content_id\": \"6e12388ad7c04cb7f3a0fe4ed7bf2b06fb189c58\",\n \"detected_licenses\": [\n \"MIT\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"C#\",\n \"length_bytes\": 4410,\n \"license_type\": \"permissive\",\n \"max_line_length\": 154,\n \"num_lines\": 142,\n \"path\": \"/src/TorchSharp/NN/ModuleList.cs\",\n \"repo_name\": \"NiklasGustafsson/TorchSharp\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"// Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information.\\nusing System.Collections;\\nusing System.Collections.Generic;\\nusing static TorchSharp.torch.nn;\\n\\nnamespace TorchSharp\\n{\\n using Modules;\\n\\n namespace Modules\\n {\\n /// <summary>\\n /// Holds modules in a list.\\n /// ModuleList can be indexed like a regular list, but modules it contains are properly registered, and will be visible by all Module methods.\\n /// </summary>\\n public class ModuleList<T> : Module, IList<T> where T: Module\\n {\\n public ModuleList(params T[] modules) : base(nameof(ModuleList))\\n {\\n if (modules != null && modules.Length > 0) {\\n _list.AddRange(modules);\\n }\\n }\\n\\n protected override void RegisterComponents()\\n {\\n if (_registered) return;\\n\\n for (int i = 0; i < _list.Count; i++) {\\n register_module($\\\"{i}\\\", _list[i]);\\n }\\n _registered = true;\\n }\\n\\n private bool _registered = false;\\n\\n public T this[int index] {\\n get => _list[index];\\n set => _list[index] = value;\\n }\\n\\n public int Count => _list.Count;\\n\\n public bool IsReadOnly => false;\\n\\n public void Add(T item)\\n {\\n _list.Add(item);\\n }\\n\\n /// <summary>\\n /// Appends zero or more parameters to the end of the list.\\n /// </summary>\\n public void append(params T[] parameters)\\n {\\n if (parameters != null && parameters.Length > 0) {\\n _list.AddRange(parameters);\\n }\\n }\\n\\n public void Clear()\\n {\\n _list.Clear();\\n }\\n\\n public bool Contains(T item)\\n {\\n return _list.Contains(item);\\n }\\n\\n public void CopyTo(T[] array, int arrayIndex)\\n {\\n _list.CopyTo(array, arrayIndex);\\n }\\n\\n /// <summary>\\n /// Appends parameters from an enumeration to the end of the list.\\n /// </summary>\\n /// <param name=\\\"modules\\\"></param>\\n public void extend(IEnumerable<T> modules)\\n {\\n _list.AddRange(modules);\\n }\\n\\n public IEnumerator<T> GetEnumerator()\\n {\\n return _list.GetEnumerator();\\n }\\n\\n public int IndexOf(T item)\\n {\\n return _list.IndexOf(item);\\n }\\n\\n public void Insert(int index, T item)\\n {\\n _list.Insert(index, item);\\n }\\n\\n public bool Remove(T item)\\n {\\n return _list.Remove(item);\\n }\\n\\n public void RemoveAt(int index)\\n {\\n _list.RemoveAt(index);\\n }\\n\\n IEnumerator IEnumerable.GetEnumerator()\\n {\\n return GetEnumerator();\\n }\\n\\n private List<T> _list = new List<T>();\\n }\\n }\\n\\n public static partial class torch\\n {\\n public static partial class nn\\n {\\n /// <summary>\\n /// Create a ModuleList instance from an array of modules.\\n /// </summary>\\n /// <param name=\\\"modules\\\">A list of modules.</param>\\n /// <remarks>\\n /// ModuleList can be indexed like a regular list, but modules it contains are properly registered, and will be visible by all Module methods.\\n /// </remarks>\\n public static ModuleList<Module> ModuleList(params Module[] modules) => new ModuleList<Module>(modules);\\n\\n /// <summary>\\n /// Create a ModuleList instance from an array of modules.\\n /// </summary>\\n /// <param name=\\\"modules\\\">A list of modules.</param>\\n /// <remarks>\\n /// ModuleList can be indexed like a regular list, but modules it contains are properly registered, and will be visible by all Module methods.\\n /// </remarks>\\n public static ModuleList<T> ModuleList<T>(params T[] modules) where T : Module => new ModuleList<T>(modules);\\n }\\n }\\n}\\n\"\n },\n {\n \"alpha_fraction\": 0.5707030892372131,\n \"alphanum_fraction\": 0.5756229162216187,\n \"avg_line_length\": 52.3983039855957,\n \"blob_id\": \"74c26008748dadefd102624a017f2c0d6ecdae02\",\n \"content_id\": \"7575c6169297af5991e6a5f5bb610005a108e84c\",\n \"detected_licenses\": [\n \"MIT\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"C#\",\n \"length_bytes\": 6301,\n \"license_type\": \"permissive\",\n \"max_line_length\": 180,\n \"num_lines\": 118,\n \"path\": \"/src/TorchSharp/NN/Unfold.cs\",\n \"repo_name\": \"NiklasGustafsson/TorchSharp\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"// Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information.\\nusing System;\\nusing static TorchSharp.torch;\\nusing static TorchSharp.PInvoke.NativeMethods;\\n\\n#nullable enable\\nnamespace TorchSharp\\n{\\n using System.Security.Cryptography;\\n using Modules;\\n\\n namespace Modules\\n {\\n public sealed class Unfold : torch.nn.Module<Tensor, Tensor>\\n {\\n internal Unfold((long, long) kernel_size, (long, long) dilation, (long, long) padding, (long, long) stride) : base(nameof(Unfold))\\n {\\n this.kernelSize = kernel_size;\\n this.dilation = dilation;\\n this.padding = padding;\\n this.stride = stride;\\n }\\n\\n public override Tensor forward(Tensor tensor)\\n {\\n return torch.nn.functional.unfold(tensor, kernelSize, dilation, padding, stride);\\n }\\n\\n // Rather than spending cycles only to discover that this module has neither\\n // parameters nor buffers, just shortcut the move completely.\\n protected internal override nn.Module _to(Device device, ScalarType dtype) => this;\\n protected internal override nn.Module _to(DeviceType deviceType, int deviceIndex = -1) => this;\\n protected internal override nn.Module _to(ScalarType dtype) => this;\\n\\n private (long, long) kernelSize;\\n private (long, long) dilation;\\n private (long, long) padding;\\n private (long, long) stride;\\n }\\n }\\n\\n public static partial class torch\\n {\\n public static partial class nn\\n {\\n /// <summary>\\n /// Extracts sliding local blocks from a batched input tensor.\\n /// </summary>\\n /// <param name=\\\"kernel_size\\\">The size of the sliding blocks</param>\\n /// <param name=\\\"dilation\\\">A parameter that controls the stride of elements within the neighborhood.</param>\\n /// <param name=\\\"padding\\\">Implicit zero padding to be added on both sides of input.</param>\\n /// <param name=\\\"stride\\\">The stride of the sliding blocks in the input spatial dimensions.</param>\\n /// <remarks>Currently, only 4-D input tensors (batched image-like tensors) are supported.</remarks>\\n public unsafe static Unfold Unfold(long kernel_size, long dilation = 1, long padding = 0, long stride = 1)\\n {\\n return new Unfold((kernel_size, kernel_size), (dilation, dilation), (padding, padding), (stride, stride));\\n }\\n\\n /// <summary>\\n /// Extracts sliding local blocks from a batched input tensor.\\n /// </summary>\\n /// <param name=\\\"kernel_size\\\">The size of the sliding blocks</param>\\n /// <param name=\\\"dilation\\\">A parameter that controls the stride of elements within the neighborhood.</param>\\n /// <param name=\\\"padding\\\">Implicit zero padding to be added on both sides of input.</param>\\n /// <param name=\\\"stride\\\">The stride of the sliding blocks in the input spatial dimensions.</param>\\n /// <remarks>Currently, only 4-D input tensors (batched image-like tensors) are supported.</remarks>\\n public unsafe static Unfold Unfold((long, long) kernel_size, (long, long)? dilation = null, (long, long)? padding = null, (long, long)? stride = null)\\n {\\n dilation ??= (1, 1);\\n stride ??= (1, 1);\\n padding ??= (0, 0);\\n\\n return new Unfold(kernel_size, dilation.Value, padding.Value, stride.Value);\\n }\\n\\n public static partial class functional\\n {\\n /// <summary>\\n /// Extracts sliding local blocks from a batched input tensor.\\n /// </summary>\\n /// <param name=\\\"input\\\">The input tensor. Must be 4-D (batched images).</param>\\n /// <param name=\\\"kernel_size\\\">The size of the sliding blocks</param>\\n /// <param name=\\\"dilation\\\">A parameter that controls the stride of elements within the neighborhood.</param>\\n /// <param name=\\\"padding\\\">Implicit zero padding to be added on both sides of input.</param>\\n /// <param name=\\\"stride\\\">The stride of the sliding blocks in the input spatial dimensions.</param>\\n public unsafe static Tensor unfold(Tensor input, long kernel_size, long dilation = 1, long padding = 0, long stride = 1)\\n {\\n var res = THSNN_unfold(input.Handle, kernel_size, kernel_size, stride, stride, padding, padding, dilation, dilation);\\n if (res == IntPtr.Zero) { torch.CheckForErrors(); }\\n return new Tensor(res);\\n }\\n\\n /// <summary>\\n /// Extracts sliding local blocks from a batched input tensor.\\n /// </summary>\\n /// <param name=\\\"input\\\">The input tensor. Must be 4-D (batched images).</param>\\n /// <param name=\\\"kernel_size\\\">The size of the sliding blocks</param>\\n /// <param name=\\\"dilation\\\">A parameter that controls the stride of elements within the neighborhood.</param>\\n /// <param name=\\\"padding\\\">Implicit zero padding to be added on both sides of input.</param>\\n /// <param name=\\\"stride\\\">The stride of the sliding blocks in the input spatial dimensions.</param>\\n public unsafe static Tensor unfold(Tensor input, (long, long) kernel_size, (long, long)? dilation = null, (long, long)? padding = null, (long, long)? stride = null)\\n {\\n dilation ??= (1, 1);\\n stride ??= (1, 1);\\n padding ??= (0, 0);\\n\\n var res = THSNN_unfold(input.Handle,\\n kernel_size.Item1, kernel_size.Item2,\\n stride.Value.Item1, stride.Value.Item2,\\n padding.Value.Item1, padding.Value.Item2,\\n dilation.Value.Item1, dilation.Value.Item2);\\n if (res == IntPtr.Zero) { torch.CheckForErrors(); }\\n return new Tensor(res);\\n }\\n }\\n }\\n }\\n}\\n\"\n },\n {\n \"alpha_fraction\": 0.47071465849876404,\n \"alphanum_fraction\": 0.47071465849876404,\n \"avg_line_length\": 31.086206436157227,\n \"blob_id\": \"6a4db713467c7a6e4044a837288b99875b58f0bb\",\n \"content_id\": \"31395a918b23624c3875c502cf9fc006107e62ba\",\n \"detected_licenses\": [\n \"MIT\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"C#\",\n \"length_bytes\": 1861,\n \"license_type\": \"permissive\",\n \"max_line_length\": 130,\n \"num_lines\": 58,\n \"path\": \"/src/TorchSharp/JIT/Type/TensorType.cs\",\n \"repo_name\": \"NiklasGustafsson/TorchSharp\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"// Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information.\\nusing System;\\nusing static TorchSharp.PInvoke.NativeMethods;\\n\\nnamespace TorchSharp\\n{\\n public static partial class torch\\n {\\n public static partial class jit\\n {\\n public sealed class TensorType : Type\\n {\\n internal TensorType(IntPtr handle) : base(handle, TypeKind.TensorType)\\n {\\n this.handle = new HType(handle, true, TypeKind.TensorType);\\n }\\n\\n internal TensorType(Type type) : base()\\n {\\n handle = type.handle;\\n type.handle = new HType(IntPtr.Zero, true, TypeKind.TensorType);\\n type.Dispose();\\n }\\n\\n public ScalarType GetScalarType()\\n {\\n return (ScalarType)THSJIT_TensorType_dtype(handle);\\n }\\n\\n /// <summary>\\n /// Retrieves the sizes of all dimensions of the tensor.\\n /// </summary>\\n public long[] size()\\n {\\n long[] ptrArray;\\n\\n using (var pa = new PinnedArray<long>()) {\\n THSJIT_TensorType_sizes(handle, pa.CreateArray);\\n CheckForErrors();\\n ptrArray = pa.Array;\\n }\\n\\n return ptrArray;\\n }\\n\\n public int GetDimensions()\\n {\\n return THSJIT_getDimensionedTensorTypeDimensions(handle);\\n }\\n\\n public string GetDevice()\\n {\\n return THSJIT_getDimensionedTensorDevice(handle);\\n }\\n }\\n }\\n }\\n}\\n\"\n },\n {\n \"alpha_fraction\": 0.4978165924549103,\n \"alphanum_fraction\": 0.5,\n \"avg_line_length\": 21.950000762939453,\n \"blob_id\": \"38f6fdba7803615e410f38a99a77887aeefe1df5\",\n \"content_id\": \"5af1b21d4b7518c0107d99bae1906701e07d13f8\",\n \"detected_licenses\": [\n \"MIT\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"C#\",\n \"length_bytes\": 458,\n \"license_type\": \"permissive\",\n \"max_line_length\": 130,\n \"num_lines\": 20,\n \"path\": \"/src/TorchAudio/AudioFormat.cs\",\n \"repo_name\": \"NiklasGustafsson/TorchSharp\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"// Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information.\\nnamespace TorchSharp\\n{\\n public static partial class torchaudio\\n {\\n /// <summary>\\n /// Audio format\\n /// </summary>\\n public enum AudioFormat\\n {\\n WAV,\\n MP3,\\n FLAC,\\n OGG,\\n OPUS,\\n SPHERE,\\n AMR_NB\\n }\\n }\\n}\"\n },\n {\n \"alpha_fraction\": 0.4525687098503113,\n \"alphanum_fraction\": 0.46666666865348816,\n \"avg_line_length\": 45.5,\n \"blob_id\": \"a0c81d142e541bac57691b7b46d31f9763c6f3a3\",\n \"content_id\": \"987f022f73d8616e233f2754dd809e2ec30899b9\",\n \"detected_licenses\": [\n \"MIT\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"C#\",\n \"length_bytes\": 4185,\n \"license_type\": \"permissive\",\n \"max_line_length\": 240,\n \"num_lines\": 90,\n \"path\": \"/src/TorchVision/Ops.cs\",\n \"repo_name\": \"NiklasGustafsson/TorchSharp\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"// Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information.\\n\\nusing static TorchSharp.torch;\\nusing System.Collections.Generic;\\n\\n#nullable enable\\nnamespace TorchSharp\\n{\\n public static partial class torchvision\\n {\\n public static partial class ops\\n {\\n public static Tensor sigmoid_focal_loss(Tensor inputs, Tensor targets, float alpha = 0.25f, float gamma = 2.0f, nn.Reduction reduction = nn.Reduction.None)\\n {\\n using (var _ = torch.NewDisposeScope()) {\\n\\n var p = torch.sigmoid(inputs);\\n var ce_loss = nn.functional.binary_cross_entropy_with_logits(inputs, targets, reduction: reduction);\\n var p_t = p * targets + (1 - p) * (1 - targets);\\n\\n var loss = ce_loss * (1 - p_t).pow(gamma);\\n\\n if (alpha >= 0) {\\n var alpha_t = alpha * targets + (1 - alpha) * (1 - targets);\\n loss = alpha_t * loss;\\n }\\n\\n if (reduction == nn.Reduction.Mean) {\\n loss = loss.mean();\\n } else if (reduction == nn.Reduction.Sum) {\\n loss = loss.sum();\\n }\\n\\n return loss.MoveToOuterDisposeScope();\\n }\\n }\\n\\n /// <summary>\\n /// Performs non-maximum suppression (NMS) on the boxes according to their intersection-over-union (IoU). NMS iteratively removes lower scoring boxes which have an IoU greater than iou_threshold with another(higher scoring) box.\\n /// </summary>\\n /// <param name=\\\"boxes\\\">Boxes (Tensor[N, 4]) to perform NMS on. They are expected to be in (left, top, right, down) format.</param>\\n /// <param name=\\\"scores\\\">Scores (Tensor[N]) for each one of the boxes.</param>\\n /// <param name=\\\"iou_threshold\\\">Discards all overlapping boxes with IoU > iou_threshold.</param>\\n /// <returns>The indices (Tensor) of the elements that have been kept by NMS, sorted in decreasing order of scores.</returns>\\n public static Tensor nms(Tensor boxes, Tensor scores, double iou_threshold = 0.5)\\n {\\n using (var _ = torch.NewDisposeScope()) {\\n var x1 = boxes.select(1, 0);\\n var y1 = boxes.select(1, 1);\\n var x2 = boxes.select(1, 2);\\n var y2 = boxes.select(1, 3);\\n var areas = (x2 - x1) * (y2 - y1);\\n var (_, order) = scores.sort(0, descending: true);\\n\\n var keep = new List<long>();\\n while (order.numel() > 0) {\\n long i;\\n if (order.numel() == 1) {\\n i = order.cpu().item<long>();\\n keep.Add(i);\\n break;\\n } else {\\n i = order[0].cpu().item<long>();\\n keep.Add(i);\\n }\\n\\n var indices = torch.arange(1, order.shape[0], dtype: ScalarType.Int64);\\n order = order[indices];\\n var xx1 = x1[order].clamp(min: x1[i]);\\n var yy1 = y1[order].clamp(min: y1[i]);\\n var xx2 = x2[order].clamp(max: x2[i]);\\n var yy2 = y2[order].clamp(max: y2[i]);\\n var inter = (xx2 - xx1).clamp(min: 0) * (yy2 - yy1).clamp(min: 0);\\n\\n var iou = inter / (areas[i] + areas[order] - inter);\\n var idx = (iou <= iou_threshold).nonzero().squeeze();\\n if (idx.numel() == 0) {\\n break;\\n }\\n\\n order = order[idx];\\n }\\n\\n var ids = torch.from_array(keep.ToArray()).to_type(ScalarType.Int64).to(device: boxes.device);\\n return ids.MoveToOuterDisposeScope();\\n }\\n }\\n }\\n }\\n}\\n\"\n },\n {\n \"alpha_fraction\": 0.5684428215026855,\n \"alphanum_fraction\": 0.5856683254241943,\n \"avg_line_length\": 44.35416793823242,\n \"blob_id\": \"37b2503eff09b602118f85d565f5f2cce9492d45\",\n \"content_id\": \"77f711d44b409edbf1c1a6dfa2539f063ae6bdac\",\n \"detected_licenses\": [\n \"MIT\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"C#\",\n \"length_bytes\": 4354,\n \"license_type\": \"permissive\",\n \"max_line_length\": 136,\n \"num_lines\": 96,\n \"path\": \"/src/TorchSharp/Distributions/Kumaraswamy.cs\",\n \"repo_name\": \"NiklasGustafsson/TorchSharp\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"// Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information.\\n\\nusing static TorchSharp.torch;\\n\\nnamespace TorchSharp\\n{\\n using Modules;\\n using static torch.distributions;\\n\\n namespace Modules\\n {\\n public class Kumaraswamy : TransformedDistribution\\n {\\n internal Kumaraswamy(Tensor concentration1, Tensor concentration0, torch.Generator generator = null) : base(generator)\\n {\\n var c1c0 = broadcast_tensors(concentration1, concentration0);\\n this.concentration1 = c1c0[0].DetachFromDisposeScope();\\n this.concentration0 = c1c0[1].DetachFromDisposeScope();\\n\\n _init(Uniform(torch.full_like(this.concentration0, 0), torch.full_like(this.concentration0, 1)),\\n new distributions.transforms.Transform[] {\\n new torch.distributions.transforms.PowerTransform(exponent: this.concentration0.reciprocal()),\\n new torch.distributions.transforms.AffineTransform(loc:1.0, scale:-1.0),\\n new torch.distributions.transforms.PowerTransform(exponent: this.concentration1.reciprocal())\\n });\\n }\\n\\n public Tensor concentration1 { get; private set; }\\n\\n public Tensor concentration0 { get; private set; }\\n\\n public override Tensor mean => moments(concentration1, concentration0, 1);\\n\\n public override Tensor mode {\\n get {\\n using var _ = torch.NewDisposeScope();\\n var log_mode = concentration0.reciprocal() * (-concentration0).log1p() - (-concentration0 * concentration1).log1p();\\n log_mode[(concentration0 < 1) | (concentration1 < 1)] = double.NaN;\\n return log_mode.exp().MoveToOuterDisposeScope();\\n }\\n }\\n\\n public override Tensor variance => moments(concentration1, concentration0, 2) - torch.pow(mean, 2);\\n\\n public override Tensor entropy()\\n {\\n using var _ = torch.NewDisposeScope();\\n var t1 = (1 - concentration1.reciprocal());\\n var t0 = (1 - concentration0.reciprocal());\\n var H0 = torch.digamma(concentration0 + 1) + euler_constant;\\n return (t0 + t1 * H0 - torch.log(concentration1) - torch.log(concentration0)).MoveToOuterDisposeScope();\\n }\\n\\n public override Distribution expand(Size batch_shape, Distribution instance = null)\\n {\\n var newDistribution = ((instance == null)\\n ? new Kumaraswamy(concentration1.expand(batch_shape), concentration0.expand(batch_shape), generator)\\n : instance) as Kumaraswamy;\\n\\n if (newDistribution == instance) {\\n newDistribution.concentration0 = concentration0.expand(batch_shape);\\n newDistribution.concentration1 = concentration1.expand(batch_shape);\\n }\\n return base.expand(batch_shape, newDistribution);\\n }\\n\\n private Tensor moments(Tensor a, Tensor b, int n)\\n {\\n using var _ = torch.NewDisposeScope();\\n var arg1 = 1 + n / a;\\n var log_value = torch.lgamma(arg1) + torch.lgamma(b) - torch.lgamma(arg1 + b);\\n return (b * torch.exp(log_value)).MoveToOuterDisposeScope();\\n }\\n }\\n }\\n\\n\\n public static partial class torch\\n {\\n public static partial class distributions\\n {\\n /// <summary>\\n /// Samples from a Kumaraswamy distribution.\\n /// </summary>\\n /// <param name=\\\"concentration1\\\">1st concentration parameter of the distribution</param>\\n /// <param name=\\\"concentration0\\\">2nd concentration parameter of the distribution</param>\\n /// <param name=\\\"generator\\\">An optional random number generator object.</param>\\n /// <returns></returns>\\n public static Kumaraswamy Kumaraswamy(Tensor concentration1, Tensor concentration0, torch.Generator generator = null)\\n {\\n \\n return new Kumaraswamy(concentration1, concentration0, generator);\\n }\\n }\\n }\\n}\\n\"\n },\n {\n \"alpha_fraction\": 0.5536637306213379,\n \"alphanum_fraction\": 0.5588324666023254,\n \"avg_line_length\": 40.632911682128906,\n \"blob_id\": \"8c811e67fb20ecee36caa6408a68cb5b0c528a32\",\n \"content_id\": \"28966487438889f9f06186301a65db1963d176d2\",\n \"detected_licenses\": [\n \"MIT\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"C#\",\n \"length_bytes\": 3289,\n \"license_type\": \"permissive\",\n \"max_line_length\": 130,\n \"num_lines\": 79,\n \"path\": \"/src/TorchSharp/NN/Activation/RReLU.cs\",\n \"repo_name\": \"NiklasGustafsson/TorchSharp\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"// Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information.\\nusing System;\\nusing static TorchSharp.torch;\\nusing static TorchSharp.PInvoke.NativeMethods;\\n\\nnamespace TorchSharp\\n{\\n using Modules;\\n\\n namespace Modules\\n {\\n /// <summary>\\n /// This class is used to represent a RReLU module.\\n /// </summary>\\n public sealed class RReLU : torch.nn.Module<Tensor, Tensor>\\n {\\n internal RReLU(IntPtr handle, IntPtr boxedHandle) : base(handle, boxedHandle) { }\\n\\n public override Tensor forward(Tensor tensor)\\n {\\n var res = THSNN_RReLU_forward(handle, tensor.Handle);\\n if (res == IntPtr.Zero) { torch.CheckForErrors(); }\\n return new Tensor(res);\\n }\\n\\n public override string GetName()\\n {\\n return typeof(RReLU).Name;\\n }\\n\\n // Rather than spending cycles only to discover that this module has neither\\n // parameters nor buffers, just shortcut the move completely.\\n protected internal override nn.Module _to(Device device, ScalarType dtype) => this;\\n protected internal override nn.Module _to(DeviceType deviceType, int deviceIndex = -1) => this;\\n protected internal override nn.Module _to(ScalarType dtype) => this;\\n }\\n }\\n\\n public static partial class torch\\n {\\n public static partial class nn\\n {\\n /// <summary>\\n /// Randomized Rectified Linear Unit\\n /// </summary>\\n /// <param name=\\\"lower\\\">Lower bound of the uniform distribution. Default: 1/8</param>\\n /// <param name=\\\"upper\\\">Upper bound of the uniform distribution. Default: 1/3</param>\\n /// <param name=\\\"inplace\\\">Do the operation in-place. Default: False</param>\\n /// <returns></returns>\\n public static RReLU RReLU(double lower = one_eighth, double upper = one_third, bool inplace = false)\\n {\\n var handle = THSNN_RReLU_ctor(lower, upper, inplace, out var boxedHandle);\\n if (handle == IntPtr.Zero) { torch.CheckForErrors(); }\\n return new RReLU(handle, boxedHandle);\\n }\\n\\n private const double one_eighth = 1.0 / 8.0;\\n private const double one_third = 1.0 / 3.0;\\n\\n public static partial class functional\\n {\\n /// <summary>\\n /// Randomized Rectified Linear Unit\\n /// </summary>\\n /// <param name=\\\"x\\\">The input tensor</param>\\n /// <param name=\\\"lower\\\">Lower bound of the uniform distribution. Default: 1/8</param>\\n /// <param name=\\\"upper\\\">Upper bound of the uniform distribution. Default: 1/3</param>\\n /// <param name=\\\"inplace\\\">Do the operation in-place. Default: False</param>\\n /// <returns></returns>\\n public static Tensor rrelu(Tensor x, double lower, double upper, bool inplace = false)\\n {\\n using (var m = nn.RReLU(lower, upper, inplace)) {\\n return m.call(x);\\n }\\n }\\n }\\n }\\n }\\n}\\n\"\n },\n {\n \"alpha_fraction\": 0.5682742595672607,\n \"alphanum_fraction\": 0.5732132196426392,\n \"avg_line_length\": 44.893333435058594,\n \"blob_id\": \"c1e6b19d5cab8dc429b643ae91b8d26c650935fd\",\n \"content_id\": \"2d8d7e908da6354761e5bc2a922f70f78ead8ca5\",\n \"detected_licenses\": [\n \"MIT\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"C#\",\n \"length_bytes\": 3442,\n \"license_type\": \"permissive\",\n \"max_line_length\": 162,\n \"num_lines\": 75,\n \"path\": \"/src/TorchSharp/NN/Pooling/MaxUnpool1d.cs\",\n \"repo_name\": \"NiklasGustafsson/TorchSharp\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"// Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information.\\nusing System;\\nusing static TorchSharp.torch;\\nusing static TorchSharp.PInvoke.NativeMethods;\\n\\nnamespace TorchSharp\\n{\\n using Modules;\\n\\n namespace Modules\\n {\\n /// <summary>\\n /// This class is used to represent a MaxUnpool1D module.\\n /// </summary>\\n public sealed class MaxUnpool1d : torch.nn.Module<Tensor, Tensor, long[], Tensor>\\n {\\n internal MaxUnpool1d(IntPtr handle, IntPtr boxedHandle) : base(handle, boxedHandle)\\n {\\n }\\n\\n public override Tensor forward(Tensor tensor, Tensor indices, long[] output_size = null)\\n {\\n unsafe {\\n fixed (long* pOutSize = output_size) {\\n var res = THSNN_MaxUnpool1d_forward(handle, tensor.Handle, indices.Handle, (IntPtr)pOutSize);\\n if (res == IntPtr.Zero) { torch.CheckForErrors(); }\\n return new Tensor(res);\\n }\\n }\\n }\\n\\n public new Tensor call(Tensor tensor, Tensor indices, long[] output_size = null)\\n {\\n return base.call(tensor, indices, output_size);\\n }\\n\\n // Rather than spending cycles only to discover that this module has neither\\n // parameters nor buffers, just shortcut the move completely.\\n protected internal override nn.Module _to(Device device, ScalarType dtype) => this;\\n protected internal override nn.Module _to(DeviceType deviceType, int deviceIndex = -1) => this;\\n protected internal override nn.Module _to(ScalarType dtype) => this;\\n }\\n }\\n\\n public static partial class torch\\n {\\n public static partial class nn\\n {\\n /// <summary>\\n /// Applies a 1D max pooling over an input signal composed of several input planes.\\n /// </summary>\\n /// <param name=\\\"kernelSize\\\">The size of the sliding window, must be > 0.</param>\\n /// <param name=\\\"stride\\\">The stride of the sliding window, must be > 0. Default value is kernel_size.</param>\\n /// <param name=\\\"padding\\\">Implicit negative infinity padding to be added on both sides, must be >= 0 and less than or equal to kernel_size / 2</param>\\n /// <returns></returns>\\n public static MaxUnpool1d MaxUnpool1d(long kernelSize, long? stride = null, long? padding = null)\\n {\\n var pStride = stride.HasValue ? new long[] { stride.Value } : null;\\n var pPadding = padding.HasValue ? new long[] { padding.Value } : null;\\n return MaxUnpool1d(new long[] { kernelSize }, pStride, pPadding);\\n }\\n\\n private static MaxUnpool1d MaxUnpool1d(long[] kernelSize, long[] strides = null, long[] padding = null)\\n {\\n unsafe {\\n fixed (long* pkernelSize = kernelSize, pstrides = strides, pPadding = padding) {\\n var handle = THSNN_MaxUnpool1d_ctor((IntPtr)pkernelSize, (IntPtr)pstrides, (IntPtr)pPadding, out var boxedHandle);\\n if (handle == IntPtr.Zero) { torch.CheckForErrors(); }\\n return new MaxUnpool1d(handle, boxedHandle);\\n }\\n }\\n }\\n }\\n }\\n}\\n\"\n },\n {\n \"alpha_fraction\": 0.5577172636985779,\n \"alphanum_fraction\": 0.5629053115844727,\n \"avg_line_length\": 34.04545593261719,\n \"blob_id\": \"18eb8ed14989b1fca9ac861265e60ddb77be9e34\",\n \"content_id\": \"68abb553c3e4bed28825656b628b52e17365a625\",\n \"detected_licenses\": [\n \"MIT\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"C#\",\n \"length_bytes\": 1542,\n \"license_type\": \"permissive\",\n \"max_line_length\": 130,\n \"num_lines\": 44,\n \"path\": \"/src/TorchVision/AdjustSharpness.cs\",\n \"repo_name\": \"NiklasGustafsson/TorchSharp\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"// Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information.\\nusing System;\\nusing static TorchSharp.torch;\\n\\nnamespace TorchSharp\\n{\\n public static partial class torchvision\\n {\\n internal class AdjustSharpness : ITransform\\n {\\n internal AdjustSharpness(double sharpness)\\n {\\n if (sharpness < 0.0)\\n throw new ArgumentException($\\\"The sharpness factor ({sharpness}) must be non-negative.\\\");\\n this.sharpness = sharpness;\\n }\\n\\n public Tensor call(Tensor input)\\n {\\n return transforms.functional.adjust_sharpness(input, sharpness);\\n }\\n\\n private double sharpness;\\n }\\n\\n public static partial class transforms\\n {\\n /// <summary>\\n /// Adjust the sharpness of the image. \\n /// </summary>\\n /// <param name=\\\"sharpness\\\">\\n /// How much to adjust the sharpness. Can be any non negative number.\\n /// 0 gives a blurred image, 1 gives the original image while 2 increases the sharpness by a factor of 2.\\n /// </param>\\n /// <returns></returns>\\n static public ITransform AdjustSharpness(double sharpness)\\n {\\n if (sharpness < 0.0)\\n throw new ArgumentException(\\\"Negative sharpness factor\\\");\\n return new AdjustSharpness(sharpness);\\n }\\n }\\n }\\n}\\n\"\n },\n {\n \"alpha_fraction\": 0.6791037321090698,\n \"alphanum_fraction\": 0.6887807846069336,\n \"avg_line_length\": 28.77503776550293,\n \"blob_id\": \"3013dc56507137965c01bcc9a83f43b566a4d010\",\n \"content_id\": \"6d0e6d97e98f6b4cfbcc0448407f308390cc1fb2\",\n \"detected_licenses\": [\n \"MIT\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"C++\",\n \"length_bytes\": 19324,\n \"license_type\": \"permissive\",\n \"max_line_length\": 208,\n \"num_lines\": 649,\n \"path\": \"/src/Native/LibTorchSharp/THSNormalization.cpp\",\n \"repo_name\": \"NiklasGustafsson/TorchSharp\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"// Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information.\\n#include \\\"THSNN.h\\\"\\n\\n#include <torch/nn/init.h>\\n\\nNNModule THSNN_BatchNorm1d_ctor(const int64_t features, const double eps, const double momentum, const bool affine, const bool track_running_stats, NNAnyModule* outAsAnyModule)\\n{\\n CATCH_RETURN_NNModule(\\n auto opts = torch::nn::BatchNorm1dOptions(features)\\n .eps(eps)\\n .momentum(momentum)\\n .affine(affine)\\n .track_running_stats(track_running_stats);\\n\\n res = create_module<torch::nn::BatchNorm1dImpl>(opts, outAsAnyModule);\\n );\\n}\\n\\nTensor THSNN_BatchNorm1d_forward(const NNModule module, const Tensor tensor)\\n{\\n CATCH_TENSOR((*module)->as<torch::nn::BatchNorm1d>()->forward(*tensor));\\n}\\n\\nNNModule THSNN_BatchNorm2d_ctor(const int64_t features, const double eps, const double momentum, const bool affine, const bool track_running_stats, NNAnyModule* outAsAnyModule)\\n{\\n CATCH_RETURN_NNModule(\\n auto opts = torch::nn::BatchNorm2dOptions(features)\\n .eps(eps)\\n .momentum(momentum)\\n .affine(affine)\\n .track_running_stats(track_running_stats);\\n\\n res = create_module<torch::nn::BatchNorm2dImpl>(opts, outAsAnyModule);\\n );\\n}\\n\\nTensor THSNN_BatchNorm2d_forward(const NNModule module, const Tensor tensor)\\n{\\n CATCH_TENSOR((*module)->as<torch::nn::BatchNorm2d>()->forward(*tensor));\\n}\\n\\nNNModule THSNN_BatchNorm3d_ctor(const int64_t features, const double eps, const double momentum, const bool affine, const bool track_running_stats, NNAnyModule* outAsAnyModule)\\n{\\n CATCH_RETURN_NNModule(\\n auto opts = torch::nn::BatchNorm3dOptions(features)\\n .eps(eps)\\n .momentum(momentum)\\n .affine(affine)\\n .track_running_stats(track_running_stats);\\n\\n res = create_module<torch::nn::BatchNorm3dImpl>(opts, outAsAnyModule);\\n );\\n}\\n\\nTensor THSNN_BatchNorm3d_forward(const NNModule module, const Tensor tensor)\\n{\\n CATCH_TENSOR((*module)->as<torch::nn::BatchNorm3d>()->forward(*tensor));\\n}\\n\\nNNModule THSNN_GroupNorm_ctor(const int64_t num_groups, const int64_t num_channels, const double eps, const bool affine, NNAnyModule* outAsAnyModule)\\n{\\n CATCH_RETURN_NNModule(\\n auto opts = torch::nn::GroupNormOptions(num_groups, num_channels).eps(eps).affine(affine);\\n res = create_module<torch::nn::GroupNormImpl>(opts, outAsAnyModule);\\n );\\n}\\n\\nTensor THSNN_GroupNorm_forward(const NNModule module, const Tensor tensor)\\n{\\n CATCH_TENSOR((*module)->as<torch::nn::GroupNorm>()->forward(*tensor));\\n}\\n\\nTensor THSNN_GroupNorm_bias(const NNModule module)\\n{\\n return get_bias<torch::nn::GroupNorm>(module);\\n}\\n\\nvoid THSNN_GroupNorm_set_bias(const NNModule module, const Tensor bias)\\n{\\n set_bias<torch::nn::GroupNorm>(module, bias);\\n}\\n\\nTensor THSNN_GroupNorm_weight(const NNModule module)\\n{\\n return get_weight<torch::nn::GroupNorm>(module);\\n}\\n\\nvoid THSNN_GroupNorm_set_weight(const NNModule module, const Tensor weight)\\n{\\n set_weight<torch::nn::GroupNorm>(module, weight);\\n}\\n\\n\\nNNModule THSNN_InstanceNorm1d_ctor(const int64_t features, const double eps, const double momentum, const bool affine, const bool track_running_stats, NNAnyModule* outAsAnyModule)\\n{\\n CATCH_RETURN_NNModule(\\n auto opts = torch::nn::InstanceNorm1dOptions(features)\\n .eps(eps)\\n .momentum(momentum)\\n .affine(affine)\\n .track_running_stats(track_running_stats);\\n\\n res = create_module<torch::nn::InstanceNorm1dImpl>(opts, outAsAnyModule);\\n );\\n}\\n\\nTensor THSNN_InstanceNorm1d_forward(const NNModule module, const Tensor tensor)\\n{\\n CATCH_TENSOR((*module)->as<torch::nn::InstanceNorm1d>()->forward(*tensor));\\n}\\n\\nNNModule THSNN_InstanceNorm2d_ctor(const int64_t features, const double eps, const double momentum, const bool affine, const bool track_running_stats, NNAnyModule* outAsAnyModule)\\n{\\n CATCH_RETURN_NNModule(\\n auto opts = torch::nn::InstanceNorm2dOptions(features)\\n .eps(eps)\\n .momentum(momentum)\\n .affine(affine)\\n .track_running_stats(track_running_stats);\\n\\n res = create_module<torch::nn::InstanceNorm2dImpl>(opts, outAsAnyModule);\\n );\\n}\\n\\nTensor THSNN_InstanceNorm2d_forward(const NNModule module, const Tensor tensor)\\n{\\n CATCH_TENSOR((*module)->as<torch::nn::InstanceNorm2d>()->forward(*tensor));\\n}\\n\\nNNModule THSNN_InstanceNorm3d_ctor(const int64_t features, const double eps, const double momentum, const bool affine, const bool track_running_stats, NNAnyModule* outAsAnyModule)\\n{\\n CATCH_RETURN_NNModule(\\n auto opts = torch::nn::InstanceNorm3dOptions(features)\\n .eps(eps)\\n .momentum(momentum)\\n .affine(affine)\\n .track_running_stats(track_running_stats);\\n\\n res = create_module<torch::nn::InstanceNorm3dImpl>(opts, outAsAnyModule);\\n );\\n}\\n\\nTensor THSNN_InstanceNorm3d_forward(const NNModule module, const Tensor tensor)\\n{\\n CATCH_TENSOR((*module)->as<torch::nn::InstanceNorm3d>()->forward(*tensor));\\n}\\n\\nNNModule THSNN_LayerNorm_ctor(const int64_t* norm_shape, const int64_t norm_shape_len, const double eps, const bool elementwise_affine, NNAnyModule* outAsAnyModule)\\n{\\n CATCH_RETURN_NNModule(\\n std::vector<int64_t> normalized_shape;\\n for (int64_t i = 0; i < norm_shape_len; ++i)\\n {\\n normalized_shape.push_back(norm_shape[i]);\\n }\\n auto opts = torch::nn::LayerNormOptions(normalized_shape).eps(eps).elementwise_affine(elementwise_affine);\\n res = create_module<torch::nn::LayerNormImpl>(opts, outAsAnyModule);\\n );\\n}\\n\\nTensor THSNN_LayerNorm_forward(const NNModule module, const Tensor tensor)\\n{\\n CATCH_TENSOR((*module)->as<torch::nn::LayerNorm>()->forward(*tensor));\\n}\\n\\nTensor THSNN_LayerNorm_bias(const NNModule module)\\n{\\n return get_bias<torch::nn::LayerNorm>(module);\\n}\\n\\nvoid THSNN_LayerNorm_set_bias(const NNModule module, const Tensor bias)\\n{\\n set_bias<torch::nn::LayerNorm>(module, bias);\\n}\\n\\nTensor THSNN_LayerNorm_weight(const NNModule module)\\n{\\n return get_weight<torch::nn::LayerNorm>(module);\\n}\\n\\nvoid THSNN_LayerNorm_set_weight(const NNModule module, const Tensor weight)\\n{\\n set_weight<torch::nn::LayerNorm>(module, weight);\\n}\\n\\n\\nNNModule THSNN_LocalResponseNorm_ctor(const int64_t size, const double alpha, const double beta, const double k, NNAnyModule* outAsAnyModule)\\n{\\n CATCH_RETURN_NNModule(\\n auto opts = torch::nn::LocalResponseNormOptions(size)\\n .alpha(alpha)\\n .beta(beta)\\n .k(k);\\n res = create_module<torch::nn::LocalResponseNormImpl>(opts, outAsAnyModule);\\n );\\n}\\n\\nTensor THSNN_LocalResponseNorm_forward(const NNModule module, const Tensor tensor)\\n{\\n CATCH_TENSOR((*module)->as<torch::nn::LocalResponseNorm>()->forward(*tensor));\\n}\\n\\nvoid THSNN_BatchNorm1d_reset_stats(const NNModule module)\\n{\\n CATCH((*module)->as<torch::nn::BatchNorm1d>()->reset_running_stats(););\\n}\\n\\nTensor THSNN_BatchNorm1d_get_mean(const NNModule module)\\n{\\n CATCH(\\n auto m = (*module)->as<torch::nn::BatchNorm1d>()->running_mean;\\n return m.defined() ? ResultTensor(m) : nullptr;\\n );\\n return nullptr;\\n}\\n\\nTensor THSNN_BatchNorm1d_get_var(const NNModule module)\\n{\\n CATCH(\\n auto v = (*module)->as<torch::nn::BatchNorm1d>()->running_var;\\n return v.defined() ? ResultTensor(v) : nullptr;\\n );\\n return nullptr;\\n}\\n\\nTensor THSNN_BatchNorm1d_get_batches(const NNModule module)\\n{\\n CATCH(\\n auto v = (*module)->as<torch::nn::BatchNorm1d>()->num_batches_tracked;\\n return v.defined() ? ResultTensor(v) : nullptr;\\n );\\n return nullptr;\\n}\\n\\nvoid THSNN_BatchNorm1d_set_mean(const NNModule module, const Tensor bias)\\n{\\n CATCH(\\n (*module)->as<torch::nn::BatchNorm1d>()->running_mean = *bias;\\n );\\n}\\n\\nvoid THSNN_BatchNorm1d_set_var(const NNModule module, const Tensor bias)\\n{\\n CATCH(\\n (*module)->as<torch::nn::BatchNorm1d>()->running_var = *bias;\\n );\\n}\\n\\nTensor THSNN_BatchNorm1d_bias(const NNModule module)\\n{\\n return get_bias<torch::nn::BatchNorm1d>(module);\\n}\\n\\nvoid THSNN_BatchNorm1d_set_bias(const NNModule module, const Tensor bias)\\n{\\n set_bias<torch::nn::BatchNorm1d>(module, bias);\\n}\\n\\nTensor THSNN_BatchNorm1d_weight(const NNModule module)\\n{\\n return get_weight<torch::nn::BatchNorm1d>(module);\\n}\\n\\nvoid THSNN_BatchNorm1d_set_weight(const NNModule module, const Tensor weight)\\n{\\n set_weight<torch::nn::BatchNorm1d>(module, weight);\\n}\\n\\nvoid THSNN_BatchNorm2d_reset_stats(const NNModule module)\\n{\\n CATCH((*module)->as<torch::nn::BatchNorm2d>()->reset_running_stats(););\\n}\\n\\nTensor THSNN_BatchNorm2d_get_mean(const NNModule module)\\n{\\n CATCH(\\n auto m = (*module)->as<torch::nn::BatchNorm2d>()->running_mean;\\n return m.defined() ? ResultTensor(m) : nullptr;\\n );\\n return nullptr;\\n}\\n\\nTensor THSNN_BatchNorm2d_get_var(const NNModule module)\\n{\\n CATCH(\\n auto v = (*module)->as<torch::nn::BatchNorm2d>()->running_var;\\n return v.defined() ? ResultTensor(v) : nullptr;\\n );\\n return nullptr;\\n}\\n\\nTensor THSNN_BatchNorm2d_get_batches(const NNModule module)\\n{\\n CATCH(\\n auto v = (*module)->as<torch::nn::BatchNorm2d>()->num_batches_tracked;\\n return v.defined() ? ResultTensor(v) : nullptr;\\n );\\n return nullptr;\\n}\\n\\nvoid THSNN_BatchNorm2d_set_mean(const NNModule module, const Tensor bias)\\n{\\n CATCH(\\n (*module)->as<torch::nn::BatchNorm2d>()->running_mean = *bias;\\n );\\n}\\n\\nvoid THSNN_BatchNorm2d_set_var(const NNModule module, const Tensor bias)\\n{\\n CATCH(\\n (*module)->as<torch::nn::BatchNorm2d>()->running_var = *bias;\\n );\\n}\\n\\nTensor THSNN_BatchNorm2d_bias(const NNModule module)\\n{\\n return get_bias<torch::nn::BatchNorm2d>(module);\\n}\\n\\nvoid THSNN_BatchNorm2d_set_bias(const NNModule module, const Tensor bias)\\n{\\n set_bias<torch::nn::BatchNorm2d>(module, bias);\\n}\\n\\nTensor THSNN_BatchNorm2d_weight(const NNModule module)\\n{\\n return get_weight<torch::nn::BatchNorm2d>(module);\\n}\\n\\nvoid THSNN_BatchNorm2d_set_weight(const NNModule module, const Tensor weight)\\n{\\n set_weight<torch::nn::BatchNorm2d>(module, weight);\\n}\\n\\nvoid THSNN_BatchNorm3d_reset_stats(const NNModule module)\\n{\\n CATCH((*module)->as<torch::nn::BatchNorm3d>()->reset_running_stats(););\\n}\\n\\nTensor THSNN_BatchNorm3d_get_mean(const NNModule module)\\n{\\n CATCH(\\n auto m = (*module)->as<torch::nn::BatchNorm3d>()->running_mean;\\n return m.defined() ? ResultTensor(m) : nullptr;\\n );\\n return nullptr;\\n}\\n\\nTensor THSNN_BatchNorm3d_get_var(const NNModule module)\\n{\\n CATCH(\\n auto v = (*module)->as<torch::nn::BatchNorm3d>()->running_var;\\n return v.defined() ? ResultTensor(v) : nullptr;\\n );\\n return nullptr;\\n}\\n\\nTensor THSNN_BatchNorm3d_get_batches(const NNModule module)\\n{\\n CATCH(\\n auto v = (*module)->as<torch::nn::BatchNorm3d>()->num_batches_tracked;\\n return v.defined() ? ResultTensor(v) : nullptr;\\n );\\n return nullptr;\\n}\\n\\nvoid THSNN_BatchNorm3d_set_mean(const NNModule module, const Tensor bias)\\n{\\n CATCH(\\n (*module)->as<torch::nn::BatchNorm3d>()->running_mean = *bias;\\n );\\n}\\n\\nvoid THSNN_BatchNorm3d_set_var(const NNModule module, const Tensor bias)\\n{\\n CATCH(\\n (*module)->as<torch::nn::BatchNorm3d>()->running_var = *bias;\\n );\\n}\\n\\nTensor THSNN_BatchNorm3d_bias(const NNModule module)\\n{\\n return get_bias<torch::nn::BatchNorm3d>(module);\\n}\\n\\nvoid THSNN_BatchNorm3d_set_bias(const NNModule module, const Tensor bias)\\n{\\n set_bias<torch::nn::BatchNorm3d>(module, bias);\\n}\\n\\nTensor THSNN_BatchNorm3d_weight(const NNModule module)\\n{\\n return get_weight<torch::nn::BatchNorm3d>(module);\\n}\\n\\nvoid THSNN_BatchNorm3d_set_weight(const NNModule module, const Tensor weight)\\n{\\n set_weight<torch::nn::BatchNorm3d>(module, weight);\\n}\\n\\nvoid THSNN_InstanceNorm1d_reset_stats(const NNModule module)\\n{\\n CATCH((*module)->as<torch::nn::InstanceNorm1d>()->reset_running_stats(););\\n}\\n\\nTensor THSNN_InstanceNorm1d_get_mean(const NNModule module)\\n{\\n CATCH(\\n auto m = (*module)->as<torch::nn::InstanceNorm1d>()->running_mean;\\n return m.defined() ? ResultTensor(m) : nullptr;\\n );\\n return nullptr;\\n}\\n\\nTensor THSNN_InstanceNorm1d_get_var(const NNModule module)\\n{\\n CATCH(\\n auto v = (*module)->as<torch::nn::InstanceNorm1d>()->running_var;\\n return v.defined() ? ResultTensor(v) : nullptr;\\n );\\n return nullptr;\\n}\\n\\nTensor THSNN_InstanceNorm1d_get_batches(const NNModule module)\\n{\\n CATCH(\\n auto v = (*module)->as<torch::nn::InstanceNorm1d>()->num_batches_tracked;\\n return v.defined() ? ResultTensor(v) : nullptr;\\n );\\n return nullptr;\\n}\\n\\nvoid THSNN_InstanceNorm1d_set_mean(const NNModule module, const Tensor bias)\\n{\\n CATCH(\\n (*module)->as<torch::nn::InstanceNorm1d>()->running_mean = *bias;\\n );\\n}\\n\\nvoid THSNN_InstanceNorm1d_set_var(const NNModule module, const Tensor bias)\\n{\\n CATCH(\\n (*module)->as<torch::nn::InstanceNorm1d>()->running_var = *bias;\\n );\\n}\\n\\nTensor THSNN_InstanceNorm1d_bias(const NNModule module)\\n{\\n return get_bias<torch::nn::InstanceNorm1d>(module);\\n}\\n\\nvoid THSNN_InstanceNorm1d_set_bias(const NNModule module, const Tensor bias)\\n{\\n set_bias<torch::nn::InstanceNorm1d>(module, bias);\\n}\\n\\nTensor THSNN_InstanceNorm1d_weight(const NNModule module)\\n{\\n return get_weight<torch::nn::InstanceNorm1d>(module);\\n}\\n\\nvoid THSNN_InstanceNorm1d_set_weight(const NNModule module, const Tensor weight)\\n{\\n set_weight<torch::nn::InstanceNorm1d>(module, weight);\\n}\\n\\nvoid THSNN_InstanceNorm2d_reset_stats(const NNModule module)\\n{\\n CATCH((*module)->as<torch::nn::InstanceNorm2d>()->reset_running_stats(););\\n}\\n\\nTensor THSNN_InstanceNorm2d_get_mean(const NNModule module)\\n{\\n CATCH(\\n auto m = (*module)->as<torch::nn::InstanceNorm2d>()->running_mean;\\n return m.defined() ? ResultTensor(m) : nullptr;\\n );\\n return nullptr;\\n}\\n\\nTensor THSNN_InstanceNorm2d_get_var(const NNModule module)\\n{\\n CATCH(\\n auto v = (*module)->as<torch::nn::InstanceNorm2d>()->running_var;\\n return v.defined() ? ResultTensor(v) : nullptr;\\n );\\n return nullptr;\\n}\\n\\nTensor THSNN_InstanceNorm2d_get_batches(const NNModule module)\\n{\\n CATCH(\\n auto v = (*module)->as<torch::nn::InstanceNorm2d>()->num_batches_tracked;\\n return v.defined() ? ResultTensor(v) : nullptr;\\n );\\n return nullptr;\\n}\\n\\nvoid THSNN_InstanceNorm2d_set_mean(const NNModule module, const Tensor bias)\\n{\\n CATCH(\\n (*module)->as<torch::nn::InstanceNorm2d>()->running_mean = *bias;\\n );\\n}\\n\\nvoid THSNN_InstanceNorm2d_set_var(const NNModule module, const Tensor bias)\\n{\\n CATCH(\\n (*module)->as<torch::nn::InstanceNorm2d>()->running_var = *bias;\\n );\\n}\\n\\nTensor THSNN_InstanceNorm2d_bias(const NNModule module)\\n{\\n return get_bias<torch::nn::InstanceNorm2d>(module);\\n}\\n\\nvoid THSNN_InstanceNorm2d_set_bias(const NNModule module, const Tensor bias)\\n{\\n set_bias<torch::nn::InstanceNorm2d>(module, bias);\\n}\\n\\nTensor THSNN_InstanceNorm2d_weight(const NNModule module)\\n{\\n return get_weight<torch::nn::InstanceNorm2d>(module);\\n}\\n\\nvoid THSNN_InstanceNorm2d_set_weight(const NNModule module, const Tensor weight)\\n{\\n set_weight<torch::nn::InstanceNorm2d>(module, weight);\\n}\\n\\nvoid THSNN_InstanceNorm3d_reset_stats(const NNModule module)\\n{\\n CATCH((*module)->as<torch::nn::InstanceNorm3d>()->reset_running_stats(););\\n}\\n\\nTensor THSNN_InstanceNorm3d_get_mean(const NNModule module)\\n{\\n CATCH(\\n auto m = (*module)->as<torch::nn::InstanceNorm3d>()->running_mean;\\n return m.defined() ? ResultTensor(m) : nullptr;\\n );\\n return nullptr;\\n}\\n\\nTensor THSNN_InstanceNorm3d_get_var(const NNModule module)\\n{\\n CATCH(\\n auto v = (*module)->as<torch::nn::InstanceNorm3d>()->running_var;\\n return v.defined() ? ResultTensor(v) : nullptr;\\n );\\n return nullptr;\\n}\\n\\nTensor THSNN_InstanceNorm3d_get_batches(const NNModule module)\\n{\\n CATCH(\\n auto v = (*module)->as<torch::nn::InstanceNorm3d>()->num_batches_tracked;\\n return v.defined() ? ResultTensor(v) : nullptr;\\n );\\n return nullptr;\\n}\\n\\nvoid THSNN_InstanceNorm3d_set_mean(const NNModule module, const Tensor bias)\\n{\\n CATCH(\\n (*module)->as<torch::nn::InstanceNorm3d>()->running_mean = *bias;\\n );\\n}\\n\\nvoid THSNN_InstanceNorm3d_set_var(const NNModule module, const Tensor bias)\\n{\\n CATCH(\\n (*module)->as<torch::nn::InstanceNorm3d>()->running_var = *bias;\\n );\\n}\\n\\nTensor THSNN_InstanceNorm3d_bias(const NNModule module)\\n{\\n return get_bias<torch::nn::InstanceNorm3d>(module);\\n}\\n\\nvoid THSNN_InstanceNorm3d_set_bias(const NNModule module, const Tensor bias)\\n{\\n set_bias<torch::nn::InstanceNorm3d>(module, bias);\\n}\\n\\nTensor THSNN_InstanceNorm3d_weight(const NNModule module)\\n{\\n return get_weight<torch::nn::InstanceNorm3d>(module);\\n}\\n\\nvoid THSNN_InstanceNorm3d_set_weight(const NNModule module, const Tensor weight)\\n{\\n set_weight<torch::nn::InstanceNorm3d>(module, weight);\\n}\\n\\nTensor THSNN_batch_norm(const Tensor input, Tensor running_mean, const Tensor running_var, const Tensor weight, const Tensor bias, const bool training, const double momentum, const double eps)\\n{\\n auto opts = torch::nn::functional::BatchNormFuncOptions()\\n .training(training)\\n .momentum(momentum)\\n .eps(eps);\\n if (weight != nullptr) opts.weight(*weight);\\n if (bias != nullptr) opts.bias(*bias);\\n CATCH_TENSOR(torch::nn::functional::batch_norm(*input, *running_mean, *running_var, opts));\\n}\\n\\nTensor THSNN_group_norm(const Tensor input, const int64_t num_groups, const Tensor weight, const Tensor bias, const double eps)\\n{\\n auto opts = torch::nn::functional::GroupNormFuncOptions(num_groups)\\n .eps(eps);\\n if (weight != nullptr) opts.weight(*weight);\\n if (bias != nullptr) opts.bias(*bias);\\n CATCH_TENSOR(torch::nn::functional::group_norm(*input, opts));\\n}\\n\\nTensor THSNN_instance_norm(const Tensor input, const Tensor running_mean, const Tensor running_var, const Tensor weight, const Tensor bias, const bool use_input_stats, const double momentum, const double eps)\\n{\\n auto opts = torch::nn::functional::InstanceNormFuncOptions()\\n .use_input_stats(use_input_stats)\\n .momentum(momentum)\\n .eps(eps);\\n if (running_mean != nullptr) opts.running_mean(*running_mean);\\n if (running_var != nullptr) opts.running_var(*running_var);\\n if (weight != nullptr) opts.weight(*weight);\\n if (bias != nullptr) opts.bias(*bias);\\n CATCH_TENSOR(torch::nn::functional::instance_norm(*input, opts));\\n}\\n\\nTensor THSNN_layer_norm(const Tensor input, const int64_t* normalized_shape, const int64_t normalized_shape_len, const Tensor weight, const Tensor bias, const double eps)\\n{\\n auto opts = torch::nn::functional::LayerNormFuncOptions(\\n std::vector<int64_t>(normalized_shape, normalized_shape + normalized_shape_len))\\n .eps(eps);\\n if (weight != nullptr) opts.weight(*weight);\\n if (bias != nullptr) opts.bias(*bias);\\n CATCH_TENSOR(torch::nn::functional::layer_norm(*input, opts));\\n}\\n\\nTensor THSNN_local_response_norm(const Tensor input, const int64_t size, const double alpha, const double beta, const double k)\\n{\\n auto opts = torch::nn::functional::LocalResponseNormFuncOptions(size)\\n .alpha(alpha)\\n .beta(beta)\\n .k(k);\\n CATCH_TENSOR(torch::nn::functional::local_response_norm(*input, opts));\\n}\\n\"\n },\n {\n \"alpha_fraction\": 0.49947553873062134,\n \"alphanum_fraction\": 0.5054195523262024,\n \"avg_line_length\": 45.8934440612793,\n \"blob_id\": \"6e1b9f7bce99738dcb0de128dc2f3ab995999265\",\n \"content_id\": \"5890fa39ca0435b54c207b5edff438d196250e13\",\n \"detected_licenses\": [\n \"MIT\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"C#\",\n \"length_bytes\": 5720,\n \"license_type\": \"permissive\",\n \"max_line_length\": 183,\n \"num_lines\": 122,\n \"path\": \"/src/TorchSharp/Hub.cs\",\n \"repo_name\": \"NiklasGustafsson/TorchSharp\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"// Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information.\\nusing System;\\nusing System.IO;\\nusing System.Net.Http;\\nusing System.Security.Cryptography;\\nusing System.Text;\\nusing System.Threading;\\nusing System.Threading.Tasks;\\n\\nnamespace TorchSharp\\n{\\n public static partial class torch\\n {\\n public static partial class hub\\n {\\n private static Func<bool, IProgressBar> createProgressBarFunc = null;\\n\\n /// <summary>\\n /// Set a function to create a progress bar.\\n /// </summary>\\n /// <param name=\\\"func\\\">A progress bar function or null to set default the console progress bar</param>\\n public static void set_create_progress_bar_func(Func<bool, IProgressBar> func)\\n {\\n createProgressBarFunc = func;\\n }\\n\\n /// <summary>\\n /// Create a progress bar.\\n /// </summary>\\n /// <param name=\\\"hidden\\\">Make a hidden progress bar.</param>\\n /// <returns>A progress bar</returns>\\n public static IProgressBar CreateProgressBar(bool hidden)\\n {\\n IProgressBar progress_bar;\\n if (createProgressBarFunc == null) {\\n progress_bar = new ConsoleProgressBar(hidden);\\n } else {\\n progress_bar = createProgressBarFunc(hidden);\\n }\\n return progress_bar;\\n }\\n\\n /// <summary>\\n /// Download the url to a file \\n /// </summary>\\n /// <param name=\\\"url\\\">The URL to download</param>\\n /// <param name=\\\"dst\\\">The file path to download the URL into</param>\\n /// <param name=\\\"hash_prefix\\\">If non null, the SHA256 hash of downloaded content must match this prefix</param>\\n /// <param name=\\\"progress\\\">whether or not to display a progress bar to stderr</param>\\n /// <exception cref=\\\"InvalidDataException\\\">SHA256 hash doesn't match</exception>\\n public static void download_url_to_file(string url, string dst, string hash_prefix = null, bool progress = true)\\n {\\n try {\\n Task.Run(async () => {\\n await download_url_to_file_async(url, dst, hash_prefix, progress);\\n }).Wait();\\n } catch (AggregateException ex) {\\n throw ex.InnerException;\\n }\\n }\\n\\n /// <summary>\\n /// Download the url to a file \\n /// </summary>\\n /// <param name=\\\"url\\\">The URL to download</param>\\n /// <param name=\\\"dst\\\">The file path to download the URL into</param>\\n /// <param name=\\\"cancellationToken\\\">A cancellation token</param>\\n /// <param name=\\\"hash_prefix\\\">If non null, the SHA256 hash of downloaded content must match this prefix</param>\\n /// <param name=\\\"progress\\\">whether or not to display a progress bar to stderr</param>\\n /// <exception cref=\\\"InvalidDataException\\\">SHA256 hash doesn't match</exception>\\n public static async Task download_url_to_file_async(string url, string dst, string hash_prefix = null, bool progress = true, CancellationToken cancellationToken = default)\\n {\\n try {\\n using (var progress_bar = CreateProgressBar(!progress))\\n using (var httpClient = new HttpClient())\\n using (var response = await httpClient.GetAsync(url, HttpCompletionOption.ResponseHeadersRead, cancellationToken)) {\\n response.EnsureSuccessStatusCode();\\n progress_bar.Maximum = response.Content.Headers.ContentLength;\\n using (var writer = File.OpenWrite(dst)) {\\n byte[] buffer = new byte[64 * 1024];\\n if (progress) {\\n var reader = await response.Content.ReadAsStreamAsync();\\n while (true) {\\n int ret = await reader.ReadAsync(buffer, 0, buffer.Length, cancellationToken);\\n if (ret == 0) break;\\n await writer.WriteAsync(buffer, 0, ret, cancellationToken);\\n progress_bar.Value += ret;\\n }\\n } else {\\n await response.Content.CopyToAsync(writer);\\n }\\n }\\n }\\n } catch (Exception) {\\n if (File.Exists(dst)) {\\n File.Delete(dst);\\n }\\n throw;\\n }\\n\\n string fileHash = GetFileChecksum(dst);\\n if (hash_prefix != null && !fileHash.StartsWith(hash_prefix)) {\\n throw new InvalidDataException($\\\"invalid hash value (expected \\\\\\\"{hash_prefix}\\\\\\\", got \\\\\\\"{fileHash}\\\\\\\"\\\");\\n }\\n }\\n\\n private static string GetFileChecksum(string path)\\n {\\n using (SHA256 sha256 = SHA256.Create()) {\\n using (var stream = File.OpenRead(path)) {\\n var hashValue = sha256.ComputeHash(stream);\\n var sb = new StringBuilder();\\n foreach (var value in hashValue) {\\n sb.Append($\\\"{value:x2}\\\");\\n }\\n return sb.ToString();\\n }\\n }\\n }\\n }\\n }\\n}\"\n },\n {\n \"alpha_fraction\": 0.5187896490097046,\n \"alphanum_fraction\": 0.5192776918411255,\n \"avg_line_length\": 38.403846740722656,\n \"blob_id\": \"6b8b343c12438371835421952aadb89f0dd87a23\",\n \"content_id\": \"f8b7ac7e9721a4464f29703f3f5e8d8e8d143c50\",\n \"detected_licenses\": [\n \"MIT\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"C#\",\n \"length_bytes\": 2049,\n \"license_type\": \"permissive\",\n \"max_line_length\": 130,\n \"num_lines\": 52,\n \"path\": \"/src/TorchSharp/Distributions/ExponentialFamily.cs\",\n \"repo_name\": \"NiklasGustafsson/TorchSharp\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"// Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information.\\nusing System;\\nusing System.Collections.Generic;\\nusing System.Linq;\\n\\nnamespace TorchSharp\\n{\\n public static partial class torch\\n {\\n public static partial class distributions\\n {\\n /// <summary>\\n /// Base class for all distributions in the exponential family.\\n /// </summary>\\n public abstract class ExponentialFamily : Distribution\\n {\\n protected ExponentialFamily(torch.Generator generator) : base(generator)\\n {\\n }\\n\\n protected abstract IList<Tensor> NaturalParams { get; }\\n\\n protected virtual Tensor LogNormalizer(params Tensor[] parameters)\\n {\\n throw new NotImplementedException();\\n }\\n\\n protected abstract Tensor MeanCarrierMeasure { get; }\\n\\n /// <summary>\\n /// Returns entropy of distribution, batched over batch_shape.\\n /// </summary>\\n public override Tensor entropy()\\n {\\n // Method to compute the entropy using Bregman divergence of the log normalizer.\\n using var _ = NewDisposeScope();\\n var result = -MeanCarrierMeasure;\\n var nparams = NaturalParams.Select(p => p.detach().requires_grad_()).ToArray();\\n var lg_normal = LogNormalizer(nparams);\\n var gradients = torch.autograd.grad(new Tensor[] { lg_normal.sum() }, nparams, create_graph: true);\\n result += lg_normal;\\n for (int i = 0; i < gradients.Count; i++) {\\n var np = nparams[i];\\n var g = gradients[i];\\n result -= np * g;\\n }\\n return result.MoveToOuterDisposeScope();\\n }\\n }\\n }\\n }\\n}\\n\"\n },\n {\n \"alpha_fraction\": 0.5200901627540588,\n \"alphanum_fraction\": 0.5263025760650635,\n \"avg_line_length\": 48.28395080566406,\n \"blob_id\": \"ee0304572d954b16c435775b6cf80e6e683f49f2\",\n \"content_id\": \"c8177e0a44874f3490ec41a8f8bba094ff92bed3\",\n \"detected_licenses\": [\n \"MIT\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"C#\",\n \"length_bytes\": 19961,\n \"license_type\": \"permissive\",\n \"max_line_length\": 251,\n \"num_lines\": 405,\n \"path\": \"/src/TorchSharp/Optimizers/RMSprop.cs\",\n \"repo_name\": \"NiklasGustafsson/TorchSharp\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"// Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information.\\nusing System;\\nusing System.Collections.Generic;\\nusing System.Linq;\\nusing static TorchSharp.torch;\\n\\n\\nnamespace TorchSharp\\n{\\n using System.Data;\\n using System.IO;\\n using Modules;\\n\\n public static partial class torch\\n {\\n public static partial class optim\\n {\\n\\n /// <summary>\\n /// Implements the RMSprop algorithm.\\n ///\\n /// Proposed by G.Hinton in his course.\\n /// https://www.cs.toronto.edu/~tijmen/csc321/slides/lecture_slides_lec6.pdf\\n /// </summary>\\n /// <param name=\\\"parameters\\\">Parameters to optimize</param>\\n /// <param name=\\\"lr\\\">Learning rate (default: 1e-2)</param>\\n /// <param name=\\\"eps\\\">Term added to the denominator to improve numerical stability (default: 1e-8)</param>\\n /// <param name=\\\"alpha\\\">Smoothing constant (default: 0.99)</param>\\n /// <param name=\\\"weight_decay\\\">Weight decay (L2 penalty) (default: 0)</param>\\n /// <param name=\\\"momentum\\\">Momentum factor (default: 0)</param>\\n /// <param name=\\\"centered\\\">if true, compute the centered RMSProp, the gradient is normalized by an estimation of its variance</param>\\n /// <param name=\\\"maximize\\\">Maximize the params based on the objective, instead of minimizing.</param>\\n public static RMSProp RMSProp(IEnumerable<Parameter> parameters, double lr = 0.01, double alpha = 0.99, double eps = 1e-8, double weight_decay = 0, double momentum = 0, bool centered = false, bool maximize = false)\\n {\\n return new RMSProp(parameters, lr, alpha, eps, weight_decay, momentum, centered, maximize);\\n }\\n\\n /// <summary>\\n /// Implements the RMSprop algorithm.\\n ///\\n /// Proposed by G.Hinton in his course.\\n /// https://www.cs.toronto.edu/~tijmen/csc321/slides/lecture_slides_lec6.pdf\\n /// </summary>\\n /// <param name=\\\"parameters\\\">Parameters to optimize</param>\\n /// <param name=\\\"lr\\\">Learning rate (default: 1e-2)</param>\\n /// <param name=\\\"eps\\\">Term added to the denominator to improve numerical stability (default: 1e-8)</param>\\n /// <param name=\\\"alpha\\\">Smoothing constant (default: 0.99)</param>\\n /// <param name=\\\"weight_decay\\\">Weight decay (L2 penalty) (default: 0)</param>\\n /// <param name=\\\"momentum\\\">Momentum factor (default: 0)</param>\\n /// <param name=\\\"centered\\\">if true, compute the centered RMSProp, the gradient is normalized by an estimation of its variance</param>\\n /// <param name=\\\"maximize\\\">Maximize the params based on the objective, instead of minimizing.</param>\\n public static RMSProp RMSProp(IEnumerable<(string name, Parameter parameter)> parameters, double lr = 0.01, double alpha = 0.99, double eps = 1e-8, double weight_decay = 0, double momentum = 0, bool centered = false, bool maximize = false)\\n {\\n return new RMSProp(parameters.Select(np => np.parameter), lr, alpha, eps, weight_decay, momentum, centered, maximize);\\n }\\n\\n /// <summary>\\n /// Implements the RMSprop algorithm.\\n ///\\n /// Proposed by G.Hinton in his course.\\n /// https://www.cs.toronto.edu/~tijmen/csc321/slides/lecture_slides_lec6.pdf\\n /// </summary>\\n /// <param name=\\\"parameters\\\">Parameters to optimize</param>\\n /// <param name=\\\"lr\\\">Learning rate (default: 1e-2)</param>\\n /// <param name=\\\"eps\\\">Term added to the denominator to improve numerical stability (default: 1e-8)</param>\\n /// <param name=\\\"alpha\\\">Smoothing constant (default: 0.99)</param>\\n /// <param name=\\\"weight_decay\\\">Weight decay (L2 penalty) (default: 0)</param>\\n /// <param name=\\\"momentum\\\">Momentum factor (default: 0)</param>\\n /// <param name=\\\"centered\\\">if true, compute the centered RMSProp, the gradient is normalized by an estimation of its variance</param>\\n /// <param name=\\\"maximize\\\">Maximize the params based on the objective, instead of minimizing.</param>\\n public static RMSProp RMSProp(IEnumerable<RMSProp.ParamGroup> parameters, double lr = 0.01, double alpha = 0.99, double eps = 1e-8, double weight_decay = 0, double momentum = 0, bool centered = false, bool maximize = false)\\n {\\n return new RMSProp(parameters, lr, alpha, eps, weight_decay, momentum, centered, maximize);\\n }\\n }\\n }\\n\\n namespace Modules\\n {\\n using static torch.optim;\\n\\n public class RMSProp : OptimizerHelper, IMomentum\\n {\\n\\n /// <summary>\\n /// Implements RMSprop algorithm.\\n ///\\n /// Proposed by G.Hinton in his course.\\n /// </summary>\\n /// <param name=\\\"parameters\\\">Parameters to optimize. This optimizer requires the parameters collection.</param>\\n /// <param name=\\\"lr\\\">Learning rate</param>\\n /// <param name=\\\"alpha\\\">Smoothing constant.</param>\\n /// <param name=\\\"momentum\\\">Momentum factor (default: 0)</param>\\n /// <param name=\\\"eps\\\">Term added to the denominator to improve numerical stability</param>\\n /// <param name=\\\"weight_decay\\\">Weight decay (L2 penalty) (default: 0)</param>\\n /// <param name=\\\"centered\\\">if ``True``, compute the centered RMSProp, the gradient is normalized by an estimation of its variance</param>\\n /// <param name=\\\"maximize\\\">Maximize the params based on the objective, instead of minimizing.</param>\\n public RMSProp(IEnumerable<Parameter> parameters, double lr = 1e-3, double alpha = 0.99, double eps = 1e-8, double weight_decay = 0, double momentum = 0.0, bool centered = false, bool maximize = false)\\n : this(new ParamGroup[] { new ParamGroup { Parameters = parameters } }, lr, alpha, eps, weight_decay, momentum, centered, maximize)\\n {\\n }\\n\\n /// <summary>\\n /// Implements RMSprop algorithm.\\n ///\\n /// Proposed by G.Hinton in his course.\\n /// </summary>\\n /// <param name=\\\"parameters\\\">Parameters to optimize. This optimizer requires the parameters collection.</param>\\n /// <param name=\\\"lr\\\">Learning rate</param>\\n /// <param name=\\\"alpha\\\">Smoothing constant.</param>\\n /// <param name=\\\"momentum\\\">Momentum factor (default: 0)</param>\\n /// <param name=\\\"eps\\\">Term added to the denominator to improve numerical stability</param>\\n /// <param name=\\\"weight_decay\\\">Weight decay (L2 penalty) (default: 0)</param>\\n /// <param name=\\\"centered\\\">if ``True``, compute the centered RMSProp, the gradient is normalized by an estimation of its variance</param>\\n /// <param name=\\\"maximize\\\">Maximize the params based on the objective, instead of minimizing.</param>\\n public RMSProp(IEnumerable<ParamGroup> parameters, double lr = 1e-3, double alpha = 0.99, double eps = 1e-8, double weight_decay = 0, double momentum = 0.0, bool centered = false, bool maximize = false)\\n {\\n if (lr < 0) throw new ArgumentException($\\\"Invalid learning rate: {lr}\\\");\\n if (eps < 0) throw new ArgumentException($\\\"Invalid ε: {eps}\\\");\\n if (alpha < 0) throw new ArgumentException($\\\"Invalid alpha: {alpha}\\\");\\n if (momentum < 0.0) throw new ArgumentException($\\\"Invalid momentum value: {momentum}\\\");\\n if (weight_decay < 0.0) throw new ArgumentException($\\\"Invalid weight_decay value: {weight_decay}\\\");\\n\\n var options = new Options {\\n LearningRate = lr,\\n InitialLearningRate = lr,\\n maximize = maximize,\\n eps = eps,\\n alpha = alpha,\\n momentum = momentum,\\n centered = centered,\\n weight_decay = weight_decay\\n };\\n\\n _defaults = options;\\n _parameter_groups = new List<Modules.ParamGroup>();\\n\\n foreach (var g in parameters) {\\n add_param_group(g);\\n }\\n }\\n\\n /// <summary>\\n /// Performs a single optimization step (parameter update).\\n /// </summary>\\n /// <param name=\\\"closure\\\">A closure that reevaluates the model and returns the loss. Optional for most optimizers.</param>\\n /// <returns></returns>\\n public override Tensor step(Func<Tensor> closure = null)\\n {\\n return _step<ParamGroup>(group => {\\n\\n var options = group.Options as Options;\\n var maximize = options.maximize.Value;\\n var momentum = options.momentum.Value;\\n var alpha = options.alpha.Value;\\n var weight_decay = options.weight_decay.Value;\\n var centered = options.centered.Value;\\n var eps = options.eps.Value;\\n var lr = options.LearningRate.Value;\\n\\n foreach (var param in group.Parameters) {\\n\\n var state = (State)_state[param.handle];\\n\\n var grad = param.grad();\\n\\n if (grad is null) continue;\\n\\n if (maximize) grad = -grad;\\n\\n state.step += 1;\\n\\n if (weight_decay != 0) {\\n grad = grad.add(param, alpha: weight_decay);\\n }\\n\\n state.square_avg.mul_(alpha).addcmul_(grad, grad, value: 1 - alpha);\\n\\n Tensor avg = null;\\n\\n if (centered) {\\n var grad_avg = state.grad_avg;\\n grad_avg.mul_(alpha).add_(grad, alpha: 1 - alpha);\\n avg = state.square_avg.addcmul(grad_avg, grad_avg, value: -1).sqrt_().add_(eps);\\n } else {\\n avg = state.square_avg.sqrt().add_(eps);\\n }\\n\\n if (momentum > 0) {\\n var buf = state.momentum_buffer;\\n buf.mul_(momentum).addcdiv_(grad, avg);\\n param.add_(buf, alpha: -lr);\\n } else {\\n param.addcdiv_(grad, avg, -lr);\\n }\\n }\\n }, closure);\\n }\\n\\n protected override void Dispose(bool disposing)\\n {\\n base.Dispose(disposing);\\n foreach (var kvp in _state) {\\n ((State)kvp.Item2).Dispose();\\n }\\n _state.Clear();\\n }\\n\\n public sealed class State : OptimizerState, IDisposable\\n {\\n public long step;\\n public Tensor square_avg;\\n public Tensor momentum_buffer;\\n public Tensor grad_avg;\\n\\n public void Dispose()\\n {\\n Dispose(true);\\n GC.SuppressFinalize(this);\\n }\\n\\n private void Dispose(bool disposing)\\n {\\n if (disposing) {\\n momentum_buffer?.Dispose();\\n square_avg.Dispose();\\n grad_avg?.Dispose();\\n }\\n }\\n\\n /// <summary>\\n /// Move all the state to the indicated device.\\n /// </summary>\\n /// <param name=\\\"device\\\">The device to move all state to.</param>\\n public override void to(Device device)\\n {\\n square_avg = square_avg.to(device);\\n momentum_buffer = momentum_buffer?.to(device);\\n grad_avg = grad_avg?.to(device);\\n }\\n\\n /// <summary>\\n /// Load the optimizer parameter state from a stream.\\n /// </summary>\\n /// <param name=\\\"reader\\\">A binary reader connected to a stream open for reading.</param>\\n public override void LoadStateDict(BinaryReader reader)\\n {\\n step = reader.ReadInt64();\\n square_avg.Load(reader);\\n LoadConditionalStateTensor(reader, ref momentum_buffer);\\n LoadConditionalStateTensor(reader, ref grad_avg);\\n }\\n /// <summary>\\n /// Save the optimizer parameter state to a stream.\\n /// </summary>\\n /// <param name=\\\"writer\\\">A binary writer connected to a stream open for writing.</param>\\n public override void SaveStateDict(BinaryWriter writer)\\n {\\n writer.Write(this.step);\\n square_avg.Save(writer);\\n SaveConditionalStateTensor(writer, momentum_buffer);\\n SaveConditionalStateTensor(writer, grad_avg);\\n }\\n\\n /// <summary>\\n /// Load optimizer parameter state from another optimizer.\\n /// </summary>\\n /// <param name=\\\"source\\\">An optimizer state record.</param>\\n public override void LoadStateDict(OptimizerState source)\\n {\\n var st_state = source as State;\\n square_avg.Dispose();\\n grad_avg.Dispose();\\n momentum_buffer.Dispose();\\n\\n step = st_state.step;\\n square_avg = st_state.square_avg;\\n grad_avg = st_state.grad_avg;\\n momentum_buffer = st_state.momentum_buffer;\\n }\\n\\n /// <summary>\\n /// Useful for tests, allows comparison of one state with another.\\n /// </summary>\\n /// <param name=\\\"other\\\">The other optimizer state</param>\\n /// <returns></returns>\\n public override bool ApproximatelyEquals(OptimizerState other)\\n {\\n var rhs = other as State;\\n return (rhs is not null) && step == rhs.step &&\\n square_avg.allclose(rhs.square_avg) &&\\n grad_avg.allclose(rhs.grad_avg) &&\\n momentum_buffer.allclose(rhs.momentum_buffer);\\n }\\n }\\n\\n /// <summary>\\n /// Add a param group to the Optimizer s param_groups.\\n /// </summary>\\n /// <param name=\\\"param_group\\\"></param>\\n /// <remarks>This can be useful when fine tuning a pre-trained network as frozen layers can be made trainable and added to the Optimizer as training progresses.</remarks>\\n public override void add_param_group(Modules.ParamGroup param_group)\\n {\\n var def = _defaults as Options;\\n if (param_group.Options is null) {\\n param_group.Options = new Options();\\n }\\n\\n var opt = param_group.Options as Options;\\n\\n // Make sure all the options are set.\\n if (!opt.maximize.HasValue) opt.maximize = def.maximize;\\n if (!opt.LearningRate.HasValue) opt.LearningRate = def.LearningRate;\\n if (!opt.momentum.HasValue) opt.momentum = def.momentum;\\n if (!opt.eps.HasValue) opt.eps = def.eps;\\n if (!opt.alpha.HasValue) opt.alpha = def.alpha;\\n if (!opt.weight_decay.HasValue) opt.weight_decay = def.weight_decay;\\n if (!opt.centered.HasValue) opt.centered = def.centered;\\n\\n opt.InitialLearningRate = opt.LearningRate.Value;\\n\\n _parameter_groups.Add(param_group);\\n\\n foreach (var p in param_group.Parameters) {\\n var state = new State();\\n _state[p.Handle] = state;\\n state.square_avg = torch.zeros_like(p).DetachFromDisposeScope();\\n state.grad_avg = torch.zeros_like(p).DetachFromDisposeScope();\\n state.momentum_buffer = torch.zeros_like(p).DetachFromDisposeScope();\\n }\\n }\\n public class Options : Modules.OptimizerOptions\\n {\\n public bool? maximize;\\n public double? momentum;\\n public double? alpha;\\n public double? eps;\\n public double? weight_decay;\\n public bool? centered;\\n\\n /// <summary>\\n /// Load optimizer options (param-group hyperparameters) from another optimizer.\\n /// </summary>\\n /// <param name=\\\"source\\\">An optimizer options record.</param>\\n public override void LoadStateDict(OptimizerOptions source)\\n {\\n base.LoadStateDict(source);\\n var opts = source as Options;\\n maximize = opts.maximize;\\n momentum = opts.momentum;\\n alpha = opts.alpha;\\n weight_decay = opts.weight_decay;\\n eps = opts.eps;\\n centered = opts.centered;\\n }\\n\\n /// <summary>\\n /// Load the optimizer options (param-group hyperparameters) from a stream.\\n /// </summary>\\n /// <param name=\\\"reader\\\">A binary reader connected to a stream open for reading.</param>\\n public override void LoadStateDict(BinaryReader reader)\\n {\\n base.LoadStateDict(reader);\\n maximize = reader.ReadBoolean();\\n momentum = reader.ReadDouble();\\n alpha = reader.ReadDouble();\\n eps = reader.ReadDouble();\\n weight_decay = reader.ReadDouble();\\n centered = reader.ReadBoolean();\\n }\\n\\n /// <summary>\\n /// Save the optimizer options (param-group hyperparameters) to a stream.\\n /// </summary>\\n /// <param name=\\\"writer\\\">A binary writer connected to a stream open for writing.</param>\\n public override void SaveStateDict(BinaryWriter writer)\\n {\\n base.SaveStateDict(writer);\\n writer.Write(maximize.Value);\\n writer.Write(momentum.Value);\\n writer.Write(alpha.Value);\\n writer.Write(eps.Value);\\n writer.Write(weight_decay.Value);\\n writer.Write(centered.Value);\\n }\\n }\\n\\n public class ParamGroup : ParamGroup<Options>, IMomentum\\n {\\n public ParamGroup() { }\\n\\n public ParamGroup(IEnumerable<Parameter> parameters, Options options) : base(parameters, options) { }\\n\\n public ParamGroup(IEnumerable<Parameter> parameters, double lr = 1e-3, double eps = 1e-8, double alpha = 0.99, double weight_decay = 0, double momentum = 0.0, bool centered = false)\\n : base(parameters, new RMSProp.Options { LearningRate = lr, eps = eps, alpha = alpha, weight_decay = weight_decay, momentum = momentum, centered = centered })\\n {\\n }\\n\\n public double Momentum { get => Options.momentum.Value; set => Options.momentum = value; }\\n }\\n\\n public double Momentum { get => (_defaults as Options).momentum.Value; set => (_defaults as Options).momentum = value; }\\n }\\n }\\n}\\n\"\n },\n {\n \"alpha_fraction\": 0.6516147255897522,\n \"alphanum_fraction\": 0.6612610220909119,\n \"avg_line_length\": 28.315574645996094,\n \"blob_id\": \"10a535ed36e7806d532005171fa9e73b874ade3a\",\n \"content_id\": \"388f7594dfc04ae6a47cb3f648f611be5871f3d9\",\n \"detected_licenses\": [\n \"MIT\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"C++\",\n \"length_bytes\": 7153,\n \"license_type\": \"permissive\",\n \"max_line_length\": 142,\n \"num_lines\": 244,\n \"path\": \"/src/Native/LibTorchSharp/THSModule.cpp\",\n \"repo_name\": \"NiklasGustafsson/TorchSharp\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"// Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information.\\n#include \\\"THSNN.h\\\"\\n\\n#include <torch/nn/init.h>\\n\\n// General Module functions\\n\\nint THSNN_Module_is_training(NNModule module)\\n{\\n return (*module)->is_training();\\n}\\n\\nvoid THSNN_Module_train(NNModule module, bool on)\\n{\\n (*module)->train(on);\\n}\\n\\nconst char* THSNN_Module_name(const NNModule module)\\n{\\n return make_sharable_string((*module)->name());\\n}\\n\\nvoid THSNN_Module_zero_grad(const NNModule module)\\n{\\n (*module)->zero_grad();\\n}\\n\\nvoid THSNN_Module_to_device(NNModule module, int64_t device, int64_t index)\\n{\\n c10::DeviceType dev = c10::kCPU;\\n if (device == 1)\\n dev = c10::kCUDA;\\n (*module)->to(torch::Device(dev, index));\\n}\\n\\nvoid THSNN_Module_to_dtype(NNModule module, int8_t dtype)\\n{\\n (*module)->to((at::ScalarType)dtype);\\n}\\n\\nvoid THSNN_Module_to_device_dtype(NNModule module, int8_t dtype, int64_t device, int64_t index)\\n{\\n c10::DeviceType dev = c10::kCPU;\\n if (device == 1)\\n dev = c10::kCUDA;\\n (*module)->to(torch::Device(dev, index), (at::ScalarType)dtype);\\n}\\n\\nvoid THSNN_Module_dispose(const NNModule module)\\n{\\n delete module; // NOTE: this only deletes the shared_ptr\\n}\\n\\nvoid THSNN_AnyModule_dispose(const NNAnyModule module)\\n{\\n delete module; // NOTE: this only deletes the shared_ptr\\n}\\n\\n//NNModule THSNN_AnyModule_get(const NNAnyModule module)\\n//{\\n//\\treturn new std::shared_ptr< torch::nn::Module>(&( (*module)->get<torch::nn::Module>()));\\n//}\\n\\n// Sub-module handling, parameters, etc.\\n\\nvoid THSNN_Module_register_module(const NNModule module, const char* name, const NNModule submodule)\\n{\\n CATCH(\\n (*module)->register_module(name, *submodule);\\n );\\n}\\n\\nvoid THSNN_Module_register_parameter(const NNModule module, const char* name, const Tensor tensor, bool requires_grad)\\n{\\n CATCH(\\n (*module)->register_parameter(name, (tensor == nullptr) ? at::Tensor() : *tensor, requires_grad);\\n );\\n}\\n\\nvoid THSNN_Module_register_buffer(const NNModule module, const char* name, const Tensor tensor)\\n{\\n CATCH(\\n (*module)->register_buffer(name, *tensor);\\n );\\n}\\n\\nint THSNN_Module_has_parameter(const NNModule module, const char* name)\\n{\\n CATCH_RETURN(int, 0, (*module)->named_parameters().contains(name));\\n}\\n\\nTensor THSNN_Module_get_parameter(const NNModule module, const char* name)\\n{\\n CATCH_TENSOR(*(*module)->named_parameters().find(name));\\n}\\n\\nvoid THSNN_Module_get_parameters(const NNModule module, Tensor* (*allocator1)(size_t length), bool recurse)\\n{\\n auto parameters = (*module)->parameters(recurse);\\n Tensor* result1 = allocator1(parameters.size());\\n\\n for (size_t i = 0; i < parameters.size(); i++)\\n {\\n result1[i] = ResultTensor(parameters[i]);\\n }\\n}\\n\\nvoid THSNN_Module_get_named_parameters(const NNModule module, Tensor* (*allocator1)(size_t length), const char** (*allocator2)(size_t length))\\n{\\n auto parameters = (*module)->named_parameters();\\n Tensor* result1 = allocator1(parameters.size());\\n const char** result2 = allocator2(parameters.size());\\n\\n for (size_t i = 0; i < parameters.size(); i++)\\n {\\n result1[i] = ResultTensor(parameters[i].value());\\n result2[i] = make_sharable_string(parameters[i].key());\\n }\\n}\\n\\nvoid THSNN_Module_get_named_buffers(const NNModule module, Tensor* (*allocator1)(size_t length), const char** (*allocator2)(size_t length))\\n{\\n auto buffers = (*module)->named_buffers();\\n Tensor* result1 = allocator1(buffers.size());\\n const char** result2 = allocator2(buffers.size());\\n\\n for (size_t i = 0; i < buffers.size(); i++)\\n {\\n result1[i] = ResultTensor(buffers[i].value());\\n result2[i] = make_sharable_string(buffers[i].key());\\n }\\n}\\n\\nvoid THSNN_Module_get_named_children(const NNModule module, NNModule* (*allocator1)(size_t length), const char** (*allocator2)(size_t length))\\n{\\n auto buffers = (*module)->named_children();\\n NNModule* result1 = allocator1(buffers.size());\\n const char** result2 = allocator2(buffers.size());\\n\\n for (size_t i = 0; i < buffers.size(); i++)\\n {\\n result1[i] = new std::shared_ptr<torch::nn::Module>(buffers[i].value());\\n result2[i] = make_sharable_string(buffers[i].key());\\n }\\n}\\n\\nvoid THSNN_Module_get_named_modules(const NNModule module, NNModule* (*allocator1)(size_t length), const char** (*allocator2)(size_t length))\\n{\\n auto buffers = (*module)->named_modules();\\n NNModule* result1 = allocator1(buffers.size());\\n const char** result2 = allocator2(buffers.size());\\n\\n for (size_t i = 0; i < buffers.size(); i++)\\n {\\n result1[i] = new std::shared_ptr<torch::nn::Module>(buffers[i].value());\\n result2[i] = make_sharable_string(buffers[i].key());\\n }\\n}\\n\\nlong THSNN_Module_children_size(const NNModule module)\\n{\\n return (*module)->children().size();\\n}\\n\\nNNModule THSNN_Module_child(const NNModule module, const int index)\\n{\\n return new std::shared_ptr<torch::nn::Module>((*module)->children()[index]);\\n}\\n\\n\\n// Save and restore\\n\\nNNModule THSNN_Module_load(const char* location)\\n{\\n CATCH_RETURN_NNModule(\\n auto module = new torch::nn::Module();\\n auto input = torch::serialize::InputArchive();\\n\\n input.load_from(location);\\n module->load(input);\\n return new std::shared_ptr<torch::nn::Module>(module);\\n );\\n}\\n\\nvoid THSNN_Module_save(const NNModule module, const char* location)\\n{\\n CATCH(\\n auto output = torch::serialize::OutputArchive();\\n\\n (*module)->save(output);\\n output.save_to(location);\\n );\\n}\\n\\n\\n// Wrapper class used to enable .NET definitions ot new modules describing parameters and with delegates to implement forward function\\nclass CustomModule : public torch::nn::Module\\n{\\npublic:\\n CustomModule(\\n const char* name,\\n Tensor(*forward)(Tensor))\\n : torch::nn::Module(name), _forward(forward)\\n {\\n }\\n\\n Tensor(*_forward)(Tensor);\\n\\n at::Tensor forward(at::Tensor input) {\\n return *(*_forward)(&input);\\n }\\n\\n};\\n\\nNNModule THSNN_custom_module(const char* name,\\n Tensor(*forward)(Tensor),\\n NNAnyModule* outAsAnyModule)\\n{\\n CATCH_RETURN_NNModule(\\n auto mod = new CustomModule(name, forward);\\n\\n // Keep a boxed version of the module in case we add it to a Sequential later (the C++ templating means\\n // a Module can only be boxed to AnyModule at the point its static type is known).\\n if (outAsAnyModule != nullptr)\\n {\\n auto modShared = new std::shared_ptr<CustomModule>(mod);\\n auto wrapped = std::make_shared<torch::nn::AnyModule>(torch::nn::ModuleHolder<CustomModule>(*modShared));\\n *outAsAnyModule = new std::shared_ptr<torch::nn::AnyModule>(wrapped);\\n }\\n res = new std::shared_ptr<torch::nn::Module>((torch::nn::Module*)mod);\\n );\\n}\\n\\n#if 0\\nstruct TORCH_API ModuleBackWardHook1 : public torch::autograd::FunctionPostHook {\\n\\n virtual ~ModuleBackWardHook1() { }\\n virtual torch::autograd::variable_list operator()(\\n const torch::autograd::variable_list& outputs /* grad_inputs */,\\n const torch::autograd::variable_list& inputs /* grad_outputs */)\\n {\\n } \\n};\\n#endif\\n\"\n },\n {\n \"alpha_fraction\": 0.5029027462005615,\n \"alphanum_fraction\": 0.5199840664863586,\n \"avg_line_length\": 50.30430221557617,\n \"blob_id\": \"00c0106fa7e715998f6f792e190e968e9c8aaaea\",\n \"content_id\": \"647e362047e22d2ef451665fb1c6851b226b5c2d\",\n \"detected_licenses\": [\n \"MIT\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"C#\",\n \"length_bytes\": 47713,\n \"license_type\": \"permissive\",\n \"max_line_length\": 193,\n \"num_lines\": 930,\n \"path\": \"/src/TorchAudio/Wav2Vec2Models.cs\",\n \"repo_name\": \"NiklasGustafsson/TorchSharp\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"// Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information.\\n\\n// A number of implementation details in this file have been translated from the Python version of torchaudio,\\n// largely located in the files found in this folder:\\n//\\n// https://github.com/pytorch/audio/blob/76fca37ac8941b72a509a6e58d623632efe04543/torchaudio/models/wav2vec2/model.py\\n//\\n// The origin has the following copyright notice and license:\\n//\\n// https://github.com/pytorch/audio/blob/main/LICENSE\\n//\\n\\nusing static TorchSharp.torch;\\nusing static TorchSharp.torch.nn;\\nusing TorchSharp.Modules;\\n\\n#nullable enable\\nnamespace TorchSharp\\n{\\n public static partial class torchaudio\\n {\\n public static partial class models\\n {\\n /// <summary>\\n /// Build a custom Wav2Vec2Model\\n /// \\n /// Note:\\n /// The \\\"feature extractor\\\" below corresponds to\\n /// `ConvFeatureExtractionModel` https://github.com/pytorch/fairseq/blob/dd3bd3c0497ae9a7ae7364404a6b0a4c501780b3/fairseq/models/wav2vec/wav2vec2.py#L736\\n /// in the original ``fairseq`` implementation.\\n /// This is referred as \\\"(convolutional) feature encoder\\\" in the *wav2vec 2.0*\\n /// [:footcite:`baevski2020wav2vec`] paper.\\n /// \\n /// The \\\"encoder\\\" below corresponds to `TransformerEncoder` https://github.com/pytorch/fairseq/blob/dd3bd3c0497ae9a7ae7364404a6b0a4c501780b3/fairseq/models/wav2vec/wav2vec2.py#L817,\\n /// and this is referred as \\\"Transformer\\\" in the paper.\\n /// </summary>\\n /// <param name=\\\"extractor_mode\\\">Operation mode of feature extractor.\\n /// Valid values are ``\\\"group_norm\\\"`` or ``\\\"layer_norm\\\"``.\\n /// If ``\\\"group_norm\\\"``, then a single normalization is applied\\n /// in the first convolution block. Otherwise, all the convolution\\n /// blocks will have layer normalization.\\n /// \\n /// This option corresponds to ``extractor_mode`` from ``fairseq``.\\n /// </param>\\n /// <param name=\\\"extractor_conv_layer_config\\\">\\n /// Configuration of convolution layers in feature extractor.\\n /// List of convolution configuration,\\n /// i.e. ``[(output_channel, kernel_size, stride), ...]``\\n /// \\n /// If ``None`` is provided, then the following default value is used.\\n /// \\n /// .. code-block:: python\\n /// \\n /// [\\n /// (512, 10, 5),\\n /// (512, 3, 2),\\n /// (512, 3, 2),\\n /// (512, 3, 2),\\n /// (512, 3, 2),\\n /// (512, 2, 2),\\n /// (512, 2, 2),\\n /// ]\\n /// \\n /// This option corresponds to ``conv_feature_layers`` from ``fairseq``.\\n /// </param>\\n /// <param name=\\\"extractor_conv_bias\\\">\\n /// Whether to include bias term to each convolution operation.\\n /// </param>\\n /// This option corresponds to ``conv_bias`` from ``fairseq``.\\n /// <param name=\\\"encoder_embed_dim\\\">\\n /// The dimension of embedding in encoder.\\n /// \\n /// This option corresponds to ``encoder_embed_dim`` from ``fairseq``.\\n /// </param>\\n /// <param name=\\\"encoder_projection_dropout\\\">\\n /// The dropout probability applied after the input feature is projected\\n /// to ``encoder_embed_dim``.\\n /// \\n /// This option corresponds to ``dropout_input`` from ``fairseq``.\\n /// </param>\\n /// <param name=\\\"encoder_pos_conv_kernel\\\">\\n /// The kernel size of convolutional positional embeddings.\\n /// \\n /// This option corresponds to ``conv_pos`` from ``fairseq``.\\n /// </param>\\n /// <param name=\\\"encoder_pos_conv_groups\\\">\\n /// The number of groups of convolutional positional embeddings.\\n /// \\n /// This option corresponds to ``conv_pos_groups`` from ``fairseq``.\\n /// </param>\\n /// <param name=\\\"encoder_num_layers\\\">\\n /// The number of self attention layers in transformer block.\\n /// \\n /// This option corresponds to ``encoder_layers`` from ``fairseq``.\\n /// </param>\\n /// <param name=\\\"encoder_num_heads\\\">\\n /// The number of heads in self attention layers.\\n /// \\n /// This option corresponds to ``encoder_attention_heads`` from ``fairseq``.\\n /// </param>\\n /// <param name=\\\"encoder_attention_dropout\\\">\\n /// The dropout probability applied after softmax in self-attention layer.\\n /// \\n /// This option corresponds to ``attention_dropout`` from ``fairseq``.\\n /// </param>\\n /// <param name=\\\"encoder_ff_interm_features\\\">\\n /// The dimension of hidden features in feed forward layer.\\n /// \\n /// This option corresponds to ``encoder_ffn_embed_dim`` from ``fairseq``.\\n /// </param>\\n /// <param name=\\\"encoder_ff_interm_dropout\\\">\\n /// The dropout probability applied in feedforward layer.\\n /// \\n /// This option correspinds to ``activation_dropout`` from ``fairseq``.\\n /// </param>\\n /// <param name=\\\"encoder_dropout\\\">\\n /// The dropout probability applied at the end of feed forward layer.\\n /// \\n /// This option corresponds to ``dropout`` from ``fairseq``.\\n /// </param>\\n /// <param name=\\\"encoder_layer_norm_first\\\">\\n /// Control the order of layer norm in transformer layer and each encoder layer.\\n /// If True, in transformer layer, layer norm is applied before features are fed\\n /// to encoder layers. In encoder layer, two layer norms are applied before and after\\n /// self attention.\\n /// If False, in transformer layer, layer norm is applied after features are fed\\n /// to encoder layers. In encoder layer, two layer norms are applied after self\\n /// attention, before and after feed forward.\\n /// \\n /// This option corresponds to ``layer_norm_first`` from ``fairseq``.\\n /// </param>\\n /// <param name=\\\"encoder_layer_drop\\\">\\n /// Probability to drop each encoder layer during training.\\n /// \\n /// This option corresponds to ``layerdrop`` from ``fairseq``.\\n /// </param>\\n /// <param name=\\\"aux_num_out\\\">\\n /// When provided, attach an extra linear layer on top of encoder, which can be\\n /// used for fine-tuning.\\n /// </param>\\n /// <returns>\\n /// The resulting model.\\n /// </returns>\\n public static Wav2Vec2Model wav2vec2_model(\\n FeatureExtractorNormMode extractor_mode,\\n long[][]? extractor_conv_layer_config,\\n bool extractor_conv_bias,\\n int encoder_embed_dim,\\n double encoder_projection_dropout,\\n int encoder_pos_conv_kernel,\\n int encoder_pos_conv_groups,\\n int encoder_num_layers,\\n int encoder_num_heads,\\n double encoder_attention_dropout,\\n int encoder_ff_interm_features,\\n double encoder_ff_interm_dropout,\\n double encoder_dropout,\\n bool encoder_layer_norm_first,\\n double encoder_layer_drop,\\n long? aux_num_out)\\n {\\n // Overriding the signature so that the return type is correct on Sphinx\\n if (extractor_conv_layer_config == null) {\\n extractor_conv_layer_config = new long[][] {\\n new long[] { 512, 10, 5 },\\n\\n new long[] { 512, 3, 2 },\\n new long[] { 512, 3, 2 },\\n new long[] { 512, 3, 2 },\\n new long[] { 512, 3, 2 },\\n\\n new long[] { 512, 2, 2 },\\n new long[] { 512, 2, 2 },\\n };\\n }\\n\\n var feature_extractor = Wav2Vec2Model._get_feature_extractor(\\n extractor_mode, extractor_conv_layer_config, extractor_conv_bias);\\n\\n var encoder = Wav2Vec2Model._get_encoder(\\n in_features: extractor_conv_layer_config[extractor_conv_layer_config.Length - 1][0],\\n embed_dim: encoder_embed_dim,\\n dropout_input: encoder_projection_dropout,\\n pos_conv_kernel: encoder_pos_conv_kernel,\\n pos_conv_groups: encoder_pos_conv_groups,\\n num_layers: encoder_num_layers,\\n num_heads: encoder_num_heads,\\n attention_dropout: encoder_attention_dropout,\\n ff_interm_features: encoder_ff_interm_features,\\n ff_interm_dropout: encoder_ff_interm_dropout,\\n dropout: encoder_dropout,\\n layer_norm_first: encoder_layer_norm_first,\\n layer_drop: encoder_layer_drop);\\n Module<Tensor, Tensor>? aux = null;\\n if (aux_num_out != null) {\\n aux = torch.nn.Linear(inputSize: encoder_embed_dim, outputSize: aux_num_out.Value);\\n }\\n return new Wav2Vec2Model(\\\"Wav2Vec2Model\\\", feature_extractor, encoder, aux);\\n }\\n\\n /// <summary>\\n /// Build Wav2Vec2Model with \\\"base\\\" architecture from *wav2vec 2.0* [:footcite:`baevski2020wav2vec`]\\n /// </summary>\\n /// <param name=\\\"encoder_projection_dropout\\\">See `wav2vec2_model`.</param>\\n /// <param name=\\\"encoder_attention_dropout\\\">See `wav2vec2_model`.</param>\\n /// <param name=\\\"encoder_dropout\\\">See `wav2vec2_model`.</param>\\n /// <param name=\\\"encoder_ff_interm_dropout\\\">See `wav2vec2_model`.</param>\\n /// <param name=\\\"encoder_layer_drop\\\">See `wav2vec2_model`.</param>\\n /// <param name=\\\"aux_num_out\\\">See `wav2vec2_model`.</param>\\n /// <returns>\\n /// The resulting model.\\n /// </returns>\\n public static Wav2Vec2Model wav2vec2_base(\\n double encoder_projection_dropout = 0.1,\\n double encoder_attention_dropout = 0.1,\\n double encoder_ff_interm_dropout = 0.1,\\n double encoder_dropout = 0.1,\\n double encoder_layer_drop = 0.1,\\n long? aux_num_out = null)\\n {\\n // Overriding the signature so that the return type is correct on Sphinx\\n return wav2vec2_model(\\n extractor_mode: FeatureExtractorNormMode.group_norm,\\n extractor_conv_layer_config: null,\\n extractor_conv_bias: false,\\n encoder_embed_dim: 768,\\n encoder_projection_dropout: encoder_projection_dropout,\\n encoder_pos_conv_kernel: 128,\\n encoder_pos_conv_groups: 16,\\n encoder_num_layers: 12,\\n encoder_num_heads: 12,\\n encoder_attention_dropout: encoder_attention_dropout,\\n encoder_ff_interm_features: 3072,\\n encoder_ff_interm_dropout: encoder_ff_interm_dropout,\\n encoder_dropout: encoder_dropout,\\n encoder_layer_norm_first: false,\\n encoder_layer_drop: encoder_layer_drop,\\n aux_num_out: aux_num_out);\\n }\\n\\n /// <summary>\\n /// Build Wav2Vec2Model with \\\"large\\\" architecture from *wav2vec 2.0* [:footcite:`baevski2020wav2vec`]\\n /// </summary>\\n /// <param name=\\\"encoder_projection_dropout\\\">See `wav2vec2_model`.</param>\\n /// <param name=\\\"encoder_attention_dropout\\\">See `wav2vec2_model`.</param>\\n /// <param name=\\\"encoder_dropout\\\">See `wav2vec2_model`.</param>\\n /// <param name=\\\"encoder_ff_interm_dropout\\\">See `wav2vec2_model`.</param>\\n /// <param name=\\\"encoder_layer_drop\\\">See `wav2vec2_model`.</param>\\n /// <param name=\\\"aux_num_out\\\">See `wav2vec2_model`.</param>\\n /// <returns>\\n /// The resulting model.\\n /// </returns>\\n public static Wav2Vec2Model wav2vec2_large(\\n double encoder_projection_dropout = 0.1,\\n double encoder_attention_dropout = 0.1,\\n double encoder_ff_interm_dropout = 0.1,\\n double encoder_dropout = 0.1,\\n double encoder_layer_drop = 0.1,\\n long? aux_num_out = null)\\n {\\n // Overriding the signature so that the return type is correct on Sphinx\\n return wav2vec2_model(\\n extractor_mode: FeatureExtractorNormMode.group_norm,\\n extractor_conv_layer_config: null,\\n extractor_conv_bias: false,\\n encoder_embed_dim: 1024,\\n encoder_projection_dropout: encoder_projection_dropout,\\n encoder_pos_conv_kernel: 128,\\n encoder_pos_conv_groups: 16,\\n encoder_num_layers: 24,\\n encoder_num_heads: 16,\\n encoder_attention_dropout: encoder_attention_dropout,\\n encoder_ff_interm_features: 4096,\\n encoder_ff_interm_dropout: encoder_ff_interm_dropout,\\n encoder_dropout: encoder_dropout,\\n encoder_layer_norm_first: false,\\n encoder_layer_drop: encoder_layer_drop,\\n aux_num_out: aux_num_out);\\n }\\n\\n /// <summary>\\n /// Build Wav2Vec2Model with \\\"large lv-60k\\\" architecture from *wav2vec 2.0* [:footcite:`baevski2020wav2vec`]\\n /// </summary>\\n /// <param name=\\\"encoder_projection_dropout\\\">See `wav2vec2_model`.</param>\\n /// <param name=\\\"encoder_attention_dropout\\\">See `wav2vec2_model`.</param>\\n /// <param name=\\\"encoder_dropout\\\">See `wav2vec2_model`.</param>\\n /// <param name=\\\"encoder_ff_interm_dropout\\\">See `wav2vec2_model`.</param>\\n /// <param name=\\\"encoder_layer_drop\\\">See `wav2vec2_model`.</param>\\n /// <param name=\\\"aux_num_out\\\">See `wav2vec2_model`.</param>\\n /// <returns>\\n /// The resulting model.\\n /// </returns>\\n public static Wav2Vec2Model wav2vec2_large_lv60k(\\n double encoder_projection_dropout = 0.1,\\n double encoder_attention_dropout = 0.0,\\n double encoder_ff_interm_dropout = 0.1,\\n double encoder_dropout = 0.0,\\n double encoder_layer_drop = 0.1,\\n long? aux_num_out = null)\\n {\\n // Overriding the signature so that the return type is correct on Sphinx\\n return wav2vec2_model(\\n extractor_mode: FeatureExtractorNormMode.layer_norm,\\n extractor_conv_layer_config: null,\\n extractor_conv_bias: true,\\n encoder_embed_dim: 1024,\\n encoder_projection_dropout: encoder_projection_dropout,\\n encoder_pos_conv_kernel: 128,\\n encoder_pos_conv_groups: 16,\\n encoder_num_layers: 24,\\n encoder_num_heads: 16,\\n encoder_attention_dropout: encoder_attention_dropout,\\n encoder_ff_interm_features: 4096,\\n encoder_ff_interm_dropout: encoder_ff_interm_dropout,\\n encoder_dropout: encoder_dropout,\\n encoder_layer_norm_first: true,\\n encoder_layer_drop: encoder_layer_drop,\\n aux_num_out: aux_num_out);\\n }\\n\\n /// <summary>\\n /// Build HuBERT model with \\\"base\\\" architecture from *HuBERT* [:footcite:`hsu2021hubert`]\\n /// </summary>\\n /// <param name=\\\"encoder_projection_dropout\\\">See `wav2vec2_model`.</param>\\n /// <param name=\\\"encoder_attention_dropout\\\">See `wav2vec2_model`.</param>\\n /// <param name=\\\"encoder_dropout\\\">See `wav2vec2_model`.</param>\\n /// <param name=\\\"encoder_ff_interm_dropout\\\">See `wav2vec2_model`.</param>\\n /// <param name=\\\"encoder_layer_drop\\\">See `wav2vec2_model`.</param>\\n /// <param name=\\\"aux_num_out\\\">See `wav2vec2_model`.</param>\\n /// <returns>\\n /// The resulting model.\\n /// </returns>\\n public static Wav2Vec2Model hubert_base(\\n double encoder_projection_dropout = 0.1,\\n double encoder_attention_dropout = 0.1,\\n double encoder_ff_interm_dropout = 0.0,\\n double encoder_dropout = 0.1,\\n double encoder_layer_drop = 0.05,\\n long? aux_num_out = null)\\n {\\n // Overriding the signature so that the return type is correct on Sphinx\\n return wav2vec2_model(\\n extractor_mode: FeatureExtractorNormMode.group_norm,\\n extractor_conv_layer_config: null,\\n extractor_conv_bias: false,\\n encoder_embed_dim: 768,\\n encoder_projection_dropout: encoder_projection_dropout,\\n encoder_pos_conv_kernel: 128,\\n encoder_pos_conv_groups: 16,\\n encoder_num_layers: 12,\\n encoder_num_heads: 12,\\n encoder_attention_dropout: encoder_attention_dropout,\\n encoder_ff_interm_features: 3072,\\n encoder_ff_interm_dropout: encoder_ff_interm_dropout,\\n encoder_dropout: encoder_dropout,\\n encoder_layer_norm_first: false,\\n encoder_layer_drop: encoder_layer_drop,\\n aux_num_out: aux_num_out);\\n }\\n\\n /// <summary>\\n /// Build HuBERT model with \\\"large\\\" architecture from *HuBERT* [:footcite:`hsu2021hubert`]\\n /// </summary>\\n /// <param name=\\\"encoder_projection_dropout\\\">See `wav2vec2_model`.</param>\\n /// <param name=\\\"encoder_attention_dropout\\\">See `wav2vec2_model`.</param>\\n /// <param name=\\\"encoder_dropout\\\">See `wav2vec2_model`.</param>\\n /// <param name=\\\"encoder_ff_interm_dropout\\\">See `wav2vec2_model`.</param>\\n /// <param name=\\\"encoder_layer_drop\\\">See `wav2vec2_model`.</param>\\n /// <param name=\\\"aux_num_out\\\">See `wav2vec2_model`.</param>\\n /// <returns>\\n /// The resulting model.\\n /// </returns>\\n public static Wav2Vec2Model hubert_large(\\n double encoder_projection_dropout = 0.0,\\n double encoder_attention_dropout = 0.0,\\n double encoder_ff_interm_dropout = 0.0,\\n double encoder_dropout = 0.0,\\n double encoder_layer_drop = 0.0,\\n long? aux_num_out = null)\\n {\\n // Overriding the signature so that the return type is correct on Sphinx\\n return wav2vec2_model(\\n extractor_mode: FeatureExtractorNormMode.layer_norm,\\n extractor_conv_layer_config: null,\\n extractor_conv_bias: false,\\n encoder_embed_dim: 1024,\\n encoder_projection_dropout: encoder_projection_dropout,\\n encoder_pos_conv_kernel: 128,\\n encoder_pos_conv_groups: 16,\\n encoder_num_layers: 24,\\n encoder_num_heads: 16,\\n encoder_attention_dropout: encoder_attention_dropout,\\n encoder_ff_interm_features: 4096,\\n encoder_ff_interm_dropout: encoder_ff_interm_dropout,\\n encoder_dropout: encoder_dropout,\\n encoder_layer_norm_first: true,\\n encoder_layer_drop: encoder_layer_drop,\\n aux_num_out: aux_num_out);\\n }\\n\\n /// <summary>\\n /// Build HuBERT model with \\\"extra large\\\" architecture from *HuBERT* [:footcite:`hsu2021hubert`]\\n /// </summary>\\n /// <param name=\\\"encoder_projection_dropout\\\">See `wav2vec2_model`.</param>\\n /// <param name=\\\"encoder_attention_dropout\\\">See `wav2vec2_model`.</param>\\n /// <param name=\\\"encoder_dropout\\\">See `wav2vec2_model`.</param>\\n /// <param name=\\\"encoder_ff_interm_dropout\\\">See `wav2vec2_model`.</param>\\n /// <param name=\\\"encoder_layer_drop\\\">See `wav2vec2_model`.</param>\\n /// <param name=\\\"aux_num_out\\\">See `wav2vec2_model`.</param>\\n /// <returns>\\n /// The resulting model.\\n /// </returns>\\n public static Wav2Vec2Model hubert_xlarge(\\n double encoder_projection_dropout = 0.0,\\n double encoder_attention_dropout = 0.0,\\n double encoder_ff_interm_dropout = 0.0,\\n double encoder_dropout = 0.0,\\n double encoder_layer_drop = 0.0,\\n long? aux_num_out = null)\\n {\\n // Overriding the signature so that the return type is correct on Sphinx\\n return wav2vec2_model(\\n extractor_mode: FeatureExtractorNormMode.layer_norm,\\n extractor_conv_layer_config: null,\\n extractor_conv_bias: false,\\n encoder_embed_dim: 1280,\\n encoder_projection_dropout: encoder_projection_dropout,\\n encoder_pos_conv_kernel: 128,\\n encoder_pos_conv_groups: 16,\\n encoder_num_layers: 48,\\n encoder_num_heads: 16,\\n encoder_attention_dropout: encoder_attention_dropout,\\n encoder_ff_interm_features: 5120,\\n encoder_ff_interm_dropout: encoder_ff_interm_dropout,\\n encoder_dropout: encoder_dropout,\\n encoder_layer_norm_first: true,\\n encoder_layer_drop: encoder_layer_drop,\\n aux_num_out: aux_num_out);\\n }\\n\\n /// <summary>\\n /// Build a custom HuBERTPretrainModel for training from scratch\\n /// \\n /// Note:\\n /// The \\\"feature extractor\\\" below corresponds to\\n /// `ConvFeatureExtractionModel` https://github.com/pytorch/fairseq/blob/dd3bd3c0497ae9a7ae7364404a6b0a4c501780b3/fairseq/models/wav2vec/wav2vec2.py#L736\\n /// in the original ``fairseq`` implementation.\\n /// This is referred as \\\"(convolutional) feature encoder\\\" in the *wav2vec 2.0*\\n /// [:footcite:`baevski2020wav2vec`] paper.\\n /// \\n /// The \\\"encoder\\\" below corresponds to `TransformerEncoder` https://github.com/pytorch/fairseq/blob/dd3bd3c0497ae9a7ae7364404a6b0a4c501780b3/fairseq/models/wav2vec/wav2vec2.py#L817,\\n /// and this is referred as \\\"Transformer\\\" in the paper.\\n /// </summary>\\n /// <param name=\\\"extractor_mode\\\">\\n /// Operation mode of feature extractor.\\n /// Valid values are ``\\\"group_norm\\\"`` or ``\\\"layer_norm\\\"``.\\n /// If ``\\\"group_norm\\\"``, then a single normalization is applied\\n /// in the first convolution block. Otherwise, all the convolution\\n /// blocks will have layer normalization.\\n /// \\n /// This option corresponds to ``extractor_mode`` from ``fairseq``.\\n /// </param>\\n /// <param name=\\\"extractor_conv_layer_config\\\">\\n /// Configuration of convolution layers in feature extractor.\\n /// List of convolution configuration,\\n /// i.e. ``[(output_channel, kernel_size, stride), ...]``\\n /// \\n /// If ``None`` is provided, then the following default value is used.\\n /// \\n /// .. code-block:: python\\n /// \\n /// [\\n /// (512, 10, 5),\\n /// (512, 3, 2),\\n /// (512, 3, 2),\\n /// (512, 3, 2),\\n /// (512, 3, 2),\\n /// (512, 2, 2),\\n /// (512, 2, 2),\\n /// ]\\n /// \\n /// This option corresponds to ``conv_feature_layers`` from ``fairseq``.\\n /// </param>\\n /// <param name=\\\"extractor_conv_bias\\\">\\n /// Whether to include bias term to each convolution operation.\\n /// \\n /// This option corresponds to ``conv_bias`` from ``fairseq``.\\n /// </param>\\n /// <param name=\\\"encoder_embed_dim\\\">\\n /// The dimension of embedding in encoder.\\n /// \\n /// This option corresponds to ``encoder_embed_dim`` from ``fairseq``.\\n /// </param>\\n /// <param name=\\\"encoder_projection_dropout\\\">\\n /// The dropout probability applied after the input feature is projected\\n /// to ``encoder_embed_dim``.\\n /// \\n /// This option corresponds to ``dropout_input`` from ``fairseq``.\\n /// </param>\\n /// <param name=\\\"encoder_pos_conv_kernel\\\">\\n /// The kernel size of convolutional positional embeddings.\\n /// \\n /// This option corresponds to ``conv_pos`` from ``fairseq``.\\n /// </param>\\n /// <param name=\\\"encoder_pos_conv_groups\\\">\\n /// The number of groups of convolutional positional embeddings.\\n /// \\n /// This option corresponds to ``conv_pos_groups`` from ``fairseq``.\\n /// </param>\\n /// <param name=\\\"encoder_num_layers\\\">\\n /// The number of self attention layers in transformer block.\\n /// \\n /// This option corresponds to ``encoder_layers`` from ``fairseq``.\\n /// </param>\\n /// <param name=\\\"encoder_num_heads\\\">\\n /// The number of heads in self attention layers.\\n /// \\n /// This option corresponds to ``encoder_attention_heads`` from ``fairseq``.\\n /// </param>\\n /// <param name=\\\"encoder_attention_dropout\\\">\\n /// The dropout probability applied after softmax in self-attention layer.\\n /// \\n /// This option corresponds to ``attention_dropout`` from ``fairseq``.\\n /// </param>\\n /// <param name=\\\"encoder_ff_interm_features\\\">\\n /// The dimension of hidden features in feed forward layer.\\n /// \\n /// This option corresponds to ``encoder_ffn_embed_dim`` from ``fairseq``.\\n /// </param>\\n /// <param name=\\\"encoder_ff_interm_dropout\\\">\\n /// The dropout probability applied in feedforward layer.\\n /// \\n /// This option correspinds to ``activation_dropout`` from ``fairseq``.\\n /// </param>\\n /// <param name=\\\"encoder_dropout\\\">\\n /// The dropout probability applied at the end of feed forward layer.\\n /// \\n /// This option corresponds to ``dropout`` from ``fairseq``.\\n /// </param>\\n /// <param name=\\\"encoder_layer_norm_first\\\">\\n /// Control the order of layer norm in transformer layer and each encoder layer.\\n /// If True, in transformer layer, layer norm is applied before features are fed\\n /// to encoder layers. In encoder layer, two layer norms are applied before and after\\n /// self attention.\\n /// If False, in transformer layer, layer norm is applied after features are fed\\n /// to encoder layers. In encoder layer, two layer norms are applied after self\\n /// attention, before and after feed forward.\\n /// \\n /// This option corresponds to ``layer_norm_first`` from ``fairseq``.\\n /// </param>\\n /// <param name=\\\"encoder_layer_drop\\\">\\n /// Probability to drop each encoder layer during training.\\n /// \\n /// This option corresponds to ``layerdrop`` from ``fairseq``.\\n /// </param>\\n /// <param name=\\\"mask_prob\\\">\\n /// Probability for each token to be chosen as start of the span to be masked. this will be multiplied by\\n /// number of timesteps divided by length of mask span to mask approximately this percentage of all elements.\\n /// However due to overlaps, the actual number will be smaller (unless no_overlap is True).\\n /// \\n /// This option corresponds to ``mask_prob`` from ``fairseq``.\\n /// </param>\\n /// <param name=\\\"mask_selection\\\">\\n /// How to choose the mask length. Options: [``static``, ``uniform``, ``normal``, ``poisson``].\\n /// \\n /// This option corresponds to ``mask_selection`` from ``fairseq``.\\n /// </param>\\n /// <param name=\\\"mask_other\\\">\\n /// Secondary mask argument (used for more complex distributions).\\n /// \\n /// This option corresponds to ``mask_other`` from ``fairseq``.\\n /// </param>\\n /// <param name=\\\"mask_length\\\">\\n /// The lengths of the mask.\\n /// \\n /// This option corresponds to ``mask_length`` from ``fairseq``.\\n /// </param>\\n /// <param name=\\\"no_mask_overlap\\\">\\n /// Whether to allow masks to overlap.\\n /// \\n /// This option corresponds to ``no_mask_overlap`` from ``fairseq``.\\n /// </param>\\n /// <param name=\\\"mask_min_space\\\">\\n /// Minimum space between spans (if no overlap is enabled).\\n /// \\n /// This option corresponds to ``mask_min_space`` from ``fairseq``.\\n /// </param>\\n /// <param name=\\\"mask_channel_prob\\\">\\n /// The probability of replacing a feature with 0.\\n /// \\n /// This option corresponds to ``mask_channel_prob`` from ``fairseq``.\\n /// </param>\\n /// <param name=\\\"mask_channel_selection\\\">\\n /// How to choose the mask length for channel masking. Options: [``static``, ``uniform``, ``normal``, ``poisson``].\\n /// \\n /// This option corresponds to ``mask_channel_selection`` from ``fairseq``.\\n /// </param>\\n /// <param name=\\\"mask_channel_other\\\">\\n /// Secondary mask argument for channel masking(used for more complex distributions).\\n /// \\n /// This option corresponds to ``mask_channel_other`` from ``fairseq``.\\n /// </param>\\n /// <param name=\\\"mask_channel_length\\\">\\n /// Minimum space between spans (if no overlap is enabled) for channel masking.\\n /// \\n /// This option corresponds to ``mask_channel_length`` from ``fairseq``.\\n /// </param>\\n /// <param name=\\\"no_mask_channel_overlap\\\">\\n /// Whether to allow channel masks to overlap.\\n /// \\n /// This option corresponds to ``no_mask_channel_overlap`` from ``fairseq``.\\n /// </param>\\n /// <param name=\\\"mask_channel_min_space\\\">\\n /// Minimum space between spans for channel masking(if no overlap is enabled).\\n /// \\n /// This option corresponds to ``mask_channel_min_space`` from ``fairseq``.\\n /// </param>\\n /// <param name=\\\"skip_masked\\\">\\n /// If True, skip computing losses over masked frames.\\n /// \\n /// This option corresponds to ``skip_masked`` from ``fairseq``.\\n /// </param>\\n /// <param name=\\\"skip_nomask\\\">\\n /// If True, skip computing losses over unmasked frames.\\n /// \\n /// This option corresponds to ``skip_nomask`` from ``fairseq``.\\n /// </param>\\n /// <param name=\\\"num_classes\\\">\\n /// The number of classes in the labels.\\n /// </param>\\n /// <param name=\\\"final_dim\\\">\\n /// Project final representations and targets to `final_dim`.\\n /// \\n /// This option corresponds to ``final_dim`` from ``fairseq``.\\n /// </param>\\n /// <param name=\\\"feature_grad_mult\\\">\\n /// The factor to scale the convolutional feature extraction layer gradients by.\\n /// The scale factor will not affect the forward pass.\\n /// \\n /// This option corresponds to ``feature_grad_mult`` from ``fairseq``.\\n /// </param>\\n /// <returns>\\n /// The resulting model.\\n /// </returns>\\n public static HuBERTPretrainModel hubert_pretrain_model(\\n FeatureExtractorNormMode extractor_mode,\\n long[][]? extractor_conv_layer_config,\\n bool extractor_conv_bias,\\n int encoder_embed_dim,\\n double encoder_projection_dropout,\\n int encoder_pos_conv_kernel,\\n int encoder_pos_conv_groups,\\n int encoder_num_layers,\\n int encoder_num_heads,\\n double encoder_attention_dropout,\\n int encoder_ff_interm_features,\\n double encoder_ff_interm_dropout,\\n double encoder_dropout,\\n bool encoder_layer_norm_first,\\n double encoder_layer_drop,\\n double mask_prob,\\n string mask_selection,\\n double mask_other,\\n int mask_length,\\n bool no_mask_overlap,\\n int mask_min_space,\\n double mask_channel_prob,\\n string mask_channel_selection,\\n double mask_channel_other,\\n long mask_channel_length,\\n bool no_mask_channel_overlap,\\n int mask_channel_min_space,\\n bool skip_masked,\\n bool skip_nomask,\\n long num_classes,\\n int final_dim,\\n double? feature_grad_mult)\\n {\\n // Overriding the signature so that the return type is correct on Sphinx\\n if (extractor_conv_layer_config == null) {\\n extractor_conv_layer_config = new long[][] {\\n new long[] { 512, 10, 5 },\\n\\n new long[] { 512, 3, 2 },\\n new long[] { 512, 3, 2 },\\n new long[] { 512, 3, 2 },\\n new long[] { 512, 3, 2 },\\n\\n new long[] { 512, 2, 2 },\\n new long[] { 512, 2, 2 },\\n };\\n }\\n\\n var feature_extractor = Wav2Vec2Model._get_feature_extractor(\\n extractor_mode, extractor_conv_layer_config, extractor_conv_bias);\\n var encoder = Wav2Vec2Model._get_encoder(\\n in_features: extractor_conv_layer_config[extractor_conv_layer_config.Length - 1][0],\\n embed_dim: encoder_embed_dim,\\n dropout_input: encoder_projection_dropout,\\n pos_conv_kernel: encoder_pos_conv_kernel,\\n pos_conv_groups: encoder_pos_conv_groups,\\n num_layers: encoder_num_layers,\\n num_heads: encoder_num_heads,\\n attention_dropout: encoder_attention_dropout,\\n ff_interm_features: encoder_ff_interm_features,\\n ff_interm_dropout: encoder_ff_interm_dropout,\\n dropout: encoder_dropout,\\n layer_norm_first: encoder_layer_norm_first,\\n layer_drop: encoder_layer_drop);\\n var wav2vec2 = new Wav2Vec2Model(\\\"Wav2Vec2Model\\\", feature_extractor, encoder);\\n var mask_generator = new Wav2Vec2Model.MaskGenerator(\\n \\\"MaskGenerator\\\",\\n encoder_embed_dim,\\n mask_prob,\\n mask_selection,\\n mask_other,\\n mask_length,\\n no_mask_overlap,\\n mask_min_space,\\n mask_channel_prob,\\n mask_channel_selection,\\n mask_channel_other,\\n mask_channel_length,\\n no_mask_channel_overlap,\\n mask_channel_min_space);\\n var logit_generator = new Wav2Vec2Model.LogitGenerator(\\n \\\"LogitGenerator\\\",\\n encoder_embed_dim,\\n num_classes,\\n final_dim,\\n skip_masked,\\n skip_nomask);\\n return new HuBERTPretrainModel(\\n \\\"HuBERTPretrainModel\\\",\\n wav2vec2: wav2vec2,\\n mask_generator: mask_generator,\\n logit_generator: logit_generator,\\n feature_grad_mult: feature_grad_mult);\\n }\\n\\n /// <summary>\\n /// Build HuBERTPretrainModel model with \\\"base\\\" architecture from *HuBERT* [:footcite:`hsu2021hubert`]\\n /// </summary>\\n /// <param name=\\\"encoder_projection_dropout\\\">See `hubert_pretrain_model`.</param>\\n /// <param name=\\\"encoder_attention_dropout\\\">See `hubert_pretrain_model`.</param>\\n /// <param name=\\\"encoder_dropout\\\">See `hubert_pretrain_model`.</param>\\n /// <param name=\\\"encoder_ff_interm_dropout\\\">See `hubert_pretrain_model`.</param>\\n /// <param name=\\\"encoder_layer_drop\\\">See `hubert_pretrain_model`.</param>\\n /// <param name=\\\"mask_prob\\\">See `hubert_pretrain_model`.</param>\\n /// <param name=\\\"mask_channel_prob\\\">See `hubert_pretrain_model`.</param>\\n /// <param name=\\\"mask_channel_length\\\">See `hubert_pretrain_model`.</param>\\n /// <param name=\\\"feature_grad_mult\\\">See `hubert_pretrain_model`.</param>\\n /// <param name=\\\"num_classes\\\">See `hubert_pretrain_model`.</param>\\n /// <returns>\\n /// The resulting model.\\n /// </returns>\\n public static HuBERTPretrainModel hubert_pretrain_base(\\n double encoder_projection_dropout = 0.1,\\n double encoder_attention_dropout = 0.1,\\n double encoder_ff_interm_dropout = 0.0,\\n double encoder_dropout = 0.1,\\n double encoder_layer_drop = 0.05,\\n double mask_prob = 0.8,\\n double mask_channel_prob = 0.0,\\n long mask_channel_length = 10,\\n double? feature_grad_mult = 0.1,\\n long num_classes = 100)\\n {\\n // Overriding the signature so that the return type is correct on Sphinx\\n return hubert_pretrain_model(\\n extractor_mode: FeatureExtractorNormMode.group_norm,\\n extractor_conv_layer_config: null,\\n extractor_conv_bias: false,\\n encoder_embed_dim: 768,\\n encoder_projection_dropout: encoder_projection_dropout,\\n encoder_pos_conv_kernel: 128,\\n encoder_pos_conv_groups: 16,\\n encoder_num_layers: 12,\\n encoder_num_heads: 12,\\n encoder_attention_dropout: encoder_attention_dropout,\\n encoder_ff_interm_features: 3072,\\n encoder_ff_interm_dropout: encoder_ff_interm_dropout,\\n encoder_dropout: encoder_dropout,\\n encoder_layer_norm_first: false,\\n encoder_layer_drop: encoder_layer_drop,\\n mask_prob: mask_prob,\\n mask_selection: \\\"static\\\",\\n mask_other: 0.0,\\n mask_length: 10,\\n no_mask_overlap: false,\\n mask_min_space: 1,\\n mask_channel_prob: mask_channel_prob,\\n mask_channel_selection: \\\"static\\\",\\n mask_channel_other: 0.0,\\n mask_channel_length: mask_channel_length,\\n no_mask_channel_overlap: false,\\n mask_channel_min_space: 1,\\n skip_masked: false,\\n skip_nomask: false,\\n num_classes: num_classes,\\n final_dim: 256,\\n feature_grad_mult: feature_grad_mult);\\n }\\n\\n /// <summary>\\n /// Build HuBERTPretrainModel model for pre-training with \\\"large\\\" architecture from *HuBERT* [:footcite:`hsu2021hubert`]\\n /// </summary>\\n /// <param name=\\\"encoder_projection_dropout\\\">See `hubert_pretrain_model`.</param>\\n /// <param name=\\\"encoder_attention_dropout\\\">See `hubert_pretrain_model`.</param>\\n /// <param name=\\\"encoder_dropout\\\">See `hubert_pretrain_model`.</param>\\n /// <param name=\\\"encoder_ff_interm_dropout\\\">See `hubert_pretrain_model`.</param>\\n /// <param name=\\\"encoder_layer_drop\\\">See `hubert_pretrain_model`.</param>\\n /// <param name=\\\"mask_prob\\\">See `hubert_pretrain_model`.</param>\\n /// <param name=\\\"mask_channel_prob\\\">See `hubert_pretrain_model`.</param>\\n /// <param name=\\\"mask_channel_length\\\">See `hubert_pretrain_model`.</param>\\n /// <param name=\\\"feature_grad_mult\\\">See `hubert_pretrain_model`.</param>\\n /// <returns>\\n /// The resulting model.\\n /// </returns>\\n public static HuBERTPretrainModel hubert_pretrain_large(\\n double encoder_projection_dropout = 0.0,\\n double encoder_attention_dropout = 0.0,\\n double encoder_ff_interm_dropout = 0.0,\\n double encoder_dropout = 0.0,\\n double encoder_layer_drop = 0.0,\\n double mask_prob = 0.8,\\n double mask_channel_prob = 0.0,\\n long mask_channel_length = 10,\\n double? feature_grad_mult = null)\\n {\\n // Overriding the signature so that the return type is correct on Sphinx\\n return hubert_pretrain_model(\\n extractor_mode: FeatureExtractorNormMode.layer_norm,\\n extractor_conv_layer_config: null,\\n extractor_conv_bias: false,\\n encoder_embed_dim: 1024,\\n encoder_projection_dropout: encoder_projection_dropout,\\n encoder_pos_conv_kernel: 128,\\n encoder_pos_conv_groups: 16,\\n encoder_num_layers: 24,\\n encoder_num_heads: 16,\\n encoder_attention_dropout: encoder_attention_dropout,\\n encoder_ff_interm_features: 4096,\\n encoder_ff_interm_dropout: encoder_ff_interm_dropout,\\n encoder_dropout: encoder_dropout,\\n encoder_layer_norm_first: true,\\n encoder_layer_drop: encoder_layer_drop,\\n mask_prob: mask_prob,\\n mask_selection: \\\"static\\\",\\n mask_other: 0.0,\\n mask_length: 10,\\n no_mask_overlap: false,\\n mask_min_space: 1,\\n mask_channel_prob: mask_channel_prob,\\n mask_channel_selection: \\\"static\\\",\\n mask_channel_other: 0.0,\\n mask_channel_length: mask_channel_length,\\n no_mask_channel_overlap: false,\\n mask_channel_min_space: 1,\\n skip_masked: false,\\n skip_nomask: false,\\n num_classes: 500,\\n final_dim: 768,\\n feature_grad_mult: feature_grad_mult);\\n }\\n\\n /// <summary>\\n /// Build HuBERTPretrainModel model for pre-training with \\\"extra large\\\" architecture from *HuBERT* [:footcite:`hsu2021hubert`]\\n /// </summary>\\n /// <param name=\\\"encoder_projection_dropout\\\">See `hubert_pretrain_model`.</param>\\n /// <param name=\\\"encoder_attention_dropout\\\">See `hubert_pretrain_model`.</param>\\n /// <param name=\\\"encoder_dropout\\\">See `hubert_pretrain_model`.</param>\\n /// <param name=\\\"encoder_ff_interm_dropout\\\">See `hubert_pretrain_model`.</param>\\n /// <param name=\\\"encoder_layer_drop\\\">See `hubert_pretrain_model`.</param>\\n /// <param name=\\\"mask_prob\\\">See `hubert_pretrain_model`.</param>\\n /// <param name=\\\"mask_channel_prob\\\">See `hubert_pretrain_model`.</param>\\n /// <param name=\\\"mask_channel_length\\\">See `hubert_pretrain_model`.</param>\\n /// <param name=\\\"feature_grad_mult\\\">See `hubert_pretrain_model`.</param>\\n /// <returns>\\n /// The resulting model.\\n /// </returns>\\n public static HuBERTPretrainModel hubert_pretrain_xlarge(\\n double encoder_projection_dropout = 0.0,\\n double encoder_attention_dropout = 0.0,\\n double encoder_ff_interm_dropout = 0.0,\\n double encoder_dropout = 0.0,\\n double encoder_layer_drop = 0.0,\\n double mask_prob = 0.8,\\n double mask_channel_prob = 0.0,\\n long mask_channel_length = 10,\\n double? feature_grad_mult = null)\\n {\\n // Overriding the signature so that the return type is correct on Sphinx\\n return hubert_pretrain_model(\\n extractor_mode: FeatureExtractorNormMode.layer_norm,\\n extractor_conv_layer_config: null,\\n extractor_conv_bias: false,\\n encoder_embed_dim: 1280,\\n encoder_projection_dropout: encoder_projection_dropout,\\n encoder_pos_conv_kernel: 128,\\n encoder_pos_conv_groups: 16,\\n encoder_num_layers: 48,\\n encoder_num_heads: 16,\\n encoder_attention_dropout: encoder_attention_dropout,\\n encoder_ff_interm_features: 5120,\\n encoder_ff_interm_dropout: encoder_ff_interm_dropout,\\n encoder_dropout: encoder_dropout,\\n encoder_layer_norm_first: true,\\n encoder_layer_drop: encoder_layer_drop,\\n mask_prob: mask_prob,\\n mask_selection: \\\"static\\\",\\n mask_other: 0.0,\\n mask_length: 10,\\n no_mask_overlap: false,\\n mask_min_space: 1,\\n mask_channel_prob: mask_channel_prob,\\n mask_channel_selection: \\\"static\\\",\\n mask_channel_other: 0.0,\\n mask_channel_length: mask_channel_length,\\n no_mask_channel_overlap: false,\\n mask_channel_min_space: 1,\\n skip_masked: false,\\n skip_nomask: false,\\n num_classes: 500,\\n final_dim: 1024,\\n feature_grad_mult: feature_grad_mult);\\n }\\n }\\n }\\n}\\n\"\n },\n {\n \"alpha_fraction\": 0.4577777683734894,\n \"alphanum_fraction\": 0.4624338746070862,\n \"avg_line_length\": 46.25,\n \"blob_id\": \"2e202864b33e19a58835ebec26c1eb0c88abf811\",\n \"content_id\": \"2f8bcd1e4b1dc8f8948e0b6ada7b703d39ad6c29\",\n \"detected_licenses\": [\n \"MIT\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"C#\",\n \"length_bytes\": 4725,\n \"license_type\": \"permissive\",\n \"max_line_length\": 232,\n \"num_lines\": 100,\n \"path\": \"/src/TorchSharp/NN/Normalization/Functional.cs\",\n \"repo_name\": \"NiklasGustafsson/TorchSharp\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"// Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information.\\nusing System;\\nusing static TorchSharp.PInvoke.NativeMethods;\\n\\nnamespace TorchSharp\\n{\\n public static partial class torch\\n {\\n public static partial class nn\\n {\\n public static partial class functional\\n {\\n /// <summary>\\n /// Applies Batch Normalization for each channel across a batch of data.\\n /// </summary>\\n public static Tensor batch_norm(Tensor input, Tensor running_mean, Tensor running_var, Tensor weight = null, Tensor bias = null, bool training = false, double momentum = 0.1, double eps = 1e-5)\\n {\\n var res = THSNN_batch_norm(\\n input.Handle,\\n running_mean.Handle,\\n running_var.Handle,\\n weight is not null ? weight.Handle : IntPtr.Zero,\\n bias is not null ? bias.Handle : IntPtr.Zero,\\n training,\\n momentum, eps);\\n if (res == IntPtr.Zero)\\n torch.CheckForErrors();\\n return new Tensor(res);\\n }\\n\\n /// <summary>\\n /// Applies Group Normalization for last certain number of dimensions.\\n /// </summary>\\n public static Tensor group_norm(Tensor input, long num_groups, Tensor weight = null, Tensor bias = null, double eps = 1e-5)\\n {\\n var res = THSNN_group_norm(\\n input.Handle,\\n num_groups,\\n weight is not null ? weight.Handle : IntPtr.Zero,\\n bias is not null ? bias.Handle : IntPtr.Zero,\\n eps);\\n if (res == IntPtr.Zero)\\n torch.CheckForErrors();\\n return new Tensor(res);\\n }\\n\\n /// <summary>\\n /// Applies Instance Normalization for each channel in each data sample in a batch.\\n /// </summary>\\n public static Tensor instance_norm(Tensor input, Tensor running_mean = null, Tensor running_var = null, Tensor weight = null, Tensor bias = null, bool use_input_stats = true, double momentum = 0.1, double eps = 1e-5)\\n {\\n var res = THSNN_instance_norm(\\n input.Handle,\\n running_mean is not null ? running_mean.Handle : IntPtr.Zero,\\n running_var is not null ? running_var.Handle : IntPtr.Zero,\\n weight is not null ? weight.Handle : IntPtr.Zero,\\n bias is not null ? bias.Handle : IntPtr.Zero,\\n use_input_stats,\\n momentum, eps);\\n if (res == IntPtr.Zero)\\n torch.CheckForErrors();\\n return new Tensor(res);\\n }\\n\\n /// <summary>\\n /// Applies Layer Normalization for last certain number of dimensions.\\n /// </summary>\\n public static Tensor layer_norm(Tensor input, long[] normalized_shape, Tensor weight = null, Tensor bias = null, double eps = 1e-5)\\n {\\n IntPtr res;\\n unsafe {\\n fixed (long* normalized_shape_ptr = normalized_shape) {\\n res = THSNN_layer_norm(\\n input.Handle,\\n normalized_shape_ptr,\\n normalized_shape.LongLength,\\n weight is not null ? weight.Handle : IntPtr.Zero,\\n bias is not null ? bias.Handle : IntPtr.Zero,\\n eps);\\n }\\n }\\n if (res == IntPtr.Zero)\\n torch.CheckForErrors();\\n return new Tensor(res);\\n }\\n\\n /// <summary>\\n /// Applies Local Normalization.\\n /// </summary>\\n public static Tensor local_response_norm(Tensor input, long size, double alpha = 0.0001, double beta = 0.75, double k = 1.0)\\n {\\n var res = THSNN_local_response_norm(input.Handle, size, alpha, beta, k);\\n if (res == IntPtr.Zero)\\n torch.CheckForErrors();\\n return new Tensor(res);\\n }\\n }\\n }\\n }\\n}\\n\"\n },\n {\n \"alpha_fraction\": 0.41532114148139954,\n \"alphanum_fraction\": 0.4296486973762512,\n \"avg_line_length\": 34.7274169921875,\n \"blob_id\": \"4cfc5bd987a6e41c4c2c76283ca05f85d21a5e98\",\n \"content_id\": \"9514003f2da95ad0d520e24dc6c62b9103c9647d\",\n \"detected_licenses\": [\n \"MIT\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"C#\",\n \"length_bytes\": 22544,\n \"license_type\": \"permissive\",\n \"max_line_length\": 157,\n \"num_lines\": 631,\n \"path\": \"/src/TorchSharp/Utils/TensorAccessor.cs\",\n \"repo_name\": \"NiklasGustafsson/TorchSharp\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"using System;\\nusing System.Collections;\\nusing System.Collections.Generic;\\nusing System.Diagnostics;\\nusing System.Linq;\\nusing static TorchSharp.PInvoke.NativeMethods;\\n\\nnamespace TorchSharp.Utils\\n{\\n /// <summary>\\n /// TensorAccessor is used to present the contents of a tensor or tensor view to the .NET world as an ordered collection\\n /// of values that integrates well with things like LINQ and foreach loops in the .NET world.\\n /// </summary>\\n /// <typeparam name=\\\"T\\\">The type of the tensor elements.</typeparam>\\n public sealed class TensorAccessor<T> : IDisposable, IEnumerable<T> where T : unmanaged\\n {\\n internal TensorAccessor(torch.Tensor tensor)\\n {\\n if (tensor.device_type != DeviceType.CPU) {\\n throw new InvalidOperationException(\\\"Reading data from non-CPU memory is not supported. Move or copy the tensor to the cpu before reading.\\\");\\n }\\n\\n var strides = tensor.stride();\\n for (var i = 0; i < strides.Length; i++) {\\n if (strides[i] < 0)\\n throw new NotImplementedException($\\\"Negative tensor strides are not currently supported. tensor.strides({i}) == {strides[i]}\\\");\\n }\\n\\n // Get the data from native code.\\n\\n unsafe {\\n var res = THSTensor_data(tensor.Handle);\\n if (res == IntPtr.Zero) { torch.CheckForErrors(); }\\n // NOTE: there is no safety here.\\n _tensor_data_ptr = res;\\n }\\n\\n _tensor = tensor; // Keep the tensor alive now that everything is alright.\\n }\\n\\n public long Count => (_tensor is not null ? _tensor.numel() : 0);\\n\\n public bool IsReadOnly => false;\\n\\n public T[] ToArray()\\n {\\n if (_tensor.ndim < 2)\\n return (T[])ToNDArray();\\n\\n var result = new T[Count];\\n CopyTo(result);\\n return result;\\n }\\n\\n /// <summary>\\n /// Extract tensor data as a multi-dimensional .NET array, with the same number of dimensions as the tensor.\\n /// </summary>\\n /// <returns>An array object, which should be cast to the concrete array type.</returns>\\n public Array ToNDArray()\\n {\\n var shape = _tensor.shape;\\n var strides = _tensor.stride();\\n switch (_tensor.ndim) {\\n default:\\n return ToNDArray(shape, strides);\\n case 0:\\n unsafe {\\n var result = new T[1];\\n T* ptr = (T*)_tensor_data_ptr;\\n result[0] = ptr[0];\\n return result;\\n }\\n case 1:\\n unsafe {\\n var result = new T[shape[0]];\\n T* ptr = (T*)_tensor_data_ptr;\\n for (long i0 = 0, off0 = 0; i0 < shape[0]; i0++, off0 += strides[0]) {\\n result[i0] = ptr[off0];\\n }\\n return result;\\n }\\n case 2:\\n unsafe {\\n var result = new T[shape[0], shape[1]];\\n T* ptr = (T*)_tensor_data_ptr;\\n for (long i0 = 0, off0 = 0; i0 < shape[0]; i0++, off0 += strides[0]) {\\n for (long i1 = 0, off1 = off0; i1 < shape[1]; i1++, off1 += strides[1]) {\\n result[i0, i1] = ptr[off1];\\n }\\n }\\n return result;\\n }\\n case 3:\\n unsafe {\\n var result = new T[shape[0], shape[1], shape[2]];\\n T* ptr = (T*)_tensor_data_ptr;\\n for (long i0 = 0, off0 = 0; i0 < shape[0]; i0++, off0 += strides[0]) {\\n for (long i1 = 0, off1 = off0; i1 < shape[1]; i1++, off1 += strides[1]) {\\n for (long i2 = 0, off2 = off1; i2 < shape[2]; i2++, off2 += strides[2]) {\\n result[i0, i1, i2] = ptr[off2];\\n }\\n }\\n }\\n return result;\\n }\\n case 4:\\n unsafe {\\n var result = new T[shape[0], shape[1], shape[2], shape[3]];\\n T* ptr = (T*)_tensor_data_ptr;\\n for (long i0 = 0, off0 = 0; i0 < shape[0]; i0++, off0 += strides[0]) {\\n for (long i1 = 0, off1 = off0; i1 < shape[1]; i1++, off1 += strides[1]) {\\n for (long i2 = 0, off2 = off1; i2 < shape[2]; i2++, off2 += strides[2]) {\\n for (long i3 = 0, off3 = off2; i3 < shape[3]; i3++, off3 += strides[3]) {\\n result[i0, i1, i2, i3] = ptr[off3];\\n }\\n }\\n }\\n }\\n return result;\\n }\\n case 5:\\n unsafe {\\n var result = new T[shape[0], shape[1], shape[2], shape[3], shape[4]];\\n T* ptr = (T*)_tensor_data_ptr;\\n for (long i0 = 0, off0 = 0; i0 < shape[0]; i0++, off0 += strides[0]) {\\n for (long i1 = 0, off1 = off0; i1 < shape[1]; i1++, off1 += strides[1]) {\\n for (long i2 = 0, off2 = off1; i2 < shape[2]; i2++, off2 += strides[2]) {\\n for (long i3 = 0, off3 = off2; i3 < shape[3]; i3++, off3 += strides[3]) {\\n for (long i4 = 0, off4 = off3; i4 < shape[4]; i4++, off4 += strides[4]) {\\n result[i0, i1, i2, i3, i4] = ptr[off4];\\n }\\n }\\n }\\n }\\n }\\n return result;\\n }\\n case 6:\\n unsafe {\\n var result = new T[shape[0], shape[1], shape[2], shape[3], shape[4], shape[5]];\\n T* ptr = (T*)_tensor_data_ptr;\\n for (long i0 = 0, off0 = 0; i0 < shape[0]; i0++, off0 += strides[0]) {\\n for (long i1 = 0, off1 = off0; i1 < shape[1]; i1++, off1 += strides[1]) {\\n for (long i2 = 0, off2 = off1; i2 < shape[2]; i2++, off2 += strides[2]) {\\n for (long i3 = 0, off3 = off2; i3 < shape[3]; i3++, off3 += strides[3]) {\\n for (long i4 = 0, off4 = off3; i4 < shape[4]; i4++, off4 += strides[4]) {\\n for (long i5 = 0, off5 = off4; i5 < shape[5]; i5++, off5 += strides[5]) {\\n result[i0, i1, i2, i3, i4, i5] = ptr[off5];\\n }\\n }\\n }\\n }\\n }\\n }\\n return result;\\n }\\n }\\n }\\n\\n private Array ToNDArray(long[] shape, long[] strides)\\n {\\n Array array = Array.CreateInstance(typeof(T), shape);\\n long[] indexes = new long[_tensor.ndim];\\n long[] off = new long[_tensor.ndim];\\n\\n while (true) {\\n unsafe {\\n T* ptr = (T*)_tensor_data_ptr;\\n array.SetValue(ptr[off[array.Rank - 1]], indexes);\\n }\\n\\n for (int i = array.Rank - 1; i >= 0; i--) {\\n if (indexes[i] < shape[i] - 1) {\\n indexes[i]++;\\n off[i] += strides[i];\\n for (int j = i; j < array.Rank - 1; j++)\\n off[j + 1] = off[j];\\n break;\\n } else {\\n if (i == 0) {\\n return array;\\n }\\n indexes[i] = 0;\\n }\\n }\\n }\\n }\\n\\n /// <summary>\\n /// Access elements of the underlying tensor / tensor view.\\n /// </summary>\\n /// <param name=\\\"indices\\\">A linear index into the data.</param>\\n /// <returns></returns>\\n public T this[params long[] indices] {\\n get {\\n long index = 0;\\n if (indices.Length == 1) {\\n index = indices[0];\\n validate(index);\\n unsafe {\\n T* ptr = (T*)_tensor_data_ptr;\\n return ptr[TranslateIndex(index, _tensor)];\\n }\\n } else {\\n unsafe {\\n T* ptr = (T*)_tensor_data_ptr;\\n return ptr[TranslateIndex(indices, _tensor)];\\n }\\n }\\n }\\n set {\\n long index = 0;\\n if (indices.Length == 1) {\\n index = indices[0];\\n validate(index);\\n unsafe {\\n T* ptr = (T*)_tensor_data_ptr;\\n ptr[TranslateIndex(indices, _tensor)] = value;\\n }\\n } else {\\n unsafe {\\n T* ptr = (T*)_tensor_data_ptr;\\n ptr[TranslateIndex(indices, _tensor)] = value;\\n }\\n }\\n }\\n }\\n\\n private void validate(long index)\\n {\\n if (index >= Count) throw new IndexOutOfRangeException();\\n }\\n\\n public void CopyTo(T[] array, int arrayIndex = 0, long tensorIndex = 0)\\n {\\n int idx = arrayIndex;\\n foreach (int offset in GetSubsequentIndices(tensorIndex)) {\\n if (idx >= array.Length) break;\\n unsafe { array[idx] = ((T*)_tensor_data_ptr)[offset]; }\\n idx += 1;\\n }\\n }\\n\\n public void CopyFrom(T[] array, int arrayIndex = 0, long tensorIndex = 0)\\n {\\n int idx = arrayIndex;\\n foreach (int offset in GetSubsequentIndices(tensorIndex)) {\\n if (idx >= array.Length) break;\\n unsafe { ((T*)_tensor_data_ptr)[offset] = array[idx]; }\\n idx += 1;\\n }\\n }\\n\\n /// <summary>\\n /// Translates a linear index within the span represented by the accessor to a linear index\\n /// used by the underlying tensor. The two should only be different if the tensor is a view\\n /// rather than an allocated tensor.\\n /// </summary>\\n private static long TranslateIndex(long idx, torch.Tensor tensor)\\n {\\n if (idx >= tensor.numel() || idx < 0)\\n throw new ArgumentOutOfRangeException($\\\"{idx} in a collection of ${tensor.numel()} elements.\\\");\\n\\n if (tensor.is_contiguous() || idx == 0) return idx;\\n\\n long result = 0;\\n var shape = tensor.shape;\\n var strides = tensor.stride();\\n\\n for (var i = shape.Length - 1; i >= 0; i--) {\\n idx = Math.DivRem(idx, shape[i], out long s);\\n result += s * strides[i];\\n }\\n\\n return result;\\n }\\n\\n private static long TranslateIndex(long[] idx, torch.Tensor tensor)\\n {\\n long result = 0;\\n var shape = tensor.shape;\\n var strides = tensor.stride();\\n\\n for (var i = shape.Length - 1; i >= 0; i--) {\\n if (idx[i] >= shape[i] || idx[i] < 0)\\n throw new IndexOutOfRangeException($\\\"{idx[i]} >= {shape[i]} in dimension {i}.\\\");\\n result += idx[i] * strides[i];\\n }\\n\\n return result;\\n }\\n\\n internal static T ReadItemAt(torch.Tensor tensor, long index)\\n {\\n if (tensor.device_type != DeviceType.CPU) {\\n throw new InvalidOperationException(\\\"Reading data from non-CPU memory is not supported. Move or copy the tensor to the cpu before reading.\\\");\\n }\\n\\n tensor.ValidateType(typeof(T));\\n\\n var strides = tensor.stride();\\n for (var i = 0; i < strides.Length; i++) {\\n if (strides[i] < 0)\\n throw new NotImplementedException($\\\"Negative tensor strides are not currently supported. tensor.strides({i}) == {strides[i]}\\\");\\n }\\n\\n unsafe {\\n var res = THSTensor_data(tensor.Handle);\\n if (res == IntPtr.Zero) { torch.CheckForErrors(); }\\n // NOTE: there is no safety here.\\n T* ptr = (T*)res;\\n return ptr[TranslateIndex(index, tensor)];\\n }\\n }\\n\\n /// <summary>\\n /// Compare two tensors element-wise.\\n /// </summary>\\n /// <param name=\\\"left\\\">A tensor</param>\\n /// <param name=\\\"right\\\">Another tensor</param>\\n /// <returns></returns>\\n public static bool operator ==(TensorAccessor<T> left, TensorAccessor<T> right)\\n {\\n if (left.Count != right.Count) return false;\\n\\n var lEnum = left.GetEnumerator();\\n var rEnum = right.GetEnumerator();\\n\\n while (lEnum.MoveNext() && rEnum.MoveNext()) {\\n if (!lEnum.Current.Equals(rEnum.Current))\\n return false;\\n }\\n return true;\\n }\\n\\n /// <summary>\\n /// Compare two tensors element-wise.\\n /// </summary>\\n /// <param name=\\\"left\\\">A tensor</param>\\n /// <param name=\\\"right\\\">Another tensor</param>\\n /// <returns></returns>\\n public static bool operator !=(TensorAccessor<T> left, TensorAccessor<T> right)\\n {\\n return !(left == right);\\n }\\n\\n\\n private IEnumerable<long> GetSubsequentIndices(long startingIndex)\\n {\\n if (startingIndex < 0 || startingIndex >= Count)\\n throw new ArgumentOutOfRangeException(nameof(startingIndex));\\n\\n if (Count <= 1) {\\n if (Count == 0) {\\n return Enumerable.Empty<long>();\\n }\\n\\n return (new long[] { 0 }).AsEnumerable<long>();\\n }\\n\\n if (_tensor.is_contiguous()) {\\n return ContiguousIndices(startingIndex);\\n }\\n\\n var stride = _tensor.stride();\\n Debug.Assert(stride.Length > 0);\\n\\n if (stride.Length == 1) {\\n return SimpleIndices(startingIndex, stride[0]);\\n }\\n\\n return MultiDimensionIndices(startingIndex);\\n }\\n\\n private IEnumerable<long> MultiDimensionIndices(long startingIndex)\\n {\\n long[] shape = _tensor.shape;\\n long[] stride = _tensor.stride();\\n long[] inds = new long[stride.Length];\\n\\n long index = startingIndex;\\n long offset = TranslateIndex(startingIndex, _tensor);\\n\\n while (true) {\\n\\n index += 1;\\n\\n yield return offset;\\n\\n if (index >= Count) break;\\n\\n for (int i = inds.Length - 1; ; i--) {\\n Debug.Assert(i >= 0);\\n offset += stride[i];\\n if (++inds[i] < shape[i])\\n break;\\n\\n // Overflow of current dimension so rewind accordingly.\\n // Can't overflow the final (left-most) dimension.\\n Debug.Assert(i > 0);\\n // Note: for perf, this multiplication could be done once up front and cached in an array.\\n offset -= inds[i] * stride[i];\\n inds[i] = 0;\\n }\\n }\\n }\\n\\n private IEnumerable<long> SimpleIndices(long startingIndex, long stride)\\n {\\n long index = startingIndex;\\n long offset = TranslateIndex(startingIndex, _tensor);\\n\\n while (index < Count) {\\n yield return offset;\\n offset += stride;\\n index += 1;\\n }\\n }\\n private IEnumerable<long> ContiguousIndices(long startingIndex)\\n {\\n // If there was an overload for Enumerable.Range that\\n // produced long integers, we wouldn't need this implementation.\\n\\n long index = startingIndex;\\n while (index < Count) {\\n yield return index;\\n index += 1;\\n }\\n }\\n\\n\\n /// <summary>\\n /// Compare two tensors element-wise.\\n /// </summary>\\n /// <param name=\\\"obj\\\">Another tensor</param>\\n /// <returns></returns>\\n public override bool Equals(object obj)\\n {\\n var left = this;\\n var right = obj as TensorAccessor<T>;\\n if (right == null) return false;\\n\\n if (left._tensor_data_ptr == right._tensor_data_ptr) return true;\\n if (left.Count != right.Count) return false;\\n for (long i = 0; i < left.Count; i++) {\\n if (!left[i].Equals(right[i])) return false;\\n }\\n return true;\\n }\\n\\n public override int GetHashCode()\\n {\\n return base.GetHashCode();\\n }\\n\\n IEnumerator IEnumerable.GetEnumerator()\\n {\\n return GetEnumerator();\\n }\\n\\n public void Dispose()\\n {\\n Dispose(true);\\n GC.SuppressFinalize(this);\\n }\\n\\n private void Dispose(bool disposing)\\n {\\n _tensor_data_ptr = IntPtr.Zero;\\n // Clear the tensor that we've been keeping alive.\\n _tensor = null;\\n }\\n\\n private torch.Tensor _tensor; // Keeping it alive.\\n private IntPtr _tensor_data_ptr;\\n\\n#if true\\n public IEnumerator<T> GetEnumerator()\\n {\\n if (Count <= 1) {\\n if (Count == 0)\\n return Enumerable.Empty<T>().GetEnumerator();\\n return new T[1] { this[0] }.AsEnumerable<T>().GetEnumerator();\\n }\\n\\n if (_tensor.is_contiguous()) {\\n return new SimpleAtorImpl(this, 1);\\n }\\n\\n var stride = _tensor.stride();\\n Debug.Assert(stride.Length > 0);\\n\\n if (stride.Length == 1) {\\n return new SimpleAtorImpl(this, stride[0]);\\n }\\n\\n return new GeneralAtorImpl(this, stride);\\n }\\n\\n private class SimpleAtorImpl : IEnumerator<T>\\n {\\n private TensorAccessor<T> _span;\\n private readonly long _count;\\n private readonly long _stride;\\n\\n // State.\\n private long _index;\\n private long _offset;\\n private T _current;\\n\\n public SimpleAtorImpl(TensorAccessor<T> span, long stride)\\n {\\n _span = span;\\n _count = span.Count;\\n Debug.Assert(_count > 0);\\n _stride = stride;\\n Reset();\\n }\\n\\n public T Current => _current;\\n object IEnumerator.Current => Current;\\n\\n public void Dispose()\\n {\\n _span = null;\\n Reset();\\n }\\n\\n public bool MoveNext()\\n {\\n if (_index < 0) {\\n _index = 0;\\n _offset = 0;\\n } else if (++_index >= _count) {\\n Reset();\\n return false;\\n } else {\\n _offset += _stride;\\n }\\n\\n unsafe { _current = ((T*)_span._tensor_data_ptr)[_offset]; }\\n return true;\\n }\\n\\n public void Reset()\\n {\\n _index = -1;\\n _offset = -1;\\n _current = default;\\n }\\n }\\n\\n private class GeneralAtorImpl : IEnumerator<T>\\n {\\n private TensorAccessor<T> _span;\\n private readonly long _count;\\n private readonly long[] _shape;\\n private readonly long[] _stride;\\n private readonly long[] _inds;\\n\\n // State.\\n private long _index;\\n private long _offset;\\n\\n public GeneralAtorImpl(TensorAccessor<T> span, long[] stride)\\n {\\n Debug.Assert(stride.Length > 1);\\n _span = span;\\n _count = span.Count;\\n Debug.Assert(_count > 0);\\n _shape = span._tensor.shape;\\n Debug.Assert(_shape.Length == stride.Length);\\n _stride = stride;\\n _inds = new long[stride.Length];\\n Reset();\\n }\\n\\n public T Current { get; private set; }\\n\\n object IEnumerator.Current => Current;\\n\\n public void Dispose()\\n {\\n // Just clear the span field.\\n _span = null;\\n }\\n\\n public bool MoveNext()\\n {\\n if (_index < 0) {\\n _index = 0;\\n _offset = 0;\\n Array.Clear(_inds, 0, _inds.Length);\\n } else if (++_index >= _count) {\\n Reset();\\n return false;\\n } else {\\n for (int i = _inds.Length - 1; ; i--) {\\n Debug.Assert(i >= 0);\\n _offset += _stride[i];\\n if (++_inds[i] < _shape[i])\\n break;\\n\\n // Overflow of current dimension so rewind accordingly.\\n // Can't overflow the final (left-most) dimension.\\n Debug.Assert(i > 0);\\n // Note: for perf, this multiplication could be done once up front and cached in an array.\\n _offset -= _inds[i] * _stride[i];\\n _inds[i] = 0;\\n }\\n }\\n\\n unsafe { Current = ((T*)_span._tensor_data_ptr)[_offset]; }\\n return true;\\n }\\n\\n public void Reset()\\n {\\n _index = -1;\\n _offset = -1;\\n Current = default;\\n }\\n }\\n#else\\n public IEnumerator<T> GetEnumerator()\\n {\\n return new TensorAccessorEnumerator(this);\\n }\\n#endif\\n }\\n}\\n\"\n },\n {\n \"alpha_fraction\": 0.5476118922233582,\n \"alphanum_fraction\": 0.5522679686546326,\n \"avg_line_length\": 32.04218292236328,\n \"blob_id\": \"90594c3edaceb60dda20ab631a13eead8c8a7713\",\n \"content_id\": \"cf95ac47f017103031032726c2da77f2a17a1322\",\n \"detected_licenses\": [\n \"MIT\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"C#\",\n \"length_bytes\": 13316,\n \"license_type\": \"permissive\",\n \"max_line_length\": 130,\n \"num_lines\": 403,\n \"path\": \"/src/TorchSharp/Scalar.cs\",\n \"repo_name\": \"NiklasGustafsson/TorchSharp\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"// Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information.\\nusing System;\\nusing static TorchSharp.PInvoke.NativeMethods;\\n\\nnamespace TorchSharp\\n{\\n /// <summary>\\n /// Represents a dynamically typed scalar value to the LibTorch runtime.\\n /// </summary>\\n public sealed class Scalar : IDisposable\\n {\\n internal IntPtr Handle {\\n get {\\n if (handle == IntPtr.Zero)\\n throw new InvalidOperationException(\\\"Scalar invalid -- empty handle.\\\");\\n return handle;\\n }\\n private set { handle = value; }\\n }\\n private IntPtr handle;\\n\\n internal Scalar(IntPtr handle)\\n {\\n Handle = handle;\\n }\\n\\n /// <summary>\\n /// Implicitly convert a .NET scalar value to Scalar\\n /// </summary>\\n /// <param name=\\\"value\\\">The scalar value.</param>\\n public static implicit operator Scalar(byte value)\\n {\\n return value.ToScalar();\\n }\\n\\n /// <summary>\\n /// Implicitly convert a .NET scalar value to Scalar\\n /// </summary>\\n /// <param name=\\\"value\\\">The scalar value.</param>\\n public static implicit operator Scalar(sbyte value)\\n {\\n return value.ToScalar();\\n }\\n\\n /// <summary>\\n /// Implicitly convert a .NET scalar value to Scalar\\n /// </summary>\\n /// <param name=\\\"value\\\">The scalar value.</param>\\n public static implicit operator Scalar(short value)\\n {\\n return value.ToScalar();\\n }\\n\\n /// <summary>\\n /// Implicitly convert a .NET scalar value to Scalar\\n /// </summary>\\n /// <param name=\\\"value\\\">The scalar value.</param>\\n public static implicit operator Scalar(int value)\\n {\\n return value.ToScalar();\\n }\\n\\n /// <summary>\\n /// Implicitly convert a .NET scalar value to Scalar\\n /// </summary>\\n /// <param name=\\\"value\\\">The scalar value.</param>\\n public static implicit operator Scalar(long value)\\n {\\n return value.ToScalar();\\n }\\n\\n#if NET6_0_OR_GREATER\\n /// <summary>\\n /// Implicitly convert a .NET scalar value to Scalar\\n /// </summary>\\n /// <param name=\\\"value\\\">The scalar value.</param>\\n public static implicit operator Scalar(Half value)\\n {\\n return value.ToScalar();\\n }\\n#endif\\n\\n /// <summary>\\n /// Implicitly convert a .NET scalar value to Scalar\\n /// </summary>\\n /// <param name=\\\"value\\\">The scalar value.</param>\\n public static implicit operator Scalar(float value)\\n {\\n return value.ToScalar();\\n }\\n\\n /// <summary>\\n /// Implicitly convert a .NET scalar value to Scalar\\n /// </summary>\\n /// <param name=\\\"value\\\">The scalar value.</param>\\n public static implicit operator Scalar(double value)\\n {\\n return value.ToScalar();\\n }\\n\\n /// <summary>\\n /// Implicitly convert a .NET scalar value to Scalar\\n /// </summary>\\n /// <param name=\\\"value\\\">The scalar value.</param>\\n public static implicit operator Scalar(bool value)\\n {\\n return value.ToScalar();\\n }\\n\\n /// <summary>\\n /// Implicitly convert a .NET scalar value to Scalar\\n /// </summary>\\n /// <param name=\\\"value\\\">The scalar value.</param>\\n public static implicit operator Scalar((float, float) value)\\n {\\n return value.ToScalar();\\n }\\n\\n /// <summary>\\n /// Implicitly convert a .NET scalar value to Scalar\\n /// </summary>\\n /// <param name=\\\"value\\\">The scalar value.</param>\\n public static implicit operator Scalar(System.Numerics.Complex value)\\n {\\n return value.ToScalar();\\n }\\n\\n /// <summary>\\n /// Gets the actual type of the Scalar value\\n /// </summary>\\n public torch.ScalarType Type {\\n get {\\n return (torch.ScalarType)THSTorch_scalar_type(Handle);\\n }\\n }\\n\\n /// <summary>\\n /// Finalize the tensor. Releases the tensor and its associated data.\\n /// </summary>\\n ~Scalar() => Dispose(false);\\n\\n public void Dispose()\\n {\\n Dispose(true);\\n GC.SuppressFinalize(this);\\n }\\n\\n /// <summary>\\n /// Implements the .NET Dispose pattern.\\n /// </summary>\\n void Dispose(bool disposing)\\n {\\n if (handle != IntPtr.Zero) {\\n THSTorch_dispose_scalar(handle);\\n handle = IntPtr.Zero;\\n }\\n }\\n }\\n\\n public static class ScalarExtensionMethods\\n {\\n /// <summary>\\n /// Explcitly construct a Scalar from a .NET scalar.\\n /// </summary>\\n /// <param name=\\\"value\\\">The input scalar value</param>\\n public static Scalar ToScalar(this byte value)\\n {\\n torch.InitializeDeviceType(DeviceType.CPU);\\n return new Scalar(THSTorch_uint8_to_scalar(value));\\n }\\n\\n /// <summary>\\n /// Explcitly construct a Scalar from a .NET scalar.\\n /// </summary>\\n /// <param name=\\\"value\\\">The input scalar value</param>\\n public static Scalar ToScalar(this sbyte value)\\n {\\n torch.InitializeDeviceType(DeviceType.CPU);\\n return new Scalar(THSTorch_int8_to_scalar(value));\\n }\\n\\n /// <summary>\\n /// Explcitly construct a Scalar from a .NET scalar.\\n /// </summary>\\n /// <param name=\\\"value\\\">The input scalar value</param>\\n public static Scalar ToScalar(this short value)\\n {\\n torch.InitializeDeviceType(DeviceType.CPU);\\n return new Scalar(THSTorch_int16_to_scalar(value));\\n }\\n\\n /// <summary>\\n /// Explcitly construct a Scalar from a .NET scalar.\\n /// </summary>\\n /// <param name=\\\"value\\\">The input scalar value</param>\\n public static Scalar ToScalar(this int value)\\n {\\n torch.InitializeDeviceType(DeviceType.CPU);\\n return new Scalar(THSTorch_int32_to_scalar(value));\\n }\\n\\n /// <summary>\\n /// Explcitly construct a Scalar from a .NET scalar.\\n /// </summary>\\n /// <param name=\\\"value\\\">The input scalar value</param>\\n public static Scalar ToScalar(this long value)\\n {\\n torch.InitializeDeviceType(DeviceType.CPU);\\n return new Scalar(THSTorch_int64_to_scalar(value));\\n }\\n\\n /// <summary>\\n /// Explcitly construct a Scalar from a .NET scalar.\\n /// </summary>\\n /// <param name=\\\"value\\\">The input scalar value</param>\\n public static Scalar ToScalar(this float value)\\n {\\n torch.InitializeDeviceType(DeviceType.CPU);\\n return new Scalar(THSTorch_float32_to_scalar(value));\\n }\\n\\n /// <summary>\\n /// Explcitly construct a Scalar from a .NET scalar.\\n /// </summary>\\n /// <param name=\\\"value\\\">The input scalar value</param>\\n public static Scalar ToScalar(this double value)\\n {\\n torch.InitializeDeviceType(DeviceType.CPU);\\n return new Scalar(THSTorch_float64_to_scalar(value));\\n }\\n\\n /// <summary>\\n /// Explcitly construct a Scalar from a .NET scalar.\\n /// </summary>\\n /// <param name=\\\"value\\\">The input scalar value</param>\\n public static Scalar ToScalar(this (float, float) value)\\n {\\n torch.InitializeDeviceType(DeviceType.CPU);\\n return new Scalar(THSTorch_complex32_to_scalar(value.Item1, value.Item2));\\n }\\n\\n /// <summary>\\n /// Explcitly construct a Scalar from a .NET scalar.\\n /// </summary>\\n /// <param name=\\\"value\\\">The input scalar value</param>\\n public static Scalar ToScalar(this System.Numerics.Complex value)\\n {\\n torch.InitializeDeviceType(DeviceType.CPU);\\n return new Scalar(THSTorch_complex64_to_scalar(value.Real, value.Imaginary));\\n }\\n\\n /// <summary>\\n /// Explcitly construct a Scalar from a .NET scalar.\\n /// </summary>\\n /// <param name=\\\"value\\\">The input scalar value</param>\\n public static Scalar ToScalar(this bool value)\\n {\\n torch.InitializeDeviceType(DeviceType.CPU);\\n return new Scalar(THSTorch_bool_to_scalar(value));\\n }\\n\\n#if NET6_0_OR_GREATER\\n /// <summary>\\n /// Explcitly construct a Scalar from a .NET scalar.\\n /// </summary>\\n /// <param name=\\\"value\\\">The input scalar value</param>\\n public static Scalar ToScalar(this Half value)\\n {\\n torch.InitializeDeviceType(DeviceType.CPU);\\n return new Scalar(THSTorch_float16_to_scalar((float)value));\\n }\\n#endif\\n\\n /// <summary>\\n /// Explcitly construct a Scalar from a .NET scalar.\\n /// </summary>\\n /// <param name=\\\"value\\\">The input scalar value</param>\\n public static Scalar ToBFloat16Scalar(this float value)\\n {\\n torch.InitializeDeviceType(DeviceType.CPU);\\n return new Scalar(THSTorch_bfloat16_to_scalar(value));\\n }\\n\\n#if NET6_0_OR_GREATER\\n /// <summary>\\n /// Explicitly convert a Scalar value to a .NET scalar\\n /// </summary>\\n /// <param name=\\\"value\\\">The input value.</param>\\n public static Half ToHalf(this Scalar value)\\n {\\n Half res;\\n THSTorch_scalar_to_float16(value.Handle, out res);\\n return res;\\n }\\n#endif\\n\\n /// <summary>\\n /// Explicitly convert a Scalar value to a .NET scalar\\n /// </summary>\\n /// <param name=\\\"value\\\">The input value.</param>\\n public static float ToSingle(this Scalar value)\\n {\\n return THSTorch_scalar_to_float32(value.Handle);\\n }\\n\\n /// <summary>\\n /// Explicitly convert a Scalar value to a .NET scalar\\n /// </summary>\\n /// <param name=\\\"value\\\">The input value.</param>\\n public static double ToDouble(this Scalar value)\\n {\\n return THSTorch_scalar_to_float64(value.Handle);\\n }\\n\\n /// <summary>\\n /// Explicitly convert a Scalar value to a .NET scalar\\n /// </summary>\\n /// <param name=\\\"value\\\">The input value.</param>\\n public static sbyte ToSByte(this Scalar value)\\n {\\n return THSTorch_scalar_to_int8(value.Handle);\\n }\\n\\n /// <summary>\\n /// Explicitly convert a Scalar value to a .NET scalar\\n /// </summary>\\n /// <param name=\\\"value\\\">The input value.</param>\\n public static byte ToByte(this Scalar value)\\n {\\n return THSTorch_scalar_to_uint8(value.Handle);\\n }\\n\\n /// <summary>\\n /// Explicitly convert a Scalar value to a .NET scalar\\n /// </summary>\\n /// <param name=\\\"value\\\">The input value.</param>\\n public static short ToInt16(this Scalar value)\\n {\\n return THSTorch_scalar_to_int16(value.Handle);\\n }\\n\\n /// <summary>\\n /// Explicitly convert a Scalar value to a .NET scalar\\n /// </summary>\\n /// <param name=\\\"value\\\">The input value.</param>\\n public static int ToInt32(this Scalar value)\\n {\\n return THSTorch_scalar_to_int32(value.Handle);\\n }\\n\\n /// <summary>\\n /// Explicitly convert a Scalar value to a .NET scalar\\n /// </summary>\\n /// <param name=\\\"value\\\">The input value.</param>\\n public static long ToInt64(this Scalar value)\\n {\\n return THSTorch_scalar_to_int64(value.Handle);\\n }\\n\\n /// <summary>\\n /// Explicitly convert a Scalar value to a .NET scalar\\n /// </summary>\\n /// <param name=\\\"value\\\">The input value.</param>\\n public static bool ToBoolean(this Scalar value)\\n {\\n return THSTorch_scalar_to_bool(value.Handle);\\n }\\n\\n /// <summary>\\n /// Explicitly convert a Scalar value to a .NET scalar\\n /// </summary>\\n /// <param name=\\\"value\\\">The input value.</param>\\n public static (float Real, float Imaginary) ToComplexFloat32(this Scalar value)\\n {\\n float[] floatArray;\\n\\n using (var pa = new PinnedArray<float>()) {\\n THSTorch_scalar_to_complex32(value.Handle, pa.CreateArray);\\n torch.CheckForErrors();\\n floatArray = pa.Array;\\n }\\n\\n return (floatArray[0], floatArray[1]);\\n }\\n\\n /// <summary>\\n /// Explicitly convert a Scalar value to a .NET scalar\\n /// </summary>\\n /// <param name=\\\"value\\\">The input value.</param>\\n public static System.Numerics.Complex ToComplexFloat64(this Scalar value)\\n {\\n double[] floatArray;\\n\\n using (var pa = new PinnedArray<double>()) {\\n THSTorch_scalar_to_complex64(value.Handle, pa.CreateArray);\\n torch.CheckForErrors();\\n floatArray = pa.Array;\\n }\\n\\n return new System.Numerics.Complex(floatArray[0], floatArray[1]);\\n }\\n }\\n}\\n\"\n },\n {\n \"alpha_fraction\": 0.44416046142578125,\n \"alphanum_fraction\": 0.4861218333244324,\n \"avg_line_length\": 45.34986114501953,\n \"blob_id\": \"f02e214827122828eb18f9e775b990d92571f576\",\n \"content_id\": \"be2fad4e2924ad40f71ebfb613866ddd7655077d\",\n \"detected_licenses\": [\n \"MIT\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"C#\",\n \"length_bytes\": 16825,\n \"license_type\": \"permissive\",\n \"max_line_length\": 235,\n \"num_lines\": 363,\n \"path\": \"/src/TorchVision/models/GoogleNet.cs\",\n \"repo_name\": \"NiklasGustafsson/TorchSharp\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"// Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information.\\n\\nusing static TorchSharp.torch;\\nusing static TorchSharp.torch.nn;\\n\\n#nullable enable\\nnamespace TorchSharp\\n{\\n public static partial class torchvision\\n {\\n public static partial class models\\n {\\n /// <summary>\\n /// ResNet-18\\n /// </summary>\\n /// <param name=\\\"num_classes\\\">The number of output classes.</param>\\n /// <param name=\\\"transform_input\\\"></param>\\n /// <param name=\\\"weights_file\\\">The location of a file containing pre-trained weights for the model.</param>\\n /// <param name=\\\"skipfc\\\">If true, the last linear layer of the classifier will not be loaded from the weights file.</param>\\n /// <param name=\\\"dropout\\\">The dropout rate for most blocks.</param>\\n /// <param name=\\\"dropout_aux\\\">The dropout rate for the aux blocks.</param>\\n /// <param name=\\\"device\\\">The device to locate the model on.</param>\\n /// <remarks>\\n /// Pre-trained weights may be retrieved by using Pytorch and saving the model state-dict\\n /// using the exportsd.py script, then loading into the .NET instance:\\n ///\\n /// from torchvision import models\\n /// import exportsd\\n /// \\n /// model = models.inception_v3(pretrained=True)\\n /// f = open(\\\"model_weights.dat\\\", \\\"wb\\\")\\n /// exportsd.save_state_dict(model.state_dict(), f)\\n /// f.close()\\n ///\\n /// See also: https://github.com/dotnet/TorchSharp/blob/main/docfx/articles/saveload.md\\n ///\\n /// In order for the weights to be loaded, the number of classes has to be the same as\\n /// in the pre-trained model, which is 1000.\\n ///\\n /// It is also possible to skip loading the last linear layer and use it for transfer-learning\\n /// with a different number of output classes. To do so, pass skipfc=true.\\n ///\\n /// All pre-trained models expect input images normalized in the same way, i.e. mini-batches of 3-channel RGB\\n /// images of shape (3 x H x W), where H and W are expected to be 299x299. The images have to be loaded\\n /// in to a range of [0, 1] and then normalized using mean = [0.485, 0.456, 0.406] and std = [0.229, 0.224, 0.225].\\n /// </remarks>\\n public static Modules.GoogleNet googlenet(\\n int num_classes = 1000,\\n bool transform_input = false,\\n string? weights_file = null,\\n bool skipfc = true,\\n float dropout = 0.2f,\\n float dropout_aux = 0.7f,\\n Device? device = null)\\n {\\n return new Modules.GoogleNet(num_classes, transform_input, weights_file, skipfc, dropout, dropout_aux, device);\\n }\\n }\\n }\\n\\n namespace Modules\\n {\\n public class GoogleNet : Module<Tensor, Tensor>\\n {\\n // The code here is based on\\n // https://github.com/pytorch/vision/blob/main/torchvision/models/googlenet.py\\n // Licence and copypright notice at: https://github.com/pytorch/vision/blob/main/LICENSE\\n\\n private readonly Module<Tensor, Tensor> conv1;\\n private readonly Module<Tensor, Tensor> maxpool1;\\n private readonly Module<Tensor, Tensor> conv2;\\n private readonly Module<Tensor, Tensor> conv3;\\n private readonly Module<Tensor, Tensor> maxpool2;\\n private readonly Module<Tensor, Tensor> inception3a;\\n private readonly Module<Tensor, Tensor> inception3b;\\n private readonly Module<Tensor, Tensor> maxpool3;\\n private readonly Module<Tensor, Tensor> inception4a;\\n private readonly Module<Tensor, Tensor> inception4b;\\n private readonly Module<Tensor, Tensor> inception4c;\\n private readonly Module<Tensor, Tensor> inception4d;\\n private readonly Module<Tensor, Tensor> inception4e;\\n private readonly Module<Tensor, Tensor> maxpool4;\\n private readonly Module<Tensor, Tensor> inception5a;\\n private readonly Module<Tensor, Tensor> inception5b;\\n //private readonly Module aux1;\\n //private readonly Module aux2;\\n\\n private readonly AdaptiveAvgPool2d avgpool;\\n private Dropout dropout;\\n private readonly Linear fc;\\n\\n bool transform_input = false;\\n\\n protected override void Dispose(bool disposing)\\n {\\n if (disposing) {\\n conv1.Dispose(); conv2.Dispose(); conv3.Dispose();\\n maxpool1.Dispose(); maxpool2.Dispose(); maxpool3.Dispose(); maxpool4.Dispose();\\n inception3a.Dispose(); inception3b.Dispose();\\n inception4a.Dispose(); inception5a.Dispose();\\n inception4b.Dispose(); inception5b.Dispose();\\n inception4c.Dispose(); inception4d.Dispose();\\n inception4e.Dispose();\\n avgpool.Dispose();\\n dropout.Dispose();\\n fc.Dispose();\\n }\\n base.Dispose(disposing);\\n }\\n\\n public GoogleNet(int numClasses = 1000,\\n bool transform_input = false,\\n string? weights_file = null,\\n bool skipfc = true,\\n float dropout = 0.2f,\\n float dropout_aux = 0.7f,\\n Device? device = null) : base(nameof(GoogleNet))\\n {\\n this.transform_input = transform_input;\\n\\n conv1 = conv_block(3, 64, kernel_size: 7, stride: 2, padding: 3);\\n maxpool1 = MaxPool2d(kernelSize: 3, stride: 2, ceilMode: true);\\n conv2 = conv_block(64, 64, kernel_size: 1);\\n conv3 = conv_block(64, 192, kernel_size: 3, padding: 1);\\n maxpool2 = MaxPool2d(kernelSize: 3, stride: 2, ceilMode: true);\\n\\n inception3a = inception_block(192, 64, 96, 128, 16, 32, 32);\\n inception3b = inception_block(256, 128, 128, 192, 32, 96, 64);\\n maxpool3 = nn.MaxPool2d(3, stride: 2, ceilMode: true);\\n\\n inception4a = inception_block(480, 192, 96, 208, 16, 48, 64);\\n inception4b = inception_block(512, 160, 112, 224, 24, 64, 64);\\n inception4c = inception_block(512, 128, 128, 256, 24, 64, 64);\\n inception4d = inception_block(512, 112, 144, 288, 32, 64, 64);\\n inception4e = inception_block(528, 256, 160, 320, 32, 128, 128);\\n maxpool4 = nn.MaxPool2d(2, stride: 2, ceilMode: true);\\n\\n inception5a = inception_block(832, 256, 160, 320, 32, 128, 128);\\n inception5b = inception_block(832, 384, 192, 384, 48, 128, 128);\\n\\n //aux1 = inception_aux_block(512, numClasses, dropout_aux);\\n //aux2 = inception_aux_block(528, numClasses, dropout_aux);\\n\\n avgpool = nn.AdaptiveAvgPool2d((1, 1));\\n this.dropout = nn.Dropout(p: dropout);\\n fc = nn.Linear(1024, numClasses);\\n\\n RegisterComponents();\\n\\n if (string.IsNullOrEmpty(weights_file)) {\\n\\n foreach (var (_, m) in named_modules()) {\\n switch (m) {\\n case TorchSharp.Modules.Conv2d conv:\\n torch.nn.init.kaiming_normal_(conv.weight, mode: init.FanInOut.FanOut, nonlinearity: init.NonlinearityType.ReLU);\\n break;\\n case TorchSharp.Modules.BatchNorm2d bn:\\n torch.nn.init.constant_(bn.weight, 1);\\n torch.nn.init.constant_(bn.bias, 0);\\n break;\\n }\\n }\\n } else {\\n\\n foreach (var (_, m) in named_modules()) {\\n switch (m) {\\n case TorchSharp.Modules.Linear ln:\\n torch.nn.init.normal_(ln.weight, 0, 0.01);\\n torch.nn.init.constant_(ln.bias, 0);\\n break;\\n }\\n }\\n this.load(weights_file, skip: skipfc ? new[] { \\\"fc.weight\\\", \\\"fc.bias\\\", \\\"AuxLogits.fc.weight\\\", \\\"AuxLogits.fc.bias\\\" } : null);\\n }\\n\\n if (device != null && device.type == DeviceType.CUDA)\\n this.to(device);\\n }\\n\\n\\n private static Module<Tensor, Tensor> conv_block(int in_channels, int out_channels, int kernel_size, int stride = 1, int padding = 0)\\n {\\n return Sequential(\\n (\\\"conv\\\", Conv2d(in_channels, out_channels, bias: false, kernelSize: kernel_size, stride: stride, padding: padding)),\\n (\\\"bn\\\", BatchNorm2d(out_channels, eps: 0.001)),\\n (\\\"relu\\\", ReLU(true))\\n );\\n }\\n\\n private static Module<Tensor, Tensor> conv_block(int in_channels, int out_channels, (long, long) kernel_size, (long, long)? stride = null, (long, long)? padding = null)\\n {\\n return Sequential(\\n (\\\"conv\\\", Conv2d(in_channels, out_channels, bias: false, kernelSize: kernel_size, stride: stride, padding: padding)),\\n (\\\"bn\\\", BatchNorm2d(out_channels, eps: 0.001)),\\n (\\\"relu\\\", ReLU(true))\\n );\\n }\\n\\n private Module<Tensor, Tensor> inception_block(int in_channels, int ch1x1, int ch3x3red, int ch3x3, int ch5x5red, int ch5x5, int pool_proj) => new Inception(in_channels, ch1x1, ch3x3red, ch3x3, ch5x5red, ch5x5, pool_proj);\\n private Module<Tensor, Tensor> inception_aux_block(int in_channels, int num_classes, float dropout) => new InceptionAux(in_channels, num_classes, dropout);\\n\\n public override Tensor forward(Tensor x)\\n {\\n // Transform\\n using (var scope = NewDisposeScope()) {\\n if (transform_input) {\\n\\n var x_ch0 = torch.unsqueeze(x[(null, null), 0], 1) * (0.229f / 0.5f) + (0.485f - 0.5f) / 0.5f;\\n var x_ch1 = torch.unsqueeze(x[(null, null), 1], 1) * (0.224f / 0.5f) + (0.456f - 0.5f) / 0.5f;\\n var x_ch2 = torch.unsqueeze(x[(null, null), 2], 1) * (0.225f / 0.5f) + (0.406f - 0.5f) / 0.5f;\\n x = torch.cat(new[] { x_ch0, x_ch1, x_ch2 }, 1);\\n }\\n\\n // N x 3 x 224 x 224\\n x = conv1.call(x);\\n // N x 64 x 112 x 112\\n x = maxpool1.call(x);\\n // N x 64 x 56 x 56\\n x = conv2.call(x);\\n // N x 64 x 56 x 56\\n x = conv3.call(x);\\n // N x 192 x 56 x 56\\n x = maxpool2.call(x);\\n\\n // N x 192 x 28 x 28\\n x = inception3a.call(x);\\n // N x 256 x 28 x 28\\n x = inception3b.call(x);\\n // N x 480 x 28 x 28\\n x = maxpool3.call(x);\\n // N x 480 x 14 x 14\\n x = inception4a.call(x);\\n // N x 512 x 14 x 14\\n //Tensor aux1;\\n //if (this.aux1 is not null)\\n // aux1 = this.aux1.call(x);\\n\\n x = inception4b.call(x);\\n // N x 512 x 14 x 14\\n x = inception4c.call(x);\\n // N x 512 x 14 x 14\\n x = inception4d.call(x);\\n // N x 528 x 14 x 14\\n //Tensor aux2;\\n //if (this.aux2 is not null)\\n // aux2 = this.aux2.call(x);\\n\\n x = inception4e.call(x);\\n // N x 832 x 14 x 14\\n x = maxpool4.call(x);\\n // N x 832 x 7 x 7\\n x = inception5a.call(x);\\n // N x 832 x 7 x 7\\n x = inception5b.call(x);\\n // N x 1024 x 7 x 7\\n\\n x = avgpool.call(x);\\n // N x 1024 x 1 x 1\\n x = torch.flatten(x, 1);\\n // N x 1024\\n x = dropout.call(x);\\n x = fc.call(x);\\n // N x 1000 .call(num_classes);\\n\\n return x.MoveToOuterDisposeScope();\\n }\\n }\\n\\n class Inception : Module<Tensor, Tensor>\\n {\\n public Inception(int in_channels, int ch1x1, int ch3x3red, int ch3x3, int ch5x5red, int ch5x5, int pool_proj) : base(\\\"Inception\\\")\\n {\\n branch1 = conv_block(in_channels, ch1x1, kernel_size: 1);\\n branch2 = nn.Sequential(\\n conv_block(in_channels, ch3x3red, kernel_size: 1),\\n conv_block(ch3x3red, ch3x3, kernel_size: 3, padding: 1)\\n );\\n branch3 = nn.Sequential(\\n conv_block(in_channels, ch5x5red, kernel_size: 1),\\n conv_block(ch5x5red, ch5x5, kernel_size: 3, padding: 1)\\n );\\n branch4 = nn.Sequential(\\n nn.MaxPool2d(kernelSize: 3, stride: 1, padding: 1, ceilMode: true),\\n conv_block(in_channels, pool_proj, kernel_size: 1)\\n );\\n RegisterComponents();\\n }\\n\\n public override Tensor forward(Tensor x)\\n {\\n using var branch1 = this.branch1.call(x);\\n using var branch2 = this.branch2.call(x);\\n using var branch3 = this.branch3.call(x);\\n using var branch4 = this.branch4.call(x);\\n\\n var outputs = new[] { branch1, branch2, branch3, branch4 };\\n return torch.cat(outputs, 1);\\n }\\n\\n protected override void Dispose(bool disposing)\\n {\\n if (disposing) {\\n branch1.Dispose(); branch2.Dispose(); branch3.Dispose();\\n branch4.Dispose();\\n }\\n base.Dispose(disposing);\\n }\\n\\n private readonly Module<Tensor, Tensor> branch1;\\n private readonly Module<Tensor, Tensor> branch2;\\n private readonly Module<Tensor, Tensor> branch3;\\n private readonly Module<Tensor, Tensor> branch4;\\n }\\n\\n class InceptionAux : Module<Tensor, Tensor>\\n {\\n private readonly Module<Tensor, Tensor> conv;\\n private readonly Module<Tensor, Tensor> fc1;\\n private readonly Module<Tensor, Tensor> fc2;\\n private readonly Module<Tensor, Tensor> dropout;\\n\\n protected override void Dispose(bool disposing)\\n {\\n if (disposing) {\\n conv.Dispose(); fc1.Dispose(); fc2.Dispose();\\n dropout.Dispose();\\n }\\n base.Dispose(disposing);\\n }\\n\\n public InceptionAux(int in_channels, int num_classes, float dropout = 0.7f) : base(\\\"InceptionAux\\\")\\n {\\n conv = conv_block(in_channels, 128, kernel_size: 1);\\n fc1 = nn.Linear(2048, 1024);\\n fc2 = nn.Linear(1024, num_classes);\\n this.dropout = nn.Dropout(p: dropout);\\n\\n RegisterComponents();\\n }\\n\\n public override Tensor forward(Tensor x)\\n {\\n // aux1: N x 512 x 14 x 14, aux2: N x 528 x 14 x 14\\n x = functional.adaptive_avg_pool2d(x, 4);\\n // aux1: N x 512 x 4 x 4, aux2: N x 528 x 4 x 4\\n x = conv.call(x);\\n // N x 128 x 4 x 4\\n x = torch.flatten(x);\\n // N x 2048\\n // Adaptive average pooling\\n x = functional.relu(fc1.call(x), inplace:true);\\n // N x 1024\\n x = dropout.call(x);\\n // N x 1024\\n x = fc2.call(x);\\n // N x 1000\\n\\n return x;\\n }\\n }\\n }\\n }\\n}\\n\"\n },\n {\n \"alpha_fraction\": 0.5854326486587524,\n \"alphanum_fraction\": 0.5854326486587524,\n \"avg_line_length\": 37.87234115600586,\n \"blob_id\": \"051e11ecf7a44413afa66d1a1066be5eae358df8\",\n \"content_id\": \"254aa14ccd72201b50669d441bfab849fff35c64\",\n \"detected_licenses\": [\n \"MIT\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"C#\",\n \"length_bytes\": 1826,\n \"license_type\": \"permissive\",\n \"max_line_length\": 173,\n \"num_lines\": 47,\n \"path\": \"/src/TorchVision/IO/NotImplemented.cs\",\n \"repo_name\": \"NiklasGustafsson/TorchSharp\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"// Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information.\\nusing System.IO;\\nusing System.Threading;\\nusing System.Threading.Tasks;\\nusing static TorchSharp.torch;\\n\\nnamespace TorchSharp\\n{\\n public static partial class torchvision\\n {\\n public static partial class io\\n {\\n public class NotImplementedImager : Imager\\n {\\n System.Exception exp = new System.NotImplementedException(\\\"You need to provide your own DefaultImager or specify the Imager for all image I/O method calls\\\");\\n public override Tensor DecodeImage(Stream image, ImageReadMode mode = ImageReadMode.UNCHANGED)\\n {\\n throw exp;\\n }\\n\\n public override Tensor DecodeImage(byte[] data, ImageReadMode mode = ImageReadMode.UNCHANGED)\\n {\\n throw new System.NotImplementedException();\\n }\\n\\n public override Task<Tensor> DecodeImageAsync(Stream stream, ImageReadMode mode = ImageReadMode.UNCHANGED, CancellationToken cancellationToken = default)\\n {\\n throw exp;\\n }\\n public override void EncodeImage(Tensor image, ImageFormat format, Stream stream)\\n {\\n throw exp;\\n }\\n\\n public override byte[] EncodeImage(Tensor image, ImageFormat format)\\n {\\n throw new System.NotImplementedException();\\n }\\n\\n public override Task EncodeImageAsync(Tensor image, ImageFormat format, Stream stream, CancellationToken cancellationToken = default)\\n {\\n throw exp;\\n }\\n }\\n }\\n }\\n}\"\n },\n {\n \"alpha_fraction\": 0.8109452724456787,\n \"alphanum_fraction\": 0.8109452724456787,\n \"avg_line_length\": 24.25,\n \"blob_id\": \"ae30e327ebfa4849ba8e74cbcd4513127c79e406\",\n \"content_id\": \"56333c2e4a772142dd9f056b3d1d38997498976e\",\n \"detected_licenses\": [\n \"MIT\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"C#\",\n \"length_bytes\": 203,\n \"license_type\": \"permissive\",\n \"max_line_length\": 58,\n \"num_lines\": 8,\n \"path\": \"/src/TorchSharp/PInvoke/GCHandleDeleter.cs\",\n \"repo_name\": \"NiklasGustafsson/TorchSharp\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"using System;\\nusing System.Runtime.InteropServices;\\n\\nnamespace TorchSharp.PInvoke\\n{\\n [UnmanagedFunctionPointer(CallingConvention.Cdecl)]\\n internal delegate void GCHandleDeleter(IntPtr memory);\\n}\"\n },\n {\n \"alpha_fraction\": 0.5684669613838196,\n \"alphanum_fraction\": 0.572912335395813,\n \"avg_line_length\": 48.323768615722656,\n \"blob_id\": \"edbb69927d273f37965e55b59d1099c4bbda8504\",\n \"content_id\": \"d20234aef74d6927312235a04261617957a039c6\",\n \"detected_licenses\": [\n \"MIT\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"C#\",\n \"length_bytes\": 24070,\n \"license_type\": \"permissive\",\n \"max_line_length\": 250,\n \"num_lines\": 488,\n \"path\": \"/src/TorchSharp/Tensor/Factories/Tensor.Factories.cs\",\n \"repo_name\": \"NiklasGustafsson/TorchSharp\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"// Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information.\\n#nullable enable\\nusing System;\\nusing System.Collections.Concurrent;\\nusing System.Diagnostics.Contracts;\\nusing System.Linq;\\nusing System.Runtime.InteropServices;\\nusing TorchSharp.Utils;\\nusing static TorchSharp.PInvoke.NativeMethods;\\n\\nnamespace TorchSharp\\n{\\n public static partial class torch\\n {\\n /// <summary>\\n /// Creates 1-D tensor of size [(stop - start) / step] with values from interval [start, stop) and\\n /// common difference step, starting from start\\n /// </summary>\\n /// <param name=\\\"start\\\">The starting value for the set of points.</param>\\n /// <param name=\\\"stop\\\">The ending value for the set of points</param>\\n /// <param name=\\\"step\\\">The gap between each pair of adjacent points.</param>\\n /// <param name=\\\"dtype\\\">the desired data type of returned tensor.\\n /// Default: if null, uses a global default (see torch.set_default_tensor_type()).\\n /// If dtype is not given, infer the data type from the other input arguments.\\n /// If any of start, end, or stop are floating-point, the dtype is inferred to be the default dtype, see get_default_dtype().\\n /// Otherwise, the dtype is inferred to be torch.int64.</param>\\n /// <param name=\\\"device\\\"></param>\\n /// <param name=\\\"requires_grad\\\"> If autograd should record operations on the returned tensor. Default: false.</param>\\n public static Tensor arange(Scalar start, Scalar stop, Scalar step, ScalarType? dtype = null, Device? device = null, bool requires_grad = false)\\n {\\n device = InitializeDevice(device);\\n\\n if (!dtype.HasValue) {\\n // Determine the element type dynamically.\\n if (start.Type.IsIntegral() && stop.Type.IsIntegral() && step.Type.IsIntegral()) {\\n dtype = ScalarType.Int64;\\n } else {\\n dtype = get_default_dtype();\\n }\\n }\\n\\n if (dtype == ScalarType.ComplexFloat32) {\\n return ComplexFloat32Tensor.arange(start, stop, step, device, requires_grad);\\n } else if (dtype == ScalarType.ComplexFloat64) {\\n return ComplexFloat64Tensor.arange(start, stop, step, device, requires_grad);\\n }\\n\\n var handle = THSTensor_arange(start.Handle, stop.Handle, step.Handle, (sbyte)dtype, (int)device.type, device.index, requires_grad);\\n if (handle == IntPtr.Zero) {\\n GC.Collect();\\n GC.WaitForPendingFinalizers();\\n handle = THSTensor_arange(start.Handle, stop.Handle, step.Handle, (sbyte)dtype, (int)device.type, device.index, requires_grad);\\n }\\n if (handle == IntPtr.Zero) { CheckForErrors(); }\\n return new Tensor(handle);\\n }\\n\\n /// <summary>\\n /// Creates 1-D tensor of size [(stop - start) / step] with values from interval [start, stop) and\\n\\t\\t/// common difference step, starting from start\\n /// </summary>\\n public static Tensor arange(Scalar start, Scalar stop, ScalarType? dtype = null, Device? device = null, bool requires_grad = false)\\n {\\n return arange(start, stop, 1, dtype, device, requires_grad);\\n }\\n\\n /// <summary>\\n /// Creates 1-D tensor of size [(stop - 0)] with values from interval [0, stop), starting from 0\\n /// </summary>\\n public static Tensor arange(Scalar stop, ScalarType? dtype = null, Device? device = null, bool requires_grad = false)\\n {\\n return arange(0, stop, 1, dtype, device, requires_grad);\\n }\\n\\n /// <summary>\\n /// Create a 2-D tensor with ones on the diagonal and zeros elsewhere.\\n /// </summary>\\n public static Tensor eye(long rows, long columns = -1L, ScalarType? dtype = null, Device? device = null, bool requires_grad = false, string[]? names = null)\\n {\\n device = InitializeDevice(device);\\n if (!dtype.HasValue) {\\n // Determine the element type dynamically.\\n dtype = get_default_dtype();\\n }\\n\\n columns = (columns == -1) ? rows : columns;\\n\\n var handle = THSTensor_eye(rows, columns, (sbyte)dtype, (int)device.type, device.index, requires_grad);\\n if (handle == IntPtr.Zero) {\\n GC.Collect();\\n GC.WaitForPendingFinalizers();\\n handle = THSTensor_eye(rows, columns, (sbyte)dtype, (int)device.type, device.index, requires_grad);\\n }\\n if (handle == IntPtr.Zero) { CheckForErrors(); }\\n var result = new Tensor(handle);\\n\\n if (names != null && names.Length > 0) {\\n\\n result.rename_(names);\\n }\\n\\n return result;\\n }\\n\\n /// <summary>\\n /// Similar to the function above, but the means and standard deviations are shared among all drawn elements. The resulting tensor has size given by size.\\n /// </summary>\\n /// <param name=\\\"mean\\\">The mean for all distributions</param>\\n /// <param name=\\\"std\\\">The standard deviation for all distributions</param>\\n /// <param name=\\\"size\\\">A sequence of integers defining the shape of the output tensor.</param>\\n /// <param name=\\\"dtype\\\"></param>\\n /// <param name=\\\"device\\\"></param>\\n /// <param name=\\\"requires_grad\\\"></param>\\n /// <param name=\\\"generator\\\">An optional random number generator</param>\\n /// <param name=\\\"names\\\">Names of the dimensions of the tensor.</param>\\n /// <returns></returns>\\n public static Tensor normal(double mean, double std, ReadOnlySpan<long> size, ScalarType? dtype = null, Device? device = null, bool requires_grad = false, Generator? generator = null, string[]? names = null)\\n {\\n return randn(size, dtype: dtype, device: device, requires_grad: requires_grad, generator: generator) * std + mean;\\n }\\n\\n private static Tensor _tensor_generic(Array rawArray, ReadOnlySpan<long> dimensions, sbyte origType, ScalarType? dtype, Device? device, bool requires_grad, bool clone = true, string[]? names = null)\\n {\\n {\\n // Validate the sizes before handing over storage to native code...\\n var prod = 1L;\\n foreach (var sz in dimensions) prod *= sz;\\n\\n if (origType == (sbyte)ScalarType.ComplexFloat32)\\n prod *= 2;\\n\\n if (prod != rawArray.LongLength)\\n throw new ArgumentException($\\\"mismatched total size creating a tensor from an array: {prod} vs. {rawArray.LongLength}\\\");\\n }\\n\\n device = InitializeDevice(device);\\n\\n if (clone) { rawArray = (Array)rawArray.Clone(); }\\n\\n var dataHandle = GCHandle.Alloc(rawArray, GCHandleType.Pinned);\\n var dataArrayAddr = dataHandle.AddrOfPinnedObject();\\n var gchp = GCHandle.ToIntPtr(dataHandle);\\n TorchSharp.PInvoke.GCHandleDeleter deleter = null!;\\n deleter = new TorchSharp.PInvoke.GCHandleDeleter((IntPtr ptr) => {\\n GCHandle.FromIntPtr(gchp).Free();\\n deleters.TryRemove(deleter, out deleter!);\\n });\\n deleters.TryAdd(deleter, deleter); // keep the delegate alive\\n\\n unsafe {\\n fixed (long* shape = dimensions) {\\n var handle = THSTensor_new(dataArrayAddr, deleter, (IntPtr)shape, dimensions.Length, origType, requires_grad);\\n\\n if (handle == IntPtr.Zero) {\\n GC.Collect();\\n GC.WaitForPendingFinalizers();\\n handle = THSTensor_new(dataArrayAddr, deleter, (IntPtr)shape, dimensions.Length, origType, requires_grad);\\n }\\n\\n if (handle == IntPtr.Zero) { CheckForErrors(); }\\n var tensor = new Tensor(handle);\\n\\n var needsConversion = dtype.HasValue && dtype.Value != (ScalarType)origType;\\n\\n if (device is not null) {\\n tensor = needsConversion ? tensor.to(dtype!.Value, device) : tensor.to(device);\\n } else if (needsConversion) {\\n tensor = tensor.to_type(dtype!.Value);\\n }\\n if (names != null && names.Length > 0) {\\n tensor.rename_(names);\\n }\\n return tensor;\\n }\\n }\\n }\\n\\n /// <summary>\\n /// Creates a <see cref=\\\"torch.Tensor\\\">torch tensor</see> from an arbitrary <see cref=\\\"Array\\\">array</see>.\\n /// </summary>\\n /// <param name=\\\"rawArray\\\">The arbitrary array to create the tensor from.</param>\\n /// <param name=\\\"device\\\">The device where the tensor is to be located. Defaults to 'cpu'.</param>\\n /// <returns>A <see cref=\\\"torch.Tensor\\\">torch tensor</see></returns>\\n /// <remarks>\\n /// This function roughly corresponds to torch.from_numpy(). It shares the underlying buffer between the input and output.\\n /// torch.tensor() always makes a copy, which can be orders of magnitude slower.\\n /// </remarks>\\n /// <exception cref=\\\"InvalidOperationException\\\">\\n /// When <see cref=\\\"Type.GetElementType()\\\">Array.GetType().GetElementType()</see> does not return the .NET element type.\\n /// </exception>\\n /// <exception cref=\\\"NotSupportedException\\\">\\n /// When <see cref=\\\"Type.GetElementType()\\\">Array.GetType().GetElementType()</see> returns an unsupported .NET element type.\\n /// Supported element types are <see cref=\\\"bool\\\" />, <see cref=\\\"byte\\\" />, <see cref=\\\"sbyte\\\" />, <see cref=\\\"short\\\" />,\\n /// <see cref=\\\"int\\\" />, <see cref=\\\"long\\\" />, <see cref=\\\"float\\\" />, <see cref=\\\"double\\\" />,\\n /// and <see cref=\\\"System.Numerics.Complex\\\" />.\\n /// </exception>\\n /// <example>\\n /// Tensor from array of rank 1\\n /// <code>\\n /// var array = new double[] { { 1, 2, 3, 4, 5, 6, 7, 8 } };\\n /// var tensor = torch.from_array(rawArray: array, dtype: ScalarType.Float64, device: Device.CPU, requires_grad: false);\\n /// </code>\\n /// Tensor from array of rank 2\\n /// <code>\\n /// var array = new double[,] { { 1, 2, 3, 4 }, { 5, 6, 7, 8 } };\\n /// var tensor = torch.from_array(rawArray: array, dtype: ScalarType.Float64, device: Device.CPU, requires_grad: false);\\n /// </code>\\n /// Tensor from array of rank 3\\n /// <code>\\n /// var array = new double[,,] { { { 1, 2 }, { 3, 4 } }, { { 5, 6 }, { 7, 8 } } };\\n /// var tensor = torch.from_array(rawArray: array, dtype: ScalarType.Float64, device: Device.CPU, requires_grad: false);\\n /// </code>\\n /// </example>\\n [Pure]\\n public static Tensor from_array(Array rawArray, Device? device = null)\\n {\\n var t = rawArray.GetType().GetElementType();\\n if (t is null) throw new InvalidOperationException($\\\"{nameof(rawArray)}.GetType().GetElementType() returned null.\\\");\\n if (t == typeof((float, float))) throw new NotImplementedException(\\\"from_array() for (float,float) elements.\\\");\\n\\n var dtype = ToScalarType(t!);\\n\\n return from_array(rawArray, dtype, device is null ? CPU : device);\\n }\\n\\n /// <summary>\\n /// Creates a <see cref=\\\"torch.Tensor\\\">torch tensor</see> from an arbitrary <see cref=\\\"Array\\\">array</see>.\\n /// </summary>\\n /// <param name=\\\"rawArray\\\">The arbitrary array to create the tensor from.</param>\\n /// <param name=\\\"dtype\\\">The element type to use in the created tensor. This can be different from the element type of the input.</param>\\n /// <param name=\\\"device\\\">The device where the tensor is to be located. Defaults to 'cpu'.</param>\\n /// <param name=\\\"requires_grad\\\"></param>\\n /// <param name=\\\"names\\\"></param>\\n /// <returns></returns>\\n /// <exception cref=\\\"InvalidOperationException\\\"></exception>\\n [Pure]\\n public static Tensor from_array(Array rawArray, ScalarType? dtype, Device? device = null, bool requires_grad = false, string[]? names = null)\\n {\\n // enumerates over all dimensions of the arbitrary array\\n // and returns the length of the dimension\\n [Pure]\\n static long[] GetShape(Array arr)\\n {\\n var shape = new long[arr.Rank];\\n for (var dim = 0; dim < arr.Rank; dim++)\\n shape[dim] = arr.GetLength(dim);\\n return shape;\\n }\\n var shape = GetShape(rawArray);\\n\\n var t = rawArray.GetType().GetElementType();\\n if (t is null) throw new InvalidOperationException($\\\"{nameof(rawArray)}.GetType().GetElementType() returned null.\\\");\\n\\n // call the existing factory methods to construct the tensor\\n\\n if (t == typeof((float, float))) return tensor(rawArray.Cast<(float, float)>().ToArray(), shape, dtype, device, requires_grad, names: names);\\n\\n var origType = ToScalarType(t);\\n return _tensor_generic(rawArray, shape, (sbyte)origType, dtype, device, requires_grad, false, names);\\n }\\n\\n // https://pytorch.org/docs/stable/generated/torch.as_tensor\\n public static Tensor as_tensor(Array data, ScalarType? dtype = null, Device? device = null) => from_array(data, dtype, device, false);\\n\\n /// <summary>\\n /// Creates a 1-dimensional Tensor from an array of n dimensions.\\n ///\\n /// Skips the first offset bytes in the buffer, and interprets the rest of the raw bytes as a 1-dimensional tensor of type dtype with count elements.\\n /// </summary>\\n /// <param name=\\\"rawArray\\\">The input array</param>\\n /// <param name=\\\"dtype\\\">The torch data type.</param>\\n /// <param name=\\\"count\\\"></param>\\n /// <param name=\\\"offset\\\"></param>\\n /// <param name=\\\"requires_grad\\\">Set <value>true</value> if gradients need to be computed for this Tensor; <value>false</value> otherwise.</param>\\n /// <returns></returns>\\n /// <exception cref=\\\"ArgumentException\\\"></exception>\\n /// <exception cref=\\\"IndexOutOfRangeException\\\"></exception>\\n /// <remarks>The returned tensor and buffer share the same memory. Modifications to the tensor will be reflected in the buffer and vice versa.</remarks>\\n public static Tensor frombuffer(Array rawArray, ScalarType dtype, long count = -1, long offset = 0, bool requires_grad = false)\\n {\\n InitializeDeviceType(DeviceType.CPU);\\n\\n var lLength = rawArray.LongLength;\\n\\n if (offset < 0 || offset >= lLength) {\\n throw new ArgumentException($\\\"invalid value for 'offset': {offset}\\\");\\n }\\n\\n if (count < 0) { count = lLength - offset; }\\n\\n if (count > lLength - offset) {\\n throw new IndexOutOfRangeException($\\\"element count is too large: {count}\\\");\\n }\\n\\n var t = rawArray.GetType().GetElementType();\\n ScalarType origType = ToScalarType(t!);\\n\\n switch (origType) {\\n case ScalarType.Int16:\\n offset *= 2;\\n break;\\n case ScalarType.Int32:\\n case ScalarType.Float32:\\n offset *= 4;\\n break;\\n case ScalarType.Int64:\\n case ScalarType.Float64:\\n offset *= 8;\\n break;\\n case ScalarType.ComplexFloat64:\\n offset *= 16;\\n break;\\n case ScalarType.ComplexFloat32:\\n // Since we are not allowed to make a copy of the buffer, it's currently not\\n // feasible to support complex types, which GCHandle.Alloc() doesn't like.\\n throw new NotImplementedException(\\\"frombuffer(ComplexFloat32)\\\");\\n }\\n\\n var dataHandle = GCHandle.Alloc(rawArray, GCHandleType.Pinned);\\n var dataArrayAddr = dataHandle.AddrOfPinnedObject();\\n var gchp = GCHandle.ToIntPtr(dataHandle);\\n TorchSharp.PInvoke.GCHandleDeleter deleter = null!;\\n deleter = new TorchSharp.PInvoke.GCHandleDeleter((IntPtr ptr) => {\\n GCHandle.FromIntPtr(gchp).Free();\\n deleters.TryRemove(deleter, out deleter!);\\n });\\n deleters.TryAdd(deleter, deleter); // keep the delegate alive\\n\\n unsafe {\\n var handle = THSTensor_frombuffer(dataArrayAddr, deleter, count, offset, (sbyte)origType, requires_grad);\\n\\n if (handle == IntPtr.Zero) {\\n GC.Collect();\\n GC.WaitForPendingFinalizers();\\n handle = THSTensor_frombuffer(dataArrayAddr, deleter, count, offset, (sbyte)origType, requires_grad);\\n }\\n\\n if (handle == IntPtr.Zero) { CheckForErrors(); }\\n var tensor = new Tensor(handle);\\n\\n var needsConversion = dtype != origType;\\n\\n if (needsConversion) {\\n tensor = tensor.to_type(dtype);\\n }\\n return tensor;\\n }\\n }\\n\\n /// <summary>\\n /// Create a sparse tensor by indexing into an existing dense tensor.\\n /// </summary>\\n public static Tensor sparse(Tensor indices, Tensor values, long[] size, ScalarType? dtype = null, Device? device = null, bool requires_grad = false, string[]? names = null)\\n {\\n device = InitializeDevice(device);\\n if (!dtype.HasValue) {\\n // Determine the element type dynamically.\\n dtype = values.dtype;\\n }\\n\\n unsafe {\\n fixed (long* psizes = size) {\\n var handle = THSTensor_sparse(indices.Handle, values.Handle, (IntPtr)psizes, size.Length, (sbyte)dtype, (int)device.type, device.index, requires_grad);\\n if (handle == IntPtr.Zero) {\\n GC.Collect();\\n GC.WaitForPendingFinalizers();\\n handle = THSTensor_sparse(indices.Handle, values.Handle, (IntPtr)psizes, size.Length, (sbyte)dtype, (int)device.type, device.index, requires_grad);\\n }\\n if (handle == IntPtr.Zero) { CheckForErrors(); }\\n var tensor = new Tensor(handle);\\n if (names != null && names.Length > 0) {\\n tensor.rename_(names);\\n }\\n return tensor;\\n }\\n }\\n }\\n\\n /// <summary>\\n /// Constructs a complex tensor with its real part equal to real and its imaginary part equal to imag.\\n /// </summary>\\n public static Tensor complex(Tensor real, Tensor imag)\\n {\\n var res = THSTensor_complex(real.Handle, imag.Handle);\\n if (res == IntPtr.Zero)\\n CheckForErrors();\\n return new Tensor(res);\\n }\\n\\n /// <summary>\\n /// Constructs a complex tensor whose elements are Cartesian coordinates corresponding to the polar coordinates with absolute value 'abs' and angle 'angle'.\\n /// </summary>\\n public static Tensor polar(Tensor abs, Tensor angle)\\n {\\n var res = THSTensor_polar(abs.Handle, angle.Handle);\\n if (res == IntPtr.Zero)\\n CheckForErrors();\\n return new Tensor(res);\\n }\\n\\n public static Tensor from_file(string filename, bool? shared = null, long? size = 0, ScalarType? dtype = null, Device? device = null, bool requires_grad = false)\\n {\\n device = InitializeDevice(device);\\n if (!dtype.HasValue) {\\n // Determine the element type dynamically.\\n dtype = get_default_dtype();\\n }\\n\\n var handle = THSTensor_from_file(StringEncoder.GetNullTerminatedUTF8ByteArray(filename), (sbyte)(!shared.HasValue ? -1 : shared.Value ? 1 : 0), size.HasValue ? size.Value : -1, (sbyte)dtype, (int)device.type, device.index, requires_grad);\\n if (handle == IntPtr.Zero) { CheckForErrors(); }\\n return new Tensor(handle);\\n }\\n\\n /// <summary>\\n /// Create a one-dimensional tensor of size steps whose values are evenly spaced from start to end, inclusive.\\n /// </summary>\\n public static Tensor linspace(double start, double end, long steps, ScalarType? dtype = null, Device? device = null, bool requires_grad = false)\\n {\\n device = InitializeDevice(device);\\n if (!dtype.HasValue) {\\n // Determine the element type dynamically.\\n dtype = get_default_dtype();\\n }\\n\\n var handle = THSTensor_linspace(start, end, steps, (sbyte)dtype, (int)device.type, device.index, requires_grad);\\n if (handle == IntPtr.Zero) {\\n GC.Collect();\\n GC.WaitForPendingFinalizers();\\n handle = THSTensor_linspace(start, end, steps, (sbyte)dtype, (int)device.type, device.index, requires_grad);\\n }\\n if (handle == IntPtr.Zero) { CheckForErrors(); }\\n return new Tensor(handle);\\n }\\n\\n /// <summary>\\n /// Creates a one-dimensional tensor of size steps whose values are evenly spaced from base^start to base^end, inclusive, on a logarithmic scale with base 'base.'\\n /// </summary>\\n public static Tensor logspace(double start, double end, long steps, double @base = 10, ScalarType? dtype = null, Device? device = null, bool requires_grad = false)\\n {\\n device = InitializeDevice(device);\\n if (!dtype.HasValue) {\\n // Determine the element type dynamically.\\n dtype = get_default_dtype();\\n }\\n\\n var handle = THSTensor_logspace(start, end, steps, @base, (sbyte)dtype, (int)device.type, device.index, requires_grad);\\n if (handle == IntPtr.Zero) {\\n GC.Collect();\\n GC.WaitForPendingFinalizers();\\n handle = THSTensor_logspace(start, end, steps, @base, (sbyte)dtype, (int)device.type, device.index, requires_grad);\\n }\\n if (handle == IntPtr.Zero) { CheckForErrors(); }\\n return new Tensor(handle);\\n }\\n\\n private static void ValidateIntegerRange(long value, ScalarType dtype, string argument)\\n {\\n switch (dtype) {\\n case ScalarType.Byte:\\n if (value < byte.MinValue || value > byte.MaxValue)\\n throw new ArgumentOutOfRangeException(argument, value, $\\\"The value is outside the range of {dtype}\\\");\\n break;\\n case ScalarType.Int8:\\n if (value < sbyte.MinValue || value > sbyte.MaxValue)\\n throw new ArgumentOutOfRangeException(argument, value, $\\\"The value is outside the range of {dtype}\\\");\\n break;\\n case ScalarType.Int16:\\n if (value < short.MinValue || value > short.MaxValue)\\n throw new ArgumentOutOfRangeException(argument, value, $\\\"The value is outside the range of {dtype}\\\");\\n break;\\n case ScalarType.Int32:\\n if (value < int.MinValue || value > int.MaxValue)\\n throw new ArgumentOutOfRangeException(argument, value, $\\\"The value is outside the range of {dtype}\\\");\\n break;\\n default:\\n break;\\n }\\n }\\n\\n private static ConcurrentDictionary<TorchSharp.PInvoke.GCHandleDeleter, TorchSharp.PInvoke.GCHandleDeleter> deleters;\\n private static ScalarType default_dtype = ScalarType.Float32;\\n\\n static torch()\\n {\\n deleters = new ConcurrentDictionary<TorchSharp.PInvoke.GCHandleDeleter, TorchSharp.PInvoke.GCHandleDeleter>();\\n }\\n }\\n}\\n\"\n },\n {\n \"alpha_fraction\": 0.4561717212200165,\n \"alphanum_fraction\": 0.4561717212200165,\n \"avg_line_length\": 20.538461685180664,\n \"blob_id\": \"e25b81e1d10c1b85635d13fcc62499a651fc1e57\",\n \"content_id\": \"f6d0f975077e0fef9f2e250f6d34e9ff073b34a4\",\n \"detected_licenses\": [\n \"MIT\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"C#\",\n \"length_bytes\": 559,\n \"license_type\": \"permissive\",\n \"max_line_length\": 130,\n \"num_lines\": 26,\n \"path\": \"/src/TorchVision/IO/ImageFormat.cs\",\n \"repo_name\": \"NiklasGustafsson/TorchSharp\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"// Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information.\\n\\nnamespace TorchSharp\\n{\\n public static partial class torchvision\\n {\\n /// <summary>\\n /// File format for an image.\\n /// </summary>\\n public enum ImageFormat\\n {\\n Unknown,\\n Png,\\n Jpeg,\\n Bmp,\\n Gif,\\n Tiff,\\n Emf,\\n Exif,\\n Wmf,\\n Pbm,\\n Tga,\\n Webp,\\n }\\n }\\n}\"\n },\n {\n \"alpha_fraction\": 0.5085046887397766,\n \"alphanum_fraction\": 0.5126935839653015,\n \"avg_line_length\": 51.87248229980469,\n \"blob_id\": \"1a8a132d93d296f28561375304f92014979d2535\",\n \"content_id\": \"b4d59b5ea44799a406302cd9b65dcd3912089b39\",\n \"detected_licenses\": [\n \"MIT\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"C#\",\n \"length_bytes\": 7878,\n \"license_type\": \"permissive\",\n \"max_line_length\": 199,\n \"num_lines\": 149,\n \"path\": \"/src/TorchSharp/NN/Pooling/MaxPool1D.cs\",\n \"repo_name\": \"NiklasGustafsson/TorchSharp\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"// Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information.\\nusing System;\\nusing System.Linq;\\nusing static TorchSharp.torch;\\nusing static TorchSharp.PInvoke.NativeMethods;\\n\\nnamespace TorchSharp\\n{\\n using Modules;\\n\\n namespace Modules\\n {\\n /// <summary>\\n /// This class is used to represent a MaxPool1D module.\\n /// </summary>\\n public sealed class MaxPool1d : torch.nn.Module<Tensor, Tensor>\\n {\\n internal MaxPool1d(IntPtr handle, IntPtr boxedHandle) : base(handle, boxedHandle)\\n {\\n }\\n\\n public override Tensor forward(Tensor tensor)\\n {\\n var res = THSNN_MaxPool1d_forward(handle, tensor.Handle);\\n if (res == IntPtr.Zero) { torch.CheckForErrors(); }\\n return new Tensor(res);\\n }\\n\\n public (Tensor Values, Tensor Indices) forward_with_indices(Tensor tensor)\\n {\\n var res = THSNN_MaxPool1d_forward_with_indices(handle, tensor.Handle, out var indices);\\n if (res == IntPtr.Zero || indices == IntPtr.Zero) { torch.CheckForErrors(); }\\n return (new Tensor(res), new Tensor(indices));\\n }\\n\\n // Rather than spending cycles only to discover that this module has neither\\n // parameters nor buffers, just shortcut the move completely.\\n protected internal override nn.Module _to(Device device, ScalarType dtype) => this;\\n protected internal override nn.Module _to(DeviceType deviceType, int deviceIndex = -1) => this;\\n protected internal override nn.Module _to(ScalarType dtype) => this;\\n }\\n }\\n\\n public static partial class torch\\n {\\n public static partial class nn\\n {\\n /// <summary>\\n /// Applies a 1D max pooling over an input signal composed of several input planes.\\n /// </summary>\\n /// <param name=\\\"kernelSize\\\">The size of the sliding window, must be > 0.</param>\\n /// <param name=\\\"stride\\\">The stride of the sliding window, must be > 0. Default value is kernel_size.</param>\\n /// <param name=\\\"padding\\\">Implicit negative infinity padding to be added on both sides, must be >= 0 and less than or equal to kernel_size / 2</param>\\n /// <param name=\\\"dilation\\\">The stride between elements within a sliding window, must be > 0.</param>\\n /// <param name=\\\"ceilMode\\\">If true, will use ceil instead of floor to compute the output shape. This ensures that every element in the input tensor is covered by a sliding window.</param>\\n /// <returns></returns>\\n public static MaxPool1d MaxPool1d(long kernelSize, long? stride = null, long? padding = null, long? dilation = null, bool ceilMode = false)\\n {\\n var pStride = stride.HasValue ? new long[] { stride.Value } : null;\\n var pPadding = padding.HasValue ? new long[] { padding.Value } : null;\\n var pDilation = dilation.HasValue ? new long[] { dilation.Value } : null;\\n return MaxPool1d(new long[] { kernelSize }, pStride, pPadding, pDilation, ceilMode);\\n }\\n\\n private static MaxPool1d MaxPool1d(long[] kernelSize, long[] strides = null, long[] padding = null, long[] dilation = null, bool ceilMode = false)\\n {\\n unsafe {\\n fixed (long* pkernelSize = kernelSize, pstrides = strides, pPadding = padding, pDilation = dilation) {\\n var handle = THSNN_MaxPool1d_ctor((IntPtr)pkernelSize, (IntPtr)pstrides, (IntPtr)pPadding, (IntPtr)pDilation, ceilMode, out var boxedHandle);\\n if (handle == IntPtr.Zero) { torch.CheckForErrors(); }\\n return new MaxPool1d(handle, boxedHandle);\\n }\\n }\\n }\\n\\n public static partial class functional\\n {\\n /// <summary>\\n /// Applies a 1D max pooling over an input signal composed of several input planes.\\n /// </summary>\\n /// <param name=\\\"input\\\">The input tensor.</param>\\n /// <param name=\\\"kernelSize\\\"></param>\\n /// <param name=\\\"stride\\\"></param>\\n /// <param name=\\\"padding\\\"></param>\\n /// <param name=\\\"dilation\\\"></param>\\n /// <param name=\\\"ceil_mode\\\"></param>\\n /// <returns></returns>\\n public static Tensor max_pool1d(Tensor input, long kernelSize, long? stride = null,\\n long? padding = null, long? dilation = null, bool ceil_mode = false)\\n {\\n var kernelSizes = new long[] { kernelSize };\\n var strides = new long[] { stride ?? 1 };\\n var paddings = new long[] { padding ?? 0 };\\n var dilations = new long[] { dilation ?? 1 };\\n unsafe {\\n fixed (long* pkernelSize = kernelSizes, pstrides = strides, ppadding = paddings, pdilation = dilations) {\\n var res =\\n THSTensor_max_pool1d(input.Handle,\\n (IntPtr)pkernelSize, kernelSizes.Length,\\n (IntPtr)pstrides, strides.Length,\\n (IntPtr)ppadding, paddings.Length,\\n (IntPtr)pdilation, dilations.Length,\\n ceil_mode);\\n if (res == IntPtr.Zero) { torch.CheckForErrors(); }\\n return new Tensor(res);\\n }\\n }\\n }\\n\\n /// <summary>\\n /// Applies a 1D max pooling over an input signal composed of several input planes.\\n /// </summary>\\n /// <param name=\\\"input\\\">The input tensor.</param>\\n /// <param name=\\\"kernelSize\\\"></param>\\n /// <param name=\\\"stride\\\"></param>\\n /// <param name=\\\"padding\\\"></param>\\n /// <param name=\\\"dilation\\\"></param>\\n /// <param name=\\\"ceil_mode\\\"></param>\\n /// <returns></returns>\\n public static (Tensor output, Tensor indices) max_pool1d_with_indices(Tensor input, long kernelSize, long? stride = null,\\n long? padding = null, long? dilation = null, bool ceil_mode = false)\\n {\\n var kernelSizes = new long[] { kernelSize };\\n var strides = new long[] { stride ?? 1 };\\n var paddings = new long[] { padding ?? 0 };\\n var dilations = new long[] { dilation ?? 1 };\\n IntPtr[] ptrArray;\\n\\n using (var pa = new PinnedArray<IntPtr>()) {\\n unsafe {\\n fixed (long* pkernelSize = kernelSizes, pstrides = strides, ppadding = paddings, pdilation = dilations) {\\n THSTensor_max_pool1d_with_indices(input.Handle,\\n pa.CreateArray,\\n (IntPtr)pkernelSize, kernelSizes.Length,\\n (IntPtr)pstrides, strides.Length,\\n (IntPtr)ppadding, paddings.Length,\\n (IntPtr)pdilation, dilations.Length,\\n ceil_mode);\\n torch.CheckForErrors();\\n }\\n }\\n ptrArray = pa.Array;\\n }\\n return (new Tensor(ptrArray[0]), new Tensor(ptrArray[1]));\\n }\\n }\\n }\\n }\\n}\\n\"\n },\n {\n \"alpha_fraction\": 0.5512006282806396,\n \"alphanum_fraction\": 0.5541440844535828,\n \"avg_line_length\": 41.74834442138672,\n \"blob_id\": \"fe54f96d11498a33751ac17fbceb1bbc969c1ee3\",\n \"content_id\": \"3de2d6ea51d04bc545525839a59a4675880c9afc\",\n \"detected_licenses\": [\n \"MIT\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"C#\",\n \"length_bytes\": 6459,\n \"license_type\": \"permissive\",\n \"max_line_length\": 147,\n \"num_lines\": 151,\n \"path\": \"/src/TorchSharp/Distributions/Poisson.cs\",\n \"repo_name\": \"NiklasGustafsson/TorchSharp\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"// Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information.\\nusing System;\\nusing System.Collections.Generic;\\nusing static TorchSharp.torch;\\n\\nnamespace TorchSharp\\n{\\n using Modules;\\n\\n namespace Modules\\n {\\n /// <summary>\\n /// A Poisson distribution parameterized by `rate`.\\n /// </summary>\\n public class Poisson : distributions.ExponentialFamily\\n {\\n public override Tensor mean => rate;\\n\\n public override Tensor variance => rate;\\n\\n public override Tensor mode => rate.floor();\\n\\n /// <summary>\\n /// Constructor\\n /// </summary>\\n /// <param name=\\\"rate\\\">rate = 1 / scale of the distribution (often referred to as 'β')</param>\\n /// <param name=\\\"generator\\\">An optional random number generator object.</param>\\n public Poisson(Tensor rate, Generator generator = null) : base(generator)\\n {\\n var locScale = broadcast_tensors(rate);\\n batch_shape = rate.size();\\n this.rate = locScale[0].alias().DetachFromDisposeScope();\\n }\\n\\n private Tensor rate;\\n\\n /// <summary>\\n /// Generates a sample_shape shaped reparameterized sample or sample_shape shaped batch of reparameterized samples\\n /// if the distribution parameters are batched.\\n /// </summary>\\n /// <param name=\\\"sample_shape\\\">The sample shape.</param>\\n public override Tensor rsample(params long[] sample_shape)\\n {\\n using var _ = NewDisposeScope();\\n var shape = ExtendedShape(sample_shape);\\n using(no_grad())\\n return torch.poisson(rate.expand(shape), generator: generator).MoveToOuterDisposeScope();\\n }\\n\\n /// <summary>\\n /// Returns the log of the probability density/mass function evaluated at `value`.\\n /// </summary>\\n /// <param name=\\\"value\\\"></param>\\n public override Tensor log_prob(Tensor value)\\n {\\n using var _ = NewDisposeScope();\\n var bcast = broadcast_tensors(rate, value);\\n var r = bcast[0];\\n var v = bcast[1];\\n return (value.xlogy(r) - r - (value + 1).lgamma()).MoveToOuterDisposeScope();\\n }\\n\\n /// <summary>\\n /// Returns the cumulative density/mass function evaluated at `value`.\\n /// </summary>\\n /// <param name=\\\"value\\\"></param>\\n public override Tensor cdf(Tensor value)\\n {\\n return WrappedTensorDisposeScope(() => 1 - exp(-rate * value));\\n }\\n\\n /// <summary>\\n /// Returns the inverse cumulative density/mass function evaluated at `value`.\\n /// </summary>\\n /// <param name=\\\"value\\\"></param>\\n public override Tensor icdf(Tensor value)\\n {\\n return WrappedTensorDisposeScope(() => -log(1 - value) / rate);\\n }\\n\\n /// <summary>\\n /// Returns tensor containing all values supported by a discrete distribution. The result will enumerate over dimension 0, so the shape\\n /// of the result will be `(cardinality,) + batch_shape + event_shape` (where `event_shape = ()` for univariate distributions).\\n ///\\n /// Note that this enumerates over all batched tensors in lock-step `[[0, 0], [1, 1], ...]`. With `expand=False`, enumeration happens\\n /// along dim 0, but with the remaining batch dimensions being singleton dimensions, `[[0], [1], ..`\\n /// </summary>\\n public override distributions.Distribution expand(Size batch_shape, distributions.Distribution instance = null)\\n {\\n if (instance != null && !(instance is Poisson))\\n throw new ArgumentException(\\\"expand(): 'instance' must be a Poisson distribution\\\");\\n\\n var r = rate.expand(batch_shape);\\n\\n var newDistribution = ((instance == null) ? new Poisson(r, generator) : instance) as Poisson;\\n\\n newDistribution.batch_shape = batch_shape;\\n if (newDistribution == instance) {\\n newDistribution.rate = r;\\n }\\n return newDistribution;\\n }\\n\\n protected override IList<Tensor> NaturalParams => new Tensor[] { rate.log() };\\n\\n protected override Tensor MeanCarrierMeasure => new Tensor(IntPtr.Zero);\\n\\n protected override Tensor LogNormalizer(params Tensor[] parameters) => parameters[0].exp();\\n }\\n\\n }\\n\\n public static partial class torch\\n {\\n public static partial class distributions\\n {\\n /// <summary>\\n /// Creates a Poisson distribution parameterized by `rate`.\\n /// </summary>\\n /// <param name=\\\"rate\\\">rate = 1 / scale of the distribution (often referred to as 'β')</param>\\n /// <param name=\\\"generator\\\">An optional random number generator object.</param>\\n /// <returns></returns>\\n public static Poisson Poisson(Tensor rate, Generator generator = null)\\n {\\n return new Poisson(rate, generator);\\n }\\n\\n /// <summary>\\n /// Creates a Poisson distribution parameterized by `rate`.\\n /// </summary>\\n /// <param name=\\\"rate\\\">rate = 1 / scale of the distribution (often referred to as 'β')</param>\\n /// <param name=\\\"generator\\\">An optional random number generator object.</param>\\n /// <returns></returns>\\n public static Poisson Poisson(float rate, Generator generator = null)\\n {\\n return new Poisson(tensor(rate), generator);\\n }\\n\\n /// <summary>\\n /// Creates a Poisson distribution parameterized by `rate`.\\n /// </summary>\\n /// <param name=\\\"rate\\\">rate = 1 / scale of the distribution (often referred to as 'β')</param>\\n /// <param name=\\\"generator\\\">An optional random number generator object.</param>\\n /// <returns></returns>\\n public static Poisson Poisson(double rate, Generator generator = null)\\n {\\n return new Poisson(tensor(rate), generator);\\n }\\n }\\n }\\n}\\n\"\n },\n {\n \"alpha_fraction\": 0.4765501916408539,\n \"alphanum_fraction\": 0.49291783571243286,\n \"avg_line_length\": 32.79787063598633,\n \"blob_id\": \"249c575d90f65d23658cc190c246fd5cf605cd79\",\n \"content_id\": \"9c5247c644e8464cabde5c3ba9453155951113b3\",\n \"detected_licenses\": [\n \"MIT\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"C#\",\n \"length_bytes\": 3177,\n \"license_type\": \"permissive\",\n \"max_line_length\": 130,\n \"num_lines\": 94,\n \"path\": \"/src/TorchSharp/Utils/LEB128Codec.cs\",\n \"repo_name\": \"NiklasGustafsson/TorchSharp\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"// Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information.\\nusing System;\\nusing System.IO;\\nusing System.Collections.Generic;\\n\\nnamespace TorchSharp.Utils\\n{\\n /// <summary>\\n /// LEB128 encoder / decoder\\n ///\\n /// LEB128 is the compression format used by BinaryWriter/Reader to encode string lengths,\\n /// and it is convenient to use it for other lengths in the encoding of tensors and module\\n /// state dictionaries.\\n /// \\n /// https://en.wikipedia.org/wiki/LEB128\\n /// </summary>\\n internal static class LEB128Codec\\n {\\n /// <summary>\\n /// Encode a long value.\\n /// </summary>\\n /// <param name=\\\"value\\\">The input value.</param>\\n /// <returns>The encoded value as a sequence of bytes.</returns>\\n public static IList<byte> Encode(long value)\\n {\\n if (value < 0)\\n throw new NotImplementedException(\\\"LEB128 encoding of negative numbers\\\");\\n\\n var result = new List<byte>();\\n while (true) {\\n long b = value & 0x7f;\\n value >>= 7;\\n if (value == 0) {\\n result.Add((byte)b);\\n return result;\\n }\\n result.Add((byte)(b | 0x80));\\n }\\n }\\n\\n /// <summary>\\n /// Encode a long value into a binary writer.\\n /// </summary>\\n /// <param name=\\\"writer\\\">A BinaryWriter instance</param>\\n /// <param name=\\\"value\\\">The input value.</param>\\n public static void Encode(this BinaryWriter writer, long value)\\n {\\n if (value < 0)\\n throw new NotImplementedException(\\\"LEB128 encoding of negative numbers\\\");\\n\\n while (true) {\\n long b = value & 0x7f;\\n value >>= 7;\\n if (value == 0) {\\n writer.Write((byte)b);\\n return;\\n }\\n writer.Write((byte)(b | 0x80));\\n }\\n }\\n\\n /// <summary>\\n /// Decode a long value from a binary reader\\n /// </summary>\\n /// <param name=\\\"reader\\\">A BinaryReader instance used for input.</param>\\n /// <returns>The decoded value</returns>\\n public static long Decode(this BinaryReader reader)\\n {\\n long result = 0;\\n for (int i = 0; true; ++i) {\\n long b = reader.ReadByte();\\n result += ((b & 0x7f) << (i * 7));\\n if ((b & 0x80) == 0) break;\\n }\\n return result;\\n }\\n\\n /// <summary>\\n /// Decode a long value from a sequence of bytes\\n /// </summary>\\n /// <param name=\\\"input\\\">A sequence of bytes used for input.</param>\\n /// <returns>The decoded value</returns>\\n public static long Decode(IList<byte> input)\\n {\\n long result = 0;\\n for (int i = 0; i < input.Count; ++i) {\\n long b = input[i];\\n result += ((b & 0x7f) << (i * 7));\\n if ((b & 0x80) == 0) break;\\n }\\n return result;\\n }\\n }\\n}\\n\"\n },\n {\n \"alpha_fraction\": 0.49643704295158386,\n \"alphanum_fraction\": 0.5002639293670654,\n \"avg_line_length\": 45.20731735229492,\n \"blob_id\": \"e3c8518859e692cb3caacb22b6dbd52518a5b998\",\n \"content_id\": \"aa9ba4ca42e31b078fd6be06ecf1f74f5c22d571\",\n \"detected_licenses\": [\n \"MIT\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"C#\",\n \"length_bytes\": 7578,\n \"license_type\": \"permissive\",\n \"max_line_length\": 139,\n \"num_lines\": 164,\n \"path\": \"/src/TorchVision/IO/SkiaSharpImager.cs\",\n \"repo_name\": \"NiklasGustafsson/TorchSharp\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"// Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information.\\nusing System;\\nusing System.IO;\\nusing System.Runtime.InteropServices;\\nusing SkiaSharp;\\nusing static TorchSharp.torch;\\nusing static TorchSharp.NativeMethods;\\n\\nnamespace TorchSharp\\n{\\n public static partial class torchvision\\n {\\n public static partial class io\\n {\\n public sealed class SkiaImager : Imager\\n {\\n\\n public SkiaImager(int quality = 75)\\n {\\n this.quality = quality;\\n }\\n private int quality;\\n\\n public override Tensor DecodeImage(byte[] bytes, ImageReadMode mode = ImageReadMode.UNCHANGED)\\n {\\n using var bitmap = SKBitmap.Decode(bytes);\\n return ToTensor(mode, bitmap);\\n }\\n\\n public override Tensor DecodeImage(Stream stream, ImageReadMode mode = ImageReadMode.UNCHANGED)\\n {\\n using var bitmap = SKBitmap.Decode(stream);\\n return ToTensor(mode, bitmap);\\n }\\n\\n public override void EncodeImage(Tensor image, ImageFormat format, Stream stream)\\n {\\n var skiaFormat = TranslateImageFormat(format); // Better to take the exception early.\\n\\n var result = new SKBitmap();\\n\\n var lstIdx = image.shape.Length;\\n\\n var channels = image.shape[lstIdx - 3];\\n var height = image.shape[lstIdx - 2];\\n var width = image.shape[lstIdx - 1];\\n\\n var imageSize = height * width;\\n\\n var isGrey = channels == 1;\\n\\n byte[] outBytes = null;\\n\\n if (isGrey) {\\n outBytes = image.bytes.ToArray();\\n } else {\\n outBytes = new byte[4 * imageSize];\\n\\n unsafe {\\n fixed (byte* input = image.bytes, output = outBytes) {\\n THSVision_RGB_BRGA((IntPtr)input, (IntPtr)output, channels, imageSize);\\n }\\n }\\n }\\n\\n SKColorType colorType = isGrey ? SKColorType.Gray8 : SKColorType.Bgra8888;\\n\\n // pin the managed array so that the GC doesn't move it\\n var gcHandle = GCHandle.Alloc(outBytes, GCHandleType.Pinned);\\n\\n // install the pixels with the color type of the pixel data\\n var info = new SKImageInfo((int)width, (int)height, colorType, SKAlphaType.Unpremul);\\n result.InstallPixels(info, gcHandle.AddrOfPinnedObject(), info.RowBytes, delegate { gcHandle.Free(); }, null);\\n\\n result.Encode(stream, skiaFormat, this.quality);\\n stream.Flush();\\n }\\n\\n public override byte[] EncodeImage(Tensor image, ImageFormat format)\\n {\\n using var memStream = new MemoryStream();\\n EncodeImage(image, format, memStream);\\n return memStream.ToArray();\\n }\\n\\n private static Tensor ToTensor(ImageReadMode mode, SKBitmap bitmap)\\n {\\n if (mode == ImageReadMode.UNCHANGED) {\\n mode = bitmap.ColorType == SKColorType.Gray8 ? ImageReadMode.GRAY : ImageReadMode.RGB;\\n }\\n\\n if (bitmap.ColorType == SKColorType.Gray8 && mode == ImageReadMode.GRAY)\\n return torch.tensor(bitmap.Bytes, 1, bitmap.Height, bitmap.Width);\\n\\n using var scope = NewDisposeScope();\\n\\n if (bitmap.ColorType == SKColorType.Gray8 && mode == ImageReadMode.RGB) {\\n Tensor t = torch.tensor(bitmap.Bytes, 1, bitmap.Height, bitmap.Width);\\n return t.expand(3, bitmap.Height, bitmap.Width).MoveToOuterDisposeScope();\\n }\\n\\n if (bitmap.BytesPerPixel != 4 && bitmap.BytesPerPixel != 1)\\n throw new ArgumentException(\\\"Conversion only supports grayscale and ARGB\\\");\\n\\n var imageSize = bitmap.Height * bitmap.Width;\\n\\n byte[] redBytes = new byte[imageSize], greenBytes = new byte[imageSize], blueBytes = new byte[imageSize];\\n byte[] alphaBytes = null;\\n\\n bool outputGrayScale = mode == ImageReadMode.GRAY_ALPHA || mode == ImageReadMode.GRAY;\\n\\n Tensor result;\\n\\n unsafe {\\n if (mode == ImageReadMode.GRAY || mode == ImageReadMode.RGB) {\\n fixed (byte* inputs = bitmap.Bytes, red = redBytes, blue = blueBytes, green = greenBytes) {\\n THSVision_BRGA_RGB((IntPtr)inputs, (IntPtr)red, (IntPtr)green, (IntPtr)blue, 4, imageSize);\\n }\\n result = torch.vstack( new[] {\\n torch.tensor(redBytes, 1, bitmap.Height, bitmap.Width),\\n torch.tensor(greenBytes, 1, bitmap.Height, bitmap.Width),\\n torch.tensor(blueBytes, 1, bitmap.Height, bitmap.Width) });\\n } else if (mode == ImageReadMode.RGB_ALPHA || mode == ImageReadMode.GRAY_ALPHA) {\\n alphaBytes = new byte[imageSize];\\n fixed (byte* inputs = bitmap.Bytes, red = redBytes, blue = blueBytes, green = greenBytes, alpha = alphaBytes) {\\n THSVision_BRGA_RGBA((IntPtr)inputs, (IntPtr)red, (IntPtr)green, (IntPtr)blue, (IntPtr)alpha, 4, imageSize);\\n result = torch.vstack(new[] {\\n torch.tensor(alphaBytes, 1, bitmap.Height, bitmap.Width),\\n torch.tensor(redBytes, 1, bitmap.Height, bitmap.Width),\\n torch.tensor(greenBytes, 1, bitmap.Height, bitmap.Width),\\n torch.tensor(blueBytes, 1, bitmap.Height, bitmap.Width) });\\n }\\n } else {\\n throw new NotImplementedException();\\n }\\n }\\n\\n if (outputGrayScale) {\\n result = torchvision.transforms.functional.rgb_to_grayscale(result);\\n }\\n\\n return result.MoveToOuterDisposeScope();\\n }\\n\\n private SKEncodedImageFormat TranslateImageFormat(ImageFormat format)\\n {\\n switch (format) {\\n case ImageFormat.Png:\\n return SKEncodedImageFormat.Png;\\n case ImageFormat.Jpeg:\\n return SKEncodedImageFormat.Jpeg;\\n case ImageFormat.Bmp:\\n return SKEncodedImageFormat.Bmp;\\n case ImageFormat.Gif:\\n return SKEncodedImageFormat.Gif;\\n case ImageFormat.Webp:\\n return SKEncodedImageFormat.Webp;\\n }\\n throw new NotSupportedException($\\\"SkiaSharp does not support encoding images in the '{format}' format.\\\");\\n }\\n }\\n }\\n }\\n}\\n\"\n },\n {\n \"alpha_fraction\": 0.7265625,\n \"alphanum_fraction\": 0.7265625,\n \"avg_line_length\": 32.68421173095703,\n \"blob_id\": \"3268098432601e8d5cff98d71f4d33758005d78b\",\n \"content_id\": \"7cf494b7a7bb569c3602b1f8261ca1c61cc1129d\",\n \"detected_licenses\": [\n \"MIT\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"C#\",\n \"length_bytes\": 640,\n \"license_type\": \"permissive\",\n \"max_line_length\": 130,\n \"num_lines\": 19,\n \"path\": \"/src/TorchSharp/PInvoke/LibTorchSharp.THSStorage.cs\",\n \"repo_name\": \"NiklasGustafsson/TorchSharp\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"// Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information.\\n#nullable enable\\nusing System;\\nusing System.Runtime.InteropServices;\\n\\nnamespace TorchSharp.PInvoke\\n{\\n internal static partial class NativeMethods\\n {\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern ulong THSStorage_nbytes(IntPtr tensor);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern void THSStorage_set_nbytes(IntPtr tensor, ulong nbytes);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern IntPtr THSStorage_data_ptr(IntPtr tensor);\\n }\\n}\\n\"\n },\n {\n \"alpha_fraction\": 0.45060285925865173,\n \"alphanum_fraction\": 0.46771037578582764,\n \"avg_line_length\": 73.02336120605469,\n \"blob_id\": \"b4d21105bd7862aa9beae83b287e83cfd20ba086\",\n \"content_id\": \"b6e2f5b6e746eb34cc5e3c8f09afec21901fb3d2\",\n \"detected_licenses\": [\n \"MIT\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"C#\",\n \"length_bytes\": 15841,\n \"license_type\": \"permissive\",\n \"max_line_length\": 236,\n \"num_lines\": 214,\n \"path\": \"/pkg/FileRestitcher/FileRestitcher/FileRestitcher.cs\",\n \"repo_name\": \"NiklasGustafsson/TorchSharp\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"using System;\\nusing System.IO;\\n\\nnamespace ConsoleApp2\\n{\\n public class Program\\n {\\n\\n public static void Restitch(string RestitcherPackage)\\n {\\n // !!!!!!!------------------------------NOTE------------------------------------!!!!!!\\n // !!!!!!! This code is manually copied into pkg\\\\common\\\\RestitchPackage.targets !!!!!!\\n // !!!!!!!------------------------------NOTE------------------------------------!!!!!!\\n //\\n // vvvvvvvvvvvvvvvvvvvvvvvvvvvvv START HERE vvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvv\\n try {\\n if (Directory.Exists(RestitcherPackage)) {\\n //System.Console.WriteLine(\\\"Searching for primary files in {0}\\\", RestitcherPackage);\\n foreach (var p in Directory.EnumerateFiles(RestitcherPackage, \\\"*\\\", SearchOption.AllDirectories)) {\\n\\n var primaryFile = Path.GetFullPath(p);\\n //Console.WriteLine(\\\"Found primary file at {0}\\\", primaryFile);\\n\\n // See if there are fragments in the parallel nuget packages. If the primary is \\n // some-package-primary\\\\runtimes\\\\....\\\\a.so \\n // some-package-primary\\\\runtimes\\\\....\\\\a.so.sha\\n // then the expected fragments are\\n // some-package-fragment1\\\\fragments\\\\....\\\\a.so \\n // some-package-fragment2\\\\fragments\\\\....\\\\a.so \\n // some-package-fragment3\\\\fragments\\\\....\\\\a.so \\n // some-package-fragment4\\\\fragments\\\\....\\\\a.so \\n // some-package-fragment5\\\\fragments\\\\....\\\\a.so \\n // some-package-fragment6\\\\fragments\\\\....\\\\a.so \\n // some-package-fragment7\\\\fragments\\\\....\\\\a.so \\n // some-package-fragment8\\\\fragments\\\\....\\\\a.so \\n // some-package-fragment9\\\\fragments\\\\....\\\\a.so \\n // some-package-fragment10\\\\fragments\\\\....\\\\a.so \\n var shaFile = primaryFile + \\\".sha\\\";\\n var fragmentFile1 = primaryFile.Replace(\\\"-primary\\\", \\\"-fragment1\\\").Replace(\\\"runtimes\\\", \\\"fragments\\\") + \\\".fragment1\\\";\\n var fragmentFile2 = primaryFile.Replace(\\\"-primary\\\", \\\"-fragment2\\\").Replace(\\\"runtimes\\\", \\\"fragments\\\") + \\\".fragment2\\\";\\n var fragmentFile3 = primaryFile.Replace(\\\"-primary\\\", \\\"-fragment3\\\").Replace(\\\"runtimes\\\", \\\"fragments\\\") + \\\".fragment3\\\";\\n var fragmentFile4 = primaryFile.Replace(\\\"-primary\\\", \\\"-fragment4\\\").Replace(\\\"runtimes\\\", \\\"fragments\\\") + \\\".fragment4\\\";\\n var fragmentFile5 = primaryFile.Replace(\\\"-primary\\\", \\\"-fragment5\\\").Replace(\\\"runtimes\\\", \\\"fragments\\\") + \\\".fragment5\\\";\\n var fragmentFile6 = primaryFile.Replace(\\\"-primary\\\", \\\"-fragment6\\\").Replace(\\\"runtimes\\\", \\\"fragments\\\") + \\\".fragment6\\\";\\n var fragmentFile7 = primaryFile.Replace(\\\"-primary\\\", \\\"-fragment7\\\").Replace(\\\"runtimes\\\", \\\"fragments\\\") + \\\".fragment7\\\";\\n var fragmentFile8 = primaryFile.Replace(\\\"-primary\\\", \\\"-fragment8\\\").Replace(\\\"runtimes\\\", \\\"fragments\\\") + \\\".fragment8\\\";\\n var fragmentFile9 = primaryFile.Replace(\\\"-primary\\\", \\\"-fragment9\\\").Replace(\\\"runtimes\\\", \\\"fragments\\\") + \\\".fragment9\\\";\\n var fragmentFile10 = primaryFile.Replace(\\\"-primary\\\", \\\"-fragment10\\\").Replace(\\\"runtimes\\\", \\\"fragments\\\") + \\\".fragment10\\\";\\n\\n if (File.Exists(fragmentFile1)) Console.WriteLine(\\\"Found fragment file at {0}\\\", fragmentFile1);\\n if (File.Exists(fragmentFile2)) Console.WriteLine(\\\"Found fragment file at {0}\\\", fragmentFile2);\\n if (File.Exists(fragmentFile3)) Console.WriteLine(\\\"Found fragment file at {0}\\\", fragmentFile3);\\n if (File.Exists(fragmentFile4)) Console.WriteLine(\\\"Found fragment file at {0}\\\", fragmentFile4);\\n if (File.Exists(fragmentFile5)) Console.WriteLine(\\\"Found fragment file at {0}\\\", fragmentFile5);\\n if (File.Exists(fragmentFile6)) Console.WriteLine(\\\"Found fragment file at {0}\\\", fragmentFile6);\\n if (File.Exists(fragmentFile7)) Console.WriteLine(\\\"Found fragment file at {0}\\\", fragmentFile7);\\n if (File.Exists(fragmentFile8)) Console.WriteLine(\\\"Found fragment file at {0}\\\", fragmentFile8);\\n if (File.Exists(fragmentFile9)) Console.WriteLine(\\\"Found fragment file at {0}\\\", fragmentFile9);\\n if (File.Exists(fragmentFile10)) Console.WriteLine(\\\"Found fragment file at {0}\\\", fragmentFile10);\\n\\n if (File.Exists(fragmentFile1)) {\\n var tmpFile = Path.GetTempFileName();\\n\\n {\\n Console.WriteLine(\\\"Writing restored primary file at {0}\\\", tmpFile);\\n using (var os = File.OpenWrite(tmpFile)) {\\n\\n Console.WriteLine(\\\"Writing bytes from {0} to {1}\\\", primaryFile, tmpFile);\\n var primaryBytes = File.ReadAllBytes(primaryFile);\\n\\n os.Write(primaryBytes, 0, primaryBytes.Length);\\n if (File.Exists(fragmentFile1)) {\\n Console.WriteLine(\\\"Writing fragment bytes from {0} to {1}\\\", fragmentFile1, tmpFile);\\n var fragmentBytes1 = File.ReadAllBytes(fragmentFile1);\\n os.Write(fragmentBytes1, 0, fragmentBytes1.Length);\\n }\\n if (File.Exists(fragmentFile2)) {\\n Console.WriteLine(\\\"Writing fragment bytes from {0} to {1}\\\", fragmentFile2, tmpFile);\\n var fragmentBytes2 = File.ReadAllBytes(fragmentFile2);\\n os.Write(fragmentBytes2, 0, fragmentBytes2.Length);\\n }\\n if (File.Exists(fragmentFile3)) {\\n Console.WriteLine(\\\"Writing fragment bytes from {0} to {1}\\\", fragmentFile3, tmpFile);\\n var fragmentBytes3 = File.ReadAllBytes(fragmentFile3);\\n os.Write(fragmentBytes3, 0, fragmentBytes3.Length);\\n }\\n if (File.Exists(fragmentFile4)) {\\n Console.WriteLine(\\\"Writing fragment bytes from {0} to {1}\\\", fragmentFile4, tmpFile);\\n var fragmentBytes4 = File.ReadAllBytes(fragmentFile4);\\n os.Write(fragmentBytes4, 0, fragmentBytes4.Length);\\n }\\n if (File.Exists(fragmentFile5)) {\\n Console.WriteLine(\\\"Writing fragment bytes from {0} to {1}\\\", fragmentFile5, tmpFile);\\n var fragmentBytes5 = File.ReadAllBytes(fragmentFile5);\\n os.Write(fragmentBytes5, 0, fragmentBytes5.Length);\\n }\\n if (File.Exists(fragmentFile6)) {\\n Console.WriteLine(\\\"Writing fragment bytes from {0} to {1}\\\", fragmentFile6, tmpFile);\\n var fragmentBytes6 = File.ReadAllBytes(fragmentFile6);\\n os.Write(fragmentBytes6, 0, fragmentBytes6.Length);\\n }\\n if (File.Exists(fragmentFile7)) {\\n Console.WriteLine(\\\"Writing fragment bytes from {0} to {1}\\\", fragmentFile7, tmpFile);\\n var fragmentBytes7 = File.ReadAllBytes(fragmentFile7);\\n os.Write(fragmentBytes7, 0, fragmentBytes7.Length);\\n }\\n if (File.Exists(fragmentFile8)) {\\n Console.WriteLine(\\\"Writing fragment bytes from {0} to {1}\\\", fragmentFile8, tmpFile);\\n var fragmentBytes8 = File.ReadAllBytes(fragmentFile8);\\n os.Write(fragmentBytes8, 0, fragmentBytes8.Length);\\n }\\n if (File.Exists(fragmentFile9)) {\\n Console.WriteLine(\\\"Writing fragment bytes from {0} to {1}\\\", fragmentFile9, tmpFile);\\n var fragmentBytes9 = File.ReadAllBytes(fragmentFile9);\\n os.Write(fragmentBytes9, 0, fragmentBytes9.Length);\\n }\\n if (File.Exists(fragmentFile10)) {\\n Console.WriteLine(\\\"Writing fragment bytes from {0} to {1}\\\", fragmentFile10, tmpFile);\\n var fragmentBytes10 = File.ReadAllBytes(fragmentFile10);\\n os.Write(fragmentBytes10, 0, fragmentBytes10.Length);\\n }\\n }\\n }\\n\\n var shaExpected = File.Exists(shaFile) ? File.ReadAllText(shaFile) : \\\"\\\";\\n\\n using (var sha256Hash = System.Security.Cryptography.SHA256.Create()) {\\n using (var os2 = File.OpenRead(tmpFile)) {\\n\\n byte[] bytes = sha256Hash.ComputeHash(os2);\\n var builder = new System.Text.StringBuilder();\\n for (int i = 0; i < bytes.Length; i++) {\\n builder.Append(bytes[i].ToString(\\\"x2\\\"));\\n }\\n var shaReconstituted = builder.ToString();\\n if (shaExpected != shaReconstituted) {\\n string msg =\\n $\\\"Error downloading and reviving packages. Reconsituted file contents have incorrect SHA\\\\n\\\\tExpected SHA: ${shaExpected}\\\\n\\\\tActual SHA: ${shaReconstituted}\\\\n\\\\tFile was reconstituted from:\\\"\\n + $\\\"\\\\n\\\\t{primaryFile} (length ${new FileInfo(primaryFile).Length})\\\"\\n + (File.Exists(fragmentFile1) ? $\\\"\\\\n\\\\t{fragmentFile1} (length ${new FileInfo(fragmentFile1).Length})\\\" : \\\"\\\")\\n + (File.Exists(fragmentFile2) ? $\\\"\\\\n\\\\t{fragmentFile2} (length ${new FileInfo(fragmentFile2).Length})\\\" : \\\"\\\")\\n + (File.Exists(fragmentFile3) ? $\\\"\\\\n\\\\t{fragmentFile3} (length ${new FileInfo(fragmentFile3).Length})\\\" : \\\"\\\")\\n + (File.Exists(fragmentFile4) ? $\\\"\\\\n\\\\t{fragmentFile4} (length ${new FileInfo(fragmentFile4).Length})\\\" : \\\"\\\")\\n + (File.Exists(fragmentFile5) ? $\\\"\\\\n\\\\t{fragmentFile5} (length ${new FileInfo(fragmentFile5).Length})\\\" : \\\"\\\")\\n + (File.Exists(fragmentFile6) ? $\\\"\\\\n\\\\t{fragmentFile6} (length ${new FileInfo(fragmentFile6).Length})\\\" : \\\"\\\")\\n + (File.Exists(fragmentFile7) ? $\\\"\\\\n\\\\t{fragmentFile7} (length ${new FileInfo(fragmentFile7).Length})\\\" : \\\"\\\")\\n + (File.Exists(fragmentFile8) ? $\\\"\\\\n\\\\t{fragmentFile8} (length ${new FileInfo(fragmentFile8).Length})\\\" : \\\"\\\")\\n + (File.Exists(fragmentFile9) ? $\\\"\\\\n\\\\t{fragmentFile9} (length ${new FileInfo(fragmentFile9).Length})\\\" : \\\"\\\")\\n + (File.Exists(fragmentFile10) ? $\\\"\\\\n\\\\t{fragmentFile10} (length ${new FileInfo(fragmentFile10).Length})\\\" : \\\"\\\");\\n Console.Error.WriteLine(msg);\\n throw new Exception(msg);\\n }\\n }\\n\\n }\\n\\n Console.WriteLine(\\\"Deleting {0}\\\", primaryFile);\\n File.Delete(primaryFile);\\n if (File.Exists(primaryFile))\\n throw new Exception(\\\"wtf?\\\");\\n\\n Console.WriteLine(\\\"Moving {0} --> {1}\\\", tmpFile, primaryFile);\\n File.Move(tmpFile, primaryFile);\\n\\n Console.WriteLine(\\\"Deleting {0}\\\", fragmentFile1);\\n File.Delete(fragmentFile1); // free up space and prevent us doing this again \\n\\n Console.WriteLine(\\\"Deleting {0}\\\", fragmentFile2);\\n if (File.Exists(fragmentFile2))\\n File.Delete(fragmentFile2); // free up space and prevent us doing this again \\n\\n Console.WriteLine(\\\"Deleting {0}\\\", fragmentFile3);\\n if (File.Exists(fragmentFile3))\\n File.Delete(fragmentFile3); // free up space and prevent us doing this again \\n\\n Console.WriteLine(\\\"Deleting {0}\\\", fragmentFile4);\\n if (File.Exists(fragmentFile4))\\n File.Delete(fragmentFile4); // free up space and prevent us doing this again \\n\\n Console.WriteLine(\\\"Deleting {0}\\\", fragmentFile5);\\n if (File.Exists(fragmentFile5))\\n File.Delete(fragmentFile5); // free up space and prevent us doing this again \\n\\n Console.WriteLine(\\\"Deleting {0}\\\", fragmentFile6);\\n if (File.Exists(fragmentFile6))\\n File.Delete(fragmentFile6); // free up space and prevent us doing this again \\n\\n Console.WriteLine(\\\"Deleting {0}\\\", fragmentFile7);\\n if (File.Exists(fragmentFile7))\\n File.Delete(fragmentFile7); // free up space and prevent us doing this again \\n\\n Console.WriteLine(\\\"Deleting {0}\\\", fragmentFile8);\\n if (File.Exists(fragmentFile8))\\n File.Delete(fragmentFile8); // free up space and prevent us doing this again \\n\\n Console.WriteLine(\\\"Deleting {0}\\\", fragmentFile9);\\n if (File.Exists(fragmentFile9))\\n File.Delete(fragmentFile9); // free up space and prevent us doing this again \\n\\n Console.WriteLine(\\\"Deleting {0}\\\", fragmentFile10);\\n if (File.Exists(fragmentFile10))\\n File.Delete(fragmentFile10); // free up space and prevent us doing this again \\n }\\n }\\n }\\n }\\n catch (Exception ex) {\\n Console.Error.WriteLine(ex.ToString());\\n Console.Error.WriteLine(ex.StackTrace);\\n }\\n // ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ END HERE^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\\n }\\n }\\n}\\n\"\n },\n {\n \"alpha_fraction\": 0.628696084022522,\n \"alphanum_fraction\": 0.6432380080223083,\n \"avg_line_length\": 33.966102600097656,\n \"blob_id\": \"8b70dca3a288ab65fa0c0bf5ca4de0b7889a62aa\",\n \"content_id\": \"a14815c2818dee57d8c7baed234abb4724a9056f\",\n \"detected_licenses\": [\n \"MIT\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"C#\",\n \"length_bytes\": 2063,\n \"license_type\": \"permissive\",\n \"max_line_length\": 130,\n \"num_lines\": 59,\n \"path\": \"/src/TorchVision/Ops/Permute.cs\",\n \"repo_name\": \"NiklasGustafsson/TorchSharp\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"// Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information.\\n\\n// A number of implementation details in this file have been translated from the Python version of torchvision,\\n// largely located in the files found in this folder:\\n//\\n// https://github.com/pytorch/vision/blob/a4f53308b2d0f1aa9191686e326f45c26053f686/torchvision/ops/misc.py\\n//\\n// The origin has the following copyright notice and license:\\n//\\n// https://github.com/pytorch/vision/blob/main/LICENSE\\n//\\n\\nusing System;\\nusing System.Linq;\\nusing System.Collections.Generic;\\nusing System.Reflection;\\nusing System.Runtime.CompilerServices;\\nusing TorchSharp.Modules;\\nusing static TorchSharp.torch;\\n\\n#nullable enable\\nnamespace TorchSharp\\n{\\n public static partial class torchvision\\n {\\n public sealed class Permute : nn.Module<Tensor, Tensor>\\n {\\n public Permute(IEnumerable<long> dims) : base(nameof(Permute))\\n {\\n this.dims = dims.ToArray();\\n }\\n\\n public override Tensor forward(Tensor input)\\n {\\n return torch.permute(input, dims);\\n }\\n\\n // Rather than spending cycles only to discover that this module has neither\\n // parameters nor buffers, just shortcut the move completely.\\n protected override nn.Module _to(Device device, ScalarType dtype) => this;\\n protected override nn.Module _to(DeviceType deviceType, int deviceIndex = -1) => this;\\n protected override nn.Module _to(ScalarType dtype) => this;\\n\\n private long[] dims;\\n }\\n\\n public static partial class ops\\n {\\n /// <summary>\\n /// This module returns a view of the tensor input with its dimensions permuted.\\n /// </summary>\\n /// <param name=\\\"dims\\\">The desired ordering of dimensions</param>\\n public static Permute Permute(params long[] dims)\\n {\\n return new Permute(dims);\\n }\\n }\\n }\\n}\\n\"\n },\n {\n \"alpha_fraction\": 0.5785866975784302,\n \"alphanum_fraction\": 0.5837259292602539,\n \"avg_line_length\": 56.89256286621094,\n \"blob_id\": \"550bd4eca832f1d7eeb90979383c81e885f63aa5\",\n \"content_id\": \"5c4de0ff01f100babf53fc64a40c4b741eeec0ed\",\n \"detected_licenses\": [\n \"MIT\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"C#\",\n \"length_bytes\": 7005,\n \"license_type\": \"permissive\",\n \"max_line_length\": 203,\n \"num_lines\": 121,\n \"path\": \"/src/TorchSharp/NN/Fold.cs\",\n \"repo_name\": \"NiklasGustafsson/TorchSharp\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"// Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information.\\nusing System;\\nusing static TorchSharp.torch;\\nusing static TorchSharp.PInvoke.NativeMethods;\\n\\n#nullable enable\\nnamespace TorchSharp\\n{\\n using System.Security.Cryptography;\\n using Modules;\\n\\n namespace Modules\\n {\\n public sealed class Fold : torch.nn.Module<Tensor, Tensor>\\n {\\n internal Fold((long, long) output_size, (long, long) kernel_size, (long, long) dilation, (long, long) padding, (long, long) stride) : base(nameof(Fold))\\n {\\n this.outputSize = output_size;\\n this.kernelSize = kernel_size;\\n this.dilation = dilation;\\n this.padding = padding;\\n this.stride = stride;\\n }\\n\\n public override Tensor forward(Tensor tensor)\\n {\\n return torch.nn.functional.fold(tensor, outputSize , kernelSize, dilation, padding, stride);\\n }\\n\\n private (long, long) outputSize;\\n private (long, long) kernelSize;\\n private (long, long) dilation;\\n private (long, long) padding;\\n private (long, long) stride;\\n }\\n }\\n\\n public static partial class torch\\n {\\n public static partial class nn\\n {\\n /// <summary>\\n /// Combines an array of sliding local blocks into a large containing tensor.\\n /// </summary>\\n /// <param name=\\\"output_size\\\">Describes the spatial shape of the large containing tensor of the sliding local blocks.</param>\\n /// <param name=\\\"kernel_size\\\">The size of the sliding blocks</param>\\n /// <param name=\\\"dilation\\\">A parameter that controls the stride of elements within the neighborhood.</param>\\n /// <param name=\\\"padding\\\">Implicit zero padding to be added on both sides of input.</param>\\n /// <param name=\\\"stride\\\">The stride of the sliding blocks in the input spatial dimensions.</param>\\n /// <remarks>Currently, only unbatched (3D) or batched (4D) image-like output tensors are supported.</remarks>\\n public unsafe static Fold Fold(long output_size, long kernel_size, long dilation = 1, long padding = 0, long stride = 1)\\n {\\n return new Fold((output_size, output_size), (kernel_size, kernel_size), (dilation, dilation), (padding, padding), (stride, stride));\\n }\\n\\n /// <summary>\\n /// Combines an array of sliding local blocks into a large containing tensor.\\n /// </summary>\\n /// <param name=\\\"output_size\\\">Describes the spatial shape of the large containing tensor of the sliding local blocks.</param>\\n /// <param name=\\\"kernel_size\\\">The size of the sliding blocks</param>\\n /// <param name=\\\"dilation\\\">A parameter that controls the stride of elements within the neighborhood.</param>\\n /// <param name=\\\"padding\\\">Implicit zero padding to be added on both sides of input.</param>\\n /// <param name=\\\"stride\\\">The stride of the sliding blocks in the input spatial dimensions.</param>\\n /// <remarks>Currently, only unbatched (3D) or batched (4D) image-like output tensors are supported.</remarks>\\n public unsafe static Fold Fold((long, long) output_size, (long, long) kernel_size, (long, long)? dilation = null, (long, long)? padding = null, (long, long)? stride = null)\\n {\\n dilation ??= (1, 1);\\n stride ??= (1, 1);\\n padding ??= (0, 0);\\n\\n return new Fold(output_size, kernel_size, dilation.Value, padding.Value, stride.Value);\\n }\\n\\n public static partial class functional\\n {\\n /// <summary>\\n /// Combines an array of sliding local blocks into a large containing tensor.\\n /// </summary>\\n /// <param name=\\\"input\\\">The input tensor.</param>\\n /// <param name=\\\"output_size\\\">Describes the spatial shape of the large containing tensor of the sliding local blocks.</param>\\n /// <param name=\\\"kernel_size\\\">The size of the sliding blocks</param>\\n /// <param name=\\\"dilation\\\">A parameter that controls the stride of elements within the neighborhood.</param>\\n /// <param name=\\\"padding\\\">Implicit zero padding to be added on both sides of input.</param>\\n /// <param name=\\\"stride\\\">The stride of the sliding blocks in the input spatial dimensions.</param>\\n /// <remarks>Currently, only unbatched (3D) or batched (4D) image-like output tensors are supported.</remarks>\\n public unsafe static Tensor fold(Tensor input, long output_size, long kernel_size, long dilation = 1, long padding = 0, long stride = 1)\\n {\\n var res = THSNN_fold(input.Handle, output_size, output_size, kernel_size, kernel_size, stride, stride, padding, padding, dilation, dilation);\\n if (res == IntPtr.Zero) { torch.CheckForErrors(); }\\n return new Tensor(res);\\n }\\n\\n /// <summary>\\n /// Combines an array of sliding local blocks into a large containing tensor.\\n /// </summary>\\n /// <param name=\\\"input\\\">The input tensor.</param>\\n /// <param name=\\\"output_size\\\">Describes the spatial shape of the large containing tensor of the sliding local blocks.</param>\\n /// <param name=\\\"kernel_size\\\">The size of the sliding blocks</param>\\n /// <param name=\\\"dilation\\\">A parameter that controls the stride of elements within the neighborhood.</param>\\n /// <param name=\\\"padding\\\">Implicit zero padding to be added on both sides of input.</param>\\n /// <param name=\\\"stride\\\">The stride of the sliding blocks in the input spatial dimensions.</param>\\n /// <remarks>Currently, only unbatched (3D) or batched (4D) image-like output tensors are supported.</remarks>\\n public unsafe static Tensor fold(Tensor input, (long,long) output_size, (long, long) kernel_size, (long, long)? dilation = null, (long, long)? padding = null, (long, long)? stride = null)\\n {\\n dilation ??= (1, 1);\\n stride ??= (1, 1);\\n padding ??= (0, 0);\\n\\n var res = THSNN_fold(input.Handle,\\n output_size.Item1, output_size.Item2,\\n kernel_size.Item1, kernel_size.Item2,\\n stride.Value.Item1, stride.Value.Item2,\\n padding.Value.Item1, padding.Value.Item2,\\n dilation.Value.Item1, dilation.Value.Item2);\\n if (res == IntPtr.Zero) { torch.CheckForErrors(); }\\n return new Tensor(res);\\n }\\n }\\n }\\n }\\n}\\n\"\n },\n {\n \"alpha_fraction\": 0.542768657207489,\n \"alphanum_fraction\": 0.545866072177887,\n \"avg_line_length\": 42.9476432800293,\n \"blob_id\": \"fd5df4db9722e65ffafac99a1fa429f70f2fe628\",\n \"content_id\": \"b537a6be4b81d2f7a5feda1a07e8247754289c8c\",\n \"detected_licenses\": [\n \"MIT\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"C#\",\n \"length_bytes\": 8394,\n \"license_type\": \"permissive\",\n \"max_line_length\": 156,\n \"num_lines\": 191,\n \"path\": \"/src/TorchSharp/Distributions/Normal.cs\",\n \"repo_name\": \"NiklasGustafsson/TorchSharp\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"// Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information.\\nusing System;\\nusing static TorchSharp.torch;\\n\\nnamespace TorchSharp\\n{\\n using Modules;\\n\\n namespace Modules\\n {\\n /// <summary>\\n /// A Normal (Gaussian) distribution.\\n /// </summary>\\n public class Normal : distributions.Distribution\\n {\\n /// <summary>\\n /// The mean of the distribution.\\n /// </summary>\\n public override Tensor mean => loc;\\n\\n /// <summary>\\n /// The mode of the distribution.\\n /// </summary>\\n public override Tensor mode => loc;\\n\\n /// <summary>\\n /// The standard deviation of the distribution\\n /// </summary>\\n public override Tensor stddev => scale;\\n\\n /// <summary>\\n /// The variance of the distribution\\n /// </summary>\\n public override Tensor variance => scale.pow(2);\\n\\n /// <summary>\\n /// Constructor\\n /// </summary>\\n /// <param name=\\\"loc\\\">Mode or median of the distribution.</param>\\n /// <param name=\\\"scale\\\">Standard deviation.</param>\\n /// <param name=\\\"generator\\\">An optional random number generator object.</param>\\n public Normal(Tensor loc, Tensor scale, Generator generator = null) : base(generator)\\n {\\n var locScale = broadcast_tensors(loc, scale);\\n this.loc = locScale[0].DetachFromDisposeScope();\\n this.scale = locScale[1].DetachFromDisposeScope();\\n this.batch_shape = this.loc.size();\\n }\\n\\n private Tensor loc;\\n private Tensor scale;\\n\\n /// <summary>\\n /// Generates a sample_shape shaped sample or sample_shape shaped batch of samples if the distribution parameters are batched.\\n /// </summary>\\n /// <param name=\\\"sample_shape\\\"></param>\\n public override Tensor sample(params long[] sample_shape)\\n {\\n var shape = ExtendedShape(sample_shape);\\n using (no_grad()) {\\n return normal(loc.expand(shape), scale.expand(shape), generator);\\n }\\n }\\n\\n\\n /// <summary>\\n /// Generates a sample_shape shaped reparameterized sample or sample_shape shaped batch of reparameterized samples\\n /// if the distribution parameters are batched.\\n /// </summary>\\n /// <param name=\\\"sample_shape\\\">The sample shape.</param>\\n public override Tensor rsample(params long[] sample_shape)\\n {\\n var shape = ExtendedShape(sample_shape);\\n var eps = empty(shape, dtype: loc.dtype, device: loc.device).normal_(generator: generator);\\n return loc + eps * scale;\\n }\\n\\n /// <summary>\\n /// Returns the log of the probability density/mass function evaluated at `value`.\\n /// </summary>\\n /// <param name=\\\"value\\\"></param>\\n public override Tensor log_prob(Tensor value)\\n {\\n using var _ = NewDisposeScope();\\n var v = scale.pow(2);\\n var log_scale = scale.log();\\n return (-((value - loc).pow(2)) / (2 * v) - log_scale - Math.Log(Math.Sqrt(2 * Math.PI))).MoveToOuterDisposeScope();\\n }\\n\\n /// <summary>\\n /// Returns entropy of distribution, batched over batch_shape.\\n /// </summary>\\n public override Tensor entropy()\\n {\\n return WrappedTensorDisposeScope(() =>\\n 0.5 + 0.5 * Math.Log(2 * Math.PI) + log(scale)\\n );\\n }\\n\\n /// <summary>\\n /// Returns the cumulative density/mass function evaluated at `value`.\\n /// </summary>\\n /// <param name=\\\"value\\\"></param>\\n public override Tensor cdf(Tensor value)\\n {\\n return WrappedTensorDisposeScope(() =>\\n 0.5 * (1 + special.erf((value - loc) * scale.reciprocal() / Math.Sqrt(2)))\\n );\\n }\\n\\n /// <summary>\\n /// Returns the inverse cumulative density/mass function evaluated at `value`.\\n /// </summary>\\n /// <param name=\\\"value\\\"></param>\\n public override Tensor icdf(Tensor value)\\n {\\n return WrappedTensorDisposeScope(() =>\\n loc + scale * special.erfinv(2 * value - 1) * Math.Sqrt(2)\\n );\\n }\\n\\n /// <summary>\\n /// Returns a new distribution instance (or populates an existing instance provided by a derived class) with batch dimensions expanded to\\n /// `batch_shape`. This method calls `torch.Tensor.expand()` on the distribution's parameters. As such, this does not allocate new\\n /// memory for the expanded distribution instance.\\n /// </summary>\\n /// <param name=\\\"batch_shape\\\">Tthe desired expanded size.</param>\\n /// <param name=\\\"instance\\\">new instance provided by subclasses that need to override `.expand`.</param>\\n public override distributions.Distribution expand(Size batch_shape, distributions.Distribution instance = null)\\n {\\n if (instance != null && !(instance is Normal))\\n throw new ArgumentException(\\\"expand(): 'instance' must be a Normal distribution\\\");\\n\\n var newDistribution = ((instance == null) ? new Normal(loc.expand(batch_shape), scale.expand(batch_shape), generator) : instance) as Normal;\\n\\n newDistribution.batch_shape = batch_shape;\\n if (newDistribution == instance) {\\n newDistribution.loc = loc.expand(batch_shape);\\n newDistribution.scale = scale.expand(batch_shape);\\n }\\n return newDistribution;\\n }\\n }\\n\\n }\\n public static partial class torch\\n {\\n public static partial class distributions\\n {\\n /// <summary>\\n /// Samples from a Normal (Gaussian) distribution. The distribution of the ratio of\\n /// independent normally distributed random variables with means `0` follows a Normal distribution.\\n /// </summary>\\n /// <param name=\\\"loc\\\">Mode or median of the distribution.</param>\\n /// <param name=\\\"scale\\\">Standard deviation.</param>\\n /// <param name=\\\"generator\\\">An optional random number generator object.</param>\\n /// <returns></returns>\\n public static Normal Normal(Tensor loc, Tensor scale, Generator generator = null)\\n {\\n return new Normal(loc, scale, generator);\\n }\\n\\n /// <summary>\\n /// Samples from a Normal (Gaussian) distribution. The distribution of the ratio of\\n /// independent normally distributed random variables with means `0` follows a Normal distribution.\\n /// </summary>\\n /// <param name=\\\"loc\\\">Mode or median of the distribution.</param>\\n /// <param name=\\\"scale\\\">Standard deviation.</param>\\n /// <param name=\\\"generator\\\">An optional random number generator object.</param>\\n /// <returns></returns>\\n public static Normal Normal(float loc, float scale = 1.0f, Generator generator = null)\\n {\\n return new Normal(tensor(loc), tensor(scale), generator);\\n }\\n\\n\\n /// <summary>\\n /// Samples from a Normal (Gaussian) distribution. The distribution of the ratio of\\n /// independent normally distributed random variables with means `0` follows a Normal distribution.\\n /// </summary>\\n /// <param name=\\\"loc\\\">Mode or median of the distribution.</param>\\n /// <param name=\\\"scale\\\">Standard deviation.</param>\\n /// <param name=\\\"generator\\\">An optional random number generator object.</param>\\n /// <returns></returns>\\n public static Normal Normal(double loc, double scale = 1.0, Generator generator = null)\\n {\\n return new Normal(tensor(loc), tensor(scale), generator);\\n }\\n }\\n }\\n}\\n\"\n },\n {\n \"alpha_fraction\": 0.5440503358840942,\n \"alphanum_fraction\": 0.5450723767280579,\n \"avg_line_length\": 55.131683349609375,\n \"blob_id\": \"148f962c520666c158dd38f9ac4f7fbbdc3a34c9\",\n \"content_id\": \"e11b5661f715b722e7211b3393ec82d24d467ee1\",\n \"detected_licenses\": [\n \"MIT\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"C#\",\n \"length_bytes\": 43067,\n \"license_type\": \"permissive\",\n \"max_line_length\": 183,\n \"num_lines\": 767,\n \"path\": \"/src/TorchSharp/LinearAlgebra.cs\",\n \"repo_name\": \"NiklasGustafsson/TorchSharp\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"// Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information.\\nusing System;\\nusing System.Linq;\\nusing System.Collections.Generic;\\nusing static TorchSharp.PInvoke.NativeMethods;\\n\\n#nullable enable\\nnamespace TorchSharp\\n{\\n public static partial class torch\\n {\\n public static class linalg\\n {\\n /// <summary>\\n /// Computes the Cholesky decomposition of a complex Hermitian or real symmetric positive-definite matrix.\\n /// </summary>\\n /// <param name=\\\"input\\\">The input tensor.</param>\\n /// <returns></returns>\\n public static Tensor cholesky(Tensor input)\\n {\\n var res = THSLinalg_cholesky(input.Handle);\\n if (res == IntPtr.Zero)\\n torch.CheckForErrors();\\n return new Tensor(res);\\n }\\n\\n /// <summary>\\n /// Computes the Cholesky decomposition of a complex Hermitian or real symmetric positive-definite matrix.\\n /// This function skips the(slow) error checking and error message construction of torch.linalg.cholesky(),\\n /// instead directly returning the LAPACK error codes as part of a named tuple(L, info).\\n /// This makes this function a faster way to check if a matrix is positive-definite, and it provides an opportunity to handle\\n /// decomposition errors more gracefully or performantly than torch.linalg.cholesky() does.\\n /// </summary>\\n /// <param name=\\\"input\\\">The input tensor.</param>\\n /// <param name=\\\"check_errors\\\">Controls whether to check the content of infos.</param>\\n /// <returns></returns>\\n public static (Tensor L, Tensor info) cholesky_ex(Tensor input, bool check_errors = false)\\n {\\n var res = THSLinalg_cholesky_ex(input.Handle, check_errors, out var pInfo);\\n if (res == IntPtr.Zero || pInfo == IntPtr.Zero)\\n torch.CheckForErrors();\\n return (new Tensor(res), new Tensor(pInfo));\\n }\\n\\n public static Tensor cond(Tensor input, int p)\\n {\\n var res = THSLinalg_cond_int(input.Handle, p);\\n if (res == IntPtr.Zero)\\n torch.CheckForErrors();\\n return new Tensor(res);\\n }\\n\\n /// <summary>\\n /// Computes the condition number of a matrix with respect to a matrix norm.\\n /// </summary>\\n /// <param name=\\\"input\\\">The input tensor.</param>\\n /// <param name=\\\"p\\\">The type of the matrix norm to use in the computations</param>\\n /// <returns></returns>\\n public static Tensor cond(Tensor input, double p)\\n {\\n var res = THSLinalg_cond_float(input.Handle, p);\\n if (res == IntPtr.Zero)\\n torch.CheckForErrors();\\n return new Tensor(res);\\n }\\n\\n /// <summary>\\n /// Computes the condition number of a matrix with respect to a matrix norm.\\n /// </summary>\\n /// <param name=\\\"input\\\">The input tensor.</param>\\n /// <param name=\\\"p\\\">The type of the matrix norm to use in the computations</param>\\n public static Tensor cond(Tensor input, string p)\\n {\\n var res = THSLinalg_cond_str(input.Handle, p);\\n if (res == IntPtr.Zero)\\n torch.CheckForErrors();\\n return new Tensor(res);\\n }\\n\\n /// <summary>\\n /// Computes the condition number of a matrix with respect to a matrix norm.\\n /// </summary>\\n /// <param name=\\\"input\\\">The input tensor.</param>\\n public static Tensor cond(Tensor input)\\n {\\n var res = THSLinalg_cond_none(input.Handle);\\n if (res == IntPtr.Zero)\\n torch.CheckForErrors();\\n return new Tensor(res);\\n }\\n\\n /// <summary>\\n /// Returns the cross product of vectors in dimension dim of input and other.\\n /// input and other must have the same size, and the size of their dim dimension should be 3.\\n /// </summary>\\n public static Tensor cross(Tensor input, Tensor other, long dim = -1)\\n {\\n var res = THSLinalg_cross(input.Handle, other.Handle, dim);\\n if (res == IntPtr.Zero) { torch.CheckForErrors(); }\\n return new Tensor(res);\\n }\\n\\n /// <summary>\\n /// Computes the determinant of a square matrix.\\n /// </summary>\\n /// <param name=\\\"input\\\">The input tensor.</param>\\n public static Tensor det(Tensor input)\\n {\\n var res = THSLinalg_det(input.Handle);\\n if (res == IntPtr.Zero)\\n torch.CheckForErrors();\\n return new Tensor(res);\\n }\\n\\n /// <summary>\\n /// Computes the sign and natural logarithm of the absolute value of the determinant of a square matrix.\\n /// For complex A, it returns the angle and the natural logarithm of the modulus of the determinant, that is, a logarithmic polar decomposition of the determinant.\\n /// </summary>\\n /// <param name=\\\"input\\\">The input tensor.</param>\\n /// <returns></returns>\\n public static (Tensor, Tensor) slogdet(Tensor input)\\n {\\n var res = THSLinalg_slogdet(input.Handle, out var logabsdet);\\n if (res == IntPtr.Zero || logabsdet == IntPtr.Zero)\\n torch.CheckForErrors();\\n return (new Tensor(res), new Tensor(logabsdet));\\n }\\n\\n /// <summary>\\n /// Returns a partial view of input with the its diagonal elements with respect to dim1 and dim2 appended as a dimension at the end of the shape.\\n /// The argument offset controls which diagonal to consider:\\n ///\\n /// If offset == 0, it is the main diagonal.\\n /// If offset > 0, it is above the main diagonal.\\n /// If offset < 0, it is below the main diagonal.\\n /// </summary>\\n /// <param name=\\\"input\\\">The input tensor</param>\\n /// <param name=\\\"offset\\\">Which diagonal to consider. Default: 0 (main diagonal).</param>\\n /// <param name=\\\"dim1\\\">First dimension with respect to which to take diagonal. Default: -1.</param>\\n /// <param name=\\\"dim2\\\">Second dimension with respect to which to take diagonal. Default: -2.</param>\\n /// <remarks>\\n /// Applying torch.diag_embed() to the output of this function with the same arguments yields a diagonal matrix with the diagonal entries of the input.\\n /// However, torch.diag_embed() has different default dimensions, so those need to be explicitly specified.\\n /// </remarks>\\n public static Tensor diagonal(Tensor input, int offset = 0, int dim1 = -2, int dim2 = -1) => input.diagonal(offset, dim1, dim2);\\n\\n /// <summary>\\n /// Computes the eigenvalue decomposition of a square matrix if it exists.\\n /// </summary>\\n /// <param name=\\\"input\\\">The input tensor.</param>\\n /// <returns></returns>\\n public static (Tensor, Tensor) eig(Tensor input)\\n {\\n var res = THSLinalg_eig(input.Handle, out var vectors);\\n if (res == IntPtr.Zero || vectors == IntPtr.Zero)\\n torch.CheckForErrors();\\n return (new Tensor(res), new Tensor(vectors));\\n }\\n\\n /// <summary>\\n /// Computes the eigenvalue decomposition of a complex Hermitian or real symmetric matrix.\\n /// </summary>\\n /// <param name=\\\"input\\\">The input tensor.</param>\\n /// <param name=\\\"UPLO\\\">Controls whether to use the upper or lower triangular part of A in the computations. </param>\\n /// <returns></returns>\\n public static (Tensor, Tensor) eigh(Tensor input, char UPLO = 'L')\\n {\\n var res = THSLinalg_eigh(input.Handle, (byte)UPLO, out var vectors);\\n if (res == IntPtr.Zero || vectors == IntPtr.Zero)\\n torch.CheckForErrors();\\n return (new Tensor(res), new Tensor(vectors));\\n }\\n\\n /// <summary>\\n /// Computes the eigenvalues of a square matrix.\\n /// </summary>\\n /// <param name=\\\"input\\\">The input tensor.</param>\\n /// <returns></returns>\\n public static Tensor eigvals(Tensor input)\\n {\\n var res = THSLinalg_eigvals(input.Handle);\\n if (res == IntPtr.Zero)\\n torch.CheckForErrors();\\n return new Tensor(res);\\n }\\n\\n /// <summary>\\n /// Computes the eigenvalues of a complex Hermitian or real symmetric matrix.\\n /// </summary>\\n /// <param name=\\\"input\\\">The input tensor.</param>\\n /// <param name=\\\"UPLO\\\">Controls whether to use the upper or lower triangular part of A in the computations. </param>\\n /// <returns></returns>\\n public static Tensor eigvalsh(Tensor input, char UPLO = 'L')\\n {\\n var res = THSLinalg_eigvalsh(input.Handle, (byte)UPLO);\\n if (res == IntPtr.Zero)\\n torch.CheckForErrors();\\n return new Tensor(res);\\n }\\n\\n /// <summary>\\n /// Computes the first n columns of a product of Householder matrices.\\n /// </summary>\\n /// <param name=\\\"A\\\">tensor of shape (*, m, n) where * is zero or more batch dimensions.</param>\\n /// <param name=\\\"tau\\\">tensor of shape (*, k) where * is zero or more batch dimensions.</param>\\n public static Tensor householder_product(Tensor A, Tensor tau)\\n {\\n var res = THSLinalg_householder_product(A.Handle, tau.Handle);\\n if (res == IntPtr.Zero)\\n torch.CheckForErrors();\\n return new Tensor(res);\\n }\\n\\n /// <summary>\\n /// Computes the inverse of a square matrix if it exists.\\n /// </summary>\\n /// <param name=\\\"input\\\">The input tensor.</param>\\n /// <returns></returns>\\n /// <remarks>Throws a RuntimeError if the matrix is not invertible.</remarks>\\n public static Tensor inv(Tensor input)\\n {\\n var res = THSLinalg_inv(input.Handle);\\n if (res == IntPtr.Zero)\\n torch.CheckForErrors();\\n return new Tensor(res);\\n }\\n\\n /// <summary>\\n /// Computes the inverse of a square matrix if it is invertible.\\n /// Returns a named tuple(inverse, info). inverse contains the result of inverting A and info stores the LAPACK error codes.\\n /// If A is not an invertible matrix, or if it’s a batch of matrices and one or more of them is not an invertible matrix,\\n /// then info stores a positive integer for the corresponding matrix.The positive integer indicates the diagonal element of\\n /// the LU decomposition of the input matrix that is exactly zero. info filled with zeros indicates that the inversion was successful.\\n /// If check_errors = True and info contains positive integers, then a RuntimeError is thrown.\\n /// </summary>\\n /// <param name=\\\"input\\\">The input tensor.</param>\\n /// <param name=\\\"check_errors\\\">Controls whether to check the content of info. controls whether to check the content of info. </param>\\n /// <returns></returns>\\n /// <remarks>Throws a RuntimeError if the matrix is not invertible.</remarks>\\n public static (Tensor L, Tensor info) inv_ex(Tensor input, bool check_errors = false)\\n {\\n var res = THSLinalg_cholesky_ex(input.Handle, check_errors, out var pInfo);\\n if (res == IntPtr.Zero || pInfo == IntPtr.Zero)\\n torch.CheckForErrors();\\n return (new Tensor(res), new Tensor(pInfo));\\n }\\n\\n /// <summary>\\n /// Computes a solution to the least squares problem of a system of linear equations.\\n /// </summary>\\n /// <param name=\\\"input\\\">lhs tensor of shape (*, m, n) where * is zero or more batch dimensions.</param>\\n /// <param name=\\\"other\\\">rhs tensor of shape (*, m, k) where * is zero or more batch dimensions.</param>\\n /// <returns></returns>\\n public static (Tensor Solution, Tensor Residuals, Tensor Rank, Tensor SingularValues) lstsq(Tensor input, Tensor other)\\n {\\n var solution = THSLinalg_lstsq_none(input.Handle, other.Handle, out var residuals, out var rank, out var singularValues);\\n if (solution == IntPtr.Zero || residuals == IntPtr.Zero || rank == IntPtr.Zero || singularValues == IntPtr.Zero)\\n torch.CheckForErrors();\\n return (new Tensor(solution), new Tensor(residuals), new Tensor(rank), new Tensor(singularValues));\\n }\\n\\n /// <summary>\\n /// Computes the LU decomposition with partial pivoting of a matrix.\\n /// </summary>\\n /// <param name=\\\"input\\\">Tensor of shape (*, m, n) where * is zero or more batch dimensions.</param>\\n /// <param name=\\\"pivot\\\">Controls whether to compute the LU decomposition with partial pivoting or no pivoting</param>\\n /// <returns></returns>\\n public static (Tensor P, Tensor L, Tensor U) lu(Tensor input, bool pivot = true)\\n {\\n var solution = THSLinalg_lu(input.Handle, pivot, out var pL, out var pU);\\n if (solution == IntPtr.Zero)\\n torch.CheckForErrors();\\n return (new Tensor(solution), new Tensor(pL), new Tensor(pU));\\n }\\n\\n /// <summary>\\n /// Computes a compact representation of the LU factorization with partial pivoting of a matrix.\\n /// </summary>\\n /// <param name=\\\"input\\\">Tensor of shape (*, m, n) where * is zero or more batch dimensions.</param>\\n /// <param name=\\\"pivot\\\">Controls whether to compute the LU decomposition with partial pivoting or no pivoting</param>\\n /// <returns></returns>\\n public static (Tensor LU, Tensor? Pivots) lu_factor(Tensor input, bool pivot = true)\\n {\\n var solution = THSLinalg_lu_factor(input.Handle, pivot, out var pivots);\\n if (solution == IntPtr.Zero)\\n torch.CheckForErrors();\\n return (new Tensor(solution), pivots == IntPtr.Zero ? null : new Tensor(pivots));\\n }\\n\\n /// <summary>\\n /// Computes a compact representation of the LU factorization with partial pivoting of a matrix.\\n /// </summary>\\n /// <param name=\\\"input\\\">Tensor of shape (*, m, n) where * is zero or more batch dimensions.</param>\\n /// <param name=\\\"hermitian\\\">Controls whether to consider the input to be Hermitian or symmetric. For real-valued matrices, this switch has no effect.</param>\\n /// <returns></returns>\\n public static (Tensor LU, Tensor? Pivots) ldl_factor(Tensor input, bool hermitian = true)\\n {\\n var solution = THSLinalg_ldl_factor(input.Handle, hermitian, out var pivots);\\n if (solution == IntPtr.Zero)\\n torch.CheckForErrors();\\n return (new Tensor(solution), pivots == IntPtr.Zero ? null : new Tensor(pivots));\\n }\\n\\n /// <summary>\\n /// Computes a compact representation of the LU factorization with partial pivoting of a matrix.\\n /// </summary>\\n /// <param name=\\\"input\\\">Tensor of shape (*, m, n) where * is zero or more batch dimensions.</param>\\n /// <param name=\\\"hermitian\\\">Controls whether to consider the input to be Hermitian or symmetric. For real-valued matrices, this switch has no effect.</param>\\n /// <param name=\\\"check_errors\\\">Controls whether to check the content of info and raise an error if it is non-zero.</param>\\n /// <returns></returns>\\n public static (Tensor LU, Tensor? Pivots, Tensor? Info) ldl_factor_ex(Tensor input, bool hermitian = true, bool check_errors = false)\\n {\\n var solution = THSLinalg_ldl_factor_ex(input.Handle, hermitian, check_errors, out var pivots, out var info);\\n if (solution == IntPtr.Zero)\\n torch.CheckForErrors();\\n return (new Tensor(solution), pivots == IntPtr.Zero ? null : new Tensor(pivots), info == IntPtr.Zero ? null : new Tensor(info));\\n }\\n\\n /// <summary>\\n /// Computes the solution of a system of linear equations using the LDL factorization.\\n /// </summary>\\n /// <param name=\\\"LD\\\">the n times n matrix or the batch of such matrices of size (*, n, n) where * is one or more batch dimensions</param>\\n /// <param name=\\\"pivots\\\">the pivots corresponding to the LDL factorization of LD</param>\\n /// <param name=\\\"B\\\">Right-hand side tensor of shape (*, n, k)</param>\\n /// <param name=\\\"hermitian\\\">Whether to consider the decomposed matrix to be Hermitian or symmetric. For real-valued matrices, this switch has no effect</param>\\n /// <returns></returns>\\n public static Tensor ldl_solve(Tensor LD, Tensor pivots, Tensor B, bool hermitian = false)\\n {\\n var res = THSLinalg_ldl_solve(LD.Handle, pivots.Handle, B.Handle, hermitian);\\n if (res == IntPtr.Zero)\\n torch.CheckForErrors();\\n return new Tensor(res);\\n }\\n\\n /// <summary>\\n /// Computes a solution to the least squares problem of a system of linear equations.\\n /// </summary>\\n /// <param name=\\\"input\\\">lhs tensor of shape (*, m, n) where * is zero or more batch dimensions.</param>\\n /// <param name=\\\"other\\\">rhs tensor of shape (*, m, k) where * is zero or more batch dimensions.</param>\\n /// <param name=\\\"rcond\\\">Used to determine the effective rank of A. If rcond= None, rcond is set to the machine precision of the dtype of A times max(m, n).</param>\\n public static (Tensor Solution, Tensor Residuals, Tensor Rank, Tensor SingularValues) lstsq(Tensor input, Tensor other, double rcond)\\n {\\n var solution = THSLinalg_lstsq_rcond(input.Handle, other.Handle, rcond, out var residuals, out var rank, out var singularValues);\\n if (solution == IntPtr.Zero || residuals == IntPtr.Zero || rank == IntPtr.Zero || singularValues == IntPtr.Zero)\\n torch.CheckForErrors();\\n return (new Tensor(solution), new Tensor(residuals), new Tensor(rank), new Tensor(singularValues));\\n }\\n\\n /// <summary>\\n /// Computes the matrix exponential of a square matrix or of each square matrix in a batch.\\n /// </summary>\\n public static Tensor matrix_exp(Tensor input) => input.matrix_exp();\\n\\n /// <summary>\\n /// Computes a matrix norm.\\n /// </summary>\\n /// <param name=\\\"input\\\">tensor with two or more dimensions.\\n /// By default its shape is interpreted as (*, m, n) where * is zero or more batch dimensions, but this behavior can be controlled using dims.</param>\\n /// <param name=\\\"ord\\\">Order of norm. Default: \\\"fro\\\"</param>\\n /// <param name=\\\"dims\\\">Dimensions over which to compute the norm.</param>\\n /// <param name=\\\"keepdim\\\">If set to true, the reduced dimensions are retained in the result as dimensions with size one. </param>\\n /// <returns></returns>\\n public static Tensor matrix_norm(Tensor input, string ord = \\\"fro\\\", long[]? dims = null, bool keepdim = false)\\n {\\n if (dims == null) dims = new long[] { -2, -1 };\\n unsafe {\\n fixed (long* pdims = dims) {\\n var res = THSLinalg_matrix_norm_fronuc(input.Handle, ord == \\\"fro\\\" ? (byte)0 : (byte)1, (IntPtr)pdims, dims.Length, keepdim);\\n if (res == IntPtr.Zero) { torch.CheckForErrors(); }\\n return new Tensor(res);\\n }\\n }\\n }\\n\\n /// <summary>\\n /// Computes a matrix norm.\\n /// </summary>\\n /// <param name=\\\"input\\\">tensor with two or more dimensions.\\n /// By default its shape is interpreted as (*, m, n) where * is zero or more batch dimensions, but this behavior can be controlled using dims.</param>\\n /// <param name=\\\"ord\\\">Order of norm.</param>\\n /// <param name=\\\"dims\\\">Dimensions over which to compute the norm.</param>\\n /// <param name=\\\"keepdim\\\">If set to true, the reduced dimensions are retained in the result as dimensions with size one. </param>\\n /// <returns></returns>\\n public static Tensor matrix_norm(Tensor input, double ord, long[]? dims = null, bool keepdim = false)\\n {\\n if (dims == null) dims = new long[] { -2, -1 };\\n unsafe {\\n fixed (long* pdims = dims) {\\n var res = THSLinalg_matrix_norm(input.Handle, ord.ToScalar().Handle, (IntPtr)pdims, dims.Length, keepdim);\\n if (res == IntPtr.Zero) { torch.CheckForErrors(); }\\n return new Tensor(res);\\n }\\n }\\n }\\n\\n /// <summary>\\n /// Computes the numerical rank of a matrix.\\n /// The matrix rank is computed as the number of singular values(or eigenvalues in absolute value when hermitian = True) that are greater than the specified tol threshold.\\n /// </summary>\\n /// <param name=\\\"input\\\">Tensor of shape (*, m, n) where * is zero or more batch dimensions.</param>\\n /// <param name=\\\"atol\\\">The absolute tolerance value.</param>\\n /// <param name=\\\"rtol\\\">The relative tolerance value.</param>\\n /// <param name=\\\"hermitian\\\">Indicates whether A is Hermitian if complex or symmetric if real</param>\\n /// <returns></returns>\\n public static Tensor matrix_rank(Tensor input, double? atol = null, double? rtol = null, bool hermitian = false)\\n {\\n unsafe {\\n var res = THSLinalg_matrix_rank(input.Handle, atol ?? double.NegativeInfinity, atol.HasValue, rtol ?? double.NegativeInfinity, rtol.HasValue, hermitian);\\n if (res == IntPtr.Zero) { torch.CheckForErrors(); }\\n return new Tensor(res);\\n }\\n }\\n\\n /// <summary>\\n /// Computes the numerical rank of a matrix.\\n /// The matrix rank is computed as the number of singular values(or eigenvalues in absolute value when hermitian = True) that are greater than the specified tol threshold.\\n /// </summary>\\n /// <param name=\\\"input\\\">Tensor of shape (*, m, n) where * is zero or more batch dimensions.</param>\\n /// <param name=\\\"atol\\\">The absolute tolerance value.</param>\\n /// <param name=\\\"rtol\\\">The relative tolerance value.</param>\\n /// <param name=\\\"hermitian\\\">Indicates whether A is Hermitian if complex or symmetric if real</param>\\n /// <returns></returns>\\n public static Tensor matrix_rank(Tensor input, Tensor atol, Tensor? rtol = null, bool hermitian = false)\\n {\\n unsafe {\\n var res = THSLinalg_matrix_rank_tensor(input.Handle, atol is null ? IntPtr.Zero : atol.Handle, rtol is null ? IntPtr.Zero : rtol.Handle, hermitian);\\n if (res == IntPtr.Zero) { torch.CheckForErrors(); }\\n return new Tensor(res);\\n }\\n }\\n\\n /// <summary>\\n /// Efficiently multiplies two or more matrices by reordering the multiplications so that the fewest arithmetic operations are performed.\\n /// </summary>\\n /// <param name=\\\"tensors\\\">Two or more tensors to multiply. The first and last tensors may be 1D or 2D. Every other tensor must be 2D.</param>\\n /// <returns></returns>\\n public static Tensor multi_dot(IList<Tensor> tensors)\\n {\\n if (tensors.Count == 0) {\\n throw new ArgumentException(nameof(tensors));\\n }\\n if (tensors.Count == 1) {\\n return tensors[0];\\n }\\n\\n using (var parray = new PinnedArray<IntPtr>()) {\\n IntPtr tensorsRef = parray.CreateArray(tensors.Select(p => p.Handle).ToArray());\\n var res = THSLinalg_multi_dot(tensorsRef, parray.Array.Length);\\n if (res == IntPtr.Zero)\\n torch.CheckForErrors();\\n return new Tensor(res);\\n }\\n }\\n\\n /// <summary>\\n /// Computes a vector or matrix norm.\\n /// If A is complex valued, it computes the norm of A.abs()\\n /// </summary>\\n /// <param name=\\\"input\\\">Tensor of shape (*, n) or (*, m, n) where * is zero or more batch dimensions</param>\\n /// <param name=\\\"ord\\\">Order of norm. </param>\\n /// <param name=\\\"dims\\\">Dimensions over which to compute the vector or matrix norm.</param>\\n /// <param name=\\\"keepdim\\\">If set to true, the reduced dimensions are retained in the result as dimensions with size one.</param>\\n /// <returns></returns>\\n public static Tensor norm(Tensor input, string ord, long[]? dims = null, bool keepdim = false)\\n {\\n unsafe {\\n fixed (long* pdims = dims) {\\n var res = THSLinalg_norm_str(input.Handle, ord, (IntPtr)pdims, dims is null ? 0 : dims.Length, keepdim);\\n if (res == IntPtr.Zero) { torch.CheckForErrors(); }\\n return new Tensor(res);\\n }\\n }\\n }\\n\\n /// <summary>\\n /// Computes a vector or matrix norm.\\n /// If A is complex valued, it computes the norm of A.abs()\\n /// </summary>\\n /// <param name=\\\"input\\\">Tensor of shape (*, n) or (*, m, n) where * is zero or more batch dimensions</param>\\n /// <param name=\\\"ord\\\">Order of norm. </param>\\n /// <param name=\\\"dims\\\">Dimensions over which to compute the vector or matrix norm.</param>\\n /// <param name=\\\"keepdim\\\">If set to true, the reduced dimensions are retained in the result as dimensions with size one.</param>\\n public static Tensor norm(Tensor input, double ord, long[]? dims = null, bool keepdim = false)\\n {\\n unsafe {\\n fixed (long* pdims = dims) {\\n var res = THSLinalg_norm_float(input.Handle, ord, (IntPtr)pdims, dims is null ? 0 : dims.Length, keepdim);\\n if (res == IntPtr.Zero) { torch.CheckForErrors(); }\\n return new Tensor(res);\\n }\\n }\\n }\\n\\n /// <summary>\\n /// Computes a vector or matrix norm.\\n /// If A is complex valued, it computes the norm of A.abs()\\n /// </summary>\\n /// <param name=\\\"input\\\">Tensor of shape (*, n) or (*, m, n) where * is zero or more batch dimensions</param>\\n /// <param name=\\\"ord\\\">Order of norm. </param>\\n /// <param name=\\\"dims\\\">Dimensions over which to compute the vector or matrix norm.</param>\\n /// <param name=\\\"keepdim\\\">If set to true, the reduced dimensions are retained in the result as dimensions with size one.</param>\\n public static Tensor norm(Tensor input, int ord, long[]? dims = null, bool keepdim = false)\\n {\\n unsafe {\\n fixed (long* pdims = dims) {\\n var res = THSLinalg_norm_int(input.Handle, ord, (IntPtr)pdims, dims is null ? 0 : dims.Length, keepdim);\\n if (res == IntPtr.Zero) { torch.CheckForErrors(); }\\n return new Tensor(res);\\n }\\n }\\n }\\n\\n /// <summary>\\n /// Computes a vector or matrix norm.\\n /// If A is complex valued, it computes the norm of A.abs()\\n /// </summary>\\n /// <param name=\\\"input\\\">Tensor of shape (*, n) or (*, m, n) where * is zero or more batch dimensions</param>\\n /// <param name=\\\"dims\\\">Dimensions over which to compute the vector or matrix norm.</param>\\n /// <param name=\\\"keepdim\\\">If set to true, the reduced dimensions are retained in the result as dimensions with size one.</param>\\n public static Tensor norm(Tensor input, long[]? dims = null, bool keepdim = false)\\n {\\n unsafe {\\n fixed (long* pdims = dims) {\\n var res = THSLinalg_norm_opt(input.Handle, (IntPtr)pdims, dims is null ? 0 : dims.Length, keepdim);\\n if (res == IntPtr.Zero) { torch.CheckForErrors(); }\\n return new Tensor(res);\\n }\\n }\\n }\\n\\n /// <summary>\\n /// Computes the numerical rank of a matrix.\\n /// The matrix rank is computed as the number of singular values(or eigenvalues in absolute value when hermitian = True) that are greater than the specified tol threshold.\\n /// </summary>\\n /// <param name=\\\"input\\\">Tensor of shape (*, m, n) where * is zero or more batch dimensions.</param>\\n /// <param name=\\\"atol\\\">The absolute tolerance value.</param>\\n /// <param name=\\\"rtol\\\">The relative tolerance value.</param>\\n /// <param name=\\\"hermitian\\\">Indicates whether A is Hermitian if complex or symmetric if real</param>\\n /// <returns></returns>\\n public static Tensor pinv(Tensor input, double? atol = null, double? rtol = null, bool hermitian = false)\\n {\\n unsafe {\\n var res = THSLinalg_pinv(input.Handle, atol ?? double.NegativeInfinity, atol.HasValue, rtol ?? double.NegativeInfinity, rtol.HasValue, hermitian);\\n if (res == IntPtr.Zero) { torch.CheckForErrors(); }\\n return new Tensor(res);\\n }\\n }\\n\\n /// <summary>\\n /// Computes the numerical rank of a matrix.\\n /// The matrix rank is computed as the number of singular values(or eigenvalues in absolute value when hermitian = True) that are greater than the specified tol threshold.\\n /// </summary>\\n /// <param name=\\\"input\\\">Tensor of shape (*, m, n) where * is zero or more batch dimensions.</param>\\n /// <param name=\\\"atol\\\">The absolute tolerance value.</param>\\n /// <param name=\\\"rtol\\\">The relative tolerance value.</param>\\n /// <param name=\\\"hermitian\\\">Indicates whether A is Hermitian if complex or symmetric if real</param>\\n /// <returns></returns>\\n public static Tensor pinv(Tensor input, Tensor atol, Tensor? rtol = null, bool hermitian = false)\\n {\\n unsafe {\\n var res = THSLinalg_pinv_tensor(input.Handle, atol is null ? IntPtr.Zero : atol.Handle, rtol is null ? IntPtr.Zero : rtol.Handle, hermitian);\\n if (res == IntPtr.Zero) { torch.CheckForErrors(); }\\n return new Tensor(res);\\n }\\n }\\n\\n public enum QRMode\\n {\\n Reduced = 0,\\n Complete = 1,\\n R = 2\\n }\\n\\n /// <summary>\\n /// Computes the QR decomposition of a matrix.\\n /// </summary>\\n /// <param name=\\\"input\\\">Tensor of shape (*, m, n) where * is zero or more batch dimensions.</param>\\n /// <param name=\\\"mode\\\">Controls the shape of the returned tensors. One of ‘Reduced’, ‘Complete’, ‘R’.</param>\\n /// <returns></returns>\\n public static (Tensor Q, Tensor R) qr(Tensor input, QRMode mode = QRMode.Reduced)\\n {\\n var Q = THSLinalg_qr(input.Handle, (byte)mode, out var R);\\n if (Q == IntPtr.Zero || R == IntPtr.Zero)\\n torch.CheckForErrors();\\n return (new Tensor(Q), new Tensor(R));\\n }\\n\\n /// <summary>\\n /// Computes the solution of a square system of linear equations with a unique solution.\\n /// </summary>\\n /// <param name=\\\"A\\\">Tensor of shape (*, n, n) where * is zero or more batch dimensions.</param>\\n /// <param name=\\\"B\\\">Right-hand side tensor of shape (*, n) or (*, n, k) or (n,) or (n, k)</param>\\n /// <param name=\\\"left\\\">whether to solve the system AX = B or XA = B.</param>\\n /// <returns></returns>\\n public static Tensor solve(Tensor A, Tensor B, bool left = true)\\n {\\n var res = THSLinalg_solve(A.Handle, B.Handle, left);\\n if (res == IntPtr.Zero)\\n torch.CheckForErrors();\\n return new Tensor(res);\\n }\\n\\n /// <summary>\\n /// Computes the solution of a square system of linear equations with a unique solution.\\n /// </summary>\\n /// <param name=\\\"A\\\">Ttensor of shape (*, n, n) where * is zero or more batch dimensions.</param>\\n /// <param name=\\\"B\\\">Right-hand side tensor of shape (*, n) or (*, n, k) or (n,) or (n, k)</param>\\n /// <param name=\\\"left\\\">whether to solve the system AX = B or XA = B.</param>\\n /// <param name=\\\"check_errors\\\">controls whether to check the content of infos and raise an error if it is non-zero</param>\\n /// <returns></returns>\\n public static (Tensor result, Tensor info) solve_ex(Tensor A, Tensor B, bool left = true, bool check_errors = false)\\n {\\n var res = THSLinalg_solve_ex(A.Handle, B.Handle, left, check_errors, out var infos);\\n if (res == IntPtr.Zero)\\n torch.CheckForErrors();\\n return (new Tensor(res), new Tensor(infos));\\n }\\n\\n /// <summary>\\n /// Computes the solution of a square system of linear equations with a unique solution.\\n /// </summary>\\n /// <param name=\\\"A\\\">Tensor of shape (*, n, n) where * is zero or more batch dimensions.</param>\\n /// <param name=\\\"B\\\">Right-hand side tensor of shape (*, n) or (*, n, k) or (n,) or (n, k)</param>\\n /// <param name=\\\"upper\\\">Whether A is an upper or lower triangular matrix</param>\\n /// <param name=\\\"left\\\">Whether to solve the system AX = B or XA = B.</param>\\n /// <param name=\\\"unitriangular\\\">If true, the diagonal elements of A are assumed to be all equal to 1.</param>\\n /// <param name=\\\"out\\\">Output tensor. B may be passed as out and the result is computed in-place on B.</param>\\n /// <returns></returns>\\n public static Tensor solve_triangular(Tensor A, Tensor B, bool upper, bool left = true, bool unitriangular = false, Tensor? @out = null)\\n {\\n var res = (@out is null)\\n ? THSLinalg_solve_triangular(A.Handle, B.Handle, upper, left, unitriangular)\\n : THSLinalg_solve_triangular_out(A.Handle, B.Handle, upper, left, unitriangular, @out.Handle);\\n if (res == IntPtr.Zero)\\n torch.CheckForErrors();\\n return new Tensor(res);\\n }\\n\\n /// <summary>\\n /// Computes the singular value decomposition (SVD) of a matrix.\\n /// </summary>\\n /// <param name=\\\"input\\\">Tensor of shape (*, m, n) where * is zero or more batch dimensions.</param>\\n /// <param name=\\\"fullMatrices\\\">Controls whether to compute the full or reduced SVD, and consequently, the shape of the returned tensors U and Vh.</param>\\n /// <returns></returns>\\n public static (Tensor U, Tensor S, Tensor Vh) svd(Tensor input, bool fullMatrices = true)\\n {\\n var U = THSLinalg_svd(input.Handle, fullMatrices, out var S, out var Vh);\\n if (U == IntPtr.Zero || S == IntPtr.Zero || Vh == IntPtr.Zero)\\n torch.CheckForErrors();\\n return (new Tensor(U), new Tensor(S), new Tensor(Vh));\\n }\\n\\n /// <summary>\\n /// Computes the singular values of a matrix.\\n /// </summary>\\n /// <param name=\\\"input\\\">The input matrix</param>\\n /// <returns></returns>\\n public static Tensor svdvals(Tensor input)\\n {\\n var res = THSLinalg_svdvals(input.Handle);\\n if (res == IntPtr.Zero)\\n torch.CheckForErrors();\\n return new Tensor(res);\\n }\\n\\n /// <summary>\\n /// Computes the multiplicative inverse of torch.tensordot().\\n /// </summary>\\n /// <param name=\\\"input\\\">Tensor to invert. </param>\\n /// <param name=\\\"ind\\\">Index at which to compute the inverse of torch.tensordot()</param>\\n /// <returns></returns>\\n public static Tensor tensorinv(Tensor input, long ind)\\n {\\n var res = THSLinalg_tensorinv(input.Handle, ind);\\n if (res == IntPtr.Zero)\\n torch.CheckForErrors();\\n return new Tensor(res);\\n }\\n\\n /// <summary>\\n /// Computes the solution X to the system torch.tensordot(A, X) = B.\\n /// </summary>\\n /// <param name=\\\"A\\\">Tensor to solve for. </param>\\n /// <param name=\\\"B\\\">Another tensor, of shape a.shape[B.dim].</param>\\n /// <param name=\\\"dims\\\">Dimensions of A to be moved. If None, no dimensions are moved.</param>\\n /// <returns></returns>\\n public static Tensor tensorsolve(Tensor A, Tensor B, long[] dims)\\n {\\n unsafe {\\n fixed (long* pdims = dims) {\\n var res = THSLinalg_tensorsolve(A.Handle, B.Handle, (IntPtr)pdims, dims.Length);\\n if (res == IntPtr.Zero) { torch.CheckForErrors(); }\\n return new Tensor(res);\\n }\\n }\\n }\\n\\n /// <summary>\\n /// Computes a vector norm.\\n /// </summary>\\n /// <param name=\\\"input\\\">Tensor, flattened by default, but this behavior can be controlled using dims.</param>\\n /// <param name=\\\"ord\\\">Order of norm. Default: 2</param>\\n /// <param name=\\\"dims\\\">Dimensions over which to compute the norm.</param>\\n /// <param name=\\\"keepdim\\\">If set to true, the reduced dimensions are retained in the result as dimensions with size one. </param>\\n /// <returns></returns>\\n public static Tensor vector_norm(Tensor input, double ord, long[]? dims = null, bool keepdim = false)\\n {\\n unsafe {\\n fixed (long* pdims = dims) {\\n var res = THSLinalg_vector_norm(input.Handle, ord.ToScalar().Handle, (IntPtr)pdims, dims is null ? 0 : dims.Length, keepdim);\\n if (res == IntPtr.Zero) { torch.CheckForErrors(); }\\n return new Tensor(res);\\n }\\n }\\n }\\n\\n /// <summary>\\n /// Generates a Vandermonde matrix.\\n /// </summary>\\n /// <param name=\\\"input\\\">tensor of shape (*, n) where * is zero or more batch dimensions consisting of vectors.</param>\\n /// <param name=\\\"N\\\">Number of columns in the output. Default: x.size(-1)</param>\\n public static Tensor vander(Tensor input, long? N = null)\\n {\\n if (!N.HasValue) {\\n N = input.shape[input.ndim - 1];\\n }\\n var res = THSLinalg_vander(input.Handle, N.Value);\\n if (res == IntPtr.Zero)\\n torch.CheckForErrors();\\n return new Tensor(res);\\n }\\n\\n /// <summary>\\n /// Computes the dot product of two batches of vectors along a dimension.\\n /// </summary>\\n /// <param name=\\\"x\\\">First batch of vectors.</param>\\n /// <param name=\\\"y\\\">Second batch of vectors</param>\\n /// <param name=\\\"dim\\\">Dimension along which to compute the dot product.</param>\\n /// <param name=\\\"out\\\">Optional output tensor.</param>\\n public static Tensor vecdot(Tensor x, Tensor y, long dim = -1, Tensor? @out = null)\\n {\\n var res = THSLinalg_vecdot(x.Handle, y.Handle, dim, @out is null ? IntPtr.Zero : @out.Handle);\\n if (res == IntPtr.Zero)\\n torch.CheckForErrors();\\n return new Tensor(res);\\n }\\n\\n /// <summary>\\n /// Computes the solution of a square system of linear equations with a unique solution given an LU decomposition.\\n /// </summary>\\n /// <param name=\\\"LU\\\">Tensor of shape (*, n, n) (or (*, k, k) if left= True) where * is zero or more batch dimensions as returned by lu_factor().</param>\\n /// <param name=\\\"pivots\\\">Tensor of shape (*, n) (or (*, k) if left= True) where * is zero or more batch dimensions as returned by lu_factor().</param>\\n /// <param name=\\\"B\\\">Right-hand side tensor of shape (*, n, k).</param>\\n /// <param name=\\\"left\\\">Whether to solve the system AX=B or XA = B. Default: True.</param>\\n /// <param name=\\\"adjoint\\\">Whether to solve the adjoint system.</param>\\n /// <param name=\\\"out\\\">Optional output tensor.</param>\\n /// <returns></returns>\\n public static Tensor lu_solve(Tensor LU, Tensor pivots, Tensor B, bool left = true, bool adjoint = false, Tensor? @out = null)\\n {\\n var res = THSLinalg_lu_solve(B.Handle, LU.Handle, pivots.Handle, left, adjoint, @out is null ? IntPtr.Zero : @out.Handle);\\n if (res == IntPtr.Zero)\\n torch.CheckForErrors();\\n return new Tensor(res);\\n }\\n }\\n }\\n}\\n\"\n },\n {\n \"alpha_fraction\": 0.587868869304657,\n \"alphanum_fraction\": 0.5880327820777893,\n \"avg_line_length\": 44.52238845825195,\n \"blob_id\": \"eb756ece997bd3c29b6c1ededff0bf228f2c14bb\",\n \"content_id\": \"d7a5895b37598604a86853318d0913ae01a5a1cb\",\n \"detected_licenses\": [\n \"MIT\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"C#\",\n \"length_bytes\": 6100,\n \"license_type\": \"permissive\",\n \"max_line_length\": 165,\n \"num_lines\": 134,\n \"path\": \"/src/TorchSharp/Distributions/Independent.cs\",\n \"repo_name\": \"NiklasGustafsson/TorchSharp\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"// Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information.\\nusing System;\\nusing static TorchSharp.torch;\\n\\nnamespace TorchSharp\\n{\\n using System.Linq;\\n using Modules;\\n\\n namespace Modules\\n {\\n /// <summary>\\n /// Reinterprets some of the batch dims of a distribution as event dims.\\n /// </summary>\\n public class Independent : torch.distributions.Distribution\\n {\\n /// <summary>\\n /// The mean of the distribution.\\n /// </summary>\\n public override Tensor mean => base_dist.mean;\\n\\n public override Tensor mode => base_dist.mode;\\n\\n /// <summary>\\n /// The variance of the distribution\\n /// </summary>\\n public override Tensor variance => base_dist.variance;\\n\\n\\n /// <summary>\\n /// Constructor\\n /// </summary>\\n /// <param name=\\\"base_distribution\\\">A base distribution.</param>\\n /// <param name=\\\"reinterpreted_batch_ndims\\\">the number of batch dims to reinterpret as event dims</param>\\n /// <param name=\\\"generator\\\">An optional random number generator object.</param>\\n public Independent(torch.distributions.Distribution base_distribution, int reinterpreted_batch_ndims, torch.Generator generator = null) : base(generator)\\n {\\n var shape = base_distribution.batch_shape.Concat(base_distribution.event_shape).ToArray();\\n var event_dim = reinterpreted_batch_ndims + base_distribution.event_shape.Length;\\n var batch_shape = shape.Take(shape.Length - event_dim).ToArray();\\n var event_shape = shape.Skip(shape.Length - event_dim).ToArray();\\n\\n this.base_dist = base_distribution;\\n this.reinterpreted_batch_ndims = reinterpreted_batch_ndims;\\n\\n _init(batch_shape, event_shape);\\n }\\n\\n private distributions.Distribution base_dist;\\n private int reinterpreted_batch_ndims;\\n\\n /// <summary>\\n /// Generates a sample_shape shaped sample or sample_shape shaped batch of samples if the distribution parameters are batched.\\n /// </summary>\\n /// <param name=\\\"sample_shape\\\"></param>\\n public override Tensor sample(params long[] sample_shape)\\n {\\n return base_dist.sample(sample_shape);\\n }\\n\\n\\n /// <summary>\\n /// Generates a sample_shape shaped reparameterized sample or sample_shape shaped batch of reparameterized samples\\n /// if the distribution parameters are batched.\\n /// </summary>\\n /// <param name=\\\"sample_shape\\\">The sample shape.</param>\\n public override Tensor rsample(params long[] sample_shape)\\n {\\n return base_dist.rsample(sample_shape);\\n }\\n\\n /// <summary>\\n /// Returns the log of the probability density/mass function evaluated at `value`.\\n /// </summary>\\n /// <param name=\\\"value\\\"></param>\\n public override Tensor log_prob(Tensor value)\\n {\\n var lp = base_dist.log_prob(value);\\n return distributions.transforms.Transform._sum_rightmost(lp, reinterpreted_batch_ndims);\\n }\\n\\n /// <summary>\\n /// Returns entropy of distribution, batched over batch_shape.\\n /// </summary>\\n public override Tensor entropy()\\n {\\n var ent = base_dist.entropy();\\n return distributions.transforms.Transform._sum_rightmost(ent, reinterpreted_batch_ndims);\\n }\\n\\n /// <summary>\\n /// Returns a new distribution instance (or populates an existing instance provided by a derived class) with batch dimensions expanded to\\n /// `batch_shape`. This method calls `torch.Tensor.expand()` on the distribution's parameters. As such, this does not allocate new\\n /// memory for the expanded distribution instance.\\n /// </summary>\\n /// <param name=\\\"batch_shape\\\">Tthe desired expanded size.</param>\\n /// <param name=\\\"instance\\\">new instance provided by subclasses that need to override `.expand`.</param>\\n public override distributions.Distribution expand(Size batch_shape, distributions.Distribution instance = null)\\n {\\n if (instance != null && !(instance is Independent))\\n throw new ArgumentException(\\\"expand(): 'instance' must be a Independent distribution\\\");\\n\\n var newDistribution = ((instance == null) ? new Independent(\\n base_dist.expand(batch_shape + event_shape.Slice(0, reinterpreted_batch_ndims)),\\n reinterpreted_batch_ndims,\\n generator) : instance) as Independent;\\n\\n if (newDistribution == instance) {\\n newDistribution._init(batch_shape, event_shape);\\n }\\n return newDistribution;\\n }\\n }\\n\\n }\\n public static partial class torch\\n {\\n public static partial class distributions\\n {\\n /// <summary>\\n /// Reinterprets some of the batch dims of a distribution as event dims.\\n /// This is mainly useful for changing the shape of the result of `log_prob`.\\n /// </summary>\\n /// <param name=\\\"base_distribution\\\">A base distribution.</param>\\n /// <param name=\\\"reinterpreted_batch_dims\\\">the number of batch dims to reinterpret as event dims</param>\\n /// <param name=\\\"generator\\\">An optional random number generator object.</param>\\n /// <returns></returns>\\n public static Independent Independent(Distribution base_distribution, int reinterpreted_batch_dims, torch.Generator generator = null)\\n {\\n return new Independent(base_distribution, reinterpreted_batch_dims, generator);\\n }\\n }\\n }\\n}\\n\"\n },\n {\n \"alpha_fraction\": 0.5152718424797058,\n \"alphanum_fraction\": 0.5328344702720642,\n \"avg_line_length\": 42.6533317565918,\n \"blob_id\": \"5e6d365362f48eca33f312db9a64d49012c25e71\",\n \"content_id\": \"fe754ffe3bb6af6647e8920a48d4733f590d3c82\",\n \"detected_licenses\": [\n \"MIT\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"C#\",\n \"length_bytes\": 6548,\n \"license_type\": \"permissive\",\n \"max_line_length\": 149,\n \"num_lines\": 150,\n \"path\": \"/src/TorchSharp/Distributions/FisherSnedecor.cs\",\n \"repo_name\": \"NiklasGustafsson/TorchSharp\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"// Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information.\\nusing System;\\nusing static TorchSharp.torch;\\n\\nnamespace TorchSharp\\n{\\n using Modules;\\n\\n namespace Modules\\n {\\n /// <summary>\\n /// A Fisher-Snedecor distribution parameterized by `df1` and `df2`.\\n /// </summary>\\n public class FisherSnedecor : torch.distributions.Distribution\\n {\\n /// <summary>\\n /// The mean of the distribution.\\n /// </summary>\\n public override Tensor mean {\\n get {\\n var df2 = this.df2.clone();\\n df2[df2 <= 2] = torch.tensor(float.NaN);\\n return df2 / (df2 - 2);\\n }\\n }\\n\\n /// <summary>\\n /// The variance of the distribution\\n /// </summary>\\n public override Tensor variance {\\n get {\\n using var _ = torch.NewDisposeScope();\\n var df2 = this.df2.clone();\\n df2[df2 <= 4] = torch.tensor(float.NaN);\\n return (2 * df2.pow(2) * (this.df1 + df2 - 2) / (this.df1 * (df2 - 2).pow(2) * (df2 - 4))).MoveToOuterDisposeScope();\\n }\\n }\\n\\n /// <summary>\\n /// Constructor\\n /// </summary>\\n /// <param name=\\\"df1\\\">Degrees of freedom parameter 1</param>\\n /// <param name=\\\"df2\\\">Degrees of freedom parameter 2</param>\\n /// <param name=\\\"generator\\\">An optional random number generator object.</param>\\n public FisherSnedecor(Tensor df1, Tensor df2, torch.Generator generator = null) : base(generator)\\n {\\n var bcast = torch.broadcast_tensors(df1, df2);\\n this.df1 = bcast[0].DetachFromDisposeScope();\\n this.df2 = bcast[1].DetachFromDisposeScope();\\n this.gamma1 = new Gamma(this.df1 * 0.5, this.df1, generator);\\n this.gamma2 = new Gamma(this.df2 * 0.5, this.df2, generator);\\n this.batch_shape = this.df1.size();\\n }\\n\\n private Tensor df1;\\n private Tensor df2;\\n private Gamma gamma1;\\n private Gamma gamma2;\\n\\n /// <summary>\\n /// Generates a sample_shape shaped reparameterized sample or sample_shape shaped batch of reparameterized samples\\n /// if the distribution parameters are batched.\\n /// </summary>\\n /// <param name=\\\"sample_shape\\\">The sample shape.</param>\\n public override Tensor rsample(params long[] sample_shape)\\n {\\n using var _ = torch.NewDisposeScope();\\n var shape = ExtendedShape(sample_shape);\\n var X1 = gamma1.rsample(sample_shape).view(shape);\\n var X2 = gamma2.rsample(sample_shape).view(shape);\\n\\n var tiny = torch.finfo(X2.dtype).tiny;\\n X2.clamp_(min: tiny);\\n var Y = X1 / X2;\\n \\n return Y.clamp_(min: tiny).MoveToOuterDisposeScope();\\n }\\n\\n /// <summary>\\n /// Returns the log of the probability density/mass function evaluated at `value`.\\n /// </summary>\\n /// <param name=\\\"value\\\"></param>\\n public override Tensor log_prob(Tensor value)\\n {\\n using var _ = torch.NewDisposeScope();\\n var ct1 = this.df1 * 0.5;\\n var ct2 = this.df2 * 0.5;\\n var ct3 = this.df1 / this.df2;\\n var t1 = (ct1 + ct2).lgamma() - ct1.lgamma() - ct2.lgamma();\\n var t2 = ct1 * ct3.log() + (ct1 - 1) * torch.log(value);\\n var t3 = (ct1 + ct2) * torch.log1p(ct3 * value);\\n return (t1 + t2 - t3).MoveToOuterDisposeScope();\\n }\\n\\n /// <summary>\\n /// Returns a new distribution instance (or populates an existing instance provided by a derived class) with batch dimensions expanded to\\n /// `batch_shape`. This method calls `torch.Tensor.expand()` on the distribution's parameters. As such, this does not allocate new\\n /// memory for the expanded distribution instance.\\n /// </summary>\\n /// <param name=\\\"batch_shape\\\">Tthe desired expanded size.</param>\\n /// <param name=\\\"instance\\\">new instance provided by subclasses that need to override `.expand`.</param>\\n public override distributions.Distribution expand(Size batch_shape, distributions.Distribution instance = null)\\n {\\n if (instance != null && !(instance is FisherSnedecor))\\n throw new ArgumentException(\\\"expand(): 'instance' must be a FisherSnedecor distribution\\\");\\n\\n var df1 = this.df1.expand(batch_shape);\\n var df2 = this.df2.expand(batch_shape);\\n\\n var newDistribution = ((instance == null) ? new FisherSnedecor(df1, df2, generator) : instance) as FisherSnedecor;\\n\\n newDistribution.batch_shape = batch_shape;\\n if (newDistribution == instance) {\\n newDistribution.df1 = df1;\\n newDistribution.df2 = df2;\\n newDistribution.gamma1 = this.gamma1.expand(batch_shape) as Gamma;\\n newDistribution.gamma2 = this.gamma2.expand(batch_shape) as Gamma;\\n\\n }\\n return newDistribution;\\n }\\n\\n /// <summary>\\n /// Returns entropy of distribution, batched over batch_shape.\\n /// </summary>\\n public override Tensor entropy()\\n {\\n throw new NotImplementedException();\\n }\\n }\\n\\n }\\n public static partial class torch\\n {\\n public static partial class distributions\\n {\\n /// <summary>\\n /// Creates a Fisher-Snedecor distribution parameterized by `df1` and `df2`.\\n /// </summary>\\n /// <param name=\\\"df1\\\">Degrees of freedom parameter 1</param>\\n /// <param name=\\\"df2\\\">Degrees of freedom parameter 2</param>\\n /// <param name=\\\"generator\\\">An optional random number generator object.</param>\\n /// <returns></returns>\\n public static FisherSnedecor FisherSnedecor(Tensor df1, Tensor df2, torch.Generator generator = null)\\n {\\n return new FisherSnedecor(df1, df2, generator);\\n }\\n }\\n }\\n}\\n\"\n },\n {\n \"alpha_fraction\": 0.7052238583564758,\n \"alphanum_fraction\": 0.7077114582061768,\n \"avg_line_length\": 33.956520080566406,\n \"blob_id\": \"d630445cd725c592e3614ff3d13fb136ef8c65c9\",\n \"content_id\": \"fc67a88de47acc9ef191cccdd65a17317b439531\",\n \"detected_licenses\": [\n \"MIT\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"C#\",\n \"length_bytes\": 804,\n \"license_type\": \"permissive\",\n \"max_line_length\": 130,\n \"num_lines\": 23,\n \"path\": \"/src/TorchSharp/PInvoke/LibTorchSharp.THSTorchCuda.cs\",\n \"repo_name\": \"NiklasGustafsson/TorchSharp\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"// Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information.\\n#nullable enable\\nusing System.Runtime.InteropServices;\\n\\nnamespace TorchSharp.PInvoke\\n{\\n internal static partial class NativeMethods\\n {\\n [DllImport(\\\"LibTorchSharp\\\")]\\n [return: MarshalAs(UnmanagedType.U1)]\\n internal static extern bool THSTorchCuda_is_available();\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n [return: MarshalAs(UnmanagedType.U1)]\\n internal static extern bool THSTorchCuda_cudnn_is_available();\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern int THSTorchCuda_device_count();\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern void THSTorchCuda_synchronize(long device_index);\\n }\\n}\\n\"\n },\n {\n \"alpha_fraction\": 0.5449577569961548,\n \"alphanum_fraction\": 0.5449577569961548,\n \"avg_line_length\": 33.11864471435547,\n \"blob_id\": \"992bb272137652ad68ba5a325280bf1016c33afe\",\n \"content_id\": \"be913efd27d11dfdf7de835f59fddb31a6b9e352\",\n \"detected_licenses\": [\n \"MIT\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"C#\",\n \"length_bytes\": 2013,\n \"license_type\": \"permissive\",\n \"max_line_length\": 130,\n \"num_lines\": 59,\n \"path\": \"/src/TorchVision/RandomChoice.cs\",\n \"repo_name\": \"NiklasGustafsson/TorchSharp\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"// Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information.\\n\\nusing System;\\nusing static TorchSharp.torch;\\n\\nnamespace TorchSharp\\n{\\n public static partial class torchvision\\n {\\n internal class RandomChoice : IDisposable, ITransform\\n {\\n public RandomChoice(Generator generator, ITransform[] transforms)\\n {\\n this.transforms = transforms;\\n this.generator = generator;\\n }\\n\\n public void Dispose()\\n {\\n foreach (var t in transforms) {\\n if (t is IDisposable) {\\n ((IDisposable)t).Dispose();\\n }\\n }\\n }\\n\\n public Tensor call(Tensor input)\\n {\\n var chance = torch.randint_int(transforms.Length, generator);\\n return transforms[chance].call(input);\\n }\\n\\n private ITransform[] transforms;\\n private Generator generator;\\n }\\n\\n public static partial class transforms\\n {\\n /// <summary>\\n /// Apply a single transformation randomly picked from a list. \\n /// </summary>\\n /// <param name=\\\"transforms\\\">A list of transforms to apply.</param>\\n static public ITransform RandomChoice(params ITransform[] transforms)\\n {\\n return new RandomChoice(null, transforms);\\n }\\n\\n /// <summary>\\n /// Apply a single transformation randomly picked from a list. \\n /// </summary>\\n /// <param name=\\\"generator\\\">A random number generator instance.</param>\\n /// <param name=\\\"transforms\\\">A list of transforms to apply.</param>\\n static public ITransform RandomChoice(Generator generator, params ITransform[] transforms)\\n {\\n return new RandomChoice(generator, transforms);\\n }\\n }\\n }\\n}\\n\"\n },\n {\n \"alpha_fraction\": 0.6129446029663086,\n \"alphanum_fraction\": 0.6145948171615601,\n \"avg_line_length\": 46.846492767333984,\n \"blob_id\": \"f931d728688c30958b1de42e6ffc6bfbb82f0d6d\",\n \"content_id\": \"554eb4de11865cbfbe1f8ad30f627e026f627c09\",\n \"detected_licenses\": [\n \"MIT\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"C#\",\n \"length_bytes\": 10908,\n \"license_type\": \"permissive\",\n \"max_line_length\": 186,\n \"num_lines\": 228,\n \"path\": \"/src/TorchSharp/Tensor/torch.RandomSampling.cs\",\n \"repo_name\": \"NiklasGustafsson/TorchSharp\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"// Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information.\\n#nullable enable\\nusing System;\\nusing System.Diagnostics.Contracts;\\nusing TorchSharp.PInvoke;\\nusing static TorchSharp.PInvoke.NativeMethods;\\n\\nnamespace TorchSharp\\n{\\n // https://pytorch.org/docs/stable/torch#random-sampling\\n public static partial class torch\\n {\\n // https://pytorch.org/docs/stable/generated/torch.seed\\n /// <summary>\\n /// Sets the seed for generating random numbers to a non-deterministic random number. Returns a 64 bit number used to seed the RNG.\\n /// </summary>\\n public static long seed() => torch.random.seed();\\n\\n // https://pytorch.org/docs/stable/generated/torch.manual_seed\\n /// <summary>\\n /// Sets the seed for generating random numbers. Returns a torch.Generator object.\\n /// </summary>\\n /// <param name=\\\"seed\\\">The desired seed.</param>\\n public static Generator manual_seed(long seed) => torch.random.manual_seed(seed);\\n\\n // https://pytorch.org/docs/stable/generated/torch.initial_seed\\n /// <summary>\\n /// Returns the initial seed for generating random numbers.\\n /// </summary>\\n public static long initial_seed() => torch.random.initial_seed();\\n\\n // https://pytorch.org/docs/stable/generated/torch.get_rng_state\\n /// <summary>\\n /// Returns the random number generator state as a torch.ByteTensor.\\n /// </summary>\\n public static Tensor get_rng_state() => torch.random.get_rng_state();\\n\\n // https://pytorch.org/docs/stable/generated/torch.set_rng_state\\n /// <summary>\\n /// Sets the random number generator state.\\n /// </summary>\\n /// <param name=\\\"new_state\\\">The desired state</param>\\n public static void set_rng_state(Tensor new_state) => torch.random.set_rng_state(new_state);\\n\\n // https://pytorch.org/docs/stable/generated/torch.bernoulli\\n /// <summary>\\n /// Draws binary random numbers (0 or 1) from a Bernoulli distribution.\\n /// </summary>\\n /// <param name=\\\"input\\\">The input tensor of probability values for the Bernoulli distribution</param>\\n /// <param name=\\\"generator\\\">Optional random number generator</param>\\n /// <returns></returns>\\n public static Tensor bernoulli(Tensor input, Generator? generator = null) => input.bernoulli(generator);\\n\\n // https://pytorch.org/docs/stable/generated/torch.multinomial\\n /// <summary>\\n /// Returns a tensor where each row contains num_samples indices sampled from the multinomial probability distribution located in the corresponding row of tensor input.\\n /// </summary>\\n /// <param name=\\\"input\\\">A probabilities tensor</param>\\n /// <param name=\\\"num_samples\\\">Number of samples to draw</param>\\n /// <param name=\\\"replacement\\\">Whether to draw with replacement or not</param>\\n /// <param name=\\\"generator\\\">Optional random number generator</param>\\n public static Tensor multinomial(Tensor input, long num_samples, bool replacement = false, Generator? generator = null) => input.multinomial(num_samples, replacement, generator);\\n\\n // https://pytorch.org/docs/stable/generated/torch.normal\\n /// <summary>\\n /// Returns a tensor of random numbers drawn from separate normal distributions whose mean and standard deviation are given.\\n /// </summary>\\n /// <param name=\\\"mean\\\">The tensor of per-element means</param>\\n /// <param name=\\\"std\\\">The tensor of per-element standard deviations</param>\\n /// <param name=\\\"generator\\\">An optional random number generator</param>\\n /// <returns></returns>\\n /// <exception cref=\\\"ArgumentException\\\"></exception>\\n public static Tensor normal(Tensor mean, Tensor std, Generator? generator = null)\\n {\\n if (std.device_type != mean.device_type || (std.device_type == DeviceType.CUDA && std.device_index != mean.device_index))\\n throw new ArgumentException(\\\"The 'means' and 'stddev' tensors must be located on the same device.\\\");\\n return randn(mean.shape, null, std.device, false, generator, names: null) * std + mean;\\n }\\n\\n // https://pytorch.org/docs/stable/generated/torch.poisson\\n /// <summary>\\n /// Returns a tensor of the same size as input with each element sampled from a Poisson distribution with rate parameter given by the corresponding element in input\\n /// </summary>\\n /// <param name=\\\"input\\\">Input tensor.</param>\\n /// <param name=\\\"generator\\\">Optional random number generator</param>\\n /// <returns></returns>\\n public static Tensor poisson(Tensor input, Generator? generator = null) => input.poisson(generator);\\n\\n // https://pytorch.org/docs/stable/generated/torch.rand\\n // TODO: implement layout parameter\\n static Tensor rand(\\n long[] size,\\n ScalarType? dtype = null,\\n layout layout = layout.strided,\\n Device? device = null,\\n bool requires_grad = false)\\n => rand(size, dtype, device, requires_grad);\\n\\n // https://pytorch.org/docs/stable/generated/torch.rand_like\\n // TODO: implement layout parameter\\n // TODO: implement memory_format parameter\\n static Tensor rand_like(\\n Tensor input,\\n ScalarType? dtype=null,\\n layout layout = layout.strided,\\n Device? device=null,\\n bool requires_grad=false,\\n memory_format memory_format = memory_format.preserve_format)\\n => rand_like(input, dtype, device, requires_grad);\\n\\n // https://pytorch.org/docs/stable/generated/torch.randint\\n // TODO: implement layout parameter\\n static Tensor randint(long low, long high, Size size,\\n Generator? generator = null,\\n ScalarType? dtype = null,\\n layout layout = layout.strided,\\n Device? device = null,\\n bool requires_grad = false)\\n => randint(low, high, size, dtype, device, requires_grad, generator);\\n\\n // TODO: implement layout parameter\\n static Tensor randint(long high, Size size,\\n Generator? generator = null,\\n ScalarType? dtype = null,\\n layout layout = layout.strided,\\n Device? device = null,\\n bool requires_grad = false)\\n => randint(high, size, dtype, device, requires_grad, generator);\\n\\n // https://pytorch.org/docs/stable/generated/torch.randint_like\\n // TODO: implement layout parameter\\n // TODO: implement memory_format parameter\\n static Tensor randint_like(\\n Tensor input,\\n long low,\\n long high,\\n ScalarType? dtype = null,\\n layout layout = layout.strided,\\n Device? device = null,\\n bool requires_grad = false,\\n memory_format memory_format = memory_format.preserve_format)\\n => randint_like(input, low, high, dtype, device, requires_grad);\\n\\n // https://pytorch.org/docs/stable/generated/torch.randn\\n // TODO: implement layout parameter\\n static Tensor randn(\\n long[] size,\\n Generator? generator = null,\\n ScalarType? dtype = null,\\n layout layout = layout.strided,\\n Device? device = null,\\n bool requires_grad = false)\\n => randn(size, dtype, device, requires_grad, generator);\\n\\n // https://pytorch.org/docs/stable/generated/torch.randn_like\\n /// <summary>\\n /// Returns a tensor with the same size as input that is filled with random numbers from a uniform distribution on the interval [0,1) .\\n /// </summary>\\n // TODO: implement layout parameter\\n // TODO: implement memory_format parameter\\n static Tensor randn_like(\\n Tensor input,\\n ScalarType? dtype = null,\\n layout layout = layout.strided,\\n Device? device = null,\\n bool requires_grad = false,\\n memory_format memory_format = memory_format.preserve_format)\\n => input.rand_like(dtype, device, requires_grad);\\n\\n // https://pytorch.org/docs/stable/generated/torch.randn_like\\n /// <summary>\\n /// Returns a tensor with the same size as input that is filled with random numbers from a uniform distribution on the interval [0,1) .\\n /// </summary>\\n public static Tensor rand_like(Tensor input, ScalarType? dtype = null, Device? device = null, bool requires_grad = false)\\n => input.rand_like(dtype, device, requires_grad);\\n\\n // https://pytorch.org/docs/stable/generated/torch.randperm\\n /// <summary>\\n /// Creates 1-D tensor of size [n] with a random permutation of [0, n).\\n /// </summary>\\n public static Tensor randperm(long n, ScalarType? dtype = null, Device? device = null,\\n bool requires_grad = false, Generator? generator = null)\\n => randperm(n, generator, dtype, layout.strided, device, false);\\n\\n /// <summary>\\n /// Mutates the existing tensor to be a 1-D tensor of size [n] with a random permutation of [0, n).\\n /// </summary>\\n public static Tensor randperm(long n,\\n Tensor @out,\\n Generator? generator = null)\\n {\\n var genHandle = generator?.Handle ?? IntPtr.Zero;\\n var res = NativeMethods.THSTensor_randperm_out(genHandle, n, @out.Handle);\\n if (res == IntPtr.Zero) { CheckForErrors(); }\\n return new Tensor(res);\\n }\\n\\n // https://pytorch.org/docs/stable/generated/torch.randperm\\n /// <summary>\\n /// Creates 1-D tensor of size [n] with a random permutation of [0, n).\\n /// </summary>\\n // TODO: implement layout parameter\\n // TODO: implement pin_memory parameter\\n static Tensor randperm(\\n long n,\\n Generator? generator = null,\\n ScalarType? dtype = ScalarType.Int64,\\n layout layout = layout.strided,\\n Device? device = null,\\n bool requires_grad = false,\\n bool pin_memory = false)\\n {\\n device = InitializeDevice(device);\\n dtype ??= ScalarType.Int64;\\n\\n var genHandle = generator?.Handle ?? IntPtr.Zero;\\n\\n var handle = THSTensor_randperm(genHandle, n, (sbyte)dtype, (int)device.type, device.index, requires_grad);\\n if (handle == IntPtr.Zero) {\\n GC.Collect();\\n GC.WaitForPendingFinalizers();\\n handle = THSTensor_randperm(genHandle, n, (sbyte)dtype, (int)device.type, device.index, requires_grad);\\n }\\n if (handle == IntPtr.Zero) { CheckForErrors(); }\\n return new Tensor(handle);\\n }\\n }\\n}\"\n },\n {\n \"alpha_fraction\": 0.5832617878913879,\n \"alphanum_fraction\": 0.5836910009384155,\n \"avg_line_length\": 39.17241287231445,\n \"blob_id\": \"cb554b4a5683a579127722ca7974247e3ccc7555\",\n \"content_id\": \"71c7b6a23d98d2948bc1a5ad8840af112aa859a8\",\n \"detected_licenses\": [\n \"MIT\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"C#\",\n \"length_bytes\": 2330,\n \"license_type\": \"permissive\",\n \"max_line_length\": 134,\n \"num_lines\": 58,\n \"path\": \"/src/TorchSharp/NN/Unflatten.cs\",\n \"repo_name\": \"NiklasGustafsson/TorchSharp\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"// Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information.\\nusing System;\\nusing static TorchSharp.torch;\\nusing static TorchSharp.PInvoke.NativeMethods;\\n\\nnamespace TorchSharp\\n{\\n using Modules;\\n\\n namespace Modules\\n {\\n /// <summary>\\n /// This class is used to represent an unflattening operation.\\n /// </summary>\\n public sealed class Unflatten : torch.nn.Module<Tensor, Tensor>\\n {\\n internal Unflatten(IntPtr handle, IntPtr boxedHandle) : base(handle, boxedHandle)\\n {\\n }\\n\\n public override Tensor forward(Tensor tensor)\\n {\\n var res = THSNN_Unflatten_forward(handle, tensor.Handle);\\n if (res == IntPtr.Zero) { torch.CheckForErrors(); }\\n return new Tensor(res);\\n }\\n\\n // Rather than spending cycles only to discover that this module has neither\\n // parameters nor buffers, just shortcut the move completely.\\n protected internal override nn.Module _to(Device device, ScalarType dtype) => this;\\n protected internal override nn.Module _to(DeviceType deviceType, int deviceIndex = -1) => this;\\n protected internal override nn.Module _to(ScalarType dtype) => this;\\n }\\n }\\n\\n public static partial class torch\\n {\\n public static partial class nn\\n {\\n /// <summary>\\n /// Unflattens a tensor dim expanding it to a desired shape. For use with Sequential.\\n /// </summary>\\n /// <param name=\\\"dim\\\">Dimension to be unflattened</param>\\n /// <param name=\\\"unflattenedSize\\\">New shape of the unflattened dimension</param>\\n /// <returns></returns>\\n public static Unflatten Unflatten(long dim, long[] unflattenedSize)\\n {\\n unsafe {\\n fixed (long* pUnflattenedSize = unflattenedSize) {\\n var handle = THSNN_Unflatten_ctor(dim, (IntPtr)pUnflattenedSize, unflattenedSize.Length, out var boxedHandle);\\n if (handle == IntPtr.Zero) { CheckForErrors(); }\\n return new Unflatten(handle, boxedHandle);\\n }\\n }\\n }\\n }\\n }\\n}\\n\"\n },\n {\n \"alpha_fraction\": 0.46875,\n \"alphanum_fraction\": 0.46875,\n \"avg_line_length\": 27.935483932495117,\n \"blob_id\": \"815911655cd314a5515ee60aeb8c5d95e7e4b827\",\n \"content_id\": \"8e88cb4a4747cb1dfffc245e32ed06170c91f518\",\n \"detected_licenses\": [\n \"MIT\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"C#\",\n \"length_bytes\": 896,\n \"license_type\": \"permissive\",\n \"max_line_length\": 130,\n \"num_lines\": 31,\n \"path\": \"/src/TorchAudio/Datasets/YesnoDatasetItem.cs\",\n \"repo_name\": \"NiklasGustafsson/TorchSharp\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"// Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information.\\n\\nnamespace TorchSharp\\n{\\n public static partial class torchaudio\\n {\\n public static partial class datasets\\n {\\n /// <summary>\\n /// An item in YESNO dataset.\\n /// </summary>\\n public struct YesnoDatasetItem\\n {\\n /// <summary>\\n /// Samples of the audio clip\\n /// </summary>\\n public torch.Tensor waveform;\\n\\n /// <summary>\\n /// Sampling rate of the audio clip\\n /// </summary>\\n public int sample_rate;\\n\\n /// <summary>\\n /// Labels of the audio clip\\n /// </summary>\\n public string[] labels;\\n }\\n }\\n }\\n}\"\n },\n {\n \"alpha_fraction\": 0.5160085558891296,\n \"alphanum_fraction\": 0.5280149579048157,\n \"avg_line_length\": 41.12359619140625,\n \"blob_id\": \"7af9f3ec848919e6cd5c245772688ed4e9109a8e\",\n \"content_id\": \"bef662f42e449066b2049b887f451a3ec1348638\",\n \"detected_licenses\": [\n \"MIT\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"C#\",\n \"length_bytes\": 3748,\n \"license_type\": \"permissive\",\n \"max_line_length\": 163,\n \"num_lines\": 89,\n \"path\": \"/src/TorchVision/RandomPerspective.cs\",\n \"repo_name\": \"NiklasGustafsson/TorchSharp\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"// Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information.\\nusing System;\\nusing System.Collections.Generic;\\nusing System.Linq;\\nusing static TorchSharp.torch;\\n\\nnamespace TorchSharp\\n{\\n public static partial class torchvision\\n {\\n internal class RandomPerspective : ITransform\\n {\\n public RandomPerspective(double distortion, double p)\\n {\\n this.distortion = distortion;\\n this.p = p;\\n this.rnd = new Random();\\n }\\n\\n public Tensor call(Tensor img)\\n {\\n if (rnd.NextDouble() <= p) {\\n var _end = img.shape.Length;\\n var w = (int)img.shape[_end - 1];\\n var h = (int)img.shape[_end - 2];\\n\\n var (startpoints, endpoints) = GetParams(w, h);\\n return transforms.functional.perspective(img, startpoints, endpoints);\\n }\\n\\n return img;\\n }\\n\\n private int Adjust(double input, double min, double max)\\n {\\n return (int)(Math.Floor(input * (max - min) + min));\\n }\\n\\n private (List<IList<int>>, List<IList<int>>) GetParams(int width, int height)\\n {\\n var half_width = width / 2;\\n var half_height = height / 2;\\n\\n var randoms = torch.rand(8, ScalarType.Float64).data<double>().ToArray();\\n\\n var topleft = new int[] {\\n Adjust(randoms[0], 0, distortion * half_width + 1),\\n Adjust(randoms[1], 0, distortion * half_height + 1)\\n };\\n var topright = new int[] {\\n Adjust(randoms[2], width - (int)Math.Floor(distortion * half_width) - 1, width),\\n Adjust(randoms[3], 0, (int)Math.Floor(distortion * half_height) + 1)\\n };\\n var botright = new int[] {\\n Adjust(randoms[4], width - (int)Math.Floor(distortion * half_width) - 1, width),\\n Adjust(randoms[5], height - (int)Math.Floor(distortion * half_height) - 1, height),\\n };\\n var botleft = new int[] {\\n Adjust(randoms[6], 0, (int)Math.Floor(distortion * half_width) + 1),\\n Adjust(randoms[7], height - (int)Math.Floor(distortion * half_height) - 1, height),\\n };\\n\\n var startpoints = new int[][] { new int[] { 0, 0 }, new int[] { width - 1, 0 }, new int[] { width - 1, height - 1 }, new int[] { 0, height - 1 } };\\n var endpoints = new int[][] { topleft, topright, botright, botleft }.ToList();\\n\\n return (startpoints.Select(x => x.ToList() as IList<int>).ToList(), endpoints.Select(x => x.ToList() as IList<int>).ToList());\\n }\\n\\n private Random rnd;\\n private double p;\\n private double distortion;\\n }\\n\\n public static partial class transforms\\n {\\n /// <summary>\\n /// Performs a random perspective transformation of the given image with a given probability.\\n /// </summary>\\n /// <param name=\\\"distortion\\\">Argument to control the degree of distortion and ranges from 0 to 1. Default is 0.5.</param>\\n /// <param name=\\\"p\\\">Probability of the image being transformed. Default is 0.5.</param>\\n /// <returns></returns>\\n /// <remarks>The application and perspectives are all stochastic.</remarks>\\n static public ITransform RandomPerspective(double distortion = 0.5, double p = 0.5)\\n {\\n return new RandomPerspective(distortion, p);\\n }\\n }\\n }\\n}\"\n },\n {\n \"alpha_fraction\": 0.6189464926719666,\n \"alphanum_fraction\": 0.6189464926719666,\n \"avg_line_length\": 47.06122589111328,\n \"blob_id\": \"6d1e27c817186267dbe4236ade2187b3715276f9\",\n \"content_id\": \"e3cf8208c46e5f233794cbd5bdf4fa5f2714a30c\",\n \"detected_licenses\": [\n \"MIT\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"C#\",\n \"length_bytes\": 2354,\n \"license_type\": \"permissive\",\n \"max_line_length\": 288,\n \"num_lines\": 49,\n \"path\": \"/src/TorchVision/Rotate.cs\",\n \"repo_name\": \"NiklasGustafsson/TorchSharp\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"// Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information.\\n\\nusing System.Collections.Generic;\\nusing static TorchSharp.torch;\\n\\nnamespace TorchSharp\\n{\\n public static partial class torchvision\\n {\\n internal class Rotate : ITransform\\n {\\n internal Rotate(float angle, InterpolationMode interpolation = InterpolationMode.Nearest, bool expand = false, (int, int)? center = null, IList<float> fill = null)\\n {\\n this.angle = angle;\\n this.fill = fill;\\n this.expand = expand;\\n this.center = center;\\n this.interpolation = interpolation;\\n }\\n\\n public torch.Tensor call(torch.Tensor img)\\n {\\n return transforms.functional.rotate(img, angle, interpolation, expand, center, fill);\\n }\\n\\n private float angle;\\n private bool expand;\\n private (int, int)? center;\\n private IList<float> fill;\\n private InterpolationMode interpolation;\\n }\\n\\n public static partial class transforms\\n {\\n /// <summary>\\n /// Rotate the image by angle, counter-clockwise.\\n /// </summary>\\n /// <param name=\\\"angle\\\">Angle by which to rotate.</param>\\n /// <param name=\\\"interpolation\\\">Desired interpolation enum. Default is `InterpolationMode.NEAREST`.</param>\\n /// <param name=\\\"expand\\\">If true, expands the output to make it large enough to hold the entire rotated image. If false or omitted, make the output image the same size as the input image. Note that the expand flag assumes rotation around the center and no translation.</param>\\n /// <param name=\\\"center\\\">Center of rotation, (x, y). Origin is the upper left corner.</param>\\n /// <param name=\\\"fill\\\">Pixel fill value for the area outside the rotated. If given a number, the value is used for all bands respectively.</param>\\n static public ITransform Rotate(float angle, InterpolationMode interpolation = InterpolationMode.Nearest, bool expand = false, (int, int)? center = null, IList<float> fill = null)\\n {\\n return new Rotate(angle, interpolation, expand, center, fill);\\n }\\n }\\n }\\n}\"\n },\n {\n \"alpha_fraction\": 0.5113962292671204,\n \"alphanum_fraction\": 0.5249078869819641,\n \"avg_line_length\": 49.53792953491211,\n \"blob_id\": \"7bdf7e92ae5e45e8d1a5d2721182013ede7cfaf0\",\n \"content_id\": \"882563c74641a8196b34be15a091a6ff03cfa585\",\n \"detected_licenses\": [\n \"MIT\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"C#\",\n \"length_bytes\": 7327,\n \"license_type\": \"permissive\",\n \"max_line_length\": 182,\n \"num_lines\": 145,\n \"path\": \"/src/TorchAudio/Datasets/SpeechCommandsDataset.cs\",\n \"repo_name\": \"NiklasGustafsson/TorchSharp\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"// Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information.\\nusing System;\\nusing System.Collections.Generic;\\nusing System.IO;\\nusing System.Linq;\\n\\nnamespace TorchSharp\\n{\\n public static partial class torchaudio\\n {\\n public static partial class datasets\\n {\\n private class SpeechCommandsDataset : torch.utils.data.Dataset<SpeechCommandsDatasetItem>\\n {\\n internal const string URL = \\\"speech_commands_v0.02\\\";\\n internal const string FOLDER_IN_ARCHIVE = \\\"SpeechCommands\\\";\\n private const string HASH_DIVIDER = \\\"_nohash_\\\";\\n private const string EXCEPT_FOLDER = \\\"_background_noise_\\\";\\n internal static readonly IDictionary<string, string> _CHECKSUMS = new Dictionary<string, string> {\\n [\\\"https://storage.googleapis.com/download.tensorflow.org/data/speech_commands_v0.01.tar.gz\\\"] = \\\"743935421bb51cccdb6bdd152e04c5c70274e935c82119ad7faeec31780d811d\\\",\\n [\\\"https://storage.googleapis.com/download.tensorflow.org/data/speech_commands_v0.02.tar.gz\\\"] = \\\"af14739ee7dc311471de98f5f9d2c9191b18aedfe957f4a6ff791c709868ff58\\\",\\n };\\n\\n private string _path;\\n private string[] _walker;\\n\\n internal SpeechCommandsDataset(string path, string subset)\\n {\\n _path = path;\\n if (subset == \\\"validation\\\") {\\n _walker = LoadList(_path, \\\"validation_list.txt\\\");\\n } else if (subset == \\\"testing\\\") {\\n _walker = LoadList(_path, \\\"testing_list.txt\\\");\\n } else if (subset == \\\"training\\\") {\\n var excludes = new HashSet<string>(LoadList(_path, \\\"validation_list.txt\\\", \\\"testing_list.txt\\\"));\\n _walker = Directory.EnumerateFiles(_path, \\\"*.wav\\\", SearchOption.AllDirectories)\\n .Where(audioPath => audioPath.Contains(HASH_DIVIDER) && !audioPath.Contains(EXCEPT_FOLDER))\\n .Where(audioPath => !excludes.Contains(audioPath))\\n .OrderBy(audioPath => audioPath)\\n .ToArray();\\n } else {\\n _walker = Directory.EnumerateFiles(_path, \\\"*.wav\\\", SearchOption.AllDirectories)\\n .Where(audioPath => audioPath.Contains(HASH_DIVIDER) && !audioPath.Contains(EXCEPT_FOLDER))\\n .OrderBy(audioPath => audioPath)\\n .ToArray();\\n }\\n }\\n\\n public override long Count => _walker.LongLength;\\n\\n public override SpeechCommandsDatasetItem GetTensor(long index)\\n {\\n var audioPath = _walker[index];\\n return LoadSpeechCommandsItem(audioPath);\\n }\\n\\n private string[] LoadList(string root, params string[] filenames)\\n {\\n List<string> output = new();\\n foreach (var filename in filenames) {\\n var filepath = Path.Combine(root, filename);\\n var pathList = File.ReadAllLines(filepath)\\n .Select(line => Path.Combine(root, Path.Combine(line.Trim())));\\n output.AddRange(pathList);\\n }\\n return output.ToArray();\\n }\\n\\n private SpeechCommandsDatasetItem LoadSpeechCommandsItem(string filepath)\\n {\\n var filename = Path.GetFileName(filepath);\\n var label = Path.GetFileName(Path.GetDirectoryName(filepath));\\n\\n // Some filenames have the form of \\\"xxx.wav.wav\\\"\\n var speaker = Path.GetFileNameWithoutExtension(filename);\\n speaker = Path.GetFileNameWithoutExtension(filename);\\n\\n var parts = speaker.Split(new string[] { HASH_DIVIDER }, StringSplitOptions.None);\\n var speaker_id = parts[0];\\n int utterance_number = int.Parse(parts[1]);\\n\\n // Load audio\\n var (waveform, sample_rate) = torchaudio.load(filepath);\\n return new SpeechCommandsDatasetItem {\\n waveform = waveform,\\n sample_rate = sample_rate,\\n label = label,\\n speaker_id = speaker_id,\\n utterance_number = utterance_number\\n };\\n }\\n }\\n\\n /// <summary>\\n /// Create a Speech Commands dataset\\n /// </summary>\\n /// <param name=\\\"root\\\">The path to the dataset</param>\\n /// <param name=\\\"url\\\">The URL to download the dataset from</param>\\n /// <param name=\\\"folder_in_archive\\\">The top directory of the dataset</param>\\n /// <param name=\\\"download\\\">True to download the dataset</param>\\n /// <param name=\\\"subset\\\">Select a subset of the dataset, null, \\\"training\\\", \\\"validation\\\" or \\\"testing\\\"</param>\\n /// <returns>The dataset</returns>\\n /// <exception cref=\\\"InvalidDataException\\\"></exception>\\n public static torch.utils.data.Dataset<SpeechCommandsDatasetItem> SPEECHCOMMANDS(\\n string root,\\n string url = SpeechCommandsDataset.URL,\\n string folder_in_archive = SpeechCommandsDataset.FOLDER_IN_ARCHIVE,\\n bool download = false,\\n string subset = null)\\n {\\n if (url == \\\"speech_commands_v0.01\\\" || url == \\\"speech_commands_v0.02\\\") {\\n string base_url = \\\"https://storage.googleapis.com/download.tensorflow.org/data/\\\";\\n string ext_archive = \\\".tar.gz\\\";\\n url = base_url + url + ext_archive;\\n }\\n\\n string[] parts = url.Split('/');\\n string basename = parts[parts.Length - 1];\\n string archive = Path.Combine(root, basename);\\n int index = basename.LastIndexOf('.');\\n index = basename.LastIndexOf('.', index - 1);\\n basename = basename.Substring(0, index);\\n folder_in_archive = Path.Combine(folder_in_archive, basename);\\n\\n string path = Path.Combine(root, folder_in_archive);\\n\\n if (download) {\\n if (!Directory.Exists(path)) {\\n if (!File.Exists(archive)) {\\n string checksum = SpeechCommandsDataset._CHECKSUMS.TryGetValue(url, out checksum) ? checksum : null;\\n torch.hub.download_url_to_file(url, archive, hash_prefix: checksum);\\n }\\n utils.extract_archive(archive, path);\\n }\\n } else {\\n if (!Directory.Exists(path)) {\\n throw new InvalidDataException(\\\"Dataset not found. Please use `download=true` to download it.\\\");\\n }\\n }\\n\\n return new SpeechCommandsDataset(path, subset);\\n }\\n }\\n }\\n}\"\n },\n {\n \"alpha_fraction\": 0.5559908747673035,\n \"alphanum_fraction\": 0.5563173294067383,\n \"avg_line_length\": 39.30263137817383,\n \"blob_id\": \"2195b86025076ed9a7f91d5da7a2406647f22870\",\n \"content_id\": \"cfd9ea1c7a343b03c761495d3017338438f2db1d\",\n \"detected_licenses\": [\n \"MIT\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"C#\",\n \"length_bytes\": 3063,\n \"license_type\": \"permissive\",\n \"max_line_length\": 130,\n \"num_lines\": 76,\n \"path\": \"/src/TorchSharp/NN/Activation/Threshold.cs\",\n \"repo_name\": \"NiklasGustafsson/TorchSharp\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"// Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information.\\nusing System;\\nusing static TorchSharp.torch;\\nusing static TorchSharp.PInvoke.NativeMethods;\\n\\nnamespace TorchSharp\\n{\\n using Modules;\\n\\n namespace Modules\\n {\\n /// <summary>\\n /// This class is used to represent a Threshold module.\\n /// </summary>\\n public sealed class Threshold : torch.nn.Module<Tensor, Tensor>\\n {\\n internal Threshold(IntPtr handle, IntPtr boxedHandle) : base(handle, boxedHandle) { }\\n\\n public override Tensor forward(Tensor tensor)\\n {\\n var res = THSNN_Threshold_forward(handle, tensor.Handle);\\n if (res == IntPtr.Zero) { torch.CheckForErrors(); }\\n return new Tensor(res);\\n }\\n\\n public override string GetName()\\n {\\n return typeof(Threshold).Name;\\n }\\n\\n // Rather than spending cycles only to discover that this module has neither\\n // parameters nor buffers, just shortcut the move completely.\\n protected internal override nn.Module _to(Device device, ScalarType dtype) => this;\\n protected internal override nn.Module _to(DeviceType deviceType, int deviceIndex = -1) => this;\\n protected internal override nn.Module _to(ScalarType dtype) => this;\\n }\\n }\\n\\n public static partial class torch\\n {\\n public static partial class nn\\n {\\n /// <summary>\\n /// Threshold\\n /// </summary>\\n /// <param name=\\\"threshold\\\">The value to threshold at</param>\\n /// <param name=\\\"value\\\">The value to replace with</param>\\n /// <param name=\\\"inplace\\\">Do the operation in-place</param>\\n /// <returns></returns>\\n public static Threshold Threshold(double threshold, double value, bool inplace = false)\\n {\\n var handle = THSNN_Threshold_ctor(threshold, value, inplace, out var boxedHandle);\\n if (handle == IntPtr.Zero) { torch.CheckForErrors(); }\\n return new Threshold(handle, boxedHandle);\\n }\\n\\n public static partial class functional\\n {\\n /// <summary>\\n /// Thresholds each element of the input Tensor.\\n /// </summary>\\n /// <param name=\\\"x\\\">The input tensor</param>\\n /// <param name=\\\"threshold\\\">The value to threshold at</param>\\n /// <param name=\\\"value\\\">The value to replace with</param>\\n /// <param name=\\\"inplace\\\">Do the operation in-place</param>\\n /// <returns></returns>\\n public static Tensor Threshold(Tensor x, double threshold, double value, bool inplace = false)\\n {\\n using (var m = nn.Threshold(threshold, value, inplace)) {\\n return m.call(x);\\n }\\n }\\n }\\n }\\n }\\n}\\n\"\n },\n {\n \"alpha_fraction\": 0.4495481252670288,\n \"alphanum_fraction\": 0.49807870388031006,\n \"avg_line_length\": 46.07872772216797,\n \"blob_id\": \"7eaf00b9ecefef34492f20cc69f50ee6358b3d12\",\n \"content_id\": \"91893eda7b1e0be285989fbecab0c21141e5a5c1\",\n \"detected_licenses\": [\n \"MIT\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"C#\",\n \"length_bytes\": 28106,\n \"license_type\": \"permissive\",\n \"max_line_length\": 180,\n \"num_lines\": 597,\n \"path\": \"/src/TorchVision/models/InceptionV3.cs\",\n \"repo_name\": \"NiklasGustafsson/TorchSharp\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"// Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information.\\n\\nusing static TorchSharp.torch;\\nusing static TorchSharp.torch.nn;\\n\\n#nullable enable\\nnamespace TorchSharp\\n{\\n public static partial class torchvision\\n {\\n public static partial class models\\n {\\n /// <summary>\\n /// ResNet-18\\n /// </summary>\\n /// <param name=\\\"num_classes\\\">The number of output classes.</param>\\n /// <param name=\\\"dropout\\\"></param>\\n /// <param name=\\\"transform_input\\\"></param>\\n /// <param name=\\\"weights_file\\\">The location of a file containing pre-trained weights for the model.</param>\\n /// <param name=\\\"skipfc\\\">If true, the last linear layer of the classifier will not be loaded from the weights file.</param>\\n /// <param name=\\\"device\\\">The device to locate the model on.</param>\\n /// <remarks>\\n /// Pre-trained weights may be retrieved by using Pytorch and saving the model state-dict\\n /// using the exportsd.py script, then loading into the .NET instance:\\n ///\\n /// from torchvision import models\\n /// import exportsd\\n /// \\n /// model = models.inception_v3(pretrained=True)\\n /// f = open(\\\"model_weights.dat\\\", \\\"wb\\\")\\n /// exportsd.save_state_dict(model.state_dict(), f)\\n /// f.close()\\n ///\\n /// See also: https://github.com/dotnet/TorchSharp/blob/main/docfx/articles/saveload.md\\n ///\\n /// In order for the weights to be loaded, the number of classes has to be the same as\\n /// in the pre-trained model, which is 1000.\\n ///\\n /// It is also possible to skip loading the last linear layer and use it for transfer-learning\\n /// with a different number of output classes. To do so, pass skipfc=true.\\n ///\\n /// All pre-trained models expect input images normalized in the same way, i.e. mini-batches of 3-channel RGB\\n /// images of shape (3 x H x W), where H and W are expected to be 299x299. The images have to be loaded\\n /// in to a range of [0, 1] and then normalized using mean = [0.485, 0.456, 0.406] and std = [0.229, 0.224, 0.225].\\n /// </remarks>\\n public static Modules.InceptionV3 inception_v3(\\n int num_classes = 1000,\\n float dropout = 0.5f,\\n bool transform_input = false,\\n string? weights_file = null,\\n bool skipfc = true,\\n Device? device = null)\\n {\\n return new Modules.InceptionV3(num_classes, dropout, transform_input, weights_file, skipfc, device);\\n }\\n }\\n }\\n\\n namespace Modules\\n {\\n public class InceptionV3 : Module<Tensor, Tensor>\\n {\\n // The code here is is loosely based on\\n // https://github.com/pytorch/vision/blob/main/torchvision/models/inception.py\\n // Licence and copypright notice at: https://github.com/pytorch/vision/blob/main/LICENSE\\n\\n private readonly Module<Tensor, Tensor> Conv2d_1a_3x3;\\n private readonly Module<Tensor, Tensor> Conv2d_2a_3x3;\\n private readonly Module<Tensor, Tensor> Conv2d_2b_3x3;\\n private readonly Module<Tensor, Tensor> maxpool1;\\n private readonly Module<Tensor, Tensor> Conv2d_3b_1x1;\\n private readonly Module<Tensor, Tensor> Conv2d_4a_3x3;\\n private readonly Module<Tensor, Tensor> maxpool2;\\n\\n private readonly Module<Tensor, Tensor> Mixed_5b;\\n private readonly Module<Tensor, Tensor> Mixed_5c;\\n private readonly Module<Tensor, Tensor> Mixed_5d;\\n private readonly Module<Tensor, Tensor> Mixed_6a;\\n private readonly Module<Tensor, Tensor> Mixed_6b;\\n private readonly Module<Tensor, Tensor> Mixed_6c;\\n private readonly Module<Tensor, Tensor> Mixed_6d;\\n private readonly Module<Tensor, Tensor> Mixed_6e;\\n private readonly Module<Tensor, Tensor> AuxLogits;\\n private readonly Module<Tensor, Tensor> Mixed_7a;\\n private readonly Module<Tensor, Tensor> Mixed_7b;\\n private readonly Module<Tensor, Tensor> Mixed_7c;\\n private readonly AdaptiveAvgPool2d avgpool;\\n private Dropout dropout;\\n private readonly Linear fc;\\n\\n bool transform_input = false;\\n\\n protected override void Dispose(bool disposing)\\n {\\n if (disposing) {\\n Conv2d_1a_3x3.Dispose(); Conv2d_2a_3x3.Dispose(); Conv2d_2b_3x3.Dispose();\\n Conv2d_3b_1x1.Dispose(); Conv2d_4a_3x3.Dispose();\\n Mixed_5b.Dispose(); Mixed_5c.Dispose(); Mixed_5d.Dispose();\\n Mixed_6a.Dispose(); Mixed_6b.Dispose(); Mixed_6c.Dispose();\\n Mixed_6d.Dispose(); Mixed_6e.Dispose();\\n AuxLogits.Dispose();\\n Mixed_7a.Dispose(); Mixed_7b.Dispose(); Mixed_7c.Dispose();\\n maxpool1.Dispose(); maxpool2.Dispose(); avgpool.Dispose();\\n dropout.Dispose(); fc.Dispose();\\n\\n }\\n base.Dispose(disposing);\\n }\\n\\n public InceptionV3(int numClasses = 1000,\\n float dropout = 0.5f,\\n bool transform_input = false,\\n string? weights_file = null,\\n bool skipfc = true,\\n Device? device = null) : base(nameof(InceptionV3))\\n {\\n this.transform_input = transform_input;\\n\\n Conv2d_1a_3x3 = conv_block(3, 32, kernel_size: 3, stride: 2);\\n Conv2d_2a_3x3 = conv_block(32, 32, kernel_size: 3);\\n Conv2d_2b_3x3 = conv_block(32, 64, kernel_size: 3, padding: 1);\\n maxpool1 = MaxPool2d(kernelSize: 3, stride: 2);\\n Conv2d_3b_1x1 = conv_block(64, 80, kernel_size: 1);\\n Conv2d_4a_3x3 = conv_block(80, 192, kernel_size: 3);\\n maxpool2 = MaxPool2d(kernelSize: 3, stride: 2);\\n\\n Mixed_5b = inception_a(192, pool_features: 32);\\n Mixed_5c = inception_a(256, pool_features: 64);\\n Mixed_5d = inception_a(288, pool_features: 64);\\n Mixed_6a = inception_b(288);\\n Mixed_6b = inception_c(768, channels_7x7: 128);\\n Mixed_6c = inception_c(768, channels_7x7: 160);\\n Mixed_6d = inception_c(768, channels_7x7: 160);\\n Mixed_6e = inception_c(768, channels_7x7: 192);\\n AuxLogits = inception_aux(768, numClasses);\\n Mixed_7a = inception_d(768);\\n Mixed_7b = inception_e(1280);\\n Mixed_7c = inception_e(2048);\\n avgpool = nn.AdaptiveAvgPool2d((1, 1));\\n this.dropout = nn.Dropout(p: dropout);\\n fc = nn.Linear(2048, numClasses);\\n\\n RegisterComponents();\\n\\n if (string.IsNullOrEmpty(weights_file)) {\\n\\n foreach (var (_, m) in named_modules()) {\\n switch (m) {\\n case TorchSharp.Modules.Conv2d conv:\\n torch.nn.init.kaiming_normal_(conv.weight, mode: init.FanInOut.FanOut, nonlinearity: init.NonlinearityType.ReLU);\\n break;\\n case TorchSharp.Modules.Linear ln:\\n torch.nn.init.normal_(ln.weight, 0, 0.01);\\n torch.nn.init.constant_(ln.bias, 0);\\n break;\\n case TorchSharp.Modules.BatchNorm2d bn:\\n torch.nn.init.constant_(bn.weight, 1);\\n torch.nn.init.constant_(bn.bias, 0);\\n break;\\n }\\n }\\n }\\n else {\\n\\n foreach (var (_, m) in named_modules()) {\\n switch (m) {\\n case TorchSharp.Modules.Linear ln:\\n torch.nn.init.normal_(ln.weight, 0, 0.01);\\n torch.nn.init.constant_(ln.bias, 0);\\n break;\\n }\\n }\\n this.load(weights_file, skip: skipfc ? new[] { \\\"fc.weight\\\", \\\"fc.bias\\\", \\\"AuxLogits.fc.weight\\\", \\\"AuxLogits.fc.bias\\\" } : null);\\n }\\n\\n if (device != null && device.type == DeviceType.CUDA)\\n this.to(device);\\n }\\n\\n\\n private static Module<Tensor, Tensor> conv_block(int in_channels, int out_channels, int kernel_size, int stride = 1, int padding = 0)\\n {\\n return Sequential(\\n (\\\"conv\\\", Conv2d(in_channels, out_channels, bias: false, kernelSize: kernel_size, stride: stride, padding: padding)),\\n (\\\"bn\\\", BatchNorm2d(out_channels, eps: 0.001)),\\n (\\\"relu\\\", ReLU(true))\\n );\\n }\\n\\n private static Module<Tensor, Tensor> conv_block(int in_channels, int out_channels, (long, long) kernel_size, (long, long)? stride = null, (long, long)? padding = null)\\n {\\n return Sequential(\\n (\\\"conv\\\", Conv2d(in_channels, out_channels, bias: false, kernelSize: kernel_size, stride: stride, padding: padding)),\\n (\\\"bn\\\", BatchNorm2d(out_channels, eps: 0.001)),\\n (\\\"relu\\\", ReLU(true))\\n );\\n }\\n\\n private Module<Tensor, Tensor> inception_a(int in_channels, int pool_features) => new InceptionA(in_channels, pool_features);\\n private Module<Tensor, Tensor> inception_b(int in_channels) => new InceptionB(in_channels);\\n private Module<Tensor, Tensor> inception_c(int in_channels, int channels_7x7) => new InceptionC(in_channels, channels_7x7);\\n private Module<Tensor, Tensor> inception_d(int in_channels) => new InceptionD(in_channels);\\n private Module<Tensor, Tensor> inception_e(int in_channels) => new InceptionE(in_channels);\\n private Module<Tensor, Tensor> inception_aux(int in_channels, int num_classes) => new InceptionAux(in_channels, num_classes);\\n\\n public override Tensor forward(Tensor x)\\n {\\n // Transform\\n using (var scope = NewDisposeScope()) {\\n if (transform_input) {\\n\\n var x_ch0 = torch.unsqueeze(x[(null, null), 0], 1) * (0.229f / 0.5f) + (0.485f - 0.5f) / 0.5f;\\n var x_ch1 = torch.unsqueeze(x[(null, null), 1], 1) * (0.224f / 0.5f) + (0.456f - 0.5f) / 0.5f;\\n var x_ch2 = torch.unsqueeze(x[(null, null), 2], 1) * (0.225f / 0.5f) + (0.406f - 0.5f) / 0.5f;\\n x = torch.cat(new[] { x_ch0, x_ch1, x_ch2 }, 1);\\n }\\n\\n // N x 3 x 299 x 299\\n x = Conv2d_1a_3x3.call(x);\\n // N x 32 x 149 x 149\\n x = Conv2d_2a_3x3.call(x);\\n // N x 32 x 147 x 147\\n x = Conv2d_2b_3x3.call(x);\\n // N x 64 x 147 x 147\\n x = maxpool1.call(x);\\n // N x 64 x 73 x 73\\n x = Conv2d_3b_1x1.call(x);\\n // N x 80 x 73 x 73\\n x = Conv2d_4a_3x3.call(x);\\n // N x 192 x 71 x 71\\n x = maxpool2.call(x);\\n // N x 192 x 35 x 35\\n x = Mixed_5b.call(x);\\n // N x 256 x 35 x 35\\n x = Mixed_5c.call(x);\\n // N x 288 x 35 x 35\\n x = Mixed_5d.call(x);\\n // N x 288 x 35 x 35\\n x = Mixed_6a.call(x);\\n // N x 768 x 17 x 17\\n x = Mixed_6b.call(x);\\n // N x 768 x 17 x 17\\n x = Mixed_6c.call(x);\\n // N x 768 x 17 x 17\\n x = Mixed_6d.call(x);\\n // N x 768 x 17 x 17\\n x = Mixed_6e.call(x);\\n // N x 768 x 17 x 17\\n x = Mixed_7a.call(x);\\n // N x 1280 x 8 x 8\\n x = Mixed_7b.call(x);\\n // N x 2048 x 8 x 8\\n x = Mixed_7c.call(x);\\n // N x 2048 x 8 x 8\\n // Adaptive average pooling\\n x = avgpool.call(x);\\n // N x 2048 x 1 x 1\\n x = dropout.call(x);\\n // N x 2048 x 1 x 1\\n x = torch.flatten(x, 1);\\n // N x 2048\\n x = fc.call(x);\\n // N x num_classes\\n\\n return x.MoveToOuterDisposeScope();\\n }\\n }\\n\\n class InceptionA : Module<Tensor, Tensor>\\n {\\n public InceptionA(int in_channels, int pool_features) : base(\\\"InceptionA\\\")\\n {\\n branch1x1 = conv_block(in_channels, 64, kernel_size: 1);\\n branch5x5_1 = conv_block(in_channels, 48, kernel_size: 1);\\n branch5x5_2 = conv_block(48, 64, kernel_size:5, padding: 2);\\n branch3x3dbl_1 = conv_block(in_channels, 64, kernel_size:1);\\n branch3x3dbl_2 = conv_block(64, 96, kernel_size:3, padding: 1);\\n branch3x3dbl_3 = conv_block(96, 96, kernel_size:3, padding: 1);\\n branch_pool = conv_block(in_channels, pool_features, kernel_size:1);\\n\\n RegisterComponents();\\n }\\n\\n public override Tensor forward(Tensor x)\\n {\\n var branch1x1_ = branch1x1.call(x);\\n\\n var branch5x5 = branch5x5_1.call(x);\\n branch5x5 = branch5x5_2.call(branch5x5);\\n\\n var branch3x3dbl = branch3x3dbl_1.call(x);\\n branch3x3dbl = branch3x3dbl_2.call(branch3x3dbl);\\n branch3x3dbl = branch3x3dbl_3.call(branch3x3dbl);\\n\\n var branch_pool_ = functional.avg_pool2d(x, kernelSize: 3, stride: 1, padding: 1);\\n branch_pool_ = branch_pool.call(branch_pool_);\\n\\n var outputs = new [] { branch1x1_, branch5x5, branch3x3dbl, branch_pool_ };\\n return torch.cat(outputs, 1);\\n }\\n\\n protected override void Dispose(bool disposing)\\n {\\n if (disposing) {\\n branch1x1.Dispose();\\n branch3x3dbl_1.Dispose(); branch3x3dbl_2.Dispose(); branch3x3dbl_3.Dispose();\\n branch_pool.Dispose();\\n branch5x5_1.Dispose(); branch5x5_2.Dispose();\\n }\\n base.Dispose(disposing);\\n }\\n\\n private readonly Module<Tensor, Tensor> branch1x1;\\n private readonly Module<Tensor, Tensor> branch5x5_1;\\n private readonly Module<Tensor, Tensor> branch5x5_2;\\n private readonly Module<Tensor, Tensor> branch3x3dbl_1;\\n private readonly Module<Tensor, Tensor> branch3x3dbl_2;\\n private readonly Module<Tensor, Tensor> branch3x3dbl_3;\\n private readonly Module<Tensor, Tensor> branch_pool;\\n }\\n\\n class InceptionB : Module<Tensor, Tensor>\\n {\\n public InceptionB(int in_channels) : base(\\\"InceptionB\\\")\\n {\\n\\n branch3x3 = conv_block(in_channels, 384, kernel_size: 3, stride: 2);\\n branch3x3dbl_1 = conv_block(in_channels, 64, kernel_size: 1);\\n branch3x3dbl_2 = conv_block(64, 96, kernel_size: 3, padding: 1);\\n branch3x3dbl_3 = conv_block(96, 96, kernel_size: 3, stride: 2);\\n\\n RegisterComponents();\\n }\\n\\n public override Tensor forward(Tensor x)\\n {\\n\\n var branch3x3_ = branch3x3.call(x);\\n\\n var branch3x3dbl = branch3x3dbl_1.call(x);\\n branch3x3dbl = branch3x3dbl_2.call(branch3x3dbl);\\n branch3x3dbl = branch3x3dbl_3.call(branch3x3dbl);\\n\\n var branch_pool = functional.max_pool2d(x, kernelSize: 3, stride: 2);\\n\\n var outputs = new[] { branch3x3_, branch3x3dbl, branch_pool };\\n\\n return torch.cat(outputs, 1);\\n }\\n\\n protected override void Dispose(bool disposing)\\n {\\n if (disposing) {\\n branch3x3.Dispose();\\n branch3x3dbl_1.Dispose();\\n branch3x3dbl_2.Dispose();\\n branch3x3dbl_3.Dispose();\\n }\\n base.Dispose(disposing);\\n }\\n\\n private readonly Module<Tensor, Tensor> branch3x3;\\n private readonly Module<Tensor, Tensor> branch3x3dbl_1;\\n private readonly Module<Tensor, Tensor> branch3x3dbl_2;\\n private readonly Module<Tensor, Tensor> branch3x3dbl_3;\\n }\\n\\n class InceptionC : Module<Tensor, Tensor>\\n {\\n private readonly Module<Tensor, Tensor> branch1x1;\\n private readonly Module<Tensor, Tensor> branch7x7_1;\\n private readonly Module<Tensor, Tensor> branch7x7_2;\\n private readonly Module<Tensor, Tensor> branch7x7_3;\\n private readonly Module<Tensor, Tensor> branch7x7dbl_1;\\n private readonly Module<Tensor, Tensor> branch7x7dbl_2;\\n private readonly Module<Tensor, Tensor> branch7x7dbl_3;\\n private readonly Module<Tensor, Tensor> branch7x7dbl_4;\\n private readonly Module<Tensor, Tensor> branch7x7dbl_5;\\n private readonly Module<Tensor, Tensor> branch_pool;\\n\\n protected override void Dispose(bool disposing)\\n {\\n if (disposing) {\\n branch1x1.Dispose();\\n branch_pool.Dispose();\\n branch7x7_1.Dispose(); branch7x7_2.Dispose(); branch7x7_3?.Dispose();\\n branch7x7dbl_1.Dispose(); branch7x7dbl_2.Dispose(); branch7x7dbl_3.Dispose();\\n branch7x7dbl_4.Dispose(); branch7x7dbl_5.Dispose();\\n }\\n base.Dispose(disposing);\\n }\\n\\n public InceptionC(int in_channels, int channels_7x7) : base(\\\"InceptionC\\\")\\n {\\n branch1x1 = conv_block(in_channels, 192, kernel_size: 1);\\n\\n var c7 = channels_7x7;\\n branch7x7_1 = conv_block(in_channels, c7, kernel_size: 1);\\n branch7x7_2 = conv_block(c7, c7, kernel_size: (1, 7), padding: (0, 3));\\n branch7x7_3 = conv_block(c7, 192, kernel_size: (7, 1), padding: (3, 0));\\n\\n branch7x7dbl_1 = conv_block(in_channels, c7, kernel_size: 1);\\n branch7x7dbl_2 = conv_block(c7, c7, kernel_size: (7, 1), padding: (3, 0));\\n branch7x7dbl_3 = conv_block(c7, c7, kernel_size: (1, 7), padding: (0, 3));\\n branch7x7dbl_4 = conv_block(c7, c7, kernel_size: (7, 1), padding: (3, 0));\\n branch7x7dbl_5 = conv_block(c7, 192, kernel_size: (1, 7), padding: (0, 3));\\n\\n branch_pool = conv_block(in_channels, 192, kernel_size: 1);\\n\\n RegisterComponents();\\n }\\n\\n public override Tensor forward(Tensor x)\\n {\\n\\n var branch1x1_ = branch1x1.call(x);\\n\\n var branch7x7 = branch7x7_1.call(x);\\n branch7x7 = branch7x7_2.call(branch7x7);\\n branch7x7 = branch7x7_3.call(branch7x7);\\n\\n var branch7x7dbl = branch7x7dbl_1.call(x);\\n branch7x7dbl = branch7x7dbl_2.call(branch7x7dbl);\\n branch7x7dbl = branch7x7dbl_3.call(branch7x7dbl);\\n branch7x7dbl = branch7x7dbl_4.call(branch7x7dbl);\\n branch7x7dbl = branch7x7dbl_5.call(branch7x7dbl);\\n\\n var branch_pool_ = functional.avg_pool2d(x, kernelSize: 3, stride: 1, padding: 1);\\n branch_pool_ = branch_pool.call(branch_pool_);\\n\\n var outputs = new[] { branch1x1_, branch7x7, branch7x7dbl, branch_pool_ };\\n return torch.cat(outputs, 1);\\n }\\n }\\n\\n class InceptionD : Module<Tensor, Tensor>\\n {\\n private readonly Module<Tensor, Tensor> branch3x3_1;\\n private readonly Module<Tensor, Tensor> branch3x3_2;\\n private readonly Module<Tensor, Tensor> branch7x7x3_1;\\n private readonly Module<Tensor, Tensor> branch7x7x3_2;\\n private readonly Module<Tensor, Tensor> branch7x7x3_3;\\n private readonly Module<Tensor, Tensor> branch7x7x3_4;\\n\\n protected override void Dispose(bool disposing)\\n {\\n if (disposing) {\\n branch3x3_1.Dispose(); branch3x3_2.Dispose();\\n branch7x7x3_1.Dispose(); branch7x7x3_2.Dispose();\\n branch7x7x3_3.Dispose(); branch7x7x3_4.Dispose();\\n }\\n base.Dispose(disposing);\\n }\\n\\n public InceptionD(int in_channels) : base(\\\"InceptionD\\\")\\n {\\n branch3x3_1 = conv_block(in_channels, 192, kernel_size: 1);\\n branch3x3_2 = conv_block(192, 320, kernel_size: 3, stride: 2);\\n\\n branch7x7x3_1 = conv_block(in_channels, 192, kernel_size: 1);\\n branch7x7x3_2 = conv_block(192, 192, kernel_size: (1, 7), padding: (0, 3));\\n branch7x7x3_3 = conv_block(192, 192, kernel_size: (7, 1), padding: (3, 0));\\n branch7x7x3_4 = conv_block(192, 192, kernel_size: 3, stride: 2);\\n\\n RegisterComponents();\\n }\\n\\n public override Tensor forward(Tensor x)\\n {\\n var branch3x3 = branch3x3_1.call(x);\\n branch3x3 = branch3x3_2.call(branch3x3);\\n\\n var branch7x7x3 = branch7x7x3_1.call(x);\\n branch7x7x3 = branch7x7x3_2.call(branch7x7x3);\\n branch7x7x3 = branch7x7x3_3.call(branch7x7x3);\\n branch7x7x3 = branch7x7x3_4.call(branch7x7x3);\\n\\n var branch_pool = functional.max_pool2d(x, kernelSize: 3, stride: 2);\\n var outputs = new[] { branch3x3, branch7x7x3, branch_pool };\\n\\n\\n return torch.cat(outputs, 1);\\n }\\n }\\n\\n class InceptionE : Module<Tensor, Tensor>\\n {\\n private readonly Module<Tensor, Tensor> branch1x1;\\n private readonly Module<Tensor, Tensor> branch3x3_1;\\n private readonly Module<Tensor, Tensor> branch3x3_2a;\\n private readonly Module<Tensor, Tensor> branch3x3_2b;\\n private readonly Module<Tensor, Tensor> branch3x3dbl_1;\\n private readonly Module<Tensor, Tensor> branch3x3dbl_2;\\n private readonly Module<Tensor, Tensor> branch3x3dbl_3a;\\n private readonly Module<Tensor, Tensor> branch3x3dbl_3b;\\n private readonly Module<Tensor, Tensor> branch_pool;\\n\\n protected override void Dispose(bool disposing)\\n {\\n if (disposing) {\\n branch1x1.Dispose(); branch_pool.Dispose();\\n branch3x3_1.Dispose(); branch3x3_2a.Dispose(); branch3x3_2b.Dispose();\\n branch3x3dbl_1.Dispose(); branch3x3dbl_2.Dispose();\\n branch3x3dbl_3a.Dispose(); branch3x3dbl_3b.Dispose();\\n }\\n base.Dispose(disposing);\\n }\\n\\n public InceptionE(int in_channels) : base(\\\"InceptionE\\\")\\n {\\n\\n branch1x1 = conv_block(in_channels, 320, kernel_size: 1);\\n\\n branch3x3_1 = conv_block(in_channels, 384, kernel_size: 1);\\n branch3x3_2a = conv_block(384, 384, kernel_size: (1, 3), padding: (0, 1));\\n branch3x3_2b = conv_block(384, 384, kernel_size: (3, 1), padding: (1, 0));\\n\\n branch3x3dbl_1 = conv_block(in_channels, 448, kernel_size: 1);\\n branch3x3dbl_2 = conv_block(448, 384, kernel_size: 3, padding: 1);\\n branch3x3dbl_3a = conv_block(384, 384, kernel_size: (1, 3), padding: (0, 1));\\n branch3x3dbl_3b = conv_block(384, 384, kernel_size: (3, 1), padding: (1, 0));\\n\\n branch_pool = conv_block(in_channels, 192, kernel_size: 1);\\n\\n RegisterComponents();\\n }\\n\\n public override Tensor forward(Tensor x)\\n {\\n\\n var branch1x1_ = branch1x1.call(x);\\n\\n var branch3x3 = branch3x3_1.call(x);\\n branch3x3 = torch.cat(new[] { branch3x3_2a.call(branch3x3), branch3x3_2b.call(branch3x3)}, 1);\\n\\n var branch3x3dbl = branch3x3dbl_1.call(x);\\n branch3x3dbl = branch3x3dbl_2.call(branch3x3dbl);\\n branch3x3dbl = torch.cat(new[] { branch3x3dbl_3a.call(branch3x3dbl), branch3x3dbl_3b.call(branch3x3dbl) }, 1);\\n\\n var branch_pool_ = functional.avg_pool2d(x, kernelSize: 3, stride: 1, padding: 1);\\n branch_pool_ = branch_pool.call(branch_pool_);\\n\\n var outputs = new[] { branch1x1_, branch3x3, branch3x3dbl, branch_pool_ };\\n\\n return torch.cat(outputs, 1);\\n }\\n }\\n\\n class InceptionAux : Module<Tensor, Tensor>\\n {\\n private readonly Module<Tensor, Tensor> conv0;\\n private readonly Module<Tensor, Tensor> conv1;\\n private readonly Module<Tensor, Tensor> fc;\\n\\n protected override void Dispose(bool disposing)\\n {\\n if (disposing) {\\n conv0.Dispose();\\n conv1.Dispose();\\n fc.Dispose();\\n }\\n base.Dispose(disposing);\\n }\\n\\n public InceptionAux(int in_channels, int num_classes) : base(\\\"InceptionAux\\\")\\n {\\n\\n conv0 = conv_block(in_channels, 128, kernel_size: 1);\\n conv1 = conv_block(128, 768, kernel_size: 5);\\n fc = nn.Linear(768, num_classes);\\n\\n RegisterComponents();\\n }\\n\\n public override Tensor forward(Tensor x)\\n {\\n // N x 768 x 17 x 17\\n x = functional.avg_pool2d(x, kernelSize: 5, stride: 3);\\n // N x 768 x 5 x 5\\n x = conv0.call(x);\\n // N x 128 x 5 x 5\\n x = conv1.call(x);\\n // N x 768 x 1 x 1\\n // Adaptive average pooling\\n x = functional.adaptive_avg_pool2d(x, (1, 1));\\n // N x 768 x 1 x 1\\n x = x.flatten(1);\\n // N x 768\\n x = fc.call(x);\\n // N x 1000\\n\\n return x;\\n }\\n }\\n }\\n }\\n}\\n\"\n },\n {\n \"alpha_fraction\": 0.6480686664581299,\n \"alphanum_fraction\": 0.6566523313522339,\n \"avg_line_length\": 25,\n \"blob_id\": \"847f983e4f0dc6a3c4139ebef0f05929fd0052ea\",\n \"content_id\": \"e3e480120aea26849b9bbc5954ead108ca083602\",\n \"detected_licenses\": [\n \"MIT\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"C#\",\n \"length_bytes\": 235,\n \"license_type\": \"permissive\",\n \"max_line_length\": 131,\n \"num_lines\": 9,\n \"path\": \"/src/TorchSharp/Tensor/Enums/DebugMode.cs\",\n \"repo_name\": \"NiklasGustafsson/TorchSharp\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"// Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information.\\nnamespace TorchSharp\\n{\\n public enum DebugMode\\n {\\n @default = 0,\\n warn = 1\\n }\\n}\"\n },\n {\n \"alpha_fraction\": 0.4951785206794739,\n \"alphanum_fraction\": 0.5121188163757324,\n \"avg_line_length\": 35.894229888916016,\n \"blob_id\": \"74d4dace427f8ab9ef2a35a9456a34b71c531dd2\",\n \"content_id\": \"41f84dddf05f1d5d3cbc8f48550bce5c4e46117c\",\n \"detected_licenses\": [\n \"MIT\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"C#\",\n \"length_bytes\": 3837,\n \"license_type\": \"permissive\",\n \"max_line_length\": 83,\n \"num_lines\": 104,\n \"path\": \"/test/TorchSharpTest/TestSaveSD.cs\",\n \"repo_name\": \"NiklasGustafsson/TorchSharp\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"using System.Collections.Generic;\\nusing TorchSharp.Modules;\\nusing static TorchSharp.torch;\\nusing static TorchSharp.torch.nn;\\nusing Xunit;\\n\\n\\nnamespace TorchSharp\\n{\\n [Collection(\\\"Sequential\\\")]\\n public class TestSaveSD\\n {\\n private class LSTMModel : nn.Module<Tensor, Tensor>\\n {\\n public static int NUM_WORDS = 100;\\n public static int EMBEDDING_VEC_LEN = 100;\\n public static int HIDDEN_SIZE = 128;\\n\\n private Module<Tensor, Tensor> embedding;\\n private LSTM lstm;\\n private Module<Tensor, Tensor> dropout;\\n private Module<Tensor, Tensor> dense;\\n private Module<Tensor, Tensor> sigmoid;\\n private Device _device;\\n\\n public LSTMModel(string name, Device device = null) : base(name)\\n {\\n _device = device;\\n embedding = Embedding(NUM_WORDS, EMBEDDING_VEC_LEN);\\n lstm = LSTM(EMBEDDING_VEC_LEN, HIDDEN_SIZE, batchFirst: true);\\n dropout = Dropout(0.5);\\n dense = Linear(HIDDEN_SIZE, 1);\\n sigmoid = Sigmoid();\\n\\n RegisterComponents();\\n if (device != null && device.type == DeviceType.CUDA)\\n this.to(device);\\n }\\n\\n public override Tensor forward(Tensor input)\\n {\\n var x_embed = embedding.call(input);\\n var h0 = zeros(1, input.shape[0], HIDDEN_SIZE, device: _device);\\n var c0 = zeros(1, input.shape[0], HIDDEN_SIZE, device: _device);\\n var (x_rnn, _, _) = lstm.call(x_embed, (h0, c0));\\n var x_rnn_last_seq = x_rnn[(null,null), -1, (null,null)];\\n x_rnn_last_seq = dropout.call(x_rnn_last_seq);\\n var logits = dense.call(x_rnn_last_seq);\\n return sigmoid.call(logits);\\n }\\n }\\n \\n [Fact]\\n public void TestSaveSDData_LSTM()\\n {\\n var lstm = new LSTMModel(\\\"lstm\\\", torch.CPU);\\n lstm.save(\\\"./lstm.dat\\\");\\n }\\n \\n class LeNet1Model : Module<Tensor, Tensor>\\n {\\n // The names of properties should be the same in C# and Python\\n // in this case, we both name the Sequential as layers\\n private readonly Module<Tensor, Tensor> layers;\\n private Device _device;\\n\\n public LeNet1Model(string name, Device device = null) : base(name)\\n {\\n _device = device;\\n\\n // the names of each layer should also be the same in C# and Python\\n var modules = new List<(string, Module<Tensor, Tensor>)>();\\n modules.Add((\\\"conv-1\\\", Conv2d(1, 4, 5, padding: 2)));\\n modules.Add((\\\"bnrm2d-1\\\", BatchNorm2d(4)));\\n modules.Add((\\\"relu-1\\\", ReLU()));\\n modules.Add((\\\"maxpool-1\\\", MaxPool2d(2, stride: 2)));\\n modules.Add((\\\"conv-2\\\", Conv2d(4, 12, 5)));\\n modules.Add((\\\"bnrm2d-2\\\", BatchNorm2d(12)));\\n modules.Add((\\\"relu-2\\\", ReLU()));\\n modules.Add((\\\"maxpool-2\\\", MaxPool2d(2, stride: 2)));\\n modules.Add((\\\"flatten\\\", Flatten()));\\n modules.Add((\\\"linear\\\", Linear(300, 10)));\\n layers = Sequential(modules);\\n\\n RegisterComponents();\\n if (device != null && device.type == TorchSharp.DeviceType.CUDA)\\n this.to(device);\\n }\\n\\n public override Tensor forward(Tensor input)\\n {\\n return layers.call(input);\\n }\\n }\\n \\n \\n [Fact]\\n public void TestSaveSDData_LeNet1()\\n {\\n var lenet1 = new LeNet1Model(\\\"lenet1\\\", torch.CPU);\\n lenet1.save(\\\"./lenet1.dat\\\");\\n }\\n }\\n}\\n\"\n },\n {\n \"alpha_fraction\": 0.6036188006401062,\n \"alphanum_fraction\": 0.6161640286445618,\n \"avg_line_length\": 33.264461517333984,\n \"blob_id\": \"9735217276e93d296990a720b55a586353b5765c\",\n \"content_id\": \"d04636db14a7bd3adfc21102b2fd0e4772c81b1d\",\n \"detected_licenses\": [\n \"MIT\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Shell\",\n \"length_bytes\": 4145,\n \"license_type\": \"permissive\",\n \"max_line_length\": 225,\n \"num_lines\": 121,\n \"path\": \"/src/Native/build.sh\",\n \"repo_name\": \"NiklasGustafsson/TorchSharp\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"#!/usr/bin/env bash\\nset -e\\n\\nusage()\\n{\\n echo \\\"Usage: $0 --arch <Architecture> \\\"\\n echo \\\"\\\"\\n echo \\\"Options:\\\"\\n echo \\\" --arch <Architecture> Target Architecture (x64, x86)\\\"\\n echo \\\" --configuration <Configuration> Build Configuration (Debug, Release)\\\"\\n echo \\\" --stripsymbols Enable symbol stripping (to external file)\\\"\\n echo \\\" --libtorchpath <PathToLibtorch> Path to libtorch TorchConfig.cmake\\\"\\n exit 1\\n}\\n\\nSOURCE=\\\"${BASH_SOURCE[0]}\\\"\\nwhile [ -h \\\"$SOURCE\\\" ]; do # resolve $SOURCE until the file is no longer a symlink\\n DIR=\\\"$( cd -P \\\"$( dirname \\\"$SOURCE\\\" )\\\" && pwd )\\\"\\n SOURCE=\\\"$(readlink \\\"$SOURCE\\\")\\\"\\n [[ \\\"$SOURCE\\\" != /* ]] && SOURCE=\\\"$DIR/$SOURCE\\\" # if $SOURCE was a relative symlink, we need to resolve it relative to the path where the symlink file was located\\ndone\\nDIR=\\\"$( cd -P \\\"$( dirname \\\"$SOURCE\\\" )\\\" && pwd )\\\"\\nRootRepo=\\\"$DIR/../..\\\"\\n\\n__build_arch=\\n__strip_argument=\\n__libtorchpath=\\n__configuration=Debug\\n__rootBinPath=\\\"$RootRepo/bin\\\"\\n__baseIntermediateOutputPath=\\\"$__rootBinPath/obj\\\"\\n__versionSourceFile=\\\"$__baseIntermediateOutputPath/version.c\\\"\\n\\nwhile [ \\\"$1\\\" != \\\"\\\" ]; do\\n lowerI=\\\"$(echo $1 | awk '{print tolower($0)}')\\\"\\n case $lowerI in\\n -h|--help)\\n usage\\n exit 1\\n ;;\\n --arch)\\n shift\\n __build_arch=$1\\n ;;\\n --configuration)\\n shift\\n __configuration=$1\\n ;; \\n --stripsymbols)\\n __strip_argument=\\\"-DSTRIP_SYMBOLS=true\\\"\\n ;;\\n --libtorchpath)\\n shift\\n __libtorchpath=$1\\n ;;\\n *)\\n echo \\\"Unknown argument to build.sh $1\\\"; usage; exit 1\\n esac\\n shift\\ndone\\n\\n# Force the build to be release since libtorch is in release.\\n__cmake_defines=\\\"-DCMAKE_BUILD_TYPE=${__configuration} ${__strip_argument} -DLIBTORCH_PATH=${__libtorchpath}\\\"\\n\\n__IntermediatesDir=\\\"$__baseIntermediateOutputPath/$__build_arch.$__configuration/Native\\\"\\n__BinDir=\\\"$__rootBinPath/$__build_arch.$__configuration/Native\\\"\\n\\nmkdir -p \\\"$__BinDir\\\"\\nmkdir -p \\\"$__IntermediatesDir\\\"\\n\\n# Set up the environment to be used for building with clang.\\nif command -v \\\"clang-6.0\\\" > /dev/null 2>&1; then\\n export CC=\\\"$(command -v clang-6.0)\\\"\\n export CXX=\\\"$(command -v clang++-6.0)\\\"\\nelif command -v \\\"clang-5.0\\\" > /dev/null 2>&1; then\\n export CC=\\\"$(command -v clang-5.0)\\\"\\n export CXX=\\\"$(command -v clang++-5.0)\\\"\\nelif command -v \\\"clang-4.0\\\" > /dev/null 2>&1; then\\n export CC=\\\"$(command -v clang-4.0)\\\"\\n export CXX=\\\"$(command -v clang++-4.0)\\\"\\nelif command -v clang > /dev/null 2>&1; then\\n export CC=\\\"$(command -v clang)\\\"\\n export CXX=\\\"$(command -v clang++)\\\"\\nelse\\n echo \\\"Unable to find Clang Compiler\\\"\\n echo \\\"Install clang-6.0, clang-5.0, or clang-4.0\\\"\\n exit 1\\nfi\\n\\n# Specify path to be set for CMAKE_INSTALL_PREFIX.\\n# This is where all built native libraries will copied to.\\nexport __CMakeBinDir=\\\"$__BinDir\\\"\\n\\nif [ ! -f $__versionSourceFile ]; then\\n __versionSourceLine=\\\"static char sccsid[] __attribute__((used)) = \\\\\\\"@(#)No version information produced\\\\\\\";\\\"\\n echo $__versionSourceLine > $__versionSourceFile\\nfi\\n\\nOSName=$(uname -s)\\ncase $OSName in\\n Darwin)\\n \\n # PyTorch is specifyin options that require OpenMP support but AppleClang's OpenMP support is lacking e.g. -fopenmp not supported\\n # See https://github.com/oneapi-src/oneDNN/issues/591 for this potential workaround, though it may be better\\n # to switch to brew clang.\\n #LIBOMP=/usr/local/opt/libomp\\n #__cmake_defines=${__cmake_defines} -DCMAKE_CXX_FLAGS=\\\"-I$LIBOMP/include\\\" -DCMAKE_C_FLAGS=\\\"-I$LIBOMP/include\\\" -DCMAKE_SHARED_LINKER_FLAGS=\\\"$LIBOMP/lib/libomp.dylib\\\" -DCMAKE_EXE_LINKER_FLAGS=\\\"$LIBOMP/lib/libomp.dylib\\\"\\n ;;\\n *)\\n echo \\\"Unsupported OS '$OSName' detected. Downloading linux-$__PKG_ARCH tools.\\\"\\n OS=Linux\\n __PKG_RID=linux\\n ;;\\nesac\\n\\ncd \\\"$__IntermediatesDir\\\"\\n\\necho \\\"Building Machine Learning native components from $DIR to $(pwd)\\\"\\nset -x # turn on trace\\ncmake \\\"$DIR\\\" -G \\\"Unix Makefiles\\\" $__cmake_defines\\nset +x # turn off trace\\nmake install\"\n },\n {\n \"alpha_fraction\": 0.6977401375770569,\n \"alphanum_fraction\": 0.6977401375770569,\n \"avg_line_length\": 31.272727966308594,\n \"blob_id\": \"c82e3d4873d722dd32bcd5035bdafd27ff553383\",\n \"content_id\": \"2fe9222715dec60edd412a86f3e12a0bb3053729\",\n \"detected_licenses\": [\n \"MIT\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"C#\",\n \"length_bytes\": 356,\n \"license_type\": \"permissive\",\n \"max_line_length\": 131,\n \"num_lines\": 11,\n \"path\": \"/src/TorchSharp/Tensor/Enums/memory_format.cs\",\n \"repo_name\": \"NiklasGustafsson/TorchSharp\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"// Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information.\\nnamespace TorchSharp\\n{\\n // https://pytorch.org/docs/stable/tensor_attributes.html#torch.memory_format\\n public enum memory_format\\n {\\n preserve_format,\\n contiguous_format,\\n channels_last\\n }\\n}\"\n },\n {\n \"alpha_fraction\": 0.5151839256286621,\n \"alphanum_fraction\": 0.5194610953330994,\n \"avg_line_length\": 46.47208023071289,\n \"blob_id\": \"db0703a130bd069424f3340637efe50df83fbc60\",\n \"content_id\": \"aacdc6f02ebea4086b29fcb8642fab273950d3fe\",\n \"detected_licenses\": [\n \"MIT\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"C#\",\n \"length_bytes\": 9352,\n \"license_type\": \"permissive\",\n \"max_line_length\": 153,\n \"num_lines\": 197,\n \"path\": \"/src/TorchSharp/Distributions/Distribution.cs\",\n \"repo_name\": \"NiklasGustafsson/TorchSharp\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"// Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information.\\nusing System;\\nusing System.Collections.Generic;\\n\\nnamespace TorchSharp\\n{\\n public static partial class torch\\n {\\n public static partial class distributions\\n {\\n public abstract class Distribution\\n {\\n public Distribution(torch.Generator generator, long[] batch_shape = null, long[] event_shape = null)\\n {\\n this.generator = generator;\\n _init(batch_shape != null ? batch_shape : Size.Empty,\\n event_shape != null ? event_shape : Size.Empty);\\n }\\n\\n public Distribution(torch.Generator generator, Size batch_shape, Size? event_shape = null)\\n {\\n this.generator = generator;\\n _init(batch_shape,\\n event_shape != null ? event_shape : Size.Empty);\\n }\\n\\n protected void _init(Size? batch_shape = null, Size? event_shape = null)\\n {\\n this.batch_shape = batch_shape != null ? batch_shape.Value : Size.Empty;\\n this.event_shape = event_shape != null ? event_shape.Value : Size.Empty;\\n }\\n\\n /// <summary>\\n /// The shape over which parameters are batched.\\n /// </summary>\\n public Size batch_shape { get; protected set; }\\n\\n /// <summary>\\n /// The shape of a single sample (without batching).\\n /// </summary>\\n public Size event_shape { get; protected set; }\\n\\n /// <summary>\\n /// The mean of the distribution.\\n /// </summary>\\n public abstract Tensor mean { get; }\\n\\n /// <summary>\\n /// The mode of the distribution.\\n /// </summary>\\n public virtual Tensor mode { get { return new Tensor(IntPtr.Zero); } } \\n\\n /// <summary>\\n /// The variance of the distribution\\n /// </summary>\\n public abstract Tensor variance { get; }\\n\\n /// <summary>\\n /// The standard deviation of the distribution\\n /// </summary>\\n public virtual Tensor stddev => variance.sqrt();\\n\\n /// <summary>\\n /// Generates a sample_shape shaped sample or sample_shape shaped batch of samples if the distribution parameters are batched.\\n /// </summary>\\n /// <param name=\\\"sample_shape\\\">A list of dimension sizes</param>\\n /// <returns>A tensor containing the sample.</returns>\\n public virtual Tensor sample(params long[] sample_shape)\\n {\\n return rsample(sample_shape);\\n }\\n\\n /// <summary>\\n /// Generates a sample_shape shaped sample or sample_shape shaped batch of samples if the distribution parameters are batched.\\n /// </summary>\\n /// <param name=\\\"sample_shape\\\">A list of dimension sizes</param>\\n /// <returns>A tensor containing the sample.</returns>\\n public Tensor sample(Size sample_shape) => sample(sample_shape.Shape);\\n\\n /// <summary>\\n /// Generates a sample_shape shaped reparameterized sample or sample_shape shaped batch of reparameterized samples\\n /// if the distribution parameters are batched.\\n /// </summary>\\n /// <param name=\\\"sample_shape\\\">The sample shape.</param>\\n /// <returns>A tensor containing the sample.</returns>\\n public abstract Tensor rsample(params long[] sample_shape);\\n\\n /// <summary>\\n /// Generates a sample_shape shaped reparameterized sample or sample_shape shaped batch of reparameterized samples\\n /// if the distribution parameters are batched.\\n /// </summary>\\n /// <param name=\\\"sample_shape\\\">The sample shape.</param>\\n /// <returns>A tensor containing the sample.</returns>\\n public Tensor rsample(Size sample_shape) => rsample(sample_shape.Shape);\\n\\n /// <summary>\\n /// Returns the log of the probability density/mass function evaluated at `value`.\\n /// </summary>\\n /// <param name=\\\"value\\\"></param>\\n /// <returns></returns>\\n public abstract Tensor log_prob(Tensor value);\\n\\n /// <summary>\\n /// Returns entropy of distribution, batched over batch_shape.\\n /// </summary>\\n /// <returns></returns>\\n public abstract Tensor entropy();\\n\\n /// <summary>\\n /// Returns a new distribution instance (or populates an existing instance provided by a derived class) with batch dimensions expanded to\\n /// `batch_shape`. This method calls `torch.Tensor.expand()` on the distribution's parameters. As such, this does not allocate new\\n /// memory for the expanded distribution instance.\\n /// </summary>\\n /// <param name=\\\"batch_shape\\\">Tthe desired expanded size.</param>\\n /// <param name=\\\"instance\\\">new instance provided by subclasses that need to override `.expand`.</param>\\n /// <returns></returns>\\n public abstract Distribution expand(Size batch_shape, Distribution instance = null);\\n\\n /// <summary>\\n /// Returns the cumulative density/mass function evaluated at `value`.\\n /// </summary>\\n /// <param name=\\\"value\\\"></param>\\n /// <returns></returns>\\n public virtual Tensor cdf(Tensor value)\\n {\\n throw new NotImplementedException(\\\"Distribution.cdf()\\\");\\n }\\n\\n /// <summary>\\n /// Returns the inverse cumulative density/mass function evaluated at `value`.\\n /// </summary>\\n /// <param name=\\\"value\\\"></param>\\n /// <returns></returns>\\n public virtual Tensor icdf(Tensor value)\\n {\\n throw new NotImplementedException(\\\"Distribution.icdf()\\\");\\n }\\n\\n /// <summary>\\n /// Returns tensor containing all values supported by a discrete distribution. The result will enumerate over dimension 0, so the shape\\n /// of the result will be `(cardinality,) + batch_shape + event_shape` (where `event_shape = ()` for univariate distributions).\\n ///\\n /// Note that this enumerates over all batched tensors in lock-step `[[0, 0], [1, 1], ...]`. With `expand=False`, enumeration happens\\n /// along dim 0, but with the remaining batch dimensions being singleton dimensions, `[[0], [1], ..`\\n /// </summary>\\n /// <param name=\\\"expand\\\">Whether to expand the support over the batch dims to match the distribution's `batch_shape`.</param>\\n /// <returns></returns>\\n public virtual Tensor enumerate_support(bool expand = true)\\n {\\n throw new NotImplementedException();\\n }\\n\\n /// <summary>\\n /// Returns perplexity of distribution, batched over batch_shape.\\n /// </summary>\\n /// <returns></returns>\\n public virtual Tensor perplexity() => torch.exp(entropy());\\n\\n protected long[] ExtendedShape(params long[] sample_shape)\\n {\\n if (batch_shape.Length == 0 && event_shape.Length == 0)\\n return sample_shape;\\n\\n var result = new List<long>();\\n if (sample_shape.Length > 0) result.AddRange(sample_shape);\\n if (batch_shape.Length > 0) result.AddRange(batch_shape);\\n if (event_shape.Length > 0) result.AddRange(event_shape);\\n\\n return result.ToArray();\\n }\\n\\n protected Tensor LogitsToProbs(Tensor logits, bool isBinary = false)\\n {\\n return (isBinary) ? torch.sigmoid(logits) : torch.nn.functional.softmax(logits, dim: -1);\\n }\\n\\n protected Tensor ProbsToLogits(Tensor probs, bool isBinary = false)\\n {\\n probs = ClampProbs(probs);\\n return (isBinary) ? (torch.log(probs) - torch.log1p(-probs)) : torch.log(probs);\\n }\\n\\n protected Tensor ClampProbs(Tensor probs)\\n {\\n var eps = torch.finfo(probs.dtype).eps;\\n return probs.clamp(eps, 1 - eps);\\n }\\n\\n protected Tensor ClampByZero(Tensor x) => (x.clamp_min(0) + x - x.clamp_max(0)) / 2;\\n\\n protected torch.Generator generator;\\n\\n protected const double euler_constant = 0.57721566490153286060; // Euler Mascheroni Constant\\n }\\n }\\n }\\n}\\n\"\n },\n {\n \"alpha_fraction\": 0.6988155841827393,\n \"alphanum_fraction\": 0.7021996378898621,\n \"avg_line_length\": 23.58333396911621,\n \"blob_id\": \"ab775b7454db8fd3e3c4ccd6ce55113048cee370\",\n \"content_id\": \"c966e0e97d088b78ba78a82173a8638a25ae45e7\",\n \"detected_licenses\": [\n \"MIT\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"C++\",\n \"length_bytes\": 591,\n \"license_type\": \"permissive\",\n \"max_line_length\": 132,\n \"num_lines\": 24,\n \"path\": \"/src/Native/LibTorchSharp/THSStorage.cpp\",\n \"repo_name\": \"NiklasGustafsson/TorchSharp\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"//// Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information.\\n#include \\\"THSStorage.h\\\"\\n\\nint64_t THSTensor_storage_offset(const Tensor tensor)\\n{\\n return tensor->storage_offset();\\n}\\n\\nsize_t THSStorage_nbytes(const Tensor tensor)\\n{\\n return tensor->storage().nbytes();\\n}\\n\\nvoid THSStorage_set_nbytes(const Tensor tensor, size_t nbytes)\\n{\\n tensor->storage().set_nbytes(nbytes);\\n}\\n\\nvoid* THSStorage_data_ptr(const Tensor tensor)\\n{\\n auto &st = tensor->storage();\\n auto &dp = st.data_ptr();\\n return dp.get();\\n}\\n\\n\"\n },\n {\n \"alpha_fraction\": 0.4516339898109436,\n \"alphanum_fraction\": 0.4516339898109436,\n \"avg_line_length\": 31.5744686126709,\n \"blob_id\": \"c20e7b19f88ea78896e72fec44bc46b4a376b5fc\",\n \"content_id\": \"84c4997a12274577d9d7433662599e60011d3e20\",\n \"detected_licenses\": [\n \"MIT\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"C#\",\n \"length_bytes\": 1530,\n \"license_type\": \"permissive\",\n \"max_line_length\": 130,\n \"num_lines\": 47,\n \"path\": \"/src/TorchSharp/Dataset.cs\",\n \"repo_name\": \"NiklasGustafsson/TorchSharp\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"// Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information.\\nusing System;\\nusing System.Collections.Generic;\\n\\nnamespace TorchSharp\\n{\\n public static partial class torch\\n {\\n public static partial class utils\\n {\\n public static partial class data\\n {\\n public abstract class Dataset : Dataset<Dictionary<string, torch.Tensor>>\\n {\\n }\\n\\n /// <summary>\\n /// Interface for Dataloader\\n /// </summary>\\n public abstract class Dataset<T> : IDisposable\\n {\\n public void Dispose()\\n {\\n Dispose(true);\\n GC.SuppressFinalize(this);\\n }\\n\\n /// <summary>\\n /// Size of dataset\\n /// </summary>\\n public abstract long Count { get; }\\n\\n /// <summary>\\n /// Get tensor according to index\\n /// </summary>\\n /// <param name=\\\"index\\\">Index for tensor</param>\\n /// <returns>Tensors of index. DataLoader will catenate these tensors.</returns>\\n public abstract T GetTensor(long index);\\n\\n protected virtual void Dispose(bool disposing)\\n {\\n }\\n }\\n }\\n }\\n }\\n}\"\n },\n {\n \"alpha_fraction\": 0.44726622104644775,\n \"alphanum_fraction\": 0.4784882068634033,\n \"avg_line_length\": 38.37647247314453,\n \"blob_id\": \"0556093ea13715edeb080837a845a1e71eacef61\",\n \"content_id\": \"cbc74acf4c3e159cf74bbe5c1dcdac0c97f441b4\",\n \"detected_licenses\": [\n \"MIT\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"C#\",\n \"length_bytes\": 6694,\n \"license_type\": \"permissive\",\n \"max_line_length\": 134,\n \"num_lines\": 170,\n \"path\": \"/src/TorchVision/Ops/Utils.cs\",\n \"repo_name\": \"NiklasGustafsson/TorchSharp\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"// Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information.\\n\\n// A number of implementation details in this file have been translated from the Python version of torchvision,\\n// largely located in the files found in this folder:\\n//\\n// https://github.com/pytorch/vision/blob/a4f53308b2d0f1aa9191686e326f45c26053f686/torchvision/ops/utils.py\\n//\\n// The origin has the following copyright notice and license:\\n//\\n// https://github.com/pytorch/vision/blob/main/LICENSE\\n\\n\\nusing System;\\nusing System.Collections.Generic;\\nusing static TorchSharp.torch;\\n\\nnamespace TorchSharp\\n{\\n public static partial class torchvision\\n {\\n public static partial class ops\\n {\\n internal static Tensor _upcast(Tensor t)\\n {\\n switch (t.dtype) {\\n case ScalarType.BFloat16:\\n case ScalarType.Float16:\\n return t.@float();\\n case ScalarType.Bool:\\n case ScalarType.Byte:\\n case ScalarType.Int8:\\n case ScalarType.Int16:\\n return t.@int();\\n }\\n return t.alias();\\n }\\n\\n internal static Tensor _upcast_non_float(Tensor t) => (!t.is_floating_point() ? t.@float() : t.alias());\\n\\n internal static (Tensor, Tensor, Tensor, Tensor) unwrap4(Tensor[] t)\\n {\\n if (t.Length != 4) throw new ArgumentException(\\\"Not the right length\\\");\\n return (t[0], t[1], t[2], t[3]);\\n }\\n\\n internal static (Tensor, Tensor) _loss_inter_union(Tensor boxes1, Tensor boxes2)\\n {\\n var (x1, y1, x2, y2) = unwrap4(boxes1.unbind(dimension: -1));\\n var (x1g, y1g, x2g, y2g) = unwrap4(boxes2.unbind(dimension: -1));\\n\\n // Intersection keypoints\\n var xkis1 = max(x1, x1g);\\n var ykis1 = max(y1, y1g);\\n var xkis2 = min(x2, x2g);\\n var ykis2 = min(y2, y2g);\\n\\n var intsctk = zeros_like(x1);\\n var mask = (ykis2 > ykis1) & (xkis2 > xkis1);\\n intsctk[mask] = (xkis2[mask] - xkis1[mask]) * (ykis2[mask] - ykis1[mask]);\\n var unionk = (x2 - x1) * (y2 - y1) + (x2g - x1g) * (y2g - y1g) - intsctk;\\n\\n return (intsctk, unionk);\\n }\\n\\n\\n internal static (Tensor, Tensor) _diou_iou_loss(Tensor boxes1, Tensor boxes2, double eps = 1e-7)\\n {\\n var (intsct, union) = _loss_inter_union(boxes1, boxes2);\\n var iou = intsct / (union + eps);\\n // smallest enclosing box\\n var (x1, y1, x2, y2) = unwrap4(boxes1.unbind(dimension: -1));\\n var (x1g, y1g, x2g, y2g) = unwrap4(boxes2.unbind(dimension: -1));\\n\\n var xc1 = min(x1, x1g);\\n var yc1 = min(y1, y1g);\\n var xc2 = max(x2, x2g);\\n var yc2 = max(y2, y2g);\\n\\n var diagonal_distance_squared = (xc2 - xc1).pow(2) + (yc2 - yc1).pow(2) + eps;\\n // centers of boxes\\n var x_p = (x2 + x1) / 2;\\n var y_p = (y2 + y1) / 2;\\n var x_g = (x1g + x2g) / 2;\\n var y_g = (y1g + y2g) / 2;\\n\\n // The distance between boxes' centers squared.\\n var centers_distance_squared = (x_p - x_g).pow(2) + (y_p - y_g).pow(2);\\n // The distance IoU is the IoU penalized by a normalized\\n // distance between boxes' centers squared.\\n var loss = 1 - iou + (centers_distance_squared / diagonal_distance_squared);\\n return (loss, iou);\\n }\\n\\n internal static Tensor _cat(IList<Tensor> tensors, int dim = 0)\\n {\\n if (tensors.Count == 1)\\n return tensors[0];\\n return cat(tensors, dim);\\n }\\n\\n internal static Tensor convert_boxes_to_roi_format(IList<Tensor> boxes)\\n {\\n var concat_boxes = _cat(boxes);\\n var temp = new List<Tensor>();\\n var idx_1 = TensorIndex.Ellipsis;\\n var idx_2 = TensorIndex.Slice(stop: 1);\\n for (var i = 0; i < boxes.Count; i++) {\\n var b = boxes[i];\\n temp.Add(full_like(b[idx_1, idx_2], i));\\n }\\n var ids = _cat(temp, dim: 0);\\n var rois = cat(new Tensor[] { ids, concat_boxes }, dim: 1);\\n return rois;\\n }\\n\\n internal static void check_roi_boxes_shape(Tensor boxes)\\n {\\n if (boxes.size(1) != 5)\\n throw new ArgumentException(\\\"The boxes tensor shape is not correct as Tensor[K, 5]\\\");\\n }\\n\\n internal static void check_roi_boxes_shape(IList<Tensor> boxes)\\n {\\n foreach (var _tensor in boxes) {\\n if (_tensor.size(1) != 4)\\n throw new ArgumentException(\\\"The shape of the tensor in the boxes list is not correct as List[Tensor[L, 4]]\\\");\\n }\\n }\\n\\n internal static Tensor _box_cxcywh_to_xyxy(Tensor boxes)\\n {\\n var (cx, cy, w, h) = unwrap4(boxes.unbind(dimension: -1));\\n var x1 = cx - 0.5 * w;\\n var y1 = cy - 0.5 * h;\\n var x2 = cx + 0.5 * w;\\n var y2 = cy + 0.5 * h;\\n boxes = torch.stack(new[] { x1, y1, x2, y2 }, dim: -1);\\n return boxes;\\n }\\n\\n internal static Tensor _box_xyxy_to_cxcywh(Tensor boxes)\\n {\\n var (x1, y1, x2, y2) = unwrap4(boxes.unbind(dimension: -1));\\n var cx = (x1 + x2) / 2;\\n var cy = (y1 + y2) / 2;\\n var w = x2 - x1;\\n var h = y2 - y1;\\n\\n boxes = torch.stack(new[] { cx, cy, w, h }, dim: -1);\\n return boxes;\\n }\\n\\n internal static Tensor _box_xywh_to_xyxy(Tensor boxes)\\n {\\n var (x, y, w, h) = unwrap4(boxes.unbind(dimension: -1));\\n boxes = torch.stack(new[] { x, y, x + w, y + h }, dim: -1);\\n return boxes;\\n }\\n\\n internal static Tensor _box_xyxy_to_xywh(Tensor boxes)\\n {\\n var (x1, y1, x2, y2) = unwrap4(boxes.unbind(dimension: -1));\\n var w = x2 - x1;\\n var h = y2 - y1;\\n boxes = torch.stack(new[] { x1, y1, w, h }, dim: -1);\\n return boxes;\\n }\\n }\\n }\\n}\\n\"\n },\n {\n \"alpha_fraction\": 0.48641976714134216,\n \"alphanum_fraction\": 0.48641976714134216,\n \"avg_line_length\": 26,\n \"blob_id\": \"5e618dc8a7e6d5fa3cbcd78fa3200d73eaf386e8\",\n \"content_id\": \"489f6e04a3b7364f535905dee51f8d4aace22b6a\",\n \"detected_licenses\": [\n \"MIT\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"C#\",\n \"length_bytes\": 810,\n \"license_type\": \"permissive\",\n \"max_line_length\": 130,\n \"num_lines\": 30,\n \"path\": \"/src/TorchAudio/BackendUtils.cs\",\n \"repo_name\": \"NiklasGustafsson/TorchSharp\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"// Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information.\\n\\nnamespace TorchSharp\\n{\\n public static partial class torchaudio\\n {\\n public static partial class backend\\n {\\n public static partial class utils\\n {\\n internal static AudioBackend _backend;\\n\\n static utils()\\n {\\n _backend = new NoBackend();\\n }\\n\\n public static void set_audio_backend(AudioBackend backend)\\n {\\n _backend = backend;\\n }\\n\\n public static AudioBackend get_audio_backend()\\n {\\n return _backend;\\n }\\n }\\n }\\n }\\n}\\n\"\n },\n {\n \"alpha_fraction\": 0.5483773350715637,\n \"alphanum_fraction\": 0.5784046053886414,\n \"avg_line_length\": 39.703704833984375,\n \"blob_id\": \"204c5fce8a4065bfc2f4bcf15fb49d1829ddd56a\",\n \"content_id\": \"5d551415d306b6d9561b0a7ed5945a6003a2f169\",\n \"detected_licenses\": [\n \"MIT\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"C#\",\n \"length_bytes\": 3297,\n \"license_type\": \"permissive\",\n \"max_line_length\": 151,\n \"num_lines\": 81,\n \"path\": \"/src/Examples/MobileNet.cs\",\n \"repo_name\": \"NiklasGustafsson/TorchSharp\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"// Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information.\\nusing System;\\nusing System.Collections.Generic;\\n\\nusing static TorchSharp.torch;\\nusing static TorchSharp.torch.nn;\\n\\nnamespace TorchSharp.Examples\\n{\\n /// <summary>\\n /// Modified version of MobileNet to classify CIFAR10 32x32 images.\\n /// </summary>\\n /// <remarks>\\n /// With an unaugmented CIFAR-10 data set, the author of this saw training converge\\n /// at roughly 75% accuracy on the test set, over the course of 1500 epochs.\\n /// </remarks>\\n class MobileNet : Module<Tensor, Tensor>\\n {\\n // The code here is is loosely based on https://github.com/kuangliu/pytorch-cifar/blob/master/models/mobilenet.py\\n // Licence and copypright notice at: https://github.com/kuangliu/pytorch-cifar/blob/master/LICENSE\\n\\n private readonly long[] planes = new long[] { 64, 128, 128, 256, 256, 512, 512, 512, 512, 512, 512, 1024, 1024 };\\n private readonly long[] strides = new long[] { 1, 2, 1, 2, 1, 2, 1, 1, 1, 1, 1, 2, 1 };\\n\\n private readonly Module<Tensor, Tensor> layers;\\n\\n public MobileNet(string name, int numClasses, Device device = null) : base(name)\\n {\\n if (planes.Length != strides.Length) throw new ArgumentException(\\\"'planes' and 'strides' must have the same length.\\\");\\n\\n var modules = new List<(string, Module<Tensor, Tensor>)>();\\n\\n modules.Add(($\\\"conv2d-first\\\", Conv2d(3, 32, kernelSize: 3, stride: 1, padding: 1, bias: false)));\\n modules.Add(($\\\"bnrm2d-first\\\", BatchNorm2d(32)));\\n modules.Add(($\\\"relu-first\\\", ReLU()));\\n MakeLayers(modules, 32);\\n modules.Add((\\\"avgpool\\\", AvgPool2d(new long[] { 2, 2 })));\\n modules.Add((\\\"flatten\\\", Flatten()));\\n modules.Add(($\\\"linear\\\", Linear(planes[planes.Length - 1], numClasses)));\\n\\n layers = Sequential(modules);\\n\\n RegisterComponents();\\n\\n if (device != null && device.type == DeviceType.CUDA)\\n this.to(device);\\n }\\n\\n private void MakeLayers(List<(string, Module<Tensor, Tensor>)> modules, long in_planes)\\n {\\n\\n for (var i = 0; i < strides.Length; i++) {\\n var out_planes = planes[i];\\n var stride = strides[i];\\n\\n modules.Add(($\\\"conv2d-{i}a\\\", Conv2d(in_planes, in_planes, kernelSize: 3, stride: stride, padding: 1, groups: in_planes, bias: false)));\\n modules.Add(($\\\"bnrm2d-{i}a\\\", BatchNorm2d(in_planes)));\\n modules.Add(($\\\"relu-{i}a\\\", ReLU()));\\n modules.Add(($\\\"conv2d-{i}b\\\", Conv2d(in_planes, out_planes, kernelSize: 1L, stride: 1L, padding: 0L, bias: false)));\\n modules.Add(($\\\"bnrm2d-{i}b\\\", BatchNorm2d(out_planes)));\\n modules.Add(($\\\"relu-{i}b\\\", ReLU()));\\n\\n in_planes = out_planes;\\n }\\n }\\n\\n public override Tensor forward(Tensor input)\\n {\\n return layers.forward(input);\\n }\\n\\n protected override void Dispose(bool disposing)\\n {\\n if(disposing) {\\n layers.Dispose();\\n ClearModules();\\n }\\n base.Dispose(disposing);\\n }\\n }\\n}\\n\"\n },\n {\n \"alpha_fraction\": 0.5681625604629517,\n \"alphanum_fraction\": 0.5726785063743591,\n \"avg_line_length\": 56.60975646972656,\n \"blob_id\": \"9cf4375fcc30905261f66fdfcd00028e21639803\",\n \"content_id\": \"d69ff96de62f04a4818a2d6131b01dcfcd6211f1\",\n \"detected_licenses\": [\n \"MIT\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"C#\",\n \"length_bytes\": 7090,\n \"license_type\": \"permissive\",\n \"max_line_length\": 240,\n \"num_lines\": 123,\n \"path\": \"/src/TorchSharp/NN/Transformer.cs\",\n \"repo_name\": \"NiklasGustafsson/TorchSharp\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"// Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information.\\nusing System;\\nusing static TorchSharp.torch;\\nusing static TorchSharp.PInvoke.NativeMethods;\\n\\n#nullable enable\\nnamespace TorchSharp\\n{\\n using System.Runtime.InteropServices;\\n using Modules;\\n\\n namespace Modules\\n {\\n public sealed class Transformer : torch.nn.Module<Tensor, Tensor, Tensor>\\n {\\n internal Transformer(IntPtr handle, IntPtr boxedHandle) : base(handle, boxedHandle) { }\\n\\n /// <summary>\\n /// Take in and process masked source/target sequences.\\n /// </summary>\\n /// <param name=\\\"src\\\">The sequence to the encoder (required).</param>\\n /// <param name=\\\"tgt\\\">The sequence to the decoder (required).</param>\\n /// <param name=\\\"src_mask\\\">The additive mask for the src sequence (optional).</param>\\n /// <param name=\\\"tgt_mask\\\">The additive mask for the tgt sequence (optional).</param>\\n /// <param name=\\\"memory_mask\\\">The additive mask for the encoder output (optional).</param>\\n /// <param name=\\\"src_key_padding_mask\\\">The ByteTensor mask for src keys per batch (optional).</param>\\n /// <param name=\\\"tgt_key_padding_mask\\\">The ByteTensor mask for tgt keys per batch (optional).</param>\\n /// <param name=\\\"memory_key_padding_mask\\\">The ByteTensor mask for memory keys per batch (optional).</param>\\n /// <returns></returns>\\n public Tensor call(Tensor src, Tensor tgt, Tensor src_mask, Tensor? tgt_mask = null, Tensor? memory_mask = null, Tensor? src_key_padding_mask = null, Tensor? tgt_key_padding_mask = null, Tensor? memory_key_padding_mask = null)\\n {\\n var res = THSNN_Transformer_forward(handle,\\n src.Handle,\\n tgt.Handle,\\n src_mask?.Handle ?? IntPtr.Zero,\\n tgt_mask?.Handle ?? IntPtr.Zero,\\n memory_mask?.Handle ?? IntPtr.Zero,\\n src_key_padding_mask?.Handle ?? IntPtr.Zero,\\n tgt_key_padding_mask?.Handle ?? IntPtr.Zero,\\n memory_key_padding_mask?.Handle ?? IntPtr.Zero);\\n if (res == IntPtr.Zero) { torch.CheckForErrors(); }\\n return new Tensor(res);\\n }\\n\\n /// <summary>\\n /// Take in and process masked source/target sequences.\\n /// </summary>\\n /// <param name=\\\"src\\\">The sequence to the encoder (required).</param>\\n /// <param name=\\\"tgt\\\">The sequence to the decoder (required).</param>\\n public override Tensor forward(Tensor src, Tensor tgt)\\n {\\n var res = THSNN_Transformer_forward(handle,\\n src.Handle,\\n tgt.Handle,\\n IntPtr.Zero,\\n IntPtr.Zero,\\n IntPtr.Zero,\\n IntPtr.Zero,\\n IntPtr.Zero,\\n IntPtr.Zero);\\n if (res == IntPtr.Zero) { torch.CheckForErrors(); }\\n return new Tensor(res);\\n }\\n }\\n }\\n\\n public static partial class torch\\n {\\n public static partial class nn\\n {\\n public enum Activations\\n {\\n ReLU = 0,\\n GELU = 1\\n }\\n\\n /// <summary>\\n /// A transformer model. User is able to modify the attributes as needed. The architecture is based on the paper “Attention Is All You Need”.\\n /// </summary>\\n /// <param name=\\\"d_model\\\">The number of expected features in the encoder/decoder inputs (default=512).</param>\\n /// <param name=\\\"nhead\\\">The number of heads in the multiheadattention models (default=8).</param>\\n /// <param name=\\\"num_encoder_layers\\\">The number of sub-encoder-layers in the encoder (default=6).</param>\\n /// <param name=\\\"num_decoder_layers\\\">The number of sub-decoder-layers in the decoder (default=6).</param>\\n /// <param name=\\\"dim_feedforward\\\">The dimension of the feedforward network model (default=2048).</param>\\n /// <param name=\\\"dropout\\\">The dropout value (default=0.1).</param>\\n /// <param name=\\\"activation\\\">The activation function of encoder/decoder intermediate layer, relu or gelu (default=relu).</param>\\n /// <returns></returns>\\n public static Transformer Transformer(long d_model = 512, long nhead = 8, long num_encoder_layers = 6, long num_decoder_layers = 6, long dim_feedforward = 2048, double dropout = 0.1, Activations activation = nn.Activations.ReLU)\\n {\\n var res = THSNN_Transformer_ctor(d_model, nhead, num_encoder_layers, num_decoder_layers, dim_feedforward, dropout, (long)activation, out var boxedHandle);\\n if (res == IntPtr.Zero) { torch.CheckForErrors(); }\\n return new Transformer(res, boxedHandle);\\n }\\n\\n public static partial class functional\\n {\\n /// <summary>\\n /// Computes scaled dot product attention on query, key and value tensors, using an optional attention mask if passed, and applying dropout if a probability greater than 0.0 is specified.\\n /// </summary>\\n /// <param name=\\\"query\\\">Query tensor, shaped (N, ..., L, E)</param>\\n /// <param name=\\\"key\\\">Key tensor, shaped (N, ..., S, E)</param>\\n /// <param name=\\\"value\\\">Value tensor, shaped (N, ..., S, Ev)</param>\\n /// <param name=\\\"attn_mask\\\">\\n /// Attention mask, shaped (N, ..., L, S).\\n /// Two types of masks are supported:\\n /// A boolean mask where a value of True indicates that the element should take part in attention.\\n /// A float mask of the same type as query, key, value that is added to the attention score.\\n /// </param>\\n /// <param name=\\\"p\\\">Dropout probability</param>\\n /// <param name=\\\"is_casual\\\">If true, assumes causal attention masking and errors if both attn_mask and is_causal are set.</param>\\n /// <returns></returns>\\n public static Tensor scaled_dot_product_attention(Tensor query, Tensor key, Tensor value, Tensor? attn_mask = null, double p = 0.0, [MarshalAs(UnmanagedType.U1)] bool is_casual = false)\\n {\\n if (p < 0) throw new ArgumentException(\\\"Dropout probability must be greater than or equal to zero.\\\");\\n if (is_casual && attn_mask is not null) throw new ArgumentException(\\\"Casual attention masking cannot pass a mask.\\\");\\n var res = THSNN_scaled_dot_product_attention(query.Handle, key.Handle, value.Handle, attn_mask is null ? IntPtr.Zero : attn_mask.Handle, p, is_casual);\\n if (res == IntPtr.Zero) { torch.CheckForErrors(); }\\n return new Tensor(res);\\n }\\n }\\n }\\n }\\n}\\n\"\n },\n {\n \"alpha_fraction\": 0.5679244995117188,\n \"alphanum_fraction\": 0.5679244995117188,\n \"avg_line_length\": 27.675676345825195,\n \"blob_id\": \"8b43c3b4da4d2ec476a327f8faf9a0e810793b4a\",\n \"content_id\": \"a106064df77514dc888aecfa057f11150b1f6508\",\n \"detected_licenses\": [\n \"MIT\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"C#\",\n \"length_bytes\": 1060,\n \"license_type\": \"permissive\",\n \"max_line_length\": 130,\n \"num_lines\": 37,\n \"path\": \"/src/TorchVision/Solarize.cs\",\n \"repo_name\": \"NiklasGustafsson/TorchSharp\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"// Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information.\\n\\nusing static TorchSharp.torch;\\n\\n\\nnamespace TorchSharp\\n{\\n public static partial class torchvision\\n {\\n internal class Solarize : ITransform\\n {\\n internal Solarize(double threshold)\\n {\\n this.threshold = threshold;\\n }\\n\\n public Tensor call(Tensor input)\\n {\\n return transforms.functional.solarize(input, threshold);\\n }\\n\\n private double threshold;\\n }\\n\\n public static partial class transforms\\n {\\n /// <summary>\\n /// Solarize the image by inverting all pixel values above a threshold.\\n /// </summary>\\n /// <param name=\\\"threshold\\\">All pixels equal or above this value are inverted.</param>\\n static public ITransform Solarize(double threshold)\\n {\\n return new Solarize(threshold);\\n }\\n }\\n }\\n}\"\n },\n {\n \"alpha_fraction\": 0.6311787366867065,\n \"alphanum_fraction\": 0.6425855755805969,\n \"avg_line_length\": 25.399999618530273,\n \"blob_id\": \"f9759eeff22e157cef190ed3566126bfd3dffbd0\",\n \"content_id\": \"c6707d8f83b6dd3cd0631cdc02af30edd3da3e32\",\n \"detected_licenses\": [\n \"MIT\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"C#\",\n \"length_bytes\": 265,\n \"license_type\": \"permissive\",\n \"max_line_length\": 131,\n \"num_lines\": 10,\n \"path\": \"/src/TorchSharp/Tensor/Enums/PrintOptionsProfile.cs\",\n \"repo_name\": \"NiklasGustafsson/TorchSharp\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"// Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information.\\nnamespace TorchSharp\\n{\\n public enum PrintOptionsProfile\\n {\\n @default = 0,\\n @short = 1,\\n full = 2\\n }\\n}\"\n },\n {\n \"alpha_fraction\": 0.4884297549724579,\n \"alphanum_fraction\": 0.5034238696098328,\n \"avg_line_length\": 41.349998474121094,\n \"blob_id\": \"db0f258d5c48f7757d3c0a06eff29cbc5f20e171\",\n \"content_id\": \"a01824120b72a56d733887ca19434e6b6d29031d\",\n \"detected_licenses\": [\n \"MIT\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"C#\",\n \"length_bytes\": 8470,\n \"license_type\": \"permissive\",\n \"max_line_length\": 134,\n \"num_lines\": 200,\n \"path\": \"/src/TorchVision/Ops/Losses.cs\",\n \"repo_name\": \"NiklasGustafsson/TorchSharp\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"// Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information.\\nusing System;\\nusing System.Drawing;\\nusing System.Text;\\nusing System.Threading.Tasks;\\nusing static TorchSharp.torch;\\nusing static TorchSharp.torch.nn.functional;\\nusing static TorchSharp.torchvision;\\n\\nnamespace TorchSharp\\n{\\n public static partial class torchvision\\n {\\n public static partial class ops\\n {\\n // Ported from https://github.com/pytorch/vision/blob/main/torchvision/ops/focal_loss.py\\n\\n /// <summary>\\n /// Loss used in RetinaNet for dense detection: https://arxiv.org/abs/1708.02002.\\n /// </summary>\\n /// <param name=\\\"inputs\\\">A float tensor of arbitrary shape. The predictions for each example. </param>\\n /// <param name=\\\"targets\\\">A float tensor with the same shape as inputs.\\n /// Stores the binary classification label for each element in inputs\\n /// (0 for the negative class and 1 for the positive class).\\n /// </param>\\n /// <param name=\\\"alpha\\\">Weighting factor in range (0,1) to balance positive vs negative examples or -1 for ignore.</param>\\n /// <param name=\\\"gamma\\\">Exponent of the modulating factor (1 - p_t) to balance easy vs hard examples.</param>\\n /// <param name=\\\"reduction\\\">The kind of reduction to apply to the result.</param>\\n /// <returns></returns>\\n public static Tensor sigmoid_focal_loss(\\n Tensor inputs,\\n Tensor targets,\\n double alpha = 0.25, double gamma = 2.0,\\n nn.Reduction reduction = nn.Reduction.None)\\n {\\n var p = torch.sigmoid(inputs);\\n var ce_loss = binary_cross_entropy_with_logits(inputs, targets, reduction: nn.Reduction.None);\\n var p_t = p * targets + (1 - p) * (1 - targets);\\n var loss = ce_loss * (1 - p_t).pow(gamma);\\n\\n if (alpha >= 0) {\\n var alpha_t = alpha * targets + (1 - alpha) * (1 - targets);\\n loss = alpha_t * loss;\\n }\\n\\n switch (reduction) {\\n case nn.Reduction.None:\\n break;\\n case nn.Reduction.Mean:\\n loss = loss.mean();\\n break;\\n case nn.Reduction.Sum:\\n loss = loss.sum();\\n break;\\n }\\n\\n return loss;\\n }\\n\\n /// <summary>\\n /// Gradient-friendly IoU loss with an additional penalty that is non-zero when the\\n /// boxes do not overlap.This loss function considers important geometrical\\n /// factors such as overlap area, normalized central point distance and aspect ratio.\\n /// This loss is symmetric, so the boxes1 and boxes2 arguments are interchangeable.\\n /// </summary>\\n /// <param name=\\\"boxes1\\\">(Tensor[N, 4] or Tensor[4]) first set of boxes</param>\\n /// <param name=\\\"boxes2\\\">(Tensor[N, 4] or Tensor[4]) second set of boxes</param>\\n /// <param name=\\\"reduction\\\">The kind of reduction to apply to the result.</param>\\n /// <param name=\\\"eps\\\">Small number to prevent division by zero.</param>\\n public static Tensor complete_box_iou_loss(\\n Tensor boxes1,\\n Tensor boxes2,\\n nn.Reduction reduction = nn.Reduction.None,\\n double eps = 1e-7)\\n {\\n boxes1 = _upcast_non_float(boxes1);\\n boxes2 = _upcast_non_float(boxes2);\\n\\n var (diou_loss, iou) = _diou_iou_loss(boxes1, boxes2);\\n\\n var xy = boxes1.unbind(dimension: -1);\\n var xyg = boxes2.unbind(dimension: -1);\\n\\n // width and height of boxes\\n var w_pred = xy[2] - xy[0];\\n var h_pred = xy[3] - xy[1];\\n var w_gt = xyg[2] - xyg[0];\\n var h_gt = xyg[3] - xyg[1];\\n\\n var v = (4 / Math.Pow(Math.PI, 2)) * torch.pow((torch.atan(w_gt / h_gt) - torch.atan(w_pred / h_pred)), 2);\\n\\n Tensor alpha = null;\\n\\n using (var _ = torch.no_grad()) {\\n alpha = v / (1 - iou + v + eps);\\n }\\n\\n var loss = diou_loss + alpha * v;\\n\\n switch (reduction) {\\n case nn.Reduction.None:\\n break;\\n case nn.Reduction.Mean:\\n loss = loss.mean();\\n break;\\n case nn.Reduction.Sum:\\n loss = loss.sum();\\n break;\\n }\\n\\n return loss;\\n }\\n\\n /// <summary>\\n /// Gradient-friendly IoU loss with an additional penalty that is non-zero when the\\n /// distance between boxes' centers isn't zero.Indeed, for two exactly overlapping\\n /// boxes, the distance IoU is the same as the IoU loss.\\n /// This loss is symmetric, so the boxes1 and boxes2 arguments are interchangeable.\\n /// </summary>\\n /// <param name=\\\"boxes1\\\">(Tensor[N, 4] or Tensor[4]) first set of boxes</param>\\n /// <param name=\\\"boxes2\\\">(Tensor[N, 4] or Tensor[4]) second set of boxes</param>\\n /// <param name=\\\"reduction\\\">The kind of reduction to apply to the result.</param>\\n /// <param name=\\\"eps\\\">Small number to prevent division by zero.</param>\\n public static Tensor distance_box_iou_loss(\\n Tensor boxes1,\\n Tensor boxes2,\\n nn.Reduction reduction = nn.Reduction.None,\\n double eps = 1e-7)\\n {\\n boxes1 = _upcast_non_float(boxes1);\\n boxes2 = _upcast_non_float(boxes2);\\n\\n var (loss, _) = _diou_iou_loss(boxes1, boxes2);\\n\\n switch (reduction) {\\n case nn.Reduction.None:\\n break;\\n case nn.Reduction.Mean:\\n loss = loss.mean();\\n break;\\n case nn.Reduction.Sum:\\n loss = loss.sum();\\n break;\\n }\\n\\n return loss;\\n }\\n\\n /// <summary>\\n /// Gradient-friendly IoU loss with an additional penalty that is non-zero when the\\n /// boxes do not overlap and scales with the size of their smallest enclosing box.\\n /// This loss is symmetric, so the boxes1 and boxes2 arguments are interchangeable.\\n /// </summary>\\n /// <param name=\\\"boxes1\\\">(Tensor[N, 4] or Tensor[4]) first set of boxes</param>\\n /// <param name=\\\"boxes2\\\">(Tensor[N, 4] or Tensor[4]) second set of boxes</param>\\n /// <param name=\\\"reduction\\\">The kind of reduction to apply to the result.</param>\\n /// <param name=\\\"eps\\\">Small number to prevent division by zero.</param>\\n public static Tensor generalized_box_iou_loss(\\n Tensor boxes1,\\n Tensor boxes2,\\n nn.Reduction reduction = nn.Reduction.None,\\n double eps = 1e-7)\\n {\\n boxes1 = _upcast_non_float(boxes1);\\n boxes2 = _upcast_non_float(boxes2);\\n\\n var (intsctk, unionk) = _loss_inter_union(boxes1, boxes2);\\n var iouk = intsctk / (unionk + eps);\\n\\n var (x1, y1, x2, y2) = unwrap4(boxes1.unbind(dimension: -1));\\n var (x1g, y1g, x2g, y2g) = unwrap4(boxes2.unbind(dimension: -1));\\n\\n // smallest enclosing box\\n\\n var xc1 = torch.min(x1, x1g);\\n var yc1 = torch.min(y1, y1g);\\n var xc2 = torch.max(x2, x2g);\\n var yc2 = torch.max(y2, y2g);\\n\\n var area_c = (xc2 - xc1) * (yc2 - yc1);\\n var miouk = iouk - ((area_c - unionk) / (area_c + eps));\\n\\n var loss = 1 - miouk;\\n\\n switch (reduction) {\\n case nn.Reduction.None:\\n break;\\n case nn.Reduction.Mean:\\n loss = loss.mean();\\n break;\\n case nn.Reduction.Sum:\\n loss = loss.sum();\\n break;\\n }\\n\\n return loss;\\n }\\n }\\n }\\n}\\n\"\n },\n {\n \"alpha_fraction\": 0.5421236753463745,\n \"alphanum_fraction\": 0.543523907661438,\n \"avg_line_length\": 43.404144287109375,\n \"blob_id\": \"f5300b88f4ee7c621bea8996b591f75d2d1d0420\",\n \"content_id\": \"e607e9069d624100dc6a46ec7c0d0db872aa6376\",\n \"detected_licenses\": [\n \"MIT\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"C#\",\n \"length_bytes\": 8570,\n \"license_type\": \"permissive\",\n \"max_line_length\": 149,\n \"num_lines\": 193,\n \"path\": \"/src/TorchSharp/Distributions/OneHotCategorical.cs\",\n \"repo_name\": \"NiklasGustafsson/TorchSharp\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"// Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information.\\nusing System;\\nusing static TorchSharp.torch;\\n\\nnamespace TorchSharp\\n{\\n using Modules;\\n\\n namespace Modules\\n {\\n /// <summary>\\n /// A Bernoulli distribution parameterized by `probs` or `logits` (but not both).\\n /// </summary>\\n public class OneHotCategorical : torch.distributions.Distribution\\n {\\n /// <summary>\\n /// The mean of the distribution.\\n /// </summary>\\n public override Tensor mean => _categorical._probs;\\n\\n /// <summary>\\n /// The mode of the distribution.\\n /// </summary>\\n public override Tensor mode {\\n get {\\n var probs = _categorical.probs;\\n var mode = probs.argmax(-1);\\n return torch.nn.functional.one_hot(mode, num_classes: probs.shape[probs.ndim - 1]).to(probs);\\n }\\n }\\n\\n /// <summary>\\n /// The variance of the distribution\\n /// </summary>\\n public override Tensor variance => probs * (1 - _categorical.probs);\\n\\n /// <summary>\\n /// Constructor\\n /// </summary>\\n /// <param name=\\\"p\\\"></param>\\n /// <param name=\\\"l\\\"></param>\\n /// <param name=\\\"generator\\\"></param>\\n public OneHotCategorical(Tensor p = null, Tensor l = null, torch.Generator generator = null) : base(generator)\\n {\\n _categorical = new Categorical(p, l, generator);\\n\\n if ((p is null && logits is null) || (p is not null && l is not null))\\n throw new ArgumentException(\\\"One and only one of 'probs' and logits should be provided.\\\");\\n\\n this.batch_shape = p is null ? l.size() : p.size();\\n this._probs = p?.alias().DetachFromDisposeScope();\\n this._logits = l?.alias().DetachFromDisposeScope();\\n }\\n\\n /// <summary>\\n /// The probability of sampling 1\\n /// </summary>\\n public Tensor probs {\\n get {\\n return _probs ?? LogitsToProbs(_logits, true);\\n }\\n }\\n\\n /// <summary>\\n /// The log-odds of sampling 1\\n /// </summary>\\n public Tensor logits {\\n get {\\n return _logits ?? ProbsToLogits(_probs);\\n }\\n }\\n\\n private Categorical _categorical;\\n private Tensor _probs;\\n private Tensor _logits;\\n\\n /// <summary>\\n /// Generates a sample_shape shaped reparameterized sample or sample_shape shaped batch of reparameterized samples\\n /// if the distribution parameters are batched.\\n /// </summary>\\n /// <param name=\\\"sample_shape\\\">The sample shape.</param>\\n /// <returns></returns>\\n public override Tensor rsample(params long[] sample_shape)\\n {\\n var probs = _categorical.probs;\\n var num_events = _categorical.num_events;\\n var indices = _categorical.sample(sample_shape);\\n return torch.nn.functional.one_hot(indices, num_events).to(probs);\\n }\\n\\n /// <summary>\\n /// Returns the log of the probability density/mass function evaluated at `value`.\\n /// </summary>\\n /// <param name=\\\"value\\\"></param>\\n /// <returns></returns>\\n\\n public override Tensor log_prob(Tensor value)\\n {\\n using var _ = torch.NewDisposeScope();\\n var indices = value.max(-1).indexes;\\n return _categorical.log_prob(indices).MoveToOuterDisposeScope();\\n }\\n\\n /// <summary>\\n /// Returns entropy of distribution, batched over batch_shape.\\n /// </summary>\\n public override Tensor entropy()\\n {\\n return _categorical.entropy();\\n }\\n\\n /// <summary>\\n /// Returns a new distribution instance (or populates an existing instance provided by a derived class) with batch dimensions expanded to\\n /// `batch_shape`. This method calls `torch.Tensor.expand()` on the distribution's parameters. As such, this does not allocate new\\n /// memory for the expanded distribution instance.\\n /// </summary>\\n /// <param name=\\\"batch_shape\\\">Tthe desired expanded size.</param>\\n /// <param name=\\\"instance\\\">new instance provided by subclasses that need to override `.expand`.</param>\\n /// <returns></returns>\\n public override distributions.Distribution expand(Size batch_shape, distributions.Distribution instance = null)\\n {\\n if (instance != null && !(instance is Bernoulli))\\n throw new ArgumentException(\\\"expand(): 'instance' must be a Bernoulli distribution\\\");\\n\\n var p = _probs?.expand(batch_shape);\\n var l = _logits?.expand(batch_shape);\\n\\n var newDistribution = ((instance == null) ? new OneHotCategorical(p, l, generator) : instance) as OneHotCategorical;\\n\\n newDistribution.batch_shape = batch_shape;\\n if (newDistribution == instance) {\\n newDistribution._probs = p;\\n newDistribution._logits = l;\\n newDistribution._categorical = _categorical.expand(batch_shape) as Categorical;\\n }\\n return newDistribution;\\n }\\n }\\n\\n }\\n public static partial class torch\\n {\\n public static partial class distributions\\n {\\n /// <summary>\\n /// Creates a Bernoulli distribution parameterized by `probs` or `logits` (but not both).\\n /// </summary>\\n /// <param name=\\\"probs\\\">The probability of sampling '1'</param>\\n /// <param name=\\\"logits\\\">The log-odds of sampling '1'</param>\\n /// <param name=\\\"generator\\\">An optional random number generator object.</param>\\n /// <returns></returns>\\n public static OneHotCategorical OneHotCategorical(Tensor probs = null, Tensor logits = null, torch.Generator generator = null)\\n {\\n return new OneHotCategorical(probs, logits, generator);\\n }\\n\\n /// <summary>\\n /// Creates a Bernoulli distribution parameterized by `probs` or `logits` (but not both).\\n /// </summary>\\n /// <param name=\\\"probs\\\">The probability of sampling '1'</param>\\n /// <param name=\\\"logits\\\">The log-odds of sampling '1'</param>\\n /// <param name=\\\"generator\\\">An optional random number generator object.</param>\\n /// <returns></returns>\\n public static OneHotCategorical OneHotCategorical(float? probs, float? logits, torch.Generator generator = null)\\n {\\n if (probs.HasValue && !logits.HasValue)\\n return new OneHotCategorical(torch.tensor(probs.Value), null, generator);\\n else if (!probs.HasValue && logits.HasValue)\\n return new OneHotCategorical(null, torch.tensor(logits.Value), generator);\\n else\\n throw new ArgumentException(\\\"One and only one of 'probs' and logits should be provided.\\\");\\n }\\n\\n\\n /// <summary>\\n /// Creates a Bernoulli distribution parameterized by `probs` or `logits` (but not both).\\n /// </summary>\\n /// <param name=\\\"probs\\\">The probability of sampling '1'</param>\\n /// <param name=\\\"logits\\\">The log-odds of sampling '1'</param>\\n /// <param name=\\\"generator\\\">An optional random number generator object.</param>\\n /// <returns></returns>\\n public static OneHotCategorical OneHotCategorical(double? probs, double? logits, torch.Generator generator = null)\\n {\\n if (probs.HasValue && !logits.HasValue)\\n return new OneHotCategorical(torch.tensor(probs.Value), null, generator);\\n else if (!probs.HasValue && logits.HasValue)\\n return new OneHotCategorical(null, torch.tensor(logits.Value), generator);\\n else\\n throw new ArgumentException(\\\"One and only one of 'probs' and 'logits' should be non-null\\\");\\n }\\n }\\n }\\n}\\n\"\n },\n {\n \"alpha_fraction\": 0.7004733681678772,\n \"alphanum_fraction\": 0.7033941149711609,\n \"avg_line_length\": 47.19902801513672,\n \"blob_id\": \"834d40b704c6e3a33bdc762e29ce1bcc4823262f\",\n \"content_id\": \"394898ec8c659b5d1cc5dc707c5c5f06eb4c7963\",\n \"detected_licenses\": [\n \"MIT\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"C#\",\n \"length_bytes\": 9929,\n \"license_type\": \"permissive\",\n \"max_line_length\": 130,\n \"num_lines\": 206,\n \"path\": \"/src/TorchSharp/PInvoke/LibTorchSharp.THSSpecial.cs\",\n \"repo_name\": \"NiklasGustafsson/TorchSharp\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"// Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information.\\n#nullable enable\\nusing System;\\nusing System.Runtime.InteropServices;\\n\\nnamespace TorchSharp.PInvoke\\n{\\n internal static partial class NativeMethods\\n {\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern IntPtr THSSpecial_airy_ai(IntPtr tensor);\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern IntPtr THSSpecial_airy_ai_out(IntPtr tensor, IntPtr _out);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern IntPtr THSSpecial_bessel_j0(IntPtr tensor);\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern IntPtr THSSpecial_bessel_j0_out(IntPtr tensor, IntPtr _out);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern IntPtr THSSpecial_bessel_j1(IntPtr tensor);\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern IntPtr THSSpecial_bessel_j1_out(IntPtr tensor, IntPtr _out);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern IntPtr THSSpecial_bessel_y0(IntPtr tensor);\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern IntPtr THSSpecial_bessel_y0_out(IntPtr tensor, IntPtr _out);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern IntPtr THSSpecial_bessel_y1(IntPtr tensor);\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern IntPtr THSSpecial_bessel_y1_out(IntPtr tensor, IntPtr _out);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern IntPtr THSSpecial_modified_bessel_i0(IntPtr tensor);\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern IntPtr THSSpecial_modified_bessel_i0_out(IntPtr tensor, IntPtr _out);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern IntPtr THSSpecial_modified_bessel_i1(IntPtr tensor);\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern IntPtr THSSpecial_modified_bessel_i1_out(IntPtr tensor, IntPtr _out);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern IntPtr THSSpecial_modified_bessel_k0(IntPtr tensor);\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern IntPtr THSSpecial_modified_bessel_k0_out(IntPtr tensor, IntPtr _out);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern IntPtr THSSpecial_modified_bessel_k1(IntPtr tensor);\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern IntPtr THSSpecial_modified_bessel_k1_out(IntPtr tensor, IntPtr _out);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern IntPtr THSSpecial_scaled_modified_bessel_k0(IntPtr tensor);\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern IntPtr THSSpecial_scaled_modified_bessel_k0_out(IntPtr tensor, IntPtr _out);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern IntPtr THSSpecial_scaled_modified_bessel_k1(IntPtr tensor);\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern IntPtr THSSpecial_scaled_modified_bessel_k1_out(IntPtr tensor, IntPtr _out);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern IntPtr THSSpecial_spherical_bessel_j0(IntPtr tensor);\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern IntPtr THSSpecial_spherical_bessel_j0_out(IntPtr tensor, IntPtr _out);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern IntPtr THSSpecial_chebyshev_polynomial_t(IntPtr x, IntPtr n);\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern IntPtr THSSpecial_chebyshev_polynomial_t_out(IntPtr x, IntPtr n, IntPtr @out);\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern IntPtr THSSpecial_chebyshev_polynomial_u(IntPtr x, IntPtr n);\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern IntPtr THSSpecial_chebyshev_polynomial_u_out(IntPtr x, IntPtr n, IntPtr @out);\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern IntPtr THSSpecial_chebyshev_polynomial_v(IntPtr x, IntPtr n);\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern IntPtr THSSpecial_chebyshev_polynomial_v_out(IntPtr x, IntPtr n, IntPtr @out);\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern IntPtr THSSpecial_chebyshev_polynomial_w(IntPtr x, IntPtr n);\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern IntPtr THSSpecial_chebyshev_polynomial_w_out(IntPtr x, IntPtr n, IntPtr @out);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern IntPtr THSSpecial_shifted_chebyshev_polynomial_t(IntPtr x, IntPtr n);\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern IntPtr THSSpecial_shifted_chebyshev_polynomial_t_out(IntPtr x, IntPtr n, IntPtr @out);\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern IntPtr THSSpecial_shifted_chebyshev_polynomial_u(IntPtr x, IntPtr n);\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern IntPtr THSSpecial_shifted_chebyshev_polynomial_u_out(IntPtr x, IntPtr n, IntPtr @out);\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern IntPtr THSSpecial_shifted_chebyshev_polynomial_v(IntPtr x, IntPtr n);\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern IntPtr THSSpecial_shifted_chebyshev_polynomial_v_out(IntPtr x, IntPtr n, IntPtr @out);\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern IntPtr THSSpecial_shifted_chebyshev_polynomial_w(IntPtr x, IntPtr n);\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern IntPtr THSSpecial_shifted_chebyshev_polynomial_w_out(IntPtr x, IntPtr n, IntPtr @out);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern IntPtr THSSpecial_hermite_polynomial_h(IntPtr x, IntPtr n);\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern IntPtr THSSpecial_hermite_polynomial_h_out(IntPtr x, IntPtr n, IntPtr @out);\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern IntPtr THSSpecial_hermite_polynomial_he(IntPtr x, IntPtr n);\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern IntPtr THSSpecial_hermite_polynomial_he_out(IntPtr x, IntPtr n, IntPtr @out);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern IntPtr THSSpecial_laguerre_polynomial_l(IntPtr x, IntPtr n);\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern IntPtr THSSpecial_laguerre_polynomial_l_out(IntPtr x, IntPtr n, IntPtr @out);\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern IntPtr THSSpecial_legendre_polynomial_p(IntPtr x, IntPtr n);\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern IntPtr THSSpecial_legendre_polynomial_p_out(IntPtr x, IntPtr n, IntPtr @out);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern IntPtr THSSpecial_entr(IntPtr tensor);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern IntPtr THSSpecial_erf(IntPtr tensor);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern IntPtr THSSpecial_erfc(IntPtr tensor);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern IntPtr THSSpecial_erfcx(IntPtr tensor);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern IntPtr THSSpecial_erfinv(IntPtr tensor);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern IntPtr THSSpecial_expit(IntPtr tensor);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern IntPtr THSSpecial_expm1(IntPtr tensor);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern IntPtr THSSpecial_exp2(IntPtr tensor);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern IntPtr THSSpecial_gammaln(IntPtr tensor);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern IntPtr THSSpecial_gammainc(IntPtr tensor, IntPtr other);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern IntPtr THSSpecial_gammaincc(IntPtr tensor, IntPtr other);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern IntPtr THSSpecial_polygamma(long n, IntPtr tensor);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern IntPtr THSSpecial_multigammaln(IntPtr tensor, long p);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern IntPtr THSSpecial_digamma(IntPtr tensor);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern IntPtr THSSpecial_i0(IntPtr tensor);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern IntPtr THSSpecial_i0e(IntPtr tensor);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern IntPtr THSSpecial_i1(IntPtr tensor);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern IntPtr THSSpecial_i1e(IntPtr tensor);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern IntPtr THSSpecial_logit(IntPtr tensor);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern IntPtr THSSpecial_log_softmax(IntPtr tensor, long dim, sbyte scalar_type);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern IntPtr THSSpecial_softmax(IntPtr tensor, long dim, sbyte scalar_type);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern IntPtr THSSpecial_ndtr(IntPtr tensor);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern IntPtr THSSpecial_ndtri(IntPtr tensor);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern IntPtr THSSpecial_sinc(IntPtr tensor);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern IntPtr THSSpecial_xlog1py(IntPtr tensor, IntPtr other);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern IntPtr THSSpecial_zeta(IntPtr tensor, IntPtr other);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern double THSSpecial_erf_scalar(double x);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern double THSSpecial_erfc_scalar(double x);\\n }\\n}\\n\"\n },\n {\n \"alpha_fraction\": 0.5633528232574463,\n \"alphanum_fraction\": 0.5698505640029907,\n \"avg_line_length\": 34.79069900512695,\n \"blob_id\": \"aae537606737be2746ed36854e0b1c789b26a76c\",\n \"content_id\": \"8679c97dd3df0a40eed01855ba1314e235f0b698\",\n \"detected_licenses\": [\n \"MIT\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"C#\",\n \"length_bytes\": 1539,\n \"license_type\": \"permissive\",\n \"max_line_length\": 141,\n \"num_lines\": 43,\n \"path\": \"/src/TorchVision/Grayscale.cs\",\n \"repo_name\": \"NiklasGustafsson/TorchSharp\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"// Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information.\\nusing System;\\nusing static TorchSharp.torch;\\n\\n\\nnamespace TorchSharp\\n{\\n public static partial class torchvision\\n {\\n internal class Grayscale : ITransform\\n {\\n internal Grayscale(int outputChannels = 1)\\n {\\n if (outputChannels != 1 && outputChannels != 3) throw new ArgumentException(\\\"The number of output channels must be 1 or 3.\\\");\\n this.outputChannels = outputChannels;\\n }\\n\\n public Tensor call(Tensor input)\\n {\\n int cDim = (int)input.Dimensions - 3;\\n if (input.shape[cDim] == 1) return input.alias();\\n if (input.shape[cDim] != 3)\\n throw new ArgumentException(\\\"RGB input to 'Grayscale' transform must have 3 channels.\\\");\\n return transforms.functional.rgb_to_grayscale(input, outputChannels);\\n }\\n\\n protected int outputChannels;\\n }\\n\\n public static partial class transforms\\n {\\n /// <summary>\\n /// Convert image to grayscale. \\n /// </summary>\\n /// <param name=\\\"outputChannels\\\">The number of channels in the output image tensor.</param>\\n /// <returns></returns>\\n static public ITransform Grayscale(int outputChannels = 1)\\n {\\n return new Grayscale(outputChannels);\\n }\\n }\\n }\\n}\\n\"\n },\n {\n \"alpha_fraction\": 0.5795321464538574,\n \"alphanum_fraction\": 0.5826510787010193,\n \"avg_line_length\": 47.40565872192383,\n \"blob_id\": \"52144a5704fde98b5e62ad5f75147b9cafae5c3b\",\n \"content_id\": \"1cfe0a2f2e3957f48604aa66bea7cf5c0506c269\",\n \"detected_licenses\": [\n \"MIT\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"C#\",\n \"length_bytes\": 5130,\n \"license_type\": \"permissive\",\n \"max_line_length\": 160,\n \"num_lines\": 106,\n \"path\": \"/src/TorchVision/GaussianBlur.cs\",\n \"repo_name\": \"NiklasGustafsson/TorchSharp\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"// Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information.\\nusing System;\\nusing System.Collections.Generic;\\nusing System.Linq;\\nusing static TorchSharp.torch;\\n\\n\\nnamespace TorchSharp\\n{\\n public static partial class torchvision\\n {\\n internal class GaussianBlur : ITransform\\n {\\n internal GaussianBlur(IList<long> kernelSize, float min, float max)\\n {\\n if (kernelSize == null || kernelSize.Count != 2 || kernelSize.Any(x => x <= 0)) {\\n throw new ArgumentException(\\\"Invalid kernel size argument.\\\");\\n }\\n if (min < 0 || max < 0 || min >= max) {\\n throw new ArgumentException(\\\"Invalid GaussianBlur arguments.\\\");\\n }\\n this.sigma = (min == max) ?\\n min :\\n (float)(new Random().NextDouble() * (max - min) + min);\\n this.kernelSize = kernelSize.ToArray();\\n }\\n\\n public Tensor call(Tensor input)\\n {\\n return transforms.functional.gaussian_blur(input, kernelSize, new float[] { sigma });\\n }\\n\\n protected long[] kernelSize;\\n protected float sigma;\\n }\\n\\n public static partial class transforms\\n {\\n /// <summary>\\n /// Apply a Gaussian blur effect to the image.\\n /// </summary>\\n /// <param name=\\\"kernelSize\\\">Gaussian kernel size</param>\\n /// <param name=\\\"sigma\\\">Gaussian kernel standard deviation</param>\\n /// <returns></returns>\\n static public ITransform GaussianBlur(IList<long> kernelSize, float sigma)\\n {\\n return new GaussianBlur(kernelSize, sigma, sigma);\\n }\\n\\n /// <summary>\\n /// Apply a Gaussian blur effect to the image.\\n /// </summary>\\n /// <param name=\\\"kernelSize\\\">Gaussian kernel size</param>\\n /// <param name=\\\"min\\\">Minimum value of the range for the uniform distribution from which the Gaussian kernel standard deviation will sampled</param>\\n /// <param name=\\\"max\\\">Maximum value of the range for the uniform distribution from which the Gaussian kernel standard deviation will sampled</param>\\n static public ITransform GaussianBlur(IList<long> kernelSize, float min = 0.1f, float max = 2.0f)\\n {\\n return new GaussianBlur(kernelSize, min, max);\\n }\\n\\n /// <summary>\\n /// Apply a Gaussian blur effect to the image.\\n /// </summary>\\n /// <param name=\\\"kernelSize\\\">Gaussian kernel size</param>\\n /// <param name=\\\"sigma\\\">Gaussian kernel standard deviation</param>\\n static public ITransform GaussianBlur(long kernelSize, float sigma)\\n {\\n return new GaussianBlur(new long[] { kernelSize, kernelSize }, sigma, sigma);\\n }\\n\\n /// <summary>\\n /// Apply a Gaussian blur effect to the image.\\n /// </summary>\\n /// <param name=\\\"kernelSize\\\">Gaussian kernel size</param>\\n /// <param name=\\\"min\\\">Minimum value of the range for the uniform distribution from which the Gaussian kernel standard deviation will sampled</param>\\n /// <param name=\\\"max\\\">Maximum value of the range for the uniform distribution from which the Gaussian kernel standard deviation will sampled</param>\\n static public ITransform GaussianBlur(long kernelSize, float min = 0.1f, float max = 2.0f)\\n {\\n return new GaussianBlur(new long[] { kernelSize, kernelSize }, min, max);\\n }\\n\\n /// <summary>\\n /// Apply a Gaussian blur effect to the image.\\n /// </summary>\\n /// <param name=\\\"kernelHeight\\\">Gaussian kernel height</param>\\n /// <param name=\\\"kernelWidth\\\">Gaussian kernel width</param>\\n /// <param name=\\\"sigma\\\">Gaussian kernel standard deviation</param>\\n static public ITransform GaussianBlur(long kernelHeight, long kernelWidth, float sigma)\\n {\\n return new GaussianBlur(new long[] { kernelHeight, kernelWidth }, sigma, sigma);\\n }\\n\\n /// <summary>\\n /// Apply a Gaussian blur effect to the image.\\n /// </summary>\\n /// <param name=\\\"kernelHeight\\\">Gaussian kernel height</param>\\n /// <param name=\\\"kernelWidth\\\">Gaussian kernel width</param>\\n /// <param name=\\\"min\\\">Minimum value of the range for the uniform distribution from which the Gaussian kernel standard deviation will sampled</param>\\n /// <param name=\\\"max\\\">Minimum value of the range for the uniform distribution from which the Gaussian kernel standard deviation will sampled</param>\\n static public ITransform GaussianBlur(long kernelHeight, long kernelWidth, float min = 0.1f, float max = 2.0f)\\n {\\n return new GaussianBlur(new long[] { kernelHeight, kernelWidth }, min, max);\\n }\\n }\\n }\\n}\"\n },\n {\n \"alpha_fraction\": 0.45181626081466675,\n \"alphanum_fraction\": 0.4575202763080597,\n \"avg_line_length\": 33.340206146240234,\n \"blob_id\": \"e4947a432e287f760d67e84bf80de2c4a0e373d6\",\n \"content_id\": \"132721ee72a404b6bf8a59cbe62c86c78b83388d\",\n \"detected_licenses\": [\n \"MIT\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"C#\",\n \"length_bytes\": 9993,\n \"license_type\": \"permissive\",\n \"max_line_length\": 130,\n \"num_lines\": 291,\n \"path\": \"/src/TorchSharp/Tensor/Size.cs\",\n \"repo_name\": \"NiklasGustafsson/TorchSharp\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"// Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information.\\n\\nusing System.Linq;\\nusing System.Collections.Generic;\\nusing System.Collections;\\n\\n#nullable enable\\nnamespace TorchSharp\\n{\\n public static partial class torch\\n {\\n /// <summary>\\n /// Represents the dimensions of a tensor, that is, its shape.\\n /// </summary>\\n /// <remarks>\\n /// The primary purpose of this type, at the moment, is to avoid having to declare\\n /// too many overloads on tensor factories that take input sizes.\\n /// The name was chosen to coincide with 'torch.Size' in PyTorch. It may later be\\n /// used as the return value of 'Tensor.shape' and 'Tensor.size()'\\n /// </remarks>\\n public struct Size : IEnumerable<long>\\n {\\n /// <summary>\\n /// Represents an empty size.\\n /// </summary>\\n public static Size Empty = new Size(System.Array.Empty<long>());\\n\\n /// <summary>\\n /// Constructor\\n /// </summary>\\n /// <param name=\\\"shape\\\">An array of longs, the size of an N-D tensor.</param>\\n public Size(long[] shape)\\n {\\n _shape = (long[])shape.Clone();\\n }\\n\\n /// <summary>\\n /// Constructor\\n /// </summary>\\n /// <param name=\\\"shape\\\">An enumerable of longs, the size of an N-D tensor.</param>\\n public Size(IEnumerable<long> shape)\\n {\\n _shape = shape.ToArray();\\n }\\n\\n /// <summary>\\n /// Constructor\\n /// </summary>\\n /// <param name=\\\"shape\\\">An array of longs, the size of an N-D tensor.</param>\\n public Size(int[] shape)\\n {\\n _shape = shape.Select(i => (long)i).ToArray();\\n }\\n\\n /// <summary>\\n /// Constructor\\n /// </summary>\\n /// <param name=\\\"size\\\">The size of a 1D tensor.</param>\\n public Size(long size)\\n {\\n _shape = new long[] { size };\\n }\\n\\n /// <summary>\\n /// Constructor\\n /// </summary>\\n /// <param name=\\\"size\\\">The size of a 2D tensor.</param>\\n public Size((long, long) size)\\n {\\n _shape = new long[] { size.Item1, size.Item2 };\\n }\\n\\n /// <summary>\\n /// Constructor\\n /// </summary>\\n /// <param name=\\\"size\\\">The size of a 3D tensor.</param>\\n public Size((long, long, long) size)\\n {\\n _shape = new long[] { size.Item1, size.Item2, size.Item3 };\\n }\\n\\n /// <summary>\\n /// Constructor\\n /// </summary>\\n /// <param name=\\\"size\\\">The size of a 4D tensor.</param>\\n public Size((long, long, long, long) size)\\n {\\n _shape = new long[] { size.Item1, size.Item2, size.Item3, size.Item4 };\\n }\\n\\n /// <summary>\\n /// Constructor\\n /// </summary>\\n /// <param name=\\\"size\\\">The size of a 5D tensor.</param>\\n public Size((long, long, long, long, long) size)\\n {\\n _shape = new long[] { size.Item1, size.Item2, size.Item3, size.Item4, size.Item5 };\\n }\\n\\n /// <summary>\\n /// Constructor\\n /// </summary>\\n /// <param name=\\\"size\\\">The size of a 6D tensor.</param>\\n public Size((long, long, long, long, long, long) size)\\n {\\n _shape = new long[] { size.Item1, size.Item2, size.Item3, size.Item4, size.Item5, size.Item6 };\\n }\\n\\n\\n /// <summary>\\n /// Constructor\\n /// </summary>\\n /// <param name=\\\"size\\\">The size of a 1D tensor.</param>\\n public Size(int size)\\n {\\n _shape = new long[] { size };\\n }\\n\\n /// <summary>\\n /// Constructor\\n /// </summary>\\n /// <param name=\\\"size\\\">The size of a 2D tensor.</param>\\n public Size((int, int) size)\\n {\\n _shape = new long[] { size.Item1, size.Item2 };\\n }\\n\\n /// <summary>\\n /// Constructor\\n /// </summary>\\n /// <param name=\\\"size\\\">The size of a 3D tensor.</param>\\n public Size((int, int, int) size)\\n {\\n _shape = new long[] { size.Item1, size.Item2, size.Item3 };\\n }\\n /// <summary>\\n /// Constructor\\n /// </summary>\\n /// <param name=\\\"size\\\">The size of a 4D tensor.</param>\\n public Size((int, int, int, int) size)\\n {\\n _shape = new long[] { size.Item1, size.Item2, size.Item3, size.Item4 };\\n }\\n\\n /// <summary>\\n /// Constructor\\n /// </summary>\\n /// <param name=\\\"size\\\">The size of a 5D tensor.</param>\\n public Size((int, int, int, int, int) size)\\n {\\n _shape = new long[] { size.Item1, size.Item2, size.Item3, size.Item4, size.Item5 };\\n }\\n\\n /// <summary>\\n /// Constructor\\n /// </summary>\\n /// <param name=\\\"size\\\">The size of a 6D tensor.</param>\\n public Size((int, int, int, int, int, int) size)\\n {\\n _shape = new long[] { size.Item1, size.Item2, size.Item3, size.Item4, size.Item5, size.Item6 };\\n }\\n\\n /// <summary>\\n /// Implicit conversion operators. Useful to avoid overloads everywhere.\\n /// </summary>\\n public static implicit operator Size(long size) => new Size(size);\\n public static implicit operator Size((long, long) size) => new Size(size);\\n public static implicit operator Size((long, long, long) size) => new Size(size);\\n public static implicit operator Size((long, long, long, long) size) => new Size(size);\\n public static implicit operator Size((long, long, long, long, long) size) => new Size(size);\\n public static implicit operator Size((long, long, long, long, long, long) size) => new Size(size);\\n\\n public static implicit operator Size(long[] size) => new Size(size);\\n\\n public static Size operator +(Size left, Size right)\\n {\\n return new Size(left._shape.AsEnumerable<long>().Concat<long>(right._shape).ToArray());\\n }\\n\\n public static Size operator +(Size left, IEnumerable<long> right)\\n {\\n return new Size(left._shape.AsEnumerable<long>().Concat<long>(right).ToArray());\\n }\\n\\n //public static implicit operator Size(long[] size) => new Size(size);\\n //public static implicit operator long[](Size size) => size._shape;\\n\\n public static bool operator ==(Size left, Size right) => left._shape == right._shape;\\n\\n public static bool operator !=(Size left, Size right) => left._shape != right._shape;\\n\\n public override bool Equals(object? obj)\\n {\\n if (obj is null || !(obj is Size)) return false;\\n\\n return _shape.Equals(((Size)obj)._shape);\\n }\\n\\n public Size Slice(int first, int next)\\n {\\n if (next < 0) {\\n next = _shape.Length + next;\\n }\\n\\n if (first < 0) {\\n first = _shape.Length + first;\\n }\\n\\n if (next <= first) {\\n return Size.Empty;\\n }\\n\\n if (first == 0) {\\n if (next == _shape.Length) {\\n return this;\\n }\\n else {\\n return new Size(_shape.Take(next));\\n }\\n }\\n else {\\n if (next == _shape.Length) {\\n return new Size(_shape.Skip(first));\\n } else {\\n return new Size(_shape.Skip(first).Take(next-first));\\n }\\n }\\n }\\n\\n public override int GetHashCode()\\n {\\n return _shape.GetHashCode();\\n }\\n\\n /// <summary>\\n /// The number of elements in a tensor, the product of its shape elements.\\n /// </summary>\\n /// <returns></returns>\\n public long numel()\\n {\\n long size = 1;\\n foreach (var s in _shape) {\\n size *= s;\\n }\\n return size;\\n }\\n\\n public bool IsEmpty => _shape.Length == 0;\\n\\n public bool IsScalar => IsEmpty;\\n\\n public int Length => _shape.Length;\\n\\n public IEnumerable<long> Take(int i) => _shape.Take<long>(i);\\n\\n public IEnumerator<long> GetEnumerator()\\n {\\n return _shape.AsEnumerable<long>().GetEnumerator();\\n }\\n\\n IEnumerator IEnumerable.GetEnumerator()\\n {\\n return this.GetEnumerator();\\n }\\n\\n /// <summary>\\n /// Element accessor\\n /// </summary>\\n /// <param name=\\\"idx\\\">The element index</param>\\n /// <returns></returns>\\n public long this[int idx] {\\n get { return _shape[idx]; }\\n }\\n\\n /// <summary>\\n /// Element accessor\\n /// </summary>\\n /// <param name=\\\"idx\\\">The element index</param>\\n /// <returns></returns>\\n public long this[long idx] {\\n get { return _shape[(int)idx]; }\\n }\\n\\n public long[] Shape { get { return _shape; } }\\n\\n private long[] _shape;\\n }\\n\\n\\n }\\n}\\n\"\n },\n {\n \"alpha_fraction\": 0.5404923558235168,\n \"alphanum_fraction\": 0.5419193506240845,\n \"avg_line_length\": 39.62318801879883,\n \"blob_id\": \"f0b863ec67b2532966e2e0584f867ac097f894a7\",\n \"content_id\": \"fe1d94bd532d41d3987fc502104cc688cf884957\",\n \"detected_licenses\": [\n \"MIT\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"C#\",\n \"length_bytes\": 2803,\n \"license_type\": \"permissive\",\n \"max_line_length\": 151,\n \"num_lines\": 69,\n \"path\": \"/src/TorchSharp/NN/PixelShuffle.cs\",\n \"repo_name\": \"NiklasGustafsson/TorchSharp\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"// Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information.\\nusing System;\\nusing static TorchSharp.torch;\\nusing static TorchSharp.PInvoke.NativeMethods;\\n\\nnamespace TorchSharp\\n{\\n using Modules;\\n\\n namespace Modules\\n {\\n /// <summary>\\n /// This class is used to represent a dropout module.\\n /// </summary>\\n public sealed class PixelShuffle : torch.nn.Module<Tensor, Tensor>\\n {\\n internal PixelShuffle(IntPtr handle, IntPtr boxedHandle) : base(handle, boxedHandle) { }\\n\\n /// <summary>\\n /// Forward pass.\\n /// </summary>\\n /// <param name=\\\"tensor\\\">Input tensor</param>\\n /// <returns></returns>\\n public override Tensor forward(Tensor tensor)\\n {\\n var res = THSNN_PixelShuffle_forward(handle, tensor.Handle);\\n if (res == IntPtr.Zero) { torch.CheckForErrors(); }\\n return new Tensor(res);\\n }\\n }\\n }\\n\\n public static partial class torch\\n {\\n public static partial class nn\\n {\\n /// <summary>\\n /// Rearranges elements in a tensor of shape (*, C * r^2, H, W) to a tensor of shape(*, C, H * r, W * r), where r is an upscale factor.\\n /// This is useful for implementing efficient sub-pixel convolution with a stride of 1/r.\\n /// </summary>\\n /// <param name=\\\"upscaleFactor\\\">Factor to increase spatial resolution by</param>\\n /// <returns></returns>\\n public static PixelShuffle PixelShuffle(long upscaleFactor)\\n {\\n var handle = THSNN_PixelShuffle_ctor(upscaleFactor, out var boxedHandle);\\n if (handle == IntPtr.Zero) { torch.CheckForErrors(); }\\n return new PixelShuffle(handle, boxedHandle);\\n }\\n\\n public static partial class functional\\n {\\n /// <summary>\\n /// Rearranges elements in a tensor of shape (*, C * r^2, H, W) to a tensor of shape(*, C, H * r, W * r), where r is an upscale factor.\\n /// This is useful for implementing efficient sub-pixel convolution with a stride of 1/r.\\n /// </summary>\\n /// <param name=\\\"x\\\">Input tensor</param>\\n /// <param name=\\\"upscaleFactor\\\">Factor to increase spatial resolution by</param>\\n /// <returns></returns>\\n /// <returns></returns>\\n public static Tensor pixel_shuffle(Tensor x, long upscaleFactor)\\n {\\n using (var d = nn.PixelShuffle(upscaleFactor)) {\\n return d.call(x);\\n }\\n }\\n }\\n }\\n }\\n}\\n\"\n },\n {\n \"alpha_fraction\": 0.6810643672943115,\n \"alphanum_fraction\": 0.7021636962890625,\n \"avg_line_length\": 37.554405212402344,\n \"blob_id\": \"c1b1f31364fa1153b8a3affa31ad03ba3e1400aa\",\n \"content_id\": \"e1500d939016fb20da8539ac042dec4511f255bf\",\n \"detected_licenses\": [\n \"MIT\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"C++\",\n \"length_bytes\": 37205,\n \"license_type\": \"permissive\",\n \"max_line_length\": 259,\n \"num_lines\": 965,\n \"path\": \"/src/Native/LibTorchSharp/THSConvolution.cpp\",\n \"repo_name\": \"NiklasGustafsson/TorchSharp\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"// Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information.\\n#include \\\"THSNN.h\\\"\\n\\n#include <torch/nn/init.h>\\n\\n\\n\\nNNModule THSNN_AvgPool1d_ctor(const int64_t* kernelSize, const int64_t* stride, const int64_t* padding,\\n bool ceil_mode, bool count_include_pad, int64_t divisor_override,\\n NNAnyModule* outAsAnyModule)\\n{\\n CATCH_RETURN_NNModule(\\n auto opts = torch::nn::AvgPool1dOptions(at::ArrayRef<int64_t>(kernelSize, 1)).ceil_mode(ceil_mode).count_include_pad(count_include_pad);\\n if (stride)\\n opts = opts.stride(at::ArrayRef<int64_t>(stride, 1));\\n if (padding)\\n opts = opts.padding(at::ArrayRef<int64_t>(padding, 1));\\n if (divisor_override > 0)\\n opts = opts.divisor_override(divisor_override);\\n res = create_module<torch::nn::AvgPool1dImpl>(opts, outAsAnyModule);\\n );\\n}\\n\\nTensor THSNN_AvgPool1d_forward(const NNModule module, const Tensor tensor)\\n{\\n CATCH_TENSOR((*module)->as<torch::nn::AvgPool1d>()->forward(*tensor));\\n}\\n\\nNNModule THSNN_AvgPool2d_ctor(const int64_t* kernelSize, const int kernelSizeLength, const int64_t* stride, const int strideLength, const int64_t* padding, const int paddingLength,\\n bool ceil_mode, bool count_include_pad, int64_t divisor_override,\\n NNAnyModule* outAsAnyModule)\\n{\\n CATCH_RETURN_NNModule(\\n auto opts = torch::nn::AvgPool2dOptions(at::ArrayRef<int64_t>(kernelSize, kernelSizeLength)).ceil_mode(ceil_mode).count_include_pad(count_include_pad);\\n if (stride)\\n opts = opts.stride(at::ArrayRef<int64_t>(stride, strideLength));\\n if (padding)\\n opts = opts.padding(at::ArrayRef<int64_t>(padding, paddingLength));\\n if (divisor_override > 0)\\n opts = opts.divisor_override(divisor_override);\\n res = create_module<torch::nn::AvgPool2dImpl>(opts, outAsAnyModule);\\n );\\n}\\n\\nTensor THSNN_AvgPool2d_forward(const NNModule module, const Tensor tensor)\\n{\\n CATCH_TENSOR((*module)->as<torch::nn::AvgPool2d>()->forward(*tensor));\\n}\\n\\nNNModule THSNN_AvgPool3d_ctor(const int64_t* kernelSize, const int kernelSizeLength, const int64_t* stride, const int strideLength, const int64_t* padding, const int paddingLength,\\n bool ceil_mode, bool count_include_pad, int64_t divisor_override,\\n NNAnyModule* outAsAnyModule)\\n{\\n CATCH_RETURN_NNModule(\\n auto opts = torch::nn::AvgPool3dOptions(at::ArrayRef<int64_t>(kernelSize, kernelSizeLength)).ceil_mode(ceil_mode).count_include_pad(count_include_pad);\\n if (stride)\\n opts = opts.stride(at::ArrayRef<int64_t>(stride, strideLength));\\n if (padding)\\n opts = opts.padding(at::ArrayRef<int64_t>(padding, paddingLength));\\n if (divisor_override > 0)\\n opts = opts.divisor_override(divisor_override);\\n res = create_module<torch::nn::AvgPool3dImpl>(opts, outAsAnyModule);\\n );\\n}\\n\\nTensor THSNN_AvgPool3d_forward(const NNModule module, const Tensor tensor)\\n{\\n CATCH_TENSOR((*module)->as<torch::nn::AvgPool3d>()->forward(*tensor));\\n}\\n\\nNNModule THSNN_AdaptiveAvgPool1d_ctor(const int64_t* kernelSize, const int kernelSizeLength,\\n NNAnyModule* outAsAnyModule)\\n{\\n CATCH_RETURN_NNModule(\\n auto opts = torch::nn::AdaptiveAvgPool1dOptions(at::ArrayRef<int64_t>(kernelSize, kernelSizeLength));\\n res = create_module<torch::nn::AdaptiveAvgPool1dImpl>(opts, outAsAnyModule);\\n );\\n}\\n\\nTensor THSNN_AdaptiveAvgPool1d_forward(const NNModule module, const Tensor tensor)\\n{\\n CATCH_TENSOR((*module)->as<torch::nn::AdaptiveAvgPool1d>()->forward(*tensor));\\n}\\n\\nNNModule THSNN_AdaptiveAvgPool2d_ctor(const int64_t* kernelSize, const int kernelSizeLength,\\n NNAnyModule* outAsAnyModule)\\n{\\n CATCH_RETURN_NNModule(\\n auto opts = torch::nn::AdaptiveAvgPool2dOptions(at::ArrayRef<int64_t>(kernelSize, kernelSizeLength));\\n res = create_module<torch::nn::AdaptiveAvgPool2dImpl>(opts, outAsAnyModule);\\n );\\n}\\n\\nTensor THSNN_AdaptiveAvgPool2d_forward(const NNModule module, const Tensor tensor)\\n{\\n CATCH_TENSOR((*module)->as<torch::nn::AdaptiveAvgPool2d>()->forward(*tensor));\\n}\\n\\nNNModule THSNN_AdaptiveAvgPool3d_ctor(const int64_t* kernelSize, const int kernelSizeLength,\\n NNAnyModule* outAsAnyModule)\\n{\\n CATCH_RETURN_NNModule(\\n auto opts = torch::nn::AdaptiveAvgPool3dOptions(at::ArrayRef<int64_t>(kernelSize, kernelSizeLength));\\n res = create_module<torch::nn::AdaptiveAvgPool3dImpl>(opts, outAsAnyModule);\\n );\\n}\\n\\nTensor THSNN_AdaptiveAvgPool3d_forward(const NNModule module, const Tensor tensor)\\n{\\n CATCH_TENSOR((*module)->as<torch::nn::AdaptiveAvgPool3d>()->forward(*tensor));\\n}\\n\\nNNModule THSNN_AdaptiveMaxPool1d_ctor(const int64_t* kernelSize, const int kernelSizeLength,\\n NNAnyModule* outAsAnyModule)\\n{\\n CATCH_RETURN_NNModule(\\n auto opts = torch::nn::AdaptiveMaxPool1dOptions(at::ArrayRef<int64_t>(kernelSize, kernelSizeLength));\\n res = create_module<torch::nn::AdaptiveMaxPool1dImpl>(opts, outAsAnyModule);\\n );\\n}\\n\\nTensor THSNN_AdaptiveMaxPool1d_forward(const NNModule module, const Tensor tensor)\\n{\\n CATCH_TENSOR((*module)->as<torch::nn::AdaptiveMaxPool1d>()->forward(*tensor));\\n}\\n\\nNNModule THSNN_AdaptiveMaxPool2d_ctor(const int64_t* kernelSize, const int kernelSizeLength,\\n NNAnyModule* outAsAnyModule)\\n{\\n CATCH_RETURN_NNModule(\\n auto opts = torch::nn::AdaptiveMaxPool2dOptions(at::ArrayRef<int64_t>(kernelSize, kernelSizeLength));\\n res = create_module<torch::nn::AdaptiveMaxPool2dImpl>(opts, outAsAnyModule);\\n );\\n}\\n\\nTensor THSNN_AdaptiveMaxPool2d_forward(const NNModule module, const Tensor tensor)\\n{\\n CATCH_TENSOR((*module)->as<torch::nn::AdaptiveMaxPool2d>()->forward(*tensor));\\n}\\n\\nNNModule THSNN_AdaptiveMaxPool3d_ctor(const int64_t* kernelSize, const int kernelSizeLength,\\n NNAnyModule* outAsAnyModule)\\n{\\n CATCH_RETURN_NNModule(\\n auto opts = torch::nn::AdaptiveMaxPool3dOptions(at::ArrayRef<int64_t>(kernelSize, kernelSizeLength));\\n res = create_module<torch::nn::AdaptiveMaxPool3dImpl>(opts, outAsAnyModule);\\n );\\n}\\n\\nTensor THSNN_AdaptiveMaxPool3d_forward(const NNModule module, const Tensor tensor)\\n{\\n CATCH_TENSOR((*module)->as<torch::nn::AdaptiveMaxPool3d>()->forward(*tensor));\\n}\\n\\nNNModule THSNN_LPPool1d_ctor(double norm_type, const int64_t* kernelSize, const int64_t* stride, const bool ceil_mode,\\n NNAnyModule* outAsAnyModule)\\n{\\n CATCH_RETURN_NNModule(\\n auto opts = torch::nn::LPPool1dOptions(norm_type, at::ArrayRef<int64_t>(kernelSize, 1)).ceil_mode(ceil_mode);\\n if (stride)\\n opts = opts.stride(at::ArrayRef<int64_t>(stride, 1));\\n res = create_module<torch::nn::LPPool1dImpl>(opts, outAsAnyModule);\\n );\\n}\\n\\nTensor THSNN_LPPool1d_forward(const NNModule module, const Tensor tensor)\\n{\\n CATCH_TENSOR((*module)->as<torch::nn::LPPool1d>()->forward(*tensor));\\n}\\n\\nNNModule THSNN_LPPool2d_ctor(double norm_type, const int64_t* kernelSize, const int kernelSizeLength, const int64_t* stride, const int strideLength, const bool ceil_mode,\\n NNAnyModule* outAsAnyModule)\\n{\\n CATCH_RETURN_NNModule(\\n auto opts = torch::nn::LPPool2dOptions(norm_type, at::ArrayRef<int64_t>(kernelSize, kernelSizeLength)).ceil_mode(ceil_mode);\\n if (stride)\\n opts = opts.stride(at::ArrayRef<int64_t>(stride, strideLength));\\n res = create_module<torch::nn::LPPool2dImpl>(opts, outAsAnyModule);\\n );\\n}\\n\\nTensor THSNN_LPPool2d_forward(const NNModule module, const Tensor tensor)\\n{\\n CATCH_TENSOR((*module)->as<torch::nn::LPPool2d>()->forward(*tensor));\\n}\\n\\nNNModule THSNN_MaxPool1d_ctor(const int64_t* kernelSize, const int64_t* stride, const int64_t* padding, const int64_t* dilation, bool ceil_mode,\\n NNAnyModule* outAsAnyModule)\\n{\\n CATCH_RETURN_NNModule(\\n auto opts = torch::nn::MaxPool1dOptions(at::ArrayRef<int64_t>(kernelSize, 1)).ceil_mode(ceil_mode);\\n if (stride)\\n opts = opts.stride(at::ArrayRef<int64_t>(stride, 1));\\n if (padding)\\n opts = opts.padding(at::ArrayRef<int64_t>(padding, 1));\\n if (dilation)\\n opts = opts.dilation(at::ArrayRef<int64_t>(dilation, 1));\\n\\n res = create_module<torch::nn::MaxPool1dImpl>(opts, outAsAnyModule);\\n );\\n}\\n\\nTensor THSNN_MaxPool1d_forward(const NNModule module, const Tensor tensor)\\n{\\n CATCH_TENSOR((*module)->as<torch::nn::MaxPool1d>()->forward(*tensor));\\n}\\n\\nTensor THSNN_MaxPool1d_forward_with_indices(const NNModule module, const Tensor tensor, Tensor* indices)\\n{\\n std::tuple<at::Tensor, at::Tensor> res;\\n CATCH(res = (*module)->as<torch::nn::MaxPool1d>()->forward_with_indices(*tensor););\\n *indices = ResultTensor(std::get<1>(res));\\n return ResultTensor(std::get<0>(res));\\n}\\n\\nNNModule THSNN_MaxPool2d_ctor(const int64_t* kernelSize, const int kernelSizeLength, const int64_t* stride, const int strideLength,\\n const int64_t* padding, const int paddingLength, const int64_t* dilation, const int dilationLength, bool ceil_mode,\\n NNAnyModule* outAsAnyModule)\\n{\\n CATCH_RETURN_NNModule(\\n auto opts = torch::nn::MaxPool2dOptions(at::ArrayRef<int64_t>(kernelSize, kernelSizeLength)).ceil_mode(ceil_mode);\\n if (stride)\\n opts = opts.stride(at::ArrayRef<int64_t>(stride, strideLength));\\n if (padding)\\n opts = opts.padding(at::ArrayRef<int64_t>(padding, paddingLength));\\n if (dilation)\\n opts = opts.dilation(at::ArrayRef<int64_t>(dilation, dilationLength));\\n\\n res = create_module<torch::nn::MaxPool2dImpl>(opts, outAsAnyModule);\\n );\\n}\\n\\nTensor THSNN_MaxPool2d_forward(const NNModule module, const Tensor tensor)\\n{\\n CATCH_TENSOR((*module)->as<torch::nn::MaxPool2d>()->forward(*tensor));\\n}\\n\\nTensor THSNN_MaxPool2d_forward_with_indices(const NNModule module, const Tensor tensor, Tensor* indices)\\n{\\n std::tuple<at::Tensor, at::Tensor> res;\\n CATCH(res = (*module)->as<torch::nn::MaxPool2d>()->forward_with_indices(*tensor););\\n *indices = ResultTensor(std::get<1>(res));\\n return ResultTensor(std::get<0>(res));\\n}\\n\\nNNModule THSNN_MaxPool3d_ctor(const int64_t* kernelSize, const int kernelSizeLength, const int64_t* stride, const int strideLength,\\n const int64_t* padding, const int paddingLength, const int64_t* dilation, const int dilationLength, bool ceil_mode,\\n NNAnyModule* outAsAnyModule)\\n{\\n CATCH_RETURN_NNModule(\\n auto opts = torch::nn::MaxPool3dOptions(at::ArrayRef<int64_t>(kernelSize, kernelSizeLength)).ceil_mode(ceil_mode);\\n if (stride)\\n opts = opts.stride(at::ArrayRef<int64_t>(stride, strideLength));\\n if (padding)\\n opts = opts.padding(at::ArrayRef<int64_t>(padding, paddingLength));\\n if (dilation)\\n opts = opts.dilation(at::ArrayRef<int64_t>(dilation, dilationLength));\\n\\n res = create_module<torch::nn::MaxPool3dImpl>(opts, outAsAnyModule);\\n );\\n}\\n\\nTensor THSNN_MaxPool3d_forward(const NNModule module, const Tensor tensor)\\n{\\n CATCH_TENSOR((*module)->as<torch::nn::MaxPool3d>()->forward(*tensor));\\n}\\n\\nTensor THSNN_MaxPool3d_forward_with_indices(const NNModule module, const Tensor tensor, Tensor* indices)\\n{\\n std::tuple<at::Tensor, at::Tensor> res;\\n CATCH(res = (*module)->as<torch::nn::MaxPool3d>()->forward_with_indices(*tensor););\\n *indices = ResultTensor(std::get<1>(res));\\n return ResultTensor(std::get<0>(res));\\n}\\n\\nNNModule THSNN_MaxUnpool1d_ctor(const int64_t* kernelSize, const int64_t* stride, const int64_t* padding, NNAnyModule* outAsAnyModule)\\n{\\n CATCH_RETURN_NNModule(\\n auto opts = torch::nn::MaxUnpool1dOptions(at::ArrayRef<int64_t>(kernelSize, 1));\\n if (stride)\\n opts = opts.stride(at::ArrayRef<int64_t>(stride, 1));\\n if (padding)\\n opts = opts.padding(at::ArrayRef<int64_t>(padding, 1));\\n\\n res = create_module<torch::nn::MaxUnpool1dImpl>(opts, outAsAnyModule);\\n );\\n}\\n\\nTensor THSNN_MaxUnpool1d_forward(const NNModule module, const Tensor tensor, const Tensor indices, const int64_t* outputSize)\\n{\\n if (outputSize != nullptr) {\\n std::vector<int64_t> outSize;\\n outSize.push_back(*outputSize);\\n\\n CATCH_TENSOR((*module)->as<torch::nn::MaxUnpool1d>()->forward(*tensor, *indices, outSize));\\n }\\n else {\\n CATCH_TENSOR((*module)->as<torch::nn::MaxUnpool1d>()->forward(*tensor, *indices));\\n }\\n}\\n\\nNNModule THSNN_MaxUnpool2d_ctor(const int64_t* kernelSize, const int kernelSizeLength, const int64_t* stride, const int strideLength, const int64_t* padding, const int paddingLength, NNAnyModule* outAsAnyModule)\\n{\\n CATCH_RETURN_NNModule(\\n auto opts = torch::nn::MaxUnpool2dOptions(at::ArrayRef<int64_t>(kernelSize, kernelSizeLength));\\n if (stride)\\n opts = opts.stride(at::ArrayRef<int64_t>(stride, strideLength));\\n if (padding)\\n opts = opts.padding(at::ArrayRef<int64_t>(padding, paddingLength));\\n\\n res = create_module<torch::nn::MaxUnpool2dImpl>(opts, outAsAnyModule);\\n );\\n}\\n\\nTensor THSNN_MaxUnpool2d_forward(const NNModule module, const Tensor tensor, const Tensor indices, const int64_t* outputSize, const int outputSizeLength)\\n{\\n if (outputSize != nullptr) {\\n std::vector<int64_t> outSize;\\n for (auto i = 0L; i < outputSizeLength; i++) {\\n outSize.push_back(outputSize[i]);\\n }\\n\\n CATCH_TENSOR((*module)->as<torch::nn::MaxUnpool2d>()->forward(*tensor, *indices, outSize));\\n }\\n else {\\n CATCH_TENSOR((*module)->as<torch::nn::MaxUnpool2d>()->forward(*tensor, *indices));\\n }\\n}\\n\\nNNModule THSNN_MaxUnpool3d_ctor(const int64_t* kernelSize, const int kernelSizeLength, const int64_t* stride, const int strideLength, const int64_t* padding, const int paddingLength, NNAnyModule* outAsAnyModule)\\n{\\n CATCH_RETURN_NNModule(\\n auto opts = torch::nn::MaxUnpool3dOptions(at::ArrayRef<int64_t>(kernelSize, kernelSizeLength));\\n if (stride)\\n opts = opts.stride(at::ArrayRef<int64_t>(stride, strideLength));\\n if (padding)\\n opts = opts.padding(at::ArrayRef<int64_t>(padding, paddingLength));\\n\\n res = create_module<torch::nn::MaxUnpool3dImpl>(opts, outAsAnyModule);\\n );\\n}\\n\\nTensor THSNN_MaxUnpool3d_forward(const NNModule module, const Tensor tensor, const Tensor indices, const int64_t* outputSize, const int outputSizeLength)\\n{\\n if (outputSize != nullptr) {\\n std::vector<int64_t> outSize;\\n for (auto i = 0L; i < outputSizeLength; i++) {\\n outSize.push_back(outputSize[i]);\\n }\\n\\n CATCH_TENSOR((*module)->as<torch::nn::MaxUnpool3d>()->forward(*tensor, *indices, outSize));\\n }\\n else {\\n CATCH_TENSOR((*module)->as<torch::nn::MaxUnpool3d>()->forward(*tensor, *indices));\\n }\\n}\\n\\n\\nNNModule THSNN_FractionalMaxPool2d_ctor(const int64_t* kernelSize, const int kernelSizeLength, const int64_t* outputSize, const int outputSizeLength, const double* outputRatio, const int outputRatioLength, NNAnyModule* outAsAnyModule)\\n{\\n CATCH_RETURN_NNModule(\\n auto opts = torch::nn::FractionalMaxPool2dOptions(at::ArrayRef<int64_t>(kernelSize, kernelSizeLength));\\n if (outputSize)\\n opts = opts.output_size(at::ArrayRef<int64_t>(outputSize, outputSizeLength));\\n if (outputRatio)\\n opts = opts.output_ratio(at::ArrayRef<double>(outputRatio, outputRatioLength));\\n\\n res = create_module<torch::nn::FractionalMaxPool2dImpl>(opts, outAsAnyModule);\\n );\\n}\\n\\nTensor THSNN_FractionalMaxPool2d_forward(const NNModule module, const Tensor tensor)\\n{\\n CATCH_TENSOR((*module)->as<torch::nn::FractionalMaxPool2d>()->forward(*tensor));\\n}\\n\\nTensor THSNN_FractionalMaxPool2d_forward_with_indices(const NNModule module, const Tensor tensor, Tensor* indices)\\n{\\n std::tuple<at::Tensor, at::Tensor> res;\\n CATCH(res = (*module)->as<torch::nn::FractionalMaxPool2d>()->forward_with_indices(*tensor););\\n *indices = ResultTensor(std::get<1>(res));\\n return ResultTensor(std::get<0>(res));\\n}\\n\\nNNModule THSNN_FractionalMaxPool3d_ctor(const int64_t* kernelSize, const int kernelSizeLength, const int64_t* outputSize, const int outputSizeLength, const double* outputRatio, const int outputRatioLength, NNAnyModule* outAsAnyModule)\\n{\\n CATCH_RETURN_NNModule(\\n auto opts = torch::nn::FractionalMaxPool3dOptions(at::ArrayRef<int64_t>(kernelSize, kernelSizeLength));\\n if (outputSize)\\n opts = opts.output_size(at::ArrayRef<int64_t>(outputSize, outputSizeLength));\\n if (outputRatio)\\n opts = opts.output_ratio(at::ArrayRef<double>(outputRatio, outputRatioLength));\\n\\n res = create_module<torch::nn::FractionalMaxPool3dImpl>(opts, outAsAnyModule);\\n );\\n}\\n\\nTensor THSNN_FractionalMaxPool3d_forward(const NNModule module, const Tensor tensor)\\n{\\n CATCH_TENSOR((*module)->as<torch::nn::FractionalMaxPool3d>()->forward(*tensor));\\n}\\n\\nTensor THSNN_FractionalMaxPool3d_forward_with_indices(const NNModule module, const Tensor tensor, Tensor* indices)\\n{\\n std::tuple<at::Tensor, at::Tensor> res;\\n CATCH(res = (*module)->as<torch::nn::FractionalMaxPool3d>()->forward_with_indices(*tensor););\\n *indices = ResultTensor(std::get<1>(res));\\n return ResultTensor(std::get<0>(res));\\n}\\n\\nNNModule THSNN_ZeroPad2d_ctor(const int64_t padding, NNAnyModule* outAsAnyModule)\\n{\\n CATCH_RETURN_NNModule(\\n auto opts = torch::nn::ZeroPad2dOptions(padding);\\n res = create_module<torch::nn::ZeroPad2dImpl>(opts, outAsAnyModule);\\n );\\n}\\n\\nNNModule THSNN_ZeroPad2d_ctor_tuple(const int64_t padding_left, const int64_t padding_right, const int64_t padding_top, const int64_t padding_bottom, NNAnyModule* outAsAnyModule)\\n{\\n CATCH_RETURN_NNModule(\\n auto opts = torch::nn::ZeroPad2dOptions({ padding_left, padding_right, padding_top, padding_bottom });\\n res = create_module<torch::nn::ZeroPad2dImpl>(opts, outAsAnyModule);\\n );\\n}\\n\\nTensor THSNN_ZeroPad2d_forward(const NNModule module, const Tensor tensor)\\n{\\n CATCH_TENSOR((*module)->as<torch::nn::ZeroPad2d>()->forward(*tensor));\\n}\\n\\nNNModule THSNN_ConstantPad1d_ctor(const double value, const int64_t padding, NNAnyModule* outAsAnyModule)\\n{\\n CATCH_RETURN_NNModule(\\n auto opts = torch::nn::ConstantPad1dOptions(padding, value);\\n res = create_module<torch::nn::ConstantPad1dImpl>(opts, outAsAnyModule);\\n );\\n}\\n\\nTensor THSNN_ConstantPad1d_forward(const NNModule module, const Tensor tensor)\\n{\\n CATCH_TENSOR((*module)->as<torch::nn::ConstantPad1d>()->forward(*tensor));\\n}\\n\\nNNModule THSNN_ConstantPad2d_ctor(const double value, const int64_t padding, NNAnyModule* outAsAnyModule)\\n{\\n CATCH_RETURN_NNModule(\\n auto opts = torch::nn::ConstantPad2dOptions(padding, value);\\n res = create_module<torch::nn::ConstantPad2dImpl>(opts, outAsAnyModule);\\n );\\n}\\n\\nTensor THSNN_ConstantPad2d_forward(const NNModule module, const Tensor tensor)\\n{\\n CATCH_TENSOR((*module)->as<torch::nn::ConstantPad2d>()->forward(*tensor));\\n}\\n\\nNNModule THSNN_ConstantPad3d_ctor(const double value, const int64_t padding, NNAnyModule* outAsAnyModule)\\n{\\n CATCH_RETURN_NNModule(\\n auto opts = torch::nn::ConstantPad3dOptions(padding, value);\\n res = create_module<torch::nn::ConstantPad3dImpl>(opts, outAsAnyModule);\\n );\\n}\\n\\nTensor THSNN_ConstantPad3d_forward(const NNModule module, const Tensor tensor)\\n{\\n CATCH_TENSOR((*module)->as<torch::nn::ConstantPad3d>()->forward(*tensor));\\n}\\n\\nNNModule THSNN_ConstantPad1d_ctor_tuple(const double value, const int64_t padding_left, const int64_t padding_right, NNAnyModule* outAsAnyModule)\\n{\\n CATCH_RETURN_NNModule(\\n auto opts = torch::nn::ConstantPad1dOptions({ padding_left, padding_right }, value);\\n res = create_module<torch::nn::ConstantPad1dImpl>(opts, outAsAnyModule);\\n );\\n}\\n\\nNNModule THSNN_ConstantPad2d_ctor_tuple(const double value, const int64_t padding_left, const int64_t padding_right, const int64_t padding_top, const int64_t padding_bottom, NNAnyModule* outAsAnyModule)\\n{\\n CATCH_RETURN_NNModule(\\n auto opts = torch::nn::ConstantPad2dOptions({ padding_left, padding_right, padding_top, padding_bottom }, value);\\n res = create_module<torch::nn::ConstantPad2dImpl>(opts, outAsAnyModule);\\n );\\n}\\n\\nNNModule THSNN_ConstantPad3d_ctor_tuple(const double value, const int64_t padding_left, const int64_t padding_right, const int64_t padding_top, const int64_t padding_bottom, const int64_t padding_front, const int64_t padding_back, NNAnyModule* outAsAnyModule)\\n{\\n CATCH_RETURN_NNModule(\\n auto opts = torch::nn::ConstantPad3dOptions({ padding_left, padding_right, padding_top, padding_bottom, padding_front, padding_back }, value);\\n res = create_module<torch::nn::ConstantPad3dImpl>(opts, outAsAnyModule);\\n );\\n}\\n\\nNNModule THSNN_ReplicationPad1d_ctor(const int64_t padding, NNAnyModule* outAsAnyModule)\\n{\\n CATCH_RETURN_NNModule(\\n auto opts = torch::nn::ReplicationPad1dOptions(padding);\\n res = create_module<torch::nn::ReplicationPad1dImpl>(opts, outAsAnyModule);\\n );\\n}\\n\\nTensor THSNN_ReplicationPad1d_forward(const NNModule module, const Tensor tensor)\\n{\\n CATCH_TENSOR((*module)->as<torch::nn::ReplicationPad1d>()->forward(*tensor));\\n}\\n\\nNNModule THSNN_ReplicationPad2d_ctor(const int64_t padding, NNAnyModule* outAsAnyModule)\\n{\\n CATCH_RETURN_NNModule(\\n auto opts = torch::nn::ReplicationPad2dOptions(padding);\\n res = create_module<torch::nn::ReplicationPad2dImpl>(opts, outAsAnyModule);\\n );\\n}\\n\\nTensor THSNN_ReplicationPad2d_forward(const NNModule module, const Tensor tensor)\\n{\\n CATCH_TENSOR((*module)->as<torch::nn::ReplicationPad2d>()->forward(*tensor));\\n}\\n\\nNNModule THSNN_ReplicationPad3d_ctor(const int64_t padding, NNAnyModule* outAsAnyModule)\\n{\\n CATCH_RETURN_NNModule(\\n auto opts = torch::nn::ReplicationPad3dOptions(padding);\\n res = create_module<torch::nn::ReplicationPad3dImpl>(opts, outAsAnyModule);\\n );\\n}\\n\\n\\nTensor THSNN_ReplicationPad3d_forward(const NNModule module, const Tensor tensor)\\n{\\n CATCH_TENSOR((*module)->as<torch::nn::ReplicationPad3d>()->forward(*tensor));\\n}\\n\\nNNModule THSNN_ReplicationPad1d_ctor_tuple(const int64_t padding_left, const int64_t padding_right, NNAnyModule* outAsAnyModule)\\n{\\n CATCH_RETURN_NNModule(\\n auto opts = torch::nn::ReplicationPad1dOptions({ padding_left, padding_right });\\n res = create_module<torch::nn::ReplicationPad1dImpl>(opts, outAsAnyModule);\\n );\\n}\\n\\nNNModule THSNN_ReplicationPad2d_ctor_tuple(const int64_t padding_left, const int64_t padding_right, const int64_t padding_top, const int64_t padding_bottom, NNAnyModule* outAsAnyModule)\\n{\\n CATCH_RETURN_NNModule(\\n auto opts = torch::nn::ReplicationPad2dOptions({ padding_left, padding_right, padding_top, padding_bottom });\\n res = create_module<torch::nn::ReplicationPad2dImpl>(opts, outAsAnyModule);\\n );\\n}\\n\\nNNModule THSNN_ReplicationPad3d_ctor_tuple(const int64_t padding_left, const int64_t padding_right, const int64_t padding_top, const int64_t padding_bottom, const int64_t padding_front, const int64_t padding_back, NNAnyModule* outAsAnyModule)\\n{\\n CATCH_RETURN_NNModule(\\n auto opts = torch::nn::ReplicationPad3dOptions({ padding_left, padding_right, padding_top, padding_bottom, padding_front, padding_back });\\n res = create_module<torch::nn::ReplicationPad3dImpl>(opts, outAsAnyModule);\\n );\\n}\\n\\nNNModule THSNN_ReflectionPad1d_ctor(const int64_t padding, NNAnyModule* outAsAnyModule)\\n{\\n CATCH_RETURN_NNModule(\\n auto opts = torch::nn::ReflectionPad1dOptions(padding);\\n res = create_module<torch::nn::ReflectionPad1dImpl>(opts, outAsAnyModule);\\n );\\n}\\n\\nTensor THSNN_ReflectionPad1d_forward(const NNModule module, const Tensor tensor)\\n{\\n CATCH_TENSOR((*module)->as<torch::nn::ReflectionPad1d>()->forward(*tensor));\\n}\\n\\nNNModule THSNN_ReflectionPad2d_ctor(const int64_t padding, NNAnyModule* outAsAnyModule)\\n{\\n CATCH_RETURN_NNModule(\\n auto opts = torch::nn::ReflectionPad2dOptions(padding);\\n res = create_module<torch::nn::ReflectionPad2dImpl>(opts, outAsAnyModule);\\n );\\n}\\n\\nTensor THSNN_ReflectionPad2d_forward(const NNModule module, const Tensor tensor)\\n{\\n CATCH_TENSOR((*module)->as<torch::nn::ReflectionPad2d>()->forward(*tensor));\\n}\\n\\nNNModule THSNN_ReflectionPad3d_ctor(const int64_t padding, NNAnyModule* outAsAnyModule)\\n{\\n CATCH_RETURN_NNModule(\\n auto opts = torch::nn::ReflectionPad3dOptions(padding);\\n res = create_module<torch::nn::ReflectionPad3dImpl>(opts, outAsAnyModule);\\n );\\n}\\n\\nTensor THSNN_ReflectionPad3d_forward(const NNModule module, const Tensor tensor)\\n{\\n CATCH_TENSOR((*module)->as<torch::nn::ReflectionPad3d>()->forward(*tensor));\\n}\\n\\nNNModule THSNN_ReflectionPad1d_ctor_tuple(const int64_t padding_left, const int64_t padding_right, NNAnyModule* outAsAnyModule)\\n{\\n CATCH_RETURN_NNModule(\\n auto opts = torch::nn::ReflectionPad1dOptions({ padding_left, padding_right });\\n res = create_module<torch::nn::ReflectionPad1dImpl>(opts, outAsAnyModule);\\n );\\n}\\n\\nNNModule THSNN_ReflectionPad2d_ctor_tuple(const int64_t padding_left, const int64_t padding_right, const int64_t padding_top, const int64_t padding_bottom, NNAnyModule* outAsAnyModule)\\n{\\n CATCH_RETURN_NNModule(\\n auto opts = torch::nn::ReflectionPad2dOptions({ padding_left, padding_right, padding_top, padding_bottom });\\n res = create_module<torch::nn::ReflectionPad2dImpl>(opts, outAsAnyModule);\\n );\\n}\\n\\nNNModule THSNN_ReflectionPad3d_ctor_tuple(const int64_t padding_left, const int64_t padding_right, const int64_t padding_top, const int64_t padding_bottom, const int64_t padding_front, const int64_t padding_back, NNAnyModule* outAsAnyModule)\\n{\\n CATCH_RETURN_NNModule(\\n auto opts = torch::nn::ReflectionPad3dOptions({ padding_left, padding_right, padding_top, padding_bottom, padding_front, padding_back });\\n res = create_module<torch::nn::ReflectionPad3dImpl>(opts, outAsAnyModule);\\n );\\n}\\n\\n\\ntemplate<typename T>\\nvoid ApplyPaddingMode(T& opts, const int64_t padding)\\n{\\n if (padding == 0)\\n opts = opts.padding_mode(torch::kZeros);\\n if (padding == 1)\\n opts = opts.padding_mode(torch::kReflect);\\n if (padding == 2)\\n opts = opts.padding_mode(torch::kReplicate);\\n if (padding == 3)\\n opts = opts.padding_mode(torch::kCircular);\\n}\\n\\nNNModule THSNN_Conv1d_ctor(const int64_t inputChannel, const int64_t outputChannel,\\n const int64_t kernelSize, const int64_t stride, const int64_t padding,\\n const int64_t dilation, const int64_t paddingMode, const int64_t groups, const bool bias,\\n NNAnyModule* outAsAnyModule)\\n{\\n torch::nn::Conv1dOptions::padding_t padd(padding);\\n if (padding == -1)\\n {\\n padd = torch::kSame;\\n }\\n\\n CATCH_RETURN_NNModule(\\n auto opts = torch::nn::Conv1dOptions(inputChannel, outputChannel, kernelSize)\\n .stride(stride)\\n .padding(padd)\\n .dilation(dilation)\\n .groups(groups)\\n .bias(bias);\\n\\n ApplyPaddingMode(opts, paddingMode);\\n\\n res = create_module<torch::nn::Conv1dImpl>(opts, outAsAnyModule);\\n );\\n}\\n\\nTensor THSNN_Conv1d_forward(const NNModule module, const Tensor tensor)\\n{\\n CATCH_TENSOR((*module)->as<torch::nn::Conv1d>()->forward(*tensor));\\n}\\n\\nTensor THSNN_Conv1d_bias(const NNModule module)\\n{\\n return get_bias<torch::nn::Conv1d>(module);\\n}\\n\\nvoid THSNN_Conv1d_set_bias(const NNModule module, const Tensor bias)\\n{\\n set_bias<torch::nn::Conv1d>(module, bias);\\n}\\n\\nTensor THSNN_Conv1d_weight(const NNModule module)\\n{\\n return get_weight<torch::nn::Conv1d>(module);\\n}\\n\\nvoid THSNN_Conv1d_set_weight(const NNModule module, const Tensor weight)\\n{\\n set_weight<torch::nn::Conv1d>(module, weight);\\n}\\n\\nNNModule THSNN_Conv2d_ctor(const int64_t inputChannel, const int64_t outputChannel,\\n const int64_t kernelSize, const int64_t stride, const int64_t padding,\\n const int64_t dilation, const int64_t paddingMode, const int64_t groups, const bool bias,\\n NNAnyModule* outAsAnyModule)\\n{\\n torch::nn::Conv2dOptions::padding_t padd =\\n (padding == -1) ? torch::kSame :\\n (padding == 0) ? torch::kValid :\\n torch::nn::Conv2dOptions::padding_t(padding);\\n\\n CATCH_RETURN_NNModule(\\n auto opts = torch::nn::Conv2dOptions(inputChannel, outputChannel, kernelSize)\\n .stride(stride)\\n .padding(padd)\\n .dilation(dilation)\\n .groups(groups)\\n .bias(bias);\\n ApplyPaddingMode(opts, paddingMode);\\n\\n res = create_module<torch::nn::Conv2dImpl>(opts, outAsAnyModule);\\n );\\n}\\n\\nNNModule THSNN_Conv2d_ctor_1(const int64_t inputChannel, const int64_t outputChannel,\\n const int64_t kernelX, const int64_t kernelY,\\n const int64_t strideX, const int64_t strideY,\\n const int64_t paddingX, const int64_t paddingY,\\n const int64_t dilationX, const int64_t dilationY,\\n const int64_t paddingMode, const int64_t groups, const bool bias,\\n NNAnyModule* outAsAnyModule)\\n{\\n torch::nn::Conv2dOptions::padding_t padd =\\n (paddingX == -1) && (paddingY == 0) ? torch::kSame :\\n (paddingX == 0) && (paddingY == 0) ? torch::kValid :\\n (torch::nn::Conv2dOptions::padding_t)torch::ExpandingArray<2>({ paddingX, paddingY });\\n\\n CATCH_RETURN_NNModule(\\n auto opts = torch::nn::Conv2dOptions(inputChannel, outputChannel, { kernelX, kernelY })\\n .stride({ strideX, strideY })\\n .padding(padd)\\n .dilation({ dilationX, dilationY })\\n .groups(groups)\\n .bias(bias);\\n ApplyPaddingMode(opts, paddingMode);\\n\\n res = create_module<torch::nn::Conv2dImpl>(opts, outAsAnyModule);\\n );\\n}\\n\\nTensor THSNN_Conv2d_forward(const NNModule module, const Tensor tensor)\\n{\\n CATCH_TENSOR((*module)->as<torch::nn::Conv2d>()->forward(*tensor));\\n}\\n\\nTensor THSNN_Conv2d_bias(const NNModule module)\\n{\\n return get_bias<torch::nn::Conv2d>(module);\\n}\\n\\nvoid THSNN_Conv2d_set_bias(const NNModule module, const Tensor bias)\\n{\\n set_bias<torch::nn::Conv2d>(module, bias);\\n}\\n\\nTensor THSNN_Conv2d_weight(const NNModule module)\\n{\\n return get_weight<torch::nn::Conv2d>(module);\\n}\\n\\nvoid THSNN_Conv2d_set_weight(const NNModule module, const Tensor weight)\\n{\\n set_weight<torch::nn::Conv2d>(module, weight);\\n}\\n\\nNNModule THSNN_Conv3d_ctor(const int64_t inputChannel, const int64_t outputChannel,\\n const int64_t kernelSize, const int64_t stride, const int64_t padding,\\n const int64_t dilation, const int64_t paddingMode, const int64_t groups, const bool bias,\\n NNAnyModule* outAsAnyModule)\\n{\\n torch::nn::Conv3dOptions::padding_t padd =\\n (padding == -1) ? torch::kSame :\\n (padding == 0) ? torch::kValid :\\n torch::nn::Conv3dOptions::padding_t(padding);\\n\\n CATCH_RETURN_NNModule(\\n auto opts = torch::nn::Conv3dOptions(inputChannel, outputChannel, kernelSize)\\n .stride(stride)\\n .padding(padd)\\n .dilation(dilation)\\n .groups(groups)\\n .bias(bias);\\n ApplyPaddingMode(opts, paddingMode);\\n\\n res = create_module<torch::nn::Conv3dImpl>(opts, outAsAnyModule);\\n );\\n}\\n\\nNNModule THSNN_Conv3d_ctor_1(const int64_t inputChannel, const int64_t outputChannel,\\n const int64_t kernelX, const int64_t kernelY, const int64_t kernelZ,\\n const int64_t strideX, const int64_t strideY, const int64_t strideZ,\\n const int64_t paddingX, const int64_t paddingY, const int64_t paddingZ,\\n const int64_t dilationX, const int64_t dilationY, const int64_t dilationZ,\\n const int64_t paddingMode, const int64_t groups, const bool bias,\\n NNAnyModule* outAsAnyModule)\\n{\\n torch::nn::Conv3dOptions::padding_t padd =\\n (paddingX == -1) && (paddingY == 0) && (paddingZ == 0) ? torch::kSame :\\n (paddingX == 0) && (paddingY == 0) && (paddingZ == 0) ? torch::kValid :\\n (torch::nn::Conv3dOptions::padding_t)torch::ExpandingArray<3>({ paddingX, paddingY, paddingZ });\\n\\n CATCH_RETURN_NNModule(\\n auto opts = torch::nn::Conv3dOptions(inputChannel, outputChannel, { kernelX, kernelY, kernelZ })\\n .stride({ strideX, strideY, strideZ })\\n .padding(padd)\\n .dilation({ dilationX, dilationY, dilationZ })\\n .groups(groups)\\n .bias(bias);\\n ApplyPaddingMode(opts, paddingMode);\\n\\n res = create_module<torch::nn::Conv3dImpl>(opts, outAsAnyModule);\\n );\\n}\\n\\nTensor THSNN_Conv3d_bias(const NNModule module)\\n{\\n return get_bias<torch::nn::Conv3d>(module);\\n}\\n\\nvoid THSNN_Conv3d_set_bias(const NNModule module, const Tensor bias)\\n{\\n set_bias<torch::nn::Conv3d>(module, bias);\\n}\\n\\nTensor THSNN_Conv3d_forward(const NNModule module, const Tensor tensor)\\n{\\n CATCH_TENSOR((*module)->as<torch::nn::Conv3d>()->forward(*tensor));\\n}\\n\\nTensor THSNN_Conv3d_weight(const NNModule module)\\n{\\n return get_weight<torch::nn::Conv3d>(module);\\n}\\n\\nvoid THSNN_Conv3d_set_weight(const NNModule module, const Tensor weight)\\n{\\n set_weight<torch::nn::Conv3d>(module, weight);\\n}\\n\\n\\nNNModule THSNN_ConvTranspose1d_ctor(const int64_t inputChannel, const int64_t outputChannel,\\n const int64_t kernelSize, const int64_t stride, const int64_t padding, const int64_t output_padding,\\n const int64_t dilation, const int64_t paddingMode, const int64_t groups, const bool bias,\\n NNAnyModule* outAsAnyModule)\\n{\\n CATCH_RETURN_NNModule(\\n auto opts = torch::nn::ConvTranspose1dOptions(inputChannel, outputChannel, kernelSize)\\n .stride(stride)\\n .padding(padding)\\n .dilation(dilation)\\n .groups(groups)\\n .bias(bias)\\n .output_padding(output_padding);\\n ApplyPaddingMode(opts, paddingMode);\\n\\n res = create_module<torch::nn::ConvTranspose1dImpl>(opts, outAsAnyModule);\\n );\\n}\\n\\nTensor THSNN_ConvTranspose1d_forward(const NNModule module, const Tensor tensor)\\n{\\n CATCH_TENSOR((*module)->as<torch::nn::ConvTranspose1d>()->forward(*tensor));\\n}\\n\\nTensor THSNN_ConvTranspose1d_bias(const NNModule module)\\n{\\n return get_bias<torch::nn::ConvTranspose1d>(module);\\n}\\n\\nvoid THSNN_ConvTranspose1d_set_bias(const NNModule module, const Tensor bias)\\n{\\n set_bias<torch::nn::ConvTranspose1d>(module, bias);\\n}\\n\\nTensor THSNN_ConvTranspose1d_weight(const NNModule module)\\n{\\n return get_weight<torch::nn::ConvTranspose1d>(module);\\n}\\n\\nvoid THSNN_ConvTranspose1d_set_weight(const NNModule module, const Tensor weight)\\n{\\n set_weight<torch::nn::ConvTranspose1d>(module, weight);\\n}\\n\\nNNModule THSNN_ConvTranspose2d_ctor(const int64_t inputChannel, const int64_t outputChannel,\\n const int64_t kernelSize, const int64_t stride, const int64_t padding, const int64_t output_padding,\\n const int64_t dilation, const int64_t paddingMode, const int64_t groups, const bool bias,\\n NNAnyModule* outAsAnyModule)\\n{\\n CATCH_RETURN_NNModule(\\n auto opts = torch::nn::ConvTranspose2dOptions(inputChannel, outputChannel, kernelSize)\\n .stride(stride)\\n .padding(padding)\\n .dilation(dilation)\\n .groups(groups)\\n .bias(bias)\\n .output_padding(output_padding);\\n ApplyPaddingMode(opts, paddingMode);\\n\\n res = create_module<torch::nn::ConvTranspose2dImpl>(opts, outAsAnyModule);\\n );\\n}\\n\\nTensor THSNN_ConvTranspose2d_forward(const NNModule module, const Tensor tensor)\\n{\\n CATCH_TENSOR((*module)->as<torch::nn::ConvTranspose2d>()->forward(*tensor));\\n}\\n\\nTensor THSNN_ConvTranspose2d_bias(const NNModule module)\\n{\\n return get_bias<torch::nn::ConvTranspose2d>(module);\\n}\\n\\nvoid THSNN_ConvTranspose2d_set_bias(const NNModule module, const Tensor bias)\\n{\\n set_bias<torch::nn::ConvTranspose2d>(module, bias);\\n}\\n\\nTensor THSNN_ConvTranspose2d_weight(const NNModule module)\\n{\\n return get_weight<torch::nn::ConvTranspose2d>(module);\\n}\\n\\nvoid THSNN_ConvTranspose2d_set_weight(const NNModule module, const Tensor weight)\\n{\\n set_weight<torch::nn::ConvTranspose2d>(module, weight);\\n}\\n\\nNNModule THSNN_ConvTranspose3d_ctor(const int64_t inputChannel, const int64_t outputChannel,\\n const int64_t kernelSize, const int64_t stride, const int64_t padding, const int64_t output_padding,\\n const int64_t dilation, const int64_t paddingMode, const int64_t groups, const bool bias,\\n NNAnyModule* outAsAnyModule)\\n{\\n CATCH_RETURN_NNModule(\\n auto opts = torch::nn::ConvTranspose3dOptions(inputChannel, outputChannel, kernelSize)\\n .stride(stride)\\n .padding(padding)\\n .dilation(dilation)\\n .groups(groups)\\n .bias(bias)\\n .output_padding(output_padding);\\n ApplyPaddingMode(opts, paddingMode);\\n\\n res = create_module<torch::nn::ConvTranspose3dImpl>(opts, outAsAnyModule);\\n );\\n}\\n\\nTensor THSNN_ConvTranspose3d_bias(const NNModule module)\\n{\\n return get_bias<torch::nn::ConvTranspose3d>(module);\\n}\\n\\nvoid THSNN_ConvTranspose3d_set_bias(const NNModule module, const Tensor bias)\\n{\\n set_bias<torch::nn::ConvTranspose3d>(module, bias);\\n}\\n\\nTensor THSNN_ConvTranspose3d_forward(const NNModule module, const Tensor tensor)\\n{\\n CATCH_TENSOR((*module)->as<torch::nn::ConvTranspose3d>()->forward(*tensor));\\n}\\n\\nTensor THSNN_ConvTranspose3d_weight(const NNModule module)\\n{\\n return get_weight<torch::nn::ConvTranspose3d>(module);\\n}\\n\\nvoid THSNN_ConvTranspose3d_set_weight(const NNModule module, const Tensor weight)\\n{\\n set_weight<torch::nn::ConvTranspose3d>(module, weight);\\n}\\n\"\n },\n {\n \"alpha_fraction\": 0.7513944506645203,\n \"alphanum_fraction\": 0.7641434073448181,\n \"avg_line_length\": 43.29411697387695,\n \"blob_id\": \"ad82228860773a71eb344eccd4a8592acc6c7816\",\n \"content_id\": \"a2610eca553080496e1d69c83d4979668d55b100\",\n \"detected_licenses\": [\n \"MIT\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"C\",\n \"length_bytes\": 3765,\n \"license_type\": \"permissive\",\n \"max_line_length\": 241,\n \"num_lines\": 85,\n \"path\": \"/src/Native/LibTorchSharp/THSJIT.h\",\n \"repo_name\": \"NiklasGustafsson/TorchSharp\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"// Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information.\\n#pragma once\\n\\n#include \\\"../Stdafx.h\\\"\\n\\n#include \\\"torch/script.h\\\"\\n\\n#include \\\"Utils.h\\\"\\n\\n// Copied from libtorch to share the type as an int8_t.\\nenum TypeKind : int8_t {\\n#define DEFINE_TYPE(T) T,\\n C10_FORALL_TYPES(DEFINE_TYPE)\\n#undef DEFINE_TYPE\\n};\\n\\n// API.\\n\\nstruct TensorOrScalar\\n{\\n int64_t TypeCode;\\n int64_t ArrayIndex;\\n ptrdiff_t Handle;\\n};\\n\\n\\nEXPORT_API(JITModule) THSJIT_load(const char* filename, int64_t device, int64_t index);\\nEXPORT_API(void) THSJIT_save(JITModule module, const char* filename);\\nEXPORT_API(JITCompilationUnit) THSJIT_compile(const char* script);\\n\\nEXPORT_API(void) THSJIT_Module_dispose(const JITModule module);\\nEXPORT_API(void) THSJIT_CompilationUnit_dispose(const JITCompilationUnit module);\\n\\nEXPORT_API(int) THSJIT_Module_num_inputs(const JITModule method);\\nEXPORT_API(int) THSJIT_Module_num_outputs(const JITModule method);\\n\\nEXPORT_API(void) THSJIT_Module_forward(const JITModule module, const TensorOrScalar* tensorPtrs, const int length, TensorOrScalar* (*allocator)(int32_t idx, size_t length), int8_t* typeCode, int32_t idx);\\nEXPORT_API(void) THSJIT_Module_invoke(const JITModule module, const char* name, const TensorOrScalar* tensorPtrs, const int length, TensorOrScalar* (*allocator)(int32_t idx, size_t length), int8_t* typeCode, int32_t idx);\\n\\nEXPORT_API(void) THSJIT_CompilationUnit_Invoke(const JITCompilationUnit module, const char* method, const TensorOrScalar* tensorPtrs, const int length, TensorOrScalar* (*allocator)(int32_t idx, size_t length), int8_t* typeCode, int32_t idx);\\n\\nEXPORT_API(int) THSJIT_Module_is_training(JITModule module);\\nEXPORT_API(void) THSJIT_Module_train(JITModule module, bool on);\\nEXPORT_API(void) THSJIT_Module_eval(JITModule module);\\n\\nEXPORT_API(void) THSJIT_Module_to_device_dtype(JITModule module, int8_t dtype, int64_t device, int64_t index);\\nEXPORT_API(void) THSJIT_Module_to_device(JITModule module, int64_t device, int64_t index);\\nEXPORT_API(void) THSJIT_Module_to_dtype(JITModule module, int8_t dtype);\\n\\nEXPORT_API(JITType) THSJIT_Module_getInputType(JITModule module, int8_t dtype);\\n\\nEXPORT_API(int8_t) THSJIT_Type_kind(JITType handle);\\nEXPORT_API(void*) THSJIT_Type_cast(const JITType type);\\n\\nEXPORT_API(int8_t) THSJIT_TensorType_dtype(const JITTensorType type);\\nEXPORT_API(void) THSJIT_TensorType_sizes(const JITTensorType type, int64_t* (*allocator)(int64_t length));\\n\\nEXPORT_API(void) THSJIT_Type_dispose(const JITType type);\\nEXPORT_API(void) THSJIT_TensorType_dispose(const JITTensorType type);\\n\\nEXPORT_API(void) THSJIT_Module_modules(const JITModule module, JITModule* (*allocator)(size_t length));\\nEXPORT_API(void) THSJIT_Module_named_modules(const JITModule module,\\n JITModule* (*allocator)(size_t length),\\n const char** (*allocator2)(size_t length));\\n\\nEXPORT_API(void) THSJIT_Module_named_children(const JITModule module,\\n JITModule* (*allocator)(size_t length),\\n const char** (*allocator2)(size_t length));\\n\\nEXPORT_API(JITMethod) THSJIT_Module_get_method(const JITModule module, const char* name);\\n\\nEXPORT_API(void) THSJIT_Module_parameters(const JITModule module, Tensor* (*allocator)(size_t length));\\nEXPORT_API(void) THSJIT_Module_named_parameters(const JITModule module,\\n Tensor* (*allocator)(size_t length),\\n const char** (*allocator2)(size_t length));\\n\\nEXPORT_API(void) THSJIT_Module_named_buffers(const JITModule module,\\n Tensor* (*allocator)(size_t length),\\n const char** (*allocator2)(size_t length));\\n\\nEXPORT_API(int) THSJIT_Method_num_inputs(const JITMethod method);\\n\\nEXPORT_API(void) THSJIT_Method_dispose(const JITMethod method);\\n\\nEXPORT_API(const char*) THSJIT_Method_name(const JITMethod method);\\n\"\n },\n {\n \"alpha_fraction\": 0.6331987977027893,\n \"alphanum_fraction\": 0.6352169513702393,\n \"avg_line_length\": 40.3125,\n \"blob_id\": \"07afdd45ed80d06eac1a840ebb05273477fcea3b\",\n \"content_id\": \"4a2b244a97522255f43d3f133d199bd10dbcce53\",\n \"detected_licenses\": [\n \"MIT\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"C#\",\n \"length_bytes\": 1982,\n \"license_type\": \"permissive\",\n \"max_line_length\": 117,\n \"num_lines\": 48,\n \"path\": \"/src/TorchSharp/ThreadDisposeScopeStatistics.cs\",\n \"repo_name\": \"NiklasGustafsson/TorchSharp\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"#nullable enable\\nnamespace TorchSharp\\n{\\n /// <summary>\\n /// Keeps track of statistics for a dispose scope. Can be queried to figure out performance/memory issues.\\n /// </summary>\\n public class ThreadDisposeScopeStatistics\\n {\\n /// <summary>\\n /// The number of disposables that were created on this thread, but weren't captured by a DisposeScope.\\n /// </summary>\\n public long CreatedOutsideScopeCount { get; internal set; }\\n\\n /// <summary>\\n /// The number of disposables that were created on this thread and were captured by a DisposeScope.\\n /// </summary>\\n public long CreatedInScopeCount { get; internal set; }\\n\\n /// <summary>\\n /// The number of disposables that were disposed on this thread and were disposed while in a DisposeScope.\\n /// </summary>\\n public long DisposedInScopeCount { get; internal set; }\\n\\n /// <summary>\\n /// Number of disposables that were once included in the scope, but were subsequently detached.\\n /// </summary>\\n public long DetachedFromScopeCount { get; internal set; }\\n\\n /// <summary>\\n /// The number of disposables that are currently live on the current thread. If a It's aproximate, see\\n /// Tensor.TotalCount. Disposables that weren't created within a DisposeScope, or detached from the dispose\\n /// scope, will not be counted as alive.\\n /// </summary>\\n public long ThreadTotalLiveCount => CreatedInScopeCount - DisposedInScopeCount - DetachedFromScopeCount;\\n\\n /// <summary>\\n /// Resets the counts for the current thread. See ThreadTotalLiveCount etc. Mainly used in tests to make sure\\n /// we get a clean slate on the thread.\\n /// </summary>\\n public void Reset()\\n {\\n CreatedOutsideScopeCount = 0;\\n CreatedInScopeCount = 0;\\n DisposedInScopeCount = 0;\\n DetachedFromScopeCount = 0;\\n }\\n }\\n}\"\n },\n {\n \"alpha_fraction\": 0.5151354074478149,\n \"alphanum_fraction\": 0.5264205932617188,\n \"avg_line_length\": 42.2931022644043,\n \"blob_id\": \"be2b0ba50ddf365ffbcc0593ba07837aef4ce7b5\",\n \"content_id\": \"53cccf5fa827536eea4ebdcc9b6fcb196152bee0\",\n \"detected_licenses\": [\n \"MIT\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"C#\",\n \"length_bytes\": 7532,\n \"license_type\": \"permissive\",\n \"max_line_length\": 140,\n \"num_lines\": 174,\n \"path\": \"/src/TorchAudio/Transforms/InverseMelScale.cs\",\n \"repo_name\": \"NiklasGustafsson/TorchSharp\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"// Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information.\\n\\n// A number of implementation details in this file have been translated from the Python version of torchaudio,\\n// largely located in the files found in this folder:\\n//\\n// https://github.com/pytorch/audio/blob/bb77cbebb620a46fdc0dc7e6dae2253eef3f37e2/torchaudio/transforms/_transforms.py\\n//\\n// The origin has the following copyright notice and license:\\n//\\n// https://github.com/pytorch/audio/blob/main/LICENSE\\n//\\n\\nusing System;\\nusing System.Collections.Generic;\\nusing static TorchSharp.torch;\\nusing static TorchSharp.torch.nn;\\nusing static TorchSharp.torchaudio;\\nusing F = TorchSharp.torchaudio.functional;\\n\\nnamespace TorchSharp\\n{\\n namespace Transforms\\n {\\n public sealed class InverseMelScale : Module<Tensor, Tensor>, ITransform\\n {\\n public readonly double f_max;\\n public readonly double f_min;\\n public readonly long max_iter;\\n public readonly long n_mels;\\n public readonly long sample_rate;\\n public readonly double tolerance_change;\\n public readonly double tolerance_loss;\\n public readonly Tensor fb;\\n\\n protected override void Dispose(bool disposing)\\n {\\n if (disposing) {\\n fb.Dispose();\\n }\\n base.Dispose(disposing);\\n }\\n\\n internal InverseMelScale(\\n string name,\\n long n_stft,\\n long n_mels = 128,\\n long sample_rate = 16000,\\n double f_min = 0.0,\\n double? f_max = null,\\n long max_iter = 100000,\\n double tolerance_loss = 1e-05,\\n double tolerance_change = 1e-08,\\n MelNorm norm = MelNorm.none,\\n torchaudio.MelScale mel_scale = torchaudio.MelScale.htk) : base(name)\\n {\\n this.n_mels = n_mels;\\n this.sample_rate = sample_rate;\\n this.f_max = f_max ?? (sample_rate / 2);\\n this.f_min = f_min;\\n this.max_iter = max_iter;\\n this.tolerance_loss = tolerance_loss;\\n this.tolerance_change = tolerance_change;\\n\\n if (f_min > this.f_max) {\\n throw new ArgumentException($\\\"Require f_min: {this.f_min} <= f_max: {this.f_max}\\\");\\n }\\n\\n this.fb = F.melscale_fbanks(n_stft, this.f_min, this.f_max, this.n_mels, this.sample_rate, norm, mel_scale);\\n this.register_buffer(\\\"fb\\\", this.fb);\\n }\\n\\n /// <param name=\\\"melspec\\\">A Mel frequency spectrogram of dimension (..., ``n_mels``, time)</param>\\n /// <returns>Linear scale spectrogram of size (..., freq, time)</returns>\\n /// <exception cref=\\\"ArgumentException\\\"></exception>\\n public override Tensor forward(Tensor melspec)\\n {\\n // pack batch\\n var shape = melspec.size();\\n melspec = melspec.view(-1, shape[shape.Length - 2], shape[shape.Length - 1]);\\n\\n var n_mels = shape[shape.Length - 2];\\n var time = shape[shape.Length - 1];\\n var freq = this.fb.size(0); // (freq, n_mels)\\n melspec = melspec.transpose(-1, -2);\\n if (this.n_mels != n_mels) {\\n throw new ArgumentException($\\\"Expected an input with {this.n_mels} mel bins. Found: {n_mels}\\\");\\n }\\n\\n var specgram = nn.Parameter(torch.rand(\\n melspec.size(0), time, freq, requires_grad: true, dtype: melspec.dtype, device: melspec.device));\\n\\n var optim = torch.optim.SGD(\\n new List<Modules.Parameter> { specgram },\\n learningRate: 0.1, momentum: 0.9);\\n\\n var loss = float.PositiveInfinity;\\n for (long i = 0; i < this.max_iter; i++) {\\n optim.zero_grad();\\n var diff = melspec - specgram.matmul(this.fb);\\n var new_loss = diff.pow(2).sum(dim: -1).mean();\\n // take sum over mel-frequency then average over other dimensions\\n // so that loss threshold is applied par unit timeframe\\n new_loss.backward();\\n optim.step();\\n using (torch.no_grad())\\n specgram.set_(specgram.clamp(min: 0));\\n\\n var new_loss_value = new_loss.item<float>();\\n if (new_loss_value < this.tolerance_loss || Math.Abs(loss - new_loss_value) < this.tolerance_change) {\\n break;\\n }\\n loss = new_loss_value;\\n }\\n\\n specgram.requires_grad_(false);\\n var specgram_tensor = specgram.clamp(min: 0).transpose(-1, -2);\\n\\n // unpack batch\\n shape[shape.Length - 2] = freq;\\n shape[shape.Length - 1] = time;\\n specgram_tensor = specgram_tensor.view(shape);\\n return specgram_tensor;\\n }\\n }\\n }\\n\\n public partial class torchaudio\\n {\\n public partial class transforms\\n {\\n /// <summary>\\n /// Estimate a STFT in normal frequency domain from mel frequency domain.\\n /// It minimizes the euclidian norm between the input mel-spectrogram and the product between\\n /// the estimated spectrogram and the filter banks using SGD.\\n /// </summary>\\n /// <param name=\\\"n_stft\\\">Number of bins in STFT. See ``n_fft`` in :class:`Spectrogram`.</param>\\n /// <param name=\\\"n_mels\\\">Number of mel filterbanks.</param>\\n /// <param name=\\\"sample_rate\\\">Sample rate of audio signal.</param>\\n /// <param name=\\\"f_min\\\">Minimum frequency.</param>\\n /// <param name=\\\"f_max\\\">Maximum frequency.</param>\\n /// <param name=\\\"max_iter\\\">Maximum number of optimization iterations.</param>\\n /// <param name=\\\"tolerance_loss\\\">Value of loss to stop optimization at.</param>\\n /// <param name=\\\"tolerance_change\\\">Difference in losses to stop optimization at.</param>\\n /// <param name=\\\"norm\\\">If 'slaney', divide the triangular mel weights by the width of the mel band (area normalization).</param>\\n /// <param name=\\\"mel_scale\\\">Scale to use: ``htk`` or ``slaney``.</param>\\n /// <returns></returns>\\n public static Transforms.InverseMelScale InverseMelScale(\\n long n_stft,\\n long n_mels = 128,\\n long sample_rate = 16000,\\n double f_min = 0.0,\\n double? f_max = null,\\n long max_iter = 100000,\\n double tolerance_loss = 1e-05,\\n double tolerance_change = 1e-08,\\n MelNorm norm = MelNorm.none,\\n torchaudio.MelScale mel_scale = torchaudio.MelScale.htk)\\n {\\n return new Transforms.InverseMelScale(\\n \\\"InverseMelScale\\\",\\n n_stft,\\n n_mels,\\n sample_rate,\\n f_min,\\n f_max,\\n max_iter,\\n tolerance_loss,\\n tolerance_change,\\n norm,\\n mel_scale);\\n }\\n }\\n }\\n}\"\n },\n {\n \"alpha_fraction\": 0.523976743221283,\n \"alphanum_fraction\": 0.5435646176338196,\n \"avg_line_length\": 48.87557601928711,\n \"blob_id\": \"8fe05f4dcd57c2692e5e27bc9d344ba7b5c5fef3\",\n \"content_id\": \"134e89a63eecc85729dff5c9d3d64a0b8eaa87f2\",\n \"detected_licenses\": [\n \"MIT\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"C#\",\n \"length_bytes\": 10823,\n \"license_type\": \"permissive\",\n \"max_line_length\": 169,\n \"num_lines\": 217,\n \"path\": \"/src/TorchVision/Ops/Boxes.cs\",\n \"repo_name\": \"NiklasGustafsson/TorchSharp\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"// Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information.\\n\\n// A number of implementation details in this file have been translated from the Python version of torchvision,\\n// largely located in the files found in this folder:\\n//\\n// https://github.com/pytorch/vision/blob/a4f53308b2d0f1aa9191686e326f45c26053f686/torchvision/ops/boxes.py\\n//\\n// The origin has the following copyright notice and license:\\n//\\n// https://github.com/pytorch/vision/blob/main/LICENSE\\n//\\n\\nusing System;\\nusing System.Collections.Generic;\\nusing System.Reflection;\\nusing System.Runtime.CompilerServices;\\nusing static TorchSharp.torch;\\n\\nnamespace TorchSharp\\n{\\n public static partial class torchvision\\n {\\n\\n public static partial class ops\\n {\\n /// <summary>\\n /// Converts boxes from given in_fmt to out_fmt.\\n /// Supported in_fmt and out_fmt are:\\n ///\\n /// 'xyxy': boxes are represented via corners, x1, y1 being top left and x2, y2 being bottom right. This is the format that torchvision utilities expect.\\n /// 'xywh' : boxes are represented via corner, width and height, x1, y2 being top left, w, h being width and height.\\n /// 'cxcywh' : boxes are represented via centre, width and height, cx, cy being center of box, w, h being width and height.\\n /// </summary>\\n /// <param name=\\\"boxes\\\">Boxes which will be converted</param>\\n /// <param name=\\\"in_fmt\\\">Input format of given boxes.</param>\\n /// <param name=\\\"out_fmt\\\">Output format of given boxes.</param>\\n /// <returns></returns>\\n public static Tensor box_convert(Tensor boxes, BoxFormats in_fmt, BoxFormats out_fmt)\\n {\\n if (in_fmt == out_fmt) return boxes.clone();\\n\\n if (in_fmt != BoxFormats.xyxy && out_fmt != BoxFormats.xyxy) {\\n boxes = (in_fmt == BoxFormats.xywh) ? _box_xywh_to_xyxy(boxes) : _box_cxcywh_to_xyxy(boxes);\\n in_fmt = BoxFormats.xyxy;\\n }\\n\\n if (in_fmt == BoxFormats.xyxy) {\\n boxes = (out_fmt == BoxFormats.xywh) ? _box_xyxy_to_xywh(boxes) : _box_xyxy_to_cxcywh(boxes);\\n } else if (out_fmt == BoxFormats.xyxy) {\\n boxes = (in_fmt == BoxFormats.xywh) ? _box_xywh_to_xyxy(boxes) : _box_cxcywh_to_xyxy(boxes);\\n }\\n\\n return boxes;\\n }\\n\\n /// <summary>\\n /// Computes the area of a set of bounding boxes, which are specified by their (x1, y1, x2, y2) coordinates.\\n /// </summary>\\n /// <param name=\\\"boxes\\\">Boxes for which the area will be computed. They are expected to be in (x1, y1, x2, y2) format.</param>\\n /// <returns></returns>\\n public static Tensor box_area(Tensor boxes)\\n {\\n boxes = _upcast(boxes);\\n return (boxes[colon, 2] - boxes[colon, 0]) * (boxes[colon, 3] - boxes[colon, 1]);\\n }\\n\\n /// <summary>\\n /// Return intersection-over-union (Jaccard index) between two sets of boxes.\\n /// </summary>\\n /// <param name=\\\"boxes1\\\">First set of boxes</param>\\n /// <param name=\\\"boxes2\\\">Second set of boxes</param>\\n /// <returns>The NxM matrix containing the pairwise IoU values for every element in boxes1 and boxes2</returns>\\n public static Tensor box_iou(Tensor boxes1, Tensor boxes2)\\n {\\n var inter = _box_inter_union(boxes1, boxes2, out var union);\\n return inter / union;\\n }\\n\\n /// <summary>\\n /// Return generalized intersection-over-union (Jaccard index) between two sets of boxes.\\n /// </summary>\\n /// <param name=\\\"boxes1\\\">First set of boxes</param>\\n /// <param name=\\\"boxes2\\\">Second set of boxes</param>\\n /// <returns>The NxM matrix containing the pairwise generalized IoU values for every element in boxes1 and boxes2</returns>\\n public static Tensor generalized_box_iou(Tensor boxes1, Tensor boxes2)\\n {\\n using var _ = NewDisposeScope();\\n var inter = _box_inter_union(boxes1, boxes2, out var union);\\n var iou = inter / union;\\n\\n var lti = torch.min(boxes1[colon, None, (null, 2)], boxes2[colon, (null, 2)]);\\n var rbi = torch.max(boxes1[colon, None, (2, null)], boxes2[colon, (2, null)]);\\n\\n var whi = _upcast(rbi - lti).clamp(min: 0); // [N,M,2]\\n var areai = whi[colon, colon, 0] * whi[colon, colon, 1];\\n\\n return (iou - (areai - union) / areai).MoveToOuterDisposeScope();\\n }\\n\\n /// <summary>\\n /// Return complete intersection-over-union (Jaccard index) between two sets of boxes.\\n /// </summary>\\n /// <param name=\\\"boxes1\\\">First set of boxes</param>\\n /// <param name=\\\"boxes2\\\">Second set of boxes</param>\\n /// <param name=\\\"eps\\\">Small number to prevent division by zero</param>\\n /// <returns>The NxM matrix containing the complete distance IoU values for every element in boxes1 and boxes2</returns>\\n public static Tensor complete_box_iou(Tensor boxes1, Tensor boxes2, double eps = 1e-7)\\n {\\n using var _ = NewDisposeScope();\\n boxes1 = _upcast(boxes1);\\n boxes2 = _upcast(boxes2);\\n\\n var diou = _box_diou_iou(boxes1, boxes2, out Tensor iou, eps);\\n\\n var w_pred = boxes1[colon, None, 2] - boxes1[colon, None, 0];\\n var h_pred = boxes1[colon, None, 3] - boxes1[colon, None, 1];\\n\\n var w_gt = boxes2[colon, 2] - boxes2[colon, 0];\\n var h_gt = boxes2[colon, 3] - boxes2[colon, 1];\\n\\n var v = (4 / (Math.Pow(Math.PI, 2))) * torch.pow(torch.atan(w_pred / h_pred) - torch.atan(w_gt / h_gt), 2);\\n\\n Tensor alpha;\\n\\n using (var ng = torch.no_grad()) {\\n alpha = v / (1 - iou + v + eps);\\n }\\n return (diou - alpha * v).MoveToOuterDisposeScope();\\n }\\n\\n\\n /// <summary>\\n /// Return generalized intersection-over-union (Jaccard index) between two sets of boxes.\\n /// </summary>\\n /// <param name=\\\"boxes1\\\">First set of boxes</param>\\n /// <param name=\\\"boxes2\\\">Second set of boxes</param>\\n /// <param name=\\\"eps\\\">Small number to prevent division by zero</param>\\n /// <returns>The NxM matrix containing the pairwise distance IoU values for every element in boxes1 and boxes2</returns>\\n public static Tensor distance_box_iou(Tensor boxes1, Tensor boxes2, double eps = 1e-7)\\n {\\n using var _ = NewDisposeScope();\\n boxes1 = _upcast(boxes1);\\n boxes2 = _upcast(boxes2);\\n var diou = _box_diou_iou(boxes1, boxes2, out var _, eps: eps);\\n return diou.MoveToOuterDisposeScope();\\n }\\n\\n /// <summary>\\n /// Compute the bounding boxes around the provided masks.\\n /// </summary>\\n /// <param name=\\\"masks\\\">masks to transform where N is the number of masks and (H, W) are the spatial dimensions.</param>\\n /// <returns>A [N, 4] tensor containing bounding boxes. The boxes are in the (x1, y1, x2, y2) format</returns>\\n /// <exception cref=\\\"ArgumentException\\\">Raised if the input is not a three-dimensional tensor.</exception>\\n public static Tensor masks_to_boxes(Tensor masks)\\n {\\n if (masks.ndim != 3) throw new ArgumentException(\\\"'masks' should have three dimensions: NxHxW\\\");\\n if (masks.numel() == 0) {\\n return torch.zeros(0, 4, device: masks.device, dtype: torch.float32);\\n }\\n\\n var n = masks.shape[0];\\n var bounding_boxes = torch.zeros(n, 4, device: masks.device, dtype: torch.float32);\\n\\n for (int i = 0;i < n; i++) {\\n var yx = torch.where(masks[i] != 0);\\n bounding_boxes[i, 0] = torch.min(yx[1]);\\n bounding_boxes[i, 1] = torch.min(yx[0]);\\n bounding_boxes[i, 2] = torch.max(yx[1]);\\n bounding_boxes[i, 3] = torch.max(yx[0]);\\n }\\n return bounding_boxes;\\n }\\n\\n private static Tensor _box_inter_union(Tensor boxes1, Tensor boxes2, out Tensor union)\\n {\\n var area1 = box_area(boxes1);\\n var area2 = box_area(boxes2);\\n\\n var lt = torch.max(boxes1[colon, None, (null, 2)], boxes2[colon, (null, 2)]); // [N,M,2];\\n var rb = torch.min(boxes1[colon, None, (2, null)], boxes2[colon, (2, null)]); // [N,M,2];\\n\\n var wh = _upcast(rb - lt).clamp(min: 0); // [N,M,2];\\n var inter = wh[colon, colon, 0] * wh[colon, colon, 1]; // [N,M];\\n\\n union = area1[colon, None] + area2 - inter;\\n return inter;\\n }\\n\\n private static Tensor _box_diou_iou(Tensor boxes1, Tensor boxes2, out Tensor iou, double eps = 1e-7)\\n {\\n iou = box_iou(boxes1, boxes2);\\n var lti = torch.min(boxes1[colon, None, (null, 2)], boxes2[colon, (null, 2)]);\\n var rbi = torch.max(boxes1[colon, None, (2, null)], boxes2[colon, (2, null)]);\\n var whi = _upcast(rbi - lti).clamp(min: 0); // [N,M,2];\\n var diagonal_distance_squared = whi[colon, colon, 0].pow(2) + whi[colon, colon, 1].pow(2) + eps;\\n // centers of boxes\\n var x_p = (boxes1[colon, 0] + boxes1[colon, 2]) / 2;\\n var y_p = (boxes1[colon, 1] + boxes1[colon, 3]) / 2;\\n var x_g = (boxes2[colon, 0] + boxes2[colon, 2]) / 2;\\n var y_g = (boxes2[colon, 1] + boxes2[colon, 3]) / 2;\\n // The distance between boxes' centers squared.\\n var centers_distance_squared = _upcast((x_p[colon, None] - x_g[None, colon])).pow(2) + _upcast((y_p[colon, None] - y_g[None, colon])).pow(2);\\n // The distance IoU is the IoU penalized by a normalized\\n // distance between boxes' centers squared.\\n return iou - (centers_distance_squared / diagonal_distance_squared);\\n }\\n\\n public enum BoxFormats\\n {\\n xyxy, xywh, cxcywh\\n }\\n\\n private static TensorIndex colon = TensorIndex.Colon;\\n private static TensorIndex None = TensorIndex.None;\\n }\\n }\\n}\\n\"\n },\n {\n \"alpha_fraction\": 0.5940752625465393,\n \"alphanum_fraction\": 0.6172938346862793,\n \"avg_line_length\": 32.53020095825195,\n \"blob_id\": \"2c92de7a2557dbdcc81b9be1ca2150af183fbbe4\",\n \"content_id\": \"1bf14cab930ead617b095000c7e8e12984aee989\",\n \"detected_licenses\": [\n \"MIT\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 4996,\n \"license_type\": \"permissive\",\n \"max_line_length\": 99,\n \"num_lines\": 149,\n \"path\": \"/test/TorchSharpTest/pyimporttest.py\",\n \"repo_name\": \"NiklasGustafsson/TorchSharp\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"from collections import OrderedDict\\n\\nimport numpy as np\\nimport torch\\nimport torch.nn as nn\\nimport torch.optim as optim\\n\\nimport importsd\\n\\n\\nNUM_WORDS = 100\\nEMBEDDING_VEC_LEN = 100\\nHIDDEN_SIZE = 128\\n\\n\\nclass LSTMModel(nn.Module):\\n # The names of properties should be the same in C# and Python\\n # otherwise, you have to manually change the key name in the state_dict\\n def __init__(self):\\n super(LSTMModel, self).__init__()\\n self.embedding = nn.Embedding(NUM_WORDS, EMBEDDING_VEC_LEN)\\n self.lstm = nn.LSTM(EMBEDDING_VEC_LEN, HIDDEN_SIZE, batch_first=True)\\n self.dropout = nn.Dropout(0.5)\\n self.dense = nn.Linear(HIDDEN_SIZE, 1)\\n self.sigmoid = nn.Sigmoid()\\n\\n def forward(self, x):\\n x_embed = self.embedding(x)\\n x_lstm, _ = self.lstm(x_embed)\\n x_lstm_last_seq = x_lstm[:, -1, :]\\n x_lstm_last_seq = self.dropout(x_lstm_last_seq)\\n logits = self.dense(x_lstm_last_seq)\\n out = self.sigmoid(logits)\\n return out\\n\\n\\nclass LeNet1Model(nn.Module):\\n # The names of properties should be the same in C# and Python\\n # in this case, we both name the Sequential as layers\\n def __init__(self):\\n super(LeNet1Model, self).__init__()\\n # the names of each layer should also be the same in C# and Python\\n modules = OrderedDict([\\n (\\\"conv-1\\\", nn.Conv2d(1, 4, 5, padding=2)),\\n (\\\"bnrm2d-1\\\", nn.BatchNorm2d(4)),\\n (\\\"relu-1\\\", nn.ReLU()),\\n (\\\"maxpool-1\\\", nn.MaxPool2d(2, stride=2)),\\n (\\\"conv-2\\\", nn.Conv2d(4, 12, 5)),\\n (\\\"bnrm2d-2\\\", nn.BatchNorm2d(12)),\\n (\\\"relu-2\\\", nn.ReLU()),\\n (\\\"maxpool-2\\\", nn.MaxPool2d(2, stride=2)),\\n (\\\"flatten\\\", nn.Flatten()),\\n (\\\"linear\\\", nn.Linear(300, 10)),\\n ])\\n self.layers = nn.Sequential(modules)\\n\\n def forward(self, x):\\n return self.layers.forward(x)\\n\\n\\ndef testLSTM():\\n print(\\\"testing LSTM\\\")\\n mylstm = LSTMModel()\\n with open(\\\"lstm.dat\\\", \\\"rb\\\") as f:\\n sd = importsd.load_state_dict(f)\\n # you can change the loaded key names here, for example:\\n # sd = {k + \\\"py\\\": v for k, v in sd}\\n # you can check the key names of state_dict in python by:\\n # print(mylstm.state_dict())\\n mylstm.load_state_dict(sd)\\n\\n # init values & functions\\n torch.manual_seed(0)\\n np.random.seed(0)\\n labels = torch.tensor(np.random.randint(0, 1, [100, 1]), dtype=torch.float)\\n inputs = torch.tensor(np.random.randint(0, 100, [100, 100]))\\n opt = optim.Adam(mylstm.parameters(), lr=8e-5)\\n loss_func = nn.BCELoss()\\n\\n # evaluation before training\\n mylstm.eval()\\n preds = mylstm.forward(inputs)\\n preds = torch.round(preds)\\n correct_num = torch.sum(preds == labels).item()\\n print(f\\\"before training: {correct_num} corrected\\\")\\n\\n # training for 50 steps\\n mylstm.train()\\n for i in range(50):\\n opt.zero_grad() # Reset the gradient in every iteration\\n outputs = mylstm(inputs)\\n loss = loss_func(outputs, labels) # Loss forward pass\\n loss.backward() # Loss backaed pass\\n opt.step() # Update all the parameters by the given learnig rule\\n\\n # evaluation after training\\n mylstm.eval()\\n preds = mylstm.forward(inputs)\\n preds = torch.round(preds)\\n correct_num = torch.sum(preds == labels).item()\\n print(f\\\"after training: {correct_num} corrected\\\")\\n\\n\\ndef testLeNet1():\\n print(\\\"testing LeNet1\\\")\\n mylenet = LeNet1Model()\\n with open(\\\"lenet1.dat\\\", \\\"rb\\\") as f:\\n sd = importsd.load_state_dict(f)\\n # you can change the loaded key names here, for example:\\n # sd = {k + \\\"py\\\": v for k, v in sd}\\n # you can check the key names of state_dict in python by:\\n # print(mylenet.state_dict())\\n mylenet.load_state_dict(sd)\\n\\n # init values & functions\\n torch.manual_seed(0)\\n np.random.seed(0)\\n labels = torch.tensor(np.random.randint(0, 10, [100]))\\n inputs = torch.tensor(np.random.randint(0, 255, [100, 1, 28, 28]) / 255.0, dtype=torch.float32)\\n opt = optim.Adam(mylenet.parameters(), lr=8e-5)\\n loss_func = nn.CrossEntropyLoss()\\n\\n # evaluation before training\\n mylenet.eval()\\n output = mylenet.forward(inputs)\\n _, preds = torch.max(output.data, dim=1)\\n correct_num = torch.sum(preds == labels).item()\\n print(f\\\"before training: {correct_num} corrected\\\")\\n\\n # training for 200 steps\\n mylenet.train()\\n for i in range(200):\\n opt.zero_grad() # Reset the gradient in every iteration\\n outputs = mylenet(inputs)\\n loss = loss_func(outputs, labels) # Loss forward pass\\n loss.backward() # Loss backaed pass\\n opt.step() # Update all the parameters by the given learnig rule\\n\\n # evaluation after training\\n mylenet.eval()\\n output = mylenet.forward(inputs)\\n _, preds = torch.max(output.data, dim=1)\\n correct_num = torch.sum(preds == labels).item()\\n print(f\\\"after training: {correct_num} corrected\\\")\\n\\n\\nif __name__ == '__main__':\\n testLSTM()\\n testLeNet1()\\n\"\n },\n {\n \"alpha_fraction\": 0.5116005539894104,\n \"alphanum_fraction\": 0.5163080096244812,\n \"avg_line_length\": 37.6298713684082,\n \"blob_id\": \"4e6bfd60bf576dcac142404bfac714616492790b\",\n \"content_id\": \"0e153f7e037a2eff04c00cb6e5aeed9c23bae806\",\n \"detected_licenses\": [\n \"MIT\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"C#\",\n \"length_bytes\": 5948,\n \"license_type\": \"permissive\",\n \"max_line_length\": 130,\n \"num_lines\": 154,\n \"path\": \"/src/TorchVision/Randomizer.cs\",\n \"repo_name\": \"NiklasGustafsson/TorchSharp\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"// Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information.\\n\\nusing static TorchSharp.torch;\\n\\n\\nnamespace TorchSharp\\n{\\n public static partial class torchvision\\n {\\n internal class Randomizer : ITransform\\n {\\n internal Randomizer(ITransform transform, double p = 0.1)\\n {\\n this.p = p;\\n this.transform = transform;\\n }\\n\\n public Tensor call(Tensor input)\\n {\\n using (var chance = torch.rand(1))\\n\\n if (chance.item<float>() < p) {\\n return transform.call(input);\\n } else {\\n return input;\\n }\\n }\\n\\n private ITransform transform;\\n private double p;\\n }\\n\\n public static partial class transforms\\n {\\n /// <summary>\\n /// Randomly apply a transform given a probability.\\n /// </summary>\\n /// <param name=\\\"transform\\\">The transform that will be applied randomly.</param>\\n /// <param name=\\\"p\\\">The probablity of applying the transform</param>\\n /// <returns></returns>\\n /// <remarks>This uses the default TorchSharp RNG.</remarks>\\n static public ITransform Randomize(ITransform transform, double p = 0.1)\\n {\\n return new Randomizer(transform, p);\\n }\\n\\n /// <summary>\\n /// Posterize the image randomly with a given probability by reducing the number of bits for each color channel. \\n /// </summary>\\n /// <param name=\\\"bits\\\">Number of bits to keep for each channel (0-8)</param>\\n /// <param name=\\\"p\\\">Probability of the transform being applied.</param>\\n /// <returns></returns>\\n /// <remarks>The tensor must be an integer tensor</remarks>\\n static public ITransform RandomPosterize(int bits, double p = 0.5)\\n {\\n return new Randomizer(Posterize(bits), p);\\n }\\n\\n /// <summary>\\n /// Equalize the image randomly with a given probability. \\n /// </summary>\\n /// <param name=\\\"p\\\">Probability of the transform being applied.</param>\\n /// <returns></returns>\\n /// <remarks>The tensor must be an integer tensor</remarks>\\n static public ITransform Equalize(double p = 0.5)\\n {\\n return new Randomizer(Equalize(), p);\\n }\\n\\n /// <summary>\\n /// Solarize the image randomly with a given probability by inverting all pixel values above a threshold.\\n /// </summary>\\n /// <param name=\\\"threshold\\\">All pixels equal or above this value are inverted.</param>\\n /// <param name=\\\"p\\\">Probability of the transform being applied.</param>\\n /// <returns></returns>\\n static public ITransform RandomSolarize(double threshold, double p = 0.5)\\n {\\n return new Randomizer(Solarize(threshold), p);\\n }\\n\\n /// <summary>\\n /// \\n /// </summary>\\n /// <param name=\\\"p\\\">Probability of the transform being applied.</param>\\n /// <returns></returns>\\n static public ITransform RandomVerticalFlip(double p = 0.5)\\n {\\n return new Randomizer(VerticalFlip(), p);\\n }\\n\\n /// <summary>\\n /// \\n /// </summary>\\n /// <param name=\\\"p\\\">Probability of the transform being applied.</param>\\n /// <returns></returns>\\n static public ITransform RandomHorizontalFlip(double p = 0.5)\\n {\\n return new Randomizer(HorizontalFlip(), p);\\n }\\n\\n /// <summary>\\n /// \\n /// </summary>\\n /// <param name=\\\"p\\\">Probability of the transform being applied.</param>\\n /// <returns></returns>\\n static public ITransform RandomGrayscale(double p = 0.1)\\n {\\n return new Randomizer(Grayscale(3), p);\\n }\\n\\n /// <summary>\\n /// Adjust the sharpness of the image randomly with a given probability. \\n /// </summary>\\n /// <param name=\\\"sharpness\\\">The sharpness factor</param>\\n /// <param name=\\\"p\\\">Probability of the transform being applied.</param>\\n /// <returns></returns>\\n static public ITransform RandomAdjustSharpness(double sharpness, double p = 0.5)\\n {\\n return new Randomizer(AdjustSharpness(sharpness), p);\\n }\\n\\n /// <summary>\\n /// \\n /// </summary>\\n /// <param name=\\\"p\\\">Probability of the transform being applied.</param>\\n /// <returns></returns>\\n static public ITransform RandomAutoContrast(double p = 0.5)\\n {\\n return new Randomizer(AutoContrast(), p);\\n }\\n\\n /// <summary>\\n /// \\n /// </summary>\\n /// <param name=\\\"p\\\">Probability of the transform being applied.</param>\\n /// <returns></returns>\\n static public ITransform RandomInvert(double p = 0.5)\\n {\\n return new Randomizer(Invert(), p);\\n }\\n\\n /// <summary>\\n /// Apply randomly a list of transformations with a given probability.\\n /// </summary>\\n /// <param name=\\\"transforms\\\">A list of transforms to compose serially.</param>\\n /// <param name=\\\"p\\\">Probability of the transforms being applied.</param>\\n /// <returns></returns>\\n static public ITransform RandomApply(ITransform[] transforms, double p = 0.5)\\n {\\n return new Randomizer(Compose(transforms), p);\\n }\\n }\\n }\\n}\"\n },\n {\n \"alpha_fraction\": 0.5785340070724487,\n \"alphanum_fraction\": 0.5785340070724487,\n \"avg_line_length\": 24.53333282470703,\n \"blob_id\": \"850f2d67e96d73c6e54789304ea3903d34539b8a\",\n \"content_id\": \"42d5ee63e86aa67f0b007befdbe432dae9c045ed\",\n \"detected_licenses\": [\n \"MIT\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"C#\",\n \"length_bytes\": 382,\n \"license_type\": \"permissive\",\n \"max_line_length\": 130,\n \"num_lines\": 15,\n \"path\": \"/src/TorchAudio/MelScale.cs\",\n \"repo_name\": \"NiklasGustafsson/TorchSharp\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"// Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information.\\nnamespace TorchSharp\\n{\\n public static partial class torchaudio\\n {\\n /// <summary>\\n /// Scale type of mel filterbanks\\n /// </summary>\\n public enum MelScale\\n {\\n slaney,\\n htk\\n }\\n }\\n}\"\n },\n {\n \"alpha_fraction\": 0.7017045617103577,\n \"alphanum_fraction\": 0.7017045617103577,\n \"avg_line_length\": 24.214284896850586,\n \"blob_id\": \"7f197adec0be9a871b7c046cbf103f45f993681c\",\n \"content_id\": \"4505451f652ccebecc58ce450c5a3ba2a18ebbf9\",\n \"detected_licenses\": [\n \"MIT\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"C#\",\n \"length_bytes\": 352,\n \"license_type\": \"permissive\",\n \"max_line_length\": 130,\n \"num_lines\": 14,\n \"path\": \"/src/TorchAudio/Transforms/ITransform.cs\",\n \"repo_name\": \"NiklasGustafsson/TorchSharp\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"// Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information.\\n\\nusing static TorchSharp.torch;\\nusing static TorchSharp.torch.nn;\\n\\nnamespace TorchSharp\\n{\\n public partial class torchaudio\\n {\\n public interface ITransform : IModule<Tensor, Tensor>\\n {\\n }\\n }\\n}\"\n },\n {\n \"alpha_fraction\": 0.6907563209533691,\n \"alphanum_fraction\": 0.6941176652908325,\n \"avg_line_length\": 26.045454025268555,\n \"blob_id\": \"f9e25e86405b9b91a2f7adf77f3d9cfca4e24108\",\n \"content_id\": \"740fc71d2a1e639d4cf824d1eee1675c13950f4b\",\n \"detected_licenses\": [\n \"MIT\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"C++\",\n \"length_bytes\": 595,\n \"license_type\": \"permissive\",\n \"max_line_length\": 130,\n \"num_lines\": 22,\n \"path\": \"/src/Native/LibTorchSharp/Utils.cpp\",\n \"repo_name\": \"NiklasGustafsson/TorchSharp\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"// Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information.\\n#include \\\"Utils.h\\\"\\n\\n#include <cstring>\\n#include <fstream>\\n#if _WINDOWS\\n#include <combaseapi.h>\\n#define TP_CoTaskMemAlloc(t) CoTaskMemAlloc(t)\\n#else\\n#define TP_CoTaskMemAlloc(t) malloc(t)\\n#endif\\n\\nthread_local char * torch_last_err = nullptr;\\n\\nconst char * make_sharable_string(const std::string str)\\n{\\n size_t n = str.length();\\n char* result = (char *)TP_CoTaskMemAlloc(n + 1); \\n strncpy(result, str.c_str(), n);\\n result[n] = '\\\\0';\\n return result;\\n}\\n\"\n },\n {\n \"alpha_fraction\": 0.5925925970077515,\n \"alphanum_fraction\": 0.595678985118866,\n \"avg_line_length\": 31.399999618530273,\n \"blob_id\": \"3b28b40671e0eaca3666fefca066870f738848b6\",\n \"content_id\": \"8d93f7de1dccb3b78515b0f6d06b3ca4b64b9d17\",\n \"detected_licenses\": [\n \"MIT\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"C#\",\n \"length_bytes\": 648,\n \"license_type\": \"permissive\",\n \"max_line_length\": 130,\n \"num_lines\": 20,\n \"path\": \"/src/Examples/Program.cs\",\n \"repo_name\": \"NiklasGustafsson/TorchSharp\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"// Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information.\\n\\nnamespace TorchSharp.Examples\\n{\\n public static class Program\\n {\\n public static void Main(string[] args)\\n {\\n //MNIST.Main(args);\\n //AdversarialExampleGeneration.Main(args);\\n CIFAR10.Main(args);\\n //SequenceToSequence.Main(args);\\n //TextClassification.Main(args);\\n //ImageTransforms.Main(args);\\n //SpeechCommands.Main(args);\\n IOReadWrite.Main(args);\\n Tensorboard.Main(args).Wait();\\n }\\n }\\n}\\n\"\n },\n {\n \"alpha_fraction\": 0.6174419522285461,\n \"alphanum_fraction\": 0.621430516242981,\n \"avg_line_length\": 52.49214553833008,\n \"blob_id\": \"9fdcf0e95ca2bb4c20b3e73902726533fce1d061\",\n \"content_id\": \"fb4568b5c73292578f63e34c7406b7e4ec92ddd0\",\n \"detected_licenses\": [\n \"MIT\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"C#\",\n \"length_bytes\": 40897,\n \"license_type\": \"permissive\",\n \"max_line_length\": 254,\n \"num_lines\": 764,\n \"path\": \"/src/TorchSharp/Tensor/torch.OtherOperations.cs\",\n \"repo_name\": \"NiklasGustafsson/TorchSharp\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"// Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information.\\n#nullable enable\\nusing System;\\nusing System.Collections.Generic;\\nusing System.Linq;\\nusing TorchSharp.PInvoke;\\nusing static TorchSharp.PInvoke.NativeMethods;\\n\\nnamespace TorchSharp\\n{\\n // https://pytorch.org/docs/stable/torch#other-operations\\n public static partial class torch\\n {\\n // https://pytorch.org/docs/stable/generated/torch.atleast_1d\\n /// <summary>\\n /// Returns a 1-dimensional view of each input tensor with zero dimensions. Input tensors with one or more dimensions are returned as-is.\\n /// </summary>\\n public static IEnumerable<Tensor> atleast_1d(params Tensor[] input) => input.Select(t => t.atleast_1d());\\n\\n // https://pytorch.org/docs/stable/generated/torch.atleast_2d\\n /// <summary>\\n /// Returns a 2-dimensional view of each input tensor with zero or one dimensions. Input tensors with two or more dimensions are returned as-is.\\n /// </summary>\\n public static IEnumerable<Tensor> atleast_2d(params Tensor[] input) => input.Select(t => t.atleast_2d());\\n\\n // https://pytorch.org/docs/stable/generated/torch.atleast_3d\\n /// <summary>\\n /// Returns a 1-dimensional view of each input tensor with fewer than three dimensions. Input tensors with three or more dimensions are returned as-is.\\n /// </summary>\\n public static IEnumerable<Tensor> atleast_3d(params Tensor[] input) => input.Select(t => t.atleast_3d());\\n\\n // https://pytorch.org/docs/stable/generated/torch.bincount\\n /// <summary>\\n /// Count the frequency of each value in an array of non-negative ints.\\n /// </summary>\\n public static Tensor bincount(Tensor input, Tensor? weights = null, long minlength = 0) => input.bincount(weights, minlength);\\n\\n // https://pytorch.org/docs/stable/generated/torch.block_diag\\n /// <summary>\\n /// Create a block diagonal matrix from provided tensors.\\n /// </summary>\\n /// <param name=\\\"tensors\\\">One or more tensors with 0, 1, or 2 dimensions.</param>\\n /// <returns></returns>\\n public static Tensor block_diag(params Tensor[] tensors)\\n {\\n using var parray = new PinnedArray<IntPtr>();\\n IntPtr tensorsRef = parray.CreateArray(tensors.Select(p => p.Handle).ToArray());\\n\\n var res = THSTensor_block_diag(tensorsRef, parray.Array.Length);\\n if (res == IntPtr.Zero) { CheckForErrors(); }\\n return new Tensor(res);\\n }\\n\\n // https://pytorch.org/docs/stable/generated/torch.broadcast_tensors\\n /// <summary>\\n /// Broadcasts the given tensors according to Torch broadcasting semantics.\\n /// </summary>\\n /// <param name=\\\"tensors\\\">Any number of tensors of the same type</param>\\n public static IList<Tensor> broadcast_tensors(params Tensor[] tensors)\\n {\\n if (tensors.Length == 0) {\\n throw new ArgumentException(nameof(tensors));\\n }\\n if (tensors.Length == 1) {\\n return tensors;\\n }\\n\\n IntPtr[] ptrArray;\\n\\n using (var pa = new PinnedArray<IntPtr>())\\n using (var parray = new PinnedArray<IntPtr>()) {\\n\\n IntPtr tensorsRef = parray.CreateArray(tensors.Select(p => p.Handle).ToArray());\\n\\n THSTensor_broadcast_tensors(tensorsRef, tensors.Length, pa.CreateArray);\\n CheckForErrors();\\n ptrArray = pa.Array;\\n }\\n\\n return ptrArray.Select(x => new Tensor(x)).ToList();\\n }\\n\\n // https://pytorch.org/docs/stable/generated/torch.broadcast_to\\n public static Tensor broadcast_to(Tensor input, params long[] shape) => input.broadcast_to(shape);\\n\\n // https://pytorch.org/docs/stable/generated/torch.broadcast_shapes\\n /// <summary>\\n /// This is equivalent to <code>torch.broadcast_tensors(*map(torch.empty, shapes))[0].shape</code>\\n /// but avoids the need create to intermediate tensors.\\n /// This is useful for broadcasting tensors of common batch shape but different rightmost shape,\\n /// e.g. to broadcast mean vectors with covariance matrices.\\n /// </summary>\\n /// <param name=\\\"shapes\\\">Shapes of tensors</param>\\n /// <returns>A shape compatible with all input shapes</returns>\\n /// <exception cref=\\\"ArgumentException\\\">If shapes are incompatible.</exception>\\n public static Size broadcast_shapes(params long[][] shapes)\\n {\\n var max_len = 0;\\n foreach (var shape in shapes) {\\n var s = shape.Length;\\n if (s > max_len) max_len = s;\\n }\\n\\n var result = Enumerable.Repeat<long>(1, max_len).ToArray();\\n\\n foreach (var shape in shapes) {\\n for (var i = shape.Length - 1; i >= 0; i--) {\\n if (shape.Length == 0 || shape[i] == 1 || shape[i] == result[i])\\n continue;\\n if (result[i] != 1)\\n throw new ArgumentException(\\\"Shape mismatch: objects cannot be broadcast to a single shape\\\");\\n result[i] = shape[i];\\n }\\n }\\n return result;\\n }\\n\\n // https://pytorch.org/docs/stable/generated/torch.bucketize\\n public static Tensor bucketize(Tensor input, Tensor boundaries, bool outInt32 = false, bool right = false)\\n => input.bucketize(boundaries, outInt32, right);\\n\\n // https://pytorch.org/docs/stable/generated/torch.cartesian_prod\\n /// <summary>\\n /// Do cartesian product of the given sequence of tensors.\\n /// </summary>\\n /// <param name=\\\"tensors\\\"></param>\\n public static Tensor cartesian_prod(IList<Tensor> tensors)\\n {\\n using var parray = new PinnedArray<IntPtr>();\\n IntPtr tensorsRef = parray.CreateArray(tensors.Select(p => p.Handle).ToArray());\\n\\n var res = THSTensor_cartesian_prod(tensorsRef, parray.Array.Length);\\n if (res == IntPtr.Zero) { torch.CheckForErrors(); }\\n return new Tensor(res);\\n }\\n\\n // https://pytorch.org/docs/stable/generated/torch.cartesian_prod\\n /// <summary>\\n /// Do cartesian product of the given sequence of tensors.\\n /// </summary>\\n /// <param name=\\\"tensors\\\"></param>\\n public static Tensor cartesian_prod(params Tensor[] tensors) => cartesian_prod((IList<Tensor>)tensors);\\n\\n // https://pytorch.org/docs/stable/generated/torch.cdist\\n /// <summary>\\n /// Computes batched the p-norm distance between each pair of the two collections of row vectors.\\n /// </summary>\\n /// <param name=\\\"x1\\\">Input tensor of shape BxPxM</param>\\n /// <param name=\\\"x2\\\">Input tensor of shape BxRxM</param>\\n /// <param name=\\\"p\\\">p value for the p-norm distance to calculate between each vector (p > 0)</param>\\n /// <param name=\\\"compute_mode\\\">\\n /// use_mm_for_euclid_dist_if_necessary - will use matrix multiplication approach to calculate euclidean distance (p = 2) if P > 25 or R > 25\\n /// use_mm_for_euclid_dist - will always use matrix multiplication approach to calculate euclidean distance (p = 2)\\n /// donot_use_mm_for_euclid_dist - will never use matrix multiplication approach to calculate euclidean distance (p = 2)\\n /// </param>\\n /// <exception cref=\\\"ArgumentException\\\"></exception>\\n public static Tensor cdist(\\n Tensor x1,\\n Tensor x2,\\n double p = 2.0,\\n compute_mode compute_mode = compute_mode.use_mm_for_euclid_dist_if_necessary)\\n {\\n if (p < 0)\\n throw new ArgumentException($\\\"p must be non-negative\\\");\\n\\n var res = THSTensor_cdist(x1.Handle, x2.Handle, p, (long)compute_mode);\\n if (res == IntPtr.Zero)\\n CheckForErrors();\\n return new Tensor(res);\\n }\\n\\n // https://pytorch.org/docs/stable/generated/torch.clone\\n public static Tensor clone(Tensor input) => input.clone();\\n\\n // https://pytorch.org/docs/stable/generated/torch.combinations\\n /// <summary>\\n /// Compute combinations of length r of the given tensor\\n /// </summary>\\n /// <param name=\\\"input\\\">1D vector.</param>\\n /// <param name=\\\"r\\\">Number of elements to combine</param>\\n /// <param name=\\\"with_replacement\\\">Whether to allow duplication in combination</param>\\n /// <returns></returns>\\n public static Tensor combinations(Tensor input, int r = 2, bool with_replacement = false)\\n {\\n if (input.ndim != 1)\\n throw new ArgumentException($\\\"Expected a 1D vector, but got one with {input.ndim} dimensions.\\\");\\n if (r < 0)\\n throw new ArgumentException($\\\"r must be non-negative\\\");\\n\\n var res = THSTensor_combinations(input.Handle, r, with_replacement);\\n if (res == IntPtr.Zero)\\n CheckForErrors();\\n return new Tensor(res);\\n }\\n\\n\\n\\n // https://pytorch.org/docs/stable/generated/torch.corrcoef\\n public static Tensor corrcoef(Tensor input) => input.corrcoef();\\n\\n // https://pytorch.org/docs/stable/generated/torch.cov\\n /// <summary>\\n /// Estimates the covariance matrix of the variables given by the input matrix, where rows are the variables and columns are the observations.\\n /// </summary>\\n /// <param name=\\\"input\\\">The input tensor</param>\\n /// <param name=\\\"correction\\\">\\n /// Difference between the sample size and sample degrees of freedom.\\n /// Defaults to Bessel’s correction, correction = 1 which returns the unbiased estimate,\\n /// even if both fweights and aweights are specified.\\n /// Correction = 0 will return the simple average.\\n /// </param>\\n /// <param name=\\\"fweights\\\">\\n /// A Scalar or 1D tensor of observation vector frequencies representing the number of times each observation should be repeated.\\n /// Its numel must equal the number of columns of input.\\n /// Must have integral dtype.</param>\\n /// <param name=\\\"aweights\\\">A Scalar or 1D array of observation vector weights.\\n /// These relative weights are typically large for observations considered “important” and smaller for\\n /// observations considered less “important”.\\n /// Its numel must equal the number of columns of input.\\n /// Must have floating point dtype.</param>\\n public static Tensor cov(Tensor input, long correction = 1, Tensor? fweights = null, Tensor? aweights = null)\\n => input.cov(correction, fweights, aweights);\\n\\n // https://pytorch.org/docs/stable/generated/torch.cross\\n /// <summary>\\n /// Returns the cross product of vectors in dimension dim of input and other.\\n /// input and other must have the same size, and the size of their dim dimension should be 3.\\n /// </summary>\\n public static Tensor cross(Tensor input, Scalar other, long dim = 0L) => input.cross(other, dim);\\n\\n // https://pytorch.org/docs/stable/generated/torch.cummax\\n public static (Tensor values, Tensor indices) cummax(Tensor input, long dim) => input.cummax(dim);\\n\\n // https://pytorch.org/docs/stable/generated/torch.cummin\\n public static (Tensor values, Tensor indices) cummin(Tensor input, long dim) => input.cummin(dim);\\n\\n // https://pytorch.org/docs/stable/generated/torch.cumprod\\n public static Tensor cumprod(Tensor input, long dim, ScalarType? dtype = null) => input.cumprod(dim, dtype);\\n\\n // https://pytorch.org/docs/stable/generated/torch.cumsum\\n /// <summary>\\n /// Returns the cumulative sum of elements of input in the dimension dim.\\n /// </summary>\\n /// <param name=\\\"input\\\">The input tensor.</param>\\n /// <param name=\\\"dim\\\">The dimension to do the operation over</param>\\n /// <param name=\\\"type\\\">The desired data type of returned tensor. If specified, the input tensor is casted to dtype before the operation is performed.\\n /// This is useful for preventing data type overflows.</param>\\n public static Tensor cumsum(Tensor input, long dim, ScalarType? type = null) => input.cumsum(dim, type);\\n\\n // https://pytorch.org/docs/stable/generated/torch.diag\\n /// <summary>\\n /// If input is a vector (1-D tensor), then returns a 2-D square tensor with the elements of input as the diagonal.\\n /// If input is a matrix (2-D tensor), then returns a 1-D tensor with the diagonal elements of input.\\n /// </summary>\\n /// <param name=\\\"input\\\">The input tensor.</param>\\n /// <param name=\\\"diagonal\\\">\\n /// The argument diagonal controls which diagonal to consider:\\n /// If diagonal is 0, it is the main diagonal.\\n /// If diagonal is greater than 0, it is above the main diagonal.\\n /// If diagonal is less than 0, it is below the main diagonal.\\n /// </param>\\n public static Tensor diag(Tensor input, long diagonal = 0) => input.diag(diagonal);\\n\\n // https://pytorch.org/docs/stable/generated/torch.diag_embed\\n /// <summary>\\n /// Creates a tensor whose diagonals of certain 2D planes (specified by dim1 and dim2) are filled by input.\\n /// To facilitate creating batched diagonal matrices, the 2D planes formed by the last two dimensions of the returned tensor are chosen by default.\\n ///\\n /// The argument offset controls which diagonal to consider:\\n /// If offset is equal to 0, it is the main diagonal.\\n /// If offset is greater than 0, it is above the main diagonal.\\n /// If offset is less than 0, it is below the main diagonal.\\n ///\\n /// The size of the new matrix will be calculated to make the specified diagonal of the size of the last input dimension.Note that for offset other than 0,\\n ///\\n /// the order of dim1 and dim2 matters.Exchanging them is equivalent to changing the sign of offset.\\n /// </summary>\\n /// <param name=\\\"input\\\">The input tensor.</param>\\n /// <param name=\\\"offset\\\">Which diagonal to consider.</param>\\n /// <param name=\\\"dim1\\\">First dimension with respect to which to take diagonal. </param>\\n /// <param name=\\\"dim2\\\">Second dimension with respect to which to take diagonal</param>\\n public static Tensor diag_embed(Tensor input, long offset = 0L, long dim1 = -2L, long dim2 = -1L)\\n => input.diag_embed(offset, dim1, dim2);\\n\\n // https://pytorch.org/docs/stable/generated/torch.diagflat\\n /// <summary>\\n /// If input is a vector (1-D tensor), then returns a 2-D square tensor with the elements of input as the diagonal.\\n /// If input is a matrix (2-D tensor), then returns a 2-D tensor with diagonal elements equal to a flattened input.\\n /// </summary>\\n /// <param name=\\\"input\\\">The input tensor.</param>\\n /// <param name=\\\"offset\\\">\\n /// The argument diagonal controls which diagonal to consider:\\n /// If diagonal is 0, it is the main diagonal.\\n /// If diagonal is greater than 0, it is above the main diagonal.\\n /// If diagonal is less than 0, it is below the main diagonal.\\n /// </param>\\n public static Tensor diagflat(Tensor input, long offset = 0) => input.diagflat(offset);\\n\\n // https://pytorch.org/docs/stable/generated/torch.diagonal\\n /// <summary>\\n /// Returns a partial view of input with the its diagonal elements with respect to dim1 and dim2 appended as a dimension at the end of the shape.\\n /// The argument offset controls which diagonal to consider:\\n ///\\n /// If offset == 0, it is the main diagonal.\\n /// If offset > 0, it is above the main diagonal.\\n /// If offset < 0, it is below the main diagonal.\\n /// </summary>\\n /// <param name=\\\"input\\\">The input tensor</param>\\n /// <param name=\\\"offset\\\">Which diagonal to consider. Default: 0 (main diagonal).</param>\\n /// <param name=\\\"dim1\\\">First dimension with respect to which to take diagonal. Default: 0.</param>\\n /// <param name=\\\"dim2\\\">Second dimension with respect to which to take diagonal. Default: 1.</param>\\n /// <remarks>\\n /// Applying torch.diag_embed() to the output of this function with the same arguments yields a diagonal matrix with the diagonal entries of the input.\\n /// However, torch.diag_embed() has different default dimensions, so those need to be explicitly specified.\\n /// </remarks>\\n public static Tensor diagonal(Tensor input, long offset = 0, long dim1 = 0, long dim2 = 0) => input.diagonal(offset, dim1, dim2);\\n\\n // https://pytorch.org/docs/stable/generated/torch.diff\\n /// <summary>\\n /// Computes the n-th forward difference along the given dimension.\\n /// </summary>\\n /// <param name=\\\"input\\\">The input tensor.</param>\\n /// <param name=\\\"n\\\">The number of times to recursively compute the difference</param>\\n /// <param name=\\\"dim\\\">The dimension to compute the difference along. Default is the last dimension.</param>\\n /// <param name=\\\"prepend\\\">\\n /// Values to prepend or append to input along dim before computing the difference.\\n /// Their dimensions must be equivalent to that of input, and their shapes must match input’s shape except on dim.\\n /// </param>\\n /// <param name=\\\"append\\\">\\n /// Values to prepend or append to input along dim before computing the difference.\\n /// Their dimensions must be equivalent to that of input, and their shapes must match input’s shape except on dim.\\n /// </param>\\n public static Tensor diff(Tensor input, long n = 1, long dim = -1, Tensor? prepend = null, Tensor? append = null) => input.diff(n, dim, prepend, append);\\n\\n // https://pytorch.org/docs/stable/generated/torch.einsum\\n /// <summary>\\n /// Sums the product of the elements of the input operands along dimensions specified using a notation based on the Einstein summation convention.\\n /// </summary>\\n /// <param name=\\\"equation\\\">The subscripts for the Einstein summation.</param>\\n /// <param name=\\\"tensors\\\">The operands to compute the Einstein sum of.</param>\\n /// <remarks>\\n /// Einsum allows computing many common multi-dimensional linear algebraic array operations by representing them in a short-hand format based on the\\n /// Einstein summation convention, given by equation.The details of this format are described below, but the general idea is to label every dimension\\n /// of the input operands with some subscript and define which subscripts are part of the output. The output is then computed by summing the product\\n /// of the elements of the operands along the dimensions whose subscripts are not part of the output.For example, matrix multiplication can be computed\\n /// using einsum as torch.einsum(“ij,jk->ik”, A, B). Here, j is the summation subscript and i and k the output subscripts(see section below for more details on why).\\n /// </remarks>\\n /// <returns></returns>\\n public static Tensor einsum(string equation, params Tensor[] tensors)\\n {\\n using var parray = new PinnedArray<IntPtr>();\\n IntPtr tensorsRef = parray.CreateArray(tensors.Select(p => p.Handle).ToArray());\\n\\n var res = THSTensor_einsum(equation, tensorsRef, parray.Array.Length);\\n if (res == IntPtr.Zero) { CheckForErrors(); }\\n return new Tensor(res);\\n }\\n\\n // https://pytorch.org/docs/stable/generated/torch.flatten\\n /// <summary>\\n /// Flattens input by reshaping it into a one-dimensional tensor.\\n /// </summary>\\n /// <param name=\\\"input\\\">The input tensor</param>\\n /// <param name=\\\"start_dim\\\">The first dim to flatten</param>\\n /// <param name=\\\"end_dim\\\">The last dim to flatten.</param>\\n /// <remarks>Flattening a zero-dimensional tensor will return a one-dimensional view.</remarks>\\n public static Tensor flatten(Tensor input, long start_dim = 0, long end_dim = -1) => input.flatten(start_dim, end_dim);\\n\\n // https://pytorch.org/docs/stable/generated/torch.flip\\n public static Tensor flip(Tensor input, params long[] dims) => input.flip(dims);\\n\\n // https://pytorch.org/docs/stable/generated/torch.fliplr\\n public static Tensor fliplr(Tensor input) => input.fliplr();\\n\\n // https://pytorch.org/docs/stable/generated/torch.flipud\\n public static Tensor flipud(Tensor input) => input.flipud();\\n\\n // https://pytorch.org/docs/stable/generated/torch.kron\\n /// <summary>\\n /// Computes the Kronecker product of input and other.\\n /// </summary>\\n /// <param name=\\\"input\\\">The input tensor.</param>\\n /// <param name=\\\"other\\\">The second tensor</param>\\n public static Tensor kron(Tensor input, Tensor other) => input.kron(other);\\n\\n // https://pytorch.org/docs/stable/generated/torch.rot90\\n /// <summary>\\n /// Rotate a n-D tensor by 90 degrees in the plane specified by dims axis.\\n /// Rotation direction is from the first towards the second axis if k is greater than 0,\\n /// and from the second towards the first for k less than 0.\\n /// </summary>\\n /// <param name=\\\"input\\\">The input tensor</param>\\n /// <param name=\\\"k\\\">The number of times to rotate.</param>\\n /// <param name=\\\"dims\\\">Axes to rotate</param>\\n public static Tensor rot90(Tensor input, long k = 1, (long, long)? dims = null) => input.rot90(k, dims);\\n\\n // https://pytorch.org/docs/stable/generated/torch.gcd\\n /// <summary>\\n /// Computes the element-wise greatest common divisor (GCD) of input and other.\\n /// </summary>\\n /// <param name=\\\"left\\\">The left-hand operand.</param>\\n /// <param name=\\\"right\\\">The right-hand operand.</param>\\n public static Tensor gcd(Tensor left, Tensor right) => left.gcd(right);\\n\\n // https://pytorch.org/docs/stable/generated/torch.gcd\\n /// <summary>\\n /// Computes the element-wise greatest common divisor (GCD) of input and other.\\n /// </summary>\\n /// <param name=\\\"left\\\">The left-hand operand.</param>\\n /// <param name=\\\"right\\\">The right-hand operand.</param>\\n public static Tensor gcd_(Tensor left, Tensor right) => left.gcd_(right);\\n\\n // https://pytorch.org/docs/stable/generated/torch.histc\\n /// <summary>\\n /// Computes the histogram of a tensor.\\n /// The elements are sorted into equal width bins between min and max.If min and max are both zero, the minimum and maximum values of the data are used.\\n /// Elements lower than min and higher than max are ignored.\\n /// </summary>\\n /// <param name=\\\"input\\\">The input tensor.</param>\\n /// <param name=\\\"bins\\\">Number of histogram bins</param>\\n /// <param name=\\\"min\\\">Lower end of the range (inclusive)</param>\\n /// <param name=\\\"max\\\">Upper end of the range (inclusive)</param>\\n public static Tensor histc(Tensor input, long bins = 100, long min = 0, long max = 0) => input.histc(bins, min, max);\\n\\n // https://pytorch.org/docs/stable/generated/torch.histogram\\n [Obsolete(\\\"not implemented\\\", true)]\\n static Tensor histogram(\\n Tensor input,\\n long bins,\\n (float min, float max)? range = null,\\n Tensor? weight = null,\\n bool density = false)\\n => throw new NotImplementedException();\\n\\n // https://pytorch.org/docs/stable/generated/torch.histogram\\n [Obsolete(\\\"not implemented\\\", true)]\\n static Tensor histogram(\\n Tensor input,\\n long[] bins,\\n (float min, float max)? range = null,\\n Tensor? weight = null,\\n bool density = false)\\n => throw new NotImplementedException();\\n\\n // https://pytorch.org/docs/stable/generated/torch.histogram\\n [Obsolete(\\\"not implemented\\\", true)]\\n static Tensor histogram(\\n Tensor input,\\n Tensor[] bins,\\n (float min, float max)? range = null,\\n Tensor? weight = null,\\n bool density = false)\\n => throw new NotImplementedException();\\n\\n // https://pytorch.org/docs/stable/generated/torch.histogramdd\\n [Obsolete(\\\"not implemented\\\", true)]\\n static (Tensor hist_values, Tensor[] edges) histogramdd(\\n Tensor input,\\n long bins,\\n (float min, float max)? range = null,\\n Tensor? weight = null,\\n bool density = false)\\n => throw new NotImplementedException();\\n\\n // https://pytorch.org/docs/stable/generated/torch.histogramdd\\n [Obsolete(\\\"not implemented\\\", true)]\\n static (Tensor hist_values, Tensor[] edges) histogramdd(\\n Tensor input,\\n long[] bins,\\n (float min, float max)? range = null,\\n Tensor? weight = null,\\n bool density = false)\\n => throw new NotImplementedException();\\n\\n // https://pytorch.org/docs/stable/generated/torch.histogramdd\\n [Obsolete(\\\"not implemented\\\", true)]\\n static (Tensor hist_values, Tensor[] edges) histogramdd(\\n Tensor input,\\n Tensor[] bins,\\n (float min, float max)? range = null,\\n Tensor? weight = null,\\n bool density = false)\\n => throw new NotImplementedException();\\n\\n // https://pytorch.org/docs/stable/generated/torch.meshgrid\\n /// <summary>\\n /// Creates grids of coordinates specified by the 1D inputs in tensors.\\n /// This is helpful when you want to visualize data over some range of inputs.\\n /// </summary>\\n /// <returns></returns>\\n /// <remarks>All tensors need to be of the same size.</remarks>\\n static IEnumerable<Tensor> meshgrid(IEnumerable<Tensor> tensors, indexing indexing = indexing.ij)\\n {\\n var idx = indexing switch {\\n indexing.ij => \\\"ij\\\",\\n indexing.xy => \\\"xy\\\",\\n _ => throw new ArgumentOutOfRangeException()\\n };\\n return meshgrid(tensors, idx);\\n }\\n\\n // https://pytorch.org/docs/stable/generated/torch.meshgrid\\n /// <summary>\\n /// Creates grids of coordinates specified by the 1D inputs in tensors.\\n /// This is helpful when you want to visualize data over some range of inputs.\\n /// </summary>\\n /// <returns></returns>\\n /// <remarks>All tensors need to be of the same size.</remarks>\\n public static Tensor[] meshgrid(IEnumerable<Tensor> tensors, string indexing = \\\"ij\\\")\\n {\\n IntPtr[] ptrArray;\\n\\n using (var parray = new PinnedArray<IntPtr>()) {\\n IntPtr tensorsRef = parray.CreateArray(tensors.Select(p => p.Handle).ToArray());\\n _ = THSTensor_meshgrid(tensorsRef, parray.Array.Length, indexing, parray.CreateArray);\\n CheckForErrors();\\n ptrArray = parray.Array;\\n }\\n return ptrArray.Select(x => new Tensor(x)).ToArray();\\n }\\n\\n // https://pytorch.org/docs/stable/generated/torch.lcm\\n /// <summary>\\n /// Computes the element-wise least common multiple (LCM) of input and other.\\n /// </summary>\\n /// <param name=\\\"input\\\">The first input tensor.</param>\\n /// <param name=\\\"other\\\">The second input tensor.</param>\\n /// <remarks>Both input and other must have integer types.</remarks>\\n public static Tensor lcm(Tensor input, Tensor other) => input.lcm(other);\\n\\n // https://pytorch.org/docs/stable/generated/torch.lcm\\n /// <summary>\\n /// Computes the element-wise least common multiple (LCM) of input and other in place.\\n /// </summary>\\n /// <param name=\\\"input\\\">The first input tensor.</param>\\n /// <param name=\\\"other\\\">The second input tensor.</param>\\n /// <remarks>Both input and other must have integer types.</remarks>\\n public static Tensor lcm_(Tensor input, Tensor other) => input.lcm_(other);\\n\\n // https://pytorch.org/docs/stable/generated/torch.logcumsumexp\\n /// <summary>\\n /// Returns the logarithm of the cumulative summation of the exponentiation of elements of input in the dimension dim.\\n /// </summary>\\n /// <param name=\\\"input\\\">The input tensor.</param>\\n /// <param name=\\\"dim\\\">The dimension to do the operation over</param>\\n public static Tensor logcumsumexp(Tensor input, long dim) => input.logcumsumexp(dim);\\n\\n // https://pytorch.org/docs/stable/generated/torch.ravel\\n public static Tensor ravel(Tensor input) => input.ravel();\\n\\n // https://pytorch.org/docs/stable/generated/torch.renorm\\n /// <summary>\\n /// Returns a tensor where each sub-tensor of input along dimension dim is normalized such that the p-norm of the sub-tensor is lower than the value maxnorm\\n /// </summary>\\n /// <param name=\\\"input\\\">The input tensor.</param>\\n /// <param name=\\\"p\\\">The power for the norm computation</param>\\n /// <param name=\\\"dim\\\">The dimension to slice over to get the sub-tensors</param>\\n /// <param name=\\\"maxnorm\\\">The maximum norm to keep each sub-tensor under</param>\\n public static Tensor renorm(Tensor input, float p, long dim, float maxnorm) => input.renorm(p, dim, maxnorm);\\n\\n // https://pytorch.org/docs/stable/generated/torch.repeat_interleave\\n /// <summary>\\n /// Repeat elements of a tensor.\\n /// </summary>\\n /// <param name=\\\"input\\\">The input tensor</param>\\n /// <param name=\\\"repeats\\\">The number of repeats</param>\\n /// <param name=\\\"dim\\\">The dimension to repeat</param>\\n /// <param name=\\\"output_size\\\">The size of output</param>\\n /// <returns></returns>\\n public static Tensor repeat_interleave(Tensor input, long repeats, long? dim = null, long? output_size = null) => input.repeat_interleave(repeats, dim, output_size);\\n\\n // https://pytorch.org/docs/stable/generated/torch.repeat_interleave\\n /// <summary>\\n /// Repeat elements of a tensor.\\n /// </summary>\\n /// <param name=\\\"input\\\">The input tensor</param>\\n /// <param name=\\\"repeats\\\">The number of repeats</param>\\n /// <param name=\\\"dim\\\">The dimension to repeat</param>\\n /// <param name=\\\"output_size\\\">The size of output</param>\\n /// <returns></returns>\\n public static Tensor repeat_interleave(Tensor input, Tensor repeats, long? dim = null, long? output_size = null) => input.repeat_interleave(repeats, dim, output_size);\\n\\n // https://pytorch.org/docs/stable/generated/torch.roll\\n /// <summary>\\n /// Roll the tensor along the given dimension(s).\\n /// Elements that are shifted beyond the last position are re-introduced at the first position.\\n /// If a dimension is not specified, the tensor will be flattened before rolling and then restored to the original shape.\\n /// </summary>\\n public static Tensor roll(Tensor input, long shifts, long? dims = null) => input.roll(shifts, dims);\\n\\n // https://pytorch.org/docs/stable/generated/torch.roll\\n /// <summary>\\n /// Roll the tensor along the given dimension(s).\\n /// Elements that are shifted beyond the last position are re-introduced at the first position.\\n /// If a dimension is not specified, the tensor will be flattened before rolling and then restored to the original shape.\\n /// </summary>\\n public static Tensor roll(Tensor input, (long, long) shifts, (long, long) dims) => input.roll(shifts, dims);\\n\\n // https://pytorch.org/docs/stable/generated/torch.roll\\n /// <summary>\\n /// Roll the tensor along the given dimension(s).\\n /// Elements that are shifted beyond the last position are re-introduced at the first position.\\n /// If a dimension is not specified, the tensor will be flattened before rolling and then restored to the original shape.\\n /// </summary>\\n public static Tensor roll(Tensor input, (long, long, long) shifts, (long, long, long) dims) => input.roll(shifts, dims);\\n\\n // https://pytorch.org/docs/stable/generated/torch.roll\\n /// <summary>\\n /// Roll the tensor along the given dimension(s).\\n /// Elements that are shifted beyond the last position are re-introduced at the first position.\\n /// If a dimension is not specified, the tensor will be flattened before rolling and then restored to the original shape.\\n /// </summary>\\n public static Tensor roll(Tensor input, long[] shifts, long[] dims) => input.roll(shifts, dims);\\n\\n // https://pytorch.org/docs/stable/generated/torch.roll\\n /// <summary>\\n /// Roll the tensor along the given dimension(s).\\n /// Elements that are shifted beyond the last position are re-introduced at the first position.\\n /// If a dimension is not specified, the tensor will be flattened before rolling and then restored to the original shape.\\n /// </summary>\\n public static Tensor roll(Tensor input, long[] shifts) => input.roll(shifts);\\n\\n // https://pytorch.org/docs/stable/generated/torch.roll\\n /// <summary>\\n /// Roll the tensor along the given dimension(s).\\n /// Elements that are shifted beyond the last position are re-introduced at the first position.\\n /// If a dimension is not specified, the tensor will be flattened before rolling and then restored to the original shape.\\n /// </summary>\\n public static Tensor roll(Tensor input, ReadOnlySpan<long> shifts, ReadOnlySpan<long> dims = default) => input.roll(shifts, dims);\\n\\n // https://pytorch.org/docs/stable/generated/torch.tensordot\\n /// <summary>\\n /// Returns a contraction of <paramref name=\\\"a\\\"/> and <paramref name=\\\"b\\\"/> over multiple dimensions.\\n /// tensordot implements a generalized matrix product.\\n /// </summary>\\n /// <param name=\\\"a\\\">Left tensor to contract</param>\\n /// <param name=\\\"b\\\">Right tensor to contract</param>\\n /// <param name=\\\"dims\\\">number of dimensions to contract for <paramref name=\\\"a\\\"/> and <paramref name=\\\"b\\\"/></param>\\n /// <returns>contraction</returns>\\n public static Tensor tensordot(Tensor a, Tensor b, long dims = 2) => a.tensordot(b, dims);\\n\\n // https://pytorch.org/docs/stable/generated/torch.tensordot\\n /// <summary>\\n /// Returns a contraction of <paramref name=\\\"a\\\"/> and <paramref name=\\\"b\\\"/> over multiple dimensions.\\n /// tensordot implements a generalized matrix product.\\n /// </summary>\\n /// <param name=\\\"a\\\">Left tensor to contract</param>\\n /// <param name=\\\"b\\\">Right tensor to contract</param>\\n /// <param name=\\\"dims1\\\">dimensions to contract for <paramref name=\\\"a\\\"/></param>\\n /// <param name=\\\"dims2\\\">dimensions to contract for <paramref name=\\\"b\\\"/></param>\\n /// <returns>contraction</returns>\\n public static Tensor tensordot(Tensor a, Tensor b, long[] dims1, long[] dims2) => a.tensordot(b, dims1, dims2);\\n\\n // https://pytorch.org/docs/stable/generated/torch.tensordot\\n /// <summary>\\n /// Returns a contraction of <paramref name=\\\"a\\\"/> and <paramref name=\\\"b\\\"/> over multiple dimensions.\\n /// tensordot implements a generalized matrix product.\\n /// </summary>\\n /// <param name=\\\"a\\\">Left tensor to contract</param>\\n /// <param name=\\\"b\\\">Right tensor to contract</param>\\n /// <param name=\\\"dims\\\">dimensions to contract for <paramref name=\\\"a\\\"/> and <paramref name=\\\"b\\\"/> respectively</param>\\n /// <returns>contraction</returns>\\n public static Tensor tensordot(Tensor a, Tensor b, (long, long)[] dims) => a.tensordot(b, dims);\\n\\n // https://pytorch.org/docs/stable/generated/torch.trace\\n /// <summary>\\n /// Returns the sum of the elements of the diagonal of the input 2-D matrix.\\n /// </summary>\\n /// <param name=\\\"input\\\">The input tensor</param>\\n /// <returns></returns>\\n public static Tensor trace(Tensor input) => input.trace();\\n\\n // https://pytorch.org/docs/stable/generated/torch.tril\\n public static Tensor tril(Tensor input, long diagonal = 0) => input.tril(diagonal);\\n\\n public static Tensor tril_(Tensor input, long diagonal = 0) => input.tril_(diagonal);\\n\\n // https://pytorch.org/docs/stable/generated/torch.tril_indices\\n public static Tensor tril_indices(\\n long row,\\n long col,\\n long offset = 0L,\\n ScalarType dtype = ScalarType.Int64,\\n Device? device = null)\\n {\\n if (!torch.is_integral(dtype))\\n throw new ArgumentException(\\\"dtype must be integral.\\\");\\n\\n if (device == null) {\\n device = torch.CPU;\\n }\\n\\n var res = NativeMethods.THSTensor_tril_indices(row, col, offset, (sbyte)dtype, (int)device.type, device.index);\\n if (res == IntPtr.Zero)\\n CheckForErrors();\\n return new Tensor(res);\\n }\\n\\n // https://pytorch.org/docs/stable/generated/torch.triu\\n public static Tensor triu(Tensor input, long diagonal = 0L) => input.triu(diagonal);\\n\\n public static Tensor triu_(Tensor input, long diagonal = 0L) => input.triu_(diagonal);\\n\\n // https://pytorch.org/docs/stable/generated/torch.triu_indices\\n public static Tensor triu_indices(\\n long row,\\n long col,\\n long offset = 0L,\\n ScalarType dtype = ScalarType.Int64,\\n Device? device = null)\\n {\\n if (!torch.is_integral(dtype))\\n throw new ArgumentException(\\\"dtype must be integral.\\\");\\n\\n if (device == null) {\\n device = torch.CPU;\\n }\\n\\n var res = NativeMethods.THSTensor_triu_indices(row, col, offset, (sbyte)dtype, (int)device.type, device.index);\\n if (res == IntPtr.Zero)\\n CheckForErrors();\\n return new Tensor(res);\\n }\\n\\n // https://pytorch.org/docs/stable/generated/torch.vander\\n public static Tensor vander(Tensor x, long N = -1, bool increasing = false) => x.vander(N, increasing);\\n\\n // https://pytorch.org/docs/stable/generated/torch.view_as_real\\n /// <summary>\\n /// Returns a view of input as a real tensor.\\n /// For an input complex tensor of size m1, m2, …, mi, this function returns a new real tensor of size m1, m2, …, mi, 2, where the last dimension of size 2 represents the real and imaginary components of complex numbers.\\n /// </summary>\\n /// <param name=\\\"input\\\">The input tensor</param>\\n public static Tensor view_as_real(Tensor input) => input.view_as_real();\\n\\n // https://pytorch.org/docs/stable/generated/torch.view_as_complex\\n /// <summary>\\n /// Returns a view of input as a complex tensor.\\n /// For an input complex tensor of size m1, m2, …, mi, 2, this function returns a new complex tensor of size m1, m2, …, mi where the last dimension of the input tensor is expected to represent the real and imaginary components of complex numbers.\\n /// </summary>\\n /// <param name=\\\"input\\\">The input tensor</param>\\n public static Tensor view_as_complex(Tensor input) => input.view_as_complex();\\n\\n // https://pytorch.org/docs/stable/generated/torch.resolve_conj\\n /// <summary>\\n /// Returns a new tensor with materialized conjugation if input’s conjugate bit is set to True, else returns input.\\n /// The output tensor will always have its conjugate bit set to False.\\n /// </summary>\\n /// <param name=\\\"input\\\">The input tensor.</param>\\n public static Tensor resolve_conj(Tensor input) => input.resolve_conj();\\n\\n // https://pytorch.org/docs/stable/generated/torch.resolve_neg\\n /// <summary>\\n /// Returns a new tensor with materialized negation if input’s negative bit is set to True, else returns input.\\n /// The output tensor will always have its negative bit set to False.\\n /// </summary>\\n public static Tensor resolve_neg(Tensor input) => input.resolve_neg();\\n\\n /// <summary>\\n /// Returns true if the input's negative bit is set to True.\\n /// </summary>\\n public static Tensor is_neg(Tensor input) => input.is_neg();\\n }\\n}\"\n },\n {\n \"alpha_fraction\": 0.668683648109436,\n \"alphanum_fraction\": 0.6760084629058838,\n \"avg_line_length\": 37.448978424072266,\n \"blob_id\": \"756fb9fdb47cc2aa7f658aed5896b784fbdc41e1\",\n \"content_id\": \"5bb6d76619f601b1f67ab57fb823fbe53563bd27\",\n \"detected_licenses\": [\n \"MIT\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"C++\",\n \"length_bytes\": 9420,\n \"license_type\": \"permissive\",\n \"max_line_length\": 224,\n \"num_lines\": 245,\n \"path\": \"/src/Native/LibTorchSharp/THSLoss.cpp\",\n \"repo_name\": \"NiklasGustafsson/TorchSharp\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"// Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information.\\n#include \\\"THSNN.h\\\"\\n\\n#include <torch/nn/init.h>\\n\\n\\ntemplate<typename T>\\nvoid ApplyReduction(T& opts, const int64_t reduction)\\n{\\n if (reduction == 0)\\n opts = opts.reduction(torch::kNone);\\n if (reduction == 1)\\n opts = opts.reduction(torch::kMean);\\n if (reduction == 2)\\n opts = opts.reduction(torch::kSum);\\n}\\n\\nTensor THSNN_binary_cross_entropy(const Tensor input, const Tensor target, const Tensor weight, const int64_t reduction)\\n{\\n CATCH_RETURN_Tensor(\\n auto opts = torch::nn::functional::BinaryCrossEntropyFuncOptions();\\n ApplyReduction(opts, reduction);\\n if (weight != nullptr)\\n opts = opts.weight(*weight);\\n res = ResultTensor(torch::nn::functional::binary_cross_entropy(*input, *target, opts));\\n )\\n}\\n\\nTensor THSNN_binary_cross_entropy_with_logits(const Tensor input, const Tensor target, const Tensor weight, const int64_t reduction, const Tensor pos_weights_wrapper)\\n{\\n CATCH_RETURN_Tensor(\\n auto opts = torch::nn::BCEWithLogitsLossOptions();\\n ApplyReduction(opts, reduction);\\n if (pos_weights_wrapper != nullptr)\\n opts = opts.pos_weight(*pos_weights_wrapper);\\n if (weight != nullptr)\\n opts = opts.weight(*weight);\\n res = ResultTensor(torch::nn::functional::binary_cross_entropy_with_logits(*input, *target, opts));\\n )\\n}\\n\\nTensor THSNN_cosine_embedding_loss(const Tensor input1, const Tensor input2, const Tensor target, const double margin, const int64_t reduction)\\n{\\n CATCH_RETURN_Tensor(\\n auto opts = torch::nn::functional::CosineEmbeddingLossFuncOptions().margin(margin);\\n ApplyReduction(opts, reduction);\\n res = ResultTensor(torch::nn::functional::cosine_embedding_loss(*input1, *input2, *target, opts));\\n )\\n}\\n\\nTensor THSNN_cross_entropy(const Tensor input, const Tensor target, const Tensor weight, const int64_t ignore_index, const bool has_ii, const int64_t reduction, const double smoothing)\\n{\\n CATCH_RETURN_Tensor(\\n auto opts = torch::nn::functional::CrossEntropyFuncOptions();\\n ApplyReduction(opts, reduction);\\n opts.label_smoothing(smoothing);\\n if (has_ii)\\n opts = opts.ignore_index(ignore_index);\\n if (weight != nullptr)\\n opts = opts.weight(*weight);\\n res = ResultTensor(torch::nn::functional::cross_entropy(*input, *target, opts));\\n )\\n}\\n\\nTensor THSNN_hinge_embedding_loss(const Tensor input, const Tensor target, const double margin, const int64_t reduction)\\n{\\n CATCH_RETURN_Tensor(\\n auto opts = torch::nn::functional::HingeEmbeddingLossFuncOptions().margin(margin);\\n ApplyReduction(opts, reduction);\\n res = ResultTensor(torch::nn::functional::hinge_embedding_loss(*input, *target, opts));\\n )\\n}\\n\\nTensor THSNN_huber_loss(const Tensor input, const Tensor target, const double delta, const int64_t reduction)\\n{\\n CATCH_RETURN_Tensor(\\n auto opts = torch::nn::functional::HuberLossFuncOptions().delta(delta);\\n ApplyReduction(opts, reduction);\\n res = ResultTensor(torch::nn::functional::huber_loss(*input, *target, opts));\\n )\\n}\\n\\n\\nTensor THSNN_kl_div_loss(const Tensor input, const Tensor target, const int64_t reduction, const bool log_target)\\n{\\n CATCH_RETURN_Tensor(\\n auto opts = torch::nn::functional::KLDivFuncOptions().log_target(log_target);\\n ApplyReduction(opts, reduction);\\n\\n res = ResultTensor(torch::nn::functional::kl_div(*input, *target, opts));\\n )\\n}\\n\\nTensor THSNN_l1_loss(const Tensor input, const Tensor target, const int64_t reduction)\\n{\\n CATCH_RETURN_Tensor(\\n auto opts = torch::nn::functional::MSELossFuncOptions();\\n ApplyReduction(opts, reduction);\\n\\n res = ResultTensor(torch::nn::functional::mse_loss(*input, *target, opts));\\n )\\n}\\n\\nTensor THSNN_margin_ranking_loss(const Tensor input1, const Tensor input2, const Tensor target, const double margin, const int64_t reduction)\\n{\\n CATCH_RETURN_Tensor(\\n auto opts = torch::nn::functional::MarginRankingLossFuncOptions().margin(margin);\\n ApplyReduction(opts, reduction);\\n res = ResultTensor(torch::nn::functional::margin_ranking_loss(*input1, *input2, *target, opts));\\n )\\n}\\n\\nTensor THSNN_mse_loss(const Tensor input, const Tensor target, const int64_t reduction)\\n{\\n CATCH_RETURN_Tensor(\\n auto opts = torch::nn::functional::MSELossFuncOptions();\\n ApplyReduction(opts, reduction);\\n\\n res = ResultTensor(torch::nn::functional::mse_loss(*input, *target, opts));\\n )\\n}\\n\\nTensor THSNN_nll_loss(const Tensor input, const Tensor target, const Tensor weight, const int64_t reduction)\\n{\\n CATCH_RETURN_Tensor(\\n auto opts = torch::nn::functional::NLLLossFuncOptions();\\n ApplyReduction(opts, reduction);\\n if (weight != nullptr)\\n opts = opts.weight(*weight);\\n\\n res = ResultTensor(torch::nn::functional::nll_loss(*input, *target, opts));\\n )\\n}\\n\\nTensor THSNN_poisson_loss(const Tensor input, const Tensor target, const bool logInput, const bool full, const double eps, const int64_t reduction)\\n{\\n CATCH_RETURN_Tensor(\\n auto opts = torch::nn::functional::PoissonNLLLossFuncOptions().log_input(logInput).full(full).eps(eps);\\n ApplyReduction(opts, reduction);\\n\\n res = ResultTensor(torch::nn::functional::poisson_nll_loss(*input, *target, opts));\\n )\\n}\\n\\nTensor THSNN_smooth_l1_loss(const Tensor input, const Tensor target, const int64_t reduction, const double beta)\\n{\\n CATCH_RETURN_Tensor(\\n auto opts = torch::nn::functional::SmoothL1LossFuncOptions();\\n ApplyReduction(opts, reduction);\\n\\n res = ResultTensor(torch::nn::functional::smooth_l1_loss(*input, *target, opts, beta));\\n )\\n}\\n\\nTensor THSNN_soft_margin_loss(const Tensor input, const Tensor target, const int64_t reduction)\\n{\\n CATCH_RETURN_Tensor(\\n auto opts = torch::nn::functional::SoftMarginLossFuncOptions();\\n ApplyReduction(opts, reduction);\\n\\n res = ResultTensor(torch::nn::functional::soft_margin_loss(*input, *target, opts));\\n )\\n}\\n\\nTensor THSNN_ctc_loss(const Tensor log_probs, const Tensor targets, const Tensor input_lengths, const Tensor target_lengths, int64_t blank, bool zero_infinity, const int64_t reduction)\\n{\\n CATCH_RETURN_Tensor(\\n auto opts = torch::nn::functional::CTCLossFuncOptions().blank(blank).zero_infinity(zero_infinity);\\n ApplyReduction(opts, reduction);\\n\\n res = ResultTensor(torch::nn::functional::ctc_loss(*log_probs, *targets, *input_lengths, *target_lengths, opts));\\n )\\n}\\n\\nTensor THSNN_multilabel_margin_loss(const Tensor input, const Tensor target, const int64_t reduction)\\n{\\n CATCH_RETURN_Tensor(\\n auto opts = torch::nn::functional::MultilabelMarginLossFuncOptions();\\n ApplyReduction(opts, reduction);\\n\\n res = ResultTensor(torch::nn::functional::multilabel_margin_loss(*input, *target, opts));\\n )\\n}\\n\\nTensor THSNN_multilabel_soft_margin_loss(const Tensor input, const Tensor target, const Tensor weight, const int64_t reduction)\\n{\\n CATCH_RETURN_Tensor(\\n auto opts = torch::nn::functional::MultilabelSoftMarginLossFuncOptions();\\n ApplyReduction(opts, reduction);\\n if (weight != nullptr)\\n opts = opts.weight(*weight);\\n\\n res = ResultTensor(torch::nn::functional::multilabel_soft_margin_loss(*input, *target, opts));\\n )\\n}\\n\\nTensor THSNN_multi_margin_loss(const Tensor input, const Tensor target, const int64_t p, const double margin, const Tensor weight, const int64_t reduction)\\n{\\n CATCH_RETURN_Tensor(\\n auto opts = torch::nn::functional::MultiMarginLossFuncOptions()\\n .p(p)\\n .margin(margin);\\n ApplyReduction(opts, reduction);\\n if (weight != nullptr)\\n opts = opts.weight(*weight);\\n\\n res = ResultTensor(torch::nn::functional::multi_margin_loss(*input, *target, opts));\\n )\\n}\\n\\nTensor THSNN_triplet_margin_loss(const Tensor anchor, const Tensor positive, const Tensor negative, double margin, int64_t p, double eps, bool swap, const int64_t reduction)\\n{\\n CATCH_RETURN_Tensor(\\n auto opts = torch::nn::functional::TripletMarginLossFuncOptions()\\n .p(p)\\n .eps(eps)\\n .margin(margin)\\n .swap(swap);\\n ApplyReduction(opts, reduction);\\n\\n res = ResultTensor(torch::nn::functional::triplet_margin_loss(*anchor, *positive, *negative, opts));\\n )\\n}\\n\\nTensor THSNN_triplet_margin_with_distance_loss(const Tensor anchor, const Tensor positive, const Tensor negative, Tensor(*distance_function)(const Tensor x, const Tensor y), double margin, bool swap, const int64_t reduction)\\n{\\n CATCH_RETURN_Tensor(\\n auto opts = torch::nn::functional::TripletMarginWithDistanceLossFuncOptions()\\n .margin(margin)\\n .swap(swap);\\n\\n ApplyReduction(opts, reduction);\\n\\n if (distance_function != nullptr) {\\n opts = opts.distance_function(\\n [=](const at::Tensor& x, const at::Tensor& y) -> const at::Tensor& {\\n auto x1 = ResultTensor(x);\\n auto y1 = ResultTensor(y);\\n return *distance_function(x1, y1);\\n });\\n }\\n\\n res = ResultTensor(torch::nn::functional::triplet_margin_with_distance_loss(*anchor, *positive, *negative, opts));\\n )\\n}\\n\"\n },\n {\n \"alpha_fraction\": 0.6013590097427368,\n \"alphanum_fraction\": 0.601925253868103,\n \"avg_line_length\": 35.79166793823242,\n \"blob_id\": \"c930e90ec5bec98691f0c32da6c8193f72f3a84c\",\n \"content_id\": \"7296a52a14c582cf2428d250f63f3215fede9115\",\n \"detected_licenses\": [\n \"MIT\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"C#\",\n \"length_bytes\": 1766,\n \"license_type\": \"permissive\",\n \"max_line_length\": 130,\n \"num_lines\": 48,\n \"path\": \"/src/TorchSharp/NN/Identity.cs\",\n \"repo_name\": \"NiklasGustafsson/TorchSharp\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"// Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information.\\nusing System;\\nusing static TorchSharp.torch;\\nusing static TorchSharp.PInvoke.NativeMethods;\\n\\n#nullable enable\\nnamespace TorchSharp\\n{\\n using Modules;\\n\\n namespace Modules\\n {\\n public sealed class Identity : torch.nn.Module<Tensor, Tensor>\\n {\\n internal Identity(IntPtr handle, IntPtr boxedHandle) : base(handle, boxedHandle) { }\\n\\n public override Tensor forward(Tensor tensor)\\n {\\n var res = THSNN_Identity_forward(handle, tensor.Handle);\\n if (res == IntPtr.Zero) { torch.CheckForErrors(); }\\n return new Tensor(res);\\n }\\n\\n // Rather than spending cycles only to discover that this module has neither\\n // parameters nor buffers, just shortcut the move completely.\\n protected internal override nn.Module _to(Device device, ScalarType dtype) => this;\\n protected internal override nn.Module _to(DeviceType deviceType, int deviceIndex = -1) => this;\\n protected internal override nn.Module _to(ScalarType dtype) => this;\\n }\\n }\\n\\n public static partial class torch\\n {\\n public static partial class nn\\n {\\n /// <summary>\\n /// A placeholder identity operator.\\n /// </summary>\\n /// <returns>The same tensor as is input.</returns>\\n public static Identity Identity()\\n {\\n var res = THSNN_Identity_ctor(out var boxedHandle);\\n if (res == IntPtr.Zero) { torch.CheckForErrors(); }\\n return new Identity(res, boxedHandle);\\n }\\n }\\n }\\n}\\n\"\n },\n {\n \"alpha_fraction\": 0.5044258832931519,\n \"alphanum_fraction\": 0.5215146541595459,\n \"avg_line_length\": 36.311927795410156,\n \"blob_id\": \"9b952b0b986168d478b2a5b4a70fac663b9c9120\",\n \"content_id\": \"215e4f74d52b55e633c8ae1d8847ff95cedcd414\",\n \"detected_licenses\": [\n \"MIT\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"C#\",\n \"length_bytes\": 8134,\n \"license_type\": \"permissive\",\n \"max_line_length\": 197,\n \"num_lines\": 218,\n \"path\": \"/src/Examples/CIFAR10.cs\",\n \"repo_name\": \"NiklasGustafsson/TorchSharp\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"// Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information.\\nusing System;\\nusing System.Diagnostics;\\nusing static TorchSharp.torch.nn;\\nusing static TorchSharp.torch.nn.functional;\\nusing static TorchSharp.torch.utils.data;\\n\\nnamespace TorchSharp.Examples\\n{\\n /// <summary>\\n /// Driver for various models trained and evaluated on the CIFAR10 small (32x32) color image data set.\\n /// </summary>\\n /// <remarks>\\n /// The dataset for this example can be found at: https://www.cs.toronto.edu/~kriz/cifar.html\\n /// Download the binary file, and place it in a dedicated folder, e.g. 'CIFAR10,' then edit\\n /// the '_dataLocation' definition below to point at the right folder.\\n ///\\n /// Note: so far, CIFAR10 is supported, but not CIFAR100.\\n /// </remarks>\\n class CIFAR10\\n {\\n private static int _epochs = 8;\\n private static int _trainBatchSize = 64;\\n private static int _testBatchSize = 128;\\n\\n private readonly static int _logInterval = 25;\\n private readonly static int _numClasses = 100;\\n\\n private readonly static int _timeout = 3600; // One hour by default.\\n\\n internal static void Main(string[] args)\\n {\\n var datasetPath = Environment.GetFolderPath(Environment.SpecialFolder.LocalApplicationData);\\n\\n torch.random.manual_seed(1);\\n\\n var device =\\n // This worked on a GeForce RTX 2080 SUPER with 8GB, for all the available network architectures.\\n // It may not fit with less memory than that, but it's worth modifying the batch size to fit in memory.\\n torch.cuda.is_available() ? torch.CUDA :\\n torch.CPU;\\n\\n if (device.type == DeviceType.CUDA) {\\n _trainBatchSize *= 8;\\n _testBatchSize *= 8;\\n _epochs *= 16;\\n }\\n\\n var modelName = args.Length > 0 ? args[0] : \\\"AlexNet\\\";\\n var epochs = args.Length > 1 ? int.Parse(args[1]) : _epochs;\\n var timeout = args.Length > 2 ? int.Parse(args[2]) : _timeout;\\n\\n Console.WriteLine();\\n Console.WriteLine($\\\"\\\\tRunning {modelName} with CIFAR100 on {device.type.ToString()} for {epochs} epochs, terminating after {TimeSpan.FromSeconds(timeout)}.\\\");\\n Console.WriteLine();\\n\\n Console.WriteLine($\\\"\\\\tCreating the model...\\\");\\n\\n Module<torch.Tensor, torch.Tensor> model = null;\\n\\n switch (modelName.ToLower()) {\\n case \\\"alexnet\\\":\\n model = new AlexNet(modelName, _numClasses, device);\\n break;\\n case \\\"mobilenet\\\":\\n model = new MobileNet(modelName, _numClasses, device);\\n break;\\n case \\\"vgg11\\\":\\n case \\\"vgg13\\\":\\n case \\\"vgg16\\\":\\n case \\\"vgg19\\\":\\n model = new VGG(modelName, _numClasses, device);\\n break;\\n case \\\"resnet18\\\":\\n model = ResNet.ResNet18(_numClasses, device);\\n break;\\n case \\\"resnet34\\\":\\n _testBatchSize /= 4;\\n model = ResNet.ResNet34(_numClasses, device);\\n break;\\n case \\\"resnet50\\\":\\n _trainBatchSize /= 6;\\n _testBatchSize /= 8;\\n model = ResNet.ResNet50(_numClasses, device);\\n break;\\n#if false\\n // The following is disabled, because they require big CUDA processors in order to run.\\n case \\\"resnet101\\\":\\n _trainBatchSize /= 6;\\n _testBatchSize /= 8;\\n model = ResNet.ResNet101(_numClasses, device);\\n break;\\n case \\\"resnet152\\\":\\n _testBatchSize /= 4;\\n model = ResNet.ResNet152(_numClasses, device);\\n break;\\n#endif\\n }\\n\\n Console.WriteLine($\\\"\\\\tPreparing training and test data...\\\");\\n Console.WriteLine();\\n\\n using (Dataset train_data = torchvision.datasets.CIFAR100(datasetPath, true, download: true),\\n test_data = torchvision.datasets.CIFAR100(datasetPath, false, download: true))\\n {\\n using var train = new DataLoader(train_data, _trainBatchSize, device: device, shuffle: true);\\n using var test = new DataLoader(test_data, _testBatchSize, device: device, shuffle: false);\\n\\n using (var optimizer = torch.optim.Adam(model.parameters(), 0.001)) {\\n\\n Stopwatch totalSW = new Stopwatch();\\n totalSW.Start();\\n\\n for (var epoch = 1; epoch <= epochs; epoch++) {\\n\\n Stopwatch epchSW = new Stopwatch();\\n epchSW.Start();\\n\\n Train(model, optimizer, torch.nn.NLLLoss(), train, epoch, _trainBatchSize, train_data.Count);\\n Test(model, torch.nn.NLLLoss(), test, test_data.Count);\\n\\n epchSW.Stop();\\n Console.WriteLine($\\\"Elapsed time for this epoch: {epchSW.Elapsed.TotalSeconds} s.\\\");\\n\\n if (totalSW.Elapsed.TotalSeconds > timeout) break;\\n }\\n\\n totalSW.Stop();\\n Console.WriteLine($\\\"Elapsed training time: {totalSW.Elapsed} s.\\\");\\n }\\n }\\n\\n model.Dispose();\\n }\\n\\n private static void Train(\\n Module<torch.Tensor, torch.Tensor> model,\\n torch.optim.Optimizer optimizer,\\n Loss<torch.Tensor, torch.Tensor, torch.Tensor> loss,\\n DataLoader dataLoader,\\n int epoch,\\n long batchSize,\\n long size)\\n {\\n model.train();\\n\\n int batchId = 1;\\n long total = 0;\\n long correct = 0;\\n\\n Console.WriteLine($\\\"Epoch: {epoch}...\\\");\\n\\n using (var d = torch.NewDisposeScope()) {\\n\\n foreach (var data in dataLoader) {\\n\\n optimizer.zero_grad();\\n\\n var target = data[\\\"label\\\"];\\n var prediction = model.call(data[\\\"data\\\"]);\\n var lsm = log_softmax(prediction, 1);\\n var output = loss.call(lsm, target);\\n\\n output.backward();\\n\\n optimizer.step();\\n\\n total += target.shape[0];\\n\\n var predicted = prediction.argmax(1);\\n correct += predicted.eq(target).sum().ToInt64();\\n\\n if (batchId % _logInterval == 0 || total == size) {\\n Console.WriteLine($\\\"\\\\rTrain: epoch {epoch} [{total} / {size}] Loss: {output.ToSingle().ToString(\\\"0.000000\\\")} | Accuracy: { ((float)correct / total).ToString(\\\"0.000000\\\") }\\\");\\n }\\n\\n batchId++;\\n\\n d.DisposeEverything();\\n }\\n }\\n }\\n\\n private static void Test(\\n Module<torch.Tensor, torch.Tensor> model,\\n Loss<torch.Tensor, torch.Tensor, torch.Tensor> loss,\\n DataLoader dataLoader,\\n long size)\\n {\\n model.eval();\\n\\n double testLoss = 0;\\n long correct = 0;\\n int batchCount = 0;\\n\\n using (var d = torch.NewDisposeScope()) {\\n\\n foreach (var data in dataLoader) {\\n\\n var target = data[\\\"label\\\"];\\n var prediction = model.call(data[\\\"data\\\"]);\\n var lsm = log_softmax(prediction, 1);\\n var output = loss.call(lsm, target);\\n\\n testLoss += output.ToSingle();\\n batchCount += 1;\\n\\n var predicted = prediction.argmax(1);\\n correct += predicted.eq(target).sum().ToInt64();\\n\\n d.DisposeEverything();\\n }\\n }\\n\\n Console.WriteLine($\\\"\\\\rTest set: Average loss {(testLoss / batchCount).ToString(\\\"0.0000\\\")} | Accuracy {((float)correct / size).ToString(\\\"0.0000\\\")}\\\");\\n }\\n }\\n}\\n\"\n },\n {\n \"alpha_fraction\": 0.40059059858322144,\n \"alphanum_fraction\": 0.4575561583042145,\n \"avg_line_length\": 32.95050811767578,\n \"blob_id\": \"865a8c99e3cd4d61d62338f84b027274b2942e00\",\n \"content_id\": \"a27582bbba6821bbbb2b559ed8bd9604fed713fe\",\n \"detected_licenses\": [\n \"MIT\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"C#\",\n \"length_bytes\": 26753,\n \"license_type\": \"permissive\",\n \"max_line_length\": 240,\n \"num_lines\": 788,\n \"path\": \"/test/TorchSharpTest/LinearAlgebra.cs\",\n \"repo_name\": \"NiklasGustafsson/TorchSharp\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"// Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information.\\nusing System;\\nusing System.Linq;\\nusing System.Runtime.InteropServices;\\nusing Xunit;\\nusing static TorchSharp.torch;\\n\\n#nullable enable\\n\\nnamespace TorchSharp\\n{\\n [Collection(\\\"Sequential\\\")]\\n public class LinearAlgebra\\n {\\n [Fact]\\n [TestOf(nameof(tensordot))]\\n public void TestTensorDot()\\n {\\n var a = arange(60).reshape(3, 4, 5);\\n var b = arange(24).reshape(4, 3, 2);\\n var res = tensordot(a, b, new []{ 1L, 0L }, new []{ 0L, 1L });\\n\\n var expected = from_array(new long[,]\\n {\\n {4400, 4730},\\n {4532, 4874},\\n {4664, 5018},\\n {4796, 5162},\\n {4928, 5306}\\n });\\n\\n Assert.True(allclose(res, expected));\\n }\\n\\n [Fact]\\n [TestOf(nameof(lu))]\\n public void TestLUSolve()\\n {\\n var A = randn(2, 3, 3);\\n var b = randn(2, 3, 1);\\n\\n {\\n var (A_LU, pivots, infos) = lu(A);\\n\\n Assert.NotNull(A_LU);\\n Assert.NotNull(pivots);\\n Assert.Null(infos);\\n\\n Assert.Equal(new long[] { 2, 3, 3 }, A_LU.shape);\\n Assert.Equal(new long[] { 2, 3 }, pivots.shape);\\n\\n var x = lu_solve(b, A_LU, pivots);\\n Assert.Equal(new long[] { 2, 3, 1 }, x.shape);\\n\\n var y = norm(bmm(A, x) - b);\\n Assert.Empty(y.shape);\\n }\\n\\n {\\n var (A_LU, pivots, infos) = lu(A, get_infos: true);\\n\\n Assert.NotNull(A_LU);\\n Assert.NotNull(pivots);\\n Assert.NotNull(infos);\\n\\n Assert.Equal(new long[] { 2, 3, 3 }, A_LU.shape);\\n Assert.Equal(new long[] { 2, 3 }, pivots.shape);\\n Assert.Equal(new long[] { 2 }, infos.shape);\\n\\n var x = lu_solve(b, A_LU, pivots);\\n Assert.Equal(new long[] { 2, 3, 1 }, x.shape);\\n\\n var y = norm(bmm(A, x) - b);\\n Assert.Empty(y.shape);\\n }\\n }\\n\\n [Fact]\\n [TestOf(nameof(lu_unpack))]\\n public void TestLUUnpack()\\n {\\n var A = randn(2, 3, 3);\\n\\n {\\n var (A_LU, pivots, infos) = lu(A);\\n\\n Assert.NotNull(A_LU);\\n Assert.NotNull(pivots);\\n Assert.Null(infos);\\n\\n var (P, A_L, A_U) = lu_unpack(A_LU, pivots);\\n\\n Assert.NotNull(P);\\n Assert.NotNull(A_L);\\n Assert.NotNull(A_U);\\n\\n Assert.Equal(new long[] { 2, 3, 3 }, P.shape);\\n Assert.Equal(new long[] { 2, 3, 3 }, A_L!.shape);\\n Assert.Equal(new long[] { 2, 3, 3 }, A_U!.shape);\\n }\\n }\\n\\n [Fact]\\n [TestOf(nameof(Tensor.mul))]\\n public void TestMul()\\n {\\n var x = ones(new long[] { 100, 100 });\\n\\n var y = x.mul(0.5f.ToScalar());\\n\\n var ydata = y.data<float>();\\n var xdata = x.data<float>();\\n\\n for (int i = 0; i < 100; i++) {\\n for (int j = 0; j < 100; j++) {\\n Assert.Equal(ydata[i + j], xdata[i + j] * 0.5f);\\n }\\n }\\n }\\n\\n void TestMmGen(Device device)\\n {\\n {\\n var x1 = ones(new long[] { 1, 2 }, device: device);\\n var x2 = ones(new long[] { 2, 1 }, device: device);\\n\\n var y = x1.mm(x2).to(DeviceType.CPU);\\n\\n var ydata = y.data<float>();\\n\\n Assert.Equal(2.0f, ydata[0]);\\n }\\n //System.Runtime.InteropServices.ExternalException : addmm for CUDA tensors only supports floating - point types.Try converting the tensors with.float() at C:\\\\w\\\\b\\\\windows\\\\pytorch\\\\aten\\\\src\\\\THC / generic / THCTensorMathBlas.cu:453\\n if (device.type == DeviceType.CPU) {\\n var x1 = ones(new long[] { 1, 2 }, int64, device: device);\\n var x2 = ones(new long[] { 2, 1 }, int64, device: device);\\n\\n var y = x1.mm(x2).to(DeviceType.CPU);\\n\\n var ydata = y.data<long>();\\n\\n Assert.Equal(2L, ydata[0]);\\n }\\n }\\n\\n [Fact]\\n [TestOf(nameof(CPU))]\\n public void TestMmCpu()\\n {\\n TestMmGen(CPU);\\n }\\n\\n [Fact]\\n [TestOf(nameof(CUDA))]\\n public void TestMmCuda()\\n {\\n if (cuda.is_available()) {\\n TestMmGen(CUDA);\\n }\\n }\\n\\n void TestMVGen(Device device)\\n {\\n {\\n var mat1 = ones(new long[] { 4, 3 }, device: device);\\n var vec1 = ones(new long[] { 3 }, device: device);\\n\\n var y = mat1.mv(vec1).to(DeviceType.CPU);\\n\\n Assert.Equal(4, y.shape[0]);\\n }\\n }\\n\\n void TestAddMVGen(Device device)\\n {\\n {\\n var x1 = ones(new long[] { 4 }, device: device);\\n var mat1 = ones(new long[] { 4, 3 }, device: device);\\n var vec1 = ones(new long[] { 3 }, device: device);\\n\\n var y = x1.addmv(mat1, vec1).to(DeviceType.CPU);\\n\\n Assert.Equal(4, y.shape[0]);\\n }\\n }\\n\\n [Fact]\\n [TestOf(nameof(CPU))]\\n public void TestMVCpu()\\n {\\n TestMVGen(CPU);\\n }\\n\\n [Fact]\\n [TestOf(nameof(CUDA))]\\n public void TestMVCuda()\\n {\\n if (cuda.is_available()) {\\n TestMVGen(CUDA);\\n }\\n }\\n\\n [Fact]\\n public void TestAddMVCpu()\\n {\\n TestAddMVGen(CPU);\\n }\\n\\n [Fact]\\n [TestOf(nameof(CUDA))]\\n public void TestAddMVCuda()\\n {\\n if (cuda.is_available()) {\\n TestAddMVGen(CUDA);\\n }\\n }\\n\\n void TestAddRGen(Device device)\\n {\\n {\\n var x1 = ones(new long[] { 4, 3 }, device: device);\\n var vec1 = ones(new long[] { 4 }, device: device);\\n var vec2 = ones(new long[] { 3 }, device: device);\\n\\n var y = x1.addr(vec1, vec2).to(DeviceType.CPU);\\n\\n Assert.Equal(new long[] { 4, 3 }, y.shape);\\n }\\n }\\n\\n [Fact]\\n [TestOf(nameof(CPU))]\\n public void TestAddRCpu()\\n {\\n TestAddRGen(CPU);\\n }\\n\\n [Fact]\\n [TestOf(nameof(CUDA))]\\n public void TestAddRCuda()\\n {\\n if (cuda.is_available()) {\\n TestAddRGen(CUDA);\\n }\\n }\\n\\n [Fact]\\n [TestOf(nameof(Tensor.vdot))]\\n public void VdotTest()\\n {\\n var a = new float[] { 1.0f, 2.0f, 3.0f };\\n var b = new float[] { 1.0f, 2.0f, 3.0f };\\n var expected = tensor(a.Zip(b).Select(x => x.First * x.Second).Sum());\\n var res = tensor(a).vdot(tensor(b));\\n Assert.True(res.allclose(expected));\\n }\\n\\n [Fact]\\n [TestOf(nameof(Tensor.vander))]\\n public void VanderTest()\\n {\\n var x = tensor(new int[] { 1, 2, 3, 5 });\\n {\\n var res = x.vander();\\n var expected = tensor(new long[] { 1, 1, 1, 1, 8, 4, 2, 1, 27, 9, 3, 1, 125, 25, 5, 1 }, 4, 4);\\n Assert.Equal(expected, res);\\n }\\n {\\n var res = x.vander(3);\\n var expected = tensor(new long[] { 1, 1, 1, 4, 2, 1, 9, 3, 1, 25, 5, 1 }, 4, 3);\\n Assert.Equal(expected, res);\\n }\\n {\\n var res = x.vander(3, true);\\n var expected = tensor(new long[] { 1, 1, 1, 1, 2, 4, 1, 3, 9, 1, 5, 25 }, 4, 3);\\n Assert.Equal(expected, res);\\n }\\n }\\n\\n [Fact]\\n [TestOf(nameof(linalg.vander))]\\n public void LinalgVanderTest()\\n {\\n var x = tensor(new int[] { 1, 2, 3, 5 });\\n {\\n var res = linalg.vander(x);\\n var expected = tensor(new long[] { 1, 1, 1, 1, 1, 2, 4, 8, 1, 3, 9, 27, 1, 5, 25, 125 }, 4, 4);\\n Assert.Equal(expected, res);\\n }\\n {\\n var res = linalg.vander(x, 3);\\n var expected = tensor(new long[] { 1, 1, 1, 1, 2, 4, 1, 3, 9, 1, 5, 25 }, 4, 3);\\n Assert.Equal(expected, res);\\n }\\n }\\n\\n [Fact]\\n [TestOf(nameof(linalg.cholesky))]\\n public void CholeskyTest()\\n {\\n var a = randn(new long[] { 3, 2, 2 }, float64);\\n a = a.matmul(a.swapdims(-2, -1)); // Worked this in to get it tested. Alias for 'transpose'\\n var l = linalg.cholesky(a);\\n\\n Assert.True(a.allclose(l.matmul(l.swapaxes(-2, -1)))); // Worked this in to get it tested. Alias for 'transpose'\\n }\\n\\n [Fact]\\n [TestOf(nameof(linalg.cholesky_ex))]\\n public void CholeskyExTest()\\n {\\n var a = randn(new long[] { 3, 2, 2 }, float64);\\n a = a.matmul(a.swapdims(-2, -1)); // Worked this in to get it tested. Alias for 'transpose'\\n var (l, info) = linalg.cholesky_ex(a);\\n\\n Assert.True(a.allclose(l.matmul(l.swapaxes(-2, -1))));\\n }\\n\\n [Fact]\\n [TestOf(nameof(linalg.inv))]\\n public void InvTest()\\n {\\n var a = randn(new long[] { 3, 2, 2 }, float64);\\n var l = linalg.inv(a);\\n\\n Assert.Equal(a.shape, l.shape);\\n }\\n\\n [Fact]\\n [TestOf(nameof(linalg.inv_ex))]\\n public void InvExTest()\\n {\\n var a = randn(new long[] { 3, 2, 2 }, float64);\\n var (l, info) = linalg.inv_ex(a);\\n\\n Assert.Equal(a.shape, l.shape);\\n }\\n\\n [Fact]\\n [TestOf(nameof(linalg.cond))]\\n public void CondTestF64()\\n {\\n {\\n var a = randn(new long[] { 3, 3, 3 }, float64);\\n // The following mostly checks that the runtime interop doesn't blow up.\\n _ = linalg.cond(a);\\n _ = linalg.cond(a, \\\"fro\\\");\\n _ = linalg.cond(a, \\\"nuc\\\");\\n _ = linalg.cond(a, 1);\\n _ = linalg.cond(a, -1);\\n _ = linalg.cond(a, 2);\\n _ = linalg.cond(a, -2);\\n _ = linalg.cond(a, Double.PositiveInfinity);\\n _ = linalg.cond(a, Double.NegativeInfinity);\\n }\\n }\\n\\n [Fact]\\n [TestOf(nameof(linalg.cond))]\\n public void CondTestCF64()\\n {\\n {\\n var a = randn(new long[] { 3, 3, 3 }, complex128);\\n // The following mostly checks that the runtime interop doesn't blow up.\\n _ = linalg.cond(a);\\n _ = linalg.cond(a, \\\"fro\\\");\\n _ = linalg.cond(a, \\\"nuc\\\");\\n _ = linalg.cond(a, 1);\\n _ = linalg.cond(a, -1);\\n _ = linalg.cond(a, 2);\\n _ = linalg.cond(a, -2);\\n _ = linalg.cond(a, Double.PositiveInfinity);\\n _ = linalg.cond(a, Double.NegativeInfinity);\\n }\\n }\\n\\n [Fact]\\n [TestOf(nameof(linalg.qr))]\\n public void QRTest()\\n {\\n var a = randn(new long[] { 4, 25, 25 });\\n\\n var l = linalg.qr(a);\\n\\n Assert.Equal(a.shape, l.Q.shape);\\n Assert.Equal(a.shape, l.R.shape);\\n }\\n\\n [Fact]\\n [TestOf(nameof(linalg.solve))]\\n public void SolveTest()\\n {\\n var A = randn(3, 3);\\n var b = randn(3);\\n var x = linalg.solve(A, b);\\n Assert.True(A.matmul(x).allclose(b, rtol: 1e-03, atol: 1e-06));\\n }\\n\\n [Fact]\\n [TestOf(nameof(linalg.solve_triangular))]\\n public void SolveTriangularTest()\\n {\\n {\\n var A = randn(3, 3).triu_();\\n var b = randn(3, 4);\\n var x = linalg.solve_triangular(A, b, upper: true);\\n Assert.True(A.matmul(x).allclose(b, rtol: 1e-03, atol: 1e-06));\\n }\\n {\\n var A = randn(2, 3, 3).tril_();\\n var b = randn(2, 3, 4);\\n var x = linalg.solve_triangular(A, b, upper: false);\\n Assert.True(A.matmul(x).allclose(b, rtol: 1e-03, atol: 1e-06));\\n }\\n {\\n var A = randn(2, 4, 4).tril_();\\n var b = randn(2, 3, 4);\\n var x = linalg.solve_triangular(A, b, upper: false, left: false);\\n Assert.True(x.matmul(A).allclose(b, rtol: 1e-03, atol: 1e-06));\\n }\\n }\\n\\n [Fact]\\n [TestOf(nameof(linalg.svd))]\\n public void SVDTest()\\n {\\n var a = randn(new long[] { 4, 25, 15 });\\n\\n var l = linalg.svd(a);\\n\\n Assert.Equal(new long[] { 4, 25, 25 }, l.U.shape);\\n Assert.Equal(new long[] { 4, 15 }, l.S.shape);\\n Assert.Equal(new long[] { 4, 15, 15 }, l.Vh.shape);\\n\\n l = linalg.svd(a, fullMatrices: false);\\n\\n Assert.Equal(a.shape, l.U.shape);\\n Assert.Equal(new long[] { 4, 15 }, l.S.shape);\\n Assert.Equal(new long[] { 4, 15, 15 }, l.Vh.shape);\\n }\\n\\n\\n [Fact]\\n [TestOf(nameof(linalg.svdvals))]\\n public void SVDValsTest()\\n {\\n var a = tensor(new double[] { -1.3490, -0.1723, 0.7730,\\n -1.6118, -0.3385, -0.6490,\\n 0.0908, 2.0704, 0.5647,\\n -0.6451, 0.1911, 0.7353,\\n 0.5247, 0.5160, 0.5110}, 5, 3);\\n\\n var l = linalg.svdvals(a);\\n Assert.True(l.allclose(tensor(new double[] { 2.5138929972840613, 2.1086555338402455, 1.1064930672223237 }), rtol: 1e-04, atol: 1e-07));\\n }\\n\\n [Fact]\\n [TestOf(nameof(linalg.lstsq))]\\n public void LSTSQTest()\\n {\\n var a = randn(new long[] { 4, 25, 15 });\\n var b = randn(new long[] { 4, 25, 10 });\\n\\n var l = linalg.lstsq(a, b);\\n\\n Assert.Equal(new long[] { 4, 15, 10 }, l.Solution.shape);\\n Assert.Equal(0, l.Residuals.shape[0]);\\n Assert.Equal(new long[] { 4 }, l.Rank.shape);\\n Assert.Equal(new long[] { 4, 15, 10 }, l.Solution.shape);\\n Assert.Equal(0, l.SingularValues.shape[0]);\\n }\\n\\n [Fact]\\n [TestOf(nameof(linalg.lu))]\\n public void LUTest()\\n {\\n var A = randn(2, 3, 3);\\n var A_factor = linalg.lu(A);\\n // For right now, pretty much just checking that it's not blowing up.\\n Assert.Multiple(\\n () => Assert.NotNull(A_factor.P),\\n () => Assert.NotNull(A_factor.L),\\n () => Assert.NotNull(A_factor.U)\\n );\\n }\\n\\n [Fact]\\n [TestOf(nameof(linalg.lu_factor))]\\n public void LUFactorTest()\\n {\\n var A = randn(2, 3, 3);\\n var A_factor = linalg.lu_factor(A);\\n // For right now, pretty much just checking that it's not blowing up.\\n Assert.Multiple(\\n () => Assert.NotNull(A_factor.LU),\\n () => Assert.NotNull(A_factor.Pivots)\\n );\\n }\\n\\n [Fact]\\n [TestOf(nameof(linalg.ldl_factor))]\\n public void LDLFactorTest()\\n {\\n var A = randn(2, 3, 3);\\n var A_factor = linalg.ldl_factor(A);\\n // For right now, pretty much just checking that it's not blowing up.\\n Assert.Multiple(\\n () => Assert.NotNull(A_factor.LU),\\n () => Assert.NotNull(A_factor.Pivots)\\n );\\n }\\n\\n [Fact]\\n [TestOf(nameof(linalg.ldl_factor))]\\n public void LDLFactorExTest()\\n {\\n var A = randn(2, 3, 3);\\n var A_factor = linalg.ldl_factor_ex(A);\\n // For right now, pretty much just checking that it's not blowing up.\\n Assert.Multiple(\\n () => Assert.NotNull(A_factor.LU),\\n () => Assert.NotNull(A_factor.Pivots),\\n () => Assert.NotNull(A_factor.Info)\\n );\\n }\\n\\n [Fact]\\n [TestOf(nameof(Tensor.matrix_power))]\\n public void MatrixPowerTest()\\n {\\n var a = randn(new long[] { 25, 25 });\\n var b = a.matrix_power(3);\\n Assert.Equal(new long[] { 25, 25 }, b.shape);\\n }\\n\\n [Fact]\\n [TestOf(nameof(Tensor.matrix_exp))]\\n public void MatrixExpTest1()\\n {\\n var a = randn(new long[] { 25, 25 });\\n var b = a.matrix_exp();\\n Assert.Equal(new long[] { 25, 25 }, b.shape);\\n\\n var c = matrix_exp(a);\\n Assert.Equal(new long[] { 25, 25 }, c.shape);\\n }\\n\\n [Fact]\\n [TestOf(nameof(matrix_exp))]\\n public void MatrixExpTest2()\\n {\\n var a = randn(new long[] { 16, 25, 25 });\\n var b = a.matrix_exp();\\n Assert.Equal(new long[] { 16, 25, 25 }, b.shape);\\n var c = matrix_exp(a);\\n Assert.Equal(new long[] { 16, 25, 25 }, c.shape);\\n }\\n\\n [Fact]\\n [TestOf(nameof(linalg.matrix_rank))]\\n public void MatrixRankTest()\\n {\\n var mr1 = linalg.matrix_rank(randn(4, 3, 2));\\n Assert.Equal(new long[] { 4 }, mr1.shape);\\n\\n var mr2 = linalg.matrix_rank(randn(2, 4, 3, 2));\\n Assert.Equal(new long[] { 2, 4 }, mr2.shape);\\n\\n // Really just testing that it doesn't blow up in interop for the following lines:\\n\\n mr2 = linalg.matrix_rank(randn(2, 4, 3, 2), atol: 1.0);\\n Assert.Equal(new long[] { 2, 4 }, mr2.shape);\\n\\n mr2 = linalg.matrix_rank(randn(2, 4, 3, 2), atol: 1.0, rtol: 0.0);\\n Assert.Equal(new long[] { 2, 4 }, mr2.shape);\\n\\n mr2 = linalg.matrix_rank(randn(2, 4, 3, 2), atol: tensor(1.0));\\n Assert.Equal(new long[] { 2, 4 }, mr2.shape);\\n\\n mr2 = linalg.matrix_rank(randn(2, 4, 3, 2), atol: tensor(1.0), rtol: tensor(0.0));\\n Assert.Equal(new long[] { 2, 4 }, mr2.shape);\\n }\\n\\n [Fact]\\n [TestOf(nameof(linalg.multi_dot))]\\n public void MultiDotTest()\\n {\\n var a = randn(new long[] { 25, 25 });\\n var b = randn(new long[] { 25, 25 });\\n var c = randn(new long[] { 25, 25 });\\n var d = linalg.multi_dot(new Tensor[] { a, b, c });\\n Assert.Equal(new long[] { 25, 25 }, d.shape);\\n }\\n\\n [Fact]\\n [TestOf(nameof(linalg.det))]\\n public void DeterminantTest()\\n {\\n {\\n var a = tensor(\\n new float[] { 0.9478f, 0.9158f, -1.1295f,\\n 0.9701f, 0.7346f, -1.8044f,\\n -0.2337f, 0.0557f, 0.6929f }, 3, 3);\\n var l = linalg.det(a);\\n Assert.True(l.allclose(tensor(0.09335048f)));\\n }\\n {\\n var a = tensor(\\n new float[] { 0.9254f, -0.6213f, -0.5787f, 1.6843f, 0.3242f, -0.9665f,\\n 0.4539f, -0.0887f, 1.1336f, -0.4025f, -0.7089f, 0.9032f }, 3, 2, 2);\\n var l = linalg.det(a);\\n Assert.True(l.allclose(tensor(new float[] { 1.19910491f, 0.4099378f, 0.7385352f })));\\n }\\n }\\n\\n [Fact]\\n [TestOf(nameof(linalg.matrix_norm))]\\n public void MatrixNormTest()\\n {\\n {\\n var a = arange(9, float32).view(3, 3);\\n\\n var b = linalg.matrix_norm(a);\\n var c = linalg.matrix_norm(a, ord: -1);\\n\\n Assert.Equal(14.282857f, b.item<float>());\\n Assert.Equal(9.0f, c.item<float>());\\n }\\n }\\n\\n [Fact]\\n [TestOf(nameof(linalg.vector_norm))]\\n public void VectorNormTest()\\n {\\n {\\n var a = tensor(\\n new float[] { -4.0f, -3.0f, -2.0f, -1.0f, 0, 1.0f, 2.0f, 3.0f, 4.0f });\\n\\n var b = linalg.vector_norm(a, ord: 3.5);\\n var c = linalg.vector_norm(a.view(3, 3), ord: 3.5);\\n\\n Assert.Equal(5.4344883f, b.item<float>());\\n Assert.Equal(5.4344883f, c.item<float>());\\n }\\n }\\n\\n [Fact]\\n [TestOf(nameof(linalg.pinv))]\\n public void PinvTest()\\n {\\n var mr1 = linalg.pinv(randn(4, 3, 5));\\n Assert.Equal(new long[] { 4, 5, 3 }, mr1.shape);\\n\\n // Really just testing that it doesn't blow up in interop for the following lines:\\n\\n mr1 = linalg.pinv(randn(4, 3, 5), atol: 1.0);\\n Assert.Equal(new long[] { 4, 5, 3 }, mr1.shape);\\n\\n mr1 = linalg.pinv(randn(4, 3, 5), atol: 1.0, rtol: 0.0);\\n Assert.Equal(new long[] { 4, 5, 3 }, mr1.shape);\\n\\n mr1 = linalg.pinv(randn(4, 3, 5), atol: tensor(1.0));\\n Assert.Equal(new long[] { 4, 5, 3 }, mr1.shape);\\n\\n mr1 = linalg.pinv(randn(4, 3, 5), atol: tensor(1.0), rtol: tensor(0.0));\\n Assert.Equal(new long[] { 4, 5, 3 }, mr1.shape);\\n }\\n\\n [Fact]\\n [TestOf(nameof(linalg.eig))]\\n public void EigTest32()\\n {\\n {\\n var a = tensor(\\n new float[] { 2.8050f, -0.3850f, -0.3850f, 3.2376f, -1.0307f, -2.7457f, -2.7457f, -1.7517f, 1.7166f }, 3, 3);\\n\\n var expected = tensor(\\n new (float, float)[] { (3.44288778f, 0.0f), (2.17609453f, 0.0f), (-2.128083f, 0.0f) });\\n\\n {\\n var (values, vectors) = linalg.eig(a);\\n Assert.NotNull(vectors);\\n Assert.True(values.allclose(expected));\\n }\\n }\\n }\\n\\n [Fact]\\n [TestOf(nameof(linalg.eigvals))]\\n public void EighvalsTest32()\\n {\\n var a = tensor(\\n new float[] { 2.8050f, -0.3850f, -0.3850f, 3.2376f, -1.0307f, -2.7457f, -2.7457f, -1.7517f, 1.7166f }, 3, 3);\\n var expected = tensor(\\n new (float, float)[] { (3.44288778f, 0.0f), (2.17609453f, 0.0f), (-2.128083f, 0.0f) });\\n var l = linalg.eigvals(a);\\n Assert.True(l.allclose(expected));\\n }\\n\\n // TODO: (Skip = \\\"Intermittently failing on MacOS or Linux (note: may now be working, we need to recheck)\\\")\\n [FactIgnoreOnPlatform(\\\"Intermittently fails\\\", \\\"OSX\\\", \\\"Linux\\\")]\\n [TestOf(nameof(linalg.eigvals))]\\n public void EighvalsTest64()\\n {\\n var a = tensor(\\n new double[] { 2.8050f, -0.3850f, -0.3850f, 3.2376f, -1.0307f, -2.7457f, -2.7457f, -1.7517f, 1.7166f }, 3, 3);\\n var expected = tensor(\\n new System.Numerics.Complex[] { new System.Numerics.Complex(3.44288778f, 0.0f), new System.Numerics.Complex(2.17609453f, 0.0f), new System.Numerics.Complex(-2.128083f, 0.0f) });\\n var l = linalg.eigvals(a);\\n Assert.True(l.allclose(expected));\\n }\\n\\n // TODO: (Skip = \\\"Intermittently failing on MacOS or Linux (note: may now be working, we need to recheck)\\\")\\n [FactIgnoreOnPlatform(\\\"Intermittently fails\\\", \\\"OSX\\\", \\\"Linux\\\")]\\n [TestOf(nameof(linalg.eigvalsh))]\\n public void EighvalshTest32()\\n {\\n var a = tensor(\\n new float[] { 2.8050f, -0.3850f, -0.3850f, 3.2376f, -1.0307f, -2.7457f,\\n -2.7457f, -1.7517f, 1.7166f, 2.2207f, 2.2207f, -2.0898f }, 3, 2, 2);\\n var expected = tensor(\\n new float[] { 2.5797f, 3.46290016f, -4.16046524f, 1.37806475f, -3.11126733f, 2.73806715f }, 3, 2);\\n var l = linalg.eigvalsh(a);\\n Assert.True(l.allclose(expected));\\n }\\n\\n // TODO: (Skip = \\\"Intermittently failing on MacOS or Linux (note: may now be working, we need to recheck)\\\")\\n [FactIgnoreOnPlatform(\\\"Intermittently fails\\\", \\\"OSX\\\", \\\"Linux\\\")]\\n [TestOf(nameof(linalg.eigvalsh))]\\n public void EighvalshTest64()\\n {\\n var a = tensor(\\n new double[] { 2.8050, -0.3850, -0.3850, 3.2376, -1.0307, -2.7457,\\n -2.7457, -1.7517, 1.7166, 2.2207, 2.2207, -2.0898 }, 3, 2, 2);\\n var expected = tensor(\\n new double[] { 2.5797, 3.46290016, -4.16046524, 1.37806475, -3.11126733, 2.73806715 }, 3, 2);\\n var l = linalg.eigvalsh(a);\\n Assert.True(l.allclose(expected));\\n }\\n\\n [Fact]\\n [TestOf(nameof(linalg.norm))]\\n public void LinalgNormTest()\\n {\\n {\\n var a = tensor(\\n new float[] { -4.0f, -3.0f, -2.0f, -1.0f, 0.0f, 1.0f, 2.0f, 3.0f, 4.0f });\\n var b = a.reshape(3, 3);\\n\\n Assert.True(linalg.norm(a).allclose(tensor(7.7460f)));\\n Assert.True(linalg.norm(b).allclose(tensor(7.7460f)));\\n Assert.True(linalg.norm(b, \\\"fro\\\").allclose(tensor(7.7460f)));\\n\\n Assert.True(linalg.norm(a, float.PositiveInfinity).allclose(tensor(4.0f)));\\n Assert.True(linalg.norm(b, float.PositiveInfinity).allclose(tensor(9.0f)));\\n Assert.True(linalg.norm(a, float.NegativeInfinity).allclose(tensor(0.0f)));\\n Assert.True(linalg.norm(b, float.NegativeInfinity).allclose(tensor(2.0f)));\\n\\n Assert.True(linalg.norm(a, 1).allclose(tensor(20.0f)));\\n Assert.True(linalg.norm(b, 1).allclose(tensor(7.0f)));\\n Assert.True(linalg.norm(a, -1).allclose(tensor(0.0f)));\\n Assert.True(linalg.norm(b, -1).allclose(tensor(6.0f)));\\n\\n Assert.True(linalg.norm(a, 2).allclose(tensor(7.7460f)));\\n Assert.True(linalg.norm(b, 2).allclose(tensor(7.3485f)));\\n Assert.True(linalg.norm(a, 3).allclose(tensor(5.8480f)));\\n Assert.True(linalg.norm(a, -2).allclose(tensor(0.0f)));\\n Assert.True(linalg.norm(a, -3).allclose(tensor(0.0f)));\\n }\\n }\\n\\n [Fact]\\n public void TestTrilIndex()\\n {\\n var a = tril_indices(3, 3);\\n var expected = new long[] { 0, 1, 1, 2, 2, 2, 0, 0, 1, 0, 1, 2 };\\n Assert.Equal(expected, a.data<long>().ToArray());\\n }\\n\\n [Fact]\\n public void TestTriuIndex()\\n {\\n var a = triu_indices(3, 3);\\n var expected = new long[] { 0, 0, 0, 1, 1, 2, 0, 1, 2, 1, 2, 2 };\\n Assert.Equal(expected, a.data<long>().ToArray());\\n }\\n }\\n}\\n\"\n },\n {\n \"alpha_fraction\": 0.6116677522659302,\n \"alphanum_fraction\": 0.6152304410934448,\n \"avg_line_length\": 58.092105865478516,\n \"blob_id\": \"2193a8c2faa8681546edda60ec36f27244284a7a\",\n \"content_id\": \"0473ddb98051c9ff5a8cf567bcdaa941706cee72\",\n \"detected_licenses\": [\n \"MIT\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"C#\",\n \"length_bytes\": 4499,\n \"license_type\": \"permissive\",\n \"max_line_length\": 360,\n \"num_lines\": 76,\n \"path\": \"/src/TorchSharp/NN/MultiheadAttention.cs\",\n \"repo_name\": \"NiklasGustafsson/TorchSharp\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"// Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See License.txt in the project root for license information.\\nusing System;\\nusing static TorchSharp.PInvoke.NativeMethods;\\nusing static TorchSharp.torch;\\n\\n#nullable enable\\nnamespace TorchSharp\\n{\\n using Modules;\\n\\n namespace Modules\\n {\\n public sealed class MultiheadAttention : torch.nn.Module<Tensor, Tensor, Tensor, Tensor?, bool, Tensor?, Tuple<Tensor,Tensor>>\\n {\\n internal MultiheadAttention(IntPtr handle, IntPtr boxedHandle) : base(handle, boxedHandle) { }\\n\\n /// <summary>\\n /// Applies the MultiheadAttention function element-wise.\\n /// </summary>\\n /// <param name=\\\"query\\\">map a query and a set of key-value pairs to an output. See “Attention Is All You Need” for more details</param>\\n /// <param name=\\\"key\\\"></param>\\n /// <param name=\\\"value\\\"></param>\\n /// <param name=\\\"key_padding_mask\\\">if provided, specified padding elements in the key will be ignored by the attention. When given a binary mask and a value is True, the corresponding value on the attention layer will be ignored. When given a byte mask and a value is non-zero, the corresponding value on the attention layer will be ignored</param>\\n /// <param name=\\\"need_weights\\\">output attn_output_weights</param>\\n /// <param name=\\\"attn_mask\\\">2D or 3D mask that prevents attention to certain positions. A 2D mask will be broadcasted for all the batches while a 3D mask allows to specify a different mask for the entries of each batch</param>\\n /// <returns>attn_output, attn_ouput_weights</returns>\\n\\n public override Tuple<Tensor,Tensor> forward(Tensor query, Tensor key, Tensor value, Tensor? key_padding_mask, bool need_weights, Tensor? attn_mask)\\n {\\n THSNN_MultiheadAttention_forward(handle,\\n query.Handle,\\n key.Handle,\\n value.Handle,\\n key_padding_mask?.Handle ?? IntPtr.Zero,\\n need_weights,\\n attn_mask?.Handle ?? IntPtr.Zero,\\n out var res1,\\n out var res2);\\n if (res1 == IntPtr.Zero || (need_weights && res2 == IntPtr.Zero)) { torch.CheckForErrors(); }\\n return Tuple.Create(new Tensor(res1), new Tensor(res2));\\n }\\n\\n public new Tuple<Tensor, Tensor> call(Tensor query, Tensor key, Tensor value, Tensor? key_padding_mask = null, bool need_weights = true, Tensor? attn_mask = null)\\n {\\n return base.call(query, key, value, key_padding_mask, need_weights, attn_mask);\\n }\\n }\\n }\\n\\n public static partial class torch\\n {\\n public static partial class nn\\n {\\n /// <summary>\\n /// Allows the model to jointly attend to information from different representation subspaces (based on the paper “Attention Is All You Need”).\\n /// </summary>\\n /// <param name=\\\"embedded_dim\\\">total dimension of the model</param>\\n /// <param name=\\\"num_heads\\\">parallel attention heads</param>\\n /// <param name=\\\"dropout\\\">a Dropout layer on attn_output_weights. Default: 0.0</param>\\n /// <param name=\\\"bias\\\">add bias as module parameter. Default: true</param>\\n /// <param name=\\\"add_bias_kv\\\">add bias to the key and value sequences at dim=0</param>\\n /// <param name=\\\"add_zero_attn\\\">add a new batch of zeros to the key and value sequences at dim=1</param>\\n /// <param name=\\\"kdim\\\">total number of features in key</param>\\n /// <param name=\\\"vdim\\\">total number of features in value</param>\\n /// <returns></returns>\\n public static MultiheadAttention MultiheadAttention(long embedded_dim, long num_heads, double dropout = 0.0, bool bias = true, bool add_bias_kv = false, bool add_zero_attn = false, long? kdim=null, long? vdim=null)\\n {\\n var _kdim = kdim.HasValue ? kdim.Value : embedded_dim;\\n var _vdim = vdim.HasValue ? vdim.Value : embedded_dim;\\n var res = THSNN_MultiheadAttention_ctor(embedded_dim, num_heads, dropout, bias, add_bias_kv, add_zero_attn, _kdim, _vdim, out var boxedHandle);\\n if (res == IntPtr.Zero) { torch.CheckForErrors(); }\\n return new MultiheadAttention(res, boxedHandle);\\n }\\n }\\n }\\n}\\n\"\n },\n {\n \"alpha_fraction\": 0.45609214901924133,\n \"alphanum_fraction\": 0.472366064786911,\n \"avg_line_length\": 47.0355339050293,\n \"blob_id\": \"3e495b8f05fa894f7f63b7bc470422c63adcedc7\",\n \"content_id\": \"f06f92fdc865ab213ff13ac5daa838dc5e738ebe\",\n \"detected_licenses\": [\n \"MIT\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"C#\",\n \"length_bytes\": 9463,\n \"license_type\": \"permissive\",\n \"max_line_length\": 125,\n \"num_lines\": 197,\n \"path\": \"/src/TorchVision/AutoAugment.cs\",\n \"repo_name\": \"NiklasGustafsson/TorchSharp\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"using System;\\nusing System.Collections.Generic;\\nusing System.Linq;\\nusing static TorchSharp.torch;\\nusing F = TorchSharp.torchvision.transforms.functional;\\n\\n#nullable enable\\nnamespace TorchSharp\\n{\\n public static partial class torchvision\\n {\\n internal abstract class AutoAugmentBase\\n {\\n protected enum opType {\\n ShearX,\\n ShearY,\\n TranslateX,\\n TranslateY,\\n Rotate,\\n Brightness,\\n Color,\\n Contrast,\\n Sharpness,\\n Posterize,\\n Solarize,\\n AutoContrast,\\n Equalize,\\n Invert,\\n Identity\\n }\\n\\n protected static Tensor apply_op(\\n Tensor img,\\n opType op_name,\\n double magnitude,\\n InterpolationMode interpolation,\\n IList<float>? fill)\\n {\\n switch(op_name) {\\n case opType.ShearX:\\n return F.affine(\\n img,\\n angle: 0.0f,\\n translate: new[] { 0, 0 },\\n scale: 1.0f,\\n shear: new[] { (float)((180.0 / Math.PI) * Math.Atan(magnitude)), 0.0f },\\n interpolation: interpolation,\\n fill: fill?.FirstOrDefault());\\n case opType.ShearY:\\n return F.affine(\\n img,\\n angle: 0.0f,\\n translate: new[] { 0, 0 },\\n scale: 1.0f,\\n shear: new[] { 0.0f, (float)((180.0 / Math.PI) * Math.Atan(magnitude)) },\\n interpolation: interpolation,\\n fill: fill?.FirstOrDefault());\\n case opType.TranslateX:\\n return F.affine(\\n img,\\n angle: 0.0f,\\n translate: new[] { (int)(magnitude), 0 },\\n scale: 1.0f,\\n interpolation: interpolation,\\n shear: new[] { 0.0f, 0.0f },\\n fill: fill?.FirstOrDefault());\\n case opType.TranslateY:\\n return F.affine(\\n img,\\n angle: 0.0f,\\n translate: new[] { 0, (int)(magnitude) },\\n scale: 1.0f,\\n interpolation: interpolation,\\n shear: new[] { 0.0f, 0.0f },\\n fill: fill?.FirstOrDefault());\\n case opType.Rotate:\\n return F.rotate(img, (float)magnitude, interpolation, fill: fill);\\n case opType.Brightness:\\n return F.adjust_brightness(img, 1.0 + magnitude);\\n case opType.Color:\\n return F.adjust_saturation(img, 1.0 + magnitude);\\n case opType.Contrast:\\n return F.adjust_contrast(img, 1.0 + magnitude);\\n case opType.Sharpness:\\n return F.adjust_sharpness(img, 1.0 + magnitude);\\n case opType.Posterize:\\n return F.posterize(img, (int)magnitude);\\n case opType.Solarize:\\n return F.solarize(img, magnitude);\\n case opType.AutoContrast:\\n return F.autocontrast(img);\\n case opType.Equalize:\\n return F.equalize(img);\\n case opType.Invert:\\n return F.invert(img);\\n case opType.Identity:\\n return img; // Pass\\n default:\\n throw new ArgumentException($\\\"The provided operator {op_name} is not recognized.\\\");\\n }\\n }\\n }\\n\\n /* Original implementation from:\\n * https://pytorch.org/vision/main/_modules/torchvision/transforms/autoaugment.html#RandAugment */\\n internal class RandAugment : AutoAugmentBase, ITransform\\n {\\n public RandAugment(\\n int num_ops = 2,\\n int magnitude = 9,\\n int num_magnitude_bins = 31,\\n InterpolationMode interpolation = InterpolationMode.Nearest,\\n IList<float>? fill = null)\\n {\\n this.num_ops = num_ops;\\n this.magnitude = magnitude;\\n this.num_magnitude_bins = num_magnitude_bins;\\n this.interpolation = interpolation;\\n this.fill = fill;\\n }\\n\\n public Tensor call(Tensor img)\\n {\\n using var _ = torch.NewDisposeScope();\\n var cDim = img.Dimensions - 3;\\n var height = img.shape[cDim + 1];\\n var width = img.shape[cDim + 2];\\n var op_meta = augmentation_space(num_magnitude_bins, (height, width));\\n for (int i = 0; i < num_ops; ++i) {\\n var op_index = torch.randint(0, op_meta.Count, 1).ToInt32();\\n var op_name = op_meta.Keys.ToList()[op_index];\\n var (magnitudes, signed) = op_meta[op_name];\\n var magnitude = magnitudes.Dimensions > 0 ? magnitudes[this.magnitude].ToDouble() : 0.0;\\n\\n if (signed && torch.randint(0, 2, 1).ToBoolean())\\n magnitude *= -1.0;\\n\\n img = apply_op(img, op_name, magnitude, interpolation, this.fill);\\n }\\n return img.MoveToOuterDisposeScope();\\n }\\n\\n private Dictionary<opType, (Tensor, bool)> augmentation_space(int num_bins, (long height, long width) image_size)\\n {\\n return new Dictionary<opType, (Tensor, bool)> {\\n { opType.Identity, (torch.tensor(0.0), false) },\\n { opType.ShearX, (torch.linspace(0.0, 0.3, num_bins), true) },\\n { opType.ShearY, (torch.linspace(0.0, 0.3, num_bins), true) },\\n { opType.TranslateX, (torch.linspace(0.0, 150.0 / 331.0 * image_size.height, num_bins), true) },\\n { opType.TranslateY, (torch.linspace(0.0, 150.0 / 331.0 * image_size.width, num_bins), true) },\\n { opType.Rotate, (torch.linspace(0.0, 30.0, num_bins), true) },\\n { opType.Brightness, (torch.linspace(0.0, 0.9, num_bins), true) },\\n { opType.Color, (torch.linspace(0.0, 0.9, num_bins), true) },\\n { opType.Contrast, (torch.linspace(0.0, 0.9, num_bins), true) },\\n { opType.Sharpness, (torch.linspace(0.0, 0.9, num_bins), true) },\\n { opType.Posterize, (8 - (torch.arange(num_bins) / ((num_bins - 1) / 4)).round().@int(), false)},\\n { opType.Solarize, (torch.linspace(255.0, 0.0, num_bins), false) },\\n { opType.AutoContrast, (torch.tensor(0.0), false) },\\n { opType.Equalize, (torch.tensor(0.0), false) }\\n };\\n }\\n\\n private readonly int num_ops;\\n private readonly int magnitude;\\n private readonly int num_magnitude_bins;\\n private readonly InterpolationMode interpolation;\\n private readonly IList<float>? fill;\\n }\\n\\n public static partial class transforms\\n {\\n /// <summary>\\n /// RandAugment data augmentation method based on\\n /// \\\"RandAugment: Practical automated data augmentation with a reduced search space\\\"\\n /// https://arxiv.org/abs/1909.13719\\n /// The image is expected to be a torch Tensor, it should be of type torch.uint8, and it is expected\\n /// to have [..., 1 or 3, H, W] shape, where ... means an arbitrary number of leading dimensions.\\n /// </summary>\\n /// <param name=\\\"num_ops\\\">Number of augmentation transformations to apply sequentially. Default: 2</param>\\n /// <param name=\\\"magnitude\\\">Magnitude for all the transformations. Default: 9</param>\\n /// <param name=\\\"num_magnitude_bins\\\">The number of different magnitude values. Default: 31</param>\\n /// <param name=\\\"interpolation\\\">Desired interpolation enum defined by\\n /// torchvision.transforms.InterpolationMode. Default: InterpolationMode.NEAREST.</param>\\n /// <param name=\\\"fill\\\">Pixel fill value for the area outside the transformed\\n /// image. If given a number, the value is used for all bands respectively. Default: null</param>\\n static public ITransform RandAugment(\\n int num_ops = 2,\\n int magnitude = 9,\\n int num_magnitude_bins = 31,\\n InterpolationMode interpolation = InterpolationMode.Nearest,\\n IList<float>? fill = null)\\n {\\n return new RandAugment(num_ops, magnitude, num_magnitude_bins, interpolation, fill);\\n }\\n }\\n }\\n}\\n\"\n },\n {\n \"alpha_fraction\": 0.5736501812934875,\n \"alphanum_fraction\": 0.5752227902412415,\n \"avg_line_length\": 42.35606002807617,\n \"blob_id\": \"0f4ed65c2fb91ee2821da415f1600f77afa9d99e\",\n \"content_id\": \"4fbb086a3cb0d8e783ebdbf8b4ffb6cc6f417e50\",\n \"detected_licenses\": [\n \"MIT\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"C#\",\n \"length_bytes\": 5725,\n \"license_type\": \"permissive\",\n \"max_line_length\": 149,\n \"num_lines\": 132,\n \"path\": \"/src/TorchSharp/Distributions/Exponential.cs\",\n \"repo_name\": \"NiklasGustafsson/TorchSharp\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"// Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information.\\nusing System;\\nusing System.Collections.Generic;\\nusing static TorchSharp.torch;\\n\\nnamespace TorchSharp\\n{\\n using Modules;\\n\\n namespace Modules\\n {\\n /// <summary>\\n /// An Exponential distribution parameterized by `rate`.\\n /// </summary>\\n public class Exponential : torch.distributions.ExponentialFamily\\n {\\n /// <summary>\\n /// The mean of the distribution.\\n /// </summary>\\n public override Tensor mean => rate.reciprocal();\\n\\n public override Tensor mode => torch.zeros_like(rate);\\n\\n /// <summary>\\n /// The variance of the distribution\\n /// </summary>\\n public override Tensor variance => rate.pow(2);\\n\\n /// <summary>\\n /// The standard deviation of the distribution\\n /// </summary>\\n public override Tensor stddev => rate.reciprocal();\\n\\n\\n /// <summary>\\n /// Constructor\\n /// </summary>\\n /// <param name=\\\"rate\\\">rate = 1 / scale of the distribution (often referred to as 'β')</param>\\n /// <param name=\\\"generator\\\">An optional random number generator object.</param>\\n public Exponential(Tensor rate, torch.Generator generator = null) : base(generator)\\n {\\n var locScale = torch.broadcast_tensors(rate);\\n this.batch_shape = rate.size();\\n this.rate = locScale[0].DetachFromDisposeScope();\\n }\\n\\n private Tensor rate;\\n\\n /// <summary>\\n /// Generates a sample_shape shaped reparameterized sample or sample_shape shaped batch of reparameterized samples\\n /// if the distribution parameters are batched.\\n /// </summary>\\n /// <param name=\\\"sample_shape\\\">The sample shape.</param>\\n public override Tensor rsample(params long[] sample_shape)\\n {\\n var shape = ExtendedShape(sample_shape);\\n return rate.new_empty(shape).exponential_(generator: generator) / rate;\\n }\\n\\n /// <summary>\\n /// Returns the log of the probability density/mass function evaluated at `value`.\\n /// </summary>\\n /// <param name=\\\"value\\\"></param>\\n public override Tensor log_prob(Tensor value) => WrappedTensorDisposeScope(() => rate.log() - rate * value);\\n\\n /// <summary>\\n /// Returns entropy of distribution, batched over batch_shape.\\n /// </summary>\\n /// <returns></returns>\\n public override Tensor entropy() => WrappedTensorDisposeScope(() => 1 - rate.log());\\n\\n /// <summary>\\n /// Returns the cumulative density/mass function evaluated at `value`.\\n /// </summary>\\n /// <param name=\\\"value\\\"></param>\\n public override Tensor cdf(Tensor value) => WrappedTensorDisposeScope(() => 1 - torch.exp(-rate * value));\\n\\n /// <summary>\\n /// Returns the inverse cumulative density/mass function evaluated at `value`.\\n /// </summary>\\n /// <param name=\\\"value\\\"></param>\\n public override Tensor icdf(Tensor value) => WrappedTensorDisposeScope(() => -torch.log(1 - value) / rate);\\n\\n /// <summary>\\n /// Returns a new distribution instance (or populates an existing instance provided by a derived class) with batch dimensions expanded to\\n /// `batch_shape`. This method calls `torch.Tensor.expand()` on the distribution's parameters. As such, this does not allocate new\\n /// memory for the expanded distribution instance.\\n /// </summary>\\n /// <param name=\\\"batch_shape\\\">Tthe desired expanded size.</param>\\n /// <param name=\\\"instance\\\">new instance provided by subclasses that need to override `.expand`.</param>\\n public override distributions.Distribution expand(Size batch_shape, distributions.Distribution instance = null)\\n {\\n if (instance != null && !(instance is Exponential))\\n throw new ArgumentException(\\\"expand(): 'instance' must be a Exponential distribution\\\");\\n\\n var r = rate.expand(batch_shape);\\n\\n var newDistribution = ((instance == null) ? new Exponential(r, generator) : instance) as Exponential;\\n\\n newDistribution.batch_shape = batch_shape;\\n if (newDistribution == instance) {\\n newDistribution.rate = r;\\n }\\n return newDistribution;\\n }\\n\\n protected override IList<Tensor> NaturalParams => new Tensor[] { -rate };\\n\\n protected override Tensor MeanCarrierMeasure => torch.tensor(0, dtype:rate.dtype, device: rate.device);\\n\\n protected override Tensor LogNormalizer(params Tensor[] parameters) => -torch.log(-parameters[0]);\\n }\\n\\n }\\n\\n public static partial class torch\\n {\\n public static partial class distributions\\n {\\n /// <summary>\\n /// Creates a Exponential distribution parameterized by `rate`.\\n /// </summary>\\n /// <param name=\\\"rate\\\">rate = 1 / scale of the distribution (often referred to as 'β')</param>\\n /// <param name=\\\"generator\\\">An optional random number generator object.</param>\\n /// <returns></returns>\\n public static Exponential Exponential(Tensor rate, torch.Generator generator = null)\\n {\\n return new Exponential(rate, generator);\\n }\\n }\\n }\\n}\\n\"\n },\n {\n \"alpha_fraction\": 0.4319537281990051,\n \"alphanum_fraction\": 0.4340375065803528,\n \"avg_line_length\": 40.053096771240234,\n \"blob_id\": \"7ca88a51d0afb4d1c39b18d05f17be1c96005708\",\n \"content_id\": \"48f58f31f3a8f1ae7db47195c872176e18157155\",\n \"detected_licenses\": [\n \"MIT\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"C#\",\n \"length_bytes\": 27834,\n \"license_type\": \"permissive\",\n \"max_line_length\": 200,\n \"num_lines\": 678,\n \"path\": \"/src/TorchSharp/Distributions/Transforms.cs\",\n \"repo_name\": \"NiklasGustafsson/TorchSharp\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"// Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information.\\nusing System;\\nusing System.Collections.Generic;\\nusing System.Linq;\\n\\nnamespace TorchSharp\\n{\\n\\n public static partial class torch\\n {\\n public static partial class distributions\\n {\\n public static partial class transforms\\n {\\n\\n /// <summary>\\n /// Abstract class for invertable transformations with computable log det jacobians.\\n ///\\n /// They are primarily used in torch.distributions.TransformedDistribution.\\n /// </summary>\\n /// <remarks>\\n /// Derived classes should implement one or both of 'forward()' or 'inverse()'.\\n /// Derived classes that set `bijective=true` should also implement 'log_abs_det_jacobian()'\\n /// </remarks>\\n public abstract class Transform\\n {\\n protected bool _bijective = false;\\n\\n protected constraints.Constraint _domain;\\n\\n protected constraints.Constraint _codomain;\\n\\n protected Transform _inv = null;\\n\\n public virtual int event_dim {\\n get {\\n if (_domain.event_dim == codomain.event_dim)\\n return _domain.event_dim;\\n throw new InvalidOperationException(\\\"Please use either .domain.event_dim or .codomain.event_dim\\\");\\n }\\n }\\n\\n public virtual Transform inv {\\n get {\\n Transform result = null;\\n if (this._inv != null)\\n result = _inv;\\n if (result == null) {\\n result = new _InverseTransform(this);\\n _inv = result;\\n }\\n return result;\\n }\\n }\\n\\n public virtual constraints.Constraint domain {\\n get {\\n return _domain;\\n }\\n }\\n\\n public virtual constraints.Constraint codomain {\\n get {\\n return _codomain;\\n }\\n }\\n\\n public virtual bool bijective {\\n get {\\n return _bijective;\\n }\\n }\\n\\n public Tensor sign {\\n get {\\n return _sign();\\n }\\n }\\n\\n protected internal virtual Tensor _sign()\\n {\\n throw new NotImplementedException();\\n }\\n\\n protected internal abstract Tensor _call(Tensor x);\\n\\n protected internal abstract Tensor _inverse(Tensor y);\\n\\n protected internal virtual Tensor log_abs_det_jacobian(Tensor x, Tensor y)\\n {\\n throw new NotImplementedException();\\n }\\n\\n public Tensor forward(Tensor x) => this._call(x);\\n\\n public virtual long[] forward_shape(long[] shape) => shape;\\n\\n public virtual long[] inverse_shape(long[] shape) => shape;\\n\\n protected internal static Tensor _sum_rightmost(Tensor value, int dim)\\n {\\n if (dim == 0) return value;\\n var required_shape = new long[value.shape.Length - dim + 1];\\n var i = 0;\\n for (; i < value.shape.Length - dim; i++) required_shape[i] = value.shape[i];\\n required_shape[i] = -1;\\n return value.reshape(required_shape).sum(-1);\\n }\\n }\\n\\n\\n internal class _InverseTransform : Transform\\n {\\n public _InverseTransform(torch.distributions.transforms.Transform transform)\\n {\\n this._inv = transform;\\n }\\n\\n public override constraints.Constraint domain {\\n get {\\n return _inv.domain;\\n }\\n }\\n\\n public override constraints.Constraint codomain {\\n get {\\n return _inv.codomain;\\n }\\n }\\n\\n public override bool bijective {\\n get {\\n return _inv.bijective;\\n }\\n }\\n\\n public override int event_dim => base.event_dim;\\n\\n public override Transform inv => base.inv;\\n\\n protected internal override Tensor log_abs_det_jacobian(Tensor x, Tensor y)\\n {\\n return -_inv.log_abs_det_jacobian(y, x);\\n }\\n\\n protected internal override Tensor _call(Tensor x)\\n {\\n return _inv._inverse(x);\\n }\\n\\n protected internal override Tensor _inverse(Tensor y)\\n {\\n return _inv._call(y);\\n }\\n\\n protected internal override Tensor _sign()\\n {\\n return _inv.sign;\\n }\\n\\n public override long[] forward_shape(long[] shape)\\n {\\n return _inv.forward_shape(shape);\\n }\\n\\n public override long[] inverse_shape(long[] shape)\\n {\\n return _inv.inverse_shape(shape);\\n }\\n }\\n\\n public class ComposeTransform : Transform\\n {\\n public ComposeTransform(IEnumerable<Transform> parts, int cache_size = 0)\\n {\\n if (parts == null) throw new ArgumentNullException(\\\"parts cannot be null\\\");\\n\\n _parts = parts.ToArray();\\n _reverse_parts = parts.Reverse().ToArray();\\n }\\n\\n private Transform[] _parts;\\n private Transform[] _reverse_parts;\\n\\n public override int event_dim => base.event_dim;\\n\\n public override Transform inv {\\n get {\\n Transform i = _inv;\\n\\n if (i == null) {\\n i = new ComposeTransform(_reverse_parts.Select(p => p.inv));\\n _inv = i;\\n }\\n return _inv;\\n }\\n }\\n\\n public override constraints.Constraint domain {\\n get {\\n if (_parts == null) return constraints.real;\\n\\n var cnt = _parts.Length;\\n var d = _parts[0].domain;\\n\\n var ed = _parts[cnt - 1].codomain.event_dim;\\n foreach (var part in _reverse_parts) {\\n ed += part.domain.event_dim - part.codomain.event_dim;\\n ed = ed < part.domain.event_dim ? part.domain.event_dim : ed;\\n }\\n if (ed > d.event_dim) {\\n d = constraints.independent(domain, ed - domain.event_dim);\\n }\\n return d;\\n }\\n }\\n\\n public override constraints.Constraint codomain {\\n get {\\n if (_parts == null) return constraints.real;\\n\\n var cnt = _parts.Length;\\n var cod = _parts[cnt - 1].domain;\\n\\n var ed = _parts[0].domain.event_dim;\\n foreach (var part in _parts) {\\n ed += part.codomain.event_dim - part.domain.event_dim;\\n ed = ed < part.codomain.event_dim ? part.codomain.event_dim : ed;\\n }\\n if (ed > cod.event_dim) {\\n cod = constraints.independent(codomain, ed - codomain.event_dim);\\n }\\n return cod;\\n }\\n }\\n\\n public override bool bijective => _parts.All(p => p.bijective);\\n\\n protected internal override Tensor _sign()\\n {\\n Tensor s = 1;\\n foreach (var p in _parts) s *= p.sign;\\n return s;\\n }\\n\\n protected internal override Tensor _call(Tensor x)\\n {\\n using var _ = torch.NewDisposeScope();\\n foreach (var p in _parts) {\\n x = p._call(x);\\n }\\n return x.MoveToOuterDisposeScope();\\n }\\n\\n protected internal override Tensor _inverse(Tensor y)\\n {\\n throw new NotImplementedException();\\n }\\n\\n protected internal override Tensor log_abs_det_jacobian(Tensor x, Tensor y)\\n {\\n if (_parts == null) {\\n return torch.zeros_like(x);\\n }\\n\\n using var _ = torch.NewDisposeScope();\\n\\n var xs = new List<Tensor>();\\n xs.Add(x);\\n for (int i = 0; i < _parts.Length-1; i++) {\\n var part = _parts[i];\\n xs.Add(part._call(xs[i]));\\n }\\n xs.Add(y);\\n\\n var terms = new List<Tensor>();\\n var event_dim = domain.event_dim;\\n\\n for (int i = 0; i < _parts.Length - 1; i++) {\\n var part = _parts[i];\\n var x1 = xs[i];\\n var y1 = xs[i + 1];\\n\\n terms.Add(_sum_rightmost(part.log_abs_det_jacobian(x1, y), event_dim - part.domain.event_dim));\\n event_dim += part.codomain.event_dim - part.domain.event_dim;\\n }\\n\\n Tensor result = terms[0];\\n for (var i = 1; i < terms.Count; i++) {\\n result = result + terms[i];\\n }\\n return result.MoveToOuterDisposeScope();\\n }\\n\\n public override long[] forward_shape(long[] shape)\\n {\\n foreach (var p in _parts) {\\n shape = p.forward_shape(shape);\\n }\\n return shape;\\n }\\n\\n public override long[] inverse_shape(long[] shape)\\n {\\n foreach (var p in _reverse_parts) {\\n shape = p.forward_shape(shape);\\n }\\n return shape;\\n }\\n }\\n\\n public class IndepdenentTransform : Transform\\n {\\n private Transform base_transform;\\n private int reinterpreted_batch_dims;\\n\\n public IndepdenentTransform(Transform base_transform, int reinterpreted_batch_dims)\\n {\\n this.base_transform = base_transform;\\n this.reinterpreted_batch_dims = reinterpreted_batch_dims;\\n }\\n\\n public override int event_dim => base.event_dim;\\n\\n public override Transform inv => base.inv;\\n\\n public override constraints.Constraint domain => constraints.independent(base_transform.domain, reinterpreted_batch_dims);\\n\\n public override constraints.Constraint codomain => constraints.independent(base_transform.codomain, reinterpreted_batch_dims);\\n\\n public override bool bijective => base_transform.bijective;\\n\\n public override long[] forward_shape(long[] shape)\\n {\\n return base_transform.forward_shape(shape);\\n }\\n\\n public override long[] inverse_shape(long[] shape)\\n {\\n return base_transform.inverse_shape(shape);\\n }\\n\\n protected internal override Tensor _sign() => base_transform._sign();\\n\\n protected internal override Tensor log_abs_det_jacobian(Tensor x, Tensor y)\\n {\\n using var _ = torch.NewDisposeScope();\\n var result = base_transform.log_abs_det_jacobian(x, y);\\n result = _sum_rightmost(result, reinterpreted_batch_dims);\\n return result.MoveToOuterDisposeScope();\\n }\\n\\n protected internal override Tensor _call(Tensor x)\\n {\\n if (x.dim() < domain.event_dim)\\n throw new ArgumentException(\\\"Too few dimensions on input\\\");\\n return base_transform._call(x);\\n }\\n\\n protected internal override Tensor _inverse(Tensor y)\\n {\\n if (y.dim() < codomain.event_dim)\\n throw new ArgumentException(\\\"Too few dimensions on input\\\");\\n return base_transform._inverse(y);\\n }\\n }\\n\\n public class ReshapeTransform : Transform\\n {\\n private Size in_shape;\\n private Size out_shape;\\n\\n public ReshapeTransform(long[] in_shape, long[] out_shape)\\n {\\n this.in_shape = in_shape;\\n this.out_shape = out_shape;\\n if (this.in_shape.numel() != this.out_shape.numel())\\n throw new ArgumentException(\\\"in_shape, out_shape have different numbers of elements\\\");\\n\\n }\\n public override bool bijective => true;\\n\\n public override constraints.Constraint domain => constraints.independent(constraints.real, in_shape.Length);\\n\\n public override constraints.Constraint codomain => constraints.independent(constraints.real, out_shape.Length);\\n\\n public override long[] forward_shape(long[] shape)\\n {\\n return base.forward_shape(shape);\\n }\\n\\n public override long[] inverse_shape(long[] shape)\\n {\\n return base.inverse_shape(shape);\\n }\\n\\n protected internal override Tensor log_abs_det_jacobian(Tensor x, Tensor y)\\n {\\n long inLen = x.shape.Length - (x.dim() - in_shape.Length);\\n var batch_shape = new long[inLen];\\n for (var i = 0; i < inLen; i++) {\\n batch_shape[i] = x.shape[i];\\n }\\n return x.new_zeros(batch_shape);\\n }\\n\\n protected internal override Tensor _call(Tensor x)\\n {\\n long inLen = x.shape.Length - (x.dim() - in_shape.Length);\\n long otLen = out_shape.Length;\\n var batch_shape = new long[inLen+otLen];\\n for (var i = 0; i < inLen; i++) {\\n batch_shape[i] = x.shape[i];\\n }\\n for (var i = 0; i < otLen; i++) {\\n batch_shape[i+inLen] = out_shape[i];\\n }\\n return x.reshape(batch_shape);\\n }\\n\\n protected internal override Tensor _inverse(Tensor y)\\n {\\n long otLen = y.shape.Length - (y.dim() - out_shape.Length);\\n long inLen = in_shape.Length;\\n var batch_shape = new long[inLen + otLen];\\n for (var i = 0; i < otLen; i++) {\\n batch_shape[i] = y.shape[i];\\n }\\n for (var i = 0; i < inLen; i++) {\\n batch_shape[i + otLen] = in_shape[i];\\n }\\n return y.reshape(batch_shape);\\n }\\n }\\n\\n public class ExpTransform : Transform\\n {\\n public override constraints.Constraint domain => constraints.real;\\n\\n public override constraints.Constraint codomain => constraints.positive;\\n\\n public override bool bijective => true;\\n\\n protected internal override Tensor _sign() => 1;\\n\\n protected internal override Tensor log_abs_det_jacobian(Tensor x, Tensor y) => x;\\n protected internal override Tensor _call(Tensor x) => x.exp();\\n\\n protected internal override Tensor _inverse(Tensor y) => y.log();\\n }\\n\\n public class LogTransform : Transform\\n {\\n public override constraints.Constraint domain => constraints.positive;\\n\\n public override constraints.Constraint codomain => constraints.real;\\n\\n public override bool bijective => true;\\n\\n protected internal override Tensor _sign() => 1;\\n\\n protected internal override Tensor log_abs_det_jacobian(Tensor x, Tensor y) => -x;\\n\\n protected internal override Tensor _call(Tensor x) => x.log();\\n\\n protected internal override Tensor _inverse(Tensor y) => y.exp();\\n }\\n\\n public class PowerTransform : Transform\\n {\\n private Tensor exponent;\\n\\n public PowerTransform(Tensor exponent)\\n {\\n this.exponent = exponent;\\n }\\n\\n public override constraints.Constraint domain => constraints.positive;\\n\\n public override constraints.Constraint codomain => constraints.positive;\\n\\n public override bool bijective => true;\\n\\n protected internal override Tensor _sign() => 1;\\n\\n public override long[] forward_shape(long[] shape)\\n {\\n return torch.broadcast_shapes(shape, exponent.shape).Shape;\\n }\\n\\n public override long[] inverse_shape(long[] shape)\\n {\\n return torch.broadcast_shapes(shape, exponent.shape).Shape;\\n }\\n\\n protected internal override Tensor log_abs_det_jacobian(Tensor x, Tensor y)\\n {\\n return torch.WrappedTensorDisposeScope(() => (exponent * y / x).abs().log());\\n }\\n\\n protected internal override Tensor _call(Tensor x)\\n {\\n return x.pow(exponent);\\n }\\n\\n protected internal override Tensor _inverse(Tensor y)\\n {\\n return y.pow(1 / exponent);\\n }\\n }\\n\\n public class SigmoidTransform : Transform\\n {\\n public override constraints.Constraint domain => constraints.real;\\n\\n public override constraints.Constraint codomain => constraints.unit_interval;\\n\\n public override bool bijective => true;\\n\\n protected internal override Tensor _sign() => 1;\\n\\n protected internal override Tensor log_abs_det_jacobian(Tensor x, Tensor y) => -nn.functional.softplus(-x) - nn.functional.softplus(x);\\n\\n protected internal override Tensor _call(Tensor x)\\n {\\n var finfo = torch.finfo(x.dtype);\\n return torch.WrappedTensorDisposeScope(() => torch.clamp(torch.sigmoid(x), min: finfo.tiny, max: 1 - finfo.eps));\\n }\\n\\n protected internal override Tensor _inverse(Tensor y)\\n {\\n using var _ = torch.NewDisposeScope();\\n var finfo = torch.finfo(y.dtype);\\n y = y.clamp(min: finfo.tiny, max: 1 - finfo.eps);\\n return (y.log() - (-y).log1p()).MoveToOuterDisposeScope();\\n }\\n }\\n\\n public class SoftplusTransform : Transform\\n {\\n public override constraints.Constraint domain => constraints.real;\\n\\n public override constraints.Constraint codomain => constraints.positive;\\n\\n public override bool bijective => true;\\n\\n protected internal override Tensor _sign() => 1;\\n\\n protected internal override Tensor log_abs_det_jacobian(Tensor x, Tensor y) => -nn.functional.softplus(-x);\\n\\n protected internal override Tensor _call(Tensor x) => nn.functional.softplus(-x);\\n\\n protected internal override Tensor _inverse(Tensor y) => (-y).expm1().neg().log() + y;\\n }\\n\\n public class SoftmaxTransform : Transform\\n {\\n public override constraints.Constraint domain => constraints.real_vector;\\n\\n public override constraints.Constraint codomain => constraints.simplex;\\n\\n public override bool bijective => true;\\n\\n protected internal override Tensor _call(Tensor x)\\n {\\n var logprobs = x;\\n var probs = (logprobs - logprobs.max(-1, true).values).exp();\\n return probs / probs.sum(-1, true);\\n }\\n\\n protected internal override Tensor _inverse(Tensor y)\\n {\\n return y.log();\\n }\\n }\\n\\n\\n public class TanhTransform : Transform\\n {\\n public override constraints.Constraint domain => constraints.real;\\n\\n public override constraints.Constraint codomain => constraints.interval(-1.0, 1.0);\\n\\n public override bool bijective => true;\\n\\n protected internal override Tensor _sign() => 1;\\n\\n protected internal override Tensor log_abs_det_jacobian(Tensor x, Tensor y) => torch.WrappedTensorDisposeScope(() => 2.0 * (Math.Log(2.0) - x - -nn.functional.softplus(-2.0 * x)));\\n\\n protected internal override Tensor _call(Tensor x) => x.tanh();\\n\\n protected internal override Tensor _inverse(Tensor y) => y.atanh();\\n }\\n\\n public class AbsTransform : Transform\\n {\\n public override constraints.Constraint domain => constraints.real;\\n\\n public override constraints.Constraint codomain => constraints.positive;\\n\\n protected internal override Tensor _call(Tensor x) => x.abs();\\n\\n protected internal override Tensor _inverse(Tensor y) => y;\\n }\\n\\n public class AffineTransform : Transform\\n {\\n private Tensor loc;\\n private Tensor scale;\\n private int _event_dim;\\n\\n public AffineTransform(double loc, double scale, int event_dim = 0)\\n {\\n this._event_dim = event_dim;\\n this.loc = loc;\\n this.scale = scale;\\n }\\n\\n public AffineTransform (Tensor loc, Tensor scale, int event_dim = 0)\\n {\\n this._event_dim = event_dim;\\n this.loc = loc;\\n this.scale = scale;\\n }\\n\\n public override int event_dim => _event_dim;\\n\\n public override bool bijective => true;\\n\\n public override constraints.Constraint domain =>\\n _event_dim == 0 ? constraints.real : constraints.independent(constraints.real, event_dim);\\n\\n public override constraints.Constraint codomain =>\\n _event_dim == 0 ? constraints.real : constraints.independent(constraints.real, event_dim);\\n\\n public override long[] forward_shape(long[] shape)\\n {\\n return torch.broadcast_shapes(shape, loc.shape, scale.shape).Shape;\\n }\\n\\n public override long[] inverse_shape(long[] shape)\\n {\\n return torch.broadcast_shapes(shape, loc.shape, scale.shape).Shape;\\n }\\n\\n protected internal override Tensor log_abs_det_jacobian(Tensor x, Tensor y)\\n {\\n using var _ = torch.NewDisposeScope();\\n var shape = x.shape;\\n var scale = this.scale;\\n var result = torch.abs(scale).log();\\n\\n if (_event_dim > 0) {\\n var result_size = new long[result.shape.Length - _event_dim + 1];\\n int i = 0;\\n for (; i < result.shape.Length - _event_dim; i++) result_size[i] = result.shape[i];\\n result_size[i] = -1;\\n result = result.view(result_size).sum(-1);\\n var nshape = new long[shape.Length];\\n for (; i < shape.Length - _event_dim; i++) nshape[i] = shape[i];\\n shape = nshape;\\n }\\n return result.expand(shape).MoveToOuterDisposeScope();\\n }\\n\\n protected internal override Tensor _call(Tensor x) => torch.WrappedTensorDisposeScope(() => loc + scale * x);\\n\\n protected internal override Tensor _inverse(Tensor y) => torch.WrappedTensorDisposeScope(() => (y - loc) / scale);\\n\\n protected internal override Tensor _sign()\\n {\\n return scale.sign();\\n }\\n }\\n }\\n }\\n }\\n}\\n\"\n },\n {\n \"alpha_fraction\": 0.6935985088348389,\n \"alphanum_fraction\": 0.7016780376434326,\n \"avg_line_length\": 39.224998474121094,\n \"blob_id\": \"f788fa7e0caaf70a7fa0398e2ae4b69a1eb8b438\",\n \"content_id\": \"bc54870cd554a8893b69066c4bcce17356955df5\",\n \"detected_licenses\": [\n \"MIT\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"C#\",\n \"length_bytes\": 1609,\n \"license_type\": \"permissive\",\n \"max_line_length\": 130,\n \"num_lines\": 40,\n \"path\": \"/src/TorchSharp/PInvoke/LibTorchSharp.THSData.cs\",\n \"repo_name\": \"NiklasGustafsson/TorchSharp\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"// Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information.\\n#nullable enable\\nusing System;\\nusing System.Runtime.InteropServices;\\n\\nnamespace TorchSharp.PInvoke\\n{\\n internal static partial class NativeMethods\\n {\\n#pragma warning disable CA2101\\n [DllImport(\\\"LibTorchSharp\\\", CharSet = CharSet.Ansi, BestFitMapping = false, ThrowOnUnmappableChar = true)]\\n internal static extern IntPtr THSData_loaderMNIST(\\n [MarshalAs(UnmanagedType.LPStr)] string filename,\\n long batchSize,\\n [MarshalAs(UnmanagedType.U1)] bool isTrain);\\n\\n [DllImport(\\\"LibTorchSharp\\\", CharSet = CharSet.Ansi, BestFitMapping = false, ThrowOnUnmappableChar = true)]\\n internal static extern IntPtr THSData_loaderCIFAR10(\\n [MarshalAs(UnmanagedType.LPStr)] string path,\\n long batchSize,\\n [MarshalAs(UnmanagedType.U1)] bool isTrain);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern IntPtr THSData_current(IntPtr iterator, IntPtr data, IntPtr target);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n [return: MarshalAs(UnmanagedType.U1)]\\n internal static extern bool THSData_moveNext(IntPtr iterator);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern long THSData_size(IntPtr iterator);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern void THSData_reset(IntPtr iterator);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern void THSData_dispose(IntPtr iterator);\\n }\\n#pragma warning restore CA2101\\n}\\n\"\n },\n {\n \"alpha_fraction\": 0.5785619020462036,\n \"alphanum_fraction\": 0.5825566053390503,\n \"avg_line_length\": 34.78571319580078,\n \"blob_id\": \"edbc10770974a49626d6a0d4677e3d08ee68af0b\",\n \"content_id\": \"a9ee0ebe6bf3317ca1e3e115f971a3e9b27841df\",\n \"detected_licenses\": [\n \"MIT\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"C#\",\n \"length_bytes\": 1502,\n \"license_type\": \"permissive\",\n \"max_line_length\": 130,\n \"num_lines\": 42,\n \"path\": \"/src/TorchVision/AdjustSaturation.cs\",\n \"repo_name\": \"NiklasGustafsson/TorchSharp\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"// Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information.\\nusing System;\\nusing static TorchSharp.torch;\\n\\nnamespace TorchSharp\\n{\\n public static partial class torchvision\\n {\\n internal class AdjustSaturation : ITransform\\n {\\n internal AdjustSaturation(double saturation_factor)\\n {\\n if (saturation_factor < 0.0)\\n throw new ArgumentException($\\\"The saturation factor ({saturation_factor}) must be non-negative.\\\");\\n this.saturation_factor = saturation_factor;\\n }\\n\\n public Tensor call(Tensor img)\\n {\\n return transforms.functional.adjust_saturation(img, saturation_factor);\\n }\\n\\n private double saturation_factor;\\n }\\n\\n public static partial class transforms\\n {\\n /// <summary>\\n /// Adjust the color saturation of an image.\\n /// </summary>\\n /// <param name=\\\"saturation_factor\\\">\\n /// How much to adjust the saturation. 0 will give a black and white image, 1 will give the original image\\n /// while 2 will enhance the saturation by a factor of 2.\\n /// </param>\\n /// <returns></returns>\\n static public ITransform AdjustSaturation(double saturation_factor)\\n {\\n return new AdjustSaturation(saturation_factor);\\n }\\n }\\n }\\n}\"\n },\n {\n \"alpha_fraction\": 0.5086056590080261,\n \"alphanum_fraction\": 0.5247212052345276,\n \"avg_line_length\": 39.3984375,\n \"blob_id\": \"38cc94d081a917e7ddbdb63ab623d68c1a7148fb\",\n \"content_id\": \"7d12f5cc60032d2d00e14ac07ff3af4e0fa61875\",\n \"detected_licenses\": [\n \"MIT\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"C#\",\n \"length_bytes\": 15513,\n \"license_type\": \"permissive\",\n \"max_line_length\": 149,\n \"num_lines\": 384,\n \"path\": \"/src/TorchAudio/Modules/WaveRNN.cs\",\n \"repo_name\": \"NiklasGustafsson/TorchSharp\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"// Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information.\\n\\n// A number of implementation details in this file have been translated from the Python version of torchaudio,\\n// largely located in the files found in this folder:\\n//\\n// https://github.com/pytorch/audio/blob/c15eee23964098f88ab0afe25a8d5cd9d728af54/torchaudio/models/wavernn.py\\n//\\n// The origin has the following copyright notice and license:\\n//\\n// https://github.com/pytorch/audio/blob/main/LICENSE\\n//\\n\\nusing System;\\nusing System.Collections.Generic;\\nusing static TorchSharp.torch;\\nusing F = TorchSharp.torch.nn.functional;\\n\\n#nullable enable\\nnamespace TorchSharp.Modules\\n{\\n /// <summary>\\n /// This class is used to represent a WaveRNN module.\\n /// </summary>\\n public class WaveRNN : nn.Module<Tensor, Tensor, Tensor>\\n {\\n private readonly int _pad;\\n public readonly nn.Module<Tensor, Tensor> fc;\\n public readonly nn.Module<Tensor, Tensor> fc1;\\n public readonly nn.Module<Tensor, Tensor> fc2;\\n public readonly nn.Module<Tensor, Tensor> fc3;\\n public readonly int hop_length;\\n public readonly int kernel_size;\\n public readonly int n_aux;\\n public readonly int n_bits;\\n public readonly int n_classes;\\n public readonly int n_rnn;\\n public readonly nn.Module<Tensor, Tensor> relu1;\\n public readonly nn.Module<Tensor, Tensor> relu2;\\n public readonly GRU rnn1;\\n public readonly GRU rnn2;\\n internal readonly UpsampleNetwork upsample;\\n\\n protected override void Dispose(bool disposing)\\n {\\n if (disposing) {\\n fc.Dispose();\\n fc1.Dispose();\\n fc2.Dispose();\\n fc3.Dispose();\\n relu1.Dispose();\\n relu2.Dispose();\\n rnn1.Dispose();\\n rnn2.Dispose();\\n upsample.Dispose();\\n }\\n base.Dispose(disposing);\\n }\\n\\n internal WaveRNN(\\n string name,\\n long[] upsample_scales,\\n int n_classes,\\n int hop_length,\\n int n_res_block = 10,\\n int n_rnn = 512,\\n int n_fc = 512,\\n int kernel_size = 5,\\n int n_freq = 128,\\n int n_hidden = 128,\\n int n_output = 128) : base(name)\\n {\\n this.kernel_size = kernel_size;\\n this._pad = (kernel_size % 2 == 1 ? kernel_size - 1 : kernel_size) / 2;\\n this.n_rnn = n_rnn;\\n this.n_aux = n_output / 4;\\n this.hop_length = hop_length;\\n this.n_classes = n_classes;\\n this.n_bits = (int)(Math.Log(this.n_classes) / Math.Log(2) + 0.5);\\n\\n long total_scale = 1;\\n foreach (var upsample_scale in upsample_scales) {\\n total_scale *= upsample_scale;\\n }\\n if (total_scale != this.hop_length) {\\n throw new ArgumentException($\\\"Expected: total_scale == hop_length, but found {total_scale} != {hop_length}\\\");\\n }\\n\\n this.upsample = new UpsampleNetwork(\\\"upsamplenetwork\\\", upsample_scales, n_res_block, n_freq, n_hidden, n_output, kernel_size);\\n this.fc = nn.Linear(n_freq + this.n_aux + 1, n_rnn);\\n\\n this.rnn1 = nn.GRU(n_rnn, n_rnn, batchFirst: true);\\n this.rnn2 = nn.GRU(n_rnn + this.n_aux, n_rnn, batchFirst: true);\\n\\n this.relu1 = nn.ReLU(inplace: true);\\n this.relu2 = nn.ReLU(inplace: true);\\n\\n this.fc1 = nn.Linear(n_rnn + this.n_aux, n_fc);\\n this.fc2 = nn.Linear(n_fc + this.n_aux, n_fc);\\n this.fc3 = nn.Linear(n_fc, this.n_classes);\\n\\n this.RegisterComponents();\\n }\\n\\n /// <summary>\\n /// Pass the input through the WaveRNN model.\\n /// </summary>\\n /// <param name=\\\"waveform\\\">The input waveform to the WaveRNN layer</param>\\n /// <param name=\\\"specgram\\\">The input spectrogram to the WaveRNN layer</param>\\n /// <returns></returns>\\n /// <exception cref=\\\"ArgumentException\\\"></exception>\\n public override Tensor forward(Tensor waveform, Tensor specgram)\\n {\\n if (waveform.size(1) != 1) {\\n throw new ArgumentException(\\\"Require the input channel of waveform is 1\\\");\\n }\\n if (specgram.size(1) != 1) {\\n throw new ArgumentException(\\\"Require the input channel of specgram is 1\\\");\\n }\\n // remove channel dimension until the end\\n waveform = waveform.squeeze(1);\\n specgram = specgram.squeeze(1);\\n\\n var batch_size = waveform.size(0);\\n var h1 = torch.zeros(1, batch_size, this.n_rnn, dtype: waveform.dtype, device: waveform.device);\\n var h2 = torch.zeros(1, batch_size, this.n_rnn, dtype: waveform.dtype, device: waveform.device);\\n // output of upsample:\\n // specgram: (n_batch, n_freq, (n_time - kernel_size + 1) * total_scale)\\n // aux: (n_batch, n_output, (n_time - kernel_size + 1) * total_scale)\\n Tensor aux;\\n (specgram, aux) = this.upsample.call(specgram);\\n specgram = specgram.transpose(1, 2);\\n aux = aux.transpose(1, 2);\\n\\n var aux_idx = new long[5];\\n for (int i = 0; i < aux_idx.Length; i++) {\\n aux_idx[i] = this.n_aux * i;\\n }\\n var a1 = aux[TensorIndex.Colon, TensorIndex.Colon, TensorIndex.Slice(aux_idx[0], aux_idx[1])];\\n var a2 = aux[TensorIndex.Colon, TensorIndex.Colon, TensorIndex.Slice(aux_idx[1], aux_idx[2])];\\n var a3 = aux[TensorIndex.Colon, TensorIndex.Colon, TensorIndex.Slice(aux_idx[2], aux_idx[3])];\\n var a4 = aux[TensorIndex.Colon, TensorIndex.Colon, TensorIndex.Slice(aux_idx[3], aux_idx[4])];\\n\\n var x = torch.cat(new Tensor[] { waveform.unsqueeze(-1), specgram, a1 }, dim: -1);\\n x = this.fc.call(x);\\n var res = x;\\n (x, _) = this.rnn1.call(x, h1);\\n\\n x = x + res;\\n res = x;\\n x = torch.cat(new Tensor[] { x, a2 }, dim: -1);\\n (x, _) = this.rnn2.call(x, h2);\\n\\n x = x + res;\\n x = torch.cat(new Tensor[] { x, a3 }, dim: -1);\\n x = this.fc1.call(x);\\n x = this.relu1.call(x);\\n\\n x = torch.cat(new Tensor[] { x, a4 }, dim: -1);\\n x = this.fc2.call(x);\\n x = this.relu2.call(x);\\n x = this.fc3.call(x);\\n\\n // bring back channel dimension\\n return x.unsqueeze(1);\\n }\\n\\n /// <summary>\\n /// Inference method of WaveRNN.\\n /// </summary>\\n /// <param name=\\\"specgram\\\">Batch of spectrograms.</param>\\n /// <param name=\\\"lengths\\\">Indicates the valid length of each audio in the batch.</param>\\n /// <returns>The inferred waveform and the valid length in time axis of the output Tensor.</returns>\\n public virtual (Tensor, Tensor?) infer(Tensor specgram, Tensor? lengths = null)\\n {\\n var device = specgram.device;\\n var dtype = specgram.dtype;\\n\\n specgram = torch.nn.functional.pad(specgram, (this._pad, this._pad));\\n Tensor aux;\\n (specgram, aux) = this.upsample.call(specgram);\\n if (lengths is not null) {\\n lengths = lengths * this.upsample.total_scale;\\n }\\n\\n var output = new List<Tensor>();\\n long b_size = specgram.size(0);\\n long seq_len = specgram.size(2);\\n\\n var h1 = torch.zeros(new long[] { 1, b_size, this.n_rnn }, device: device, dtype: dtype);\\n var h2 = torch.zeros(new long[] { 1, b_size, this.n_rnn }, device: device, dtype: dtype);\\n var x = torch.zeros(new long[] { b_size, 1 }, device: device, dtype: dtype);\\n\\n var aux_split = new Tensor[4];\\n for (int i = 0; i < 4; i++) {\\n aux_split[i] = aux[TensorIndex.Colon, TensorIndex.Slice(this.n_aux * i, this.n_aux * (i + 1)), TensorIndex.Colon];\\n }\\n\\n for (int i = 0; i < seq_len; i++) {\\n\\n var m_t = specgram[TensorIndex.Colon, TensorIndex.Colon, i];\\n\\n var a1_t = aux_split[0][TensorIndex.Colon, TensorIndex.Colon, i];\\n var a2_t = aux_split[1][TensorIndex.Colon, TensorIndex.Colon, i];\\n var a3_t = aux_split[2][TensorIndex.Colon, TensorIndex.Colon, i];\\n var a4_t = aux_split[3][TensorIndex.Colon, TensorIndex.Colon, i];\\n\\n x = torch.cat(new Tensor[] { x, m_t, a1_t }, dim: 1);\\n x = this.fc.call(x);\\n (_, h1) = this.rnn1.call(x.unsqueeze(1), h1);\\n\\n x = x + h1[0];\\n var inp = torch.cat(new Tensor[] { x, a2_t }, dim: 1);\\n (_, h2) = this.rnn2.call(inp.unsqueeze(1), h2);\\n\\n x = x + h2[0];\\n x = torch.cat(new Tensor[] { x, a3_t }, dim: 1);\\n x = F.relu(this.fc1.call(x));\\n\\n x = torch.cat(new Tensor[] { x, a4_t }, dim: 1);\\n x = F.relu(this.fc2.call(x));\\n\\n var logits = this.fc3.call(x);\\n\\n var posterior = F.softmax(logits, dim: 1);\\n\\n x = torch.multinomial(posterior, 1).@float();\\n // Transform label [0, 2 ** n_bits - 1] to waveform [-1, 1]\\n\\n x = 2 * x / ((1 << this.n_bits) - 1.0) - 1.0;\\n\\n output.Add(x);\\n }\\n return (torch.stack(output).permute(1, 2, 0), lengths);\\n }\\n\\n private class ResBlock : nn.Module<Tensor, Tensor>\\n {\\n public nn.Module<Tensor, Tensor> resblock_model;\\n\\n protected override void Dispose(bool disposing)\\n {\\n if (disposing) {\\n resblock_model.Dispose();\\n }\\n base.Dispose(disposing);\\n }\\n\\n public ResBlock(string name, int n_freq = 128) : base(name)\\n {\\n this.resblock_model = nn.Sequential(\\n nn.Conv1d(inputChannel: n_freq, outputChannel: n_freq, kernelSize: 1, bias: false),\\n nn.BatchNorm1d(n_freq),\\n nn.ReLU(inplace: true),\\n nn.Conv1d(inputChannel: n_freq, outputChannel: n_freq, kernelSize: 1, bias: false),\\n nn.BatchNorm1d(n_freq));\\n RegisterComponents();\\n }\\n\\n public override Tensor forward(Tensor specgram)\\n {\\n return this.resblock_model.call(specgram) + specgram;\\n }\\n }\\n\\n internal class MelResNet : nn.Module<Tensor, Tensor>\\n {\\n public readonly nn.Module<Tensor, Tensor> melresnet_model;\\n\\n protected override void Dispose(bool disposing)\\n {\\n if (disposing) {\\n melresnet_model.Dispose();\\n }\\n base.Dispose(disposing);\\n }\\n\\n public MelResNet(\\n string name,\\n int n_res_block = 10,\\n int n_freq = 128,\\n int n_hidden = 128,\\n int n_output = 128,\\n int kernel_size = 5) : base(name)\\n {\\n var modules = new List<nn.Module<Tensor, Tensor>>();\\n modules.Add(nn.Conv1d(inputChannel: n_freq, outputChannel: n_hidden, kernelSize: kernel_size, bias: false));\\n modules.Add(nn.BatchNorm1d(n_hidden));\\n modules.Add(nn.ReLU(inplace: true));\\n for (int i = 0; i < n_res_block; i++) {\\n modules.Add(new ResBlock(\\\"resblock\\\", n_hidden));\\n }\\n modules.Add(nn.Conv1d(inputChannel: n_hidden, outputChannel: n_output, kernelSize: 1));\\n this.melresnet_model = nn.Sequential(modules);\\n RegisterComponents();\\n }\\n\\n public override Tensor forward(Tensor specgram)\\n {\\n return this.melresnet_model.call(specgram);\\n }\\n }\\n\\n public class Stretch2d : nn.Module<Tensor, Tensor>\\n {\\n public long freq_scale;\\n public long time_scale;\\n\\n public Stretch2d(string name, long time_scale, long freq_scale) : base(name)\\n {\\n this.freq_scale = freq_scale;\\n this.time_scale = time_scale;\\n this.RegisterComponents();\\n }\\n\\n public override Tensor forward(Tensor specgram)\\n {\\n return specgram.repeat_interleave(this.freq_scale, -2).repeat_interleave(this.time_scale, -1);\\n }\\n }\\n\\n internal class UpsampleNetwork : nn.Module<Tensor, (Tensor,Tensor)>\\n {\\n public readonly long indent;\\n public readonly MelResNet resnet;\\n public readonly Stretch2d resnet_stretch;\\n public readonly long total_scale;\\n public readonly nn.Module<Tensor, Tensor> upsample_layers;\\n\\n protected override void Dispose(bool disposing)\\n {\\n if (disposing) {\\n resnet.Dispose();\\n resnet_stretch.Dispose();\\n upsample_layers .Dispose();\\n }\\n base.Dispose(disposing);\\n }\\n\\n public UpsampleNetwork(\\n string name,\\n long[] upsample_scales,\\n int n_res_block = 10,\\n int n_freq = 128,\\n int n_hidden = 128,\\n int n_output = 128,\\n int kernel_size = 5) : base(name)\\n {\\n long total_scale = 1;\\n foreach (var upsample_scale in upsample_scales) {\\n total_scale *= upsample_scale;\\n }\\n this.total_scale = total_scale;\\n\\n this.indent = (kernel_size - 1) / 2 * total_scale;\\n this.resnet = new MelResNet(\\\"melresnet\\\", n_res_block, n_freq, n_hidden, n_output, kernel_size);\\n this.resnet_stretch = new Stretch2d(\\\"stretch2d\\\", total_scale, 1);\\n\\n var up_layers = new List<nn.Module<Tensor, Tensor>>();\\n foreach (var scale in upsample_scales) {\\n var stretch = new Stretch2d(\\\"stretch2d\\\", scale, 1);\\n var conv = nn.Conv2d(inputChannel: 1, outputChannel: 1, kernelSize: (1, scale * 2 + 1), padding: (0, scale), bias: false);\\n torch.nn.init.constant_(conv.weight, 1.0 / (scale * 2 + 1));\\n up_layers.Add(stretch);\\n up_layers.Add(conv);\\n }\\n this.upsample_layers = nn.Sequential(up_layers);\\n this.RegisterComponents();\\n }\\n\\n public override (Tensor, Tensor) forward(Tensor specgram)\\n {\\n var resnet_output = this.resnet.call(specgram).unsqueeze(1);\\n resnet_output = this.resnet_stretch.call(resnet_output);\\n resnet_output = resnet_output.squeeze(1);\\n\\n specgram = specgram.unsqueeze(1);\\n var upsampling_output = this.upsample_layers.call(specgram);\\n upsampling_output = upsampling_output.squeeze(1)[TensorIndex.Colon, TensorIndex.Colon, TensorIndex.Slice(this.indent, -this.indent)];\\n\\n return (upsampling_output, resnet_output);\\n }\\n }\\n }\\n}\\n\"\n },\n {\n \"alpha_fraction\": 0.5384615659713745,\n \"alphanum_fraction\": 0.5384615659713745,\n \"avg_line_length\": 26.736841201782227,\n \"blob_id\": \"42003a6ad9d51768ecf613f46814f4353df34c7e\",\n \"content_id\": \"7cb598edad11660fbe13f0dbcfaf82eb971e374a\",\n \"detected_licenses\": [\n \"MIT\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"C#\",\n \"length_bytes\": 1053,\n \"license_type\": \"permissive\",\n \"max_line_length\": 130,\n \"num_lines\": 38,\n \"path\": \"/src/TorchVision/Lambda.cs\",\n \"repo_name\": \"NiklasGustafsson/TorchSharp\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"// Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information.\\nusing System;\\nusing static TorchSharp.torch;\\n\\n\\nnamespace TorchSharp\\n{\\n public static partial class torchvision\\n {\\n internal class Lambda : ITransform\\n {\\n internal Lambda(Func<Tensor, Tensor> lambda)\\n {\\n this.lambda = lambda;\\n }\\n\\n public Tensor call(Tensor img)\\n {\\n return lambda(img);\\n }\\n\\n private Func<Tensor, Tensor> lambda;\\n }\\n\\n public static partial class transforms\\n {\\n /// <summary>\\n /// Apply a user-defined function as a transform. \\n /// </summary>\\n /// <param name=\\\"lambda\\\">Lambda/function to be used for transform.</param>\\n /// <returns></returns>\\n static public ITransform Lambda(Func<Tensor, Tensor> lambda)\\n {\\n return new Lambda(lambda);\\n }\\n }\\n }\\n}\"\n },\n {\n \"alpha_fraction\": 0.57302325963974,\n \"alphanum_fraction\": 0.5801550149917603,\n \"avg_line_length\": 42.58108139038086,\n \"blob_id\": \"afb5d19e7142226a9e7999f8353927175bd9ef44\",\n \"content_id\": \"363cb40d59f29b76c3c54271c99d91a6e40ac5a1\",\n \"detected_licenses\": [\n \"MIT\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"C#\",\n \"length_bytes\": 3225,\n \"license_type\": \"permissive\",\n \"max_line_length\": 169,\n \"num_lines\": 74,\n \"path\": \"/src/TorchSharp/NN/Dropout2d.cs\",\n \"repo_name\": \"NiklasGustafsson/TorchSharp\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"// Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information.\\nusing System;\\nusing static TorchSharp.torch;\\nusing static TorchSharp.PInvoke.NativeMethods;\\n\\nnamespace TorchSharp\\n{\\n using Modules;\\n\\n namespace Modules\\n {\\n /// <summary>\\n /// This class is used to represent a Dropout2d module.\\n /// </summary>\\n public sealed class Dropout2d : torch.nn.Module<Tensor, Tensor>\\n {\\n internal Dropout2d(double p = 0.5, bool inplace = false) : base(nameof(Dropout2d))\\n {\\n this.p = p;\\n this.inplace = inplace;\\n }\\n\\n public override Tensor forward(Tensor input)\\n {\\n var res = THSNN_dropout2d(input.Handle, p, this.training, inplace);\\n if (res == IntPtr.Zero) { torch.CheckForErrors(); }\\n return new Tensor(res);\\n }\\n\\n // Rather than spending cycles only to discover that this module has neither\\n // parameters nor buffers, just shortcut the move completely.\\n protected internal override nn.Module _to(Device device, ScalarType dtype) => this;\\n protected internal override nn.Module _to(DeviceType deviceType, int deviceIndex = -1) => this;\\n protected internal override nn.Module _to(ScalarType dtype) => this;\\n\\n private bool inplace;\\n private double p;\\n }\\n }\\n\\n public static partial class torch\\n {\\n public static partial class nn\\n {\\n /// <summary>\\n /// Randomly zero out entire channels (a channel is a 2D feature map, e.g., the jj -th channel of the ii -th sample in the batched input is a 2D tensor).\\n /// Each channel will be zeroed out independently on every forward call with probability p using samples from a Bernoulli distribution.\\n /// </summary>\\n /// <param name=\\\"p\\\">Probability of an element to be zeroed. Default: 0.5</param>\\n /// <param name=\\\"inplace\\\">If set to true, will do this operation in-place. Default: false</param>\\n /// <returns></returns>\\n public static Dropout2d Dropout2d(double p = 0.5, bool inplace = false)\\n {\\n return new Dropout2d(p, inplace);\\n }\\n\\n public static partial class functional\\n {\\n\\n /// <summary>\\n /// Randomly zero out entire channels (a channel is a 2D feature map, e.g., the jj -th channel of the ii -th sample in the batched input is a 2D tensor).\\n /// Each channel will be zeroed out independently on every forward call with probability p using samples from a Bernoulli distribution.\\n /// </summary>\\n /// <returns></returns>\\n public static Tensor dropout2d(Tensor input, double p = 0.5, bool training = true, bool inplace = false)\\n {\\n var res = THSNN_dropout2d(input.Handle, p, training, inplace);\\n if (res == IntPtr.Zero) { torch.CheckForErrors(); }\\n return new Tensor(res);\\n }\\n }\\n }\\n }\\n}\\n\"\n },\n {\n \"alpha_fraction\": 0.5326876640319824,\n \"alphanum_fraction\": 0.5332929491996765,\n \"avg_line_length\": 43.64864730834961,\n \"blob_id\": \"507babe20d3e2fa97bff484f79d739eaeb463752\",\n \"content_id\": \"d73678789bf3c90fe7b65d2d582c9d4e5bf56570\",\n \"detected_licenses\": [\n \"MIT\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"C#\",\n \"length_bytes\": 4956,\n \"license_type\": \"permissive\",\n \"max_line_length\": 144,\n \"num_lines\": 111,\n \"path\": \"/src/TorchSharp/NN/Recurrent/GRUCell.cs\",\n \"repo_name\": \"NiklasGustafsson/TorchSharp\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"// Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information.\\nusing System;\\nusing static TorchSharp.torch;\\nusing static TorchSharp.torch.nn;\\nusing static TorchSharp.PInvoke.NativeMethods;\\n\\n#nullable enable\\nnamespace TorchSharp\\n{\\n using Modules;\\n\\n namespace Modules\\n {\\n public sealed class GRUCell : torch.nn.Module<Tensor, Tensor, Tensor>\\n {\\n internal GRUCell(IntPtr handle, IntPtr boxedHandle) : base(handle, boxedHandle) { }\\n\\n public new static GRUCell Load(string modelPath)\\n {\\n var res = Module<Tensor, Tensor>.Load(modelPath);\\n return new GRUCell(res.handle.DangerousGetHandle(), IntPtr.Zero);\\n }\\n\\n /// <summary>\\n /// Apply the GRU cell to an input tensor.\\n /// </summary>\\n /// <param name=\\\"input\\\">Tensor of shape (batch, input_size) containing the features of the input sequence.</param>\\n /// <param name=\\\"h0\\\">Tensor of shape (batch, hidden_size) containing the initial hidden state for each element in the batch.</param>\\n /// <returns></returns>\\n public override Tensor forward(Tensor input, Tensor? h0 = null)\\n {\\n var hN = THSNN_GRUCell_forward(handle, input.Handle, h0?.Handle ?? IntPtr.Zero);\\n if (hN == IntPtr.Zero) { torch.CheckForErrors(); }\\n return new Tensor(hN);\\n }\\n\\n public Parameter? bias_ih {\\n get {\\n var res = THSNN_GRUCell_bias_ih(handle);\\n if (res == IntPtr.Zero) { torch.CheckForErrors(); }\\n return ((res == IntPtr.Zero) ? null : new Parameter(res));\\n }\\n set {\\n THSNN_GRUCell_set_bias_ih(handle, (value is null ? IntPtr.Zero : value.Handle));\\n torch.CheckForErrors();\\n ConditionallyRegisterParameter(\\\"bias_ih\\\", value);\\n }\\n }\\n\\n public Parameter? bias_hh {\\n get {\\n var res = THSNN_GRUCell_bias_hh(handle);\\n if (res == IntPtr.Zero) { torch.CheckForErrors(); }\\n return ((res == IntPtr.Zero) ? null : new Parameter(res));\\n }\\n set {\\n THSNN_GRUCell_set_bias_hh(handle, (value is null ? IntPtr.Zero : value.Handle));\\n torch.CheckForErrors();\\n ConditionallyRegisterParameter(\\\"bias_hh\\\", value);\\n }\\n }\\n\\n public Parameter? weight_ih {\\n get {\\n var res = THSNN_GRUCell_weight_ih(handle);\\n if (res == IntPtr.Zero) { torch.CheckForErrors(); }\\n return (res == IntPtr.Zero) ? null : new Parameter(res);\\n }\\n set {\\n THSNN_GRUCell_set_weight_ih(handle, value is null ? IntPtr.Zero : value.Handle);\\n torch.CheckForErrors();\\n ConditionallyRegisterParameter(\\\"weight_ih\\\", value);\\n }\\n }\\n\\n public Parameter? weight_hh {\\n get {\\n var res = THSNN_GRUCell_weight_hh(handle);\\n if (res == IntPtr.Zero) { torch.CheckForErrors(); }\\n return (res == IntPtr.Zero) ? null : new Parameter(res);\\n }\\n set {\\n THSNN_GRUCell_set_weight_hh(handle, value is null ? IntPtr.Zero : value.Handle);\\n torch.CheckForErrors();\\n ConditionallyRegisterParameter(\\\"weight_hh\\\", value);\\n }\\n }\\n }\\n }\\n\\n public static partial class torch\\n {\\n public static partial class nn\\n {\\n /// <summary>\\n /// A gated recurrent unit (GRU) cell\\n /// </summary>\\n /// <param name=\\\"inputSize\\\">The number of expected features in the input x</param>\\n /// <param name=\\\"hiddenSize\\\">The number of features in the hidden state h</param>\\n /// <param name=\\\"device\\\">The desired device of the parameters and buffers in this module</param>\\n /// <param name=\\\"dtype\\\">The desired floating point or complex dtype of the parameters and buffers in this module</param>\\n /// <param name=\\\"bias\\\">If False, then the layer does not use bias weights b_ih and b_hh. Default: True</param>\\n public static GRUCell GRUCell(long inputSize, long hiddenSize, bool bias = true, Device? device = null, ScalarType? dtype = null)\\n {\\n var res = THSNN_GRUCell_ctor(inputSize, hiddenSize, bias, out var boxedHandle);\\n if (res == IntPtr.Zero) { torch.CheckForErrors(); }\\n return new GRUCell(res, boxedHandle).MoveModule<GRUCell>(device, dtype);\\n }\\n }\\n }\\n}\\n\"\n },\n {\n \"alpha_fraction\": 0.4594695568084717,\n \"alphanum_fraction\": 0.4998132288455963,\n \"avg_line_length\": 35.17567443847656,\n \"blob_id\": \"3d59ed9521a3a99455658f06d901141a8195daeb\",\n \"content_id\": \"3a76b9e32b6a1b432385cf0e61faed7f2949aa28\",\n \"detected_licenses\": [\n \"MIT\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"C#\",\n \"length_bytes\": 2677,\n \"license_type\": \"permissive\",\n \"max_line_length\": 130,\n \"num_lines\": 74,\n \"path\": \"/src/Examples/AlexNet.cs\",\n \"repo_name\": \"NiklasGustafsson/TorchSharp\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"// Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information.\\n\\nusing static TorchSharp.torch;\\nusing static TorchSharp.torch.nn;\\n\\nnamespace TorchSharp.Examples\\n{\\n /// <summary>\\n /// Modified version of original AlexNet to fix CIFAR10 32x32 images.\\n /// </summary>\\n class AlexNet : Module<Tensor, Tensor>\\n {\\n private readonly Module<Tensor, Tensor> features;\\n private readonly Module<Tensor, Tensor> avgPool;\\n private readonly Module<Tensor, Tensor> classifier;\\n\\n public AlexNet(string name, int numClasses, torch.Device device = null) : base(name)\\n {\\n features = Sequential(\\n (\\\"c1\\\", Conv2d(3, 64, kernelSize: 3, stride: 2, padding: 1)),\\n (\\\"r1\\\", ReLU(inplace: true)),\\n (\\\"mp1\\\", MaxPool2d(kernelSize: new long[] { 2, 2 })),\\n (\\\"c2\\\", Conv2d(64, 192, kernelSize: 3, padding: 1)),\\n (\\\"r2\\\", ReLU(inplace: true)),\\n (\\\"mp2\\\", MaxPool2d(kernelSize: new long[] { 2, 2 })),\\n (\\\"c3\\\", Conv2d(192, 384, kernelSize: 3, padding: 1)),\\n (\\\"r3\\\", ReLU(inplace: true)),\\n (\\\"c4\\\", Conv2d(384, 256, kernelSize: 3, padding: 1)),\\n (\\\"r4\\\", ReLU(inplace: true)),\\n (\\\"c5\\\", Conv2d(256, 256, kernelSize: 3, padding: 1)),\\n (\\\"r5\\\", ReLU(inplace: true)),\\n (\\\"mp3\\\", MaxPool2d(kernelSize: new long[] { 2, 2 })));\\n\\n avgPool = AdaptiveAvgPool2d(new long[] { 2, 2 });\\n\\n classifier = Sequential(\\n (\\\"d1\\\", Dropout()),\\n (\\\"l1\\\", Linear(256 * 2 * 2, 4096)),\\n (\\\"r1\\\", ReLU(inplace: true)),\\n (\\\"d2\\\", Dropout()),\\n (\\\"l2\\\", Linear(4096, 4096)),\\n (\\\"r3\\\", ReLU(inplace: true)),\\n (\\\"d3\\\", Dropout()),\\n (\\\"l3\\\", Linear(4096, numClasses))\\n );\\n\\n RegisterComponents();\\n\\n if (device != null && device.type == DeviceType.CUDA)\\n this.to(device);\\n }\\n\\n public override Tensor forward(Tensor input)\\n {\\n var f = features.call(input);\\n var avg = avgPool.call(f);\\n\\n using (var x = avg.reshape(new long[] { avg.shape[0], 256 * 2 * 2 }))\\n return classifier.call(x);\\n }\\n\\n protected override void Dispose(bool disposing)\\n {\\n if (disposing) {\\n features.Dispose();\\n avgPool.Dispose();\\n classifier.Dispose();\\n ClearModules();\\n }\\n base.Dispose(disposing);\\n }\\n }\\n\\n}\\n\"\n },\n {\n \"alpha_fraction\": 0.5558139681816101,\n \"alphanum_fraction\": 0.561012327671051,\n \"avg_line_length\": 44.6875,\n \"blob_id\": \"df6a5e536593f6f6d695a6fe488fd38804d3c8f8\",\n \"content_id\": \"bda99fb0923cb2af153b66be1db02d7bdc8a53f9\",\n \"detected_licenses\": [\n \"MIT\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"C#\",\n \"length_bytes\": 7310,\n \"license_type\": \"permissive\",\n \"max_line_length\": 205,\n \"num_lines\": 160,\n \"path\": \"/src/TorchSharp/Tensor/Factories/empty.cs\",\n \"repo_name\": \"NiklasGustafsson/TorchSharp\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"// Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information.\\nusing System;\\nusing static TorchSharp.PInvoke.NativeMethods;\\n\\n#nullable enable\\nnamespace TorchSharp\\n{\\n public static partial class torch\\n {\\n /// <summary>\\n /// Create a new tensor filled with empty\\n /// </summary>\\n public static Tensor empty(long[] size, ScalarType? dtype = null, Device? device = null, bool requires_grad = false, string[]? names = null)\\n {\\n return _empty(size, dtype, device, requires_grad, names: names);\\n }\\n\\n /// <summary>\\n /// Create a new tensor filled with empty\\n /// </summary>\\n public static Tensor empty(ReadOnlySpan<long> size, ScalarType? dtype = null, Device? device = null, bool requires_grad = false, string[]? names = null)\\n {\\n return _empty(size, dtype, device, requires_grad, names: names);\\n }\\n\\n /// <summary>\\n /// Create a new 1-D tensor filled with empty\\n /// </summary>\\n public static Tensor empty(long size, ScalarType? dtype = null, Device? device = null, bool requires_grad = false, string[]? names = null)\\n {\\n return _empty(stackalloc long[] { size }, dtype, device, requires_grad, names: names);\\n }\\n\\n /// <summary>\\n /// Create a new 2-D tensor filled with empty\\n /// </summary>\\n public static Tensor empty(long rows, long columns, ScalarType? dtype = null, Device? device = null, bool requires_grad = false, string[]? names = null)\\n {\\n return _empty(stackalloc long[] { rows, columns }, dtype, device, requires_grad, names: names);\\n }\\n\\n /// <summary>\\n /// Create a new 3-D tensor filled with empty\\n /// </summary>\\n public static Tensor empty(long dim0, long dim1, long dim2, ScalarType? dtype = null, Device? device = null, bool requires_grad = false, string[]? names = null)\\n {\\n return _empty(stackalloc long[] { dim0, dim1, dim2 }, dtype, device, requires_grad, names: names);\\n }\\n\\n /// <summary>\\n /// Create a new 4-D tensor filled with empty\\n /// </summary>\\n public static Tensor empty(long dim0, long dim1, long dim2, long dim3, ScalarType? dtype = null, Device? device = null, bool requires_grad = false, string[]? names = null)\\n {\\n return _empty(stackalloc long[] { dim0, dim1, dim2, dim3 }, dtype, device, requires_grad, names: names);\\n }\\n\\n /// <summary>\\n /// Create a new 1-D tensor filled with empty\\n /// </summary>\\n public static Tensor empty(int size, ScalarType? dtype = null, Device? device = null, bool requires_grad = false, string[]? names = null)\\n {\\n return _empty(stackalloc long[] { size }, dtype, device, requires_grad, names: names);\\n }\\n\\n /// <summary>\\n /// Create a new 2-D tensor filled with empty\\n /// </summary>\\n public static Tensor empty(int rows, int columns, ScalarType? dtype = null, Device? device = null, bool requires_grad = false, string[]? names = null)\\n {\\n return _empty(stackalloc long[] { rows, columns }, dtype, device, requires_grad, names: names);\\n }\\n\\n /// <summary>\\n /// Create a new 3-D tensor filled with empty\\n /// </summary>\\n public static Tensor empty(int dim0, int dim1, int dim2, ScalarType? dtype = null, Device? device = null, bool requires_grad = false, string[]? names = null)\\n {\\n return _empty(stackalloc long[] { dim0, dim1, dim2 }, dtype, device, requires_grad, names: names);\\n }\\n\\n /// <summary>\\n /// Create a new 4-D tensor filled with empty\\n /// </summary>\\n public static Tensor empty(int dim0, int dim1, int dim2, int dim3, ScalarType? dtype = null, Device? device = null, bool requires_grad = false, string[]? names = null)\\n {\\n return _empty(stackalloc long[] { dim0, dim1, dim2, dim3 }, dtype, device, requires_grad, names: names);\\n }\\n\\n /// <summary>\\n /// Returns a tensor filled with uninitialized data, with the same size as input.\\n /// </summary>\\n public static Tensor empty_like(Tensor input, ScalarType? dtype = null, Device? device = null, bool requires_grad = false, string[]? names = null) => input.empty_like(dtype, device, requires_grad);\\n\\n\\n /// <summary>\\n /// Returns a tensor filled with uninitialized data. The shape and strides of the tensor is defined by the variable argument size and stride respectively.\\n /// </summary>\\n public static Tensor empty_strided(long[] size, long[] strides, ScalarType? dtype = null, Device? device = null, bool requires_grad = false, string[]? names = null)\\n {\\n device = InitializeDevice(device);\\n if (!dtype.HasValue) {\\n // Determine the element type dynamically.\\n dtype = get_default_dtype();\\n }\\n\\n unsafe {\\n fixed (long* psizes = size, pstrides = strides) {\\n var handle = THSTensor_empty_strided((IntPtr)psizes, size.Length, (IntPtr)pstrides, strides.Length, (sbyte)dtype, (int)device.type, device.index, requires_grad);\\n if (handle == IntPtr.Zero) {\\n GC.Collect();\\n GC.WaitForPendingFinalizers();\\n handle = THSTensor_empty_strided((IntPtr)psizes, size.Length, (IntPtr)pstrides, strides.Length, (sbyte)dtype, (int)device.type, device.index, requires_grad);\\n }\\n if (handle == IntPtr.Zero) { CheckForErrors(); }\\n var result = new Tensor(handle);\\n\\n if (names != null && names.Length > 0) {\\n\\n result.rename_(names);\\n }\\n\\n return result;\\n }\\n }\\n }\\n\\n /// <summary>\\n /// Create a new tensor filled with empty\\n /// </summary>\\n private static Tensor _empty(ReadOnlySpan<long> size, ScalarType? dtype = null, Device? device = null, bool requires_grad = false, string[]? names = null)\\n {\\n device = InitializeDevice(device);\\n if (!dtype.HasValue) {\\n // Determine the element type dynamically.\\n dtype = get_default_dtype();\\n }\\n\\n unsafe {\\n fixed (long* psizes = size) {\\n var handle = THSTensor_empty((IntPtr)psizes, size.Length, (sbyte)dtype, (int)device.type, device.index, requires_grad);\\n if (handle == IntPtr.Zero) {\\n GC.Collect();\\n GC.WaitForPendingFinalizers();\\n handle = THSTensor_empty((IntPtr)psizes, size.Length, (sbyte)dtype, (int)device.type, device.index, requires_grad);\\n }\\n if (handle == IntPtr.Zero) { CheckForErrors(); }\\n var result = new Tensor(handle);\\n\\n if (names != null && names.Length > 0) {\\n\\n result.rename_(names);\\n }\\n\\n return result;\\n }\\n }\\n }\\n }\\n}\\n\"\n },\n {\n \"alpha_fraction\": 0.5628982782363892,\n \"alphanum_fraction\": 0.5657304525375366,\n \"avg_line_length\": 46.340782165527344,\n \"blob_id\": \"bfc2ca728e291fa5ca1d78fb83443c90d0c9daa0\",\n \"content_id\": \"ff4258652cde445a8cba21abb0041bde11ba568b\",\n \"detected_licenses\": [\n \"MIT\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"C#\",\n \"length_bytes\": 8474,\n \"license_type\": \"permissive\",\n \"max_line_length\": 149,\n \"num_lines\": 179,\n \"path\": \"/src/TorchSharp/Distributions/Multinomial.cs\",\n \"repo_name\": \"NiklasGustafsson/TorchSharp\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"// Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information.\\nusing System;\\nusing System.Collections.Generic;\\nusing System.Linq;\\nusing static TorchSharp.torch;\\n\\nnamespace TorchSharp\\n{\\n using Modules;\\n\\n namespace Modules\\n {\\n /// <summary>\\n /// A Multinomial distribution parameterized by `probs` or `logits` (but not both).\\n /// `total_count` must be broadcastable with `probs`/`logits`.\\n /// </summary>\\n public class Multinomial : torch.distributions.Distribution\\n {\\n /// <summary>\\n /// The mean of the distribution.\\n /// </summary>\\n public override Tensor mean => total_count * probs;\\n\\n /// <summary>\\n /// The variance of the distribution\\n /// </summary>\\n public override Tensor variance => WrappedTensorDisposeScope(() => total_count * probs * (1 - probs));\\n\\n /// <summary>\\n /// Constructor\\n /// </summary>\\n /// <param name=\\\"total_count\\\">Number of Bernoulli trials</param>\\n /// <param name=\\\"probs\\\">The probability of sampling '1'</param>\\n /// <param name=\\\"logits\\\">The log-odds of sampling '1'</param>\\n /// <param name=\\\"generator\\\">An optional random number generator object.</param>\\n public Multinomial(int total_count, Tensor probs = null, Tensor logits = null, torch.Generator generator = null) : base(generator) \\n {\\n this.total_count = total_count;\\n this.categorical = new Categorical(probs, logits);\\n this.batch_shape = this.categorical.batch_shape;\\n var ps = this.categorical.param_shape;\\n this.event_shape = new long[] { ps[ps.Length-1] };\\n }\\n\\n private Multinomial(int total_count, Categorical categorical, torch.Generator generator = null) : base(generator)\\n {\\n this.total_count = total_count;\\n this.categorical = categorical;\\n this.batch_shape = categorical.batch_shape;\\n var ps = categorical.param_shape;\\n this.event_shape = new long[] { ps[ps.Length - 1] };\\n }\\n\\n /// <summary>\\n /// Event probabilities\\n /// </summary>\\n public Tensor probs => categorical.probs;\\n\\n /// <summary>\\n /// Event log-odds\\n /// </summary>\\n public Tensor logits => categorical.logits;\\n\\n\\n public long[] param_shape => categorical.param_shape;\\n\\n private int total_count;\\n private Categorical categorical;\\n\\n /// <summary>\\n /// Generates a sample_shape shaped reparameterized sample or sample_shape shaped batch of reparameterized samples\\n /// if the distribution parameters are batched.\\n /// </summary>\\n /// <param name=\\\"sample_shape\\\">The sample shape.</param>\\n public override Tensor rsample(params long[] sample_shape)\\n {\\n var cShape = new List<long>(); cShape.Add(total_count); cShape.AddRange(sample_shape);\\n\\n using var _ = NewDisposeScope();\\n var samples = categorical.sample(cShape.ToArray());\\n var shifted_idx = Enumerable.Range(0, (int)samples.dim()).ToList();\\n var tc = shifted_idx[0];\\n shifted_idx.RemoveAt(0);\\n shifted_idx.Add(tc);\\n samples = samples.permute(shifted_idx.Select(i => (long)i).ToArray());\\n var counts = samples.new_zeros(ExtendedShape(sample_shape));\\n counts.scatter_add_(-1, samples, torch.ones_like(samples));\\n return counts.type_as(probs).MoveToOuterDisposeScope();\\n }\\n\\n /// <summary>\\n /// Returns the log of the probability density/mass function evaluated at `value`.\\n /// </summary>\\n /// <param name=\\\"value\\\"></param>\\n public override Tensor log_prob(Tensor value)\\n {\\n using var _ = NewDisposeScope();\\n var bcast = torch.broadcast_tensors(logits, value);\\n var l = bcast[0].clone();\\n value = bcast[1];\\n var log_factorial_n = torch.lgamma(value.sum(-1) + 1);\\n var log_factorial_xs = torch.lgamma(value + 1).sum(-1);\\n l[(value == 0) & (l == float.NegativeInfinity)] = torch.tensor(0.0f);\\n var log_powers = (logits * value).sum(-1);\\n return (log_factorial_n - log_factorial_xs + log_powers).MoveToOuterDisposeScope();\\n }\\n\\n /// <summary>\\n /// Returns a new distribution instance (or populates an existing instance provided by a derived class) with batch dimensions expanded to\\n /// `batch_shape`. This method calls `torch.Tensor.expand()` on the distribution's parameters. As such, this does not allocate new\\n /// memory for the expanded distribution instance.\\n /// </summary>\\n /// <param name=\\\"batch_shape\\\">Tthe desired expanded size.</param>\\n /// <param name=\\\"instance\\\">new instance provided by subclasses that need to override `.expand`.</param>\\n public override distributions.Distribution expand(Size batch_shape, distributions.Distribution instance = null)\\n {\\n if (instance != null && !(instance is Multinomial))\\n throw new ArgumentException(\\\"expand(): 'instance' must be a Multinomial distribution\\\");\\n\\n var newDistribution = ((instance == null) ?\\n new Multinomial(total_count, categorical.expand(batch_shape) as Categorical, generator) :\\n instance) as Multinomial;\\n\\n if (newDistribution == instance) {\\n newDistribution.total_count = total_count;\\n newDistribution.categorical = categorical.expand(batch_shape) as Categorical;\\n newDistribution.batch_shape = newDistribution.categorical.batch_shape;\\n var ps = newDistribution.categorical.param_shape;\\n newDistribution.event_shape = new long[] { ps[ps.Length - 1] };\\n }\\n\\n return newDistribution;\\n }\\n\\n /// <summary>\\n /// Returns entropy of distribution, batched over batch_shape.\\n /// </summary>\\n /// <returns></returns>\\n public override Tensor entropy()\\n {\\n throw new NotImplementedException();\\n }\\n }\\n\\n }\\n public static partial class torch\\n {\\n public static partial class distributions\\n {\\n /// <summary>\\n /// Creates a Multinomial distribution parameterized by `probs` or `logits` (but not both).\\n /// `total_count` must be broadcastable with `probs`/`logits`.\\n /// </summary>\\n /// <param name=\\\"total_count\\\">Number of Bernoulli trials</param>\\n /// <param name=\\\"probs\\\">The probability of sampling '1'</param>\\n /// <param name=\\\"logits\\\">The log-odds of sampling '1'</param>\\n /// <param name=\\\"generator\\\">An optional random number generator object.</param>\\n /// <returns></returns>\\n public static Multinomial Multinomial(int total_count, Tensor probs = null, Tensor logits = null, torch.Generator generator = null)\\n {\\n return new Multinomial(total_count, probs, logits, generator);\\n }\\n\\n /// <summary>\\n /// Creates an equal-probability multinomial distribution parameterized by the number of categories.\\n /// `total_count` must be broadcastable with `probs`/`logits`.\\n /// </summary>\\n /// <param name=\\\"total_count\\\">Number of Bernoulli trials</param>\\n /// <param name=\\\"categories\\\">The number of categories.</param>\\n /// <param name=\\\"generator\\\">An optional random number generator object.</param>\\n /// <returns></returns>\\n public static Multinomial Multinomial(int total_count, int categories, torch.Generator generator = null)\\n {\\n var probs = torch.tensor(1.0 / categories).expand(categories);\\n return new Multinomial(total_count, probs, null, generator);\\n }\\n }\\n }\\n}\\n\"\n },\n {\n \"alpha_fraction\": 0.5633964538574219,\n \"alphanum_fraction\": 0.565691351890564,\n \"avg_line_length\": 36.91304397583008,\n \"blob_id\": \"31f9c867490f7ed2e7c3be3aea7f9d3c48c95ee2\",\n \"content_id\": \"d6cd833be9a51d1606f8b7a5ffc0dc74b9438c36\",\n \"detected_licenses\": [\n \"MIT\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"C#\",\n \"length_bytes\": 1747,\n \"license_type\": \"permissive\",\n \"max_line_length\": 131,\n \"num_lines\": 46,\n \"path\": \"/src/TorchVision/Pad.cs\",\n \"repo_name\": \"NiklasGustafsson/TorchSharp\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"// Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information.\\n\\nusing static TorchSharp.torch;\\n\\nnamespace TorchSharp\\n{\\n public static partial class torchvision\\n {\\n internal class Pad : ITransform\\n {\\n internal Pad(long[] pad, PaddingModes mode = PaddingModes.Constant, double value = 0)\\n {\\n this.pad = pad;\\n this.mode = mode;\\n this.value = value;\\n }\\n\\n public Tensor call(Tensor input)\\n {\\n return TorchSharp.torch.nn.functional.pad(input, pad, mode, value);\\n }\\n\\n private long[] pad;\\n private PaddingModes mode;\\n private double value;\\n }\\n\\n public static partial class transforms\\n {\\n /// <summary>\\n /// Pad the given image on all sides with the given “pad” value.\\n /// </summary>\\n /// <param name=\\\"padding\\\">\\n /// Padding on each border. If a single int is provided this is used to pad all borders.\\n /// If sequence of length 2 is provided this is the padding on left/right and top/bottom respectively.\\n /// If a sequence of length 4 is provided this is the padding for the left, top, right and bottom borders respectively.\\n /// </param>\\n /// <param name=\\\"fill\\\">Pixel fill value for constant fill.</param>\\n /// <param name=\\\"mode\\\">Type of padding.</param>\\n static public ITransform Pad(long[] padding, PaddingModes mode = PaddingModes.Constant, double fill = 0)\\n {\\n return new Pad(padding, mode, fill);\\n }\\n }\\n }\\n}\"\n },\n {\n \"alpha_fraction\": 0.5328271985054016,\n \"alphanum_fraction\": 0.5441445112228394,\n \"avg_line_length\": 42.638038635253906,\n \"blob_id\": \"71a9a10d8f19ee1c9c79dafe06a408654d4e63bf\",\n \"content_id\": \"2ddb738f816682f3182214cbad2396ce1f779815\",\n \"detected_licenses\": [\n \"MIT\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"C#\",\n \"length_bytes\": 14226,\n \"license_type\": \"permissive\",\n \"max_line_length\": 187,\n \"num_lines\": 326,\n \"path\": \"/src/TorchVision/dsets/CIFAR.cs\",\n \"repo_name\": \"NiklasGustafsson/TorchSharp\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"// Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information.\\nusing System;\\nusing System.Collections.Generic;\\nusing System.IO;\\nusing System.Net;\\nusing System.Net.Http;\\nusing static TorchSharp.torch;\\nusing static TorchSharp.torch.utils.data;\\n\\nnamespace TorchSharp \\n{\\n public static partial class torchvision\\n {\\n public static partial class datasets\\n {\\n /// <summary>\\n /// CIFAR10 Dataset.\\n /// </summary>\\n /// <remarks>\\n /// The dataset for this example can be found at: https://www.cs.toronto.edu/~kriz/cifar.html\\n /// </remarks>\\n /// <param name=\\\"root\\\">Root directory of dataset where directory CIFAR{10,100} exists or will be saved to if download is set to true.</param>\\n /// <param name=\\\"train\\\">If true, creates dataset from training set, otherwise creates from test set</param>\\n /// <param name=\\\"download\\\">If true, downloads the dataset from the internet and puts it in root directory. If dataset is already downloaded, it is not downloaded again</param>\\n /// <param name=\\\"target_transform\\\">A transform that takes in the target tensor and transforms it.</param>\\n /// <returns>A CIFAR dataset.</returns>\\n public static Dataset CIFAR10(string root, bool train, bool download = false, torchvision.ITransform target_transform = null)\\n {\\n return new Modules.CIFAR10(root, train, \\\"https://www.cs.toronto.edu/~kriz/\\\", download, target_transform);\\n }\\n\\n /// <summary>\\n /// CIFAR10 Dataset.\\n /// </summary>\\n /// <remarks>\\n /// The dataset for this example can be found at: https://www.cs.toronto.edu/~kriz/cifar.html\\n /// </remarks>\\n /// <param name=\\\"root\\\">Root directory of dataset where directory CIFAR{10,100} exists or will be saved to if download is set to true.</param>\\n /// <param name=\\\"train\\\">If true, creates dataset from training set, otherwise creates from test set</param>\\n /// <param name=\\\"download\\\">If true, downloads the dataset from the internet and puts it in root directory. If dataset is already downloaded, it is not downloaded again</param>\\n /// <param name=\\\"target_transform\\\">A function that takes in the target tensor and transforms it.</param>\\n /// <returns>A CIFAR dataset.</returns>\\n public static Dataset CIFAR10(string root, bool train, bool download, Func<Tensor, Tensor> target_transform)\\n {\\n return new Modules.CIFAR10(root, train, \\\"https://www.cs.toronto.edu/~kriz/\\\", download, new torchvision.Lambda(target_transform));\\n }\\n\\n /// <summary>\\n /// CIFAR100 Dataset.\\n /// </summary>\\n /// <remarks>\\n /// The dataset for this example can be found at: https://www.cs.toronto.edu/~kriz/cifar.html\\n /// </remarks>\\n /// <param name=\\\"root\\\">Root directory of dataset where directory CIFAR{10,100} exists or will be saved to if download is set to true.</param>\\n /// <param name=\\\"train\\\">If true, creates dataset from training set, otherwise creates from test set</param>\\n /// <param name=\\\"download\\\">If true, downloads the dataset from the internet and puts it in root directory. If dataset is already downloaded, it is not downloaded again</param>\\n /// <param name=\\\"target_transform\\\">A transform that takes in the target and transforms it.</param>\\n /// <returns>A CIFAR dataset.</returns>\\n public static Dataset CIFAR100(string root, bool train, bool download = false, torchvision.ITransform target_transform = null)\\n {\\n return new Modules.CIFAR100(root, train, \\\"https://www.cs.toronto.edu/~kriz/\\\", download, target_transform);\\n }\\n\\n /// <summary>\\n /// CIFAR100 Dataset.\\n /// </summary>\\n /// <remarks>\\n /// The dataset for this example can be found at: https://www.cs.toronto.edu/~kriz/cifar.html\\n /// </remarks>\\n /// <param name=\\\"root\\\">Root directory of dataset where directory CIFAR{10,100} exists or will be saved to if download is set to true.</param>\\n /// <param name=\\\"train\\\">If true, creates dataset from training set, otherwise creates from test set</param>\\n /// <param name=\\\"download\\\">If true, downloads the dataset from the internet and puts it in root directory. If dataset is already downloaded, it is not downloaded again</param>\\n /// <param name=\\\"target_transform\\\">A function that takes in the target and transforms it.</param>\\n /// <returns>A CIFAR dataset.</returns>\\n public static Dataset CIFAR100(string root, bool train, bool download, Func<Tensor, Tensor> target_transform)\\n {\\n return new Modules.CIFAR100(root, train, \\\"https://www.cs.toronto.edu/~kriz/\\\", download, new torchvision.Lambda(target_transform));\\n }\\n }\\n }\\n\\n namespace Modules\\n {\\n internal abstract class DatasetHelper : Dataset\\n {\\n protected void DownloadFile(string file, string target, string baseUrl)\\n {\\n var filePath = JoinPaths(target, file);\\n\\n var netPath = baseUrl.EndsWith('/') ? $\\\"{baseUrl}{file}\\\" : $\\\"{baseUrl}/{file}\\\";\\n\\n if (!File.Exists(filePath)) {\\n lock (_httpClient) {\\n using var s = _httpClient.GetStreamAsync(netPath).Result;\\n using var fs = new FileStream(file, FileMode.CreateNew);\\n s.CopyToAsync(fs).Wait();\\n }\\n }\\n }\\n\\n protected static string JoinPaths(string directory, string file)\\n {\\n#if NETSTANDARD2_0_OR_GREATER\\n return NSPath.Join(directory, file);\\n#else\\n return Path.Join(directory, file);\\n#endif // NETSTANDARD2_0_OR_GREATER\\n }\\n\\n // Sharing a client among all dataset downloads, based on this article's advice:\\n // https://www.aspnetmonsters.com/2016/08/2016-08-27-httpclientwrong/\\n //\\n private static HttpClient _httpClient = new HttpClient();\\n }\\n\\n internal abstract class CIFAR : DatasetHelper\\n {\\n protected void Download(string targetDir, string baseUrl, string dataset, string fileName)\\n {\\n if (!Directory.Exists(targetDir)) {\\n Directory.CreateDirectory(targetDir);\\n }\\n\\n DownloadFile(fileName, targetDir, baseUrl);\\n\\n if (!File.Exists(Path.Combine(targetDir, $\\\"{dataset}-binary.bin\\\"))) {\\n DecompressFile(fileName, targetDir, targetDir);\\n }\\n }\\n\\n private static void DecompressFile(string file, string sourceDir, string targetDir)\\n {\\n Utils.Decompress.ExtractTGZ(Path.Combine(sourceDir, file), targetDir);\\n }\\n }\\n\\n internal class CIFAR10 : CIFAR, IDisposable\\n {\\n public CIFAR10(string root, bool train, string baseUrl, bool download, torchvision.ITransform transform)\\n {\\n var targetDir = JoinPaths(root, \\\"CIFAR10\\\");\\n\\n if (download) Download(targetDir, baseUrl, \\\"cifar-10\\\", \\\"cifar-10-binary.tar.gz\\\");\\n\\n this.transform = transform;\\n\\n var dataPath = Path.Combine(targetDir, \\\"cifar-10-batches-bin\\\");\\n\\n if (!train) {\\n _count = ReadSingleFile(Path.Combine(dataPath, \\\"test_batch.bin\\\"));\\n } else {\\n _count += ReadSingleFile(Path.Combine(dataPath, \\\"data_batch_1.bin\\\"));\\n _count += ReadSingleFile(Path.Combine(dataPath, \\\"data_batch_2.bin\\\"));\\n _count += ReadSingleFile(Path.Combine(dataPath, \\\"data_batch_3.bin\\\"));\\n _count += ReadSingleFile(Path.Combine(dataPath, \\\"data_batch_4.bin\\\"));\\n _count += ReadSingleFile(Path.Combine(dataPath, \\\"data_batch_5.bin\\\"));\\n }\\n\\n }\\n\\n private int ReadSingleFile(string path)\\n {\\n const int height = 32;\\n const int width = 32;\\n const int channels = 3;\\n const int count = 10000;\\n\\n byte[] dataBytes = File.ReadAllBytes(path);\\n\\n var imgSize = channels * height * width + 1;\\n\\n if (dataBytes.Length != imgSize * count)\\n throw new InvalidDataException($\\\"Not a proper CIFAR10 file: {path}\\\");\\n\\n // Go through the data and create tensors\\n\\n // A previous version of this relied on LINQ expressions, but it was about 20% slower.\\n\\n for (var i = 0; i < count; i++) {\\n\\n var imgStart = i * imgSize;\\n\\n labels.Add(tensor(dataBytes[imgStart], int64));\\n\\n var floats = new float[imgSize-1];\\n for (int j = 0; j < imgSize-1; j++) {\\n floats[j] = dataBytes[1 + j + imgStart] / 256.0f;\\n }\\n\\n data.Add(tensor(floats, new long[] { channels, width, height }));\\n }\\n\\n return count;\\n }\\n\\n protected override void Dispose(bool disposing)\\n {\\n if (disposing) {\\n data.ForEach(d => d.Dispose());\\n labels.ForEach(d => d.Dispose());\\n }\\n }\\n\\n /// <summary>\\n /// Get tensor according to index\\n /// </summary>\\n /// <param name=\\\"index\\\">Index for tensor</param>\\n /// <returns>Tensors of index. DataLoader will catenate these tensors into batches.</returns>\\n public override Dictionary<string, Tensor> GetTensor(long index)\\n {\\n var rdic = new Dictionary<string, Tensor>();\\n if (transform is not null) {\\n rdic.Add(\\\"data\\\", transform.call(data[(int)index]));\\n }\\n else {\\n rdic.Add(\\\"data\\\", data[(int)index]);\\n }\\n rdic.Add(\\\"label\\\", labels[(int)index]);\\n return rdic;\\n }\\n\\n private List<Tensor> data = new();\\n private List<Tensor> labels = new();\\n\\n private torchvision.ITransform transform;\\n public override long Count => _count;\\n\\n private int _count = 0;\\n }\\n\\n internal class CIFAR100 : CIFAR, IDisposable\\n {\\n public CIFAR100(string root, bool train, string baseUrl, bool download, torchvision.ITransform transform)\\n {\\n var targetDir = JoinPaths(root, \\\"CIFAR100\\\");\\n\\n if (download) Download(targetDir, baseUrl, \\\"cifar-100\\\", \\\"cifar-100-binary.tar.gz\\\");\\n\\n this.transform = transform;\\n\\n var dataPath = Path.Combine(targetDir, \\\"cifar-100-binary\\\");\\n\\n if (train) {\\n _count = ReadSingleFile(Path.Combine(dataPath, \\\"train.bin\\\"));\\n } else {\\n _count = ReadSingleFile(Path.Combine(dataPath, \\\"test.bin\\\"));\\n }\\n\\n }\\n\\n private int ReadSingleFile(string path)\\n {\\n const int height = 32;\\n const int width = 32;\\n const int channels = 3;\\n\\n byte[] dataBytes = File.ReadAllBytes(path);\\n\\n var imgSize = channels * height * width;\\n var recSize = imgSize + 2;\\n\\n if (dataBytes.Length % recSize != 0)\\n throw new InvalidDataException($\\\"Not a proper CIFAR100 file: {path}\\\");\\n\\n int count = dataBytes.Length / recSize;\\n\\n // Go through the data and create tensors\\n\\n for (var i = 0; i < count; i++) {\\n\\n var recStart = i * recSize;\\n var imgStart = recStart + 2;\\n\\n // CIFAR100 has two labels -- one, which is 20 categories, one which is 100 entities.\\n coarse_labels.Add(tensor(dataBytes[recStart], int64));\\n fine_labels.Add(tensor(dataBytes[recStart+1], int64));\\n\\n var floats = new float[imgSize];\\n for (int j = 0; j < imgSize; j++) {\\n floats[j] = dataBytes[j + imgStart] / 256.0f;\\n }\\n\\n data.Add(tensor(floats, new long[] { channels, width, height }));\\n }\\n\\n return count;\\n }\\n\\n protected override void Dispose(bool disposing)\\n {\\n if (disposing) {\\n data.ForEach(d => d.Dispose());\\n fine_labels.ForEach(d => d.Dispose());\\n coarse_labels.ForEach(d => d.Dispose());\\n }\\n }\\n\\n /// <summary>\\n /// Get tensor according to index\\n /// </summary>\\n /// <param name=\\\"index\\\">Index for tensor</param>\\n /// <returns>Tensors of index. DataLoader will catenate these tensors into batches.</returns>\\n public override Dictionary<string, Tensor> GetTensor(long index)\\n {\\n var rdic = new Dictionary<string, Tensor>();\\n if (transform is not null) {\\n rdic.Add(\\\"data\\\", transform.call(data[(int)index]));\\n } else {\\n rdic.Add(\\\"data\\\", data[(int)index]);\\n }\\n rdic.Add(\\\"label\\\", fine_labels[(int)index]);\\n rdic.Add(\\\"categories\\\", coarse_labels[(int)index]);\\n return rdic;\\n }\\n\\n private List<Tensor> data = new();\\n private List<Tensor> coarse_labels = new();\\n private List<Tensor> fine_labels = new();\\n\\n private torchvision.ITransform transform;\\n public override long Count => _count;\\n\\n private int _count = 0;\\n }\\n }\\n}\\n\"\n },\n {\n \"alpha_fraction\": 0.5431309938430786,\n \"alphanum_fraction\": 0.5445367693901062,\n \"avg_line_length\": 43.460227966308594,\n \"blob_id\": \"c22a87526d411890b389f5ec241e9a22078d97f7\",\n \"content_id\": \"3a1eb8dc8c227963ec0fb1856b3916823c52f617\",\n \"detected_licenses\": [\n \"MIT\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"C#\",\n \"length_bytes\": 7825,\n \"license_type\": \"permissive\",\n \"max_line_length\": 149,\n \"num_lines\": 176,\n \"path\": \"/src/TorchSharp/Distributions/Bernoulli.cs\",\n \"repo_name\": \"NiklasGustafsson/TorchSharp\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"// Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information.\\nusing System;\\nusing static TorchSharp.torch;\\n\\nnamespace TorchSharp\\n{\\n using Modules;\\n\\n namespace Modules\\n {\\n /// <summary>\\n /// A Bernoulli distribution parameterized by `probs` or `logits` (but not both).\\n /// </summary>\\n public class Bernoulli : torch.distributions.Distribution\\n {\\n /// <summary>\\n /// The mean of the distribution.\\n /// </summary>\\n public override Tensor mean => probs;\\n\\n /// <summary>\\n /// The variance of the distribution\\n /// </summary>\\n public override Tensor variance =>\\n WrappedTensorDisposeScope(() => probs * (1 - probs));\\n\\n /// <summary>\\n /// Constructor\\n /// </summary>\\n /// <param name=\\\"p\\\"></param>\\n /// <param name=\\\"l\\\"></param>\\n /// <param name=\\\"generator\\\"></param>\\n public Bernoulli(Tensor p = null, Tensor l = null, torch.Generator generator = null) : base(generator)\\n {\\n if ((p is null && l is null) || (p is not null && l is not null))\\n throw new ArgumentException(\\\"One and only one of 'probs' and logits should be provided.\\\");\\n\\n this.batch_shape = p is null ? l.size() : p.size();\\n this._probs = p?.alias().DetachFromDisposeScope();\\n this._logits = l?.alias().DetachFromDisposeScope();\\n }\\n\\n /// <summary>\\n /// The probability of sampling 1\\n /// </summary>\\n public Tensor probs {\\n get {\\n return _probs ?? LogitsToProbs(_logits, true);\\n }\\n }\\n\\n /// <summary>\\n /// The log-odds of sampling 1\\n /// </summary>\\n public Tensor logits {\\n get {\\n return _logits ?? ProbsToLogits(_probs, true);\\n }\\n }\\n\\n private Tensor _probs;\\n private Tensor _logits;\\n\\n /// <summary>\\n /// Generates a sample_shape shaped reparameterized sample or sample_shape shaped batch of reparameterized samples\\n /// if the distribution parameters are batched.\\n /// </summary>\\n /// <param name=\\\"sample_shape\\\">The sample shape.</param>\\n /// <returns></returns>\\n public override Tensor rsample(params long[] sample_shape)\\n {\\n var shape = ExtendedShape(sample_shape);\\n return torch.bernoulli(probs.expand(shape), generator);\\n }\\n\\n /// <summary>\\n /// Returns the log of the probability density/mass function evaluated at `value`.\\n /// </summary>\\n /// <param name=\\\"value\\\"></param>\\n /// <returns></returns>\\n\\n public override Tensor log_prob(Tensor value)\\n {\\n var logitsValue = torch.broadcast_tensors(logits, value);\\n return -torch.nn.functional.binary_cross_entropy_with_logits(logitsValue[0], logitsValue[1], reduction: nn.Reduction.None);\\n }\\n\\n /// <summary>\\n /// Returns entropy of distribution, batched over batch_shape.\\n /// </summary>\\n public override Tensor entropy()\\n {\\n return torch.nn.functional.binary_cross_entropy_with_logits(logits, probs, reduction: nn.Reduction.None);\\n }\\n\\n /// <summary>\\n /// Returns a new distribution instance (or populates an existing instance provided by a derived class) with batch dimensions expanded to\\n /// `batch_shape`. This method calls `torch.Tensor.expand()` on the distribution's parameters. As such, this does not allocate new\\n /// memory for the expanded distribution instance.\\n /// </summary>\\n /// <param name=\\\"batch_shape\\\">Tthe desired expanded size.</param>\\n /// <param name=\\\"instance\\\">new instance provided by subclasses that need to override `.expand`.</param>\\n /// <returns></returns>\\n public override distributions.Distribution expand(Size batch_shape, distributions.Distribution instance = null)\\n {\\n if (instance != null && !(instance is Bernoulli))\\n throw new ArgumentException(\\\"expand(): 'instance' must be a Bernoulli distribution\\\");\\n\\n var p = _probs?.expand(batch_shape);\\n var l = _logits?.expand(batch_shape);\\n\\n var newDistribution = ((instance == null) ? new Bernoulli(p, l, generator) : instance) as Bernoulli;\\n\\n newDistribution.batch_shape = batch_shape;\\n if (newDistribution == instance) {\\n newDistribution._probs = p;\\n newDistribution._logits = l;\\n }\\n return newDistribution;\\n }\\n }\\n\\n }\\n public static partial class torch\\n {\\n public static partial class distributions\\n {\\n /// <summary>\\n /// Creates a Bernoulli distribution parameterized by `probs` or `logits` (but not both).\\n /// </summary>\\n /// <param name=\\\"probs\\\">The probability of sampling '1'</param>\\n /// <param name=\\\"logits\\\">The log-odds of sampling '1'</param>\\n /// <param name=\\\"generator\\\">An optional random number generator object.</param>\\n /// <returns></returns>\\n public static Bernoulli Bernoulli(Tensor probs = null, Tensor logits = null, torch.Generator generator = null)\\n {\\n return new Bernoulli(probs, logits, generator);\\n }\\n\\n /// <summary>\\n /// Creates a Bernoulli distribution parameterized by `probs` or `logits` (but not both).\\n /// </summary>\\n /// <param name=\\\"probs\\\">The probability of sampling '1'</param>\\n /// <param name=\\\"logits\\\">The log-odds of sampling '1'</param>\\n /// <param name=\\\"generator\\\">An optional random number generator object.</param>\\n /// <returns></returns>\\n public static Bernoulli Bernoulli(float? probs, float? logits, torch.Generator generator = null)\\n {\\n if (probs.HasValue && !logits.HasValue)\\n return new Bernoulli(torch.tensor(probs.Value), null, generator);\\n else if (!probs.HasValue && logits.HasValue)\\n return new Bernoulli(null, torch.tensor(logits.Value), generator);\\n else\\n throw new ArgumentException(\\\"One and only one of 'probs' and logits should be provided.\\\");\\n }\\n\\n\\n /// <summary>\\n /// Creates a Bernoulli distribution parameterized by `probs` or `logits` (but not both).\\n /// </summary>\\n /// <param name=\\\"probs\\\">The probability of sampling '1'</param>\\n /// <param name=\\\"logits\\\">The log-odds of sampling '1'</param>\\n /// <param name=\\\"generator\\\">An optional random number generator object.</param>\\n /// <returns></returns>\\n public static Bernoulli Bernoulli(double? probs, double? logits, torch.Generator generator = null)\\n {\\n if (probs.HasValue && !logits.HasValue)\\n return new Bernoulli(torch.tensor(probs.Value), null, generator);\\n else if (!probs.HasValue && logits.HasValue)\\n return new Bernoulli(null, torch.tensor(logits.Value), generator);\\n else\\n throw new ArgumentException(\\\"One and only one of 'probs' and 'logits' should be non-null\\\");\\n }\\n }\\n }\\n}\\n\"\n },\n {\n \"alpha_fraction\": 0.5323854684829712,\n \"alphanum_fraction\": 0.5331753492355347,\n \"avg_line_length\": 36.264705657958984,\n \"blob_id\": \"c6a3db3a9163cc9ddf2f4fec8ee53fbedc6e277a\",\n \"content_id\": \"57c7b454cd46963008dfdf510b3fbc68102c98a5\",\n \"detected_licenses\": [\n \"MIT\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"C#\",\n \"length_bytes\": 1266,\n \"license_type\": \"permissive\",\n \"max_line_length\": 130,\n \"num_lines\": 34,\n \"path\": \"/src/TorchAudio/Datasets/Utils.cs\",\n \"repo_name\": \"NiklasGustafsson/TorchSharp\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"// Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information.\\n\\nusing System.IO;\\nusing System.Text;\\nusing ICSharpCode.SharpZipLib.GZip;\\nusing ICSharpCode.SharpZipLib.Tar;\\n\\nnamespace TorchSharp\\n{\\n public static partial class torchaudio\\n {\\n public static partial class datasets\\n {\\n public static partial class utils\\n {\\n /// <summary>\\n /// Extract an archive.\\n /// </summary>\\n /// <param name=\\\"from_path\\\">The path of the archive</param>\\n /// <param name=\\\"to_path\\\">The path to extract the archive</param>\\n internal static void extract_archive(string from_path, string to_path)\\n {\\n using (var fileStream = File.OpenRead(from_path)) {\\n using (var inputStream = new GZipInputStream(fileStream)) {\\n using (TarArchive tarArchive = TarArchive.CreateInputTarArchive(inputStream, Encoding.UTF8)) {\\n tarArchive.ExtractContents(to_path);\\n }\\n }\\n }\\n }\\n }\\n }\\n }\\n}\"\n },\n {\n \"alpha_fraction\": 0.5554831624031067,\n \"alphanum_fraction\": 0.5606974363327026,\n \"avg_line_length\": 47.70634841918945,\n \"blob_id\": \"1383722dd689bae865daa41fb40c5fff6871d414\",\n \"content_id\": \"58d9229c4ecd53a0e84a7031efc92ec7b44819e9\",\n \"detected_licenses\": [\n \"MIT\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"C#\",\n \"length_bytes\": 6137,\n \"license_type\": \"permissive\",\n \"max_line_length\": 228,\n \"num_lines\": 126,\n \"path\": \"/src/TorchSharp/NN/Normalization/InstanceNorm1d.cs\",\n \"repo_name\": \"NiklasGustafsson/TorchSharp\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"// Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information.\\nusing System;\\nusing static TorchSharp.torch;\\nusing static TorchSharp.PInvoke.NativeMethods;\\n\\n#nullable enable\\nnamespace TorchSharp\\n{\\n using Modules;\\n\\n namespace Modules\\n {\\n\\n /// <summary>\\n /// This class is used to represent a InstanceNorm1D module.\\n /// </summary>\\n public sealed class InstanceNorm1d : torch.nn.Module<Tensor, Tensor>\\n {\\n internal InstanceNorm1d(IntPtr handle, IntPtr boxedHandle) : base(handle, boxedHandle)\\n {\\n }\\n\\n public override Tensor forward(Tensor tensor)\\n {\\n if (tensor.Dimensions < 2 || tensor.Dimensions > 3) throw new ArgumentException($\\\"Invalid number of dimensions for InstanceNorm argument: {tensor.Dimensions}\\\");\\n var res = THSNN_InstanceNorm1d_forward(handle.DangerousGetHandle(), tensor.Handle);\\n if (res == IntPtr.Zero) { torch.CheckForErrors(); }\\n return new Tensor(res);\\n }\\n\\n public Parameter? bias {\\n get {\\n var res = THSNN_InstanceNorm1d_bias(handle);\\n if (res == IntPtr.Zero) { torch.CheckForErrors(); }\\n return (res == IntPtr.Zero) ? null : new Parameter(res);\\n }\\n set {\\n THSNN_InstanceNorm1d_set_bias(handle, (value is null ? IntPtr.Zero : value.Handle));\\n torch.CheckForErrors();\\n ConditionallyRegisterParameter(\\\"bias\\\", value);\\n }\\n }\\n\\n public Parameter? weight {\\n get {\\n var res = THSNN_InstanceNorm1d_weight(handle);\\n if (res == IntPtr.Zero) { torch.CheckForErrors(); }\\n return (res == IntPtr.Zero) ? null : new Parameter(res);\\n }\\n set {\\n THSNN_InstanceNorm1d_set_weight(handle, value is null ? IntPtr.Zero : value.Handle);\\n torch.CheckForErrors();\\n ConditionallyRegisterParameter(\\\"weight\\\", value);\\n }\\n }\\n\\n public Tensor? running_mean {\\n get {\\n var res = THSNN_InstanceNorm1d_get_mean(handle);\\n if (res == IntPtr.Zero) { torch.CheckForErrors(); return null; }\\n return new Tensor(res);\\n }\\n set {\\n THSNN_InstanceNorm1d_set_mean(handle, (value is null ? IntPtr.Zero : value.Handle));\\n torch.CheckForErrors();\\n ConditionallyRegisterBuffer(\\\"running_mean\\\", value);\\n }\\n }\\n\\n public Tensor? running_var {\\n get {\\n var res = THSNN_InstanceNorm1d_get_var(handle);\\n if (res == IntPtr.Zero) { torch.CheckForErrors(); return null; }\\n return new Tensor(res);\\n }\\n set {\\n THSNN_InstanceNorm1d_set_var(handle, (value is null ? IntPtr.Zero : value.Handle));\\n torch.CheckForErrors();\\n ConditionallyRegisterBuffer(\\\"running_var\\\", value);\\n }\\n }\\n\\n public Tensor? num_batches_tracked {\\n get {\\n var res = THSNN_InstanceNorm1d_get_batches(handle);\\n if (res == IntPtr.Zero) { torch.CheckForErrors(); return null; }\\n return new Tensor(res);\\n }\\n }\\n\\n public void reset_running_stats()\\n {\\n THSNN_InstanceNorm1d_reset_stats(handle);\\n torch.CheckForErrors();\\n }\\n }\\n }\\n\\n public static partial class torch\\n {\\n public static partial class nn\\n {\\n /// <summary>\\n /// Applies Instance Normalization over a 3D input (a mini-batch of 1D inputs with optional additional channel dimension) as described in the paper Instance Normalization: The Missing Ingredient for Fast Stylization.\\n /// </summary>\\n /// <param name=\\\"features\\\">C from an expected input of size (N,C,L) or LL from input of size (N, L)</param>\\n /// <param name=\\\"eps\\\">A value added to the denominator for numerical stability. Default: 1e-5</param>\\n /// <param name=\\\"momentum\\\">The value used for the running_mean and running_var computation. Can be set to None for cumulative moving average (i.e. simple average). Default: 0.1</param>\\n /// <param name=\\\"affine\\\">A boolean value that when set to True, this module has learnable affine parameters. Default: true</param>\\n /// <param name=\\\"track_running_stats\\\">A boolean value that when set to True, this module tracks the running mean and variance, and when set to False,\\n /// this module does not track such statistics, and initializes statistics buffers running_mean and running_var as None.\\n /// When these buffers are None, this module always uses batch statistics. in both training and eval modes. Default: true</param>\\n /// <param name=\\\"device\\\">The desired device of the parameters and buffers in this module</param>\\n /// <param name=\\\"dtype\\\">The desired floating point or complex dtype of the parameters and buffers in this module</param>\\n /// <returns></returns>\\n public static InstanceNorm1d InstanceNorm1d(long features, double eps = 1e-05, double momentum = 0.1, bool affine = false, bool track_running_stats = false, Device? device = null, ScalarType? dtype = null)\\n {\\n unsafe {\\n var handle = THSNN_InstanceNorm1d_ctor(features, eps, momentum, affine, track_running_stats, out var boxedHandle);\\n if (handle == IntPtr.Zero) { torch.CheckForErrors(); }\\n return new InstanceNorm1d(handle, boxedHandle).MoveModule<InstanceNorm1d>(device, dtype);\\n }\\n }\\n }\\n }\\n}\\n\"\n },\n {\n \"alpha_fraction\": 0.32987022399902344,\n \"alphanum_fraction\": 0.3371623456478119,\n \"avg_line_length\": 48.14643478393555,\n \"blob_id\": \"031d1bb0cbcbdde798f5af040f3ee43d6e38b953\",\n \"content_id\": \"328427b1abab7ef4ae03ee2d3d43cc39418774eb\",\n \"detected_licenses\": [\n \"MIT\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"C#\",\n \"length_bytes\": 25507,\n \"license_type\": \"permissive\",\n \"max_line_length\": 192,\n \"num_lines\": 519,\n \"path\": \"/src/TorchSharp/Utils/tensorboard/GifEncoder/Encoder.cs\",\n \"repo_name\": \"NiklasGustafsson/TorchSharp\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"using System;\\nusing System.IO;\\nusing SkiaSharp;\\n\\nnamespace TorchSharp\\n{\\n public static partial class torch\\n {\\n public static partial class utils\\n {\\n public static partial class tensorboard\\n {\\n internal static partial class GifEncoder\\n {\\n /// <summary>\\n /// Class AnimatedGifEncoder - Encodes a GIF file consisting of one or more frames.\\n ///\\n /// No copyright asserted on the source code of this class. May be used for any\\n /// purpose, however, refer to the Unisys LZW patent for restrictions on use of\\n /// the associated LZWEncoder class. Please forward any corrections to\\n /// kweiner@fmsware.com.\\n ///\\n /// @author Kevin Weiner, FM Software\\n /// @version 1.03 November 2003\\n ///\\n /// https://cs.android.com/android/platform/superproject/+/master:external/glide/third_party/gif_encoder/src/main/java/com/bumptech/glide/gifencoder/AnimatedGifEncoder.java\\n /// </summary>\\n internal class Encoder : IDisposable\\n {\\n protected int width; // image size\\n protected int height;\\n protected SKColor transparent = SKColor.Empty; // transparent color if given\\n protected int transIndex; // transparent index in color table\\n protected int repeat = -1; // no repeat\\n protected int delay = 0; // frame delay (hundredths)\\n protected bool started = false; // ready to output frames\\n //\\tprotected BinaryWriter bw;\\n protected MemoryStream ms;\\n //\\t\\tprotected FileStream fs;\\n\\n protected SKBitmap image; // current frame\\n protected byte[] pixels; // BGR byte array from frame\\n protected byte[] indexedPixels; // converted frame indexed to palette\\n protected int colorDepth; // number of bit planes\\n protected byte[] colorTab; // RGB palette\\n protected bool[] usedEntry = new bool[256]; // active palette entries\\n protected int palSize = 7; // color table size (bits-1)\\n protected int dispose = -1; // disposal code (-1 = use default)\\n protected bool closeStream = false; // close stream when finished\\n protected bool firstFrame = true;\\n protected bool sizeSet = false; // if false, get size from first frame\\n protected int sample = 10; // default sample interval for quantizer\\n private bool disposedValue;\\n\\n /// <summary>\\n /// Sets the delay time between each frame, or changes it\\n /// for subsequent frames (applies to last frame added).\\n /// </summary>\\n /// <param name=\\\"ms\\\"> delay time in milliseconds </param>\\n public void SetDelay(int ms)\\n {\\n delay = (int)Math.Round(ms / 10.0f);\\n }\\n\\n /// <summary>\\n /// Sets the GIF frame disposal code for the last added frame\\n /// and any subsequent frames. Default is 0 if no transparent\\n /// color has been set, otherwise 2.\\n /// </summary>\\n /// <param name=\\\"code\\\"> disposal code. </param>\\n public void SetDispose(int code)\\n {\\n if (code >= 0) {\\n dispose = code;\\n }\\n }\\n\\n /// <summary>\\n /// Sets the number of times the set of GIF frames\\n /// should be played. Default is 1; 0 means play\\n /// indefinitely. Must be invoked before the first\\n /// image is added.\\n /// </summary>\\n /// <param name=\\\"iter\\\"> number of iterations. </param>\\n public void SetRepeat(int iter)\\n {\\n if (iter >= 0) {\\n repeat = iter;\\n }\\n }\\n\\n /// <summary>\\n /// Sets the transparent color for the last added frame\\n /// and any subsequent frames.\\n /// Since all colors are subject to modification\\n /// in the quantization process, the color in the final\\n /// palette for each frame closest to the given color\\n /// becomes the transparent color for that frame.\\n /// May be set to null to indicate no transparent color.\\n /// </summary>\\n /// <param name=\\\"c\\\"> Color to be treated as transparent on display. </param>\\n public void SetTransparent(SKColor c)\\n {\\n transparent = c;\\n }\\n\\n /// <summary>\\n /// Adds next GIF frame. The frame is not written immediately, but is\\n /// actually deferred until the next frame is received so that timing\\n /// data can be inserted. Invoking <code>finish()</code> flushes all\\n /// frames. If <code>setSize</code> was not invoked, the size of the\\n /// first image is used for all subsequent frames.\\n /// </summary>\\n /// <param name=\\\"im\\\"> BufferedImage containing frame to write. </param>\\n /// <returns> true if successful. </returns>\\n public bool AddFrame(SKBitmap im)\\n {\\n if ((im == null) || !started) {\\n return false;\\n }\\n bool ok = true;\\n try {\\n if (!sizeSet) {\\n // use first frame's size\\n SetSize(im.Width, im.Height);\\n }\\n image = im;\\n GetImagePixels(); // convert to correct format if necessary\\n AnalyzePixels(); // build color table & map pixels\\n if (firstFrame) {\\n WriteLSD(); // logical screen descriptior\\n WritePalette(); // global color table\\n if (repeat >= 0) {\\n // use NS app extension to indicate reps\\n WriteNetscapeExt();\\n }\\n }\\n WriteGraphicCtrlExt(); // write graphic control extension\\n WriteImageDesc(); // image descriptor\\n if (!firstFrame) {\\n WritePalette(); // local color table\\n }\\n WritePixels(); // encode and write pixel data\\n firstFrame = false;\\n } catch (IOException) {\\n ok = false;\\n }\\n\\n return ok;\\n }\\n\\n /// <summary>\\n /// Flushes any pending data and closes output file.\\n /// If writing to an OutputStream, the stream is not\\n /// closed.\\n /// </summary>\\n /// <returns></returns>\\n public bool Finish()\\n {\\n if (!started) return false;\\n bool ok = true;\\n started = false;\\n try {\\n ms.WriteByte(0x3b); // gif trailer\\n ms.Flush();\\n } catch (IOException) {\\n ok = false;\\n }\\n\\n // reset for subsequent use\\n transIndex = 0;\\n //\\t\\t\\tfs = null;\\n image = null;\\n pixels = null;\\n indexedPixels = null;\\n colorTab = null;\\n closeStream = false;\\n firstFrame = true;\\n\\n return ok;\\n }\\n\\n /// <summary>\\n /// Sets frame rate in frames per second. Equivalent to\\n /// <code>setDelay(1000/fps)</code>.\\n /// </summary>\\n /// <param name=\\\"fps\\\"> fps float frame rate (frames per second) </param>\\n public void SetFrameRate(float fps)\\n {\\n if (fps != 0f) {\\n delay = (int)Math.Round(100f / fps);\\n }\\n }\\n\\n /// <summary>\\n /// Sets the GIF frame size. The default size is the\\n /// size of the first frame added if this method is\\n /// not invoked.\\n /// </summary>\\n /// <param name=\\\"w\\\"> frame width </param>\\n /// <param name=\\\"h\\\"> frame width </param>\\n public void SetSize(int w, int h)\\n {\\n if (started && !firstFrame) return;\\n width = w;\\n height = h;\\n if (width < 1) width = 320;\\n if (height < 1) height = 240;\\n sizeSet = true;\\n }\\n\\n /// <summary>\\n /// Initiates GIF file creation on the given stream. The stream\\n /// is not closed automatically.\\n /// </summary>\\n /// <param name=\\\"os\\\"> OutputStream on which GIF images are written. </param>\\n /// <returns> false if initial write failed. </returns>\\n public bool Start(MemoryStream os)\\n {\\n if (os == null) return false;\\n bool ok = true;\\n closeStream = false;\\n ms = os;\\n try {\\n WriteString(\\\"GIF89a\\\"); // header\\n } catch (IOException) {\\n ok = false;\\n }\\n return started = ok;\\n }\\n\\n /// <summary>\\n /// Initiates writing of a GIF file to a memory stream.\\n /// </summary>\\n /// <returns></returns>\\n public bool Start()\\n {\\n bool ok;\\n try {\\n ok = Start(new MemoryStream(10 * 1024));\\n closeStream = true;\\n } catch (IOException) {\\n ok = false;\\n }\\n return started = ok;\\n }\\n\\n /// <summary>\\n /// Initiates writing of a GIF file with the specified name.\\n /// </summary>\\n /// <param name=\\\"file\\\"></param>\\n /// <returns></returns>\\n public bool Output(string file)\\n {\\n try {\\n var fs = new FileStream(file, FileMode.OpenOrCreate, FileAccess.Write, FileShare.None);\\n fs.Write(ms.ToArray(), 0, (int)ms.Length);\\n fs.Close();\\n } catch (IOException) {\\n return false;\\n }\\n return true;\\n }\\n\\n public MemoryStream Output()\\n {\\n return ms;\\n }\\n\\n /// <summary>\\n /// Analyzes image colors and creates color map.\\n /// </summary>\\n protected void AnalyzePixels()\\n {\\n int len = pixels.Length;\\n int nPix = len / 3;\\n indexedPixels = new byte[nPix];\\n var nq = new NeuQuant(pixels, len, sample);\\n // initialize quantizer\\n colorTab = nq.Process(); // create reduced palette\\n // convert map from BGR to RGB\\n //\\t\\t\\tfor (int i = 0; i < colorTab.Length; i += 3) \\n //\\t\\t\\t{\\n //\\t\\t\\t\\tbyte temp = colorTab[i];\\n //\\t\\t\\t\\tcolorTab[i] = colorTab[i + 2];\\n //\\t\\t\\t\\tcolorTab[i + 2] = temp;\\n //\\t\\t\\t\\tusedEntry[i / 3] = false;\\n //\\t\\t\\t}\\n // map image pixels to new palette\\n int k = 0;\\n for (int i = 0; i < nPix; i++) {\\n int index =\\n nq.Map(pixels[k++] & 0xff,\\n pixels[k++] & 0xff,\\n pixels[k++] & 0xff);\\n usedEntry[index] = true;\\n indexedPixels[i] = (byte)index;\\n }\\n pixels = null;\\n colorDepth = 8;\\n palSize = 7;\\n // get closest match to transparent color if specified\\n if (transparent != SKColor.Empty) {\\n //transIndex = FindClosest(transparent);\\n transIndex = nq.Map(transparent.Blue, transparent.Green, transparent.Red);\\n }\\n }\\n\\n /// <summary>\\n /// Returns index of palette color closest to c\\n /// </summary>\\n /// <param name=\\\"c\\\"></param>\\n /// <returns></returns>\\n protected int FindClosest(SKColor c)\\n {\\n if (colorTab == null) return -1;\\n int r = c.Red;\\n int g = c.Green;\\n int b = c.Blue;\\n int minpos = 0;\\n int dmin = 256 * 256 * 256;\\n int len = colorTab.Length;\\n for (int i = 0; i < len;) {\\n int dr = r - (colorTab[i++] & 0xff);\\n int dg = g - (colorTab[i++] & 0xff);\\n int db = b - (colorTab[i] & 0xff);\\n int d = dr * dr + dg * dg + db * db;\\n int index = i / 3;\\n if (usedEntry[index] && (d < dmin)) {\\n dmin = d;\\n minpos = index;\\n }\\n i++;\\n }\\n return minpos;\\n }\\n\\n /// <summary>\\n /// Extracts image pixels into byte array \\\"pixels\\\"\\n /// </summary>\\n protected void GetImagePixels()\\n {\\n pixels = new byte[3 * image.Width * image.Height];\\n int count = 0;\\n\\n for (int th = 0; th < image.Height; th++) {\\n for (int tw = 0; tw < image.Width; tw++) {\\n SKColor color = image.GetPixel(tw, th);\\n pixels[count] = color.Red;\\n count++;\\n pixels[count] = color.Green;\\n count++;\\n pixels[count] = color.Blue;\\n count++;\\n }\\n }\\n\\n //\\t\\tpixels = ((DataBufferByte) image.getRaster().getDataBuffer()).getData();\\n }\\n\\n /// <summary>\\n /// Writes Graphic Control Extension\\n /// </summary>\\n protected void WriteGraphicCtrlExt()\\n {\\n ms.WriteByte(0x21); // extension introducer\\n ms.WriteByte(0xf9); // GCE label\\n ms.WriteByte(4); // data block size\\n int transp, disp;\\n if (transparent == SKColor.Empty) {\\n transp = 0;\\n disp = 0; // dispose = no action\\n } else {\\n transp = 1;\\n disp = 2; // force clear if using transparent color\\n }\\n if (dispose >= 0) {\\n disp = dispose & 7; // user override\\n }\\n disp <<= 2;\\n\\n // packed fields\\n ms.WriteByte(Convert.ToByte(0 | // 1:3 reserved\\n disp | // 4:6 disposal\\n 0 | // 7 user input - 0 = none\\n transp)); // 8 transparency flag\\n\\n WriteShort(delay); // delay x 1/100 sec\\n ms.WriteByte(Convert.ToByte(transIndex)); // transparent color index\\n ms.WriteByte(0); // block terminator\\n }\\n\\n /// <summary>\\n /// Writes Image Descriptor\\n /// </summary>\\n protected void WriteImageDesc()\\n {\\n ms.WriteByte(0x2c); // image separator\\n WriteShort(0); // image position x,y = 0,0\\n WriteShort(0);\\n WriteShort(width); // image size\\n WriteShort(height);\\n // packed fields\\n if (firstFrame) {\\n // no LCT - GCT is used for first (or only) frame\\n ms.WriteByte(0);\\n } else {\\n // specify normal LCT\\n ms.WriteByte(Convert.ToByte(0x80 | // 1 local color table 1=yes\\n 0 | // 2 interlace - 0=no\\n 0 | // 3 sorted - 0=no\\n 0 | // 4-5 reserved\\n palSize)); // 6-8 size of color table\\n }\\n }\\n\\n /// <summary>\\n /// Writes Logical Screen Descriptor\\n /// </summary>\\n protected void WriteLSD()\\n {\\n // logical screen size\\n WriteShort(width);\\n WriteShort(height);\\n // packed fields\\n ms.WriteByte(Convert.ToByte(0x80 | // 1 : global color table flag = 1 (gct used)\\n 0x70 | // 2-4 : color resolution = 7\\n 0x00 | // 5 : gct sort flag = 0\\n palSize)); // 6-8 : gct size\\n\\n ms.WriteByte(0); // background color index\\n ms.WriteByte(0); // pixel aspect ratio - assume 1:1\\n }\\n\\n /// <summary>\\n /// Writes Netscape application extension to define repeat count.\\n /// </summary>\\n protected void WriteNetscapeExt()\\n {\\n ms.WriteByte(0x21); // extension introducer\\n ms.WriteByte(0xff); // app extension label\\n ms.WriteByte(11); // block size\\n WriteString(\\\"NETSCAPE\\\" + \\\"2.0\\\"); // app id + auth code\\n ms.WriteByte(3); // sub-block size\\n ms.WriteByte(1); // loop sub-block id\\n WriteShort(repeat); // loop count (extra iterations, 0=repeat forever)\\n ms.WriteByte(0); // block terminator\\n }\\n\\n /// <summary>\\n /// Writes color table\\n /// </summary>\\n protected void WritePalette()\\n {\\n ms.Write(colorTab, 0, colorTab.Length);\\n int n = (3 * 256) - colorTab.Length;\\n for (int i = 0; i < n; i++) {\\n ms.WriteByte(0);\\n }\\n }\\n\\n /// <summary>\\n /// Encodes and writes pixel data\\n /// </summary>\\n protected void WritePixels()\\n {\\n var encoder = new LZWEncoder(indexedPixels, colorDepth);\\n encoder.Encode(ms);\\n }\\n\\n /// <summary>\\n /// Write 16-bit value to output stream, LSB first\\n /// </summary>\\n /// <param name=\\\"value\\\"></param>\\n protected void WriteShort(int value)\\n {\\n ms.WriteByte(Convert.ToByte(value & 0xff));\\n ms.WriteByte(Convert.ToByte((value >> 8) & 0xff));\\n }\\n\\n /// <summary>\\n /// Writes string to output stream\\n /// </summary>\\n /// <param name=\\\"s\\\"></param>\\n protected void WriteString(string s)\\n {\\n char[] chars = s.ToCharArray();\\n for (int i = 0; i < chars.Length; i++) {\\n ms.WriteByte((byte)chars[i]);\\n }\\n }\\n\\n protected virtual void Dispose(bool disposing)\\n {\\n if (!disposedValue) {\\n if (disposing) {\\n ms.Dispose();\\n }\\n\\n disposedValue = true;\\n }\\n }\\n\\n ~Encoder()\\n {\\n Dispose(disposing: false);\\n }\\n\\n public void Dispose()\\n {\\n Dispose(disposing: true);\\n GC.SuppressFinalize(this);\\n }\\n }\\n }\\n }\\n }\\n }\\n}\\n\"\n },\n {\n \"alpha_fraction\": 0.4467984735965729,\n \"alphanum_fraction\": 0.47587883472442627,\n \"avg_line_length\": 32.08258819580078,\n \"blob_id\": \"01b30a22e3773b7acca800c8b16d684a32688431\",\n \"content_id\": \"cca0434fb0783d8358ad95514efcc85442a247c1\",\n \"detected_licenses\": [\n \"MIT\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"C#\",\n \"length_bytes\": 14821,\n \"license_type\": \"permissive\",\n \"max_line_length\": 207,\n \"num_lines\": 448,\n \"path\": \"/test/TorchSharpTest/TestJIT.cs\",\n \"repo_name\": \"NiklasGustafsson/TorchSharp\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"// Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information.\\nusing System;\\nusing System.IO;\\nusing System.Linq;\\nusing static TorchSharp.torch;\\nusing static TorchSharp.torch.nn;\\nusing Xunit;\\n\\n#nullable enable\\n\\nnamespace TorchSharp\\n{\\n [Collection(\\\"Sequential\\\")]\\n public class TestJIT\\n {\\n [Fact]\\n public void TestLoadJIT_Func()\\n {\\n // One linear layer followed by ReLU.\\n using var m = torch.jit.load<Tensor, Tensor, Tensor>(@\\\"func.script.dat\\\");\\n\\n var sms = m.named_modules().ToArray();\\n Assert.Empty(sms);\\n\\n var kids = m.named_children().ToArray();\\n Assert.Empty(kids);\\n\\n var t = m.call(torch.ones(10), torch.ones(10));\\n\\n Assert.Equal(new long[] { 10 }, t.shape);\\n Assert.Equal(torch.float32, t.dtype);\\n Assert.True(torch.tensor(new float[] { 2, 2, 2, 2, 2, 2, 2, 2, 2, 2 }).allclose(t));\\n }\\n\\n [Fact]\\n public void TestLoadJIT_1()\\n {\\n var input = torch.ones(10);\\n var expected = torch.tensor(new float[] { 0.313458264f, 0, 0.9996568f, 0, 0, 0 });\\n\\n // One linear layer followed by ReLU.\\n var m = torch.jit.load<Tensor, Tensor>(@\\\"linrelu.script.dat\\\");\\n if (torch.cuda.is_available()) {\\n m = m.to(torch.CUDA);\\n input = input.to(torch.CUDA);\\n expected = expected.to(torch.CUDA);\\n }\\n\\n var t = m.call(input);\\n\\n Assert.Equal(new long[] { 6 }, t.shape);\\n Assert.Equal(torch.float32, t.dtype);\\n Assert.True(expected.allclose(t));\\n }\\n\\n [Fact]\\n public void TestSaveJIT()\\n {\\n var location = \\\"TestSaveJIT.ts\\\";\\n if (File.Exists(location)) File.Delete(location);\\n\\n try {\\n\\n // One linear layer followed by ReLU.\\n using var m1 = torch.jit.load<Tensor, Tensor>(@\\\"linrelu.script.dat\\\");\\n\\n torch.jit.save(m1, location);\\n using var m2 = torch.jit.load<Tensor, Tensor>(location);\\n\\n var t = m2.call(torch.ones(10));\\n\\n Assert.Equal(new long[] { 6 }, t.shape);\\n Assert.Equal(torch.float32, t.dtype);\\n Assert.True(torch.tensor(new float[] { 0.313458264f, 0, 0.9996568f, 0, 0, 0 }).allclose(t));\\n\\n } finally {\\n if (File.Exists(location)) File.Delete(location);\\n }\\n }\\n\\n [Fact]\\n public void TestLoadJIT_2()\\n {\\n // One linear layer followed by ReLU.\\n using var m = torch.jit.load<Tensor, Tensor>(@\\\"scripted.script.dat\\\");\\n var t = m.call(torch.ones(6));\\n\\n Assert.Equal(new long[] { 6 }, t.shape);\\n Assert.Equal(torch.float32, t.dtype);\\n Assert.True(torch.tensor(new float[] { 1.554085f, 1.01024628f, -1.35086036f, -1.84021854f, 0.0127189457f, 0.5994258f }).allclose(t));\\n }\\n\\n [Fact]\\n public void TestLoadJIT_3()\\n {\\n // Two linear layers, nested Sequential, ReLU in between.\\n using var m = torch.jit.load<Tensor, Tensor>(@\\\"l1000_100_10.script.dat\\\");\\n\\n var sms = m.named_modules().ToArray();\\n Assert.Equal(4, sms.Length);\\n\\n var kids = m.named_children().ToArray();\\n Assert.Equal(2, kids.Length);\\n\\n var t = m.call(torch.ones(1000));\\n\\n Assert.Equal(new long[] { 10 }, t.shape);\\n Assert.Equal(torch.float32, t.dtype);\\n Assert.True(torch.tensor(new float[] { 0.564213157f, -0.04519982f, -0.005117342f, 0.395530462f, -0.3780813f, -0.004734449f, -0.3221216f, -0.289159119f, 0.268511474f, 0.180702567f }).allclose(t));\\n\\n Assert.Throws<System.Runtime.InteropServices.ExternalException>(() => m.call(torch.ones(100)));\\n }\\n\\n [Fact]\\n public void TestLoadJIT_4()\\n {\\n // Definitely not a TorchScript file. Let's see what the runtime does with it.\\n Assert.Throws<System.Runtime.InteropServices.ExternalException>(() => torch.jit.load(@\\\"bug510.dat\\\"));\\n }\\n\\n [Fact]\\n public void TestSaveLoadJITCUDA()\\n {\\n if (torch.cuda.is_available()) {\\n\\n {\\n using var m = torch.jit.load<Tensor, Tensor>(@\\\"linrelu.script.dat\\\");\\n\\n m.to(DeviceType.CUDA);\\n var params0 = m.parameters().ToArray();\\n foreach (var p in params0)\\n Assert.Equal(DeviceType.CUDA, p.device_type);\\n\\n var t = m.call(torch.ones(10).cuda()).cpu();\\n\\n Assert.Equal(new long[] { 6 }, t.shape);\\n Assert.Equal(torch.float32, t.dtype);\\n Assert.True(torch.tensor(new float[] { 0.313458264f, 0, 0.9996568f, 0, 0, 0 }).allclose(t));\\n }\\n {\\n using var m = torch.jit.load<Tensor, Tensor>(@\\\"linrelu.script.dat\\\", DeviceType.CUDA);\\n\\n var params0 = m.parameters().ToArray();\\n foreach (var p in params0)\\n Assert.Equal(DeviceType.CUDA, p.device_type);\\n\\n var t = m.call(torch.ones(10).cuda()).cpu();\\n\\n Assert.Equal(new long[] { 6 }, t.shape);\\n Assert.Equal(torch.float32, t.dtype);\\n Assert.True(torch.tensor(new float[] { 0.313458264f, 0, 0.9996568f, 0, 0, 0 }).allclose(t));\\n }\\n }\\n }\\n\\n [Fact]\\n public void TestJIT_TupleOut()\\n {\\n // def a(x, y):\\n // return x + y, x - y\\n //\\n using var m = torch.jit.load<(Tensor, Tensor)>(@\\\"tuple_out.dat\\\");\\n\\n var x = torch.rand(3, 4);\\n var y = torch.rand(3, 4);\\n var output = m.call(x, y);\\n\\n Assert.Multiple(\\n () => Assert.Equal(x.shape, output.Item1.shape),\\n () => Assert.Equal(x.shape, output.Item2.shape),\\n () => Assert.Equal(x + y, output.Item1),\\n () => Assert.Equal(x - y, output.Item2)\\n );\\n }\\n\\n [Fact]\\n public void TestJIT_TupleOutError()\\n {\\n // def a(x, y):\\n // return x + y, x - y\\n //\\n using var m = torch.jit.load<(Tensor, Tensor)>(@\\\"func.script.dat\\\");\\n\\n var x = torch.rand(3, 4);\\n var y = torch.rand(3, 4);\\n Assert.Throws<InvalidCastException>(() => m.call(x, y));\\n }\\n\\n [Fact]\\n public void TestJIT_ListOut()\\n {\\n // def a(x, y):\\n // return [x + y, x - y]\\n //\\n using var m = torch.jit.load<Tensor[]>(@\\\"list_out.dat\\\");\\n\\n var x = torch.rand(3, 4);\\n var y = torch.rand(3, 4);\\n var output = m.call(x, y);\\n\\n Assert.Multiple(\\n () => Assert.Equal(x.shape, output[0].shape),\\n () => Assert.Equal(x.shape, output[1].shape),\\n () => Assert.Equal(x + y, output[0]),\\n () => Assert.Equal(x - y, output[1])\\n );\\n }\\n\\n [Fact]\\n public void TestJIT_ListOutError()\\n {\\n // def a(x, y):\\n // return x + y, x - y\\n //\\n using var m = torch.jit.load<Tensor[]>(@\\\"func.script.dat\\\");\\n\\n var x = torch.rand(3, 4);\\n var y = torch.rand(3, 4);\\n Assert.Throws<InvalidCastException>(() => m.call(x, y));\\n }\\n\\n\\n\\n [Fact]\\n public void TestLoadJIT_Methods()\\n {\\n // class MyModule(nn.Module):\\n // def __init__(self):\\n // super().__init__()\\n // self.p = nn.Parameter(torch.rand(10))\\n // def forward(self, x: Tensor, y: Tensor) -> Tuple[Tensor, Tensor]:\\n // return x + y, x - y\\n //\\n // @torch.jit.export\\n // def predict(self, x: Tensor) -> Tensor:\\n // return x + self.p\\n // @torch.jit.export\\n // def add_scalar(self, x: Tensor, i: int) -> Tensor:\\n // return x + i\\n\\n using var m = new TestScriptModule(@\\\"exported.method.dat\\\");\\n\\n var x = torch.rand(3, 4);\\n var y = torch.rand(3, 4);\\n var output = m.call(x, y);\\n\\n Assert.Multiple(\\n () => Assert.Equal(x.shape, output.Item1.shape),\\n () => Assert.Equal(x.shape, output.Item2.shape),\\n () => Assert.Equal(x + y, output.Item1),\\n () => Assert.Equal(x - y, output.Item2)\\n );\\n\\n var ones = m.add_scalar(torch.zeros(10), 1);\\n\\n Assert.Equal(torch.ones(10), ones);\\n\\n var a = torch.rand(10);\\n var predict = m.predict(a);\\n\\n Assert.Multiple(\\n () => Assert.NotEqual(a, predict)\\n );\\n }\\n\\n internal class TestScriptModule : Module<Tensor, Tensor, (Tensor, Tensor)>\\n {\\n internal TestScriptModule(string filename) : base(nameof(TestScriptModule))\\n {\\n m = torch.jit.load<(Tensor, Tensor)> (filename);\\n }\\n\\n public override (Tensor, Tensor) forward(Tensor input1, Tensor input2)\\n {\\n return m.call(input1, input2);\\n }\\n\\n public Tensor predict(Tensor input)\\n {\\n return m.invoke<Tensor>(\\\"predict\\\", input);\\n }\\n\\n public Tensor add_scalar(Tensor input, int i)\\n {\\n return m.invoke<Tensor>(\\\"add_scalar\\\", input, i);\\n }\\n\\n private torch.jit.ScriptModule<(Tensor, Tensor)> m;\\n }\\n\\n [Fact]\\n public void TestJITCompile_1()\\n {\\n string script = @\\\"\\n def relu_script(a, b):\\n return torch.relu(a + b)\\n def relu6_script(a, b):\\n return torch.relu6(a + b)\\n def add_i(x: Tensor, i: int) -> Tensor:\\n return x + i\\n def add_d(x: Tensor, i: float) -> Tensor:\\n return x + i\\n def add_ii(x: int, i: int) -> Tuple[int,int]:\\n return (x + i,x-i)\\n\\\";\\n\\n using var cu = torch.jit.compile(script);\\n\\n Assert.NotNull(cu);\\n\\n var x = torch.randn(3, 4);\\n var y = torch.randn(3, 4);\\n\\n var zeros = torch.zeros(3, 4);\\n var ones = torch.ones(3, 4);\\n\\n var z = (Tensor)cu.invoke(\\\"relu_script\\\", x, y);\\n Assert.Equal(torch.nn.functional.relu(x + y), z);\\n z = cu.invoke<Tensor>(\\\"relu6_script\\\", x, y);\\n Assert.Equal(torch.nn.functional.relu6(x + y), z);\\n z = cu.invoke<Tensor>(\\\"add_i\\\", zeros, 1);\\n Assert.Equal(ones, z);\\n z = cu.invoke<Tensor>(\\\"add_d\\\", zeros, 1.0);\\n Assert.Equal(ones, z);\\n\\n var ss = cu.invoke<(Scalar,Scalar)>(\\\"add_ii\\\", 3, 1);\\n Assert.Multiple(\\n () => Assert.Equal(4, ss.Item1.ToInt32()),\\n () => Assert.Equal(2, ss.Item2.ToInt32())\\n );\\n }\\n\\n [Fact]\\n public void TestJITCompile_2()\\n {\\n string script = @\\\"\\n def none_script(a: Any, b: Any):\\n return a\\n def none_tuple(a: Any, b: Any):\\n return (a, None)\\n def tuple_tuple(a: Any, b: Any, c:Any):\\n return (a, (b, None))\\n def list_tuple(a: Any, b: Any, c:Any):\\n return [a, (b, c)]\\n def list_tuple_list(a: Any, b: Any, c:Any):\\n return [a, (b, [c, None])]\\n\\\";\\n\\n using var cu = torch.jit.compile(script);\\n\\n Assert.NotNull(cu);\\n\\n var x = torch.randn(3, 4);\\n var y = torch.randn(3, 5);\\n var w = torch.randn(3, 6);\\n\\n var z = cu.invoke(\\\"none_script\\\", null, null);\\n Assert.Null(z);\\n z = cu.invoke(\\\"none_script\\\", null, y);\\n Assert.Null(z);\\n z = cu.invoke(\\\"none_script\\\", x, null);\\n Assert.NotNull(z);\\n\\n {\\n var zArr = cu.invoke<(object, object)>(\\\"none_tuple\\\", null, null);\\n Assert.Null(zArr.Item1);\\n Assert.Null(zArr.Item2);\\n }\\n\\n {\\n var zArr = cu.invoke<(object, object)>(\\\"none_tuple\\\", x, null);\\n Assert.NotNull(zArr.Item1);\\n Assert.Null(zArr.Item2);\\n }\\n\\n {\\n var zArr = cu.invoke<(object, object)>(\\\"tuple_tuple\\\", x, y, w);\\n Assert.NotNull(zArr.Item1);\\n Assert.Equal(x, (Tensor)zArr.Item1);\\n //Assert.NotNull(zArr.Item2);\\n var (a,b) = ((object, object))zArr.Item2;\\n Assert.NotNull(a);\\n Assert.Null(b);\\n }\\n\\n {\\n var zArr = cu.invoke<object[]>(\\\"list_tuple\\\", x, y, w);\\n Assert.NotNull(zArr);\\n Assert.NotNull(zArr[0]);\\n Assert.IsType<Tensor>(zArr[0]);\\n Assert.Equal(x, zArr[0]);\\n Assert.NotNull(zArr[1]);\\n Assert.IsType<(Tensor,Tensor)>(zArr[1]);\\n }\\n\\n {\\n var zArr = cu.invoke<object[]>(\\\"list_tuple_list\\\", x, y, w);\\n Assert.NotNull(z);\\n Assert.NotNull(zArr[0]);\\n Assert.Equal(x, zArr[0]);\\n Assert.NotNull(zArr[1]);\\n }\\n }\\n\\n [Fact]//(Skip =\\\"Doesn't work yet.\\\")]\\n public void TestJITCompile_3()\\n {\\n string script = @\\\"\\n def list_first(a: List[Tensor]) -> Tensor:\\n return a[0]\\n def list_two(a: List[Tensor]) -> List[Tensor]:\\n return [a[0],a[1]]\\n def list_from_two(a: List[Tensor], b: List[Tensor]) -> List[Tensor]:\\n return [a[0],a[1],b[0],b[1]]\\n\\\";\\n\\n using var cu = torch.jit.compile(script);\\n\\n Assert.NotNull(cu);\\n\\n var x = torch.randn(3, 4);\\n var y = torch.randn(3, 5);\\n var w = torch.randn(3, 6);\\n\\n {\\n var zArr = cu.invoke<Tensor>(\\\"list_first\\\", new[] { new[] { x, y } });\\n Assert.NotNull(zArr);\\n Assert.Equal(x, zArr);\\n }\\n {\\n var zArr = cu.invoke<Tensor[]>(\\\"list_two\\\", new [] { new[] { x, w } });\\n Assert.NotNull(zArr);\\n Assert.Equal(2, zArr.Length);\\n Assert.Equal(x, zArr[0]);\\n Assert.Equal(w, zArr[1]);\\n }\\n {\\n var zArr = cu.invoke<Tensor[]>(\\\"list_from_two\\\", new[] { x, y }, new[] { y, w });\\n Assert.NotNull(zArr);\\n Assert.Equal(4, zArr.Length);\\n Assert.Equal(x, zArr[0]);\\n Assert.Equal(y, zArr[1]);\\n Assert.Equal(y, zArr[2]);\\n Assert.Equal(w, zArr[3]);\\n }\\n }\\n }\\n}\\n\"\n },\n {\n \"alpha_fraction\": 0.6138077974319458,\n \"alphanum_fraction\": 0.6138077974319458,\n \"avg_line_length\": 40.36800003051758,\n \"blob_id\": \"d7dcc65168281c62ac0b6ac35ec7755c686f46cd\",\n \"content_id\": \"162eda077cfa06d85cb15a1b17eb6feb4f9ba49a\",\n \"detected_licenses\": [\n \"MIT\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"C#\",\n \"length_bytes\": 5171,\n \"license_type\": \"permissive\",\n \"max_line_length\": 140,\n \"num_lines\": 125,\n \"path\": \"/src/TorchSharp/Tensor/Factories/as_tensor.cs\",\n \"repo_name\": \"NiklasGustafsson/TorchSharp\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"// Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information.\\nusing System.Collections.Generic;\\nusing System.Linq;\\n\\n#nullable enable\\nnamespace TorchSharp\\n{\\n public static partial class torch\\n {\\n // as_tensor()\\n\\n public static Tensor as_tensor(Tensor data, ScalarType? dtype = null, Device? device = null)\\n {\\n if (dtype != null && device != null && (data.dtype != dtype || data.device != device)) {\\n return data.to(dtype.Value, device).requires_grad_(data.requires_grad);\\n } else if (dtype != null && data.dtype != dtype) {\\n return data.to(dtype.Value).requires_grad_(data.requires_grad);\\n } else if (device != null && data.device != device) {\\n return data.to(device).requires_grad_(data.requires_grad);\\n } else {\\n return data.alias();\\n }\\n }\\n\\n public static Tensor as_tensor(IList<bool> rawArray, ScalarType? dtype = null, Device? device = null)\\n {\\n return torch.from_array(rawArray.ToArray(), dtype, device);\\n }\\n\\n public static Tensor as_tensor(bool[] rawArray, torch.ScalarType? dtype = null, torch.Device? device = null)\\n {\\n return torch.from_array(rawArray, dtype, device);\\n }\\n\\n public static Tensor as_tensor(IList<byte> rawArray, torch.ScalarType? dtype = null, torch.Device? device = null)\\n {\\n return torch.from_array(rawArray.ToArray(), dtype, device);\\n }\\n\\n public static Tensor as_tensor(byte[] rawArray, torch.ScalarType? dtype = null, torch.Device? device = null)\\n {\\n return torch.from_array(rawArray, dtype, device);\\n }\\n\\n public static Tensor as_tensor(IList<sbyte> rawArray, torch.ScalarType? dtype = null, torch.Device? device = null)\\n {\\n return torch.from_array(rawArray.ToArray(), dtype, device);\\n }\\n\\n public static Tensor as_tensor(sbyte[] rawArray, torch.ScalarType? dtype = null, torch.Device? device = null)\\n {\\n return torch.from_array(rawArray, dtype, device);\\n }\\n\\n public static Tensor as_tensor(IList<short> rawArray, torch.ScalarType? dtype = null, torch.Device? device = null)\\n {\\n return torch.from_array(rawArray.ToArray(), dtype, device);\\n }\\n\\n public static Tensor as_tensor(short[] rawArray, torch.ScalarType? dtype = null, torch.Device? device = null)\\n {\\n return torch.from_array(rawArray, dtype, device);\\n }\\n\\n public static Tensor as_tensor(IList<int> rawArray, torch.ScalarType? dtype = null, torch.Device? device = null)\\n {\\n return torch.from_array(rawArray.ToArray(), dtype, device);\\n }\\n\\n public static Tensor as_tensor(int[] rawArray, torch.ScalarType? dtype = null, torch.Device? device = null)\\n {\\n return torch.from_array(rawArray, dtype, device);\\n }\\n\\n public static Tensor as_tensor(IList<long> rawArray, torch.ScalarType? dtype = null, torch.Device? device = null)\\n {\\n return torch.from_array(rawArray.ToArray(), dtype, device);\\n }\\n\\n public static Tensor as_tensor(long[] rawArray, torch.ScalarType? dtype = null, torch.Device? device = null)\\n {\\n return torch.from_array(rawArray, dtype, device);\\n }\\n\\n public static Tensor as_tensor(IList<float> rawArray, torch.ScalarType? dtype = null, torch.Device? device = null)\\n {\\n return torch.from_array(rawArray.ToArray(), dtype, device);\\n }\\n\\n public static Tensor as_tensor(float[] rawArray, torch.ScalarType? dtype = null, torch.Device? device = null)\\n {\\n return torch.from_array(rawArray, dtype, device);\\n }\\n\\n public static Tensor as_tensor(IList<double> rawArray, torch.ScalarType? dtype = null, torch.Device? device = null)\\n {\\n return torch.from_array(rawArray.ToArray(), dtype, device);\\n }\\n\\n public static Tensor as_tensor(double[] rawArray, torch.ScalarType? dtype = null, torch.Device? device = null)\\n {\\n return torch.from_array(rawArray, dtype, device);\\n }\\n\\n public static Tensor as_tensor(IList<(float, float)> rawArray, torch.ScalarType? dtype = null, torch.Device? device = null)\\n {\\n return torch.from_array(rawArray.ToArray(), dtype, device);\\n }\\n\\n public static Tensor as_tensor((float, float)[] rawArray, torch.ScalarType? dtype = null, torch.Device? device = null)\\n {\\n return torch.from_array(rawArray, dtype, device);\\n }\\n\\n public static Tensor as_tensor(IList<System.Numerics.Complex> rawArray, torch.ScalarType? dtype = null, torch.Device? device = null)\\n {\\n return torch.from_array(rawArray.ToArray(), dtype, device);\\n }\\n\\n public static Tensor as_tensor(System.Numerics.Complex[] rawArray, torch.ScalarType? dtype = null, torch.Device? device = null)\\n {\\n return torch.from_array(rawArray, dtype, device);\\n }\\n }\\n}\\n\"\n },\n {\n \"alpha_fraction\": 0.561601996421814,\n \"alphanum_fraction\": 0.5667617917060852,\n \"avg_line_length\": 52.5293083190918,\n \"blob_id\": \"cd4abbdaec593fd0eabe05e42dc63a6a009f105f\",\n \"content_id\": \"5e514bef5e2ed4c80d2c36d70b4cf99afee6e37e\",\n \"detected_licenses\": [\n \"MIT\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"C#\",\n \"length_bytes\": 60300,\n \"license_type\": \"permissive\",\n \"max_line_length\": 244,\n \"num_lines\": 1126,\n \"path\": \"/src/TorchSharp/NN/Losses.cs\",\n \"repo_name\": \"NiklasGustafsson/TorchSharp\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"// Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information.\\nusing System;\\nusing static TorchSharp.torch;\\nusing static TorchSharp.torch.nn;\\nusing static TorchSharp.PInvoke.NativeMethods;\\n\\n#nullable enable\\n\\nnamespace TorchSharp\\n{\\n using Modules;\\n\\n public abstract class Loss<T1, T2, TResult> : nn.Module<T1, T2, TResult>\\n {\\n public Loss(torch.nn.Reduction reduction = nn.Reduction.Mean) : base(nameof(Loss<T1, T2, TResult>))\\n {\\n this.reduction = reduction;\\n }\\n\\n public torch.nn.Reduction reduction { get; }\\n }\\n\\n public abstract class Loss<T1, T2, T3, TResult> : nn.Module<T1, T2, T3, TResult>\\n {\\n public Loss(torch.nn.Reduction reduction = nn.Reduction.Mean) : base(nameof(Loss<T1, T2, T3, TResult>))\\n {\\n this.reduction = reduction;\\n }\\n\\n public torch.nn.Reduction reduction { get; }\\n }\\n public abstract class Loss<T1, T2, T3, T4, TResult> : nn.Module<T1, T2, T3, T4, TResult>\\n {\\n public Loss(torch.nn.Reduction reduction = nn.Reduction.Mean) : base(nameof(Loss<T1, T2, T3, T4, TResult>))\\n {\\n this.reduction = reduction;\\n }\\n\\n public torch.nn.Reduction reduction { get; }\\n }\\n\\n public abstract class WeightedLoss<T1, T2, TResult> : Loss<T1, T2, TResult>\\n {\\n public WeightedLoss(Tensor? weight = null, torch.nn.Reduction reduction = nn.Reduction.Mean) : base(reduction)\\n {\\n this.weight = weight;\\n }\\n\\n public Tensor? weight { get; }\\n }\\n\\n public abstract class WeightedLoss<T1, T2, T3, TResult> : Loss<T1, T2, T3, TResult>\\n {\\n public WeightedLoss(Tensor? weight = null, torch.nn.Reduction reduction = nn.Reduction.Mean) : base(reduction)\\n {\\n this.weight = weight;\\n }\\n\\n public Tensor? weight { get; }\\n }\\n\\n public abstract class WeightedLoss<T1, T2, T3, T4, TResult> : Loss<T1, T2, T3, T4, TResult>\\n {\\n public WeightedLoss(Tensor? weight = null, torch.nn.Reduction reduction = nn.Reduction.Mean) : base(reduction)\\n {\\n this.weight = weight;\\n }\\n\\n public Tensor? weight { get; }\\n }\\n\\n public static partial class torch\\n {\\n public static partial class nn\\n {\\n /// <summary>\\n /// This criterion combines log_softmax and nll_loss in a single function.\\n /// </summary>\\n /// <param name=\\\"weight\\\">A manual rescaling weight if provided it’s repeated to match input tensor shape</param>\\n /// <param name=\\\"ignore_index\\\">Specifies a target value that is ignored and does not contribute to the input gradient.</param>\\n /// <param name=\\\"reduction\\\">Specifies the reduction to apply to the output</param>\\n /// <returns></returns>\\n public static Modules.CrossEntropyLoss CrossEntropyLoss(Tensor? weight = null, long? ignore_index = null, Reduction reduction = Reduction.Mean)\\n {\\n return new Modules.CrossEntropyLoss(weight, ignore_index, reduction);\\n }\\n\\n /// <summary>\\n /// Measures the Binary Cross Entropy between the target and the output.\\n /// </summary>\\n /// <param name=\\\"weight\\\">A manual rescaling weight if provided it’s repeated to match input tensor shape</param>\\n /// <param name=\\\"reduction\\\">Specifies the reduction to apply to the output</param>\\n /// <returns></returns>\\n public static Modules.BCELoss BCELoss(Tensor? weight = null, Reduction reduction = Reduction.Mean)\\n {\\n return new Modules.BCELoss(weight, reduction);\\n }\\n\\n /// <summary>\\n /// Measures Binary Cross Entropy between target and output logits.\\n /// </summary>\\n /// <param name=\\\"weight\\\">A manual rescaling weight if provided it’s repeated to match input tensor shape</param>\\n /// <param name=\\\"reduction\\\">Specifies the reduction to apply to the output</param>\\n /// <param name=\\\"pos_weights\\\">A weight of positive examples. Must be a vector with length equal to the number of classes.</param>\\n /// <returns></returns>\\n public static Modules.BCEWithLogitsLoss BCEWithLogitsLoss(Tensor? weight = null, Reduction reduction = Reduction.Mean, Tensor? pos_weights = null)\\n {\\n return new Modules.BCEWithLogitsLoss(weight, reduction, pos_weights);\\n }\\n\\n /// <summary>\\n /// Measures the loss given two input tensor and a lable tensor with values 1 or -1.\\n ///\\n /// See: https://pytorch.org/docs/stable/generated/torch.nn.CosineEmbeddingLoss.html#torch.nn.CosineEmbeddingLoss\\n /// </summary>\\n /// <param name=\\\"margin\\\"> Should be a number from -1 to 1, 0 to 0.5 is suggested</param>\\n /// <param name=\\\"reduction\\\">Specifies the reduction to apply to the output</param>\\n /// <returns></returns>\\n public static Modules.CosineEmbeddingLoss CosineEmbeddingLoss(double margin = 0.0, Reduction reduction = Reduction.Mean)\\n {\\n return new Modules.CosineEmbeddingLoss(margin, reduction);\\n }\\n\\n\\n /// <summary>\\n /// The Connectionist Temporal Classification loss.\\n ///\\n /// Calculates loss between a continuous (unsegmented) time series and a target sequence.\\n /// CTCLoss sums over the probability of possible alignments of input to target, producing a\\n /// loss value which is differentiable with respect to each input node. The alignment of input to\\n /// target is assumed to be “many-to-one”, which limits the length of the target sequence such that\\n /// it must be less than the input length.\\n /// </summary>\\n /// <returns></returns>\\n public static Modules.CTCLoss CTCLoss(long blank = 0, bool zero_infinity = false, Reduction reduction = Reduction.Mean)\\n {\\n return new Modules.CTCLoss(blank, zero_infinity, reduction);\\n }\\n\\n /// <summary>\\n /// Measures the loss given an input tensor x and a labels tensor y (containing 1 or -1).\\n ///\\n /// See: https://pytorch.org/docs/stable/generated/torch.nn.HingeEmbeddingLoss.html#torch.nn.HingeEmbeddingLoss\\n /// </summary>\\n /// <param name=\\\"margin\\\"> Should be a number from -1 to 1, 0 to 0.5 is suggested</param>\\n /// <param name=\\\"reduction\\\">Specifies the reduction to apply to the output</param>\\n /// <returns></returns>\\n public static Modules.HingeEmbeddingLoss HingeEmbeddingLoss(double margin = 1.0, Reduction reduction = Reduction.Mean)\\n {\\n return new Modules.HingeEmbeddingLoss(margin, reduction);\\n }\\n\\n /// <summary>\\n /// Creates a criterion that uses a squared term if the absolute element-wise error falls below delta and a delta-scaled L1 term otherwise.\\n ///\\n /// See: https://pytorch.org/docs/stable/generated/torch.nn.HuberLoss.html#torch.nn.HuberLoss\\n /// </summary>\\n /// <param name=\\\"delta\\\">Specifies the threshold at which to change between delta-scaled L1 and L2 loss. The value must be positive. Default: 1.0</param>\\n /// <param name=\\\"reduction\\\">Specifies the reduction to apply to the output</param>\\n /// <returns></returns>\\n public static Modules.HuberLoss HuberLoss(double delta = 1.0, Reduction reduction = Reduction.Mean)\\n {\\n return new Modules.HuberLoss(delta, reduction);\\n }\\n\\n /// <summary>\\n /// Creates a criterion that measures the loss given inputs x1, x2, two 1D mini-batch or 0D Tensors, and a label 1D mini-batch or 0D Tensor y (containing 1 or -1).\\n ///\\n /// See: https://pytorch.org/docs/stable/generated/torch.nn.MarginRankingLoss.html#torch.nn.MarginRankingLoss\\n /// </summary>\\n /// <param name=\\\"margin\\\">Has a default value of 0.</param>\\n /// <param name=\\\"reduction\\\">Specifies the reduction to apply to the output</param>\\n /// <returns></returns>\\n public static Modules.MarginRankingLoss MarginRankingLoss(double margin = 0, Reduction reduction = Reduction.Mean)\\n {\\n return new Modules.MarginRankingLoss(margin, reduction);\\n }\\n\\n /// <summary>\\n /// Creates a criterion that optimizes a multi-label one-versus-all loss based on max-entropy, between input x and target y of size NxC.\\n ///\\n /// See: https://pytorch.org/docs/stable/generated/torch.nn.MultiLabelSoftMarginLoss.html#torch.nn.MultiLabelSoftMarginLoss\\n /// </summary>\\n /// <param name=\\\"weight\\\">A manual rescaling weight given to each class. If given, it has to be a Tensor of size C. Otherwise, it is treated as if having all ones.</param>\\n /// <param name=\\\"reduction\\\">Specifies the reduction to apply to the output</param>\\n /// <returns></returns>\\n public static MultiLabelSoftMarginLoss MultiLabelSoftMarginLoss(Tensor? weight = null, Reduction reduction = Reduction.Mean)\\n {\\n return new MultiLabelSoftMarginLoss(weight, reduction);\\n }\\n\\n /// <summary>\\n /// Creates a criterion that optimizes a multi-class classification hinge loss.\\n ///\\n /// See: https://pytorch.org/docs/stable/generated/torch.nn.MultiMarginLoss.html#torch.nn.MultiMarginLoss\\n /// </summary>\\n /// <param name=\\\"p\\\">Has a default value of 1. 1 and 2 are the only supported values.</param>\\n /// <param name=\\\"margin\\\">Has a default value of 1</param>\\n /// <param name=\\\"weight\\\">A manual rescaling weight given to each class. If given, it has to be a Tensor of size C. Otherwise, it is treated as if having all ones.</param>\\n /// <param name=\\\"reduction\\\">Specifies the reduction to apply to the output</param>\\n public static MultiMarginLoss MultiMarginLoss(int p = 1, double margin = 1.0, Tensor? weight = null, Reduction reduction = Reduction.Mean)\\n {\\n return new MultiMarginLoss(p, margin, weight, reduction);\\n }\\n\\n /// <summary>\\n /// Measures the element-wise mean squared error.\\n /// </summary>\\n /// <param name=\\\"reduction\\\">Specifies the reduction to apply to the output</param>\\n /// <returns></returns>\\n public static MSELoss MSELoss(Reduction reduction = Reduction.Mean)\\n {\\n return new MSELoss(reduction);\\n }\\n\\n /// <summary>\\n /// Function that takes the mean element-wise absolute value difference.\\n /// </summary>\\n /// <param name=\\\"reduction\\\">Specifies the reduction to apply to the output</param>\\n /// <returns></returns>\\n public static L1Loss L1Loss(Reduction reduction = Reduction.Mean)\\n {\\n return new L1Loss(reduction);\\n }\\n\\n /// <summary>\\n /// The negative log likelihood loss.\\n /// </summary>\\n /// <param name=\\\"weight\\\">A manual rescaling weight if provided it’s repeated to match input tensor shape</param>\\n /// <param name=\\\"reduction\\\">Specifies the reduction to apply to the output</param>\\n /// <returns></returns>\\n public static NLLLoss NLLLoss(Tensor? weight = null, Reduction reduction = Reduction.Mean)\\n {\\n return new NLLLoss(weight, reduction);\\n }\\n\\n /// <summary>\\n /// Poisson negative log likelihood loss.\\n /// </summary>\\n /// <param name=\\\"log_input\\\"></param>\\n /// <param name=\\\"full\\\">Whether to compute full loss, i. e. to add the Stirling approximation term.</param>\\n /// <param name=\\\"eps\\\">Small value to avoid evaluation of log(0)</param>\\n /// <param name=\\\"reduction\\\">Specifies the reduction to apply to the output</param>\\n /// <returns></returns>\\n public static PoissonNLLLoss PoissonNLLLoss(bool log_input = true, bool full = false, float eps = 1e-8f, Reduction reduction = Reduction.Mean)\\n {\\n return new PoissonNLLLoss(log_input, full, eps, reduction);\\n }\\n\\n /// <summary>\\n /// The Kullback-Leibler divergence Loss\\n /// </summary>\\n /// <param name=\\\"log_target\\\">A flag indicating whether target is passed in the log space.</param>\\n /// <param name=\\\"reduction\\\">Specifies the reduction to apply to the output</param>\\n /// <returns></returns>\\n public static KLDivLoss KLDivLoss(bool log_target = true, Reduction reduction = Reduction.Mean)\\n {\\n return new KLDivLoss(log_target, reduction);\\n }\\n\\n /// <summary>\\n /// Optimizes a two-class classification logistic loss between input tensor xx and target tensor yy (containing 1 or -1).\\n /// </summary>\\n /// <param name=\\\"reduction\\\">Specifies the reduction to apply to the output</param>\\n /// <returns></returns>\\n public static SoftMarginLoss SoftMarginLoss(Reduction reduction = Reduction.Mean)\\n {\\n return new SoftMarginLoss(reduction);\\n }\\n\\n /// <summary>\\n /// Creates a criterion that measures the triplet loss given an input tensors x1, x2, x3 and a margin with a value greater than 0.\\n /// This is used for measuring a relative similarity between samples.\\n ///\\n /// See: https://pytorch.org/docs/stable/generated/torch.nn.TripletMarginLoss.html#torch.nn.TripletMarginLoss\\n /// </summary>\\n /// <param name=\\\"margin\\\">\\n /// A nonnegative margin representing the minimum difference between the positive and negative distances required for the loss to be 0.\\n /// Larger margins penalize cases where the negative examples are not distant enough from the anchors, relative to the positives.\\n /// </param>\\n /// <param name=\\\"p\\\">The norm degree for pairwise distance. </param>\\n /// <param name=\\\"eps\\\"></param>\\n /// <param name=\\\"swap\\\">\\n /// If true, and if the positive example is closer to the negative example than the anchor is, swaps the positive example and the anchor in the loss computation.\\n /// The distance swap is described in detail in the paper Learning shallow convolutional feature descriptors with triplet losses by V. Balntas, E. Riba et al.\\n /// </param>\\n /// <param name=\\\"reduction\\\">Specifies the reduction to apply to the output</param>\\n /// <returns></returns>\\n public static TripletMarginLoss TripletMarginLoss(double margin = 1.0, long p = 2, double eps = 1e-06, bool swap = false, Reduction reduction = Reduction.Mean)\\n {\\n return new TripletMarginLoss(margin, p, eps, swap, reduction);\\n }\\n\\n /// <summary>\\n /// Creates a criterion that measures the triplet loss given input tensors a, p, and n (representing anchor, positive, and negative examples, respectively),\\n /// and a nonnegative, real-valued function (\\\"distance function\\\") used to compute the relationship between the anchor and positive example (\\\"positive distance\\\")\\n /// and the anchor and negative example (\\\"negative distance\\\").\\n /// </summary>\\n /// <param name=\\\"distance\\\"> A nonnegative, real-valued function that quantifies the closeness of two tensors. If not specified, nn.PairwiseDistance will be used.</param>\\n /// <param name=\\\"margin\\\">\\n /// A nonnegative margin representing the minimum difference between the positive and negative distances required for the loss to be 0.\\n /// Larger margins penalize cases where the negative examples are not distant enough from the anchors, relative to the positives.\\n /// </param>\\n /// <param name=\\\"swap\\\">\\n /// If true, and if the positive example is closer to the negative example than the anchor is, swaps the positive example and the anchor in the loss computation.\\n /// The distance swap is described in detail in the paper Learning shallow convolutional feature descriptors with triplet losses by V. Balntas, E. Riba et al.\\n /// </param>\\n /// <param name=\\\"reduction\\\">Specifies the reduction to apply to the output</param>\\n /// <returns></returns>\\n public static TripletMarginWithDistanceLoss TripletMarginWithDistanceLoss(Func<Tensor, Tensor, Tensor>? distance = null, double margin = 1.0, bool swap = false, Reduction reduction = Reduction.Mean)\\n {\\n return new TripletMarginWithDistanceLoss(distance, margin, swap, reduction);\\n }\\n\\n /// <summary>\\n /// Gaussian negative log likelihood loss.\\n /// </summary>\\n /// <param name=\\\"full\\\">Include the constant term in the loss calculation</param>\\n /// <param name=\\\"eps\\\">Value used to clamp var (see note below), for stability.</param>\\n /// <param name=\\\"reduction\\\">Specifies the reduction to apply to the output</param>\\n /// <returns></returns>\\n public static GaussianNLLLoss GaussianNLLLoss(bool full = false, float eps = 1e-8f, Reduction reduction = Reduction.Mean)\\n {\\n return new GaussianNLLLoss(full, eps, reduction);\\n }\\n\\n /// <summary>\\n /// Creates a criterion that optimizes a multi-class multi-classification hinge loss (margin-based loss) between input x (a 2D mini-batch Tensor)\\n /// and output y (which is a 2D Tensor of target class indices).\\n /// </summary>\\n /// <param name=\\\"reduction\\\">Specifies the reduction to apply to the output</param>\\n /// <returns></returns>\\n public static MultiLabelMarginLoss MultiLabelMarginLoss(Reduction reduction = Reduction.Mean)\\n {\\n return new MultiLabelMarginLoss(reduction);\\n }\\n\\n /// <summary>\\n /// Function that uses a squared term if the absolute element-wise error falls below beta and an L1 term otherwise.\\n /// </summary>\\n /// <param name=\\\"beta\\\">Specifies the threshold at which to change between L1 and L2 loss. The value must be non-negative. Default: 1.0</param>\\n /// <param name=\\\"reduction\\\">Specifies the reduction to apply to the output</param>\\n /// <returns></returns>\\n public static SmoothL1Loss SmoothL1Loss(Reduction reduction = Reduction.Mean, double beta = 1.0)\\n {\\n return new SmoothL1Loss(reduction, beta);\\n }\\n\\n\\n /// <summary>\\n /// Class maintaing the supported loss functions.\\n /// </summary>\\n public static partial class functional\\n {\\n /// <summary>\\n /// Function that measures Binary Cross Entropy between target and input logits.\\n /// </summary>\\n /// <param name=\\\"input\\\">Tensor of arbitrary shape as unnormalized scores (often referred to as logits).</param>\\n /// <param name=\\\"target\\\">Tensor of the same shape as input with values between 0 and 1</param>\\n /// <param name=\\\"weight\\\">A manual rescaling weight if provided it’s repeated to match input tensor shape</param>\\n /// <param name=\\\"reduction\\\">Specifies the reduction to apply to the output</param>\\n /// <param name=\\\"pos_weights\\\">A weight of positive examples. Must be a vector with length equal to the number of classes.</param>\\n /// <returns></returns>\\n public static Tensor binary_cross_entropy_with_logits(Tensor input, Tensor target, Tensor? weight = null, Reduction reduction = Reduction.Mean, Tensor? pos_weights = null)\\n {\\n var res = THSNN_binary_cross_entropy_with_logits(input.Handle, target.Handle, weight?.Handle ?? IntPtr.Zero, (long)reduction, pos_weights?.Handle ?? IntPtr.Zero);\\n if (res == IntPtr.Zero) { torch.CheckForErrors(); }\\n return new Tensor(res);\\n }\\n\\n /// <summary>\\n /// Function that measures the Binary Cross Entropy between the target and input probabilities.\\n /// </summary>\\n /// <param name=\\\"input\\\">Tensor of arbitrary shape as probabilities.</param>\\n /// <param name=\\\"target\\\">Tensor of the same shape as input with values between 0 and 1</param>\\n /// <param name=\\\"weight\\\">A manual rescaling weight if provided it’s repeated to match input tensor shape</param>\\n /// <param name=\\\"reduction\\\">Specifies the reduction to apply to the output</param>\\n /// <returns></returns>\\n public static Tensor binary_cross_entropy(Tensor input, Tensor target, Tensor? weight = null, Reduction reduction = Reduction.Mean)\\n {\\n var res = THSNN_binary_cross_entropy(input.Handle, target.Handle, weight?.Handle ?? IntPtr.Zero, (long)reduction);\\n if (res == IntPtr.Zero) { torch.CheckForErrors(); }\\n return new Tensor(res);\\n }\\n\\n /// <summary>\\n /// Computes the cross entropy loss between input and target.\\n /// </summary>\\n /// <param name=\\\"input\\\">Tensor of arbitrary shape as unnormalized scores (often referred to as logits).</param>\\n /// <param name=\\\"target\\\">Ground truth class indices or class probabilities; see Shape section below for supported shapes.</param>\\n /// <param name=\\\"weight\\\">A manual rescaling weight if provided it’s repeated to match input tensor shape</param>\\n /// <param name=\\\"ignore_index\\\">\\n /// Specifies a target value that is ignored and does not contribute to the input gradient.\\n /// Note that ignore_index is only applicable when the target contains class indices.\\n /// </param>\\n /// <param name=\\\"reduction\\\">Specifies the reduction to apply to the output</param>\\n /// <param name=\\\"label_smoothing\\\">A float in [0.0, 1.0].\\n /// Specifies the amount of smoothing when computing the loss, where 0.0 means no smoothing.\\n /// The targets become a mixture of the original ground truth and a uniform distribution.</param>\\n /// <returns></returns>\\n public static Tensor cross_entropy(Tensor input, Tensor target, Tensor? weight = null, long ignore_index = -100, Reduction reduction = Reduction.Mean, double label_smoothing = 0.0)\\n {\\n var res = THSNN_cross_entropy(input.Handle, target.Handle, weight?.Handle ?? IntPtr.Zero, ignore_index, true, (long)reduction, label_smoothing);\\n if (res == IntPtr.Zero) { torch.CheckForErrors(); }\\n return new Tensor(res);\\n }\\n\\n /// <summary>\\n /// Poisson negative log likelihood loss.\\n /// </summary>\\n /// <param name=\\\"input\\\">Expectation of underlying Poisson distribution.</param>\\n /// <param name=\\\"target\\\">Random sample target.</param>\\n /// <param name=\\\"log_input\\\"></param>\\n /// <param name=\\\"full\\\">Whether to compute full loss, i.e. to add the Stirling approximation term.</param>\\n /// <param name=\\\"eps\\\">Small value to avoid evaluation of log(0)</param>\\n /// <param name=\\\"reduction\\\">Specifies the reduction to apply to the output</param>\\n /// <returns></returns>\\n public static Tensor poisson_nll_loss(Tensor input, Tensor target, bool log_input = true, bool full = false, float eps = 1e-8f, Reduction reduction = Reduction.Mean)\\n {\\n var res = THSNN_poisson_loss(input.Handle, target.Handle, log_input, full, eps, (long)reduction);\\n if (res == IntPtr.Zero) { torch.CheckForErrors(); }\\n return new Tensor(res);\\n }\\n\\n /// <summary>\\n ///\\n /// </summary>\\n /// <param name=\\\"input1\\\">(N,D) or (D), where N is the batch size and D is the embedding dimension.</param>\\n /// <param name=\\\"input2\\\">Same shape as input1</param>\\n /// <param name=\\\"target\\\">N or ()</param>\\n /// <param name=\\\"margin\\\">Should be a number from -1−1 to 11, 00 to 0.50.5 is suggested</param>\\n /// <param name=\\\"reduction\\\">Specifies the reduction to apply to the output</param>\\n /// <returns></returns>\\n public static Tensor cosine_embedding_loss(Tensor input1, Tensor input2, Tensor target, double margin = 0.0, Reduction reduction = Reduction.Mean)\\n {\\n var res = THSNN_cosine_embedding_loss(input1.Handle, input2.Handle, target.Handle, margin, (long)reduction);\\n if (res == IntPtr.Zero) { torch.CheckForErrors(); }\\n return new Tensor(res);\\n }\\n\\n /// <summary>\\n /// Computes the Connectionist Temporal Classification loss.\\n /// </summary>\\n /// <param name=\\\"log_probs\\\">The logarithmized probabilities of the outputs.</param>\\n /// <param name=\\\"targets\\\"></param>\\n /// <param name=\\\"input_lengths\\\">Lengths of the inputs.</param>\\n /// <param name=\\\"target_lengths\\\">Lengths of the targets.</param>\\n /// <param name=\\\"blank\\\">Blank label.</param>\\n /// <param name=\\\"zero_infinity\\\">Whether to zero infinite losses and the associated gradients.</param>\\n /// <param name=\\\"reduction\\\">Specifies the reduction to apply to the output</param>\\n /// <returns></returns>\\n public static Tensor ctc_loss(Tensor log_probs, Tensor targets, Tensor input_lengths, Tensor target_lengths, long blank = 0, bool zero_infinity = false, Reduction reduction = Reduction.Mean)\\n {\\n var res = THSNN_ctc_loss(log_probs.Handle, targets.Handle, input_lengths.Handle, target_lengths.Handle, blank, zero_infinity, (long)reduction);\\n if (res == IntPtr.Zero) { torch.CheckForErrors(); }\\n return new Tensor(res);\\n }\\n\\n /// <summary>\\n /// Measures the loss given an input tensor x and a labels tensor y (containing 1 or -1).\\n /// </summary>\\n /// <param name=\\\"input\\\"></param>\\n /// <param name=\\\"target\\\"></param>\\n /// <param name=\\\"margin\\\"> Should be a number from -1 to 1, 0 to 0.5 is suggested</param>\\n /// <param name=\\\"reduction\\\">Specifies the reduction to apply to the output</param>\\n /// <returns></returns>\\n public static Tensor hinge_embedding_loss(Tensor input, Tensor target, double margin = 0.0, Reduction reduction = Reduction.Mean)\\n {\\n var res = THSNN_hinge_embedding_loss(input.Handle, target.Handle, margin, (long)reduction);\\n if (res == IntPtr.Zero) { torch.CheckForErrors(); }\\n return new Tensor(res);\\n }\\n\\n /// <summary>\\n /// Function that uses a squared term if the absolute element-wise error falls below delta and a delta-scaled L1 term otherwise.\\n /// </summary>\\n /// <param name=\\\"input\\\"></param>\\n /// <param name=\\\"target\\\"></param>\\n /// <param name=\\\"delta\\\">Specifies the threshold at which to change between delta-scaled L1 and L2 loss. The value must be positive. Default: 1.0</param>\\n /// <param name=\\\"reduction\\\">Specifies the reduction to apply to the output</param>\\n /// <returns></returns>\\n public static Tensor huber_loss(Tensor input, Tensor target, double delta = 1.0, Reduction reduction = Reduction.Mean)\\n {\\n var res = THSNN_huber_loss(input.Handle, target.Handle, delta, (long)reduction);\\n if (res == IntPtr.Zero) { torch.CheckForErrors(); }\\n return new Tensor(res);\\n }\\n\\n /// <summary>\\n /// Creates a criterion that measures the loss given inputs x1, x2, two 1D mini-batch or 0D Tensors, and a label 1D mini-batch or 0D Tensor y (containing 1 or -1).\\n /// </summary>\\n /// <param name=\\\"input1\\\"></param>\\n /// <param name=\\\"input2\\\"></param>\\n /// <param name=\\\"target\\\"></param>\\n /// <param name=\\\"margin\\\">Has a default value of 0.</param>\\n /// <param name=\\\"reduction\\\">Specifies the reduction to apply to the output</param>\\n /// <returns></returns>\\n public static Tensor margin_ranking_loss(Tensor input1, Tensor input2, Tensor target, double margin = 0.0, Reduction reduction = Reduction.Mean)\\n {\\n var res = THSNN_margin_ranking_loss(input1.Handle, input2.Handle, target.Handle, margin, (long)reduction);\\n if (res == IntPtr.Zero) { torch.CheckForErrors(); }\\n return new Tensor(res);\\n }\\n\\n /// <summary>\\n /// Creates a criterion that optimizes a multi-class multi-classification hinge loss (margin-based loss) between input x (a 2D mini-batch Tensor)\\n /// and output y (which is a 2D Tensor of target class indices).\\n /// </summary>\\n /// <param name=\\\"input\\\"></param>\\n /// <param name=\\\"target\\\"></param>\\n /// <param name=\\\"reduction\\\">Specifies the reduction to apply to the output</param>\\n /// <returns></returns>\\n public static Tensor multi_label_margin_loss(Tensor input, Tensor target, Reduction reduction = Reduction.Mean)\\n {\\n var res = THSNN_multilabel_margin_loss(input.Handle, target.Handle, (long)reduction);\\n if (res == IntPtr.Zero) { torch.CheckForErrors(); }\\n return new Tensor(res);\\n }\\n\\n /// <summary>\\n /// Creates a criterion that optimizes a multi-label one-versus-all loss based on max-entropy, between input x and target y of size NxC.\\n /// </summary>\\n /// <param name=\\\"input\\\"></param>\\n /// <param name=\\\"target\\\"></param>\\n /// <param name=\\\"weight\\\">A manual rescaling weight if provided it’s repeated to match input tensor shape</param>\\n /// <param name=\\\"reduction\\\">Specifies the reduction to apply to the output</param>\\n /// <returns></returns>\\n public static Tensor multilabel_soft_margin_loss(Tensor input, Tensor target, Tensor? weight = null,Reduction reduction = Reduction.Mean)\\n {\\n var res = THSNN_multilabel_soft_margin_loss(input.Handle, target.Handle, weight?.Handle ?? IntPtr.Zero, (long)reduction);\\n if (res == IntPtr.Zero) { torch.CheckForErrors(); }\\n return new Tensor(res);\\n }\\n\\n /// <summary>\\n /// Creates a criterion that optimizes a multi-class classification hinge loss.\\n /// </summary>\\n /// <param name=\\\"input\\\"></param>\\n /// <param name=\\\"target\\\"></param>\\n /// <param name=\\\"p\\\">Has a default value of 1. 1 and 2 are the only supported values.</param>\\n /// <param name=\\\"margin\\\">Has a default value of 1</param>\\n /// <param name=\\\"weight\\\">A manual rescaling weight if provided it’s repeated to match input tensor shape</param>\\n /// <param name=\\\"reduction\\\">Specifies the reduction to apply to the output</param>\\n /// <returns></returns>\\n public static Tensor multi_margin_loss(Tensor input, Tensor target, int p = 1, double margin = 1.0, Tensor? weight = null, Reduction reduction = Reduction.Mean)\\n {\\n IntPtr h = (weight is null) ? IntPtr.Zero : weight.Handle;\\n var res = THSNN_multi_margin_loss(input.Handle, target.Handle, p, margin, h, (long)reduction);\\n if (res == IntPtr.Zero) { torch.CheckForErrors(); }\\n return new Tensor(res);\\n }\\n\\n /// <summary>\\n ///\\tMeasures the element-wise mean squared error.\\n /// </summary>\\n /// <param name=\\\"input\\\">Tensor of any shape.</param>\\n /// <param name=\\\"target\\\">Tensor of the same shape as 'input'</param>\\n /// <param name=\\\"reduction\\\">Specifies the reduction to apply to the output</param>\\n /// <returns></returns>\\n public static Tensor mse_loss(Tensor input, Tensor target, Reduction reduction = Reduction.Mean)\\n {\\n var res = THSNN_mse_loss(input.Handle, target.Handle, (long)reduction);\\n if (res == IntPtr.Zero) { torch.CheckForErrors(); }\\n return new Tensor(res);\\n }\\n\\n /// <summary>\\n /// Function that takes the mean element-wise absolute value difference.\\n /// </summary>\\n /// <param name=\\\"input\\\">Tensor of any shape.</param>\\n /// <param name=\\\"target\\\">Tensor of the same shape as 'input'</param>\\n /// <param name=\\\"reduction\\\">Specifies the reduction to apply to the output</param>\\n /// <returns></returns>\\n public static Tensor l1_loss(Tensor input, Tensor target, Reduction reduction = Reduction.Mean)\\n {\\n var res = THSNN_l1_loss(input.Handle, target.Handle, (long)reduction);\\n if (res == IntPtr.Zero) { torch.CheckForErrors(); }\\n return new Tensor(res);\\n }\\n\\n /// <summary>\\n /// Computes the negative log likelihood loss.\\n /// </summary>\\n /// <param name=\\\"input\\\"></param>\\n /// <param name=\\\"target\\\"></param>\\n /// <param name=\\\"weight\\\">A manual rescaling weight if provided it’s repeated to match input tensor shape</param>\\n /// <param name=\\\"reduction\\\">Specifies the reduction to apply to the output</param>\\n /// <returns></returns>\\n public static Tensor nll_loss(Tensor input, Tensor target, Tensor? weight = null, Reduction reduction = Reduction.Mean)\\n {\\n var res = THSNN_nll_loss(input.Handle, target.Handle, weight?.Handle ?? IntPtr.Zero, (long)reduction);\\n if (res == IntPtr.Zero) { torch.CheckForErrors(); }\\n return new Tensor(res);\\n }\\n\\n /// <summary>\\n /// Gaussian negative log likelihood loss.\\n /// </summary>\\n /// <param name=\\\"input\\\"></param>\\n /// <param name=\\\"target\\\"></param>\\n /// <param name=\\\"variance\\\">Tensor of positive variance(s), one for each of the expectations in the input (heteroscedastic), or a single one (homoscedastic).</param>\\n /// <param name=\\\"full\\\">Include the constant term in the loss calculation. </param>\\n /// <param name=\\\"eps\\\">Value added to var, for stability</param>\\n /// <param name=\\\"reduction\\\">Specifies the reduction to apply to the output</param>\\n /// <returns></returns>\\n public static Tensor gaussian_nll_loss(Tensor input, Tensor target, Tensor variance, bool full = false, float eps = 1e-6f, Reduction reduction = Reduction.Mean)\\n {\\n return new Modules.GaussianNLLLoss(full, eps, reduction).call(input, target, variance);\\n }\\n\\n /// <summary>\\n /// Computes the Kullback-Leibler divergence Loss\\n /// </summary>\\n /// <param name=\\\"input\\\"></param>\\n /// <param name=\\\"target\\\"></param>\\n /// <param name=\\\"log_target\\\">A flag indicating whether target is passed in the log space.</param>\\n /// <param name=\\\"reduction\\\">Specifies the reduction to apply to the output</param>\\n /// <returns></returns>\\n public static Tensor kl_div(Tensor input, Tensor target, bool log_target = true, Reduction reduction = Reduction.Mean)\\n {\\n var res = THSNN_kl_div_loss(input.Handle, target.Handle, (long)reduction, log_target);\\n if (res == IntPtr.Zero) { torch.CheckForErrors(); }\\n return new Tensor(res);\\n }\\n\\n /// <summary>\\n /// Function that uses a squared term if the absolute element-wise error falls below beta and an L1 term otherwise.\\n /// </summary>\\n /// <param name=\\\"input\\\"></param>\\n /// <param name=\\\"target\\\"></param>\\n /// <param name=\\\"reduction\\\">Specifies the reduction to apply to the output</param>\\n /// <param name=\\\"beta\\\">Specifies the threshold at which to change between L1 and L2 loss. The value must be non-negative.</param>\\n /// <returns></returns>\\n public static Tensor smooth_l1_loss(Tensor input, Tensor target, Reduction reduction = Reduction.Mean, double beta = 1.0)\\n {\\n var res = THSNN_smooth_l1_loss(input.Handle, target.Handle, (long)reduction, beta);\\n if (res == IntPtr.Zero) { torch.CheckForErrors(); }\\n return new Tensor(res);\\n }\\n\\n /// <summary>\\n /// Optimizes a two-class classification logistic loss between input tensor x and target tensor y (containing 1 or -1).\\n /// </summary>\\n /// <param name=\\\"input\\\"></param>\\n /// <param name=\\\"target\\\"></param>\\n /// <param name=\\\"reduction\\\">Specifies the reduction to apply to the output</param>\\n /// <returns></returns>\\n public static Tensor soft_margin_loss(Tensor input, Tensor target, Reduction reduction = Reduction.Mean)\\n {\\n var res = THSNN_soft_margin_loss(input.Handle, target.Handle, (long)reduction);\\n if (res == IntPtr.Zero) { torch.CheckForErrors(); }\\n return new Tensor(res);\\n }\\n\\n /// <summary>\\n /// Creates a criterion that measures the triplet loss given an input tensors x1, x2, x3 and a margin with a value greater than 0.\\n /// This is used for measuring a relative similarity between samples.\\n /// </summary>\\n /// <param name=\\\"anchor\\\"></param>\\n /// <param name=\\\"positive\\\"></param>\\n /// <param name=\\\"negative\\\"></param>\\n /// <param name=\\\"margin\\\">\\n /// A nonnegative margin representing the minimum difference between the positive and negative distances required for the loss to be 0.\\n /// Larger margins penalize cases where the negative examples are not distant enough from the anchors, relative to the positives.\\n /// </param>\\n /// <param name=\\\"p\\\">The norm degree for pairwise distance. </param>\\n /// <param name=\\\"eps\\\"></param>\\n /// <param name=\\\"swap\\\">\\n /// If true, and if the positive example is closer to the negative example than the anchor is, swaps the positive example and the anchor in the loss computation.\\n /// The distance swap is described in detail in the paper Learning shallow convolutional feature descriptors with triplet losses by V. Balntas, E. Riba et al.\\n /// </param>\\n /// <param name=\\\"reduction\\\">Specifies the reduction to apply to the output</param>\\n /// <returns></returns>\\n public static Tensor triplet_margin_loss(Tensor anchor, Tensor positive, Tensor negative, double margin = 1.0, long p = 2, double eps = 1e-06, bool swap = false, Reduction reduction = Reduction.Mean)\\n {\\n var res = THSNN_triplet_margin_loss(anchor.Handle, positive.Handle, negative.Handle, margin, p, eps, swap, (long)reduction);\\n if (res == IntPtr.Zero) { torch.CheckForErrors(); }\\n return new Tensor(res);\\n }\\n\\n /// <summary>\\n /// Creates a criterion that measures the triplet loss given input tensors a, p, and n (representing anchor, positive, and negative examples, respectively),\\n /// and a nonnegative, real-valued function (\\\"distance function\\\") used to compute the relationship between the anchor and positive example (\\\"positive distance\\\")\\n /// and the anchor and negative example (\\\"negative distance\\\").\\n /// </summary>\\n /// <param name=\\\"anchor\\\"></param>\\n /// <param name=\\\"positive\\\"></param>\\n /// <param name=\\\"negative\\\"></param>\\n /// <param name=\\\"distance\\\"> A nonnegative, real-valued function that quantifies the closeness of two tensors. If not specified, nn.PairwiseDistance will be used.</param>\\n /// <param name=\\\"margin\\\">\\n /// A nonnegative margin representing the minimum difference between the positive and negative distances required for the loss to be 0.\\n /// Larger margins penalize cases where the negative examples are not distant enough from the anchors, relative to the positives.\\n /// </param>\\n /// <param name=\\\"swap\\\">\\n /// If true, and if the positive example is closer to the negative example than the anchor is, swaps the positive example and the anchor in the loss computation.\\n /// The distance swap is described in detail in the paper Learning shallow convolutional feature descriptors with triplet losses by V. Balntas, E. Riba et al.\\n /// </param>\\n /// <param name=\\\"reduction\\\">Specifies the reduction to apply to the output</param>\\n /// <returns></returns>\\n public static Tensor triplet_margin_with_distance_loss(Tensor anchor, Tensor positive, Tensor negative, Func<Tensor, Tensor, Tensor>? distance = null, double margin = 1.0, bool swap = false, Reduction reduction = Reduction.Mean)\\n {\\n DistanceFunctionNative? func = null;\\n\\n if (distance != null) {\\n func = (IntPtr x, IntPtr y) => {\\n var x1 = new Tensor(x);\\n var y1 = new Tensor(y);\\n var res = distance(x1, y1);\\n\\n GC.SuppressFinalize(x1);\\n GC.SuppressFinalize(y1);\\n GC.SuppressFinalize(res);\\n\\n return res.Handle;\\n };\\n }\\n var res = THSNN_triplet_margin_with_distance_loss(anchor.Handle, positive.Handle, negative.Handle, func, margin, swap, (long)reduction);\\n if (res == IntPtr.Zero) { torch.CheckForErrors(); }\\n return new Tensor(res);\\n }\\n }\\n\\n public enum Reduction : long\\n {\\n None = 0,\\n Mean = 1,\\n Sum = 2\\n }\\n }\\n }\\n\\n namespace Modules\\n {\\n public sealed class CrossEntropyLoss : WeightedLoss<Tensor, Tensor, Tensor>\\n {\\n public CrossEntropyLoss(Tensor? weight = null, long? ignore_index = null, Reduction reduction = Reduction.Mean, double label_smoothing = 0.0) : base(weight, reduction)\\n {\\n this.ignore_index = ignore_index;\\n this.label_smoothing = label_smoothing;\\n }\\n\\n public override Tensor forward(Tensor input, Tensor target)\\n {\\n var ii = ignore_index.HasValue ? ignore_index.Value : -100;\\n var res = THSNN_cross_entropy(input.Handle, target.Handle, weight?.Handle ?? IntPtr.Zero, ii, ignore_index.HasValue, (long)reduction, label_smoothing);\\n if (res == IntPtr.Zero) { torch.CheckForErrors(); }\\n return new Tensor(res);\\n }\\n\\n public long? ignore_index { get; }\\n public double label_smoothing { get; }\\n }\\n\\n public sealed class BCELoss : WeightedLoss<Tensor, Tensor, Tensor>\\n {\\n public BCELoss(Tensor? weight = null, Reduction reduction = Reduction.Mean) : base(weight, reduction)\\n {\\n }\\n\\n public override Tensor forward(Tensor input, Tensor target)\\n {\\n var res = THSNN_binary_cross_entropy(input.Handle, target.Handle, weight?.Handle ?? IntPtr.Zero, (long)reduction);\\n if (res == IntPtr.Zero) { torch.CheckForErrors(); }\\n return new Tensor(res);\\n }\\n }\\n\\n public sealed class BCEWithLogitsLoss : WeightedLoss<Tensor, Tensor, Tensor>\\n {\\n public BCEWithLogitsLoss(Tensor? weight = null, Reduction reduction = Reduction.Mean, Tensor? pos_weights = null) : base(weight, reduction)\\n {\\n this.pos_weights = pos_weights;\\n }\\n\\n public override Tensor forward(Tensor input, Tensor target)\\n {\\n var res = THSNN_binary_cross_entropy_with_logits(input.Handle, target.Handle, weight?.Handle ?? IntPtr.Zero, (long)reduction, pos_weights?.Handle ?? IntPtr.Zero);\\n if (res == IntPtr.Zero) { torch.CheckForErrors(); }\\n return new Tensor(res);\\n }\\n\\n public Tensor? pos_weights { get; }\\n }\\n\\n public sealed class CosineEmbeddingLoss : Loss<Tensor, Tensor, Tensor, Tensor>\\n {\\n public CosineEmbeddingLoss(double margin = 0.0, Reduction reduction = Reduction.Mean) : base(reduction)\\n {\\n this.margin = margin;\\n }\\n\\n public override Tensor forward(Tensor input1, Tensor input2, Tensor target)\\n {\\n var res = THSNN_cosine_embedding_loss(input1.Handle, input2.Handle, target.Handle, margin, (long)reduction);\\n if (res == IntPtr.Zero) { torch.CheckForErrors(); }\\n return new Tensor(res);\\n }\\n\\n public double margin { get; }\\n }\\n\\n public sealed class CTCLoss : Loss<Tensor, Tensor, Tensor, Tensor, Tensor>\\n {\\n public CTCLoss(long blank = 0, bool zero_infinity = false, Reduction reduction = Reduction.Mean) : base(reduction)\\n {\\n this.blank = blank;\\n this.zero_infinity = zero_infinity;\\n }\\n\\n public override Tensor forward(Tensor log_probs, Tensor targets, Tensor input_lengths, Tensor target_lengths)\\n {\\n var res = THSNN_ctc_loss(log_probs.Handle, targets.Handle, input_lengths.Handle, target_lengths.Handle, blank, zero_infinity, (long)reduction);\\n if (res == IntPtr.Zero) { torch.CheckForErrors(); }\\n return new Tensor(res);\\n }\\n\\n public long blank { get; }\\n public bool zero_infinity { get; }\\n }\\n\\n public sealed class HingeEmbeddingLoss : Loss<Tensor, Tensor, Tensor>\\n {\\n public HingeEmbeddingLoss(double margin = 0.0, Reduction reduction = Reduction.Mean) : base(reduction)\\n {\\n this.margin = margin;\\n }\\n\\n public override Tensor forward(Tensor input, Tensor target)\\n {\\n var res = THSNN_hinge_embedding_loss(input.Handle, target.Handle, margin, (long)reduction);\\n if (res == IntPtr.Zero) { torch.CheckForErrors(); }\\n return new Tensor(res);\\n }\\n\\n public double margin { get; }\\n }\\n\\n public sealed class HuberLoss : Loss<Tensor, Tensor, Tensor>\\n {\\n public HuberLoss(double delta = 1.0, Reduction reduction = Reduction.Mean) : base(reduction)\\n {\\n this.delta = delta;\\n }\\n\\n public override Tensor forward(Tensor input, Tensor target)\\n {\\n var res = THSNN_huber_loss(input.Handle, target.Handle, delta, (long)reduction);\\n if (res == IntPtr.Zero) { torch.CheckForErrors(); }\\n return new Tensor(res);\\n }\\n\\n public double delta { get; }\\n }\\n\\n public sealed class MarginRankingLoss : Loss<Tensor, Tensor, Tensor, Tensor>\\n {\\n public MarginRankingLoss(double margin = 0.0, Reduction reduction = Reduction.Mean) : base(reduction)\\n {\\n this.margin = margin;\\n }\\n\\n public override Tensor forward(Tensor input1, Tensor input2, Tensor target)\\n {\\n var res = THSNN_margin_ranking_loss(input1.Handle, input2.Handle, target.Handle, margin, (long)reduction);\\n if (res == IntPtr.Zero) { torch.CheckForErrors(); }\\n return new Tensor(res);\\n }\\n\\n public double margin { get; }\\n }\\n\\n public sealed class MultiLabelMarginLoss : Loss<Tensor, Tensor, Tensor>\\n {\\n public MultiLabelMarginLoss(Reduction reduction = Reduction.Mean) : base(reduction)\\n {\\n }\\n\\n public override Tensor forward(Tensor input, Tensor target)\\n {\\n var res = THSNN_multilabel_margin_loss(input.Handle, target.Handle, (long)reduction);\\n if (res == IntPtr.Zero) { torch.CheckForErrors(); }\\n return new Tensor(res);\\n }\\n }\\n\\n public sealed class MultiLabelSoftMarginLoss : WeightedLoss<Tensor, Tensor, Tensor>\\n {\\n public MultiLabelSoftMarginLoss(Tensor? weight = null, Reduction reduction = Reduction.Mean) : base(weight, reduction)\\n {\\n }\\n\\n public override Tensor forward(Tensor input, Tensor target)\\n {\\n var res = THSNN_multilabel_soft_margin_loss(input.Handle, target.Handle, weight?.Handle ?? IntPtr.Zero, (long)reduction);\\n if (res == IntPtr.Zero) { torch.CheckForErrors(); }\\n return new Tensor(res);\\n }\\n }\\n\\n public sealed class MultiMarginLoss : WeightedLoss<Tensor, Tensor, Tensor>\\n {\\n public MultiMarginLoss(int p = 1, double margin = 1.0, Tensor? weight = null, Reduction reduction = Reduction.Mean) : base(weight, reduction)\\n {\\n this.margin = margin;\\n this.p = p;\\n }\\n\\n public override Tensor forward(Tensor input, Tensor target)\\n {\\n IntPtr h = (weight is null) ? IntPtr.Zero : weight.Handle;\\n\\n var res = THSNN_multi_margin_loss(input.Handle, target.Handle, p, margin, h, (long)reduction);\\n if (res == IntPtr.Zero) { torch.CheckForErrors(); }\\n return new Tensor(res);\\n }\\n\\n public double margin { get; }\\n public int p { get; }\\n }\\n\\n public sealed class MSELoss : Loss<Tensor, Tensor, Tensor>\\n {\\n public MSELoss(Reduction reduction = Reduction.Mean) : base(reduction)\\n {\\n }\\n\\n public override Tensor forward(Tensor input, Tensor target)\\n {\\n var res = THSNN_mse_loss(input.Handle, target.Handle, (long)reduction);\\n if (res == IntPtr.Zero) { torch.CheckForErrors(); }\\n return new Tensor(res);\\n }\\n }\\n\\n public sealed class L1Loss : Loss<Tensor, Tensor, Tensor>\\n {\\n public L1Loss(Reduction reduction = Reduction.Mean) : base(reduction)\\n {\\n }\\n\\n public override Tensor forward(Tensor input, Tensor target)\\n {\\n var res = THSNN_l1_loss(input.Handle, target.Handle, (long)reduction);\\n if (res == IntPtr.Zero) { torch.CheckForErrors(); }\\n return new Tensor(res);\\n }\\n }\\n\\n public sealed class NLLLoss : WeightedLoss<Tensor, Tensor, Tensor>\\n {\\n public NLLLoss(Tensor? weight = null, Reduction reduction = Reduction.Mean) : base(weight, reduction)\\n {\\n }\\n\\n public override Tensor forward(Tensor input, Tensor target)\\n {\\n var res = THSNN_nll_loss(input.Handle, target.Handle, weight?.Handle ?? IntPtr.Zero, (long)reduction);\\n if (res == IntPtr.Zero) { torch.CheckForErrors(); }\\n return new Tensor(res);\\n }\\n }\\n\\n public sealed class PoissonNLLLoss : Loss<Tensor, Tensor, Tensor>\\n {\\n public PoissonNLLLoss(bool log_input = true, bool full = false, float eps = 1e-8f, Reduction reduction = Reduction.Mean) : base(reduction)\\n {\\n this.full = full;\\n this.log_input = log_input;\\n this.eps = eps;\\n }\\n\\n public override Tensor forward(Tensor input, Tensor target)\\n {\\n var res = THSNN_poisson_loss(input.Handle, target.Handle, log_input, full, eps, (long)reduction);\\n if (res == IntPtr.Zero) { torch.CheckForErrors(); }\\n return new Tensor(res);\\n }\\n\\n public bool log_input { get; }\\n public bool full { get; }\\n public float eps { get; }\\n\\n }\\n\\n public sealed class GaussianNLLLoss : Loss<Tensor, Tensor, Tensor, Tensor>\\n {\\n public GaussianNLLLoss(bool full = false, float eps = 1e-8f, Reduction reduction = Reduction.Mean) : base(reduction)\\n {\\n this.full = full;\\n this.eps = eps;\\n }\\n\\n public override Tensor forward(Tensor input, Tensor target, Tensor variance)\\n {\\n input = input.view(input.shape[0], -1);\\n target = target.view(target.shape[0], -1);\\n if (target.shape == input.shape) throw new ArgumentException(\\\"input and target must have the same shape\\\");\\n\\n variance = variance.view(target.shape[0], -1);\\n if (variance.shape[1] != input.shape[1] && variance.shape[1] != 1) throw new ArgumentException(\\\"variance has the wrong shape\\\");\\n\\n if ((variance < 0).any().cpu().item<bool>()) throw new ArgumentException(\\\"variance has negative entry/entries\\\");\\n\\n using (var _ = torch.no_grad())\\n variance = variance.clamp_min(eps);\\n\\n var loss = 0.5 * (variance.log() + (input - target).square() / variance).view(input.shape[0], -1).sum(dim: stackalloc long[] { 1 });\\n\\n if (full) {\\n loss = loss + 0.5 * input.shape[1] * MathF.Log(2 * MathF.PI);\\n }\\n\\n return (reduction == Reduction.Mean) ? loss.mean() : (reduction == Reduction.Sum) ? loss.sum() : loss;\\n }\\n\\n public bool full { get; }\\n public float eps { get; }\\n\\n }\\n\\n public sealed class KLDivLoss : Loss<Tensor, Tensor, Tensor>\\n {\\n public KLDivLoss(bool log_target = true, Reduction reduction = Reduction.Mean) : base(reduction)\\n {\\n this.log_target = log_target;\\n }\\n\\n public override Tensor forward(Tensor input, Tensor target)\\n {\\n var res = THSNN_kl_div_loss(input.Handle, target.Handle, (long)reduction, log_target);\\n if (res == IntPtr.Zero) { torch.CheckForErrors(); }\\n return new Tensor(res);\\n }\\n\\n public bool log_target { get; }\\n }\\n\\n public sealed class SmoothL1Loss : Loss<Tensor, Tensor, Tensor>\\n {\\n public SmoothL1Loss(Reduction reduction = Reduction.Mean, double beta = 1.0) : base(reduction)\\n {\\n this.beta = beta;\\n }\\n\\n public override Tensor forward(Tensor input, Tensor target)\\n {\\n var res = THSNN_smooth_l1_loss(input.Handle, target.Handle, (long)reduction, beta);\\n if (res == IntPtr.Zero) { torch.CheckForErrors(); }\\n return new Tensor(res);\\n }\\n\\n public double beta { get; }\\n }\\n\\n public sealed class SoftMarginLoss : Loss<Tensor, Tensor, Tensor>\\n {\\n public SoftMarginLoss(Reduction reduction = Reduction.Mean) : base(reduction)\\n {\\n }\\n\\n public override Tensor forward(Tensor input, Tensor target)\\n {\\n var res = THSNN_soft_margin_loss(input.Handle, target.Handle, (long)reduction);\\n if (res == IntPtr.Zero) { torch.CheckForErrors(); }\\n return new Tensor(res);\\n }\\n }\\n\\n public sealed class TripletMarginLoss : Loss<Tensor, Tensor, Tensor, Tensor>\\n {\\n public TripletMarginLoss(double margin = 1.0, long p = 2, double eps = 1e-06, bool swap = false, Reduction reduction = Reduction.Mean) : base(reduction)\\n {\\n this.margin = margin;\\n this.p = p;\\n this.eps = eps;\\n this.swap = swap;\\n }\\n\\n public override Tensor forward(Tensor anchor, Tensor positive, Tensor negative)\\n {\\n var res = THSNN_triplet_margin_loss(anchor.Handle, positive.Handle, negative.Handle, margin, p, eps, swap, (long)reduction);\\n if (res == IntPtr.Zero) { torch.CheckForErrors(); }\\n return new Tensor(res);\\n }\\n\\n public double margin { get; }\\n public long p { get; }\\n double eps { get; }\\n bool swap { get; }\\n }\\n\\n public sealed class TripletMarginWithDistanceLoss : Loss<Tensor, Tensor, Tensor, Tensor>\\n {\\n public TripletMarginWithDistanceLoss(Func<Tensor, Tensor, Tensor>? distance = null, double margin = 1.0, bool swap = false, Reduction reduction = Reduction.Mean) : base(reduction)\\n {\\n if (distance != null) {\\n this.distance = (IntPtr x, IntPtr y) => {\\n var x1 = new Tensor(x);\\n var y1 = new Tensor(y);\\n var res = distance(x1, y1);\\n\\n GC.SuppressFinalize(x1);\\n GC.SuppressFinalize(y1);\\n GC.SuppressFinalize(res);\\n\\n return res.Handle;\\n };\\n }\\n\\n this.margin = margin;\\n this.swap = swap;\\n }\\n\\n public override Tensor forward(Tensor anchor, Tensor positive, Tensor negative)\\n {\\n var res = THSNN_triplet_margin_with_distance_loss(anchor.Handle, positive.Handle, negative.Handle, distance, margin, swap, (long)reduction);\\n if (res == IntPtr.Zero) { torch.CheckForErrors(); }\\n return new Tensor(res);\\n }\\n\\n DistanceFunctionNative? distance { get; }\\n public double margin { get; }\\n bool swap { get; }\\n }\\n }\\n}\\n\"\n },\n {\n \"alpha_fraction\": 0.5615906715393066,\n \"alphanum_fraction\": 0.571774959564209,\n \"avg_line_length\": 38.653846740722656,\n \"blob_id\": \"ecc96a1ede36505931832c95a316d4111d424671\",\n \"content_id\": \"31eee07674fed9f8df8ff36a32f47beb74d70f29\",\n \"detected_licenses\": [\n \"MIT\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"C#\",\n \"length_bytes\": 2062,\n \"license_type\": \"permissive\",\n \"max_line_length\": 135,\n \"num_lines\": 52,\n \"path\": \"/src/TorchVision/AdjustHue.cs\",\n \"repo_name\": \"NiklasGustafsson/TorchSharp\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"// Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information.\\n\\nusing static TorchSharp.torch;\\n\\nnamespace TorchSharp\\n{\\n public static partial class torchvision\\n {\\n internal class AdjustHue : ITransform\\n {\\n internal AdjustHue(double hue_factor)\\n {\\n hue_factor %= 1.0;\\n\\n this.hue_factor = hue_factor;\\n }\\n\\n public Tensor call(Tensor img)\\n {\\n return transforms.functional.adjust_hue(img, hue_factor);\\n }\\n\\n private double hue_factor;\\n }\\n\\n public static partial class transforms\\n {\\n /// <summary>\\n /// Adjust hue of an image.\\n /// The image hue is adjusted by converting the image to HSV and cyclically shifting the intensities in the hue channel(H).\\n /// The image is then converted back to original image mode.\\n /// </summary>\\n /// <param name=\\\"hue_factor\\\">\\n /// How much to shift the hue channel. 0 means no shift in hue.\\n /// Hue is often defined in degrees, with 360 being a full turn on the color wheel.\\n /// In this library, 1.0 is a full turn, which means that 0.5 and -0.5 give complete reversal of\\n /// the hue channel in HSV space in positive and negative direction respectively.\\n /// </param>\\n /// <returns></returns>\\n /// <remarks>\\n /// Unlike Pytorch, TorchSharp will allow the hue_factor to lie outside the range [-0.5,0.5].\\n /// A factor of 0.75 has the same effect as -.25\\n /// Note that adjusting the hue is a very expensive operation, and may therefore not be suitable as a method\\n /// for data augmentation when training speed is important.\\n /// </remarks>\\n static public ITransform AdjustHue(double hue_factor)\\n {\\n return new AdjustHue(hue_factor);\\n }\\n }\\n }\\n}\\n\"\n },\n {\n \"alpha_fraction\": 0.5561426877975464,\n \"alphanum_fraction\": 0.575957715511322,\n \"avg_line_length\": 58.76315689086914,\n \"blob_id\": \"df780ee75e893b03a70d7e2bd036027c655185c8\",\n \"content_id\": \"020ace0222c83784e3e911a58cf2d52f7c5821d3\",\n \"detected_licenses\": [\n \"MIT\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"C#\",\n \"length_bytes\": 2271,\n \"license_type\": \"permissive\",\n \"max_line_length\": 160,\n \"num_lines\": 38,\n \"path\": \"/test/TorchSharpTest/netstandardTests.cs\",\n \"repo_name\": \"NiklasGustafsson/TorchSharp\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"using System.IO;\\nusing Xunit;\\n\\nnamespace TorchSharp\\n{\\n public class NetStandardTests\\n {\\n [Theory]\\n [InlineData(@\\\"c:\\\\users\\\\me\\\", @\\\"middle\\\", @\\\"c:\\\\users\\\\me\\\\middle\\\")]\\n [InlineData(@\\\"c:\\\\users\\\\me\\\", null, @\\\"c:\\\\users\\\\me\\\")]\\n [InlineData(null, @\\\"last\\\", @\\\"last\\\")]\\n [InlineData(@\\\"c:\\\\users\\\\me\\\", \\\"\\\", @\\\"c:\\\\users\\\\me\\\")]\\n [InlineData(\\\"\\\", @\\\"last\\\", @\\\"last\\\")]\\n [InlineData(@\\\"c:\\\\users\\\\me\\\\\\\", @\\\"middle\\\\\\\", @\\\"c:\\\\users\\\\me\\\\middle\\\\\\\")]\\n [InlineData(@\\\"c:\\\\users\\\\me\\\", @\\\"\\\\middle\\\\\\\", @\\\"c:\\\\users\\\\me\\\\middle\\\\\\\")]\\n public static void TestNSPath2Parts(string s1, string s2, string expected) => Assert.Equal(expected, NSPath.Join(s1, s2));\\n\\n [Theory]\\n [InlineData(@\\\"c:\\\\users\\\\me\\\", @\\\"middle\\\", @\\\"last\\\", @\\\"c:\\\\users\\\\me\\\\middle\\\\last\\\")]\\n [InlineData(null, @\\\"middle\\\", @\\\"last\\\", @\\\"middle\\\\last\\\")]\\n [InlineData(@\\\"c:\\\\users\\\\me\\\", null, @\\\"last\\\", @\\\"c:\\\\users\\\\me\\\\last\\\")]\\n [InlineData(@\\\"c:\\\\users\\\\me\\\", @\\\"middle\\\", null, @\\\"c:\\\\users\\\\me\\\\middle\\\")]\\n [InlineData(@\\\"c:\\\\users\\\\me\\\\\\\", @\\\"middle\\\", @\\\"last\\\", @\\\"c:\\\\users\\\\me\\\\middle\\\\last\\\")]\\n [InlineData(@\\\"c:\\\\users\\\\me\\\", @\\\"\\\\middle\\\\\\\", @\\\"last\\\", @\\\"c:\\\\users\\\\me\\\\middle\\\\last\\\")]\\n [InlineData(@\\\"c:\\\\users\\\\me\\\", @\\\"\\\\middle\\\", @\\\"\\\\last\\\", @\\\"c:\\\\users\\\\me\\\\middle\\\\last\\\")]\\n public static void TestNSPath3Parts(string s1, string s2, string s3, string expected) => Assert.Equal(expected, NSPath.Join(s1, s2, s3));\\n\\n [Theory]\\n [InlineData(@\\\"c:\\\\users\\\\me\\\", @\\\"middle1\\\", @\\\"middle2\\\", @\\\"last\\\", @\\\"c:\\\\users\\\\me\\\\middle1\\\\middle2\\\\last\\\")]\\n [InlineData(null, @\\\"middle1\\\", @\\\"middle2\\\", @\\\"last\\\", @\\\"middle1\\\\middle2\\\\last\\\")]\\n [InlineData(@\\\"c:\\\\users\\\\me\\\", null, @\\\"middle2\\\", @\\\"last\\\", @\\\"c:\\\\users\\\\me\\\\middle2\\\\last\\\")]\\n [InlineData(@\\\"c:\\\\users\\\\me\\\", @\\\"middle1\\\", null, @\\\"last\\\", @\\\"c:\\\\users\\\\me\\\\middle1\\\\last\\\")]\\n [InlineData(@\\\"c:\\\\users\\\\me\\\", @\\\"middle1\\\", @\\\"middle2\\\", null, @\\\"c:\\\\users\\\\me\\\\middle1\\\\middle2\\\")]\\n [InlineData(@\\\"c:\\\\users\\\\me\\\\\\\", @\\\"middle1\\\\\\\", @\\\"middle2\\\\\\\", @\\\"last\\\", @\\\"c:\\\\users\\\\me\\\\middle1\\\\middle2\\\\last\\\")]\\n [InlineData(@\\\"c:\\\\users\\\\me\\\", @\\\"\\\\middle1\\\", @\\\"\\\\middle2\\\", @\\\"\\\\last\\\", @\\\"c:\\\\users\\\\me\\\\middle1\\\\middle2\\\\last\\\")]\\n public static void TestNSPath4Parts(string s1, string s2, string s3, string s4, string expected) => Assert.Equal(expected, NSPath.Join(s1, s2, s3, s4));\\n }\\n}\\n\"\n },\n {\n \"alpha_fraction\": 0.5096489191055298,\n \"alphanum_fraction\": 0.5152289867401123,\n \"avg_line_length\": 32.34108352661133,\n \"blob_id\": \"93365016f0dbdb79ada3eaf47eb318e8dbe5fa4f\",\n \"content_id\": \"10c75c4193ca580119512807d978b587dd5cedde\",\n \"detected_licenses\": [\n \"MIT\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"C#\",\n \"length_bytes\": 4301,\n \"license_type\": \"permissive\",\n \"max_line_length\": 149,\n \"num_lines\": 129,\n \"path\": \"/src/Examples.Utils/AG_NEWSReader.cs\",\n \"repo_name\": \"NiklasGustafsson/TorchSharp\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"// Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information.\\nusing System;\\nusing System.Collections.Generic;\\nusing System.IO;\\nusing System.Linq;\\nusing TorchSharp;\\nusing static TorchSharp.torch;\\n\\nnamespace TorchText.Data\\n{\\n public class AG_NEWSReader : IDisposable\\n {\\n public static AG_NEWSReader AG_NEWS(string split, Device device, string root = \\\".data\\\")\\n {\\n var dataPath = Path.Combine(root, $\\\"{split}.csv\\\");\\n return new AG_NEWSReader(dataPath, device);\\n }\\n\\n private AG_NEWSReader(string path, Device device)\\n {\\n _path = path;\\n _device = device;\\n }\\n\\n private string _path;\\n private Device _device;\\n\\n public IEnumerable<(int, string)> Enumerate()\\n {\\n return File.ReadLines(_path).Select(line => ParseLine(line));\\n }\\n\\n public IEnumerable<(Tensor, Tensor, Tensor)> GetBatches(Func<string, IEnumerable<string>> tokenizer, Vocab.Vocab vocab, long batch_size)\\n {\\n // This data set fits in memory, so we will simply load it all and cache it between epochs.\\n\\n var inputs = new List<(int, string)>();\\n\\n if (_data == null) {\\n\\n _data = new List<(Tensor, Tensor, Tensor)>();\\n\\n var counter = 0;\\n var lines = Enumerate().ToList();\\n var left = lines.Count;\\n\\n foreach (var line in lines) {\\n\\n inputs.Add(line);\\n left -= 1;\\n\\n if (++counter == batch_size || left == 0) {\\n _data.Add(Batchifier(inputs, tokenizer, vocab));\\n inputs.Clear();\\n counter = 0;\\n }\\n }\\n }\\n\\n return _data;\\n }\\n\\n private List<(Tensor, Tensor, Tensor)> _data;\\n private bool disposedValue;\\n\\n private (Tensor, Tensor, Tensor) Batchifier(IEnumerable<(int, string)> input, Func<string, IEnumerable<string>> tokenizer, Vocab.Vocab vocab)\\n {\\n var label_list = new List<long>();\\n var text_list = new List<Tensor>();\\n var offsets = new List<long>();\\n offsets.Add(0);\\n\\n long last = 0;\\n\\n foreach (var (label, text) in input) {\\n label_list.Add(label);\\n var processed_text = torch.tensor(tokenizer(text).Select(t => (long)vocab[t]).ToArray(),dtype:torch.int64);\\n text_list.Add(processed_text);\\n last += processed_text.size(0);\\n offsets.Add(last);\\n }\\n\\n var labels = torch.tensor(label_list.ToArray(), dtype: torch.int64).to(_device);\\n var texts = torch.cat(text_list.ToArray(), 0).to(_device);\\n var offs = torch.tensor(offsets.Take(label_list.Count).ToArray(), dtype:torch.int64).to(_device);\\n\\n return (labels, texts, offs);\\n }\\n\\n public (int, string) ParseLine(string line)\\n {\\n int label = 0;\\n string text = \\\"\\\";\\n\\n int firstComma = line.IndexOf(\\\"\\\\\\\",\\\\\\\"\\\");\\n label = int.Parse(line.Substring(1, firstComma - 1));\\n text = line.Substring(firstComma + 2, line.Length - firstComma - 2);\\n int secondComma = text.IndexOf(\\\"\\\\\\\",\\\\\\\"\\\");\\n text = text.Substring(secondComma + 2, text.Length - secondComma - 2);\\n int thirdComma = text.IndexOf(\\\"\\\\\\\",\\\\\\\"\\\");\\n\\n text = text.Substring(thirdComma + 2, text.Length - thirdComma - 3);\\n\\n return (label-1, text);\\n }\\n\\n protected virtual void Dispose(bool disposing)\\n {\\n if (!disposedValue) {\\n if (disposing && _data != null) {\\n foreach (var (l, t, o) in _data) {\\n l.Dispose();\\n t.Dispose();\\n o.Dispose();\\n }\\n }\\n\\n disposedValue = true;\\n }\\n }\\n\\n public void Dispose()\\n {\\n // Do not change this code. Put cleanup code in 'Dispose(bool disposing)' method\\n Dispose(disposing: true);\\n GC.SuppressFinalize(this);\\n }\\n }\\n}\\n\"\n },\n {\n \"alpha_fraction\": 0.5693780183792114,\n \"alphanum_fraction\": 0.5707602500915527,\n \"avg_line_length\": 48.24083709716797,\n \"blob_id\": \"418b89fe66b2b9b3777dcd551b37920de68895c7\",\n \"content_id\": \"750f8ebc1a36ab048ab363cec490dcf4d2c2b950\",\n \"detected_licenses\": [\n \"MIT\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"C#\",\n \"length_bytes\": 9405,\n \"license_type\": \"permissive\",\n \"max_line_length\": 156,\n \"num_lines\": 191,\n \"path\": \"/src/TorchSharp/Distributions/NegativeBinomial.cs\",\n \"repo_name\": \"NiklasGustafsson/TorchSharp\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"// Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information.\\nusing System;\\nusing static TorchSharp.torch;\\nusing static TorchSharp.torch.nn.functional;\\n\\nnamespace TorchSharp\\n{\\n using Modules;\\n\\n namespace Modules\\n {\\n /// <summary>\\n /// A NegativeBinomial distribution parameterized by total_count and either probs or logits (but not both).\\n ///\\n /// This is a distribution of the number of successful independent and identical Bernoulli trials\\n /// before `total_count` failures are achieved. The probability of success of each Bernoulli trial is `probs`.\\n /// </summary>\\n public class NegativeBinomial : torch.distributions.Distribution\\n {\\n /// <summary>\\n /// The mean of the distribution.\\n /// </summary>\\n public override Tensor mean => total_count * torch.exp(logits);\\n\\n /// <summary>\\n /// Mode of the negative binomial distribution.\\n /// </summary>\\n public override Tensor mode =>\\n WrappedTensorDisposeScope(() => ((total_count - 1) * logits.exp()).floor_().clamp(min: 0));\\n\\n /// <summary>\\n /// The variance of the distribution\\n /// </summary>\\n public override Tensor variance =>\\n WrappedTensorDisposeScope(() => mean / torch.sigmoid(-logits));\\n\\n public NegativeBinomial(Tensor total_count, Tensor p = null, Tensor l = null, torch.Generator generator = null) : base(generator)\\n {\\n this.batch_shape = p is null ? l.size() : p.size();\\n this._probs = p ?? LogitsToProbs(l, true).DetachFromDisposeScope();\\n this._logits = l ?? ProbsToLogits(p, true).DetachFromDisposeScope();\\n this.generator = generator;\\n\\n var broadcast = (p is null) ? torch.broadcast_tensors(total_count, l) : torch.broadcast_tensors(total_count, p);\\n this.total_count = broadcast[0].type_as(p ?? l).DetachFromDisposeScope();\\n }\\n\\n /// <summary>\\n /// Event probabilities\\n /// </summary>\\n public Tensor probs {\\n get {\\n return _probs;\\n }\\n }\\n\\n /// <summary>\\n /// Event log-odds\\n /// </summary>\\n public Tensor logits {\\n get {\\n return _logits;\\n }\\n }\\n\\n private Tensor _probs;\\n private Tensor _logits;\\n private Tensor total_count;\\n\\n /// <summary>\\n /// Generates a sample_shape shaped reparameterized sample or sample_shape shaped batch of reparameterized samples\\n /// if the distribution parameters are batched.\\n /// </summary>\\n /// <param name=\\\"sample_shape\\\">The sample shape.</param>\\n public override Tensor rsample(params long[] sample_shape)\\n {\\n using var scope = NewDisposeScope();\\n using (var _ = torch.no_grad()) {\\n var gamma = distributions.Gamma(concentration:total_count, rate: torch.exp(-_logits));\\n var rate = gamma.sample(sample_shape);\\n return torch.poisson(rate, generator).MoveToOuterDisposeScope();\\n }\\n }\\n\\n /// <summary>\\n /// Returns the log of the probability density/mass function evaluated at `value`.\\n /// </summary>\\n /// <param name=\\\"value\\\"></param>\\n public override Tensor log_prob(Tensor value)\\n {\\n using var _ = NewDisposeScope();\\n var log_unnormalized_prob = (total_count * (-_logits).log_sigmoid() + value * logits.log_sigmoid());\\n var log_normalization = (-torch.lgamma(total_count + value) + torch.lgamma(1.0 + value) + torch.lgamma(total_count));\\n log_normalization = log_normalization.masked_fill(total_count + value == 0, 0);\\n\\n return (log_unnormalized_prob - log_normalization).MoveToOuterDisposeScope();\\n }\\n\\n /// <summary>\\n /// Returns entropy of distribution, batched over batch_shape.\\n /// </summary>\\n public override Tensor entropy()\\n {\\n throw new NotImplementedException(nameof(entropy));\\n }\\n\\n /// <summary>\\n /// Returns a new distribution instance (or populates an existing instance provided by a derived class) with batch dimensions expanded to\\n /// `batch_shape`. This method calls `torch.Tensor.expand()` on the distribution's parameters. As such, this does not allocate new\\n /// memory for the expanded distribution instance.\\n /// </summary>\\n /// <param name=\\\"batch_shape\\\">Tthe desired expanded size.</param>\\n /// <param name=\\\"instance\\\">new instance provided by subclasses that need to override `.expand`.</param>\\n public override distributions.Distribution expand(Size batch_shape, distributions.Distribution instance = null)\\n {\\n if (instance != null && !(instance is NegativeBinomial))\\n throw new ArgumentException(\\\"expand(): 'instance' must be a NegativeBinomial distribution\\\");\\n\\n var newDistribution = ((instance == null) ?\\n new NegativeBinomial(total_count.expand(batch_shape), p: _probs?.expand(batch_shape), l: logits?.expand(batch_shape), generator) :\\n instance) as NegativeBinomial;\\n\\n newDistribution.batch_shape = batch_shape;\\n if (newDistribution == instance) {\\n newDistribution.total_count = total_count.expand(batch_shape);\\n newDistribution._probs = _probs?.expand(batch_shape);\\n newDistribution._logits = _logits?.expand(batch_shape);\\n }\\n return newDistribution;\\n }\\n }\\n\\n }\\n public static partial class torch\\n {\\n public static partial class distributions\\n {\\n /// <summary>\\n /// Creates a NegativeBinomial distribution parameterized by `probs` or `logits` (but not both).\\n /// `total_count` must be broadcastable with `probs`/`logits`.\\n /// </summary>\\n /// <param name=\\\"total_count\\\">Number of Bernoulli trials</param>\\n /// <param name=\\\"probs\\\">The probability of sampling '1'</param>\\n /// <param name=\\\"logits\\\">The log-odds of sampling '1'</param>\\n /// <param name=\\\"generator\\\">An optional random number generator object.</param>\\n /// <returns></returns>\\n public static NegativeBinomial NegativeBinomial(Tensor total_count, Tensor probs = null, Tensor logits = null, torch.Generator generator = null)\\n {\\n return new NegativeBinomial(total_count, probs, logits);\\n }\\n\\n /// <summary>\\n /// Creates a NegativeBinomial distribution parameterized by `probs` or `logits` (but not both).\\n /// </summary>\\n /// <param name=\\\"total_count\\\">Number of Bernoulli trials</param>\\n /// <param name=\\\"probs\\\">The probability of sampling '1'</param>\\n /// <param name=\\\"logits\\\">The log-odds of sampling '1'</param>\\n /// <param name=\\\"generator\\\">An optional random number generator object.</param>\\n /// <returns></returns>\\n public static NegativeBinomial NegativeBinomial(int total_count, float? probs, float? logits, torch.Generator generator = null)\\n {\\n if (probs.HasValue && !logits.HasValue)\\n return new NegativeBinomial(torch.tensor(total_count), torch.tensor(probs.Value), null);\\n else if (!probs.HasValue && logits.HasValue)\\n return new NegativeBinomial(torch.tensor(total_count), null, torch.tensor(logits.Value));\\n else\\n throw new ArgumentException(\\\"One and only one of 'probs' and logits should be provided.\\\");\\n }\\n\\n\\n /// <summary>\\n /// Creates a NegativeBinomial distribution parameterized by `probs` or `logits` (but not both).\\n /// </summary>\\n /// <param name=\\\"total_count\\\">Number of Bernoulli trials</param>\\n /// <param name=\\\"probs\\\">The probability of sampling '1'</param>\\n /// <param name=\\\"logits\\\">The log-odds of sampling '1'</param>\\n /// <param name=\\\"generator\\\">An optional random number generator object.</param>\\n /// <returns></returns>\\n public static NegativeBinomial NegativeBinomial(int total_count, double? probs, double? logits, torch.Generator generator = null)\\n {\\n if (probs.HasValue && !logits.HasValue)\\n return new NegativeBinomial(torch.tensor(total_count), torch.tensor(probs.Value), null, generator);\\n else if (!probs.HasValue && logits.HasValue)\\n return new NegativeBinomial(torch.tensor(total_count), null, torch.tensor(logits.Value), generator);\\n else\\n throw new ArgumentException(\\\"One and only one of 'probs' and logits should be provided.\\\");\\n }\\n\\n }\\n }\\n}\\n\"\n },\n {\n \"alpha_fraction\": 0.5229358077049255,\n \"alphanum_fraction\": 0.5275229215621948,\n \"avg_line_length\": 15.769230842590332,\n \"blob_id\": \"a1c0eb9defdf9b7a8cdaa900f081fd45b5cec240\",\n \"content_id\": \"1917bda876596786b466ed006d6d1cc7b600558b\",\n \"detected_licenses\": [\n \"MIT\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"C#\",\n \"length_bytes\": 218,\n \"license_type\": \"permissive\",\n \"max_line_length\": 44,\n \"num_lines\": 13,\n \"path\": \"/src/TorchSharp/Utils/tensorboard/Enums/HistogramBinSelector.cs\",\n \"repo_name\": \"NiklasGustafsson/TorchSharp\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"namespace TorchSharp.Utils.tensorboard.Enums\\n{\\n public enum HistogramBinSelector : byte\\n {\\n Doane = 0,\\n Rice,\\n Scott,\\n Sqrt,\\n Stone,\\n Sturges,\\n Tensorflow\\n }\\n}\\n\"\n },\n {\n \"alpha_fraction\": 0.5300502777099609,\n \"alphanum_fraction\": 0.5330653190612793,\n \"avg_line_length\": 38.79999923706055,\n \"blob_id\": \"e82238826a2b03c1c547988fc25fc4909895cdca\",\n \"content_id\": \"b4c2f030ee85ace47dd110d5d4f47ce4c538c8ae\",\n \"detected_licenses\": [\n \"MIT\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"C#\",\n \"length_bytes\": 4975,\n \"license_type\": \"permissive\",\n \"max_line_length\": 144,\n \"num_lines\": 125,\n \"path\": \"/src/TorchSharp/Device.cs\",\n \"repo_name\": \"NiklasGustafsson/TorchSharp\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"// Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information.\\nusing System;\\n\\nnamespace TorchSharp\\n{\\n public static partial class torch\\n {\\n /// <summary>\\n /// A torch.Device is an object representing the device on which a torch.Tensor is or will be allocated.\\n /// </summary>\\n public class Device\\n {\\n public DeviceType type { get; private set; } = DeviceType.CPU;\\n public int index { get; private set; } = -1;\\n\\n /// <summary>\\n /// Constructor\\n /// </summary>\\n /// <param name=\\\"description\\\">A device descriptor 'device[:index]'</param>\\n public Device(string description)\\n {\\n var splits = description.Split(':');\\n if (splits.Length == 1) {\\n // Interpret as a device type\\n type = (DeviceType)Enum.Parse(typeof(DeviceType), splits[0].ToUpper());\\n } else if (splits.Length == 2) {\\n // Interpret as a device type and index\\n type = (DeviceType)Enum.Parse(typeof(DeviceType), splits[0].ToUpper());\\n index = int.Parse(splits[1]);\\n }\\n }\\n\\n /// <summary>\\n /// Constructor\\n /// </summary>\\n /// <param name=\\\"deviceType\\\">CPU or CUDA</param>\\n /// <param name=\\\"index\\\">For CUDA, the device index</param>\\n public Device(string deviceType, int index = -1)\\n {\\n type = (DeviceType)Enum.Parse(typeof(DeviceType), deviceType.ToUpper());\\n this.index = index;\\n }\\n\\n /// <summary>\\n /// Constructor\\n /// </summary>\\n /// <param name=\\\"deviceType\\\">CPU or CUDA</param>\\n /// <param name=\\\"index\\\">For CUDA, the device index</param>\\n public Device(DeviceType deviceType, int index = -1)\\n {\\n type = deviceType;\\n this.index = index;\\n }\\n\\n /// <summary>\\n /// Constructor\\n /// </summary>\\n /// <param name=\\\"index\\\">The CUDA device index</param>\\n public Device(int index)\\n {\\n type = DeviceType.CUDA;\\n this.index = index;\\n }\\n\\n /// <summary>\\n /// Return the device descriptor using the input format.\\n /// </summary>\\n /// <returns></returns>\\n public override string ToString()\\n {\\n return type == DeviceType.CPU ? \\\"cpu\\\" : (index == -1) ? $\\\"{type.ToString().ToLower()}\\\" : $\\\"{type.ToString().ToLower()}:{index}\\\";\\n }\\n\\n public static implicit operator Device(string description)\\n {\\n return new Device(description);\\n }\\n }\\n\\n /// <summary>\\n /// Convenience declaration of a CPU device accessible everywhere.\\n /// </summary>\\n public static Device CPU = new Device(DeviceType.CPU, -1);\\n\\n /// <summary>\\n /// Convenience declaration of a CUDA device accessible everywhere.\\n /// </summary>\\n public static Device CUDA = new Device(DeviceType.CUDA, -1);\\n\\n /// <summary>\\n /// Convenience declaration of a META device accessible everywhere.\\n /// </summary>\\n public static Device META = new Device(DeviceType.META, -1);\\n\\n /// <summary>\\n /// Factory for a device object, following the Pytorch API.\\n /// </summary>\\n /// <param name=\\\"description\\\">String description of the device, e.g. 'cpu' or 'cuda:0'</param>\\n /// <returns></returns>\\n public static Device device(string description) => new Device(description);\\n\\n /// <summary>\\n /// Factory for a device object, following the Pytorch API.\\n /// </summary>\\n /// <param name=\\\"deviceType\\\">The device type, e.g. 'cpu' or 'cuda'</param>\\n /// <param name=\\\"index\\\">The device index. Ignored for CPUs</param>\\n /// <returns></returns>\\n public static Device device(string deviceType, int index = -1) => new Device(deviceType, index);\\n\\n /// <summary>\\n /// Factory for a device object, following the Pytorch API.\\n /// </summary>\\n /// <param name=\\\"deviceType\\\">The device type, e.g. DeviceType.CPU or DeviceType.CUDA.</param>\\n /// <param name=\\\"index\\\">The device index. Ignored for CPUs</param>\\n /// <returns></returns>\\n public static Device device(DeviceType deviceType, int index = -1) => new Device(deviceType, index);\\n\\n /// <summary>\\n /// Factory for a CUDA device object, following the Pytorch API.\\n /// </summary>\\n /// <param name=\\\"index\\\">The CUDA device ordinal.</param>\\n /// <returns></returns>\\n public static Device device(int index) => new Device(DeviceType.CUDA, index);\\n }\\n}\\n\"\n },\n {\n \"alpha_fraction\": 0.5669390559196472,\n \"alphanum_fraction\": 0.5686675310134888,\n \"avg_line_length\": 45.79411697387695,\n \"blob_id\": \"711085ff192d874631e58f284a7d7635b57e6909\",\n \"content_id\": \"63a03c1790e18930c639520ec1f8093df3f9c6ab\",\n \"detected_licenses\": [\n \"MIT\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"C#\",\n \"length_bytes\": 6364,\n \"license_type\": \"permissive\",\n \"max_line_length\": 156,\n \"num_lines\": 136,\n \"path\": \"/src/TorchSharp/Distributions/Cauchy.cs\",\n \"repo_name\": \"NiklasGustafsson/TorchSharp\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"// Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information.\\nusing System;\\nusing static TorchSharp.torch;\\n\\nnamespace TorchSharp\\n{\\n using Modules;\\n\\n namespace Modules\\n {\\n /// <summary>\\n /// A Cauchy (Lorentz) distribution. The distribution of the ratio of\\n /// independent normally distributed random variables with means `0` follows a Cauchy distribution.\\n /// </summary>\\n public class Cauchy : torch.distributions.Distribution\\n {\\n /// <summary>\\n /// The mode of the distribution.\\n /// </summary>\\n public override Tensor mode => loc;\\n\\n /// <summary>\\n /// The mean of the distribution.\\n /// </summary>\\n public override Tensor mean => _mean;\\n\\n /// <summary>\\n /// The variance of the distribution\\n /// </summary>\\n public override Tensor variance => _variance;\\n\\n /// <summary>\\n /// Constructor\\n /// </summary>\\n /// <param name=\\\"loc\\\">Mode or median of the distribution.</param>\\n /// <param name=\\\"scale\\\">Half width at half maximum.</param>\\n /// <param name=\\\"generator\\\">An optional random number generator object.</param>\\n public Cauchy(Tensor loc, Tensor scale, torch.Generator generator = null) : base(generator)\\n {\\n var locScale = torch.broadcast_tensors(loc, scale);\\n this.loc = locScale[0].DetachFromDisposeScope();\\n this.scale = locScale[1].DetachFromDisposeScope();\\n this._mean = torch.full(ExtendedShape(), double.NaN, dtype: loc.dtype, device: loc.device).DetachFromDisposeScope();\\n this._variance = torch.full(ExtendedShape(), double.PositiveInfinity, dtype: loc.dtype, device: loc.device).DetachFromDisposeScope();\\n this.batch_shape = this.loc.size();\\n }\\n\\n private Tensor loc;\\n private Tensor scale;\\n private Tensor _mean, _variance;\\n\\n /// <summary>\\n /// Generates a sample_shape shaped reparameterized sample or sample_shape shaped batch of reparameterized samples\\n /// if the distribution parameters are batched.\\n /// </summary>\\n /// <param name=\\\"sample_shape\\\">The sample shape.</param>\\n /// <returns></returns>\\n public override Tensor rsample(params long[] sample_shape)\\n {\\n var shape = ExtendedShape(sample_shape);\\n var eps = loc.new_empty(shape).cauchy_(generator: generator);\\n return loc + eps * scale;\\n }\\n\\n /// <summary>\\n /// Returns the log of the probability density/mass function evaluated at `value`.\\n /// </summary>\\n /// <param name=\\\"value\\\"></param>\\n public override Tensor log_prob(Tensor value) =>\\n WrappedTensorDisposeScope(() => -Math.Log(Math.PI) - scale.log() - (((value - loc) / scale).pow(2)).log1p()); \\n\\n /// <summary>\\n /// Returns entropy of distribution, batched over batch_shape.\\n /// </summary>\\n /// <returns></returns>\\n public override Tensor entropy() =>\\n WrappedTensorDisposeScope(() => Math.Log(Math.PI * 4) + scale.log());\\n\\n /// <summary>\\n /// Returns the cumulative density/mass function evaluated at `value`.\\n /// </summary>\\n /// <param name=\\\"value\\\"></param>\\n public override Tensor cdf(Tensor value) =>\\n WrappedTensorDisposeScope(() => torch.atan((value - loc) / scale) / Math.PI + 0.5);\\n\\n /// <summary>\\n /// Returns the inverse cumulative density/mass function evaluated at `value`.\\n /// </summary>\\n /// <param name=\\\"value\\\"></param>\\n public override Tensor icdf(Tensor value) =>\\n WrappedTensorDisposeScope(() => torch.tan(Math.PI * (value - 0.5)) * scale + loc);\\n\\n\\n /// <summary>\\n /// Returns a new distribution instance (or populates an existing instance provided by a derived class) with batch dimensions expanded to\\n /// `batch_shape`. This method calls `torch.Tensor.expand()` on the distribution's parameters. As such, this does not allocate new\\n /// memory for the expanded distribution instance.\\n /// </summary>\\n /// <param name=\\\"batch_shape\\\">Tthe desired expanded size.</param>\\n /// <param name=\\\"instance\\\">new instance provided by subclasses that need to override `.expand`.</param>\\n public override distributions.Distribution expand(Size batch_shape, distributions.Distribution instance = null)\\n {\\n if (instance != null && !(instance is Cauchy))\\n throw new ArgumentException(\\\"expand(): 'instance' must be a Cauchy distribution\\\");\\n\\n var newDistribution = ((instance == null) ? new Cauchy(loc.expand(batch_shape), scale.expand(batch_shape), generator) : instance) as Cauchy;\\n\\n newDistribution.batch_shape = batch_shape;\\n if (newDistribution == instance) {\\n newDistribution.loc = loc.expand(batch_shape);\\n newDistribution.scale = scale.expand(batch_shape);\\n }\\n return newDistribution;\\n }\\n }\\n\\n }\\n public static partial class torch\\n {\\n public static partial class distributions\\n {\\n /// <summary>\\n /// Samples from a Cauchy (Lorentz) distribution. The distribution of the ratio of\\n /// independent normally distributed random variables with means `0` follows a Cauchy distribution.\\n /// </summary>\\n /// <param name=\\\"loc\\\">Mode or median of the distribution.</param>\\n /// <param name=\\\"scale\\\">Half width at half maximum.</param>\\n /// <param name=\\\"generator\\\">An optional random number generator object.</param>\\n /// <returns></returns>\\n public static Cauchy Cauchy(Tensor loc, Tensor scale, torch.Generator generator = null)\\n {\\n return new Cauchy(loc, scale, generator);\\n }\\n }\\n }\\n}\\n\"\n },\n {\n \"alpha_fraction\": 0.5930420756340027,\n \"alphanum_fraction\": 0.5930420756340027,\n \"avg_line_length\": 49.79452133178711,\n \"blob_id\": \"3dcc95d41e74123530e4794bbb5b6165845dd10d\",\n \"content_id\": \"620b8ac555def849e73efe782e978ca56a5db584\",\n \"detected_licenses\": [\n \"MIT\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"C#\",\n \"length_bytes\": 3708,\n \"license_type\": \"permissive\",\n \"max_line_length\": 188,\n \"num_lines\": 73,\n \"path\": \"/src/TorchSharp/NN/TransformerDecoder.cs\",\n \"repo_name\": \"NiklasGustafsson/TorchSharp\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"// Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information.\\nusing System;\\nusing static TorchSharp.torch;\\nusing static TorchSharp.PInvoke.NativeMethods;\\n\\nnamespace TorchSharp\\n{\\n using Modules;\\n\\n namespace Modules\\n {\\n public sealed class TransformerDecoder : torch.nn.Module<Tensor, Tensor, Tensor, Tensor, Tensor, Tensor, Tensor>\\n {\\n internal TransformerDecoder(IntPtr handle, IntPtr boxedHandle) : base(handle, boxedHandle) { }\\n\\n /// <summary>\\n /// Pass the inputs (and mask) through the decoder layers in turn.\\n /// </summary>\\n /// <param name=\\\"tgt\\\">The sequence to the decoder layer (required).</param>\\n /// <param name=\\\"memory\\\">The sequence from the last layer of the encoder (required).</param>\\n /// <param name=\\\"tgt_mask\\\">The mask for the tgt sequence (optional).</param>\\n /// <param name=\\\"memory_mask\\\">The mask for the memory sequence (optional).</param>\\n /// <param name=\\\"tgt_key_padding_mask\\\">The mask for the tgt keys per batch (optional).</param>\\n /// <param name=\\\"memory_key_padding_mask\\\">The mask for the memory keys per batch (optional).</param>\\n /// <returns></returns>\\n public override Tensor forward(Tensor tgt, Tensor memory, Tensor tgt_mask, Tensor memory_mask = null, Tensor tgt_key_padding_mask = null, Tensor memory_key_padding_mask = null)\\n {\\n var res = THSNN_TransformerDecoder_forward(handle,\\n tgt.Handle,\\n memory.Handle,\\n tgt_mask?.Handle ?? IntPtr.Zero,\\n memory_mask?.Handle ?? IntPtr.Zero,\\n tgt_key_padding_mask?.Handle ?? IntPtr.Zero,\\n memory_key_padding_mask?.Handle ?? IntPtr.Zero);\\n if (res == IntPtr.Zero) { torch.CheckForErrors(); }\\n return new Tensor(res);\\n }\\n public new Tensor call(Tensor tgt, Tensor memory, Tensor tgt_mask, Tensor memory_mask = null, Tensor tgt_key_padding_mask = null, Tensor memory_key_padding_mask = null)\\n {\\n return base.call(tgt, memory, tgt_mask, memory_mask, tgt_key_padding_mask, memory_key_padding_mask);\\n }\\n\\n /// <summary>\\n /// Pass the inputs (and mask) through the decoder layers in turn.\\n /// </summary>\\n /// <param name=\\\"tgt\\\">The sequence to the decoder layer (required).</param>\\n /// <param name=\\\"memory\\\">The sequence from the last layer of the encoder (required).</param>\\n public Tensor call(Tensor tgt, Tensor memory)\\n {\\n return base.call(tgt, memory, null, null, null, null);\\n }\\n }\\n }\\n\\n public static partial class torch\\n {\\n public static partial class nn\\n {\\n /// <summary>\\n /// TransformerDecoder is a stack of N decoder layers\\n /// </summary>\\n /// <param name=\\\"decoder_layer\\\">An instance of the TransformerDecoderLayer class (required).</param>\\n /// <param name=\\\"num_layers\\\">The number of sub-decoder-layers in the decoder (required).</param>\\n /// <returns></returns>\\n public static TransformerDecoder TransformerDecoder(TransformerDecoderLayer decoder_layer, long num_layers)\\n {\\n var res = THSNN_TransformerDecoder_ctor(decoder_layer.handle, num_layers, out var boxedHandle);\\n if (res == IntPtr.Zero) { torch.CheckForErrors(); }\\n return new TransformerDecoder(res, boxedHandle);\\n }\\n }\\n }\\n}\\n\"\n },\n {\n \"alpha_fraction\": 0.6298515200614929,\n \"alphanum_fraction\": 0.6306329965591431,\n \"avg_line_length\": 57.181819915771484,\n \"blob_id\": \"09a53f021f44c8559b840de351fee80d12dc393a\",\n \"content_id\": \"c5b3ae593b7ef653a6554acb2d222fb153d09f60\",\n \"detected_licenses\": [\n \"MIT\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"C#\",\n \"length_bytes\": 3839,\n \"license_type\": \"permissive\",\n \"max_line_length\": 288,\n \"num_lines\": 66,\n \"path\": \"/src/TorchVision/RandomRotation.cs\",\n \"repo_name\": \"NiklasGustafsson/TorchSharp\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"// Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information.\\nusing System;\\nusing System.Collections.Generic;\\nusing static TorchSharp.torch;\\n\\nnamespace TorchSharp\\n{\\n public static partial class torchvision\\n {\\n internal class RandomRotation : ITransform\\n {\\n public RandomRotation((double, double) degrees, InterpolationMode interpolation = InterpolationMode.Nearest, bool expand = false, (int, int)? center = null, IList<float> fill = null)\\n {\\n this.degrees = degrees;\\n this.interpolation = interpolation;\\n this.center = center;\\n this.expand = expand;\\n this.fill = fill;\\n }\\n\\n public Tensor call(Tensor input)\\n {\\n var random = new Random();\\n var angle = random.NextDouble() * (degrees.Item2 - degrees.Item1) + degrees.Item1;\\n\\n var rotate = torchvision.transforms.Rotate((float)angle, interpolation, expand, center, fill);\\n return rotate.call(input);\\n }\\n\\n private (double, double) degrees;\\n private bool expand;\\n private (int, int)? center;\\n private IList<float> fill;\\n private InterpolationMode interpolation;\\n }\\n\\n public static partial class transforms\\n {\\n /// <summary>\\n /// Rotate the image by a random angle. \\n /// </summary>\\n /// <param name=\\\"degrees\\\">Range of degrees to select from (-degrees, +degrees).</param>\\n /// <param name=\\\"interpolation\\\">Desired interpolation enum. Default is `InterpolationMode.NEAREST`.</param>\\n /// <param name=\\\"expand\\\">If true, expands the output to make it large enough to hold the entire rotated image. If false or omitted, make the output image the same size as the input image. Note that the expand flag assumes rotation around the center and no translation.</param>\\n /// <param name=\\\"center\\\">Center of rotation, (x, y). Origin is the upper left corner.</param>\\n /// <param name=\\\"fill\\\">Pixel fill value for the area outside the rotated. If given a number, the value is used for all bands respectively.</param>\\n static public ITransform RandomRotation(double degrees, InterpolationMode interpolation = InterpolationMode.Nearest, bool expand = false, (int, int)? center = null, IList<float> fill = null)\\n {\\n return new RandomRotation((-degrees, degrees), interpolation, expand, center, fill);\\n }\\n\\n /// <summary>\\n ///Rotate the image by a random angle. \\n /// </summary>\\n /// <param name=\\\"degrees\\\">Range of degrees to select from</param>\\n /// <param name=\\\"interpolation\\\">Desired interpolation enum. Default is `InterpolationMode.NEAREST`.</param>\\n /// <param name=\\\"expand\\\">If true, expands the output to make it large enough to hold the entire rotated image. If false or omitted, make the output image the same size as the input image. Note that the expand flag assumes rotation around the center and no translation.</param>\\n /// <param name=\\\"center\\\">Center of rotation, (x, y). Origin is the upper left corner.</param>\\n /// <param name=\\\"fill\\\">Pixel fill value for the area outside the rotated. If given a number, the value is used for all bands respectively.</param>\\n static public ITransform RandomRotation((double, double) degrees, InterpolationMode interpolation = InterpolationMode.Nearest, bool expand = false, (int, int)? center = null, IList<float> fill = null)\\n {\\n return new RandomRotation(degrees, interpolation, expand, center, fill);\\n }\\n }\\n }\\n}\"\n },\n {\n \"alpha_fraction\": 0.6136853694915771,\n \"alphanum_fraction\": 0.62109375,\n \"avg_line_length\": 50.55555725097656,\n \"blob_id\": \"f802ca6420e5fcff10309b248ea52e530ad9e644\",\n \"content_id\": \"31ad430b51ad7a021554cd667c9f57227d0f7df0\",\n \"detected_licenses\": [\n \"MIT\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"C#\",\n \"length_bytes\": 7424,\n \"license_type\": \"permissive\",\n \"max_line_length\": 215,\n \"num_lines\": 144,\n \"path\": \"/src/TorchSharp/Tensor/Factories/tensor_long.cs\",\n \"repo_name\": \"NiklasGustafsson/TorchSharp\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"// Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information.\\nusing System;\\nusing System.Collections.Generic;\\nusing System.Diagnostics.Contracts;\\nusing System.Linq;\\nusing static TorchSharp.PInvoke.NativeMethods;\\n\\n#nullable enable\\nnamespace TorchSharp\\n{\\n public static partial class torch\\n {\\n /// <summary>\\n /// Create a scalar tensor from a single value\\n /// </summary>\\n public static Tensor tensor(long scalar, ScalarType? dtype = null, Device? device = null, bool requires_grad = false)\\n {\\n device = InitializeDevice(device);\\n var handle = THSTensor_newInt64Scalar(scalar, (int)device.type, device.index, requires_grad);\\n if (handle == IntPtr.Zero) { CheckForErrors(); }\\n var tensor = new Tensor(handle);\\n if (device is { }) {\\n tensor = dtype.HasValue ? tensor.to(dtype.Value, device) : tensor.to(device);\\n } else if (dtype.HasValue) {\\n tensor = tensor.to_type(dtype.Value);\\n }\\n return tensor;\\n }\\n\\n /// <summary>\\n /// Create a tensor from an array of values, shaping it based on the shape passed in.\\n /// </summary>\\n [Pure]\\n public static Tensor tensor(IList<long> rawArray, ReadOnlySpan<long> dimensions, ScalarType? dtype = null, Device? device = null, bool requires_grad = false, string[]? names = null)\\n {\\n return _tensor_i64(rawArray.ToArray(), dimensions, dtype, device, requires_grad, false, names);\\n }\\n\\n /// <summary>\\n /// Create a tensor from an array of values, shaping it based on the shape passed in.\\n /// </summary>\\n [Pure]\\n public static Tensor tensor(long[] dataArray, ScalarType? dtype = null, Device? device = null, bool requires_grad = false, string[]? names = null)\\n {\\n return _tensor_i64(dataArray, stackalloc long[] { dataArray.LongLength }, dtype, device, requires_grad, names: names);\\n }\\n\\n /// <summary>\\n /// Create a tensor from an array of values, shaping it based on the shape passed in.\\n /// </summary>\\n [Pure]\\n public static Tensor tensor(long[] dataArray, ReadOnlySpan<long> dimensions, ScalarType? dtype = null, Device? device = null, bool requires_grad = false, string[]? names = null)\\n {\\n return _tensor_i64(dataArray, dimensions, dtype, device, requires_grad, names: names);\\n }\\n\\n [Pure]\\n private static Tensor _tensor_i64(Array rawArray, ReadOnlySpan<long> dimensions, ScalarType? dtype, Device? device, bool requires_grad, bool clone = false, string[]? names = null) =>\\n _tensor_generic(rawArray, dimensions, (sbyte)ScalarType.Int64, dtype, device, requires_grad, names: names);\\n\\n /// <summary>\\n /// Create a 1-D tensor from an array of values, shaping it based on the input array.\\n /// </summary>\\n /// <remarks>The Torch runtime does not take ownership of the data, so there is no device argument.</remarks>\\n [Pure]\\n public static Tensor tensor(IList<long> rawArray, ScalarType? dtype = null, Device? device = null, bool requires_grad = false, string[]? names = null)\\n {\\n return tensor(rawArray, stackalloc long[] { (long)rawArray.Count }, dtype, device, requires_grad, names: names);\\n }\\n\\n /// <summary>\\n /// Create a tensor from an array of values, organizing it as a two-dimensional tensor.\\n /// </summary>\\n /// <remarks>\\n /// The Torch runtime does not take ownership of the data, so there is no device argument.\\n /// The input array must have rows * columns elements.\\n /// </remarks>\\n [Pure]\\n public static Tensor tensor(IList<long> rawArray, long rows, long columns, ScalarType? dtype = null, Device? device = null, bool requires_grad = false, string[]? names = null)\\n {\\n return tensor(rawArray, stackalloc long[] { rows, columns }, dtype, device, requires_grad, names: names);\\n }\\n\\n /// <summary>\\n /// Create a tensor from an array of values, organizing it as a three-dimensional tensor.\\n /// </summary>\\n /// <remarks>\\n /// The Torch runtime does not take ownership of the data, so there is no device argument.\\n /// The input array must have dim0*dim1*dim2 elements.\\n /// </remarks>\\n [Pure]\\n public static Tensor tensor(IList<long> rawArray, long dim0, long dim1, long dim2, ScalarType? dtype = null, Device? device = null, bool requires_grad = false, string[]? names = null)\\n {\\n return tensor(rawArray, stackalloc long[] { dim0, dim1, dim2 }, dtype, device, requires_grad, names: names);\\n }\\n\\n /// <summary>\\n /// Create a tensor from an array of values, organizing it as a four-dimensional tensor.\\n /// </summary>\\n /// <remarks>\\n /// The Torch runtime does not take ownership of the data, so there is no device argument.\\n /// The input array must have dim0*dim1*dim2*dim3 elements.\\n /// </remarks>\\n [Pure]\\n public static Tensor tensor(IList<long> rawArray, long dim0, long dim1, long dim2, long dim3, ScalarType? dtype = null, Device? device = null, bool requires_grad = false, string[]? names = null)\\n {\\n return tensor(rawArray, stackalloc long[] { dim0, dim1, dim2, dim3 }, dtype, device, requires_grad, names: names);\\n }\\n\\n /// <summary>\\n /// Create a two-dimensional tensor from a two-dimensional array of values.\\n /// </summary>\\n [Pure]\\n public static Tensor tensor(long[,] rawArray, ScalarType? dtype = null, Device? device = null, bool requires_grad = false, string[]? names = null)\\n {\\n return _tensor_i64(rawArray, stackalloc long[] { rawArray.GetLongLength(0), rawArray.GetLongLength(1) }, dtype, device, requires_grad, names: names);\\n }\\n\\n /// <summary>\\n /// Create a three-dimensional tensor from a three-dimensional array of values.\\n /// </summary>\\n [Pure]\\n public static Tensor tensor(long[,,] rawArray, ScalarType? dtype = null, Device? device = null, bool requires_grad = false, string[]? names = null)\\n {\\n return _tensor_i64(rawArray, stackalloc long[] { rawArray.GetLongLength(0), rawArray.GetLongLength(1), rawArray.GetLongLength(2) }, dtype, device, requires_grad, names: names);\\n }\\n\\n /// <summary>\\n /// Create a four-dimensional tensor from a four-dimensional array of values.\\n /// </summary>\\n [Pure]\\n public static Tensor tensor(long[,,,] rawArray, ScalarType? dtype = null, Device? device = null, bool requires_grad = false, string[]? names = null)\\n {\\n return _tensor_i64(rawArray, stackalloc long[] { rawArray.GetLongLength(0), rawArray.GetLongLength(1), rawArray.GetLongLength(2), rawArray.GetLongLength(3) }, dtype, device, requires_grad, names: names);\\n }\\n\\n /// <summary>\\n /// Cast a tensor to a 64-bit integer tensor.\\n /// </summary>\\n /// <param name=\\\"t\\\">The input tensor</param>\\n /// <returns>'this' if the tensor is already a 64-bit integer tensor; otherwise, a new tensor.</returns>\\n public static Tensor LongTensor(Tensor t) => t.to(ScalarType.Int64);\\n }\\n}\\n\"\n },\n {\n \"alpha_fraction\": 0.5257009267807007,\n \"alphanum_fraction\": 0.5257009267807007,\n \"avg_line_length\": 22.83333396911621,\n \"blob_id\": \"5a5330876f07ec5050be2ce1027629e6d0fefd45\",\n \"content_id\": \"729959af11ba810ecedfb8f4c9fecfc6cdf9b2ad\",\n \"detected_licenses\": [\n \"MIT\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"C#\",\n \"length_bytes\": 428,\n \"license_type\": \"permissive\",\n \"max_line_length\": 130,\n \"num_lines\": 18,\n \"path\": \"/src/TorchAudio/AudioEncoding.cs\",\n \"repo_name\": \"NiklasGustafsson/TorchSharp\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"// Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information.\\nnamespace TorchSharp\\n{\\n public static partial class torchaudio\\n {\\n /// <summary>\\n /// Audio encoding\\n /// </summary>\\n public enum AudioEncoding\\n {\\n PCM_S,\\n PCM_U,\\n PCM_F,\\n ULAW,\\n ALAW\\n }\\n }\\n}\"\n },\n {\n \"alpha_fraction\": 0.6038913130760193,\n \"alphanum_fraction\": 0.6116005778312683,\n \"avg_line_length\": 44.400001525878906,\n \"blob_id\": \"c70c03e698bd8ffb8d2b36a6c5a308756f1f2acc\",\n \"content_id\": \"d602ee499aaf0c25244b56b361ecf87b1047d2bf\",\n \"detected_licenses\": [\n \"MIT\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"C#\",\n \"length_bytes\": 5448,\n \"license_type\": \"permissive\",\n \"max_line_length\": 130,\n \"num_lines\": 120,\n \"path\": \"/src/TorchAudio/Modules/Wav2Vec2Model.cs\",\n \"repo_name\": \"NiklasGustafsson/TorchSharp\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"// Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information.\\n\\n// A number of implementation details in this file have been translated from the Python version of torchaudio,\\n// largely located in the files found in this folder:\\n//\\n// https://github.com/pytorch/audio/blob/76fca37ac8941b72a509a6e58d623632efe04543/torchaudio/models/wav2vec2/model.py\\n//\\n// The origin has the following copyright notice and license:\\n//\\n// https://github.com/pytorch/audio/blob/main/LICENSE\\n//\\n\\nusing static TorchSharp.torch;\\n\\n#nullable enable\\nnamespace TorchSharp.Modules\\n{\\n /// <summary>\\n /// Encoder model used in *wav2vec 2.0* [:footcite:`baevski2020wav2vec`].\\n /// Note:\\n /// To build the model, please use one of the factory functions.\\n /// </summary>\\n public partial class Wav2Vec2Model : nn.Module<Tensor, Tensor?, (Tensor, Tensor?)>\\n {\\n internal readonly FeatureExtractor feature_extractor;\\n internal readonly Encoder encoder;\\n private readonly nn.Module<Tensor, Tensor>? aux;\\n\\n /// <param name=\\\"name\\\"></param>\\n /// <param name=\\\"feature_extractor\\\">Feature extractor that extracts feature vectors from raw audio Tensor.</param>\\n /// <param name=\\\"encoder\\\">Encoder that converts the audio features into the sequence of probability\\n /// distribution (in negative log-likelihood) over labels.</param>\\n /// <param name=\\\"aux\\\">Auxiliary module. If provided, the output from encoder is passed to this module.</param>\\n internal Wav2Vec2Model(\\n string name,\\n FeatureExtractor feature_extractor,\\n Encoder encoder,\\n nn.Module<Tensor, Tensor>? aux = null) : base(name)\\n {\\n this.feature_extractor = feature_extractor;\\n this.encoder = encoder;\\n this.aux = aux;\\n RegisterComponents();\\n }\\n\\n /// <summary>\\n /// Extract feature vectors from raw waveforms\\n /// \\n /// This returns the list of outputs from the intermediate layers of\\n /// transformer block in encoder.\\n /// </summary>\\n /// <param name=\\\"waveforms\\\">Audio tensor of shape `(batch, frames)`.</param>\\n /// <param name=\\\"lengths\\\">Indicates the valid length of each audio in the batch.\\n /// Shape: `(batch, )`.\\n /// When the ``waveforms`` contains audios with different durations,\\n /// by providing ``lengths`` argument, the model will compute\\n /// the corresponding valid output lengths and apply proper mask in\\n /// transformer attention layer.\\n /// If ``None``, it is assumed that the entire audio waveform\\n /// length is valid.</param>\\n /// <param name=\\\"num_layers\\\">\\n /// If given, limit the number of intermediate layers to go through.\\n /// Providing `1` will stop the computation after going through one\\n /// intermediate layers. If not given, the outputs from all the\\n /// intermediate layers are returned.\\n /// </param>\\n /// <returns>\\n /// List of Tensors\\n /// Features from requested layers.\\n /// Each Tensor is of shape: `(batch, time frame, feature dimension)`\\n /// If ``lengths`` argument was provided, a Tensor of shape `(batch, )`\\n /// is returned.\\n /// It indicates the valid length in time axis of each feature Tensor.\\n /// </returns>\\n public (Tensor[], Tensor?) extract_features(\\n Tensor waveforms,\\n Tensor? lengths = null,\\n int? num_layers = null)\\n {\\n Tensor x;\\n (x, lengths) = this.feature_extractor.call(waveforms, lengths);\\n var xs = this.encoder.extract_features(x, lengths, num_layers);\\n return (xs, lengths);\\n }\\n\\n /// <summary>\\n /// Compute the sequence of probability distribution over labels.\\n /// </summary>\\n /// <param name=\\\"waveforms\\\">Audio tensor of shape `(batch, frames)`.\\n /// waveforms (Tensor): Audio tensor of shape `(batch, frames)`.</param>\\n /// <param name=\\\"lengths\\\">Indicates the valid length of each audio in the batch.\\n /// Shape: `(batch, )`.\\n /// When the ``waveforms`` contains audios with different durations,\\n /// by providing ``lengths`` argument, the model will compute\\n /// the corresponding valid output lengths and apply proper mask in\\n /// transformer attention layer.\\n /// If ``None``, it is assumed that all the audio in ``waveforms``\\n /// have valid length. Default: ``None``.</param>\\n /// <returns>\\n /// The sequences of probability distribution (in logit) over labels.\\n /// If ``lengths`` argument was provided, a Tensor of shape `(batch, )`\\n /// is returned.\\n /// It indicates the valid length in time axis of the output Tensor.\\n /// </returns>\\n public override (Tensor, Tensor?) forward(\\n Tensor waveforms,\\n Tensor? lengths = null)\\n {\\n Tensor x;\\n (x, lengths) = this.feature_extractor.call(waveforms, lengths);\\n x = this.encoder.call(x, lengths);\\n if (this.aux != null) {\\n x = this.aux.call(x);\\n }\\n return (x, lengths);\\n }\\n\\n public new (Tensor, Tensor?) call(Tensor waveforms, Tensor? lengths = null) => base.call(waveforms, lengths);\\n }\\n}\\n\"\n },\n {\n \"alpha_fraction\": 0.7279344797134399,\n \"alphanum_fraction\": 0.7388535141944885,\n \"avg_line_length\": 32.33333206176758,\n \"blob_id\": \"b261bb4756fe03993c0b2cc038edac62327de90a\",\n \"content_id\": \"ff42768386cb125603fdf87ad819937b16bf816a\",\n \"detected_licenses\": [\n \"MIT\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"C\",\n \"length_bytes\": 1099,\n \"license_type\": \"permissive\",\n \"max_line_length\": 130,\n \"num_lines\": 33,\n \"path\": \"/src/Native/LibTorchSharp/THSAutograd.h\",\n \"repo_name\": \"NiklasGustafsson/TorchSharp\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"// Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information.\\n#pragma once\\n\\n#include \\\"../Stdafx.h\\\"\\n\\n#include \\\"Utils.h\\\"\\n\\n// Returns whether the grad is enabled or not.\\nEXPORT_API(bool) THSAutograd_isGradEnabled();\\n\\n// Enables / disables grad.\\nEXPORT_API(void) THSAutograd_setGrad(bool enabled);\\n\\n// Returns whether the grad is enabled or not.\\nEXPORT_API(bool) THSAutograd_isAnomalyEnabled();\\n\\nEXPORT_API(bool) THSAutograd_shouldCheckNaN();\\n\\n// Enables / disables grad.\\nEXPORT_API(void) THSAutograd_setAnomaly(bool enabled, bool check_nan);\\n\\nEXPORT_API(void) THSAutograd_grad(\\n Tensor* outputs, const int64_t oLength,\\n Tensor* inputs, const int64_t iLength,\\n Tensor* grad_outs, const int64_t gLenght,\\n bool retain_graph, bool create_graph, bool allow_unused,\\n Tensor* (*allocator)(size_t length));\\n\\nEXPORT_API(void) THSAutograd_backward(\\n Tensor* tensors, const int64_t tLength,\\n Tensor* grad_tensors, const int64_t gtLength,\\n bool retain_graph, bool create_graph,\\n Tensor* inputs, const int64_t iLength);\"\n },\n {\n \"alpha_fraction\": 0.7593501210212708,\n \"alphanum_fraction\": 0.7624157071113586,\n \"avg_line_length\": 52.47541046142578,\n \"blob_id\": \"a44f837a01472ea6a1f43fe990762e2cbc9bf4ba\",\n \"content_id\": \"f1d28cebbb045c44d6775e968a8c07dbc1b1db5a\",\n \"detected_licenses\": [\n \"MIT\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Markdown\",\n \"length_bytes\": 3262,\n \"license_type\": \"permissive\",\n \"max_line_length\": 284,\n \"num_lines\": 61,\n \"path\": \"/CONTRIBUTING.md\",\n \"repo_name\": \"NiklasGustafsson/TorchSharp\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"Contributing to TorchSharp\\n==========================\\n\\nIf you are here, it means you are interested in helping us out. A hearty welcome and thank you! There are many ways you can contribute to the ML.NET project:\\n\\n* Offer PR's to fix bugs or implement new features.\\n* Give us feedback and bug reports regarding the software or the documentation.\\n* Improve our examples, tutorials, and documentation.\\n\\nThis document describes contribution guidelines that are specific to TorchSharp. Please read [.NET Core Guidelines](https://github.com/dotnet/coreclr/blob/main/Documentation/project-docs/contributing.md) for more general .NET Core contribution guidelines.\\n\\n## Developers\\n\\nSee the [Developer Guide](DEVGUIDE.md) for details about building and developing in this repo.\\n\\n\\n## Pull Requests\\n\\nIf you send us a PR, whether for documentation, examples, or library code, we require that you sign a digital Contributor License Agreement (CLA), so that we know you have the right to contribute. Once you have signed it, future PRs should go through without further requests to sign.\\n\\n* **DO** use your own forked repository for all development, and submit cross-fork PRs.\\n* **DO** resolve merge conflicts early, by merging recent changes to 'main' into your development fork before submitting a PR.\\n* **DO** submit all code changes via pull requests (PRs). PRs will be reviewed and potentially merged by the repo maintainers after a peer review that includes at least one maintainer.\\n* **DO** give PRs short-but-descriptive names (for example, \\\"Improve code coverage for System.Console by 10%\\\", not \\\"Fix #1234\\\")\\n* **DO** refer to any relevant issues, and include [keywords](https://help.github.com/articles/closing-issues-via-commit-messages/) that automatically close issues when the PR is merged.\\n* **DO** tag any users that should know about and/or review the change.\\n* **DO** ensure each commit successfully builds. The entire PR must pass all tests in the Continuous Integration (CI) system before it'll be merged.\\n* **DO** add a brief description to the RELEASENOTES.md file at the top under the heading of the upcoming release.\\n* **DO** address PR feedback in an additional commit(s) rather than amending the existing commits, and only rebase/squash them when necessary. This makes it easier for reviewers to track changes.\\n* **DO** assume that [\\\"Squash and Merge\\\"](https://github.com/blog/2141-squash-your-commits) will be used to merge your commit unless you request otherwise in the PR.\\n* **DO NOT** fix merge conflicts using a merge commit. Prefer `git rebase`.\\n* **DO NOT** mix independent, unrelated changes in one PR. Separate unrelated fixes into separate PRs.\\n\\n\\n## A Useful Tip\\n\\nA useful tip from the Tensorflow.NET repo:\\n\\nAfter you fork, add dotnet/TorchSharp as 'upstream' to your local repo ...\\n\\n```git\\ngit remote add upstream https://github.com/dotnet/TorchSharp.git\\n```\\n\\nThis makes it easy to keep your fork up to date by regularly pulling and merging from upstream.\\n\\nAssuming that you do all your development off your main branch, keep your main updated\\nwith these commands:\\n\\n```git\\ngit checkout main\\ngit pull upstream main\\ngit push origin main\\n```\\n\\nThen, you merge onto your dev branch:\\n\\n```git\\ngit checkout <<your dev branch>>\\ngit merge main\\n```\\n\"\n },\n {\n \"alpha_fraction\": 0.5750093460083008,\n \"alphanum_fraction\": 0.5757575631141663,\n \"avg_line_length\": 38.89552307128906,\n \"blob_id\": \"b6eba5bf64249ca3e13f827da0a88f45ed369aff\",\n \"content_id\": \"dc4fea3fe317eb299ba9f622acbbe26e50ef050c\",\n \"detected_licenses\": [\n \"MIT\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"C#\",\n \"length_bytes\": 2673,\n \"license_type\": \"permissive\",\n \"max_line_length\": 139,\n \"num_lines\": 67,\n \"path\": \"/src/TorchSharp/NN/Activation/Softmax.cs\",\n \"repo_name\": \"NiklasGustafsson/TorchSharp\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"// Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information.\\nusing System;\\nusing static TorchSharp.torch;\\nusing static TorchSharp.PInvoke.NativeMethods;\\n\\nnamespace TorchSharp\\n{\\n using Modules;\\n\\n namespace Modules\\n {\\n /// <summary>\\n /// This class is used to represent a Softmax module.\\n /// </summary>\\n public sealed class Softmax : torch.nn.Module<Tensor, Tensor>\\n {\\n internal Softmax(IntPtr handle, IntPtr boxedHandle) : base(handle, boxedHandle) { }\\n\\n public override Tensor forward(Tensor tensor)\\n {\\n var res = THSNN_Softmax_forward(handle, tensor.Handle);\\n if (res == IntPtr.Zero) { torch.CheckForErrors(); }\\n return new Tensor(res);\\n }\\n\\n public override string GetName()\\n {\\n return typeof(Softmax).Name;\\n }\\n\\n // Rather than spending cycles only to discover that this module has neither\\n // parameters nor buffers, just shortcut the move completely.\\n protected internal override nn.Module _to(Device device, ScalarType dtype) => this;\\n protected internal override nn.Module _to(DeviceType deviceType, int deviceIndex = -1) => this;\\n protected internal override nn.Module _to(ScalarType dtype) => this;\\n }\\n }\\n\\n public static partial class torch\\n {\\n public static partial class nn\\n {\\n /// <summary>\\n /// Softmax\\n /// </summary>\\n /// <param name=\\\"dim\\\">A dimension along which Softmax will be computed (so every slice along dim will sum to 1)</param>\\n /// <returns></returns>\\n public static Softmax Softmax(long dim)\\n {\\n var handle = THSNN_Softmax_ctor(dim, out var boxedHandle);\\n if (handle == IntPtr.Zero) { torch.CheckForErrors(); }\\n return new Softmax(handle, boxedHandle);\\n }\\n\\n public static partial class functional\\n {\\n /// <summary>\\n /// Computes the softmax function for the input tensor.\\n /// </summary>\\n /// <param name=\\\"input\\\">The input tensor</param>\\n /// <param name=\\\"dim\\\">A dimension along which softmax will be computed.</param>\\n /// <param name=\\\"dtype\\\">The desired data type of returned tensor.</param>\\n public static Tensor softmax(Tensor input, long dim, ScalarType? dtype = null) => torch.special.softmax(input, dim, dtype);\\n }\\n }\\n }\\n}\\n\"\n },\n {\n \"alpha_fraction\": 0.5465657114982605,\n \"alphanum_fraction\": 0.5545369982719421,\n \"avg_line_length\": 34.6729850769043,\n \"blob_id\": \"33fdeb6653b51580c7e58f804c8ada514b0af26a\",\n \"content_id\": \"8fb175718ec201652a76b0d93010f49c06815762\",\n \"detected_licenses\": [\n \"MIT\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"C#\",\n \"length_bytes\": 7527,\n \"license_type\": \"permissive\",\n \"max_line_length\": 208,\n \"num_lines\": 211,\n \"path\": \"/src/Examples/TextClassification.cs\",\n \"repo_name\": \"NiklasGustafsson/TorchSharp\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"// Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information.\\nusing System;\\nusing System.IO;\\nusing System.Linq;\\nusing System.Collections.Generic;\\nusing System.Diagnostics;\\n\\nusing static TorchSharp.torch;\\nusing static TorchSharp.torch.nn;\\n\\nnamespace TorchSharp.Examples\\n{\\n\\n /// <summary>\\n /// This example is based on the PyTorch tutorial at:\\n ///\\n /// https://pytorch.org/tutorials/beginner/text_sentiment_ngrams_tutorial.html\\n ///\\n /// It relies on the AG_NEWS dataset, which can be downloaded in CSV form at:\\n ///\\n /// https://github.com/mhjabreel/CharCnn_Keras/tree/master/data/ag_news_csv\\n ///\\n /// Download the two files, and place them in a folder called \\\"AG_NEWS\\\" in\\n /// accordance with the file path below (Windows only).\\n ///\\n /// </summary>\\n public class TextClassification\\n {\\n private const long emsize = 200;\\n\\n private const long batch_size = 128;\\n private const long eval_batch_size = 128;\\n\\n private const int epochs = 15;\\n\\n // This path assumes that you're running this on Windows.\\n#if NET472_OR_GREATER\\n private readonly static string _dataLocation = NSPath.Join(Environment.GetFolderPath(Environment.SpecialFolder.DesktopDirectory), \\\"..\\\", \\\"Downloads\\\", \\\"AG_NEWS\\\");\\n#else\\n private readonly static string _dataLocation = Path.Join(Environment.GetFolderPath(Environment.SpecialFolder.DesktopDirectory), \\\"..\\\", \\\"Downloads\\\", \\\"AG_NEWS\\\");\\n#endif // NET472_OR_GREATER\\n internal static void Main(string[] args)\\n\\n {\\n torch.random.manual_seed(1);\\n\\n var cwd = Environment.CurrentDirectory;\\n\\n var device = torch.cuda.is_available() ? torch.CUDA : torch.CPU;\\n Console.WriteLine($\\\"Running TextClassification on {device.type.ToString()}\\\");\\n\\n using (var reader = TorchText.Data.AG_NEWSReader.AG_NEWS(\\\"train\\\", (Device)device, _dataLocation)) {\\n\\n var dataloader = reader.Enumerate();\\n\\n var tokenizer = TorchText.Data.Utils.get_tokenizer(\\\"basic_english\\\");\\n\\n var counter = new TorchText.Vocab.Counter<string>();\\n foreach (var (label, text) in dataloader) {\\n counter.update(tokenizer(text));\\n }\\n\\n var vocab = new TorchText.Vocab.Vocab(counter);\\n\\n var model = new TextClassificationModel(vocab.Count, emsize, 4).to((Device)device);\\n\\n var loss = CrossEntropyLoss();\\n var lr = 5.0;\\n var optimizer = torch.optim.SGD(model.parameters(), lr);\\n var scheduler = torch.optim.lr_scheduler.StepLR(optimizer, 1, 0.2, last_epoch: 5);\\n\\n // This data set is small enough that we can get away with\\n // collecting memory only once per epoch.\\n\\n using (var d = torch.NewDisposeScope()) {\\n\\n foreach (var epoch in Enumerable.Range(1, epochs)) {\\n\\n var sw = new Stopwatch();\\n sw.Start();\\n\\n train(epoch, reader.GetBatches(tokenizer, vocab, batch_size), model, loss, optimizer);\\n\\n sw.Stop();\\n\\n var pgFirst = optimizer.ParamGroups.First();\\n\\n Console.WriteLine($\\\"\\\\nEnd of epoch: {epoch} | lr: {pgFirst.LearningRate:0.00} | time: {sw.Elapsed.TotalSeconds:0.0}s\\\\n\\\");\\n scheduler.step();\\n }\\n }\\n\\n using (var d = torch.NewDisposeScope()) {\\n\\n using (var test_reader = TorchText.Data.AG_NEWSReader.AG_NEWS(\\\"test\\\", (Device)device, _dataLocation)) {\\n\\n var sw = new Stopwatch();\\n sw.Start();\\n\\n var accuracy = evaluate(test_reader.GetBatches(tokenizer, vocab, eval_batch_size), model, loss);\\n\\n sw.Stop();\\n\\n Console.WriteLine($\\\"\\\\nEnd of training: test accuracy: {accuracy:0.00} | eval time: {sw.Elapsed.TotalSeconds:0.0}s\\\\n\\\");\\n scheduler.step();\\n }\\n }\\n }\\n }\\n\\n static void train(int epoch, IEnumerable<(Tensor, Tensor, Tensor)> train_data, TextClassificationModel model, Loss<torch.Tensor, torch.Tensor, torch.Tensor> criterion, torch.optim.Optimizer optimizer)\\n {\\n model.train();\\n\\n double total_acc = 0.0;\\n long total_count = 0;\\n long log_interval = 250;\\n\\n var batch = 0;\\n\\n var batch_count = train_data.Count();\\n\\n foreach (var (labels, texts, offsets) in train_data) {\\n\\n optimizer.zero_grad();\\n\\n using (var predicted_labels = model.call(texts, offsets)) {\\n\\n var loss = criterion.call(predicted_labels, labels);\\n loss.backward();\\n torch.nn.utils.clip_grad_norm_(model.parameters().ToArray(), 0.5);\\n optimizer.step();\\n\\n total_acc += (predicted_labels.argmax(1) == labels).sum().to(torch.CPU).item<long>();\\n total_count += labels.size(0);\\n }\\n\\n if (batch % log_interval == 0 && batch > 0) {\\n var accuracy = total_acc / total_count;\\n Console.WriteLine($\\\"epoch: {epoch} | batch: {batch} / {batch_count} | accuracy: {accuracy:0.00}\\\");\\n }\\n\\n batch += 1;\\n }\\n }\\n\\n static double evaluate(IEnumerable<(Tensor, Tensor, Tensor)> test_data, TextClassificationModel model, Loss<Tensor, Tensor, Tensor> criterion)\\n {\\n model.eval();\\n\\n double total_acc = 0.0;\\n long total_count = 0;\\n\\n foreach (var (labels, texts, offsets) in test_data) {\\n\\n using (var predicted_labels = model.call(texts, offsets)) {\\n var loss = criterion.call(predicted_labels, labels);\\n\\n total_acc += (predicted_labels.argmax(1) == labels).sum().to(torch.CPU).item<long>();\\n total_count += labels.size(0);\\n }\\n }\\n\\n return total_acc / total_count;\\n }\\n }\\n\\n class TextClassificationModel : Module<Tensor, Tensor>\\n {\\n private Modules.EmbeddingBag embedding;\\n private Modules.Linear fc;\\n\\n public TextClassificationModel(long vocab_size, long embed_dim, long num_class) : base(\\\"TextClassification\\\")\\n {\\n embedding = EmbeddingBag(vocab_size, embed_dim, sparse: false);\\n fc = Linear(embed_dim, num_class);\\n InitWeights();\\n\\n RegisterComponents();\\n }\\n\\n private void InitWeights()\\n {\\n var initrange = 0.5;\\n\\n init.uniform_(embedding.weight, -initrange, initrange);\\n init.uniform_(fc.weight, -initrange, initrange);\\n init.zeros_(fc.bias);\\n }\\n\\n public override Tensor forward(Tensor t)\\n {\\n throw new NotImplementedException();\\n }\\n\\n public Tensor call(Tensor input, Tensor offsets)\\n {\\n return fc.call(embedding.call(input, offsets));\\n }\\n\\n protected override void Dispose(bool disposing)\\n {\\n if (disposing) {\\n embedding.Dispose();\\n fc.Dispose();\\n ClearModules();\\n }\\n base.Dispose(disposing);\\n }\\n }\\n}\\n\"\n },\n {\n \"alpha_fraction\": 0.5338345766067505,\n \"alphanum_fraction\": 0.5371407270431519,\n \"avg_line_length\": 46.72010040283203,\n \"blob_id\": \"ed558b1e96236a328e8cdee987a9c62078f697e6\",\n \"content_id\": \"3dc444bec3e731c1427e33c81044d74f184015a5\",\n \"detected_licenses\": [\n \"MIT\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"C#\",\n \"length_bytes\": 18761,\n \"license_type\": \"permissive\",\n \"max_line_length\": 207,\n \"num_lines\": 393,\n \"path\": \"/src/TorchSharp/Utils/tensorboard/SummaryWriter.cs\",\n \"repo_name\": \"NiklasGustafsson/TorchSharp\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"// Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information.\\n\\nusing System;\\nusing System.Collections.Generic;\\nusing System.Diagnostics;\\nusing System.IO;\\nusing System.Threading;\\nusing Google.Protobuf;\\nusing Tensorboard;\\n\\nnamespace TorchSharp\\n{\\n public static partial class torch\\n {\\n public static partial class utils\\n {\\n public static partial class tensorboard\\n {\\n /// <summary>\\n /// Writes entries directly to event files in the log_dir to be consumed by TensorBoard.\\n /// \\n /// The SummaryWriter class provides a high-level API to create an event file in a given directory and add summaries and events to it.The class updates the file contents asynchronously.\\n ///\\n /// This allows a training program to call methods to add data to the file directly from the training loop, without slowing down training.\\n /// </summary>\\n /// <param name=\\\"log_dir\\\">\\n /// Save directory location. Default is runs/CURRENT_DATETIME_HOSTNAME, which changes after each run.\\n /// Use hierarchical folder structure to compare between runs easily. e.g. pass in ‘runs/exp1’, ‘runs/exp2’, etc.\\n /// for each new experiment to compare across them\\n /// </param>\\n /// <param name=\\\"filename_suffix\\\">Suffix added to all event filenames in the log_dir directory.</param>\\n /// <param name=\\\"createRunName\\\">Create a time-based run name, even if log_dir is specified.</param>\\n public static Modules.SummaryWriter SummaryWriter(string log_dir = null, string filename_suffix = null, bool createRunName = false)\\n {\\n if (createRunName && !string.IsNullOrEmpty(log_dir)) {\\n log_dir = CreateRunName(log_dir);\\n }\\n\\n return new Modules.SummaryWriter(log_dir, filename_suffix);\\n }\\n\\n internal static string CreateRunName(string log_dir)\\n {\\n var now = DateTime.Now;\\n var _months = System.Globalization.CultureInfo.InvariantCulture.DateTimeFormat.AbbreviatedMonthNames;\\n var name = $\\\"{_months[now.Month - 1]}{now.Day}_{now.Hour}-{now.Minute}-{now.Second}_{System.Net.Dns.GetHostName()}\\\";\\n log_dir = Path.Combine(log_dir, name);\\n return log_dir;\\n }\\n }\\n }\\n }\\n\\n namespace Modules\\n {\\n /// <summary>\\n /// Writes entries directly to event files in the log_dir to be consumed by TensorBoard.\\n /// \\n /// The SummaryWriter class provides a high-level API to create an event file in a given directory and add summaries and events to it.The class updates the file contents asynchronously.\\n ///\\n /// This allows a training program to call methods to add data to the file directly from the training loop, without slowing down training.\\n /// </summary>\\n public class SummaryWriter\\n {\\n internal SummaryWriter(string log_dir, string filename_suffix)\\n {\\n _suffix = filename_suffix;\\n\\n if (string.IsNullOrEmpty(log_dir)) {\\n _log_dir = torch.utils.tensorboard.CreateRunName(\\\"runs\\\");\\n } else {\\n _log_dir = log_dir;\\n }\\n\\n if (!Directory.Exists(_log_dir)) {\\n Directory.CreateDirectory(_log_dir);\\n }\\n\\n var fileName = Path.Combine(_log_dir, $\\\"events.out.tfevents.{DateTime.Now.Ticks}.{System.Net.Dns.GetHostName()}.{Process.GetCurrentProcess().Id}.{Interlocked.Increment(ref _global_uid)}\\\");\\n if (!string.IsNullOrEmpty(_suffix)) {\\n fileName = fileName + \\\".\\\" + _suffix;\\n }\\n\\n if (File.Exists(fileName)) {\\n File.Delete(fileName);\\n }\\n\\n InitFile(fileName);\\n\\n _fileNames[\\\"__default__\\\"] = fileName;\\n }\\n\\n /// <summary>\\n /// The directory/folder where logging is made.\\n /// </summary>\\n public string LogDir { get { return _log_dir; } }\\n\\n /// <summary>\\n /// Add scalar data to summary.\\n /// </summary>\\n /// <param name=\\\"tag\\\">Data identifier</param>\\n /// <param name=\\\"scalar_value\\\">Value to save</param>\\n /// <param name=\\\"global_step\\\">Global step value to record</param>\\n /// <param name=\\\"walltime\\\">Optional override default walltime (DateTimeOffset.Now.ToUnixTimeSeconds())</param>\\n public void add_scalar(string tag, float scalar_value, int global_step, long? walltime = null)\\n {\\n var fileName = InitDefaultFile();\\n SetWalltime(ref walltime);\\n\\n var summary = new Summary();\\n summary.Value.Add(new Summary.Types.Value() { SimpleValue = scalar_value, Tag = tag });\\n var evnt = new Event() { Step = global_step, WallTime = walltime.Value, Summary = summary };\\n\\n WriteEvent(fileName, evnt);\\n }\\n\\n /// <summary>\\n /// Adds many scalar data points to summary.\\n /// </summary>\\n /// <param name=\\\"main_tag\\\">Data identifier</param>\\n /// <param name=\\\"tag_scalar_dict\\\">Dictionary storing the tag and corresponding values</param>\\n /// <param name=\\\"global_step\\\">Global step value to record</param>\\n /// <param name=\\\"walltime\\\">Optional override default walltime (DateTimeOffset.Now.ToUnixTimeSeconds())</param>\\n public void add_scalars(string main_tag, IDictionary<string, float> tag_scalar_dict, int global_step, long? walltime = null)\\n {\\n SetWalltime(ref walltime);\\n\\n foreach (var kv in tag_scalar_dict) {\\n\\n var key = kv.Key;\\n var scalar_value = kv.Value;\\n\\n var summary = new Summary();\\n summary.Value.Add(new Summary.Types.Value() { SimpleValue = scalar_value, Tag = main_tag });\\n var evnt = new Event() { Step = global_step, WallTime = walltime.Value, Summary = summary };\\n\\n if (!_fileNames.TryGetValue(key, out var fileName)) {\\n\\n // We haven't logged this tag from this session, yet. Create a clean file.\\n\\n var fileDir = Path.Combine(_log_dir, $\\\"{main_tag}_{key}\\\");\\n if (!Directory.Exists(fileDir)) {\\n Directory.CreateDirectory(fileDir);\\n }\\n\\n fileName = Path.Combine(fileDir, $\\\"events.out.tfevents.{DateTime.Now.Ticks}.{System.Net.Dns.GetHostName()}.{Process.GetCurrentProcess().Id}.{Interlocked.Increment(ref _global_uid)}\\\");\\n\\n if (File.Exists(fileName)) {\\n File.Delete(fileName);\\n }\\n\\n InitFile(fileName);\\n\\n _fileNames[key] = fileName;\\n }\\n\\n WriteEvent(fileName, evnt);\\n }\\n }\\n\\n\\n /// <summary>\\n /// Adds many scalar data points to summary.\\n /// </summary>\\n /// <param name=\\\"main_tag\\\">Data identifier</param>\\n /// <param name=\\\"tag_scalar_dict\\\">List of tuples storing the tag and corresponding values</param>\\n /// <param name=\\\"global_step\\\">Global step value to record</param>\\n /// <param name=\\\"walltime\\\">Optional override default walltime (DateTimeOffset.Now.ToUnixTimeSeconds())</param>\\n public void add_scalars(string main_tag, IList<(string, float)> tag_scalar_dict, int global_step, long? walltime = null)\\n {\\n SetWalltime(ref walltime);\\n\\n foreach (var (key, scalar_value) in tag_scalar_dict) {\\n\\n var summary = new Summary();\\n summary.Value.Add(new Summary.Types.Value() { SimpleValue = scalar_value, Tag = main_tag });\\n var evnt = new Event() { Step = global_step, WallTime = walltime.Value, Summary = summary };\\n\\n if (!_fileNames.TryGetValue(key, out var fileName)) {\\n\\n var fileDir = Path.Combine(_log_dir, $\\\"{main_tag}_{key}\\\");\\n if (!Directory.Exists(fileDir)) {\\n Directory.CreateDirectory(fileDir);\\n }\\n\\n fileName = Path.Combine(fileDir, $\\\"events.out.tfevents.{DateTime.Now.Ticks}.{System.Net.Dns.GetHostName()}.{Process.GetCurrentProcess().Id}.{Interlocked.Increment(ref _global_uid)}\\\");\\n\\n if (!File.Exists(fileName)) {\\n InitFile(fileName);\\n }\\n\\n _fileNames[key] = fileName;\\n }\\n\\n WriteEvent(fileName, evnt);\\n }\\n }\\n\\n /// <summary>\\n /// Add histogram to summary.\\n ///\\n /// https://pytorch.org/docs/stable/_modules/torch/utils/tensorboard/writer.html#SummaryWriter.add_histogram\\n /// </summary>\\n /// <param name=\\\"tag\\\"> Data identifier </param>\\n /// <param name=\\\"values\\\"> Values to build histogram </param>\\n /// <param name=\\\"global_step\\\"> Global step value to record </param>\\n /// <param name=\\\"bins\\\"> This determines how the bins are made </param>\\n /// <param name=\\\"walltime\\\"> Optional override default walltime (DateTimeOffset.Now.ToUnixTimeSeconds()) </param>\\n /// <param name=\\\"max_bins\\\"></param>\\n public void add_histogram(string tag,\\n torch.Tensor values,\\n int global_step,\\n Utils.tensorboard.Enums.HistogramBinSelector bins = Utils.tensorboard.Enums.HistogramBinSelector.Tensorflow,\\n long? walltime = null,\\n long? max_bins = null)\\n {\\n static torch.Tensor default_bins()\\n {\\n double v = 1e-12;\\n var buckets = new List<double>();\\n var neg_buckets = new List<double>();\\n while (v < 1e20) {\\n buckets.Add(v);\\n neg_buckets.Add(-v);\\n v *= 1.1;\\n }\\n neg_buckets.Reverse();\\n var result = new List<double>();\\n result.AddRange(neg_buckets); result.Add(0); result.AddRange(buckets);\\n return torch.tensor(result);\\n }\\n\\n var fileName = InitDefaultFile();\\n SetWalltime(ref walltime);\\n Summary summary = bins == Utils.tensorboard.Enums.HistogramBinSelector.Tensorflow ?\\n torch.utils.tensorboard.Summary.histogram(tag, values, default_bins(), max_bins) :\\n torch.utils.tensorboard.Summary.histogram(tag, values, (HistogramBinSelector)(byte)bins, max_bins);\\n var evnt = new Event() { Step = global_step, WallTime = walltime.Value, Summary = summary };\\n WriteEvent(fileName, evnt);\\n }\\n\\n /// <summary>\\n /// Add batched image data to summary.\\n ///\\n /// https://pytorch.org/docs/stable/tensorboard.html#torch.utils.tensorboard.writer.SummaryWriter.add_image\\n /// </summary>\\n /// <param name=\\\"tag\\\"> Data identifier </param>\\n /// <param name=\\\"img_tensor\\\"> Image data </param>\\n /// <param name=\\\"global_step\\\"> Global step value to record </param>\\n /// <param name=\\\"walltime\\\"> Optional override default walltime (DateTimeOffset.Now.ToUnixTimeSeconds()) </param>\\n /// <param name=\\\"dataformats\\\"> Image data format specification of the form CHW, HWC, HW, WH, etc. </param>\\n public void add_img(string tag, torch.Tensor img_tensor, int global_step, long? walltime = null, string dataformats = \\\"CHW\\\")\\n {\\n var fileName = InitDefaultFile();\\n SetWalltime(ref walltime);\\n Summary summary = torch.utils.tensorboard.Summary.image(tag, img_tensor, dataformats: dataformats);\\n var evnt = new Event() { Step = global_step, WallTime = walltime.Value, Summary = summary };\\n WriteEvent(fileName, evnt);\\n }\\n\\n /// <summary>\\n /// Add batched image data to summary.\\n ///\\n /// https://pytorch.org/docs/stable/tensorboard.html#torch.utils.tensorboard.writer.SummaryWriter.add_image\\n /// </summary>\\n /// <param name=\\\"tag\\\"> Data identifier </param>\\n /// <param name=\\\"file_name\\\"> Image file </param>\\n /// <param name=\\\"global_step\\\"> Global step value to record </param>\\n /// <param name=\\\"walltime\\\"> Optional override default walltime (DateTimeOffset.Now.ToUnixTimeSeconds()) </param>\\n public void add_img(string tag, string file_name, int global_step, long? walltime = null)\\n {\\n var fileName = InitDefaultFile();\\n SetWalltime(ref walltime);\\n Summary summary = torch.utils.tensorboard.Summary.image(tag, file_name);\\n var evnt = new Event() { Step = global_step, WallTime = walltime.Value, Summary = summary };\\n WriteEvent(fileName, evnt);\\n }\\n\\n /// <summary>\\n /// Add video data to summary.\\n ///\\n /// https://pytorch.org/docs/stable/tensorboard.html#torch.utils.tensorboard.writer.SummaryWriter.add_video\\n /// </summary>\\n /// <param name=\\\"tag\\\"> Data identifier </param>\\n /// <param name=\\\"vid_tensor\\\">\\n /// Video data\\n ///\\n /// Shape: (N,T,C,H,W). The values should lie in [0, 255] for type uint8 or [0, 1] for type float.\\n /// </param>\\n /// <param name=\\\"global_step\\\"> Global step value to record </param>\\n /// <param name=\\\"fps\\\"> Frames per second </param>\\n /// <param name=\\\"walltime\\\"> Optional override default walltime (DateTimeOffset.Now.ToUnixTimeSeconds()) </param>\\n public void add_video(string tag, torch.Tensor vid_tensor, int global_step, int fps = 4, long? walltime = null)\\n {\\n var fileName = InitDefaultFile();\\n SetWalltime(ref walltime);\\n Summary summary = torch.utils.tensorboard.Summary.video(tag, vid_tensor, fps);\\n var evnt = new Event() { Step = global_step, WallTime = walltime.Value, Summary = summary };\\n WriteEvent(fileName, evnt);\\n }\\n\\n /// <summary>\\n /// Add text data to summary.\\n /// \\n /// https://pytorch.org/docs/stable/_modules/torch/utils/tensorboard/writer.html#SummaryWriter.add_text\\n /// </summary>\\n /// <param name=\\\"tag\\\"> Data identifier </param>\\n /// <param name=\\\"text_string\\\"> String to save </param>\\n /// <param name=\\\"global_step\\\"> Global step value to record </param>\\n /// <param name=\\\"walltime\\\"> Optional override default walltime (DateTimeOffset.Now.ToUnixTimeSeconds()) </param>\\n public void add_text(string tag, string text_string, int global_step, long? walltime = null)\\n {\\n var fileName = InitDefaultFile();\\n SetWalltime(ref walltime);\\n\\n var evnt = new Event() { Step = global_step, WallTime = walltime.Value, Summary = torch.utils.tensorboard.Summary.text(tag, text_string) };\\n WriteEvent(fileName, evnt);\\n }\\n\\n private static void InitFile(string fileName)\\n {\\n var evnt = new Event() { FileVersion = \\\"brain.Event:2\\\", WallTime = DateTime.Now.Ticks };\\n WriteEvent(fileName, evnt);\\n }\\n\\n private string InitDefaultFile()\\n {\\n var fileName = _fileNames[\\\"__default__\\\"];\\n\\n if (!File.Exists(fileName)) {\\n InitFile(fileName);\\n }\\n\\n return fileName;\\n }\\n\\n private static void SetWalltime(ref long? walltime)\\n => walltime ??= DateTimeOffset.Now.ToUnixTimeSeconds();\\n\\n private static void WriteEvent(string fileName, Event evnt)\\n {\\n var bytes = evnt.ToByteArray();\\n\\n long header = bytes.LongLength;\\n uint header_crc = GetMaskedCrc(header);\\n uint footer_crc = GetMaskedCrc(bytes);\\n\\n using (var fStream = File.Open(fileName, FileMode.OpenOrCreate, FileAccess.Write, FileShare.Read)) {\\n fStream.Seek(0, SeekOrigin.End);\\n\\n using (var writers = new BinaryWriter(fStream)) {\\n writers.Write(header);\\n writers.Write(header_crc);\\n using (var stream = new Google.Protobuf.CodedOutputStream(fStream, true)) {\\n evnt.WriteTo(stream);\\n }\\n fStream.Seek(0, SeekOrigin.End);\\n writers.Write(footer_crc);\\n fStream.Flush();\\n }\\n }\\n }\\n\\n private string _log_dir = null;\\n private Dictionary<string, string> _fileNames = new Dictionary<string, string>();\\n\\n private string _suffix = null;\\n\\n private int _global_uid;\\n\\n private static uint GetMaskedCrc(byte[] data)\\n {\\n var value = TorchSharp.Utils.CRC32C.process(data);\\n // Rotate right by 15 bits and add a constant.\\n return ((value >> 15) | (value << 17)) + 0xa282ead8U;\\n }\\n\\n private static uint GetMaskedCrc(int data)\\n {\\n var value = TorchSharp.Utils.CRC32C.process(data);\\n // Rotate right by 15 bits and add a constant.\\n return ((value >> 15) | (value << 17)) + 0xa282ead8U;\\n }\\n\\n private static uint GetMaskedCrc(long data)\\n {\\n var value = TorchSharp.Utils.CRC32C.process(data);\\n // Rotate right by 15 bits and add a constant.\\n return ((value >> 15) | (value << 17)) + 0xa282ead8U;\\n }\\n }\\n }\\n}\"\n },\n {\n \"alpha_fraction\": 0.4700726568698883,\n \"alphanum_fraction\": 0.5074143409729004,\n \"avg_line_length\": 34.14495086669922,\n \"blob_id\": \"cc8c0fe28dfdbec371ce590d040e987f7c3d4f7b\",\n \"content_id\": \"84f7bea9a0e3047583cf31c957a5583c58454fc2\",\n \"detected_licenses\": [\n \"MIT\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"C#\",\n \"length_bytes\": 40732,\n \"license_type\": \"permissive\",\n \"max_line_length\": 173,\n \"num_lines\": 1159,\n \"path\": \"/test/TorchSharpTest/TestTorchTensorBugs.cs\",\n \"repo_name\": \"NiklasGustafsson/TorchSharp\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"// Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information.\\nusing System;\\nusing System.Collections.Generic;\\nusing System.Linq;\\nusing System.IO;\\n\\nusing System.Threading;\\nusing System.Runtime.CompilerServices;\\n\\nusing static TorchSharp.torch.nn;\\nusing Xunit;\\n\\nusing static TorchSharp.torch;\\nusing static TorchSharp.torchvision.models;\\n\\n\\nusing System.Numerics;\\n\\n#nullable enable\\n\\nnamespace TorchSharp\\n{\\n // The tests in this file are all derived from reported GitHub Issues, serving\\n // as regression tests.\\n\\n [Collection(\\\"Sequential\\\")]\\n public class TestTorchTensorBugs\\n {\\n\\n [Fact]\\n public void ValidateAddInplace()\\n {\\n using var _ = NewDisposeScope();\\n\\n var x = torch.zeros(10, 10);\\n var y = torch.ones(10).expand(10, 10);\\n\\n x.add_(y, 1);\\n\\n Assert.Equal(x, y);\\n }\\n\\n [Fact(Skip = \\\"No longer throws an exception, and it doesn't seem like ever should have.\\\")]\\n public void ValidateIssue145()\\n {\\n // Tensor.DataItem gives a hard crash on GPU tensor\\n\\n if (torch.cuda.is_available()) {\\n\\n using var _ = NewDisposeScope();\\n\\n var scalar = torch.tensor(3.14f, torch.CUDA);\\n Assert.Throws<InvalidOperationException>(() => scalar.item<float>());\\n var tensor = torch.zeros(new long[] { 10, 10 }, device: torch.CUDA);\\n Assert.Throws<InvalidOperationException>(() => tensor.data<float>());\\n Assert.Throws<InvalidOperationException>(() => { var _ = tensor.bytes; });\\n }\\n }\\n\\n class DoubleIt : nn.Module<Tensor, Tensor>\\n {\\n public DoubleIt() : base(\\\"double\\\") { }\\n\\n public override Tensor forward(Tensor t) => t * 2;\\n }\\n\\n [Fact]\\n public void ValidateIssue315_1()\\n {\\n using var _ = NewDisposeScope();\\n\\n // https://github.com/dotnet/TorchSharp/issues/315\\n // custom module crash in GC thread\\n\\n // make Torch call our custom module by adding a ReLU in front of it\\n using var net = nn.Sequential(\\n (\\\"relu\\\", nn.ReLU()),\\n (\\\"double\\\", new DoubleIt())\\n );\\n\\n using var @in = torch.tensor(3);\\n using var @out = net.call(@in);\\n GC.Collect();\\n GC.WaitForPendingFinalizers();\\n }\\n\\n [Fact]\\n public void ValidateIssue315_2()\\n {\\n using var _ = NewDisposeScope();\\n\\n Func<Tensor, Tensor, Tensor> distance =\\n (x, y) => {\\n return (x - y).abs();\\n };\\n\\n using (Tensor anchor = torch.rand(new long[] { 15, 5 }, requires_grad: true).neg())\\n using (Tensor positive = torch.randn(new long[] { 15, 5 }, requires_grad: true))\\n using (Tensor negative = torch.randn(new long[] { 15, 5 })) {\\n\\n var output = nn.TripletMarginWithDistanceLoss(distance);\\n using (var result = output.call(anchor, positive, negative)) { }\\n }\\n GC.Collect();\\n GC.WaitForPendingFinalizers();\\n }\\n\\n\\n\\n [Fact]\\n public void ValidateIssue315_3()\\n {\\n using var _ = NewDisposeScope();\\n\\n var lin1 = Linear(1000, 100);\\n var lin2 = Linear(100, 10);\\n var seq = Sequential((\\\"lin1\\\", lin1), (\\\"relu1\\\", ReLU()), (\\\"lin2\\\", lin2));\\n\\n using var x = torch.randn(new long[] { 64, 1000 });\\n using var y = torch.randn(new long[] { 64, 10 });\\n\\n double learning_rate = 0.00004f;\\n var optimizer = torch.optim.LBFGS(seq.parameters(), learning_rate);\\n var loss = nn.MSELoss(Reduction.Sum);\\n\\n Func<Tensor> closure = () => {\\n using var eval = seq.call(x);\\n var output = loss.call(eval, y);\\n\\n var l = output.ToSingle();\\n\\n optimizer.zero_grad();\\n\\n output.backward();\\n return output;\\n };\\n\\n optimizer.step(closure);\\n\\n GC.Collect();\\n GC.WaitForPendingFinalizers();\\n }\\n\\n\\n private void ThreadFunc()\\n {\\n using var _ = NewDisposeScope();\\n\\n using var net = nn.Sequential(\\n (\\\"relu\\\", nn.ReLU()),\\n (\\\"double\\\", new DoubleIt())\\n );\\n\\n using var @in = torch.tensor(3);\\n\\n for (var i = 0; i < 1000; i++) {\\n using var @out = net.call(@in);\\n }\\n }\\n\\n [Fact]\\n public void ValidateIssue315_4()\\n {\\n // Is CustomModule thread-safe?\\n // See: https://github.com/pytorch/pytorch/issues/19029\\n\\n var threads = new List<Thread>();\\n\\n for (var i = 0; i < 10; i++) {\\n var t = new Thread(ThreadFunc);\\n threads.Add(t);\\n }\\n\\n foreach (var t in threads) {\\n t.Start();\\n }\\n foreach (var t in threads) {\\n t.Join();\\n }\\n }\\n\\n class TestModule : Module<Tensor, Tensor>\\n {\\n public TestModule() : base(nameof(TestModule)) { }\\n\\n public override torch.Tensor forward(torch.Tensor t) => t.clone();\\n\\n public static void Reproduce()\\n {\\n Tensor t = torch.zeros(10);\\n\\n var seq = Make();\\n GC.Collect();\\n GC.WaitForPendingFinalizers();\\n for (var i = 0; i < 100; i++) {\\n GC.Collect();\\n GC.WaitForPendingFinalizers();\\n t = seq.call(t);\\n }\\n }\\n\\n [MethodImpl(MethodImplOptions.NoInlining)]\\n static Module<Tensor, Tensor> Make() => Sequential((\\\"t\\\", new TestModule()), (\\\"d\\\", Linear(10, 10)));\\n }\\n\\n [Fact]\\n void ValidateIssue321()\\n {\\n TestModule.Reproduce();\\n }\\n\\n [Fact]\\n public void ValidateIssue353()\\n {\\n using var _ = NewDisposeScope();\\n\\n //\\n // Just validating that the methods are there.\\n //\\n using var x = torch.zeros(3, 3);\\n using var y = torch.ones(3, 3);\\n\\n var mx1 = x.max();\\n Assert.Equal(0, mx1.item<float>());\\n\\n var mx2 = x.maximum(y);\\n Assert.True(mx2.allclose(y));\\n\\n var mn1 = x.min();\\n Assert.Equal(0, mn1.item<float>());\\n\\n var mn2 = x.minimum(y);\\n Assert.True(mn2.allclose(x));\\n }\\n\\n [Fact]\\n public void ValidateIssue399_1()\\n {\\n using var _ = NewDisposeScope();\\n\\n // Create a contiguous 3x4 matrix and fill with auto-inc values starting at zero.\\n // 0 1 2 3\\n // 4 5 6 7\\n // 8 9 10 11\\n using var contig = torch.arange(12, int32).reshape(3, 4).contiguous();\\n var data1 = contig.data<int>();\\n\\n // Create a 3x2 slice of this, which should contain:\\n // 1 2\\n // 5 6\\n // 9 10\\n using var sub = contig.slice(1, 1, 3, 1);\\n var data2 = sub.data<int>();\\n\\n Assert.Equal(12, data1.Count);\\n Assert.Equal(6, data2.Count);\\n\\n Assert.False(sub.is_contiguous());\\n Assert.True(sub.contiguous().data<int>() == data2);\\n Assert.False(sub.contiguous().data<int>() != data2);\\n Assert.Equal(sub.contiguous().data<int>(), data2);\\n Assert.Equal(sub.contiguous().data<int>().ToArray(), data2.ToArray());\\n }\\n\\n [Fact]\\n public void ValidateIssue399_2()\\n {\\n using var _ = NewDisposeScope();\\n\\n // Create a contiguous 3x4 matrix and fill with auto-inc values starting at zero.\\n // 0 1 2 3\\n // 4 5 6 7\\n // 8 9 10 11\\n using var contig = torch.arange(12, int32).reshape(3, 4).contiguous();\\n var data1 = contig.data<int>();\\n\\n // Creating the transpose, which should look like:\\n // 0 4 8\\n // 1 5 9\\n // 2 6 10\\n // 3 7 11\\n using var trans = contig.t();\\n var data2 = trans.data<int>();\\n\\n Assert.False(trans.is_contiguous());\\n\\n Assert.Equal(12, data1.Count);\\n Assert.Equal(12, data2.Count);\\n\\n Assert.Equal(6, data2[2, 1]);\\n Assert.Equal(7, data2[3, 1]);\\n\\n Assert.True(trans.contiguous().data<int>() == data2);\\n Assert.False(trans.contiguous().data<int>() != data2);\\n Assert.Equal(trans.contiguous().data<int>(), data2);\\n Assert.Equal(trans.contiguous().data<int>().ToArray(), data2.ToArray());\\n }\\n\\n [Fact]\\n public void ValidateIssue399_3()\\n {\\n using var _ = NewDisposeScope();\\n\\n using var contig = torch.arange(27, int32).reshape(3, 3, 3).contiguous();\\n using var trans = contig.permute(2, 0, 1);\\n\\n Assert.False(trans.is_contiguous());\\n Assert.Equal<int>(trans.contiguous().data<int>(), trans.data<int>());\\n Assert.Equal<int>(trans.contiguous().data<int>().ToArray(), trans.data<int>().ToArray());\\n }\\n\\n [Fact]\\n public void ValidateIssue399_4()\\n {\\n using var _ = NewDisposeScope();\\n\\n using var contig = torch.arange(12, int32).reshape(3, 4).contiguous();\\n using var flipped = contig.t().flip(1);\\n var strides = flipped.stride();\\n\\n Assert.False(flipped.is_contiguous());\\n Assert.Equal<int>(flipped.contiguous().data<int>(), flipped.data<int>());\\n Assert.Equal<int>(flipped.contiguous().data<int>().ToArray(), flipped.data<int>().ToArray());\\n }\\n\\n [Fact]\\n public void ValidateIssue399_5()\\n {\\n using var _ = NewDisposeScope();\\n\\n using var contig = torch.arange(12, int32).reshape(3, 4).contiguous();\\n using var strided = contig.as_strided(new long[] { 3, 2, 4 }, new long[] { 4, 0, 1 });\\n\\n Assert.False(strided.is_contiguous());\\n Assert.Equal<int>(strided.contiguous().data<int>(), strided.data<int>());\\n Assert.Equal(new long[] { 3, 2, 4 }, strided.shape);\\n }\\n\\n [Fact]\\n public void ValidateIssue399_6()\\n {\\n using var _ = NewDisposeScope();\\n\\n using var contig = torch.arange(3, int32).reshape(3, 1).contiguous();\\n using var strided = contig.expand(3, 4);\\n\\n Assert.False(strided.is_contiguous());\\n Assert.Equal<int>(strided.contiguous().data<int>(), strided.data<int>());\\n Assert.Equal(new long[] { 3, 4 }, strided.shape);\\n }\\n\\n [Fact]\\n public void ValidateIssue399_7()\\n {\\n using var _ = NewDisposeScope();\\n\\n using var contig = torch.arange(27, int32).reshape(3, 3, 3).contiguous();\\n using var trans = contig.permute(2, 0, 1);\\n\\n Assert.False(trans.is_contiguous());\\n\\n // Test the enumerators.\\n var data1 = trans.contiguous().data<int>();\\n var data2 = trans.data<int>();\\n\\n var expected = new int[] { 0, 3, 6, 9, 12, 15, 18, 21, 24, 1, 4, 7, 10, 13, 16, 19, 22, 25, 2, 5, 8, 11, 14, 17, 20, 23, 26 };\\n\\n long idx = 0;\\n foreach (var value in data1) {\\n Assert.Equal(expected[idx], value);\\n idx += 1;\\n }\\n Assert.Equal(27, idx);\\n\\n idx = 0;\\n foreach (var value in data2) {\\n Assert.Equal(expected[idx], value);\\n idx += 1;\\n }\\n Assert.Equal(27, idx);\\n\\n var arr1 = data1.AsEnumerable<int>().ToArray();\\n var arr2 = data2.AsEnumerable<int>().ToArray();\\n\\n Assert.Equal(expected, arr1);\\n Assert.Equal(arr1, arr2);\\n }\\n\\n [Fact]\\n public void ValidateIssue399_8()\\n {\\n using var _ = NewDisposeScope();\\n\\n // We need to test something that has rank 1, because the TensorAccessor uses\\n // seprate enumeration logic for that.\\n using var contig = torch.arange(48, int32).reshape(12, 4).contiguous();\\n using var sub = contig.slice(1, 1, 2, 1).squeeze(1);\\n\\n var data1 = sub.contiguous().data<int>();\\n var data2 = sub.data<int>();\\n\\n Assert.True(data1 == data2);\\n Assert.False(data1 != data2);\\n\\n Assert.Equal(data1.ToArray(), data2.ToArray());\\n\\n var expected = new int[] { 1, 5, 9, 13, 17, 21, 25, 29, 33, 37, 41, 45 };\\n\\n long idx = 0;\\n foreach (var value in data1) {\\n Assert.Equal(expected[idx], value);\\n idx += 1;\\n }\\n Assert.Equal(12, idx);\\n\\n idx = 0;\\n foreach (var value in data2) {\\n Assert.Equal(expected[idx], value);\\n idx += 1;\\n }\\n Assert.Equal(12, idx);\\n\\n var arr1 = data1.AsEnumerable<int>().ToArray();\\n var arr2 = data2.AsEnumerable<int>().ToArray();\\n\\n Assert.Equal(expected, arr1);\\n Assert.Equal(arr1, arr2);\\n }\\n\\n [Fact]\\n public void ValidateRandThrowsOnWrongType()\\n {\\n using var _ = NewDisposeScope();\\n\\n Assert.Throws<ArgumentException>(() => torch.rand(3, 4, dtype: torch.int8));\\n Assert.Throws<ArgumentException>(() => torch.rand(3, 4, dtype: torch.uint8));\\n Assert.Throws<ArgumentException>(() => torch.rand(3, 4, dtype: torch.int16));\\n Assert.Throws<ArgumentException>(() => torch.rand(3, 4, dtype: torch.int32));\\n Assert.Throws<ArgumentException>(() => torch.rand(3, 4, dtype: torch.int64));\\n Assert.Throws<ArgumentException>(() => torch.rand(3, 4, dtype: torch.@bool));\\n Assert.Throws<ArgumentException>(() => torch.randn(3, 4, dtype: torch.int8));\\n Assert.Throws<ArgumentException>(() => torch.randn(3, 4, dtype: torch.uint8));\\n Assert.Throws<ArgumentException>(() => torch.randn(3, 4, dtype: torch.int16));\\n Assert.Throws<ArgumentException>(() => torch.randn(3, 4, dtype: torch.int32));\\n Assert.Throws<ArgumentException>(() => torch.randn(3, 4, dtype: torch.int64));\\n Assert.Throws<ArgumentException>(() => torch.randn(3, 4, dtype: torch.@bool));\\n }\\n\\n [Fact]\\n public void ValidateIssue496()\\n {\\n using var _ = NewDisposeScope();\\n\\n var c2 = torch.nn.Conv2d(3, 16, kernelSize: (1, 7), stride: (1, 1), padding: (0, 3));\\n var Win = torch.rand(16, 3, 8, 8);\\n var s = c2.call(Win).shape;\\n Assert.Equal(new long[] { 16, 16, 8, 8 }, s);\\n }\\n\\n [Fact]\\n public void ValidateIssue500()\\n {\\n using var _ = NewDisposeScope();\\n\\n using (var pool = BatchNorm1d(28)) {\\n pool.eval();\\n pool.call(torch.ones(1, 28));\\n }\\n using (var pool = BatchNorm1d(28))\\n using (var seq = Sequential(pool)) {\\n seq.eval();\\n seq.call(torch.ones(1, 28));\\n }\\n using (var seq = new Module500()) {\\n seq.eval();\\n seq.call(torch.ones(1, 28));\\n }\\n }\\n\\n class Module500 : Module<Tensor, Tensor>\\n {\\n private Module<Tensor, Tensor> bn1 = BatchNorm1d(28);\\n\\n public Module500() : base(nameof(TestModule)) { RegisterComponents(); }\\n\\n public override torch.Tensor forward(torch.Tensor t) => bn1.call(t);\\n }\\n\\n [Fact]\\n public void ValidateIssue510()\\n {\\n using var _ = NewDisposeScope();\\n\\n var model = new Module510(1, 32);\\n model.call(torch.randn(16, 1, 32));\\n\\n var w0 = model.get_parameter(\\\"stack.0.weight\\\").clone();\\n var w1 = model.get_parameter(\\\"stack.1.weight\\\").clone();\\n var b1 = model.get_parameter(\\\"stack.1.bias\\\").clone();\\n var rm = model.get_buffer(\\\"stack.1.running_mean\\\").clone();\\n var rv = model.get_buffer(\\\"stack.1.running_var\\\").clone();\\n var nm = model.get_buffer(\\\"stack.1.num_batches_tracked\\\").clone();\\n\\n model.load(\\\"bug510.dat\\\");\\n\\n var w0_ = model.get_parameter(\\\"stack.0.weight\\\");\\n var w1_ = model.get_parameter(\\\"stack.1.weight\\\");\\n var b1_ = model.get_parameter(\\\"stack.1.bias\\\");\\n var rm_ = model.get_buffer(\\\"stack.1.running_mean\\\");\\n var rv_ = model.get_buffer(\\\"stack.1.running_var\\\");\\n var nm_ = model.get_buffer(\\\"stack.1.num_batches_tracked\\\");\\n\\n Assert.NotEqual(w0, w0_);\\n Assert.NotEqual(w1, w1_);\\n Assert.NotEqual(b1, b1_);\\n Assert.NotEqual(rm, rm_);\\n Assert.NotEqual(rv, rv_);\\n Assert.Equal(1, nm.item<long>());\\n Assert.Equal(0, nm_.item<long>());\\n }\\n\\n internal class Module510 : Module<Tensor, Tensor>\\n {\\n private readonly Module<Tensor, Tensor> stack;\\n\\n public Module510(int in_channels, int out_channels, int kernel_size = 3, int stride = 1, int padding = 0) : base(String.Empty)\\n {\\n var temp = BatchNorm1d(out_channels);\\n this.stack = Sequential(\\n Conv1d(in_channels, out_channels, 3, stride: stride, padding: padding, bias: false),\\n temp,\\n ReLU(inplace: true)\\n );\\n\\n temp.weight = Parameter(torch.randn(temp.weight!.shape));\\n temp.bias = Parameter(torch.randn(temp.bias!.shape));\\n if (temp.running_mean is not null) temp.running_mean = torch.randn(temp.running_mean.shape);\\n if (temp.running_var is not null) temp.running_var = torch.randn(temp.running_var.shape);\\n\\n this.RegisterComponents();\\n }\\n\\n public override torch.Tensor forward(torch.Tensor t)\\n {\\n return this.stack.call(t);\\n }\\n }\\n\\n [Fact]\\n public void ValidateIssue516()\\n {\\n if (torch.cuda.is_available()) {\\n using var _ = NewDisposeScope();\\n\\n var model = new TestGradWarningModel();\\n model.cuda();\\n\\n var optimizer = torch.optim.Adam(model.parameters());\\n optimizer.zero_grad();\\n\\n var x = torch.ones(5, 3).cuda();\\n var y = torch.ones(5, 4).cuda();\\n\\n var z = model.call(x);\\n var lossFunc = torch.nn.CrossEntropyLoss();\\n var loss = lossFunc.call(y, z);\\n loss.backward();\\n optimizer.step();\\n\\n var grad1 = optimizer.parameters().ToArray()[0].grad();\\n Assert.NotNull(grad1);\\n\\n var grad2 = model.Weight.grad();\\n Assert.NotNull(grad2);\\n }\\n }\\n\\n internal abstract class BaseModule : torch.nn.Module<Tensor, Tensor>\\n {\\n public int? InstanceId = null;\\n\\n protected BaseModule(string name) : base(name)\\n {\\n }\\n }\\n\\n public class TestGradWarningModel : torch.nn.Module<Tensor, Tensor>\\n {\\n public readonly Modules.Parameter Weight;\\n\\n public TestGradWarningModel() : base(nameof(TestGradWarningModel))\\n {\\n Weight = torch.zeros(new long[] { 3, 4 }).AsParameter();\\n RegisterComponents();\\n }\\n\\n public override torch.Tensor forward(torch.Tensor t)\\n {\\n return torch.matmul(t, Weight);\\n }\\n }\\n\\n [Fact]\\n public void Validate532()\\n {\\n using var _ = NewDisposeScope();\\n var module = new Module532(1000, 100);\\n\\n var p = module.parameters().ToArray();\\n var pB = module.batch.parameters().ToArray();\\n var pC = module.conv.parameters().ToArray();\\n\\n Assert.Equal(pB.Length + pC.Length, p.Length);\\n }\\n\\n internal class Module532 : Module<Tensor, Tensor>\\n {\\n public Module<Tensor, Tensor> conv;\\n public Module<Tensor, Tensor> batch;\\n private Module<Tensor, Tensor> seq;\\n\\n public Module532(int in_channels, int out_channels) : base(String.Empty)\\n {\\n conv = Conv1d(in_channels, out_channels, 3);\\n batch = BatchNorm1d(out_channels);\\n seq = Sequential(\\n conv,\\n batch,\\n ReLU(inplace: true)\\n );\\n\\n this.RegisterComponents();\\n }\\n\\n public override torch.Tensor forward(torch.Tensor t)\\n {\\n return this.seq.call(t);\\n }\\n }\\n\\n\\n\\n [Fact]\\n public void Validate538()\\n {\\n var module = new Module538(1000, 100);\\n\\n var sd = module.state_dict();\\n\\n Assert.Equal(7, sd.Count);\\n\\n if (File.Exists(\\\"bug538.dat\\\")) File.Delete(\\\"bug538.dat\\\");\\n\\n module.save(\\\"bug538.dat\\\");\\n module.load(\\\"bug538.dat\\\");\\n\\n File.Delete(\\\"bug538.dat\\\");\\n }\\n\\n internal class Module538 : Module<Tensor, Tensor>\\n {\\n private Module<Tensor, Tensor> seq;\\n\\n public Module538(int in_channels, int out_channels) : base(String.Empty)\\n {\\n seq = Sequential(Conv2d(1, 32, 3),\\n BatchNorm2d(32),\\n ReLU(),\\n Flatten(),\\n LogSoftmax(1)\\n );\\n\\n RegisterComponents();\\n }\\n\\n public override torch.Tensor forward(torch.Tensor t)\\n {\\n return this.seq.call(t);\\n }\\n }\\n\\n [Fact]\\n public void GruModuleWorksWithoutPassingH0()\\n {\\n var device = torch.cuda.is_available() ? torch.CUDA : torch.CPU;\\n using var _ = NewDisposeScope();\\n using (Tensor input = torch.randn(new long[] { 5, 3, 10 }, device: device),\\n h0 = torch.randn(new long[] { 1, 3, 20 }, device: device))\\n using (var gru = GRU(10, 20)) {\\n gru.to(device);\\n var (output, hN) = gru.call(input);\\n Assert.Equal(h0.shape, hN.shape);\\n Assert.Equal(new long[] { input.shape[0], input.shape[1], 20 }, output.shape);\\n }\\n }\\n\\n [Fact]\\n public void LstmModuleWorksWithoutPassingH0C0()\\n {\\n var device = torch.cuda.is_available() ? torch.CUDA : torch.CPU;\\n using var _ = NewDisposeScope();\\n using (Tensor input = torch.randn(new long[] { 5, 3, 10 }, device: device),\\n h0 = torch.randn(new long[] { 1, 3, 20 }, device: device))\\n using (var lstm = LSTM(10, 20)) {\\n lstm.to(device);\\n var (output, hN, hX) = lstm.call(input);\\n Assert.Equal(h0.shape, hN.shape);\\n Assert.Equal(h0.shape, hX.shape);\\n Assert.Equal(new long[] { input.shape[0], input.shape[1], 20 }, output.shape);\\n }\\n }\\n\\n [Fact]\\n public void RnnModuleWorksWithoutPassingH0()\\n {\\n var device = torch.cuda.is_available() ? torch.CUDA : torch.CPU;\\n using var _ = NewDisposeScope();\\n using (Tensor input = torch.randn(new long[] { 5, 3, 10 }, device: device),\\n h0 = torch.randn(new long[] { 1, 3, 20 }, device: device))\\n using (var rnn = RNN(10, 20)) {\\n rnn.to(device);\\n var (output, hN) = rnn.call(input);\\n Assert.Equal(h0.shape, hN.shape);\\n Assert.Equal(new long[] { input.shape[0], input.shape[1], 20 }, output.shape);\\n }\\n }\\n\\n [Fact]\\n public void ValidateBug618()\\n {\\n if (torch.cuda.is_available()) {\\n using var _ = NewDisposeScope();\\n var mean = torch.randn(new long[] { 32, 3, 3 }, device: torch.CUDA);\\n var tmp = torch.randn(new long[] { 3, 3 }, device: torch.CUDA);\\n var log_std = tmp.expand_as(mean);\\n var std = exp(log_std);\\n var dist = torch.distributions.Normal(mean, std);\\n dist.sample();// error\\n }\\n }\\n\\n [Fact]\\n public void ValidateBug653()\\n {\\n var x = torch.linspace(0, 1, 100, dtype: torch.float32);\\n var y = torch.linspace(0, 1, 100, dtype: torch.float64);\\n var z = x.to(y);\\n Assert.Equal(torch.float64, z.dtype);\\n }\\n\\n [Fact]\\n public void ValidateBug679()\\n {\\n // Per bug report.\\n // https://github.com/dotnet/TorchSharp/issues/679\\n //\\n var dtype = torch.complex64;\\n var spec = torch.rand(1, 1024, 500, dtype: dtype);\\n Assert.Throws<System.Runtime.InteropServices.ExternalException>(() => torch.istft(spec, 512, 160, 400, null));\\n\\n spec = torch.rand(1, 512, 500, dtype: dtype);\\n var x = torch.istft(spec, 512, 160, 400, null);\\n\\n spec = torch.rand(1, 257, 500, dtype: dtype);\\n x = torch.istft(spec, 512, 160, 400, null);\\n\\n dtype = torch.complex128;\\n spec = torch.rand(1, 1024, 500, dtype: dtype);\\n Assert.Throws<System.Runtime.InteropServices.ExternalException>(() => torch.istft(spec, 512, 160, 400, null));\\n\\n spec = torch.rand(1, 512, 500, dtype: dtype);\\n x = torch.istft(spec, 512, 160, 400, null);\\n\\n spec = torch.rand(1, 257, 500, dtype: dtype);\\n x = torch.istft(spec, 512, 160, 400, null);\\n }\\n\\n [Fact]\\n public void ValidateBug715()\\n {\\n var resnet = resnet18();\\n var resnetlist = resnet.named_children();\\n var list = resnetlist.Take(6).Select(x => (x.name, (nn.Module<Tensor, Tensor>)x.module));\\n var bone = nn.Sequential(list);\\n\\n var x = torch.zeros(1, 3, 64, 160);\\n\\n // This should not blow up.\\n var tmp = bone.call(x);\\n }\\n\\n [Fact]\\n [TestOf(nameof(Modules.Categorical.probs))]\\n public void ValidateBug836()\\n {\\n int nSamples = Convert.ToInt32(5e3);\\n random.manual_seed(1);\\n Tensor actionLogits = ones(nSamples, 2);\\n Modules.Categorical distribution = new(logits: actionLogits);\\n Tensor actions = distribution.sample();\\n Tensor entropy = distribution.entropy();\\n Tensor log_prob = distribution.log_prob(actions);\\n var eMean = entropy.mean().ToSingle();\\n var lMean = log_prob.mean().ToSingle();\\n\\n Assert.Equal(0.693147, eMean, 0.0001);\\n Assert.Equal(-0.693147, lMean, 0.0001);\\n }\\n\\n [Fact]\\n [TestOf(nameof(torch.distributions.Bernoulli))]\\n public void ValidateBug838()\\n {\\n int nSamples = Convert.ToInt32(5e6);\\n random.manual_seed(1);\\n Tensor actionLogits = rand(nSamples, 2);\\n // This should not blow up.\\n var distribution = distributions.Bernoulli(logits: actionLogits);\\n }\\n\\n [Fact]\\n public void ValidateMultiStepLR()\\n {\\n var gen = new Generator(4711);\\n TestTraining.CreateLinearLayers(gen, out var lin1, out var lin2);\\n TestTraining.CreateDataAndLabels(gen, out var x, out var y);\\n\\n var seq = Sequential((\\\"lin1\\\", lin1), (\\\"relu1\\\", ReLU()), (\\\"lin2\\\", lin2));\\n\\n double learning_rate = 0.1;\\n var optimizer = torch.optim.SGD(seq.parameters(), learning_rate);\\n var scheduler = torch.optim.lr_scheduler.MultiStepLR(optimizer, new int[] { 3, 5, 7 }, 0.9);\\n\\n optimizer.zero_grad();\\n optimizer.step();\\n scheduler.step(1);\\n Assert.Equal(0.1, optimizer.ParamGroups.First().LearningRate);\\n scheduler.step(2);\\n Assert.Equal(0.1, optimizer.ParamGroups.First().LearningRate);\\n scheduler.step(3);\\n Assert.Equal(0.09, optimizer.ParamGroups.First().LearningRate, 0.00001);\\n scheduler.step(4);\\n Assert.Equal(0.09, optimizer.ParamGroups.First().LearningRate, 0.00001);\\n scheduler.step(5);\\n Assert.Equal(0.09 * 0.9, optimizer.ParamGroups.First().LearningRate, 0.00001);\\n scheduler.step(6);\\n scheduler.step(7);\\n Assert.Equal(0.09 * 0.9 * 0.9, optimizer.ParamGroups.First().LearningRate, 0.00001);\\n scheduler.step(8);\\n Assert.Equal(0.09 * 0.9 * 0.9, optimizer.ParamGroups.First().LearningRate, 0.00001);\\n scheduler.step(9);\\n Assert.Equal(0.09 * 0.9 * 0.9, optimizer.ParamGroups.First().LearningRate, 0.00001);\\n scheduler.step(10);\\n Assert.Equal(0.09 * 0.9 * 0.9, optimizer.ParamGroups.First().LearningRate, 0.00001);\\n }\\n\\n\\n\\n [Fact]\\n public void ValidatePolynomialLR()\\n {\\n {\\n // Linear decay\\n var gen = new Generator(4711);\\n TestTraining.CreateLinearLayers(gen, out var lin1, out var lin2);\\n TestTraining.CreateDataAndLabels(gen, out var x, out var y);\\n\\n var seq = Sequential((\\\"lin1\\\", lin1), (\\\"relu1\\\", ReLU()), (\\\"lin2\\\", lin2));\\n\\n double learning_rate = 0.1;\\n var optimizer = torch.optim.SGD(seq.parameters(), learning_rate);\\n var scheduler = torch.optim.lr_scheduler.PolynomialLR(optimizer, 10, 1);\\n\\n optimizer.zero_grad();\\n optimizer.step();\\n scheduler.step(1);\\n Assert.Equal(0.09, optimizer.ParamGroups.First().LearningRate, 0.00001);\\n scheduler.step(2);\\n Assert.Equal(0.08, optimizer.ParamGroups.First().LearningRate, 0.00001);\\n scheduler.step(3);\\n Assert.Equal(0.07, optimizer.ParamGroups.First().LearningRate, 0.00001);\\n scheduler.step(4);\\n Assert.Equal(0.06, optimizer.ParamGroups.First().LearningRate, 0.00001);\\n scheduler.step(5);\\n Assert.Equal(0.05, optimizer.ParamGroups.First().LearningRate, 0.00001);\\n scheduler.step(6);\\n scheduler.step(7);\\n Assert.Equal(0.03, optimizer.ParamGroups.First().LearningRate, 0.00001);\\n scheduler.step(8);\\n Assert.Equal(0.02, optimizer.ParamGroups.First().LearningRate, 0.00001);\\n scheduler.step(9);\\n Assert.Equal(0.01, optimizer.ParamGroups.First().LearningRate, 0.00001);\\n scheduler.step(10);\\n Assert.Equal(0.0, optimizer.ParamGroups.First().LearningRate, 0.00001);\\n }\\n {\\n // Squared decay\\n var gen = new Generator(4711);\\n TestTraining.CreateLinearLayers(gen, out var lin1, out var lin2);\\n TestTraining.CreateDataAndLabels(gen, out var x, out var y);\\n\\n var seq = Sequential((\\\"lin1\\\", lin1), (\\\"relu1\\\", ReLU()), (\\\"lin2\\\", lin2));\\n\\n double learning_rate = 0.1;\\n var optimizer = torch.optim.SGD(seq.parameters(), learning_rate);\\n var scheduler = torch.optim.lr_scheduler.PolynomialLR(optimizer, 10, 2);\\n\\n optimizer.zero_grad();\\n optimizer.step();\\n scheduler.step(1);\\n Assert.Equal(0.081, optimizer.ParamGroups.First().LearningRate, 0.00001);\\n scheduler.step(2);\\n Assert.Equal(0.064, optimizer.ParamGroups.First().LearningRate, 0.00001);\\n scheduler.step(3);\\n Assert.Equal(0.049, optimizer.ParamGroups.First().LearningRate, 0.00001);\\n scheduler.step(4);\\n Assert.Equal(0.036, optimizer.ParamGroups.First().LearningRate, 0.00001);\\n scheduler.step(5);\\n Assert.Equal(0.025, optimizer.ParamGroups.First().LearningRate, 0.00001);\\n scheduler.step(6);\\n scheduler.step(7);\\n Assert.Equal(0.009, optimizer.ParamGroups.First().LearningRate, 0.00001);\\n scheduler.step(8);\\n Assert.Equal(0.004, optimizer.ParamGroups.First().LearningRate, 0.00001);\\n scheduler.step(9);\\n Assert.Equal(0.001, optimizer.ParamGroups.First().LearningRate, 0.00001);\\n scheduler.step(10);\\n Assert.Equal(0.0, optimizer.ParamGroups.First().LearningRate, 0.00001);\\n }\\n }\\n\\n\\n [Fact]\\n public void Validate845()\\n {\\n var module1 = new Module845(10, 10);\\n var module2 = new Module845(10, 10);\\n var module3 = new Module845(10, 10);\\n\\n var dev = torch.cuda.is_available() ? CUDA : CPU;\\n\\n module1.to(dev);\\n module1.validate(float32, dev.type);\\n\\n module2.to(dev, float64);\\n module2.validate(float64, dev.type);\\n\\n module3.to(float64);\\n module3.validate(float64, CPU.type);\\n }\\n\\n internal class Module845 : Module<Tensor, Tensor>\\n {\\n private Module<Tensor, Tensor> seq;\\n\\n public Module845(int in_channels, int out_channels) : base(String.Empty)\\n {\\n seq = Sequential(Conv2d(1, 32, 3),\\n ReLU(),\\n Flatten(),\\n LogSoftmax(1)\\n );\\n\\n register_buffer(\\\"test\\\", torch.ones(10, 10));\\n RegisterComponents();\\n }\\n\\n public override torch.Tensor forward(torch.Tensor t)\\n {\\n return this.seq.call(t);\\n }\\n\\n public void validate(ScalarType expected, DeviceType devType)\\n {\\n foreach (var (name, buffer) in named_buffers()) {\\n Assert.Equal(expected, buffer.dtype);\\n Assert.Equal(devType, buffer.device_type);\\n }\\n }\\n }\\n\\n [Fact]\\n public void Validate851()\\n {\\n var a = torch.tensor(new long[] { 100, 200, 300, 400 }, new long[] { 1, 4 });\\n a.print();\\n var str = a.ToString(TorchSharp.TensorStringStyle.Numpy);\\n Assert.Equal(\\\"[[100, 200, 300, 400]]\\\", str);\\n }\\n\\n [Fact]\\n public void Validate852()\\n {\\n float[] a = new float[12];\\n var x = torch.as_tensor(a);\\n }\\n\\n [Fact]\\n public void Validate877()\\n {\\n if (torch.cuda.is_available()) {\\n var device = torch.CUDA;\\n torch.TryInitializeDeviceType(device.type);\\n var train = new GitBlockTest(\\\"test\\\", device);\\n\\n var p0 = train.named_parameters().Select(p => p.name).ToArray();\\n\\n train.to(device);\\n\\n int named_parameters_1 = train.named_parameters().Count();\\n train.to(torch.CUDA);\\n int named_parameters_2 = train.named_parameters().Count();\\n\\n Assert.Equal(named_parameters_1, named_parameters_2);\\n }\\n }\\n\\n\\n class GitTestCnn : Module<Tensor, Tensor>\\n {\\n private readonly TorchSharp.Modules.Sequential layers0;\\n\\n public GitTestCnn(string name, Device? device = null) : base(name)\\n {\\n var modules = new List<(string, Module<Tensor, Tensor>)>();\\n modules.Add(($\\\"{name}-conv2d-1\\\", Conv2d(1, 4, kernelSize: (1L, 1L), stride: (1L, 1L), padding: (0L, 0L), paddingMode: PaddingModes.Replicate, bias: false)));\\n layers0 = Sequential(modules);\\n\\n RegisterComponents();\\n }\\n\\n public override Tensor forward(Tensor t)\\n {\\n var t1 = layers0.call(t).squeeze_(3);\\n return t1;\\n }\\n }\\n\\n class GitTestGru : Module<Tensor, Tensor>\\n {\\n private TorchSharp.Modules.GRU layers1;\\n private TorchSharp.Modules.GRU layers2;\\n private Tensor init_h0;\\n private Tensor init_h1;\\n\\n public GitTestGru(string name, Device? device = null) : base(name)\\n {\\n layers1 = nn.GRU(1, 4, batchFirst: true);\\n layers2 = nn.GRU(4, 4, batchFirst: true);\\n\\n var state_size = new long[] { 1, 1, 4 };\\n init_h0 = torch.nn.Parameter(torch.zeros(state_size, device: device));\\n init_h1 = torch.nn.Parameter(torch.zeros(state_size, device: device));\\n\\n RegisterComponents();\\n }\\n\\n public override Tensor forward(Tensor input)\\n {\\n var (rnn_output, states_h0) = layers1.call(input, init_h0);\\n init_h0 = states_h0.detach_();\\n var (_, states_h1) = layers2.call(rnn_output, init_h1);\\n init_h1 = states_h1.detach_();\\n var x2 = states_h1[-1];\\n return x2;\\n }\\n }\\n\\n class GitBlockTest : Module<Tensor, Tensor>\\n {\\n private readonly Module<Tensor, Tensor> sequence_layers;\\n\\n public GitBlockTest(string name, Device? device = null) : base(name)\\n {\\n var modules = nn.ModuleDict<Module<Tensor, Tensor>>();\\n modules.Add((\\\"cnn-1\\\", new GitTestCnn(\\\"GitTest\\\", device)));\\n modules.Add((\\\"rnn-2\\\", new GitTestGru(\\\"GitTestGru\\\", device)));\\n sequence_layers = Sequential(modules.values());\\n RegisterComponents();\\n }\\n\\n public override Tensor forward(Tensor input)\\n {\\n var t0 = sequence_layers.call(input);\\n return t0;\\n }\\n }\\n\\n\\n [Fact]\\n public void Validate912()\\n {\\n var test = new Test_912(2);\\n Dictionary<string, Tensor> sd = test.state_dict(); // No layer2.modules keyword\\n Assert.Contains(sd.Keys, k => k.StartsWith(\\\"layer2.modules\\\"));\\n }\\n\\n public class Test_912 : nn.Module\\n {\\n public nn.Module layer;\\n public nn.Module layer2;\\n\\n public Test_912(int layernum) : base(\\\"Test_912\\\")\\n {\\n layer = nn.Linear(16, 2);\\n layer2 = new Test2_912((from l in Enumerable.Range(0, layernum)\\n select new Test3_912()).ToArray(),\\n (from l in Enumerable.Range(0, layernum)\\n select new Test3_912()).ToArray());\\n this.RegisterComponents();\\n }\\n }\\n public class Test2_912 : nn.Module\\n {\\n public new Modules.ModuleList<Test3_912> modules;\\n public Modules.ModuleList<nn.Module> modules2;\\n public nn.Module layer;\\n public Test2_912(Test3_912[] ms, nn.Module[] ms2) : base(\\\"Test2_912\\\")\\n {\\n layer = nn.Linear(16, 2);\\n modules = nn.ModuleList(ms);\\n modules2 = nn.ModuleList(ms2);\\n this.RegisterComponents();\\n }\\n }\\n public class Test3_912 : nn.Module\\n {\\n public nn.Module layer;\\n public Test3_912() : base(\\\"Test3_912\\\")\\n {\\n layer = nn.Linear(16, 2);\\n this.RegisterComponents();\\n }\\n }\\n\\n [Fact]\\n public void Validate971()\\n {\\n var t = torch.tensor(new double[] { 0, 1, 2 });\\n var std_dev = t.std(false).item<double>();\\n\\n Assert.Equal(0.8165, std_dev, 0.0001);\\n }\\n\\n [Fact(Skip = \\\"Takes too long to run to completion.\\\")]\\n static void Validate1057()\\n {\\n if (torch.cuda_is_available()) {\\n var device = torch.CUDA;\\n\\n for (int i = 0; i < 25; i++) {\\n using (var _ = torch.NewDisposeScope()) {\\n var data = torch.randn(200000, 3, 32, 32).to(device).requires_grad_(true);\\n }\\n }\\n }\\n }\\n }\\n}\"\n },\n {\n \"alpha_fraction\": 0.5044707655906677,\n \"alphanum_fraction\": 0.505094587802887,\n \"avg_line_length\": 40.10256576538086,\n \"blob_id\": \"7c952ddcb84dd69e220e002731fd0d0550a52e77\",\n \"content_id\": \"992f951b9f2d0357343dac1797d74c6ab75010c0\",\n \"detected_licenses\": [\n \"MIT\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"C#\",\n \"length_bytes\": 4809,\n \"license_type\": \"permissive\",\n \"max_line_length\": 133,\n \"num_lines\": 117,\n \"path\": \"/src/TorchSharp/JIT/CompilationUnit.cs\",\n \"repo_name\": \"NiklasGustafsson/TorchSharp\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"// Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information.\\nusing System;\\nusing TorchSharp.PInvoke;\\nusing static TorchSharp.PInvoke.NativeMethods;\\n\\nnamespace TorchSharp\\n{\\n public static partial class torch\\n {\\n public static partial class jit\\n {\\n /// <summary>\\n /// Represents a TorchScript compilation unit, i.e. a Python script file.\\n /// </summary>\\n /// <example>\\n /// var cu = torch.jit.compile(@\\\"\\n /// def relu_script(a, b):\\n /// return torch.relu(a + b)\\n /// \\\");\\n ///\\n /// var y = cu.invoke(\\\"relu_script\\\", torch.randn(10));\\n /// </example>\\n /// <remarks>\\n /// Currently, scripts are limited to defining functions. Classes will be ignored.\\n /// </remarks>\\n public class CompilationUnit : IDisposable\\n {\\n internal CompilationUnit(IntPtr handle)\\n {\\n this.handle = handle;\\n }\\n\\n ~CompilationUnit() => Dispose(false);\\n\\n /// <summary>\\n /// Releases the storage.\\n /// </summary>\\n public void Dispose()\\n {\\n Dispose(true);\\n GC.SuppressFinalize(this);\\n }\\n\\n /// <summary>\\n /// Implements the .NET Dispose pattern.\\n /// </summary>\\n protected virtual void Dispose(bool disposing)\\n {\\n if (disposing && handle != IntPtr.Zero) {\\n handle = IntPtr.Zero;\\n }\\n }\\n\\n internal IntPtr handle;\\n\\n /// <summary>\\n /// Invoke a function from the compilation unit.\\n /// </summary>\\n /// <param name=\\\"name\\\">The name of the function.</param>\\n /// <param name=\\\"objs\\\">Function arguments.</param>\\n public object invoke(string name, params object[] objs)\\n {\\n if (string.IsNullOrEmpty(name)) throw new ArgumentNullException(\\\"method name\\\");\\n\\n TensorOrScalar[] ptrArray = null;\\n sbyte typeCode = 0;\\n\\n using (var parray = new IndexedPinnedArrays<TensorOrScalar>()) {\\n\\n var tRefsHandle = ScriptModule.DetermineArgumentTypeRefs(objs, out var count, parray);\\n\\n var allocated = parray.Count;\\n\\n THSJIT_CompilationUnit_Invoke(handle, name, tRefsHandle, count, parray.CreateArray, out typeCode, allocated);\\n torch.CheckForErrors();\\n ptrArray = parray[allocated];\\n\\n return ScriptModule.ProcessReturnValue(name, parray, ptrArray, typeCode);\\n }\\n }\\n\\n /// <summary>\\n /// Invoke a function from the compilation unit.\\n /// </summary>\\n /// <typeparam name=\\\"TResult\\\">The return type of the TorchScript function.</typeparam>\\n /// <param name=\\\"name\\\">The name of the function.</param>\\n /// <param name=\\\"inputs\\\">Function arguments.</param>\\n public TResult invoke<TResult>(string name, params object[] inputs) => (TResult)invoke(name, inputs);\\n\\n /// <summary>\\n /// Invoke a function from the compilation unit.\\n /// </summary>\\n /// <typeparam name=\\\"T\\\">The type of all function arguments.</typeparam>\\n /// <typeparam name=\\\"TResult\\\">The return type of the TorchScript function.</typeparam>\\n /// <param name=\\\"name\\\">The name of the function.</param>\\n /// <param name=\\\"inputs\\\">Function arguments.</param>\\n public TResult invoke<T, TResult>(string name, params T[] inputs) => (TResult)invoke(name, inputs);\\n }\\n\\n /// <summary>\\n /// Create a TorchScript compilation unit containing TorchScript-compliant Python from a string.\\n /// </summary>\\n /// <param name=\\\"script\\\">A string with Python code expressing a set of TorchScript functions.</param>\\n /// <returns></returns>\\n public static CompilationUnit compile(string script)\\n {\\n if (string.IsNullOrEmpty(script))\\n throw new ArgumentNullException(\\\"empty script\\\");\\n\\n var result = THSJIT_compile(script);\\n if (result == IntPtr.Zero)\\n CheckForErrors();\\n return new CompilationUnit(result);\\n }\\n }\\n }\\n}\\n\"\n },\n {\n \"alpha_fraction\": 0.5798611044883728,\n \"alphanum_fraction\": 0.5819978713989258,\n \"avg_line_length\": 39.25806427001953,\n \"blob_id\": \"92d5292413ef9a197ebc4f6e473e64722d84a8c2\",\n \"content_id\": \"68bff2b606ff3d1079d5939b2eb016ccdf3d80fe\",\n \"detected_licenses\": [\n \"MIT\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"C#\",\n \"length_bytes\": 3744,\n \"license_type\": \"permissive\",\n \"max_line_length\": 149,\n \"num_lines\": 93,\n \"path\": \"/src/TorchSharp/Distributions/HalfCauchy.cs\",\n \"repo_name\": \"NiklasGustafsson/TorchSharp\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"// Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information.\\nusing System;\\nusing static TorchSharp.torch;\\n\\nnamespace TorchSharp\\n{\\n using Modules;\\n using static torch.distributions;\\n\\n namespace Modules\\n {\\n /// <summary>\\n /// Creates a half-Cauchy distribution parameterized by `scale`\\n /// </summary>\\n public class HalfCauchy : TransformedDistribution\\n {\\n internal HalfCauchy(Tensor scale, torch.Generator generator = null) :\\n base(Cauchy(torch.tensor(0).to(scale.dtype), scale, generator), new torch.distributions.transforms.AbsTransform(), generator)\\n {\\n this.scale = scale?.alias().DetachFromDisposeScope();\\n }\\n\\n public Tensor scale { get; private set; }\\n\\n public override Tensor mean => torch.full(ExtendedShape(), double.PositiveInfinity, dtype: scale.dtype, device: scale.device);\\n\\n public override Tensor mode => torch.zeros_like(scale);\\n\\n public override Tensor variance => base_distribution.variance;\\n\\n public override Tensor log_prob(Tensor value)\\n {\\n using var _ = torch.NewDisposeScope();\\n value = torch.as_tensor(value, scale.dtype, scale.device);\\n var lp = base_distribution.log_prob(value) + Math.Log(2);\\n lp = torch.where(value >= 0, lp, double.NegativeInfinity);\\n return lp.MoveToOuterDisposeScope();\\n }\\n\\n public override Tensor cdf(Tensor value)\\n {\\n return 2 * base_distribution.cdf(value) - 1;\\n }\\n\\n public override Tensor icdf(Tensor value)\\n {\\n return base_distribution.icdf((value + 1) / 2);\\n }\\n\\n /// <summary>\\n /// Returns entropy of distribution, batched over batch_shape.\\n /// </summary>\\n public override Tensor entropy()\\n {\\n return base_distribution.entropy() - Math.Log(2);\\n }\\n\\n /// <summary>\\n /// Returns a new distribution instance (or populates an existing instance provided by a derived class) with batch dimensions expanded to\\n /// `batch_shape`. This method calls `torch.Tensor.expand()` on the distribution's parameters. As such, this does not allocate new\\n /// memory for the expanded distribution instance.\\n /// </summary>\\n /// <param name=\\\"batch_shape\\\">Tthe desired expanded size.</param>\\n /// <param name=\\\"instance\\\">new instance provided by subclasses that need to override `.expand`.</param>\\n public override Distribution expand(Size batch_shape, Distribution instance = null)\\n {\\n var newDistribution = ((instance == null)\\n ? new HalfCauchy(scale.expand(batch_shape), generator)\\n : instance) as HalfCauchy;\\n return base.expand(batch_shape, newDistribution);\\n }\\n }\\n }\\n\\n\\n public static partial class torch\\n {\\n public static partial class distributions\\n {\\n /// <summary>\\n /// Creates a half-Cauchy distribution parameterized by `scale`\\n /// </summary>\\n /// <param name=\\\"scale\\\">Scale parameter of the distribution.</param>\\n /// <param name=\\\"generator\\\">An optional random number generator object.</param>\\n /// <returns></returns>\\n public static HalfCauchy HalfCauchy(Tensor scale, torch.Generator generator = null)\\n {\\n\\n return new HalfCauchy(scale, generator);\\n }\\n }\\n }\\n}\\n\"\n },\n {\n \"alpha_fraction\": 0.5810214281082153,\n \"alphanum_fraction\": 0.5895333886146545,\n \"avg_line_length\": 43.05555725097656,\n \"blob_id\": \"2e9c9148c01ddcfbb74ec7390dc91c8e9407c38f\",\n \"content_id\": \"e06eecafa156e8d3ec4894a11ff3aff1382bf5d6\",\n \"detected_licenses\": [\n \"MIT\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"C#\",\n \"length_bytes\": 3172,\n \"license_type\": \"permissive\",\n \"max_line_length\": 169,\n \"num_lines\": 72,\n \"path\": \"/src/TorchSharp/NN/Dropout1d.cs\",\n \"repo_name\": \"NiklasGustafsson/TorchSharp\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"// Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information.\\nusing System;\\nusing static TorchSharp.torch;\\nusing static TorchSharp.PInvoke.NativeMethods;\\n\\nnamespace TorchSharp\\n{\\n using Modules;\\n\\n namespace Modules\\n {\\n /// <summary>\\n /// This class is used to represent a Dropout2d module.\\n /// </summary>\\n public sealed class Dropout1d : torch.nn.Module<Tensor, Tensor>\\n {\\n internal Dropout1d(double p = 0.5, bool inplace = false) : base(nameof(Dropout1d))\\n {\\n this.p = p;\\n this.inplace = inplace;\\n }\\n\\n public override Tensor forward(Tensor tensor)\\n {\\n if (tensor.ndim != 3)\\n throw new ArgumentException(\\\"tensor passed to Dropout1d must be of the shape (N,C,L\\\");\\n return torch.nn.functional.dropout1d(tensor, this.p, this.training, this.inplace);\\n }\\n\\n // Rather than spending cycles only to discover that this module has neither\\n // parameters nor buffers, just shortcut the move completely.\\n protected internal override nn.Module _to(Device device, ScalarType dtype) => this;\\n protected internal override nn.Module _to(DeviceType deviceType, int deviceIndex = -1) => this;\\n protected internal override nn.Module _to(ScalarType dtype) => this;\\n\\n private bool inplace;\\n private double p;\\n }\\n }\\n\\n public static partial class torch\\n {\\n public static partial class nn\\n {\\n /// <summary>\\n /// Randomly zero out entire channels (a channel is a 2D feature map, e.g., the jj -th channel of the ii -th sample in the batched input is a 2D tensor).\\n /// Each channel will be zeroed out independently on every forward call with probability p using samples from a Bernoulli distribution.\\n /// </summary>\\n /// <param name=\\\"p\\\">Probability of an element to be zeroed. Default: 0.5</param>\\n /// <param name=\\\"inplace\\\">If set to true, will do this operation in-place. Default: false</param>\\n /// <returns></returns>\\n public static Dropout1d Dropout1d(double p = 0.5, bool inplace = false)\\n {\\n return new Dropout1d(p, inplace);\\n }\\n\\n public static partial class functional\\n {\\n\\n /// <summary>\\n /// Randomly zero out entire channels (a channel is a 2D feature map, e.g., the jj -th channel of the ii -th sample in the batched input is a 2D tensor).\\n /// Each channel will be zeroed out independently on every forward call with probability p using samples from a Bernoulli distribution.\\n /// </summary>\\n /// <returns></returns>\\n public static Tensor dropout1d(Tensor input, double p = 0.5, bool training = true, bool inplace = false)\\n {\\n return dropout2d(input.unsqueeze(-1), p, training, inplace).squeeze(-1);\\n }\\n }\\n }\\n }\\n}\\n\"\n },\n {\n \"alpha_fraction\": 0.653333306312561,\n \"alphanum_fraction\": 0.653333306312561,\n \"avg_line_length\": 29.882352828979492,\n \"blob_id\": \"34210d34ce6bcbcf692763b5f024f55128db13dc\",\n \"content_id\": \"22956e9b3b88cef5015cb37011ee0697624b5ebf\",\n \"detected_licenses\": [\n \"MIT\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"C#\",\n \"length_bytes\": 525,\n \"license_type\": \"permissive\",\n \"max_line_length\": 130,\n \"num_lines\": 17,\n \"path\": \"/src/TorchVision/ITransform.cs\",\n \"repo_name\": \"NiklasGustafsson/TorchSharp\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"// Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information.\\n\\nusing static TorchSharp.torch;\\n\\nnamespace TorchSharp\\n{\\n public static partial class torchvision\\n {\\n /// <summary>\\n /// This is essentially an alias. We're keeping it because it was\\n /// introduced and thus leaving it in will avoid a breaking change.\\n /// </summary>\\n public interface ITransform : nn.IModule<Tensor, Tensor>\\n {\\n }\\n }\\n}\\n\"\n },\n {\n \"alpha_fraction\": 0.4776082932949066,\n \"alphanum_fraction\": 0.48599597811698914,\n \"avg_line_length\": 61.72922897338867,\n \"blob_id\": \"3ef1848b6a52f29192832d6835a59db4e24c58b4\",\n \"content_id\": \"dd43d825d25a8ffed0cae3943d69322edb6a62f2\",\n \"detected_licenses\": [\n \"MIT\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"C#\",\n \"length_bytes\": 20387,\n \"license_type\": \"permissive\",\n \"max_line_length\": 198,\n \"num_lines\": 325,\n \"path\": \"/src/TorchSharp/Utils/tensorboard/Summary.cs\",\n \"repo_name\": \"NiklasGustafsson/TorchSharp\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"// Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information.\\nusing System;\\nusing System.IO;\\nusing Google.Protobuf;\\nusing SkiaSharp;\\n\\nnamespace TorchSharp\\n{\\n public static partial class torch\\n {\\n public static partial class utils\\n {\\n public static partial class tensorboard\\n {\\n public static partial class Summary\\n {\\n private static int calc_scale_factor(Tensor tensor)\\n => tensor.dtype == ScalarType.Byte || tensor.dtype == ScalarType.Int8 ? 1 : 255;\\n\\n /// <summary>\\n /// Outputs a `Summary` protocol buffer with a histogram.\\n /// The generated\\n /// [`Summary`](https://www.tensorflow.org/code/tensorflow/core/framework/summary.proto)\\n /// has one summary value containing a histogram for `values`.\\n /// This op reports an `InvalidArgument` error if any value is not finite.\\n ///\\n /// https://github.com/pytorch/pytorch/blob/1.7/torch/utils/tensorboard/summary.py#L283\\n /// </summary>\\n /// <param name=\\\"name\\\"> A name for the generated node. Will also serve as a series name in TensorBoard. </param>\\n /// <param name=\\\"values\\\"> A real numeric `Tensor`. Any shape. Values to use to build the histogram. </param>\\n /// <param name=\\\"bins\\\"></param>\\n /// <param name=\\\"max_bins\\\"></param>\\n /// <returns> A scalar `Tensor` of type `string`. The serialized `Summary` protocol buffer. </returns>\\n public static Tensorboard.Summary histogram(string name, Tensor values, Tensor bins, long? max_bins = null)\\n {\\n if (values.device.type != DeviceType.CPU)\\n values = values.cpu();\\n Tensorboard.HistogramProto hist = make_histogram(values, bins, max_bins);\\n var summary = new Tensorboard.Summary();\\n summary.Value.Add(new Tensorboard.Summary.Types.Value() { Tag = name, Histo = hist });\\n return summary;\\n }\\n\\n /// <summary>\\n /// Outputs a `Summary` protocol buffer with a histogram.\\n /// The generated\\n /// [`Summary`](https://www.tensorflow.org/code/tensorflow/core/framework/summary.proto)\\n /// has one summary value containing a histogram for `values`.\\n /// This op reports an `InvalidArgument` error if any value is not finite.\\n ///\\n /// https://github.com/pytorch/pytorch/blob/1.7/torch/utils/tensorboard/summary.py#L283\\n /// </summary>\\n /// <param name=\\\"name\\\"> A name for the generated node. Will also serve as a series name in TensorBoard. </param>\\n /// <param name=\\\"values\\\"> A real numeric `Tensor`. Any shape. Values to use to build the histogram. </param>\\n /// <param name=\\\"bins\\\"></param>\\n /// <param name=\\\"max_bins\\\"></param>\\n /// <returns> A scalar `Tensor` of type `string`. The serialized `Summary` protocol buffer. </returns>\\n public static Tensorboard.Summary histogram(string name, Tensor values, HistogramBinSelector bins, long? max_bins = null)\\n {\\n Tensorboard.HistogramProto hist = make_histogram(values, bins, max_bins);\\n var summary = new Tensorboard.Summary();\\n summary.Value.Add(new Tensorboard.Summary.Types.Value() { Tag = name, Histo = hist });\\n return summary;\\n }\\n\\n /// <summary>\\n /// Convert values into a histogram proto using logic from histogram.cc.\\n ///\\n /// https://github.com/pytorch/pytorch/blob/1.7/torch/utils/tensorboard/summary.py#L304\\n /// </summary>\\n /// <param name=\\\"values\\\"></param>\\n /// <param name=\\\"bins\\\"></param>\\n /// <param name=\\\"max_bins\\\"></param>\\n /// <returns></returns>\\n /// <exception cref=\\\"ArgumentException\\\"></exception>\\n public static Tensorboard.HistogramProto make_histogram(Tensor values, Tensor bins, long? max_bins = null)\\n {\\n if (values.numel() == 0)\\n throw new ArgumentException(\\\"The input has no element.\\\");\\n if (values.dtype != ScalarType.Float64)\\n values = values.to_type(ScalarType.Float64);\\n values = values.reshape(-1);\\n (Tensor counts, Tensor limits) = torch.histogram(values, bins);\\n return make_histogram(values, counts, limits, max_bins);\\n }\\n\\n /// <summary>\\n /// Convert values into a histogram proto using logic from histogram.cc.\\n ///\\n /// https://github.com/pytorch/pytorch/blob/1.7/torch/utils/tensorboard/summary.py#L304\\n /// </summary>\\n /// <param name=\\\"values\\\"></param>\\n /// <param name=\\\"bins\\\"></param>\\n /// <param name=\\\"max_bins\\\"></param>\\n /// <returns></returns>\\n /// <exception cref=\\\"ArgumentException\\\"></exception>\\n public static Tensorboard.HistogramProto make_histogram(Tensor values, HistogramBinSelector bins, long? max_bins = null)\\n {\\n if (values.numel() == 0)\\n throw new ArgumentException(\\\"The input has no element.\\\");\\n if (values.dtype != ScalarType.Float64)\\n values = values.to_type(ScalarType.Float64);\\n values = values.reshape(-1);\\n (Tensor counts, Tensor limits) = torch.histogram(values, bins);\\n return make_histogram(values, counts, limits, max_bins);\\n }\\n\\n private static Tensorboard.HistogramProto make_histogram(Tensor values, Tensor counts, Tensor limits, long? max_bins = null)\\n {\\n long num_bins = counts.shape[0];\\n if (max_bins != null && num_bins > max_bins) {\\n long subsampling = num_bins / max_bins.Value;\\n long subsampling_remainder = num_bins % subsampling;\\n if (subsampling_remainder != 0)\\n counts = nn.functional.pad(counts, (0, subsampling - subsampling_remainder), PaddingModes.Constant, 0);\\n counts = counts.reshape(-1, subsampling).sum(1);\\n Tensor new_limits = empty(new long[] { counts.numel() + 1 }, limits.dtype);\\n new_limits[TensorIndex.Slice(null, -1)] = limits[TensorIndex.Slice(null, -1, subsampling)];\\n new_limits[-1] = limits[-1];\\n limits = new_limits;\\n }\\n\\n Tensor cum_counts = cumsum(greater(counts, 0), 0);\\n Tensor search_value = empty(2, cum_counts.dtype);\\n search_value[0] = 0;\\n search_value[1] = cum_counts[-1] - 1;\\n Tensor search_result = searchsorted(cum_counts, search_value, right: true);\\n long start = search_result[0].item<long>();\\n long end = search_result[1].item<long>() + 1;\\n counts = start > 0 ? counts[TensorIndex.Slice(start - 1, end)] : concatenate(new Tensor[] { tensor(new[] { 0 }), counts[TensorIndex.Slice(stop: end)] });\\n limits = limits[TensorIndex.Slice(start, end + 1)];\\n\\n if (counts.numel() == 0 || limits.numel() == 0)\\n throw new ArgumentException(\\\"The histogram is empty, please file a bug report.\\\");\\n\\n Tensor sum_sq = values.dot(values);\\n Tensorboard.HistogramProto histogramProto = new Tensorboard.HistogramProto() {\\n Min = values.min().item<double>(),\\n Max = values.max().item<double>(),\\n Num = values.shape[0],\\n Sum = values.sum().item<double>(),\\n SumSquares = sum_sq.item<double>(),\\n };\\n histogramProto.BucketLimit.AddRange(limits.data<double>().ToArray());\\n histogramProto.Bucket.AddRange(counts.data<double>().ToArray());\\n return histogramProto;\\n }\\n\\n /// <summary>\\n /// Outputs a `Summary` protocol buffer with images.\\n /// The summary has up to `max_images` summary values containing images. The\\n /// images are built from `tensor` which must be 3-D with shape `[height, width,\\n /// channels]` and where `channels` can be:\\n /// * 1: `tensor` is interpreted as Grayscale.\\n /// * 3: `tensor` is interpreted as RGB.\\n /// * 4: `tensor` is interpreted as RGBA.\\n /// The `name` in the outputted Summary.Value protobufs is generated based on the\\n /// name, with a suffix depending on the max_outputs setting:\\n /// * If `max_outputs` is 1, the summary value tag is '*name*/image'.\\n /// * If `max_outputs` is greater than 1, the summary value tags are\\n /// generated sequentially as '*name*/image/0', '*name*/image/1', etc.\\n /// </summary>\\n /// <param name=\\\"tag\\\"> A name for the generated node. Will also serve as a series name in TensorBoard. </param>\\n /// <param name=\\\"tensor\\\">\\n /// A 3-D `uint8` or `float32` `Tensor` of shape `[height, width,\\n /// channels]` where `channels` is 1, 3, or 4.\\n /// 'tensor' can either have values in [0, 1] (float32) or [0, 255] (uint8).\\n /// The image() function will scale the image values to [0, 255] by applying\\n /// a scale factor of either 1 (uint8) or 255 (float32). Out-of-range values\\n /// will be clipped.\\n /// </param>\\n /// <param name=\\\"rescale\\\"> Rescale image size </param>\\n /// <param name=\\\"dataformats\\\"> Image data format specification of the form CHW, HWC, HW, WH, etc. </param>\\n /// <returns> A scalar `Tensor` of type `string`. The serialized `Summary` protocol buffer. </returns>\\n public static Tensorboard.Summary image(string tag, Tensor tensor, double rescale = 1, string dataformats = \\\"NCHW\\\")\\n {\\n tensor = utils.convert_to_HWC(tensor, dataformats);\\n int scale_factor = calc_scale_factor(tensor);\\n tensor = tensor.to_type(ScalarType.Float32);\\n tensor = (tensor * scale_factor).clip(0, 255).to_type(ScalarType.Byte);\\n Tensorboard.Summary.Types.Image image = make_image(tensor, rescale);\\n var summary = new Tensorboard.Summary();\\n summary.Value.Add(new Tensorboard.Summary.Types.Value() { Tag = tag, Image = image });\\n return summary;\\n }\\n\\n /// <summary>\\n /// Outputs a `Summary` protocol buffer with images.\\n /// </summary>\\n /// <param name=\\\"tag\\\"> A name for the generated node. Will also serve as a series name in TensorBoard. </param>\\n /// <param name=\\\"file_name\\\"> local image filename </param>\\n /// <param name=\\\"rescale\\\"> Rescale image size </param>\\n /// <returns> A scalar `Tensor` of type `string`. The serialized `Summary` protocol buffer. </returns>\\n public static Tensorboard.Summary image(string tag, string file_name, double rescale = 1)\\n {\\n using var img = SKBitmap.Decode(file_name);\\n Tensorboard.Summary.Types.Image image = make_image(img, rescale);\\n var summary = new Tensorboard.Summary();\\n summary.Value.Add(new Tensorboard.Summary.Types.Value() { Tag = tag, Image = image });\\n return summary;\\n }\\n\\n /// <summary>\\n /// Convert a tensor representation of an image to Image protobuf\\n /// \\n /// https://github.com/pytorch/pytorch/blob/master/torch/utils/tensorboard/summary.py#L481\\n /// </summary>\\n /// <param name=\\\"tensor\\\"> HWC(0~255) image tensor </param>\\n /// <param name=\\\"rescale\\\"> Rescale image size </param>\\n /// <returns></returns>\\n public static Tensorboard.Summary.Types.Image make_image(Tensor tensor, double rescale = 1)\\n {\\n using SKBitmap skBmp = TensorToSKBitmap(tensor);\\n return make_image(skBmp, rescale);\\n }\\n\\n /// <summary>\\n /// Convert an image to Image protobuf\\n /// \\n /// https://github.com/pytorch/pytorch/blob/master/torch/utils/tensorboard/summary.py#L495\\n /// </summary>\\n /// <param name=\\\"img\\\"> Image </param>\\n /// <param name=\\\"rescale\\\"> Rescale image size </param>\\n /// <returns></returns>\\n internal static Tensorboard.Summary.Types.Image make_image(SKBitmap img, double rescale = 1)\\n {\\n using var image = img.Copy();\\n byte[] bmpData = image.Resize(new SKSizeI((int)(image.Width * rescale), (int)(image.Height * rescale)), SKFilterQuality.High).Encode(SKEncodedImageFormat.Png, 100).ToArray();\\n return new Tensorboard.Summary.Types.Image() { Height = image.Height, Width = image.Width, Colorspace = 4, EncodedImageString = ByteString.CopyFrom(bmpData) };\\n }\\n\\n /// <summary>\\n /// https://github.com/pytorch/pytorch/blob/master/torch/utils/tensorboard/summary.py#L509\\n /// </summary>\\n /// <param name=\\\"tag\\\"> A name for the generated node. Will also serve as a series name in TensorBoard. </param>\\n /// <param name=\\\"tensor\\\"> Video data </param>\\n /// <param name=\\\"fps\\\"> Frames per second </param>\\n /// <returns></returns>\\n public static Tensorboard.Summary video(string tag, Tensor tensor, int fps)\\n {\\n tensor = utils.prepare_video(tensor);\\n int scale_factor = calc_scale_factor(tensor);\\n tensor = tensor.to_type(ScalarType.Float32);\\n tensor = (tensor * scale_factor).clip(0, 255).to_type(ScalarType.Byte);\\n Tensorboard.Summary.Types.Image video = make_video(tensor, fps);\\n var summary = new Tensorboard.Summary();\\n summary.Value.Add(new Tensorboard.Summary.Types.Value() { Tag = tag, Image = video });\\n return summary;\\n }\\n\\n /// <summary>\\n /// https://github.com/pytorch/pytorch/blob/master/torch/utils/tensorboard/summary.py#L520\\n /// </summary>\\n /// <param name=\\\"tensor\\\"> Video data </param>\\n /// <param name=\\\"fps\\\"> Frames per second </param>\\n /// <returns></returns>\\n public static Tensorboard.Summary.Types.Image make_video(Tensor tensor, int fps)\\n {\\n int h = (int)tensor.shape[1];\\n int w = (int)tensor.shape[2];\\n using GifEncoder.Encoder encoder = new GifEncoder.Encoder();\\n encoder.Start();\\n encoder.SetRepeat(0);\\n encoder.SetFrameRate(fps);\\n foreach (var t in tensor.split(1)) {\\n using SKBitmap bitmap = TensorToSKBitmap(t.squeeze());\\n encoder.AddFrame(bitmap);\\n }\\n encoder.Finish();\\n Stream stream = encoder.Output();\\n stream.Position = 0;\\n return new Tensorboard.Summary.Types.Image() { Height = h, Width = w, Colorspace = 4, EncodedImageString = ByteString.FromStream(stream) };\\n }\\n\\n private static SKBitmap TensorToSKBitmap(Tensor tensor)\\n {\\n int h = (int)tensor.shape[0];\\n int w = (int)tensor.shape[1];\\n int c = (int)tensor.shape[2];\\n\\n byte[,,] data = tensor.cpu().data<byte>().ToNDArray() as byte[,,];\\n var skBmp = new SKBitmap(w, h, SKColorType.Rgba8888, SKAlphaType.Opaque);\\n int pixelSize = 4;\\n unsafe {\\n byte* pSkBmp = (byte*)skBmp.GetPixels().ToPointer();\\n for (int i = 0; i < h; i++) {\\n for (int j = 0; j < w; j++) {\\n pSkBmp[j * pixelSize] = data[i, j, 0];\\n pSkBmp[j * pixelSize + 1] = data[i, j, 1];\\n pSkBmp[j * pixelSize + 2] = data[i, j, 2];\\n pSkBmp[j * pixelSize + 3] = c == 4 ? data[i, j, 3] : (byte)255;\\n }\\n pSkBmp += skBmp.Info.RowBytes;\\n }\\n }\\n\\n return skBmp;\\n }\\n\\n /// <summary>\\n /// https://github.com/pytorch/pytorch/blob/master/torch/utils/tensorboard/summary.py#L630\\n /// </summary>\\n /// <param name=\\\"tag\\\"> Data identifier </param>\\n /// <param name=\\\"text\\\"> String to save </param>\\n /// <returns></returns>\\n public static Tensorboard.Summary text(string tag, string text)\\n {\\n // TextPluginData(version=0).SerializeToString()\\n // output: b''\\n var pluginData = new Tensorboard.SummaryMetadata.Types.PluginData() { PluginName = \\\"text\\\", Content = ByteString.CopyFromUtf8(\\\"\\\") };\\n var smd = new Tensorboard.SummaryMetadata() { PluginData = pluginData };\\n var shapeProto = new Tensorboard.TensorShapeProto();\\n shapeProto.Dim.Add(new Tensorboard.TensorShapeProto.Types.Dim() { Size = 1 });\\n var tensor = new Tensorboard.TensorProto() { Dtype = Tensorboard.DataType.DtString, TensorShape = shapeProto };\\n tensor.StringVal.Add(ByteString.CopyFromUtf8(text));\\n\\n var summary = new Tensorboard.Summary();\\n summary.Value.Add(new Tensorboard.Summary.Types.Value() { Tag = tag + \\\"/text_summary\\\", Metadata = smd, Tensor = tensor });\\n return summary;\\n }\\n }\\n }\\n }\\n }\\n}\\n\"\n },\n {\n \"alpha_fraction\": 0.48758187890052795,\n \"alphanum_fraction\": 0.488153338432312,\n \"avg_line_length\": 35.39680099487305,\n \"blob_id\": \"a578ff80f7aa6e51bd378213265001222032b3e8\",\n \"content_id\": \"b066a165a737a33ddd61a1bbb171c37ada852dfc\",\n \"detected_licenses\": [\n \"MIT\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"C#\",\n \"length_bytes\": 22749,\n \"license_type\": \"permissive\",\n \"max_line_length\": 186,\n \"num_lines\": 625,\n \"path\": \"/src/TorchSharp/Optimizers/Optimizer.cs\",\n \"repo_name\": \"NiklasGustafsson/TorchSharp\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"// Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information.\\nusing System;\\nusing System.Collections.Generic;\\nusing System.Linq;\\nusing System.Runtime.InteropServices;\\nusing static TorchSharp.torch;\\nusing static TorchSharp.PInvoke.NativeMethods;\\n\\nnamespace TorchSharp\\n{\\n using System.IO;\\n using Modules;\\n using TorchSharp.Utils;\\n\\n public static partial class torch\\n {\\n public static partial class optim\\n {\\n /// <summary>\\n /// Base class for all optimizers.\\n /// </summary>\\n public abstract partial class Optimizer : IDisposable\\n {\\n /// <summary>\\n /// Class wrapping PyTorch's optimzer object reference.\\n /// </summary>\\n internal sealed class HType : SafeHandle\\n {\\n public HType(IntPtr preexistingHandle, bool ownsHandle) : base(IntPtr.Zero, ownsHandle)\\n {\\n SetHandle(preexistingHandle);\\n }\\n\\n public override bool IsInvalid => handle == IntPtr.Zero;\\n\\n // This is just for marshalling\\n internal HType() : base(IntPtr.Zero, true)\\n {\\n }\\n\\n protected override bool ReleaseHandle()\\n {\\n THSNN_Optimizer_dispose(this);\\n return true;\\n }\\n\\n protected override void Dispose(bool disposing)\\n {\\n if (disposing) {\\n ReleaseHandle();\\n }\\n }\\n }\\n\\n internal HType handle;\\n\\n /// <summary>\\n /// Constructor used for optimizers implemented in native code.\\n /// </summary>\\n /// <param name=\\\"handle\\\"></param>\\n protected Optimizer(IntPtr handle)\\n {\\n if (handle != IntPtr.Zero) {\\n this.handle = new HType(handle, true);\\n }\\n }\\n\\n ~Optimizer()\\n {\\n Dispose(false);\\n }\\n\\n /// <summary>\\n /// Releases the storage.\\n /// </summary>\\n public void Dispose()\\n {\\n Dispose(true);\\n GC.SuppressFinalize(this);\\n }\\n\\n /// <summary>\\n /// Implements the .NET Dispose pattern.\\n /// </summary>\\n protected virtual void Dispose(bool disposing)\\n {\\n if (disposing && handle != null && !handle.IsInvalid) {\\n handle.Dispose();\\n handle.SetHandleAsInvalid();\\n }\\n }\\n\\n /// <summary>\\n /// Sets the gradients of all parameters to zero.\\n /// </summary>\\n public virtual void zero_grad()\\n {\\n THSNN_Optimizer_zero_grad(handle);\\n torch.CheckForErrors();\\n }\\n\\n [UnmanagedFunctionPointer(CallingConvention.Cdecl)]\\n public delegate IntPtr LossClosure();\\n\\n /// <summary>\\n /// Add a param group to the Optimizer s param_groups.\\n /// </summary>\\n /// <param name=\\\"param_group\\\"></param>\\n /// <remarks>This can be useful when fine tuning a pre-trained network as frozen layers can be made trainable and added to the Optimizer as training progresses.</remarks>\\n public virtual void add_param_group(ParamGroup param_group)\\n {\\n throw new NotImplementedException($\\\"add_param_group\\\");\\n }\\n\\n\\n /// <summary>\\n /// Performs a single optimization step (parameter update).\\n /// </summary>\\n /// <param name=\\\"closure\\\">A closure that reevaluates the model and returns the loss. Optional for most optimizers.</param>\\n /// <returns></returns>\\n public virtual Tensor step(Func<Tensor> closure = null)\\n {\\n IntPtr res = (closure == null) ?\\n THSNN_Optimizer_step(handle, null) :\\n THSNN_Optimizer_step(handle, () => {\\n return closure().DecoupleFromNativeHandle();\\n });\\n\\n if (res == IntPtr.Zero)\\n torch.CheckForErrors();\\n\\n return (res == IntPtr.Zero) ? null : new Tensor(res);\\n }\\n\\n /// <summary>\\n /// Get the parameters that the optimizer is handling.\\n /// </summary>\\n public virtual IEnumerable<Parameter> parameters()\\n {\\n IntPtr[] ptrArray;\\n\\n using (var pa = new PinnedArray<IntPtr>()) {\\n THSNN_Optimizer_getParameters(handle, pa.CreateArray);\\n torch.CheckForErrors();\\n ptrArray = pa.Array;\\n }\\n return ptrArray.Select(x => new Parameter(x));\\n }\\n\\n public class StateDictionary\\n {\\n internal StateDictionary() { Options = new List<OptimizerOptions>(); State = new List<OptimizerState>(); }\\n\\n public List<OptimizerOptions> Options { get; private set; }\\n\\n public List<OptimizerState> State { get; private set; }\\n }\\n\\n public virtual IEnumerable<ILearningRateController> ParamGroups {\\n get => _parameter_groups;\\n }\\n\\n protected IList<ParamGroup> _parameter_groups;\\n }\\n\\n /// <summary>\\n /// This interfce is used by learning rate schedulers to access and control\\n /// the rates used by optimizers.\\n /// </summary>\\n public interface ILearningRateController\\n {\\n /// <summary>\\n /// The current LR\\n /// </summary>\\n double LearningRate { set; get; }\\n\\n /// <summary>\\n /// The initial LR\\n /// </summary>\\n double InitialLearningRate { set; get; }\\n }\\n\\n /// <summary>\\n /// Indicates optimizers with support for momentum, which some LR schedulers require.\\n /// </summary>\\n public interface IMomentum\\n {\\n double Momentum { get; set; }\\n }\\n\\n /// <summary>\\n /// Indicates optimizers with support for betas instead of momentum.\\n /// </summary>\\n public interface IBetas\\n {\\n (double, double) Betas { get; set; }\\n }\\n }\\n }\\n\\n namespace Modules\\n {\\n using static torch.optim;\\n\\n /// <summary>\\n /// Base class to help with a couple of the things that managed-code implementations need.\\n /// </summary>\\n public abstract class OptimizerHelper : Optimizer\\n {\\n public OptimizerHelper() : base(IntPtr.Zero)\\n {\\n }\\n\\n\\n protected enum TypeCode\\n {\\n Double = 0,\\n Long = 1,\\n Tensor = 2,\\n Options = 3,\\n State = 4,\\n }\\n\\n /// <summary>\\n /// Saves the optimizer state.\\n /// </summary>\\n /// <param name=\\\"location\\\">The name of a file where optimizer state data will be stored.</param>\\n public void save_state_dict(string location)\\n {\\n using var stream = System.IO.File.Create(location);\\n using (var writer = new System.IO.BinaryWriter(stream)) {\\n\\n save_state_dict(writer);\\n writer.Flush();\\n stream.Flush();\\n }\\n }\\n\\n /// <summary>\\n /// Loads the optimizer state.\\n /// </summary>\\n /// <param name=\\\"location\\\">The name of a file where optimizer state data is stored.</param>\\n /// <remarks>\\n /// Optimizer state saved from PyTorch cannot be restored -- the file format is unique to TorchSharp.\\n /// </remarks>\\n public void load_state_dict(string location)\\n {\\n using var stream = System.IO.File.OpenRead(location);\\n using (var reader = new System.IO.BinaryReader(stream)) {\\n\\n load_state_dict(reader);\\n }\\n }\\n\\n /// <summary>\\n /// Saves the optimizer state.\\n /// </summary>\\n /// <param name=\\\"writer\\\">A binary writer connected to a stream where .</param>\\n public void save_state_dict(System.IO.BinaryWriter writer)\\n {\\n // Save the name of the optimizer, so that we can avoid problems.\\n writer.Write(this.GetType().Name);\\n\\n var sd = state_dict();\\n\\n writer.Encode(sd.Options.Count); // 4 bytes\\n writer.Encode(sd.State.Count); // 4 bytes\\n\\n foreach (var opts in sd.Options) {\\n\\n opts.SaveStateDict(writer);\\n }\\n\\n foreach (var state in sd.State) {\\n state.SaveStateDict(writer);\\n }\\n }\\n\\n /// <summary>\\n /// Loads the optimizer state.\\n /// </summary>\\n /// <param name=\\\"reader\\\">A binary reader connected to a stream containing saved optimizer state.</param>\\n /// <remarks>\\n /// Optimizer state saved from PyTorch cannot be restored -- the file format is unique to TorchSharp.\\n /// </remarks>\\n public void load_state_dict(System.IO.BinaryReader reader)\\n {\\n var optName = reader.ReadString();\\n if (optName != this.GetType().Name) {\\n throw new InvalidDataException($\\\"Mismatched optimizer type: expected '{this.GetType().Name}', but found '{optName}' in the loaded stream.\\\");\\n }\\n\\n // First, figure out how many entries.\\n var options = reader.Decode();\\n var states = reader.Decode();\\n\\n var sd = state_dict();\\n\\n if (options != sd.Options.Count) throw new ArgumentException(\\\"Invalid optimizer state -- different number of parameter groups.\\\");\\n if (states != sd.State.Count) throw new ArgumentException(\\\"Invalid optimizer state -- different number of states.\\\");\\n\\n for (var i = 0; i < options; i++) {\\n var opts = sd.Options[i] as OptimizerOptions;\\n opts.LoadStateDict(reader);\\n }\\n\\n for (var i = 0; i < states; i++) {\\n\\n var state = sd.State[i] as OptimizerState;\\n state.LoadStateDict(reader);\\n }\\n }\\n\\n /// <summary>\\n /// Loads the optimizer state.\\n /// </summary>\\n /// <param name=\\\"state_dict\\\">Optimizer state. Should be an object returned from a call to state_dict().</param>\\n /// <remarks>\\n /// The format of the optimizer state dict is different from PyTorch's. Instead of a dictionary with two entries,\\n /// the state is represented as record with two entries, both containing lists with state.\\n /// </remarks>\\n public virtual void load_state_dict(StateDictionary state_dict)\\n {\\n var sd = this.state_dict();\\n\\n if (state_dict.Options.Count != sd.Options.Count) throw new ArgumentException(\\\"Invalid optimizer state -- different number of parameter groups.\\\");\\n if (state_dict.State.Count != sd.State.Count) throw new ArgumentException(\\\"Invalid optimizer state -- different number of states.\\\");\\n\\n for (var i = 0; i < state_dict.Options.Count; i++) {\\n\\n var st_opts = sd.Options[i] as OptimizerOptions;\\n if (st_opts != state_dict.Options[i]) {\\n st_opts.LoadStateDict(state_dict.Options[i]);\\n }\\n }\\n\\n for (var i = 0; i < state_dict.State.Count; i++) {\\n\\n var st_state = sd.State[i] as OptimizerState;\\n if (st_state != state_dict.State[i]) {\\n st_state.LoadStateDict(state_dict.State[i]);\\n }\\n }\\n }\\n\\n /// <summary>\\n /// Returns the state of the optimizer as a dict.\\n /// </summary>\\n /// <returns></returns>\\n /// <remarks>\\n /// The format of the optimizer state dict is different from PyTorch's. Instead of a dictionary with two entries,\\n /// the state is represented as record with two entries, both containing lists with state.\\n /// </remarks>\\n public virtual StateDictionary state_dict()\\n {\\n var dict = new StateDictionary();\\n\\n int pgidx = -1;\\n int pridx = 0;\\n foreach (var pg in _parameter_groups) {\\n\\n dict.Options.Add(pg.Options);\\n\\n foreach (var p in pg.Parameters) {\\n\\n dict.State.Add(_state[p.Handle]);\\n pridx++;\\n }\\n pgidx--;\\n }\\n return dict;\\n }\\n\\n /// <summary>\\n /// Move all the state to the indicated device.\\n /// </summary>\\n /// <param name=\\\"device\\\">The device to move all state to.</param>\\n public void to(Device device)\\n {\\n foreach (var pg in _parameter_groups) {\\n foreach (var p in pg.Parameters) {\\n var state = _state[p.Handle];\\n state.to(device);\\n }\\n }\\n }\\n\\n /// <summary>\\n /// Sets the gradients of all parameters to zero.\\n /// </summary>\\n public override void zero_grad()\\n {\\n foreach (var g in _parameter_groups) {\\n\\n foreach (var p in g.Parameters) {\\n\\n using var grad = p.grad();\\n\\n if (grad is null) continue;\\n\\n grad.zero_().Dispose();\\n }\\n }\\n }\\n\\n /// <summary>\\n /// Support routine for implementation of step() in all optimizers that support parameter groups.\\n /// </summary>\\n /// <typeparam name=\\\"T\\\">The ParamGroup type in use</typeparam>\\n /// <param name=\\\"body\\\">The body of the step update.</param>\\n /// <param name=\\\"loss_closure\\\">The closure, if any, for computing the loss.</param>\\n /// <returns></returns>\\n protected Tensor _step<T>(Action<T> body, Func<Tensor> loss_closure = null) where T : ParamGroup\\n {\\n Tensor loss = null;\\n\\n if (loss_closure != null) {\\n using (var _ = torch.enable_grad())\\n loss = loss_closure();\\n }\\n\\n using (var _ = torch.no_grad()) {\\n\\n using (var d = torch.NewDisposeScope()) {\\n\\n foreach (var group in _parameter_groups) {\\n\\n body(group as T);\\n\\n }\\n }\\n }\\n\\n return loss;\\n }\\n\\n /// <summary>\\n /// Get the parameters that the optimizer is handling.\\n /// </summary>\\n public override IEnumerable<Parameter> parameters()\\n {\\n return _parameter_groups.SelectMany(pg => pg.Parameters);\\n }\\n\\n /// <summary>\\n /// Add a param group to the Optimizer s param_groups.\\n /// </summary>\\n /// <param name=\\\"param_group\\\"></param>\\n /// <remarks>This can be useful when fine tuning a pre-trained network as frozen layers can be made trainable and added to the Optimizer as training progresses.</remarks>\\n public override void add_param_group(ParamGroup param_group)\\n {\\n _parameter_groups.Add(param_group);\\n }\\n\\n protected OptimizerOptions _defaults;\\n\\n protected Utils.OrderedDict<IntPtr, OptimizerState> _state = new Utils.OrderedDict<IntPtr, OptimizerState>();\\n }\\n\\n /// <summary>\\n /// Base class for optimizer options.\\n /// </summary>\\n public class OptimizerOptions\\n {\\n public double? LearningRate { get; set; }\\n public double InitialLearningRate { get; set; }\\n\\n /// <summary>\\n /// Save the optimizer options (param-group hyperparameters) to a stream.\\n /// </summary>\\n /// <param name=\\\"writer\\\">A binary writer connected to a stream open for writing.</param>\\n public virtual void SaveStateDict(BinaryWriter writer)\\n {\\n writer.Write(InitialLearningRate);\\n writer.Write(LearningRate.Value);\\n }\\n\\n /// <summary>\\n /// Load the optimizer options (param-group hyperparameters) from a stream.\\n /// </summary>\\n /// <param name=\\\"reader\\\">A binary reader connected to a stream open for reading.</param>\\n public virtual void LoadStateDict(BinaryReader reader)\\n {\\n InitialLearningRate = reader.ReadDouble();\\n LearningRate = reader.ReadDouble();\\n }\\n\\n /// <summary>\\n /// Load optimizer options (param-group hyperparameters) from another optimizer.\\n /// </summary>\\n /// <param name=\\\"source\\\">An optimizer options record.</param>\\n public virtual void LoadStateDict(OptimizerOptions source)\\n {\\n InitialLearningRate = source.InitialLearningRate;\\n LearningRate = source.LearningRate;\\n }\\n }\\n\\n /// <summary>\\n /// Base class for optimizer options.\\n /// </summary>\\n public abstract class OptimizerState\\n {\\n /// <summary>\\n /// Save the optimizer parameter state to a stream.\\n /// </summary>\\n /// <param name=\\\"writer\\\">A binary writer connected to a stream open for writing.</param>\\n public abstract void SaveStateDict(BinaryWriter writer);\\n\\n /// <summary>\\n /// Load the optimizer parameter state from a stream.\\n /// </summary>\\n /// <param name=\\\"reader\\\">A binary reader connected to a stream open for reading.</param>\\n public abstract void LoadStateDict(BinaryReader reader);\\n\\n /// <summary>\\n /// Load optimizer parameter state from another optimizer.\\n /// </summary>\\n /// <param name=\\\"source\\\">An optimizer state record.</param>\\n public abstract void LoadStateDict(OptimizerState source);\\n\\n /// <summary>\\n /// Useful for tests, allows comparison of one state with another.\\n /// </summary>\\n /// <param name=\\\"other\\\">The other optimizer state</param>\\n /// <returns></returns>\\n public virtual bool ApproximatelyEquals(OptimizerState other)\\n {\\n return false;\\n }\\n\\n /// <summary>\\n /// Move all the state to the indicated device.\\n /// </summary>\\n /// <param name=\\\"device\\\">The device to move all state to.</param>\\n public virtual void to(Device device) { }\\n\\n protected static void LoadConditionalStateTensor(BinaryReader reader, ref Tensor result)\\n {\\n var hasTensor = reader.ReadBoolean();\\n\\n if (hasTensor) {\\n TensorExtensionMethods.Load(ref result, reader);\\n } else {\\n if (result is not null)\\n result.Dispose();\\n result = null;\\n }\\n }\\n\\n protected static void SaveConditionalStateTensor(BinaryWriter writer, Tensor tensor)\\n {\\n if (tensor is not null) {\\n writer.Write(true);\\n tensor.Save(writer);\\n } else {\\n writer.Write(false);\\n }\\n }\\n }\\n\\n /// <summary>\\n /// Base class for parameter groups\\n /// </summary>\\n public class ParamGroup : ILearningRateController\\n {\\n public IEnumerable<Parameter> Parameters { get; set; }\\n\\n public OptimizerOptions Options { get; set; }\\n\\n public double LearningRate { get => Options.LearningRate.Value; set => Options.LearningRate = value; }\\n public double InitialLearningRate { get => Options.InitialLearningRate; set => Options.InitialLearningRate = value; }\\n }\\n\\n /// <summary>\\n /// Generic-typed version of ParamGroup\\n /// </summary>\\n /// <typeparam name=\\\"TOptions\\\">The type of options used for the parameter group.</typeparam>\\n public class ParamGroup<TOptions> : ParamGroup where TOptions : OptimizerOptions\\n {\\n /// <summary>\\n /// Constructor\\n /// </summary>\\n public ParamGroup()\\n {\\n }\\n\\n /// <summary>\\n /// Constructor\\n /// </summary>\\n /// <param name=\\\"parameters\\\">The parameters of the parameter group</param>\\n /// <param name=\\\"options\\\">The options of the parameter group</param>\\n public ParamGroup(IEnumerable<Parameter> parameters, TOptions options = null)\\n {\\n base.Parameters = parameters;\\n base.Options = options;\\n }\\n\\n /// <summary>\\n /// Parameter group options / hyperparameters, used to control the optimizer algorithm.\\n /// </summary>\\n public new TOptions Options { get => (TOptions)base.Options; set => base.Options = value; }\\n }\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n }\\n}\\n\\n\"\n },\n {\n \"alpha_fraction\": 0.5719128251075745,\n \"alphanum_fraction\": 0.5807909369468689,\n \"avg_line_length\": 43.25,\n \"blob_id\": \"a8e08fbc253d7fb6ad589ca2e079b3f927a8ce09\",\n \"content_id\": \"76176217c1541cfdc23d0ae099f5fe31c6d8b0eb\",\n \"detected_licenses\": [\n \"MIT\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"C#\",\n \"length_bytes\": 6197,\n \"license_type\": \"permissive\",\n \"max_line_length\": 149,\n \"num_lines\": 140,\n \"path\": \"/src/TorchSharp/Distributions/Beta.cs\",\n \"repo_name\": \"NiklasGustafsson/TorchSharp\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"// Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information.\\nusing System;\\nusing System.Collections.Generic;\\nusing static TorchSharp.torch;\\n\\nnamespace TorchSharp\\n{\\n using Modules;\\n\\n namespace Modules\\n {\\n /// <summary>\\n /// A Beta distribution parameterized by concentration1 and concentration0.\\n /// </summary>\\n public class Beta : torch.distributions.ExponentialFamily\\n {\\n /// <summary>\\n /// The mean of the distribution.\\n /// </summary>\\n public override Tensor mean => concentration1 / (concentration1 + concentration0);\\n\\n public override Tensor mode => dirichlet.mode[TensorIndex.Ellipsis, 0];\\n\\n /// <summary>\\n /// The variance of the distribution\\n /// </summary>\\n public override Tensor variance {\\n get {\\n using var _ = NewDisposeScope();\\n var total = concentration0 + concentration1;\\n return (concentration1 * concentration0 / (total.pow(2) * (total + 1))).MoveToOuterDisposeScope();\\n }\\n }\\n\\n // Note that the order of the arguments is not a mistake -- the original source has them\\n // ordered this way.\\n\\n public Beta(Tensor concentration1, Tensor concentration0, torch.Generator generator = null) : base(generator)\\n {\\n var bcast = torch.broadcast_tensors(concentration1, concentration0);\\n this.concentration1 = bcast[0].DetachFromDisposeScope();\\n this.concentration0 = bcast[1].DetachFromDisposeScope();\\n this.dirichlet = new Dirichlet(torch.stack(bcast, -1), generator);\\n this.batch_shape = this.dirichlet.batch_shape;\\n }\\n\\n\\n protected Dirichlet dirichlet;\\n private Tensor concentration1;\\n private Tensor concentration0;\\n\\n /// <summary>\\n /// Generates a sample_shape shaped reparameterized sample or sample_shape shaped batch of reparameterized samples\\n /// if the distribution parameters are batched.\\n /// </summary>\\n /// <param name=\\\"sample_shape\\\">The sample shape.</param>\\n public override Tensor rsample(params long[] sample_shape)\\n {\\n return dirichlet.rsample(sample_shape).select(-1, 0);\\n }\\n\\n /// <summary>\\n /// Returns the log of the probability density/mass function evaluated at `value`.\\n /// </summary>\\n /// <param name=\\\"value\\\"></param>\\n public override Tensor log_prob(Tensor value)\\n {\\n var heads_tails = torch.stack(new Tensor[] { value, 1.0 - value }, -1);\\n return dirichlet.log_prob(heads_tails);\\n }\\n\\n /// <summary>\\n /// Returns entropy of distribution, batched over batch_shape.\\n /// </summary>\\n public override Tensor entropy()\\n {\\n return dirichlet.entropy();\\n }\\n\\n /// <summary>\\n /// Returns a new distribution instance (or populates an existing instance provided by a derived class) with batch dimensions expanded to\\n /// `batch_shape`. This method calls `torch.Tensor.expand()` on the distribution's parameters. As such, this does not allocate new\\n /// memory for the expanded distribution instance.\\n /// </summary>\\n /// <param name=\\\"batch_shape\\\">Tthe desired expanded size.</param>\\n /// <param name=\\\"instance\\\">new instance provided by subclasses that need to override `.expand`.</param>\\n public override distributions.Distribution expand(Size batch_shape, distributions.Distribution instance = null)\\n {\\n if (instance != null && !(instance is Beta))\\n throw new ArgumentException(\\\"expand(): 'instance' must be a Beta distribution\\\");\\n\\n var c0 = concentration0.expand(batch_shape);\\n var c1 = concentration1.expand(batch_shape);\\n\\n var newDistribution = ((instance == null) ? new Beta(c1, c0, generator) : instance) as Beta;\\n\\n newDistribution.batch_shape = batch_shape;\\n if (newDistribution == instance) {\\n newDistribution.dirichlet = new Dirichlet(torch.stack(new Tensor[] { c1, c0 }, -1));\\n newDistribution.concentration1 = c1;\\n newDistribution.concentration0 = c0;\\n }\\n return newDistribution;\\n }\\n\\n protected override IList<Tensor> NaturalParams => new Tensor[] { concentration1, concentration0 };\\n\\n protected override Tensor MeanCarrierMeasure => new Tensor(IntPtr.Zero);\\n\\n protected override Tensor LogNormalizer(params Tensor[] parameters)\\n {\\n var x = parameters[0];\\n var y = parameters[1];\\n\\n return torch.lgamma(x) + torch.lgamma(y) - torch.lgamma(x + y);\\n }\\n }\\n\\n }\\n\\n public static partial class torch\\n {\\n public static partial class distributions\\n {\\n /// <summary>\\n /// Creates a Beta distribution parameterized by concentration1 and concentration0.\\n /// </summary>\\n /// <param name=\\\"concentration1\\\">1st concentration parameter of the distribution (often referred to as 'α')</param>\\n /// <param name=\\\"concentration0\\\">2nd concentration parameter of the distribution (often referred to as 'β')</param>\\n /// <param name=\\\"generator\\\">An optional random number generator object.</param>\\n /// <returns></returns>\\n /// <remarks>The order of the arguments is not a mistake -- the original source has them ordered this way.\\n /// </remarks>\\n public static Beta Beta(Tensor concentration1, Tensor concentration0, torch.Generator generator = null)\\n {\\n return new Beta(concentration1, concentration0, generator);\\n }\\n }\\n }\\n}\\n\"\n },\n {\n \"alpha_fraction\": 0.5364921689033508,\n \"alphanum_fraction\": 0.537065327167511,\n \"avg_line_length\": 43.735042572021484,\n \"blob_id\": \"81e18130097109206aa20ebfad3e4a9cb77a3ff5\",\n \"content_id\": \"94bf275d8f44e544fe937d6d70b899a9e87e2369\",\n \"detected_licenses\": [\n \"MIT\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"C#\",\n \"length_bytes\": 5234,\n \"license_type\": \"permissive\",\n \"max_line_length\": 195,\n \"num_lines\": 117,\n \"path\": \"/src/TorchSharp/NN/Recurrent/RNNCell.cs\",\n \"repo_name\": \"NiklasGustafsson/TorchSharp\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"// Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information.\\nusing System;\\nusing static TorchSharp.torch;\\nusing static TorchSharp.torch.nn;\\nusing static TorchSharp.PInvoke.NativeMethods;\\n\\n#nullable enable\\nnamespace TorchSharp\\n{\\n using Modules;\\n\\n namespace Modules\\n {\\n public sealed class RNNCell : torch.nn.Module<Tensor, Tensor, Tensor>\\n {\\n internal RNNCell(IntPtr handle, IntPtr boxedHandle) : base(handle, boxedHandle)\\n {\\n }\\n\\n public new static RNNCell Load(string modelPath)\\n {\\n var res = Module<Tensor, Tensor>.Load(modelPath);\\n return new RNNCell(res.handle.DangerousGetHandle(), IntPtr.Zero);\\n }\\n\\n /// <summary>\\n /// Apply the RNN cell to an input tensor.\\n /// </summary>\\n /// <param name=\\\"input\\\">Tensor of shape (batch, input_size) containing the features of the input sequence.</param>\\n /// <param name=\\\"h0\\\">Tensor of shape (batch, hidden_size) containing the initial hidden state for each element in the batch.</param>\\n /// <returns></returns>\\n public override Tensor forward(Tensor input, Tensor? h0 = null)\\n {\\n var hN = THSNN_RNNCell_forward(handle, input.Handle, h0?.Handle ?? IntPtr.Zero);\\n if (hN == IntPtr.Zero) { torch.CheckForErrors(); }\\n return new Tensor(hN);\\n }\\n\\n public Parameter? bias_ih {\\n get {\\n var res = THSNN_RNNCell_bias_ih(handle);\\n if (res == IntPtr.Zero) { torch.CheckForErrors(); }\\n return ((res == IntPtr.Zero) ? null : new Parameter(res));\\n }\\n set {\\n THSNN_RNNCell_set_bias_ih(handle, (value is null ? IntPtr.Zero : value.Handle));\\n torch.CheckForErrors();\\n ConditionallyRegisterParameter(\\\"bias_ih\\\", value);\\n\\n }\\n }\\n\\n public Parameter? bias_hh {\\n get {\\n var res = THSNN_RNNCell_bias_hh(handle);\\n if (res == IntPtr.Zero) { torch.CheckForErrors(); }\\n return ((res == IntPtr.Zero) ? null : new Parameter(res));\\n }\\n set {\\n THSNN_RNNCell_set_bias_hh(handle, (value is null ? IntPtr.Zero : value.Handle));\\n torch.CheckForErrors();\\n ConditionallyRegisterParameter(\\\"bias_hh\\\", value);\\n\\n }\\n }\\n\\n public Parameter? weight_ih {\\n get {\\n var res = THSNN_RNNCell_weight_ih(handle);\\n if (res == IntPtr.Zero) { torch.CheckForErrors(); }\\n return (res == IntPtr.Zero) ? null : new Parameter(res);\\n }\\n set {\\n THSNN_RNNCell_set_weight_ih(handle, value is null ? IntPtr.Zero : value.Handle);\\n torch.CheckForErrors();\\n ConditionallyRegisterParameter(\\\"weight_ih\\\", value);\\n }\\n }\\n\\n public Parameter? weight_hh {\\n get {\\n var res = THSNN_RNNCell_weight_hh(handle);\\n if (res == IntPtr.Zero) { torch.CheckForErrors(); }\\n return (res == IntPtr.Zero) ? null : new Parameter(res);\\n }\\n set {\\n THSNN_RNNCell_set_weight_hh(handle, value is null ? IntPtr.Zero : value.Handle);\\n torch.CheckForErrors();\\n ConditionallyRegisterParameter(\\\"weight_hh\\\", value);\\n }\\n }\\n }\\n }\\n\\n public static partial class torch\\n {\\n public static partial class nn\\n {\\n /// <summary>\\n /// An Elman RNN cell with tanh or ReLU non-linearity.\\n /// </summary>\\n /// <param name=\\\"inputSize\\\">The number of expected features in the input x</param>\\n /// <param name=\\\"hiddenSize\\\">The number of features in the hidden state h</param>\\n /// <param name=\\\"nonLinearity\\\">The non-linearity to use. Can be either 'tanh' or 'relu'. Default: 'tanh'</param>\\n /// <param name=\\\"bias\\\">If False, then the layer does not use bias weights b_ih and b_hh. Default: True</param>\\n /// <param name=\\\"device\\\">The desired device of the parameters and buffers in this module</param>\\n /// <param name=\\\"dtype\\\">The desired floating point or complex dtype of the parameters and buffers in this module</param>\\n /// <returns></returns>\\n public static RNNCell RNNCell(long inputSize, long hiddenSize, NonLinearities nonLinearity = nn.NonLinearities.Tanh, bool bias = true, Device? device = null, ScalarType? dtype = null)\\n {\\n var res = THSNN_RNNCell_ctor(inputSize, hiddenSize, (long)nonLinearity, bias, out var boxedHandle);\\n if (res == IntPtr.Zero) { torch.CheckForErrors(); }\\n return new RNNCell(res, boxedHandle).MoveModule<RNNCell>(device, dtype);\\n }\\n }\\n }\\n}\\n\"\n },\n {\n \"alpha_fraction\": 0.4843258559703827,\n \"alphanum_fraction\": 0.48990610241889954,\n \"avg_line_length\": 47.12702941894531,\n \"blob_id\": \"1a6bd61fd5bb62cc7bd8207a5f7fa8e1292f0f29\",\n \"content_id\": \"fdf7cb47cda8967cba8593dd67522429b2a15ad2\",\n \"detected_licenses\": [\n \"MIT\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"C#\",\n \"length_bytes\": 82991,\n \"license_type\": \"permissive\",\n \"max_line_length\": 319,\n \"num_lines\": 1724,\n \"path\": \"/src/TorchSharp/NN/Module.cs\",\n \"repo_name\": \"NiklasGustafsson/TorchSharp\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"// Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information.\\nusing System;\\nusing System.Collections.Generic;\\nusing System.Diagnostics;\\nusing System.Linq;\\nusing System.Reflection;\\nusing System.Runtime.InteropServices;\\nusing TorchSharp.Modules;\\nusing static TorchSharp.torch;\\nusing static TorchSharp.Utils.LEB128Codec;\\nusing static TorchSharp.PInvoke.NativeMethods;\\n\\nnamespace TorchSharp\\n{\\n public static partial class torch\\n {\\n public static partial class nn\\n {\\n /// <summary>\\n /// Base class for all neural network modules.\\n /// Your models should subclass this class.\\n /// </summary>\\n /// <remarks>\\n /// Modules can also contain other Modules, allowing to nest them in a tree structure.\\n /// You can assign the submodules as regular fields of the derived Module class. Submodules assigned\\n /// to fields will be registered, and will have their parameters converted and moved when you call to(),\\n /// and saved to disk when calling save().\\n /// </remarks>\\n public class Module : IDisposable\\n {\\n /// <summary>\\n /// Class wrapping PyTorch's module object reference.\\n /// </summary>\\n protected internal sealed class HType : SafeHandle\\n {\\n public HType(IntPtr preexistingHandle, bool ownsHandle, Action<HType> dispose = null)\\n : base(IntPtr.Zero, ownsHandle)\\n {\\n _dispose = dispose ?? THSNN_Module_dispose;\\n SetHandle(preexistingHandle);\\n }\\n\\n public override bool IsInvalid => handle == IntPtr.Zero;\\n\\n // This is just for marshalling\\n internal HType() : base(IntPtr.Zero, true)\\n {\\n }\\n\\n protected override bool ReleaseHandle()\\n {\\n if (!IsInvalid) {\\n _dispose(this);\\n }\\n SetHandle(IntPtr.Zero);\\n return true;\\n }\\n\\n protected override void Dispose(bool disposing)\\n {\\n if (disposing) {\\n ReleaseHandle();\\n }\\n }\\n\\n private Action<HType> _dispose;\\n }\\n\\n internal HType handle;\\n\\n /// Stores the AnyModule corresponding to this module.\\n internal BoxedModule boxedModule;\\n\\n internal BoxedModule BoxedModule {\\n get {\\n if (boxedModule == null)\\n throw new InvalidOperationException(\\\"A Sequential or Loaded module may not be added to a Sequential\\\");\\n return boxedModule;\\n }\\n }\\n\\n internal Module(HType handle, IntPtr? boxedHandle)\\n {\\n this.handle = handle;\\n boxedModule = boxedHandle.HasValue ? new BoxedModule(boxedHandle.Value) : null;\\n\\n if (handle.IsInvalid) return;\\n register_p_and_b();\\n }\\n\\n internal Module(IntPtr handle, IntPtr? boxedHandle, bool ownsHandle = true)\\n {\\n this.handle = new HType(handle, ownsHandle);\\n boxedModule = boxedHandle.HasValue ? new BoxedModule(boxedHandle.Value) : null;\\n\\n if (handle == IntPtr.Zero) return;\\n register_p_and_b();\\n }\\n\\n private void register_p_and_b()\\n {\\n foreach (var (parameterName, parameter) in _named_parameters()) {\\n register_parameter(parameterName, parameter);\\n }\\n foreach (var (bufferName, buffer) in _named_buffers()) {\\n register_buffer(bufferName, buffer);\\n }\\n }\\n\\n ~Module() => Dispose(false);\\n\\n /// <summary>\\n /// Releases the storage.\\n /// </summary>\\n public void Dispose()\\n {\\n Dispose(true);\\n GC.SuppressFinalize(this);\\n }\\n\\n /// <summary>\\n /// Implements the .NET Dispose pattern.\\n /// </summary>\\n protected virtual void Dispose(bool disposing)\\n {\\n if (disposing && !handle.IsInvalid) {\\n\\n foreach (var (_, p) in named_buffers(false)) {\\n p.Dispose();\\n }\\n foreach (var (_, b) in named_parameters(false)) {\\n b.Dispose();\\n }\\n\\n foreach (var (_, m) in named_modules()) {\\n m.Dispose();\\n }\\n\\n handle.Dispose();\\n handle.SetHandleAsInvalid();\\n boxedModule?.Dispose();\\n }\\n }\\n\\n /// <summary>\\n /// Moves and converts the parameters and buffers.\\n /// </summary>\\n /// <param name=\\\"device\\\">The target device.</param>\\n /// <param name=\\\"dtype\\\">The target element type.</param>\\n protected internal virtual Module _to(Device device, ScalarType dtype)\\n {\\n if (device.type != DeviceType.CUDA) { device = new Device(device.type, -1); };\\n\\n if (device.type == DeviceType.CUDA && !torch.cuda.is_available()) throw new InvalidOperationException(\\\"CUDA is not available.\\\");\\n\\n InitializeDeviceType(device.type);\\n THSNN_Module_to_device_dtype(handle, (sbyte)dtype, (int)device.type, device.index);\\n CheckForErrors();\\n\\n _toEpilog(device, dtype);\\n\\n return this;\\n }\\n\\n protected void _toEpilog(Device device, ScalarType dtype)\\n {\\n foreach (var (_, sm) in named_children()) sm._to(device, dtype);\\n\\n var alreadyHandled = new HashSet<IntPtr>();\\n\\n foreach (var field in GetType().GetFields(BindingFlags.NonPublic | BindingFlags.Public | BindingFlags.Instance)) {\\n\\n var fieldName = field.Name;\\n var value = field.GetValue(this);\\n\\n switch (value) {\\n // This order in which these cases are arranged is significant.\\n case Parameter param when dtype == param.dtype && device.type == param.device_type && device.index == param.device_index:\\n alreadyHandled.Add(param.handle);\\n continue;\\n\\n case Parameter param: {\\n var t = param.to(dtype, device);\\n t.retain_grad();\\n var p = new Parameter(t, param.requires_grad);\\n field.SetValue(this, p);\\n ConditionallyRegisterParameter(fieldName, p);\\n alreadyHandled.Add(p.handle);\\n break;\\n }\\n\\n case Tensor tensor when (device.type != tensor.device_type || device.index != tensor.device_index): {\\n var t = tensor.to(dtype, device);\\n field.SetValue(this, t);\\n ConditionallyRegisterBuffer(fieldName, t);\\n alreadyHandled.Add(t.handle);\\n break;\\n }\\n\\n case Tensor tensor:\\n alreadyHandled.Add(tensor.handle);\\n break;\\n }\\n }\\n\\n foreach (var (name, param) in named_parameters(false).ToList()) {\\n if (alreadyHandled.Contains(param.handle)) continue;\\n var t = param.to(dtype, device);\\n ConditionallyRegisterParameter(name, t);\\n }\\n\\n foreach (var (name, buffer) in named_buffers(false).ToList()) {\\n if (alreadyHandled.Contains(buffer.handle)) continue;\\n var t = buffer.to(dtype, device);\\n ConditionallyRegisterBuffer(name, t);\\n }\\n\\n _deviceType = device.type;\\n _deviceIndex = device.index;\\n\\n Debug.Assert(_deviceType == DeviceType.CUDA || _deviceIndex == -1);\\n }\\n\\n\\n /// <summary>\\n /// Moves the parameters and buffers.\\n /// </summary>\\n /// <param name=\\\"deviceType\\\">The device type, e.g. 'CPU' or 'CUDA'.</param>\\n /// <param name=\\\"deviceIndex\\\">The optional device index.</param>\\n /// <returns></returns>\\n protected internal virtual Module _to(DeviceType deviceType, int deviceIndex = -1)\\n {\\n if (deviceType != DeviceType.CUDA) deviceIndex = -1;\\n\\n if (deviceType == DeviceType.CUDA && !torch.cuda.is_available()) throw new InvalidOperationException(\\\"CUDA is not available.\\\");\\n\\n if (deviceType != _deviceType || deviceIndex != _deviceIndex) {\\n\\n InitializeDeviceType(deviceType);\\n THSNN_Module_to_device(handle, (int)deviceType, deviceIndex);\\n CheckForErrors();\\n\\n _toEpilog(deviceType, deviceIndex);\\n }\\n\\n Debug.Assert(_deviceType == DeviceType.CUDA || _deviceIndex == -1);\\n\\n return this;\\n }\\n\\n protected void _toEpilog(DeviceType deviceType, int deviceIndex)\\n {\\n foreach (var (_, sm) in named_children()) sm._to(deviceType, deviceIndex);\\n\\n var alreadyHandled = new HashSet<IntPtr>();\\n\\n foreach (var field in GetType().GetFields(BindingFlags.NonPublic | BindingFlags.Public | BindingFlags.Instance)) {\\n\\n var fieldName = field.Name;\\n var value = field.GetValue(this);\\n\\n switch (value) {\\n // This order in which these cases are arranged is significant.\\n case Parameter param when deviceType == param.device_type && deviceIndex == param.device_index:\\n alreadyHandled.Add(param.handle);\\n continue;\\n\\n case Parameter param: {\\n var t = param.to(deviceType, deviceIndex);\\n t.retain_grad();\\n var p = new Parameter(t, param.requires_grad);\\n field.SetValue(this, p);\\n ConditionallyRegisterParameter(fieldName, p);\\n alreadyHandled.Add(p.handle);\\n break;\\n }\\n\\n case Tensor tensor when (deviceType != tensor.device_type || deviceIndex != tensor.device_index): {\\n var t = tensor.to(deviceType, deviceIndex);\\n field.SetValue(this, t);\\n ConditionallyRegisterBuffer(fieldName, t);\\n alreadyHandled.Add(t.handle);\\n break;\\n }\\n\\n case Tensor tensor:\\n alreadyHandled.Add(tensor.handle);\\n break;\\n }\\n }\\n\\n foreach (var (name, param) in named_parameters(false).ToList()) {\\n if (alreadyHandled.Contains(param.handle)) continue;\\n var t = param.to(deviceType, deviceIndex);\\n ConditionallyRegisterParameter(name, t);\\n }\\n\\n foreach (var (name, buffer) in named_buffers(false).ToList()) {\\n if (alreadyHandled.Contains(buffer.handle)) continue;\\n var t = buffer.to(deviceType, deviceIndex);\\n ConditionallyRegisterBuffer(name, t);\\n }\\n\\n _deviceType = deviceType;\\n _deviceIndex = deviceIndex;\\n }\\n\\n private DeviceType _deviceType = DeviceType.CPU;\\n private int _deviceIndex = -1;\\n\\n /// <summary>\\n /// Convert the parameters and buffers.\\n /// </summary>\\n /// <returns></returns>\\n protected internal virtual Module _to(ScalarType dtype)\\n {\\n THSNN_Module_to_dtype(handle, (sbyte)dtype);\\n CheckForErrors();\\n\\n _toEpilog(dtype);\\n\\n return this;\\n }\\n\\n protected void _toEpilog(ScalarType dtype)\\n {\\n foreach (var (_, sm) in named_children()) sm._to(dtype);\\n\\n var alreadyHandled = new HashSet<IntPtr>();\\n\\n foreach (var field in GetType().GetFields(BindingFlags.NonPublic | BindingFlags.Public | BindingFlags.Instance)) {\\n\\n var fieldName = field.Name;\\n var value = field.GetValue(this);\\n\\n switch (value) {\\n // This order in which these cases are arranged is significant.\\n case Parameter param when dtype == param.dtype:\\n alreadyHandled.Add(param.handle);\\n continue;\\n\\n case Parameter param: {\\n var t = param.to(dtype);\\n t.retain_grad();\\n var p = new Parameter(t, param.requires_grad);\\n field.SetValue(this, p);\\n ConditionallyRegisterParameter(fieldName, p);\\n alreadyHandled.Add(p.handle);\\n break;\\n }\\n\\n case Tensor tensor when dtype == tensor.dtype:\\n alreadyHandled.Add(tensor.handle);\\n continue;\\n\\n case Tensor tensor: {\\n var t = tensor.to(dtype);\\n field.SetValue(this, t);\\n ConditionallyRegisterBuffer(fieldName, t);\\n alreadyHandled.Add(t.handle);\\n break;\\n }\\n }\\n }\\n\\n foreach (var (name, param) in named_parameters(false).ToList()) {\\n if (alreadyHandled.Contains(param.handle)) continue;\\n var t = param.to(dtype);\\n ConditionallyRegisterParameter(name, t);\\n }\\n\\n foreach (var (name, buffer) in named_buffers(false).ToList()) {\\n if (alreadyHandled.Contains(buffer.handle)) continue;\\n var t = buffer.to(dtype);\\n ConditionallyRegisterBuffer(name, t);\\n }\\n }\\n\\n /// <summary>\\n /// Moves and converts the parameters and buffers.\\n /// </summary>\\n /// <param name=\\\"other\\\">The tensor serving as a template.</param>\\n /// <returns></returns>\\n public Module _to(Tensor other)\\n {\\n _to(other.dtype);\\n return _to(other.device_type, other.device_index);\\n }\\n\\n\\n\\n /// <summary>\\n /// Applies a function recursively to every submodule as well as this.\\n /// </summary>\\n /// <param name=\\\"fn\\\">Function to be applied to each submodule</param>\\n /// <returns></returns>\\n public virtual Module apply(Action<Module> fn)\\n {\\n foreach (var (_, m) in _internal_submodules) m.apply(fn);\\n fn(this);\\n return this;\\n }\\n\\n public static Module Load(string filename)\\n {\\n if (!System.IO.File.Exists(filename))\\n throw new System.IO.FileNotFoundException(filename);\\n\\n var handle = THSNN_Module_load(filename);\\n if (handle == IntPtr.Zero) { CheckForErrors(); }\\n return new Module(handle, IntPtr.Zero);\\n }\\n\\n public virtual void Save(string modelPath)\\n => THSNN_Module_save(handle, modelPath);\\n\\n /// <summary>\\n /// Sets the module in training mode.\\n /// </summary>\\n /// <remarks>\\n /// This has any effect only on certain modules.See documentations of particular modules for details of their behaviors in training/evaluation mode, if they are affected, e.g.Dropout, BatchNorm, etc.\\n /// </remarks>\\n public virtual void train(bool train = true)\\n {\\n THSNN_Module_train(handle, train);\\n CheckForErrors();\\n foreach (var (_, m) in named_children()) { m.train(train); }\\n }\\n\\n /// <summary>\\n /// Sets the module in evaluation mode.\\n /// </summary>\\n /// <remarks>\\n /// This has any effect only on certain modules.See documentations of particular modules for details of their behaviors in training/evaluation mode, if they are affected, e.g.Dropout, BatchNorm, etc.\\n /// </remarks>\\n public virtual void eval()\\n {\\n train(false);\\n }\\n\\n /// <summary>\\n /// Check whether the module is set to training or evaluation mode.\\n /// </summary>\\n public virtual bool training {\\n get {\\n var res = THSNN_Module_is_training(handle);\\n CheckForErrors();\\n return res;\\n }\\n }\\n\\n public virtual void zero_grad()\\n {\\n THSNN_Module_zero_grad(handle);\\n CheckForErrors();\\n }\\n\\n /// <summary>\\n /// Returns an enumerable of module buffers, yielding both the name of the buffer as well as the buffer itself.\\n /// </summary>\\n /// <param name=\\\"recurse\\\">If true, then yields buffers of this module and all submodules. Otherwise, yields only buffers that are direct members of this module.</param>\\n /// <returns>(string, torch.Tensor) – Tuple containing the name and buffer</returns>\\n public virtual IEnumerable<(string name, Tensor buffer)> named_buffers(bool recurse = true)\\n {\\n var seen = new HashSet<IntPtr>();\\n seen.Add(IntPtr.Zero); // Ignore invalid buffers.\\n\\n foreach (var nsm in _internal_buffers) {\\n if (seen.Contains(nsm.Item2.handle)) continue;\\n seen.Add(nsm.Item2.handle);\\n yield return nsm;\\n }\\n\\n if (!recurse) yield break;\\n\\n foreach (var (submoduleName, subModule) in _internal_submodules) {\\n foreach (var (bufferName, buffer) in subModule.named_buffers(true)) {\\n if (seen.Contains(buffer.handle)) continue;\\n seen.Add(buffer.handle);\\n yield return ($\\\"{submoduleName}.{bufferName}\\\", buffer);\\n }\\n }\\n\\n }\\n\\n /// <summary>\\n /// Returns an enumerable of buffers.\\n /// </summary>\\n public virtual IEnumerable<Tensor> buffers(bool recurse = true) => named_buffers(recurse).Select(np => np.buffer);\\n\\n /// <summary>\\n /// Returns an enumerable of immediate children modules, yielding both the name of the module as well as the module itself.\\n /// </summary>\\n /// <returns>(string, Module) – Tuple containing a name and child module</returns>\\n public virtual IEnumerable<(string name, Module module)> named_children() => _internal_submodules;\\n\\n /// <summary>\\n /// Returns an enumerable of all modules in the network, yielding both the name of the module as well as the module itself.\\n /// </summary>\\n /// <returns>(string, Module) – Tuple of name and module</returns>\\n public virtual IEnumerable<(string name, Module module)> named_modules()\\n {\\n foreach (var nsm in _internal_submodules) {\\n yield return nsm;\\n }\\n\\n foreach (var (submoduleName, sm) in _internal_submodules) {\\n foreach (var (n, p) in sm.named_modules()) {\\n yield return ($\\\"{submoduleName}.{n}\\\", p);\\n }\\n }\\n }\\n\\n /// <summary>\\n /// Returns an enumerable of modules.\\n /// </summary>\\n public virtual IEnumerable<Module> modules() => named_modules().Select(np => np.module);\\n\\n /// <summary>\\n /// Returns an enumerable of immediate modules.\\n /// </summary>\\n public virtual IEnumerable<Module> children() => named_children().Select(np => np.module);\\n\\n /// <summary>\\n /// Returns a dictionary containing a whole state of the module.\\n ///\\n /// Both parameters and persistent buffers(e.g.running averages) are included.Keys are corresponding parameter and buffer names.\\n /// Parameters and buffers set to null are not included.\\n /// </summary>\\n /// <param name=\\\"destination\\\">An optional dictionary where the state should be accumulated.</param>\\n /// <param name=\\\"prefix\\\">A prefix string to use when entering the name of entries into the dictionary.</param>\\n /// <returns></returns>\\n public virtual Dictionary<string, Tensor> state_dict(Dictionary<string, Tensor> destination = null, string prefix = null)\\n {\\n destination ??= new Dictionary<string, Tensor>();\\n\\n foreach (var p in named_parameters()) {\\n var key = string.IsNullOrEmpty(prefix) ? $\\\"{p.name}\\\" : $\\\"{prefix}.{p.name}\\\";\\n destination.TryAdd(key, p.Item2);\\n }\\n\\n foreach (var p in named_buffers()) {\\n var key = string.IsNullOrEmpty(prefix) ? $\\\"{p.name}\\\" : $\\\"{prefix}.{p.name}\\\";\\n destination.TryAdd(key, p.Item2);\\n }\\n\\n foreach (var (n, p) in _internal_submodules) {\\n var key = string.IsNullOrEmpty(prefix) ? $\\\"{n}\\\" : $\\\"{prefix}.{n}\\\";\\n p.state_dict(destination, key);\\n }\\n\\n return destination;\\n }\\n\\n /// <summary>\\n /// Copies parameters and buffers from state_dict into this module and its descendants.\\n ///\\n /// If strict is True, then the keys of state_dict must exactly match the keys returned by this module’s state_dict() function.\\n /// </summary>\\n /// <param name=\\\"source\\\">A dict containing parameters and persistent buffers.</param>\\n /// <param name=\\\"strict\\\">Whether to strictly enforce that the keys in state_dict match the keys returned by this module’s state_dict() function.</param>\\n /// <param name=\\\"skip\\\">A list of keys not to consider when loading the dictionary.</param>\\n /// <returns></returns>\\n public virtual (IList<string> missing_keys, IList<string> unexpected_keyes) load_state_dict(Dictionary<string, Tensor> source, bool strict = true, IList<string> skip = null)\\n {\\n List<string> missing = new List<string>();\\n List<string> unexpected = new List<string>();\\n skip ??= Array.Empty<string>();\\n\\n var destination = state_dict();\\n\\n foreach (var key in source.Keys) {\\n if (skip.Contains(key)) continue;\\n if (!destination.ContainsKey(key)) {\\n unexpected.Add(key);\\n }\\n }\\n\\n foreach (var key in destination.Keys) {\\n if (skip.Contains(key)) continue;\\n if (!source.ContainsKey(key)) {\\n missing.Add(key);\\n }\\n }\\n\\n if (strict && (missing.Count > 0 || unexpected.Count > 0))\\n throw new InvalidOperationException(\\\"The loaded state_dict is not identical to the target dictionary.\\\");\\n\\n foreach (var key in source.Keys) {\\n if (skip.Contains(key)) continue;\\n if (destination.ContainsKey(key)) {\\n destination[key].bytes = source[key].bytes;\\n }\\n }\\n\\n return (missing, unexpected);\\n }\\n\\n protected virtual (string name, Parameter parameter)[] _named_parameters()\\n {\\n using var pa = new PinnedArray<IntPtr>();\\n using var sa = new PinnedArray<IntPtr>();\\n THSNN_Module_get_named_parameters(handle, pa.CreateArray, sa.CreateArray);\\n CheckForErrors();\\n var ptrArray = pa.Array;\\n var strArray = sa.Array;\\n\\n return ptrArray.Select((x, i) => (Marshal.PtrToStringAnsi(strArray[i]), new Parameter(x))).ToArray();\\n }\\n\\n protected virtual (string name, Tensor buffer)[] _named_buffers()\\n {\\n using var pa = new PinnedArray<IntPtr>();\\n using var sa = new PinnedArray<IntPtr>();\\n THSNN_Module_get_named_buffers(handle, pa.CreateArray, sa.CreateArray);\\n CheckForErrors();\\n var ptrArray = pa.Array;\\n var strArray = sa.Array;\\n\\n return ptrArray.Select((x, i) => (Marshal.PtrToStringAnsi(strArray[i]), new Tensor(x))).ToArray();\\n }\\n\\n /// <summary>\\n /// Returns an enumerable of module parameters, yielding both the name of the parameter as well as the parameter itself.\\n /// </summary>\\n /// <param name=\\\"recurse\\\">If true, then yields parameters of this module and all submodules. Otherwise, yields only parameters that are direct members of this module.</param>\\n /// <returns>(string, Parameter) – Tuple containing the name and parameter</returns>\\n public virtual IEnumerable<(string name, Parameter parameter)> named_parameters(bool recurse = true)\\n {\\n var seen = new HashSet<IntPtr>();\\n seen.Add(IntPtr.Zero); // Ignore invalid parameters.\\n\\n foreach (var nsm in _internal_params) {\\n if (seen.Contains(nsm.Item2.handle)) continue;\\n seen.Add(nsm.Item2.handle);\\n yield return nsm;\\n }\\n\\n if (!recurse) yield break;\\n foreach (var (submoduleName, subModule) in _internal_submodules) {\\n foreach (var (parameterName, parameter) in subModule.named_parameters(true)) {\\n if (seen.Contains(parameter.handle)) continue;\\n seen.Add(parameter.handle);\\n yield return ($\\\"{submoduleName}.{parameterName}\\\", parameter);\\n }\\n }\\n }\\n\\n protected virtual Parameter[] _parameters(bool recurse = true)\\n {\\n IntPtr[] ptrArray;\\n\\n using (var pa = new PinnedArray<IntPtr>()) {\\n AllocatePinnedArray allocator = pa.CreateArray;\\n THSNN_Module_get_parameters(handle, allocator, recurse);\\n CheckForErrors();\\n ptrArray = pa.Array;\\n }\\n return ptrArray.Select(x => new Parameter(x)).ToArray();\\n }\\n\\n /// <summary>\\n /// Returns an enumerable of module parameters.\\n /// </summary>\\n /// <param name=\\\"recurse\\\">If true, then yields parameters of this module and all submodules. Otherwise, yields only parameters that are direct members of this module.</param>\\n /// <returns></returns>\\n public virtual IEnumerable<Parameter> parameters(bool recurse = true)\\n => named_parameters(recurse).Select(np => np.parameter);\\n\\n public virtual bool has_buffer(string target)\\n {\\n if (_internal_buffers.TryGetValue(target, out var buffer)) {\\n return true;\\n }\\n\\n var splits = target.Split('.');\\n if (splits.Length <= 1) return false;\\n foreach (var child in named_children().Where(nc => nc.name == splits[0])) {\\n if (child.module.has_buffer(target.Remove(0, splits[0].Length + 1)))\\n return true;\\n }\\n return false;\\n }\\n\\n public virtual bool has_parameter(string target)\\n {\\n if (_internal_params.TryGetValue(target, out var parameter)) {\\n return true;\\n }\\n\\n var splits = target.Split('.');\\n if (splits.Length <= 1) return false;\\n foreach (var child in named_children().Where(nc => nc.name == splits[0])) {\\n if (child.module.has_parameter(target.Remove(0, splits[0].Length + 1)))\\n return true;\\n }\\n return false;\\n }\\n\\n /// <summary>\\n /// Returns the buffer given by target if it exists, otherwise throws an error.\\n /// </summary>\\n /// <param name=\\\"target\\\">The fully-qualified string name of the buffer to look for.</param>\\n /// <returns>The tensor referenced by target</returns>\\n public virtual Tensor get_buffer(string target)\\n {\\n if (target is null) throw new ArgumentNullException(\\\"target\\\");\\n if (_internal_buffers.TryGetValue(target, out var buffer)) {\\n return buffer;\\n }\\n\\n var splits = target.Split('.');\\n if (splits.Length <= 1) return null;\\n foreach (var child in named_children().Where(nc => nc.name == splits[0])) {\\n var p = child.module.get_buffer(target.Remove(0, splits[0].Length + 1));\\n if (p is not null)\\n return p;\\n }\\n return null;\\n }\\n\\n /// <summary>\\n /// Returns the parameter given by target if it exists, otherwise throws an error.\\n /// </summary>\\n /// <param name=\\\"target\\\">The fully-qualified string name of the Parameter to look for.</param>\\n /// <returns>The Parameter referenced by target</returns>\\n public virtual Parameter get_parameter(string target)\\n {\\n if (_internal_params.TryGetValue(target, out var parameter)) {\\n return parameter;\\n }\\n\\n var splits = target.Split('.');\\n if (splits.Length <= 1) return null;\\n foreach (var child in named_children().Where(nc => nc.name == splits[0])) {\\n var p = child.module.get_parameter(target.Remove(0, splits[0].Length + 1));\\n if (p is not null)\\n return p;\\n }\\n return null;\\n }\\n\\n /// <summary>\\n /// Adds a buffer to the module.\\n ///\\n /// This is typically used to register a buffer that should not to be considered a model parameter.For example, BatchNorm’s running_mean is not a parameter,\\n /// but is part of the module’s state.Buffers, by default, are persistent and will be saved alongside parameters.\\n /// </summary>\\n /// <param name=\\\"name\\\">Name of the buffer. The buffer can be accessed from this module using the given name</param>\\n /// <param name=\\\"tensor\\\">\\n /// Buffer to be registered. If null, then operations that run on buffers, such as cuda(), are ignored.\\n /// If null, the buffer is not included in the module’s state_dict.</param>\\n /// <exception cref=\\\"ArgumentNullException\\\"></exception>\\n /// <exception cref=\\\"InvalidOperationException\\\"></exception>\\n public virtual void register_buffer(string name, Tensor tensor)\\n {\\n if (tensor is null || tensor.handle == IntPtr.Zero)\\n throw new ArgumentNullException(nameof(tensor), \\\"A null tensor cannot be registered as a buffer.\\\");\\n\\n if (!_internal_buffers.TryAdd(name, tensor))\\n throw new InvalidOperationException($\\\"Tensor {name} is already registered.\\\");\\n }\\n\\n /// <summary>\\n /// Adds a parameter to the module.\\n /// </summary>\\n /// <param name=\\\"name\\\">Name of the parameter. The parameter can be accessed from this module using the given name</param>\\n /// <param name=\\\"param\\\">\\n /// Buffer to be registered.\\n /// If null, then operations that run on buffers, such as cuda(), are ignored.\\n /// If null, the buffer is not included in the module’s state_dict.</param>\\n /// <exception cref=\\\"ArgumentNullException\\\"></exception>\\n /// <exception cref=\\\"InvalidOperationException\\\"></exception>\\n public virtual void register_parameter(string name, Parameter param)\\n {\\n if (param is null || param.handle == IntPtr.Zero)\\n throw new ArgumentNullException(nameof(param), \\\"A null tensor cannot be registered as a parameter.\\\");\\n\\n if (!_internal_params.TryAdd(name, param))\\n throw new InvalidOperationException($\\\"Parameter {name} is already registered.\\\");\\n }\\n\\n /// <summary>\\n /// Alias for register_module()\\n /// </summary>\\n /// <param name=\\\"name\\\">\\n /// name of the child module.\\n /// The child module can be accessed from this module using the given name\\n /// </param>\\n /// <param name=\\\"module\\\">child module to be added to the module.</param>\\n /// <exception cref=\\\"ArgumentException\\\"></exception>\\n /// <exception cref=\\\"InvalidOperationException\\\"></exception>\\n public void add_module(string name, Module module)\\n => register_module(name, module);\\n\\n /// <summary>\\n /// Register a submodule.\\n /// </summary>\\n /// <param name=\\\"name\\\">Name of the submodule.</param>\\n /// <param name=\\\"submodule\\\">The module to register.</param>\\n /// <exception cref=\\\"ArgumentException\\\"></exception>\\n /// <exception cref=\\\"InvalidOperationException\\\"></exception>\\n public virtual void register_module(string name, Module submodule)\\n {\\n if (submodule is null || submodule.handle.IsInvalid) {\\n if (_internal_submodules.ContainsKey(name)) {\\n _internal_submodules.Remove(name);\\n }\\n } else {\\n if (name.Contains(\\\".\\\")) {\\n throw new ArgumentException($\\\"module name can't contain \\\\\\\".\\\\\\\", got: {name}\\\");\\n }\\n if (string.IsNullOrEmpty(name)) {\\n throw new ArgumentException(\\\"module name can't be empty string \\\\\\\"\\\\\\\"\\\");\\n }\\n if (_internal_submodules.ContainsKey(name)) {\\n throw new InvalidOperationException($\\\"Sub-module {name} is already registered.\\\");\\n }\\n\\n submodule.RegisterComponents();\\n\\n _internal_submodules.Add(name, submodule);\\n }\\n }\\n\\n protected void ConditionallyRegisterParameter(string name, Tensor value)\\n {\\n if (value is null) {\\n if (_internal_params.ContainsKey(name)) {\\n _internal_params.Remove(name);\\n }\\n } else {\\n var p = value is Parameter parameter\\n ? parameter\\n : new Parameter(value, requires_grad: true);\\n\\n if (_internal_params.ContainsKey(name)) {\\n _internal_params[name] = p;\\n } else {\\n _internal_params.Add(name, p);\\n }\\n }\\n }\\n\\n protected void ConditionallyRegisterBuffer(string name, Tensor value)\\n {\\n if (value is null) {\\n if (_internal_buffers.ContainsKey(name)) {\\n _internal_buffers.Remove(name);\\n }\\n } else {\\n if (_internal_buffers.ContainsKey(name)) {\\n _internal_buffers[name] = value;\\n } else {\\n _internal_buffers.Add(name, value);\\n }\\n }\\n }\\n\\n public virtual string GetName()\\n {\\n var res = THSNN_Module_name(handle);\\n CheckForErrors();\\n return res;\\n }\\n\\n /// <summary>\\n /// Save the parameters and buffers of the module to a disk location.\\n /// </summary>\\n /// <param name=\\\"location\\\">The file path.</param>\\n /// <param name=\\\"skip\\\">A list of keys not to consider when saving the weights.</param>\\n /// <returns></returns>\\n public Module save(string location, IList<string> skip = null)\\n {\\n using var stream = System.IO.File.Create(location);\\n using var writer = new System.IO.BinaryWriter(stream);\\n save(writer, skip);\\n\\n return this;\\n }\\n\\n /// <summary>\\n /// Save the parameters and buffers of the module to a disk location.\\n /// </summary>\\n /// <param name=\\\"writer\\\">A binary writer instance.</param>\\n /// <param name=\\\"skip\\\">A list of keys not to consider when saving the weights.</param>\\n /// <returns></returns>\\n public Module save(System.IO.BinaryWriter writer, IList<string> skip = null)\\n {\\n var sd = state_dict();\\n\\n // First, write how many entries.\\n save_state_dict(writer, sd, skip);\\n\\n return this;\\n }\\n\\n /// <summary>\\n /// Save the parameters and buffers of the module to a disk location.\\n /// </summary>\\n /// <param name=\\\"stream\\\">A writable stream instance.</param>\\n /// <param name=\\\"skip\\\">A list of keys not to consider when saving the weights.</param>\\n /// <returns></returns>\\n public Module save(System.IO.Stream stream, IList<string> skip = null)\\n {\\n using var writer = new System.IO.BinaryWriter(stream);\\n return save(writer, skip);\\n }\\n\\n /// <summary>\\n ///\\n /// </summary>\\n /// <param name=\\\"writer\\\">A binary writer instance.</param>\\n /// <param name=\\\"skip\\\">A list of keys not to consider when saving the weights.</param>\\n /// <param name=\\\"sd\\\">A dictionary containing all the buffers and parameters of the module.</param>\\n public static void save_state_dict(System.IO.BinaryWriter writer, Dictionary<string, Tensor> sd, IList<string> skip = null)\\n {\\n if (skip is not null && skip.Count > 0) {\\n // We need to make a copy, so that the passed-in 'sd' isn't modified.\\n var tmp = new Dictionary<string, Tensor>();\\n foreach (var kv in sd.Where(kv => !skip.Contains(kv.Key))) {\\n tmp.Add(kv.Key, kv.Value);\\n }\\n sd = tmp;\\n }\\n\\n writer.Encode(sd.Count); // 4 bytes\\n\\n foreach (var kvp in sd) {\\n writer.Write(kvp.Key);\\n kvp.Value.Save(writer);\\n }\\n }\\n\\n /// <summary>\\n /// Load the parameters and buffers\\n /// </summary>\\n /// <param name=\\\"location\\\">The file path.</param>\\n /// <param name=\\\"strict\\\">\\n /// If true, will only load a module if it exactly corresponds to the current module's state.\\n /// If false, will load the parameters and buffers that it finds in the saved file,\\n /// leaving everything else alone.\\n /// </param>\\n /// <param name=\\\"skip\\\">A list of keys not to consider when loading the dictionary.</param>\\n /// <returns>The module, with parameters and buffers loaded.</returns>\\n /// <remarks>\\n /// Using a skip list only prevents tensors in the target module from being modified, it\\n /// does not alter any logic related to checking for matching tensor element types or entries.\\n /// It may be necessary to also pass 'strict=false' to avoid exceptions.\\n /// </remarks>\\n public virtual Module load(string location, bool strict = true, IList<string> skip = null)\\n {\\n if (!System.IO.File.Exists(location))\\n throw new System.IO.FileNotFoundException(location);\\n\\n using var stream = System.IO.File.OpenRead(location);\\n using var reader = new System.IO.BinaryReader(stream);\\n load(reader, strict, skip);\\n\\n return this;\\n }\\n\\n /// <summary>\\n /// Load the parameters and buffers\\n /// </summary>\\n /// <param name=\\\"reader\\\">A binary reader instance.</param>\\n /// <param name=\\\"strict\\\">\\n /// If true, will only load a module if it exactly corresponds to the current module's state.\\n /// If false, will load the parameters and buffers that it finds in the saved file,\\n /// leaving everything else alone.\\n /// </param>\\n /// <param name=\\\"skip\\\">A list of keys not to consider when loading the dictionary.</param>\\n /// <returns>The module, with parameters and buffers loaded.</returns>\\n /// <remarks>\\n /// Using a skip list only prevents tensors in the target module from being modified, it\\n /// does not alter any logic related to checking for matching tensor element types or entries.\\n /// It may be necessary to also pass 'strict=false' to avoid exceptions.\\n /// </remarks>\\n public virtual Module load(System.IO.BinaryReader reader, bool strict = true, IList<string> skip = null)\\n {\\n skip ??= Array.Empty<string>();\\n\\n var dt = _deviceType;\\n var di = _deviceIndex;\\n\\n if (dt != DeviceType.CPU) this.cpu();\\n\\n try {\\n var sd = state_dict();\\n\\n // First, figure out how many entries.\\n var streamEntries = reader.Decode();\\n\\n if (streamEntries != sd.Count && strict)\\n throw new ArgumentException($\\\"Mismatched state_dict sizes: expected {sd.Count}, but found {streamEntries} entries.\\\");\\n\\n for (int i = 0; i < streamEntries; ++i) {\\n var key = reader.ReadString();\\n var found = sd.ContainsKey(key);\\n if (!found && strict)\\n throw new ArgumentException($\\\"Mismatched module state names: the target modules does not have a submodule or buffer named '{key}'\\\");\\n\\n if (found) {\\n sd[key].Load(reader, skip: skip.Contains(key));\\n }\\n }\\n } finally {\\n if (dt != DeviceType.CPU) _to(dt, di);\\n }\\n\\n return this;\\n }\\n\\n /// <summary>\\n /// Load the parameters and buffers\\n /// </summary>\\n /// <param name=\\\"stream\\\">A readable stream instance.</param>\\n /// <param name=\\\"strict\\\">\\n /// If true, will only load a module if it exactly corresponds to the current module's state.\\n /// If false, will load the parameters and buffers that it finds in the saved file,\\n /// leaving everything else alone.\\n /// </param>\\n /// <param name=\\\"skip\\\">A list of keys not to consider when loading the dictionary.</param>\\n /// <returns>The module, with parameters and buffers loaded.</returns>\\n /// <remarks>\\n /// Using a skip list only prevents tensors in the target module from being modified, it\\n /// does not alter any logic related to checking for matching tensor element types or entries.\\n /// It may be necessary to also pass 'strict=false' to avoid exceptions.\\n /// </remarks>\\n public Module load(System.IO.Stream stream, bool strict = true, IList<string> skip = null)\\n {\\n using var reader = new System.IO.BinaryReader(stream);\\n return load(reader, strict, skip);\\n }\\n\\n /// <summary>\\n /// Create a module and load its weights from disk.\\n /// </summary>\\n /// <typeparam name=\\\"T\\\"></typeparam>\\n /// <param name=\\\"path\\\"></param>\\n /// <returns></returns>\\n public static Module Create<T>(string path)\\n where T : Module, new()\\n {\\n var model = new T();\\n return model.load(path);\\n }\\n\\n /// <summary>\\n /// Constructor for custom modules, i.e. those defined outside of TorchSharp.\\n /// </summary>\\n /// <param name=\\\"name\\\">The name of the module. Useful for debugging purposes, mostly.</param>\\n protected Module(string name) : this(IntPtr.Zero, IntPtr.Zero)\\n {\\n this.name = name;\\n\\n IntPtr ForwardNative(IntPtr t)\\n {\\n var input = new Tensor(t);\\n var output = ((nn.Module<Tensor, Tensor>)this).call(input);\\n\\n // handles must live on - we don't own them, but\\n // the managed objects should go away.\\n input.DecoupleFromNativeHandle();\\n\\n return output.DecoupleFromNativeHandle();\\n }\\n\\n var res = THSNN_custom_module(name, ForwardNative, out var boxedHandle);\\n CheckForErrors();\\n handle = new HType(res, true);\\n this._forwardNative = ForwardNative;\\n boxedModule = new BoxedModule(boxedHandle);\\n\\n _init_parameters();\\n }\\n\\n private void _init_parameters()\\n { \\n // In this case, the parameter registration was not done yet.\\n foreach (var (parameterName, parameter) in _named_parameters()) {\\n register_parameter(parameterName, parameter);\\n }\\n }\\n\\n protected virtual void RegisterComponents()\\n {\\n if (_areComponentsRegistered) return;\\n\\n foreach (var field in GetType().GetFields(BindingFlags.Public | BindingFlags.NonPublic | BindingFlags.Instance)) {\\n\\n var fieldName = field.Name;\\n if (_internal_submodules.ContainsKey(fieldName) || _internal_params.ContainsKey(fieldName) || _internal_buffers.ContainsKey(fieldName)) continue;\\n\\n var value = field.GetValue(this);\\n\\n switch (value) {\\n case Module module:\\n register_module(fieldName, module);\\n break;\\n case Parameter param: // This test must come before the Tensor test\\n register_parameter(fieldName, param);\\n break;\\n case Tensor tensor:\\n register_buffer(fieldName, tensor);\\n break;\\n }\\n }\\n\\n _areComponentsRegistered = true;\\n }\\n\\n protected static (Device device, ScalarType dtype) GetDefaultDeviceAndType(Device device = null, ScalarType? dtype = null)\\n {\\n if (!dtype.HasValue)\\n dtype = get_default_dtype();\\n\\n if (device == null)\\n device = torch.CPU;\\n\\n return (device, dtype.Value);\\n }\\n\\n internal T MoveModule<T>(Device device, ScalarType? dtype) where T : Module\\n {\\n T module = (T)this;\\n\\n return device != null ?\\n (dtype.HasValue ? (T)module._to(device, dtype.Value) : (T)module._to(device.type, device.index)) :\\n (dtype.HasValue ? (T)module._to(dtype.Value) : module);\\n }\\n\\n protected void ClearModules() { _internal_submodules.clear(); }\\n\\n private bool _areComponentsRegistered;\\n\\n protected Utils.OrderedDict<string, Module> _internal_submodules = new Utils.OrderedDict<string, Module>();\\n protected Utils.OrderedDict<string, Tensor> _internal_buffers = new Utils.OrderedDict<string, Tensor>();\\n protected Utils.OrderedDict<string, Parameter> _internal_params = new Utils.OrderedDict<string, Parameter>();\\n\\n /// Keeps the callback delegate alive\\n private ForwardFunctionC _forwardNative;\\n protected string name;\\n }\\n\\n internal class BoxedModule : IDisposable\\n {\\n internal sealed class HType : SafeHandle\\n {\\n public HType(IntPtr preexistingHandle, bool ownsHandle) : base(IntPtr.Zero, ownsHandle)\\n => SetHandle(preexistingHandle);\\n\\n public override bool IsInvalid => handle == IntPtr.Zero;\\n\\n // This is just for marshalling\\n internal HType() : base(IntPtr.Zero, true)\\n {\\n }\\n\\n protected override bool ReleaseHandle()\\n {\\n if (!IsInvalid) THSNN_AnyModule_dispose(this);\\n handle = IntPtr.Zero;\\n return true;\\n }\\n\\n protected override void Dispose(bool disposing)\\n {\\n if (disposing) {\\n ReleaseHandle();\\n }\\n }\\n }\\n\\n internal HType handle;\\n\\n internal BoxedModule(IntPtr handle)\\n {\\n this.handle = new HType(handle, true);\\n }\\n\\n ~BoxedModule() => Dispose(false);\\n\\n /// <summary>\\n /// Releases the storage.\\n /// </summary>\\n public void Dispose()\\n {\\n Dispose(true);\\n GC.SuppressFinalize(this);\\n }\\n\\n /// <summary>\\n /// Implements the .NET Dispose pattern.\\n /// </summary>\\n protected void Dispose(bool disposing)\\n {\\n if (disposing) {\\n handle.Dispose();\\n handle.SetHandleAsInvalid();\\n }\\n }\\n }\\n\\n /// <summary>\\n /// Interface for concrete modules with a forward() that takes a single argument.\\n /// </summary>\\n /// <typeparam name=\\\"T\\\">The argument type of the module's forward() function.</typeparam>\\n /// <typeparam name=\\\"TResult\\\">The return type of the module's forward() function.</typeparam>\\n public interface IModule<T, TResult>\\n {\\n public TResult call(T input1);\\n }\\n\\n /// <summary>\\n /// Interface for concrete modules with a forward() that takes two arguments.\\n /// </summary>\\n /// <typeparam name=\\\"T1\\\">The first argument type of the module's forward() function.</typeparam>\\n /// <typeparam name=\\\"T2\\\">The second argument type of the module's forward() function.</typeparam>\\n /// <typeparam name=\\\"TResult\\\">The return type of the module's forward() function.</typeparam>\\n public interface IModule<T1, T2, TResult>\\n {\\n public abstract TResult call(T1 input1, T2 input2);\\n }\\n\\n /// <summary>\\n /// Interface for concrete modules with a forward() that takes three arguments.\\n /// </summary>\\n /// <typeparam name=\\\"T1\\\">The first argument type of the module's forward() function.</typeparam>\\n /// <typeparam name=\\\"T2\\\">The second argument type of the module's forward() function.</typeparam>\\n /// <typeparam name=\\\"T3\\\">The third argument type of the module's forward() function.</typeparam>\\n /// <typeparam name=\\\"TResult\\\">The return type of the module's forward() function.</typeparam>\\n public interface IModule<T1, T2, T3, TResult>\\n {\\n public abstract TResult call(T1 input1, T2 input2, T3 input3);\\n }\\n\\n /// <summary>\\n /// Interface for concrete modules with a forward() that takes four arguments.\\n /// </summary>\\n /// <typeparam name=\\\"T1\\\">The first argument type of the module's forward() function.</typeparam>\\n /// <typeparam name=\\\"T2\\\">The second argument type of the module's forward() function.</typeparam>\\n /// <typeparam name=\\\"T3\\\">The third argument type of the module's forward() function.</typeparam>\\n /// <typeparam name=\\\"T4\\\">The fourth argument type of the module's forward() function.</typeparam>\\n /// <typeparam name=\\\"TResult\\\">The return type of the module's forward() function.</typeparam>\\n public interface IModule<T1, T2, T3, T4, TResult>\\n {\\n public abstract TResult call(T1 input1, T2 input2, T3 input3, T4 input4);\\n }\\n\\n /// <summary>\\n /// Interface for concrete modules with a forward() that takes five arguments.\\n /// </summary>\\n /// <typeparam name=\\\"T1\\\">The first argument type of the module's forward() function.</typeparam>\\n /// <typeparam name=\\\"T2\\\">The second argument type of the module's forward() function.</typeparam>\\n /// <typeparam name=\\\"T3\\\">The third argument type of the module's forward() function.</typeparam>\\n /// <typeparam name=\\\"T4\\\">The fourth argument type of the module's forward() function.</typeparam>\\n /// <typeparam name=\\\"T5\\\">The fifth argument type of the module's forward() function.</typeparam>\\n /// <typeparam name=\\\"TResult\\\">The return type of the module's forward() function.</typeparam>\\n public interface IModule<T1, T2, T3, T4, T5, TResult>\\n {\\n public abstract TResult call(T1 input1, T2 input2, T3 input3, T4 input4, T5 input5);\\n }\\n\\n /// <summary>\\n /// Interface for concrete modules with a forward() that takes six arguments.\\n /// </summary>\\n /// <typeparam name=\\\"T1\\\">The first argument type of the module's forward() function.</typeparam>\\n /// <typeparam name=\\\"T2\\\">The second argument type of the module's forward() function.</typeparam>\\n /// <typeparam name=\\\"T3\\\">The third argument type of the module's forward() function.</typeparam>\\n /// <typeparam name=\\\"T4\\\">The fourth argument type of the module's forward() function.</typeparam>\\n /// <typeparam name=\\\"T5\\\">The fifth argument type of the module's forward() function.</typeparam>\\n /// <typeparam name=\\\"T6\\\">The sixth argument type of the module's forward() function.</typeparam>\\n /// <typeparam name=\\\"TResult\\\">The return type of the module's forward() function.</typeparam>\\n public interface IModule<T1, T2, T3, T4, T5, T6, TResult>\\n {\\n public abstract TResult call(T1 input1, T2 input2, T3 input3, T4 input4, T5 input5, T6 input6);\\n }\\n\\n /// <summary>\\n /// Represents a module that accepts 'hook' to the module logic.\\n /// </summary>\\n public class HookableModule<TPreHook,TPostHook> : Module\\n {\\n protected HookableModule(string name) : base(name) { }\\n\\n protected HookableModule(IntPtr handle, IntPtr boxedHandle) : base(handle, boxedHandle) { }\\n internal HookableModule(HType handle, IntPtr? boxedHandle) : base(handle, boxedHandle) { }\\n\\n public HookRemover register_forward_hook(TPostHook hook)\\n {\\n var key = Guid.NewGuid().ToString();\\n post_hooks.Add(key, hook);\\n return new HookRemover(this, key);\\n }\\n\\n public HookRemover register_forward_pre_hook(TPreHook hook)\\n {\\n var key = Guid.NewGuid().ToString();\\n pre_hooks.Add(key, hook);\\n return new HookRemover(this, key);\\n }\\n\\n private void remove(string key)\\n {\\n if (pre_hooks.ContainsKey(key)) pre_hooks.Remove(key);\\n if (post_hooks.ContainsKey(key)) post_hooks.Remove(key);\\n }\\n\\n protected Dictionary<string, TPreHook> pre_hooks = new Dictionary<string, TPreHook>();\\n protected Dictionary<string, TPostHook> post_hooks = new Dictionary<string, TPostHook>();\\n\\n /// <summary>\\n /// Used to remove a specific hook, following the PyTorch API design.\\n /// </summary>\\n /// <remarks>The name and namespace of this class is not the same as in PyTorch, but serves the same purpose.</remarks>\\n public class HookRemover\\n {\\n public HookRemover(HookableModule<TPreHook, TPostHook> module, string key)\\n {\\n this.module = module;\\n this.key = key;\\n }\\n\\n public void remove()\\n {\\n module.remove(key);\\n }\\n\\n private HookableModule<TPreHook, TPostHook> module;\\n private string key;\\n }\\n }\\n\\n /// <summary>\\n /// Base class for concrete modules with a forward() that takes a single argument.\\n /// </summary>\\n /// <typeparam name=\\\"T\\\">The argument type of the module's forward() function.</typeparam>\\n /// <typeparam name=\\\"TResult\\\">The return type of the module's forward() function.</typeparam>\\n public abstract class Module<T, TResult> : HookableModule<Func<Module<T,TResult>, T, T>, Func<Module<T, TResult>, T, TResult, TResult>>, IModule<T, TResult>\\n {\\n protected Module(string name) : base(name) { }\\n protected Module(IntPtr handle, IntPtr boxedHandle) : base(handle, boxedHandle) { }\\n internal Module(HType handle, IntPtr? boxedHandle) : base(handle, boxedHandle) { }\\n\\n /// <summary>\\n /// Invoke the logic of the module.\\n /// </summary>\\n /// <remarks>`forward` will not invoke any registered hooks for the module.</remarks>\\n public abstract TResult forward(T input);\\n\\n /// <summary>\\n /// Invoke the logic of the module.\\n /// </summary>\\n /// <remarks>`call` will invoke any registered hooks for the module.</remarks>\\n public TResult call(T input)\\n {\\n // Call pre-hooks, if available.\\n\\n foreach (var hook in pre_hooks.Values) {\\n var modified = hook(this, input);\\n if (modified is not null)\\n input = modified;\\n }\\n\\n var result = forward(input);\\n\\n // Call post-hooks, if available.\\n\\n foreach (var hook in post_hooks.Values) {\\n var modified = hook(this, input, result);\\n if (modified is not null)\\n result = modified;\\n }\\n\\n return result;\\n }\\n }\\n\\n /// <summary>\\n /// Base class for concrete modules with a forward() that takes two arguments.\\n /// </summary>\\n /// <typeparam name=\\\"T1\\\">The first argument type of the module's forward() function.</typeparam>\\n /// <typeparam name=\\\"T2\\\">The second argument type of the module's forward() function.</typeparam>\\n /// <typeparam name=\\\"TResult\\\">The return type of the module's forward() function.</typeparam>\\n public abstract class Module<T1, T2, TResult> : HookableModule<Func<Module<T1, T2, TResult>, T1, T2, (T1, T2)?>, Func<Module<T1, T2, TResult>, T1, T2, TResult, TResult>>, IModule<T1, T2, TResult>\\n {\\n protected Module(string name) : base(name) { }\\n protected Module(IntPtr handle, IntPtr boxedHandle) : base(handle, boxedHandle) { }\\n internal Module(HType handle, IntPtr? boxedHandle) : base(handle, boxedHandle) { }\\n\\n /// <summary>\\n /// Invoke the logic of the module.\\n /// </summary>\\n /// <remarks>`forward` will not invoke any registered hooks for the module.</remarks>\\n public abstract TResult forward(T1 input1, T2 input2);\\n\\n /// <summary>\\n /// Invoke the logic of the module.\\n /// </summary>\\n /// <remarks>`call` will invoke any registered hooks for the module.</remarks>\\n public TResult call(T1 input1, T2 input2)\\n {\\n // Call pre-hooks, if available.\\n\\n foreach (var hook in pre_hooks.Values) {\\n var modified = hook(this, input1, input2);\\n if (modified.HasValue) {\\n input1 = modified.Value.Item1;\\n input2 = modified.Value.Item2;\\n }\\n }\\n\\n var result = forward(input1, input2);\\n\\n // Call post-hooks, if available.\\n\\n foreach (var hook in post_hooks.Values) {\\n var modified = hook(this, input1, input2, result);\\n if (modified is not null)\\n result = modified;\\n }\\n\\n return result;\\n }\\n }\\n\\n /// <summary>\\n /// Base class for concrete modules with a forward() that takes three arguments.\\n /// </summary>\\n /// <typeparam name=\\\"T1\\\">The first argument type of the module's forward() function.</typeparam>\\n /// <typeparam name=\\\"T2\\\">The second argument type of the module's forward() function.</typeparam>\\n /// <typeparam name=\\\"T3\\\">The third argument type of the module's forward() function.</typeparam>\\n /// <typeparam name=\\\"TResult\\\">The return type of the module's forward() function.</typeparam>\\n public abstract class Module<T1, T2, T3, TResult> : HookableModule<Func<Module<T1, T2, T3, TResult>, T1, T2, T3, (T1, T2, T3)?>, Func<Module<T1, T2, T3, TResult>, T1, T2, T3, TResult, TResult>>, IModule<T1, T2, T3, TResult>\\n {\\n protected Module(string name) : base(name) { }\\n protected Module(IntPtr handle, IntPtr boxedHandle) : base(handle, boxedHandle) { }\\n internal Module(HType handle, IntPtr? boxedHandle) : base(handle, boxedHandle) { }\\n\\n /// <summary>\\n /// Invoke the logic of the module.\\n /// </summary>\\n /// <remarks>`forward` will not invoke any registered hooks for the module.</remarks>\\n public abstract TResult forward(T1 input1, T2 input2, T3 input3);\\n\\n /// <summary>\\n /// Invoke the logic of the module.\\n /// </summary>\\n /// <remarks>`call` will invoke any registered hooks for the module.</remarks>\\n public TResult call(T1 input1, T2 input2, T3 input3)\\n {\\n // Call pre-hooks, if available.\\n\\n foreach (var hook in pre_hooks.Values) {\\n var modified = hook(this, input1, input2, input3);\\n if (modified.HasValue) {\\n input1 = modified.Value.Item1;\\n input2 = modified.Value.Item2;\\n input3 = modified.Value.Item3;\\n }\\n }\\n\\n var result = forward(input1, input2, input3);\\n\\n // Call post-hooks, if available.\\n\\n foreach (var hook in post_hooks.Values) {\\n var modified = hook(this, input1, input2, input3, result);\\n if (modified is not null)\\n result = modified;\\n }\\n\\n return result;\\n }\\n }\\n\\n /// <summary>\\n /// Base class for concrete modules with a forward() that takes four arguments.\\n /// </summary>\\n /// <typeparam name=\\\"T1\\\">The first argument type of the module's forward() function.</typeparam>\\n /// <typeparam name=\\\"T2\\\">The second argument type of the module's forward() function.</typeparam>\\n /// <typeparam name=\\\"T3\\\">The third argument type of the module's forward() function.</typeparam>\\n /// <typeparam name=\\\"T4\\\">The fourth argument type of the module's forward() function.</typeparam>\\n /// <typeparam name=\\\"TResult\\\">The return type of the module's forward() function.</typeparam>\\n public abstract class Module<T1, T2, T3, T4, TResult> : HookableModule<Func<Module<T1, T2, T3, T4, TResult>, T1, T2, T3, T4, (T1, T2, T3, T4)?>, Func<Module<T1, T2, T3, T4, TResult>, T1, T2, T3, T4, TResult, TResult>>, IModule<T1, T2, T3, T4, TResult>\\n {\\n protected Module(string name) : base(name) { }\\n protected Module(IntPtr handle, IntPtr boxedHandle) : base(handle, boxedHandle) { }\\n internal Module(HType handle, IntPtr? boxedHandle) : base(handle, boxedHandle) { }\\n\\n /// <summary>\\n /// Invoke the logic of the module.\\n /// </summary>\\n /// <remarks>`forward` will not invoke any registered hooks for the module.</remarks>\\n public abstract TResult forward(T1 input1, T2 input2, T3 input3, T4 input4);\\n\\n /// <summary>\\n /// Invoke the logic of the module.\\n /// </summary>\\n /// <remarks>`call` will invoke any registered hooks for the module.</remarks>\\n public TResult call(T1 input1, T2 input2, T3 input3, T4 input4)\\n {\\n // Call pre-hooks, if available.\\n\\n foreach (var hook in pre_hooks.Values) {\\n var modified = hook(this, input1, input2, input3, input4);\\n if (modified.HasValue) {\\n input1 = modified.Value.Item1;\\n input2 = modified.Value.Item2;\\n input3 = modified.Value.Item3;\\n input4 = modified.Value.Item4;\\n }\\n }\\n\\n var result = forward(input1, input2, input3, input4);\\n\\n // Call post-hooks, if available.\\n\\n foreach (var hook in post_hooks.Values) {\\n var modified = hook(this, input1, input2, input3, input4, result);\\n if (modified is not null)\\n result = modified;\\n }\\n\\n return result;\\n }\\n }\\n\\n /// <summary>\\n /// Base class for concrete modules with a forward() that takes five arguments.\\n /// </summary>\\n /// <typeparam name=\\\"T1\\\">The first argument type of the module's forward() function.</typeparam>\\n /// <typeparam name=\\\"T2\\\">The second argument type of the module's forward() function.</typeparam>\\n /// <typeparam name=\\\"T3\\\">The third argument type of the module's forward() function.</typeparam>\\n /// <typeparam name=\\\"T4\\\">The fourth argument type of the module's forward() function.</typeparam>\\n /// <typeparam name=\\\"T5\\\">The fifth argument type of the module's forward() function.</typeparam>\\n /// <typeparam name=\\\"TResult\\\">The return type of the module's forward() function.</typeparam>\\n public abstract class Module<T1, T2, T3, T4, T5,TResult> : HookableModule<Func<Module<T1, T2, T3, T4, T5, TResult>, T1, T2, T3, T4, T5, (T1, T2, T3, T4, T5)?>, Func<Module<T1, T2, T3, T4, T5, TResult>, T1, T2, T3, T4, T5, TResult, TResult>>, IModule<T1, T2, T3, T4, T5, TResult>\\n {\\n protected Module(string name) : base(name) { }\\n protected Module(IntPtr handle, IntPtr boxedHandle) : base(handle, boxedHandle) { }\\n internal Module(HType handle, IntPtr? boxedHandle) : base(handle, boxedHandle) { }\\n\\n /// <summary>\\n /// Invoke the logic of the module.\\n /// </summary>\\n /// <remarks>`forward` will not invoke any registered hooks for the module.</remarks>\\n public abstract TResult forward(T1 input1, T2 input2, T3 input3, T4 input4, T5 input5);\\n\\n /// <summary>\\n /// Invoke the logic of the module.\\n /// </summary>\\n /// <remarks>`call` will invoke any registered hooks for the module.</remarks>\\n public TResult call(T1 input1, T2 input2, T3 input3, T4 input4, T5 input5)\\n {\\n // Call pre-hooks, if available.\\n\\n foreach (var hook in pre_hooks.Values) {\\n var modified = hook(this, input1, input2, input3, input4, input5);\\n if (modified.HasValue) {\\n input1 = modified.Value.Item1;\\n input2 = modified.Value.Item2;\\n input3 = modified.Value.Item3;\\n input4 = modified.Value.Item4;\\n input5 = modified.Value.Item5;\\n }\\n }\\n\\n var result = forward(input1, input2, input3, input4, input5);\\n\\n // Call post-hooks, if available.\\n\\n foreach (var hook in post_hooks.Values) {\\n var modified = hook(this, input1, input2, input3, input4, input5, result);\\n if (modified is not null)\\n result = modified;\\n }\\n\\n return result;\\n }\\n }\\n\\n /// <summary>\\n /// Base class for concrete modules with a forward() that takes six arguments.\\n /// </summary>\\n /// <typeparam name=\\\"T1\\\">The first argument type of the module's forward() function.</typeparam>\\n /// <typeparam name=\\\"T2\\\">The second argument type of the module's forward() function.</typeparam>\\n /// <typeparam name=\\\"T3\\\">The third argument type of the module's forward() function.</typeparam>\\n /// <typeparam name=\\\"T4\\\">The fourth argument type of the module's forward() function.</typeparam>\\n /// <typeparam name=\\\"T5\\\">The fifth argument type of the module's forward() function.</typeparam>\\n /// <typeparam name=\\\"T6\\\">The sixth argument type of the module's forward() function.</typeparam>\\n /// <typeparam name=\\\"TResult\\\">The return type of the module's forward() function.</typeparam>\\n public abstract class Module<T1, T2, T3, T4, T5, T6, TResult> : HookableModule<Func<Module<T1, T2, T3, T4, T5, T6, TResult>, T1, T2, T3, T4, T5, T6, (T1, T2, T3, T4, T5, T6)?>, Func<Module<T1, T2, T3, T4, T5, T6, TResult>, T1, T2, T3, T4, T5, T6, TResult, TResult>>, IModule<T1, T2, T3, T4, T5, T6, TResult>\\n {\\n protected Module(string name) : base(name) { }\\n protected Module(IntPtr handle, IntPtr boxedHandle) : base(handle, boxedHandle) { }\\n internal Module(HType handle, IntPtr? boxedHandle) : base(handle, boxedHandle) { }\\n\\n /// <summary>\\n /// Invoke the logic of the module.\\n /// </summary>\\n /// <remarks>`forward` will not invoke any registered hooks for the module.</remarks>\\n public abstract TResult forward(T1 input1, T2 input2, T3 input3, T4 input4, T5 input5, T6 input6);\\n\\n /// <summary>\\n /// Invoke the logic of the module.\\n /// </summary>\\n /// <remarks>`call` will invoke any registered hooks for the module.</remarks>\\n public TResult call(T1 input1, T2 input2, T3 input3, T4 input4, T5 input5, T6 input6)\\n {\\n // Call pre-hooks, if available.\\n\\n foreach (var hook in pre_hooks.Values) {\\n var modified = hook(this, input1, input2, input3, input4, input5, input6);\\n if (modified.HasValue) {\\n input1 = modified.Value.Item1;\\n input2 = modified.Value.Item2;\\n input3 = modified.Value.Item3;\\n input4 = modified.Value.Item4;\\n input5 = modified.Value.Item5;\\n input6 = modified.Value.Item6;\\n }\\n }\\n\\n var result = forward(input1, input2, input3, input4, input5, input6);\\n\\n // Call post-hooks, if available.\\n\\n foreach (var hook in post_hooks.Values) {\\n var modified = hook(this, input1, input2, input3, input4, input5, input6, result);\\n if (modified is not null)\\n result = modified;\\n }\\n\\n return result;\\n }\\n }\\n }\\n }\\n\\n public static class ModuleExtensionMethods\\n {\\n /// <summary>\\n /// Converts the parameters and buffers.\\n /// </summary>\\n /// <param name=\\\"module\\\">The module to move</param>\\n /// <param name=\\\"type\\\">The target element type.</param>\\n public static T to<T>(this T module, torch.ScalarType type) where T : torch.nn.Module\\n {\\n return (T)module._to(type);\\n }\\n\\n /// <summary>\\n /// Moves and converts the parameters and buffers.\\n /// </summary>\\n /// <param name=\\\"module\\\">The module to move</param>\\n /// <param name=\\\"device\\\">The target device.</param>\\n /// <param name=\\\"type\\\">The target element type.</param>\\n public static T to<T>(this T module, torch.Device device, torch.ScalarType type) where T : torch.nn.Module\\n {\\n return (T)module._to(device, type);\\n }\\n\\n /// <summary>\\n /// Moves the parameters and buffers.\\n /// </summary>\\n /// <param name=\\\"module\\\">The module to move</param>\\n /// <param name=\\\"deviceType\\\">The device type, e.g. 'CPU' or 'CUDA'.</param>\\n /// <param name=\\\"deviceIndex\\\">The optional device index.</param>\\n public static T to<T>(this T module, DeviceType deviceType, int deviceIndex = -1) where T : torch.nn.Module\\n {\\n return (T)module._to(deviceType, deviceIndex);\\n }\\n\\n /// <summary>\\n /// Moves the parameters and buffers.\\n /// </summary>\\n /// <param name=\\\"module\\\">The module to move</param>\\\"\\n /// <param name=\\\"device\\\">The target device</param>\\n /// <returns></returns>\\n public static T to<T>(this T module, Device device) where T : torch.nn.Module => (T)module._to(device.type, device.index);\\n\\n /// <summary>\\n /// Moves the parameters and buffers.\\n /// </summary>\\n /// <param name=\\\"module\\\">The module to move</param>\\n /// <param name=\\\"device\\\">A string denoting the target device.</param>\\n /// <returns></returns>\\n /// <remarks>Relies on the Device constructor to parse the string.</remarks>\\n public static T to<T>(this T module, string device) where T : torch.nn.Module\\n {\\n var dev = new Device(device);\\n return (T)module._to(dev.type, dev.index);\\n }\\n\\n /// <summary>\\n /// Moves and converts the parameters and buffers.\\n /// </summary>\\n /// <param name=\\\"module\\\">The module to move</param>\\n /// <param name=\\\"other\\\">The tensor serving as a template.</param>\\n /// <returns></returns>\\n public static T to<T>(this T module, Tensor other) where T : torch.nn.Module\\n {\\n return (T)module._to(other.device, other.dtype);\\n }\\n\\n /// <summary>\\n /// Moves all model parameters and buffers to the CPU.\\n /// </summary>\\n public static T cpu<T>(this T module) where T : torch.nn.Module => (T)module._to(DeviceType.CPU);\\n\\n /// <summary>\\n /// Moves all model parameters and buffers to a GPU.\\n ///\\n /// This also makes associated parameters and buffers different objects.So it should be called before constructing optimizer if the module will live on GPU while being optimized.\\n /// </summary>\\n /// <param name=\\\"module\\\">The module to move</param>\\n /// <param name=\\\"deviceIndex\\\">If specified, all parameters will be copied to that device</param>\\n public static T cuda<T>(this T module, int deviceIndex = -1) where T : torch.nn.Module => (T)module._to(DeviceType.CUDA, deviceIndex);\\n }\\n\\n internal delegate IntPtr ForwardFunctionC(IntPtr tensor);\\n}\\n\"\n },\n {\n \"alpha_fraction\": 0.6053891181945801,\n \"alphanum_fraction\": 0.6099536418914795,\n \"avg_line_length\": 43.980133056640625,\n \"blob_id\": \"abdfbf99b0e55fea149856f90758f30f84d5495c\",\n \"content_id\": \"43a50d336c83fb5ff2e832e8e059620343c00e6f\",\n \"detected_licenses\": [\n \"MIT\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"C#\",\n \"length_bytes\": 13583,\n \"license_type\": \"permissive\",\n \"max_line_length\": 130,\n \"num_lines\": 302,\n \"path\": \"/src/TorchSharp/DisposeScope.cs\",\n \"repo_name\": \"NiklasGustafsson/TorchSharp\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"// Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information.\\n\\nusing System;\\nusing System.Collections.Generic;\\nusing System.Linq;\\nusing TorchSharp.Utils;\\n\\n#nullable enable\\nnamespace TorchSharp\\n{\\n /// <summary>\\n /// Keeps track of all disposables that are in the current scope - the dispose scopes can be nested and the\\n /// nesting functionality is mainly managed by DisposeScopeManager.\\n /// </summary>\\n public sealed class DisposeScope : IDisposable\\n {\\n private readonly DisposeScopeManager _disposeScopeManager;\\n\\n public DisposeScope(DisposeScopeManager disposeScopeManager)\\n {\\n _disposeScopeManager = disposeScopeManager ?? throw new ArgumentNullException(nameof(disposeScopeManager));\\n if (disposeScopeManager.DisposeScopeStack.Count > 0) {\\n OuterScope = disposeScopeManager.DisposeScopeStack.Peek();\\n }\\n }\\n\\n /// <summary>\\n /// The outer scope with relation to this scope.\\n /// </summary>\\n internal DisposeScope? OuterScope { get; }\\n\\n /// <summary>\\n /// The disposables that are scheduled for disposing.\\n /// </summary>\\n /// TODO: There is a ReferenceEqualityComparer coming in .NET 6, use that!\\n internal HashSet<IDisposable> Disposables { get; private set; } =\\n new HashSet<IDisposable>(ReferenceEqualityComparer<IDisposable>.Default);\\n\\n /// <summary>\\n /// A view of the disposables in the scope - this list will not be kept in synch with the disposables\\n /// in the scope.\\n /// </summary>\\n public IReadOnlyList<IDisposable> DisposablesView => Disposables.ToList();\\n\\n /// <summary>\\n /// The number of disposables currently held in the scope\\n /// </summary>\\n public int DisposablesCount => Disposables.Count;\\n\\n /// <summary>\\n /// Includes a disposable in the scope - for tensors this is done automatically once the scope has been\\n /// created. Use this method to add additional disposables that should be disposed, but you typically\\n /// don't need to call this method.\\n /// </summary>\\n /// <param name=\\\"disposable\\\">The disposable to keep in the scope</param>\\n /// <returns></returns>\\n public T Include<T>(T disposable) where T : IDisposable\\n {\\n Disposables.Add(disposable);\\n return disposable;\\n }\\n\\n /// <summary>\\n /// Excludes a set of tensors/disposables from the current dispose scope, and moves it up to the outer\\n /// dispose scope, if one exists. See overloaded methods. If you wish to exclude a tensor from all sccopes,\\n /// use Detach.\\n /// </summary>\\n public T MoveToOuter<T>(T disposable) where T : IDisposable\\n {\\n MoveToOuter(new IDisposable[] { disposable });\\n return disposable;\\n }\\n\\n /// <summary>\\n /// Excludes a set of tensors/disposables from the current dispose scope, and moves it up to the outer\\n /// dispose scope, if one exists. See overloaded methods. If you wish to exclude a tensor from all sccopes,\\n /// use Detach.\\n /// </summary>\\n public (T1 first, T2 second) MoveToOuter<T1, T2>(T1 first, T2 second)\\n where T1 : IDisposable where T2 : IDisposable\\n {\\n MoveToOuter(new IDisposable[] { first, second });\\n return (first, second);\\n }\\n\\n /// <summary>\\n /// Excludes a set of tensors/disposables from the current dispose scope, and moves it up to the outer\\n /// dispose scope, if one exists. See overloaded methods. If you wish to exclude a tensor from all sccopes,\\n /// use Detach.\\n /// </summary>\\n public (T1 first, T2 second, T3 third) MoveToOuter<T1, T2, T3>(T1 first, T2 second, T3 third)\\n where T1 : IDisposable where T2 : IDisposable where T3 : IDisposable\\n {\\n MoveToOuter(new IDisposable[] { first, second, third });\\n return (first, second, third);\\n }\\n\\n /// <summary>\\n /// Excludes a set of tensors/disposables from the current dispose scope, and moves it up to the outer\\n /// dispose scope, if one exists. See overloaded methods. If you wish to exclude a tensor from all sccopes,\\n /// use Detach.\\n /// </summary>\\n public void MoveToOuter(params IDisposable[] disposables) =>\\n MoveToOuter((IEnumerable<IDisposable>)disposables);\\n\\n /// <summary>\\n /// Excludes a set of tensors/disposables from the current dispose scope, and moves it up to the outer\\n /// dispose scope, if one exists. See overloaded methods. If you wish to exclude a tensor from all sccopes,\\n /// use Detach.\\n /// </summary>\\n public void MoveToOuter(IEnumerable<IDisposable> disposables)\\n {\\n foreach (var disposable in disposables) {\\n if (Disposables.Remove(disposable)) {\\n AddToParent(disposable);\\n }\\n }\\n }\\n\\n /// <summary>\\n /// Detaches/excludes a set of tensors/disposables from the all dispose scopes, see overloaded methods. See MoveToOuter\\n /// if you wish to move it to the outer dispose scope.\\n /// </summary>\\n public T Detach<T>(T disposable) where T : IDisposable\\n {\\n Detach(new IDisposable[] { disposable });\\n return disposable;\\n }\\n\\n /// <summary>\\n /// Detaches/excludes a set of tensors/disposables from the all dispose scopes, see overloaded methods. See MoveToOuter\\n /// if you wish to move it to the outer dispose scope.\\n /// </summary>\\n public (T1 first, T2 second) Detach<T1, T2>(T1 first, T2 second)\\n where T1 : IDisposable where T2 : IDisposable\\n {\\n Detach(new IDisposable[] { first, second });\\n return (first, second);\\n }\\n\\n /// <summary>\\n /// Detaches/excludes a set of tensors/disposables from the all dispose scopes, see overloaded methods. See MoveToOuter\\n /// if you wish to move it to the outer dispose scope.\\n /// </summary>\\n public (T1 first, T2 second, T3 third) Detach<T1, T2, T3>(T1 first, T2 second, T3 third)\\n where T1 : IDisposable where T2 : IDisposable where T3 : IDisposable\\n {\\n Detach(new IDisposable[] { first, second, third });\\n return (first, second, third);\\n }\\n\\n /// <summary>\\n /// Detaches/excludes a set of tensors/disposables from the all dispose scopes, see overloaded methods. See MoveToOuter\\n /// if you wish to move it to the outer dispose scope.\\n /// </summary>\\n public void Detach(params IDisposable[] disposables) => Detach((IEnumerable<IDisposable>)disposables);\\n\\n /// <summary>\\n /// Detaches/excludes a set of tensors/disposables from the all dispose scopes, see overloaded methods. See MoveToOuter\\n /// if you wish to move it to the outer dispose scope.\\n /// </summary>\\n public void Detach(IEnumerable<IDisposable> disposables)\\n {\\n foreach (var disposable in disposables) {\\n if (Disposables.Remove(disposable)) {\\n _disposeScopeManager.StatisticsInstance.DetachedFromScopeCount++;\\n if (disposable is torch.Tensor tensor) {\\n tensor.OwningDisposeScope = null;\\n }\\n }\\n }\\n }\\n\\n /// <summary>\\n /// Disposes everything currently in the dispose scope.\\n /// </summary>\\n public void DisposeEverything() => DisposeEverythingBut(Enumerable.Empty<IDisposable>());\\n\\n /// <summary>\\n /// As an intermediate step, you can dispose all the tensors/disposables currently scheduled for dispose, to\\n /// clear up some memory without creating a new scope. Note that this doesn't permanently exclude the\\n /// tensors from disposing, use Exclude for that. Also, excluded tensors don't need to be included\\n /// here.\\n /// </summary>\\n public void DisposeEverythingBut(IEnumerable<IDisposable> inKeep)\\n {\\n // Avoiding multiple enumerations\\n var oldList = Disposables;\\n Disposables = inKeep.ToHashSet(ReferenceEqualityComparer<IDisposable>.Default);\\n foreach (var disposable in oldList) {\\n if (Disposables.Contains(disposable)) {\\n continue;\\n }\\n\\n if (disposable is torch.Tensor tensor) {\\n // No need to have the disposable call back to the scope\\n tensor.OwningDisposeScope = null;\\n if (!tensor.IsInvalid) {\\n _disposeScopeManager.StatisticsInstance.DisposedInScopeCount++;\\n }\\n } else {\\n _disposeScopeManager.StatisticsInstance.DisposedInScopeCount++;\\n }\\n\\n disposable.Dispose();\\n }\\n }\\n\\n /// <summary>\\n /// As an intermediate step, you can dispose all the tensors/disposables currently scheduled for dispose, to\\n /// clear up some memory without creating a new scope. Note that this doesn't permanently exclude the\\n /// tensors from disposing, use Exclude for that. Also, excluded tensors don't need to be included\\n /// here.\\n /// </summary>\\n public void DisposeEverythingBut(params IDisposable[] keep) =>\\n DisposeEverythingBut((IEnumerable<IDisposable>)keep);\\n\\n /// <summary>\\n /// As an intermediate step, you can dispose all the tensors/disposables currently scheduled for dispose, to\\n /// clear up some memory without creating a new scope. Note that this doesn't permanently exclude the\\n /// tensors from disposing, use Exclude for that. Also, excluded tensors don't need to be included\\n /// here.\\n /// </summary>\\n public T DisposeEverythingBut<T>(T keep) where T : IDisposable\\n {\\n DisposeEverythingBut(new IDisposable[] { keep });\\n return keep;\\n }\\n\\n /// <summary>\\n /// As an intermediate step, you can dispose all the tensors/disposables currently scheduled for dispose, to\\n /// clear up some memory without creating a new scope. Note that this doesn't permanently exclude the\\n /// tensors from disposing, use Exclude for that. Also, excluded tensors don't need to be included\\n /// here.\\n /// </summary>\\n public (T1 first, T2 second) DisposeEverythingBut<T1, T2>(T1 first, T2 second)\\n where T1 : IDisposable where T2 : IDisposable\\n {\\n DisposeEverythingBut(new IDisposable[] { first, second });\\n return (first, second);\\n }\\n\\n /// <summary>\\n /// As an intermediate step, you can dispose all the tensors/disposables currently scheduled for dispose, to\\n /// clear up some memory without creating a new scope. Note that this doesn't permanently exclude the\\n /// tensors from disposing, use Exclude for that. Also, excluded tensors don't need to be included\\n /// here.\\n /// </summary>\\n public (T1 first, T2 second, T3 third) DisposeEverythingBut<T1, T2, T3>(T1 first, T2 second, T3 third)\\n where T1 : IDisposable where T2 : IDisposable where T3 : IDisposable\\n {\\n DisposeEverythingBut(new IDisposable[] { first, second, third });\\n return (first, second, third);\\n }\\n\\n /// <summary>\\n /// Disposes of the DisposeScope and all the disposables in its list. You would typically not call this method,\\n /// instead you should use a usings clause around the scope.\\n /// </summary>\\n public void Dispose()\\n {\\n DisposeEverything();\\n _disposeScopeManager.RemoveDisposeScope(this);\\n }\\n\\n /// <summary>\\n /// A method that notifies the DisposeScope that a disposable was disposed, so that it can be removed from the\\n /// tracked list. This will be called if a tensor is manually disposed, but you can also add your own\\n /// disposables to the dispose scope. If you do, and dispose them manually, you should make sure to call this\\n /// method.\\n /// </summary>\\n /// <param name=\\\"disposable\\\">The disposable that was disposed</param>\\n public void MarkAsDisposed(IDisposable disposable)\\n {\\n _disposeScopeManager.StatisticsInstance.DisposedInScopeCount++;\\n Disposables.Remove(disposable);\\n if (disposable is torch.Tensor tensor) {\\n tensor.OwningDisposeScope = null;\\n }\\n }\\n\\n /// <summary>\\n /// Checks if the DisposeScope contains the disposable\\n /// </summary>\\n /// <param name=\\\"disposable\\\">The disposable that's searched for</param>\\n /// <returns></returns>\\n public bool Contains(IDisposable disposable) => Disposables.Contains(disposable);\\n\\n private void AddToParent(IDisposable disposable)\\n {\\n if (OuterScope != null) {\\n OuterScope.Disposables.Add(disposable);\\n } else {\\n _disposeScopeManager.StatisticsInstance.DetachedFromScopeCount++;\\n }\\n\\n if (disposable is torch.Tensor tensor) {\\n tensor.OwningDisposeScope = OuterScope;\\n }\\n }\\n }\\n}\"\n },\n {\n \"alpha_fraction\": 0.6992481350898743,\n \"alphanum_fraction\": 0.6992481350898743,\n \"avg_line_length\": 19.538461685180664,\n \"blob_id\": \"9ee620badae5b6b7cd767e709773fd5390f2d0f0\",\n \"content_id\": \"42bd0ef72d3509ab1412d403281dade320d0ef64\",\n \"detected_licenses\": [\n \"MIT\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"C#\",\n \"length_bytes\": 266,\n \"license_type\": \"permissive\",\n \"max_line_length\": 41,\n \"num_lines\": 13,\n \"path\": \"/src/TorchSharp/PInvoke/TensorOrScalar.cs\",\n \"repo_name\": \"NiklasGustafsson/TorchSharp\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"using System;\\nusing System.Runtime.InteropServices;\\n\\nnamespace TorchSharp.PInvoke\\n{\\n [StructLayout(LayoutKind.Sequential)]\\n internal struct TensorOrScalar\\n {\\n public long TypeCode;\\n public long ArrayIndex;\\n public IntPtr Handle;\\n }\\n}\"\n },\n {\n \"alpha_fraction\": 0.6620370149612427,\n \"alphanum_fraction\": 0.6620370149612427,\n \"avg_line_length\": 23.11111068725586,\n \"blob_id\": \"ba6474ae9b8689ae28d619a2dc0c38332292a822\",\n \"content_id\": \"b70213e1548037a93fee73fab1f754b413015a65\",\n \"detected_licenses\": [\n \"MIT\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"C#\",\n \"length_bytes\": 218,\n \"license_type\": \"permissive\",\n \"max_line_length\": 131,\n \"num_lines\": 9,\n \"path\": \"/src/TorchSharp/Tensor/Enums/indexing.cs\",\n \"repo_name\": \"NiklasGustafsson/TorchSharp\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"// Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information.\\nnamespace TorchSharp\\n{\\n public enum indexing\\n {\\n xy,\\n ij\\n }\\n}\"\n },\n {\n \"alpha_fraction\": 0.593024730682373,\n \"alphanum_fraction\": 0.6021500825881958,\n \"avg_line_length\": 41.328041076660156,\n \"blob_id\": \"4c1a0836e6290ab2b2539895653a6449ae2740fb\",\n \"content_id\": \"2b9d44ceb74a59122ca83ac6d124e9295bb24f79\",\n \"detected_licenses\": [\n \"MIT\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"C#\",\n \"length_bytes\": 23999,\n \"license_type\": \"permissive\",\n \"max_line_length\": 200,\n \"num_lines\": 567,\n \"path\": \"/src/TorchSharp/netstandard.cs\",\n \"repo_name\": \"NiklasGustafsson/TorchSharp\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"// Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information.\\n\\n//\\n// This file contains the implementation of functionality exist in .NET and not supported in netstandard.\\n// The implementation is supported on Windows only. The goal mainly is to support .NET Framework.\\n//\\n\\nusing System.Collections.Generic;\\nusing System.IO;\\nusing System.Reflection;\\nusing System.Runtime.CompilerServices;\\nusing System.Runtime.InteropServices;\\n\\nnamespace System\\n{\\n internal static partial class NSExtension\\n {\\n public static HashSet<TSource> ToHashSet<TSource>(this IEnumerable<TSource> source) => source.ToHashSet(comparer: null);\\n\\n public static HashSet<TSource> ToHashSet<TSource>(this IEnumerable<TSource> source, IEqualityComparer<TSource> comparer)\\n {\\n if (source == null)\\n {\\n throw new ArgumentNullException(nameof(source));\\n }\\n\\n // Don't pre-allocate based on knowledge of size, as potentially many elements will be dropped.\\n return new HashSet<TSource>(source, comparer);\\n }\\n\\n public static bool TryAdd<TKey, TValue>(this IDictionary<TKey, TValue> dictionary, TKey key, TValue value)\\n {\\n if (!dictionary.TryGetValue(key, out _))\\n {\\n dictionary.Add(key, value);\\n return true;\\n }\\n\\n return false;\\n }\\n\\n public static bool Contains(this string source, char c) => !string.IsNullOrEmpty(source) && source.IndexOf(c) >= 0;\\n public static bool EndsWith(this string source, char c) => !string.IsNullOrEmpty(source) && source[source.Length - 1] == c;\\n public static bool StartsWith(this string source, char c) => !string.IsNullOrEmpty(source) && source[0] == c;\\n }\\n\\n // MathF emulation on platforms which don't support it natively.\\n internal static class MathF\\n {\\n public const float PI = (float)Math.PI;\\n\\n [MethodImpl(MethodImplOptions.AggressiveInlining)] public static float Max(float x, float y) => Math.Max(x, y);\\n [MethodImpl(MethodImplOptions.AggressiveInlining)] public static float Round(float x) => (float)Math.Round(x);\\n [MethodImpl(MethodImplOptions.AggressiveInlining)] public static float Sqrt(float x) => (float)Math.Sqrt(x);\\n [MethodImpl(MethodImplOptions.AggressiveInlining)] public static int Sign(float x) => Math.Sign(x);\\n [MethodImpl(MethodImplOptions.AggressiveInlining)] public static float Abs(float x) => Math.Abs(x);\\n [MethodImpl(MethodImplOptions.AggressiveInlining)] public static float Log10(float x) => log10f(x);\\n [MethodImpl(MethodImplOptions.AggressiveInlining)] public static float Log2(float x) => log2f(x);\\n [MethodImpl(MethodImplOptions.AggressiveInlining)] public static float Acos(float x) => acosf(x);\\n [MethodImpl(MethodImplOptions.AggressiveInlining)] public static float Cos(float x) => cosf(x);\\n [MethodImpl(MethodImplOptions.AggressiveInlining)] public static float Cosh(float x) => coshf(x);\\n [MethodImpl(MethodImplOptions.AggressiveInlining)] public static float Atan(float x) => atanf(x);\\n [MethodImpl(MethodImplOptions.AggressiveInlining)] public static float Tanh(float x) => tanhf(x);\\n [MethodImpl(MethodImplOptions.AggressiveInlining)] public static float Pow(float x, float y) => powf(x, y);\\n [MethodImpl(MethodImplOptions.AggressiveInlining)] public static float Sin(float x) => sinf(x);\\n [MethodImpl(MethodImplOptions.AggressiveInlining)] public static float Sinh(float x) => sinhf(x);\\n [MethodImpl(MethodImplOptions.AggressiveInlining)] public static float Asinh(float x) => asinhf(x);\\n [MethodImpl(MethodImplOptions.AggressiveInlining)] public static float Tan(float x) => tanf(x);\\n [MethodImpl(MethodImplOptions.AggressiveInlining)] public static float Acosh(float x) => acoshf(x);\\n [MethodImpl(MethodImplOptions.AggressiveInlining)] public static float Atanh(float x) => atanhf(x);\\n [MethodImpl(MethodImplOptions.AggressiveInlining)] public static float Asin(float x) => asinf(x);\\n [MethodImpl(MethodImplOptions.AggressiveInlining)] public static float Log(float x) => logf(x);\\n [MethodImpl(MethodImplOptions.AggressiveInlining)] public static float Exp(float x) => expf(x);\\n [MethodImpl(MethodImplOptions.AggressiveInlining)] public static float Floor(float x) => floorf(x);\\n [MethodImpl(MethodImplOptions.AggressiveInlining)] public static float Ceiling(float x) => ceilf(x);\\n [MethodImpl(MethodImplOptions.AggressiveInlining)] public static float Truncate(float x) => truncf(x);\\n\\n public static float Log(float x, float y)\\n {\\n if (float.IsNaN(x))\\n {\\n return x; // IEEE 754-2008: NaN payload must be preserved\\n }\\n\\n if (float.IsNaN(y))\\n {\\n return y; // IEEE 754-2008: NaN payload must be preserved\\n }\\n\\n if (y == 1)\\n {\\n return float.NaN;\\n }\\n\\n if ((x != 1) && ((y == 0) || float.IsPositiveInfinity(y)))\\n {\\n return float.NaN;\\n }\\n\\n return Log(x) / Log(y);\\n }\\n\\n public static float IEEERemainder(float x, float y)\\n {\\n if (float.IsNaN(x))\\n {\\n return x; // IEEE 754-2008: NaN payload must be preserved\\n }\\n\\n if (float.IsNaN(y))\\n {\\n return y; // IEEE 754-2008: NaN payload must be preserved\\n }\\n\\n float regularMod = x % y;\\n\\n if (float.IsNaN(regularMod))\\n {\\n return float.NaN;\\n }\\n\\n if ((regularMod == 0) && IsNegative(x))\\n {\\n return NegativeZero;\\n }\\n\\n float alternativeResult = (regularMod - (Abs(y) * Sign(x)));\\n\\n if (Abs(alternativeResult) == Abs(regularMod))\\n {\\n float divisionResult = x / y;\\n float roundedResult = Round(divisionResult);\\n\\n if (Abs(roundedResult) > Abs(divisionResult))\\n {\\n return alternativeResult;\\n }\\n else\\n {\\n return regularMod;\\n }\\n }\\n\\n if (Abs(alternativeResult) < Abs(regularMod))\\n {\\n return alternativeResult;\\n }\\n else\\n {\\n return regularMod;\\n }\\n }\\n\\n internal const float NegativeZero = (float)-0.0;\\n internal static bool IsNegative(float x) => (((ulong)BitConverter.DoubleToInt64Bits(x)) & 0x8000000000000000) == 0x8000000000000000;\\n\\n private const string CrtLibrary = \\\"ucrtbase.dll\\\";\\n [DllImport(CrtLibrary)] private static extern float asinhf(float x);\\n [DllImport(CrtLibrary)] private static extern float acosf(float x);\\n [DllImport(CrtLibrary)] private static extern float cosf(float x);\\n [DllImport(CrtLibrary)] private static extern float coshf(float x);\\n [DllImport(CrtLibrary)] private static extern float atanf(float x);\\n [DllImport(CrtLibrary)] private static extern float tanhf(float x);\\n [DllImport(CrtLibrary)] private static extern float powf(float x, float y);\\n [DllImport(CrtLibrary)] private static extern float sinf(float x);\\n [DllImport(CrtLibrary)] private static extern float sinhf(float x);\\n [DllImport(CrtLibrary)] private static extern float tanf(float x);\\n [DllImport(CrtLibrary)] private static extern float logf(float x);\\n [DllImport(CrtLibrary)] private static extern float acoshf(float x);\\n [DllImport(CrtLibrary)] private static extern float atanhf(float x);\\n [DllImport(CrtLibrary)] private static extern float asinf(float x);\\n [DllImport(CrtLibrary)] private static extern float log10f(float x);\\n [DllImport(CrtLibrary)] private static extern float log2f(float x);\\n [DllImport(CrtLibrary)] private static extern float expf(float x);\\n [DllImport(CrtLibrary)] private static extern float floorf(float x);\\n [DllImport(CrtLibrary)] private static extern float ceilf(float x);\\n [DllImport(CrtLibrary)] private static extern float truncf(float x);\\n }\\n\\n internal static partial class NativeLibrary\\n {\\n [DllImport(\\\"kernel32.dll\\\", CharSet=CharSet.Unicode, SetLastError = true)]\\n internal static extern IntPtr LoadLibraryEx(string lpFileName, IntPtr hReservedNull, int dwFlags);\\n\\n public static bool TryLoad(string libraryPath, out IntPtr handle)\\n {\\n if (!RuntimeInformation.IsOSPlatform(OSPlatform.Windows))\\n {\\n throw new NotSupportedException(\\\"This call is not supported on non-Windows platforms.\\\");\\n }\\n\\n if (libraryPath == null)\\n throw new ArgumentNullException(nameof(libraryPath));\\n\\n handle = LoadFromPath(libraryPath, throwOnError: false);\\n return handle != IntPtr.Zero;\\n }\\n\\n public static bool TryLoad(string libraryName, Assembly assembly, DllImportSearchPath? searchPath, out IntPtr handle)\\n {\\n if (!RuntimeInformation.IsOSPlatform(OSPlatform.Windows))\\n {\\n throw new NotSupportedException(\\\"This call is not supported on non-Windows platforms.\\\");\\n }\\n\\n if (libraryName == null)\\n throw new ArgumentNullException(nameof(libraryName));\\n if (assembly == null)\\n throw new ArgumentNullException(nameof(assembly));\\n\\n handle = LoadLibraryByName(libraryName,\\n assembly,\\n searchPath,\\n throwOnError: false);\\n return handle != IntPtr.Zero;\\n }\\n\\n internal static IntPtr LoadLibraryByName(string libraryName, Assembly assembly, DllImportSearchPath? searchPath, bool throwOnError)\\n {\\n // First checks if a default dllImportSearchPathFlags was passed in, if so, use that value.\\n // Otherwise checks if the assembly has the DefaultDllImportSearchPathsAttribute attribute.\\n // If so, use that value.\\n\\n int searchPathFlags;\\n bool searchAssemblyDirectory;\\n if (searchPath.HasValue)\\n {\\n searchPathFlags = (int)(searchPath.Value & ~DllImportSearchPath.AssemblyDirectory);\\n searchAssemblyDirectory = (searchPath.Value & DllImportSearchPath.AssemblyDirectory) != 0;\\n }\\n else\\n {\\n GetDllImportSearchPathFlags(assembly, out searchPathFlags, out searchAssemblyDirectory);\\n }\\n\\n LoadLibErrorTracker errorTracker = default;\\n IntPtr ret = LoadBySearch(assembly, searchAssemblyDirectory, searchPathFlags, ref errorTracker, libraryName);\\n if (throwOnError && ret == IntPtr.Zero)\\n {\\n errorTracker.Throw(libraryName);\\n }\\n\\n return ret;\\n }\\n\\n internal static IntPtr LoadBySearch(Assembly callingAssembly, bool searchAssemblyDirectory, int dllImportSearchPathFlags, ref LoadLibErrorTracker errorTracker, string libraryName)\\n {\\n IntPtr ret;\\n\\n int loadWithAlteredPathFlags = 0;\\n bool libNameIsRelativePath = !Path.IsPathRooted(libraryName);\\n\\n foreach (LibraryNameVariation libraryNameVariation in LibraryNameVariation.DetermineLibraryNameVariations(libraryName, libNameIsRelativePath))\\n {\\n string currLibNameVariation = libraryNameVariation.Prefix + libraryName + libraryNameVariation.Suffix;\\n\\n if (!libNameIsRelativePath)\\n {\\n int flags = loadWithAlteredPathFlags;\\n if ((dllImportSearchPathFlags & (int)DllImportSearchPath.UseDllDirectoryForDependencies) != 0)\\n {\\n // LOAD_LIBRARY_SEARCH_DLL_LOAD_DIR is the only flag affecting absolute path. Don't OR the flags\\n // unconditionally as all absolute path P/Invokes could then lose LOAD_WITH_ALTERED_SEARCH_PATH.\\n flags |= dllImportSearchPathFlags;\\n }\\n\\n ret = LoadLibraryHelper(currLibNameVariation, flags, ref errorTracker);\\n if (ret != IntPtr.Zero)\\n {\\n return ret;\\n }\\n }\\n else if ((callingAssembly != null) && searchAssemblyDirectory)\\n {\\n // Try to load the module alongside the assembly where the PInvoke was declared.\\n // This only makes sense in dynamic scenarios (JIT/interpreter), so leaving this out for now.\\n }\\n\\n ret = LoadLibraryHelper(currLibNameVariation, dllImportSearchPathFlags, ref errorTracker);\\n if (ret != IntPtr.Zero)\\n {\\n return ret;\\n }\\n }\\n\\n return IntPtr.Zero;\\n }\\n\\n internal static void GetDllImportSearchPathFlags(Assembly callingAssembly, out int searchPathFlags, out bool searchAssemblyDirectory)\\n {\\n var searchPath = DllImportSearchPath.AssemblyDirectory;\\n\\n foreach (CustomAttributeData cad in callingAssembly.CustomAttributes)\\n {\\n if (cad.AttributeType == typeof(DefaultDllImportSearchPathsAttribute))\\n {\\n searchPath = (DllImportSearchPath)cad.ConstructorArguments[0].Value!;\\n }\\n }\\n\\n searchPathFlags = (int)(searchPath & ~DllImportSearchPath.AssemblyDirectory);\\n searchAssemblyDirectory = (searchPath & DllImportSearchPath.AssemblyDirectory) != 0;\\n }\\n\\n private static IntPtr LoadFromPath(string libraryName, bool throwOnError)\\n {\\n LoadLibErrorTracker errorTracker = default;\\n IntPtr ret = LoadLibraryHelper(libraryName, 0, ref errorTracker);\\n if (throwOnError && ret == IntPtr.Zero)\\n {\\n errorTracker.Throw(libraryName);\\n }\\n\\n return ret;\\n }\\n\\n private static IntPtr LoadLibraryHelper(string libraryName, int flags, ref LoadLibErrorTracker errorTracker)\\n {\\n IntPtr ret = LoadLibraryEx(libraryName, IntPtr.Zero, flags);\\n if (ret != IntPtr.Zero)\\n {\\n return ret;\\n }\\n\\n int lastError = Marshal.GetLastWin32Error();\\n if (lastError != LoadLibErrorTracker.ERROR_INVALID_PARAMETER)\\n {\\n errorTracker.TrackErrorCode(lastError);\\n }\\n\\n return ret;\\n }\\n\\n internal struct LoadLibErrorTracker\\n {\\n internal const int ERROR_INVALID_PARAMETER = 0x57;\\n internal const int ERROR_MOD_NOT_FOUND = 126;\\n internal const int ERROR_BAD_EXE_FORMAT = 193;\\n\\n private int _errorCode;\\n\\n public void Throw(string libraryName)\\n {\\n if (_errorCode == ERROR_BAD_EXE_FORMAT)\\n {\\n throw new BadImageFormatException();\\n }\\n\\n throw new DllNotFoundException($\\\"Unable to native library '{libraryName}' or one of its dependencies.\\\");\\n }\\n\\n public void TrackErrorCode(int errorCode)\\n {\\n _errorCode = errorCode;\\n }\\n\\n }\\n\\n internal partial struct LibraryNameVariation\\n {\\n private const string LibraryNameSuffix = \\\".dll\\\";\\n\\n public string Prefix;\\n public string Suffix;\\n\\n public LibraryNameVariation(string prefix, string suffix)\\n {\\n Prefix = prefix;\\n Suffix = suffix;\\n }\\n\\n internal static IEnumerable<LibraryNameVariation> DetermineLibraryNameVariations(string libName, bool isRelativePath, bool forOSLoader = false)\\n {\\n // This is a copy of the logic in DetermineLibNameVariations in dllimport.cpp in CoreCLR\\n\\n yield return new LibraryNameVariation(string.Empty, string.Empty);\\n\\n // Follow LoadLibrary rules if forOSLoader is true\\n if (isRelativePath &&\\n (!forOSLoader || libName.Contains('.') && !libName.EndsWith('.')) &&\\n !libName.EndsWith(\\\".dll\\\", StringComparison.OrdinalIgnoreCase) &&\\n !libName.EndsWith(\\\".exe\\\", StringComparison.OrdinalIgnoreCase))\\n {\\n yield return new LibraryNameVariation(string.Empty, LibraryNameSuffix);\\n }\\n }\\n }\\n }\\n}\\n\\nnamespace System.Diagnostics.CodeAnalysis\\n{\\n [AttributeUsage(AttributeTargets.Parameter, Inherited = false)]\\n internal sealed class MaybeNullWhenAttribute : Attribute\\n {\\n /// <summary>Initializes the attribute with the specified return value condition.</summary>\\n /// <param name=\\\"returnValue\\\">\\n /// The return value condition. If the method returns this value, the associated parameter may be null.\\n /// </param>\\n public MaybeNullWhenAttribute(bool returnValue) => ReturnValue = returnValue;\\n\\n /// <summary>Gets the return value condition.</summary>\\n public bool ReturnValue { get; }\\n }\\n}\\n\\nnamespace System.Linq\\n{\\n internal static partial class NSEnumerable\\n {\\n public static IEnumerable<TResult> Zip<TFirst, TSecond, TResult>(this IEnumerable<TFirst> first, IEnumerable<TSecond> second, Func<TFirst, TSecond, TResult> resultSelector)\\n {\\n if (first is null)\\n {\\n throw new ArgumentNullException(nameof(first));\\n }\\n\\n if (second is null)\\n {\\n throw new ArgumentNullException(nameof(second));\\n }\\n\\n if (resultSelector is null)\\n {\\n throw new ArgumentNullException(nameof(resultSelector));\\n }\\n\\n return ZipIterator(first, second, resultSelector);\\n }\\n\\n public static IEnumerable<(TFirst First, TSecond Second)> Zip<TFirst, TSecond>(this IEnumerable<TFirst> first, IEnumerable<TSecond> second)\\n {\\n if (first is null)\\n {\\n throw new ArgumentNullException(nameof(first));\\n }\\n\\n if (second is null)\\n {\\n throw new ArgumentNullException(nameof(second));\\n }\\n\\n return ZipIterator(first, second);\\n }\\n\\n public static IEnumerable<(TFirst First, TSecond Second, TThird Third)> Zip<TFirst, TSecond, TThird>(this IEnumerable<TFirst> first, IEnumerable<TSecond> second, IEnumerable<TThird> third)\\n {\\n if (first is null)\\n {\\n throw new ArgumentNullException(nameof(first));\\n }\\n\\n if (second is null)\\n {\\n throw new ArgumentNullException(nameof(second));\\n }\\n\\n if (third is null)\\n {\\n throw new ArgumentNullException(nameof(third));\\n }\\n\\n return ZipIterator(first, second, third);\\n }\\n\\n private static IEnumerable<(TFirst First, TSecond Second)> ZipIterator<TFirst, TSecond>(IEnumerable<TFirst> first, IEnumerable<TSecond> second)\\n {\\n using (IEnumerator<TFirst> e1 = first.GetEnumerator())\\n using (IEnumerator<TSecond> e2 = second.GetEnumerator())\\n {\\n while (e1.MoveNext() && e2.MoveNext())\\n {\\n yield return (e1.Current, e2.Current);\\n }\\n }\\n }\\n\\n private static IEnumerable<TResult> ZipIterator<TFirst, TSecond, TResult>(IEnumerable<TFirst> first, IEnumerable<TSecond> second, Func<TFirst, TSecond, TResult> resultSelector)\\n {\\n using (IEnumerator<TFirst> e1 = first.GetEnumerator())\\n using (IEnumerator<TSecond> e2 = second.GetEnumerator())\\n {\\n while (e1.MoveNext() && e2.MoveNext())\\n {\\n yield return resultSelector(e1.Current, e2.Current);\\n }\\n }\\n }\\n\\n private static IEnumerable<(TFirst First, TSecond Second, TThird Third)> ZipIterator<TFirst, TSecond, TThird>(IEnumerable<TFirst> first, IEnumerable<TSecond> second, IEnumerable<TThird> third)\\n {\\n using (IEnumerator<TFirst> e1 = first.GetEnumerator())\\n using (IEnumerator<TSecond> e2 = second.GetEnumerator())\\n using (IEnumerator<TThird> e3 = third.GetEnumerator())\\n {\\n while (e1.MoveNext() && e2.MoveNext() && e3.MoveNext())\\n {\\n yield return (e1.Current, e2.Current, e3.Current);\\n }\\n }\\n }\\n }\\n}\\n\\nnamespace System.IO\\n{\\n internal class NSPath\\n {\\n internal const char DirectorySeparatorChar = '\\\\\\\\'; // Windows implementation\\n internal const char AltDirectorySeparatorChar = '/';\\n internal const string DirectorySeparatorCharAsString = \\\"\\\\\\\\\\\";\\n\\n public static string Join(string path1, string path2)\\n {\\n if (path1 is null || path1.Length == 0)\\n return path2;\\n if (path2 is null || path2.Length == 0)\\n return path1;\\n\\n bool hasSeparator = IsDirectorySeparator(path1[path1.Length - 1]) || IsDirectorySeparator(path2[0]);\\n return hasSeparator ? string.Concat(path1, path2) : string.Concat(path1, DirectorySeparatorCharAsString, path2);\\n }\\n\\n [MethodImpl(MethodImplOptions.AggressiveInlining)]\\n internal static bool IsDirectorySeparator(char c) => c == DirectorySeparatorChar || c == AltDirectorySeparatorChar;\\n\\n public static string Join(string path1, string path2, string path3)\\n {\\n if (path1 is null || path1.Length == 0)\\n return Join(path2, path3);\\n\\n if (path2 is null || path2.Length == 0)\\n return Join(path1, path3);\\n\\n if (path3 is null || path3.Length == 0)\\n return Join(path1, path2);\\n\\n bool firstHasSeparator = IsDirectorySeparator(path1[path1.Length - 1]) || IsDirectorySeparator(path2[0]);\\n bool secondHasSeparator = IsDirectorySeparator(path2[path2.Length - 1]) || IsDirectorySeparator(path3[0]);\\n return path1 + (firstHasSeparator ? \\\"\\\" : DirectorySeparatorCharAsString) + path2 + (secondHasSeparator ? \\\"\\\" : DirectorySeparatorCharAsString) + path3;\\n }\\n\\n public static string Join(string path1, string path2, string path3, string path4)\\n {\\n if (path1 is null || path1.Length == 0)\\n return Join(path2, path3, path4);\\n\\n if (path2 is null || path2.Length == 0)\\n return Join(path1, path3, path4);\\n\\n if (path3 is null || path3.Length == 0)\\n return Join(path1, path2, path4);\\n\\n if (path4 is null || path4.Length == 0)\\n return Join(path1, path2, path3);\\n\\n bool firstHasSeparator = IsDirectorySeparator(path1[path1.Length - 1]) || IsDirectorySeparator(path2[0]);\\n bool secondHasSeparator = IsDirectorySeparator(path2[path2.Length - 1]) || IsDirectorySeparator(path3[0]);\\n bool thirdHasSeparator = IsDirectorySeparator(path3[path3.Length - 1]) || IsDirectorySeparator(path4[0]);\\n\\n return path1 + (firstHasSeparator ? \\\"\\\" : DirectorySeparatorCharAsString) +\\n path2 + (secondHasSeparator ? \\\"\\\" : DirectorySeparatorCharAsString) +\\n path3 + (thirdHasSeparator ? \\\"\\\" : DirectorySeparatorCharAsString) +\\n path4;\\n }\\n }\\n}\"\n },\n {\n \"alpha_fraction\": 0.5804773569107056,\n \"alphanum_fraction\": 0.5829253196716309,\n \"avg_line_length\": 46.36231994628906,\n \"blob_id\": \"11ef884f5387b362ebf9ed98ddb722bd0f865f42\",\n \"content_id\": \"70a1858f09b10a684bd1d33112f2b9cd30986327\",\n \"detected_licenses\": [\n \"MIT\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"C#\",\n \"length_bytes\": 6540,\n \"license_type\": \"permissive\",\n \"max_line_length\": 149,\n \"num_lines\": 138,\n \"path\": \"/src/TorchSharp/Distributions/Gamma.cs\",\n \"repo_name\": \"NiklasGustafsson/TorchSharp\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"// Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information.\\nusing System;\\nusing System.Collections.Generic;\\nusing static TorchSharp.torch;\\n\\nnamespace TorchSharp\\n{\\n using Modules;\\n\\n namespace Modules\\n {\\n /// <summary>\\n /// A Gamma distribution parameterized by shape `concentration` and `rate`.\\n /// </summary>\\n public class Gamma : torch.distributions.ExponentialFamily\\n {\\n /// <summary>\\n /// The mean of the distribution.\\n /// </summary>\\n public override Tensor mean => WrappedTensorDisposeScope(() => concentration / rate);\\n\\n public override Tensor mode => WrappedTensorDisposeScope(() => ((concentration - 1) / rate).clamp_(min: 0));\\n\\n /// <summary>\\n /// The variance of the distribution\\n /// </summary>\\n public override Tensor variance => WrappedTensorDisposeScope(() => concentration / rate.pow(2));\\n\\n /// <summary>\\n /// Constructor\\n /// </summary>\\n /// <param name=\\\"concentration\\\">Shape parameter of the distribution (often referred to as 'α')</param>\\n /// <param name=\\\"rate\\\">rate = 1 / scale of the distribution (often referred to as 'β')</param>\\n /// <param name=\\\"generator\\\">An optional random number generator object.</param>\\n public Gamma(Tensor concentration, Tensor rate, torch.Generator generator = null) : base(generator)\\n {\\n var locScale = torch.broadcast_tensors(concentration, rate);\\n this.concentration = locScale[0].DetachFromDisposeScope();\\n this.rate = locScale[1].DetachFromDisposeScope();\\n this.batch_shape = this.concentration.size();\\n }\\n\\n protected Tensor concentration;\\n private Tensor rate;\\n\\n /// <summary>\\n /// Generates a sample_shape shaped reparameterized sample or sample_shape shaped batch of reparameterized samples\\n /// if the distribution parameters are batched.\\n /// </summary>\\n /// <param name=\\\"sample_shape\\\">The sample shape.</param>\\n public override Tensor rsample(params long[] sample_shape)\\n {\\n using var _ = torch.NewDisposeScope();\\n var shape = ExtendedShape(sample_shape);\\n var value = torch._standard_gamma(concentration.expand(shape), generator: generator) / rate.expand(shape);\\n return value.detach().clamp_(min: torch.finfo(value.dtype).tiny).MoveToOuterDisposeScope();\\n }\\n\\n /// <summary>\\n /// Returns the log of the probability density/mass function evaluated at `value`.\\n /// </summary>\\n /// <param name=\\\"value\\\"></param>\\n public override Tensor log_prob(Tensor value)\\n {\\n using var _ = torch.NewDisposeScope();\\n value = torch.as_tensor(value, dtype: rate.dtype, device: rate.device);\\n var result = concentration * rate.log() + (concentration - 1) * value.log() - rate * value - torch.lgamma(concentration);\\n return result.MoveToOuterDisposeScope();\\n }\\n\\n /// <summary>\\n /// Returns entropy of distribution, batched over batch_shape.\\n /// </summary>\\n public override Tensor entropy() =>\\n WrappedTensorDisposeScope(() =>\\n concentration - rate.log() + concentration.lgamma() + (1.0 - concentration) * concentration.digamma());\\n\\n public override Tensor cdf(Tensor value) =>\\n WrappedTensorDisposeScope(() => torch.special.gammainc(concentration, rate * value));\\n\\n /// <summary>\\n /// Returns a new distribution instance (or populates an existing instance provided by a derived class) with batch dimensions expanded to\\n /// `batch_shape`. This method calls `torch.Tensor.expand()` on the distribution's parameters. As such, this does not allocate new\\n /// memory for the expanded distribution instance.\\n /// </summary>\\n /// <param name=\\\"batch_shape\\\">Tthe desired expanded size.</param>\\n /// <param name=\\\"instance\\\">new instance provided by subclasses that need to override `.expand`.</param>\\n public override distributions.Distribution expand(Size batch_shape, distributions.Distribution instance = null)\\n {\\n if (instance != null && !(instance is Gamma))\\n throw new ArgumentException(\\\"expand(): 'instance' must be a Gamma distribution\\\");\\n\\n var c = concentration.expand(batch_shape);\\n var r = rate.expand(batch_shape);\\n\\n var newDistribution = ((instance == null) ? new Gamma(c, r, generator) : instance) as Gamma;\\n\\n newDistribution.batch_shape = batch_shape;\\n if (newDistribution == instance) {\\n newDistribution.concentration = c;\\n newDistribution.rate = r;\\n }\\n return newDistribution;\\n }\\n\\n protected override IList<Tensor> NaturalParams => new Tensor[] { concentration - 1, rate };\\n\\n protected override Tensor MeanCarrierMeasure => torch.tensor(0, dtype:rate.dtype, device: rate.device);\\n\\n protected override Tensor LogNormalizer(params Tensor[] parameters)\\n {\\n var x = parameters[0];\\n var y = parameters[1];\\n\\n return torch.lgamma(x + 1) + (x + 1) * torch.log(-y.reciprocal());\\n }\\n }\\n\\n }\\n\\n public static partial class torch\\n {\\n public static partial class distributions\\n {\\n /// <summary>\\n /// Creates a Gamma distribution parameterized by shape `concentration` and `rate`.\\n /// </summary>\\n /// <param name=\\\"concentration\\\">Shape parameter of the distribution (often referred to as 'α')</param>\\n /// <param name=\\\"rate\\\">rate = 1 / scale of the distribution (often referred to as 'β')</param>\\n /// <param name=\\\"generator\\\">An optional random number generator object.</param>\\n /// <returns></returns>\\n public static Gamma Gamma(Tensor concentration, Tensor rate, torch.Generator generator = null)\\n {\\n return new Gamma(concentration, rate, generator);\\n }\\n }\\n }\\n}\\n\"\n },\n {\n \"alpha_fraction\": 0.5351672172546387,\n \"alphanum_fraction\": 0.5367302298545837,\n \"avg_line_length\": 38.0121955871582,\n \"blob_id\": \"e14378277b260b3c9865dfdbf5012e4192c33148\",\n \"content_id\": \"9fb1d3d6e9980d8f133772689517c9375eb13e77\",\n \"detected_licenses\": [\n \"MIT\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"C#\",\n \"length_bytes\": 3199,\n \"license_type\": \"permissive\",\n \"max_line_length\": 132,\n \"num_lines\": 82,\n \"path\": \"/src/TorchSharp/NN/Activation/PReLU.cs\",\n \"repo_name\": \"NiklasGustafsson/TorchSharp\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"// Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information.\\nusing System;\\nusing static TorchSharp.torch;\\nusing static TorchSharp.PInvoke.NativeMethods;\\n\\n#nullable enable\\nnamespace TorchSharp\\n{\\n using Modules;\\n\\n namespace Modules\\n {\\n /// <summary>\\n /// This class is used to represent a PReLU module.\\n /// </summary>\\n public sealed class PReLU : torch.nn.Module<Tensor, Tensor>\\n {\\n internal PReLU(IntPtr handle, IntPtr boxedHandle) : base(handle, boxedHandle) { }\\n\\n public override Tensor forward(Tensor tensor)\\n {\\n var res = THSNN_PReLU_forward(handle, tensor.Handle);\\n if (res == IntPtr.Zero) { torch.CheckForErrors(); }\\n return new Tensor(res);\\n }\\n\\n public override string GetName()\\n {\\n return typeof(PReLU).Name;\\n }\\n\\n public Parameter? weight {\\n get {\\n var res = THSNN_PReLU_weight(handle);\\n if (res == IntPtr.Zero) { torch.CheckForErrors(); }\\n return (res == IntPtr.Zero) ? null : new Parameter(res);\\n }\\n set {\\n THSNN_PReLU_set_weight(handle, value!.Handle);\\n torch.CheckForErrors();\\n ConditionallyRegisterParameter(\\\"weight\\\", value);\\n }\\n }\\n }\\n }\\n\\n public static partial class torch\\n {\\n public static partial class nn\\n {\\n /// <summary>\\n /// Parameterized Rectified Linear Unit\\n /// </summary>\\n /// <param name=\\\"num_parameters\\\">\\n /// Number of 'a' to learn.\\n /// Although it takes an int as input, there is only two values are legitimate: 1, or the number of channels at input.\\n /// </param>\\n /// <param name=\\\"init\\\">The initial value of 'a'.</param>\\n /// <param name=\\\"device\\\">The desired device of the parameters and buffers in this module</param>\\n /// <param name=\\\"dtype\\\">The desired floating point or complex dtype of the parameters and buffers in this module</param>\\n public static PReLU PReLU(long num_parameters, double init = 0.25, Device? device = null, ScalarType? dtype = null)\\n {\\n var handle = THSNN_PReLU_ctor(num_parameters, init, out var boxedHandle);\\n if (handle == IntPtr.Zero) { torch.CheckForErrors(); }\\n return new PReLU(handle, boxedHandle).MoveModule<PReLU>(device, dtype);\\n }\\n\\n public static partial class functional\\n {\\n /// <summary>\\n /// Parameterized Rectified Linear Unit\\n /// </summary>\\n /// <param name=\\\"input\\\">The input tensor</param>\\n /// <param name=\\\"weight\\\">Weight is expected to be a scalar or 1-D tensor.</param>\\n public static Tensor prelu(Tensor input, Tensor weight)\\n {\\n return input.prelu(weight);\\n }\\n }\\n }\\n }\\n}\\n\"\n },\n {\n \"alpha_fraction\": 0.5407118201255798,\n \"alphanum_fraction\": 0.5411993861198425,\n \"avg_line_length\": 31.55555534362793,\n \"blob_id\": \"60e9be32a739f1f7d85ad6a9c8d8a2ad42d85422\",\n \"content_id\": \"8027843562e85e3f9a6f5fcab8fb4ee4cd531345\",\n \"detected_licenses\": [\n \"MIT\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"C#\",\n \"length_bytes\": 2051,\n \"license_type\": \"permissive\",\n \"max_line_length\": 130,\n \"num_lines\": 63,\n \"path\": \"/src/TorchSharp/NN/Activation/LogSigmoid.cs\",\n \"repo_name\": \"NiklasGustafsson/TorchSharp\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"// Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information.\\nusing System;\\nusing static TorchSharp.torch;\\nusing static TorchSharp.PInvoke.NativeMethods;\\n\\nnamespace TorchSharp\\n{\\n using Modules;\\n\\n namespace Modules\\n {\\n /// <summary>\\n /// This class is used to represent a LogSigmoid module.\\n /// </summary>\\n public sealed class LogSigmoid : torch.nn.Module<Tensor, Tensor>\\n {\\n internal LogSigmoid() : base(nameof(LogSigmoid)) { }\\n\\n public override Tensor forward(Tensor tensor)\\n {\\n return tensor.log_sigmoid();\\n }\\n\\n public override string GetName()\\n {\\n return typeof(LogSigmoid).Name;\\n }\\n\\n // Rather than spending cycles only to discover that this module has neither\\n // parameters nor buffers, just shortcut the move completely.\\n protected internal override nn.Module _to(Device device, ScalarType dtype) => this;\\n protected internal override nn.Module _to(DeviceType deviceType, int deviceIndex = -1) => this;\\n protected internal override nn.Module _to(ScalarType dtype) => this;\\n }\\n }\\n public static partial class torch\\n {\\n public static partial class nn\\n {\\n /// <summary>\\n /// LogSigmoid activation\\n /// </summary>\\n /// <returns></returns>\\n public static LogSigmoid LogSigmoid()\\n {\\n return new LogSigmoid();\\n }\\n\\n public static partial class functional\\n {\\n /// <summary>\\n /// LogSigmoid activation\\n /// </summary>\\n /// <param name=\\\"x\\\">The input tensor</param>\\n /// <returns></returns>\\n public static Tensor logsigmoid(Tensor x)\\n {\\n return x.log_sigmoid();\\n }\\n }\\n }\\n }\\n}\\n\"\n },\n {\n \"alpha_fraction\": 0.5108544230461121,\n \"alphanum_fraction\": 0.518925666809082,\n \"avg_line_length\": 41.779762268066406,\n \"blob_id\": \"98d550e3f7aecff71f2ae88945c3e5c0d3a88bc3\",\n \"content_id\": \"a5eefcd5ecfff5b3f3d13fcd38f79aa7793a899b\",\n \"detected_licenses\": [\n \"MIT\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"C#\",\n \"length_bytes\": 7186,\n \"license_type\": \"permissive\",\n \"max_line_length\": 178,\n \"num_lines\": 168,\n \"path\": \"/src/TorchAudio/Transforms/MelSpectrogram.cs\",\n \"repo_name\": \"NiklasGustafsson/TorchSharp\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"// Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information.\\n\\n// A number of implementation details in this file have been translated from the Python version of torchaudio,\\n// largely located in the files found in this folder:\\n//\\n// https://github.com/pytorch/audio/blob/bb77cbebb620a46fdc0dc7e6dae2253eef3f37e2/torchaudio/transforms/_transforms.py\\n//\\n// The origin has the following copyright notice and license:\\n//\\n// https://github.com/pytorch/audio/blob/main/LICENSE\\n//\\n\\nusing System;\\nusing static TorchSharp.torch;\\nusing static TorchSharp.torch.nn;\\nusing static TorchSharp.torchaudio;\\n\\nnamespace TorchSharp\\n{\\n namespace Transforms\\n {\\n public class MelSpectrogram : Module<Tensor, Tensor>\\n {\\n public readonly long sample_rate;\\n public readonly long n_fft;\\n public readonly long win_length;\\n public readonly long hop_length;\\n public readonly long pad;\\n public readonly double power;\\n public readonly bool normalized;\\n public readonly long n_mels;\\n public readonly double? f_max;\\n public readonly double f_min;\\n public readonly Spectrogram spectrogram;\\n public readonly MelScale mel_scale;\\n\\n protected override void Dispose(bool disposing)\\n {\\n if (disposing) {\\n spectrogram.Dispose();\\n mel_scale.Dispose();\\n }\\n base.Dispose(disposing);\\n }\\n\\n\\n internal MelSpectrogram(\\n string name,\\n long sample_rate = 16000,\\n long n_fft = 400,\\n long? win_length = null,\\n long? hop_length = null,\\n double f_min = 0.0,\\n double? f_max = null,\\n long pad = 0,\\n long n_mels = 128,\\n WindowFunction window_fn = null,\\n double power = 2.0,\\n bool normalized = false,\\n bool center = true,\\n PaddingModes pad_mode = PaddingModes.Reflect,\\n bool onesided = true,\\n MelNorm norm = MelNorm.none,\\n torchaudio.MelScale mel_scale = torchaudio.MelScale.htk) : base(name)\\n {\\n this.sample_rate = sample_rate;\\n this.n_fft = n_fft;\\n this.win_length = win_length ?? n_fft;\\n this.hop_length = hop_length ?? (this.win_length / 2);\\n this.pad = pad;\\n this.power = power;\\n this.normalized = normalized;\\n this.n_mels = n_mels; // number of mel frequency bins\\n this.f_max = f_max;\\n this.f_min = f_min;\\n this.spectrogram = torchaudio.transforms.Spectrogram(\\n n_fft: this.n_fft,\\n win_length: this.win_length,\\n hop_length: this.hop_length,\\n pad: this.pad,\\n window_fn: window_fn,\\n power: this.power,\\n normalized: this.normalized,\\n center: center,\\n pad_mode: pad_mode,\\n onesided: onesided);\\n this.mel_scale = torchaudio.transforms.MelScale(\\n this.n_mels, this.sample_rate, this.f_min, this.f_max, this.n_fft / 2 + 1, norm, mel_scale\\n );\\n this.RegisterComponents();\\n }\\n\\n /// <param name=\\\"waveform\\\">Tensor of audio of dimension (..., time).</param>\\n /// <returns>Mel frequency spectrogram of size (..., ``n_mels``, time).</returns>\\n public override Tensor forward(Tensor waveform)\\n {\\n var specgram = this.spectrogram.call(waveform);\\n var mel_specgram = this.mel_scale.call(specgram);\\n return mel_specgram;\\n }\\n }\\n }\\n\\n public partial class torchaudio\\n {\\n public partial class transforms\\n {\\n /// <summary>\\n /// Create MelSpectrogram for a raw audio signal.\\n /// </summary>\\n /// <param name=\\\"sample_rate\\\">Sample rate of audio signal.</param>\\n /// <param name=\\\"n_fft\\\">Size of FFT, creates ``n_fft / 2 + 1`` bins.</param>\\n /// <param name=\\\"win_length\\\">Window size.</param>\\n /// <param name=\\\"hop_length\\\">Length of hop between STFT windows.</param>\\n /// <param name=\\\"f_min\\\">Minimum frequency.</param>\\n /// <param name=\\\"f_max\\\">Maximum frequency.</param>\\n /// <param name=\\\"pad\\\">Two sided padding of signal.</param>\\n /// <param name=\\\"n_mels\\\">Number of mel filterbanks.</param>\\n /// <param name=\\\"window_fn\\\">A function to create a window tensor that is applied/multiplied to each frame/window.</param>\\n /// <param name=\\\"power\\\">Exponent for the magnitude spectrogram, (must be > 0) e.g., 1 for energy, 2 for power, etc.</param>\\n /// <param name=\\\"normalized\\\">Whether to normalize by magnitude after stft.</param>\\n /// <param name=\\\"center\\\">whether to pad :attr:`waveform` on both sides so that the :math:`t`-th frame is centered at time :math:`t \\\\times \\\\text{hop\\\\_length}`.</param>\\n /// <param name=\\\"pad_mode\\\">controls the padding method used when :attr:`center` is ``true``.</param>\\n /// <param name=\\\"onesided\\\">controls whether to return half of results to avoid redundancy.</param>\\n /// <param name=\\\"norm\\\">If \\\"slaney\\\", divide the triangular mel weights by the width of the mel band (area normalization).</param>\\n /// <param name=\\\"mel_scale\\\">Scale to use: ``htk`` or ``slaney``.</param>\\n /// <returns></returns>\\n\\n public static Transforms.MelSpectrogram MelSpectrogram(\\n long sample_rate = 16000,\\n long n_fft = 400,\\n long? win_length = null,\\n long? hop_length = null,\\n double f_min = 0.0,\\n double? f_max = null,\\n long pad = 0,\\n long n_mels = 128,\\n WindowFunction window_fn = null,\\n double power = 2.0,\\n bool normalized = false,\\n bool center = true,\\n PaddingModes pad_mode = PaddingModes.Reflect,\\n bool onesided = true,\\n MelNorm norm = MelNorm.none,\\n torchaudio.MelScale mel_scale = torchaudio.MelScale.htk)\\n {\\n return new Transforms.MelSpectrogram(\\n \\\"MelSpectrogram\\\",\\n sample_rate,\\n n_fft,\\n win_length,\\n hop_length,\\n f_min,\\n f_max,\\n pad,\\n n_mels,\\n window_fn,\\n power,\\n normalized,\\n center,\\n pad_mode,\\n onesided,\\n norm,\\n mel_scale);\\n }\\n }\\n }\\n}\"\n },\n {\n \"alpha_fraction\": 0.5135135054588318,\n \"alphanum_fraction\": 0.5147420167922974,\n \"avg_line_length\": 22.97058868408203,\n \"blob_id\": \"891957cd125641035954ca760ce019810d3e1475\",\n \"content_id\": \"46d408579eddb92837b0832097acd0a054c8be7f\",\n \"detected_licenses\": [\n \"MIT\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"C#\",\n \"length_bytes\": 814,\n \"license_type\": \"permissive\",\n \"max_line_length\": 130,\n \"num_lines\": 34,\n \"path\": \"/src/TorchVision/VerticalFlip.cs\",\n \"repo_name\": \"NiklasGustafsson/TorchSharp\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"// Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information.\\n\\nusing static TorchSharp.torch;\\n\\n\\nnamespace TorchSharp\\n{\\n public static partial class torchvision\\n {\\n internal class VerticalFlip : ITransform\\n {\\n internal VerticalFlip()\\n {\\n }\\n\\n public Tensor call(Tensor input)\\n {\\n return input.flip(-2);\\n }\\n }\\n\\n public static partial class transforms\\n {\\n /// <summary>\\n /// Flip the image vertically.\\n /// </summary>\\n /// <returns></returns>\\n static public ITransform VerticalFlip()\\n {\\n return new VerticalFlip();\\n }\\n }\\n }\\n}\"\n },\n {\n \"alpha_fraction\": 0.5217657685279846,\n \"alphanum_fraction\": 0.5223789215087891,\n \"avg_line_length\": 31,\n \"blob_id\": \"f09a9531aecef0e8c398851d5a03b15d5a9fb34a\",\n \"content_id\": \"6d5b77751166e59c07d9554757a7d150c5bfcc24\",\n \"detected_licenses\": [\n \"MIT\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"C#\",\n \"length_bytes\": 1631,\n \"license_type\": \"permissive\",\n \"max_line_length\": 130,\n \"num_lines\": 51,\n \"path\": \"/src/TorchVision/Resize.cs\",\n \"repo_name\": \"NiklasGustafsson/TorchSharp\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"// Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information.\\n\\nusing static TorchSharp.torch;\\n\\nnamespace TorchSharp\\n{\\n public static partial class torchvision\\n {\\n internal class Resize : ITransform\\n {\\n internal Resize(int height, int width, int? maxSize)\\n {\\n this.height = height;\\n this.width = width;\\n this.maxSize = maxSize;\\n }\\n\\n public Tensor call(Tensor input)\\n {\\n return transforms.functional.resize(input, height, width, maxSize);\\n }\\n\\n private int height, width;\\n private int? maxSize;\\n }\\n\\n public static partial class transforms\\n {\\n /// <summary>\\n /// Resize the input image to the given size.\\n /// </summary>\\n /// <param name=\\\"height\\\">Desired output height</param>\\n /// <param name=\\\"width\\\">Desired output width</param>\\n /// <returns></returns>\\n static public ITransform Resize(int height, int width)\\n {\\n return new Resize(height, width, null);\\n }\\n\\n /// <summary>\\n /// Resize the input image to the given size.\\n /// </summary>\\n /// <param name=\\\"size\\\">Desired output size</param>\\n /// <param name=\\\"maxSize\\\">Max size</param>\\n static public ITransform Resize(int size, int? maxSize = null)\\n {\\n return new Resize(size, -1, maxSize);\\n }\\n }\\n }\\n}\"\n },\n {\n \"alpha_fraction\": 0.6404391527175903,\n \"alphanum_fraction\": 0.6462336182594299,\n \"avg_line_length\": 47.955223083496094,\n \"blob_id\": \"67102487ac147824f8960fc83ee8e8387ae7e2d4\",\n \"content_id\": \"55cb1bf196fd09b43ce0664645d063eb3a78a7c0\",\n \"detected_licenses\": [\n \"MIT\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"C#\",\n \"length_bytes\": 3281,\n \"license_type\": \"permissive\",\n \"max_line_length\": 152,\n \"num_lines\": 67,\n \"path\": \"/src/TorchSharp/Tensor/torch.Tensors.cs\",\n \"repo_name\": \"NiklasGustafsson/TorchSharp\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"// Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information.\\n#nullable enable\\nusing System;\\nusing System.Diagnostics.Contracts;\\n\\nnamespace TorchSharp\\n{\\n // https://pytorch.org/docs/stable/torch#tensors\\n public static partial class torch\\n {\\n // https://pytorch.org/docs/stable/generated/torch.is_tensor\\n [Pure]public static bool is_tensor(object obj) => obj is Tensor;\\n\\n // https://pytorch.org/docs/stable/generated/torch.is_storage\\n [Pure]public static bool is_storage(object obj) => obj is Storage;\\n\\n // https://pytorch.org/docs/stable/generated/torch.is_complex\\n /// <summary>\\n /// Returns True if the data type of input is a complex data type i.e., one of torch.complex64, and torch.complex128.\\n /// </summary>\\n /// <param name=\\\"input\\\">The input tensor</param>\\n public static bool is_complex(Tensor input) => is_complex(input.dtype);\\n\\n // https://pytorch.org/docs/stable/generated/torch.is_floating_point\\n /// <summary>\\n /// Returns True if the data type of input is a floating point data type.\\n /// </summary>\\n /// <param name=\\\"input\\\">The input tensor</param>\\n public static bool is_floating_point(Tensor input) => is_floating_point(input.dtype);\\n\\n // https://pytorch.org/docs/stable/generated/torch.is_nonzero\\n /// <summary>\\n /// Returns True if the input is a single element tensor which is not equal to zero after type conversions,\\n /// i.e. not equal to torch.tensor([0.]) or torch.tensor([0]) or torch.tensor([False]).\\n /// Throws an InvalidOperationException if torch.numel() != 1.\\n /// </summary>\\n /// <param name=\\\"input\\\">The input tensor</param>\\n public static bool is_nonzero(Tensor input) => input.is_nonzero();\\n\\n // https://pytorch.org/docs/stable/generated/torch.set_default_dtype\\n /// <summary>\\n /// Sets the default floating point dtype to d. This dtype is:\\n /// 1. The inferred dtype for python floats in torch.tensor().\\n /// 2. Used to infer dtype for python complex numbers.\\n /// The default complex dtype is set to torch.complex128 if default floating point dtype is torch.float64, otherwise it’s set to torch.complex64\\n /// The default floating point dtype is initially torch.float32.\\n /// </summary>\\n /// <param name=\\\"dtype\\\"></param>\\n public static void set_default_dtype(ScalarType dtype) { default_dtype = dtype; }\\n\\n // https://pytorch.org/docs/stable/generated/torch.get_default_dtype\\n /// <summary>\\n /// Get the current default floating point torch.dtype.\\n /// </summary>\\n /// <returns></returns>\\n [Pure]public static ScalarType get_default_dtype() => default_dtype;\\n\\n // https://pytorch.org/docs/stable/generated/torch.set_default_tensor_type\\n public static void set_default_tensor_type(Tensor t) => set_default_dtype(t.dtype);\\n\\n // https://pytorch.org/docs/stable/generated/torch.numel\\n /// <summary>\\n /// Get the number of elements in the input tensor.\\n /// </summary>\\n [Pure]public static long numel(Tensor input) => input.numel();\\n }\\n}\"\n },\n {\n \"alpha_fraction\": 0.5494467616081238,\n \"alphanum_fraction\": 0.5608575344085693,\n \"avg_line_length\": 32.627906799316406,\n \"blob_id\": \"50b79906d4b177a753d089e6f7e0aa0e7edf69d5\",\n \"content_id\": \"087aab7b722133307ceac79301aad328fd9c28bd\",\n \"detected_licenses\": [\n \"MIT\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"C#\",\n \"length_bytes\": 2892,\n \"license_type\": \"permissive\",\n \"max_line_length\": 130,\n \"num_lines\": 86,\n \"path\": \"/src/TorchAudio/Transforms/GriffinLim.cs\",\n \"repo_name\": \"NiklasGustafsson/TorchSharp\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"// Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information.\\nusing System;\\nusing static TorchSharp.torch;\\nusing static TorchSharp.torchaudio;\\n\\n// A number of implementation details in this file have been translated from the Python version of torchaudio,\\n// largely located in the files found in this folder:\\n//\\n// https://github.com/pytorch/audio/blob/bb77cbebb620a46fdc0dc7e6dae2253eef3f37e2/torchaudio/transforms/_transforms.py\\n//\\n// The origin has the following copyright notice and license:\\n//\\n// https://github.com/pytorch/audio/blob/main/LICENSE\\n//\\n\\nnamespace TorchSharp.Transforms\\n{\\n public sealed class GriffinLim : nn.Module<Tensor, Tensor>, ITransform\\n {\\n public readonly long n_fft;\\n public readonly int n_iter;\\n public readonly long win_length;\\n public readonly long hop_length;\\n public readonly Tensor window;\\n public readonly long? length;\\n public readonly double power;\\n public readonly double momentum;\\n public readonly bool rand_init;\\n\\n protected override void Dispose(bool disposing)\\n {\\n if (disposing) {\\n window.Dispose();\\n }\\n base.Dispose(disposing);\\n }\\n\\n internal GriffinLim(\\n string name,\\n long n_fft = 400,\\n int n_iter = 32,\\n long? win_length = null,\\n long? hop_length = null,\\n WindowFunction window_fn = null,\\n double power = 2.0,\\n double momentum = 0.99,\\n long? length = null,\\n bool rand_init = true) : base(name)\\n {\\n if (momentum < 0 || 1 <= momentum) {\\n throw new ArgumentOutOfRangeException($\\\"momentum must be in the range [0, 1). Found: {momentum}\\\");\\n }\\n\\n this.n_fft = n_fft;\\n this.n_iter = n_iter;\\n this.win_length = win_length ?? n_fft;\\n this.hop_length = hop_length ?? this.win_length / 2;\\n if (window_fn != null) {\\n this.window = window_fn(this.win_length);\\n } else {\\n this.window = torch.hann_window(this.win_length);\\n }\\n this.register_buffer(\\\"window\\\", this.window);\\n this.length = length;\\n this.power = power;\\n this.momentum = momentum;\\n this.rand_init = rand_init;\\n }\\n\\n public override Tensor forward(Tensor specgram)\\n {\\n return torchaudio.functional.griffinlim(\\n specgram,\\n this.window,\\n this.n_fft,\\n this.hop_length,\\n this.win_length,\\n this.power,\\n this.n_iter,\\n this.momentum,\\n this.length,\\n this.rand_init\\n );\\n }\\n }\\n}\\n\"\n },\n {\n \"alpha_fraction\": 0.5665773749351501,\n \"alphanum_fraction\": 0.5825388431549072,\n \"avg_line_length\": 50.86111068725586,\n \"blob_id\": \"cefc3a6e112ef56095866801bcb6099e14efe450\",\n \"content_id\": \"2aece98d00a06c7fc674dd709d4f24f128dd8641\",\n \"detected_licenses\": [\n \"MIT\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"C#\",\n \"length_bytes\": 9335,\n \"license_type\": \"permissive\",\n \"max_line_length\": 182,\n \"num_lines\": 180,\n \"path\": \"/src/TorchVision/Ops/DropBlock.cs\",\n \"repo_name\": \"NiklasGustafsson/TorchSharp\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"// Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information.\\n\\n// A number of implementation details in this file have been translated from the Python version of torchvision,\\n// largely located in the files found in this folder:\\n//\\n// https://github.com/pytorch/vision/blob/a4f53308b2d0f1aa9191686e326f45c26053f686/torchvision/ops/drop_block.py\\n//\\n// The origin has the following copyright notice and license:\\n//\\n// https://github.com/pytorch/vision/blob/main/LICENSE\\n//\\n\\nusing System;\\nusing System.Collections.Generic;\\nusing System.Reflection;\\nusing System.Runtime.CompilerServices;\\nusing TorchSharp.Modules;\\nusing static TorchSharp.torch;\\n\\nnamespace TorchSharp\\n{\\n public static partial class torchvision\\n {\\n\\n public static partial class ops\\n {\\n /// <summary>\\n /// Implements DropBlock2d from `\\\"DropBlock: A regularization method for convolutional networks\\\"\\n /// https://arxiv.org/abs/1810.12890\\n /// </summary>\\n /// <param name=\\\"input\\\">The input tensor of 4 dimensions with the first one beinng its batch.</param>\\n /// <param name=\\\"p\\\">Probability of an element to be dropped.</param>\\n /// <param name=\\\"block_size\\\">Size of the block to drop.</param>\\n /// <param name=\\\"inplace\\\">If set to true, will do this operation in-place</param>\\n /// <param name=\\\"eps\\\">A small value added to the denominator for numerical stability.</param>\\n /// <param name=\\\"training\\\">Apply dropblock if is true</param>\\n /// <returns>The randomly zeroed [N, C, H, W] tensor after dropblock</returns>\\n /// <exception cref=\\\"ArgumentOutOfRangeException\\\">If p is not in the range [0,1]</exception>\\n /// <exception cref=\\\"ArgumentException\\\">If the input tensor is not 4-dimensional</exception>\\n public static Tensor drop_block2d(Tensor input, double p, long block_size, bool inplace = false, double eps = 1e-6, bool training = true)\\n {\\n if (p < 0 || p > 1) throw new ArgumentOutOfRangeException(nameof(p));\\n if (input.ndim != 4) throw new ArgumentException($\\\"input should be 4 dimensional. Got {input.ndim} dimensions.\\\");\\n if (!training || p == 0) return input.alias();\\n\\n var (N, C, H, W) = (input.shape[0], input.shape[1], input.shape[2], input.shape[3]);\\n\\n block_size = Math.Min(block_size, Math.Min(W, H));\\n // compute the gamma of Bernoulli distribution\\n var gamma = (p * H * W) / ((block_size * block_size) * ((H - block_size + 1) * (W - block_size + 1)));\\n var noise = torch.empty(N, C, H - block_size + 1, W - block_size + 1, dtype: input.dtype, device: input.device);\\n noise.bernoulli_(gamma);\\n\\n var pad = block_size / 2;\\n noise = torch.nn.functional.pad(noise, (pad, pad, pad, pad), value: 0);\\n noise = torch.nn.functional.max_pool2d(noise, stride: 1, kernelSize: block_size, padding: block_size / 2);\\n noise = 1 - noise;\\n\\n var normalize_scale = noise.numel() / (eps + noise.sum());\\n if (inplace)\\n input.mul_(noise).mul_(normalize_scale);\\n else\\n input = input * noise * normalize_scale;\\n return input;\\n }\\n\\n /// <summary>\\n /// Implements DropBlock3d from `\\\"DropBlock: A regularization method for convolutional networks\\\"\\n /// https://arxiv.org/abs/1810.12890\\n /// </summary>\\n /// <param name=\\\"input\\\">The input tensor of 5 dimensions with the first one beinng its batch.</param>\\n /// <param name=\\\"p\\\">Probability of an element to be dropped.</param>\\n /// <param name=\\\"block_size\\\">Size of the block to drop.</param>\\n /// <param name=\\\"inplace\\\">If set to true, will do this operation in-place</param>\\n /// <param name=\\\"eps\\\">A small value added to the denominator for numerical stability.</param>\\n /// <param name=\\\"training\\\">Apply dropblock if is true</param>\\n /// <returns>The randomly zeroed [N, C, D, H, W] tensor after dropblock</returns>\\n /// <exception cref=\\\"ArgumentOutOfRangeException\\\">If p is not in the range [0,1]</exception>\\n /// <exception cref=\\\"ArgumentException\\\">If the input tensor is not 5-dimensional</exception>\\n public static Tensor drop_block3d(Tensor input, double p, long block_size, bool inplace = false, double eps = 1e-6, bool training = true)\\n {\\n if (p < 0 || p > 1) throw new ArgumentOutOfRangeException(nameof(p));\\n if (input.ndim != 5) throw new ArgumentException($\\\"input should be 5-dimensional. Got {input.ndim} dimensions.\\\");\\n if (!training || p == 0) return input.alias();\\n\\n var (N, C, D, H, W) = (input.shape[0], input.shape[1], input.shape[2], input.shape[3], input.shape[4]);\\n\\n block_size = Math.Min(Math.Min(block_size, D), Math.Min(W, H));\\n // compute the gamma of Bernoulli distribution\\n var gamma = (p * D * H * W) / ((block_size * block_size * block_size) * ((D - block_size + 1) * (H - block_size + 1) * (W - block_size + 1)));\\n var noise = torch.empty(new[] { N, C, D - block_size + 1, H - block_size + 1, W - block_size + 1 }, dtype: input.dtype, device: input.device);\\n noise.bernoulli_(gamma);\\n\\n var pad = block_size / 2;\\n var padding = new[] { pad, pad, pad, pad, pad, pad };\\n noise = torch.nn.functional.pad(noise, padding, value: 0);\\n noise = torch.nn.functional.max_pool3d(noise, strides: new long[] { 1, 1, 1 }, kernelSize: new[] { block_size, block_size, block_size }, padding: new long[] { pad });\\n noise = 1 - noise;\\n\\n var normalize_scale = noise.numel() / (eps + noise.sum());\\n if (inplace)\\n input.mul_(noise).mul_(normalize_scale);\\n else\\n input = input * noise * normalize_scale;\\n return input;\\n }\\n\\n /// <summary>\\n /// Implements DropBlock2d from `\\\"DropBlock: A regularization method for convolutional networks\\\"\\n /// https://arxiv.org/abs/1810.12890\\n /// </summary>\\n public static DropBlock2d DropBlock2d(double p, long block_size, bool inplace = false, double eps = 1e-6) => new DropBlock2d(p, block_size, inplace, eps);\\n\\n /// <summary>\\n /// Implements DropBlock3d from `\\\"DropBlock: A regularization method for convolutional networks\\\"\\n /// https://arxiv.org/abs/1810.12890\\n /// </summary>\\n public static DropBlock3d DropBlock3d(double p, long block_size, bool inplace = false, double eps = 1e-6) => new DropBlock3d(p, block_size, inplace, eps);\\n }\\n }\\n\\n namespace Modules\\n {\\n public class DropBlock2d : torch.nn.Module<Tensor,Tensor>\\n {\\n public DropBlock2d(double p, long block_size, bool inplace = false, double eps = 1e-6) : base(nameof(DropBlock2d))\\n {\\n this.p = p;\\n this.block_size = block_size;\\n this.inplace = inplace;\\n this.eps = eps;\\n }\\n\\n\\n public override Tensor forward(Tensor input)\\n {\\n return torchvision.ops.drop_block2d(input, p, block_size, inplace, eps, training);\\n }\\n\\n // Rather than spending cycles only to discover that this module has neither\\n // parameters nor buffers, just shortcut the move completely.\\n protected override nn.Module _to(Device device, ScalarType dtype) => this;\\n protected override nn.Module _to(DeviceType deviceType, int deviceIndex = -1) => this;\\n protected override nn.Module _to(ScalarType dtype) => this;\\n\\n private bool inplace;\\n private double p;\\n private long block_size;\\n private double eps;\\n }\\n\\n public class DropBlock3d : torch.nn.Module<Tensor, Tensor>\\n {\\n public DropBlock3d(double p, long block_size, bool inplace = false, double eps = 1e-6) : base(nameof(DropBlock3d))\\n {\\n this.p = p;\\n this.block_size = block_size;\\n this.inplace = inplace;\\n this.eps = eps;\\n }\\n\\n\\n public override Tensor forward(Tensor input)\\n {\\n return torchvision.ops.drop_block3d(input, p, block_size, inplace, eps, training);\\n }\\n\\n // Rather than spending cycles only to discover that this module has neither\\n // parameters nor buffers, just shortcut the move completely.\\n protected override nn.Module _to(Device device, ScalarType dtype) => this;\\n protected override nn.Module _to(DeviceType deviceType, int deviceIndex = -1) => this;\\n protected override nn.Module _to(ScalarType dtype) => this;\\n\\n private bool inplace;\\n private double p;\\n private long block_size;\\n private double eps;\\n }\\n }\\n}\\n\"\n },\n {\n \"alpha_fraction\": 0.7226585745811462,\n \"alphanum_fraction\": 0.7297270894050598,\n \"avg_line_length\": 26.165332794189453,\n \"blob_id\": \"32f1bab56c75157b13ddf13c2ad0aade49a996e7\",\n \"content_id\": \"96ee551e10b40f157239fe22efde491b5f2ad8ec\",\n \"detected_licenses\": [\n \"MIT\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"C++\",\n \"length_bytes\": 10186,\n \"license_type\": \"permissive\",\n \"max_line_length\": 130,\n \"num_lines\": 375,\n \"path\": \"/src/Native/LibTorchSharp/THSSpecial.cpp\",\n \"repo_name\": \"NiklasGustafsson/TorchSharp\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"// Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information.\\n#include \\\"THSTensor.h\\\"\\n\\n#include <iostream>\\n#include <fstream>\\n\\nTensor THSSpecial_airy_ai(const Tensor tensor)\\n{\\n CATCH_TENSOR(torch::special::airy_ai(*tensor));\\n}\\n\\nTensor THSSpecial_airy_ai_out(const Tensor tensor, Tensor out)\\n{\\n CATCH_TENSOR(torch::special::airy_ai_out(*out, *tensor));\\n}\\n\\nTensor THSSpecial_bessel_j0(const Tensor tensor)\\n{\\n CATCH_TENSOR(torch::special::bessel_j0(*tensor));\\n}\\n\\nTensor THSSpecial_bessel_j0_out(const Tensor tensor, Tensor out)\\n{\\n CATCH_TENSOR(torch::special::bessel_j0_out(*out, *tensor));\\n}\\n\\nTensor THSSpecial_bessel_j1(const Tensor tensor)\\n{\\n CATCH_TENSOR(torch::special::bessel_j1(*tensor));\\n}\\n\\nTensor THSSpecial_bessel_j1_out(const Tensor tensor, Tensor out)\\n{\\n CATCH_TENSOR(torch::special::bessel_j1_out(*out, *tensor));\\n}\\n\\nTensor THSSpecial_bessel_y0(const Tensor tensor)\\n{\\n CATCH_TENSOR(torch::special::bessel_y0(*tensor));\\n}\\n\\nTensor THSSpecial_bessel_y0_out(const Tensor tensor, Tensor out)\\n{\\n CATCH_TENSOR(torch::special::bessel_y0_out(*out, *tensor));\\n}\\n\\nTensor THSSpecial_bessel_y1(const Tensor tensor)\\n{\\n CATCH_TENSOR(torch::special::bessel_y1(*tensor));\\n}\\n\\nTensor THSSpecial_bessel_y1_out(const Tensor tensor, Tensor out)\\n{\\n CATCH_TENSOR(torch::special::bessel_y1_out(*out, *tensor));\\n}\\n\\nTensor THSSpecial_modified_bessel_i0(const Tensor tensor)\\n{\\n CATCH_TENSOR(torch::special::modified_bessel_i0(*tensor));\\n}\\n\\nTensor THSSpecial_modified_bessel_i0_out(const Tensor tensor, Tensor out)\\n{\\n CATCH_TENSOR(torch::special::modified_bessel_i0_out(*out, *tensor));\\n}\\n\\nTensor THSSpecial_modified_bessel_i1(const Tensor tensor)\\n{\\n CATCH_TENSOR(torch::special::modified_bessel_i1(*tensor));\\n}\\n\\nTensor THSSpecial_modified_bessel_i1_out(const Tensor tensor, Tensor out)\\n{\\n CATCH_TENSOR(torch::special::modified_bessel_i1_out(*out, *tensor));\\n}\\n\\nTensor THSSpecial_modified_bessel_k0(const Tensor tensor)\\n{\\n CATCH_TENSOR(torch::special::modified_bessel_k0(*tensor));\\n}\\n\\nTensor THSSpecial_modified_bessel_k0_out(const Tensor tensor, Tensor out)\\n{\\n CATCH_TENSOR(torch::special::modified_bessel_k0_out(*out, *tensor));\\n}\\n\\nTensor THSSpecial_modified_bessel_k1(const Tensor tensor)\\n{\\n CATCH_TENSOR(torch::special::modified_bessel_k1(*tensor));\\n}\\n\\nTensor THSSpecial_modified_bessel_k1_out(const Tensor tensor, Tensor out)\\n{\\n CATCH_TENSOR(torch::special::modified_bessel_k1_out(*out, *tensor));\\n}\\n\\nTensor THSSpecial_scaled_modified_bessel_k0(const Tensor tensor)\\n{\\n CATCH_TENSOR(torch::special::scaled_modified_bessel_k0(*tensor));\\n}\\n\\nTensor THSSpecial_scaled_modified_bessel_k0_out(const Tensor tensor, Tensor out)\\n{\\n CATCH_TENSOR(torch::special::scaled_modified_bessel_k0_out(*out, *tensor));\\n}\\n\\nTensor THSSpecial_scaled_modified_bessel_k1(const Tensor tensor)\\n{\\n CATCH_TENSOR(torch::special::scaled_modified_bessel_k1(*tensor));\\n}\\n\\nTensor THSSpecial_scaled_modified_bessel_k1_out(const Tensor tensor, Tensor out)\\n{\\n CATCH_TENSOR(torch::special::scaled_modified_bessel_k1_out(*out, *tensor));\\n}\\n\\nTensor THSSpecial_spherical_bessel_j0(const Tensor tensor)\\n{\\n CATCH_TENSOR(torch::special::spherical_bessel_j0(*tensor));\\n}\\n\\nTensor THSSpecial_spherical_bessel_j0_out(const Tensor tensor, Tensor out)\\n{\\n CATCH_TENSOR(torch::special::spherical_bessel_j0_out(*out, *tensor));\\n}\\n\\nTensor THSSpecial_chebyshev_polynomial_t(const Tensor x, const Tensor n)\\n{\\n CATCH_TENSOR(torch::special::chebyshev_polynomial_t(*x, *n));\\n}\\n\\nTensor THSSpecial_chebyshev_polynomial_t_out(const Tensor x, const Tensor n, Tensor out)\\n{\\n CATCH_TENSOR(torch::special::chebyshev_polynomial_t_out(*out, *x, *n));\\n}\\n\\nTensor THSSpecial_chebyshev_polynomial_u(const Tensor x, const Tensor n)\\n{\\n CATCH_TENSOR(torch::special::chebyshev_polynomial_u(*x, *n));\\n}\\n\\nTensor THSSpecial_chebyshev_polynomial_u_out(const Tensor x, const Tensor n, Tensor out)\\n{\\n CATCH_TENSOR(torch::special::chebyshev_polynomial_u_out(*out, *x, *n));\\n}\\n\\nTensor THSSpecial_chebyshev_polynomial_v(const Tensor x, const Tensor n)\\n{\\n CATCH_TENSOR(torch::special::chebyshev_polynomial_v(*x, *n));\\n}\\n\\nTensor THSSpecial_chebyshev_polynomial_v_out(const Tensor x, const Tensor n, Tensor out)\\n{\\n CATCH_TENSOR(torch::special::chebyshev_polynomial_v_out(*out, *x, *n));\\n}\\n\\nTensor THSSpecial_chebyshev_polynomial_w(const Tensor x, const Tensor n)\\n{\\n CATCH_TENSOR(torch::special::chebyshev_polynomial_w(*x, *n));\\n}\\n\\nTensor THSSpecial_chebyshev_polynomial_w_out(const Tensor x, const Tensor n, Tensor out)\\n{\\n CATCH_TENSOR(torch::special::chebyshev_polynomial_w_out(*out, *x, *n));\\n}\\n\\nTensor THSSpecial_shifted_chebyshev_polynomial_t(const Tensor x, const Tensor n)\\n{\\n CATCH_TENSOR(torch::special::shifted_chebyshev_polynomial_t(*x, *n));\\n}\\n\\nTensor THSSpecial_shifted_chebyshev_polynomial_t_out(const Tensor x, const Tensor n, Tensor out)\\n{\\n CATCH_TENSOR(torch::special::shifted_chebyshev_polynomial_t_out(*out, *x, *n));\\n}\\n\\nTensor THSSpecial_shifted_chebyshev_polynomial_u(const Tensor x, const Tensor n)\\n{\\n CATCH_TENSOR(torch::special::shifted_chebyshev_polynomial_u(*x, *n));\\n}\\n\\nTensor THSSpecial_shifted_chebyshev_polynomial_u_out(const Tensor x, const Tensor n, Tensor out)\\n{\\n CATCH_TENSOR(torch::special::shifted_chebyshev_polynomial_u_out(*out, *x, *n));\\n}\\n\\nTensor THSSpecial_shifted_chebyshev_polynomial_v(const Tensor x, const Tensor n)\\n{\\n CATCH_TENSOR(torch::special::shifted_chebyshev_polynomial_v(*x, *n));\\n}\\n\\nTensor THSSpecial_shifted_chebyshev_polynomial_v_out(const Tensor x, const Tensor n, Tensor out)\\n{\\n CATCH_TENSOR(torch::special::shifted_chebyshev_polynomial_v_out(*out, *x, *n));\\n}\\n\\nTensor THSSpecial_shifted_chebyshev_polynomial_w(const Tensor x, const Tensor n)\\n{\\n CATCH_TENSOR(torch::special::shifted_chebyshev_polynomial_w(*x, *n));\\n}\\n\\nTensor THSSpecial_shifted_chebyshev_polynomial_w_out(const Tensor x, const Tensor n, Tensor out)\\n{\\n CATCH_TENSOR(torch::special::shifted_chebyshev_polynomial_w_out(*out, *x, *n));\\n}\\n\\nTensor THSSpecial_hermite_polynomial_h(const Tensor x, const Tensor n)\\n{\\n CATCH_TENSOR(torch::special::hermite_polynomial_h(*x, *n));\\n}\\n\\nTensor THSSpecial_hermite_polynomial_h_out(const Tensor x, const Tensor n, Tensor out)\\n{\\n CATCH_TENSOR(torch::special::hermite_polynomial_h_out(*out, *x, *n));\\n}\\n\\nTensor THSSpecial_hermite_polynomial_he(const Tensor x, const Tensor n)\\n{\\n CATCH_TENSOR(torch::special::hermite_polynomial_he(*x, *n));\\n}\\n\\nTensor THSSpecial_hermite_polynomial_he_out(const Tensor x, const Tensor n, Tensor out)\\n{\\n CATCH_TENSOR(torch::special::hermite_polynomial_he_out(*out, *x, *n));\\n}\\n\\nTensor THSSpecial_laguerre_polynomial_l(const Tensor x, const Tensor n)\\n{\\n CATCH_TENSOR(torch::special::laguerre_polynomial_l(*x, *n));\\n}\\n\\nTensor THSSpecial_laguerre_polynomial_l_out(const Tensor x, const Tensor n, Tensor out)\\n{\\n CATCH_TENSOR(torch::special::laguerre_polynomial_l_out(*out, *x, *n));\\n}\\n\\nTensor THSSpecial_legendre_polynomial_p(const Tensor x, const Tensor n)\\n{\\n CATCH_TENSOR(torch::special::legendre_polynomial_p(*x, *n));\\n}\\n\\nTensor THSSpecial_legendre_polynomial_p_out(const Tensor x, const Tensor n, Tensor out)\\n{\\n CATCH_TENSOR(torch::special::legendre_polynomial_p_out(*out, *x, *n));\\n}\\n\\nTensor THSSpecial_entr(const Tensor tensor)\\n{\\n CATCH_TENSOR(torch::special::entr(*tensor));\\n}\\n\\nTensor THSSpecial_erf(const Tensor tensor)\\n{\\n CATCH_TENSOR(torch::special::erf(*tensor));\\n}\\n\\nTensor THSSpecial_erfc(const Tensor tensor)\\n{\\n CATCH_TENSOR(torch::special::erfc(*tensor));\\n}\\n\\nTensor THSSpecial_erfcx(const Tensor tensor)\\n{\\n CATCH_TENSOR(torch::special::erfc(*tensor));\\n}\\n\\nTensor THSSpecial_erfinv(const Tensor tensor)\\n{\\n CATCH_TENSOR(torch::special::erfinv(*tensor));\\n}\\n\\nTensor THSSpecial_expit(const Tensor tensor)\\n{\\n CATCH_TENSOR(torch::special::expit(*tensor));\\n}\\n\\nTensor THSSpecial_expm1(const Tensor tensor)\\n{\\n CATCH_TENSOR(torch::special::expm1(*tensor));\\n}\\n\\nTensor THSSpecial_exp2(const Tensor tensor)\\n{\\n CATCH_TENSOR(torch::special::exp2(*tensor));\\n}\\n\\nTensor THSSpecial_gammaln(const Tensor tensor)\\n{\\n CATCH_TENSOR(torch::special::gammaln(*tensor));\\n}\\n\\nTensor THSSpecial_gammainc(const Tensor input, const Tensor other)\\n{\\n CATCH_TENSOR(torch::special::gammainc(*input, *other));\\n}\\n\\nTensor THSSpecial_gammaincc(const Tensor input, const Tensor other)\\n{\\n CATCH_TENSOR(torch::special::gammaincc(*input, *other));\\n}\\n\\nTensor THSSpecial_polygamma(const int64_t n, const Tensor tensor)\\n{\\n CATCH_TENSOR(torch::special::polygamma(n, *tensor));\\n}\\n\\nTensor THSSpecial_digamma(const Tensor tensor)\\n{\\n CATCH_TENSOR(torch::special::digamma(*tensor));\\n}\\n\\nTensor THSSpecial_multigammaln(const Tensor tensor, const int64_t p)\\n{\\n CATCH_TENSOR(torch::special::multigammaln(*tensor, p));\\n}\\n\\nTensor THSSpecial_i0e(const Tensor tensor)\\n{\\n CATCH_TENSOR(torch::special::i0e(*tensor));\\n}\\n\\nTensor THSSpecial_i0(const Tensor tensor)\\n{\\n CATCH_TENSOR(torch::special::i0(*tensor));\\n}\\n\\nTensor THSSpecial_i1e(const Tensor tensor)\\n{\\n CATCH_TENSOR(torch::special::i1e(*tensor));\\n}\\n\\nTensor THSSpecial_i1(const Tensor tensor)\\n{\\n CATCH_TENSOR(torch::special::i1(*tensor));\\n}\\n\\nTensor THSSpecial_logit(const Tensor tensor)\\n{\\n CATCH_TENSOR(torch::special::logit(*tensor));\\n}\\n\\nTensor THSSpecial_log_softmax(const Tensor tensor, int64_t dim, int8_t scalar_type)\\n{\\n CATCH_TENSOR(torch::special::log_softmax(*tensor, dim, c10::ScalarType(scalar_type)));\\n}\\n\\nTensor THSSpecial_ndtr(const Tensor tensor)\\n{\\n CATCH_TENSOR(torch::special::ndtr(*tensor));\\n}\\n\\nTensor THSSpecial_ndtri(const Tensor tensor)\\n{\\n CATCH_TENSOR(torch::special::ndtri(*tensor));\\n}\\n\\nTensor THSSpecial_sinc(const Tensor tensor)\\n{\\n CATCH_TENSOR(torch::special::sinc(*tensor));\\n}\\n\\nTensor THSSpecial_softmax(const Tensor tensor, int64_t dim, int8_t scalar_type)\\n{\\n CATCH_TENSOR(torch::special::softmax(*tensor, dim, c10::ScalarType(scalar_type)));\\n}\\n\\nTensor THSSpecial_xlog1py(const Tensor input, const Tensor other)\\n{\\n CATCH_TENSOR(torch::special::xlog1py(*input, *other));\\n}\\n\\nTensor THSSpecial_zeta(const Tensor input, const Tensor other)\\n{\\n CATCH_TENSOR(torch::special::zeta(*input, *other));\\n}\"\n },\n {\n \"alpha_fraction\": 0.7142857313156128,\n \"alphanum_fraction\": 0.7142857313156128,\n \"avg_line_length\": 36.264705657958984,\n \"blob_id\": \"496f723968283b1f04dc1fb493e887e3f5153cdb\",\n \"content_id\": \"8e6d4f48c78ed238d3f71e1d89ff0fa2e025296b\",\n \"detected_licenses\": [\n \"MIT\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"C#\",\n \"length_bytes\": 1267,\n \"license_type\": \"permissive\",\n \"max_line_length\": 130,\n \"num_lines\": 34,\n \"path\": \"/src/TorchSharp/PInvoke/LibTorchSharp.THSGenerator.cs\",\n \"repo_name\": \"NiklasGustafsson/TorchSharp\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"// Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information.\\n#nullable enable\\nusing System;\\nusing System.Runtime.InteropServices;\\n\\nnamespace TorchSharp.PInvoke\\n{\\n internal static partial class NativeMethods\\n {\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern long THSGenerator_initial_seed(IntPtr handle);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern IntPtr THSGenerator_get_rng_state(IntPtr handle);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern void THSGenerator_set_rng_state(IntPtr handle, IntPtr tensor);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern void THSGenerator_gen_manual_seed(IntPtr handle, long seed);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern IntPtr THSGenerator_new(ulong seed, long device_type, long device_index);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern IntPtr THSGenerator_default_generator();\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern void THSGenerator_dispose(IntPtr handle);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern IntPtr THSGenerator_manual_seed(long seed);\\n }\\n}\\n\"\n },\n {\n \"alpha_fraction\": 0.5340318083763123,\n \"alphanum_fraction\": 0.537695050239563,\n \"avg_line_length\": 47.06338119506836,\n \"blob_id\": \"6454ac651613cae5c50718b7e2d57d09cff6a3c8\",\n \"content_id\": \"53e6facfbd41664c6a26b43d990493026a0833e8\",\n \"detected_licenses\": [\n \"MIT\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"C#\",\n \"length_bytes\": 13651,\n \"license_type\": \"permissive\",\n \"max_line_length\": 236,\n \"num_lines\": 284,\n \"path\": \"/src/TorchSharp/Tensor/Tensor.LinearAlgebra.cs\",\n \"repo_name\": \"NiklasGustafsson/TorchSharp\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"// Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information.\\nusing System;\\nusing System.Linq;\\nusing static TorchSharp.PInvoke.NativeMethods;\\n\\nnamespace TorchSharp\\n{\\n public static partial class torch\\n {\\n public partial class Tensor\\n {\\n // https://pytorch.org/docs/stable/generated/torch.tensordot\\n /// <summary>\\n /// Returns a contraction of a and b over multiple dimensions.\\n /// tensordot implements a generalized matrix product.\\n /// </summary>\\n public Tensor tensordot(Tensor b, long[] dims1, long[] dims2)\\n {\\n IntPtr res;\\n unsafe {\\n fixed (long* pdims1 = dims1, pdims2 = dims2) {\\n res = THSLinalg_tensordot(Handle, b.Handle,(IntPtr)pdims1, dims1.Length,(IntPtr)pdims2, dims2.Length);\\n }\\n }\\n if (res == IntPtr.Zero) {\\n CheckForErrors();\\n }\\n return new Tensor(res);\\n }\\n\\n // https://pytorch.org/docs/stable/generated/torch.tensordot\\n /// <summary>\\n /// Returns a contraction of this tensor and <paramref name=\\\"b\\\"/> over multiple dimensions.\\n /// tensordot implements a generalized matrix product.\\n /// </summary>\\n /// <param name=\\\"b\\\">Right tensor to contract</param>\\n /// <param name=\\\"dims\\\">dimensions to contract for this tensor and <paramref name=\\\"b\\\"/> respectively</param>\\n /// <returns>contraction</returns>\\n public Tensor tensordot(Tensor b, (long, long)[] dims)\\n => tensordot(b, dims.Select(t => t.Item1).ToArray(), dims.Select(t => t.Item2).ToArray());\\n\\n // https://pytorch.org/docs/stable/generated/torch.tensordot\\n /// <summary>\\n /// Returns a contraction of this tensor and <paramref name=\\\"b\\\"/> over multiple dimensions.\\n /// tensordot implements a generalized matrix product.\\n /// </summary>\\n /// <param name=\\\"b\\\">Right tensor to contract</param>\\n /// <param name=\\\"dims\\\">number of dimensions to contract for this tensor and <paramref name=\\\"b\\\"/></param>\\n /// <returns>contraction</returns>\\n public Tensor tensordot(Tensor b, long dims = 2)\\n {\\n if (dims < 0) throw new ArgumentOutOfRangeException(nameof(dims), dims, \\\"must be >= 0\\\");\\n\\n var list = new long[dims];\\n for (long i = 0; i <= dims; i++) {\\n list[i] = i;\\n }\\n\\n return tensordot(b, list, list);\\n }\\n\\n /// <summary>\\n /// Computes the Cholesky decomposition of a symmetric positive-definite matrix AA or for batches of symmetric positive-definite matrices.\\n /// </summary>\\n /// <param name=\\\"upper\\\">If upper is true, the returned matrix U is upper-triangular. If upper is false, the returned matrix L is lower-triangular</param>\\n /// <returns></returns>\\n public Tensor cholesky(bool upper = false)\\n {\\n var res = THSTensor_cholesky(Handle, upper);\\n if (res == IntPtr.Zero) { CheckForErrors(); }\\n return new Tensor(res);\\n }\\n\\n /// <summary>\\n /// Computes the inverse of a symmetric positive-definite matrix AA using its Cholesky factor uu : returns matrix inv\\n /// </summary>\\n /// <param name=\\\"upper\\\"></param>\\n /// <returns></returns>\\n public Tensor cholesky_inverse(bool upper = false)\\n {\\n var res = THSTensor_cholesky_inverse(Handle, upper);\\n if (res == IntPtr.Zero) { CheckForErrors(); }\\n return new Tensor(res);\\n }\\n\\n /// <summary>\\n /// Solves a linear system of equations with a positive semidefinite matrix to be inverted given its Cholesky factor matrix u.\\n /// </summary>\\n /// <param name=\\\"input2\\\"></param>\\n /// <param name=\\\"upper\\\"></param>\\n /// <returns></returns>\\n public Tensor cholesky_solve(Tensor input2, bool upper = false)\\n {\\n var res = THSTensor_cholesky_solve(Handle, input2.Handle, upper);\\n if (res == IntPtr.Zero) { CheckForErrors(); }\\n return new Tensor(res);\\n }\\n\\n /// <summary>\\n /// Computes the cross product of two 3-dimensional vectors.\\n /// </summary>\\n /// <remarks>\\n /// Supports input of float, double, cfloat and cdouble dtypes. Also supports batches of vectors,\\n /// for which it computes the product along the dimension dim. In this case, the output has the\\n /// same batch dimensions as the inputs broadcast to a common shape.\\n /// </remarks>\\n public Tensor cross(Scalar other, long dim)\\n {\\n var res = THSTensor_cross(Handle, other.Handle, dim);\\n if (res == IntPtr.Zero) { CheckForErrors(); }\\n return new Tensor(res);\\n }\\n\\n /// <summary>\\n /// Computes the determinant of a square matrix.\\n /// </summary>\\n public Tensor det()\\n {\\n return linalg.det(this);\\n }\\n\\n /// <summary>\\n /// Calculates log determinant of a square matrix or batches of square matrices.\\n /// </summary>\\n /// <returns></returns>\\n public Tensor logdet()\\n {\\n var shape = this.shape;\\n var len = shape.Length;\\n if (shape[len - 1] != shape[len - 2]) throw new ArgumentException(\\\"The input tensor is not square\\\");\\n\\n var res = THSTensor_logdet(Handle);\\n if (res == IntPtr.Zero) { CheckForErrors(); }\\n return new Tensor(res);\\n }\\n\\n\\n /// <summary>\\n /// This is a low-level function for calling LAPACK’s geqrf directly.\\n /// This function returns a namedtuple (a, tau) as defined in LAPACK documentation for geqrf.\\n /// </summary>\\n /// <remarks>\\n /// Computes a QR decomposition of input. Both Q and R matrices are stored in the same output tensor a.\\n /// The elements of R are stored on and above the diagonal. Elementary reflectors (or Householder vectors)\\n /// implicitly defining matrix Q are stored below the diagonal. The results of this function can be used\\n /// together with torch.linalg.householder_product() to obtain the Q matrix or with torch.ormqr(), which\\n /// uses an implicit representation of the Q matrix, for an efficient matrix-matrix multiplication.\\n /// </remarks>\\n public (Tensor a, Tensor tau) geqrf()\\n {\\n var res = THSTensor_geqrf(Handle, out var tau);\\n if (res == IntPtr.Zero || tau == IntPtr.Zero)\\n torch.CheckForErrors();\\n return (new Tensor(res), new Tensor(tau));\\n }\\n\\n /// <summary>\\n /// Matrix product of two tensors.\\n /// </summary>\\n /// <param name=\\\"target\\\"></param>\\n /// <returns></returns>\\n /// <remarks>\\n /// The behavior depends on the dimensionality of the tensors as follows:\\n /// 1. If both tensors are 1-dimensional, the dot product (scalar) is returned\\n /// 2. If both arguments are 2-dimensional, the matrix-matrix product is returned.\\n /// 3. If the first argument is 1-dimensional and the second argument is 2-dimensional, a 1 is prepended to its dimension for the purpose of the matrix multiply. After the matrix multiply, the prepended dimension is removed.\\n /// 4. If the first argument is 2-dimensional and the second argument is 1-dimensional, the matrix-vector product is returned.\\n /// 5. If both arguments are at least 1-dimensional and at least one argument is N-dimensional (where N > 2), then a batched matrix multiply is returned.\\n /// </remarks>\\n public Tensor matmul(Tensor target)\\n {\\n var res = THSTensor_matmul(Handle, target.Handle);\\n if (res == IntPtr.Zero) { CheckForErrors(); }\\n return new Tensor(res);\\n }\\n\\n /// <summary>\\n /// Performs a matrix multiplication of the matrices input and mat2.\\n /// </summary>\\n /// <param name=\\\"target\\\"></param>\\n /// <returns></returns>\\n public Tensor mm(Tensor target)\\n {\\n var res = THSTensor_mm(Handle, target.Handle);\\n if (res == IntPtr.Zero) { CheckForErrors(); }\\n return new Tensor(res);\\n }\\n\\n /// <summary>\\n /// Performs a matrix-vector product of the matrix input and the vector vec.\\n /// </summary>\\n /// <param name=\\\"target\\\"></param>\\n /// <returns></returns>\\n public Tensor mv(Tensor target)\\n {\\n var res = THSTensor_mv(Handle, target.Handle);\\n if (res == IntPtr.Zero) { CheckForErrors(); }\\n return new Tensor(res);\\n }\\n\\n /// <summary>\\n /// Computes the matrix exponential of a square matrix or of each square matrix in a batch.\\n /// </summary>\\n public Tensor matrix_exp()\\n {\\n var res = THSTensor_matrix_exp(Handle);\\n if (res == IntPtr.Zero) { CheckForErrors(); }\\n return new Tensor(res);\\n }\\n\\n /// <summary>\\n /// Computes the n-th power of a square matrix for an integer n.\\n /// </summary>\\n /// <param name=\\\"n\\\">The exponent</param>\\n /// <returns></returns>\\n /// <remarks>Input tensor must be of shape (*, m, m) where * is zero or more batch dimensions.</remarks>\\n public Tensor matrix_power(int n)\\n {\\n var res = THSLinalg_matrix_power(Handle, n);\\n if (res == IntPtr.Zero) { CheckForErrors(); }\\n return new Tensor(res);\\n }\\n\\n /// <summary>\\n /// Computes the dot product of two 1D tensors.\\n /// </summary>\\n /// <returns></returns>\\n /// <remarks>\\n /// The vdot(a, b) function handles complex numbers differently than dot(a, b).\\n /// If the first argument is complex, the complex conjugate of the first argument is used for the calculation of the dot product.\\n /// </remarks>\\n public Tensor vdot(Tensor target)\\n {\\n if (shape.Length != 1 || target.shape.Length != 1 || shape[0] != target.shape[0]) throw new InvalidOperationException(\\\"vdot arguments must have the same shape.\\\");\\n var res = THSTensor_vdot(Handle, target.Handle);\\n if (res == IntPtr.Zero) { CheckForErrors(); }\\n return new Tensor(res);\\n }\\n\\n /// <summary>\\n /// Computes the dot product of two 1D tensors.\\n /// </summary>\\n /// <returns></returns>\\n public Tensor dot(Tensor target)\\n {\\n if (shape.Length != 1 || target.shape.Length != 1 || shape[0] != target.shape[0]) throw new InvalidOperationException(\\\"dot arguments must have the same shape.\\\");\\n var res = THSTensor_dot(Handle, target.Handle);\\n if (res == IntPtr.Zero) { CheckForErrors(); }\\n return new Tensor(res);\\n }\\n\\n /// <summary>\\n /// Computes the pseudoinverse (Moore-Penrose inverse) of a matrix.\\n /// </summary>\\n /// <param name=\\\"rcond\\\">The tolerance value to determine when is a singular value zero </param>\\n /// <param name=\\\"hermitian\\\">Indicates whether A is Hermitian if complex or symmetric if real. </param>\\n /// <remarks>Input should be tensor of shape (*, m, n) where * is zero or more batch dimensions.</remarks>\\n /// <returns></returns>\\n public Tensor pinverse(double rcond = 1e-15, bool hermitian = false)\\n {\\n var res = THSLinalg_pinverse(Handle, rcond, hermitian);\\n if (res == IntPtr.Zero)\\n CheckForErrors();\\n return new Tensor(res);\\n }\\n\\n /// <summary>\\n /// Computes the matrix-matrix multiplication of a product of Householder matrices with a general matrix.\\n /// </summary>\\n /// <param name=\\\"tau\\\">Tensor of shape (*, min(mn, k)) where * is zero or more batch dimensions.</param>\\n /// <param name=\\\"other\\\">Tensor of shape (*, m, n) where * is zero or more batch dimensions.</param>\\n /// <param name=\\\"left\\\">Controls the order of multiplication.</param>\\n /// <param name=\\\"transpose\\\">Controls whether the matrix Q is conjugate transposed or not.</param>\\n /// <returns></returns>\\n public Tensor ormqr(Tensor tau, Tensor other, bool left = true, bool transpose = false)\\n {\\n var res = THSTensor_ormqr(Handle, tau.handle, other.Handle, left, transpose);\\n if (res == IntPtr.Zero)\\n CheckForErrors();\\n return new Tensor(res);\\n }\\n }\\n }\\n}\"\n },\n {\n \"alpha_fraction\": 0.7786961793899536,\n \"alphanum_fraction\": 0.7956100702285767,\n \"avg_line_length\": 91.2676773071289,\n \"blob_id\": \"00c69f84566b348e4bd5aeff6dd0f6c071100d71\",\n \"content_id\": \"330ceb1d259f0b53bcf94344456f73c4615bb3eb\",\n \"detected_licenses\": [\n \"MIT\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"C\",\n \"length_bytes\": 54807,\n \"license_type\": \"permissive\",\n \"max_line_length\": 478,\n \"num_lines\": 594,\n \"path\": \"/src/Native/LibTorchSharp/THSNN.h\",\n \"repo_name\": \"NiklasGustafsson/TorchSharp\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"// Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information.\\n#pragma once\\n\\n#include \\\"../Stdafx.h\\\"\\n\\n#include \\\"torch/torch.h\\\"\\n\\n#include \\\"Utils.h\\\"\\n\\n// API.\\n\\nEXPORT_API(int) THSNN_Module_has_parameter(const NNModule module, const char* name);\\nEXPORT_API(Tensor) THSNN_Module_get_parameter(const NNModule module, const char* name);\\nEXPORT_API(void) THSNN_Module_get_named_parameters(const NNModule module, Tensor* (*allocator1)(size_t length), const char** (*allocator2)(size_t length));\\nEXPORT_API(void) THSNN_Module_get_named_buffers(const NNModule module, Tensor* (*allocator1)(size_t length), const char** (*allocator2)(size_t length));\\nEXPORT_API(void) THSNN_Module_get_named_children(const NNModule module, NNModule* (*allocator1)(size_t length), const char** (*allocator2)(size_t length));\\nEXPORT_API(void) THSNN_Module_get_named_modules(const NNModule module, NNModule* (*allocator1)(size_t length), const char** (*allocator2)(size_t length));\\nEXPORT_API(void) THSNN_Module_get_parameters(const NNModule module, Tensor* (*allocator1)(size_t length), bool recurse);\\nEXPORT_API(int) THSNN_Module_is_training(NNModule module);\\nEXPORT_API(void) THSNN_Module_train(NNModule module, bool on);\\nEXPORT_API(long) THSNN_Module_children_size(const NNModule module);\\nEXPORT_API(NNModule) THSNN_Module_child(const NNModule module, const int index);\\nEXPORT_API(const char*) THSNN_Module_name(const NNModule module);\\nEXPORT_API(void) THSNN_Module_zero_grad(const NNModule module);\\nEXPORT_API(void) THSNN_Module_save(const NNModule module, const char* location);\\nEXPORT_API(NNModule) THSNN_Module_load(const char* location);\\nEXPORT_API(void) THSNN_Module_register_buffer(const NNModule module, const char* name, const Tensor submodule);\\nEXPORT_API(void) THSNN_Module_register_parameter(const NNModule module, const char* name, const Tensor tensor, bool requires_grad);\\nEXPORT_API(void) THSNN_Module_register_module(const NNModule module, const char* name, const NNModule submodule);\\nEXPORT_API(void) THSNN_Module_dispose(const NNModule module);\\nEXPORT_API(void) THSNN_Module_to_device(NNModule module, int64_t device, int64_t index);\\nEXPORT_API(void) THSNN_Module_to_dtype(NNModule module, int8_t dtype);\\nEXPORT_API(void) THSNN_Module_to_device_dtype(NNModule module, int8_t dtype, int64_t device, int64_t index);\\n\\nEXPORT_API(void) THSNN_AnyModule_dispose(const NNAnyModule module);\\n//EXPORT_API(NNModule) THSNN_AnyModule_get(const NNAnyModule module);\\n\\nEXPORT_API(NNModule) THSNN_custom_module(const char* name, Tensor(*forward)(Tensor), NNAnyModule* outAsAnyModule);\\n\\n// Pooling\\n\\nEXPORT_API(NNModule) THSNN_MaxPool1d_ctor(const int64_t* kernelSize, const int64_t* stride, const int64_t* padding, const int64_t* dilation, bool ceil_mode, NNAnyModule* outAsAnyModule);\\nEXPORT_API(Tensor) THSNN_MaxPool1d_forward(const NNModule module, const Tensor tensor);\\nEXPORT_API(Tensor) THSNN_MaxPool1d_forward_with_indices(const NNModule module, const Tensor tensor, Tensor *indices);\\n\\nEXPORT_API(NNModule) THSNN_MaxPool2d_ctor(const int64_t* kernelSize, const int kernelSizeLength, const int64_t* stride, const int strideLength, const int64_t* padding, const int paddingLength, const int64_t* dilation, const int dilationLength, bool ceil_mode, NNAnyModule* outAsAnyModule);\\nEXPORT_API(Tensor) THSNN_MaxPool2d_forward(const NNModule module, const Tensor tensor);\\nEXPORT_API(Tensor) THSNN_MaxPool2d_forward_with_indices(const NNModule module, const Tensor tensor, Tensor* indices);\\n\\nEXPORT_API(NNModule) THSNN_MaxPool3d_ctor(const int64_t* kernelSize, const int kernelSizeLength, const int64_t* stride, const int strideLength, const int64_t* padding, const int paddingLength, const int64_t* dilation, const int dilationLength, bool ceil_mode, NNAnyModule* outAsAnyModule);\\nEXPORT_API(Tensor) THSNN_MaxPool3d_forward(const NNModule module, const Tensor tensor);\\nEXPORT_API(Tensor) THSNN_MaxPool3d_forward_with_indices(const NNModule module, const Tensor tensor, Tensor* indices);\\n\\nEXPORT_API(NNModule) THSNN_FractionalMaxPool2d_ctor(const int64_t* kernelSize, const int kernelSizeLength, const int64_t* outputSize, const int outputSizeLength, const double* outputRatio, const int outputRatioLength, NNAnyModule* outAsAnyModule);\\nEXPORT_API(Tensor) THSNN_FractionalMaxPool2d_forward(const NNModule module, const Tensor tensor);\\nEXPORT_API(Tensor) THSNN_FractionalMaxPool2d_forward_with_indices(const NNModule module, const Tensor tensor, Tensor* indices);\\n\\nEXPORT_API(NNModule) THSNN_FractionalMaxPool3d_ctor(const int64_t* kernelSize, const int kernelSizeLength, const int64_t* outputSize, const int outputSizeLength, const double* outputRatio, const int outputRatioLength, NNAnyModule* outAsAnyModule);\\nEXPORT_API(Tensor) THSNN_FractionalMaxPool3d_forward(const NNModule module, const Tensor tensor);\\nEXPORT_API(Tensor) THSNN_FractionalMaxPool3d_forward_with_indices(const NNModule module, const Tensor tensor, Tensor* indices);\\n\\nEXPORT_API(NNModule) THSNN_MaxUnpool1d_ctor(const int64_t* kernelSize, const int64_t* stride, const int64_t* padding, NNAnyModule* outAsAnyModule);\\nEXPORT_API(Tensor) THSNN_MaxUnpool1d_forward(const NNModule module, const Tensor tensor, const Tensor indices, const int64_t* outputSize);\\n\\nEXPORT_API(NNModule) THSNN_MaxUnpool2d_ctor(const int64_t* kernelSize, const int kernelSizeLength, const int64_t* stride, const int strideLength, const int64_t* padding, const int paddingLength, NNAnyModule* outAsAnyModule);\\nEXPORT_API(Tensor) THSNN_MaxUnpool2d_forward(const NNModule module, const Tensor tensor, const Tensor indices, const int64_t* outputSize, const int outputSizeLength);\\n\\nEXPORT_API(NNModule) THSNN_MaxUnpool3d_ctor(const int64_t* kernelSize, const int kernelSizeLength, const int64_t* stride, const int strideLength, const int64_t* padding, const int paddingLength, NNAnyModule* outAsAnyModule);\\nEXPORT_API(Tensor) THSNN_MaxUnpool3d_forward(const NNModule module, const Tensor tensor, const Tensor indices, const int64_t* outputSize, const int outputSizeLength);\\n\\nEXPORT_API(NNModule) THSNN_AdaptiveAvgPool1d_ctor(const int64_t* sizes, const int length, NNAnyModule* outAsAnyModule);\\nEXPORT_API(Tensor) THSNN_AdaptiveAvgPool1d_forward(const NNModule module, const Tensor tensor);\\nEXPORT_API(NNModule) THSNN_AdaptiveAvgPool2d_ctor(const int64_t* sizes, const int length, NNAnyModule* outAsAnyModule);\\nEXPORT_API(Tensor) THSNN_AdaptiveAvgPool2d_forward(const NNModule module, const Tensor tensor);\\nEXPORT_API(NNModule) THSNN_AdaptiveAvgPool3d_ctor(const int64_t* sizes, const int length, NNAnyModule* outAsAnyModule);\\nEXPORT_API(Tensor) THSNN_AdaptiveAvgPool3d_forward(const NNModule module, const Tensor tensor);\\n\\nEXPORT_API(NNModule) THSNN_AdaptiveMaxPool1d_ctor(const int64_t* sizes, const int length, NNAnyModule* outAsAnyModule);\\nEXPORT_API(Tensor) THSNN_AdaptiveMaxPool1d_forward(const NNModule module, const Tensor tensor);\\nEXPORT_API(NNModule) THSNN_AdaptiveMaxPool2d_ctor(const int64_t* sizes, const int length, NNAnyModule* outAsAnyModule);\\nEXPORT_API(Tensor) THSNN_AdaptiveMaxPool2d_forward(const NNModule module, const Tensor tensor);\\nEXPORT_API(NNModule) THSNN_AdaptiveMaxPool3d_ctor(const int64_t* sizes, const int length, NNAnyModule* outAsAnyModule);\\nEXPORT_API(Tensor) THSNN_AdaptiveMaxPool3d_forward(const NNModule module, const Tensor tensor);\\n\\nEXPORT_API(NNModule) THSNN_AvgPool1d_ctor(const int64_t* kernelSize, const int64_t* stride, const int64_t* padding, bool ceil_mode, bool count_include_pad, int64_t divisor_override, NNAnyModule* outAsAnyModule);\\nEXPORT_API(Tensor) THSNN_AvgPool1d_forward(const NNModule module, const Tensor tensor);\\nEXPORT_API(NNModule) THSNN_AvgPool2d_ctor(const int64_t* kernelSize, const int kernelSizeLength, const int64_t* stride, const int strideLength, const int64_t* padding, const int paddingLength, bool ceil_mode, bool count_include_pad, int64_t divisor_override, NNAnyModule* outAsAnyModule);\\nEXPORT_API(Tensor) THSNN_AvgPool2d_forward(const NNModule module, const Tensor tensor);\\nEXPORT_API(NNModule) THSNN_AvgPool3d_ctor(const int64_t* kernelSize, const int kernelSizeLength, const int64_t* stride, const int strideLength, const int64_t* padding, const int paddingLength, bool ceil_mode, bool count_include_pad, int64_t divisor_override, NNAnyModule* outAsAnyModule);\\nEXPORT_API(Tensor) THSNN_AvgPool3d_forward(const NNModule module, const Tensor tensor);\\n\\nEXPORT_API(NNModule) THSNN_LPPool1d_ctor(double norm_type, const int64_t* kernelSize, const int64_t* stride, bool ceil_mode, NNAnyModule* outAsAnyModule);\\nEXPORT_API(Tensor) THSNN_LPPool1d_forward(const NNModule module, const Tensor tensor);\\nEXPORT_API(NNModule) THSNN_LPPool2d_ctor(double norm_type, const int64_t* kernelSize, const int kernelSizeLength, const int64_t* stride, const int strideLength, bool ceil_mode, NNAnyModule* outAsAnyModule);\\nEXPORT_API(Tensor) THSNN_LPPool2d_forward(const NNModule module, const Tensor tensor);\\n\\n// Padding\\n\\nEXPORT_API(NNModule) THSNN_ZeroPad2d_ctor(const int64_t padding, NNAnyModule* outAsAnyModule);\\nEXPORT_API(NNModule) THSNN_ZeroPad2d_ctor_tuple(const int64_t padding_left, const int64_t padding_right, const int64_t padding_top, const int64_t padding_bottom, NNAnyModule* outAsAnyModule);\\nEXPORT_API(Tensor) THSNN_ZeroPad2d_forward(const NNModule module, const Tensor tensor);\\n\\nEXPORT_API(NNModule) THSNN_ConstantPad1d_ctor(const double value, const int64_t padding, NNAnyModule* outAsAnyModule);\\nEXPORT_API(NNModule) THSNN_ConstantPad1d_ctor_tuple(const double value, const int64_t padding_left, const int64_t padding_right, NNAnyModule* outAsAnyModule);\\nEXPORT_API(Tensor) THSNN_ConstantPad1d_forward(const NNModule module, const Tensor tensor);\\nEXPORT_API(NNModule) THSNN_ConstantPad2d_ctor(const double value, const int64_t padding, NNAnyModule* outAsAnyModule);\\nEXPORT_API(NNModule) THSNN_ConstantPad2d_ctor_tuple(const double value, const int64_t padding_left, const int64_t padding_right, const int64_t padding_top, const int64_t padding_bottom, NNAnyModule* outAsAnyModule);\\nEXPORT_API(Tensor) THSNN_ConstantPad2d_forward(const NNModule module, const Tensor tensor);\\nEXPORT_API(NNModule) THSNN_ConstantPad3d_ctor(const double value, const int64_t padding, NNAnyModule* outAsAnyModule);\\nEXPORT_API(NNModule) THSNN_ConstantPad3d_ctor_tuple(const double value, const int64_t padding_left, const int64_t padding_right, const int64_t padding_top, const int64_t padding_bottom, const int64_t padding_front, const int64_t padding_back, NNAnyModule* outAsAnyModule);\\nEXPORT_API(Tensor) THSNN_ConstantPad3d_forward(const NNModule module, const Tensor tensor);\\n\\nEXPORT_API(NNModule) THSNN_ReplicationPad1d_ctor(const int64_t padding, NNAnyModule* outAsAnyModule);\\nEXPORT_API(NNModule) THSNN_ReplicationPad1d_ctor_tuple(const int64_t padding_left, const int64_t padding_right, NNAnyModule* outAsAnyModule);\\nEXPORT_API(Tensor) THSNN_ReplicationPad1d_forward(const NNModule module, const Tensor tensor);\\nEXPORT_API(NNModule) THSNN_ReplicationPad2d_ctor(const int64_t padding, NNAnyModule* outAsAnyModule);\\nEXPORT_API(NNModule) THSNN_ReplicationPad2d_ctor_tuple(const int64_t padding_left, const int64_t padding_right, const int64_t padding_top, const int64_t padding_bottom, NNAnyModule* outAsAnyModule);\\nEXPORT_API(Tensor) THSNN_ReplicationPad2d_forward(const NNModule module, const Tensor tensor);\\nEXPORT_API(NNModule) THSNN_ReplicationPad3d_ctor(const int64_t padding, NNAnyModule* outAsAnyModule);\\nEXPORT_API(NNModule) THSNN_ReplicationPad3d_ctor_tuple(const int64_t padding_left, const int64_t padding_right, const int64_t padding_top, const int64_t padding_bottom, const int64_t padding_front, const int64_t padding_back, NNAnyModule* outAsAnyModule);\\nEXPORT_API(Tensor) THSNN_ReplicationPad3d_forward(const NNModule module, const Tensor tensor);\\n\\nEXPORT_API(NNModule) THSNN_ReflectionPad1d_ctor(const int64_t padding, NNAnyModule* outAsAnyModule);\\nEXPORT_API(NNModule) THSNN_ReflectionPad1d_ctor_tuple(const int64_t padding_left, const int64_t padding_right, NNAnyModule* outAsAnyModule);\\nEXPORT_API(Tensor) THSNN_ReflectionPad1d_forward(const NNModule module, const Tensor tensor);\\nEXPORT_API(NNModule) THSNN_ReflectionPad2d_ctor(const int64_t padding, NNAnyModule* outAsAnyModule);\\nEXPORT_API(NNModule) THSNN_ReflectionPad2d_ctor_tuple(const int64_t padding_left, const int64_t padding_right, const int64_t padding_top, const int64_t padding_bottom, NNAnyModule* outAsAnyModule);\\nEXPORT_API(Tensor) THSNN_ReflectionPad2d_forward(const NNModule module, const Tensor tensor);\\nEXPORT_API(NNModule) THSNN_ReflectionPad3d_ctor(const int64_t padding, NNAnyModule* outAsAnyModule);\\nEXPORT_API(NNModule) THSNN_ReflectionPad3d_ctor_tuple(const int64_t padding_left, const int64_t padding_right, const int64_t padding_top, const int64_t padding_bottom, const int64_t padding_front, const int64_t padding_back, NNAnyModule* outAsAnyModule);\\nEXPORT_API(Tensor) THSNN_ReflectionPad3d_forward(const NNModule module, const Tensor tensor);\\n\\n// Convolution\\n\\nEXPORT_API(NNModule) THSNN_Conv1d_ctor(const int64_t inputChannel, const int64_t outputChannel, const int64_t kernelSize, const int64_t stride, const int64_t padding, const int64_t dilation, const int64_t paddingMode, const int64_t groups, const bool bias, NNAnyModule* outAsAnyModule);\\nEXPORT_API(Tensor) THSNN_Conv1d_forward(const NNModule module, const Tensor tensor);\\nEXPORT_API(Tensor) THSNN_Conv1d_bias(const NNModule module);\\nEXPORT_API(void) THSNN_Conv1d_set_bias(const NNModule module, const Tensor bias);\\nEXPORT_API(Tensor) THSNN_Conv1d_weight(const NNModule module);\\nEXPORT_API(void) THSNN_Conv1d_set_weight(const NNModule module, const Tensor weight);\\nEXPORT_API(NNModule) THSNN_Conv2d_ctor(const int64_t inputChannel, const int64_t outputChannel, const int64_t kernelSize, const int64_t stride, const int64_t padding, const int64_t dilation, const int64_t paddingMode, const int64_t groups, const bool bias, NNAnyModule* outAsAnyModule);\\nEXPORT_API(NNModule) THSNN_Conv2d_ctor_1(const int64_t inputChannel, const int64_t outputChannel, const int64_t kernelX, const int64_t kernelY, const int64_t strideX, const int64_t strideY, const int64_t paddingX, const int64_t paddingY, const int64_t dilationX, const int64_t dilationY, const int64_t paddingMode, const int64_t groups, const bool bias, NNAnyModule* outAsAnyModule);\\nEXPORT_API(Tensor) THSNN_Conv2d_forward(const NNModule module, const Tensor tensor);\\nEXPORT_API(Tensor) THSNN_Conv2d_weight(const NNModule module);\\nEXPORT_API(void) THSNN_Conv2d_set_weight(const NNModule module, const Tensor weight);\\nEXPORT_API(Tensor) THSNN_Conv2d_bias(const NNModule module);\\nEXPORT_API(void) THSNN_Conv2d_set_bias(const NNModule module, const Tensor bias);\\nEXPORT_API(NNModule) THSNN_Conv3d_ctor(const int64_t inputChannel, const int64_t outputChannel, const int64_t kernelSize, const int64_t stride, const int64_t padding, const int64_t dilation, const int64_t paddingMode, const int64_t groups, const bool bias, NNAnyModule* outAsAnyModule);\\nEXPORT_API(NNModule) THSNN_Conv3d_ctor_1(const int64_t inputChannel, const int64_t outputChannel, const int64_t kernelX, const int64_t kernelY, const int64_t kernelZ, const int64_t strideX, const int64_t strideY, const int64_t strideZ, const int64_t paddingX, const int64_t paddingY, const int64_t paddingZ, const int64_t dilationX, const int64_t dilationY, const int64_t dilationZ, const int64_t paddingMode, const int64_t groups, const bool bias, NNAnyModule* outAsAnyModule);\\nEXPORT_API(Tensor) THSNN_Conv3d_forward(const NNModule module, const Tensor tensor);\\nEXPORT_API(Tensor) THSNN_Conv3d_weight(const NNModule module);\\nEXPORT_API(void) THSNN_Conv3d_set_weight(const NNModule module, const Tensor weight);\\nEXPORT_API(Tensor) THSNN_Conv3d_bias(const NNModule module);\\nEXPORT_API(void) THSNN_Conv3d_set_bias(const NNModule module, const Tensor bias);\\n\\nEXPORT_API(NNModule) THSNN_ConvTranspose1d_ctor(const int64_t inputChannel, const int64_t outputChannel, const int64_t kernelSize, const int64_t stride, const int64_t padding, const int64_t output_padding, const int64_t dilation, const int64_t paddingMode, const int64_t groups, const bool bias, NNAnyModule* outAsAnyModule);\\nEXPORT_API(Tensor) THSNN_ConvTranspose1d_forward(const NNModule module, const Tensor tensor);\\nEXPORT_API(Tensor) THSNN_ConvTranspose1d_bias(const NNModule module);\\nEXPORT_API(void) THSNN_ConvTranspose1d_set_bias(const NNModule module, const Tensor bias);\\nEXPORT_API(Tensor) THSNN_ConvTranspose1d_weight(const NNModule module);\\nEXPORT_API(void) THSNN_ConvTranspose1d_set_weight(const NNModule module, const Tensor weight);\\nEXPORT_API(NNModule) THSNN_ConvTranspose2d_ctor(const int64_t inputChannel, const int64_t outputChannel, const int64_t kernelSize, const int64_t stride, const int64_t padding, const int64_t output_padding, const int64_t dilation, const int64_t paddingMode, const int64_t groups, const bool bias, NNAnyModule* outAsAnyModule);\\nEXPORT_API(Tensor) THSNN_ConvTranspose2d_forward(const NNModule module, const Tensor tensor);\\nEXPORT_API(Tensor) THSNN_ConvTranspose2d_weight(const NNModule module);\\nEXPORT_API(void) THSNN_ConvTranspose2d_set_weight(const NNModule module, const Tensor weight);\\nEXPORT_API(Tensor) THSNN_ConvTranspose2d_bias(const NNModule module);\\nEXPORT_API(void) THSNN_ConvTranspose2d_set_bias(const NNModule module, const Tensor bias);\\nEXPORT_API(NNModule) THSNN_ConvTranspose3d_ctor(const int64_t inputChannel, const int64_t outputChannel, const int64_t kernelSize, const int64_t stride, const int64_t padding, const int64_t output_padding, const int64_t dilation, const int64_t paddingMode, const int64_t groups, const bool bias, NNAnyModule* outAsAnyModule);\\nEXPORT_API(Tensor) THSNN_ConvTranspose3d_forward(const NNModule module, const Tensor tensor);\\nEXPORT_API(Tensor) THSNN_ConvTranspose3d_weight(const NNModule module);\\nEXPORT_API(void) THSNN_ConvTranspose3d_set_weight(const NNModule module, const Tensor weight);\\nEXPORT_API(Tensor) THSNN_ConvTranspose3d_bias(const NNModule module);\\nEXPORT_API(void) THSNN_ConvTranspose3d_set_bias(const NNModule module, const Tensor bias);\\n\\n// Normalization\\n\\nEXPORT_API(NNModule) THSNN_BatchNorm1d_ctor(const int64_t features, const double eps, const double momentum, const bool affine, const bool track_running_stats, NNAnyModule* outAsAnyModule);\\nEXPORT_API(Tensor) THSNN_BatchNorm1d_forward(const NNModule module, const Tensor tensor);\\nEXPORT_API(NNModule) THSNN_BatchNorm2d_ctor(const int64_t features, const double eps, const double momentum, const bool affine, const bool track_running_stats, NNAnyModule* outAsAnyModule);\\nEXPORT_API(Tensor) THSNN_BatchNorm2d_forward(const NNModule module, const Tensor tensor);\\nEXPORT_API(NNModule) THSNN_BatchNorm3d_ctor(const int64_t features, const double eps, const double momentum, const bool affine, const bool track_running_stats, NNAnyModule* outAsAnyModule);\\nEXPORT_API(Tensor) THSNN_BatchNorm3d_forward(const NNModule module, const Tensor tensor);\\n\\nEXPORT_API(Tensor) THSNN_BatchNorm1d_bias(const NNModule module);\\nEXPORT_API(void) THSNN_BatchNorm1d_set_bias(const NNModule module, const Tensor bias);\\nEXPORT_API(Tensor) THSNN_BatchNorm1d_weight(const NNModule module);\\nEXPORT_API(void) THSNN_BatchNorm1d_set_weight(const NNModule module, const Tensor weight);\\n\\nEXPORT_API(Tensor) THSNN_BatchNorm2d_bias(const NNModule module);\\nEXPORT_API(void) THSNN_BatchNorm2d_set_bias(const NNModule module, const Tensor bias);\\nEXPORT_API(Tensor) THSNN_BatchNorm2d_weight(const NNModule module);\\nEXPORT_API(void) THSNN_BatchNorm2d_set_weight(const NNModule module, const Tensor weight);\\n\\nEXPORT_API(Tensor) THSNN_BatchNorm3d_bias(const NNModule module);\\nEXPORT_API(void) THSNN_BatchNorm3d_set_bias(const NNModule module, const Tensor bias);\\nEXPORT_API(Tensor) THSNN_BatchNorm3d_weight(const NNModule module);\\nEXPORT_API(void) THSNN_BatchNorm3d_set_weight(const NNModule module, const Tensor weight);\\n\\nEXPORT_API(void) THSNN_BatchNorm1d_reset_stats(const NNModule module);\\nEXPORT_API(void) THSNN_BatchNorm2d_reset_stats(const NNModule module);\\nEXPORT_API(void) THSNN_BatchNorm3d_reset_stats(const NNModule module);\\n\\nEXPORT_API(Tensor) THSNN_BatchNorm1d_get_mean(const NNModule module);\\nEXPORT_API(Tensor) THSNN_BatchNorm2d_get_mean(const NNModule module);\\nEXPORT_API(Tensor) THSNN_BatchNorm3d_get_mean(const NNModule module);\\n\\nEXPORT_API(void) THSNN_BatchNorm1d_set_mean(const NNModule module, const Tensor weight);\\nEXPORT_API(void) THSNN_BatchNorm2d_set_mean(const NNModule module, const Tensor weight);\\nEXPORT_API(void) THSNN_BatchNorm3d_set_mean(const NNModule module, const Tensor weight);\\n\\nEXPORT_API(Tensor) THSNN_BatchNorm1d_get_var(const NNModule module);\\nEXPORT_API(Tensor) THSNN_BatchNorm2d_get_var(const NNModule module);\\nEXPORT_API(Tensor) THSNN_BatchNorm3d_get_var(const NNModule module);\\n\\nEXPORT_API(void) THSNN_BatchNorm1d_set_var(const NNModule module, const Tensor weight);\\nEXPORT_API(void) THSNN_BatchNorm2d_set_var(const NNModule module, const Tensor weight);\\nEXPORT_API(void) THSNN_BatchNorm3d_set_var(const NNModule module, const Tensor weight);\\n\\nEXPORT_API(Tensor) THSNN_BatchNorm1d_get_batches(const NNModule module);\\nEXPORT_API(Tensor) THSNN_BatchNorm2d_get_batches(const NNModule module);\\nEXPORT_API(Tensor) THSNN_BatchNorm3d_get_batches(const NNModule module);\\n\\nEXPORT_API(NNModule) THSNN_InstanceNorm1d_ctor(const int64_t features, const double eps, const double momentum, const bool affine, const bool track_running_stats, NNAnyModule* outAsAnyModule);\\nEXPORT_API(Tensor) THSNN_InstanceNorm1d_forward(const NNModule module, const Tensor tensor);\\nEXPORT_API(NNModule) THSNN_InstanceNorm2d_ctor(const int64_t features, const double eps, const double momentum, const bool affine, const bool track_running_stats, NNAnyModule* outAsAnyModule);\\nEXPORT_API(Tensor) THSNN_InstanceNorm2d_forward(const NNModule module, const Tensor tensor);\\nEXPORT_API(NNModule) THSNN_InstanceNorm3d_ctor(const int64_t features, const double eps, const double momentum, const bool affine, const bool track_running_stats, NNAnyModule* outAsAnyModule);\\nEXPORT_API(Tensor) THSNN_InstanceNorm3d_forward(const NNModule module, const Tensor tensor);\\n\\nEXPORT_API(Tensor) THSNN_InstanceNorm1d_bias(const NNModule module);\\nEXPORT_API(void) THSNN_InstanceNorm1d_set_bias(const NNModule module, const Tensor bias);\\nEXPORT_API(Tensor) THSNN_InstanceNorm1d_weight(const NNModule module);\\nEXPORT_API(void) THSNN_InstanceNorm1d_set_weight(const NNModule module, const Tensor weight);\\n\\nEXPORT_API(Tensor) THSNN_InstanceNorm2d_bias(const NNModule module);\\nEXPORT_API(void) THSNN_InstanceNorm2d_set_bias(const NNModule module, const Tensor bias);\\nEXPORT_API(Tensor) THSNN_InstanceNorm2d_weight(const NNModule module);\\nEXPORT_API(void) THSNN_InstanceNorm2d_set_weight(const NNModule module, const Tensor weight);\\n\\nEXPORT_API(Tensor) THSNN_InstanceNorm3d_bias(const NNModule module);\\nEXPORT_API(void) THSNN_InstanceNorm3d_set_bias(const NNModule module, const Tensor bias);\\nEXPORT_API(Tensor) THSNN_InstanceNorm3d_weight(const NNModule module);\\nEXPORT_API(void) THSNN_InstanceNorm3d_set_weight(const NNModule module, const Tensor weight);\\n\\nEXPORT_API(void) THSNN_InstanceNorm1d_reset_stats(const NNModule module);\\nEXPORT_API(void) THSNN_InstanceNorm2d_reset_stats(const NNModule module);\\nEXPORT_API(void) THSNN_InstanceNorm3d_reset_stats(const NNModule module);\\n\\nEXPORT_API(Tensor) THSNN_InstanceNorm1d_get_mean(const NNModule module);\\nEXPORT_API(Tensor) THSNN_InstanceNorm2d_get_mean(const NNModule module);\\nEXPORT_API(Tensor) THSNN_InstanceNorm3d_get_mean(const NNModule module);\\n\\nEXPORT_API(void) THSNN_InstanceNorm1d_set_mean(const NNModule module, const Tensor weight);\\nEXPORT_API(void) THSNN_InstanceNorm2d_set_mean(const NNModule module, const Tensor weight);\\nEXPORT_API(void) THSNN_InstanceNorm3d_set_mean(const NNModule module, const Tensor weight);\\n\\nEXPORT_API(Tensor) THSNN_InstanceNorm1d_get_var(const NNModule module);\\nEXPORT_API(Tensor) THSNN_InstanceNorm2d_get_var(const NNModule module);\\nEXPORT_API(Tensor) THSNN_InstanceNorm3d_get_var(const NNModule module);\\n\\nEXPORT_API(void) THSNN_InstanceNorm1d_set_var(const NNModule module, const Tensor weight);\\nEXPORT_API(void) THSNN_InstanceNorm2d_set_var(const NNModule module, const Tensor weight);\\nEXPORT_API(void) THSNN_InstanceNorm3d_set_var(const NNModule module, const Tensor weight);\\n\\nEXPORT_API(Tensor) THSNN_InstanceNorm1d_get_batches(const NNModule module);\\nEXPORT_API(Tensor) THSNN_InstanceNorm2d_get_batches(const NNModule module);\\nEXPORT_API(Tensor) THSNN_InstanceNorm3d_get_batches(const NNModule module);\\n\\n\\n\\nEXPORT_API(NNModule) THSNN_LayerNorm_ctor(const int64_t* norm_shape, const int64_t norm_shape_len, const double eps, const bool elementwise_affine, NNAnyModule* outAsAnyModule);\\nEXPORT_API(Tensor) THSNN_LayerNorm_forward(const NNModule module, const Tensor tensor);\\n\\nEXPORT_API(Tensor) THSNN_LayerNorm_bias(const NNModule module);\\nEXPORT_API(void) THSNN_LayerNorm_set_bias(const NNModule module, const Tensor bias);\\nEXPORT_API(Tensor) THSNN_LayerNorm_weight(const NNModule module);\\nEXPORT_API(void) THSNN_LayerNorm_set_weight(const NNModule module, const Tensor weight);\\n\\nEXPORT_API(NNModule) THSNN_GroupNorm_ctor(const int64_t num_groups, const int64_t num_channels, const double eps, const bool affine, NNAnyModule* outAsAnyModule);\\nEXPORT_API(Tensor) THSNN_GroupNorm_forward(const NNModule module, const Tensor tensor);\\n\\nEXPORT_API(Tensor) THSNN_GroupNorm_bias(const NNModule module);\\nEXPORT_API(void) THSNN_GroupNorm_set_bias(const NNModule module, const Tensor bias);\\nEXPORT_API(Tensor) THSNN_GroupNorm_weight(const NNModule module);\\nEXPORT_API(void) THSNN_GroupNorm_set_weight(const NNModule module, const Tensor weight);\\n\\nEXPORT_API(NNModule) THSNN_LocalResponseNorm_ctor(const int64_t size, const double alpha, const double beta, const double k, NNAnyModule* outAsAnyModule);\\nEXPORT_API(Tensor) THSNN_LocalResponseNorm_forward(const NNModule module, const Tensor tensor);\\n\\nEXPORT_API(Tensor) THSNN_batch_norm(const Tensor input, const Tensor running_mean, const Tensor running_var, const Tensor weight, const Tensor bias, const bool training, const double momentum, const double eps);\\nEXPORT_API(Tensor) THSNN_group_norm(const Tensor input, int64_t num_groups, const Tensor weight, const Tensor bias, const double eps);\\nEXPORT_API(Tensor) THSNN_instance_norm(const Tensor input, const Tensor running_mean, const Tensor running_var, const Tensor weight, const Tensor bias, const bool use_input_stats, const double momentum, const double eps);\\nEXPORT_API(Tensor) THSNN_layer_norm(const Tensor input, const int64_t* normalized_shape, const int64_t normalized_shape_len, const Tensor weight, const Tensor bias, const double eps);\\nEXPORT_API(Tensor) THSNN_local_response_norm(const Tensor input, const int64_t size, const double alpha, const double beta, const double k);\\n\\n// Dropout\\n\\nEXPORT_API(NNModule) THSNN_Dropout_ctor(double probability, bool inplace, NNAnyModule* outAsAnyModule);\\nEXPORT_API(Tensor) THSNN_Dropout_forward(const NNModule module, const Tensor tensor);\\nEXPORT_API(NNModule) THSNN_Dropout1d_ctor(double probability, bool inplace, NNAnyModule* outAsAnyModule);\\nEXPORT_API(Tensor) THSNN_Dropout1d_forward(const NNModule module, const Tensor tensor);\\nEXPORT_API(NNModule) THSNN_Dropout2d_ctor(double probability, bool inplace, NNAnyModule* outAsAnyModule);\\nEXPORT_API(Tensor) THSNN_Dropout2d_forward(const NNModule module, const Tensor tensor);\\nEXPORT_API(NNModule) THSNN_Dropout3d_ctor(double probability, bool inplace, NNAnyModule* outAsAnyModule);\\nEXPORT_API(Tensor) THSNN_Dropout3d_forward(const NNModule module, const Tensor tensor);\\n\\nEXPORT_API(NNModule) THSNN_AlphaDropout_ctor(double probability, bool inplace, NNAnyModule* outAsAnyModule);\\nEXPORT_API(Tensor) THSNN_AlphaDropout_forward(const NNModule module, const Tensor tensor);\\n\\nEXPORT_API(NNModule) THSNN_FeatureAlphaDropout_ctor(double probability, NNAnyModule* outAsAnyModule);\\nEXPORT_API(Tensor) THSNN_FeatureAlphaDropout_forward(const NNModule module, const Tensor tensor);\\n\\nEXPORT_API(Tensor) THSNN_dropout(const Tensor input, const double p, bool training, bool inplace);\\nEXPORT_API(Tensor) THSNN_dropout2d(const Tensor input, const double p, bool training, bool inplace);\\nEXPORT_API(Tensor) THSNN_dropout3d(const Tensor input, const double p, bool training, bool inplace);\\n\\nEXPORT_API(Tensor) THSNN_alpha_dropout(const Tensor input, const double p, bool training, bool inplace);\\n\\nEXPORT_API(Tensor) THSNN_feature_alpha_dropout(const Tensor input, const double p, bool training, bool inplace);\\n\\nEXPORT_API(Tensor) THSNN_fold(const Tensor input, const int64_t out1, const int64_t out2, const int64_t kernel1, const int64_t kernel2, const int64_t stride1, const int64_t stride2, const int64_t pad1, const int64_t pad2, const int64_t dil1, const int64_t dil2);\\nEXPORT_API(Tensor) THSNN_unfold(const Tensor input, const int64_t kernel1, const int64_t kernel2, const int64_t stride1, const int64_t stride2, const int64_t pad1, const int64_t pad2, const int64_t dil1, const int64_t dil2);\\n\\n// Linear\\n\\nEXPORT_API(NNModule) THSNN_Identity_ctor(NNAnyModule* outAsAnyModule);\\nEXPORT_API(Tensor) THSNN_Identity_forward(const NNModule module, const Tensor tensor);\\nEXPORT_API(NNModule) THSNN_Linear_ctor(const int64_t input_size, const int64_t output_size, const bool with_bias, NNAnyModule* outAsAnyModule);\\nEXPORT_API(Tensor) THSNN_Linear_forward(const NNModule module, const Tensor tensor);\\nEXPORT_API(Tensor) THSNN_Linear_bias(const NNModule module);\\nEXPORT_API(void) THSNN_Linear_set_bias(const NNModule module, const Tensor tensor);\\nEXPORT_API(Tensor) THSNN_Linear_weight(const NNModule module);\\nEXPORT_API(void) THSNN_Linear_set_weight(const NNModule module, const Tensor tensor);\\n\\nEXPORT_API(Tensor) THSNN_functional_linear(const Tensor input, const Tensor weights, const Tensor bias);\\nEXPORT_API(Tensor) THSNN_functional_bilinear(const Tensor input1, const Tensor input2, const Tensor weights, const Tensor bias);\\n\\nEXPORT_API(NNModule) THSNN_Bilinear_ctor(const int64_t input_size_1, const int64_t input_size_2, const int64_t output_size, const bool with_bias, NNAnyModule* outAsAnyModule);\\nEXPORT_API(Tensor) THSNN_Bilinear_forward(const NNModule module, const Tensor x1, const Tensor x2);\\nEXPORT_API(Tensor) THSNN_Bilinear_bias(const NNModule module);\\nEXPORT_API(void) THSNN_Bilinear_set_bias(const NNModule module, const Tensor tensor);\\nEXPORT_API(Tensor) THSNN_Bilinear_weight(const NNModule module);\\nEXPORT_API(void) THSNN_Bilinear_set_weight(const NNModule module, const Tensor tensor);\\n\\n// Vision -- Modules\\n\\nEXPORT_API(NNModule) THSNN_PixelShuffle_ctor(const int64_t upscale_factor, NNAnyModule* outAsAnyModule);\\nEXPORT_API(Tensor) THSNN_PixelShuffle_forward(const NNModule module, const Tensor tensor);\\nEXPORT_API(NNModule) THSNN_PixelUnshuffle_ctor(const int64_t downscale_factor, NNAnyModule* outAsAnyModule);\\nEXPORT_API(Tensor) THSNN_PixelUnshuffle_forward(const NNModule module, const Tensor tensor);\\nEXPORT_API(NNModule) THSNN_Upsample_ctor(const int64_t* size, const int size_len, const double* scale_factor, const int scale_factor_len, const int8_t mode, const int8_t align_corners, NNAnyModule* outAsAnyModule);\\nEXPORT_API(Tensor) THSNN_Upsample_forward(const NNModule module, const Tensor tensor);\\n\\n// Vision -- Functions\\n\\nEXPORT_API(Tensor) THSNN_pad(const Tensor input, const int64_t* pad, const int pad_length, const int8_t mode, const double value);\\nEXPORT_API(Tensor) THSNN_interpolate(const Tensor input, const int64_t* size, const int size_len, const double* scale_factor, const int scale_factor_len, const int8_t mode, const int8_t align_corners, const bool recompute_scale_factor, NNAnyModule* outAsAnyModule);\\nEXPORT_API(Tensor) THSNN_grid_sample(const Tensor input, const Tensor grid, const int8_t mode, const int8_t padding_mode, const int8_t align_corners);\\nEXPORT_API(Tensor) THSNN_affine_grid(const Tensor theta, const int64_t* size, const int size_len, const bool align_corners);\\n\\n// Activation functions\\n\\nEXPORT_API(NNModule) THSNN_CELU_ctor(const double alpha, const bool inplace, NNAnyModule* outAsAnyModule);\\nEXPORT_API(Tensor) THSNN_CELU_forward(const NNModule module, const Tensor tensor);\\nEXPORT_API(NNModule) THSNN_ELU_ctor(const double alpha, const bool inplace, NNAnyModule* outAsAnyModule);\\nEXPORT_API(Tensor) THSNN_ELU_forward(const NNModule module, const Tensor tensor);\\nEXPORT_API(NNModule) THSNN_GELU_ctor(NNAnyModule* outAsAnyModule);\\nEXPORT_API(Tensor) THSNN_GELU_forward(const NNModule module, const Tensor tensor);\\nEXPORT_API(NNModule) THSNN_GLU_ctor(const int64_t dim, NNAnyModule* outAsAnyModule);\\nEXPORT_API(Tensor) THSNN_GLU_forward(const NNModule module, const Tensor tensor);\\nEXPORT_API(NNModule) THSNN_Hardshrink_ctor(const double lambda, NNAnyModule* outAsAnyModule);\\nEXPORT_API(Tensor) THSNN_Hardshrink_forward(const NNModule module, const Tensor tensor);\\nEXPORT_API(NNModule) THSNN_Hardtanh_ctor(const double min_val, const double max_val, const bool inplace, NNAnyModule* outAsAnyModule);\\nEXPORT_API(Tensor) THSNN_Hardtanh_forward(const NNModule module, const Tensor tensor);\\nEXPORT_API(NNModule) THSNN_LeakyReLU_ctor(const double negative_sloope, const bool inplace, NNAnyModule* outAsAnyModule);\\nEXPORT_API(Tensor) THSNN_LeakyReLU_forward(const NNModule module, const Tensor tensor);\\nEXPORT_API(NNModule) THSNN_Mish_ctor(NNAnyModule* outAsAnyModule);\\nEXPORT_API(Tensor) THSNN_Mish_forward(const NNModule module, const Tensor tensor);\\nEXPORT_API(NNModule) THSNN_PReLU_ctor(const int64_t nparams, const double init, NNAnyModule* outAsAnyModule);\\nEXPORT_API(Tensor) THSNN_PReLU_forward(const NNModule module, const Tensor tensor);\\nEXPORT_API(Tensor) THSNN_PReLU_weight(const NNModule module);\\nEXPORT_API(void) THSNN_PReLU_set_weight(const NNModule module, const Tensor weight);\\nEXPORT_API(NNModule) THSNN_ReLU_ctor(bool inplace, NNAnyModule* outAsAnyModule);\\nEXPORT_API(Tensor) THSNN_ReLU_forward(const NNModule module, const Tensor tensor);\\nEXPORT_API(NNModule) THSNN_ReLU6_ctor(bool inplace, NNAnyModule* outAsAnyModule);\\nEXPORT_API(Tensor) THSNN_ReLU6_forward(const NNModule module, const Tensor tensor);\\nEXPORT_API(NNModule) THSNN_RReLU_ctor(const double lower, const double upper, const bool inplace, NNAnyModule* outAsAnyModule);\\nEXPORT_API(Tensor) THSNN_RReLU_forward(const NNModule module, const Tensor tensor);\\nEXPORT_API(NNModule) THSNN_LogSoftmax_ctor(int64_t dim, NNAnyModule* outAsAnyModule);\\nEXPORT_API(Tensor) THSNN_LogSoftmax_forward(const NNModule module, const Tensor tensor);\\nEXPORT_API(NNModule) THSNN_SELU_ctor(bool inplace, NNAnyModule* outAsAnyModule);\\nEXPORT_API(Tensor) THSNN_SELU_forward(const NNModule module, const Tensor tensor);\\nEXPORT_API(NNModule) THSNN_Sigmoid_ctor(NNAnyModule* outAsAnyModule);\\nEXPORT_API(Tensor) THSNN_Sigmoid_forward(const NNModule module, const Tensor tensor);\\nEXPORT_API(NNModule) THSNN_SiLU_ctor(NNAnyModule* outAsAnyModule);\\nEXPORT_API(Tensor) THSNN_SiLU_forward(const NNModule module, const Tensor tensor);\\nEXPORT_API(NNModule) THSNN_Softmax_ctor(const int64_t dim, NNAnyModule* outAsAnyModule);\\nEXPORT_API(Tensor) THSNN_Softmax_forward(const NNModule module, const Tensor tensor);\\nEXPORT_API(NNModule) THSNN_Softmax2d_ctor(NNAnyModule* outAsAnyModule);\\nEXPORT_API(Tensor) THSNN_Softmax2d_forward(const NNModule module, const Tensor tensor);\\nEXPORT_API(NNModule) THSNN_Softmin_ctor(const int64_t dim, NNAnyModule* outAsAnyModule);\\nEXPORT_API(Tensor) THSNN_Softmin_forward(const NNModule module, const Tensor tensor);\\nEXPORT_API(NNModule) THSNN_Softplus_ctor(const double beta, const double threshold, NNAnyModule* outAsAnyModule);\\nEXPORT_API(Tensor) THSNN_Softplus_forward(const NNModule module, const Tensor tensor);\\nEXPORT_API(NNModule) THSNN_Softshrink_ctor(const double lambda, NNAnyModule* outAsAnyModule);\\nEXPORT_API(Tensor) THSNN_Softshrink_forward(const NNModule module, const Tensor tensor);\\nEXPORT_API(NNModule) THSNN_Softsign_ctor(NNAnyModule* outAsAnyModule);\\nEXPORT_API(Tensor) THSNN_Softsign_forward(const NNModule module, const Tensor tensor);\\nEXPORT_API(NNModule) THSNN_Tanh_ctor(NNAnyModule* outAsAnyModule);\\nEXPORT_API(Tensor) THSNN_Tanh_forward(const NNModule module, const Tensor tensor);\\nEXPORT_API(NNModule) THSNN_Tanhshrink_ctor(NNAnyModule* outAsAnyModule);\\nEXPORT_API(Tensor) THSNN_Tanhshrink_forward(const NNModule module, const Tensor tensor);\\nEXPORT_API(NNModule) THSNN_Threshold_ctor(const double threshold, const double value, const bool inplace, NNAnyModule* outAsAnyModule);\\nEXPORT_API(Tensor) THSNN_Threshold_forward(const NNModule module, const Tensor tensor);\\n\\n// Sparse\\n\\nEXPORT_API(NNModule) THSNN_Embedding_ctor(const int64_t num_embeddings, const int64_t embedding_dims, const int64_t padding_idx, bool has_pi, const double max_norm, const bool has_mn, const double norm_type, const bool scale_grad_by_freq, const bool sparse, NNAnyModule* outAsAnyModule);\\nEXPORT_API(NNModule) THSNN_Embedding_from_pretrained(const Tensor embeddings, const bool freeze, const int64_t padding_idx, bool has_pi, const double max_norm, const bool has_mn, const double norm_type, const bool scale_grad_by_freq, const bool sparse, NNAnyModule* outAsAnyModule);\\nEXPORT_API(Tensor) THSNN_Embedding_forward(const NNModule module, const Tensor weights);\\nEXPORT_API(Tensor) THSNN_Embedding_weight(const NNModule module);\\nEXPORT_API(void) THSNN_Embedding_set_weight(const NNModule module, const Tensor weights);\\n\\nEXPORT_API(NNModule) THSNN_EmbeddingBag_ctor(const int64_t num_embeddings, const int64_t embedding_dims, const double max_norm, const bool has_mn, const double norm_type, const bool scale_grad_by_freq, const int64_t mode, const bool sparse, const bool include_last_offset, const int64_t padding_idx, NNAnyModule* outAsAnyModule);\\nEXPORT_API(NNModule) THSNN_EmbeddingBag_from_pretrained(const Tensor embeddings, const bool freeze, const double max_norm, const bool has_mn, const double norm_type, const bool scale_grad_by_freq, const int64_t mode, const bool sparse, const bool include_last_offset, const int64_t padding_idx, NNAnyModule* outAsAnyModule);\\nEXPORT_API(Tensor) THSNN_EmbeddingBag_forward(const NNModule module, const Tensor input, const Tensor offsets, const Tensor per_sample_weights);\\nEXPORT_API(Tensor) THSNN_EmbeddingBag_weight(const NNModule module);\\nEXPORT_API(void) THSNN_EmbeddingBag_set_weight(const NNModule module, const Tensor weights);\\n\\n// Transformer\\n\\nEXPORT_API(NNModule) THSNN_Transformer_ctor(const int64_t d_model, const int64_t nhead, const int64_t num_encoder_layers, const int64_t num_decoder_layers, const int64_t dim_feedforward, const double dropout, const int64_t activation, NNAnyModule* outAsAnyModule);\\nEXPORT_API(Tensor) THSNN_Transformer_forward(const NNModule module, const Tensor src, const Tensor tgt, const Tensor src_mask, const Tensor tgt_mask, const Tensor memory_mask, const Tensor src_key_padding_mask, const Tensor tgt_key_padding_mask, const Tensor memory_key_padding_mask);\\nEXPORT_API(NNModule) THSNN_TransformerEncoderLayer_ctor(const int64_t d_model, const int64_t nhead, const int64_t dim_feedforward, const double dropout, const int64_t activation, NNAnyModule* outAsAnyModule);\\nEXPORT_API(Tensor) THSNN_TransformerEncoderLayer_forward(const NNModule module, const Tensor src, const Tensor src_mask, const Tensor src_key_padding_mask);\\nEXPORT_API(NNModule) THSNN_TransformerDecoderLayer_ctor(const int64_t d_model, const int64_t nhead, const int64_t dim_feedforward, const double dropout, const int64_t activation, NNAnyModule* outAsAnyModule);\\nEXPORT_API(Tensor) THSNN_TransformerDecoderLayer_forward(const NNModule module, const Tensor tgt, const Tensor memory, const Tensor tgt_mask, const Tensor memory_mask, const Tensor tgt_key_padding_mask, const Tensor memory_key_padding_mask);\\nEXPORT_API(NNModule) THSNN_TransformerEncoder_ctor(const NNModule encoder_layer, const int64_t num_layers, NNAnyModule* outAsAnyModule);\\nEXPORT_API(Tensor) THSNN_TransformerEncoder_forward(const NNModule module, const Tensor src, const Tensor src_mask, const Tensor src_key_padding_mask);\\nEXPORT_API(NNModule) THSNN_TransformerDecoder_ctor(const NNModule decoder_layer, const int64_t num_layers, NNAnyModule* outAsAnyModule);\\nEXPORT_API(Tensor) THSNN_TransformerDecoder_forward(const NNModule module, const Tensor tgt, const Tensor memory, const Tensor tgt_mask, const Tensor memory_mask, const Tensor tgt_key_padding_mask, const Tensor memory_key_padding_mask);\\nEXPORT_API(NNModule) THSNN_MultiheadAttention_ctor(const int64_t embeded_dim, const int64_t num_heads, const double dropout, const bool bias, const bool add_bias_kv, const bool add_zero_attn, const int64_t kdim, const int64_t vdim, NNAnyModule* outAsAnyModule);\\nEXPORT_API(void) THSNN_MultiheadAttention_forward(const NNModule module, const Tensor query, const Tensor key, const Tensor value, const Tensor key_padding_mask, const bool need_weights, const Tensor attn_mask, Tensor& res1, Tensor& res2);\\n\\n\\n// Recurrent\\n\\nEXPORT_API(NNModule) THSNN_RNN_ctor(const int64_t input_size, const int64_t hidden_size, const int64_t num_layers, const int64_t nonlinearity, const bool bias, const bool batchFirst, const double dropout, const bool bidirectional, NNAnyModule* outAsAnyModule);\\nEXPORT_API(Tensor) THSNN_RNN_forward(const NNModule module, const Tensor input1, const Tensor input2, Tensor* h_n);\\nEXPORT_API(PackedSequence) THSNN_RNN_forward_with_packed_input(const NNModule module, const PackedSequence input1, const Tensor input2, Tensor* h_n);\\nEXPORT_API(NNModule) THSNN_GRU_ctor(const int64_t input_size, const int64_t hidden_size, const int64_t num_layers, const bool bias, const bool batchFirst, const double dropout, const bool bidirectional, NNAnyModule* outAsAnyModule);\\nEXPORT_API(Tensor) THSNN_GRU_forward(const NNModule module, const Tensor input1, const Tensor input2, Tensor* h_n);\\nEXPORT_API(PackedSequence) THSNN_GRU_forward_with_packed_input(const NNModule module, const PackedSequence input1, const Tensor input2, Tensor* h_n);\\nEXPORT_API(NNModule) THSNN_LSTM_ctor(const int64_t input_size, const int64_t hidden_size, const int64_t num_layers, const bool bias, const bool batchFirst, const double dropout, const bool bidirectional, NNAnyModule* outAsAnyModule);\\nEXPORT_API(Tensor) THSNN_LSTM_forward(const NNModule module, const Tensor input1, const Tensor h0, const Tensor c0, Tensor* h_n, Tensor* c_n);\\nEXPORT_API(PackedSequence) THSNN_LSTM_forward_with_packed_input(const NNModule module, const PackedSequence input1, const Tensor h0, const Tensor c0, Tensor* h_n, Tensor* c_n);\\n\\nEXPORT_API(NNModule) THSNN_RNNCell_ctor(const int64_t input_size, const int64_t hidden_size, const int64_t nonlinearity, const bool bias, NNAnyModule* outAsAnyModule);\\nEXPORT_API(Tensor) THSNN_RNNCell_forward(const NNModule module, const Tensor input1, const Tensor h0);\\nEXPORT_API(NNModule) THSNN_GRUCell_ctor(const int64_t input_size, const int64_t hidden_size, const bool bias, NNAnyModule* outAsAnyModule);\\nEXPORT_API(Tensor) THSNN_GRUCell_forward(const NNModule module, const Tensor input1, const Tensor h0);\\nEXPORT_API(NNModule) THSNN_LSTMCell_ctor(const int64_t input_size, const int64_t hidden_size, const bool bias, NNAnyModule* outAsAnyModule);\\nEXPORT_API(Tensor) THSNN_LSTMCell_forward(const NNModule module, const Tensor input1, const Tensor h0, const Tensor c0, Tensor* c_n);\\n\\nEXPORT_API(void) THSNN_RNN_flatten_parameters(const NNModule module);\\nEXPORT_API(void) THSNN_GRU_flatten_parameters(const NNModule module);\\nEXPORT_API(void) THSNN_LSTM_flatten_parameters(const NNModule module);\\n\\nEXPORT_API(Tensor) THSNN_RNN_bias_ih(const NNModule module, const int64_t idx);\\nEXPORT_API(void) THSNN_RNN_set_bias_ih(const NNModule module, const Tensor bias, const int64_t idx);\\nEXPORT_API(Tensor) THSNN_RNN_weight_ih(const NNModule module, const int64_t idx);\\nEXPORT_API(void) THSNN_RNN_set_weight_ih(const NNModule module, const Tensor weight, const int64_t idx);\\nEXPORT_API(Tensor) THSNN_RNN_bias_hh(const NNModule module, const int64_t idx);\\nEXPORT_API(void) THSNN_RNN_set_bias_hh(const NNModule module, const Tensor bias, const int64_t idx);\\nEXPORT_API(Tensor) THSNN_RNN_weight_hh(const NNModule module, const int64_t idx);\\nEXPORT_API(void) THSNN_RNN_set_weight_hh(const NNModule module, const Tensor weight, const int64_t idx);\\n\\nEXPORT_API(Tensor) THSNN_RNNCell_bias_ih(const NNModule module);\\nEXPORT_API(void) THSNN_RNNCell_set_bias_ih(const NNModule module, const Tensor bias);\\nEXPORT_API(Tensor) THSNN_RNNCell_weight_ih(const NNModule module);\\nEXPORT_API(void) THSNN_RNNCell_set_weight_ih(const NNModule module, const Tensor weight);\\nEXPORT_API(Tensor) THSNN_RNNCell_bias_hh(const NNModule module);\\nEXPORT_API(void) THSNN_RNNCell_set_bias_hh(const NNModule module, const Tensor bias);\\nEXPORT_API(Tensor) THSNN_RNNCell_weight_hh(const NNModule module);\\nEXPORT_API(void) THSNN_RNNCell_set_weight_hh(const NNModule module, const Tensor weight);\\n\\nEXPORT_API(Tensor) THSNN_LSTMCell_bias_ih(const NNModule module);\\nEXPORT_API(void) THSNN_LSTMCell_set_bias_ih(const NNModule module, const Tensor bias);\\nEXPORT_API(Tensor) THSNN_LSTMCell_weight_ih(const NNModule module);\\nEXPORT_API(void) THSNN_LSTMCell_set_weight_ih(const NNModule module, const Tensor weight);\\nEXPORT_API(Tensor) THSNN_LSTMCell_bias_hh(const NNModule module);\\nEXPORT_API(void) THSNN_LSTMCell_set_bias_hh(const NNModule module, const Tensor bias);\\nEXPORT_API(Tensor) THSNN_LSTMCell_weight_hh(const NNModule module);\\nEXPORT_API(void) THSNN_LSTMCell_set_weight_hh(const NNModule module, const Tensor weight);\\n\\nEXPORT_API(Tensor) THSNN_GRUCell_bias_ih(const NNModule module);\\nEXPORT_API(void) THSNN_GRUCell_set_bias_ih(const NNModule module, const Tensor bias);\\nEXPORT_API(Tensor) THSNN_GRUCell_weight_ih(const NNModule module);\\nEXPORT_API(void) THSNN_GRUCell_set_weight_ih(const NNModule module, const Tensor weight);\\nEXPORT_API(Tensor) THSNN_GRUCell_bias_hh(const NNModule module);\\nEXPORT_API(void) THSNN_GRUCell_set_bias_hh(const NNModule module, const Tensor bias);\\nEXPORT_API(Tensor) THSNN_GRUCell_weight_hh(const NNModule module);\\nEXPORT_API(void) THSNN_GRUCell_set_weight_hh(const NNModule module, const Tensor weight);\\n\\n// Containers\\n\\nEXPORT_API(NNModule) THSNN_Sequential_ctor();\\nEXPORT_API(void) THSNN_Sequential_push_back(const NNModule module, const char* name, const NNAnyModule submodule);\\nEXPORT_API(Tensor) THSNN_Sequential_forward(const NNModule module, const Tensor tensor);\\n\\n// Loss functions\\n\\nEXPORT_API(Tensor) THSNN_binary_cross_entropy(const Tensor input, const Tensor target, const Tensor weight, const int64_t reduction);\\nEXPORT_API(Tensor) THSNN_binary_cross_entropy_with_logits(const Tensor input, const Tensor target, const Tensor weight, const int64_t reduction, const Tensor pos_weights_);\\nEXPORT_API(Tensor) THSNN_cosine_embedding_loss(const Tensor input1, const Tensor input2, const Tensor target, const double margin, const int64_t reduction);\\nEXPORT_API(Tensor) THSNN_cross_entropy(const Tensor input, const Tensor target, const Tensor weight, const int64_t ignore_index, const bool has_ii, const int64_t reduction, const double smoothing);\\nEXPORT_API(Tensor) THSNN_ctc_loss(const Tensor log_probs, const Tensor targets, const Tensor input_lengths, const Tensor target_lengths, int64_t blank, bool zero_infinity, const int64_t reduction);\\nEXPORT_API(Tensor) THSNN_hinge_embedding_loss(const Tensor input, const Tensor target, const double margin, const int64_t reduction);\\nEXPORT_API(Tensor) THSNN_huber_loss(const Tensor input, const Tensor target, const double delta, const int64_t reduction);\\nEXPORT_API(Tensor) THSNN_l1_loss(const Tensor input, const Tensor target, const int64_t reduction);\\nEXPORT_API(Tensor) THSNN_margin_ranking_loss(const Tensor input1, const Tensor input2, const Tensor target, const double margin, const int64_t reduction);\\nEXPORT_API(Tensor) THSNN_mse_loss(const Tensor input, const Tensor target, const int64_t reduction);\\nEXPORT_API(Tensor) THSNN_multilabel_margin_loss(const Tensor input, const Tensor target, const int64_t reduction);\\nEXPORT_API(Tensor) THSNN_multilabel_soft_margin_loss(const Tensor input, const Tensor target, const Tensor weight, const int64_t reduction);\\nEXPORT_API(Tensor) THSNN_multi_margin_loss(const Tensor input, const Tensor target, const int64_t p, const double margin, const Tensor weight, const int64_t reduction);\\nEXPORT_API(Tensor) THSNN_nll_loss(const Tensor input, const Tensor target, const Tensor weight, const int64_t reduction);\\nEXPORT_API(Tensor) THSNN_poisson_loss(const Tensor input, const Tensor target, const bool logInput, const bool full, const double eps, const int64_t reduction);\\nEXPORT_API(Tensor) THSNN_kl_div_loss(const Tensor input, const Tensor target, const int64_t reduction, const bool log_target);\\nEXPORT_API(Tensor) THSNN_smooth_l1_loss(const Tensor input, const Tensor target, const int64_t reduction, const double beta);\\nEXPORT_API(Tensor) THSNN_soft_margin_loss(const Tensor input, const Tensor target, const int64_t reduction);\\nEXPORT_API(Tensor) THSNN_triplet_margin_loss(const Tensor anchor, const Tensor positive, const Tensor negative, double margin, int64_t p, double eps, bool swap, const int64_t reduction);\\nEXPORT_API(Tensor) THSNN_triplet_margin_with_distance_loss(const Tensor anchor, const Tensor positive, const Tensor negative, Tensor (*distance_function)(const Tensor x, const Tensor y), double margin, bool swap, const int64_t reduction);\\n\\n// Optimizers\\n\\nEXPORT_API(Optimizer) THSNN_Adagrad_ctor(const Tensor* parameters, const int len, const double learning_rate, const double lr_decay, const double weight_decay, const double initial_accumulator_value, const double eps);\\nEXPORT_API(Optimizer) THSNN_Adam_ctor(const Tensor* parameters, const int len, const double learning_rate, const double beta1, const double beta2, const double eps, const double weight_decay, const bool amsgrad);\\nEXPORT_API(Optimizer) THSNN_AdamW_ctor(const Tensor* parameters, const int len, const double learning_rate, const double beta1, const double beta2, const double eps, const double weight_decay, const bool amsgrad);\\nEXPORT_API(Optimizer) THSNN_LBFGS_ctor(const Tensor* parameters, const int len, const double lr, const int64_t max_iter, const int64_t max_eval, const double tolerange_grad, const double tolerance_change, const int64_t history_size);\\nEXPORT_API(Optimizer) THSNN_RMSprop_ctor(const Tensor* parameters, const int length, const double learning_rate, const double alpha, const double eps, const double weight_decay, const double momentum, const bool centered);\\nEXPORT_API(Optimizer) THSNN_SGD_ctor(const Tensor* parameters, const int length, const double learning_rate, const double momentum, const double dampening, const double weight_decay, const bool nesterov);\\n\\nEXPORT_API(void) THSNN_Adam_set_betas(const Optimizer optimizer, double beta1, double beta2);\\nEXPORT_API(void) THSNN_AdamW_set_betas(const Optimizer optimizer, double beta1, double beta2);\\nEXPORT_API(void) THSNN_RMSprop_set_momentum(const Optimizer optimizer, double momentum);\\nEXPORT_API(void) THSNN_SGD_set_momentum(const Optimizer optimizer, double momentum);\\n\\nEXPORT_API(void) THSNN_Optimizer_zero_grad(const Optimizer optimizer);\\nEXPORT_API(void) THSNN_Optimizer_getParameters(const Optimizer optimizer, Tensor* (*allocator)(size_t length));\\nEXPORT_API(Tensor) THSNN_Optimizer_step(const Optimizer optimizer, Tensor(*loss_closure)());\\n\\nEXPORT_API(void) THSNN_Optimizer_dispose(const Optimizer optimizer);\\n\\nEXPORT_API(void) THSNN_Adagrad_set_lr(const Optimizer optimizer, const double lr);\\nEXPORT_API(void) THSNN_Adam_set_lr(const Optimizer optimizer, const double lr);\\nEXPORT_API(void) THSNN_AdamW_set_lr(const Optimizer optimizer, const double lr);\\nEXPORT_API(void) THSNN_LBFGS_set_lr(const Optimizer optimizer, const double lr);\\nEXPORT_API(void) THSNN_RMSprop_set_lr(const Optimizer optimizer, const double lr);\\nEXPORT_API(void) THSNN_SGD_set_lr(const Optimizer optimizer, const double lr);\\n\\n// Misc.\\n\\nEXPORT_API(Tensor) THSNN_one_hot(const Tensor self, const int64_t num_classes);\\n\\nEXPORT_API(NNModule) THSNN_Flatten_ctor(const int64_t start_dim, const int64_t end_dim, NNAnyModule* outAsAnyModule);\\nEXPORT_API(Tensor) THSNN_Flatten_forward(const NNModule module, const Tensor tensor);\\n\\nEXPORT_API(NNModule) THSNN_Unflatten_ctor(const int64_t dim, const int64_t* shape, const int64_t shape_len, NNAnyModule* outAsAnyModule);\\nEXPORT_API(Tensor) THSNN_Unflatten_forward(const NNModule module, const Tensor tensor);\\n\\nEXPORT_API(NNModule) THSNN_CosineSimilarity_ctor(const int64_t dim, double eps, NNAnyModule* outAsAnyModule);\\nEXPORT_API(Tensor) THSNN_CosineSimilarity_forward(const NNModule module, const Tensor input1, const Tensor input2);\\n\\nEXPORT_API(NNModule) THSNN_PairwiseDistance_ctor(double p, double eps, bool keep_dim, NNAnyModule* outAsAnyModule);\\nEXPORT_API(Tensor) THSNN_PairwiseDistance_forward(const NNModule module, const Tensor input1, const Tensor input2);\\n\\nEXPORT_API(Tensor) THSNN_scaled_dot_product_attention(const Tensor query, const Tensor key, const Tensor value, const Tensor attention_mask, double p, bool casual);\\n\\n// Initializers\\n\\nEXPORT_API(void) THSNN_initUniform(Tensor twrapper, double low, double high);\\nEXPORT_API(void) THSNN_initKaimingUniform(Tensor tensor, double a);\\n\\n// Utils RNN\\n\\nEXPORT_API(Tensor) THSNN_PackedSequence_data(PackedSequence sequence);\\nEXPORT_API(Tensor) THSNN_PackedSequence_batch_sizes(PackedSequence sequence);\\nEXPORT_API(Tensor) THSNN_PackedSequence_sorted_indices(PackedSequence sequence);\\nEXPORT_API(Tensor) THSNN_PackedSequence_unsorted_indices(PackedSequence sequence);\\nEXPORT_API(void) THSNN_PackedSequence_dispose(PackedSequence sequence);\\nEXPORT_API(PackedSequence) THSNN_pack_padded_sequence(Tensor input, Tensor lengths, bool batch_first, bool enforce_sorted);\\nEXPORT_API(void) THSNN_pad_packed_sequence(PackedSequence sequence, bool batch_first, double padding_value, int64_t total_length, Tensor* res1, Tensor* res2);\\nEXPORT_API(Tensor) THSNN_pad_sequence(const Tensor* sequences, const int sequences_len, bool batch_first, double padding_value);\\nEXPORT_API(PackedSequence) THSNN_pack_sequence(const Tensor* sequences, int sequences_len, bool enforce_sorted);\\n\"\n },\n {\n \"alpha_fraction\": 0.5700336694717407,\n \"alphanum_fraction\": 0.577104389667511,\n \"avg_line_length\": 43.3283576965332,\n \"blob_id\": \"e68621f6b0776cfa4e2e92b351544d06f20a8044\",\n \"content_id\": \"16360fdc6c2ccad37f338ad9bc8092fca312bc52\",\n \"detected_licenses\": [\n \"MIT\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"C#\",\n \"length_bytes\": 2970,\n \"license_type\": \"permissive\",\n \"max_line_length\": 142,\n \"num_lines\": 67,\n \"path\": \"/src/TorchSharp/NN/PairwiseDistance.cs\",\n \"repo_name\": \"NiklasGustafsson/TorchSharp\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"// Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information.\\nusing System;\\nusing static TorchSharp.torch;\\nusing static TorchSharp.PInvoke.NativeMethods;\\n\\nnamespace TorchSharp\\n{\\n using Modules;\\n\\n namespace Modules\\n {\\n /// <summary>\\n /// Computes the pairwise distance between vectors using the p-norm.\\n /// </summary>\\n public sealed class PairwiseDistance : torch.nn.Module<Tensor, Tensor, Tensor>\\n {\\n internal PairwiseDistance(IntPtr handle, IntPtr boxedHandle) : base(handle, boxedHandle)\\n {\\n }\\n\\n public override Tensor forward(Tensor input1, Tensor input2)\\n {\\n var res = THSNN_PairwiseDistance_forward(handle, input1.Handle, input2.Handle);\\n if (res == IntPtr.Zero) { torch.CheckForErrors(); }\\n return new Tensor(res);\\n }\\n\\n // Rather than spending cycles only to discover that this module has neither\\n // parameters nor buffers, just shortcut the move completely.\\n protected internal override nn.Module _to(Device device, ScalarType dtype) => this;\\n protected internal override nn.Module _to(DeviceType deviceType, int deviceIndex = -1) => this;\\n protected internal override nn.Module _to(ScalarType dtype) => this;\\n }\\n }\\n\\n public static partial class torch\\n {\\n public static partial class nn\\n {\\n public static PairwiseDistance PairwiseDistance(double p = 2.0, double eps = 1e-6, bool keep_dim = false)\\n {\\n var handle = THSNN_PairwiseDistance_ctor(p, eps, keep_dim, out var boxedHandle);\\n if (handle == IntPtr.Zero) { torch.CheckForErrors(); }\\n return new PairwiseDistance(handle, boxedHandle);\\n }\\n\\n public static partial class functional\\n {\\n /// <summary>\\n /// Computes the pairwise distance between vectors using the p-norm.\\n /// </summary>\\n /// <param name=\\\"input1\\\">(N,D) or (D) where N = batch dimension and D = vector dimension</param>\\n /// <param name=\\\"input2\\\">(N, D) or (D), same shape as the Input1</param>\\n /// <param name=\\\"p\\\">The norm degree. Default: 2</param>\\n /// <param name=\\\"eps\\\">Small value to avoid division by zero.</param>\\n /// <param name=\\\"keep_dim\\\">Determines whether or not to keep the vector dimension.</param>\\n /// <returns></returns>\\n public static Tensor pairwise_distance(Tensor input1, Tensor input2, double p = 2.0, double eps = 1e-6, bool keep_dim = false)\\n {\\n using (var f = nn.PairwiseDistance(p, eps, keep_dim)) {\\n return f.call(input1, input2);\\n }\\n }\\n }\\n }\\n }\\n}\\n\"\n },\n {\n \"alpha_fraction\": 0.6527947783470154,\n \"alphanum_fraction\": 0.66431725025177,\n \"avg_line_length\": 50.632911682128906,\n \"blob_id\": \"a0e605a034a0040ae475d0c2351586a8ba26b61e\",\n \"content_id\": \"f5ee6fe6e72a1a84e480c70db25fddb229c6d4e4\",\n \"detected_licenses\": [\n \"MIT\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Markdown\",\n \"length_bytes\": 16316,\n \"license_type\": \"permissive\",\n \"max_line_length\": 542,\n \"num_lines\": 316,\n \"path\": \"/docfx/articles/modules.md\",\n \"repo_name\": \"NiklasGustafsson/TorchSharp\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"# Creating Your Own TorchSharp Modules\\n\\nUnfortunatly, the word 'Module' is one of the most overloaded terms in software. That said, the module concept is central to TorchSharp, which means we have to define its contextual meaning. \\n\\nIn the context of TorchSharp, it means the same as in PyTorch: the fundamental building block of all models is the 'Module' class. All neural network layers are derived from Module and the way to create a model for training and inference in your code is to create a new Module. Without it, back-propagation will not work.\\n\\n## Sequential\\n\\nFor the most basic network architecture, which actually covers a surprising breadth, there is a simple way to create modules -- the 'Sequential' class. This class is created from a list of Modules (i.e. model components). When data is passed to the model in the form of a tensor, the Sequential instance will invoke each submodule in the order they were passed when the Sequential instance was created, passing the output from each layer to the next. The output of the final submodule will be the output of the Sequential instance.\\n\\n```C#\\nvar seq = Sequential((\\\"lin1\\\", Linear(100, 10)), (\\\"lin2\\\", Linear(10, 5)));\\n...\\nseq.forward(some_data);\\n```\\n\\nThere is no real performance reason to use Sequential instead of rolling your own custom module, but there is much less code to write. That said, if using a custom module (up next) is your preference, for whatever reason, that's what you should do. It can be useful to create a custom module first, debug it, and then convert to using Sequential.\\n\\nSequential can only string together modules that take a single tensor and return a single tensor -- anything else needs to be a custom module.\\n\\n## Custom Modules\\n\\nA custom module is created by deriving a subclass from torch.nn.Module<T...,TResult>. The first generic parameters denote the input types of the module's forward() method:\\n\\n```C#\\n private class TestModule1 : Module<Tensor,Tensor>\\n {\\n public TestModule1()\\n : base(\\\"TestModule1\\\")\\n {\\n lin1 = Linear(100, 10);\\n lin2 = Linear(10, 5);\\n RegisterComponents();\\n }\\n\\n public override Tensor forward(Tensor input)\\n {\\n using (var x = lin1.forward(input))\\n return lin2.forward(x);\\n }\\n\\n private Module lin1;\\n private Module lin2;\\n }\\n```\\n\\nNote that the field names in the module class correspond to the names that were passed in to the Sequential constructor in the earlier section.\\n\\nCustom modules should always call `RegisterComponents()` once all submodules and buffers (see discussion later in this document) have been created. This will register the modules and its parameters with the native runtime, which is essential for it to function correctly. For this to work properly, each submodule should have its own private field (**not** property) in the class. The only exception are submodules that do not have trainable weights, such as activation functions like ReLU or tanh.\\n\\nThe `forward()` method contains the computation of the module. It can contain a mix of TorchSharp primitives, layers, as well as any .NET code. Note, however, that only TorchSharp APIs are capable of operating on data residing in CUDA memory. Therefore, if performance is of the essence, expressing all computation in terms of TorchSharp APIs is essential. Non-TorchSharp APIs should be limited to things that aren't related to the tensor data, things like logging, for example.\\n\\nIn PyTorch, the `forward()` method takes an arbitrary number of arguments, of any type, and supports using default arguments. \\n\\nIn TorchSharp, the signature of `forward()` is determined by the type parameter signature of the Module<> base class. In most cases, the input and output are both `torch.Tensor`. As noted above, the Sequential module collection class assumes that it is passing only a single tensor along between its layers. Therefore, any model that needs to pass multiple arguments between layers will have to be custom.\\n\\nGoing back to the code inside the `forward()` method -- please note that the local variable 'x' was declared in a using statement. This is important in order to deallocate the native memory associated with it as soon as it is no longer needed. For that reason, it is important to pull out temporaries like this into local variables, especially when the code is running on a GPU. (More on this at: [Dispose vs. GC in TorchSharp](memory.md)) \\n\\nIn other words, the following code is less memory efficient, because it delays reclaiming native memory until the next time GC is run:\\n\\n```C#\\n public override Tensor forward(Tensor input)\\n {\\n return lin2.forward(lin1.forward(input));\\n }\\n```\\n\\n## Using Sequential Inside A Custome Module\\n\\nCustom modules are often combined with Sequential, which is especially useful when building long chains of repetitive blocks inside a custom module. In the C# TorchSharp examples, VGG, MobileNet, and ResNet demonstrate this well. \\n\\nTo illustrate, this is the code for MobileNet from the TorchSharp examples:\\n\\n```C#\\n class MobileNet : Module\\n {\\n private readonly long[] planes = new long[] { 64, 128, 128, 256, 256, 512, 512, 512, 512, 512, 512, 1024, 1024 };\\n private readonly long[] strides = new long[] { 1, 2, 1, 2, 1, 2, 1, 1, 1, 1, 1, 2, 1 };\\n\\n private readonly Module layers;\\n\\n public MobileNet(string name, int numClasses, Device device = null) : base(name)\\n {\\n var modules = new List<(string, Module)>();\\n\\n modules.Add((\\\"conv2d-first\\\", \\n Conv2d(3, 32, kernelSize: 3, stride: 1, padding: 1, bias: false)));\\n modules.Add((\\\"bnrm2d-first\\\", \\n BatchNorm2d(32)));\\n modules.Add((\\\"relu-first\\\", \\n ReLU()));\\n MakeLayers(modules, 32);\\n modules.Add((\\\"avgpool\\\", \\n AvgPool2d(new long[] { 2, 2 })));\\n modules.Add((\\\"flatten\\\", \\n Flatten()));\\n modules.Add((\\\"linear\\\", \\n Linear(planes[^1], numClasses)));\\n\\n layers = Sequential(modules);\\n\\n RegisterComponents();\\n }\\n\\n private void MakeLayers(List<(string, Module)> modules, long in_planes)\\n {\\n\\n for (var i = 0; i < strides.Length; i++) {\\n var out_planes = planes[i];\\n var stride = strides[i];\\n\\n modules.Add(($\\\"conv2d-{i}a\\\", \\n Conv2d(in_planes, in_planes, kernelSize: 3, stride: stride, padding: 1, groups: in_planes, bias: false)));\\n modules.Add(($\\\"bnrm2d-{i}a\\\", \\n BatchNorm2d(in_planes)));\\n modules.Add(($\\\"relu-{i}a\\\", \\n ReLU()));\\n modules.Add(($\\\"conv2d-{i}b\\\", \\n Conv2d(in_planes, out_planes, kernelSize: 1L, stride: 1L, padding: 0L, bias: false)));\\n modules.Add(($\\\"bnrm2d-{i}b\\\", \\n BatchNorm2d(out_planes)));\\n modules.Add(($\\\"relu-{i}b\\\", \\n ReLU()));\\n\\n in_planes = out_planes;\\n }\\n }\\n\\n public override Tensor forward(Tensor input)\\n {\\n return layers.forward(input);\\n }\\n }\\n```\\n\\n## ModuleList\\n\\nIn some circumstances, it's useful to define a dynamic number of modules in a custom module. It could be because you want to parameterize the network architecture, or dynamically choose which layers to run, or just that its tedious to define so many fields. This may be addressed by using a ModuleList to contain the submodules. Unlike Sequential, ModuleList itself does not suffice -- its `forward()` method will throw an exception if invoked, and you must iterate through the modules of the list directly in your `forward()` implementation.\\n\\nThe purpose is simply to provide a list implementation that automatically registers the submodules when components are registered. You have to iterate through the list in the `forward()` method:\\n\\n```C#\\n private class TestModule1 : Module<Tensor,Tensor>\\n {\\n public TestModule1()\\n : base(\\\"TestModule1\\\")\\n {\\n RegisterComponents();\\n }\\n\\n public override Tensor forward(Tensor input)\\n {\\n for (int i = 0; i < submodules.Count; i++) { // Using 'for' instead of 'foreach' can be useful for debugging.\\n input = submodules[i].forward(input); \\n }\\n }\\n\\n private ModuleList submodules = new ModuleList(Linear(100, 10), Linear(10, 5));\\n }\\n```\\n\\n## ModuleDict\\n\\nIn some circumstances, it's useful to define a dynamic number of modules in a custom module and use a dictionary to hold them. Like with `ModuleList`, it could be because you want to parameterize the network architecture, or dynamically choose which layers to run, or just that its tedious to define so many fields. This may be addressed by using a ModuleDict to contain the submodules.\\n\\nThe purpose is simply to provide a list implementation that automatically registers the submodules when components are registered. You have to iterate through the list in the `forward()` method:\\n\\n```C#\\n private class TestModule1 : Module<Tensor,Tensor>\\n {\\n public TestModule1()\\n : base(\\\"TestModule1\\\")\\n {\\n dict.Add(\\\"lin1\\\", Linear(100, 10));\\n dict.Add(\\\"lin2\\\", Linear(10, 5));\\n RegisterComponents();\\n }\\n\\n public override Tensor forward(Tensor input)\\n {\\n using (var x = submodules[\\\"lin1\\\"].forward(input))\\n return submodules[\\\"lin2\\\"].forward(x);\\n }\\n\\n private ModuleDict submodules = new ModuleDict();\\n }\\n```\\n\\nModuleDict is an ordered dictionary, so you can also iterate through the dictionary as if it were a list. If so, you will get a sequence of tuples with the submodule name and module in each iteration.\\n\\n## Parameter\\n\\nMany modules are just compositions of existing modules, but sometimes it will implement a novel algorithm. In this case, the `forward()` method will not just pass tensors from one module to another, but actually use TorchSharp operators and functions to perform arithmetic directly. If this requires training of parameters, those parameters should be declared directly in the module. The Parameter class is a wrapper around tensor; its only purpose is to make sure that it is registered with the native runtime.\\n\\nFor example, a re-implementation of 'Linear' would look something like:\\n\\n```C#\\n private class MyLinear : Module<Tensor,Tensor>\\n {\\n public MyLinear(long input_size, long output_size)\\n : base(\\\"MyLinear\\\")\\n {\\n weights = Parameter(torch.randn(input_size, output_size));\\n bias = Parameter(torch.zeros(output_size));\\n RegisterComponents();\\n }\\n\\n public override Tensor forward(Tensor input)\\n {\\n var mm = torch.matmul(input,weights);\\n return mm.add_(bias);\\n }\\n\\n private Parameter weights;\\n private Parameter bias;\\n }\\n```\\n\\nIn this case, we're not relying on 'using' in the `forward()` method, because the \\\"temporary\\\" is reused as the target by the `add_()` function.\\n\\nParameter's dirty little secret is that it will clean out the tensor that is given to its constructor. So, `Parameter()` is preferrably used with another tensor factory (such as in the example above), or a cloned tensor. Once you have passes a tensor to the Parameter constructor, the original tensor is invalidated.\\n\\n## ParameterList\\n\\nMuch like ModuleList, ParameterList is a list of Parameter instances, which is automatically registered with the runtime if found as a field of a module instance.\\n\\n## ParameterDict\\n\\nMuch like ModuleDict, ParameterDict is a dictionary of Parameter instances, which is automatically registered with the runtime if found as a field of a module instance.\\n\\n## Buffers\\n\\nSometimes, a module needs to allocate tensor that are not trainable, i.e. their values are not modified during back-propagation. An example is a random dropout mask. These are referred to as 'buffers' as opposed to 'parameters' and are treated differently by `RegisterComponents()` -- even though they are not trainable, the native runtime still wants to know about them for other purposes, such as storing them to disk, so it is important to declare them in the module.\\n\\nEach buffer should be declared as a field of type 'Tensor' (not 'Parameter'). This will ensure that the buffer is registered properly when `RegisterComponents()` is called.\\n\\n\\n## Modules, 'children()' and 'named_children()'\\n\\nIt is sometimes necessary to create a new model from an existing one and discard some of the final layers. The submodules will appear in the 'named_children' list in the same order that they are declared within the module itself, and when constructing a model based on the children, the layers may be reordered unless the submodules are declared in the same order that they are meant to be invoked.\\n\\nSo, for example:\\n\\n```C#\\n private class TestModule1 : Module<Tensor,Tensor>\\n {\\n public TestModule1()\\n : base(\\\"TestModule1\\\")\\n {\\n lin1 = Linear(100, 10);\\n lin2 = Linear(10, 5);\\n RegisterComponents();\\n }\\n\\n public override Tensor forward(Tensor input)\\n {\\n using (var x = lin1.forward(input))\\n return lin2.forward(x);\\n }\\n\\n // Correct -- the layers are declared in the same order they are invoked.\\n private Module lin1;\\n private Module lin2;\\n }\\n\\n private class TestModule2 : Module<Tensor,Tensor>\\n {\\n public TestModule2()\\n : base(\\\"TestModule2\\\")\\n {\\n lin1 = Linear(100, 10);\\n lin2 = Linear(10, 5);\\n RegisterComponents();\\n }\\n\\n public override Tensor forward(Tensor input)\\n {\\n using (var x = lin1.forward(input))\\n return lin2.forward(x);\\n }\\n\\n // Incorrect -- the layers are not declared in the same order they are invoked.\\n private Module lin2;\\n private Module lin1;\\n }\\n\\n ...\\n TestModule1 mod1 = ...\\n TestModule2 mod2 = ...\\n var seq1 = nn.Sequential(mod1.named_children());\\n seq1.forward(t); // Does the same as mod1.forward(t)\\n var seq2 = nn.Sequential(mod2.named_children());\\n seq2.forward(t); // This probably blows up.\\n```\\n\\n\\n## Moving and Converting Modules\\n\\nThere are a few ways to move and/or convert the parameters and buffers in a module, whether custom or not. The actual methods that are used are declared as extension methods on 'Module,' but the implementation is found in three virtual methods declared as part of Module itself:\\n\\n```C#\\nprotected virtual Module _to(ScalarType dtype)\\nprotected virtual Module _to(DeviceType deviceType, int deviceIndex = -1)\\nprotected virtual Module _to(Device device, ScalarType dtype)\\n```\\n\\nThe most likely reason for overriding any or all of these is to \\\"hook\\\" calls to moves and conversions rather than implementing it differently. The Module base class already goes through all children modules, declared parameters and buffers, so it should generally not be necessary to implement these methods separately. In th \\\"hook\\\" scenario, it is advisable to end the body with a call to 'base._to(...)' like the TransformerModel in the SequenceToSequence example does:\\n\\n```C#\\nprotected override Module _to(DeviceType deviceType, int deviceIndex = -1)\\n{\\n this.device = new Device(deviceType, deviceIndex);\\n return base._to(deviceType, deviceIndex);\\n}\\n```\\nIn this case, the model needs to know what device the parameters and buffers are, because it repeatedly generates a mask during training, and this mask must live on the same device as the model parameters.\\n\"\n },\n {\n \"alpha_fraction\": 0.5411071181297302,\n \"alphanum_fraction\": 0.548352837562561,\n \"avg_line_length\": 58.48850631713867,\n \"blob_id\": \"9cd3c2fae9978e47d1de2af7afa1ae7302e17649\",\n \"content_id\": \"d920eb7bce4f4322441f9ea03d2345ecae27f9af\",\n \"detected_licenses\": [\n \"MIT\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"C#\",\n \"length_bytes\": 10351,\n \"license_type\": \"permissive\",\n \"max_line_length\": 322,\n \"num_lines\": 174,\n \"path\": \"/src/TorchSharp/NN/Pooling/AvgPool3D.cs\",\n \"repo_name\": \"NiklasGustafsson/TorchSharp\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"// Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information.\\nusing System;\\nusing static TorchSharp.torch;\\nusing static TorchSharp.PInvoke.NativeMethods;\\n\\nnamespace TorchSharp\\n{\\n using Modules;\\n\\n namespace Modules\\n {\\n /// <summary>\\n /// This class is used to represent a AvgPool3D module.\\n /// </summary>\\n public sealed class AvgPool3d : torch.nn.Module<Tensor, Tensor>\\n {\\n internal AvgPool3d(IntPtr handle, IntPtr boxedHandle) : base(handle, boxedHandle)\\n {\\n }\\n\\n public override Tensor forward(Tensor tensor)\\n {\\n var res = THSNN_AvgPool3d_forward(handle.DangerousGetHandle(), tensor.Handle);\\n if (res == IntPtr.Zero) { torch.CheckForErrors(); }\\n return new Tensor(res);\\n }\\n\\n // Rather than spending cycles only to discover that this module has neither\\n // parameters nor buffers, just shortcut the move completely.\\n protected internal override nn.Module _to(Device device, ScalarType dtype) => this;\\n protected internal override nn.Module _to(DeviceType deviceType, int deviceIndex = -1) => this;\\n protected internal override nn.Module _to(ScalarType dtype) => this;\\n }\\n }\\n\\n public static partial class torch\\n {\\n public static partial class nn\\n {\\n /// <summary>\\n /// Applies a 3D average pooling over an input signal composed of several input planes.\\n /// </summary>\\n /// <param name=\\\"kernel_size\\\">The size of the window</param>\\n /// <param name=\\\"strides\\\">The stride of the window. Default value is kernel_size</param>\\n /// <param name=\\\"padding\\\">implicit zero padding to be added on both sides</param>\\n /// <param name=\\\"ceil_mode\\\">Whether to use ceil instead of floor to compute the output shape</param>\\n /// <param name=\\\"count_include_pad\\\">Whether to include the zero-padding in the averaging calculation</param>\\n /// <param name=\\\"divisor_override\\\">If specified, it will be used as divisor, otherwise size of the pooling region will be used</param>\\n public static AvgPool3d AvgPool3d(long[] kernel_size, long[] strides = null, long[] padding = null, bool ceil_mode = false, bool count_include_pad = true, long? divisor_override = null)\\n {\\n unsafe {\\n fixed (long* pkernelSize = kernel_size, pstrides = strides, ppadding = padding) {\\n var handle = THSNN_AvgPool3d_ctor((IntPtr)pkernelSize, kernel_size.Length, (IntPtr)pstrides, (strides == null ? 0 : strides.Length), (IntPtr)ppadding, (padding == null ? 0 : padding.Length), ceil_mode, count_include_pad, divisor_override.HasValue ? divisor_override.Value : 0, out var boxedHandle);\\n if (handle == IntPtr.Zero) { torch.CheckForErrors(); }\\n return new AvgPool3d(handle, boxedHandle);\\n }\\n }\\n }\\n\\n /// <summary>\\n /// Applies a 3D average pooling over an input signal composed of several input planes.\\n /// </summary>\\n /// <param name=\\\"kernel_size\\\">The size of the window</param>\\n /// <param name=\\\"stride\\\">The stride of the window. Default value is kernel_size</param>\\n /// <param name=\\\"padding\\\">implicit zero padding to be added on both sides</param>\\n /// <param name=\\\"ceil_mode\\\">Whether to use ceil instead of floor to compute the output shape</param>\\n /// <param name=\\\"count_include_pad\\\">Whether to include the zero-padding in the averaging calculation</param>\\n /// <param name=\\\"divisor_override\\\">If specified, it will be used as divisor, otherwise size of the pooling region will be used</param>\\n public static unsafe AvgPool3d AvgPool3d((long, long, long) kernel_size, (long, long, long)? stride = null, (long, long, long)? padding = null, bool ceil_mode = false, bool count_include_pad = true, long? divisor_override = null)\\n {\\n long svalue1 = (stride == null) ? kernel_size.Item1 : stride.Value.Item1;\\n long svalue2 = (stride == null) ? kernel_size.Item2 : stride.Value.Item2;\\n long svalue3 = (stride == null) ? kernel_size.Item3 : stride.Value.Item3;\\n\\n long pvalue1 = (padding == null) ? 0 : stride.Value.Item1;\\n long pvalue2 = (padding == null) ? 0 : stride.Value.Item2;\\n long pvalue3 = (padding == null) ? 0 : stride.Value.Item3;\\n\\n long* pkernelSize = stackalloc long[3] { kernel_size.Item1, kernel_size.Item2, kernel_size.Item3 };\\n long* pstrides = stackalloc long[3] { svalue1, svalue2, svalue3 };\\n long* ppadding = stackalloc long[3] { pvalue1, pvalue2, pvalue3 };\\n\\n var handle = THSNN_AvgPool3d_ctor((IntPtr)pkernelSize, 3, (IntPtr)pstrides, 3, (IntPtr)ppadding, 3, ceil_mode, count_include_pad, divisor_override.HasValue ? divisor_override.Value : 0, out var boxedHandle);\\n if (handle == IntPtr.Zero) { torch.CheckForErrors(); }\\n return new AvgPool3d(handle, boxedHandle);\\n }\\n\\n /// <summary>\\n /// Applies a 3D average pooling over an input signal composed of several input planes.\\n /// </summary>\\n /// <param name=\\\"kernel_size\\\">The size of the window</param>\\n /// <param name=\\\"stride\\\">The stride of the window. Default value is kernel_size</param>\\n /// <param name=\\\"padding\\\">implicit zero padding to be added on both sides</param>\\n /// <param name=\\\"ceil_mode\\\">Whether to use ceil instead of floor to compute the output shape</param>\\n /// <param name=\\\"count_include_pad\\\">Whether to include the zero-padding in the averaging calculation</param>\\n /// <param name=\\\"divisor_override\\\">If specified, it will be used as divisor, otherwise size of the pooling region will be used</param>\\n public static unsafe AvgPool3d AvgPool3d(long kernel_size, long? stride = null, long? padding = null, bool ceil_mode = false, bool count_include_pad = true, long? divisor_override = null)\\n {\\n long svalue = (stride == null) ? kernel_size : stride.Value;\\n long pvalue = (stride == null) ? 0 : padding.Value;\\n\\n long* pkernelSize = stackalloc long[3] { kernel_size, kernel_size, kernel_size };\\n long* pstrides = stackalloc long[3] { svalue, svalue, svalue };\\n long* ppadding = stackalloc long[3] { pvalue, pvalue, pvalue };\\n\\n var handle = THSNN_AvgPool3d_ctor((IntPtr)pkernelSize, 3, (IntPtr)pstrides, 3, (IntPtr)ppadding, 3, ceil_mode, count_include_pad, divisor_override.HasValue ? divisor_override.Value : 0, out var boxedHandle);\\n if (handle == IntPtr.Zero) { torch.CheckForErrors(); }\\n return new AvgPool3d(handle, boxedHandle);\\n }\\n\\n public static partial class functional\\n {\\n /// <summary>\\n /// Applies 3D average-pooling operation in kT x kH x kW regions by step size sT x sH x sW steps.\\n /// </summary>\\n /// <param name=\\\"input\\\">The input tensor.</param>\\n /// <param name=\\\"kernelSizes\\\"></param>\\n /// <param name=\\\"strides\\\"></param>\\n /// <param name=\\\"paddings\\\"></param>\\n /// <param name=\\\"ceil_mode\\\"></param>\\n /// <param name=\\\"count_include_pad\\\"></param>\\n /// <returns></returns>\\n public static Tensor avg_pool3d(Tensor input, long[] kernelSizes,\\n long[] strides = null,\\n long[] paddings = null,\\n bool ceil_mode = false,\\n bool count_include_pad = true)\\n {\\n strides = (strides == null) ? new long[] { 1 } : strides;\\n paddings = (paddings == null) ? new long[] { 0 } : paddings;\\n unsafe {\\n fixed (long* pkernelSize = kernelSizes, pstrides = strides, ppadding = paddings) {\\n var res =\\n THSTensor_avg_pool3d(input.Handle,\\n (IntPtr)pkernelSize, kernelSizes.Length,\\n (IntPtr)pstrides, strides.Length,\\n (IntPtr)ppadding, paddings.Length,\\n ceil_mode,\\n count_include_pad);\\n if (res == IntPtr.Zero) { torch.CheckForErrors(); }\\n return new Tensor(res);\\n }\\n }\\n }\\n\\n public static Tensor avg_pool3d_backward(Tensor input, Tensor originalInput,\\n long[] kernelSizes,\\n long[] strides = null,\\n long[] paddings = null,\\n bool ceil_mode = false,\\n bool count_include_pad = true,\\n long divisorOverride = 0)\\n {\\n strides = (strides == null) ? new long[] { 1 } : strides;\\n paddings = (paddings == null) ? new long[] { 0 } : paddings;\\n unsafe {\\n fixed (long* pkernelSize = kernelSizes, pstrides = strides, ppadding = paddings) {\\n var res =\\n THSTensor_avg_pool3d_backward(input.Handle, originalInput.Handle,\\n (IntPtr)pkernelSize, kernelSizes.Length,\\n (IntPtr)pstrides, strides.Length,\\n (IntPtr)ppadding, paddings.Length,\\n ceil_mode,\\n count_include_pad,\\n divisorOverride);\\n if (res == IntPtr.Zero) { torch.CheckForErrors(); }\\n return new Tensor(res);\\n }\\n }\\n }\\n }\\n }\\n }\\n}\\n\"\n },\n {\n \"alpha_fraction\": 0.5389890670776367,\n \"alphanum_fraction\": 0.5425450801849365,\n \"avg_line_length\": 42.74444580078125,\n \"blob_id\": \"1233e97601be87a90f079a1ffe1f4ea18dadb18c\",\n \"content_id\": \"1ce8c12a69dc2aa49a1bf1b2d31e27d3088495c2\",\n \"detected_licenses\": [\n \"MIT\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"C#\",\n \"length_bytes\": 7874,\n \"license_type\": \"permissive\",\n \"max_line_length\": 149,\n \"num_lines\": 180,\n \"path\": \"/src/TorchSharp/Distributions/Categorical.cs\",\n \"repo_name\": \"NiklasGustafsson/TorchSharp\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"// Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information.\\nusing System;\\nusing System.Collections.Generic;\\nusing System.Linq;\\nusing static TorchSharp.torch;\\n\\nnamespace TorchSharp\\n{\\n using Modules;\\n\\n namespace Modules\\n {\\n public class Categorical : torch.distributions.Distribution\\n {\\n /// <summary>\\n /// The mean of the distribution.\\n /// </summary>\\n public override Tensor mean => torch.full(ExtendedShape(), double.NaN, dtype: probs.dtype, device: probs.device);\\n\\n /// <summary>\\n /// The variance of the distribution\\n /// </summary>\\n public override Tensor variance => torch.full(ExtendedShape(), double.NaN, dtype: probs.dtype, device: probs.device);\\n\\n /// <summary>\\n /// Constructor\\n /// </summary>\\n /// <param name=\\\"probs\\\">Event probabilities</param>\\n /// <param name=\\\"logits\\\">Even log-odds</param>\\n /// <param name=\\\"generator\\\">An optional random number generator object.</param>\\n public Categorical(Tensor probs = null, Tensor logits = null, torch.Generator generator = null) : base(generator)\\n {\\n var param = probs is null ? logits : probs;\\n\\n this._probs = (probs is not null) ? (probs / probs.sum(-1, keepdim: true)).DetachFromDisposeScope() : null;\\n this._logits = (logits is not null) ? (logits - logits.logsumexp(-1, keepdim:true)).DetachFromDisposeScope() : null;\\n this.num_events = param.size(-1);\\n this.batch_shape = param.ndim > 1 ? param.shape.Take(param.shape.Length-1).ToArray() : new long[0];\\n }\\n\\n /// <summary>\\n /// Event probabilities\\n /// </summary>\\n public Tensor probs {\\n get {\\n return _probs ?? LogitsToProbs(_logits);\\n }\\n }\\n\\n /// <summary>\\n /// Event log-odds\\n /// </summary>\\n public Tensor logits {\\n get {\\n return _logits ?? ProbsToLogits(_probs);\\n }\\n }\\n\\n /// <summary>\\n /// The shape of the input parameter.\\n /// </summary>\\n public long[] param_shape {\\n get {\\n return _probs is null ? _logits.shape : _probs.shape;\\n }\\n }\\n\\n internal Tensor _probs;\\n internal Tensor _logits;\\n internal long num_events;\\n\\n /// <summary>\\n /// Generates a sample_shape shaped reparameterized sample or sample_shape shaped batch of reparameterized samples\\n /// if the distribution parameters are batched.\\n /// </summary>\\n /// <param name=\\\"sample_shape\\\">The sample shape.</param>\\n public override Tensor rsample(params long[] sample_shape)\\n {\\n var probs_2d = probs.reshape(-1, num_events);\\n var samples_2d = torch.multinomial(probs_2d, sample_shape.Aggregate<long, long>(1, (x, y) => x * y), true, generator).T;\\n return samples_2d.reshape(ExtendedShape(sample_shape));\\n }\\n\\n /// <summary>\\n /// Returns the log of the probability density/mass function evaluated at `value`.\\n /// </summary>\\n /// <param name=\\\"value\\\"></param>\\n public override Tensor log_prob(Tensor value)\\n {\\n value = value.@long().unsqueeze(-1);\\n var valLogPmf = torch.broadcast_tensors(value, logits);\\n value = valLogPmf[0][TensorIndex.Ellipsis, TensorIndex.Slice(null, 1)];\\n return valLogPmf[1].gather(-1, value).squeeze(-1);\\n }\\n\\n /// <summary>\\n /// Returns entropy of distribution, batched over batch_shape.\\n /// </summary>\\n public override Tensor entropy()\\n {\\n var min_real = torch.finfo(logits.dtype).min;\\n var logs = torch.clamp(logits, min: min_real);\\n var p_log_p = logs * probs;\\n return -p_log_p.sum(-1);\\n }\\n\\n /// <summary>\\n /// Returns a new distribution instance (or populates an existing instance provided by a derived class) with batch dimensions expanded to\\n /// `batch_shape`. This method calls `torch.Tensor.expand()` on the distribution's parameters. As such, this does not allocate new\\n /// memory for the expanded distribution instance.\\n /// </summary>\\n /// <param name=\\\"batch_shape\\\">Tthe desired expanded size.</param>\\n /// <param name=\\\"instance\\\">new instance provided by subclasses that need to override `.expand`.</param>\\n /// <returns></returns>\\n public override distributions.Distribution expand(Size batch_shape, distributions.Distribution instance = null)\\n {\\n if (instance != null && !(instance is Categorical))\\n throw new ArgumentException(\\\"expand(): 'instance' must be a Categorical distribution\\\");\\n\\n var param_shape = new List<long>();\\n param_shape.AddRange(batch_shape);\\n param_shape.Add(num_events);\\n\\n var shape = param_shape.ToArray();\\n\\n var p = _probs?.expand(shape);\\n var l = _logits?.expand(shape);\\n\\n var newDistribution = ((instance == null) ? new Categorical(p, l, generator) : instance) as Categorical;\\n\\n newDistribution.batch_shape = batch_shape;\\n if (newDistribution == instance) {\\n newDistribution.num_events = this.num_events;\\n newDistribution._probs = p;\\n newDistribution._logits = l;\\n }\\n return newDistribution;\\n }\\n }\\n\\n }\\n public static partial class torch\\n {\\n public static partial class distributions\\n {\\n /// <summary>\\n /// Creates a Categorical distribution parameterized by `probs` or `logits` (but not both).\\n ///\\n /// Samples are integers from [0, K-1]` where `K` is probs.size(-1).\\n /// \\n /// If `probs` is 1-dimensional with length- `K`, each element is the relative probability\\n /// of sampling the class at that index.\\n /// \\n /// If `probs` is N-dimensional, the first N-1 dimensions are treated as a batch of\\n /// relative probability vectors.\\n /// </summary>\\n /// <param name=\\\"probs\\\">The probability of sampling '1'</param>\\n /// <param name=\\\"logits\\\">The log-odds of sampling '1'</param>\\n /// <param name=\\\"generator\\\">An optional random number generator object.</param>\\n /// <returns></returns>\\n public static Categorical Categorical(Tensor probs = null, Tensor logits = null, torch.Generator generator = null)\\n {\\n return new Categorical(probs, logits);\\n }\\n\\n /// <summary>\\n /// Creates an equal-probability categorical distribution parameterized by the number of categories.\\n ///\\n /// </summary>\\n /// <param name=\\\"categories\\\">The number of categories.</param>\\n /// <param name=\\\"generator\\\">An optional random number generator object.</param>\\n /// <returns></returns>\\n public static Categorical Categorical(int categories, torch.Generator generator = null)\\n {\\n var probs = torch.tensor(1.0 / categories).expand(categories);\\n return new Categorical(probs, null, generator);\\n }\\n }\\n }\\n}\\n\"\n },\n {\n \"alpha_fraction\": 0.7330131530761719,\n \"alphanum_fraction\": 0.7362148761749268,\n \"avg_line_length\": 52.03773498535156,\n \"blob_id\": \"9b2476113534bcdcb768c38e889196bd1d5652e1\",\n \"content_id\": \"c0d66ba2501804f7ee99e1f6fcb3ff88f1fd5ef3\",\n \"detected_licenses\": [\n \"MIT\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"C#\",\n \"length_bytes\": 5622,\n \"license_type\": \"permissive\",\n \"max_line_length\": 195,\n \"num_lines\": 106,\n \"path\": \"/src/TorchSharp/PInvoke/LibTorchSharp.THSJIT.cs\",\n \"repo_name\": \"NiklasGustafsson/TorchSharp\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"// Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information.\\n#nullable enable\\nusing System;\\nusing System.Runtime.InteropServices;\\n\\nnamespace TorchSharp.PInvoke\\n{\\n#pragma warning disable CA2101\\n internal static partial class NativeMethods\\n {\\n [DllImport(\\\"LibTorchSharp\\\", CharSet = CharSet.Ansi, BestFitMapping = false, ThrowOnUnmappableChar = true)]\\n internal static extern void THSJIT_CompilationUnit_Invoke(IntPtr module, string name, IntPtr tensors, int length, AllocateIndexedPinnedArray allocator, out sbyte typeCode, int idx);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern void THSJIT_CompilationUnit_dispose(IntPtr handle);\\n\\n [DllImport(\\\"LibTorchSharp\\\", CharSet = CharSet.Ansi, BestFitMapping = false, ThrowOnUnmappableChar = true)]\\n internal static extern IntPtr THSJIT_compile(string script);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern void THSJIT_Module_dispose(torch.nn.Module.HType handle);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern void THSJIT_Module_named_parameters(torch.nn.Module.HType module, AllocatePinnedArray allocator1, AllocatePinnedArray allocator2);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern void THSJIT_Module_named_buffers(torch.nn.Module.HType module, AllocatePinnedArray allocator1, AllocatePinnedArray allocator2);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern void THSJIT_Module_named_modules(torch.nn.Module.HType module, AllocatePinnedArray allocator1, AllocatePinnedArray allocator2);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern void THSJIT_Module_named_children(torch.nn.Module.HType module, AllocatePinnedArray allocator1, AllocatePinnedArray allocator2);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern long THSJIT_getNumModules(torch.nn.Module.HType module);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern int THSJIT_Module_num_inputs(torch.nn.Module.HType module);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern int THSJIT_Module_num_outputs(torch.nn.Module.HType module);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern void THSJIT_Module_train(torch.nn.Module.HType module, [MarshalAs(UnmanagedType.U1)] bool on);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern void THSJIT_Module_eval(torch.nn.Module.HType module);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n [return: MarshalAs(UnmanagedType.U1)]\\n internal static extern bool THSJIT_Module_is_training(torch.nn.Module.HType module);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern void THSJIT_Module_to_device(torch.nn.Module.HType module, long deviceType, long deviceIndex);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern void THSJIT_Module_to_device_dtype(torch.nn.Module.HType module, sbyte dtype, long deviceType, long deviceIndex);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern void THSJIT_Module_to_dtype(torch.nn.Module.HType module, sbyte dtype);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern IntPtr THSJIT_Module_getInputType(torch.nn.Module.HType module, int index);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern IntPtr THSJIT_getOutputType(torch.jit.Type.HType module, int index);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern void THSJIT_Module_forward(torch.nn.Module.HType module, IntPtr tensors, int length, AllocateIndexedPinnedArray allocator, out sbyte typeCode, int idx);\\n\\n [DllImport(\\\"LibTorchSharp\\\", CharSet = CharSet.Ansi, BestFitMapping = false, ThrowOnUnmappableChar = true)]\\n internal static extern void THSJIT_Module_invoke(torch.nn.Module.HType module, string name, IntPtr tensors, int length, AllocateIndexedPinnedArray allocator, out sbyte typeCode, int idx);\\n\\n [DllImport(\\\"LibTorchSharp\\\", CharSet = CharSet.Ansi, BestFitMapping = false, ThrowOnUnmappableChar = true)]\\n internal static extern IntPtr THSJIT_load(string filename, long deviceType, long deviceIndex);\\n\\n [DllImport(\\\"LibTorchSharp\\\", CharSet = CharSet.Ansi, BestFitMapping = false, ThrowOnUnmappableChar = true)]\\n internal static extern void THSJIT_save(torch.nn.Module.HType handle, string filename);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern long THSJIT_TensorType_sizes(torch.jit.Type.HType handle, AllocatePinnedArray allocator);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern int THSJIT_getDimensionedTensorTypeDimensions(torch.jit.Type.HType handle);\\n\\n [DllImport(\\\"LibTorchSharp\\\", CharSet = CharSet.Ansi, BestFitMapping = false, ThrowOnUnmappableChar = true)]\\n internal static extern string THSJIT_getDimensionedTensorDevice(torch.jit.Type.HType handle);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern sbyte THSJIT_TensorType_dtype(torch.jit.Type.HType handle);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern void THSJIT_Type_dispose(torch.jit.Type.HType handle);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern void THSJIT_TensorType_dispose(torch.jit.Type.HType handle);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern sbyte THSJIT_Type_kind(torch.jit.Type.HType handle);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern IntPtr THSJIT_Type_cast(torch.jit.Type.HType module);\\n }\\n#pragma warning restore CA2101\\n}\\n\"\n },\n {\n \"alpha_fraction\": 0.5343691110610962,\n \"alphanum_fraction\": 0.5343691110610962,\n \"avg_line_length\": 41.47999954223633,\n \"blob_id\": \"cbf13b3685a0c3f831aa32a70641da9bec241dfd\",\n \"content_id\": \"b5aa0c65356564de366b7f8807fd6b0434a013be\",\n \"detected_licenses\": [\n \"MIT\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"C#\",\n \"length_bytes\": 4248,\n \"license_type\": \"permissive\",\n \"max_line_length\": 142,\n \"num_lines\": 100,\n \"path\": \"/src/TorchSharp/NN/Linear.cs\",\n \"repo_name\": \"NiklasGustafsson/TorchSharp\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"// Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information.\\nusing System;\\nusing static TorchSharp.torch;\\nusing static TorchSharp.torch.nn;\\nusing static TorchSharp.PInvoke.NativeMethods;\\n\\n#nullable enable\\nnamespace TorchSharp\\n{\\n using Modules;\\n\\n namespace Modules\\n {\\n public sealed class Linear : torch.nn.Module<Tensor, Tensor>\\n {\\n internal Linear(IntPtr handle, IntPtr boxedHandle) : base(handle, boxedHandle)\\n {\\n }\\n\\n public new static Linear Load(string modelPath)\\n {\\n var res = Module<Tensor, Tensor>.Load(modelPath);\\n return new Linear(res.handle.DangerousGetHandle(), IntPtr.Zero);\\n }\\n\\n public override Tensor forward(Tensor tensor)\\n {\\n var res = THSNN_Linear_forward(handle, tensor.Handle);\\n if (res == IntPtr.Zero) { torch.CheckForErrors(); }\\n return new Tensor(res);\\n }\\n\\n public Parameter? bias {\\n get {\\n var res = THSNN_Linear_bias(handle);\\n if (res == IntPtr.Zero) { torch.CheckForErrors(); }\\n return ((res == IntPtr.Zero) ? null : new Parameter(res));\\n }\\n set {\\n THSNN_Linear_set_bias(handle, value?.Handle ?? IntPtr.Zero);\\n torch.CheckForErrors();\\n ConditionallyRegisterParameter(\\\"bias\\\", value);\\n }\\n }\\n\\n public Parameter? weight {\\n get {\\n var res = THSNN_Linear_weight(handle);\\n if (res == IntPtr.Zero) { torch.CheckForErrors(); }\\n return (res == IntPtr.Zero) ? null : new Parameter(res);\\n }\\n set {\\n THSNN_Linear_set_weight(handle, value!.Handle);\\n torch.CheckForErrors();\\n ConditionallyRegisterParameter(\\\"weight\\\", value);\\n }\\n }\\n }\\n }\\n\\n public static partial class torch\\n {\\n public static partial class nn\\n {\\n /// <summary>\\n /// Applies a linear transformation to the incoming data.\\n /// </summary>\\n /// <param name=\\\"inputSize\\\">Size of each input sample</param>\\n /// <param name=\\\"outputSize\\\">Size of each output sample</param>\\n /// <param name=\\\"hasBias\\\">If set to false, the layer will not learn an additive bias.</param>\\n /// <param name=\\\"device\\\">The desired device of the parameters and buffers in this module</param>\\n /// <param name=\\\"dtype\\\">The desired floating point or complex dtype of the parameters and buffers in this module</param>\\n public static Linear Linear(long inputSize, long outputSize, bool hasBias = true, Device? device = null, ScalarType? dtype = null)\\n {\\n var res = THSNN_Linear_ctor(inputSize, outputSize, hasBias, out var boxedHandle);\\n if (res == IntPtr.Zero) { torch.CheckForErrors(); }\\n\\n return new Linear(res, boxedHandle).MoveModule<Linear>(device, dtype);\\n }\\n\\n public static partial class functional\\n {\\n /// <summary>\\n /// Applies a linear transformation to the incoming data.\\n /// </summary>\\n /// <param name=\\\"input\\\">Input tensor of shape (*,Hin)</param>\\n /// <param name=\\\"weights\\\">Weights of shape (Hout,Hin) or (Hin)</param>\\n /// <param name=\\\"bias\\\">Bias of shape (Hout) or ()</param>\\n /// <returns>A tensor of shape (*,Hout) where '*' is the same as the subshape of the input.</returns>\\n public static Tensor linear(Tensor input, Tensor weights, Tensor? bias = null)\\n {\\n IntPtr bPtr = bias?.Handle ?? IntPtr.Zero;\\n var res = THSNN_functional_linear(input.Handle, weights.Handle, bPtr);\\n if (res == IntPtr.Zero) { torch.CheckForErrors(); }\\n return new Tensor(res);\\n }\\n }\\n }\\n }\\n}\\n\"\n },\n {\n \"alpha_fraction\": 0.5198135375976562,\n \"alphanum_fraction\": 0.5225329995155334,\n \"avg_line_length\": 37.43283462524414,\n \"blob_id\": \"05cebbf97c1b1309357e33a13ff07075d87f68bc\",\n \"content_id\": \"73b6ab1d7f6aad8634e678ec5e6b8145feddff75\",\n \"detected_licenses\": [\n \"MIT\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"C#\",\n \"length_bytes\": 2574,\n \"license_type\": \"permissive\",\n \"max_line_length\": 125,\n \"num_lines\": 67,\n \"path\": \"/src/TorchVision/File.cs\",\n \"repo_name\": \"NiklasGustafsson/TorchSharp\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"using System.IO;\\nusing System.Threading.Tasks;\\n\\nusing static TorchSharp.torch;\\n\\nnamespace TorchSharp\\n{\\n public static partial class torchvision\\n {\\n partial class io\\n {\\n /// <summary>\\n /// Reads a file into a <cref>Tensor</cref>.\\n /// </summary>\\n /// <param name=\\\"filename\\\">Path to the file.</param>\\n /// <returns>\\n /// One dimensional <cref>Tensor</cref> containing the bytes of the file.\\n /// </returns>\\n public static Tensor read_file(string filename)\\n {\\n return File.ReadAllBytes(filename);\\n }\\n\\n /// <summary>\\n /// Asynchronously reads a file into a <cref>Tensor</cref>.\\n /// </summary>\\n /// <param name=\\\"filename\\\">Path to the file.</param>\\n /// <returns>\\n /// A task that represents the asynchronous read operation.\\n /// The value of the TResult parameter is a one dimensional <cref>Tensor</cref> containing the bytes of the file.\\n /// </returns>\\n public static async Task<Tensor> read_file_async(string filename)\\n {\\n byte[] data;\\n\\n using (FileStream stream = File.Open(filename, FileMode.Open)) {\\n data = new byte[stream.Length];\\n await stream.ReadAsync(data, 0, data.Length);\\n }\\n\\n return data;\\n }\\n\\n /// <summary>\\n /// Writes a one dimensional <c>uint8</c> <cref>Tensor</cref> into a file.\\n /// </summary>\\n /// <param name=\\\"filename\\\">Path to the file.</param>\\n /// <param name=\\\"data\\\">One dimensional <c>uint8</c> <cref>Tensor</cref>.</param>\\n public static void write_file(string filename, Tensor data)\\n {\\n File.WriteAllBytes(filename, data.bytes.ToArray());\\n }\\n\\n /// <summary>\\n /// Asynchronously writes a one dimensional <c>uint8</c> <cref>Tensor</cref> into a file.\\n /// </summary>\\n /// <param name=\\\"filename\\\">Path to the file.</param>\\n /// <param name=\\\"data\\\">One dimensional <c>uint8</c> <cref>Tensor</cref>.</param>\\n public static async void write_file_async(string filename, Tensor data)\\n {\\n using (FileStream stream = File.Open(filename, FileMode.OpenOrCreate)) {\\n await stream.WriteAsync(data.bytes.ToArray(), 0, (int)data.shape[0]);\\n }\\n }\\n }\\n }\\n}\"\n },\n {\n \"alpha_fraction\": 0.5470979809761047,\n \"alphanum_fraction\": 0.554709792137146,\n \"avg_line_length\": 22.886363983154297,\n \"blob_id\": \"1291b317d20afdd300825c6a6bc21fcd208c0ef6\",\n \"content_id\": \"5167c75981ff5ea409b2f107ee7be550ada57ce9\",\n \"detected_licenses\": [\n \"MIT\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"C#\",\n \"length_bytes\": 1051,\n \"license_type\": \"permissive\",\n \"max_line_length\": 130,\n \"num_lines\": 44,\n \"path\": \"/src/TorchSharp/Utils/BigEndianReader.cs\",\n \"repo_name\": \"NiklasGustafsson/TorchSharp\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"// Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information.\\nusing System;\\nusing System.IO;\\n\\nnamespace TorchSharp.Utils\\n{\\n public class BigEndianReader\\n {\\n public BigEndianReader(BinaryReader baseReader)\\n {\\n mBaseReader = baseReader;\\n }\\n\\n public int ReadInt32()\\n {\\n return BitConverter.ToInt32(ReadBigEndianBytes(4), 0);\\n }\\n\\n public byte[] ReadBigEndianBytes(int count)\\n {\\n byte[] bytes = new byte[count];\\n for (int i = count - 1; i >= 0; i--)\\n bytes[i] = mBaseReader.ReadByte();\\n\\n return bytes;\\n }\\n\\n public byte[] ReadBytes(int count)\\n {\\n return mBaseReader.ReadBytes(count);\\n }\\n\\n public void Close()\\n {\\n mBaseReader.Close();\\n }\\n\\n public Stream BaseStream {\\n get { return mBaseReader.BaseStream; }\\n }\\n\\n private BinaryReader mBaseReader;\\n }\\n}\\n\"\n },\n {\n \"alpha_fraction\": 0.5101739168167114,\n \"alphanum_fraction\": 0.5280209183692932,\n \"avg_line_length\": 35.20164489746094,\n \"blob_id\": \"fc47a1f549a0abf077c09ab9db5be28cc227455d\",\n \"content_id\": \"6069186fba0a9c79dba88e5910b53498af703b82\",\n \"detected_licenses\": [\n \"MIT\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"C#\",\n \"length_bytes\": 8797,\n \"license_type\": \"permissive\",\n \"max_line_length\": 133,\n \"num_lines\": 243,\n \"path\": \"/src/Examples/MNIST.cs\",\n \"repo_name\": \"NiklasGustafsson/TorchSharp\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"// Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information.\\nusing System;\\nusing System.Diagnostics;\\nusing static TorchSharp.torch;\\n\\nusing static TorchSharp.torch.nn;\\nusing static TorchSharp.torch.utils.data;\\n\\nnamespace TorchSharp.Examples\\n{\\n /// <summary>\\n /// Simple MNIST Convolutional model.\\n /// </summary>\\n /// <remarks>\\n /// There are at least two interesting data sets to use with this example:\\n ///\\n /// 1. The classic MNIST set of 60000 images of handwritten digits.\\n ///\\n /// It is available at: http://yann.lecun.com/exdb/mnist/\\n ///\\n /// 2. The 'fashion-mnist' data set, which has the exact same file names and format as MNIST, but is a harder\\n /// data set to train on. It's just as large as MNIST, and has the same 60/10 split of training and test\\n /// data.\\n /// It is available at: https://github.com/zalandoresearch/fashion-mnist/tree/master/data/fashion\\n /// </remarks>\\n public class MNIST\\n {\\n private static int _epochs = 1;\\n private static int _trainBatchSize = 64;\\n private static int _testBatchSize = 128;\\n\\n private readonly static int _logInterval = 100;\\n\\n internal static void Main(string[] args)\\n {\\n var dataset = args.Length > 0 ? args[0] : \\\"mnist\\\";\\n var datasetPath = Environment.GetFolderPath(Environment.SpecialFolder.LocalApplicationData);\\n\\n torch.random.manual_seed(1);\\n\\n var cwd = Environment.CurrentDirectory;\\n\\n var device = torch.cuda.is_available() ? torch.CUDA : torch.CPU;\\n Console.WriteLine($\\\"Running MNIST on {device.type.ToString()}\\\");\\n Console.WriteLine($\\\"Dataset: {dataset}\\\");\\n\\n if (device.type == DeviceType.CUDA) {\\n _trainBatchSize *= 4;\\n _testBatchSize *= 4;\\n }\\n\\n using var model = new Model(\\\"model\\\", device);\\n\\n var normImage = torchvision.transforms.Normalize(new double[] { 0.1307 }, new double[] { 0.3081 });\\n\\n using (Dataset train_data = torchvision.datasets.MNIST(datasetPath, true, download: true, target_transform: normImage),\\n test_data = torchvision.datasets.MNIST(datasetPath, false, download: true, target_transform: normImage)) {\\n\\n TrainingLoop(\\\"mnist\\\", device, model, train_data, test_data);\\n }\\n }\\n\\n internal static void TrainingLoop(string dataset, Device device, Model model, Dataset train_data, Dataset test_data)\\n {\\n using var train = new DataLoader(train_data, _trainBatchSize, device: device, shuffle: true);\\n using var test = new DataLoader(test_data, _testBatchSize, device: device, shuffle: false);\\n\\n if (device.type == DeviceType.CUDA) {\\n _epochs *= 4;\\n }\\n\\n var optimizer = torch.optim.Adam(model.parameters());\\n\\n var scheduler = torch.optim.lr_scheduler.StepLR(optimizer, 1, 0.75);\\n\\n Stopwatch sw = new Stopwatch();\\n sw.Start();\\n\\n for (var epoch = 1; epoch <= _epochs; epoch++) {\\n\\n using (var d = torch.NewDisposeScope()) {\\n\\n Train(model, optimizer, torch.nn.NLLLoss(reduction: Reduction.Mean), train, epoch, train_data.Count);\\n Test(model, torch.nn.NLLLoss(reduction: torch.nn.Reduction.Sum), test, test_data.Count);\\n\\n Console.WriteLine($\\\"End-of-epoch memory use: {GC.GetTotalMemory(false)}\\\");\\n scheduler.step();\\n }\\n }\\n\\n sw.Stop();\\n Console.WriteLine($\\\"Elapsed time: {sw.Elapsed.TotalSeconds:F1} s.\\\");\\n\\n Console.WriteLine(\\\"Saving model to '{0}'\\\", dataset + \\\".model.bin\\\");\\n model.save(dataset + \\\".model.bin\\\");\\n }\\n\\n internal class Model : Module<Tensor, Tensor>\\n {\\n private Module<Tensor, Tensor> conv1 = Conv2d(1, 32, 3);\\n private Module<Tensor, Tensor> conv2 = Conv2d(32, 64, 3);\\n private Module<Tensor, Tensor> fc1 = Linear(9216, 128);\\n private Module<Tensor, Tensor> fc2 = Linear(128, 10);\\n\\n // These don't have any parameters, so the only reason to instantiate\\n // them is performance, since they will be used over and over.\\n private Module<Tensor, Tensor> pool1 = MaxPool2d(kernelSize: new long[] { 2, 2 });\\n\\n private Module<Tensor, Tensor> relu1 = ReLU();\\n private Module<Tensor, Tensor> relu2 = ReLU();\\n private Module<Tensor, Tensor> relu3 = ReLU();\\n\\n private Module<Tensor, Tensor> dropout1 = Dropout(0.25);\\n private Module<Tensor, Tensor> dropout2 = Dropout(0.5);\\n\\n private Module<Tensor, Tensor> flatten = Flatten();\\n private Module<Tensor, Tensor> logsm = LogSoftmax(1);\\n\\n public Model(string name, torch.Device device = null) : base(name)\\n {\\n RegisterComponents();\\n\\n if (device != null && device.type == DeviceType.CUDA)\\n this.to(device);\\n }\\n\\n public override Tensor forward(Tensor input)\\n {\\n // All these modules are private to the model and won't have hooks,\\n // so we can use 'forward' instead of 'call'\\n var l11 = conv1.forward(input);\\n var l12 = relu1.forward(l11);\\n\\n var l21 = conv2.forward(l12);\\n var l22 = relu2.forward(l21);\\n var l23 = pool1.forward(l22);\\n var l24 = dropout1.forward(l23);\\n\\n var x = flatten.forward(l24);\\n\\n var l31 = fc1.forward(x);\\n var l32 = relu3.forward(l31);\\n var l33 = dropout2.forward(l32);\\n\\n var l41 = fc2.forward(l33);\\n\\n return logsm.forward(l41);\\n }\\n\\n protected override void Dispose(bool disposing)\\n {\\n if (disposing) {\\n conv1.Dispose();\\n conv2.Dispose();\\n fc1.Dispose();\\n fc2.Dispose();\\n pool1.Dispose();\\n relu1.Dispose();\\n relu2.Dispose();\\n relu3.Dispose();\\n dropout1.Dispose();\\n dropout2.Dispose();\\n flatten.Dispose();\\n logsm.Dispose();\\n ClearModules();\\n }\\n base.Dispose(disposing);\\n }\\n }\\n\\n\\n private static void Train(\\n Model model,\\n torch.optim.Optimizer optimizer,\\n Loss<torch.Tensor, torch.Tensor, torch.Tensor> loss,\\n DataLoader dataLoader,\\n int epoch,\\n long size)\\n {\\n model.train();\\n\\n int batchId = 1;\\n long total = 0;\\n\\n Console.WriteLine($\\\"Epoch: {epoch}...\\\");\\n\\n using (var d = torch.NewDisposeScope()) {\\n\\n foreach (var data in dataLoader) {\\n optimizer.zero_grad();\\n\\n var target = data[\\\"label\\\"];\\n var prediction = model.call(data[\\\"data\\\"]);\\n var output = loss.call(prediction, target);\\n\\n output.backward();\\n\\n optimizer.step();\\n\\n total += target.shape[0];\\n\\n if (batchId % _logInterval == 0 || total == size) {\\n Console.WriteLine($\\\"\\\\rTrain: epoch {epoch} [{total} / {size}] Loss: {output.ToSingle():F4}\\\");\\n }\\n\\n batchId++;\\n\\n d.DisposeEverything();\\n }\\n }\\n }\\n\\n private static void Test(\\n Model model,\\n Loss<torch.Tensor, torch.Tensor, torch.Tensor> loss,\\n DataLoader dataLoader,\\n long size)\\n {\\n model.eval();\\n\\n double testLoss = 0;\\n int correct = 0;\\n\\n using (var d = torch.NewDisposeScope()) {\\n\\n foreach (var data in dataLoader) {\\n var prediction = model.call(data[\\\"data\\\"]);\\n var output = loss.call(prediction, data[\\\"label\\\"]);\\n testLoss += output.ToSingle();\\n\\n var pred = prediction.argmax(1);\\n correct += pred.eq(data[\\\"label\\\"]).sum().ToInt32();\\n\\n d.DisposeEverything();\\n }\\n }\\n\\n Console.WriteLine($\\\"Size: {size}, Total: {size}\\\");\\n\\n Console.WriteLine($\\\"\\\\rTest set: Average loss {(testLoss / size):F4} | Accuracy {((double)correct / size):P2}\\\");\\n }\\n }\\n}\\n\"\n },\n {\n \"alpha_fraction\": 0.5167227983474731,\n \"alphanum_fraction\": 0.5228196382522583,\n \"avg_line_length\": 45.672088623046875,\n \"blob_id\": \"3ca9ff687754f9a72e57785caafefbe648ab7028\",\n \"content_id\": \"fa12f67122d842dcaabdd3d69cb85b1fab63bc5e\",\n \"detected_licenses\": [\n \"MIT\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"C#\",\n \"length_bytes\": 17222,\n \"license_type\": \"permissive\",\n \"max_line_length\": 229,\n \"num_lines\": 369,\n \"path\": \"/src/TorchSharp/Optimizers/Rprop.cs\",\n \"repo_name\": \"NiklasGustafsson/TorchSharp\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"// Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information.\\nusing System;\\nusing System.IO;\\nusing System.Collections.Generic;\\nusing System.Linq;\\nusing static TorchSharp.torch;\\n\\nnamespace TorchSharp\\n{\\n using Modules;\\n\\n public static partial class torch\\n {\\n public static partial class optim\\n {\\n\\n /// <summary>\\n /// Implements the the resilient backpropagation algorithm.\\n ///\\n /// A Direct Adaptive Method for Faster Backpropagation Learning: The RPROP Algorithm\\n /// http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.21.1417\\n /// </summary>\\n /// <param name=\\\"parameters\\\">Parameters to optimize. This optimizer requires the <b>named</b> parameters collection.</param>\\n /// <param name=\\\"lr \\\">Learning rate</param>\\n /// <param name=\\\"etaminus\\\">Multiplicative increase factor.</param>\\n /// <param name=\\\"etaplus\\\">Multiplicative decrease factor.</param>\\n /// <param name=\\\"min_step\\\">Minimum allowed step size.</param>\\n /// <param name=\\\"max_step\\\">Maximum allowed step size.</param>\\n /// <param name=\\\"maximize\\\">Maximize the params based on the objective, instead of minimizing.</param>\\n public static Rprop Rprop(IEnumerable<Parameter> parameters, double lr = 1e-2, double etaminus = 0.5, double etaplus = 1.2, double min_step = 1e-6, double max_step = 50, bool maximize = false)\\n {\\n return new Rprop(parameters, lr, etaminus, etaplus, min_step, max_step, maximize);\\n }\\n\\n /// <summary>\\n /// Implements the the resilient backpropagation algorithm.\\n ///\\n /// A Direct Adaptive Method for Faster Backpropagation Learning: The RPROP Algorithm\\n /// http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.21.1417\\n /// </summary>\\n /// <param name=\\\"parameters\\\">Parameters to optimize. This optimizer requires the <b>named</b> parameters collection.</param>\\n /// <param name=\\\"lr \\\">Learning rate</param>\\n /// <param name=\\\"etaminus\\\">Multiplicative increase factor.</param>\\n /// <param name=\\\"etaplus\\\">Multiplicative decrease factor.</param>\\n /// <param name=\\\"min_step\\\">Minimum allowed step size.</param>\\n /// <param name=\\\"max_step\\\">Maximum allowed step size.</param>\\n /// <param name=\\\"maximize\\\">Maximize the params based on the objective, instead of minimizing.</param>\\n public static Rprop Rprop(IEnumerable<(string name, Parameter parameter)> parameters, double lr = 1e-2, double etaminus = 0.5, double etaplus = 1.2, double min_step = 1e-6, double max_step = 50, bool maximize = false)\\n {\\n return new Rprop(parameters.Select(np => np.parameter), lr, etaminus, etaplus, min_step, max_step, maximize);\\n }\\n\\n /// <summary>\\n /// Implements the the resilient backpropagation algorithm.\\n ///\\n /// A Direct Adaptive Method for Faster Backpropagation Learning: The RPROP Algorithm\\n /// http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.21.1417\\n /// </summary>\\n /// <param name=\\\"parameters\\\">Parameters to optimize. This optimizer requires the <b>named</b> parameters collection.</param>\\n /// <param name=\\\"lr \\\">Learning rate</param>\\n /// <param name=\\\"etaminus\\\">Multiplicative increase factor.</param>\\n /// <param name=\\\"etaplus\\\">Multiplicative decrease factor.</param>\\n /// <param name=\\\"min_step\\\">Minimum allowed step size.</param>\\n /// <param name=\\\"max_step\\\">Maximum allowed step size.</param>\\n /// <param name=\\\"maximize\\\">Maximize the params based on the objective, instead of minimizing.</param>\\n public static Rprop Rprop(IEnumerable<Rprop.ParamGroup> parameters, double lr = 1e-2, double etaminus = 0.5, double etaplus = 1.2, double min_step = 1e-6, double max_step = 50, bool maximize = false)\\n {\\n return new Rprop(parameters, lr, etaminus, etaplus, min_step, max_step, maximize);\\n }\\n }\\n }\\n\\n namespace Modules\\n {\\n public class Rprop : OptimizerHelper\\n {\\n /// <summary>\\n /// Implements Rprop algorithm (a variant of Adam based on infinity norm).\\n ///\\n /// It has been proposed in Adam: A Method for Stochastic Optimization.\\n /// </summary>\\n /// <param name=\\\"parameters\\\">Parameters to optimize.</param>\\n /// <param name=\\\"lr \\\">Learning rate</param>\\n /// <param name=\\\"etaminus\\\">Multiplicative increase factor.</param>\\n /// <param name=\\\"etaplus\\\">Multiplicative decrease factor.</param>\\n /// <param name=\\\"min_step\\\">Minimum allowed step size.</param>\\n /// <param name=\\\"max_step\\\">Maximum allowed step size.</param>\\n /// <param name=\\\"maximize\\\">Maximize the params based on the objective, instead of minimizing.</param>\\n public Rprop(IEnumerable<Parameter> parameters, double lr = 0.01, double etaminus = 0.5, double etaplus = 1.2, double min_step = 1e-6, double max_step = 50, bool maximize = false)\\n : this(new ParamGroup[] { new() { Parameters = parameters } }, lr, etaminus, etaplus, min_step, max_step, maximize)\\n {\\n }\\n\\n /// <summary>\\n /// Implements Rprop algorithm (a variant of Adam based on infinity norm).\\n ///\\n /// It has been proposed in Adam: A Method for Stochastic Optimization.\\n /// </summary>\\n /// <param name=\\\"parameters\\\">Parameters to optimize.</param>\\n /// <param name=\\\"lr \\\">Learning rate</param>\\n /// <param name=\\\"etaminus\\\">Multiplicative increase factor.</param>\\n /// <param name=\\\"etaplus\\\">Multiplicative decrease factor.</param>\\n /// <param name=\\\"min_step\\\">Minimum allowed step size.</param>\\n /// <param name=\\\"max_step\\\">Maximum allowed step size.</param>\\n /// <param name=\\\"maximize\\\">Maximize the params based on the objective, instead of minimizing.</param>\\n public Rprop(IEnumerable<ParamGroup> parameters, double lr = 1e-2, double etaminus = 0.5, double etaplus = 1.2, double min_step = 1e-6, double max_step = 50, bool maximize = false)\\n {\\n if (lr < 0.0) throw new ArgumentException($\\\"Invalid learning rate: {lr}\\\");\\n\\n var options = new Options {\\n LearningRate = lr,\\n InitialLearningRate = lr,\\n maximize = maximize,\\n etaminus = etaminus,\\n etaplus = etaplus,\\n min_step = min_step,\\n max_step = max_step\\n };\\n\\n _defaults = options;\\n _parameter_groups = new List<Modules.ParamGroup>();\\n\\n foreach (var g in parameters) {\\n add_param_group(g);\\n }\\n }\\n\\n /// <summary>\\n /// Performs a single optimization step (parameter update).\\n /// </summary>\\n /// <param name=\\\"closure\\\">A closure that reevaluates the model and returns the loss. Optional for most optimizers.</param>\\n /// <returns></returns>\\n public override Tensor step(Func<Tensor> closure = null)\\n {\\n return _step<ParamGroup>(group => {\\n\\n var options = group.Options as Options;\\n var maximize = options.maximize.Value;\\n var etaminus = options.etaminus.Value;\\n var etaplus = options.etaplus.Value;\\n var min_step = options.min_step.Value;\\n var max_step = options.max_step.Value;\\n var lr = options.LearningRate.Value;\\n\\n foreach (var param in group.Parameters) {\\n\\n var grad = param.grad();\\n\\n if (grad is null) continue;\\n\\n if (grad.is_sparse) throw new ArgumentException(\\\"Rprop does not support sparse gradients\\\");\\n\\n if (maximize) grad = -grad;\\n\\n var state = (State)_state[param.handle];\\n\\n state.step += 1;\\n\\n grad = (max_step != 0)\\n ? grad.add(param, alpha: max_step)\\n : grad.alias();\\n\\n var sign = grad.mul(state.prev).sign();\\n sign[sign.gt(0)] = (Tensor)etaplus;\\n sign[sign.lt(0)] = (Tensor)etaminus;\\n sign[sign.eq(0)] = (Tensor)1;\\n\\n state.step_size.mul_(sign).clamp_(min_step, max_step);\\n\\n grad = grad.clone();\\n\\n grad.index_put_(0, sign.eq(etaminus));\\n\\n param.addcmul_(grad.sign(), state.step_size, -1);\\n\\n state.prev.copy_(grad);\\n }\\n\\n }, closure);\\n }\\n\\n protected override void Dispose(bool disposing)\\n {\\n base.Dispose(disposing);\\n foreach (var kvp in _state) {\\n ((State)kvp.Item2).Dispose();\\n }\\n }\\n /// <summary>\\n /// Add a param group to the Optimizer s param_groups.\\n /// </summary>\\n /// <param name=\\\"param_group\\\"></param>\\n /// <remarks>This can be useful when fine tuning a pre-trained network as frozen layers can be made trainable and added to the Optimizer as training progresses.</remarks>\\n public override void add_param_group(Modules.ParamGroup param_group)\\n {\\n var def = _defaults as Options;\\n if (param_group.Options is null) {\\n param_group.Options = new Options();\\n }\\n\\n var opt = param_group.Options as Options;\\n\\n // Make sure all the options are set.\\n if (!opt.maximize.HasValue) opt.maximize = def.maximize;\\n if (!opt.LearningRate.HasValue) opt.LearningRate = def.LearningRate;\\n if (!opt.etaminus.HasValue) opt.etaminus = def.etaminus;\\n if (!opt.etaplus.HasValue) opt.etaplus = def.etaplus;\\n if (!opt.min_step.HasValue) opt.min_step = def.min_step;\\n if (!opt.max_step.HasValue) opt.max_step = def.max_step;\\n\\n opt.InitialLearningRate = opt.LearningRate.Value;\\n\\n _parameter_groups.Add(param_group);\\n\\n foreach (var p in param_group.Parameters) {\\n var state = new State();\\n _state[p.Handle] = state;\\n state.step = 0;\\n state.prev = torch.zeros_like(p).DetachFromDisposeScope();\\n state.step_size = p.new_empty(p.shape).fill_(opt.LearningRate).DetachFromDisposeScope();\\n }\\n }\\n\\n public sealed class State : OptimizerState, IDisposable\\n {\\n public long step;\\n public Tensor prev;\\n public Tensor step_size;\\n\\n public void Dispose()\\n {\\n Dispose(true);\\n GC.SuppressFinalize(this);\\n }\\n\\n private void Dispose(bool disposing)\\n {\\n if (disposing) {\\n prev.Dispose();\\n step_size.Dispose();\\n }\\n }\\n\\n /// <summary>\\n /// Move all the state to the indicated device.\\n /// </summary>\\n /// <param name=\\\"device\\\">The device to move all state to.</param>\\n public override void to(Device device)\\n {\\n prev = prev.to(device);\\n step_size = step_size.to(device);\\n }\\n\\n /// <summary>\\n /// Load the optimizer parameter state from a stream.\\n /// </summary>\\n /// <param name=\\\"reader\\\">A binary reader connected to a stream open for reading.</param>\\n public override void LoadStateDict(BinaryReader reader)\\n {\\n step = reader.ReadInt64();\\n prev.Load(reader);\\n step_size.Load(reader);\\n }\\n\\n /// <summary>\\n /// Save the optimizer parameter state to a stream.\\n /// </summary>\\n /// <param name=\\\"writer\\\">A binary writer connected to a stream open for writing.</param>\\n public override void SaveStateDict(BinaryWriter writer)\\n {\\n writer.Write(this.step);\\n prev.Save(writer);\\n step_size.Save(writer);\\n }\\n\\n /// <summary>\\n /// Load optimizer parameter state from another optimizer.\\n /// </summary>\\n /// <param name=\\\"source\\\">An optimizer state record.</param>\\n public override void LoadStateDict(OptimizerState source)\\n {\\n var st_state = source as State;\\n prev.Dispose();\\n step_size.Dispose();\\n step = st_state.step;\\n prev = st_state.prev;\\n step_size = st_state.step_size;\\n }\\n\\n /// <summary>\\n /// Useful for tests, allows comparison of one state with another.\\n /// </summary>\\n /// <param name=\\\"other\\\">The other optimizer state</param>\\n /// <returns></returns>\\n public override bool ApproximatelyEquals(OptimizerState other)\\n {\\n var rhs = other as State;\\n return (rhs is not null) && step == rhs.step && prev.allclose(rhs.prev) && step_size.allclose(rhs.step_size);\\n }\\n }\\n\\n\\n public class Options : OptimizerOptions\\n {\\n public bool? maximize;\\n public double? etaminus;\\n public double? etaplus;\\n public double? min_step;\\n public double? max_step;\\n\\n /// <summary>\\n /// Save the optimizer options (param-group hyperparameters) to a stream.\\n /// </summary>\\n /// <param name=\\\"writer\\\">A binary writer connected to a stream open for writing.</param>\\n public override void SaveStateDict(BinaryWriter writer)\\n {\\n base.SaveStateDict(writer);\\n writer.Write(maximize.Value);\\n writer.Write(etaminus.Value);\\n writer.Write(etaplus.Value);\\n writer.Write(min_step.Value);\\n writer.Write(max_step.Value);\\n }\\n\\n /// <summary>\\n /// Load the optimizer options (param-group hyperparameters) from a stream.\\n /// </summary>\\n /// <param name=\\\"reader\\\">A binary reader connected to a stream open for reading.</param>\\n public override void LoadStateDict(BinaryReader reader)\\n {\\n base.LoadStateDict(reader);\\n maximize = reader.ReadBoolean();\\n etaminus = reader.ReadDouble();\\n etaplus = reader.ReadDouble();\\n min_step = reader.ReadDouble();\\n max_step = reader.ReadDouble();\\n }\\n\\n /// <summary>\\n /// Load optimizer options (param-group hyperparameters) from another optimizer.\\n /// </summary>\\n /// <param name=\\\"source\\\">An optimizer options record.</param>\\n public override void LoadStateDict(OptimizerOptions source)\\n {\\n base.LoadStateDict(source);\\n var opts = source as Options;\\n maximize = opts.maximize;\\n etaminus = opts.etaminus;\\n etaplus = opts.etaplus;\\n min_step = opts.min_step;\\n max_step = opts.max_step;\\n }\\n }\\n\\n public class ParamGroup : ParamGroup<Options>\\n {\\n public ParamGroup() { }\\n\\n public ParamGroup(IEnumerable<Parameter> parameters, Options options) : base(parameters, options) { }\\n\\n public ParamGroup(IEnumerable<Parameter> parameters, double lr = 1e-2, double etaminus = 0.5, double etaplus = 1.2, double min_step = 1e-6, double max_step = 50, bool maximize = false)\\n : base(parameters, new Rprop.Options { LearningRate = lr, etaminus = etaminus, etaplus = etaplus, min_step = min_step, max_step = max_step, maximize = maximize })\\n {\\n }\\n }\\n\\n }\\n }\\n}\\n\"\n },\n {\n \"alpha_fraction\": 0.6332852840423584,\n \"alphanum_fraction\": 0.6390489935874939,\n \"avg_line_length\": 42.375,\n \"blob_id\": \"cc01938f7ee6956e519cd65f24eea856cc481513\",\n \"content_id\": \"b84e5428cfbfb1149564e57a989e885cbebc4912\",\n \"detected_licenses\": [\n \"MIT\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"C#\",\n \"length_bytes\": 1388,\n \"license_type\": \"permissive\",\n \"max_line_length\": 130,\n \"num_lines\": 32,\n \"path\": \"/src/TorchSharp/Data/Loader.cs\",\n \"repo_name\": \"NiklasGustafsson/TorchSharp\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"// Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information.\\nusing static TorchSharp.PInvoke.NativeMethods;\\n\\nnamespace TorchSharp.Data\\n{\\n public class Loader\\n {\\n /// <summary>\\n /// Create an iterator scanning the MNIST dataset.\\n /// </summary>\\n /// <param name=\\\"filename\\\">The position of the MNIST dataset</param>\\n /// <param name=\\\"batchSize\\\">The required batch size</param>\\n /// <param name=\\\"isTrain\\\">Wheter the iterator is for training or testing</param>\\n /// <returns></returns>\\n public static DataIterator MNIST(string filename, long batchSize, bool isTrain = true)\\n {\\n return new DataIterator(THSData_loaderMNIST(filename, batchSize, isTrain));\\n }\\n\\n /// <summary>\\n /// Create an iterator scanning the CIFAR10 dataset.\\n /// </summary>\\n /// <param name=\\\"path\\\">The position of the CIFAR10 dataset</param>\\n /// <param name=\\\"batchSize\\\">The required batch size</param>\\n /// <param name=\\\"isTrain\\\">Wheter the iterator is for training or testing</param>\\n /// <returns></returns>\\n public static DataIterator CIFAR10(string path, long batchSize, bool isTrain = true)\\n {\\n return new DataIterator(THSData_loaderCIFAR10(path, batchSize, isTrain));\\n }\\n }\\n}\\n\"\n },\n {\n \"alpha_fraction\": 0.5405641794204712,\n \"alphanum_fraction\": 0.5449376702308655,\n \"avg_line_length\": 43.40776824951172,\n \"blob_id\": \"ca4125d90d0369aa18f6375d835108dbd2049394\",\n \"content_id\": \"316f81ea0d8f3caf3cd191ca6e2d50bf9783ba39\",\n \"detected_licenses\": [\n \"MIT\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"C#\",\n \"length_bytes\": 4573,\n \"license_type\": \"permissive\",\n \"max_line_length\": 166,\n \"num_lines\": 103,\n \"path\": \"/src/TorchSharp/NN/Bilinear.cs\",\n \"repo_name\": \"NiklasGustafsson/TorchSharp\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"// Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information.\\nusing System;\\nusing static TorchSharp.torch;\\nusing static TorchSharp.torch.nn;\\nusing static TorchSharp.PInvoke.NativeMethods;\\n\\n#nullable enable\\nnamespace TorchSharp\\n{\\n using Modules;\\n\\n namespace Modules\\n {\\n public sealed class Bilinear : Module<Tensor, Tensor, Tensor>\\n {\\n internal Bilinear(IntPtr handle, IntPtr boxedHandle) : base(handle, boxedHandle) { }\\n\\n public new static Bilinear Load(string modelPath)\\n {\\n var res = Module<Tensor, Tensor>.Load(modelPath);\\n return new Bilinear(res.handle.DangerousGetHandle(), IntPtr.Zero);\\n }\\n\\n public override Tensor forward(Tensor input1, Tensor input2)\\n {\\n var res = THSNN_Bilinear_forward(handle, input1.Handle, input2.Handle);\\n if (res == IntPtr.Zero) { CheckForErrors(); }\\n return new Tensor(res);\\n }\\n\\n public Parameter? bias {\\n get {\\n var res = THSNN_Bilinear_bias(handle);\\n if (res == IntPtr.Zero) { CheckForErrors(); }\\n return ((res == IntPtr.Zero) ? null : new Parameter(res));\\n }\\n set {\\n THSNN_Bilinear_set_bias(handle, value?.Handle ?? IntPtr.Zero);\\n CheckForErrors();\\n ConditionallyRegisterParameter(\\\"bias\\\", value);\\n }\\n }\\n\\n public Parameter? weight {\\n get {\\n var res = THSNN_Bilinear_weight(handle);\\n if (res == IntPtr.Zero) { CheckForErrors(); }\\n return (res == IntPtr.Zero) ? null : new Parameter(res);\\n }\\n set {\\n THSNN_Bilinear_set_weight(handle, value?.Handle ?? IntPtr.Zero);\\n CheckForErrors();\\n ConditionallyRegisterParameter(\\\"weight\\\", value);\\n }\\n }\\n }\\n }\\n\\n public static partial class torch\\n {\\n public static partial class nn\\n {\\n\\n /// <summary>\\n /// Applies a bilinear transformation to the incoming data\\n /// </summary>\\n /// <param name=\\\"in1Features\\\">size of each first input sample</param>\\n /// <param name=\\\"in2Features\\\">size of each second input sample</param>\\n /// <param name=\\\"outputSize\\\">size of each output sample</param>\\n /// <param name=\\\"hasBias\\\">If set to false, the layer will not learn an additive bias</param>\\n /// <param name=\\\"device\\\">The desired device of the parameters and buffers in this module</param>\\n /// <param name=\\\"dtype\\\">The desired floating point or complex dtype of the parameters and buffers in this module</param>\\n /// <returns></returns>\\n public static Bilinear Bilinear(long in1Features, long in2Features, long outputSize, bool hasBias = true, Device? device = null, ScalarType? dtype = null)\\n {\\n var res = THSNN_Bilinear_ctor(in1Features, in2Features, outputSize, hasBias, out var boxedHandle);\\n if (res == IntPtr.Zero) { CheckForErrors(); }\\n\\n return new Bilinear(res, boxedHandle).MoveModule<Bilinear>(device, dtype);\\n }\\n\\n public static partial class functional\\n {\\n /// <summary>\\n /// Applies a bilinear transformation to the incoming data\\n /// </summary>\\n /// <param name=\\\"input1\\\">Input tensor of shape (N,*,H1)</param>\\n /// <param name=\\\"input2\\\">Input tensor of shape (N,*,H2)</param>\\n /// <param name=\\\"weight\\\">Weights of shape (Hout,H1, H2)</param>\\n /// <param name=\\\"bias\\\">Optional bias of shape (Hout)</param>\\n /// <returns>Tensor of shape (N,*,Hout)</returns>\\n /// <remarks>The '*' sub-shape must be the same among the two inputs.</remarks>\\n public static Tensor bilinear(Tensor input1, Tensor input2, Tensor weight, Tensor? bias = null)\\n {\\n IntPtr bPtr = bias?.Handle ?? IntPtr.Zero;\\n var res = THSNN_functional_bilinear(input1.Handle, input2.Handle, weight.Handle, bPtr);\\n if (res == IntPtr.Zero) { CheckForErrors(); }\\n return new Tensor(res);\\n }\\n }\\n }\\n }\\n}\"\n },\n {\n \"alpha_fraction\": 0.5852211713790894,\n \"alphanum_fraction\": 0.5852211713790894,\n \"avg_line_length\": 35.35293960571289,\n \"blob_id\": \"9c68a4dd5c41496646d7643fb14f5c587fa6db5d\",\n \"content_id\": \"81e9051d8a44d3aa96d8fdcb9e5464414611700a\",\n \"detected_licenses\": [\n \"MIT\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"C#\",\n \"length_bytes\": 1858,\n \"license_type\": \"permissive\",\n \"max_line_length\": 130,\n \"num_lines\": 51,\n \"path\": \"/src/TorchSharp/NN/Parameter.cs\",\n \"repo_name\": \"NiklasGustafsson/TorchSharp\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"// Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information.\\nusing static TorchSharp.torch;\\n\\nnamespace TorchSharp\\n{\\n using Modules;\\n\\n namespace Modules\\n {\\n /// <summary>\\n /// A kind of Tensor that is to be considered a module parameter.\\n ///\\n /// Parameters are Tensor subclasses, that have a very special property when used with Modules -\\n /// when they’re assigned as Module attributes they are automatically added to the list of its parameters,\\n /// and will appear e.g. in parameters() iterator.Assigning a Tensor doesn’t have such effect.This is because\\n /// one might want to cache some temporary state, like last hidden state of the RNN, in the model.\\n /// If there was no such class as Parameter, these temporaries would get registered too.\\n /// </summary>\\n public class Parameter : Tensor\\n {\\n /// <summary>\\n /// Constructor\\n /// </summary>\\n /// <param name=\\\"data\\\">A tensor, which will become empty.</param>\\n /// <param name=\\\"requires_grad\\\"></param>\\n public Parameter(Tensor data, bool requires_grad = true) :\\n base(data.with_requires_grad(requires_grad).MoveHandle())\\n {\\n }\\n\\n /// <summary>\\n /// Constructor\\n /// </summary>\\n /// <param name=\\\"handle\\\">A tensor handle.</param>\\n internal Parameter(System.IntPtr handle) : base(handle)\\n {\\n }\\n\\n };\\n }\\n\\n public static partial class torch\\n {\\n public static partial class nn\\n {\\n public static Parameter Parameter(Tensor data, bool requires_grad = true) =>\\n new Parameter(data, requires_grad);\\n\\n }\\n }\\n}\\n\"\n },\n {\n \"alpha_fraction\": 0.6014527678489685,\n \"alphanum_fraction\": 0.6024212837219238,\n \"avg_line_length\": 35.24561309814453,\n \"blob_id\": \"99567f0fa980537144825738fc31265fd504c8b7\",\n \"content_id\": \"668fbdb002553e76864ecd6860f42a3f16cb0377\",\n \"detected_licenses\": [\n \"MIT\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"C#\",\n \"length_bytes\": 2065,\n \"license_type\": \"permissive\",\n \"max_line_length\": 130,\n \"num_lines\": 57,\n \"path\": \"/src/TorchSharp/Tensor/torch.Parallelism.cs\",\n \"repo_name\": \"NiklasGustafsson/TorchSharp\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"// Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information.\\n#nullable enable\\nusing System;\\nusing System.Diagnostics.Contracts;\\n\\nusing static TorchSharp.PInvoke.NativeMethods;\\n\\nnamespace TorchSharp\\n{\\n // https://pytorch.org/docs/stable/torch#parallelism\\n public static partial class torch\\n {\\n // https://pytorch.org/docs/stable/generated/torch.get_num_threads\\n /// <summary>\\n /// Returns the number of threads used for parallelizing CPU operations\\n /// </summary>\\n public static int get_num_threads()\\n {\\n var res = THSTorch_get_num_threads();\\n if (res == -1) CheckForErrors();\\n return res;\\n }\\n\\n // https://pytorch.org/docs/stable/generated/torch.set_num_threads\\n /// <summary>\\n /// Sets the number of threads used for parallelizing CPU operations\\n /// </summary>\\n /// <param name=\\\"num\\\">The number of threads to use.</param>\\n public static void set_num_threads(int num)\\n {\\n THSTorch_set_num_threads(num);\\n CheckForErrors();\\n }\\n\\n // https://pytorch.org/docs/stable/generated/torch.get_num_interop_threads\\n /// <summary>\\n /// Returns the number of threads used for inter-op parallelism on CPU (e.g. in JIT interpreter)\\n /// </summary>\\n public static int get_num_interop_threads()\\n {\\n var res = THSTorch_get_num_interop_threads();\\n if (res == -1) CheckForErrors();\\n return res;\\n }\\n\\n // https://pytorch.org/docs/stable/generated/torch.set_num_interop_threads\\n /// <summary>\\n /// Sets the number of threads used for inter-op parallelism on CPU (e.g. in JIT interpreter)\\n /// </summary>\\n /// <param name=\\\"num\\\">The number of threads to use.</param>\\n public static void set_num_interop_threads(int num)\\n {\\n THSTorch_set_num_interop_threads(num);\\n CheckForErrors();\\n }\\n }\\n}\"\n },\n {\n \"alpha_fraction\": 0.5210997462272644,\n \"alphanum_fraction\": 0.5223785042762756,\n \"avg_line_length\": 30.280000686645508,\n \"blob_id\": \"a85bb779728d53a1490270762f68b3453e502036\",\n \"content_id\": \"743e4c55c4553897b0d42e694da61adf8e4cbc04\",\n \"detected_licenses\": [\n \"MIT\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"C#\",\n \"length_bytes\": 3128,\n \"license_type\": \"permissive\",\n \"max_line_length\": 185,\n \"num_lines\": 100,\n \"path\": \"/src/Examples.Utils/Vocab.cs\",\n \"repo_name\": \"NiklasGustafsson/TorchSharp\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"// Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information.\\nusing System;\\nusing System.Collections;\\nusing System.Collections.Generic;\\nusing System.Diagnostics.CodeAnalysis;\\nusing System.Linq;\\nusing TorchSharp;\\n\\nusing static TorchSharp.torch.nn;\\n\\nnamespace TorchText.Vocab\\n{\\n /// <summary>\\n /// This needs a permanent place.\\n /// The functionality is based on the Python 'Counter' class.\\n /// </summary>\\n /// <typeparam name=\\\"T\\\"></typeparam>\\n public class Counter<T> : IEnumerable<KeyValuePair<T, int>>\\n {\\n private Dictionary<T, int> _dict = new Dictionary<T, int>();\\n\\n public void update(T key)\\n {\\n if (_dict.TryGetValue(key, out int count)) {\\n _dict[key] = count + 1;\\n } else {\\n _dict[key] = 1;\\n }\\n }\\n public void update(IEnumerable<T> keys)\\n {\\n foreach (T key in keys) {\\n update(key);\\n }\\n }\\n public int this[T key] { get => _dict[key]; }\\n\\n public IEnumerator<KeyValuePair<T, int>> GetEnumerator()\\n {\\n return _dict.GetEnumerator();\\n }\\n\\n IEnumerator IEnumerable.GetEnumerator()\\n {\\n return GetEnumerator();\\n }\\n }\\n\\n /// <summary>\\n /// This belongs in its own package, 'TorchText'.\\n /// For now, it's useful to keep it with the examples that use it.\\n /// </summary>\\n public class Vocab\\n {\\n public Vocab(Counter<string> counter, int? maxSize = null, int minFreq = 1, string[] specials = null, Func<torch.Tensor, torch.Tensor> unkInit = null, bool specialsFirst = true)\\n {\\n if (specials == null) specials = new string[] { \\\"<unk>\\\", \\\"<pad>\\\" };\\n if (unkInit == null) unkInit = (t => init.zeros_(t.clone()));\\n if (specialsFirst) {\\n foreach (var sp in specials) {\\n _dict.Add(sp, _last++);\\n }\\n }\\n foreach (var kv in counter.Where(kv => kv.Value >= minFreq)) {\\n if (!specials.Contains(kv.Key)) {\\n _dict.Add(kv.Key, _last++);\\n }\\n if (_last > (maxSize ?? int.MaxValue))\\n break;\\n }\\n if (!specialsFirst) {\\n foreach (var sp in specials) {\\n _dict.Add(sp, _last++);\\n }\\n }\\n }\\n\\n public int this[string key] { get => _dict.TryGetValue(key, out int value) ? value : _dict[\\\"<unk>\\\"]; }\\n\\n public int Count => _dict.Count;\\n\\n public void Add(string key, int value)\\n {\\n _dict.Add(key, value);\\n }\\n\\n public void Add(KeyValuePair<string, int> item)\\n {\\n Add(item.Key, item.Value);\\n }\\n\\n public bool TryGetValue(string key, [MaybeNullWhen(false)] out int value)\\n {\\n return _dict.TryGetValue(key, out value);\\n }\\n\\n private Dictionary<string, int> _dict = new Dictionary<string, int>();\\n private int _last = 0;\\n }\\n}\\n\"\n },\n {\n \"alpha_fraction\": 0.602297306060791,\n \"alphanum_fraction\": 0.6036486625671387,\n \"avg_line_length\": 51.85714340209961,\n \"blob_id\": \"c08600650a03d8b2be994bffdd170809050e170c\",\n \"content_id\": \"b8cda7aebb1c55fd1597fb8e94d59b3156025925\",\n \"detected_licenses\": [\n \"MIT\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"C#\",\n \"length_bytes\": 7400,\n \"license_type\": \"permissive\",\n \"max_line_length\": 172,\n \"num_lines\": 140,\n \"path\": \"/src/TorchSharp/Distributions/RelaxedOneHotCategorical.cs\",\n \"repo_name\": \"NiklasGustafsson/TorchSharp\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"// Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information.\\nusing System;\\nusing static TorchSharp.torch;\\n\\nnamespace TorchSharp\\n{\\n using Modules;\\n using static torch.distributions;\\n\\n namespace Modules\\n {\\n /// <summary>\\n /// Creates a RelaxedOneHotCategorical distribution, parametrized by `temperature`, and either `probs` or `logits` (but not both).\\n /// This is a relaxed version of the `OneHotCategorical` distribution, so its samples are on simplex, and are reparametrizable..\\n /// </summary>\\n public class RelaxedOneHotCategorical : TransformedDistribution\\n {\\n /// <summary>\\n /// Constructor\\n /// </summary>\\n /// <param name=\\\"temperature\\\">Relaxation temperature</param>\\n /// <param name=\\\"probs\\\">the probability of sampling `1`</param>\\n /// <param name=\\\"logits\\\">the log-odds of sampling `1`</param>\\n /// <param name=\\\"generator\\\"></param>\\n public RelaxedOneHotCategorical(Tensor temperature, Tensor probs = null, Tensor logits = null, torch.Generator generator = null) :\\n base(ExpRelaxedCategorical(temperature, probs, logits, generator), new distributions.transforms.ExpTransform(), generator)\\n {\\n this._probs = probs?.alias().DetachFromDisposeScope();\\n this._logits = logits?.alias().DetachFromDisposeScope();\\n }\\n\\n private Tensor _probs;\\n private Tensor _logits;\\n\\n private ExpRelaxedCategorical base_dist => this.base_distribution as ExpRelaxedCategorical;\\n\\n /// <summary>\\n /// The probability of sampling 1\\n /// </summary>\\n public Tensor probs {\\n get {\\n return base_dist.probs;\\n }\\n }\\n\\n /// <summary>\\n /// The log-odds of sampling 1\\n /// </summary>\\n public Tensor logits {\\n get {\\n return base_dist.logits;\\n }\\n }\\n\\n public Tensor temperature {\\n get {\\n return base_dist.logits;\\n }\\n }\\n\\n /// <summary>\\n /// Returns a new distribution instance (or populates an existing instance provided by a derived class) with batch dimensions expanded to\\n /// `batch_shape`. This method calls `torch.Tensor.expand()` on the distribution's parameters. As such, this does not allocate new\\n /// memory for the expanded distribution instance.\\n /// </summary>\\n /// <param name=\\\"batch_shape\\\">Tthe desired expanded size.</param>\\n /// <param name=\\\"instance\\\">new instance provided by subclasses that need to override `.expand`.</param>\\n /// <returns></returns>\\n public override distributions.Distribution expand(Size batch_shape, distributions.Distribution instance = null)\\n {\\n if (instance != null && !(instance is RelaxedOneHotCategorical))\\n throw new ArgumentException(\\\"expand(): 'instance' must be a RelaxedOneHotCategorical distribution\\\");\\n\\n var newDistribution = ((instance == null) ? new RelaxedOneHotCategorical(temperature, _probs, _logits, generator) : instance) as RelaxedOneHotCategorical;\\n\\n newDistribution.batch_shape = batch_shape;\\n return base.expand(batch_shape, newDistribution);\\n }\\n }\\n\\n }\\n public static partial class torch\\n {\\n public static partial class distributions\\n {\\n /// <summary>\\n /// Creates a RelaxedOneHotCategorical distribution, parametrized by `temperature`, and either `probs` or `logits` (but not both).\\n /// This is a relaxed version of the `OneHotCategorical` distribution, so its samples are on simplex, and are reparametrizable..\\n /// </summary>\\n /// <param name=\\\"temperature\\\">Relaxation temperature</param>\\n /// <param name=\\\"probs\\\">The probability of sampling '1'</param>\\n /// <param name=\\\"logits\\\">The log-odds of sampling '1'</param>\\n /// <param name=\\\"generator\\\">An optional random number generator object.</param>\\n /// <returns></returns>\\n public static RelaxedOneHotCategorical RelaxedOneHotCategorical(Tensor temperature, Tensor probs = null, Tensor logits = null, torch.Generator generator = null)\\n {\\n return new RelaxedOneHotCategorical(temperature, probs, logits, generator);\\n }\\n\\n /// <summary>\\n /// Creates a RelaxedOneHotCategorical distribution, parametrized by `temperature`, and either `probs` or `logits` (but not both).\\n /// This is a relaxed version of the `OneHotCategorical` distribution, so its samples are on simplex, and are reparametrizable..\\n /// </summary>\\n /// <param name=\\\"temperature\\\">Relaxation temperature</param>\\n /// <param name=\\\"probs\\\">The probability of sampling '1'</param>\\n /// <param name=\\\"logits\\\">The log-odds of sampling '1'</param>\\n /// <param name=\\\"generator\\\">An optional random number generator object.</param>\\n /// <returns></returns>\\n public static RelaxedOneHotCategorical RelaxedOneHotCategorical(Tensor temperature, float? probs, float? logits, torch.Generator generator = null)\\n {\\n if (probs.HasValue && !logits.HasValue)\\n return new RelaxedOneHotCategorical(temperature, torch.tensor(probs.Value), null, generator);\\n else if (!probs.HasValue && logits.HasValue)\\n return new RelaxedOneHotCategorical(temperature, null, torch.tensor(logits.Value), generator);\\n else\\n throw new ArgumentException(\\\"One and only one of 'probs' and logits should be provided.\\\");\\n }\\n\\n\\n /// <summary>\\n /// Creates a RelaxedOneHotCategorical distribution, parametrized by `temperature`, and either `probs` or `logits` (but not both).\\n /// This is a relaxed version of the `OneHotCategorical` distribution, so its samples are on simplex, and are reparametrizable..\\n /// </summary>\\n /// <param name=\\\"temperature\\\">Relaxation temperature</param>\\n /// <param name=\\\"probs\\\">The probability of sampling '1'</param>\\n /// <param name=\\\"logits\\\">The log-odds of sampling '1'</param>\\n /// <param name=\\\"generator\\\">An optional random number generator object.</param>\\n /// <returns></returns>\\n public static RelaxedOneHotCategorical RelaxedOneHotCategorical(Tensor temperature, double? probs, double? logits, torch.Generator generator = null)\\n {\\n if (probs.HasValue && !logits.HasValue)\\n return new RelaxedOneHotCategorical(temperature, torch.tensor(probs.Value), null, generator);\\n else if (!probs.HasValue && logits.HasValue)\\n return new RelaxedOneHotCategorical(temperature, null, torch.tensor(logits.Value), generator);\\n else\\n throw new ArgumentException(\\\"One and only one of 'probs' and 'logits' should be non-null\\\");\\n }\\n }\\n }\\n}\\n\"\n },\n {\n \"alpha_fraction\": 0.3323427438735962,\n \"alphanum_fraction\": 0.8015121817588806,\n \"avg_line_length\": 76.79247283935547,\n \"blob_id\": \"e01bc550a7443bf4b763ac6faf9e99a8a516f859\",\n \"content_id\": \"6f34e23b3016ad3384221af1257c309d74cd1f57\",\n \"detected_licenses\": [\n \"MIT\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"C\",\n \"length_bytes\": 72347,\n \"license_type\": \"permissive\",\n \"max_line_length\": 87,\n \"num_lines\": 930,\n \"path\": \"/src/Native/LibTorchSharp/crc32c.c\",\n \"repo_name\": \"NiklasGustafsson/TorchSharp\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"/* Part of CRC-32C library: https://crc32c.machinezoo.com/ */\\n/*\\n Copyright (c) 2013 - 2014, 2016 Mark Adler, Robert Vazan, Max Vysokikh\\n\\n This software is provided 'as-is', without any express or implied\\n warranty. In no event will the author be held liable for any damages\\n arising from the use of this software.\\n\\n Permission is granted to anyone to use this software for any purpose,\\n including commercial applications, and to alter it and redistribute it\\n freely, subject to the following restrictions:\\n\\n 1. The origin of this software must not be misrepresented; you must not\\n claim that you wrote the original software. If you use this software\\n in a product, an acknowledgment in the product documentation would be\\n appreciated but is not required.\\n 2. Altered source versions must be plainly marked as such, and must not be\\n misrepresented as being the original software.\\n 3. This notice may not be removed or altered from any source distribution.\\n*/\\n\\n#ifndef _CRT_SECURE_NO_WARNINGS\\n#define _CRT_SECURE_NO_WARNINGS\\n#endif\\n\\n#include \\\"crc32c.h\\\"\\n\\n// ALTERED SOURCE VERSION\\n//\\n// Per #2 in the copyright notice above:\\n// \\n// The definition of 'NONMINMAX' below was altered from the original.\\n\\n//#define NOMINMAX\\n\\n#ifdef CRC32C_GCC\\n#include <cpuid.h>\\n#include <x86intrin.h>\\n#else\\n#include <intrin.h>\\n#endif\\n\\n#define POLY 0x82f63b78\\n#define LONG_SHIFT 8192\\n#define SHORT_SHIFT 256\\n\\ntypedef const uint8_t *buffer;\\n\\nstatic union {\\n uint32_t dword_table[16][256];\\n uint64_t qword_array[2048];\\n} table = {\\n .qword_array = {\\n 0xf26b830300000000, 0x1350f3f4e13b70f7, 0x35f1141cc79a971f, 0xd4ca64eb26a1e7e8,\\n 0x78b2dbcc8ad958cf, 0x9989ab3b6be22838, 0xbf284cd34d43cfd0, 0x5e133c24ac78bf27,\\n 0xe235446c105ec76f, 0x30e349bf165b798, 0x25afd373d7c45070, 0xc494a38436ff2087,\\n 0x68ec1ca39a879fa0, 0x89d76c547bbcef57, 0xaf768bbc5d1d08bf, 0x4e4dfb4bbc267848,\\n 0xd2d60ddd20bd8ede, 0x33ed7d2ac186fe29, 0x154c9ac2e72719c1, 0xf477ea35061c6936,\\n 0x580f5512aa64d611, 0xb93425e54b5fa6e6, 0x9f95c20d6dfe410e, 0x7eaeb2fa8cc531f9,\\n 0xc288cab230e349b1, 0x23b3ba45d1d83946, 0x5125dadf779deae, 0xe4292d5a1642ae59,\\n 0x4851927dba3a117e, 0xa96ae28a5b016189, 0x8fcb05627da08661, 0x6ef075959c9bf696,\\n 0xb3109ebf417b1dbc, 0x522bee48a0406d4b, 0x748a09a086e18aa3, 0x95b1795767dafa54,\\n 0x39c9c670cba24573, 0xd8f2b6872a993584, 0xfe53516f0c38d26c, 0x1f682198ed03a29b,\\n 0xa34e59d05125dad3, 0x42752927b01eaa24, 0x64d4cecf96bf4dcc, 0x85efbe3877843d3b,\\n 0x2997011fdbfc821c, 0xc8ac71e83ac7f2eb, 0xee0d96001c661503, 0xf36e6f7fd5d65f4,\\n 0x93ad106161c69362, 0x7296609680fde395, 0x5437877ea65c047d, 0xb50cf7894767748a,\\n 0x197448aeeb1fcbad, 0xf84f38590a24bb5a, 0xdeeedfb12c855cb2, 0x3fd5af46cdbe2c45,\\n 0x83f3d70e7198540d, 0x62c8a7f990a324fa, 0x44694011b602c312, 0xa55230e65739b3e5,\\n 0x92a8fc1fb410cc2, 0xe811ff361a7a7c35, 0xceb018de3cdb9bdd, 0x2f8b6829dde0eb2a,\\n 0x709db87b82f63b78, 0x91a6c88c63cd4b8f, 0xb7072f64456cac67, 0x563c5f93a457dc90,\\n 0xfa44e0b4082f63b7, 0x1b7f9043e9141340, 0x3dde77abcfb5f4a8, 0xdce5075c2e8e845f,\\n 0x60c37f1492a8fc17, 0x81f80fe373938ce0, 0xa759e80b55326b08, 0x466298fcb4091bff,\\n 0xea1a27db1871a4d8, 0xb21572cf94ad42f, 0x2d80b0c4dfeb33c7, 0xccbbc0333ed04330,\\n 0x502036a5a24bb5a6, 0xb11b46524370c551, 0x97baa1ba65d122b9, 0x7681d14d84ea524e,\\n 0xdaf96e6a2892ed69, 0x3bc21e9dc9a99d9e, 0x1d63f975ef087a76, 0xfc5889820e330a81,\\n 0x407ef1cab21572c9, 0xa145813d532e023e, 0x87e466d5758fe5d6, 0x66df162294b49521,\\n 0xcaa7a90538cc2a06, 0x2b9cd9f2d9f75af1, 0xd3d3e1aff56bd19, 0xec064eed1e6dcdee,\\n 0x31e6a5c7c38d26c4, 0xd0ddd53022b65633, 0xf67c32d80417b1db, 0x1747422fe52cc12c,\\n 0xbb3ffd0849547e0b, 0x5a048dffa86f0efc, 0x7ca56a178ecee914, 0x9d9e1ae06ff599e3,\\n 0x21b862a8d3d3e1ab, 0xc083125f32e8915c, 0xe622f5b7144976b4, 0x7198540f5720643,\\n 0xab613a67590ab964, 0x4a5a4a90b831c993, 0x6cfbad789e902e7b, 0x8dc0dd8f7fab5e8c,\\n 0x115b2b19e330a81a, 0xf0605bee020bd8ed, 0xd6c1bc0624aa3f05, 0x37faccf1c5914ff2,\\n 0x9b8273d669e9f0d5, 0x7ab9032188d28022, 0x5c18e4c9ae7367ca, 0xbd23943e4f48173d,\\n 0x105ec76f36e6f75, 0xe03e9c8112551f82, 0xc69f7b6934f4f86a, 0x27a40b9ed5cf889d,\\n 0x8bdcb4b979b737ba, 0x6ae7c44e988c474d, 0x4c4623a6be2da0a5, 0xad7d53515f16d052,\\n 0x13a2987700000000, 0x34e7a899274530ee, 0x5d28f9ab4e8a61dc, 0x7a6dc94569cf5132,\\n 0x8eb65bcf9d14c3b8, 0xa9f36b21ba51f356, 0xc03c3a13d39ea264, 0xe7790afdf4db928a,\\n 0x2c6769f63fc5f181, 0xb2259181880c16f, 0x62ed082a714f905d, 0x45a838c4560aa0b3,\\n 0xb173aa4ea2d13239, 0x96369aa0859402d7, 0xfff9cb92ec5b53e5, 0xd8bcfb7ccb1e630b,\\n 0x6c297b757f8be302, 0x4b6c4b9b58ced3ec, 0x22a31aa9310182de, 0x5e62a471644b230,\\n 0xf13db8cde29f20ba, 0xd6788823c5da1054, 0xbfb7d911ac154166, 0x98f2e9ff8b507188,\\n 0x53ec8af4404e1283, 0x74a9ba1a670b226d, 0x1d66eb280ec4735f, 0x3a23dbc6298143b1,\\n 0xcef8494cdd5ad13b, 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0x9f5c6607cb5b3341, 0x62a3ef6236a4ba24, 0xcaad45ee9eaa10a8,\\n 0x6ac980a63eced5e0, 0xc2c72a2a96c07f6c, 0x3f38a34f6b3ff609, 0x973609c3c3315c85,\\n 0xc12bc774952c9232, 0x69256df83d2238be, 0x94dae49dc0ddb1db, 0x3cd44e1168d31b57,\\n 0x38e179f36ce62cb5, 0x90efd37fc4e88639, 0x6d105a1a39170f5c, 0xc51ef0969119a5d0,\\n 0x93033e21c7046b67, 0x3b0d94ad6f0ac1eb, 0xc6f21dc892f5488e, 0x6efcb7443afbe202,\\n 0x83d7e1a9d7d0b4ef, 0x2bd94b257fde1e63, 0xd626c24082219706, 0x7e2868cc2a2f3d8a,\\n 0x2835a67b7c32f33d, 0x803b0cf7d43c59b1, 0x7dc4859229c3d0d4, 0xd5ca2f1e81cd7a58,\\n 0xd1ff18fc85f84dba, 0x79f1b2702df6e736, 0x840e3b15d0096e53, 0x2c0091997807c4df,\\n 0x7a1d5f2e2e1a0a68, 0xd213f5a28614a0e4, 0x2fec7cc77beb2981, 0x87e2d64bd3e5830d,\\n 0x2786130373814645, 0x8f88b98fdb8fecc9, 0x727730ea267065ac, 0xda799a668e7ecf20,\\n 0x8c6454d1d8630197, 0x246afe5d706dab1b, 0xd99577388d92227e, 0x719bddb4259c88f2,\\n 0x75aeea5621a9bf10, 0xdda040da89a7159c, 0x205fc9bf74589cf9, 0x88516333dc563675,\\n 0xde4cad848a4bf8c2, 0x764207082245524e, 0x8bbd8e6ddfbadb2b, 0x23b324e177b471a7,\\n 0x678efd0100000000, 0xa8930703cf1dfa02, 0xfc597ff49bd782f5, 0x334485f654ca78f7,\\n 0x55cd8e1a3243731b, 0x9ad07418fd5e8919, 0xce1a0cefa994f1ee, 0x107f6ed66890bec,\\n 0x3081b376486e636, 0xcc15e135ab9b1c34, 0x98df99c2ff5164c3, 0x57c263c0304c9ec1,\\n 0x314b682c56c5952d, 0xfe56922e99d86f2f, 0xaa9cead9cd1217d8, 0x658110db020fedda,\\n 0xae83316dc90dcc6c, 0x619ecb6f0610366e, 0x3554b39852da4e99, 0xfa49499a9dc7b49b,\\n 0x9cc04276fb4ebf77, 0x53ddb87434534575, 0x717c08360993d82, 0xc80a3a81af84c780,\\n 0xca05d75bad8b2a5a, 0x5182d596296d058, 0x51d255ae365ca8af, 0x9ecfafacf94152ad,\\n 0xf846a4409fc85941, 0x375b5e4250d5a343, 0x639126b5041fdbb4, 0xac8cdcb7cb0221b6,\\n 0xf079132897f7ee29, 0x3f64e92a58ea142b, 0x6bae91dd0c206cdc, 0xa4b36bdfc33d96de,\\n 0xc23a6033a5b49d32, 0xd279a316aa96730, 0x59ede2c63e631fc7, 0x96f018c4f17ee5c5,\\n 0x94fff51ef371081f, 0x5be20f1c3c6cf21d, 0xf2877eb68a68aea, 0xc0358de9a7bb70e8,\\n 0xa6bc8605c1327b04, 0x69a17c070e2f8106, 0x3d6b04f05ae5f9f1, 0xf276fef295f803f3,\\n 0x3974df445efa2245, 0xf669254691e7d847, 0xa2a35db1c52da0b0, 0x6dbea7b30a305ab2,\\n 0xb37ac5f6cb9515e, 0xc42a565da3a4ab5c, 0x90e02eaaf76ed3ab, 0x5ffdd4a8387329a9,\\n 0x5df239723a7cc473, 0x92efc370f5613e71, 0xc625bb87a1ab4686, 0x93841856eb6bc84,\\n 0x6fb14a69083fb768, 0xa0acb06bc7224d6a, 0xf466c89c93e8359d, 0x3b7b329e5cf5cf9f,\\n 0x4d8d57a22a03aaa3, 0x8290ada0e51e50a1, 0xd65ad557b1d42856, 0x19472f557ec9d254,\\n 0x7fce24b91840d9b8, 0xb0d3debbd75d23ba, 0xe419a64c83975b4d, 0x2b045c4e4c8aa14f,\\n 0x290bb1944e854c95, 0xe6164b968198b697, 0xb2dc3361d552ce60, 0x7dc1c9631a4f3462,\\n 0x1b48c28f7cc63f8e, 0xd455388db3dbc58c, 0x809f407ae711bd7b, 0x4f82ba78280c4779,\\n 0x84809bcee30e66cf, 0x4b9d61cc2c139ccd, 0x1f57193b78d9e43a, 0xd04ae339b7c41e38,\\n 0xb6c3e8d5d14d15d4, 0x79de12d71e50efd6, 0x2d146a204a9a9721, 0xe209902285876d23,\\n 0xe0067df8878880f9, 0x2f1b87fa48957afb, 0x7bd1ff0d1c5f020c, 0xb4cc050fd342f80e,\\n 0xd2450ee3b5cbf3e2, 0x1d58f4e17ad609e0, 0x49928c162e1c7117, 0x868f7614e1018b15,\\n 0xda7ab98bbdf4448a, 0x1567438972e9be88, 0x41ad3b7e2623c67f, 0x8eb0c17ce93e3c7d,\\n 0xe839ca908fb73791, 0x2724309240aacd93, 0x73ee48651460b564, 0xbcf3b267db7d4f66,\\n 0xbefc5fbdd972a2bc, 0x71e1a5bf166f58be, 0x252bdd4842a52049, 0xea36274a8db8da4b,\\n 0x8cbf2ca6eb31d1a7, 0x43a2d6a4242c2ba5, 0x1768ae5370e65352, 0xd8755451bffba950,\\n 0x137775e774f988e6, 0xdc6a8fe5bbe472e4, 0x88a0f712ef2e0a13, 0x47bd0d102033f011,\\n 0x213406fc46bafbfd, 0xee29fcfe89a701ff, 0xbae38409dd6d7908, 0x75fe7e0b1270830a,\\n 0x77f193d1107f6ed0, 0xb8ec69d3df6294d2, 0xec2611248ba8ec25, 0x233beb2644b51627,\\n 0x45b2e0ca223c1dcb, 0x8aaf1ac8ed21e7c9, 0xde65623fb9eb9f3e, 0x1178983d76f6653c,\\n 0xf20c0dfe00000000, 0x13f860f3e1f46d0d, 0x3408a115c604aceb, 0xd5fccc1827f0c1e6,\\n 0x7be922d989e52f27, 0x9a1d4fd46811422a, 0xbded8e324fe183cc, 0x5c19e33fae15eec1,\\n 0xe42a2541162628bf, 0x5de484cf7d245b2, 0x222e89aad0228454, 0xc3dae4a731d6e959,\\n 0x6dcf0a669fc30798, 0x8c3b676b7e376a95, 0xabcba68d59c7ab73, 0x4a3fcb80b833c67e,\\n 0xde405c802c4c517e, 0x3fb4318dcdb83c73, 0x1844f06bea48fd95, 0xf9b09d660bbc9098,\\n 0x57a573a7a5a97e59, 0xb6511eaa445d1354, 0x91a1df4c63add2b2, 0x7055b2418259bfbf,\\n 0xc866743f3a6a79c1, 0x29921932db9e14cc, 0xe62d8d4fc6ed52a, 0xef96b5d91d9ab827,\\n 0x41835b18b38f56e6, 0xa0773615527b3beb, 0x8787f7f3758bfa0d, 0x66739afe947f9700,\\n 0xaa94af025898a2fc, 0x4b60c20fb96ccff1, 0x6c9003e99e9c0e17, 0x8d646ee47f68631a,\\n 0x23718025d17d8ddb, 0xc285ed283089e0d6, 0xe5752cce17792130, 0x48141c3f68d4c3d,\\n 0xbcb287bd4ebe8a43, 0x5d46eab0af4ae74e, 0x7ab62b5688ba26a8, 0x9b42465b694e4ba5,\\n 0x3557a89ac75ba564, 0xd4a3c59726afc869, 0xf3530471015f098f, 0x12a7697ce0ab6482,\\n 0x86d8fe7c74d4f382, 0x672c937195209e8f, 0x40dc5297b2d05f69, 0xa1283f9a53243264,\\n 0xf3dd15bfd31dca5, 0xeec9bc561cc5b1a8, 0xc9397db03b35704e, 0x28cd10bddac11d43,\\n 0x90fed6c362f2db3d, 0x710abbce8306b630, 0x56fa7a28a4f677d6, 0xb70e172545021adb,\\n 0x191bf9e4eb17f41a, 0xf8ef94e90ae39917, 0xdf1f550f2d1358f1, 0x3eeb3802cce735fc,\\n 0x433d4806b13145f8, 0xa2c9250b50c528f5, 0x8539e4ed7735e913, 0x64cd89e096c1841e,\\n 0xcad8672138d46adf, 0x2b2c0a2cd92007d2, 0xcdccbcafed0c634, 0xed28a6c71f24ab39,\\n 0x551b60b9a7176d47, 0xb4ef0db446e3004a, 0x931fcc526113c1ac, 0x72eba15f80e7aca1,\\n 0xdcfe4f9e2ef24260, 0x3d0a2293cf062f6d, 0x1afae375e8f6ee8b, 0xfb0e8e7809028386,\\n 0x6f7119789d7d1486, 0x8e8574757c89798b, 0xa975b5935b79b86d, 0x4881d89eba8dd560,\\n 0xe694365f14983ba1, 0x7605b52f56c56ac, 0x20909ab4d29c974a, 0xc164f7b93368fa47,\\n 0x795731c78b5b3c39, 0x98a35cca6aaf5134, 0xbf539d2c4d5f90d2, 0x5ea7f021acabfddf,\\n 0xf0b21ee002be131e, 0x114673ede34a7e13, 0x36b6b20bc4babff5, 0xd742df06254ed2f8,\\n 0x1ba5eafae9a9e704, 0xfa5187f7085d8a09, 0xdda146112fad4bef, 0x3c552b1cce5926e2,\\n 0x9240c5dd604cc823, 0x73b4a8d081b8a52e, 0x54446936a64864c8, 0xb5b0043b47bc09c5,\\n 0xd83c245ff8fcfbb, 0xec77af481e7ba2b6, 0xcb876eae398b6350, 0x2a7303a3d87f0e5d,\\n 0x8466ed62766ae09c, 0x6592806f979e8d91, 0x42624189b06e4c77, 0xa3962c84519a217a,\\n 0x37e9bb84c5e5b67a, 0xd61dd6892411db77, 0xf1ed176f03e11a91, 0x10197a62e215779c,\\n 0xbe0c94a34c00995d, 0x5ff8f9aeadf4f450, 0x780838488a0435b6, 0x99fc55456bf058bb,\\n 0x21cf933bd3c39ec5, 0xc03bfe363237f3c8, 0xe7cb3fd015c7322e, 0x63f52ddf4335f23,\\n 0xa82abc1c5a26b1e2, 0x49ded111bbd2dcef, 0x6e2e10f79c221d09, 0x8fda7dfa7dd67004,\\n }\\n};\\n\\nstatic union {\\n uint32_t dword_table[4][256];\\n uint64_t qword_array[512];\\n} long_shifts = {\\n .qword_array = {\\n 0xe040e0ac00000000, 0x252d5705c56db7a9, 0x6f77f90f8f3719a3, 0xaa1a4ea64a5aae0a,\\n 0xfbc2a51b1b8245b7, 0x3eaf12b2deeff21e, 0x74f5bcb894b55c14, 0xb1980b1151d8ebbd,\\n 0xd7446bc237048b6e, 0x1229dc6bf2693cc7, 0x58737261b83392cd, 0x9d1ec5c87d5e2564,\\n 0xccc62e752c86ced9, 0x9ab99dce9eb7970, 0x43f137d6a3b1d77a, 0x869c807f66dc60d3,\\n 0x8e49f6706e0916dc, 0x4b2441d9ab64a175, 0x17eefd3e13e0f7f, 0xc413587a2453b8d6,\\n 0x95cbb3c7758b536b, 0x50a6046eb0e6e4c2, 0x1afcaa64fabc4ac8, 0xdf911dcd3fd1fd61,\\n 0xb94d7d1e590d9db2, 0x7c20cab79c602a1b, 0x367a64bdd63a8411, 0xf317d314135733b8,\\n 0xa2cf38a9428fd805, 0x67a28f0087e26fac, 0x2df8210acdb8c1a6, 0xe89596a308d5760f,\\n 0x3c52cd14dc122db8, 0xf93f7abd197f9a11, 0xb365d4b75325341b, 0x7608631e964883b2,\\n 0x27d088a3c790680f, 0xe2bd3f0a02fddfa6, 0xa8e7910048a771ac, 0x6d8a26a98dcac605,\\n 0xb56467aeb16a6d6, 0xce3bf1d32e7b117f, 0x84615fd96421bf75, 0x410ce870a14c08dc,\\n 0x10d403cdf094e361, 0xd5b9b46435f954c8, 0x9fe31a6e7fa3fac2, 0x5a8eadc7bace4d6b,\\n 0x525bdbc8b21b3b64, 0x97366c6177768ccd, 0xdd6cc26b3d2c22c7, 0x180175c2f841956e,\\n 0x49d99e7fa9997ed3, 0x8cb429d66cf4c97a, 0xc6ee87dc26ae6770, 0x3833075e3c3d0d9,\\n 0x655f50a6851fb00a, 0xa032e70f407207a3, 0xea6849050a28a9a9, 0x2f05feaccf451e00,\\n 0x7edd15119e9df5bd, 0xbbb0a2b85bf04214, 0xf1ea0cb211aaec1e, 0x3487bb1bd4c75bb7,\\n 0x5d88cd2dbdc82d81, 0x98e57a8478a59a28, 0xd2bfd48e32ff3422, 0x17d26327f792838b,\\n 0x460a889aa64a6836, 0x83673f336327df9f, 0xc93d9139297d7195, 0xc502690ec10c63c,\\n 0x6a8c46438acca6ef, 0xafe1f1ea4fa11146, 0xe5bb5fe005fbbf4c, 0x20d6e849c09608e5,\\n 0x710e03f4914ee358, 0xb463b45d542354f1, 0xfe391a571e79fafb, 0x3b54adfedb144d52,\\n 0x3381dbf1d3c13b5d, 0xf6ec6c5816ac8cf4, 0xbcb6c2525cf622fe, 0x79db75fb999b9557,\\n 0x28039e46c8437eea, 0xed6e29ef0d2ec943, 0xa73487e547746749, 0x6259304c8219d0e0,\\n 0x485509fe4c5b033, 0xc1e8e73621a8079a, 0x8bb2493c6bf2a990, 0x4edffe95ae9f1e39,\\n 0x1f071528ff47f584, 0xda6aa2813a2a422d, 0x90300c8b7070ec27, 0x555dbb22b51d5b8e,\\n 0x819ae09561da0039, 0x44f7573ca4b7b790, 0xeadf936eeed199a, 0xcbc04e9f2b80ae33,\\n 0x9a18a5227a58458e, 0x5f75128bbf35f227, 0x152fbc81f56f5c2d, 0xd0420b283002eb84,\\n 0xb69e6bfb56de8b57, 0x73f3dc5293b33cfe, 0x39a97258d9e992f4, 0xfcc4c5f11c84255d,\\n 0xad1c2e4c4d5ccee0, 0x687199e588317949, 0x222b37efc26bd743, 0xe7468046070660ea,\\n 0xef93f6490fd316e5, 0x2afe41e0cabea14c, 0x60a4efea80e40f46, 0xa5c958434589b8ef,\\n 0xf411b3fe14515352, 0x317c0457d13ce4fb, 0x7b26aa5d9b664af1, 0xbe4b1df45e0bfd58,\\n 0xd8977d2738d79d8b, 0x1dfaca8efdba2a22, 0x57a06484b7e08428, 0x92cdd32d728d3381,\\n 0xc31538902355d83c, 0x6788f39e6386f95, 0x4c222133ac62c19f, 0x894f969a690f7636,\\n 0x7e7c2df300000000, 0x82847615fcf85be6, 0x8260eccefc1cc13d, 0x7e98b72800e49adb,\\n 0x83a9d978fdd5f48b, 0x7f51829e012daf6d, 0x7fb5184501c935b6, 0x834d43a3fd316e50,\\n 0x803bb214fe479fe7, 0x7cc3e9f202bfc401, 0x7c277329025b5eda, 0x80df28cffea3053c,\\n 0x7dee469f03926b6c, 0x81161d79ff6a308a, 0x81f287a2ff8eaa51, 0x7d0adc440376f1b7,\\n 0x871f64ccf963493f, 0x7be73f2a059b12d9, 0x7b03a5f1057f8802, 0x87fbfe17f987d3e4,\\n 0x7aca904704b6bdb4, 0x8632cba1f84ee652, 0x86d6517af8aa7c89, 0x7a2e0a9c0452276f,\\n 0x7958fb2b0724d6d8, 0x85a0a0cdfbdc8d3e, 0x85443a16fb3817e5, 0x79bc61f007c04c03,\\n 0x848d0fa0faf12253, 0x78755446060979b5, 0x7891ce9d06ede36e, 0x8469957bfa15b888,\\n 0x8956c97cf72ae48f, 0x75ae929a0bd2bf69, 0x754a08410b3625b2, 0x89b253a7f7ce7e54,\\n 0x74833df70aff1004, 0x887b6611f6074be2, 0x889ffccaf6e3d139, 0x7467a72c0a1b8adf,\\n 0x7711569b096d7b68, 0x8be90d7df595208e, 0x8b0d97a6f571ba55, 0x77f5cc400989e1b3,\\n 0x8ac4a210f4b88fe3, 0x763cf9f60840d405, 0x76d8632d08a44ede, 0x8a2038cbf45c1538,\\n 0x703580430e49adb0, 0x8ccddba5f2b1f656, 0x8c29417ef2556c8d, 0x70d11a980ead376b,\\n 0x8de074c8f39c593b, 0x71182f2e0f6402dd, 0x71fcb5f50f809806, 0x8d04ee13f378c3e0,\\n 0x8e721fa4f00e3257, 0x728a44420cf669b1, 0x726ede990c12f36a, 0x8e96857ff0eaa88c,\\n 0x73a7eb2f0ddbc6dc, 0x8f5fb0c9f1239d3a, 0x8fbb2a12f1c707e1, 0x734371f40d3f5c07,\\n 0x95c5921cebb9bfef, 0x693dc9fa1741e409, 0x69d9532117a57ed2, 0x952108c7eb5d2534,\\n 0x68106697166c4b64, 0x94e83d71ea941082, 0x940ca7aaea708a59, 0x68f4fc4c1688d1bf,\\n 0x6b820dfb15fe2008, 0x977a561de9067bee, 0x979eccc6e9e2e135, 0x6b669720151abad3,\\n 0x9657f970e82bd483, 0x6aafa29614d38f65, 0x6a4b384d143715be, 0x96b363abe8cf4e58,\\n 0x6ca6db2312daf6d0, 0x905e80c5ee22ad36, 0x90ba1a1eeec637ed, 0x6c4241f8123e6c0b,\\n 0x91732fa8ef0f025b, 0x6d8b744e13f759bd, 0x6d6fee951313c366, 0x9197b573efeb9880,\\n 0x92e144c4ec9d6937, 0x6e191f22106532d1, 0x6efd85f91081a80a, 0x9205de1fec79f3ec,\\n 0x6f34b04f11489dbc, 0x93cceba9edb0c65a, 0x93287172ed545c81, 0x6fd02a9411ac0767,\\n 0x62ef76931c935b60, 0x9e172d75e06b0086, 0x9ef3b7aee08f9a5d, 0x620bec481c77c1bb,\\n 0x9f3a8218e146afeb, 0x63c2d9fe1dbef40d, 0x632643251d5a6ed6, 0x9fde18c3e1a23530,\\n 0x9ca8e974e2d4c487, 0x6050b2921e2c9f61, 0x60b428491ec805ba, 0x9c4c73afe2305e5c,\\n 0x617d1dff1f01300c, 0x9d854619e3f96bea, 0x9d61dcc2e31df131, 0x619987241fe5aad7,\\n 0x9b8c3face5f0125f, 0x6774644a190849b9, 0x6790fe9119ecd362, 0x9b68a577e5148884,\\n 0x6659cb271825e6d4, 0x9aa190c1e4ddbd32, 0x9a450a1ae43927e9, 0x66bd51fc18c17c0f,\\n 0x65cba04b1bb78db8, 0x9933fbade74fd65e, 0x99d76176e7ab4c85, 0x652f3a901b531763,\\n 0x981e54c0e6627933, 0x64e60f261a9a22d5, 0x640295fd1a7eb80e, 0x98face1be686e3e8,\\n 0xd29f092f00000000, 0x724d6d80a0d264af, 0x96d7b6804448bfaf, 0x3605d22fe49adb00,\\n 0x5a0e767188917f5e, 0xfadc12de28431bf1, 0x1e46c9deccd9c0f1, 0xbe94ad716c0ba45e,\\n 0xc651816214ce884d, 0x6683e5cdb41cece2, 0x82193ecd508637e2, 0x22cb5a62f054534d,\\n 0x4ec0fe3c9c5ff713, 0xee129a933c8d93bc, 0xa884193d81748bc, 0xaa5a253c78c52c13,\\n 0xfb0219b5299d109a, 0x5bd07d1a894f7435, 0xbf4aa61a6dd5af35, 0x1f98c2b5cd07cb9a,\\n 0x739366eba10c6fc4, 0xd341024401de0b6b, 0x37dbd944e544d06b, 0x9709bdeb4596b4c4,\\n 0xefcc91f83d5398d7, 0x4f1ef5579d81fc78, 0xab842e57791b2778, 0xb564af8d9c943d7,\\n 0x675deea6b5c2e789, 0xc78f8a0915108326, 0x23155109f18a5826, 0x83c735a651583c89,\\n 0x81a5281b533a2134, 0x21774cb4f3e8459b, 0xc5ed97b417729e9b, 0x653ff31bb7a0fa34,\\n 0x9345745dbab5e6a, 0xa9e633ea7b793ac5, 0x4d7ce8ea9fe3e1c5, 0xedae8c453f31856a,\\n 0x956ba05647f4a979, 0x35b9c4f9e726cdd6, 0xd1231ff903bc16d6, 0x71f17b56a36e7279,\\n 0x1dfadf08cf65d627, 0xbd28bba76fb7b288, 0x59b260a78b2d6988, 0xf96004082bff0d27,\\n 0xa83838817aa731ae, 0x8ea5c2eda755501, 0xec70872e3eef8e01, 0x4ca2e3819e3deaae,\\n 0x20a947dff2364ef0, 0x807b237052e42a5f, 0x64e1f870b67ef15f, 0xc4339cdf16ac95f0,\\n 0xbcf6b0cc6e69b9e3, 0x1c24d463cebbdd4c, 0xf8be0f632a21064c, 0x586c6bcc8af362e3,\\n 0x3467cf92e6f8c6bd, 0x94b5ab3d462aa212, 0x702f703da2b07912, 0xd0fd149202621dbd,\\n 0x74eb4b47a6744268, 0xd4392fe806a626c7, 0x30a3f4e8e23cfdc7, 0x9071904742ee9968,\\n 0xfc7a34192ee53d36, 0x5ca850b68e375999, 0xb8328bb66aad8299, 0x18e0ef19ca7fe636,\\n 0x6025c30ab2baca25, 0xc0f7a7a51268ae8a, 0x246d7ca5f6f2758a, 0x84bf180a56201125,\\n 0xe8b4bc543a2bb57b, 0x4866d8fb9af9d1d4, 0xacfc03fb7e630ad4, 0xc2e6754deb16e7b,\\n 0x5d765bdd8fe952f2, 0xfda43f722f3b365d, 0x193ee472cba1ed5d, 0xb9ec80dd6b7389f2,\\n 0xd5e7248307782dac, 0x7535402ca7aa4903, 0x91af9b2c43309203, 0x317dff83e3e2f6ac,\\n 0x49b8d3909b27dabf, 0xe96ab73f3bf5be10, 0xdf06c3fdf6f6510, 0xad2208907fbd01bf,\\n 0xc129acce13b6a5e1, 0x61fbc861b364c14e, 0x8561136157fe1a4e, 0x25b377cef72c7ee1,\\n 0x27d16a73f54e635c, 0x87030edc559c07f3, 0x6399d5dcb106dcf3, 0xc34bb17311d4b85c,\\n 0xaf40152d7ddf1c02, 0xf927182dd0d78ad, 0xeb08aa823997a3ad, 0x4bdace2d9945c702,\\n 0x331fe23ee180eb11, 0x93cd869141528fbe, 0x77575d91a5c854be, 0xd785393e051a3011,\\n 0xbb8e9d606911944f, 0x1b5cf9cfc9c3f0e0, 0xffc622cf2d592be0, 0x5f1446608d8b4f4f,\\n 0xe4c7ae9dcd373c6, 0xae9e1e467c011769, 0x4a04c546989bcc69, 0xead6a1e93849a8c6,\\n 0x86dd05b754420c98, 0x260f6118f4906837, 0xc295ba18100ab337, 0x6247deb7b0d8d798,\\n 0x1a82f2a4c81dfb8b, 0xba50960b68cf9f24, 0x5eca4d0b8c554424, 0xfe1829a42c87208b,\\n 0x92138dfa408c84d5, 0x32c1e955e05ee07a, 0xd65b325504c43b7a, 0x768956faa4165fd5,\\n 0x4904f22100000000, 0xdb0d16639209e442, 0x68fb4c5421ffbe75, 0xfaf2a816b3f65a37,\\n 0xafb8ecb43ff7cea, 0x98f26a89d1f698a8, 0x2b0430be6200c29f, 0xb90dd4fcf00926dd,\\n 0xcefa0bf587fef9d4, 0x5cf3efb715f71d96, 0xef05b580a60147a1, 0x7d0c51c23408a3e3,\\n 0x8d05771fc401853e, 0x1f0c935d5608617c, 0xacfac96ae5fe3b4b, 0x3ef32d2877f7df09,\\n 0x431577780a118559, 0xd11c933a9818611b, 0x62eac90d2bee3b2c, 0xf0e32d4fb9e7df6e,\\n 0xea0b9249eef9b3, 0x92e3efd0dbe71df1, 0x2115b5e7681147c6, 0xb31c51a5fa18a384,\\n 0xc4eb8eac8def7c8d, 0x56e26aee1fe698cf, 0xe51430d9ac10c2f8, 0x771dd49b3e1926ba,\\n 0x8714f246ce100067, 0x151d16045c19e425, 0xa6eb4c33efefbe12, 0x34e2a8717de65a50,\\n 0x5d27f89314230ab2, 0xcf2e1cd1862aeef0, 0x7cd846e635dcb4c7, 0xeed1a2a4a7d55085,\\n 0x1ed8847957dc7658, 0x8cd1603bc5d5921a, 0x3f273a0c7623c82d, 0xad2ede4ee42a2c6f,\\n 0xdad9014793ddf366, 0x48d0e50501d41724, 0xfb26bf32b2224d13, 0x692f5b70202ba951,\\n 0x99267dadd0228f8c, 0xb2f99ef422b6bce, 0xb8d9c3d8f1dd31f9, 0x2ad0279a63d4d5bb,\\n 0x57367dca1e328feb, 0xc53f99888c3b6ba9, 0x76c9c3bf3fcd319e, 0xe4c027fdadc4d5dc,\\n 0x14c901205dcdf301, 0x86c0e562cfc41743, 0x3536bf557c324d74, 0xa73f5b17ee3ba936,\\n 0xd0c8841e99cc763f, 0x42c1605c0bc5927d, 0xf1373a6bb833c84a, 0x633ede292a3a2c08,\\n 0x9337f8f4da330ad5, 0x13e1cb6483aee97, 0xb2c84681fbccb4a0, 0x20c1a2c369c550e2,\\n 0x6142e74528461564, 0xf34b0307ba4ff126, 0x40bd593009b9ab11, 0xd2b4bd729bb04f53,\\n 0x22bd9baf6bb9698e, 0xb0b47fedf9b08dcc, 0x34225da4a46d7fb, 0x914bc198d84f33b9,\\n 0xe6bc1e91afb8ecb0, 0x74b5fad33db108f2, 0xc743a0e48e4752c5, 0x554a44a61c4eb687,\\n 0xa543627bec47905a, 0x374a86397e4e7418, 0x84bcdc0ecdb82e2f, 0x16b5384c5fb1ca6d,\\n 0x6b53621c2257903d, 0xf95a865eb05e747f, 0x4aacdc6903a82e48, 0xd8a5382b91a1ca0a,\\n 0x28ac1ef661a8ecd7, 0xbaa5fab4f3a10895, 0x953a083405752a2, 0x9b5a44c1d25eb6e0,\\n 0xecad9bc8a5a969e9, 0x7ea47f8a37a08dab, 0xcd5225bd8456d79c, 0x5f5bc1ff165f33de,\\n 0xaf52e722e6561503, 0x3d5b0360745ff141, 0x8ead5957c7a9ab76, 0x1ca4bd1555a04f34,\\n 0x7561edf73c651fd6, 0xe76809b5ae6cfb94, 0x549e53821d9aa1a3, 0xc697b7c08f9345e1,\\n 0x369e911d7f9a633c, 0xa497755fed93877e, 0x17612f685e65dd49, 0x8568cb2acc6c390b,\\n 0xf29f1423bb9be602, 0x6096f06129920240, 0xd360aa569a645877, 0x41694e14086dbc35,\\n 0xb16068c9f8649ae8, 0x23698c8b6a6d7eaa, 0x909fd6bcd99b249d, 0x29632fe4b92c0df,\\n 0x7f7068ae36749a8f, 0xed798ceca47d7ecd, 0x5e8fd6db178b24fa, 0xcc8632998582c0b8,\\n 0x3c8f1444758be665, 0xae86f006e7820227, 0x1d70aa3154745810, 0x8f794e73c67dbc52,\\n 0xf88e917ab18a635b, 0x6a87753823838719, 0xd9712f0f9075dd2e, 0x4b78cb4d027c396c,\\n 0xbb71ed90f2751fb1, 0x297809d2607cfbf3, 0x9a8e53e5d38aa1c4, 0x887b7a741834586,\\n }\\n};\\n\\nstatic union {\\n uint32_t dword_table[4][256];\\n uint64_t qword_array[512];\\n} short_shifts = {\\n .qword_array = {\\n 0xdcb17aa400000000, 0x603ff91dbc8e83b9, 0xa0400b277cf17183, 0x1cce889ec07ff23a,\\n 0x255399a2f9e2e306, 0x99dd1a1b456c60bf, 0x59a2e82185139285, 0xe52c6b98399d113c,\\n 0x2a98ca59f629b0fd, 0x961649e04aa73344, 0x5669bbda8ad8c17e, 0xeae73863365642c7,\\n 0xd37a295f0fcb53fb, 0x6ff4aae6b345d042, 0xaf8b58dc733a2278, 0x1305db65cfb4a1c1,\\n 0x350e6dafe9bf170b, 0x8980ee16553194b2, 0x49ff1c2c954e6688, 0xf5719f9529c0e531,\\n 0xccec8ea9105df40d, 0x70620d10acd377b4, 0xb01dff2a6cac858e, 0xc937c93d0220637,\\n 0xc327dd521f96a7f6, 0x7fa95eeba318244f, 0xbfd6acd16367d675, 0x3582f68dfe955cc,\\n 0x3ac53e54e67444f0, 0x864bbded5afac749, 0x46344fd79a853573, 0xfabacc6e260bb6ca,\\n 0xa232243d69258e7, 0xb6ada1fa6a1cdb5e, 0x76d253c0aa632964, 0xca5cd07916edaadd,\\n 0xf3c1c1452f70bbe1, 0x4f4f42fc93fe3858, 0x8f30b0c65381ca62, 0x33be337fef0f49db,\\n 0xfc0a92be20bbe81a, 0x408411079c356ba3, 0x80fbe33d5c4a9999, 0x3c756084e0c41a20,\\n 0x5e871b8d9590b1c, 0xb966f20165d788a5, 0x7919003ba5a87a9f, 0xc59783821926f926,\\n 0xe39c35483f2d4fec, 0x5f12b6f183a3cc55, 0x9f6d44cb43dc3e6f, 0x23e3c772ff52bdd6,\\n 0x1a7ed64ec6cfacea, 0xa6f055f77a412f53, 0x668fa7cdba3edd69, 0xda01247406b05ed0,\\n 0x15b585b5c904ff11, 0xa93b060c758a7ca8, 0x6944f436b5f58e92, 0xd5ca778f097b0d2b,\\n 0xec5766b330e61c17, 0x50d9e50a8c689fae, 0x90a617304c176d94, 0x2c289489f099ee2d,\\n 0x7479bd9ba8c8c73f, 0xc8f73e2214464486, 0x888cc18d439b6bc, 0xb4064fa168b73505,\\n 0x8d9b5e9d512a2439, 0x3115dd24eda4a780, 0xf16a2f1e2ddb55ba, 0x4de4aca79155d603,\\n 0x82500d665ee177c2, 0x3ede8edfe26ff47b, 0xfea17ce522100641, 0x422fff5c9e9e85f8,\\n 0x7bb2ee60a70394c4, 0xc73c6dd91b8d177d, 0x7439fe3dbf2e547, 0xbbcd1c5a677c66fe,\\n 0x9dc6aa904177d034, 0x21482929fdf9538d, 0xe137db133d86a1b7, 0x5db958aa8108220e,\\n 0x64244996b8953332, 0xd8aaca2f041bb08b, 0x18d53815c46442b1, 0xa45bbbac78eac108,\\n 0x6bef1a6db75e60c9, 0xd76199d40bd0e370, 0x171e6beecbaf114a, 0xab90e857772192f3,\\n 0x920df96b4ebc83cf, 0x2e837ad2f2320076, 0xeefc88e8324df24c, 0x52720b518ec371f5,\\n 0xa2ebe57c7e5a9fd8, 0x1e6566c5c2d41c61, 0xde1a94ff02abee5b, 0x62941746be256de2,\\n 0x5b09067a87b87cde, 0xe78785c33b36ff67, 0x27f877f9fb490d5d, 0x9b76f44047c78ee4,\\n 0x54c2558188732f25, 0xe84cd63834fdac9c, 0x28332402f4825ea6, 0x94bda7bb480cdd1f,\\n 0xad20b6877191cc23, 0x11ae353ecd1f4f9a, 0xd1d1c7040d60bda0, 0x6d5f44bdb1ee3e19,\\n 0x4b54f27797e588d3, 0xf7da71ce2b6b0b6a, 0x37a583f4eb14f950, 0x8b2b004d579a7ae9,\\n 0xb2b611716e076bd5, 0xe3892c8d289e86c, 0xce4760f212f61a56, 0x72c9e34bae7899ef,\\n 0xbd7d428a61cc382e, 0x1f3c133dd42bb97, 0xc18c33091d3d49ad, 0x7d02b0b0a1b3ca14,\\n 0x449fa18c982edb28, 0xf811223524a05891, 0x386ed00fe4dfaaab, 0x84e053b658512912,\\n 0x547df88f00000000, 0xfc860991a8fbf11e, 0x666c42541b94cd, 0xa89d9d5cfce065d3,\\n 0xfc4ad115a837299a, 0x54b1200b00ccd884, 0xa85145d8fc2cbd57, 0xaab4c654d74c49,\\n 0x1ffdd4a558225c5, 0xa9042c54fd79d4db, 0x55e449870199b108, 0xfd1fb899a9624016,\\n 0xa9c8f4d0fdb50c5f, 0x13305ce554efd41, 0xfdd3601da9ae9892, 0x552891030155698c,\\n 0xff79b305ab044b8a, 0x5782421b03ffba94, 0xab6227c8ff1fdf47, 0x399d6d657e42e59,\\n 0x574e9a9f03336210, 0xffb56b81abc8930e, 0x3550e525728f6dd, 0xabaeff4cffd307c3,\\n 0xaafb96c0fe866e4f, 0x20067de567d9f51, 0xfee0020daa9dfa82, 0x561bf31302660b9c,\\n 0x2ccbf5a56b147d5, 0xaa374e44fe4ab6cb, 0x56d72b9702aad318, 0xfe2cda89aa512206,\\n 0x799196a53e4e1e5, 0xaf62e874fb1f10fb, 0x53828da707ff7528, 0xfb797cb9af048436,\\n 0xafae30f0fbd3c87f, 0x755c1ee53283961, 0xfbb5a43dafc85cb2, 0x534e55230733adac,\\n 0x521b3caf0666c420, 0xfae0cdb1ae9d353e, 0x600a862527d50ed, 0xaefb597cfa86a1f3,\\n 0xfa2c1535ae51edba, 0x52d7e42b06aa1ca4, 0xae3781f8fa4a7977, 0x6cc70e652b18869,\\n 0xac9d52e0f8e0aa6f, 0x466a3fe501b5b71, 0xf886c62dacfb3ea2, 0x507d37330400cfbc,\\n 0x4aa7b7a50d783f5, 0xac518a64f82c72eb, 0x50b1efb704cc1738, 0xf84a1ea9ac37e626,\\n 0xf91f7725ad628faa, 0x51e4863b05997eb4, 0xad04e3e8f9791b67, 0x5ff12f65182ea79,\\n 0x51285ebf0555a630, 0xf9d3afa1adae572e, 0x533ca72514e32fd, 0xadc83b6cf9b5c3e3,\\n 0xf3b43b45a7c9c3ca, 0x5b4fca5b0f3232d4, 0xa7afaf88f3d25707, 0xf545e965b29a619,\\n 0x5b8312df0ffeea50, 0xf378e3c1a7051b4e, 0xf9886125be57e9d, 0xa763770cf31e8f83,\\n 0xa6361e80f24be60f, 0xecdef9e5ab01711, 0xf22d8a4da65072c2, 0x5ad67b530eab83dc,\\n 0xe01371a5a7ccf95, 0xa6fac604f2873e8b, 0x5a1aa3d70e675b58, 0xf2e152c9a69caa46,\\n 0x58b070cf0ccd8840, 0xf04b81d1a436795e, 0xcabe40258d61c8d, 0xa450151cf02ded93,\\n 0xf0875955a4faa1da, 0x587ca84b0c0150c4, 0xa49ccd98f0e13517, 0xc673c86581ac409,\\n 0xd32550a594fad85, 0xa5c9a414f1b45c9b, 0x5929c1c70d543948, 0xf1d230d9a5afc856,\\n 0xa5057c90f178841f, 0xdfe8d8e59837501, 0xf11ee85da56310d2, 0x59e519430d98e1cc,\\n 0xa050daa0f42d222f, 0x8ab2bbe5cd6d331, 0xf44b4e6da036b6e2, 0x5cb0bf7308cd47fc,\\n 0x867f33a5c1a0bb5, 0xa09c0224f4e1faab, 0x5c7c67f708019f78, 0xf48796e9a0fa6e66,\\n 0xf5d2ff65a1af07ea, 0x5d290e7b0954f6f4, 0xa1c96ba8f5b49327, 0x9329ab65d4f6239,\\n 0x5de5d6ff09982e70, 0xf51e27e1a163df6e, 0x9fe42325d83babd, 0xa105b32cf5784ba3,\\n 0xb54912a5f2969a5, 0xa3af6034f7d298bb, 0x5f4f05e70b32fd68, 0xf7b4f4f9a3c90c76,\\n 0xa363b8b0f71e403f, 0xb9849ae5fe5b121, 0xf7782c7da305d4f2, 0x5f83dd630bfe25ec,\\n 0x5ed6b4ef0aab4c60, 0xf62d45f1a250bd7e, 0xacd20225eb0d8ad, 0xa236d13cf64b29b3,\\n 0xf6e19d75a29c65fa, 0x5e1a6c6b0a6794e4, 0xa2fa09b8f687f137, 0xa01f8a65e7c0029,\\n 0x4a7ff16500000000, 0xde8013af94ffe2ca, 0x666c42002c13b365, 0xf293a0cab8ec51af,\\n 0x125897af582766ca, 0x86a77565ccd88400, 0x3e4b24ca7434d5af, 0xaab4c600e0cb3765,\\n 0xfa313cf1b04ecd94, 0x6ecede3b24b12f5e, 0xd6228f949c5d7ef1, 0x42dd6d5e08a29c3b,\\n 0xa2165a3be869ab5e, 0x36e9b8f17c964994, 0x8e05e95ec47a183b, 0x1afa0b945085faf1,\\n 0x2f0e1cbc6571edd9, 0xbbf1fe76f18e0f13, 0x31dafd949625ebc, 0x97e24d13dd9dbc76,\\n 0x77297a763d568b13, 0xe3d698bca9a969d9, 0x5b3ac91311453876, 0xcfc52bd985badabc,\\n 0x9f40d128d53f204d, 0xbbf33e241c0c287, 0xb353624df92c9328, 0x27ac80876dd371e2,\\n 0xc767b7e28d184687, 0x5398552819e7a44d, 0xeb740487a10bf5e2, 0x7f8be64d35f41728,\\n 0x809c2ad7cae3dbb2, 0x1463c81d5e1c3978, 0xac8f99b2e6f068d7, 0x38707b78720f8a1d,\\n 0xd8bb4c1d92c4bd78, 0x4c44aed7063b5fb2, 0xf4a8ff78bed70e1d, 0x60571db22a28ecd7,\\n 0x30d2e7437aad1626, 0xa42d0589ee52f4ec, 0x1cc1542656bea543, 0x883eb6ecc2414789,\\n 0x68f58189228a70ec, 0xfc0a6343b6759226, 0x44e632ec0e99c389, 0xd019d0269a662143,\\n 0xe5edc70eaf92366b, 0x711225c43b6dd4a1, 0xc9fe746b8381850e, 0x5d0196a1177e67c4,\\n 0xbdcaa1c4f7b550a1, 0x2935430e634ab26b, 0x91d912a1dba6e3c4, 0x526f06b4f59010e,\\n 0x55a30a9a1fdcfbff, 0xc15ce8508b231935, 0x79b0b9ff33cf489a, 0xed4f5b35a730aa50,\\n 0xd846c5047fb9d35, 0x997b8e9ad3047fff, 0x2197df356be82e50, 0xb5683dffff17cc9a,\\n 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}\\n};\\n\\n/* Table-driven software version as a fall-back. This is about 15 times slower\\n than using the hardware instructions. This assumes little-endian integers,\\n as is the case on Intel processors that the assembler code here is for. */\\nuint32_t crc32c_append_sw(uint32_t crci, buffer input, size_t length)\\n{\\n buffer next = input;\\n#ifdef _M_X64\\n uint64_t crc;\\n#else\\n uint32_t crc;\\n#endif\\n\\n crc = crci ^ 0xffffffff;\\n#ifdef _M_X64\\n while (length && ((uintptr_t)next & 7) != 0)\\n {\\n crc = table.dword_table[0][(crc ^ *next++) & 0xff] ^ (crc >> 8);\\n --length;\\n }\\n while (length >= 16)\\n {\\n crc ^= *(uint64_t *)next;\\n uint64_t high = *(uint64_t *)(next + 8);\\n crc = table.dword_table[15][crc & 0xff]\\n ^ table.dword_table[14][(crc >> 8) & 0xff]\\n ^ table.dword_table[13][(crc >> 16) & 0xff]\\n ^ table.dword_table[12][(crc >> 24) & 0xff]\\n ^ table.dword_table[11][(crc >> 32) & 0xff]\\n ^ table.dword_table[10][(crc >> 40) & 0xff]\\n ^ table.dword_table[9][(crc >> 48) & 0xff]\\n ^ table.dword_table[8][crc >> 56]\\n ^ table.dword_table[7][high & 0xff]\\n ^ table.dword_table[6][(high >> 8) & 0xff]\\n ^ table.dword_table[5][(high >> 16) & 0xff]\\n ^ table.dword_table[4][(high >> 24) & 0xff]\\n ^ table.dword_table[3][(high >> 32) & 0xff]\\n ^ table.dword_table[2][(high >> 40) & 0xff]\\n ^ table.dword_table[1][(high >> 48) & 0xff]\\n ^ table.dword_table[0][high >> 56];\\n next += 16;\\n length -= 16;\\n }\\n#else\\n while (length && ((uintptr_t)next & 3) != 0)\\n {\\n crc = table.dword_table[0][(crc ^ *next++) & 0xff] ^ (crc >> 8);\\n --length;\\n }\\n while (length >= 12)\\n {\\n crc ^= *(uint32_t *)next;\\n uint32_t high = *(uint32_t *)(next + 4);\\n uint32_t high2 = *(uint32_t *)(next + 8);\\n crc = table.dword_table[11][crc & 0xff]\\n ^ table.dword_table[10][(crc >> 8) & 0xff]\\n ^ table.dword_table[9][(crc >> 16) & 0xff]\\n ^ table.dword_table[8][crc >> 24]\\n ^ table.dword_table[7][high & 0xff]\\n ^ table.dword_table[6][(high >> 8) & 0xff]\\n ^ table.dword_table[5][(high >> 16) & 0xff]\\n ^ table.dword_table[4][high >> 24]\\n ^ table.dword_table[3][high2 & 0xff]\\n ^ table.dword_table[2][(high2 >> 8) & 0xff]\\n ^ table.dword_table[1][(high2 >> 16) & 0xff]\\n ^ table.dword_table[0][high2 >> 24];\\n next += 12;\\n length -= 12;\\n }\\n#endif\\n while (length)\\n {\\n crc = table.dword_table[0][(crc ^ *next++) & 0xff] ^ (crc >> 8);\\n --length;\\n }\\n return (uint32_t)crc ^ 0xffffffff;\\n}\\n\\n/* Apply the zeros operator table to crc. */\\nstatic inline uint32_t shift_crc(uint32_t shift_table[][256], uint32_t crc)\\n{\\n return shift_table[0][crc & 0xff]\\n ^ shift_table[1][(crc >> 8) & 0xff]\\n ^ shift_table[2][(crc >> 16) & 0xff]\\n ^ shift_table[3][crc >> 24];\\n}\\n\\nuint32_t crc32c_append(uint32_t crc, buffer input, size_t length)\\n{\\n\\treturn crc32c_append_sw(crc, input, length);\\n}\\n\"\n },\n {\n \"alpha_fraction\": 0.43617162108421326,\n \"alphanum_fraction\": 0.4375,\n \"avg_line_length\": 50.21088409423828,\n \"blob_id\": \"9007234a047b510c5ae12d8f58dbf856ddaec197\",\n \"content_id\": \"1b6edba218b536f3c22e6d3126a1cf71b86ef6d6\",\n \"detected_licenses\": [\n \"MIT\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"C#\",\n \"length_bytes\": 15056,\n \"license_type\": \"permissive\",\n \"max_line_length\": 228,\n \"num_lines\": 294,\n \"path\": \"/src/TorchSharp/DataLoader.cs\",\n \"repo_name\": \"NiklasGustafsson/TorchSharp\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"// Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information.\\n\\nusing System;\\nusing System.Collections;\\nusing System.Collections.Generic;\\nusing System.Diagnostics.SymbolStore;\\nusing System.Linq;\\nusing System.Threading;\\nusing System.Threading.Tasks;\\nusing TorchSharp.Utils;\\n\\nnamespace TorchSharp\\n{\\n public static partial class torch\\n {\\n public static partial class utils\\n {\\n public static partial class data\\n {\\n /// <summary>\\n /// This class makes easier to create batch. Data set must implement Dataset interface\\n /// </summary>\\n public class DataLoader : DataLoader<Dictionary<string, torch.Tensor>, Dictionary<string, torch.Tensor>>\\n {\\n /// <summary>\\n /// Pytorch style dataloader\\n /// </summary>\\n /// <param name=\\\"dataset\\\">Dataset for create batch</param>\\n /// <param name=\\\"batchSize\\\">Size of batch</param>\\n /// <param name=\\\"device\\\">device for output tensor</param>\\n /// <param name=\\\"shuffler\\\">Shuffler for dataloader</param>\\n /// <param name=\\\"num_worker\\\">Count of worker</param>\\n /// <param name=\\\"drop_last\\\">\\n /// Set to true to drop the last incomplete batch, if the dataset size is not divisible by the batch size.\\n /// If alse and the size of dataset is not divisible by the batch size, then the last batch will be smaller.\\n /// </param>\\n public DataLoader(Dataset dataset, int batchSize, IEnumerable<long> shuffler, Device device = null, int num_worker = 1, bool drop_last = false)\\n : base(dataset, batchSize, Collate, shuffler, device, num_worker, drop_last)\\n {\\n }\\n\\n /// <summary>\\n /// Pytorch style dataloader\\n /// </summary>\\n /// <param name=\\\"dataset\\\">Dataset for create batch</param>\\n /// <param name=\\\"batchSize\\\">Size of batch</param>\\n /// <param name=\\\"shuffle\\\">true if shuffle dataset, false for not</param>\\n /// <param name=\\\"device\\\">device for output tensor</param>\\n /// <param name=\\\"seed\\\">Seed for generating shuffle</param>\\n /// <param name=\\\"num_worker\\\">Count of worker</param>\\n /// <param name=\\\"drop_last\\\">\\n /// Set to true to drop the last incomplete batch, if the dataset size is not divisible by the batch size.\\n /// If alse and the size of dataset is not divisible by the batch size, then the last batch will be smaller.\\n /// </param>\\n public DataLoader(Dataset dataset, int batchSize, bool shuffle = false, Device device = null, int? seed = null, int num_worker = 1, bool drop_last = false)\\n : base(dataset, batchSize, Collate, shuffle, device, seed, num_worker, drop_last)\\n {\\n }\\n\\n private static Dictionary<string, torch.Tensor> Collate(IEnumerable<Dictionary<string, torch.Tensor>> dic, torch.Device device)\\n {\\n Dictionary<string, torch.Tensor> batch = new();\\n foreach (var x in dic.First().Keys) {\\n var t = cat(dic.Select(k => k[x].unsqueeze(0)).ToArray(), 0);\\n if (t.device_type != device.type || t.device_index != device.index)\\n t = t.to(device);\\n batch[x] = t;\\n }\\n return batch;\\n }\\n }\\n\\n /// <summary>\\n /// This class makes easier to create batch. Data set must implement Dataset interface\\n /// </summary>\\n public class DataLoader<T, S> : IEnumerable<S>, IDisposable\\n {\\n private Dataset<T> dataset;\\n private int batchSize;\\n private bool shuffle;\\n private bool drop_last;\\n private Device device;\\n private IEnumerable<long> shuffler;\\n private int num_worker;\\n private Func<IEnumerable<T>, torch.Device, S> collate_fn;\\n\\n /// <summary>\\n /// Pytorch style dataloader\\n /// </summary>\\n /// <param name=\\\"dataset\\\">Dataset for create batch</param>\\n /// <param name=\\\"batchSize\\\">Size of batch</param>\\n /// <param name=\\\"collate_fn\\\">Callback to merge items make to a batch</param>\\n /// <param name=\\\"device\\\">device for output tensor</param>\\n /// <param name=\\\"shuffler\\\">Shuffler for dataloader</param>\\n /// <param name=\\\"num_worker\\\">Count of worker</param>\\n /// <param name=\\\"drop_last\\\">\\n /// Set to true to drop the last incomplete batch, if the dataset size is not divisible by the batch size.\\n /// If alse and the size of dataset is not divisible by the batch size, then the last batch will be smaller.\\n /// </param>\\n public DataLoader(Dataset<T> dataset, int batchSize, Func<IEnumerable<T>, torch.Device, S> collate_fn, IEnumerable<long> shuffler, Device device = null, int num_worker = 1, bool drop_last = false)\\n {\\n this.dataset = dataset;\\n this.batchSize = batchSize;\\n this.shuffle = true;\\n this.drop_last = drop_last;\\n this.device = device ?? CPU;\\n this.shuffler = shuffler;\\n this.num_worker = num_worker;\\n this.collate_fn = collate_fn;\\n }\\n\\n /// <summary>\\n /// Pytorch style dataloader\\n /// </summary>\\n /// <param name=\\\"dataset\\\">Dataset for create batch</param>\\n /// <param name=\\\"batchSize\\\">Size of batch</param>\\n /// <param name=\\\"collate_fn\\\">Callback to merge items to make a batch</param>\\n /// <param name=\\\"shuffle\\\">true if shuffle dataset, false for not</param>\\n /// <param name=\\\"device\\\">device for output tensor</param>\\n /// <param name=\\\"seed\\\">Seed for generating shuffle</param>\\n /// <param name=\\\"num_worker\\\">Count of worker</param>\\n /// <param name=\\\"drop_last\\\">\\n /// Set to true to drop the last incomplete batch, if the dataset size is not divisible by the batch size.\\n /// If alse and the size of dataset is not divisible by the batch size, then the last batch will be smaller.\\n /// </param>\\n public DataLoader(Dataset<T> dataset, int batchSize, Func<IEnumerable<T>, torch.Device, S> collate_fn, bool shuffle = false, Device device = null, int? seed = null, int num_worker = 1, bool drop_last = false)\\n {\\n this.dataset = dataset;\\n this.batchSize = batchSize;\\n this.shuffle = shuffle;\\n this.drop_last = drop_last;\\n this.device = device ?? CPU;\\n this.shuffler = seed is null ? new FisherYatesShuffler(dataset.Count) : new FisherYatesShuffler(dataset.Count, seed);\\n this.num_worker = num_worker;\\n this.collate_fn = collate_fn;\\n }\\n\\n /// <summary>\\n /// Generate enumerator\\n /// </summary>\\n /// <returns>Enumerator for batch</returns>\\n public IEnumerator<S> GetEnumerator() =>\\n new DataLoaderEnumerator(dataset, batchSize, shuffle, device, shuffler, num_worker, collate_fn);\\n\\n IEnumerator IEnumerable.GetEnumerator() => GetEnumerator();\\n\\n /// <summary>\\n /// Size of batch\\n /// </summary>\\n public long Count => drop_last ? (dataset.Count / batchSize) : ((dataset.Count - 1) / batchSize + 1);\\n\\n private class DataLoaderEnumerator : IEnumerator<S>\\n {\\n private Dataset<T> dataset;\\n private int batchSize;\\n private Device device;\\n private bool shuffle;\\n private IEnumerable<long> shuffleEnumerable;\\n private IEnumerator<long> shuffler;\\n private long currentVal = 0;\\n private int num_worker = 0;\\n private IList<IDisposable> currentDisposables;\\n private Func<IEnumerable<T>, torch.Device, S> collate_fn;\\n public DataLoaderEnumerator(Dataset<T> dataset, int batchSize, bool shuffle, Device device, IEnumerable<long> shuffleEnumerable, int num_worker, Func<IEnumerable<T>, torch.Device, S> collate_fn)\\n {\\n this.dataset = dataset;\\n this.batchSize = batchSize;\\n this.device = device;\\n this.shuffle = shuffle;\\n this.shuffleEnumerable = shuffleEnumerable;\\n if (num_worker < 1) num_worker = 1;\\n this.num_worker = num_worker;\\n this.collate_fn = collate_fn;\\n Reset();\\n }\\n\\n private bool MoveNextValue()\\n {\\n if (shuffle) {\\n if (!shuffler.MoveNext()) return false;\\n currentVal = shuffler.Current;\\n return true;\\n } else {\\n currentVal++;\\n return currentVal < dataset.Count;\\n }\\n }\\n\\n /// <summary>\\n /// Get next batch\\n /// </summary>\\n /// <returns>true if batch created, false if batch has finished</returns>\\n public bool MoveNext()\\n {\\n DisposeCurrent();\\n using (var scope = DisposeScopeManager.NewDisposeScope()) {\\n if (!MoveNextValue()) return false;\\n\\n var tensorIndexList = new List<long> {currentVal};\\n for (int i = 1; i < batchSize; i++) {\\n if (!MoveNextValue()) break;\\n tensorIndexList.Add(currentVal);\\n }\\n\\n var items = new List<T>(new T[tensorIndexList.Count]);\\n var taskedBatchCount = 0;\\n\\n //Run Async\\n var tasks = new List<Task>();\\n foreach (var _ in Enumerable.Range(1, num_worker - 1))\\n tasks.Add(new(ProcessPendingBatches));\\n tasks.ForEach(x => x.Start());\\n\\n ProcessPendingBatches();\\n\\n foreach (var task in tasks)\\n task.Wait();\\n\\n using (var collate_scope = DisposeScopeManager.NewDisposeScope()) {\\n Current = collate_fn(items, device);\\n currentDisposables = collate_scope.DisposablesView.ToList();\\n collate_scope.Detach(currentDisposables);\\n }\\n\\n return true;\\n\\n void ProcessPendingBatches()\\n {\\n while (true) {\\n var idx = ScheduleBatch();\\n if (idx is null) break;\\n items[idx.Value.Item1] = dataset.GetTensor(idx.Value.Item2);\\n }\\n }\\n\\n (int, long)? ScheduleBatch()\\n {\\n var t = Interlocked.Increment(ref taskedBatchCount) - 1;\\n if (t < tensorIndexList.Count)\\n return (t, tensorIndexList[t]);\\n return null;\\n }\\n }\\n }\\n\\n /// <summary>\\n /// Reset enumerator\\n /// </summary>\\n public void Reset()\\n {\\n DisposeCurrent();\\n if(shuffle) shuffler = shuffleEnumerable.GetEnumerator();\\n currentVal = -1;\\n }\\n\\n /// <summary>\\n /// Current tensor\\n /// </summary>\\n public S Current { get; private set; }\\n\\n object IEnumerator.Current => Current;\\n\\n public void Dispose()\\n {\\n DisposeCurrent();\\n }\\n\\n private void DisposeCurrent()\\n {\\n if (currentDisposables is null) return;\\n foreach(var x in currentDisposables)\\n x.Dispose();\\n currentDisposables = null;\\n shuffler?.Dispose();\\n }\\n }\\n\\n public void Dispose()\\n {\\n Dispose(true);\\n GC.SuppressFinalize(this);\\n }\\n\\n protected virtual void Dispose(bool disposing)\\n {\\n if (disposing) {\\n dataset.Dispose();\\n }\\n }\\n }\\n }\\n }\\n }\\n}\\n\"\n },\n {\n \"alpha_fraction\": 0.4713080823421478,\n \"alphanum_fraction\": 0.47497737407684326,\n \"avg_line_length\": 59.755645751953125,\n \"blob_id\": \"cb594eb7a688753743a9bbb630116d874981c65e\",\n \"content_id\": \"ea15e2ee6173d9d94d546999e79eb284fd27d307\",\n \"detected_licenses\": [\n \"MIT\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"C#\",\n \"length_bytes\": 104943,\n \"license_type\": \"permissive\",\n \"max_line_length\": 364,\n \"num_lines\": 1727,\n \"path\": \"/src/TorchSharp/Optimizers/LRScheduler.cs\",\n \"repo_name\": \"NiklasGustafsson/TorchSharp\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"// Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information.\\nusing System;\\nusing System.Collections.Generic;\\nusing System.Diagnostics;\\nusing System.Linq;\\nusing System.Net.Security;\\nusing TorchSharp.Modules;\\n\\n// All LR schedulers in this file are directly based on the Pytorch implementation at:\\n//\\n// https://github.com/pytorch/pytorch/blob/master/torch/optim/lr_scheduler.py\\n//\\n\\nnamespace TorchSharp\\n{\\n public static partial class torch\\n {\\n public static partial class optim\\n {\\n public static partial class lr_scheduler\\n {\\n public abstract class LRScheduler\\n {\\n protected LRScheduler()\\n {\\n\\n }\\n\\n protected LRScheduler(Optimizer optimizer, int last_epoch = -1, bool verbose = false)\\n {\\n _optimizer = optimizer;\\n _last_epoch = last_epoch;\\n _verbose = verbose;\\n _base_lrs = optimizer.ParamGroups.Select(pg => pg.InitialLearningRate).ToList();\\n _last_lrs = optimizer.ParamGroups.Select(pg => pg.LearningRate).ToList();\\n\\n if (last_epoch == -1) {\\n foreach (var pg in optimizer.ParamGroups) {\\n pg.InitialLearningRate = pg.LearningRate;\\n }\\n }\\n }\\n\\n /// <summary>\\n /// Advance the learning rate schedule. \\n /// </summary>\\n /// <remarks>Typically, this is done once per epoch or once every N batches.</remarks>\\n public virtual void step()\\n {\\n step(null);\\n }\\n\\n /// <summary>\\n /// Advance the learning rate schedule. \\n /// </summary>\\n /// <remarks>Typically, this is done once per epoch or once every N batches.</remarks>\\n public virtual void step(int? epoch)\\n {\\n _step_count += 1;\\n _last_epoch = (epoch.HasValue) ? epoch.Value : _last_epoch + 1;\\n\\n // NOTE: It is super-important to use the 'get_lr()' method no more than once per step(), since\\n // for many LR schedulers, it will modify the internal state of the scheduler,\\n // as well as that of the controlled optimizer.\\n var lr = get_lr().ToList();\\n var pgs = _optimizer.ParamGroups.ToList();\\n\\n for (int i = 0; i < _base_lrs.Count; i++) {\\n\\n pgs[i].LearningRate = lr[i];\\n if (_verbose && _last_lrs[i] != lr[i])\\n Console.WriteLine($\\\"Adjusting learning rate to {lr[i]}\\\");\\n _last_lrs[i] = lr[i];\\n }\\n }\\n\\n /// <summary>\\n /// Advance the learning rate scheduler, passing in the current value of some metric.\\n /// </summary>\\n /// <remarks>\\n /// The metric value is ignored by most LR schedulers.\\n /// </remarks>\\n public virtual void step(double current, int? epoch = null)\\n {\\n // Ignore the metric\\n step(epoch);\\n }\\n\\n /// <summary>\\n /// Compute the current learning rate for the scheduler.\\n /// </summary>\\n protected virtual IEnumerable<double> get_lr() => _optimizer.ParamGroups.Select(pg => pg.LearningRate);\\n\\n /// <summary>\\n /// Return last computed learning rate by current scheduler.\\n /// </summary>\\n public IEnumerable<double> get_last_lr() => _last_lrs;\\n\\n internal Optimizer _optimizer;\\n internal int _last_epoch = -1;\\n internal bool _verbose = false;\\n protected int _step_count = 0;\\n internal IList<double> _last_lrs;\\n internal IList<double> _base_lrs;\\n }\\n\\n public static partial class impl\\n {\\n /// <summary>\\n /// Sets the learning rate of each parameter group to the initial lr times a given function.\\n /// When last_epoch=-1, sets initial lr as lr.\\n /// </summary>\\n public class LambdaLR : LRScheduler\\n {\\n /// <summary>\\n /// Constructor\\n /// </summary>\\n /// <param name=\\\"optimizer\\\">Wrapped optimizer.</param>\\n /// <param name=\\\"lr_lambda\\\">A function which computes a multiplicative factor given an integer parameter epoch.</param>\\n /// <param name=\\\"last_epoch\\\">The index of last epoch. Default: -1.</param>\\n /// <param name=\\\"verbose\\\"> If true, prints a message to stdout for each update. Default: false.</param>\\n /// <returns>A scheduler</returns>\\n public LambdaLR(Optimizer optimizer, Func<int, double> lr_lambda, int last_epoch = -1, bool verbose = false) : base(optimizer, last_epoch, verbose)\\n {\\n if (optimizer == null) throw new ArgumentNullException(\\\"optimizer\\\");\\n _lr_lambdas = Enumerable.Repeat(lr_lambda, optimizer.ParamGroups.Count()).ToList();\\n\\n step();\\n }\\n\\n /// <summary>\\n /// Constructor\\n /// </summary>\\n /// <param name=\\\"optimizer\\\">Wrapped optimizer.</param>\\n /// <param name=\\\"lr_lambdas\\\">A list of functions, one for each paramater group, which computes a multiplicative factor given an integer parameter epoch.</param>\\n /// <param name=\\\"last_epoch\\\">The index of last epoch. Default: -1.</param>\\n /// <param name=\\\"verbose\\\"> If true, prints a message to stdout for each update. Default: false.</param>\\n /// <returns>A scheduler</returns>\\n public LambdaLR(Optimizer optimizer, IEnumerable<Func<int, double>> lr_lambdas, int last_epoch = -1, bool verbose = false) : base(optimizer, last_epoch, verbose)\\n {\\n if (optimizer == null) throw new ArgumentNullException(\\\"optimizer\\\");\\n _lr_lambdas = lr_lambdas.ToList();\\n\\n step();\\n }\\n\\n /// <summary>\\n /// Compute the current learning rate for the scheduler.\\n /// </summary>\\n protected override IEnumerable<double> get_lr()\\n {\\n var pgs = _optimizer.ParamGroups.ToList();\\n return Enumerable.Range(0, pgs.Count).Select(i => _base_lrs[i] * _lr_lambdas[i](_last_epoch));\\n }\\n\\n private List<Func<int, double>> _lr_lambdas;\\n }\\n\\n /// <summary>\\n /// Multiply the learning rate of each parameter group by the factor given in the specified function.\\n /// When last_epoch = -1, sets initial lr as lr.\\n /// </summary>\\n public class MultiplicativeLR : LRScheduler\\n {\\n /// <summary>\\n /// Constructor\\n /// </summary>\\n /// <param name=\\\"optimizer\\\">Wrapped optimizer.</param>\\n /// <param name=\\\"lr_lambdas\\\">A list of functions, one for each paramater group, which computes a multiplicative factor given an integer parameter epoch.</param>\\n /// <param name=\\\"last_epoch\\\">The index of last epoch. Default: -1.</param>\\n /// <param name=\\\"verbose\\\"> If true, prints a message to stdout for each update. Default: false.</param>\\n /// <returns>A scheduler</returns>\\n public MultiplicativeLR(Optimizer optimizer, IEnumerable<Func<int, double>> lr_lambdas, int last_epoch = -1, bool verbose = false) : base(optimizer, last_epoch, verbose)\\n {\\n if (optimizer == null) throw new ArgumentNullException(\\\"optimizer\\\");\\n _lr_lambdas = lr_lambdas.ToList();\\n\\n step();\\n }\\n\\n /// <summary>\\n /// Constructor\\n /// </summary>\\n /// <param name=\\\"optimizer\\\">Wrapped optimizer.</param>\\n /// <param name=\\\"lr_lambda\\\">A function which computes a multiplicative factor given an integer parameter epoch.</param>\\n /// <param name=\\\"last_epoch\\\">The index of last epoch. Default: -1.</param>\\n /// <param name=\\\"verbose\\\"> If true, prints a message to stdout for each update. Default: false.</param>\\n /// <returns>A scheduler</returns>\\n public MultiplicativeLR(Optimizer optimizer, Func<int, double> lr_lambda, int last_epoch = -1, bool verbose = false) : base(optimizer, last_epoch, verbose)\\n {\\n if (optimizer == null) throw new ArgumentNullException(\\\"optimizer\\\");\\n _lr_lambdas = Enumerable.Repeat(lr_lambda, optimizer.ParamGroups.Count()).ToList();\\n\\n step();\\n }\\n\\n /// <summary>\\n /// Compute the current learning rate for the scheduler.\\n /// </summary>\\n protected override IEnumerable<double> get_lr()\\n {\\n var pgs = _optimizer.ParamGroups.ToList();\\n return (_last_epoch > 0)\\n ? Enumerable.Range(0, pgs.Count).Select(i => pgs[i].LearningRate * _lr_lambdas[i](_last_epoch))\\n : Enumerable.Range(0, pgs.Count).Select(i => pgs[i].LearningRate);\\n }\\n\\n private List<Func<int, double>> _lr_lambdas;\\n }\\n\\n /// <summary>\\n /// Decays the learning rate of each parameter group by gamma every step_size epochs.\\n /// Notice that such decay can happen simultaneously with other changes to the learning rate from outside this scheduler.\\n /// When last_epoch=-1, sets initial lr as lr.\\n /// </summary>\\n public class StepLR : LRScheduler\\n {\\n /// <summary>\\n /// Constructor\\n /// </summary>\\n /// <param name=\\\"optimizer\\\">Wrapped optimizer.</param>\\n /// <param name=\\\"step_size\\\">Period of learning rate decay.</param>\\n /// <param name=\\\"gamma\\\">Multiplicative factor of learning rate decay. Default: 0.1.</param>\\n /// <param name=\\\"last_epoch\\\">The index of last epoch. Default: -1.</param>\\n /// <param name=\\\"verbose\\\"> If true, prints a message to stdout for each update. Default: false.</param>\\n /// <returns>A scheduler</returns>\\n public StepLR(Optimizer optimizer, int step_size, double gamma = 0.1, int last_epoch = -1, bool verbose = false) : base(optimizer, last_epoch, verbose)\\n {\\n if (optimizer == null) throw new ArgumentNullException(\\\"optimizer\\\");\\n _step_size = step_size;\\n _gamma = gamma;\\n\\n step();\\n }\\n\\n /// <summary>\\n /// Compute the current learning rate for the scheduler.\\n /// </summary>\\n protected override IEnumerable<double> get_lr()\\n {\\n return (_last_epoch == 0) || (_last_epoch % _step_size != 0)\\n ? _optimizer.ParamGroups.Select(pg => pg.LearningRate)\\n : _optimizer.ParamGroups.Select(pg => pg.LearningRate * _gamma);\\n }\\n\\n private int _step_size;\\n private double _gamma;\\n }\\n\\n /// <summary>\\n /// Decays the learning rate of each parameter group by gamma once the number of epoch reaches one of the milestones.\\n /// Notice that such decay can happen simultaneously with other changes to the learning rate from outside this scheduler.\\n /// When last_epoch=-1, sets initial lr as lr.\\n /// </summary>\\n public class MultiStepLR : LRScheduler\\n {\\n /// <summary>\\n /// Constructor\\n /// </summary>\\n /// <param name=\\\"optimizer\\\">Wrapped optimizer.</param>\\n /// <param name=\\\"milestones\\\">List of epoch indices. Must be increasing.</param>\\n /// <param name=\\\"gamma\\\">Multiplicative factor of learning rate decay. Default: 0.1.</param>\\n /// <param name=\\\"last_epoch\\\">The index of last epoch. Default: -1.</param>\\n /// <param name=\\\"verbose\\\"> If true, prints a message to stdout for each update. Default: false.</param>\\n /// <returns>A scheduler</returns>\\n public MultiStepLR(Optimizer optimizer, IList<int> milestones, double gamma = 0.1, int last_epoch = -1, bool verbose = false) : base(optimizer, last_epoch, verbose)\\n {\\n if (optimizer == null) throw new ArgumentNullException(\\\"optimizer\\\");\\n _milestones = milestones;\\n _gamma = gamma;\\n\\n step();\\n }\\n\\n /// <summary>\\n /// Compute the current learning rate for the scheduler.\\n /// </summary>\\n protected override IEnumerable<double> get_lr()\\n {\\n var idx = _milestones.IndexOf(_last_epoch);\\n return idx == -1\\n ? _optimizer.ParamGroups.Select(pg => pg.LearningRate)\\n : _optimizer.ParamGroups.Select(pg => pg.LearningRate * _gamma);\\n }\\n\\n private IList<int> _milestones;\\n private double _gamma;\\n }\\n\\n /// <summary>\\n /// Decays the learning rate of each parameter group using a polynomial function in the given total_iters.\\n /// When last_epoch=-1, sets initial lr as lr.\\n /// </summary>\\n public class PolynomialLR : LRScheduler\\n {\\n /// <summary>\\n /// Constructor\\n /// </summary>\\n /// <param name=\\\"optimizer\\\">Wrapped optimizer.</param>\\n /// <param name=\\\"total_iters\\\">The number of steps that the scheduler decays the learning rate.</param>\\n /// <param name=\\\"power\\\">The power of the polynomial.</param>\\n /// <param name=\\\"last_epoch\\\">The index of last epoch. Default: -1.</param>\\n /// <param name=\\\"verbose\\\"> If true, prints a message to stdout for each update. Default: false.</param>\\n /// <returns>A scheduler</returns>\\n public PolynomialLR(Optimizer optimizer, int total_iters = 5, int power = 1, int last_epoch = -1, bool verbose = false) : base(optimizer, last_epoch, verbose)\\n {\\n if (optimizer == null) throw new ArgumentNullException(\\\"optimizer\\\");\\n _power = power;\\n _total_iters = total_iters;\\n step();\\n }\\n\\n /// <summary>\\n /// Compute the current learning rate for the scheduler.\\n /// </summary>\\n protected override IEnumerable<double> get_lr()\\n {\\n if (_last_epoch == 0 || _last_epoch > _total_iters) {\\n return _optimizer.ParamGroups.Select(pg => pg.LearningRate);\\n }\\n else {\\n var decay_factor = ((1.0 - _last_epoch / _total_iters) / (1.0 - (_last_epoch - 1) / _total_iters));\\n if (_power != 1) {\\n decay_factor = Math.Pow(decay_factor, _power);\\n }\\n return _optimizer.ParamGroups.Select(pg => pg.LearningRate * decay_factor);\\n }\\n }\\n\\n private double _total_iters;\\n private int _power;\\n }\\n\\n /// <summary>\\n /// Decays the learning rate of each parameter group by gamma every epoch.\\n /// Notice that such decay can happen simultaneously with other changes to the learning rate from outside this scheduler.\\n /// When last_epoch=-1, sets initial lr as lr.\\n /// </summary>\\n public class ExponentialLR : LRScheduler\\n {\\n /// <summary>\\n /// Constructor\\n /// </summary>\\n /// <param name=\\\"optimizer\\\">Wrapped optimizer.</param>\\n /// <param name=\\\"gamma\\\">Multiplicative factor of learning rate decay. Default: 0.1.</param>\\n /// <param name=\\\"last_epoch\\\">The index of last epoch. Default: -1.</param>\\n /// <param name=\\\"verbose\\\"> If true, prints a message to stdout for each update. Default: false.</param>\\n /// <returns>A scheduler</returns>\\n public ExponentialLR(Optimizer optimizer, double gamma = 0.1, int last_epoch = -1, bool verbose = false) : base(optimizer, last_epoch, verbose)\\n {\\n if (optimizer == null) throw new ArgumentNullException(\\\"optimizer\\\");\\n _gamma = gamma;\\n\\n step();\\n }\\n\\n /// <summary>\\n /// Compute the current learning rate for the scheduler.\\n /// </summary>\\n protected override IEnumerable<double> get_lr()\\n {\\n return (_last_epoch == 0)\\n ? _optimizer.ParamGroups.Select(pg => pg.LearningRate)\\n : _optimizer.ParamGroups.Select(pg => pg.LearningRate * _gamma);\\n }\\n\\n private double _gamma;\\n }\\n\\n /// <summary>\\n /// Decays the learning rate of each parameter group by a small constant factor until the number of epoch reaches a pre-defined milestone: total_iters.\\n /// Notice that such decay can happen simultaneously with other changes to the learning rate from outside this scheduler.\\n /// When last_epoch=-1, sets initial lr as lr.\\n /// </summary>\\n public class ConstantLR : LRScheduler\\n {\\n /// <summary>\\n /// Constructor\\n /// </summary>\\n /// <param name=\\\"optimizer\\\">Wrapped optimizer.</param>\\n /// <param name=\\\"factor\\\">The number we multiply learning rate until the milestone.</param>\\n /// <param name=\\\"total_iters\\\">The number of steps that the scheduler decays the learning rate.</param>\\n /// <param name=\\\"last_epoch\\\">The index of last epoch. Default: -1.</param>\\n /// <param name=\\\"verbose\\\"> If true, prints a message to stdout for each update. Default: false.</param>\\n /// <returns>A scheduler</returns>\\n public ConstantLR(Optimizer optimizer, double factor = 1.0 / 3, int total_iters = 5, int last_epoch = -1, bool verbose = false) : base(optimizer, last_epoch, verbose)\\n {\\n if (optimizer == null) throw new ArgumentNullException(\\\"optimizer\\\");\\n _factor = factor;\\n _total_iters = total_iters;\\n\\n step();\\n }\\n\\n /// <summary>\\n /// Compute the current learning rate for the scheduler.\\n /// </summary>\\n protected override IEnumerable<double> get_lr()\\n {\\n if (_last_epoch == 0) {\\n return _optimizer.ParamGroups.Select(pg => pg.LearningRate * _factor);\\n } else if (_last_epoch == _total_iters) {\\n return _optimizer.ParamGroups.Select(pg => pg.LearningRate * (1.0 / _factor));\\n } else {\\n return _optimizer.ParamGroups.Select(pg => pg.LearningRate);\\n }\\n }\\n\\n private double _factor;\\n private int _total_iters;\\n }\\n\\n /// <summary>\\n /// Decays the learning rate of each parameter group by linearly changing small multiplicative factor until the\\n /// number of epoch reaches a pre-defined milestone: total_iters.\\n /// Notice that such decay can happen simultaneously with other changes to the learning rate from outside this scheduler.\\n /// When last_epoch=-1, sets initial lr as lr.\\n /// </summary>\\n public class LinearLR : LRScheduler\\n {\\n /// <summary>\\n /// Constructor\\n /// </summary>\\n /// <param name=\\\"optimizer\\\">Wrapped optimizer.</param>\\n /// <param name=\\\"start_factor\\\">The number we multiply learning rate in the first epoch. The multiplication factor changes towards end_factor in the following epochs.</param>\\n /// <param name=\\\"end_factor\\\">The number we multiply learning rate at the end of linear changing process.</param>\\n /// <param name=\\\"total_iters\\\">The number of steps that the scheduler decays the learning rate.</param>\\n /// <param name=\\\"last_epoch\\\">The index of last epoch. Default: -1.</param>\\n /// <param name=\\\"verbose\\\"> If true, prints a message to stdout for each update. Default: false.</param>\\n /// <returns>A scheduler</returns>\\n public LinearLR(Optimizer optimizer, double start_factor = 1.0 / 3, double end_factor = 5, int total_iters = 5, int last_epoch = -1, bool verbose = false) : base(optimizer, last_epoch, verbose)\\n {\\n if (optimizer == null) throw new ArgumentNullException(\\\"optimizer\\\");\\n _start_factor = start_factor;\\n _end_factor = end_factor;\\n _total_iters = total_iters;\\n\\n step();\\n }\\n\\n /// <summary>\\n /// Compute the current learning rate for the scheduler.\\n /// </summary>\\n protected override IEnumerable<double> get_lr()\\n {\\n if (_last_epoch == 0) {\\n return _optimizer.ParamGroups.Select(pg => pg.LearningRate * _start_factor);\\n } else if (_last_epoch > _total_iters) {\\n return _optimizer.ParamGroups.Select(pg => pg.LearningRate);\\n } else {\\n var factor = (1 + (_end_factor - _start_factor)) / (_total_iters * _start_factor + (_last_epoch - 1) * (_end_factor - _start_factor));\\n return _optimizer.ParamGroups.Select(pg => pg.LearningRate * factor);\\n }\\n }\\n\\n private double _start_factor;\\n private double _end_factor;\\n private int _total_iters;\\n }\\n\\n /// <summary>\\n /// Receives the list of schedulers that is expected to be called sequentially during optimization process and milestone points that provides exact intervals to reflect which scheduler is supposed to be called at a given epoch.\\n /// </summary>\\n public class SequentialLR : LRScheduler\\n {\\n public SequentialLR(Optimizer optimizer, IEnumerable<LRScheduler> schedulers, IEnumerable<int> milestones, int last_epoch = -1) : base(optimizer, last_epoch + 1, false)\\n {\\n if (optimizer == null) throw new ArgumentNullException(\\\"optimizer\\\");\\n\\n foreach (var scheduler in schedulers) {\\n if (scheduler._optimizer != optimizer)\\n throw new ArgumentException($\\\"The SequentialLR scheduler expects all its sub-schedulers to be bound to the same optimizer.\\\");\\n }\\n\\n if (milestones.Count() != schedulers.Count() - 1) {\\n throw new ArgumentException($\\\"Received {schedulers.Count()} schedulers and {milestones.Count()} milestones. The SequentialLR scheduler expects the number of schedulers to be one more than the number of milestones. \\\");\\n }\\n\\n _schedulers = schedulers.ToArray();\\n _milestones = milestones.ToArray();\\n\\n foreach (var group in optimizer.ParamGroups) {\\n group.LearningRate = group.InitialLearningRate;\\n }\\n foreach (var scheduler in _schedulers) {\\n scheduler._last_epoch -= 1;\\n }\\n _schedulers[0].step();\\n _last_lrs = _schedulers[0]._last_lrs;\\n }\\n\\n /// <summary>\\n /// Compute the current learning rate for the scheduler.\\n /// </summary>\\n protected override IEnumerable<double> get_lr()\\n {\\n\\n var idx = _milestones.Length;\\n for (; idx > 0 ;) {\\n if (_milestones[idx-1] <= _last_epoch) break;\\n idx -= 1;\\n }\\n\\n var scheduler = _schedulers[idx];\\n if (idx > 0 && _milestones[idx - 1] == _last_epoch)\\n scheduler.step(0);\\n else\\n scheduler.step();\\n\\n return _optimizer.ParamGroups.Select(pg => pg.LearningRate);\\n }\\n\\n private LRScheduler[] _schedulers;\\n private int[] _milestones;\\n }\\n\\n /// <summary>\\n /// Set the learning rate of each parameter group using a cosine annealing schedule.\\n /// When last_epoch=-1, sets initial lr as lr.\\n /// </summary>\\n public class CosineAnnealingLR : LRScheduler\\n {\\n /// <summary>\\n /// Constructor\\n /// </summary>\\n /// <param name=\\\"optimizer\\\">Wrapped optimizer.</param>\\n /// <param name=\\\"T_max\\\">Maximum number of iterations.</param>\\n /// <param name=\\\"eta_min\\\">Minimum learning rate.</param>\\n /// <param name=\\\"last_epoch\\\">The index of last epoch. Default: -1.</param>\\n /// <param name=\\\"verbose\\\"> If true, prints a message to stdout for each update. Default: false.</param>\\n /// <returns>A scheduler</returns>\\n public CosineAnnealingLR(Optimizer optimizer, double T_max, double eta_min = 0, int last_epoch = -1, bool verbose = false) : base(optimizer, last_epoch, verbose)\\n {\\n if (optimizer == null) throw new ArgumentNullException(\\\"optimizer\\\");\\n _T_max = T_max;\\n _eta_min = eta_min;\\n\\n step();\\n }\\n\\n /// <summary>\\n /// Compute the current learning rate for the scheduler.\\n /// </summary>\\n protected override IEnumerable<double> get_lr()\\n {\\n if (_last_epoch == 0) {\\n return _optimizer.ParamGroups.Select(pg => pg.LearningRate);\\n } else if ((_last_epoch - 1 - _T_max) % (2 * _T_max) == 0) {\\n var pgs = _optimizer.ParamGroups.ToList();\\n return Enumerable.Range(0, pgs.Count).Select(i => pgs[i].LearningRate + (_base_lrs[i] - _eta_min) * (1 - Math.Cos(Math.PI / _T_max)) / 2);\\n } else {\\n return _optimizer.ParamGroups.Select(pg => (1 + Math.Cos(Math.PI * _last_epoch / _T_max)) /\\n (1 + Math.Cos(Math.PI * (_last_epoch - 1) / _T_max)) * (pg.LearningRate - _eta_min) + _eta_min);\\n }\\n }\\n\\n private double _T_max;\\n private double _eta_min;\\n }\\n\\n /// <summary>\\n /// Set the learning rate of each parameter group using a cosine annealing schedule.\\n /// When last_epoch=-1, sets initial lr as lr.\\n /// </summary>\\n public class ReduceLROnPlateau : LRScheduler\\n {\\n /// <summary>\\n /// Constructor\\n /// </summary>\\n /// <returns>A scheduler</returns>\\n public ReduceLROnPlateau(Optimizer optimizer, string mode = \\\"min\\\", double factor = 0.1, int patience = 10, double threshold = 1e-4, string threshold_mode = \\\"rel\\\", int cooldown = 0, IList<double> min_lr = null, double eps = 1e-8, bool verbose = false)\\n :base(optimizer, -1, verbose)\\n {\\n if (optimizer == null) throw new ArgumentNullException(\\\"optimizer\\\");\\n if (factor >= 1.0) throw new ArgumentException(\\\"Factor should be < 1.0\\\");\\n\\n switch (mode) {\\n case \\\"min\\\":\\n case \\\"max\\\":\\n break;\\n default:\\n throw new ArgumentException($\\\"mode {mode} is unknown!\\\");\\n }\\n switch (threshold_mode) {\\n case \\\"rel\\\":\\n case \\\"abs\\\":\\n break;\\n default:\\n throw new ArgumentException($\\\"threshold mode {threshold_mode} is unknown!\\\");\\n }\\n\\n this._optimizer = optimizer;\\n this.factor = factor;\\n\\n var pgLength = optimizer.ParamGroups.Count();\\n if (min_lr == null || min_lr.Count == 0) {\\n this.min_lrs = Enumerable.Repeat(0.0, pgLength).ToList();\\n }\\n else if (min_lr.Count == 1) {\\n this.min_lrs = Enumerable.Repeat(min_lr[0], pgLength).ToList();\\n }\\n else {\\n this.min_lrs = min_lr.ToList();\\n }\\n\\n this.patience = patience;\\n this.cooldown = cooldown;\\n this.cooldown_counter = 0;\\n this.mode = mode;\\n this.threshold = threshold;\\n this.threshold_mode = threshold_mode;\\n this.best = -1;\\n this.num_bad_epochs = -1;\\n this.eps = eps;\\n\\n this.mode_worst = (mode == \\\"min\\\") ? double.PositiveInfinity : double.NegativeInfinity;\\n\\n reset();\\n }\\n\\n /// <summary>\\n /// Advance the LR scheduler one epoch.\\n /// Unlike other LR schedulers, you're supposed to pass in the most recent value of the metric\\n /// that is used to decided whether to modify the learning rates. \\n /// </summary>\\n /// <param name=\\\"current\\\">The current value of the metric that we're interested in imvproving.</param>\\n /// <param name=\\\"epoch\\\">The current epoch.</param>\\n public override void step(double current, int? epoch = null)\\n {\\n if (epoch == null) {\\n epoch = _last_epoch + 1;\\n }\\n _last_epoch = epoch.Value;\\n\\n if (is_better(current, best)) {\\n best = current;\\n num_bad_epochs = 0;\\n }\\n else {\\n num_bad_epochs += 1;\\n }\\n\\n if (cooldown_counter > 0) {\\n cooldown_counter -= 1;\\n num_bad_epochs = 0;\\n }\\n\\n if (num_bad_epochs > patience) {\\n reduce_lr(_last_epoch);\\n cooldown_counter = cooldown;\\n num_bad_epochs = 0;\\n }\\n }\\n\\n public override void step()\\n {\\n throw new InvalidOperationException(\\\"step() should not be used with the ReduceLROnPlateau scheduler. Use step(double, int?), instead.\\\");\\n }\\n\\n public override void step(int? epoch)\\n {\\n throw new InvalidOperationException(\\\"step(int?) should not be used with the ReduceLROnPlateau scheduler. Use step(double, int?), instead.\\\");\\n }\\n\\n private bool is_better(double a, double best)\\n {\\n if (mode == \\\"min\\\" && threshold_mode == \\\"rel\\\") {\\n return a < best * (1 - threshold);\\n }\\n else if (mode == \\\"min\\\" && threshold_mode == \\\"abs\\\") {\\n return a < best - threshold;\\n }\\n else if (mode == \\\"max\\\" && threshold_mode == \\\"rel\\\") {\\n return a > best * (threshold + 1);\\n }\\n else {\\n return a > best + threshold;\\n }\\n }\\n\\n private void reduce_lr(long epoch)\\n {\\n var pgs = _optimizer.ParamGroups.ToList();\\n\\n for (var i = 0; i < pgs.Count; i++) {\\n var param_group = pgs[i];\\n\\n var old_lr = param_group.LearningRate;\\n var new_lr = Math.Max(old_lr * factor, min_lrs[i]);\\n if (old_lr - new_lr > eps) {\\n param_group.LearningRate = new_lr;\\n if (_verbose) {\\n Console.WriteLine($\\\"Epoch {epoch}: reducing learning rate of group {i} to {new_lr:g4}.\\\");\\n }\\n }\\n }\\n }\\n\\n private void reset()\\n {\\n best = mode_worst;\\n cooldown_counter = 0;\\n num_bad_epochs = 0;\\n }\\n\\n private int patience;\\n private int cooldown;\\n private int cooldown_counter;\\n private string mode;\\n private double threshold;\\n private string threshold_mode;\\n private double best;\\n private double mode_worst;\\n private int num_bad_epochs;\\n private double eps;\\n\\n private double factor;\\n private List<double> min_lrs;\\n }\\n\\n /// <summary>\\n /// Sets the learning rate of each parameter group according to cyclical learning rate policy(CLR).\\n ///\\n /// The policy cycles the learning rate between two boundaries with a constant frequency, as detailed in\\n /// the paper `Cyclical Learning Rates for Training Neural Networks`_.\\n /// \\n /// The distance between the two boundaries can be scaled on a per-iteration or per-cycle basis.\\n /// </summary>\\n public class CyclicLR : LRScheduler\\n {\\n public enum Mode\\n {\\n Triangular,\\n Triangular2,\\n ExpRange\\n }\\n public enum ScaleMode\\n {\\n Cycle,\\n Iterations\\n }\\n\\n /// <summary>\\n /// Constructor\\n /// </summary>\\n public CyclicLR(Optimizer optimizer,\\n double base_lr,\\n double max_lr,\\n int step_size_up = 2000,\\n int step_size_down = -1,\\n Mode mode = Mode.Triangular,\\n double gamma = 1.0,\\n Func<double, double> scale_fn = null,\\n ScaleMode scale_mode = ScaleMode.Cycle,\\n bool cycle_momentum = true,\\n double base_momentum = 0.8,\\n double max_momentum = 0.9,\\n int last_epoch = -1,\\n bool verbose = false) : base(optimizer, last_epoch, verbose)\\n {\\n if (optimizer == null) throw new ArgumentNullException(\\\"optimizer\\\");\\n\\n var pgCount = optimizer.ParamGroups.Count();\\n\\n Initialize(\\n optimizer,\\n Enumerable.Repeat(base_lr, pgCount),\\n Enumerable.Repeat(max_lr, pgCount),\\n step_size_up,\\n step_size_down,\\n mode,\\n gamma,\\n scale_fn,\\n scale_mode,\\n cycle_momentum,\\n Enumerable.Repeat(base_momentum, pgCount),\\n Enumerable.Repeat(max_momentum, pgCount));\\n }\\n\\n /// <summary>\\n /// Constructor\\n /// </summary>\\n public CyclicLR(Optimizer optimizer,\\n IEnumerable<double> base_lr,\\n IEnumerable<double> max_lr,\\n int step_size_up = 2000,\\n int step_size_down = -1,\\n Mode mode = Mode.Triangular,\\n double gamma = 1.0,\\n Func<double, double> scale_fn = null,\\n ScaleMode scale_mode = ScaleMode.Cycle,\\n bool cycle_momentum = true,\\n IEnumerable<double> base_momentum = null,\\n IEnumerable<double> max_momentum = null,\\n int last_epoch = -1,\\n bool verbose = false) : base(optimizer, last_epoch, verbose)\\n {\\n if (optimizer == null) throw new ArgumentNullException(\\\"optimizer\\\");\\n\\n Initialize(\\n optimizer,\\n base_lr,\\n max_lr,\\n step_size_up,\\n step_size_down,\\n mode,\\n gamma,\\n scale_fn,\\n scale_mode,\\n cycle_momentum,\\n base_momentum,\\n max_momentum);\\n }\\n\\n private void Initialize(Optimizer optimizer, IEnumerable<double> base_lr, IEnumerable<double> max_lr, int step_size_up, int step_size_down, Mode mode, double gamma, Func<double, double> scale_fn, ScaleMode scale_mode, bool cycle_momentum, IEnumerable<double> base_momentum, IEnumerable<double> max_momentum)\\n {\\n double down = (step_size_down == -1) ? step_size_up : step_size_down;\\n _total_size = step_size_up + down;\\n _step_ratio = step_size_up / _total_size;\\n\\n var pgs = optimizer.ParamGroups.ToList();\\n\\n _max_lrs = max_lr.ToList();\\n _base_lrs = base_lr.ToList();\\n\\n if (_last_epoch == -1) {\\n for (int i = 0; i < pgs.Count; i++) {\\n pgs[i].LearningRate = _base_lrs[i];\\n }\\n }\\n\\n _mode = mode;\\n _gamma = gamma;\\n _cycle_momentum = cycle_momentum;\\n\\n if (cycle_momentum) {\\n var momentum = optimizer as IMomentum;\\n if (momentum == null && cycle_momentum) throw new ArgumentException($\\\"optimizer must support momentum with `cycle_momentum` option enabled\\\");\\n\\n _base_momentum = (base_momentum is null) ? Enumerable.Repeat(0.8, pgs.Count).ToList() : base_momentum.ToList();\\n _max_momentum = (max_momentum is null) ? Enumerable.Repeat(0.9, pgs.Count).ToList() : max_momentum.ToList();\\n\\n if (_last_epoch == -1) {\\n for (int i = 0; i < pgs.Count; i++) {\\n (pgs[i] as IMomentum).Momentum = _base_momentum[i];\\n }\\n }\\n }\\n\\n\\n if (scale_fn == null) {\\n switch (mode) {\\n case Mode.Triangular:\\n _scale_func = x => 1.0;\\n _scale_mode = ScaleMode.Cycle;\\n break;\\n case Mode.Triangular2:\\n _scale_func = x => 1.0 / (Math.Pow(2, x - 1));\\n _scale_mode = ScaleMode.Cycle;\\n break;\\n case Mode.ExpRange:\\n _scale_func = x => Math.Pow(this._gamma, x);\\n _scale_mode = ScaleMode.Cycle;\\n break;\\n }\\n } else {\\n _scale_func = scale_fn;\\n _scale_mode = scale_mode;\\n }\\n\\n step();\\n }\\n\\n /// <summary>\\n /// Compute the current learning rate for the scheduler.\\n /// </summary>\\n protected override IEnumerable<double> get_lr()\\n {\\n var pgs = _optimizer.ParamGroups.ToList();\\n\\n var cycle = Math.Floor(1.0 + _last_epoch / _total_size);\\n var x = 1.0 + _last_epoch / _total_size - cycle;\\n\\n var scale_factor = (x <= _step_ratio) ? x / _step_ratio : (x - 1) / (_step_ratio - 1);\\n\\n return Enumerable.Range(0, pgs.Count).Select(i => {\\n\\n var base_height = (_max_lrs[i] - _base_lrs[i]) * scale_factor;\\n\\n var computed_lr = (_scale_mode == ScaleMode.Cycle)\\n ? _base_lrs[i] + base_height * _scale_func(cycle)\\n : _base_lrs[i] + base_height * _scale_func(_last_epoch);\\n\\n if (_cycle_momentum) {\\n\\n base_height = (_max_momentum[i] - _base_momentum[i]) * scale_factor;\\n\\n (pgs[i] as IMomentum).Momentum = (_scale_mode == ScaleMode.Cycle)\\n ? _max_momentum[i] + base_height * _scale_func(cycle)\\n : _max_momentum[i] + base_height * _scale_func(_last_epoch);\\n\\n }\\n\\n return computed_lr;\\n });\\n }\\n\\n private double _total_size;\\n private double _step_ratio;\\n\\n private Func<double, double> _scale_func;\\n private ScaleMode _scale_mode;\\n\\n private bool _cycle_momentum;\\n\\n private List<double> _max_lrs;\\n\\n private List<double> _base_momentum;\\n private List<double> _max_momentum;\\n\\n private Mode _mode;\\n private double _gamma;\\n }\\n\\n /// <summary>\\n /// Sets the learning rate of each parameter group according to the 1cycle learning rate policy.\\n /// </summary>\\n public class OneCycleLR : LRScheduler\\n {\\n public enum AnnealStrategy\\n {\\n Cos,\\n Linear\\n }\\n\\n /// <summary>\\n /// Constructor\\n /// </summary>\\n public OneCycleLR(Optimizer optimizer,\\n double max_lr,\\n int total_steps = -1,\\n int epochs = -1,\\n int steps_per_epoch = -1,\\n double pct_start = 0.3,\\n AnnealStrategy anneal_strategy = AnnealStrategy.Cos,\\n bool cycle_momentum = true,\\n double base_momentum = 0.85,\\n double max_momentum = 0.95,\\n double div_factor = 25,\\n double final_div_factor = 1e4,\\n bool three_phase = false,\\n int last_epoch = -1, bool verbose = false) : base(optimizer, last_epoch, verbose)\\n {\\n if (optimizer == null) throw new ArgumentNullException(\\\"optimizer\\\");\\n\\n var pgCount = _optimizer.ParamGroups.Count();\\n\\n Initialize(optimizer,\\n Enumerable.Repeat(max_lr, pgCount),\\n total_steps,\\n epochs,\\n steps_per_epoch,\\n pct_start, anneal_strategy,\\n cycle_momentum,\\n Enumerable.Repeat(base_momentum, pgCount),\\n Enumerable.Repeat(max_momentum, pgCount),\\n div_factor,\\n final_div_factor,\\n three_phase,\\n last_epoch);\\n }\\n\\n /// <summary>\\n /// Constructor\\n /// </summary>\\n public OneCycleLR(Optimizer optimizer,\\n IEnumerable<double> max_lr,\\n int total_steps = -1,\\n int epochs = -1,\\n int steps_per_epoch = -1,\\n double pct_start = 0.3,\\n AnnealStrategy anneal_strategy = AnnealStrategy.Cos,\\n bool cycle_momentum = true,\\n IEnumerable<double> base_momentum = null,\\n IEnumerable<double> max_momentum = null,\\n double div_factor = 25,\\n double final_div_factor = 1e4,\\n bool three_phase = false,\\n int last_epoch = -1, bool verbose = false) : base(optimizer, last_epoch, verbose)\\n {\\n if (optimizer == null) throw new ArgumentNullException(\\\"optimizer\\\");\\n\\n Initialize(optimizer,\\n max_lr,\\n total_steps,\\n epochs,\\n steps_per_epoch,\\n pct_start,\\n anneal_strategy,\\n cycle_momentum,\\n base_momentum,\\n max_momentum,\\n div_factor,\\n final_div_factor,\\n three_phase,\\n last_epoch);\\n }\\n\\n private void Initialize(Optimizer optimizer, IEnumerable<double> max_lr, int total_steps, int epochs, int steps_per_epoch, double pct_start, AnnealStrategy anneal_strategy, bool cycle_momentum, IEnumerable<double> base_momentum, IEnumerable<double> max_momentum, double div_factor, double final_div_factor, bool three_phase, int last_epoch)\\n {\\n _cycle_momentum = cycle_momentum;\\n\\n var pgs = optimizer.ParamGroups.ToList();\\n\\n if (cycle_momentum) {\\n var _momentum = optimizer as IMomentum;\\n var _betas = optimizer as IBetas;\\n if (_momentum == null && _betas == null) throw new ArgumentException($\\\"optimizer must support momentum with `cycle_momentum` option enabled\\\");\\n }\\n\\n if (pct_start < 0 || pct_start > 1) throw new ArgumentException($\\\"Expected float between 0 and 1 for pct_start, but got {pct_start}\\\");\\n\\n if (total_steps == -1 && epochs == -1 && steps_per_epoch == -1) {\\n throw new ArgumentException(\\\"You must define either total_steps OR (epochs AND steps_per_epoch)\\\");\\n } else if (total_steps != -1) {\\n if (total_steps <= 0) throw new ArgumentException($\\\"Expected positive integer total_steps, but got {total_steps}\\\");\\n _total_steps = total_steps;\\n } else {\\n if (epochs <= 0) throw new ArgumentException($\\\"Expected positive integer epochs, but got {epochs}\\\");\\n if (steps_per_epoch <= 0) throw new ArgumentException($\\\"Expected positive integer steps_per_epoch, but got {steps_per_epoch}\\\");\\n _total_steps = epochs * steps_per_epoch;\\n }\\n\\n var mlr = max_lr.ToList();\\n\\n _betas = pgs.Select(pg => pg as IBetas).ToList();\\n _momentum = pgs.Select(pg => pg as IMomentum).ToList();\\n\\n var initial_lrs = max_lr.Select(mlr => mlr / div_factor).ToList();\\n\\n _phase_values[\\\"initial_lr\\\"] = initial_lrs;\\n _phase_values[\\\"max_lr\\\"] = mlr;\\n _phase_values[\\\"min_lr\\\"] = initial_lrs.Select(ilr => ilr / final_div_factor).ToList();\\n\\n _annealing_func = (anneal_strategy == AnnealStrategy.Cos)\\n ? (start, end, pct) => end + (start - end) / 2.0 * (Math.Cos(Math.PI * pct) + 1)\\n : (start, end, pct) => (end - start) * pct + start;\\n\\n _schedule_phases = three_phase\\n ? new PhaseDescriptor[] {\\n new PhaseDescriptor { end_step = pct_start * _total_steps - 1, start_lr = \\\"initial_lr\\\", end_lr = \\\"max_lr\\\", start_momentum = \\\"max_momentum\\\", end_momentum = \\\"base_momentum\\\" },\\n new PhaseDescriptor { end_step = 2 * pct_start * _total_steps - 2, start_lr = \\\"max_lr\\\", end_lr = \\\"initial_lr\\\", start_momentum = \\\"base_momentum\\\", end_momentum = \\\"max_momentum\\\" },\\n new PhaseDescriptor { end_step = _total_steps - 1, start_lr = \\\"initial_lr\\\", end_lr = \\\"min_lr\\\", start_momentum = \\\"max_momentum\\\", end_momentum = \\\"max_momentum\\\" }\\n }\\n : new PhaseDescriptor[] {\\n new PhaseDescriptor { end_step = pct_start * _total_steps - 1, start_lr = \\\"initial_lr\\\", end_lr = \\\"max_lr\\\", start_momentum = \\\"max_momentum\\\", end_momentum = \\\"base_momentum\\\" },\\n new PhaseDescriptor { end_step = _total_steps - 1, start_lr = \\\"max_lr\\\", end_lr = \\\"min_lr\\\", start_momentum = \\\"base_momentum\\\", end_momentum = \\\"max_momentum\\\" },\\n };\\n\\n if (last_epoch == -1) {\\n\\n if (cycle_momentum) {\\n\\n var _base_momentum = (base_momentum is null) ? Enumerable.Repeat(0.85, pgs.Count).ToList() : base_momentum.ToList();\\n var _max_momentum = (max_momentum is null) ? Enumerable.Repeat(0.95, pgs.Count).ToList() : max_momentum.ToList();\\n\\n for (int i = 0; i < pgs.Count; i++) {\\n if (_betas[i] != null) {\\n var (_, beta2) = _betas[i].Betas;\\n _betas[i].Betas = (_max_momentum[i], beta2);\\n } else {\\n if (_momentum[i] != null)\\n _momentum[i].Momentum = _max_momentum[i];\\n }\\n }\\n _phase_values[\\\"max_momentum\\\"] = _max_momentum;\\n _phase_values[\\\"base_momentum\\\"] = _base_momentum;\\n }\\n }\\n step();\\n }\\n\\n /// <summary>\\n /// Compute the current learning rate for the scheduler.\\n /// </summary>\\n protected override IEnumerable<double> get_lr()\\n {\\n var step_num = _last_epoch;\\n if (step_num > _total_steps) {\\n throw new InvalidOperationException($\\\"Tried to step {step_num + 1} times. The specified number of total steps is {_total_steps}\\\");\\n }\\n\\n var pgs = _optimizer.ParamGroups.ToList();\\n\\n return Enumerable.Range(0, pgs.Count).Select(i => {\\n\\n double start_step = 0;\\n double computed_lr = 0;\\n double computed_momentum = 0;\\n\\n for (var j = 0; j < _schedule_phases.Length; j++) {\\n\\n var phase = _schedule_phases[j];\\n var end_step = phase.end_step;\\n\\n if (step_num <= end_step || i == _schedule_phases.Length - 1) {\\n var pct = (step_num - start_step) / (end_step - start_step);\\n computed_lr = _annealing_func(_phase_values[phase.start_lr][i], _phase_values[phase.end_lr][i], pct);\\n if (_cycle_momentum) {\\n computed_momentum = _annealing_func(_phase_values[phase.start_momentum][i], _phase_values[phase.end_momentum][i], pct);\\n }\\n break;\\n }\\n start_step = phase.end_step;\\n }\\n\\n if (_cycle_momentum) {\\n if (_betas[i] != null) {\\n var (_, beta2) = _betas[i].Betas;\\n _betas[i].Betas = (computed_momentum, beta2);\\n } else {\\n _momentum[i].Momentum = computed_momentum;\\n }\\n }\\n return computed_lr;\\n });\\n }\\n\\n private Func<double, double, double, double> _annealing_func;\\n\\n private PhaseDescriptor[] _schedule_phases;\\n private Dictionary<string, List<double>> _phase_values = new Dictionary<string, List<double>>();\\n\\n private bool _cycle_momentum;\\n\\n private List<IBetas> _betas;\\n private List<IMomentum> _momentum;\\n\\n private int _total_steps;\\n\\n private class PhaseDescriptor\\n {\\n public double end_step;\\n public string start_lr;\\n public string end_lr;\\n public string start_momentum;\\n public string end_momentum;\\n }\\n }\\n\\n /// <summary>\\n /// Chains list of learning rate schedulers. It takes a list of chainable learning rate schedulers and applies step() to all of them.\\n /// </summary>\\n public class ChainedLR : LRScheduler\\n {\\n /// <summary>\\n /// Constructor\\n /// </summary>\\n /// <param name=\\\"schedulers\\\">List of chained schedulers.</param>\\n /// <returns>A scheduler</returns>\\n public ChainedLR(IEnumerable<LRScheduler> schedulers)\\n {\\n _schedulers = schedulers;\\n }\\n\\n public override void step()\\n {\\n foreach (var sched in _schedulers) {\\n sched.step();\\n }\\n }\\n\\n private IEnumerable<LRScheduler> _schedulers;\\n }\\n }\\n\\n /// <summary>\\n /// Decays the learning rate of each parameter group by gamma every step_size epochs.\\n /// Notice that such decay can happen simultaneously with other changes to the learning rate from outside this scheduler.\\n /// When last_epoch=-1, sets initial lr as lr.\\n /// </summary>\\n /// <param name=\\\"optimizer\\\">Wrapped optimizer.</param>\\n /// <param name=\\\"step_size\\\">Period of learning rate decay.</param>\\n /// <param name=\\\"gamma\\\">Multiplicative factor of learning rate decay. Default: 0.1.</param>\\n /// <param name=\\\"last_epoch\\\">\\n /// The index of the last batch. This parameter is used when resuming a training job.Since `step()` should be invoked after each\\n /// batch instead of after each epoch, this number represents the total number of *batches* computed, not the total number of epochs computed.\\n /// When last_epoch = -1, the schedule is started from the beginning.\\n /// </param>\\n /// <param name=\\\"verbose\\\"> If true, prints a message to stdout for each update. Default: false.</param>\\n /// <returns>A scheduler</returns>\\n public static LRScheduler StepLR(Optimizer optimizer, int step_size, double gamma = 0.1, int last_epoch = -1, bool verbose = false)\\n {\\n return new impl.StepLR(optimizer, step_size, gamma, last_epoch, verbose);\\n }\\n\\n /// <summary>\\n /// Sets the learning rate of each parameter group to the initial lr times a given function.\\n /// When last_epoch=-1, sets initial lr as lr.\\n /// </summary>\\n /// <param name=\\\"optimizer\\\">Wrapped optimizer.</param>\\n /// <param name=\\\"lr_lambda\\\">A function which computes a multiplicative factor given an integer parameter epoch.</param>\\n /// <param name=\\\"last_epoch\\\">\\n /// The index of the last batch. This parameter is used when resuming a training job.Since `step()` should be invoked after each\\n /// batch instead of after each epoch, this number represents the total number of *batches* computed, not the total number of epochs computed.\\n /// When last_epoch = -1, the schedule is started from the beginning.\\n /// </param>\\n /// <param name=\\\"verbose\\\"> If true, prints a message to stdout for each update. Default: false.</param>\\n /// <returns>A scheduler</returns>\\n public static LRScheduler LambdaLR(Optimizer optimizer, Func<int, double> lr_lambda, int last_epoch = -1, bool verbose = false)\\n {\\n return new impl.LambdaLR(optimizer, lr_lambda, last_epoch, verbose);\\n\\n }\\n\\n /// <summary>\\n /// Decays the learning rate of each parameter group by gamma once the number of epoch reaches one of the milestones.\\n /// Notice that such decay can happen simultaneously with other changes to the learning rate from outside this scheduler.\\n /// When last_epoch=-1, sets initial lr as lr.\\n /// </summary>\\n /// <param name=\\\"optimizer\\\">Wrapped optimizer.</param>\\n /// <param name=\\\"milestones\\\">List of epoch indices. Must be increasing.</param>\\n /// <param name=\\\"gamma\\\">Multiplicative factor of learning rate decay. Default: 0.1.</param>\\n /// <param name=\\\"last_epoch\\\">\\n /// The index of the last batch. This parameter is used when resuming a training job.Since `step()` should be invoked after each\\n /// batch instead of after each epoch, this number represents the total number of *batches* computed, not the total number of epochs computed.\\n /// When last_epoch = -1, the schedule is started from the beginning.\\n /// </param>\\n /// <param name=\\\"verbose\\\"> If true, prints a message to stdout for each update. Default: false.</param>\\n /// <returns>A scheduler</returns>\\n public static LRScheduler MultiStepLR(Optimizer optimizer, IList<int> milestones, double gamma = 0.1, int last_epoch = -1, bool verbose = false)\\n {\\n return new impl.MultiStepLR(optimizer, milestones, gamma, last_epoch, verbose);\\n\\n }\\n\\n /// <summary>\\n /// Constructor\\n /// </summary>\\n /// <param name=\\\"optimizer\\\">Wrapped optimizer.</param>\\n /// <param name=\\\"total_iters\\\">The number of steps that the scheduler decays the learning rate.</param>\\n /// <param name=\\\"power\\\">The power of the polynomial.</param>\\n /// <param name=\\\"last_epoch\\\">The index of last epoch. Default: -1.</param>\\n /// <param name=\\\"verbose\\\"> If true, prints a message to stdout for each update. Default: false.</param>\\n /// <returns>A scheduler</returns>\\n public static LRScheduler PolynomialLR(Optimizer optimizer, int total_iters = 5, int power = 1, int last_epoch = -1, bool verbose = false)\\n {\\n return new impl.PolynomialLR(optimizer, total_iters, power, last_epoch, verbose);\\n }\\n\\n /// <summary>\\n /// Multiply the learning rate of each parameter group by the factor given in the specified function.\\n /// When last_epoch = -1, sets initial lr as lr.\\n /// </summary>\\n /// <param name=\\\"optimizer\\\">Wrapped optimizer.</param>\\n /// <param name=\\\"lr_lambda\\\">A function which computes a multiplicative factor given an integer parameter epoch.</param>\\n /// <param name=\\\"last_epoch\\\">\\n /// The index of the last batch. This parameter is used when resuming a training job.Since `step()` should be invoked after each\\n /// batch instead of after each epoch, this number represents the total number of *batches* computed, not the total number of epochs computed.\\n /// When last_epoch = -1, the schedule is started from the beginning.\\n /// </param>\\n /// <param name=\\\"verbose\\\"> If true, prints a message to stdout for each update. Default: false.</param>\\n /// <returns>A scheduler</returns>\\n public static LRScheduler MultiplicativeLR(Optimizer optimizer, Func<int, double> lr_lambda, int last_epoch = -1, bool verbose = false)\\n {\\n return new impl.MultiplicativeLR(optimizer, lr_lambda, last_epoch, verbose);\\n\\n }\\n\\n /// <summary>\\n /// Decays the learning rate of each parameter group by gamma every epoch.\\n /// Notice that such decay can happen simultaneously with other changes to the learning rate from outside this scheduler.\\n /// When last_epoch=-1, sets initial lr as lr.\\n /// </summary>\\n /// <param name=\\\"optimizer\\\">Wrapped optimizer.</param>\\n /// <param name=\\\"gamma\\\">Multiplicative factor of learning rate decay. Default: 0.1.</param>\\n /// <param name=\\\"last_epoch\\\">\\n /// The index of the last batch. This parameter is used when resuming a training job.Since `step()` should be invoked after each\\n /// batch instead of after each epoch, this number represents the total number of *batches* computed, not the total number of epochs computed.\\n /// When last_epoch = -1, the schedule is started from the beginning.\\n /// </param>\\n /// <param name=\\\"verbose\\\"> If true, prints a message to stdout for each update. Default: false.</param>\\n /// <returns>A scheduler</returns>\\n public static LRScheduler ExponentialLR(Optimizer optimizer, double gamma = 0.1, int last_epoch = -1, bool verbose = false)\\n {\\n return new impl.ExponentialLR(optimizer, gamma, last_epoch, verbose);\\n }\\n\\n /// <summary>\\n /// Decays the learning rate of each parameter group by a small constant factor until the number of epoch reaches a pre-defined milestone: total_iters.\\n /// Notice that such decay can happen simultaneously with other changes to the learning rate from outside this scheduler.\\n /// When last_epoch=-1, sets initial lr as lr.\\n /// </summary>\\n /// <param name=\\\"optimizer\\\">Wrapped optimizer.</param>\\n /// <param name=\\\"factor\\\">The number we multiply learning rate until the milestone.</param>\\n /// <param name=\\\"total_iters\\\">The number of steps that the scheduler decays the learning rate.</param>\\n /// <param name=\\\"last_epoch\\\">\\n /// The index of the last batch. This parameter is used when resuming a training job.Since `step()` should be invoked after each\\n /// batch instead of after each epoch, this number represents the total number of *batches* computed, not the total number of epochs computed.\\n /// When last_epoch = -1, the schedule is started from the beginning.\\n /// </param>\\n /// <param name=\\\"verbose\\\">If true, prints a message to stdout for each update. Default: false.</param>\\n /// <returns>A scheduler</returns>\\n public static LRScheduler ConstantLR(Optimizer optimizer, double factor = 1.0 / 3, int total_iters = 5, int last_epoch = -1, bool verbose = false)\\n {\\n return new impl.ConstantLR(optimizer, factor, total_iters, last_epoch, verbose);\\n }\\n\\n /// <summary>\\n /// Uses a list of schedulers, chosen based on the epoch. A sequence of milestones determines when a switch occurs from one scheduler to the next.\\n /// </summary>\\n /// <param name=\\\"optimizer\\\">Wrapped optimizer. All sub-schedulers must be bound to the same optimizer.</param>\\n /// <param name=\\\"schedulers\\\">List of chained schedulers. Should be one more than the number of milestones.</param>\\n /// <param name=\\\"milestones\\\">List of integers reflecting the milestone points.</param>\\n /// <param name=\\\"last_epoch\\\">The index of last epoch. Default: -1.</param>\\n /// <returns></returns>\\n /// <remarks>The 'verbose' flag should be passed to each individual sub-scheduler.</remarks>\\n public static LRScheduler SequentialLR(Optimizer optimizer, IEnumerable<LRScheduler> schedulers, IEnumerable<int> milestones, int last_epoch = -1)\\n {\\n return new impl.SequentialLR(optimizer, schedulers, milestones, last_epoch);\\n }\\n\\n /// <summary>\\n /// Decays the learning rate of each parameter group by linearly changing small multiplicative factor until the\\n /// number of epoch reaches a pre-defined milestone: total_iters.\\n /// </summary>\\n /// <param name=\\\"optimizer\\\">Wrapped optimizer.</param>\\n /// <param name=\\\"start_factor\\\">The number we multiply learning rate in the first epoch. The multiplication factor changes towards end_factor in the following epochs.</param>\\n /// <param name=\\\"end_factor\\\">The number we multiply learning rate at the end of linear changing process.</param>\\n /// <param name=\\\"total_iters\\\">The number of steps that the scheduler decays the learning rate.</param>\\n /// <param name=\\\"last_epoch\\\">\\n /// The index of the last batch. This parameter is used when resuming a training job.Since `step()` should be invoked after each\\n /// batch instead of after each epoch, this number represents the total number of *batches* computed, not the total number of epochs computed.\\n /// When last_epoch = -1, the schedule is started from the beginning.\\n /// </param>\\n /// <param name=\\\"verbose\\\">If true, prints a message to stdout for each update. Default: false.</param>\\n /// <returns>A scheduler</returns>\\n public static LRScheduler LinearLR(Optimizer optimizer, double start_factor = 1.0 / 3, double end_factor = 5, int total_iters = 5, int last_epoch = -1, bool verbose = false)\\n {\\n return new impl.LinearLR(optimizer, start_factor, end_factor, total_iters, last_epoch, verbose);\\n }\\n\\n /// <summary>\\n /// Chains list of learning rate schedulers. It takes a list of chainable learning rate schedulers and applies step() to all of them.\\n /// </summary>\\n ///<param name=\\\"schedulers\\\">List of chained schedulers.</param>\\n /// <returns>A scheduler</returns>\\n public static LRScheduler ChainedLR(IEnumerable<LRScheduler> schedulers)\\n {\\n return new impl.ChainedLR(schedulers);\\n }\\n\\n /// <summary>\\n /// Sets the learning rate using a cosine annealing schedule.\\n /// </summary>\\n /// <param name=\\\"optimizer\\\">Wrapped optimizer.</param>\\n /// <param name=\\\"T_max\\\">Maximum number of iterations.</param>\\n /// <param name=\\\"eta_min\\\">Minimum learning rate.</param>\\n /// <param name=\\\"last_epoch\\\">\\n /// The index of the last batch. This parameter is used when resuming a training job.Since `step()` should be invoked after each\\n /// batch instead of after each epoch, this number represents the total number of *batches* computed, not the total number of epochs computed.\\n /// When last_epoch = -1, the schedule is started from the beginning.\\n /// </param>\\n /// <param name=\\\"verbose\\\"> If true, prints a message to stdout for each update. Default: false.</param>\\n /// <returns>A scheduler</returns>\\n public static LRScheduler CosineAnnealingLR(Optimizer optimizer, double T_max, double eta_min = 0, int last_epoch = -1, bool verbose = false)\\n {\\n return new impl.CosineAnnealingLR(optimizer, T_max, eta_min, last_epoch, verbose);\\n }\\n\\n /// <summary>\\n /// Sets the learning rate of each parameter group according to the\\n /// 1cycle learning rate policy.The 1cycle policy anneals the learning\\n /// rate from an initial learning rate to some maximum learning rate and then\\n /// from that maximum learning rate to some minimum learning rate much lower\\n /// than the initial learning rate.\\n /// \\n /// This policy was initially described in the paper `Super-Convergence:\\n /// Very Fast Training of Neural Networks Using Large Learning Rates`_.\\n ///\\n /// The 1cycle learning rate policy changes the learning rate after every batch.\\n /// `step` should be called after a batch has been used for training.\\n ///\\n /// This scheduler is not chainable. \\n /// </summary>\\n /// <param name=\\\"optimizer\\\">Wrapped optimizer.</param>\\n /// <param name=\\\"max_lr\\\">Upper learning rate boundaries in the cycle</param>\\n /// <param name=\\\"total_steps\\\">\\n /// The total number of steps in the cycle.\\n /// Note that if a value is not provided here, then it must be inferred by providing a value for epochs and steps_per_epoch.\\n /// </param>\\n /// <param name=\\\"epochs\\\">\\n /// The number of epochs to train for. This is used along\\n /// with steps_per_epoch in order to infer the total number of steps in the cycle\\n /// if a value for total_steps is not provided.\\n /// </param>\\n /// <param name=\\\"steps_per_epoch\\\">\\n /// The number of steps per epoch to train for. This is\\n /// used along with epochs in order to infer the total number of steps in the\\n /// cycle if a value for total_steps is not provided.\\n /// </param>\\n /// <param name=\\\"pct_start\\\">The percentage of the cycle (in number of steps) spent increasing the learning rate.</param>\\n /// <param name=\\\"anneal_strategy\\\">Specifies the annealing strategy: \\\"cos\\\" for cosine annealing, \\\"linear\\\" for linear annealing.</param>\\n /// <param name=\\\"cycle_momentum\\\">If true, momentum is cycled inversely to learning rate between 'base_momentum' and 'max_momentum'.</param>\\n /// <param name=\\\"base_momentum\\\">\\n /// Lower momentum boundaries in the cycle\\n /// for each parameter group.Note that momentum is cycled inversely\\n /// to learning rate; at the peak of a cycle, momentum is\\n /// 'base_momentum' and learning rate is 'max_lr'.\\n /// </param>\\n /// <param name=\\\"max_momentum\\\">\\n /// Upper momentum boundaries in the cycle for each parameter group.\\n /// Functionally, it defines the cycle amplitude(max_momentum - base_momentum).\\n /// Note that momentum is cycled inversely to learning rate; at the start of a cycle, momentum is 'max_momentum'\\n /// and learning rate is 'base_lr'\\n /// </param>\\n /// <param name=\\\"div_factor\\\">Determines the initial learning rate via initial_lr = max_lr/div_factor</param>\\n /// <param name=\\\"final_div_factor\\\">Determines the minimum learning rate via min_lr = initial_lr/final_div_factor</param>\\n /// <param name=\\\"three_phase\\\">\\n /// If ``True``, use a third phase of the schedule to annihilate the\\n /// learning rate according to 'final_div_factor' instead of modifying the second\\n /// phase (the first two phases will be symmetrical about the step indicated by 'pct_start').\\n /// </param>\\n /// <param name=\\\"last_epoch\\\">\\n /// The index of the last batch. This parameter is used when resuming a training job.Since `step()` should be invoked after each\\n /// batch instead of after each epoch, this number represents the total number of *batches* computed, not the total number of epochs computed.\\n /// When last_epoch = -1, the schedule is started from the beginning.\\n /// </param>\\n /// <param name=\\\"verbose\\\"> If true, prints a message to stdout for each update. Default: false.</param>\\n /// <returns>A scheduler</returns>\\n /// <remarks>\\n /// Note also that the total number of steps in the cycle can be determined in one\\n /// of two ways (listed in order of precedence):\\n ///\\n /// #. A value for total_steps is explicitly provided.\\n /// #. A number of epochs (epochs) and a number of steps per epoch\\n /// (steps_per_epoch) are provided.\\n /// In this case, the number of total steps is inferred by\\n /// total_steps = epochs * steps_per_epoch\\n ///\\n /// You must either provide a value for total_steps or provide a value for both\\n /// epochs and steps_per_epoch.\\n ///\\n /// The default behaviour of this scheduler follows the fastai implementation of 1cycle, which\\n /// claims that \\\"unpublished work has shown even better results by using only two phases\\\". To\\n /// mimic the behaviour of the original paper instead, set ``three_phase= True``.\\n /// </remarks>\\n public static LRScheduler OneCycleLR(Optimizer optimizer,\\n double max_lr,\\n int total_steps = -1,\\n int epochs = -1,\\n int steps_per_epoch = -1,\\n double pct_start = 0.3,\\n impl.OneCycleLR.AnnealStrategy anneal_strategy = impl.OneCycleLR.AnnealStrategy.Cos,\\n bool cycle_momentum = true,\\n double base_momentum = 0.85,\\n double max_momentum = 0.95,\\n double div_factor = 25,\\n double final_div_factor = 1e4,\\n bool three_phase = false,\\n int last_epoch = -1, bool verbose = false)\\n {\\n return new impl.OneCycleLR(optimizer, max_lr, total_steps, epochs, steps_per_epoch, pct_start, anneal_strategy, cycle_momentum, base_momentum, max_momentum, div_factor, final_div_factor, three_phase, last_epoch, verbose);\\n }\\n\\n /// <summary>\\n /// Sets the learning rate of each parameter group according to the\\n /// 1cycle learning rate policy.The 1cycle policy anneals the learning\\n /// rate from an initial learning rate to some maximum learning rate and then\\n /// from that maximum learning rate to some minimum learning rate much lower\\n /// than the initial learning rate.\\n /// \\n /// This policy was initially described in the paper `Super-Convergence:\\n /// Very Fast Training of Neural Networks Using Large Learning Rates`_.\\n ///\\n /// The 1cycle learning rate policy changes the learning rate after every batch.\\n /// `step` should be called after a batch has been used for training.\\n ///\\n /// This scheduler is not chainable. \\n /// </summary>\\n /// <param name=\\\"optimizer\\\">Wrapped optimizer.</param>\\n /// <param name=\\\"max_lr\\\">Upper learning rate boundaries in the cycle</param>\\n /// <param name=\\\"total_steps\\\">\\n /// The total number of steps in the cycle.\\n /// Note that if a value is not provided here, then it must be inferred by providing a value for epochs and steps_per_epoch.\\n /// </param>\\n /// <param name=\\\"epochs\\\">\\n /// The number of epochs to train for. This is used along\\n /// with steps_per_epoch in order to infer the total number of steps in the cycle\\n /// if a value for total_steps is not provided.\\n /// </param>\\n /// <param name=\\\"steps_per_epoch\\\">\\n /// The number of steps per epoch to train for. This is\\n /// used along with epochs in order to infer the total number of steps in the\\n /// cycle if a value for total_steps is not provided.\\n /// </param>\\n /// <param name=\\\"pct_start\\\">The percentage of the cycle (in number of steps) spent increasing the learning rate.</param>\\n /// <param name=\\\"anneal_strategy\\\">Specifies the annealing strategy: \\\"cos\\\" for cosine annealing, \\\"linear\\\" for linear annealing.</param>\\n /// <param name=\\\"cycle_momentum\\\">If true, momentum is cycled inversely to learning rate between 'base_momentum' and 'max_momentum'.</param>\\n /// <param name=\\\"base_momentum\\\">\\n /// Lower momentum boundaries in the cycle\\n /// for each parameter group.Note that momentum is cycled inversely\\n /// to learning rate; at the peak of a cycle, momentum is\\n /// 'base_momentum' and learning rate is 'max_lr'.\\n /// </param>\\n /// <param name=\\\"max_momentum\\\">\\n /// Upper momentum boundaries in the cycle for each parameter group.\\n /// Functionally, it defines the cycle amplitude(max_momentum - base_momentum).\\n /// Note that momentum is cycled inversely to learning rate; at the start of a cycle, momentum is 'max_momentum'\\n /// and learning rate is 'base_lr'\\n /// </param>\\n /// <param name=\\\"div_factor\\\">Determines the initial learning rate via initial_lr = max_lr/div_factor</param>\\n /// <param name=\\\"final_div_factor\\\">Determines the minimum learning rate via min_lr = initial_lr/final_div_factor</param>\\n /// <param name=\\\"three_phase\\\">\\n /// If ``True``, use a third phase of the schedule to annihilate the\\n /// learning rate according to 'final_div_factor' instead of modifying the second\\n /// phase (the first two phases will be symmetrical about the step indicated by 'pct_start').\\n /// </param>\\n /// <param name=\\\"last_epoch\\\">\\n /// The index of the last batch. This parameter is used when resuming a training job.Since `step()` should be invoked after each\\n /// batch instead of after each epoch, this number represents the total number of *batches* computed, not the total number of epochs computed.\\n /// When last_epoch = -1, the schedule is started from the beginning.\\n /// </param>\\n /// <param name=\\\"verbose\\\"> If true, prints a message to stdout for each update. Default: false.</param>\\n /// <returns>A scheduler</returns>\\n /// <remarks>\\n /// Note also that the total number of steps in the cycle can be determined in one\\n /// of two ways (listed in order of precedence):\\n ///\\n /// #. A value for total_steps is explicitly provided.\\n /// #. A number of epochs (epochs) and a number of steps per epoch\\n /// (steps_per_epoch) are provided.\\n /// In this case, the number of total steps is inferred by\\n /// total_steps = epochs * steps_per_epoch\\n ///\\n /// You must either provide a value for total_steps or provide a value for both\\n /// epochs and steps_per_epoch.\\n ///\\n /// The default behaviour of this scheduler follows the fastai implementation of 1cycle, which\\n /// claims that \\\"unpublished work has shown even better results by using only two phases\\\". To\\n /// mimic the behaviour of the original paper instead, set ``three_phase= True``.\\n /// </remarks>\\n public static LRScheduler OneCycleLR(Optimizer optimizer,\\n IEnumerable<double> max_lr,\\n int total_steps = -1,\\n int epochs = -1,\\n int steps_per_epoch = -1,\\n double pct_start = 0.3,\\n impl.OneCycleLR.AnnealStrategy anneal_strategy = impl.OneCycleLR.AnnealStrategy.Cos,\\n bool cycle_momentum = true,\\n IEnumerable<double> base_momentum = null,\\n IEnumerable<double> max_momentum = null,\\n double div_factor = 25,\\n double final_div_factor = 1e4,\\n bool three_phase = false,\\n int last_epoch = -1, bool verbose = false)\\n {\\n return new impl.OneCycleLR(optimizer, max_lr, total_steps, epochs, steps_per_epoch, pct_start, anneal_strategy, cycle_momentum, base_momentum, max_momentum, div_factor, final_div_factor, three_phase, last_epoch, verbose);\\n }\\n\\n /// <summary>\\n /// Sets the learning rate of each parameter group according to\\n /// cyclical learning rate policy(CLR). The policy cycles the learning\\n /// rate between two boundaries with a constant frequency, as detailed in\\n /// the paper `Cyclical Learning Rates for Training Neural Networks`_.\\n /// The distance between the two boundaries can be scaled on a per-iteration\\n /// or per-cycle basis.\\n ///\\n /// Cyclical learning rate policy changes the learning rate after every batch.\\n /// `step` should be called after a batch has been used for training.\\n ///\\n /// This class has three built-in policies, as put forth in the paper:\\n /// * \\\"triangular\\\": A basic triangular cycle without amplitude scaling.\\n /// * \\\"triangular2\\\": A basic triangular cycle that scales initial amplitude by half each cycle.\\n /// * \\\"exp_range\\\": A cycle that scales initial amplitude by gamma^(cycle iterations).\\n /// }`\\n /// at each cycle iteration.\\n /// /// </summary>\\n /// <param name=\\\"optimizer\\\">Wrapped optimizer.</param>\\n /// <param name=\\\"base_lr\\\">Initial learning rate which is the lower boundary in the cycle</param>\\n /// <param name=\\\"max_lr\\\">\\n /// Upper learning rate boundaries in the cycle for each parameter group.\\n /// Functionally, it defines the cycle amplitude(max_lr - base_lr).\\n /// The lr at any cycle is the sum of base_lr and some scaling of the amplitude; therefore \\n /// max_lr may not actually be reached depending on the scaling function.\\n /// </param>\\n /// <param name=\\\"step_size_up\\\">Number of training iterations in the increasing half of a cycle.</param>\\n /// <param name=\\\"step_size_down\\\">\\n /// Number of training iterations in the decreasing half of a cycle.\\n /// If step_size_down is -1, it is set to step_size_up.\\n /// </param>\\n /// <param name=\\\"mode\\\">Values correspond to policies detailed above. If scale_fn is non-null, this argument is ignored.</param>\\n /// <param name=\\\"gamma\\\">Constant in 'exp_range' scaling function</param>\\n /// <param name=\\\"scale_fn\\\">Custom scaling policy defined by a single argument lambda function. If specified, then 'mode' is ignored.</param>\\n /// <param name=\\\"scale_mode\\\">Defines whether scale_fn is evaluated on cycle number or cycle iterations(training iterations since start of cycle)</param>\\n /// <param name=\\\"cycle_momentum\\\">If true, momentum is cycled inversely to learning rate between 'base_momentum' and 'max_momentum'.</param>\\n /// <param name=\\\"base_momentum\\\">Lower momentum boundaries in the cycle. Note that momentum is cycled inversely to learning rate</param>\\n /// <param name=\\\"max_momentum\\\">\\n /// Upper momentum boundaries in the cycle.\\n /// Functionally, it defines the cycle amplitude(max_momentum - base_momentum).\\n /// The momentum at any cycle is the difference of max_momentum and some scaling of the amplitude; therefore\\n /// base_momentum may not actually be reached depending on the scaling function.\\n /// </param>\\n /// <param name=\\\"last_epoch\\\">\\n /// The index of the last batch. This parameter is used when resuming a training job.Since `step()` should be invoked after each\\n /// batch instead of after each epoch, this number represents the total number of *batches* computed, not the total number of epochs computed.\\n /// When last_epoch = -1, the schedule is started from the beginning.\\n /// </param>\\n /// <param name=\\\"verbose\\\"> If true, prints a message to stdout for each update. Default: false.</param>\\n /// <returns>A scheduler</returns>\\n public static LRScheduler CyclicLR(Optimizer optimizer,\\n double base_lr,\\n double max_lr,\\n int step_size_up = 2000,\\n int step_size_down = -1,\\n impl.CyclicLR.Mode mode = impl.CyclicLR.Mode.Triangular,\\n double gamma = 1.0,\\n Func<double, double> scale_fn = null,\\n impl.CyclicLR.ScaleMode scale_mode = impl.CyclicLR.ScaleMode.Cycle,\\n bool cycle_momentum = true,\\n double base_momentum = 0.8,\\n double max_momentum = 0.9,\\n int last_epoch = -1,\\n bool verbose = false)\\n {\\n return new impl.CyclicLR(optimizer, base_lr, max_lr, step_size_up, step_size_down, mode, gamma, scale_fn, scale_mode, cycle_momentum, base_momentum, max_momentum, last_epoch, verbose);\\n }\\n\\n /// <summary>\\n /// Sets the learning rate of each parameter group according to\\n /// cyclical learning rate policy(CLR). The policy cycles the learning\\n /// rate between two boundaries with a constant frequency, as detailed in\\n /// the paper `Cyclical Learning Rates for Training Neural Networks`_.\\n /// The distance between the two boundaries can be scaled on a per-iteration\\n /// or per-cycle basis.\\n ///\\n /// Cyclical learning rate policy changes the learning rate after every batch.\\n /// `step` should be called after a batch has been used for training.\\n ///\\n /// This class has three built-in policies, as put forth in the paper:\\n /// * \\\"triangular\\\": A basic triangular cycle without amplitude scaling.\\n /// * \\\"triangular2\\\": A basic triangular cycle that scales initial amplitude by half each cycle.\\n /// * \\\"exp_range\\\": A cycle that scales initial amplitude by gamma^(cycle iterations).\\n /// }`\\n /// at each cycle iteration.\\n /// /// </summary>\\n /// <param name=\\\"optimizer\\\">Wrapped optimizer.</param>\\n /// <param name=\\\"base_lr\\\">Initial learning rate which is the lower boundary in the cycle</param>\\n /// <param name=\\\"max_lr\\\">\\n /// Upper learning rate boundaries in the cycle for each parameter group.\\n /// Functionally, it defines the cycle amplitude(max_lr - base_lr).\\n /// The lr at any cycle is the sum of base_lr and some scaling of the amplitude; therefore \\n /// max_lr may not actually be reached depending on the scaling function.\\n /// </param>\\n /// <param name=\\\"step_size_up\\\">Number of training iterations in the increasing half of a cycle.</param>\\n /// <param name=\\\"step_size_down\\\">\\n /// Number of training iterations in the decreasing half of a cycle.\\n /// If step_size_down is -1, it is set to step_size_up.\\n /// </param>\\n /// <param name=\\\"mode\\\">Values correspond to policies detailed above. If scale_fn is non-null, this argument is ignored.</param>\\n /// <param name=\\\"gamma\\\">Constant in 'exp_range' scaling function</param>\\n /// <param name=\\\"scale_fn\\\">Custom scaling policy defined by a single argument lambda function. If specified, then 'mode' is ignored.</param>\\n /// <param name=\\\"scale_mode\\\">Defines whether scale_fn is evaluated on cycle number or cycle iterations(training iterations since start of cycle)</param>\\n /// <param name=\\\"cycle_momentum\\\">If true, momentum is cycled inversely to learning rate between 'base_momentum' and 'max_momentum'.</param>\\n /// <param name=\\\"base_momentum\\\">Lower momentum boundaries in the cycle. Note that momentum is cycled inversely to learning rate</param>\\n /// <param name=\\\"max_momentum\\\">\\n /// Upper momentum boundaries in the cycle.\\n /// Functionally, it defines the cycle amplitude(max_momentum - base_momentum).\\n /// The momentum at any cycle is the difference of max_momentum and some scaling of the amplitude; therefore\\n /// base_momentum may not actually be reached depending on the scaling function.\\n /// </param>\\n /// <param name=\\\"last_epoch\\\">\\n /// The index of the last batch. This parameter is used when resuming a training job.Since `step()` should be invoked after each\\n /// batch instead of after each epoch, this number represents the total number of *batches* computed, not the total number of epochs computed.\\n /// When last_epoch = -1, the schedule is started from the beginning.\\n /// </param>\\n /// <param name=\\\"verbose\\\"> If true, prints a message to stdout for each update. Default: false.</param>\\n /// <returns>A scheduler</returns>\\n public static LRScheduler CyclicLR(Optimizer optimizer,\\n IEnumerable<double> base_lr,\\n IEnumerable<double> max_lr,\\n int step_size_up = 2000,\\n int step_size_down = -1,\\n impl.CyclicLR.Mode mode = impl.CyclicLR.Mode.Triangular,\\n double gamma = 1.0,\\n Func<double, double> scale_fn = null,\\n impl.CyclicLR.ScaleMode scale_mode = impl.CyclicLR.ScaleMode.Cycle,\\n bool cycle_momentum = true,\\n IEnumerable<double> base_momentum = null,\\n IEnumerable<double> max_momentum = null,\\n int last_epoch = -1,\\n bool verbose = false)\\n {\\n return new impl.CyclicLR(optimizer, base_lr, max_lr, step_size_up, step_size_down, mode, gamma, scale_fn, scale_mode, cycle_momentum, base_momentum, max_momentum, last_epoch, verbose);\\n }\\n\\n /// <summary>\\n /// Reduce learning rate when a metric has stopped improving.\\n /// Models often benefit from reducing the learning rate by a factor of 2-10 once learning stagnates.\\n /// This scheduler reads a metrics quantity and if no improvement is seen for a ‘patience’ number of epochs, the learning rate is reduced.\\n /// </summary>\\n /// <param name=\\\"optimizer\\\">Wrapped optimizer.</param>\\n /// <param name=\\\"mode\\\">\\n /// One of min, max. In min mode, lr will be reduced when the quantity monitored has stopped decreasing;\\n /// in max mode it will be reduced when the quantity monitored has stopped increasing. Default: ‘min’\\n /// </param>\\n /// <param name=\\\"factor\\\">Factor by which the learning rate will be reduced. new_lr = lr * factor.</param>\\n /// <param name=\\\"patience\\\">\\n /// Number of epochs with no improvement after which learning rate will be reduced. For example, if patience = 2,\\n /// then we will ignore the first 2 epochs with no improvement, and will only decrease the LR after the 3rd epoch if\\n /// the loss still hasn’t improved then. Default: 10.</param>\\n /// <param name=\\\"threshold\\\">Threshold for measuring the new optimum, to only focus on significant changes. </param>\\n /// <param name=\\\"threshold_mode\\\">\\n /// One of rel, abs. In rel mode, dynamic_threshold = best * ( 1 + threshold ) in ‘max’ mode or best * ( 1 - threshold ) in min mode. In abs mode, dynamic_threshold = best + threshold in max mode or best - threshold in min mode. Default: ‘rel’.\\n /// </param>\\n /// <param name=\\\"cooldown\\\">Number of epochs to wait before resuming normal operation after lr has been reduced.</param>\\n /// <param name=\\\"min_lr\\\">A scalar or a list of scalars. A lower bound on the learning rate of all param groups or each group respectively. Default: 0.</param>\\n /// <param name=\\\"eps\\\">Minimal decay applied to lr. If the difference between new and old lr is smaller than eps, the update is ignored. Default: 1e-8.</param>\\n /// <param name=\\\"verbose\\\">Indicates whether to print a message to stdout for each update</param>\\n public static LRScheduler ReduceLROnPlateau(Optimizer optimizer, string mode = \\\"min\\\", double factor = 0.1, int patience = 10, double threshold = 1e-4, string threshold_mode = \\\"rel\\\", int cooldown = 0, IList<double> min_lr = null, double eps = 1e-8, bool verbose = false)\\n {\\n return new impl.ReduceLROnPlateau(optimizer, mode, factor, patience, threshold, threshold_mode, cooldown, min_lr, eps, verbose);\\n }\\n }\\n }\\n }\\n}\\n\"\n },\n {\n \"alpha_fraction\": 0.7059859037399292,\n \"alphanum_fraction\": 0.7059859037399292,\n \"avg_line_length\": 39.64285659790039,\n \"blob_id\": \"1755d0a70f8e09c480d5933b1fdac547058547db\",\n \"content_id\": \"5e2c09e91c5415828c8995690ac86947ab7b5fd7\",\n \"detected_licenses\": [\n \"MIT\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"C#\",\n \"length_bytes\": 570,\n \"license_type\": \"permissive\",\n \"max_line_length\": 131,\n \"num_lines\": 14,\n \"path\": \"/src/TorchSharp/Tensor/torch.Serialization.cs\",\n \"repo_name\": \"NiklasGustafsson/TorchSharp\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"// Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information.\\n#nullable enable\\nnamespace TorchSharp\\n{\\n // https://pytorch.org/docs/stable/torch#serialization\\n public static partial class torch\\n {\\n // https://pytorch.org/docs/stable/generated/torch.save\\n public static void save(Tensor t, string location) => t.save(location);\\n\\n // https://pytorch.org/docs/stable/generated/torch.load\\n public static Tensor load(string location) => Tensor.load(location);\\n }\\n}\"\n },\n {\n \"alpha_fraction\": 0.6069402098655701,\n \"alphanum_fraction\": 0.6089553833007812,\n \"avg_line_length\": 47.78291320800781,\n \"blob_id\": \"8e4cc20afc504632ff9d714d4b4e5cde5a35d44b\",\n \"content_id\": \"0fccbd8ce82bb250c0ad0424b5030b0fff70e45d\",\n \"detected_licenses\": [\n \"MIT\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"C#\",\n \"length_bytes\": 83377,\n \"license_type\": \"permissive\",\n \"max_line_length\": 197,\n \"num_lines\": 1709,\n \"path\": \"/src/TorchSharp/Tensor/torch.PointwiseOps.cs\",\n \"repo_name\": \"NiklasGustafsson/TorchSharp\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"// Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information.\\n#nullable enable\\nusing System;\\nusing System.Collections.Generic;\\nusing System.Diagnostics.Contracts;\\nusing ICSharpCode.SharpZipLib.BZip2;\\n\\nnamespace TorchSharp\\n{\\n public static partial class torch\\n {\\n // https://pytorch.org/docs/stable/generated/torch.abs\\n /// <summary>\\n /// Compute the absolute value of each element in the tensor\\n /// </summary>\\n /// <param name=\\\"input\\\">The input tensor.</param>\\n [Pure]public static Tensor abs(Tensor input) => input.abs();\\n\\n // https://pytorch.org/docs/stable/generated/torch.abs\\n /// <summary>\\n /// Compute the absolute value of each element in the tensor, in-place\\n /// </summary>\\n /// <param name=\\\"input\\\">The input tensor.</param>\\n public static Tensor abs_(Tensor input) => input.abs_();\\n\\n // https://pytorch.org/docs/stable/generated/torch.absolute\\n /// <summary>\\n /// Compute the absolute value of each element in the tensor\\n /// </summary>\\n /// <param name=\\\"input\\\">The input tensor.</param>\\n [Pure]public static Tensor absolute(Tensor input) => input.absolute();\\n\\n // https://pytorch.org/docs/stable/generated/torch.absolute\\n /// <summary>\\n /// Compute the absolute value of each element in the tensor, in-place\\n /// </summary>\\n /// <param name=\\\"input\\\">The input tensor.</param>\\n public static Tensor absolute_(Tensor input) => input.absolute_();\\n\\n // https://pytorch.org/docs/stable/generated/torch.acos\\n /// <summary>\\n /// Computes the arccosine of the elements of input.\\n /// </summary>\\n /// <returns></returns>\\n [Pure]public static Tensor acos(Tensor input) => input.acos();\\n\\n // https://pytorch.org/docs/stable/generated/torch.acos\\n /// <summary>\\n /// Computes the arccosine of the elements of input.\\n /// </summary>\\n /// <returns></returns>\\n public static Tensor acos_(Tensor input) => input.acos_();\\n\\n // https://pytorch.org/docs/stable/generated/torch.arccos\\n /// <summary>\\n /// Computes the arccosine of the elements of input.\\n /// </summary>\\n /// <returns></returns>\\n [Pure]public static Tensor arccos(Tensor input) => input.arccos();\\n\\n // https://pytorch.org/docs/stable/generated/torch.arccos\\n /// <summary>\\n /// Computes the arccosine of the elements of input.\\n /// </summary>\\n /// <returns></returns>\\n public static Tensor arccos_(Tensor input) => input.arccos_();\\n\\n // https://pytorch.org/docs/stable/generated/torch.acosh\\n /// <summary>\\n /// Computes the hyperbolic arccosine of the elements of input.\\n /// </summary>\\n /// <returns></returns>\\n [Pure]public static Tensor acosh(Tensor input) => input.acosh();\\n\\n // https://pytorch.org/docs/stable/generated/torch.acosh\\n /// <summary>\\n /// Computes the hyperbolic arccosine of the elements of input.\\n /// </summary>\\n /// <returns></returns>\\n public static Tensor acosh_(Tensor input) => input.acosh_();\\n\\n // https://pytorch.org/docs/stable/generated/torch.arccosh\\n /// <summary>\\n /// Computes the hyperbolic arccosine of the elements of input.\\n /// </summary>\\n /// <returns></returns>\\n [Pure]public static Tensor arccosh(Tensor input) => input.arccosh();\\n\\n // https://pytorch.org/docs/stable/generated/torch.arccosh\\n /// <summary>\\n /// Computes the hyperbolic arccosine of the elements of input.\\n /// </summary>\\n /// <returns></returns>\\n public static Tensor arccosh_(Tensor input) => input.arccosh_();\\n\\n // https://pytorch.org/docs/stable/generated/torch.add\\n /// <summary>\\n /// Add two tensors, element-wise\\n /// </summary>\\n /// <param name=\\\"left\\\">The left-hand operand.</param>\\n /// <param name=\\\"right\\\">The right-hand operand.</param>\\n /// <returns></returns>\\n [Pure]public static Tensor add(Tensor left, Tensor right) => left.add(right);\\n\\n // https://pytorch.org/docs/stable/generated/torch.add\\n /// <summary>\\n /// Add a scalar value to each element in the target tensor.\\n /// </summary>\\n /// <param name=\\\"left\\\">The left-hand operand.</param>\\n /// <param name=\\\"right\\\">The right-hand operand.</param>\\n [Pure]public static Tensor add(Tensor left, Scalar right) => left.add(right);\\n\\n // https://pytorch.org/docs/stable/generated/torch.add\\n /// <summary>\\n /// Add two tensors, element-wise, scaling the second operator by 'alpha'\\n /// </summary>\\n /// <param name=\\\"left\\\">The left-hand operand.</param>\\n /// <param name=\\\"right\\\">The right-hand operand.</param>\\n /// <param name=\\\"alpha\\\">RHS scale factor.</param>\\n [Pure]public static Tensor add(Tensor left, Tensor right, Scalar alpha) => left.add(right, alpha);\\n\\n // https://pytorch.org/docs/stable/generated/torch.add\\n /// <summary>\\n /// Add a scalar value to each element in the target tensor, scaled by 'alpha'\\n /// </summary>\\n /// <param name=\\\"left\\\">The left-hand operand.</param>\\n /// <param name=\\\"right\\\">The right-hand operand.</param>\\n /// <param name=\\\"alpha\\\">RHS scale factor.</param>\\n [Pure]public static Tensor add(Tensor left, Scalar right, Scalar alpha) => left.add(right, alpha);\\n\\n // https://pytorch.org/docs/stable/generated/torch.add\\n /// <summary>\\n /// Add two tensors, element-wise, in place\\n /// </summary>\\n /// <param name=\\\"left\\\">The left-hand operand.</param>\\n /// <param name=\\\"right\\\">The right-hand operand.</param>\\n public static Tensor add_(Tensor left, Tensor right) => left.add_(right);\\n\\n // https://pytorch.org/docs/stable/generated/torch.add\\n /// <summary>\\n /// Add a scalar value to each element in the target tensor, in place.\\n /// </summary>\\n /// <param name=\\\"left\\\">The left-hand operand.</param>\\n /// <param name=\\\"right\\\">The right-hand operand.</param>\\n public static Tensor add_(Tensor left, Scalar right) => left.add_(right);\\n\\n // https://pytorch.org/docs/stable/generated/torch.add\\n /// <summary>\\n /// Add two tensors, element-wise, scaling the second operator by 'alpha', in place\\n /// </summary>\\n /// <param name=\\\"left\\\">The left-hand operand.</param>\\n /// <param name=\\\"right\\\">The right-hand operand.</param>\\n /// <param name=\\\"alpha\\\">RHS scale factor.</param>\\n public static Tensor add_(Tensor left, Tensor right, Scalar alpha) => left.add_(right, alpha);\\n\\n // https://pytorch.org/docs/stable/generated/torch.add\\n /// <summary>\\n /// Add a scalar value to each element in the target tensor, scaled by 'alpha', in place\\n /// </summary>\\n /// <param name=\\\"left\\\">The left-hand operand.</param>\\n /// <param name=\\\"right\\\">The right-hand operand.</param>\\n /// <param name=\\\"alpha\\\">RHS scale factor.</param>\\n public static Tensor add_(Tensor left, Scalar right, Scalar alpha) => left.add_(right, alpha);\\n\\n // https://pytorch.org/docs/stable/generated/torch.addcdiv\\n /// <summary>\\n /// Performs the element-wise division of tensor1 by tensor2, multiply the result by the scalar value and add it to input.\\n /// </summary>\\n /// <param name=\\\"input\\\">The input tensor.</param>\\n /// <param name=\\\"tensor1\\\">First tensor</param>\\n /// <param name=\\\"tensor2\\\">Second tensor</param>\\n /// <param name=\\\"value\\\">Scale factor</param>\\n [Pure]public static Tensor addcdiv(Tensor input, Tensor tensor1, Tensor tensor2, Scalar value)\\n => input.addcdiv(tensor1, tensor2, value);\\n\\n // https://pytorch.org/docs/stable/generated/torch.addcdiv\\n /// <summary>\\n /// Performs the element-wise division of tensor1 by tensor2, multiply the result by the scalar value and add it to input.\\n /// In-place version of <see cref=\\\"addcdiv\\\"/>.\\n /// </summary>\\n /// <param name=\\\"input\\\">The input tensor.</param>\\n /// <param name=\\\"tensor1\\\">First tensor</param>\\n /// <param name=\\\"tensor2\\\">Second tensor</param>\\n /// <param name=\\\"value\\\">Scale factor</param>\\n public static Tensor addcdiv_(Tensor input, Tensor tensor1, Tensor tensor2, Scalar value)\\n => input.addcdiv_(tensor1, tensor2, value);\\n\\n // https://pytorch.org/docs/stable/generated/torch.addcmul\\n /// <summary>\\n /// Performs the element-wise multiplication of tensor1 by tensor2, multiply the result by the scalar value and add it to input.\\n /// </summary>\\n /// <param name=\\\"input\\\">The input tensor.</param>\\n /// <param name=\\\"tensor1\\\">First tensor</param>\\n /// <param name=\\\"tensor2\\\">Second tensor</param>\\n /// <param name=\\\"value\\\">Scale factor</param>\\n [Pure]public static Tensor addcmul(Tensor input, Tensor tensor1, Tensor tensor2, Scalar value)\\n => input.addcmul(tensor1, tensor2, value);\\n\\n // https://pytorch.org/docs/stable/generated/torch.addcmul\\n /// <summary>\\n /// Performs the element-wise divismultiplicationion of tensor1 by tensor2, multiply the result by the scalar value and add it to input.\\n /// In-place version of <see cref=\\\"addcmul\\\"/>.\\n /// </summary>\\n /// <param name=\\\"input\\\">The input tensor.</param>\\n /// <param name=\\\"tensor1\\\">First tensor</param>\\n /// <param name=\\\"tensor2\\\">Second tensor</param>\\n /// <param name=\\\"value\\\">Scale factor</param>\\n public static Tensor addcmul_(Tensor input, Tensor tensor1, Tensor tensor2, Scalar value)\\n => input.addcmul_(tensor1, tensor2, value);\\n\\n // https://pytorch.org/docs/stable/generated/torch.angle\\n /// <summary>\\n /// Computes the element-wise angle (in radians) of the given input tensor.\\n /// </summary>\\n /// <returns></returns>\\n /// <remarks>\\n /// Starting in Torch 1.8, angle returns pi for negative real numbers, zero for non-negative real numbers, and propagates NaNs.\\n /// Previously the function would return zero for all real numbers and not propagate floating-point NaNs.\\n /// </remarks>\\n [Pure]public static Tensor angle(Tensor input) => input.angle();\\n\\n // https://pytorch.org/docs/stable/generated/torch.asin\\n /// <summary>\\n /// Computes the arcsine of the elements of input.\\n /// </summary>\\n /// <returns></returns>\\n [Pure]public static Tensor asin(Tensor input) => input.asin();\\n\\n // https://pytorch.org/docs/stable/generated/torch.asin\\n /// <summary>\\n /// Computes the arcsine of the elements of input, in place.\\n /// </summary>\\n /// <returns></returns>\\n public static Tensor asin_(Tensor input) => input.asin_();\\n\\n // https://pytorch.org/docs/stable/generated/torch.arcsin\\n /// <summary>\\n /// Computes the arcsine of the elements of input.\\n /// </summary>\\n /// <returns></returns>\\n [Pure]public static Tensor arcsin(Tensor input) => input.arcsin();\\n\\n // https://pytorch.org/docs/stable/generated/torch.arcsin\\n /// <summary>\\n /// Computes the arcsine of the elements of input.\\n /// </summary>\\n /// <returns></returns>\\n public static Tensor arcsin_(Tensor input) => input.arcsin_();\\n\\n // https://pytorch.org/docs/stable/generated/torch.asinh\\n /// <summary>\\n /// Computes the hyperbolic arcsine of the elements of input.\\n /// </summary>\\n /// <returns></returns>\\n [Pure]public static Tensor asinh(Tensor input) => input.asinh();\\n\\n // https://pytorch.org/docs/stable/generated/torch.asinh\\n /// <summary>\\n /// Computes the hyperbolic arcsine of the elements of input.\\n /// </summary>\\n /// <returns></returns>\\n public static Tensor asinh_(Tensor input) => input.asinh_();\\n\\n // https://pytorch.org/docs/stable/generated/torch.arcsinh\\n /// <summary>\\n /// Computes the hyperbolic arcsine of the elements of input.\\n /// </summary>\\n /// <returns></returns>\\n [Pure]public static Tensor arcsinh(Tensor input) => input.arcsinh();\\n\\n // https://pytorch.org/docs/stable/generated/torch.arcsinh\\n /// <summary>\\n /// Computes the hyperbolic arcsine of the elements of input.\\n /// </summary>\\n /// <returns></returns>\\n public static Tensor arcsinh_(Tensor input) => input.arcsinh_();\\n\\n // https://pytorch.org/docs/stable/generated/torch.atan\\n /// <summary>\\n /// Computes the arctangent of the elements of input.\\n /// </summary>\\n /// <returns></returns>\\n [Pure]public static Tensor atan(Tensor input) => input.atan();\\n\\n // https://pytorch.org/docs/stable/generated/torch.atan\\n /// <summary>\\n /// Computes the arctangent of the elements of input.\\n /// </summary>\\n /// <returns></returns>\\n public static Tensor atan_(Tensor input) => input.atan_();\\n\\n // https://pytorch.org/docs/stable/generated/torch.arctan\\n /// <summary>\\n /// Computes the arctangent of the elements of input.\\n /// </summary>\\n /// <returns></returns>\\n [Pure]public static Tensor arctan(Tensor input) => input.arctan();\\n\\n // https://pytorch.org/docs/stable/generated/torch.arctan\\n /// <summary>\\n /// Computes the arctangent of the elements of input.\\n /// </summary>\\n /// <returns></returns>\\n public static Tensor arctan_(Tensor input) => input.arctan_();\\n\\n // https://pytorch.org/docs/stable/generated/torch.atanh\\n /// <summary>\\n /// Computes the hyperbolic arctangent of the elements of input.\\n /// </summary>\\n /// <returns></returns>\\n [Pure]public static Tensor atanh(Tensor input) => input.atanh();\\n\\n // https://pytorch.org/docs/stable/generated/torch.atanh\\n /// <summary>\\n /// Computes the hyperbolic arctangent of the elements of input.\\n /// </summary>\\n /// <returns></returns>\\n public static Tensor atanh_(Tensor input) => input.atanh_();\\n\\n // https://pytorch.org/docs/stable/generated/torch.arctanh\\n /// <summary>\\n /// Computes the hyperbolic arctangent of the elements of input.\\n /// </summary>\\n /// <returns></returns>\\n [Pure]public static Tensor arctanh(Tensor input) => input.arctanh();\\n\\n /// <summary>\\n /// Computes the hyperbolic arctangent of the elements of input.\\n /// </summary>\\n /// <returns></returns>\\n public static Tensor arctanh_(Tensor input) => input.arctanh_();\\n\\n // https://pytorch.org/docs/stable/generated/torch.atan2\\n /// <summary>\\n /// Element-wise arctangent of input / other with consideration of the quadrant.\\n /// </summary>\\n /// <returns></returns>\\n [Pure]public static Tensor atan2(Tensor input, Tensor other) => input.atan2(other);\\n\\n /// <summary>\\n /// Element-wise arctangent of input / other with consideration of the quadrant.\\n /// </summary>\\n /// <returns></returns>\\n public static Tensor atan2_(Tensor input, Tensor other) => input.atan2_(other);\\n\\n // https://pytorch.org/docs/stable/generated/torch.arctan2\\n /// <summary>\\n /// Element-wise arctangent of input / other with consideration of the quadrant.\\n /// </summary>\\n /// <returns></returns>\\n [Pure]public static Tensor arctan2(Tensor input, Tensor other) => input.arctan2(other);\\n\\n // https://pytorch.org/docs/stable/generated/torch.arctan2\\n /// <summary>\\n /// Element-wise arctangent of input / other with consideration of the quadrant.\\n /// </summary>\\n /// <returns></returns>\\n public static Tensor arctan2_(Tensor input, Tensor other) => input.arctan2_(other);\\n\\n // https://pytorch.org/docs/stable/generated/torch.arctan\\n /// <summary>\\n /// Element-wise arctangent of input / other with consideration of the quadrant.\\n /// </summary>\\n /// <returns></returns>\\n [Pure]public static Tensor arctan(Tensor input, Tensor other) => input.arctan(other);\\n\\n // https://pytorch.org/docs/stable/generated/torch.arctan\\n /// <summary>\\n /// Element-wise arctangent of input / other with consideration of the quadrant.\\n /// </summary>\\n /// <returns></returns>\\n public static Tensor arctan_(Tensor input, Tensor other) => input.arctan_(other);\\n\\n // https://pytorch.org/docs/stable/generated/torch.bitwise_not\\n /// <summary>\\n /// Element-wise bitwise NOT\\n /// </summary>\\n /// <param name=\\\"input\\\">The input tensor.</param>\\n [Pure]public static Tensor bitwise_not(Tensor input) => input.bitwise_not();\\n\\n // https://pytorch.org/docs/stable/generated/torch.bitwise_not\\n /// <summary>\\n /// Element-wise bitwise NOT, in place.\\n /// </summary>\\n /// <param name=\\\"input\\\">The input tensor.</param>\\n public static Tensor bitwise_not_(Tensor input) => input.bitwise_not_();\\n\\n // https://pytorch.org/docs/stable/generated/torch.bitwise_and\\n /// <summary>\\n /// Element-wise bitwise AND\\n /// </summary>\\n /// <param name=\\\"left\\\">Left-hand operand.</param>\\n /// <param name=\\\"right\\\">Right-hand operand.</param>\\n [Pure]public static Tensor bitwise_and(Tensor left, Tensor right) => left.bitwise_and(right);\\n\\n // https://pytorch.org/docs/stable/generated/torch.bitwise_and\\n /// <summary>\\n /// Element-wise bitwise AND, in place.\\n /// </summary>\\n /// <param name=\\\"left\\\">Left-hand operand.</param>\\n /// <param name=\\\"right\\\">Right-hand operand.</param>\\n public static Tensor bitwise_and_(Tensor left, Tensor right) => left.bitwise_and_(right);\\n\\n // https://pytorch.org/docs/stable/generated/torch.bitwise_or\\n /// <summary>\\n /// Element-wise bitwise OR\\n /// </summary>\\n /// <param name=\\\"left\\\">Left-hand operand.</param>\\n /// <param name=\\\"right\\\">Right-hand operand.</param>\\n [Pure]public static Tensor bitwise_or(Tensor left, Tensor right) => left.bitwise_or(right);\\n\\n // https://pytorch.org/docs/stable/generated/torch.bitwise_or\\n /// <summary>\\n /// Element-wise bitwiseXOR, in place.\\n /// </summary>\\n /// <param name=\\\"left\\\">Left-hand operand.</param>\\n /// <param name=\\\"right\\\">Right-hand operand.</param>\\n public static Tensor bitwise_or_(Tensor left, Tensor right) => left.bitwise_or_(right);\\n\\n // https://pytorch.org/docs/stable/generated/torch.bitwise_xor\\n /// <summary>\\n /// Element-wise bitwise XOR\\n /// </summary>\\n /// <param name=\\\"left\\\">Left-hand operand.</param>\\n /// <param name=\\\"right\\\">Right-hand operand.</param>\\n [Pure]public static Tensor bitwise_xor(Tensor left, Tensor right) => left.bitwise_xor(right);\\n\\n // https://pytorch.org/docs/stable/generated/torch.bitwise_xor\\n /// <summary>\\n /// Element-wise bitwise XOR, in place.\\n /// </summary>\\n /// <param name=\\\"left\\\">Left-hand operand.</param>\\n /// <param name=\\\"right\\\">Right-hand operand.</param>\\n public static Tensor bitwise_xor_(Tensor left, Tensor right) => left.bitwise_xor_(right);\\n\\n // https://pytorch.org/docs/stable/generated/torch.bitwise_left_shift\\n /// <summary>\\n /// Element-wise bitwise left shift\\n /// </summary>\\n /// <param name=\\\"left\\\">Left-hand operand.</param>\\n /// <param name=\\\"right\\\">Right-hand operand.</param>\\n public static Tensor bitwise_left_shift(Tensor left, Tensor right) => left.bitwise_left_shift(right);\\n\\n // https://pytorch.org/docs/stable/generated/torch.bitwise_left_shift\\n /// <summary>\\n /// Element-wise bitwise left shift, in place.\\n /// </summary>\\n /// <param name=\\\"left\\\">Left-hand operand.</param>\\n /// <param name=\\\"right\\\">Right-hand operand.</param>\\n public static Tensor bitwise_left_shift_(Tensor left, Tensor right) => left.bitwise_left_shift_(right);\\n\\n // https://pytorch.org/docs/stable/generated/torch.bitwise_right_shift\\n /// <summary>\\n /// Element-wise bitwise right shift\\n /// </summary>\\n /// <param name=\\\"left\\\">Left-hand operand.</param>\\n /// <param name=\\\"right\\\">Right-hand operand.</param>\\n [Pure]public static Tensor bitwise_right_shift(Tensor left, Tensor right) => left.bitwise_right_shift(right);\\n\\n // https://pytorch.org/docs/stable/generated/torch.bitwise_right_shift\\n /// <summary>\\n /// Element-wise bitwise right shift, in place.\\n /// </summary>\\n /// <param name=\\\"left\\\">Left-hand operand.</param>\\n /// <param name=\\\"right\\\">Right-hand operand.</param>\\n public static Tensor bitwise_right_shift_(Tensor left, Tensor right) => left.bitwise_right_shift_(right);\\n\\n // https://pytorch.org/docs/stable/generated/torch.ceil\\n /// <summary>\\n /// Returns a new tensor with the ceil of the elements of input, the smallest integer greater than or equal to each element.\\n /// </summary>\\n /// <param name=\\\"input\\\">The input tensor.</param>\\n public static Tensor ceil(Tensor input) => input.ceil();\\n\\n // https://pytorch.org/docs/stable/generated/torch.ceil\\n /// <summary>\\n /// Replaces each element of the input with the smallest integer greater than or equal to the element.\\n /// </summary>\\n /// <param name=\\\"input\\\">The input tensor.</param>\\n public static Tensor ceil_(Tensor input) => input.ceil_();\\n\\n // https://pytorch.org/docs/stable/generated/torch.clamp\\n /// <summary>\\n /// Clamps all elements in input into the range [ min, max ].\\n /// </summary>\\n /// <param name=\\\"input\\\">The input tensor</param>\\n /// <param name=\\\"min\\\">The minimum value</param>\\n /// <param name=\\\"max\\\">The maximum value</param>\\n public static Tensor clamp(Tensor input, Scalar? min = null, Scalar? max = null) => input.clamp(min, max);\\n\\n // https://pytorch.org/docs/stable/generated/torch.clamp\\n /// <summary>\\n /// Clamps all elements in input into the range [ min, max ] in place.\\n /// </summary>\\n /// <param name=\\\"input\\\">The input tensor</param>\\n /// <param name=\\\"min\\\">The minimum value</param>\\n /// <param name=\\\"max\\\">The maximum value</param>\\n public static Tensor clamp_(Tensor input, Scalar? min = null, Scalar? max = null) => input.clamp_(min, max);\\n\\n // https://pytorch.org/docs/stable/generated/torch.clamp\\n /// <summary>\\n /// Clamps all elements in input into the range [ min, max ].\\n /// </summary>\\n /// <param name=\\\"input\\\">The input tensor</param>\\n /// <param name=\\\"min\\\">The minimum value</param>\\n /// <param name=\\\"max\\\">The maximum value</param>\\n public static Tensor clamp(Tensor input, Tensor? min = null, Tensor? max = null) => input.clamp(min, max);\\n\\n // https://pytorch.org/docs/stable/generated/torch.clamp\\n /// <summary>\\n /// Clamps all elements in input into the range [ min, max ] in place.\\n /// </summary>\\n /// <param name=\\\"input\\\">The input tensor</param>\\n /// <param name=\\\"min\\\">The minimum value</param>\\n /// <param name=\\\"max\\\">The maximum value</param>\\n public static Tensor clamp_(Tensor input, Tensor? min = null, Tensor? max = null) => input.clamp_(min, max);\\n\\n // https://pytorch.org/docs/stable/generated/torch.clip\\n public static Tensor clip(Tensor input, Scalar? min = null, Scalar? max = null) => input.clip(min, max);\\n\\n // https://pytorch.org/docs/stable/generated/torch.conj_physical\\n /// <summary>\\n /// Computes the element-wise conjugate of the given input tensor. If input has a non-complex <see cref=\\\"DeviceType\\\">dtype</see>, this function just returns input.\\n /// </summary>\\n /// <param name=\\\"input\\\">The input tensor.</param>\\n [Pure]public static Tensor conj_physical(Tensor input) => input.conj_physical();\\n\\n // https://pytorch.org/docs/stable/generated/torch.conj_physical\\n /// <summary>\\n /// In-place version of conj_physical\\n /// </summary>\\n /// <param name=\\\"input\\\">The input tensor.</param>\\n public static Tensor conj_physical_(Tensor input) => input.conj_physical_();\\n\\n // https://pytorch.org/docs/stable/generated/torch.copysign\\n /// <summary>\\n /// Create a new floating-point tensor with the magnitude of input and the sign of other, elementwise.\\n /// Supports broadcasting to a common shape, and integer and float inputs.\\n /// </summary>\\n /// <param name=\\\"input\\\">magnitudes</param>\\n /// <param name=\\\"other\\\">contains value(s) whose signbit(s) are applied to the magnitudes in <paramref name=\\\"input\\\"/>.</param>\\n /// <returns>the output tensor</returns>\\n [Pure]public static Tensor copysign(Tensor input, Tensor other) => input.copysign(other);\\n\\n // https://pytorch.org/docs/stable/generated/torch.cos\\n /// <summary>\\n /// Computes the cosine of the elements of input.\\n /// </summary>\\n /// <returns></returns>\\n [Pure]public static Tensor cos(Tensor input) => input.cos();\\n\\n /// <summary>\\n /// Computes the cosine of the elements of input.\\n /// </summary>\\n /// <returns></returns>\\n public static Tensor cos_(Tensor input) => input.cos_();\\n\\n // https://pytorch.org/docs/stable/generated/torch.cosh\\n /// <summary>\\n /// Computes the hyperbolic cosine of the elements of input.\\n /// </summary>\\n /// <returns></returns>\\n public static Tensor cosh(Tensor input) => input.cosh();\\n\\n // https://pytorch.org/docs/stable/generated/torch.cosh\\n /// <summary>\\n /// Computes the hyperbolic cosine of the elements of input.\\n /// </summary>\\n /// <returns></returns>\\n public static Tensor cosh_(Tensor input) => input.cosh_();\\n\\n // https://pytorch.org/docs/stable/generated/torch.deg2rad\\n [Pure]public static Tensor deg2rad(Tensor input) => input.deg2rad();\\n\\n // https://pytorch.org/docs/stable/generated/torch.div\\n /// <summary>\\n /// Divides each element of the input by the corresponding element of other.\\n /// </summary>\\n /// <param name=\\\"left\\\">Numerator</param>\\n /// <param name=\\\"right\\\">Denominator</param>\\n /// <param name=\\\"rounding_mode\\\">Rounding mode.</param>\\n public static Tensor div(Tensor left, Tensor right, RoundingMode rounding_mode = RoundingMode.None) => left.div(right);\\n\\n // https://pytorch.org/docs/stable/generated/torch.div\\n /// <summary>\\n /// Divides each element of the input by a scalar value.\\n /// </summary>\\n /// <param name=\\\"left\\\">Numerator</param>\\n /// <param name=\\\"right\\\">Denominator</param>\\n /// <param name=\\\"rounding_mode\\\">Rounding mode.</param>\\n public static Tensor div(Tensor left, Scalar right, RoundingMode rounding_mode = RoundingMode.None) => left.div(right);\\n\\n // https://pytorch.org/docs/stable/generated/torch.div\\n /// <summary>\\n /// Divides each element of the input by the corresponding element of other.\\n /// </summary>\\n /// <param name=\\\"left\\\">Numerator</param>\\n /// <param name=\\\"right\\\">Denominator</param>\\n /// <param name=\\\"rounding_mode\\\">Rounding mode.</param>\\n public static Tensor div_(Tensor left, Scalar right, RoundingMode rounding_mode = RoundingMode.None) => left.div_(right, rounding_mode);\\n\\n // https://pytorch.org/docs/stable/generated/torch.div\\n /// <summary>\\n /// Divides each element of the input by the corresponding element of other.\\n /// </summary>\\n /// <param name=\\\"left\\\">Numerator</param>\\n /// <param name=\\\"right\\\">Denominator</param>\\n /// <param name=\\\"rounding_mode\\\">Rounding mode.</param>\\n public static Tensor div_(Tensor left, Tensor right, RoundingMode rounding_mode = RoundingMode.None) => left.div_(right);\\n\\n // https://pytorch.org/docs/stable/generated/torch.divide\\n /// <summary>\\n /// Divides each element of the input by the corresponding element of other.\\n /// </summary>\\n /// <param name=\\\"left\\\">Numerator</param>\\n /// <param name=\\\"right\\\">Denominator</param>\\n /// <param name=\\\"rounding_mode\\\">Rounding mode.</param>\\n public static Tensor divide(Tensor left, Tensor right, RoundingMode rounding_mode = RoundingMode.None) => left.div(right);\\n\\n // https://pytorch.org/docs/stable/generated/torch.divide\\n /// <summary>\\n /// Divides each element of the input by a scalar value.\\n /// </summary>\\n /// <param name=\\\"left\\\">Numerator</param>\\n /// <param name=\\\"right\\\">Denominator</param>\\n /// <param name=\\\"rounding_mode\\\">Rounding mode.</param>\\n public static Tensor divide(Tensor left, Scalar right, RoundingMode rounding_mode = RoundingMode.None) => left.div(right);\\n\\n // https://pytorch.org/docs/stable/generated/torch.divide\\n /// <summary>\\n /// Divides each element of the input by the corresponding element of other.\\n /// </summary>\\n /// <param name=\\\"left\\\">Numerator</param>\\n /// <param name=\\\"right\\\">Denominator</param>\\n /// <param name=\\\"rounding_mode\\\">Rounding mode.</param>\\n public static Tensor divide_(Tensor left, Tensor right, RoundingMode rounding_mode = RoundingMode.None) => left.div_(right);\\n\\n // https://pytorch.org/docs/stable/generated/torch.divide\\n /// <summary>\\n /// Divides each element of the input by the corresponding element of other.\\n /// </summary>\\n /// <param name=\\\"left\\\">Numerator</param>\\n /// <param name=\\\"right\\\">Denominator</param>\\n public static Tensor divide_(Tensor left, Scalar right) => left.div_(right);\\n\\n // https://pytorch.org/docs/stable/generated/torch.digamma\\n /// <summary>\\n /// Computes the logarithmic derivative of the gamma function on input.\\n /// </summary>\\n /// <param name=\\\"input\\\">The input tensor.</param>\\n public static Tensor digamma(Tensor input) => input.digamma();\\n\\n // https://pytorch.org/docs/stable/generated/torch.digamma\\n /// <summary>\\n /// Computes the logarithmic derivative of the gamma function on input, in place.\\n /// </summary>\\n /// <param name=\\\"input\\\">The input tensor.</param>\\n public static Tensor digamma_(Tensor input) => input.digamma_();\\n\\n // https://pytorch.org/docs/stable/generated/torch.erf\\n /// <summary>\\n /// Computes the error function of the input.\\n /// </summary>\\n public static Tensor erf(Tensor input) => input.erf();\\n\\n // https://pytorch.org/docs/stable/generated/torch.erf\\n /// <summary>\\n /// Computes the error function of the input in place.\\n /// </summary>\\n public static Tensor erf_(Tensor input) => input.erf_();\\n\\n // https://pytorch.org/docs/stable/generated/torch.erfc\\n /// <summary>\\n /// Computes the error function of the input.\\n /// </summary>\\n public static Tensor erfc(Tensor input) => input.erfc();\\n\\n // https://pytorch.org/docs/stable/generated/torch.erfc\\n /// <summary>\\n /// Computes the error function of the input in place.\\n /// </summary>\\n public static Tensor erfc_(Tensor input) => input.erfc_();\\n\\n // https://pytorch.org/docs/stable/generated/torch.erfinv\\n /// <summary>\\n /// Computes the error function of the input.\\n /// </summary>\\n public static Tensor erfinv(Tensor input) => input.erf();\\n\\n // https://pytorch.org/docs/stable/generated/torch.erfinv\\n /// <summary>\\n /// Computes the error function of the input in place.\\n /// </summary>\\n public static Tensor erfinv_(Tensor input) => input.erfinv_();\\n\\n // https://pytorch.org/docs/stable/generated/torch.exp\\n /// <summary>\\n /// Returns a new tensor with the exponential of the elements of the input tensor input.\\n /// </summary>\\n /// <param name=\\\"input\\\">The input tensor.</param>\\n public static Tensor exp(Tensor input) => input.exp();\\n\\n // https://pytorch.org/docs/stable/generated/torch.exp\\n /// <summary>\\n /// Replaces each element of the input with the exponential of the elements of the input tensor input.\\n /// </summary>\\n /// <param name=\\\"input\\\">The input tensor.</param>\\n public static Tensor exp_(Tensor input) => input.exp_();\\n\\n // https://pytorch.org/docs/stable/generated/torch.exp2\\n /// <summary>\\n /// Computes the base 2 exponential function of input.\\n /// </summary>\\n /// <param name=\\\"input\\\">The input tensor.</param>\\n public static Tensor exp2(Tensor input) => input.exp2();\\n\\n // https://pytorch.org/docs/stable/generated/torch.expm1\\n /// <summary>\\n /// Returns a new tensor with the exponential of the elements minus 1 of input.\\n /// </summary>\\n /// <param name=\\\"input\\\">The input tensor.</param>\\n public static Tensor expm1(Tensor input) => input.expm1();\\n\\n // https://pytorch.org/docs/stable/generated/torch.expm1\\n /// <summary>\\n /// Replaces each element with the exponential of the element minus 1 of input.\\n /// </summary>\\n /// <param name=\\\"input\\\">The input tensor.</param>\\n public static Tensor expm1_(Tensor input) => input.expm1_();\\n\\n // https://pytorch.org/docs/stable/generated/torch.fake_quantize_per_channel_affine\\n /// <summary>\\n /// Returns a new tensor with the data in <paramref name=\\\"input\\\"/> fake quantized per channel using\\n /// <paramref name=\\\"scale\\\"/>, <paramref name=\\\"zero_point\\\"/>, <paramref name=\\\"quant_min\\\"/> and <paramref name=\\\"quant_max\\\"/>,\\n /// across the channel specified by <paramref name=\\\"axis\\\"/>.\\n /// </summary>\\n /// <param name=\\\"input\\\">the input value(s) (float32)</param>\\n /// <param name=\\\"scale\\\">quantization scale, per channel (float32)</param>\\n /// <param name=\\\"zero_point\\\">quantization zero_point, per channel (torch.int32, torch.half, or torch.float32)</param>\\n /// <param name=\\\"axis\\\">channel axis</param>\\n /// <param name=\\\"quant_min\\\">lower bound of the quantized domain</param>\\n /// <param name=\\\"quant_max\\\">upper bound of the quantized domain</param>\\n /// <returns>A newly fake_quantized per channel torch.float32 tensor</returns>\\n [Pure, Obsolete(\\\"not implemented\\\", true)]\\n public static Tensor fake_quantize_per_channel_affine(Tensor input, Tensor scale, Tensor zero_point, int axis, long quant_min, long quant_max)\\n => throw new NotImplementedException();\\n\\n // https://pytorch.org/docs/stable/generated/torch.fake_quantize_per_tensor_affine\\n /// <summary>\\n /// Returns a new tensor with the data in <paramref name=\\\"input\\\"/> fake quantized per channel using\\n /// <paramref name=\\\"scale\\\"/>, <paramref name=\\\"zero_point\\\"/>, <paramref name=\\\"quant_min\\\"/> and <paramref name=\\\"quant_max\\\"/>,\\n /// across the channel specified by axis.\\n /// </summary>\\n /// <param name=\\\"input\\\">the input value(s) (float32)</param>\\n /// <param name=\\\"scale\\\">quantization scale, per channel (float32)</param>\\n /// <param name=\\\"zero_point\\\">quantization zero_point, per channel (torch.int32, torch.half, or torch.float32)</param>\\n /// <param name=\\\"quant_min\\\">lower bound of the quantized domain</param>\\n /// <param name=\\\"quant_max\\\">upper bound of the quantized domain</param>\\n /// <returns>A newly fake_quantized per channel torch.float32 tensor</returns>\\n [Pure, Obsolete(\\\"not implemented\\\", true)]\\n public static Tensor fake_quantize_per_tensor_affine(Tensor input, Tensor scale, Tensor zero_point, long quant_min, long quant_max)\\n => throw new NotImplementedException();\\n\\n // https://pytorch.org/docs/stable/generated/torch.fix\\n /// <summary>\\n /// Returns a new tensor with the truncated integer values of the elements of input.\\n /// </summary>\\n /// <param name=\\\"input\\\">The input tensor.</param>\\n [Pure]public static Tensor fix(Tensor input) => input.fix();\\n\\n // https://pytorch.org/docs/stable/generated/torch.fix\\n /// <summary>\\n /// Replaces each element with the truncated integer values of the elements of input.\\n /// </summary>\\n /// <param name=\\\"input\\\">The input tensor.</param>\\n public static Tensor fix_(Tensor input) => input.fix_();\\n\\n // https://pytorch.org/docs/stable/generated/torch.float_power\\n /// <summary>\\n /// Raises input to the power of exponent, elementwise, in double precision.\\n /// </summary>\\n /// <param name=\\\"input\\\">The input tensor.</param>\\n /// <param name=\\\"target\\\">The exponent.</param>\\n /// <remarks> If neither input is complex returns a torch.float64 tensor, and if one or more inputs is complex returns a torch.complex128 tensor.</remarks>\\n [Pure]public static Tensor float_power(Tensor input, Tensor target) => input.float_power(target);\\n\\n // https://pytorch.org/docs/stable/generated/torch.floor\\n /// <summary>\\n /// Returns a new tensor with the floor of the elements of input, the largest integer less than or equal to each element.\\n /// </summary>\\n /// <param name=\\\"input\\\">The input tensor.</param>\\n [Pure]public static Tensor floor(Tensor input) => input.floor();\\n\\n // https://pytorch.org/docs/stable/generated/torch.floor\\n /// <summary>\\n /// Replaces each element with the floor of the input, the largest integer less than or equal to each element.\\n /// </summary>\\n /// <param name=\\\"input\\\">The input tensor.</param>\\n public static Tensor floor_(Tensor input) => input.floor_();\\n\\n // https://pytorch.org/docs/stable/generated/torch.floor_divide\\n /// <summary>\\n /// Computes input divided by other, elementwise, and floors the result.\\n /// Supports broadcasting to a common shape, type promotion, and integer and float inputs.\\n /// </summary>\\n /// <param name=\\\"input\\\">the dividend</param>\\n /// <param name=\\\"other\\\">the divisor</param>\\n /// <returns>the output tensor</returns>\\n [Pure]\\n public static Tensor floor_divide(Tensor input, Tensor other) => input.floor_divide(other);\\n\\n // https://pytorch.org/docs/stable/generated/torch.floor_divide\\n /// <summary>\\n /// Computes input divided by other, elementwise, and floors the result.\\n /// Supports broadcasting to a common shape, type promotion, and integer and float inputs.\\n /// </summary>\\n /// <param name=\\\"input\\\">the dividend</param>\\n /// <param name=\\\"other\\\">the divisor</param>\\n /// <returns>the output tensor</returns>\\n public static Tensor floor_divide_(Tensor input, Tensor other) => input.floor_divide_(other);\\n\\n // https://pytorch.org/docs/stable/generated/torch.fmod\\n /// <summary>\\n /// Computes the element-wise remainder of division.\\n /// </summary>\\n [Pure]public static Tensor fmod(Tensor left, Tensor right) => left.fmod(right);\\n\\n // https://pytorch.org/docs/stable/generated/torch.fmod\\n /// <summary>\\n /// Computes the element-wise remainder of division, in place.\\n /// </summary>\\n /// <param name=\\\"left\\\">Numerator</param>\\n /// <param name=\\\"right\\\">Denominator</param>\\n public static Tensor fmod_(Tensor left, Tensor right) => left.fmod_(right);\\n\\n // https://pytorch.org/docs/stable/generated/torch.fmod\\n /// <summary>\\n /// Computes the element-wise remainder of division.\\n /// </summary>\\n /// <param name=\\\"left\\\">Numerator</param>\\n /// <param name=\\\"right\\\">Denominator</param>\\n [Pure]public static Tensor fmod(Tensor left, Scalar right) => left.fmod(right);\\n\\n // https://pytorch.org/docs/stable/generated/torch.fmod\\n /// <summary>\\n /// Computes the element-wise remainder of division, in place.\\n /// </summary>\\n /// <param name=\\\"left\\\">Numerator</param>\\n /// <param name=\\\"right\\\">Denominator</param>\\n public static Tensor fmod_(Tensor left, Scalar right) => left.fmod_(right);\\n\\n // https://pytorch.org/docs/stable/generated/torch.frac\\n /// <summary>\\n /// Computes the fractional portion of each element in input.\\n /// </summary>\\n /// <param name=\\\"input\\\">The input tensor.</param>\\n [Pure]public static Tensor frac(Tensor input) => input.frac();\\n\\n // https://pytorch.org/docs/stable/generated/torch.frac\\n /// <summary>\\n /// Computes the fractional portion of each element in input, in-place.\\n /// </summary>\\n /// <param name=\\\"input\\\">The input tensor.</param>\\n public static Tensor frac_(Tensor input) => input.frac_();\\n\\n // https://pytorch.org/docs/stable/generated/torch.frexp\\n /// <summary>\\n /// Decomposes input into mantissa and exponent tensors\\n /// </summary>\\n /// <param name=\\\"input\\\">The input tensor.</param>\\n [Pure]public static (Tensor Mantissa, Tensor Exponent) frexp(Tensor input) => input.frexp();\\n\\n // https://pytorch.org/docs/stable/generated/torch.gradient\\n [Pure, Obsolete(\\\"not implemented\\\", true)]\\n public static ICollection<Tensor> gradient(Tensor input, int spacing = 1, long? dim = null, int edge_order = 1)\\n => throw new NotImplementedException();\\n\\n // https://pytorch.org/docs/stable/generated/torch.gradient\\n [Pure, Obsolete(\\\"not implemented\\\", true)]\\n public static ICollection<Tensor> gradient(Tensor input, int spacing = 1, long[]? dims = null, int edge_order = 1)\\n => throw new NotImplementedException();\\n\\n // https://pytorch.org/docs/stable/generated/torch.imag\\n [Pure]public static Tensor imag(Tensor input) => input.imag;\\n\\n // https://pytorch.org/docs/stable/generated/torch.ldexp\\n /// <summary>\\n /// Multiplies input by pow(2,other).\\n /// </summary>\\n /// <param name=\\\"input\\\">The input tensor.</param>\\n /// <param name=\\\"other\\\">A tensor of exponents, typically integers</param>\\n /// <remarks>Typically this function is used to construct floating point numbers by multiplying mantissas in input with integral powers of two created from the exponents in other.</remarks>\\n [Pure]public static Tensor ldexp(Tensor input, Tensor other) => input.ldexp(other);\\n\\n // https://pytorch.org/docs/stable/generated/torch.ldexp\\n /// <summary>\\n /// Multiplies input by pow(2,other) in place.\\n /// </summary>\\n /// <param name=\\\"input\\\">The input tensor.</param>\\n /// <param name=\\\"other\\\">A tensor of exponents, typically integers</param>\\n /// <remarks>Typically this function is used to construct floating point numbers by multiplying mantissas in input with integral powers of two created from the exponents in other.</remarks>\\n public static Tensor ldexp_(Tensor input, Tensor other) => input.ldexp_(other);\\n\\n // https://pytorch.org/docs/stable/generated/torch.lerp\\n /// <summary>\\n /// Does a linear interpolation of two tensors start (given by input)\\n /// and end based on a scalar or tensor weight and returns the resulting out tensor.\\n /// </summary>\\n /// <remarks>\\n /// The shapes of start and end must be broadcastable.\\n /// If weight is a tensor, then the shapes of weight, start, and end must be broadcastable.\\n /// </remarks>\\n /// <param name=\\\"input\\\">the tensor with the starting points</param>\\n /// <param name=\\\"end\\\">the tensor with the ending points</param>\\n /// <param name=\\\"weight\\\">the weight for the interpolation formula</param>\\n /// <returns>the output tensor</returns>\\n [Pure]public static Tensor lerp(Tensor input, Tensor end, Tensor weight) => input.lerp(end, weight);\\n\\n // https://pytorch.org/docs/stable/generated/torch.lgamma\\n /// <summary>\\n /// Computes the logarithm of the gamma function on input.\\n /// </summary>\\n /// <param name=\\\"input\\\">The input tensor.</param>\\n [Pure]public static Tensor lgamma(Tensor input) => input.lgamma();\\n\\n // https://pytorch.org/docs/stable/generated/torch.lgamma\\n /// <summary>\\n /// Computes the logarithm of the gamma function on input, in place.\\n /// </summary>\\n /// <param name=\\\"input\\\">The input tensor.</param>\\n public static Tensor lgamma_(Tensor input) => input.lgamma();\\n\\n // https://pytorch.org/docs/stable/generated/torch.log\\n /// <summary>\\n /// Returns a new tensor with the natural logarithm of the input elements.\\n /// </summary>\\n /// <param name=\\\"input\\\">The input tensor.</param>\\n [Pure]public static Tensor log(Tensor input) => input.log();\\n\\n // https://pytorch.org/docs/stable/generated/torch.log\\n /// <summary>\\n /// Replaces each elements with the natural logarithm of the input.\\n /// </summary>\\n /// <param name=\\\"input\\\">The input tensor.</param>\\n public static Tensor log_(Tensor input) => input.log_();\\n\\n // https://pytorch.org/docs/stable/generated/torch.log10\\n /// <summary>\\n /// Returns a new tensor with the logarithm to the base 10 of the elements of input.\\n /// </summary>\\n /// <param name=\\\"input\\\">The input tensor.</param>\\n [Pure]public static Tensor log10(Tensor input) => input.log10();\\n\\n // https://pytorch.org/docs/stable/generated/torch.log10\\n /// <summary>\\n /// Replaces each elements with the logarithm to the base 10 of the elements of input.\\n /// </summary>\\n /// <param name=\\\"input\\\">The input tensor.</param>\\n public static Tensor log10_(Tensor input) => input.log10_();\\n\\n // https://pytorch.org/docs/stable/generated/torch.log1p\\n /// <summary>\\n /// Returns a new tensor with the natural logarithm of (1 + input).\\n /// </summary>\\n /// <param name=\\\"input\\\">The input tensor.</param>\\n [Pure]public static Tensor log1p(Tensor input) => input.log1p();\\n\\n // https://pytorch.org/docs/stable/generated/torch.log1p\\n /// <summary>\\n /// Replaces each elements with the natural logarithm of (1 + input), in place.\\n /// </summary>\\n /// <param name=\\\"input\\\">The input tensor.</param>\\n public static Tensor log1p_(Tensor input) => input.log1p_();\\n\\n // https://pytorch.org/docs/stable/generated/torch.log2\\n /// <summary>\\n /// Returns a new tensor with the logarithm to the base 10 of the elements of input.\\n /// </summary>\\n /// <param name=\\\"input\\\">The input tensor.</param>\\n [Pure]public static Tensor log2(Tensor input) => input.log2();\\n\\n // https://pytorch.org/docs/stable/generated/torch.log2\\n /// <summary>\\n /// Replaces each elements with the logarithm to the base 10 of the elements of input.\\n /// </summary>\\n /// <param name=\\\"input\\\">The input tensor.</param>\\n public static Tensor log2_(Tensor input) => input.log2_();\\n\\n // https://pytorch.org/docs/stable/generated/torch.logaddexp\\n /// <summary>\\n /// Logarithm of the sum of exponentiations of the inputs.\\n /// </summary>\\n /// <param name=\\\"left\\\">The left-hand operand.</param>\\n /// <param name=\\\"right\\\">The right-hand operand.</param>\\n [Pure]public static Tensor logaddexp(Tensor left, Tensor right) => left.logaddexp(right);\\n\\n // https://pytorch.org/docs/stable/generated/torch.logaddexp2\\n /// <summary>\\n /// Logarithm of the sum of exponentiations of the inputs in base-2.\\n /// </summary>\\n /// <param name=\\\"left\\\">The left-hand operand.</param>\\n /// <param name=\\\"right\\\">The right-hand operand.</param>\\n [Pure]public static Tensor logaddexp2(Tensor left, Tensor right) => left.logaddexp2(right);\\n\\n // https://pytorch.org/docs/stable/generated/torch.logical_and\\n /// <summary>\\n /// Element-wise logical AND\\n /// </summary>\\n /// <param name=\\\"left\\\">The left-hand operand.</param>\\n /// <param name=\\\"right\\\">The right-hand operand.</param>\\n [Pure]public static Tensor logical_and(Tensor left, Tensor right) => left.logical_and(right);\\n\\n // https://pytorch.org/docs/stable/generated/torch.logical_and\\n /// <summary>\\n /// Element-wise logical AND, in place.\\n /// </summary>\\n /// <param name=\\\"left\\\">The left-hand operand.</param>\\n /// <param name=\\\"right\\\">The right-hand operand.</param>\\n public static Tensor logical_and_(Tensor left, Tensor right) => left.logical_and_(right);\\n\\n // https://pytorch.org/docs/stable/generated/torch.logical_not\\n /// <summary>\\n /// Element-wise logical NOT\\n /// </summary>\\n /// <param name=\\\"input\\\">The input tensor.</param>\\n public static Tensor logical_not(Tensor input) => input.logical_not();\\n\\n // https://pytorch.org/docs/stable/generated/torch.logical_or\\n /// <summary>\\n /// Element-wise logical OR\\n /// </summary>\\n /// <param name=\\\"left\\\">The left-hand operand.</param>\\n /// <param name=\\\"right\\\">The right-hand operand.</param>\\n [Pure]public static Tensor logical_or(Tensor left, Tensor right) => left.logical_or(right);\\n\\n // https://pytorch.org/docs/stable/generated/torch.logical_or\\n /// <summary>\\n /// Element-wise logicalXOR, in place.\\n /// </summary>\\n /// <param name=\\\"left\\\">The left-hand operand.</param>\\n /// <param name=\\\"right\\\">The right-hand operand.</param>\\n public static Tensor logical_or_(Tensor left, Tensor right) => left.logical_or_(right);\\n\\n // https://pytorch.org/docs/stable/generated/torch.logical_xor\\n /// <summary>\\n /// Element-wise logical XOR\\n /// </summary>\\n /// <param name=\\\"left\\\">The left-hand operand.</param>\\n /// <param name=\\\"right\\\">The right-hand operand.</param>\\n [Pure]public static Tensor logical_xor(Tensor left, Tensor right) => left.logical_xor(right);\\n\\n // https://pytorch.org/docs/stable/generated/torch.logical_xor\\n /// <summary>\\n /// Element-wise logical XOR\\n /// </summary>\\n /// <param name=\\\"left\\\">The left-hand operand.</param>\\n /// <param name=\\\"right\\\">The right-hand operand.</param>\\n public static Tensor logical_xor_(Tensor left, Tensor right) => left.logical_xor_(right);\\n\\n // https://pytorch.org/docs/stable/generated/torch.logit\\n /// <summary>\\n /// Returns a new tensor with the logit of the elements of input.\\n /// input is clamped to [eps, 1 - eps] when eps is not null\\n /// </summary>\\n /// <param name=\\\"input\\\">The input tensor.</param>\\n /// <param name=\\\"eps\\\">The epsilon for input clamp bound.</param>\\n [Pure]public static Tensor logit(Tensor input, double? eps = null) => input.logit(eps);\\n\\n // https://pytorch.org/docs/stable/generated/torch.hypot\\n /// <summary>\\n /// Element-wise: given the legs of a right triangle, return its hypotenuse.\\n /// </summary>\\n /// <param name=\\\"left\\\">The left-hand operand.</param>\\n /// <param name=\\\"right\\\">The right-hand operand.</param>\\n [Pure]public static Tensor hypot(Tensor left, Tensor right) => left.hypot(right);\\n\\n // https://pytorch.org/docs/stable/generated/torch.i0\\n /// <summary>\\n /// Alias for <see cref=\\\"torch.special.i0\\\"/>.\\n /// </summary>\\n /// <param name=\\\"input\\\"></param>\\n /// <returns></returns>\\n [Pure]public static Tensor i0(Tensor input) => special.i0(input);\\n\\n // https://pytorch.org/docs/stable/generated/torch.igamma\\n /// <summary>\\n /// Alias for <see cref=\\\"torch.special.gammainc\\\"/>.\\n /// </summary>\\n /// <param name=\\\"input\\\"></param>\\n /// <param name=\\\"other\\\"></param>\\n /// <returns></returns>\\n [Pure]public static Tensor igamma(Tensor input, Tensor other) => special.gammainc(input, other);\\n\\n // https://pytorch.org/docs/stable/generated/torch.igammac\\n /// <summary>\\n /// Alias for <see cref=\\\"torch.special.gammaincc\\\"/>\\\".\\n /// </summary>\\n /// <param name=\\\"input\\\"></param>\\n /// <param name=\\\"other\\\"></param>\\n /// <returns></returns>\\n [Pure]public static Tensor igammac(Tensor input, Tensor other) => special.gammaincc(input, other);\\n\\n // https://pytorch.org/docs/stable/generated/torch.mul\\n /// <summary>\\n /// Multiplies each element of the input by the corresponding element of other.\\n /// </summary>\\n /// <param name=\\\"left\\\">The left-hand operand.</param>\\n /// <param name=\\\"right\\\">The right-hand operand.</param>\\n public static Tensor mul(Tensor left, Tensor right) => left.mul(right);\\n\\n // https://pytorch.org/docs/stable/generated/torch.mul\\n /// <summary>\\n /// Divides each element of the input by a scalar value.\\n /// </summary>\\n /// <param name=\\\"left\\\">The left-hand operand.</param>\\n /// <param name=\\\"right\\\">The right-hand operand.</param>\\n public static Tensor mul(Tensor left, Scalar right) => left.mul(right);\\n\\n // https://pytorch.org/docs/stable/generated/torch.mul\\n /// <summary>\\n /// Divides each element of the input by the corresponding element of other.\\n /// </summary>\\n /// <param name=\\\"left\\\">The left-hand operand.</param>\\n /// <param name=\\\"right\\\">The right-hand operand.</param>\\n public static Tensor mul_(Tensor left, Tensor right) => left.mul_(right);\\n\\n // https://pytorch.org/docs/stable/generated/torch.mul\\n /// <summary>\\n /// Divides each element of the input by the corresponding element of other.\\n /// </summary>\\n /// <param name=\\\"left\\\">The left-hand operand.</param>\\n /// <param name=\\\"right\\\">The right-hand operand.</param>\\n public static Tensor mul_(Tensor left, Scalar right) => left.mul_(right);\\n\\n // https://pytorch.org/docs/stable/generated/torch.multiply\\n /// <summary>\\n /// Multiplies each element of the input by the corresponding element of other.\\n /// </summary>\\n /// <param name=\\\"left\\\">The left-hand operand.</param>\\n /// <param name=\\\"right\\\">The right-hand operand.</param>\\n public static Tensor multiply(Tensor left, Tensor right) => left.mul(right);\\n\\n // https://pytorch.org/docs/stable/generated/torch.multiply\\n /// <summary>\\n /// Divides each element of the input by a scalar value.\\n /// </summary>\\n /// <param name=\\\"left\\\">The left-hand operand.</param>\\n /// <param name=\\\"right\\\">The right-hand operand.</param>\\n public static Tensor multiply(Tensor left, Scalar right) => left.mul(right);\\n\\n // https://pytorch.org/docs/stable/generated/torch.multiply\\n /// <summary>\\n /// Divides each element of the input by the corresponding element of other.\\n /// </summary>\\n /// <param name=\\\"left\\\">The left-hand operand.</param>\\n /// <param name=\\\"right\\\">The right-hand operand.</param>\\n public static Tensor multiply_(Tensor left, Tensor right) => left.mul_(right);\\n\\n // https://pytorch.org/docs/stable/generated/torch.multiply\\n /// <summary>\\n /// Divides each element of the input by the corresponding element of other.\\n /// </summary>\\n /// <param name=\\\"left\\\">The left-hand operand.</param>\\n /// <param name=\\\"right\\\">The right-hand operand.</param>\\n public static Tensor multiply_(Tensor left, Scalar right) => left.mul_(right);\\n\\n // https://pytorch.org/docs/stable/generated/torch.mvlgamma\\n /// <summary>\\n /// Computes the multivariate log-gamma function) with dimension pp element-wise\\n /// </summary>\\n /// <param name=\\\"input\\\">The input tensor.</param>\\n /// <param name=\\\"p\\\">The number of dimensions</param>\\n /// <returns></returns>\\n public static Tensor mvlgamma(Tensor input, long p) => input.mvlgamma(p);\\n\\n // https://pytorch.org/docs/stable/generated/torch.mvlgamma\\n /// <summary>\\n /// Computes the multivariate log-gamma function) with dimension pp element-wise, in place.\\n /// </summary>\\n /// <param name=\\\"input\\\">The input tensor.</param>\\n /// <param name=\\\"p\\\">The number of dimensions</param>\\n /// <returns></returns>\\n public static Tensor mvlgamma_(Tensor input, long p) => input.mvlgamma_(p);\\n\\n // https://pytorch.org/docs/stable/generated/torch.nan_to_num\\n /// <summary>\\n /// Replaces NaN, positive infinity, and negative infinity values in input with the values specified by nan,\\n /// posinf, and neginf, respectively. By default, NaNs are replaced with zero,\\n /// positive infinity is replaced with the greatest finite value representable by input’s dtype,\\n /// and negative infinity is replaced with the least finite value representable by input’s dtype.\\n /// </summary>\\n /// <param name=\\\"input\\\">the input tensor</param>\\n /// <param name=\\\"nan\\\">the value to replace NaNs with. Default is zero.</param>\\n /// <param name=\\\"posinf\\\">\\n /// if a Number, the value to replace positive infinity values with.\\n /// If None, positive infinity values are replaced with the greatest finite value representable by input’s dtype.\\n /// Default is null.\\n /// </param>\\n /// <param name=\\\"neginf\\\">\\n /// if a Number, the value to replace negative infinity values with.\\n /// If None, negative infinity values are replaced with the lowest finite value representable by input’s dtype.\\n /// Default is null.\\n /// </param>\\n /// <returns></returns>\\n public static Tensor nan_to_num(Tensor input, double nan = 0d, double? posinf = null, double? neginf = null)\\n => input.nan_to_num(nan, posinf, neginf);\\n\\n // https://pytorch.org/docs/stable/generated/torch.neg\\n /// <summary>\\n /// Negation\\n /// </summary>\\n /// <param name=\\\"input\\\">The input tensor.</param>\\n public static Tensor neg(Tensor input) => input.neg();\\n\\n // https://pytorch.org/docs/stable/generated/torch.neg\\n /// <summary>\\n /// In-place negation\\n /// </summary>\\n /// <param name=\\\"input\\\">The input tensor.</param>\\n public static Tensor neg_(Tensor input) => input.neg_();\\n\\n // https://pytorch.org/docs/stable/generated/torch.negative\\n /// <summary>\\n /// Negation\\n /// </summary>\\n /// <param name=\\\"input\\\">The input tensor.</param>\\n public static Tensor negative(Tensor input) => input.neg();\\n\\n // https://pytorch.org/docs/stable/generated/torch.negative\\n /// <summary>\\n /// In-place negation\\n /// </summary>\\n /// <param name=\\\"input\\\">The input tensor.</param>\\n public static Tensor negative_(Tensor input) => input.neg_();\\n\\n\\n // https://pytorch.org/docs/stable/generated/torch.nextafter\\n public static Tensor nextafter(Tensor input, Tensor other) => input.nextafter(other);\\n\\n // https://pytorch.org/docs/stable/generated/torch.polygamma\\n /// <summary>\\n /// Computes the Nth derivative of the digamma function on input.\\n /// </summary>\\n /// <param name=\\\"input\\\">The input tensor.</param>\\n /// <param name=\\\"p\\\">The number of dimensions</param>\\n /// <returns></returns>\\n public static Tensor polygamma(Tensor input, long p) => input.polygamma(p);\\n\\n // https://pytorch.org/docs/stable/generated/torch.polygamma\\n /// <summary>\\n /// Computes the Nth derivative of the digamma function on input, in-place.\\n /// </summary>\\n /// <param name=\\\"input\\\">The input tensor.</param>\\n /// <param name=\\\"p\\\">The number of dimensions</param>\\n public static Tensor polygamma_(Tensor input, long p) => input.polygamma_(p);\\n\\n // https://pytorch.org/docs/stable/generated/torch.positive\\n public static Tensor positive(Tensor input) => input.positive();\\n\\n // https://pytorch.org/docs/stable/generated/torch.pow\\n /// <summary>\\n /// Takes the power of each element in input with exponent and returns a tensor with the result.\\n /// </summary>\\n /// <param name=\\\"left\\\">The left-hand operand.</param>\\n /// <param name=\\\"exponent\\\">The right-hand operand.</param>\\n [Pure]public static Tensor pow(Tensor left, Tensor exponent) => left.pow(exponent);\\n\\n // https://pytorch.org/docs/stable/generated/torch.pow\\n /// <summary>\\n /// Takes the power of each element in input with exponent and returns a tensor with the result.\\n /// </summary>\\n /// <param name=\\\"left\\\">The left-hand operand.</param>\\n /// <param name=\\\"exponent\\\">The right-hand operand.</param>\\n [Pure]public static Tensor pow(Tensor left, Scalar exponent) => left.pow(exponent);\\n\\n // https://pytorch.org/docs/stable/generated/torch.pow\\n /// <summary>\\n /// Replaces each element in input with the power of the element and the exponent.\\n /// </summary>\\n /// <param name=\\\"left\\\">The left-hand operand.</param>\\n /// <param name=\\\"exponent\\\">The right-hand operand.</param>\\n public static Tensor pow_(Tensor left, Tensor exponent) => left.pow_(exponent);\\n\\n // https://pytorch.org/docs/stable/generated/torch.pow\\n /// <summary>\\n /// Replaces each element in input with the power of the element and the exponent.\\n /// </summary>\\n /// <param name=\\\"left\\\">The left-hand operand.</param>\\n /// <param name=\\\"exponent\\\">The right-hand operand.</param>\\n public static Tensor pow_(Tensor left, Scalar exponent) => left.pow_(exponent);\\n\\n // https://pytorch.org/docs/stable/generated/torch.quantized_batch_norm\\n /// <summary>\\n /// Applies batch normalization on a 4D (NCHW) quantized tensor.\\n /// </summary>\\n /// <param name=\\\"input\\\">quantized tensor</param>\\n /// <param name=\\\"weight\\\">float tensor that corresponds to the gamma, size C</param>\\n /// <param name=\\\"bias\\\">float tensor that corresponds to the beta, size C</param>\\n /// <param name=\\\"mean\\\">float mean value in batch normalization, size C</param>\\n /// <param name=\\\"var\\\">float tensor for variance, size C</param>\\n /// <param name=\\\"eps\\\">a value added to the denominator for numerical stability.</param>\\n /// <param name=\\\"output_scale\\\">output quantized tensor scale</param>\\n /// <param name=\\\"output_zero_point\\\">output quantized tensor zero_point</param>\\n /// <returns>A quantized tensor with batch normalization applied.</returns>\\n [Pure, Obsolete(\\\"not implemented\\\", true)]\\n public static Tensor quantized_batch_norm(Tensor input, Tensor? weight, Tensor? bias, Tensor mean, Tensor @var, double eps, double output_scale, long output_zero_point)\\n => throw new NotImplementedException();\\n\\n // https://pytorch.org/docs/stable/generated/torch.quantized_max_pool1d\\n /// <summary>\\n /// Applies a 1D max pooling over an input quantized tensor composed of several input planes.\\n /// </summary>\\n /// <param name=\\\"input\\\">quantized tensor</param>\\n /// <param name=\\\"kernel_size\\\">the size of the sliding window</param>\\n /// <param name=\\\"stride\\\">the stride of the sliding window</param>\\n /// <param name=\\\"padding\\\">padding to be added on both sides, must be >= 0 and <= kernel_size / 2</param>\\n /// <param name=\\\"dilation\\\">the stride between elements within a sliding window, must be > 0. Default 1</param>\\n /// <param name=\\\"ceil_mode\\\">If <value>true</value>, will use ceil instead of floor to compute the output shape. Defaults to <value>false</value>.</param>\\n /// <returns>A quantized tensor with max_pool1d applied.</returns>\\n [Pure, Obsolete(\\\"not implemented\\\", true)]\\n public static Tensor quantized_max_pool1d(Tensor input, long[] kernel_size, long[]? stride, long[]? padding, long dilation=1, bool ceil_mode=false)\\n => throw new NotImplementedException();\\n\\n // https://pytorch.org/docs/stable/generated/torch.quantized_max_pool2d\\n /// <summary>\\n /// Applies a 2D max pooling over an input quantized tensor composed of several input planes.\\n /// </summary>\\n /// <param name=\\\"input\\\">quantized tensor</param>\\n /// <param name=\\\"kernel_size\\\">the size of the sliding window</param>\\n /// <param name=\\\"stride\\\">the stride of the sliding window</param>\\n /// <param name=\\\"padding\\\">padding to be added on both sides, must be >= 0 and <= kernel_size / 2</param>\\n /// <param name=\\\"dilation\\\">The stride between elements within a sliding window, must be > 0. Default 1</param>\\n /// <param name=\\\"ceil_mode\\\">If <value>true</value>, will use ceil instead of floor to compute the output shape. Defaults to <value>false</value>.</param>\\n /// <returns>A quantized tensor with max_pool2d applied.</returns>\\n [Pure, Obsolete(\\\"not implemented\\\", true)]\\n public static Tensor quantized_max_pool2d(Tensor input, long[] kernel_size, long[]? stride, long[]? padding, long dilation=1, bool ceil_mode=false)\\n => throw new NotImplementedException();\\n\\n // https://pytorch.org/docs/stable/generated/torch.rad2deg\\n /// <summary>\\n /// Returns a new tensor with each of the elements of input converted from angles in radians to degrees.\\n /// </summary>\\n /// <param name=\\\"input\\\">The input tensor.</param>\\n /// <returns>tensor with angles in radians</returns>\\n public static Tensor rad2deg(Tensor input) => input.rad2deg();\\n\\n // https://pytorch.org/docs/stable/generated/torch.real\\n /// <summary>\\n /// Returns a new tensor containing real values of the self tensor.\\n /// The returned tensor and self share the same underlying storage.\\n /// </summary>\\n /// <param name=\\\"input\\\">the input tensor.</param>\\n /// <returns>tensor containing real values</returns>\\n [Pure]public static Tensor real(Tensor input) => input.real;\\n\\n // https://pytorch.org/docs/stable/generated/torch.reciprocal\\n /// <summary>\\n /// Returns a new tensor with the reciprocal of the elements of input\\n /// </summary>\\n /// <param name=\\\"input\\\">The input tensor.</param>\\n [Pure]public static Tensor reciprocal(Tensor input) => input.reciprocal();\\n\\n // https://pytorch.org/docs/stable/generated/torch.reciprocal\\n /// <summary>\\n /// Replaces each element with the reciprocal of the input\\n /// </summary>\\n /// <param name=\\\"input\\\">The input tensor.</param>\\n public static Tensor reciprocal_(Tensor input) => input.reciprocal_();\\n\\n // https://pytorch.org/docs/stable/generated/torch.remainder\\n /// <summary>\\n /// Computes the element-wise remainder of division.\\n /// </summary>\\n /// <param name=\\\"left\\\">Numerator</param>\\n /// <param name=\\\"right\\\">Denominator</param>\\n [Pure]public static Tensor remainder(Tensor left, Tensor right) => left.remainder(right);\\n\\n // https://pytorch.org/docs/stable/generated/torch.remainder\\n /// <summary>\\n /// Computes the element-wise remainder of division.\\n /// </summary>\\n /// <param name=\\\"left\\\">Numerator</param>\\n /// <param name=\\\"right\\\">Denominator</param>\\n [Pure]public static Tensor remainder(Tensor left, Scalar right) => left.remainder(right);\\n\\n // https://pytorch.org/docs/stable/generated/torch.remainder\\n /// <summary>\\n /// Computes the element-wise remainder of division, in place\\n /// </summary>\\n /// <param name=\\\"left\\\">Numerator</param>\\n /// <param name=\\\"right\\\">Denominator</param>\\n public static Tensor remainder_(Tensor left, Tensor right) => left.remainder_(right);\\n\\n // https://pytorch.org/docs/stable/generated/torch.remainder\\n /// <summary>\\n /// Computes the element-wise remainder of division, in place\\n /// </summary>\\n /// <param name=\\\"left\\\">Numerator</param>\\n /// <param name=\\\"right\\\">Denominator</param>\\n public static Tensor remainder_(Tensor left, Scalar right) => left.remainder_(right);\\n\\n // https://pytorch.org/docs/stable/generated/torch.round\\n /// <summary>\\n /// Returns a new tensor with each of the elements of input rounded to the closest value with the given number of decimals.\\n /// </summary>\\n /// <param name=\\\"input\\\">The input tensor.</param>\\n /// <param name=\\\"decimals\\\">Number of decimal places to round to (default: 0). If decimals is negative, it specifies the number of positions to the left of the decimal point.</param>\\n [Pure]public static Tensor round(Tensor input, long decimals = 0L) => input.round(decimals);\\n\\n // https://pytorch.org/docs/stable/generated/torch.round\\n /// <summary>\\n /// Replaces each of the elements of input with the element rounded to the closest value with the given number of decimals.\\n /// </summary>\\n /// <param name=\\\"input\\\">The input tensor.</param>\\n /// <param name=\\\"decimals\\\">Number of decimal places to round to (default: 0). If decimals is negative, it specifies the number of positions to the left of the decimal point.</param>\\n public static Tensor round_(Tensor input, long decimals = 0L) => input.round_(decimals);\\n\\n // https://pytorch.org/docs/stable/generated/torch.rsqrt\\n /// <summary>\\n /// Returns a new tensor with the reciprocal of the square-root of each of the elements of input.\\n /// </summary>\\n /// <param name=\\\"input\\\">The input tensor.</param>\\n [Pure]public static Tensor rsqrt(Tensor input) => input.rsqrt();\\n\\n // https://pytorch.org/docs/stable/generated/torch.rsqrt\\n /// <summary>\\n /// Replaces each of the elements of input with the reciprocal of the square-root of each of the elements of input.\\n /// </summary>\\n /// <param name=\\\"input\\\">The input tensor.</param>\\n public static Tensor rsqrt_(Tensor input) => input.rsqrt_();\\n\\n // https://pytorch.org/docs/stable/generated/torch.sigmoid\\n /// <summary>\\n /// Computes the logistic sigmoid function of the elements of input.\\n /// </summary>\\n /// <param name=\\\"input\\\">The input tensor.</param>\\n [Pure]public static Tensor sigmoid(Tensor input) => input.sigmoid();\\n\\n // https://pytorch.org/docs/stable/generated/torch.sigmoid\\n /// <summary>\\n /// Computes the logistic sigmoid function of the elements of input.\\n /// </summary>\\n /// <param name=\\\"input\\\">The input tensor.</param>\\n public static Tensor sigmoid_(Tensor input) => input.sigmoid_();\\n\\n // https://pytorch.org/docs/stable/generated/torch.sign\\n /// <summary>\\n /// Returns a new tensor with the signs (-1, 0, 1) of the elements of input.\\n /// </summary>\\n /// <param name=\\\"input\\\">The input tensor.</param>\\n [Pure]public static Tensor sign(Tensor input) => input.sign();\\n\\n // https://pytorch.org/docs/stable/generated/torch.sign\\n /// <summary>\\n /// Returns a new tensor with the signs (-1, 0, 1) of the elements of input.\\n /// </summary>\\n /// <param name=\\\"input\\\">The input tensor.</param>\\n [Pure] public static Tensor sign_(Tensor input) => input.sign_();\\n\\n // https://pytorch.org/docs/stable/generated/torch.sgn\\n /// <summary>\\n /// This function is an extension of torch.sign() to complex tensors.\\n /// It computes a new tensor whose elements have the same angles as the corresponding\\n /// elements of input and absolute values (i.e. magnitudes) of one for complex tensors\\n /// and is equivalent to torch.sign() for non-complex tensors.\\n /// </summary>\\n /// <param name=\\\"input\\\">the input tensor.</param>\\n /// <returns>the output tensor.</returns>\\n [Pure]\\n public static Tensor sgn(Tensor input) => input.sgn();\\n\\n // https://pytorch.org/docs/stable/generated/torch.sgn\\n /// <summary>\\n /// This function is an extension of torch.sign() to complex tensors.\\n /// It computes a new tensor whose elements have the same angles as the corresponding\\n /// elements of input and absolute values (i.e. magnitudes) of one for complex tensors\\n /// and is equivalent to torch.sign() for non-complex tensors.\\n /// </summary>\\n /// <param name=\\\"input\\\">the input tensor.</param>\\n /// <returns>the output tensor.</returns>\\n [Pure]\\n public static Tensor sgn_(Tensor input) => input.sgn_();\\n\\n // https://pytorch.org/docs/stable/generated/torch.signbit\\n /// <summary>\\n /// Tests whether each element of input has its sign bit set (is less than zero) or not.\\n /// </summary>\\n /// <param name=\\\"input\\\">The input tensor.</param>\\n /// <returns>A boolean tensor of the same shape as the input.</returns>\\n [Pure]public static Tensor signbit(Tensor input) => input.signbit();\\n\\n // https://pytorch.org/docs/stable/generated/torch.sin\\n /// <summary>\\n /// Computes the sine of the elements of input.\\n /// </summary>\\n /// <returns></returns>\\n [Pure]public static Tensor sin(Tensor input) => input.sin();\\n\\n // https://pytorch.org/docs/stable/generated/torch.sin\\n /// <summary>\\n /// Computes the sine of the elements of input.\\n /// </summary>\\n /// <returns></returns>\\n public static Tensor sin_(Tensor input) => input.sin_();\\n\\n // https://pytorch.org/docs/stable/generated/torch.sinc\\n /// <summary>\\n /// Computes the normalized sinc of input.\\n /// </summary>\\n /// <returns></returns>\\n [Pure]public static Tensor sinc(Tensor input) => input.sinc();\\n\\n // https://pytorch.org/docs/stable/generated/torch.sinc\\n /// <summary>\\n /// Computes the normalized sinc of input, in place.\\n /// </summary>\\n /// <returns></returns>\\n public static Tensor sinc_(Tensor input) => input.sinc_();\\n\\n // https://pytorch.org/docs/stable/generated/torch.sinh\\n /// <summary>\\n /// Computes the hyperbolic sine of the elements of input.\\n /// </summary>\\n /// <returns></returns>\\n [Pure]public static Tensor sinh(Tensor input) => input.sinh();\\n\\n // https://pytorch.org/docs/stable/generated/torch.sinh\\n /// <summary>\\n /// Computes the hyperbolic sine of the elements of input.\\n /// </summary>\\n /// <returns></returns>\\n public static Tensor sinh_(Tensor input) => input.sinh_();\\n\\n // https://pytorch.org/docs/stable/generated/torch.sqrt\\n /// <summary>\\n /// Computes the element-wise square root\\n /// </summary>\\n /// <param name=\\\"input\\\">The input tensor.</param>\\n [Pure]public static Tensor sqrt(Tensor input) => input.sqrt();\\n\\n // https://pytorch.org/docs/stable/generated/torch.sqrt\\n /// <summary>\\n /// Computes the element-wise square root, in place\\n /// </summary>\\n /// <param name=\\\"input\\\">The input tensor.</param>\\n public static Tensor sqrt_(Tensor input) => input.sqrt_();\\n\\n // https://pytorch.org/docs/stable/generated/torch.square\\n /// <summary>\\n /// Computes the element-wise square\\n /// </summary>\\n /// <param name=\\\"input\\\">The input tensor.</param>\\n [Pure]public static Tensor square(Tensor input) => input.square();\\n\\n // https://pytorch.org/docs/stable/generated/torch.sub\\n /// <summary>\\n /// Element-wise subtraction\\n /// </summary>\\n /// <param name=\\\"left\\\">The left-hand operand.</param>\\n /// <param name=\\\"right\\\">The right-hand operand.</param>\\n [Pure]public static Tensor sub(Tensor left, Tensor right) => left.sub(right);\\n\\n // https://pytorch.org/docs/stable/generated/torch.sub\\n /// <summary>\\n /// Element-wise subtraction\\n /// </summary>\\n /// <param name=\\\"left\\\">The left-hand operand.</param>\\n /// <param name=\\\"right\\\">The right-hand operand.</param>\\n [Pure]public static Tensor sub(Tensor left, Scalar right) => left.sub(right);\\n\\n // https://pytorch.org/docs/stable/generated/torch.sub\\n /// <summary>\\n /// Element-wise subtraction\\n /// </summary>\\n /// <param name=\\\"left\\\">The left-hand operand.</param>\\n /// <param name=\\\"right\\\">The right-hand operand.</param>\\n public static Tensor sub_(Tensor left, Tensor right) => left.sub_(right);\\n\\n // https://pytorch.org/docs/stable/generated/torch.sub\\n /// <summary>\\n /// Element-wise subtraction\\n /// </summary>\\n /// <param name=\\\"left\\\">The left-hand operand.</param>\\n /// <param name=\\\"right\\\">The right-hand operand.</param>\\n public static Tensor sub_(Tensor left, Scalar right) => left.sub_(right);\\n\\n // https://pytorch.org/docs/stable/generated/torch.subtract\\n /// <summary>\\n /// Element-wise subtraction\\n /// </summary>\\n /// <param name=\\\"left\\\">The left-hand operand.</param>\\n /// <param name=\\\"right\\\">The right-hand operand.</param>\\n [Pure]public static Tensor subtract(Tensor left, Tensor right) => left.subtract(right);\\n\\n // https://pytorch.org/docs/stable/generated/torch.subtract\\n /// <summary>\\n /// Element-wise subtraction\\n /// </summary>\\n /// <param name=\\\"left\\\">The left-hand operand.</param>\\n /// <param name=\\\"right\\\">The right-hand operand.</param>\\n [Pure]public static Tensor subtract(Tensor left, Scalar right) => left.subtract(right);\\n\\n // https://pytorch.org/docs/stable/generated/torch.subtract\\n /// <summary>\\n /// Element-wise subtraction\\n /// </summary>\\n /// <returns></returns>\\n public static Tensor subtract_(Tensor left, Tensor right) => left.subtract_(right);\\n\\n // https://pytorch.org/docs/stable/generated/torch.subtract\\n /// <summary>\\n /// Element-wise subtraction\\n /// </summary>\\n /// <param name=\\\"left\\\">The left-hand operand.</param>\\n /// <param name=\\\"right\\\">The right-hand operand.</param>\\n public static Tensor subtract_(Tensor left, Scalar right) => left.subtract_(right);\\n\\n // https://pytorch.org/docs/stable/generated/torch.tan\\n /// <summary>\\n /// Computes the tangent of the elements of input.\\n /// </summary>\\n /// <returns></returns>\\n [Pure]public static Tensor tan(Tensor input) => input.tan();\\n\\n // https://pytorch.org/docs/stable/generated/torch.tan\\n /// <summary>\\n /// Computes the tangent of the elements of input. In-place version.\\n /// </summary>\\n /// <returns></returns>\\n public static Tensor tan_(Tensor input) => input.tan_();\\n\\n // https://pytorch.org/docs/stable/generated/torch.tanh\\n /// <summary>\\n /// Computes the hyperbolic tangent of the elements of input.\\n /// </summary>\\n /// <returns></returns>\\n [Pure]\\n public static Tensor tanh(Tensor input) => input.tanh();\\n\\n // https://pytorch.org/docs/stable/generated/torch.tanh\\n /// <summary>\\n /// Computes the hyperbolic tangent of the elements of input.\\n /// </summary>\\n /// <returns></returns>\\n public static Tensor tanh_(Tensor input) => input.tanh_();\\n\\n // https://pytorch.org/docs/stable/generated/torch.true_divide\\n /// <summary>\\n /// Alias for torch.div() with rounding_mode=None.\\n /// </summary>\\n [Pure]\\n public static Tensor true_divide(Tensor dividend, Tensor divisor) => dividend.true_divide(divisor);\\n\\n // https://pytorch.org/docs/stable/generated/torch.true_divide\\n /// <summary>\\n /// Alias for torch.div_() with rounding_mode=None.\\n /// </summary>\\n public static Tensor true_divide_(Tensor dividend, Tensor divisor) => dividend.true_divide_(divisor);\\n\\n // https://pytorch.org/docs/stable/generated/torch.trunc\\n /// <summary>\\n /// Returns a new tensor with the truncated integer values of the elements of input.\\n /// </summary>\\n /// <param name=\\\"input\\\">The input tensor.</param>\\n [Pure]\\n public static Tensor trunc(Tensor input) => input.trunc();\\n\\n // https://pytorch.org/docs/stable/generated/torch.trunc\\n /// <summary>\\n /// Replaces each element with the truncated integer values of the elements of input.\\n /// </summary>\\n /// <param name=\\\"input\\\">The input tensor.</param>\\n public static Tensor trunc_(Tensor input) => input.trunc_();\\n\\n // https://pytorch.org/docs/stable/generated/torch.xlogy\\n /// <summary>\\n /// Computes x * log(y)\\n /// </summary>\\n /// <param name=\\\"x\\\">The 'x' operand.</param>\\n /// <param name=\\\"y\\\">The 'y' operand.</param>\\n [Pure]\\n public static Tensor xlogy(Tensor x, Tensor y) => x.xlogy(y);\\n\\n // https://pytorch.org/docs/stable/generated/torch.xlogy\\n /// <summary>\\n /// Computes x * log(y) in place\\n /// </summary>\\n /// <param name=\\\"x\\\">The 'x' operand.</param>\\n /// <param name=\\\"y\\\">The 'y' operand.</param>\\n public static Tensor xlogy_(Tensor x, Tensor y) => x.xlogy_(y);\\n\\n // https://pytorch.org/docs/stable/generated/torch.xlogy\\n /// <summary>\\n /// Computes x * log(y)\\n /// </summary>\\n /// <param name=\\\"x\\\">The 'x' operand.</param>\\n /// <param name=\\\"y\\\">The 'y' operand.</param>\\n [Pure]\\n public static Tensor xlogy(Tensor x, Scalar y) => x.xlogy(y);\\n\\n // https://pytorch.org/docs/stable/generated/torch.xlogy\\n /// <summary>\\n /// Computes x * log(y) in place\\n /// </summary>\\n /// <param name=\\\"x\\\">The 'x' operand.</param>\\n /// <param name=\\\"y\\\">The 'y' operand.</param>\\n public static Tensor xlogy_(Tensor x, Scalar y) => x.xlogy_(y);\\n }\\n}\"\n },\n {\n \"alpha_fraction\": 0.7084447741508484,\n \"alphanum_fraction\": 0.7176156044006348,\n \"avg_line_length\": 57.17777633666992,\n \"blob_id\": \"31a107ecd71f9c25089128952e275abe1991c603\",\n \"content_id\": \"8884e02790c5ebc92391c4c96db1a2fae3d199ab\",\n \"detected_licenses\": [\n \"MIT\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"C#\",\n \"length_bytes\": 2617,\n \"license_type\": \"permissive\",\n \"max_line_length\": 206,\n \"num_lines\": 45,\n \"path\": \"/src/TorchVision/LibTorchSharp.cs\",\n \"repo_name\": \"NiklasGustafsson/TorchSharp\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"// Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information.\\nusing System;\\nusing System.Runtime.InteropServices;\\n\\nnamespace TorchSharp\\n{\\n internal static class NativeMethods\\n {\\n /* Tensor THSVision_AdjustHue(const Tensor i, const double hue_factor) */\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern IntPtr THSVision_AdjustHue(IntPtr img, double hue_factor);\\n\\n // EXPORT_API(Tensor) THSVision_ApplyGridTransform(Tensor img, Tensor grid, const int8_t m, const float* fill, const int64_t fill_length);\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern IntPtr THSVision_ApplyGridTransform(IntPtr img, IntPtr grid, sbyte mode, IntPtr fill,\\n long fill_length);\\n\\n /* Tensor THSVision_GenerateAffineGrid(Tensor theta, const int64_t w, const int64_t h, const int64_t ow, const int64_t oh); */\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern IntPtr THSVision_GenerateAffineGrid(IntPtr theta, long w, long h, long ow, long oh);\\n\\n /* Tensor THSVision_ComputeOutputSize(const float* matrix, const int64_t matrix_length, const int64_t w, const int64_t h); */\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern void THSVision_ComputeOutputSize(IntPtr matrix, long matrix_length, long w, long h,\\n out int first, out int second);\\n\\n /* Tensor THSVision_PerspectiveGrid(const float* coeffs, const int64_t coeffs_length, const int64_t ow, const int64_t oh, const int8_t scalar_type, const int device_type, const int device_index); */\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern IntPtr THSVision_PerspectiveGrid(IntPtr coeffs, long coeffs_length, long ow, long oh,\\n sbyte dtype, int device_type, int device_index);\\n\\n /* Tensor THSVision_ScaleChannel(Tensor ic); */\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern IntPtr THSVision_ScaleChannel(IntPtr img);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern void THSVision_BRGA_RGB(IntPtr inputBytes, IntPtr redBytes, IntPtr greenBytes, IntPtr blueBytes, long inputChannelCount, long imageSize);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern void THSVision_BRGA_RGBA(IntPtr inputBytes, IntPtr redBytes, IntPtr greenBytes, IntPtr blueBytes, IntPtr alphaBytes, long inputChannelCount, long imageSize);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern void THSVision_RGB_BRGA(IntPtr inputBytes, IntPtr outBytes, long inputChannelCount, long imageSize);\\n }\\n}\"\n },\n {\n \"alpha_fraction\": 0.49823907017707825,\n \"alphanum_fraction\": 0.49948206543922424,\n \"avg_line_length\": 30.34415626525879,\n \"blob_id\": \"eb584a341ac54b0943aa3f4b4c541f09749788e2\",\n \"content_id\": \"129753d35374ef60700acd8c42f4407bee937305\",\n \"detected_licenses\": [\n \"MIT\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"C#\",\n \"length_bytes\": 4827,\n \"license_type\": \"permissive\",\n \"max_line_length\": 156,\n \"num_lines\": 154,\n \"path\": \"/src/TorchSharp/NN/ParameterList.cs\",\n \"repo_name\": \"NiklasGustafsson/TorchSharp\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"// Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information.\\nusing System.Collections;\\nusing System.Collections.Generic;\\nusing static TorchSharp.torch.nn;\\n\\nnamespace TorchSharp\\n{\\n using System.Linq;\\n using Modules;\\n\\n namespace Modules\\n {\\n /// <summary>\\n /// Holds parameters in a list.\\n /// ParameterList can be indexed like a regular list, but parameters it contains are properly registered, and will be visible by all Module methods.\\n /// </summary>\\n public class ParameterList : Module, IList<Parameter>\\n {\\n public ParameterList(params Parameter[] parameters) : base(nameof(ParameterList))\\n {\\n if (parameters != null && parameters.Length > 0) {\\n _list.AddRange(parameters);\\n }\\n }\\n\\n protected override void RegisterComponents()\\n {\\n if (_registered) return;\\n\\n for (int i = 0; i < _list.Count; i++) {\\n register_parameter($\\\"{i}\\\", _list[i]);\\n }\\n _registered = true;\\n }\\n\\n public override IEnumerable<(string name, Parameter parameter)> named_parameters(bool recurse = true)\\n {\\n return Enumerable.Range(0, _list.Count).Select(i => ($\\\"{i}\\\", _list[i]));\\n }\\n\\n public override bool has_parameter(string target)\\n {\\n return int.TryParse(target, out int idx) && idx > -1 && idx < _list.Count && _list[idx] is not null;\\n }\\n\\n public override Parameter get_parameter(string target)\\n {\\n if (int.TryParse(target, out int idx) && idx > -1 && idx < _list.Count) {\\n return _list[idx];\\n }\\n return null;\\n }\\n\\n private bool _registered = false;\\n\\n public Parameter this[int index] {\\n get => _list[index];\\n set => _list[index] = value;\\n }\\n\\n public int Count => _list.Count;\\n\\n public bool IsReadOnly => false;\\n\\n public void Add(Parameter item)\\n {\\n _list.Add(item);\\n }\\n\\n // The following two funtions are here because PyTorch defines them.\\n\\n /// <summary>\\n /// Appends parameters from an enumeration to the end of the list.\\n /// </summary>\\n /// <param name=\\\"parameters\\\"></param>\\n public void extend(IEnumerable<Parameter> parameters)\\n {\\n _list.AddRange(parameters);\\n }\\n\\n /// <summary>\\n /// Appends zero or more parameters to the end of the list.\\n /// </summary>\\n public void append(params Parameter[] parameters)\\n {\\n if (parameters != null && parameters.Length > 0) {\\n _list.AddRange(parameters);\\n }\\n }\\n\\n public void Clear()\\n {\\n _list.Clear();\\n }\\n\\n public bool Contains(Parameter item)\\n {\\n return _list.Contains(item);\\n }\\n\\n public void CopyTo(Parameter[] array, int arrayIndex)\\n {\\n _list.CopyTo(array, arrayIndex);\\n }\\n\\n public IEnumerator<Parameter> GetEnumerator()\\n {\\n return _list.GetEnumerator();\\n }\\n\\n public int IndexOf(Parameter item)\\n {\\n return _list.IndexOf(item);\\n }\\n\\n public void Insert(int index, Parameter item)\\n {\\n _list.Insert(index, item);\\n }\\n\\n public bool Remove(Parameter item)\\n {\\n return _list.Remove(item);\\n }\\n\\n public void RemoveAt(int index)\\n {\\n _list.RemoveAt(index);\\n }\\n\\n IEnumerator IEnumerable.GetEnumerator()\\n {\\n return GetEnumerator();\\n }\\n\\n private List<Parameter> _list = new List<Parameter>();\\n }\\n }\\n\\n public static partial class torch\\n {\\n public static partial class nn\\n {\\n /// <summary>\\n /// Create a ParameterList instance from an array of parameter tensors.\\n /// </summary>\\n /// <param name=\\\"parameters\\\">A list of modules.</param>\\n /// <remarks>\\n /// ParameterList can be indexed like a regular list, but the parameters it contains are properly registered.\\n /// </remarks>\\n public static ParameterList ParameterList(params Parameter[] parameters) => new ParameterList(parameters);\\n }\\n }\\n}\\n\"\n },\n {\n \"alpha_fraction\": 0.546951413154602,\n \"alphanum_fraction\": 0.5482556223869324,\n \"avg_line_length\": 42.50354766845703,\n \"blob_id\": \"496925f4af86e870ef9a3b5bfee49b255b170f58\",\n \"content_id\": \"047751d0afae96ee1eccf0b0ca4d297d24339362\",\n \"detected_licenses\": [\n \"MIT\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"C#\",\n \"length_bytes\": 6134,\n \"license_type\": \"permissive\",\n \"max_line_length\": 149,\n \"num_lines\": 141,\n \"path\": \"/src/TorchSharp/Distributions/Uniform.cs\",\n \"repo_name\": \"NiklasGustafsson/TorchSharp\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"// Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information.\\nusing System;\\nusing static TorchSharp.torch;\\n\\nnamespace TorchSharp\\n{\\n using Modules;\\n\\n namespace Modules\\n {\\n /// <summary>\\n /// Generates uniformly distributed random samples from the half-open interval [low, high[.\\n /// </summary>\\n public class Uniform : torch.distributions.Distribution\\n {\\n /// <summary>\\n /// The mean of the distribution.\\n /// </summary>\\n public override Tensor mean =>\\n WrappedTensorDisposeScope(() => (high + low) / 2);\\n\\n public override Tensor mode => double.NaN * high;\\n\\n /// <summary>\\n /// The variance of the distribution\\n /// </summary>\\n public override Tensor variance =>\\n WrappedTensorDisposeScope(() => (high - low).pow(2) / 12);\\n\\n /// <summary>\\n /// Constructor\\n /// </summary>\\n /// <param name=\\\"low\\\">Lower bound (inclusive)</param>\\n /// <param name=\\\"high\\\">Upper bound (exclusive)</param>\\n /// <param name=\\\"generator\\\">An optional random number generator object.</param>\\n public Uniform(Tensor low, Tensor high, torch.Generator generator = null) : base(generator, low.size())\\n {\\n var lowHigh = torch.broadcast_tensors(low, high);\\n this.low = lowHigh[0].DetachFromDisposeScope();\\n this.high = lowHigh[1].DetachFromDisposeScope();\\n }\\n\\n private Tensor high;\\n private Tensor low;\\n\\n /// <summary>\\n /// Generates a sample_shape shaped reparameterized sample or sample_shape shaped batch of reparameterized samples\\n /// if the distribution parameters are batched.\\n /// </summary>\\n /// <param name=\\\"sample_shape\\\">The sample shape.</param>\\n public override Tensor rsample(params long[] sample_shape)\\n {\\n using var _ = torch.NewDisposeScope();\\n var shape = ExtendedShape(sample_shape);\\n var rand = torch.rand(shape, dtype: low.dtype, device: low.device, generator: generator);\\n return (low + rand * (high - low)).MoveToOuterDisposeScope();\\n }\\n\\n /// <summary>\\n /// Returns the log of the probability density/mass function evaluated at `value`.\\n /// </summary>\\n /// <param name=\\\"value\\\"></param>\\n public override Tensor log_prob(Tensor value)\\n {\\n using var _ = torch.NewDisposeScope();\\n var lb = low.le(value).type_as(low);\\n var ub = high.gt(value).type_as(low);\\n return (torch.log(lb.mul(ub)) - torch.log(high - low)).MoveToOuterDisposeScope();\\n }\\n\\n /// <summary>\\n /// Returns the cumulative density/mass function evaluated at `value`.\\n /// </summary>\\n /// <param name=\\\"value\\\"></param>\\n public override Tensor cdf(Tensor value)\\n {\\n return torch.WrappedTensorDisposeScope(() => ((value - low) / (high - low)).clamp_(0, 1));\\n }\\n\\n /// <summary>\\n /// Returns the inverse cumulative density/mass function evaluated at `value`.\\n /// </summary>\\n /// <param name=\\\"value\\\"></param>\\n public override Tensor icdf(Tensor value)\\n {\\n return torch.WrappedTensorDisposeScope(() => (value * (high - low)).add_(low));\\n }\\n\\n /// <summary>\\n /// Returns entropy of distribution, batched over batch_shape.\\n /// </summary>\\n public override Tensor entropy()\\n {\\n return torch.WrappedTensorDisposeScope(() => (high - low).log_());\\n }\\n\\n /// <summary>\\n /// Returns a new distribution instance (or populates an existing instance provided by a derived class) with batch dimensions expanded to\\n /// `batch_shape`. This method calls `torch.Tensor.expand()` on the distribution's parameters. As such, this does not allocate new\\n /// memory for the expanded distribution instance.\\n /// </summary>\\n /// <param name=\\\"batch_shape\\\">Tthe desired expanded size.</param>\\n /// <param name=\\\"instance\\\">new instance provided by subclasses that need to override `.expand`.</param>\\n public override distributions.Distribution expand(Size batch_shape, distributions.Distribution instance = null)\\n {\\n if (instance != null && !(instance is Uniform))\\n throw new ArgumentException(\\\"expand(): 'instance' must be a Uniform distribution\\\");\\n\\n var newDistribution = ((instance == null) ?\\n new Uniform(low.expand(batch_shape), high.expand(batch_shape), generator) :\\n instance) as Uniform;\\n\\n newDistribution.batch_shape = batch_shape;\\n if (newDistribution == instance)\\n {\\n newDistribution.low = low.expand(batch_shape);\\n newDistribution.high = high.expand(batch_shape);\\n }\\n return newDistribution;\\n }\\n }\\n\\n }\\n public static partial class torch\\n {\\n public static partial class distributions\\n {\\n /// <summary>\\n /// Generates uniformly distributed random samples from the half-open interval [low, high[.\\n /// </summary>\\n /// <param name=\\\"low\\\">Lower bound (inclusive)</param>\\n /// <param name=\\\"high\\\">Upper bound (exclusive)</param>\\n /// <param name=\\\"generator\\\">An optional random number generator object.</param>\\n /// <returns></returns>\\n public static Uniform Uniform(Tensor low, Tensor high, torch.Generator generator = null)\\n {\\n return new Uniform(low, high, generator);\\n }\\n }\\n }\\n}\\n\"\n },\n {\n \"alpha_fraction\": 0.6966068148612976,\n \"alphanum_fraction\": 0.7001996040344238,\n \"avg_line_length\": 36.01970291137695,\n \"blob_id\": \"d5e6f8e37ec41423493c2d075b54a69f00e71124\",\n \"content_id\": \"42fd493ee202f49d0720ccfed9dc2240296b579e\",\n \"detected_licenses\": [\n \"MIT\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"C++\",\n \"length_bytes\": 7515,\n \"license_type\": \"permissive\",\n \"max_line_length\": 234,\n \"num_lines\": 203,\n \"path\": \"/src/Native/LibTorchSharp/THSOptimizers.cpp\",\n \"repo_name\": \"NiklasGustafsson/TorchSharp\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"// Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information.\\n#include \\\"THSNN.h\\\"\\n\\n#include <torch/nn/init.h>\\n\\nvoid THSNN_Optimizer_getParameters(const Optimizer optimizer, Tensor* (*allocator)(size_t length))\\n{\\n auto parameters = (*optimizer)->parameters();\\n Tensor* result = allocator(parameters.size());\\n\\n for (size_t i = 0; i < parameters.size(); i++)\\n {\\n result[i] = ResultTensor(parameters[i]);\\n }\\n}\\n\\nTensor THSNN_Optimizer_step(const Optimizer optimizer, Tensor(*loss_closure)())\\n{\\n CATCH_TENSOR((loss_closure == nullptr) ? (*optimizer)->step() : (*optimizer)->step([loss_closure]() -> at::Tensor { return *(loss_closure()); }));\\n}\\n\\nvoid THSNN_Optimizer_zero_grad(const Optimizer optimizer)\\n{\\n (*optimizer)->zero_grad();\\n auto defaults = (*optimizer)->defaults();\\n}\\n\\nOptimizer THSNN_Adagrad_ctor(const Tensor* parameters, const int length, const double learning_rate, const double lr_decay, const double weight_decay, const double initial_accumulator_value, const double eps)\\n{\\n auto params = toTensors<at::Tensor>((torch::Tensor**)parameters, length);\\n auto options = torch::optim::AdagradOptions(learning_rate)\\n .lr_decay(lr_decay)\\n .weight_decay(weight_decay)\\n .initial_accumulator_value(initial_accumulator_value)\\n .eps(eps);\\n\\n return new std::shared_ptr<torch::optim::Optimizer>(std::make_shared<torch::optim::Adagrad>(torch::optim::Adagrad(params, options)));\\n}\\n\\nOptimizer THSNN_Adam_ctor(const Tensor* parameters, const int length, const double learning_rate, const double beta1, const double beta2, const double eps, const double weight_decay, const bool amsgrad)\\n{\\n auto params = toTensors<at::Tensor>((torch::Tensor**)parameters, length);\\n auto options = torch::optim::AdamOptions(learning_rate)\\n .betas(std::make_tuple(beta1, beta2))\\n .eps(eps)\\n .weight_decay(weight_decay)\\n .amsgrad(amsgrad);\\n\\n return new std::shared_ptr<torch::optim::Optimizer>(std::make_shared<torch::optim::Adam>(torch::optim::Adam(params, options)));\\n}\\n\\nOptimizer THSNN_AdamW_ctor(const Tensor* parameters, const int length, const double learning_rate, const double beta1, const double beta2, const double eps, const double weight_decay, const bool amsgrad)\\n{\\n auto params = toTensors<at::Tensor>((torch::Tensor**)parameters, length);\\n auto options = torch::optim::AdamWOptions(learning_rate)\\n .betas(std::make_tuple(beta1, beta2))\\n .eps(eps)\\n .weight_decay(weight_decay)\\n .amsgrad(amsgrad);\\n\\n return new std::shared_ptr<torch::optim::Optimizer>(std::make_shared<torch::optim::AdamW>(torch::optim::AdamW(params, options)));\\n}\\n\\nOptimizer THSNN_LBFGS_ctor(const Tensor* parameters, const int length, const double learning_rate, const int64_t max_iter, const int64_t max_eval, const double tolerange_grad, const double tolerance_change, const int64_t history_size)\\n{\\n auto params = toTensors<at::Tensor>((torch::Tensor**)parameters, length);\\n auto options = torch::optim::LBFGSOptions(learning_rate)\\n .max_iter(max_iter)\\n .max_eval(max_eval)\\n .tolerance_grad(tolerange_grad)\\n .tolerance_change(tolerance_change)\\n .history_size(history_size);\\n\\n return new std::shared_ptr<torch::optim::Optimizer>(std::make_shared<torch::optim::LBFGS>(torch::optim::LBFGS(params, options)));\\n}\\n\\nOptimizer THSNN_RMSprop_ctor(const Tensor* parameters, const int length, const double learning_rate, const double alpha, const double eps, const double weight_decay, const double momentum, const bool centered)\\n{\\n auto params = toTensors<at::Tensor>((torch::Tensor**)parameters, length);\\n\\n auto options = torch::optim::RMSpropOptions(learning_rate)\\n .alpha(alpha)\\n .eps(eps)\\n .weight_decay(weight_decay)\\n .momentum(momentum)\\n .centered(centered);\\n\\n return new std::shared_ptr<torch::optim::Optimizer>(std::make_shared<torch::optim::RMSprop>(torch::optim::RMSprop(params, options)));\\n}\\n\\nOptimizer THSNN_SGD_ctor(const Tensor* parameters, const int length, const double learning_rate, const double momentum, const double dampening, const double weight_decay, const bool nesterov)\\n{\\n auto params = toTensors<at::Tensor>((torch::Tensor**)parameters, length);\\n auto opts = torch::optim::SGDOptions(learning_rate)\\n .momentum(momentum)\\n .dampening(dampening)\\n .weight_decay(weight_decay)\\n .nesterov(nesterov);\\n\\n return new std::shared_ptr<torch::optim::Optimizer>(std::make_shared<torch::optim::SGD>(torch::optim::SGD(params, opts)));\\n}\\n\\n\\n// Scheduler integration\\n\\ntemplate<typename OptionsType>\\nvoid SetLearningRate(const Optimizer optimizer, const double lr)\\n{\\n auto options = dynamic_cast<OptionsType*>(&(*optimizer)->defaults());\\n options->lr(lr);\\n\\n auto& pgs = (*optimizer)->param_groups();\\n for (auto pg = pgs.begin(); pg < pgs.end(); ++pg)\\n {\\n options = dynamic_cast<OptionsType*>(&(pg->options()));\\n options->lr(lr);\\n }\\n}\\n\\ntemplate<typename OptionsType>\\nvoid SetMomentum(const Optimizer optimizer, const double momentum)\\n{\\n auto options = dynamic_cast<OptionsType*>(&(*optimizer)->defaults());\\n options->momentum(momentum);\\n\\n auto& pgs = (*optimizer)->param_groups();\\n for (auto pg = pgs.begin(); pg < pgs.end(); ++pg)\\n {\\n options = dynamic_cast<OptionsType*>(&(pg->options()));\\n options->momentum(momentum);\\n }\\n}\\n\\ntemplate<typename OptionsType>\\nvoid SetBetas(const Optimizer optimizer, const double beta1, const double beta2)\\n{\\n auto betas = std::make_tuple(beta1, beta2);\\n\\n auto options = dynamic_cast<OptionsType*>(&(*optimizer)->defaults());\\n options->betas(betas);\\n\\n auto& pgs = (*optimizer)->param_groups();\\n for (auto pg = pgs.begin(); pg < pgs.end(); ++pg)\\n {\\n options = dynamic_cast<OptionsType*>(&(pg->options()));\\n options->betas(betas);\\n }\\n}\\n\\nvoid THSNN_Adagrad_set_lr(const Optimizer optimizer, const double lr)\\n{\\n SetLearningRate<torch::optim::AdagradOptions>(optimizer, lr);\\n}\\n\\nvoid THSNN_Adam_set_lr(const Optimizer optimizer, const double lr)\\n{\\n SetLearningRate<torch::optim::AdamOptions>(optimizer, lr);\\n}\\n\\nvoid THSNN_AdamW_set_lr(const Optimizer optimizer, const double lr)\\n{\\n SetLearningRate<torch::optim::AdamWOptions>(optimizer, lr);\\n}\\n\\nvoid THSNN_RMSprop_set_lr(const Optimizer optimizer, const double lr)\\n{\\n SetLearningRate<torch::optim::RMSpropOptions>(optimizer, lr);\\n}\\n\\nvoid THSNN_LBFGS_set_lr(const Optimizer optimizer, const double lr)\\n{\\n SetLearningRate<torch::optim::LBFGSOptions>(optimizer, lr);\\n}\\n\\nvoid THSNN_SGD_set_lr(const Optimizer optimizer, const double lr)\\n{\\n SetLearningRate<torch::optim::SGDOptions>(optimizer, lr);\\n}\\n\\nvoid THSNN_Optimizer_dispose(const Optimizer optimizer)\\n{\\n delete optimizer; // NOTE: this reduces the ref count on the shared_ptr\\n}\\n\\nvoid THSNN_Adam_set_betas(const Optimizer optimizer, double beta1, double beta2)\\n{\\n SetBetas<torch::optim::AdamOptions>(optimizer, beta1, beta2);\\n}\\n\\nvoid THSNN_AdamW_set_betas(const Optimizer optimizer, double beta1, double beta2)\\n{\\n SetBetas<torch::optim::AdamWOptions>(optimizer, beta1, beta2);\\n}\\n\\nvoid THSNN_RMSprop_set_momentum(const Optimizer optimizer, double momentum)\\n{\\n SetMomentum<torch::optim::RMSpropOptions>(optimizer, momentum);\\n}\\n\\nvoid THSNN_SGD_set_momentum(const Optimizer optimizer, double momentum)\\n{\\n SetMomentum<torch::optim::SGDOptions>(optimizer, momentum);\\n}\\n\"\n },\n {\n \"alpha_fraction\": 0.6264303922653198,\n \"alphanum_fraction\": 0.6299987435340881,\n \"avg_line_length\": 52.64521408081055,\n \"blob_id\": \"caad04d8ac7c7c6fb283494d9275cc86e2166e83\",\n \"content_id\": \"8e062c7863cbfdf170583d1ac3ab11463e5df032\",\n \"detected_licenses\": [\n \"MIT\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"C#\",\n \"length_bytes\": 32509,\n \"license_type\": \"permissive\",\n \"max_line_length\": 183,\n \"num_lines\": 606,\n \"path\": \"/src/TorchSharp/Tensor/torch.IndexingSlicingJoiningMutatingOps.cs\",\n \"repo_name\": \"NiklasGustafsson/TorchSharp\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"// Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information.\\n#nullable enable\\nusing System;\\nusing System.Collections.Generic;\\nusing System.Diagnostics.Contracts;\\nusing System.Linq;\\nusing static TorchSharp.PInvoke.NativeMethods;\\n\\nnamespace TorchSharp\\n{\\n // https://pytorch.org/docs/stable/torch#indexing-slicing-joining-mutating-ops\\n public static partial class torch\\n {\\n // https://pytorch.org/docs/stable/generated/torch.adjoint\\n /// <summary>\\n /// Returns a view of the tensor conjugated and with the last two dimensions transposed.\\n /// </summary>\\n /// <param name=\\\"input\\\">The input tensor</param>\\n public static Tensor adjoint(Tensor input) => input.adjoint();\\n\\n // https://pytorch.org/docs/stable/generated/torch.argwhere\\n /// <summary>\\n /// Returns a tensor containing the indices of all non-zero elements of input.\\n /// Each row in the result contains the indices of a non-zero element in input.\\n /// The result is sorted lexicographically, with the last index changing the fastest (C-style).\\n /// If input has n dimensions, then the resulting indices tensor out is of size (z×n), where\\n /// z is the total number of non-zero elements in the input tensor.\\n /// </summary>\\n public static Tensor argwhere(Tensor input) => input.argwhere();\\n\\n // https://pytorch.org/docs/stable/generated/torch.cat\\n /// <summary>\\n /// Concatenates the given sequence of tensors in the given dimension.\\n /// </summary>\\n /// <param name=\\\"tensors\\\">A sequence of tensors of the same type. Non-empty tensors provided must have the same shape, except in the cat dimension.</param>\\n /// <param name=\\\"dim\\\">The dimension over which the tensors are concatenated</param>\\n /// <remarks> All tensors must either have the same shape (except in the concatenating dimension) or be empty.</remarks>\\n public static Tensor cat(IList<Tensor> tensors, long dim = 0)\\n {\\n switch (tensors.Count)\\n {\\n case <=0: throw new ArgumentException(nameof(tensors));\\n case 1: return tensors[0];\\n }\\n\\n using var parray = new PinnedArray<IntPtr>();\\n IntPtr tensorsRef = parray.CreateArray(tensors.Select(p => p.Handle).ToArray());\\n\\n var res = THSTensor_cat(tensorsRef, parray.Array.Length, dim);\\n if (res == IntPtr.Zero) CheckForErrors();\\n return new Tensor(res);\\n }\\n\\n // https://pytorch.org/docs/stable/generated/torch.concat\\n /// <summary>\\n /// Alias of torch.cat()\\n /// </summary>\\n /// <param name=\\\"tensors\\\">A sequence of tensors of the same type. Non-empty tensors provided must have the same shape, except in the cat dimension.</param>\\n /// <param name=\\\"dim\\\">The dimension over which the tensors are concatenated</param>\\n /// <remarks> All tensors must either have the same shape (except in the concatenating dimension) or be empty.</remarks>\\n public static Tensor concat(IList<Tensor> tensors, long dim = 0) => torch.cat(tensors, dim);\\n\\n // https://pytorch.org/docs/stable/generated/torch.conj\\n /// <summary>\\n /// Returns a view of input with a flipped conjugate bit. If input has a non-complex dtype, this function just returns input.\\n /// </summary>\\n /// <param name=\\\"input\\\">The input tensor.</param>\\n public static Tensor conj(Tensor input) => input.conj();\\n\\n // https://pytorch.org/docs/stable/generated/torch.chunk\\n public static Tensor[] chunk(Tensor input, long chunks, long dim = 0)\\n => input.chunk(chunks, dim);\\n\\n // https://pytorch.org/docs/stable/generated/torch.dsplit\\n public static Tensor[] dsplit(Tensor input, Tensor indices_or_sections)\\n => input.dsplit(indices_or_sections);\\n\\n // https://pytorch.org/docs/stable/generated/torch.dsplit\\n public static Tensor[] dsplit(Tensor input, long size)\\n => input.dsplit(size);\\n\\n // https://pytorch.org/docs/stable/generated/torch.dsplit\\n public static Tensor[] dsplit(Tensor input, long[] indices_or_sections)\\n => input.dsplit(indices_or_sections);\\n\\n // https://pytorch.org/docs/stable/generated/torch.dsplit\\n public static Tensor[] dsplit(Tensor input, (long, long) indices_or_sections)\\n => input.dsplit(indices_or_sections);\\n\\n // https://pytorch.org/docs/stable/generated/torch.dsplit\\n public static Tensor[] dsplit(Tensor input, (long, long, long) indices_or_sections)\\n => input.dsplit(indices_or_sections);\\n\\n // https://pytorch.org/docs/stable/generated/torch.dsplit\\n public static Tensor[] dsplit(Tensor input, (long, long, long, long) indices_or_sections)\\n => input.dsplit(indices_or_sections);\\n\\n // https://pytorch.org/docs/stable/generated/torch.dstack\\n /// <summary>\\n /// Stack tensors in sequence depthwise (along third axis).\\n /// </summary>\\n /// <param name=\\\"tensors\\\"></param>\\n /// <returns></returns>\\n /// <remarks>This is equivalent to concatenation along the third axis after 1-D and 2-D tensors have been reshaped by torch.atleast_3d().</remarks>\\n public static Tensor dstack(params Tensor[] tensors)\\n => dstack((IEnumerable<Tensor>)tensors);\\n\\n // https://pytorch.org/docs/stable/generated/torch.dstack\\n /// <summary>\\n /// Stack tensors in sequence depthwise (along third axis).\\n /// </summary>\\n /// <param name=\\\"tensors\\\"></param>\\n /// <returns></returns>\\n /// <remarks>This is equivalent to concatenation along the third axis after 1-D and 2-D tensors have been reshaped by torch.atleast_3d().</remarks>\\n public static Tensor dstack(IList<Tensor> tensors)\\n {\\n using (var parray = new PinnedArray<IntPtr>()) {\\n IntPtr tensorsRef = parray.CreateArray(tensors.Select(p => p.Handle).ToArray());\\n\\n var res = THSTensor_dstack(tensorsRef, parray.Array.Length);\\n if (res == IntPtr.Zero) { torch.CheckForErrors(); }\\n return new Tensor(res);\\n }\\n }\\n\\n /// <summary>\\n /// Stack tensors in sequence depthwise (along third axis).\\n /// </summary>\\n /// <param name=\\\"tensors\\\"></param>\\n /// <returns></returns>\\n /// <remarks>This is equivalent to concatenation along the third axis after 1-D and 2-D tensors have been reshaped by torch.atleast_3d().</remarks>\\n public static Tensor dstack(IEnumerable<Tensor> tensors)\\n {\\n using var parray = new PinnedArray<IntPtr>();\\n IntPtr tensorsRef = parray.CreateArray(tensors.Select(p => p.Handle).ToArray());\\n var res = THSTensor_dstack(tensorsRef, parray.Array.Length);\\n if (res == IntPtr.Zero) { CheckForErrors(); }\\n return new Tensor(res);\\n }\\n \\n // https://pytorch.org/docs/stable/generated/torch.gather\\n /// <summary>\\n /// Gathers values along an axis specified by dim.\\n /// </summary>\\n public static Tensor gather(Tensor input, long dim, Tensor index) => input.gather(dim, index);\\n\\n // https://pytorch.org/docs/stable/generated/torch.gather\\n // TODO: implement parameter sparse_grad\\n public static Tensor gather(Tensor input, long dim, Tensor index, bool sparse_grad=false)\\n => input.gather(dim, index);\\n\\n // https://pytorch.org/docs/stable/generated/torch.hsplit\\n public static Tensor[] hsplit(Tensor input, Tensor indices_or_sections)\\n => input.hsplit(indices_or_sections);\\n\\n // https://pytorch.org/docs/stable/generated/torch.hsplit\\n public static Tensor[] hsplit(Tensor input, long indices_or_sections)\\n => input.hsplit(indices_or_sections);\\n\\n // https://pytorch.org/docs/stable/generated/torch.hsplit\\n public static Tensor[] hsplit(Tensor input, long[] indices_or_sections)\\n => input.hsplit(indices_or_sections);\\n\\n // https://pytorch.org/docs/stable/generated/torch.hsplit\\n public static Tensor[] hsplit(Tensor input, (long, long) indices_or_sections)\\n => input.hsplit(new[]{\\n indices_or_sections.Item1,\\n indices_or_sections.Item2\\n });\\n\\n // https://pytorch.org/docs/stable/generated/torch.hsplit\\n public static Tensor[] hsplit(Tensor input, (long, long, long) indices_or_sections)\\n => input.hsplit(new[]{\\n indices_or_sections.Item1,\\n indices_or_sections.Item2,\\n indices_or_sections.Item3\\n });\\n\\n // https://pytorch.org/docs/stable/generated/torch.hsplit\\n public static Tensor[] hsplit(Tensor input, (long, long, long, long) indices_or_sections)\\n => input.hsplit(new[]{\\n indices_or_sections.Item1,\\n indices_or_sections.Item2,\\n indices_or_sections.Item3,\\n indices_or_sections.Item4\\n });\\n\\n // https://pytorch.org/docs/stable/generated/torch.hstack\\n /// <summary>\\n /// Stack tensors in sequence horizontally (column wise).\\n /// </summary>\\n /// <param name=\\\"tensors\\\"></param>\\n /// <returns></returns>\\n public static Tensor hstack(IList<Tensor> tensors)\\n {\\n using var parray = new PinnedArray<IntPtr>();\\n IntPtr tensorsRef = parray.CreateArray(tensors.Select(p => p.Handle).ToArray());\\n\\n var res = THSTensor_hstack(tensorsRef, parray.Array.Length);\\n if (res == IntPtr.Zero) { torch.CheckForErrors(); }\\n return new Tensor(res);\\n }\\n\\n // https://pytorch.org/docs/stable/generated/torch.hstack\\n /// <summary>\\n /// Stack tensors in sequence horizontally (column wise).\\n /// </summary>\\n /// <param name=\\\"tensors\\\"></param>\\n /// <returns></returns>\\n public static Tensor hstack(params Tensor[] tensors)\\n {\\n return hstack((IEnumerable<Tensor>)tensors);\\n }\\n\\n // https://pytorch.org/docs/stable/generated/torch.hstack\\n /// <summary>\\n /// Stack tensors in sequence horizontally (column wise).\\n /// </summary>\\n /// <param name=\\\"tensors\\\"></param>\\n /// <returns></returns>\\n public static Tensor hstack(IEnumerable<Tensor> tensors)\\n {\\n using var parray = new PinnedArray<IntPtr>();\\n IntPtr tensorsRef = parray.CreateArray(tensors.Select(p => p.Handle).ToArray());\\n\\n var res = THSTensor_hstack(tensorsRef, parray.Array.Length);\\n if (res == IntPtr.Zero) { CheckForErrors(); }\\n return new Tensor(res);\\n }\\n\\n // https://pytorch.org/docs/stable/generated/torch.index_add\\n /// <summary>\\n /// Accumulate the elements of alpha times source into the input tensor by adding to the indices in the order given in index.\\n ///\\n /// For example, if dim == 0, index[i] == j, and alpha=-1, then the ith row of source is subtracted from the jth row of the input tensor.\\n /// The dimth dimension of source must have the same size as the length of index(which must be a vector), and all other dimensions must match self, or an error will be raised.\\n /// </summary>\\n /// <param name=\\\"input\\\">The input tensor</param>\\n /// <param name=\\\"dim\\\">Dimension along which to index</param>\\n /// <param name=\\\"index\\\">Indices of source to select from, should have dtype either torch.int64 or torch.int32</param>\\n /// <param name=\\\"source\\\">The tensor containing values to add</param>\\n /// <param name=\\\"alpha\\\">The scalar multiplier for source</param>\\n /// <returns></returns>\\n public static Tensor index_add(Tensor input, long dim, Tensor index, Tensor source, Scalar alpha)\\n => input.index_add(dim, index, source, alpha);\\n\\n // https://pytorch.org/docs/stable/generated/torch.index_add\\n /// <summary>\\n /// Accumulate, in place, the elements of alpha times source into the input tensor by adding to the indices in the order given in index.\\n ///\\n /// For example, if dim == 0, index[i] == j, and alpha=-1, then the ith row of source is subtracted from the jth row of the input tensor.\\n /// The dimth dimension of source must have the same size as the length of index(which must be a vector), and all other dimensions must match self, or an error will be raised.\\n /// </summary>\\n /// <param name=\\\"input\\\">The input tensor</param>\\n /// <param name=\\\"dim\\\">Dimension along which to index</param>\\n /// <param name=\\\"index\\\">Indices of source to select from, should have dtype either torch.int64 or torch.int32</param>\\n /// <param name=\\\"source\\\">The tensor containing values to add</param>\\n /// <param name=\\\"alpha\\\">The scalar multiplier for source</param>\\n /// <returns></returns>\\n public static Tensor index_add_(Tensor input, long dim, Tensor index, Tensor source, Scalar alpha)\\n => input.index_add_(dim, index, source, alpha);\\n\\n // https://pytorch.org/docs/stable/generated/torch.index_copy\\n /// <summary>\\n /// Copies the elements of the source tensor into the input tensor by selecting the indices in the order given in index.\\n ///\\n /// For example, if dim == 0 and index[i] == j, then the ith row of tensor is copied to the jth row of the input tensor.\\n /// The dimth dimension of source must have the same size as the length of index(which must be a vector), and all other dimensions must match self, or an error will be raised.\\n /// </summary>\\n /// <param name=\\\"input\\\">The input tensor</param>\\n /// <param name=\\\"dim\\\">Dimension along which to index</param>\\n /// <param name=\\\"index\\\">Indices of source to select from, should have dtype either torch.int64 or torch.int32</param>\\n /// <param name=\\\"source\\\">The tensor containing values to copy</param>\\n /// <returns></returns>\\n public static Tensor index_copy(Tensor input, long dim, Tensor index, Tensor source)\\n => input.index_copy(dim, index, source);\\n\\n // https://pytorch.org/docs/stable/generated/torch.index_copy\\n /// <summary>\\n /// Copies, in place, the elements of the source tensor into the input tensor by selecting the indices in the order given in index.\\n ///\\n /// For example, if dim == 0 and index[i] == j, then the ith row of tensor is copied to the jth row of the input tensor.\\n /// The dimth dimension of source must have the same size as the length of index(which must be a vector), and all other dimensions must match self, or an error will be raised.\\n /// </summary>\\n /// <param name=\\\"input\\\">The input tensor</param>\\n /// <param name=\\\"dim\\\">Dimension along which to index</param>\\n /// <param name=\\\"index\\\">Indices of source to select from, should have dtype either torch.int64 or torch.int32</param>\\n /// <param name=\\\"source\\\">The tensor containing values to copy</param>\\n /// <returns></returns>\\n public static Tensor index_copy_(Tensor input, long dim, Tensor index, Tensor source)\\n => input.index_copy_(dim, index, source);\\n\\n // https://pytorch.org/docs/stable/generated/torch.index_reduce\\n [Obsolete(\\\"not implemented\\\", true)]\\n public static Tensor index_reduce(Tensor input, long dim, Tensor index, Tensor source, Reduce reduce, bool include_self=true)\\n => throw new NotImplementedException();\\n\\n // https://pytorch.org/docs/stable/generated/torch.index_select\\n /// <summary>\\n /// Returns a new tensor which indexes the input tensor along dimension dim using the entries in index which is a LongTensor.\\n /// </summary>\\n public static Tensor index_select(Tensor input, long dim, Tensor index)\\n => input.index_select(dim, index);\\n\\n // https://pytorch.org/docs/stable/generated/torch.masked_select\\n public static Tensor masked_select(Tensor input, Tensor mask)\\n => input.masked_select(mask);\\n\\n // https://pytorch.org/docs/stable/generated/torch.movedim\\n public static Tensor movedim(Tensor input, long source, long destination)\\n => input.movedim(new[]{source}, new[]{destination});\\n\\n // https://pytorch.org/docs/stable/generated/torch.movedim\\n static Tensor movedim(Tensor input, (long, long) source, (long, long) destination)\\n => input.movedim(\\n new[]{source.Item1, source.Item2},\\n new[]{destination.Item1, destination.Item2});\\n\\n // https://pytorch.org/docs/stable/generated/torch.movedim\\n static Tensor movedim(Tensor input, (long, long, long) source, (long, long, long) destination)\\n => input.movedim(\\n new[]{source.Item1, source.Item2, source.Item3},\\n new[]{destination.Item1, destination.Item2, destination.Item3});\\n\\n // https://pytorch.org/docs/stable/generated/torch.movedim\\n static Tensor movedim(Tensor input, (long, long, long, long) source, (long, long, long, long) destination)\\n => input.movedim(\\n new[]{source.Item1, source.Item2, source.Item3, source.Item4},\\n new[]{destination.Item1, destination.Item2, destination.Item3, destination.Item4});\\n\\n // https://pytorch.org/docs/stable/generated/torch.movedim\\n public static Tensor movedim(Tensor input, long[] source, long[] destination)\\n => input.movedim(source, destination);\\n\\n // https://pytorch.org/docs/stable/generated/torch.moveaxis\\n public static Tensor moveaxis(Tensor input, long source, long destination)\\n => input.moveaxis(new[]{source}, new[]{destination});\\n\\n // https://pytorch.org/docs/stable/generated/torch.moveaxis\\n public static Tensor moveaxis(Tensor input, (long, long) source, (long, long) destination)\\n => input.moveaxis(\\n new[]{source.Item1, source.Item2 },\\n new[]{ destination.Item1, destination.Item2 });\\n\\n // https://pytorch.org/docs/stable/generated/torch.moveaxis\\n public static Tensor moveaxis(Tensor input, (long, long, long) source, (long, long, long) destination)\\n => input.moveaxis(\\n new[]{source.Item1, source.Item2, source.Item3 },\\n new[]{ destination.Item1, destination.Item2, destination.Item3 });\\n\\n public static Tensor moveaxis(Tensor input, (long, long, long, long) source, (long, long, long, long) destination)\\n => input.moveaxis(\\n new[]{source.Item1, source.Item2, source.Item3, source.Item4 },\\n new[]{ destination.Item1, destination.Item2, destination.Item3, destination.Item4 });\\n\\n public static Tensor moveaxis(Tensor input, long[] source, long[] destination)\\n => input.moveaxis(source, destination);\\n\\n // https://pytorch.org/docs/stable/generated/torch.narrow\\n public static Tensor narrow(Tensor input, long dim, long start, long length)\\n => input.narrow(dim, start, length);\\n\\n // https://pytorch.org/docs/stable/generated/torch.nonzero\\n /// <summary>\\n /// Returns a tensor containing the indices of all non-zero elements of input.\\n /// Each row in the result contains the indices of a non-zero element in input.\\n /// The result is sorted lexicographically, with the last index changing the fastest (C-style).\\n /// </summary>\\n public static Tensor nonzero(Tensor input) => input.nonzero();\\n\\n // https://pytorch.org/docs/stable/generated/torch.permute\\n /// <summary>\\n /// Returns a view of the original tensor with its dimensions permuted.\\n /// </summary>\\n /// <param name=\\\"input\\\">The input tensor.</param>\\n /// <param name=\\\"permutation\\\">The desired ordering of dimensions</param>\\n public static Tensor permute(Tensor input, params long[] permutation) => input.permute(permutation);\\n\\n // https://pytorch.org/docs/stable/generated/torch.reshape\\n /// <summary>\\n /// Returns a tensor with the same data and number of elements as the input but with the specified shape.\\n /// </summary>\\n /// <param name=\\\"input\\\">The input tensor</param>\\n /// <param name=\\\"shape\\\">The new tensor shape.</param>\\n public static Tensor reshape(Tensor input, params long[] shape) => input.reshape(shape);\\n\\n // https://pytorch.org/docs/stable/generated/torch.select\\n public static Tensor select(Tensor input, long dim, long index)\\n => input.select(dim, index);\\n\\n // https://pytorch.org/docs/stable/generated/torch.scatter\\n /// <summary>\\n /// Writes all values from the tensor src into input at the indices specified in the index tensor. For each\\n /// value in src, its output index is specified by its index in src for dimension != dim and by the #\\n /// corresponding value in index for dimension = dim.\\n /// </summary>\\n public static Tensor scatter(Tensor input, long dim, Tensor index, Tensor src)\\n => input.scatter(dim, index, src);\\n\\n // https://pytorch.org/docs/stable/generated/torch.scatter\\n /// <summary>\\n /// Writes all values from the tensor src into input at the indices specified in the index tensor. For each\\n /// value in src, its output index is specified by its index in src for dimension != dim and by the #\\n /// corresponding value in index for dimension = dim.\\n /// </summary>\\n public static Tensor scatter_(Tensor input, long dim, Tensor index, Tensor src)\\n => input.scatter_(dim, index, src);\\n\\n // https://pytorch.org/docs/stable/generated/torch.diagonal_scatter\\n /// <summary>\\n /// Embeds the values of the src tensor into input along the diagonal elements of input, with respect to dim1 and dim2.\\n /// </summary>\\n /// <param name=\\\"input\\\">The input tensor.</param>\\n /// <param name=\\\"src\\\">The tensor to embed into 'this'.</param>\\n /// <param name=\\\"offset\\\">Which diagonal to consider. Default: main diagonal.</param>\\n /// <param name=\\\"dim1\\\">First dimension with respect to which to take diagonal.</param>\\n /// <param name=\\\"dim2\\\">Second dimension with respect to which to take diagonal.</param>\\n public static Tensor diagonal_scatter(Tensor input, Tensor src, long offset = 0L, long dim1 = 0L, long dim2 = 1L) => input.diagonal_scatter(src, offset, dim1, dim2);\\n\\n // https://pytorch.org/docs/stable/generated/torch.select_scatter\\n /// <summary>\\n /// Embeds the values of the src tensor into input at the given index. This function returns a tensor with fresh storage; it does not create a view.\\n /// </summary>\\n /// <param name=\\\"input\\\">The input tensor.</param>\\n /// <param name=\\\"src\\\">The tensor to embed into 'this'</param>\\n /// <param name=\\\"dim\\\">The dimension to insert the slice into</param>\\n /// <param name=\\\"index\\\">The index to select with</param>\\n /// <remarks>This function returns a tensor with fresh storage; it does not create a view.</remarks>\\n public static Tensor select_scatter(Tensor input, Tensor src, long dim, long index) => input.select_scatter(src, dim, index);\\n\\n // https://pytorch.org/docs/stable/generated/torch.slice_scatter\\n /// <summary>\\n /// Embeds the values of the src tensor into input at the given dimension.\\n /// </summary>\\n /// <param name=\\\"input\\\">The input tensor.</param>\\n /// <param name=\\\"src\\\">The tensor to embed into 'this'.</param>\\n /// <param name=\\\"dim\\\">The dimension to insert the slice into</param>\\n /// <param name=\\\"start\\\">The start index of where to insert the slice</param>\\n /// <param name=\\\"end\\\">The end index of where to insert the slice</param>\\n /// <param name=\\\"step\\\">How many elements to skip</param>\\n public static Tensor slice_scatter(Tensor input, Tensor src, long dim = 0L, long? start = null, long? end = null, long step = 1L)\\n => input.slice_scatter(src, dim, start, end, step);\\n\\n // https://pytorch.org/docs/stable/generated/torch.scatter_add\\n /// <summary>\\n /// Adds all values from the tensor other into input at the indices specified in the index tensor in a similar fashion as scatter_().\\n /// For each value in src, it is added to an index in self which is specified by its index in src for dimension != dim and by the\\n /// corresponding value in index for dimension = dim.\\n /// </summary>\\n public static Tensor scatter_add(Tensor input, long dim, Tensor index, Tensor src)\\n => input.scatter_add(dim, index, src);\\n\\n // https://pytorch.org/docs/stable/generated/torch.scatter_reduce\\n [Obsolete(\\\"not implemented\\\", true)]\\n static Tensor scatter_reduce(\\n Tensor input,\\n long dim,\\n Tensor index,\\n Tensor src,\\n Reduce reduce,\\n bool include_self = true)\\n => throw new NotImplementedException();\\n\\n // https://pytorch.org/docs/stable/generated/torch.split\\n public static Tensor[] split(Tensor tensor, long[] split_size_or_sections, long dim = 0L)\\n => tensor.split(split_size_or_sections, dim);\\n\\n // https://pytorch.org/docs/stable/generated/torch.stack\\n /// <summary>\\n /// Concatenates a sequence of tensors along a new dimension.\\n /// </summary>\\n /// <returns></returns>\\n /// <remarks>All tensors need to be of the same size.</remarks>\\n public static Tensor stack(IEnumerable<Tensor> tensors, long dim = 0)\\n {\\n using var parray = new PinnedArray<IntPtr>();\\n IntPtr tensorsRef = parray.CreateArray(tensors.Select(p => p.Handle).ToArray());\\n\\n var res = THSTensor_stack(tensorsRef, parray.Array.Length, dim);\\n if (res == IntPtr.Zero) { CheckForErrors(); }\\n return new Tensor(res);\\n }\\n\\n // https://pytorch.org/docs/stable/generated/torch.swapaxes\\n public static Tensor swapaxes(Tensor input, long axis0, long axis1)\\n => input.swapaxes(axis0, axis1);\\n\\n // https://pytorch.org/docs/stable/generated/torch.swapdims\\n public static Tensor swapdims(Tensor input, long dim0, long dim1)\\n => input.swapdims(dim0, dim1);\\n\\n // https://pytorch.org/docs/stable/generated/torch.t\\n public static Tensor t(Tensor input)\\n => input.t();\\n\\n // https://pytorch.org/docs/stable/generated/torch.take\\n public static Tensor take(Tensor input, Tensor index)\\n => input.take(index);\\n\\n // https://pytorch.org/docs/stable/generated/torch.take_along_dim\\n public static Tensor take_along_dim(Tensor input, Tensor indices, long dim = 0L)\\n => input.take_along_dim(indices, dim);\\n\\n // https://pytorch.org/docs/stable/generated/torch.take_along_dim\\n public static Tensor take_along_dim(Tensor input, IEnumerable<long> indices, long dim = 0L)\\n => input.take_along_dim(indices, dim);\\n\\n // https://pytorch.org/docs/stable/generated/torch.tensor_split\\n public static Tensor[] tensor_split(Tensor input, long indices_or_sections, long dim = 0L)\\n => input.tensor_split(indices_or_sections, dim);\\n\\n // https://pytorch.org/docs/stable/generated/torch.tensor_split\\n public static Tensor[] tensor_split(Tensor input, long[] indices_or_sections, long dim = 0L)\\n => input.tensor_split(indices_or_sections, dim);\\n\\n // https://pytorch.org/docs/stable/generated/torch.tensor_split\\n public static Tensor[] tensor_split(Tensor input, Tensor indices_or_sections, long dim = 0L)\\n => input.tensor_split(indices_or_sections, dim);\\n\\n // https://pytorch.org/docs/stable/generated/torch.tile\\n /// <summary>\\n /// Constructs a tensor by repeating the elements of input. The dims argument specifies the number of repetitions in each dimension.\\n /// </summary>\\n /// <param name=\\\"input\\\">The input tensor</param>\\n /// <param name=\\\"dims\\\">The number of repetitions per dimension.</param>\\n public static Tensor tile(Tensor input, long[] dims) => input.tile(dims);\\n\\n // https://pytorch.org/docs/stable/generated/torch.transpose\\n public static Tensor transpose(Tensor input, long dim0, long dim1)\\n => input.transpose(dim0, dim1);\\n\\n // https://pytorch.org/docs/stable/generated/torch.unbind\\n public static Tensor[] unbind(Tensor input, long dim = 0L)\\n => input.unbind(dim);\\n\\n // https://pytorch.org/docs/stable/generated/torch.unsqueeze\\n /// <summary>\\n /// Returns a new tensor with a dimension of size one inserted at the specified position.\\n /// The returned tensor shares the same underlying data with this tensor.\\n /// </summary>\\n public static Tensor unsqueeze(Tensor input, long dim)\\n => input.unsqueeze(dim);\\n\\n // https://pytorch.org/docs/stable/generated/torch.unsqueeze\\n /// <summary>\\n /// Returns a new tensor with a dimension of size one inserted at the specified position.\\n /// The returned tensor shares the same underlying data with this tensor.\\n /// </summary>\\n public static Tensor unsqueeze_(Tensor input, long dim)\\n => input.unsqueeze_(dim);\\n\\n // https://pytorch.org/docs/stable/generated/torch.vsplit\\n public static Tensor[] vsplit(Tensor input, long[] indices_or_sections)\\n => input.vsplit(indices_or_sections);\\n\\n // https://pytorch.org/docs/stable/generated/torch.vstack\\n /// <summary>\\n /// Stack tensors in sequence vertically (row wise).\\n /// </summary>\\n /// <param name=\\\"tensors\\\"></param>\\n /// <returns></returns>\\n public static Tensor vstack(IList<Tensor> tensors)\\n {\\n using var parray = new PinnedArray<IntPtr>();\\n IntPtr tensorsRef = parray.CreateArray(tensors.Select(p => p.Handle).ToArray());\\n\\n var res = THSTensor_vstack(tensorsRef, parray.Array.Length);\\n if (res == IntPtr.Zero) { CheckForErrors(); }\\n return new Tensor(res);\\n }\\n\\n // https://pytorch.org/docs/stable/generated/torch.where\\n /// <summary>\\n /// Return a tensor of elements selected from either x or y, depending on condition.\\n /// </summary>\\n /// <param name=\\\"condition\\\">When true, yield x, otherwise yield y.</param>\\n /// <param name=\\\"x\\\">Values selected at indices where condition is true</param>\\n /// <param name=\\\"y\\\">Values selected at indices where condition is false</param>\\n /// <returns></returns>\\n public static Tensor where(Tensor condition, Tensor x, Tensor y) => x.where(condition, y);\\n\\n // https://pytorch.org/docs/stable/generated/torch.where\\n /// <summary>\\n /// Returns a tuple of 1-D tensors, one for each dimension in input, each containing the indices (in that dimension) of all non-zero elements of input .\\n /// If input has nn dimensions, then the resulting tuple contains nn tensors of size zz, where zz is the total number of non-zero elements in the input tensor.\\n /// As a special case, when input has zero dimensions and a nonzero scalar value, it is treated as a one-dimensional tensor with one element.\\n /// </summary>\\n /// <param name=\\\"condition\\\">The input tensor</param>\\n /// <returns></returns>\\n /// <exception cref=\\\"ArgumentException\\\"></exception>\\n public static Tensor[] where(Tensor condition)\\n {\\n if (condition.dtype != ScalarType.Bool) throw new ArgumentException(\\\"The condition to 'where' must be a boolean tensor.\\\");\\n\\n IntPtr[] ptrArray;\\n\\n using (var pa = new PinnedArray<IntPtr>()) {\\n THSTensor_where_list(condition.Handle, pa.CreateArray);\\n CheckForErrors();\\n ptrArray = pa.Array;\\n }\\n\\n return ptrArray.Select(x => new Tensor(x)).ToArray();\\n }\\n }\\n}\"\n },\n {\n \"alpha_fraction\": 0.5130791664123535,\n \"alphanum_fraction\": 0.5197653770446777,\n \"avg_line_length\": 45.08648681640625,\n \"blob_id\": \"e8830d173acb3a031c7329a7cb33cf9190070171\",\n \"content_id\": \"e0b4c495849cdc10d11ff96aad3d0ee6cbd35d5c\",\n \"detected_licenses\": [\n \"MIT\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"C#\",\n \"length_bytes\": 8525,\n \"license_type\": \"permissive\",\n \"max_line_length\": 130,\n \"num_lines\": 185,\n \"path\": \"/src/TorchAudio/Transforms.cs\",\n \"repo_name\": \"NiklasGustafsson/TorchSharp\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"// Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information.\\nusing System;\\nusing static TorchSharp.torch;\\nusing TorchSharp.Transforms;\\n\\n// A number of implementation details in this file have been translated from the Python version of torchaudio,\\n// largely located in the files found in this folder:\\n//\\n// https://github.com/pytorch/audio/blob/bb77cbebb620a46fdc0dc7e6dae2253eef3f37e2/torchaudio/transforms/_transforms.py\\n//\\n// The origin has the following copyright notice and license:\\n//\\n// https://github.com/pytorch/audio/blob/main/LICENSE\\n//\\n\\nnamespace TorchSharp\\n{\\n public static partial class torchaudio\\n {\\n public delegate torch.Tensor WindowFunction(long win_length);\\n\\n public static partial class transforms\\n {\\n /// <summary>\\n /// Compute spectrograms from audio signals.\\n /// </summary>\\n /// <param name=\\\"n_fft\\\">The size of Fourier transform</param>\\n /// <param name=\\\"hop_length\\\">The hop length</param>\\n /// <param name=\\\"win_length\\\">The window length</param>\\n /// <param name=\\\"pad\\\">Padding on the sides</param>\\n /// <param name=\\\"window_fn\\\">The callback to create a window function</param>\\n /// <param name=\\\"window\\\">The window function</param>\\n /// <param name=\\\"power\\\">Exponent for the magnitude spectrogram</param>\\n /// <param name=\\\"normalized\\\">Whether the output is normalized, or not.</param>\\n /// <param name=\\\"center\\\">Whether the t-th frame is centered around t * hop_window, or not.</param>\\n /// <param name=\\\"pad_mode\\\">The padding mode used when center is true.</param>\\n /// <param name=\\\"onesided\\\">Whether the output is onesided or not.</param>\\n /// <param name=\\\"return_complex\\\">Deprecated and not used.</param>\\n /// <returns>ITransform to compute spectrograms of audio signals</returns>\\n public static Spectrogram Spectrogram(\\n long n_fft = 400,\\n long? win_length = null,\\n long? hop_length = null,\\n long pad = 0,\\n WindowFunction window_fn = null,\\n Tensor window = null,\\n double? power = 2.0,\\n bool normalized = false,\\n bool center = true,\\n PaddingModes pad_mode = PaddingModes.Reflect,\\n bool onesided = true,\\n bool? return_complex = null)\\n {\\n return new Spectrogram(\\n \\\"Spectrogram\\\",\\n n_fft: n_fft,\\n hop_length: hop_length,\\n win_length: win_length,\\n pad: pad,\\n window_fn: window_fn,\\n window: window,\\n power: power,\\n normalized: normalized,\\n center: center,\\n pad_mode: pad_mode,\\n onesided: onesided,\\n return_complex: return_complex);\\n }\\n\\n /// <summary>\\n /// Compute inverse of spectrogram.\\n /// </summary>\\n /// <param name=\\\"n_fft\\\">The size of Fourier transform</param>\\n /// <param name=\\\"hop_length\\\">The hop length</param>\\n /// <param name=\\\"win_length\\\">The window length</param>\\n /// <param name=\\\"pad\\\">Padding on the sides</param>\\n /// <param name=\\\"window_fn\\\">The callback to create a window function</param>\\n /// <param name=\\\"window\\\">The window function</param>\\n /// <param name=\\\"normalized\\\">Whether the output is normalized, or not.</param>\\n /// <param name=\\\"center\\\">Whether the t-th frame is centered around t * hop_window, or not.</param>\\n /// <param name=\\\"pad_mode\\\">The padding mode used when center is true.</param>\\n /// <param name=\\\"onesided\\\">Whether the output is onesided or not.</param>\\n /// <returns>ITransform to compute inverse of spectrogram</returns>\\n public static InverseSpectrogram InverseSpectrogram(\\n long n_fft = 400,\\n long? win_length = null,\\n long? hop_length = null,\\n long pad = 0,\\n WindowFunction window_fn = null,\\n Tensor window = null,\\n bool normalized = false,\\n bool center = true,\\n PaddingModes pad_mode = PaddingModes.Reflect,\\n bool onesided = true)\\n {\\n return new InverseSpectrogram(\\n \\\"InverseSpectrogram\\\",\\n n_fft: n_fft,\\n hop_length: hop_length,\\n win_length: win_length,\\n pad: pad,\\n window_fn: window_fn,\\n window: window,\\n normalized: normalized,\\n center: center,\\n pad_mode: pad_mode,\\n onesided: onesided);\\n }\\n\\n /// <summary>\\n /// Resample the waveform\\n /// </summary>\\n /// <param name=\\\"orig_freq\\\">The source sampling rate</param>\\n /// <param name=\\\"new_freq\\\">The destination sampling rate</param>\\n /// <param name=\\\"lowpass_filter_width\\\">The width of the filter</param>\\n /// <param name=\\\"rolloff\\\">The roll-off frequency</param>\\n /// <param name=\\\"resampling_method\\\">The resampling method</param>\\n /// <param name=\\\"beta\\\">Beta for Keizer window</param>\\n /// <param name=\\\"device\\\">The device</param>\\n /// <param name=\\\"dtype\\\">The scalar type</param>\\n /// <returns>The resampled waveform</returns>\\n /// <exception cref=\\\"ArgumentOutOfRangeException\\\"></exception>\\n public static Resample Resample(\\n int orig_freq = 16000,\\n int new_freq = 16000,\\n ResamplingMethod resampling_method = ResamplingMethod.sinc_interpolation,\\n int lowpass_filter_width = 6,\\n double rolloff = 0.99,\\n double? beta = null,\\n torch.Device device = null,\\n torch.ScalarType? dtype = null)\\n {\\n return new Resample(\\n \\\"Resample\\\",\\n orig_freq,\\n new_freq,\\n resampling_method,\\n lowpass_filter_width,\\n rolloff,\\n beta,\\n device,\\n dtype);\\n }\\n\\n /// <summary>\\n /// Compute waveform from a linear scale magnitude spectrogram using the Griffin-Lim transformation.\\n /// </summary>\\n /// <param name=\\\"n_fft\\\">Size of FFT, creates ``n_fft // 2 + 1`` bins.</param>\\n /// <param name=\\\"n_iter\\\">Number of iteration for phase recovery process.</param>\\n /// <param name=\\\"win_length\\\">Window size.</param>\\n /// <param name=\\\"hop_length\\\">Length of hop between STFT windows.</param>\\n /// <param name=\\\"window_fn\\\">A function to create a window tensor\\n /// that is applied/multiplied to each frame/window.</param>\\n /// <param name=\\\"power\\\">Exponent for the magnitude spectrogram,\\n /// (must be > 0) e.g., 1 for energy, 2 for power, etc.</param>\\n /// <param name=\\\"momentum\\\">The momentum parameter for fast Griffin-Lim.</param>\\n /// <param name=\\\"length\\\">Array length of the expected output.</param>\\n /// <param name=\\\"rand_init\\\">Initializes phase randomly if True and to zero otherwise.</param>\\n /// <returns></returns>\\n public static GriffinLim GriffinLim(\\n int n_fft = 400,\\n int n_iter = 32,\\n long? win_length = null,\\n long? hop_length = null,\\n WindowFunction window_fn = null,\\n double power = 2.0,\\n double momentum = 0.99,\\n int? length = null,\\n bool rand_init = true)\\n {\\n return new GriffinLim(\\n \\\"GriffinLim\\\",\\n n_fft,\\n n_iter,\\n win_length,\\n hop_length,\\n window_fn,\\n power,\\n momentum,\\n length,\\n rand_init);\\n }\\n }\\n }\\n}\"\n },\n {\n \"alpha_fraction\": 0.5553860068321228,\n \"alphanum_fraction\": 0.5590479373931885,\n \"avg_line_length\": 39.456790924072266,\n \"blob_id\": \"5b0d4fdac7d305df1ea68710fcefcdd3e307543d\",\n \"content_id\": \"5df3b78737001a5b6cc9d47e0ecdfe7122d66648\",\n \"detected_licenses\": [\n \"MIT\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"C#\",\n \"length_bytes\": 3277,\n \"license_type\": \"permissive\",\n \"max_line_length\": 172,\n \"num_lines\": 81,\n \"path\": \"/src/TorchSharp/Distributions/Pareto.cs\",\n \"repo_name\": \"NiklasGustafsson/TorchSharp\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"// Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information.\\nusing System;\\nusing static TorchSharp.torch;\\n\\nnamespace TorchSharp\\n{\\n using Modules;\\n using static torch.distributions;\\n\\n namespace Modules\\n {\\n public class Pareto : TransformedDistribution\\n {\\n internal Pareto(Tensor scale, Tensor alpha, Distribution base_distribution, torch.distributions.transforms.Transform[] transforms, Generator generator = null) :\\n base(base_distribution, transforms, generator)\\n {\\n this.scale = scale.alias().DetachFromDisposeScope();\\n this.alpha = alpha.alias().DetachFromDisposeScope();\\n }\\n\\n public Tensor scale { get; private set; }\\n\\n public Tensor alpha { get; private set; }\\n\\n public override Tensor mean {\\n get {\\n using var _ = torch.NewDisposeScope();\\n var a = alpha.clamp(min: 1);\\n return (a * scale / (a - 1)).MoveToOuterDisposeScope();\\n }\\n }\\n\\n public override Tensor mode => scale;\\n\\n public override Tensor variance {\\n get {\\n using var _ = torch.NewDisposeScope();\\n var a = alpha.clamp(min: 2);\\n return (scale.pow(2) * a / ((a - 1).pow(2) * (a - 2))).MoveToOuterDisposeScope();\\n }\\n }\\n\\n public override Distribution expand(Size batch_shape, Distribution instance = null)\\n {\\n var newDistribution = ((instance == null)\\n ? torch.distributions.Pareto(scale.expand(batch_shape), alpha.expand(batch_shape), generator)\\n : instance) as Pareto;\\n return base.expand(batch_shape, newDistribution);\\n }\\n\\n public override Tensor entropy() => torch.WrappedTensorDisposeScope(() => (scale / alpha).log() + 1 + alpha.reciprocal());\\n }\\n }\\n\\n\\n public static partial class torch\\n {\\n public static partial class distributions\\n {\\n /// <summary>\\n /// Samples from a Pareto Type 1 distribution.\\n /// </summary>\\n /// <param name=\\\"scale\\\">Scale parameter of the distribution.</param>\\n /// <param name=\\\"alpha\\\">Shape parameter of the distribution</param>\\n /// <param name=\\\"generator\\\">An optional random number generator object.</param>\\n /// <returns></returns>\\n public static Pareto Pareto(Tensor scale, Tensor alpha, torch.Generator generator = null)\\n {\\n var scaleAlpha = torch.broadcast_tensors(scale, alpha);\\n scale = scaleAlpha[0];\\n alpha = scaleAlpha[1];\\n var base_dist = Exponential(alpha, generator);\\n var transforms = new torch.distributions.transforms.Transform[] {\\n new torch.distributions.transforms.ExpTransform(),\\n new torch.distributions.transforms.AffineTransform(loc:0, scale:scale)\\n };\\n return new Pareto(scale, alpha, base_dist, transforms, generator);\\n }\\n }\\n }\\n}\\n\"\n },\n {\n \"alpha_fraction\": 0.5217464566230774,\n \"alphanum_fraction\": 0.5250704288482666,\n \"avg_line_length\": 46.97297286987305,\n \"blob_id\": \"06c0944d9719839e4b8f2522cbd24ac54eec1855\",\n \"content_id\": \"250d9ac5ed563bf8e629b51eda263aaf0c37abfd\",\n \"detected_licenses\": [\n \"MIT\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"C#\",\n \"length_bytes\": 17750,\n \"license_type\": \"permissive\",\n \"max_line_length\": 236,\n \"num_lines\": 370,\n \"path\": \"/src/TorchSharp/Optimizers/SGD.cs\",\n \"repo_name\": \"NiklasGustafsson/TorchSharp\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"// Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information.\\nusing System;\\nusing System.Collections.Generic;\\nusing System.Linq;\\nusing static TorchSharp.torch;\\n\\nnamespace TorchSharp\\n{\\n using System.IO;\\n using Modules;\\n\\n public static partial class torch\\n {\\n public static partial class optim\\n {\\n /// <summary>\\n /// Implements the stochastic gradient descent (optionally with momentum).\\n ///\\n /// The use of momentum is covered in:\\n /// http://www.cs.toronto.edu/%7Ehinton/absps/momentum.pdf\\n /// </summary>\\n /// <param name=\\\"parameters\\\">Parameters to optimize. This optimizer requires the <b>named</b> parameters collection.</param>\\n /// <param name=\\\"learningRate\\\">Learning rate</param>\\n /// <param name=\\\"momentum\\\">Momentum factor (default: 0)</param>\\n /// <param name=\\\"dampening\\\">Dampening for momentum (default: 0)</param>\\n /// <param name=\\\"weight_decay\\\">Weight decay (L2 penalty) (default: 0)</param>\\n /// <param name=\\\"nesterov\\\">Enables Nesterov momentum (default: False)</param>\\n /// <param name=\\\"maximize\\\"></param>\\n /// <returns></returns>\\n public static Modules.SGD SGD(IEnumerable<Parameter> parameters, double learningRate, double momentum = 0, double dampening = 0, double weight_decay = 0, bool nesterov = false, bool maximize = false)\\n {\\n return new Modules.SGD(parameters, learningRate, momentum, dampening, weight_decay, nesterov, maximize);\\n }\\n\\n /// <summary>\\n /// Implements the stochastic gradient descent (optionally with momentum).\\n ///\\n /// The use of momentum is covered in:\\n /// http://www.cs.toronto.edu/%7Ehinton/absps/momentum.pdf\\n /// </summary>\\n /// <param name=\\\"parameters\\\">Parameters to optimize. This optimizer requires the <b>named</b> parameters collection.</param>\\n /// <param name=\\\"learningRate\\\">Learning rate</param>\\n /// <param name=\\\"momentum\\\">Momentum factor (default: 0)</param>\\n /// <param name=\\\"dampening\\\">Dampening for momentum (default: 0)</param>\\n /// <param name=\\\"weight_decay\\\">Weight decay (L2 penalty) (default: 0)</param>\\n /// <param name=\\\"nesterov\\\">Enables Nesterov momentum (default: False)</param>\\n /// <param name=\\\"maximize\\\"></param>\\n /// <returns></returns>\\n public static Modules.SGD SGD(IEnumerable<(string name, Parameter parameter)> parameters, double learningRate, double momentum = 0, double dampening = 0, double weight_decay = 0, bool nesterov = false, bool maximize = false)\\n {\\n return new Modules.SGD(parameters.Select(np => np.parameter), learningRate, momentum, dampening, weight_decay, nesterov, maximize);\\n }\\n\\n /// <summary>\\n /// Implements the stochastic gradient descent (optionally with momentum).\\n ///\\n /// The use of momentum is covered in:\\n /// http://www.cs.toronto.edu/%7Ehinton/absps/momentum.pdf\\n /// </summary>\\n /// <param name=\\\"parameters\\\">Parameters to optimize. This optimizer requires the <b>named</b> parameters collection.</param>\\n /// <param name=\\\"learningRate\\\">Learning rate</param>\\n /// <param name=\\\"momentum\\\">Momentum factor (default: 0)</param>\\n /// <param name=\\\"dampening\\\">Dampening for momentum (default: 0)</param>\\n /// <param name=\\\"weight_decay\\\">Weight decay (L2 penalty) (default: 0)</param>\\n /// <param name=\\\"nesterov\\\">Enables Nesterov momentum (default: False)</param>\\n /// <param name=\\\"maximize\\\"></param>\\n /// <returns></returns>\\n public static Modules.SGD SGD(IEnumerable<SGD.ParamGroup> parameters, double learningRate, double momentum = 0.0, double dampening = 0, double weight_decay = 0, bool nesterov = false, bool maximize = false)\\n {\\n return new Modules.SGD(parameters, learningRate, momentum, dampening, weight_decay, nesterov, maximize);\\n }\\n }\\n }\\n namespace Modules\\n {\\n using static torch.optim;\\n\\n public class SGD : OptimizerHelper, IMomentum\\n {\\n /// <summary>\\n /// Implements stochastic gradient descent (optionally with momentum).\\n /// </summary>\\n /// <param name=\\\"parameters\\\">Parameters to optimize. This optimizer requires the <b>named</b> parameters collection.</param>\\n /// <param name=\\\"lr\\\">Learning rate</param>\\n /// <param name=\\\"momentum\\\">Momentum factor (default: 0)</param>\\n /// <param name=\\\"dampening\\\">Dampening for momentum (default: 0)</param>\\n /// <param name=\\\"weight_decay\\\">Weight decay (L2 penalty) (default: 0)</param>\\n /// <param name=\\\"nesterov\\\">Enables Nesterov momentum (default: False)</param>\\n /// <param name=\\\"maximize\\\"></param>\\n /// <returns></returns>\\n public SGD(IEnumerable<Parameter> parameters, double lr, double momentum = 0.0, double dampening = 0, double weight_decay = 0, bool nesterov = false, bool maximize = false)\\n : this(new ParamGroup[] { new ParamGroup { Parameters = parameters } }, lr, momentum, dampening, weight_decay, nesterov, maximize)\\n {\\n }\\n\\n /// <summary>\\n /// Implements stochastic gradient descent (optionally with momentum).\\n /// </summary>\\n /// <param name=\\\"parameters\\\">Parameters to optimize. This optimizer requires the <b>named</b> parameters collection.</param>\\n /// <param name=\\\"lr\\\">Learning rate</param>\\n /// <param name=\\\"momentum\\\">Momentum factor (default: 0)</param>\\n /// <param name=\\\"dampening\\\">Dampening for momentum (default: 0)</param>\\n /// <param name=\\\"weight_decay\\\">Weight decay (L2 penalty) (default: 0)</param>\\n /// <param name=\\\"nesterov\\\">Enables Nesterov momentum (default: False)</param>\\n /// <param name=\\\"maximize\\\"></param>\\n /// <returns></returns>\\n public SGD(IEnumerable<ParamGroup> parameters, double lr, double momentum = 0.0, double dampening = 0, double weight_decay = 0, bool nesterov = false, bool maximize = false)\\n {\\n if (lr < 0.0) throw new ArgumentException($\\\"Invalid learning rate: {lr}\\\");\\n if (momentum < 0.0) throw new ArgumentException($\\\"Invalid momentum value: {momentum}\\\");\\n if (weight_decay < 0.0) throw new ArgumentException($\\\"Invalid weight_decay value: {weight_decay}\\\");\\n if (nesterov && (momentum <= 0 || dampening != 0)) throw new ArgumentException(\\\"Nesterov momentum requires a momentum and zero dampening\\\");\\n\\n var options = new Options {\\n LearningRate = lr,\\n InitialLearningRate = lr,\\n dampening = dampening,\\n maximize = maximize,\\n momentum = momentum,\\n nesterov = nesterov,\\n weight_decay = weight_decay\\n };\\n\\n _defaults = options;\\n _parameter_groups = new List<Modules.ParamGroup>();\\n\\n foreach (var g in parameters) {\\n add_param_group(g);\\n }\\n }\\n\\n /// <summary>\\n /// Performs a single optimization step (parameter update).\\n /// </summary>\\n /// <param name=\\\"closure\\\">A closure that reevaluates the model and returns the loss. Optional for most optimizers.</param>\\n /// <returns></returns>\\n public override Tensor step(Func<Tensor> closure = null)\\n {\\n return _step<ParamGroup>(group => {\\n\\n var options = group.Options;\\n var momentum = options.momentum.Value;\\n var dampening = options.dampening.Value;\\n var weight_decay = options.weight_decay.Value;\\n var nesterov = options.nesterov.Value;\\n var maximize = options.maximize.Value;\\n var lr = options.LearningRate.Value;\\n\\n foreach (var param in group.Parameters) {\\n\\n var state = (State)_state[param.handle];\\n\\n var grad = param.grad();\\n\\n if (grad is null) continue;\\n\\n if (weight_decay != 0) {\\n grad = grad.add(param, alpha: weight_decay);\\n }\\n\\n if (momentum != 0) {\\n var buf = state.momentum_buffer;\\n\\n if (buf is null) {\\n buf = grad.clone().detach().DetachFromDisposeScope();\\n state.momentum_buffer = buf;\\n } else {\\n buf.mul_(momentum).add_(grad, alpha: (1 - dampening));\\n }\\n\\n if (nesterov) {\\n grad = grad.add(buf, alpha: momentum);\\n } else {\\n grad = buf;\\n }\\n\\n state.momentum_buffer = buf;\\n }\\n\\n var alpha = maximize ? lr : -lr;\\n param.add_(grad, alpha: alpha);\\n\\n }\\n }, closure);\\n }\\n\\n protected override void Dispose(bool disposing)\\n {\\n base.Dispose(disposing);\\n foreach (var kvp in _state) {\\n var st = (State)kvp.Item2;\\n if (st.momentum_buffer is not null) {\\n st.momentum_buffer.Dispose();\\n }\\n }\\n _state.Clear();\\n }\\n\\n public sealed class State : OptimizerState, IDisposable\\n {\\n public Tensor momentum_buffer;\\n\\n public void Dispose()\\n {\\n Dispose(true);\\n GC.SuppressFinalize(this);\\n }\\n\\n private void Dispose(bool disposing)\\n {\\n if (disposing) {\\n momentum_buffer?.Dispose();\\n }\\n }\\n\\n /// <summary>\\n /// Move all the state to the indicated device.\\n /// </summary>\\n /// <param name=\\\"device\\\">The device to move all state to.</param>\\n public override void to(Device device)\\n {\\n momentum_buffer = momentum_buffer?.to(device);\\n }\\n\\n /// <summary>\\n /// Load the optimizer parameter state from a stream.\\n /// </summary>\\n /// <param name=\\\"reader\\\">A binary reader connected to a stream open for reading.</param>\\n public override void LoadStateDict(BinaryReader reader)\\n {\\n LoadConditionalStateTensor(reader, ref momentum_buffer);\\n }\\n\\n /// <summary>\\n /// Save the optimizer parameter state to a stream.\\n /// </summary>\\n /// <param name=\\\"writer\\\">A binary writer connected to a stream open for writing.</param>\\n public override void SaveStateDict(BinaryWriter writer)\\n {\\n SaveConditionalStateTensor(writer, momentum_buffer);\\n }\\n\\n /// <summary>\\n /// Load optimizer parameter state from another optimizer.\\n /// </summary>\\n /// <param name=\\\"source\\\">An optimizer state record.</param>\\n public override void LoadStateDict(OptimizerState source)\\n {\\n var st_state = source as State;\\n if (momentum_buffer is not null) {\\n momentum_buffer.Dispose();\\n }\\n momentum_buffer = st_state.momentum_buffer;\\n }\\n\\n /// <summary>\\n /// Useful for tests, allows comparison of one state with another.\\n /// </summary>\\n /// <param name=\\\"other\\\">The other optimizer state</param>\\n /// <returns></returns>\\n public override bool ApproximatelyEquals(OptimizerState other)\\n {\\n var rhs = other as State;\\n return (rhs is not null) && (momentum_buffer is null || momentum_buffer.allclose(rhs.momentum_buffer));\\n }\\n }\\n\\n /// <summary>\\n /// Add a param group to the Optimizer s param_groups.\\n /// </summary>\\n /// <param name=\\\"param_group\\\"></param>\\n /// <remarks>This can be useful when fine tuning a pre-trained network as frozen layers can be made trainable and added to the Optimizer as training progresses.</remarks>\\n public override void add_param_group(Modules.ParamGroup param_group)\\n {\\n var def = _defaults as Options;\\n if (param_group.Options is null) {\\n param_group.Options = new Options();\\n }\\n\\n var opt = param_group.Options as Options;\\n\\n // Make sure all the options are set.\\n if (!opt.LearningRate.HasValue) opt.LearningRate = def.LearningRate;\\n if (!opt.momentum.HasValue) opt.momentum = def.momentum;\\n if (!opt.dampening.HasValue) opt.dampening = def.dampening;\\n if (!opt.weight_decay.HasValue) opt.weight_decay = def.weight_decay;\\n if (!opt.nesterov.HasValue) opt.nesterov = def.nesterov;\\n if (!opt.maximize.HasValue) opt.maximize = def.maximize;\\n\\n opt.InitialLearningRate = opt.LearningRate.Value;\\n\\n _parameter_groups.Add(param_group);\\n\\n foreach (var p in param_group.Parameters) {\\n var state = new State();\\n _state.Add((p.Handle,state));\\n state.momentum_buffer = null;\\n }\\n }\\n\\n public class Options : Modules.OptimizerOptions\\n {\\n public double? momentum;\\n public double? dampening;\\n public double? weight_decay;\\n public bool? nesterov;\\n public bool? maximize;\\n\\n /// <summary>\\n /// Load the optimizer options (param-group hyperparameters) from a stream.\\n /// </summary>\\n /// <param name=\\\"reader\\\">A binary reader connected to a stream open for reading.</param>\\n public override void LoadStateDict(BinaryReader reader)\\n {\\n base.LoadStateDict(reader);\\n momentum = reader.ReadDouble();\\n dampening = reader.ReadDouble();\\n weight_decay = reader.ReadDouble();\\n nesterov = reader.ReadBoolean();\\n maximize = reader.ReadBoolean();\\n }\\n\\n /// <summary>\\n /// Load optimizer options (param-group hyperparameters) from another optimizer.\\n /// </summary>\\n /// <param name=\\\"source\\\">An optimizer options record.</param>\\n public override void LoadStateDict(OptimizerOptions source)\\n {\\n base.LoadStateDict(source);\\n var opts = source as Options;\\n momentum = opts.momentum;\\n dampening = opts.dampening;\\n weight_decay = opts.weight_decay;\\n nesterov = opts.nesterov;\\n maximize = opts.maximize;\\n }\\n\\n /// <summary>\\n /// Save the optimizer options (param-group hyperparameters) to a stream.\\n /// </summary>\\n /// <param name=\\\"writer\\\">A binary writer connected to a stream open for writing.</param>\\n public override void SaveStateDict(BinaryWriter writer)\\n {\\n base.SaveStateDict(writer);\\n writer.Write(momentum.Value);\\n writer.Write(dampening.Value);\\n writer.Write(weight_decay.Value);\\n writer.Write(nesterov.Value);\\n writer.Write(maximize.Value);\\n }\\n }\\n\\n public class ParamGroup : ParamGroup<Options>, IMomentum\\n {\\n public ParamGroup() { }\\n\\n public ParamGroup(IEnumerable<Parameter> parameters, Options options) : base(parameters, options) { }\\n\\n public ParamGroup(IEnumerable<Parameter> parameters, double lr = 1e-3, double momentum = 0.0, double dampening = 0, double weight_decay = 0, bool nesterov = false, bool maximize = false)\\n : base(parameters, new SGD.Options { LearningRate = lr, dampening = dampening, momentum = momentum, weight_decay = weight_decay, nesterov = nesterov, maximize = maximize })\\n {\\n }\\n\\n public double Momentum { get => Options.momentum.Value; set => Options.momentum = value; }\\n }\\n\\n public double Momentum { get => (_defaults as Options).momentum.Value; set => (_defaults as Options).momentum = value; }\\n }\\n }\\n}\\n\"\n },\n {\n \"alpha_fraction\": 0.535237193107605,\n \"alphanum_fraction\": 0.5380009412765503,\n \"avg_line_length\": 30.463768005371094,\n \"blob_id\": \"5828959b8aa25fc858d2d99eeea6656e4b456a29\",\n \"content_id\": \"aedcf10155a9f7cad45aa0cab880239f49cc9582\",\n \"detected_licenses\": [\n \"MIT\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"C#\",\n \"length_bytes\": 2171,\n \"license_type\": \"permissive\",\n \"max_line_length\": 161,\n \"num_lines\": 69,\n \"path\": \"/src/TorchSharp/Utils/TensorDataset.cs\",\n \"repo_name\": \"NiklasGustafsson/TorchSharp\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"// Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information.\\n\\nusing System;\\nusing System.IO;\\nusing System.Collections.Generic;\\n\\nnamespace TorchSharp\\n{\\n using System.Data;\\n using System.Linq;\\n using Modules;\\n\\n public static partial class torch\\n {\\n public static partial class utils\\n {\\n public static partial class data {\\n\\n /// <summary>\\n /// Dataset wrapping tensors.\\n ///\\n /// Each sample will be retrieved by indexing tensors along the first dimension.\\n /// </summary>\\n /// <param name=\\\"tensors\\\">Tensors that have the same size of the first dimension.</param>\\n public static TensorDataset TensorDataset(params torch.Tensor[] tensors) => new TensorDataset(tensors);\\n }\\n }\\n }\\n\\n namespace Modules\\n {\\n public class TensorDataset : torch.utils.data.Dataset<IList<torch.Tensor>>\\n {\\n internal TensorDataset(torch.Tensor[] tensors)\\n {\\n if (tensors is null || tensors.Length == 0) throw new ArgumentNullException(nameof(tensors));\\n long size1 = tensors[0].shape[0];\\n if (!tensors.All(t => t.shape[0] == size1)) throw new ArgumentException(\\\"All tensors must have the same first dimension size.\\\", nameof(tensors));\\n\\n _tensors.AddRange(tensors);\\n }\\n\\n /// <summary>\\n /// Indexer\\n /// </summary>\\n public IList<torch.Tensor> this[long index] {\\n\\n get {\\n return _tensors.Select(t => t[index]).ToList();\\n }\\n }\\n\\n /// <summary>\\n /// Length of the dataset\\n /// </summary>\\n public override long Count {\\n get { return _tensors.Count; }\\n }\\n\\n public override IList<torch.Tensor> GetTensor(long index)\\n {\\n return this[index];\\n }\\n\\n private List<torch.Tensor> _tensors = new List<torch.Tensor>();\\n }\\n\\n }\\n}\\n\"\n },\n {\n \"alpha_fraction\": 0.532310426235199,\n \"alphanum_fraction\": 0.5464738607406616,\n \"avg_line_length\": 42.46154022216797,\n \"blob_id\": \"62b29d7481339376d2112fe2571fcbda72356a57\",\n \"content_id\": \"6324757a29cdfd9ec0100fc63f5b275327869d6c\",\n \"detected_licenses\": [\n \"MIT\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"C#\",\n \"length_bytes\": 3389,\n \"license_type\": \"permissive\",\n \"max_line_length\": 130,\n \"num_lines\": 78,\n \"path\": \"/src/TorchAudio/Datasets/YesnoDataset.cs\",\n \"repo_name\": \"NiklasGustafsson/TorchSharp\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"// Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information.\\nusing System;\\nusing System.IO;\\nusing System.Linq;\\n\\nnamespace TorchSharp\\n{\\n public static partial class torchaudio\\n {\\n public static partial class datasets\\n {\\n private class YesnoDataset : torch.utils.data.Dataset<YesnoDatasetItem>\\n {\\n public const string ArchiveChecksum = \\\"c3f49e0cca421f96b75b41640749167b52118f232498667ca7a5f9416aef8e73\\\";\\n\\n private readonly string[] audioPathList;\\n\\n public YesnoDataset(string directoryPathInArchive)\\n {\\n audioPathList = Directory.EnumerateFiles(directoryPathInArchive, \\\"*.wav\\\").ToArray();\\n }\\n\\n public override long Count => audioPathList.LongLength;\\n\\n public override YesnoDatasetItem GetTensor(long index)\\n {\\n var (waveform, sample_rate) = torchaudio.load(audioPathList[index]);\\n return new() {\\n waveform = waveform,\\n sample_rate = sample_rate,\\n labels = null\\n };\\n }\\n }\\n\\n /// <summary>\\n /// Create a Yesno dataset\\n /// </summary>\\n /// <param name=\\\"root\\\">The path to the dataset</param>\\n /// <param name=\\\"url\\\">The URL to download the dataset from</param>\\n /// <param name=\\\"folder_in_archive\\\">The top directory of the dataset</param>\\n /// <param name=\\\"download\\\">True to download the dataset</param>\\n /// <returns>The dataset</returns>\\n /// <exception cref=\\\"InvalidDataException\\\"></exception>\\n public static torch.utils.data.Dataset<YesnoDatasetItem> YESNO(\\n string root,\\n string url = \\\"http://www.openslr.org/resources/1/waves_yesno.tar.gz\\\",\\n string folder_in_archive = \\\"waves_yesno\\\",\\n bool download = false)\\n {\\n var directoryPathInArchive = Path.Combine(root, folder_in_archive);\\n var archiveFileName = GetFileNameFromUrl(url);\\n var archivePath = Path.Combine(root, archiveFileName);\\n if (download) {\\n if (!Directory.Exists(directoryPathInArchive)) {\\n if (!File.Exists(archivePath)) {\\n torch.hub.download_url_to_file(url, archivePath, YesnoDataset.ArchiveChecksum);\\n }\\n utils.extract_archive(archivePath, root);\\n }\\n }\\n if (!Directory.Exists(directoryPathInArchive)) {\\n throw new InvalidDataException(\\\"Dataset not found. Please use `download=true` to download it.\\\");\\n }\\n return new YesnoDataset(directoryPathInArchive);\\n }\\n\\n private static string GetFileNameFromUrl(string url)\\n {\\n int index = url.LastIndexOf('/');\\n if (index < 0) throw new ArgumentException();\\n var fileName = url.Substring(index + 1);\\n if (string.IsNullOrWhiteSpace(fileName)) throw new ArgumentException();\\n return fileName;\\n }\\n }\\n }\\n}\"\n },\n {\n \"alpha_fraction\": 0.8063063025474548,\n \"alphanum_fraction\": 0.8063063025474548,\n \"avg_line_length\": 23.77777862548828,\n \"blob_id\": \"b3676c49ea15108b35c41a4b93af7e419eac305b\",\n \"content_id\": \"7a1bee53a7cc355bdf0a9996f7d9d5036ffc180c\",\n \"detected_licenses\": [\n \"MIT\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"C#\",\n \"length_bytes\": 224,\n \"license_type\": \"permissive\",\n \"max_line_length\": 70,\n \"num_lines\": 9,\n \"path\": \"/src/TorchSharp/DistanceFunctionNative.cs\",\n \"repo_name\": \"NiklasGustafsson/TorchSharp\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"#nullable enable\\nusing System;\\nusing System.Runtime.InteropServices;\\n\\nnamespace TorchSharp\\n{\\n [UnmanagedFunctionPointer(CallingConvention.Cdecl)]\\n public delegate IntPtr DistanceFunctionNative(IntPtr x, IntPtr y);\\n}\"\n },\n {\n \"alpha_fraction\": 0.7151066064834595,\n \"alphanum_fraction\": 0.7199258804321289,\n \"avg_line_length\": 38.37956237792969,\n \"blob_id\": \"714c6ff073ebd7e04b490bd8a6ff7d33dd44f572\",\n \"content_id\": \"7b423e5a44cf0b990a3762d92384a44cb0e58628\",\n \"detected_licenses\": [\n \"MIT\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Markdown\",\n \"length_bytes\": 5395,\n \"license_type\": \"permissive\",\n \"max_line_length\": 410,\n \"num_lines\": 137,\n \"path\": \"/docfx/articles/torchscript.md\",\n \"repo_name\": \"NiklasGustafsson/TorchSharp\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"# TorchScript\\n\\nTorchSchript is a PyTorch technology that lets you save a subset of PyTorch-based Python code without a dependency on a Python runtime. Such models can be loaded into native code, and therefore into .NET code.\\n\\nTorchScript is very powerful, because it allows you to save the logic and the weights of a model together, and it furthermore allows the module to be loaded into another program, __without any dependencies on the Python runtime.__ Thus, you can load a model that has been serialized using TorchScript and have it behave as any TorchScript module -- you can use it for training, or you can use it for inference.\\n\\n## Loading TorchScript Modules\\n\\nStarting with release 0.96.9, you can load TorchScript modules and functions that have been either traced or scripted in Pytorch. It is, however, not yet possible to create a TorchScript module from scratch using TorchSharp. \\n\\nIn Python, a TorchScript module can be a class derived from 'nn.Module,' or a function that operates on tensors and stays within the constraints that TorchScript places on it. The script can be formed by tracing or by compiling the code. Refer to the [Pytorch JIT](https://pytorch.org/docs/stable/jit.html) docs for information on the details.\\n\\nFor example, the following Python code creates a TorchScript module. Note the use of type annotations\\n\\n```Python\\nfrom typing import Tuple\\nimport torch\\nfrom torch import nn\\nfrom torch import Tensor\\n\\nclass MyModule(nn.Module):\\n def __init__(self):\\n super().__init__()\\n self.p = nn.Parameter(torch.rand(10))\\n\\n def forward(self, x: Tensor, y: Tensor) -> Tuple[Tensor, Tensor]:\\n return x + y, x - y\\n \\n @torch.jit.export\\n def predict(self, x: Tensor) -> Tensor:\\n return x + self.p\\n\\n @torch.jit.export\\n def add_scalar(self, x: Tensor, i: int) -> Tensor:\\n return x + i\\n\\nm = MyModule()\\n\\nm = torch.jit.script(m)\\nm.save(\\\"exported.method.dat\\\")\\n```\\n\\nThe following can also be used to create a TorchScript script:\\n\\n```Python\\n@torch.jit.script\\ndef a(x: Tensor, y: Tensor):\\n return x + y, x - y\\n```\\n\\nOnce you have a TorchScript file, you can load it into TorchSharp using:\\n\\n```C#\\nvar m = torch.jit.load(\\\"file-name\\\");\\n```\\n\\nIt returns a ScriptModule, which behaves just like any other TorchSharp module. Whether the original script came from a module or a function, it is deserialized as a module. You can use it for training of inference by calling either `train()` or `eval()`. ScriptModules always start out on the CPU, so you have to call `cuda()` in order to move it to a GPU.\\n\\nNote that if you used __tracing__ to create the TorchScript file in Pytorch, submodules that behave differently in training and eval modes will behave according to the mode they were traced in.\\n\\nIf you use the script module to train, you may want / need to save it afterwards. \\n\\nThat is easily done using `save()`:\\n\\n```C#\\ntorch.jit.save(m, \\\"file-name\\\");\\n```\\n\\nThere's also a type-safe version of `load()` that returns a script module that implements IModule<T1...,TResult>. For the above example(s) `torch.jit.load<Tensor,Tensor,(Tensor, Tensor)>` is the appropriate one to use:\\n\\n```C#\\nvar m = torch.jit.load<Tensor,Tensor,Tensor>(\\\"file-name\\\");\\n```\\n\\nWhile it is possible to save a modified ScriptModule from TorchSharp, it is not (yet) possible to create one _from scratch_ using either tracing or scripting.\\n\\nScript modules have a main `forward()` method, even if they were created from a function rather than a Module class. However, if the module was created from a class, and other methods have the 'torch.jit.export' annotation, then those methods are also callable from TorchSharp:\\n\\n```C#\\nvar x = torch.rand(10);\\nvar y = torch.rand(10);\\nvar t0 = m.forward(x,y);\\nvar t1 = m.invoke(\\\"predict\\\", x);\\nvar t2 = m.invoke(\\\"add_scalar\\\", x, 3.14);\\n```\\n\\nNote how methods other than `forward()` must be invoked with their name passed as a string. It may be useful to wrap such modules in a hand-written class, like:\\n\\n```C#\\nclass TestScriptModule : Module<Tensor, Tensor, (Tensor, Tensor)>\\n{\\n internal TestScriptModule(string filename) : base(nameof(TestScriptModule))\\n {\\n m = torch.jit.load<(Tensor, Tensor)> (filename);\\n }\\n\\n public override (Tensor, Tensor) forward(Tensor input1, Tensor input2)\\n {\\n return m.forward(input1, input2);\\n }\\n\\n public Tensor predict(Tensor input)\\n {\\n return m.invoke<Tensor>(\\\"predict\\\", input);\\n }\\n\\n public Tensor add_scalar(Tensor input, int i)\\n {\\n return m.invoke<Tensor>(\\\"add_scalar\\\", input, i);\\n }\\n\\n private torch.jit.ScriptModule<Tensor, Tensor, (Tensor, Tensor)> m;\\n}\\n```\\n\\n## Compiling TorchScript from text\\n\\nBesides the ability to load TorchScript modules from files, TorchSharp also offers the ability to create them from Python source code directly using the `torch.jit.compile()` method. This results not in a ScriptModule, but a CompilationUnit, which can contain methods, classes, etc. \\n\\nRight now, TorchSharp only offers support for using methods compiled from source:\\n\\n```C#\\nstring script = @\\\"\\n def relu_script(a, b):\\n return torch.relu(a + b)\\n def add_i(x: Tensor, i: int) -> Tensor:\\n return x + i\\n\\\";\\n\\nusing var cu = torch.jit.compile(script);\\n\\nvar x = torch.randn(3, 4);\\nvar y = torch.randn(3, 4);\\n\\nvar z = (Tensor)cu.invoke(\\\"relu_script\\\", x, y); // Return type is 'object'\\nz = cu.invoke<Tensor>(\\\"add_i\\\", x, 1); // Type-safe return type.\\n```\\n\"\n },\n {\n \"alpha_fraction\": 0.5667069554328918,\n \"alphanum_fraction\": 0.5806126594543457,\n \"avg_line_length\": 43.702701568603516,\n \"blob_id\": \"5de9405d5b0eed44bda48eef78b1bbc45385a791\",\n \"content_id\": \"57524da88b14e80e9703b708a438387be278e80f\",\n \"detected_licenses\": [\n \"MIT\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"C#\",\n \"length_bytes\": 4962,\n \"license_type\": \"permissive\",\n \"max_line_length\": 163,\n \"num_lines\": 111,\n \"path\": \"/src/TorchVision/Ops/SqueezeExcitation.cs\",\n \"repo_name\": \"NiklasGustafsson/TorchSharp\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"// Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information.\\n\\n// A number of implementation details in this file have been translated from the Python version of torchvision,\\n// largely located in the files found in this folder:\\n//\\n// https://github.com/pytorch/vision/blob/a4f53308b2d0f1aa9191686e326f45c26053f686/torchvision/ops/misc.py\\n//\\n// The origin has the following copyright notice and license:\\n//\\n// https://github.com/pytorch/vision/blob/main/LICENSE\\n//\\n\\nusing System;\\nusing System.Collections.Generic;\\nusing System.Linq;\\nusing static TorchSharp.torch;\\n\\n#nullable enable\\nnamespace TorchSharp\\n{\\n public static partial class torchvision\\n {\\n public static partial class ops\\n {\\n /// <summary>\\n /// This block implements the Squeeze-and-Excitation block from https://arxiv.org/abs/1709.01507\\n /// Parameters ``activation``, and ``scale_activation`` correspond to ``delta`` and ``sigma`` in eq. 3.\\n /// </summary>\\n /// <param name=\\\"input_channels\\\">Number of channels in the input image</param>\\n /// <param name=\\\"squeeze_channels\\\">Number of squeeze channels</param>\\n /// <param name=\\\"activation\\\">`delta` activation.</param>\\n /// <param name=\\\"scale_activation\\\">`sigma` activation</param>\\n public static SqueezeExcitation SqueezeExcitation(\\n long input_channels,\\n long squeeze_channels,\\n Func<nn.Module<Tensor, Tensor>>? activation = null,\\n Func<nn.Module<Tensor, Tensor>>? scale_activation = null) => new SqueezeExcitation(input_channels, squeeze_channels, activation, scale_activation);\\n }\\n\\n /// <summary>\\n /// This block implements the Squeeze-and-Excitation block from https://arxiv.org/abs/1709.01507\\n /// Parameters ``activation``, and ``scale_activation`` correspond to ``delta`` and ``sigma`` in eq. 3.\\n /// </summary>\\n public class SqueezeExcitation : torch.nn.Module<Tensor, Tensor>\\n {\\n private readonly nn.Module<Tensor, Tensor> avgpool;\\n private readonly nn.Module<Tensor, Tensor> fc1;\\n private readonly nn.Module<Tensor, Tensor> fc2;\\n private readonly nn.Module<Tensor, Tensor> activation;\\n private readonly nn.Module<Tensor, Tensor> scale_activation;\\n\\n private long input_channels;\\n\\n /// <summary>\\n /// Constructor\\n /// </summary>\\n /// <param name=\\\"input_channels\\\">Number of channels in the input image</param>\\n /// <param name=\\\"squeeze_channels\\\">Number of squeeze channels</param>\\n /// <param name=\\\"activation\\\">``delta`` activation</param>\\n /// <param name=\\\"scale_activation\\\">``sigma`` activation.</param>\\n public SqueezeExcitation(\\n long input_channels,\\n long squeeze_channels,\\n Func<nn.Module<Tensor, Tensor>>? activation = null,\\n Func<nn.Module<Tensor, Tensor>>? scale_activation = null) : base(nameof(SqueezeExcitation))\\n {\\n this.input_channels = input_channels;\\n\\n this.avgpool = torch.nn.AdaptiveAvgPool2d(1);\\n this.fc1 = torch.nn.Conv2d(input_channels, squeeze_channels, 1);\\n this.fc2 = torch.nn.Conv2d(squeeze_channels, input_channels, 1);\\n this.activation = activation is null ? nn.ReLU() : activation();\\n this.scale_activation = scale_activation is null ? nn.Sigmoid() : scale_activation();\\n\\n RegisterComponents();\\n }\\n\\n private Tensor _scale(Tensor input)\\n {\\n var scale = this.avgpool.call(input);\\n scale = this.fc1.call(scale);\\n scale = this.activation.call(scale);\\n scale = this.fc2.call(scale);\\n return this.scale_activation.call(scale);\\n }\\n\\n public override Tensor forward(Tensor input)\\n {\\n if ((input.ndim == 4 && input.shape[1] == input_channels) ||\\n (input.ndim == 3 && input.shape[0] == input_channels)) {\\n using var _ = NewDisposeScope();\\n var scale = this._scale(input);\\n return (scale * input).MoveToOuterDisposeScope();\\n }\\n throw new ArgumentException(\\\"Expected 3D (unbatched) or 4D (batched) input to SqueezeExcitation\\\");\\n }\\n\\n protected override void Dispose(bool disposing)\\n {\\n if (disposing) {\\n avgpool.Dispose();\\n fc1.Dispose();\\n fc2.Dispose();\\n activation.Dispose();\\n scale_activation.Dispose();\\n }\\n base.Dispose(disposing);\\n }\\n }\\n }\\n}\\n\"\n },\n {\n \"alpha_fraction\": 0.5585940480232239,\n \"alphanum_fraction\": 0.5642493963241577,\n \"avg_line_length\": 48.889286041259766,\n \"blob_id\": \"e5c4914f0bb26e0303e461ff7c2cb091ed78b6c2\",\n \"content_id\": \"f5e50f15ff6020d805fa60d4cf0a85deab505cda\",\n \"detected_licenses\": [\n \"MIT\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"C#\",\n \"length_bytes\": 13969,\n \"license_type\": \"permissive\",\n \"max_line_length\": 200,\n \"num_lines\": 280,\n \"path\": \"/src/TorchSharp/Distributions/MultiVariateNormal.cs\",\n \"repo_name\": \"NiklasGustafsson/TorchSharp\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"// Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information.\\nusing System;\\nusing System.Collections.Generic;\\nusing System.Linq;\\nusing static TorchSharp.torch;\\n\\nnamespace TorchSharp\\n{\\n using System.Numerics;\\n using Modules;\\n\\n namespace Modules\\n {\\n /// <summary>\\n /// A multivariate normal (also called Gaussian) distribution parameterized by a mean vector and a covariance matrix.\\n ///\\n /// The multivariate normal distribution can be parameterized either in terms of a positive definite covariance matrix\\n /// or a positive definite precision matrix or a lower-triangular matrix with positive-valued diagonal entries. This triangular matrix\\n /// can be obtained via Cholesky decomposition of the covariance.\\n /// </summary>\\n public class MultivariateNormal : torch.distributions.Distribution\\n {\\n /// <summary>\\n /// The mean of the distribution.\\n /// </summary>\\n public override Tensor mean => loc;\\n\\n /// <summary>\\n /// The mode of the distribution.\\n /// </summary>\\n public override Tensor mode => loc;\\n\\n /// <summary>\\n /// The variance of the distribution\\n /// </summary>\\n public override Tensor variance =>\\n WrappedTensorDisposeScope(() => _unbroadcasted_scale_tril.pow(2).sum(-1).expand(batch_shape + event_shape));\\n\\n /// <summary>\\n /// Constructor\\n /// </summary>\\n /// <param name=\\\"loc\\\"></param>\\n /// <param name=\\\"covariance_matrix\\\">Positive-definite covariance matrix</param>\\n /// <param name=\\\"precision_matrix\\\">Positive-definite precision matrix</param>\\n /// <param name=\\\"scale_tril\\\">The lower-triangular factor of covariance, with positive-valued diagonal</param>\\n /// <param name=\\\"generator\\\">An optional random number generator object.</param>\\n /// <remarks>\\n /// Only one of `covariance_matrix` or `precision_matrix` or `scale_tril` may be specified.\\n ///\\n /// Using `scale_tril` will be more efficient: all computations internally are based on `scale_tril`.\\n /// If `covariance_matrix` or `precision_matrix` is passed instead, it is only used to compute\\n /// the corresponding lower triangular matrices using a Cholesky decomposition.\\n /// </remarks>\\n public MultivariateNormal(Tensor loc, Tensor covariance_matrix = null, Tensor precision_matrix = null, Tensor scale_tril = null, torch.Generator generator = null) : base(generator)\\n {\\n var argCount = 0;\\n argCount += (covariance_matrix is null ? 0 : 1);\\n argCount += (precision_matrix is null ? 0 : 1);\\n argCount += (scale_tril is null ? 0 : 1);\\n\\n if (argCount != 1)\\n throw new ArgumentException(\\\"Exactly one of covariance_matrix or precision_matrix or scale_tril may be specified.\\\");\\n\\n using var _ = NewDisposeScope();\\n\\n if (scale_tril is not null) {\\n if (scale_tril.dim() < 2) {\\n throw new ArgumentException(\\\"scale_tril matrix must be at least two-dimensional, with optional leading batch dimensions\\\");\\n }\\n batch_shape = torch.broadcast_shapes(TakeAllBut(scale_tril.shape, 2), TakeAllBut(loc.shape, 1));\\n this.scale_tril = scale_tril.expand(batch_shape + (-1, -1)).DetachFromDisposeScope();\\n _unbroadcasted_scale_tril = scale_tril;\\n } else if (covariance_matrix is not null) {\\n if (covariance_matrix.dim() < 2) {\\n throw new ArgumentException(\\\"covariance_matrix matrix must be at least two-dimensional, with optional leading batch dimensions\\\");\\n }\\n batch_shape = torch.broadcast_shapes(TakeAllBut(covariance_matrix.shape, 2).ToArray(), TakeAllBut(loc.shape, 1).ToArray());\\n this.covariance_matrix = covariance_matrix.expand(batch_shape + (-1, -1)).DetachFromDisposeScope();\\n _unbroadcasted_scale_tril = torch.linalg.cholesky(covariance_matrix).DetachFromDisposeScope();\\n } else {\\n if (precision_matrix.dim() < 2) {\\n throw new ArgumentException(\\\"precision_matrix matrix must be at least two-dimensional, with optional leading batch dimensions\\\");\\n }\\n batch_shape = torch.broadcast_shapes(TakeAllBut(precision_matrix.shape, 2).ToArray(), TakeAllBut(loc.shape, 1).ToArray());\\n this.precision_matrix = precision_matrix.expand(batch_shape + (-1, -1)).DetachFromDisposeScope();\\n _unbroadcasted_scale_tril = PrecisionToScaleTril(precision_matrix).DetachFromDisposeScope();\\n }\\n\\n this.loc = loc.expand(batch_shape + -1).DetachFromDisposeScope();\\n\\n this.event_shape = loc.shape[loc.shape.Length-1];\\n }\\n\\n private MultivariateNormal(torch.Generator generator = null) : base(generator) { }\\n\\n private Tensor loc;\\n private Tensor scale_tril;\\n private Tensor precision_matrix;\\n private Tensor covariance_matrix;\\n\\n private Tensor _unbroadcasted_scale_tril;\\n\\n /// <summary>\\n /// Generates a sample_shape shaped reparameterized sample or sample_shape shaped batch of reparameterized samples\\n /// if the distribution parameters are batched.\\n /// </summary>\\n /// <param name=\\\"sample_shape\\\">The sample shape.</param>\\n public override Tensor rsample(params long[] sample_shape)\\n {\\n using var _ = NewDisposeScope();\\n\\n var shape = ExtendedShape(sample_shape);\\n var eps = torch.empty(shape, dtype: loc.dtype, device: loc.device).normal_(generator:generator);\\n return (loc + BatchMV(_unbroadcasted_scale_tril, eps)).MoveToOuterDisposeScope();\\n }\\n\\n /// <summary>\\n /// Returns the log of the probability density/mass function evaluated at `value`.\\n /// </summary>\\n /// <param name=\\\"value\\\"></param>\\n public override Tensor log_prob(Tensor value)\\n {\\n using var _ = NewDisposeScope();\\n\\n var diff = value - loc;\\n var M = BatchMahalanobis(_unbroadcasted_scale_tril, diff);\\n var half_log_det = _unbroadcasted_scale_tril.diagonal(dim1: -2, dim2: -1).log().sum(-1);\\n return (-0.5 * (event_shape[0] * Math.Log(2 * Math.PI) + M) - half_log_det).MoveToOuterDisposeScope();\\n }\\n\\n /// <summary>\\n /// Returns a new distribution instance (or populates an existing instance provided by a derived class) with batch dimensions expanded to\\n /// `batch_shape`. This method calls `torch.Tensor.expand()` on the distribution's parameters. As such, this does not allocate new\\n /// memory for the expanded distribution instance.\\n /// </summary>\\n /// <param name=\\\"batch_shape\\\">Tthe desired expanded size.</param>\\n /// <param name=\\\"instance\\\">new instance provided by subclasses that need to override `.expand`.</param>\\n public override distributions.Distribution expand(Size batch_shape, distributions.Distribution instance = null)\\n {\\n if (instance != null && !(instance is MultivariateNormal))\\n throw new ArgumentException(\\\"expand(): 'instance' must be a MultivariateNormal distribution\\\");\\n\\n var newDistribution = ((instance == null) ? new MultivariateNormal(generator) : instance) as MultivariateNormal;\\n\\n var loc_shape = batch_shape + event_shape;\\n var cov_shape = batch_shape + event_shape + event_shape;\\n\\n newDistribution.loc = loc.expand(loc_shape);\\n newDistribution._unbroadcasted_scale_tril = _unbroadcasted_scale_tril;\\n newDistribution.scale_tril = scale_tril?.expand(cov_shape);\\n newDistribution.covariance_matrix = covariance_matrix?.expand(cov_shape).DetachFromDisposeScope();\\n newDistribution.precision_matrix = precision_matrix?.expand(cov_shape).DetachFromDisposeScope();\\n\\n newDistribution.batch_shape = batch_shape;\\n newDistribution.event_shape = event_shape;\\n\\n return newDistribution;\\n }\\n\\n /// <summary>\\n /// Returns entropy of distribution, batched over batch_shape.\\n /// </summary>\\n /// <returns></returns>\\n public override Tensor entropy()\\n {\\n using var _ = NewDisposeScope();\\n\\n var half_log_det = _unbroadcasted_scale_tril.diagonal(dim1: -2, dim2: -1).log().sum(-1);\\n var H = 0.5 * event_shape[0] * (1 + Math.Log(2 * Math.PI)) + half_log_det;\\n\\n return ((batch_shape.Length == 0) ? H : H.expand(batch_shape)).MoveToOuterDisposeScope();\\n }\\n\\n private Tensor BatchMV(Tensor bmat, Tensor bvec)\\n {\\n using var _ = NewDisposeScope();\\n return torch.matmul(bmat, bvec.unsqueeze(-1)).squeeze(-1).MoveToOuterDisposeScope();\\n }\\n\\n private IEnumerable<long> Range(long start, long end, long step = 1)\\n {\\n var result = new List<long>();\\n for (long i = start; i < end; i += step) {\\n result.Add(i);\\n }\\n return result;\\n }\\n\\n private long[] TakeAllBut(long[] input, int count)\\n {\\n var result = new long[input.Length - count];\\n for (int i = 0; i < result.Length; i++)\\n result[i] = input[i];\\n return result;\\n }\\n\\n private Tensor BatchMahalanobis(Tensor bL, Tensor bx)\\n {\\n using var _ = NewDisposeScope();\\n\\n var n = bx.size(-1);\\n var bx_batch_shape = TakeAllBut(bx.shape, 1);\\n\\n var bx_batch_dims = bx_batch_shape.Length;\\n var bL_batch_dims = bL.dim() - 2;\\n int outer_batch_dims = bx_batch_dims - (int)bL_batch_dims;\\n var old_batch_dims = outer_batch_dims + bL_batch_dims;\\n var new_batch_dims = outer_batch_dims + 2 * bL_batch_dims;\\n\\n var bx_new_shape = TakeAllBut(bx.shape, outer_batch_dims).ToList();\\n\\n for (int i = 0; i < bL.ndim - 2; i++) {\\n var sL = bL.shape[i];\\n var sx = bx.shape[outer_batch_dims + i];\\n bx_new_shape.Add(sx / sL);\\n bx_new_shape.Add(sL);\\n }\\n bx_new_shape.Add(n);\\n bx = bx.reshape(bx_new_shape.ToArray());\\n\\n List<long> permute_dims = new List<long>();\\n permute_dims.AddRange(Range(0, outer_batch_dims));\\n permute_dims.AddRange(Range(outer_batch_dims, new_batch_dims, 2));\\n permute_dims.AddRange(Range(outer_batch_dims + 1, new_batch_dims, 2));\\n permute_dims.Add(new_batch_dims);\\n\\n bx = bx.permute(permute_dims.ToArray());\\n\\n var flat_L = bL.reshape(-1, n, n);\\n var flat_x = bx.reshape(-1, flat_L.size(0), n);\\n var flat_x_swap = flat_x.permute(1, 2, 0);\\n\\n var M_swap = torch.linalg.solve_triangular(flat_L, flat_x_swap, upper: false).pow(2).sum(-2);\\n var M = M_swap.t();\\n\\n var permuted_M = M.reshape(TakeAllBut(bx.shape, 1));\\n var permuted_inv_dims = Range(0, outer_batch_dims).ToList();\\n for (int i = 0; i < bL_batch_dims; i++) {\\n permuted_inv_dims.Add(outer_batch_dims + i);\\n permuted_inv_dims.Add(old_batch_dims + i);\\n }\\n\\n var reshaped_M = permuted_M.permute(permuted_inv_dims);\\n\\n return reshaped_M.reshape(bx_batch_shape).MoveToOuterDisposeScope();\\n }\\n\\n private Tensor PrecisionToScaleTril(Tensor P)\\n {\\n using var _ = NewDisposeScope();\\n var Lf = torch.linalg.cholesky(torch.flip(P, -2, -1));\\n var L_inv = torch.transpose(torch.flip(Lf, -2, -1), -2, -1);\\n var Id = torch.eye(P.shape[P.shape.Length - 1], dtype: P.dtype, device: P.device);\\n var L = torch.linalg.solve_triangular(L_inv, Id, upper: false);\\n return L.MoveToOuterDisposeScope();\\n }\\n }\\n\\n }\\n public static partial class torch\\n {\\n public static partial class distributions\\n {\\n /// <summary>\\n /// Creates a MultivariateNormal distribution parameterized by `probs` or `logits` (but not both).\\n /// `total_count` must be broadcastable with `probs`/`logits`.\\n /// </summary>\\n /// <param name=\\\"loc\\\"></param>\\n /// <param name=\\\"covariance_matrix\\\"></param>\\n /// <param name=\\\"precision_matrix\\\"></param>\\n /// <param name=\\\"scale_tril\\\"></param>\\n /// <param name=\\\"generator\\\">An optional random number generator object.</param>\\n /// <returns></returns>\\n public static MultivariateNormal MultivariateNormal(Tensor loc, Tensor covariance_matrix = null, Tensor precision_matrix = null, Tensor scale_tril = null, torch.Generator generator = null)\\n {\\n return new MultivariateNormal(loc, covariance_matrix, precision_matrix, scale_tril, generator);\\n }\\n }\\n }\\n}\\n\"\n },\n {\n \"alpha_fraction\": 0.5440872311592102,\n \"alphanum_fraction\": 0.5500094294548035,\n \"avg_line_length\": 60.491329193115234,\n \"blob_id\": \"53720992fcb8093412dc8d366d603608250ddc7b\",\n \"content_id\": \"9536603d057629a83ddea7d3f52cc6a7d0e0da9d\",\n \"detected_licenses\": [\n \"MIT\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"C#\",\n \"length_bytes\": 10638,\n \"license_type\": \"permissive\",\n \"max_line_length\": 306,\n \"num_lines\": 173,\n \"path\": \"/src/TorchSharp/NN/Pooling/MaxPool3D.cs\",\n \"repo_name\": \"NiklasGustafsson/TorchSharp\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"// Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information.\\nusing System;\\nusing System.Linq;\\nusing static TorchSharp.torch;\\nusing static TorchSharp.PInvoke.NativeMethods;\\n\\nnamespace TorchSharp\\n{\\n using Modules;\\n\\n namespace Modules\\n {\\n /// <summary>\\n /// This class is used to represent a MaxPool3D module.\\n /// </summary>\\n public sealed class MaxPool3d : torch.nn.Module<Tensor, Tensor>\\n {\\n internal MaxPool3d(IntPtr handle, IntPtr boxedHandle) : base(handle, boxedHandle)\\n {\\n }\\n\\n public override Tensor forward(Tensor tensor)\\n {\\n var res = THSNN_MaxPool3d_forward(handle, tensor.Handle);\\n if (res == IntPtr.Zero) { torch.CheckForErrors(); }\\n return new Tensor(res);\\n }\\n\\n public (Tensor Values, Tensor Indices) forward_with_indices(Tensor tensor)\\n {\\n var res = THSNN_MaxPool3d_forward_with_indices(handle, tensor.Handle, out var indices);\\n if (res == IntPtr.Zero || indices == IntPtr.Zero) { torch.CheckForErrors(); }\\n return (new Tensor(res), new Tensor(indices));\\n }\\n\\n // Rather than spending cycles only to discover that this module has neither\\n // parameters nor buffers, just shortcut the move completely.\\n protected internal override nn.Module _to(Device device, ScalarType dtype) => this;\\n protected internal override nn.Module _to(DeviceType deviceType, int deviceIndex = -1) => this;\\n protected internal override nn.Module _to(ScalarType dtype) => this;\\n }\\n }\\n\\n public static partial class torch\\n {\\n public static partial class nn\\n {\\n /// <summary>\\n /// Applies a 3D max pooling over an input signal composed of several input planes.\\n /// </summary>\\n /// <param name=\\\"kernelSize\\\">The size of the sliding window, must be > 0.</param>\\n /// <param name=\\\"stride\\\">The stride of the sliding window, must be > 0. Default value is kernel_size.</param>\\n /// <param name=\\\"padding\\\">Implicit negative infinity padding to be added on both sides, must be >= 0 and less than or equal to kernel_size / 2</param>\\n /// <param name=\\\"dilation\\\">The stride between elements within a sliding window, must be > 0.</param>\\n /// <param name=\\\"ceilMode\\\">If true, will use ceil instead of floor to compute the output shape. This ensures that every element in the input tensor is covered by a sliding window.</param>\\n /// <returns></returns>\\n public static MaxPool3d MaxPool3d(long kernelSize, long? stride = null, long? padding = null, long? dilation = null, bool ceilMode = false)\\n {\\n var pStride = stride.HasValue ? new long[] { stride.Value, stride.Value, stride.Value } : null;\\n var pPadding = padding.HasValue ? new long[] { padding.Value, padding.Value, padding.Value } : null;\\n var pDilation = dilation.HasValue ? new long[] { dilation.Value, dilation.Value, dilation.Value } : null;\\n return MaxPool3d(new long[] { kernelSize, kernelSize, kernelSize }, pStride, pPadding, pDilation, ceilMode);\\n }\\n\\n /// <summary>\\n /// Applies a 3D max pooling over an input signal composed of several input planes.\\n /// </summary>\\n /// <param name=\\\"kernelSize\\\">The size of the sliding window, must be > 0.</param>\\n /// <param name=\\\"stride\\\">The stride of the sliding window, must be > 0. Default value is kernel_size.</param>\\n /// <param name=\\\"padding\\\">Implicit negative infinity padding to be added on both sides, must be >= 0 and less than or equal to kernel_size / 2</param>\\n /// <param name=\\\"dilation\\\">The stride between elements within a sliding window, must be > 0.</param>\\n /// <param name=\\\"ceilMode\\\">If true, will use ceil instead of floor to compute the output shape. This ensures that every element in the input tensor is covered by a sliding window.</param>\\n /// <returns></returns>\\n public static MaxPool3d MaxPool3d((long, long, long) kernelSize, (long, long, long)? stride = null, (long, long, long)? padding = null, (long, long, long)? dilation = null, bool ceilMode = false)\\n {\\n var pStride = stride.HasValue ? new long[] { stride.Value.Item1, stride.Value.Item2, stride.Value.Item3 } : null;\\n var pPadding = padding.HasValue ? new long[] { padding.Value.Item1, padding.Value.Item2, padding.Value.Item3 } : null;\\n var pDilation = dilation.HasValue ? new long[] { dilation.Value.Item1, dilation.Value.Item2, dilation.Value.Item3 } : null;\\n return MaxPool3d(new long[] { kernelSize.Item1, kernelSize.Item2, kernelSize.Item3 }, pStride, pPadding, pDilation, ceilMode);\\n }\\n\\n /// <summary>\\n /// Applies a 3D max pooling over an input signal composed of several input planes.\\n /// </summary>\\n /// <param name=\\\"kernelSize\\\">The size of the sliding window, must be > 0.</param>\\n /// <param name=\\\"strides\\\">The stride of the sliding window, must be > 0. Default value is kernel_size.</param>\\n /// <param name=\\\"padding\\\">Implicit negative infinity padding to be added on both sides, must be >= 0 and less than or equal to kernel_size / 2</param>\\n /// <param name=\\\"dilation\\\">The stride between elements within a sliding window, must be > 0.</param>\\n /// <param name=\\\"ceilMode\\\">If true, will use ceil instead of floor to compute the output shape. This ensures that every element in the input tensor is covered by a sliding window.</param>\\n /// <returns></returns>\\n public static MaxPool3d MaxPool3d(long[] kernelSize, long[] strides = null, long[] padding = null, long[] dilation = null, bool ceilMode = false)\\n {\\n unsafe {\\n fixed (long* pkernelSize = kernelSize, pstrides = strides, pPadding = padding, pDilation = dilation) {\\n var handle = THSNN_MaxPool3d_ctor((IntPtr)pkernelSize, kernelSize.Length, (IntPtr)pstrides, (strides == null ? 0 : strides.Length), (IntPtr)pPadding, (padding == null ? 0 : padding.Length), (IntPtr)pDilation, (dilation == null ? 0 : dilation.Length), ceilMode, out var boxedHandle);\\n if (handle == IntPtr.Zero) { torch.CheckForErrors(); }\\n return new MaxPool3d(handle, boxedHandle);\\n }\\n }\\n }\\n\\n public static partial class functional\\n {\\n /// <summary>\\n /// Applies a 3D max pooling over an input signal composed of several input planes.\\n /// </summary>\\n /// <param name=\\\"input\\\">The input tensor.</param>\\n /// <param name=\\\"kernelSize\\\"></param>\\n /// <param name=\\\"strides\\\"></param>\\n /// <param name=\\\"padding\\\"></param>\\n /// <param name=\\\"dilation\\\"></param>\\n /// <param name=\\\"ceil_mode\\\"></param>\\n /// <returns></returns>\\n public static Tensor max_pool3d(Tensor input, long[] kernelSize, long[] strides = null,\\n long[] padding = null, long[] dilation = null, bool ceil_mode = false)\\n {\\n strides = strides ?? kernelSize.Select(x => 1L).ToArray();\\n padding = padding ?? kernelSize.Select(x => 0L).ToArray();\\n dilation = dilation ?? kernelSize.Select(x => 1L).ToArray();\\n unsafe {\\n fixed (long* pkernelSize = kernelSize, pstrides = strides, ppadding = padding, pdilation = dilation) {\\n var res =\\n THSTensor_max_pool3d(input.Handle,\\n (IntPtr)pkernelSize, kernelSize.Length,\\n (IntPtr)pstrides, strides.Length,\\n (IntPtr)ppadding, padding.Length,\\n (IntPtr)pdilation, dilation.Length,\\n ceil_mode);\\n if (res == IntPtr.Zero) { torch.CheckForErrors(); }\\n return new Tensor(res);\\n }\\n }\\n }\\n\\n /// <summary>\\n /// Applies a 3D max pooling over an input signal composed of several input planes.\\n /// </summary>\\n /// <param name=\\\"input\\\">The input tensor.</param>\\n /// <param name=\\\"kernelSize\\\"></param>\\n /// <param name=\\\"strides\\\"></param>\\n /// <param name=\\\"padding\\\"></param>\\n /// <param name=\\\"dilation\\\"></param>\\n /// <param name=\\\"ceil_mode\\\"></param>\\n /// <returns></returns>\\n public static (Tensor output, Tensor indices) max_pool3d_with_indices(Tensor input, long[] kernelSize, long[] strides = null,\\n long[] padding = null, long[] dilation = null, bool ceil_mode = false)\\n {\\n strides = strides ?? kernelSize.Select(x => 1L).ToArray();\\n padding = padding ?? kernelSize.Select(x => 0L).ToArray();\\n dilation = dilation ?? kernelSize.Select(x => 1L).ToArray();\\n IntPtr[] ptrArray;\\n\\n using (var pa = new PinnedArray<IntPtr>()) {\\n unsafe {\\n fixed (long* pkernelSize = kernelSize, pstrides = strides, ppadding = padding, pdilation = dilation) {\\n THSTensor_max_pool3d_with_indices(input.Handle,\\n pa.CreateArray,\\n (IntPtr)pkernelSize, kernelSize.Length,\\n (IntPtr)pstrides, strides.Length,\\n (IntPtr)ppadding, padding.Length,\\n (IntPtr)pdilation, dilation.Length,\\n ceil_mode);\\n torch.CheckForErrors();\\n }\\n }\\n ptrArray = pa.Array;\\n }\\n return (new Tensor(ptrArray[0]), new Tensor(ptrArray[1]));\\n }\\n }\\n }\\n }\\n}\\n\"\n },\n {\n \"alpha_fraction\": 0.5308775901794434,\n \"alphanum_fraction\": 0.5373781323432922,\n \"avg_line_length\": 33.8301887512207,\n \"blob_id\": \"4db15dc01e676a64ce9aaf92ee908990b730c2b0\",\n \"content_id\": \"89cd0b5ae63ef42ef217d2a0532ebe9f52cd96df\",\n \"detected_licenses\": [\n \"MIT\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"C#\",\n \"length_bytes\": 1846,\n \"license_type\": \"permissive\",\n \"max_line_length\": 130,\n \"num_lines\": 53,\n \"path\": \"/src/TorchVision/AdjustGamma.cs\",\n \"repo_name\": \"NiklasGustafsson/TorchSharp\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"// Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information.\\nusing System;\\nusing static TorchSharp.torch;\\n\\nnamespace TorchSharp\\n{\\n public static partial class torchvision\\n {\\n internal class AdjustGamma : ITransform\\n {\\n internal AdjustGamma(double gamma, double gain = 1.0)\\n {\\n if (gamma < 0.0)\\n throw new ArgumentException($\\\"The saturation factor ({gamma}) must be non-negative.\\\");\\n this.gamma = gamma;\\n this.gain = gain;\\n }\\n\\n public Tensor call(Tensor img)\\n {\\n var dtype = img.dtype;\\n if (!torch.is_floating_point(img))\\n img = transforms.ConvertImageDtype(torch.float32).call(img);\\n\\n img = (gain * img.pow(gamma)).clamp(0, 1);\\n\\n return transforms.ConvertImageDtype(dtype).call(img); ;\\n }\\n\\n private double gamma;\\n private double gain;\\n }\\n\\n public static partial class transforms\\n {\\n /// <summary>\\n /// Perform gamma correction on an image.\\n ///\\n /// See: https://en.wikipedia.org/wiki/Gamma_correction\\n /// </summary>\\n /// <param name=\\\"gamma\\\">\\n /// Non negative real number.\\n /// gamma larger than 1 make the shadows darker, while gamma smaller than 1 make dark regions lighter.\\n /// </param>\\n /// <param name=\\\"gain\\\">The constant multiplier in the gamma correction equation.</param>\\n /// <returns></returns>\\n static public ITransform AdjustGamma(double gamma, double gain = 1.0)\\n {\\n return new AdjustGamma(gamma);\\n }\\n }\\n }\\n}\\n\"\n },\n {\n \"alpha_fraction\": 0.580381453037262,\n \"alphanum_fraction\": 0.5953678488731384,\n \"avg_line_length\": 29.625,\n \"blob_id\": \"608516b7c52ad86a70df5288f81f0524677cd051\",\n \"content_id\": \"7fa461630b74088004d0f3bbabb95c874d13ee92\",\n \"detected_licenses\": [\n \"MIT\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 734,\n \"license_type\": \"permissive\",\n \"max_line_length\": 92,\n \"num_lines\": 24,\n \"path\": \"/test/TorchSharpTest/bug510.py\",\n \"repo_name\": \"NiklasGustafsson/TorchSharp\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"import torch\\nimport src.Python.exportsd as exportsd\\n\\nclass BasicConv1d(torch.nn.Module):\\n def __init__(self, in_channels, out_channels, **kwargs):\\n super().__init__()\\n \\n self.stack = torch.nn.Sequential(\\n torch.nn.Conv1d(in_channels, out_channels, kernel_size=3, bias=False, **kwargs),\\n torch.nn.BatchNorm1d(out_channels),\\n torch.nn.ReLU(inplace=True)\\n )\\n \\n def forward(self, x):\\n return self.stack(x)\\n\\nif __name__ == '__main__':\\n # Create model\\n model = BasicConv1d(1, 32)\\n \\n #Export model to .dat file for ingestion into TorchSharp\\n f = open(\\\"bug510.dat\\\", \\\"wb\\\")\\n exportsd.save_state_dict(model.to(\\\"cpu\\\").state_dict(), f)\\n f.close()\"\n },\n {\n \"alpha_fraction\": 0.44665011763572693,\n \"alphanum_fraction\": 0.44665011763572693,\n \"avg_line_length\": 28.512195587158203,\n \"blob_id\": \"e3a9f6c8e6e1a651fbdac3d68b6919beb5581516\",\n \"content_id\": \"a09356d4a6b9af6a4b6731b665d0f9f32fd346a1\",\n \"detected_licenses\": [\n \"MIT\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"C#\",\n \"length_bytes\": 1209,\n \"license_type\": \"permissive\",\n \"max_line_length\": 130,\n \"num_lines\": 41,\n \"path\": \"/src/TorchAudio/Datasets/SpeechCommandsDatasetItem.cs\",\n \"repo_name\": \"NiklasGustafsson/TorchSharp\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"// Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information.\\n\\nnamespace TorchSharp\\n{\\n public static partial class torchaudio\\n {\\n public static partial class datasets\\n {\\n /// <summary>\\n /// An item in SPEECHCOMMANDS dataset.\\n /// </summary>\\n public class SpeechCommandsDatasetItem\\n {\\n /// <summary>\\n /// Samples of the audio clip\\n /// </summary>\\n public torch.Tensor waveform;\\n\\n /// <summary>\\n /// Sampling rate of the audio clip\\n /// </summary>\\n public int sample_rate;\\n\\n /// <summary>\\n /// Labels of the audio clip, 'bird', 'yes', ...\\n /// </summary>\\n public string label;\\n\\n /// <summary>\\n /// Speaker ID\\n /// </summary>\\n public string speaker_id;\\n\\n /// <summary>\\n /// Utterance number\\n /// </summary>\\n public int utterance_number;\\n }\\n }\\n }\\n}\"\n },\n {\n \"alpha_fraction\": 0.5736129283905029,\n \"alphanum_fraction\": 0.6063783168792725,\n \"avg_line_length\": 27.612499237060547,\n \"blob_id\": \"34a02e4e1e1660cbb2b0e026c56ea2b3043be64f\",\n \"content_id\": \"ddd5432443e86bd624c76149ec46132af1999499\",\n \"detected_licenses\": [\n \"MIT\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 2289,\n \"license_type\": \"permissive\",\n \"max_line_length\": 129,\n \"num_lines\": 80,\n \"path\": \"/src/Python/importsd.py\",\n \"repo_name\": \"NiklasGustafsson/TorchSharp\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"#\\n# Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information.\\n#\\nimport torch\\nimport leb128\\nimport numpy as np\\nfrom collections import OrderedDict\\n\\n\\n_DTYPE_SIZE_MAP = {\\n np.uint8: 1,\\n np.int8: 1,\\n np.int16: 2,\\n np.int32: 4,\\n np.int64: 8,\\n np.float16: 2,\\n np.float32: 4,\\n np.float64: 8,\\n}\\n\\n\\ndef _get_elem_type(type_num: int):\\n if type_num == 0:\\n return np.uint8\\n elif type_num == 1:\\n return np.int8\\n elif type_num == 2:\\n return np.int16\\n elif type_num == 3:\\n return np.int32\\n elif type_num == 4:\\n return np.int64\\n elif type_num == 5:\\n return np.float16\\n elif type_num == 6:\\n return np.float32\\n elif type_num == 7:\\n return np.float64\\n elif type_num == 11:\\n # return torch.bool\\n raise NotImplemented(\\\"Unsupported data type\\\")\\n elif type_num == 15:\\n # return torch.bfloat16\\n raise NotImplemented(\\\"Unsupported data type\\\")\\n elif type_num == 4711:\\n raise NotImplemented(\\\"Unsupported data type\\\")\\n else:\\n raise ValueError(\\\"cannot decode the data type\\\")\\n\\n\\ndef load_state_dict(stream):\\n \\\"\\\"\\\"\\n Loads a PyTorch state dictionary using the format that saved by TorchSharp.\\n\\n :param stream: An write stream opened for binary I/O.\\n :return sd: A dictionary can be loaded by 'model.load_state_dict()'\\n \\\"\\\"\\\"\\n sd = OrderedDict()\\n dict_len, _ = leb128.u.decode_reader(stream)\\n for i in range(dict_len):\\n key_len, _ = leb128.u.decode_reader(stream)\\n key_name = stream.read(key_len).decode(\\\"utf-8\\\")\\n\\n ele_type, _ = leb128.u.decode_reader(stream)\\n buffer_dtype = _get_elem_type(ele_type)\\n\\n buffer_shape_len, _ = leb128.u.decode_reader(stream)\\n buffer_shape = tuple(leb128.u.decode_reader(stream)[0] for _ in range(buffer_shape_len))\\n if buffer_shape:\\n data_size = np.prod(buffer_shape)\\n else:\\n data_size = 1\\n\\n data_size_bytes = data_size * _DTYPE_SIZE_MAP[buffer_dtype]\\n sd[key_name] = torch.from_numpy(\\n np.frombuffer(\\n stream.read(data_size_bytes), dtype=buffer_dtype, count=data_size\\n ).reshape(buffer_shape)\\n )\\n return sd\\n\"\n },\n {\n \"alpha_fraction\": 0.3953971564769745,\n \"alphanum_fraction\": 0.41745245456695557,\n \"avg_line_length\": 49.055999755859375,\n \"blob_id\": \"4694733fc0957452a403e0919fee2d2ebba91426\",\n \"content_id\": \"edfccd899c6ed72317f94aa63570c01c0a664d63\",\n \"detected_licenses\": [\n \"MIT\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"C#\",\n \"length_bytes\": 6257,\n \"license_type\": \"permissive\",\n \"max_line_length\": 218,\n \"num_lines\": 125,\n \"path\": \"/src/TorchSharp/Utils/tensorboard/Utils.cs\",\n \"repo_name\": \"NiklasGustafsson/TorchSharp\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"// Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information.\\n\\nusing System;\\nusing System.Diagnostics;\\nusing System.Linq;\\n\\nnamespace TorchSharp\\n{\\n public static partial class torch\\n {\\n public static partial class utils\\n {\\n public static partial class tensorboard\\n {\\n internal static partial class utils\\n {\\n /// <summary>\\n /// Converts a 5D tensor [batchsize, time(frame), channel(color), height, width]\\n /// into 4D tensor with dimension[time(frame), new_width, new_height, channel].\\n /// A batch of images are spreaded to a grid, which forms a frame.\\n /// e.g. Video with batchsize 16 will have a 4x4 grid.\\n ///\\n /// https://github.com/pytorch/pytorch/blob/master/torch/utils/tensorboard/_utils.py#L110\\n /// </summary>\\n /// <param name=\\\"V\\\"></param>\\n /// <returns></returns>\\n public static Tensor prepare_video(Tensor V)\\n {\\n long b = V.shape[0];\\n long t = V.shape[1];\\n long c = V.shape[2];\\n long h = V.shape[3];\\n long w = V.shape[4];\\n\\n if (V.dtype == ScalarType.Int8 || V.dtype == ScalarType.Byte)\\n V = V.to_type(ScalarType.Float32) / 255.0;\\n\\n bool is_power2(long num)\\n => num != 0 && ((num & (num - 1)) == 0);\\n int bit_length(long value)\\n => Convert.ToString(value, 2).Length;\\n\\n if (!is_power2(V.shape[0])) {\\n int len_addition = Convert.ToInt32(Math.Pow(2, bit_length(V.shape[0])) - V.shape[0]);\\n V = cat(new Tensor[] { V, zeros(new long[] { len_addition, t, c, h, w }, device: V.device) });\\n }\\n\\n long n_rows = Convert.ToInt32(Math.Pow(2, (bit_length(V.shape[0]) - 1) / 2));\\n long n_cols = V.shape[0] / n_rows;\\n\\n V = V.reshape(n_rows, n_cols, t, c, h, w);\\n V = V.permute(2, 0, 4, 1, 5, 3);\\n V = V.reshape(t, n_rows * h, n_cols * w, c);\\n return V;\\n }\\n\\n /// <summary>\\n /// https://github.com/pytorch/pytorch/blob/6c30dc6ceed5542351b3be4f8043b28020f93f3a/torch/utils/tensorboard/_utils.py#L69\\n /// </summary>\\n /// <param name=\\\"I\\\"></param>\\n /// <param name=\\\"ncols\\\"></param>\\n /// <returns></returns>\\n public static Tensor make_grid(Tensor I, long ncols = 8)\\n {\\n if (I.shape[1] == 1)\\n I = I.expand(-1, 3);\\n Trace.Assert(I.ndim == 4 && I.shape[1] == 3);\\n long nimg = I.shape[0];\\n long H = I.shape[2];\\n long W = I.shape[3];\\n ncols = Math.Min(ncols, nimg);\\n long nrows = (long)Math.Ceiling((double)nimg / (double)ncols);\\n Tensor canvas = zeros(new long[] { 3, H * nrows, W * ncols }, I.dtype);\\n long i = 0;\\n for (long r = 0; r < nrows; r++) {\\n for (long c = 0; c < ncols; c++) {\\n if (i >= nimg)\\n break;\\n canvas.narrow(1, r * H, H).narrow(2, c * W, W).copy_(I[i]);\\n i++;\\n }\\n }\\n return canvas;\\n }\\n\\n /// <summary>\\n /// https://github.com/pytorch/pytorch/blob/6c30dc6ceed5542351b3be4f8043b28020f93f3a/torch/utils/tensorboard/_utils.py#L95\\n /// </summary>\\n /// <param name=\\\"tensor\\\"> Image data </param>\\n /// <param name=\\\"input_format\\\"> Image data format specification of the form NCHW, NHWC, CHW, HWC, HW, WH, etc. </param>\\n /// <returns></returns>\\n public static Tensor convert_to_HWC(Tensor tensor, string input_format)\\n {\\n Trace.Assert(tensor.shape.Length == input_format.Length, $\\\"size of input tensor and input format are different. tensor shape: ({string.Join(\\\", \\\", tensor.shape)}), input_format: {input_format}\\\");\\n input_format = input_format.ToUpper();\\n\\n if (input_format.Length == 4) {\\n long[] index = \\\"NCHW\\\".Select(c => Convert.ToInt64(input_format.IndexOf(c))).ToArray();\\n Tensor tensor_NCHW = tensor.permute(index);\\n Tensor tensor_CHW = make_grid(tensor_NCHW);\\n return tensor_CHW.permute(1, 2, 0);\\n }\\n\\n if (input_format.Length == 3) {\\n long[] index = \\\"HWC\\\".Select(c => Convert.ToInt64(input_format.IndexOf(c))).ToArray();\\n Tensor tensor_HWC = tensor.permute(index);\\n if (tensor_HWC.shape[2] == 1)\\n tensor_HWC = tensor_HWC.expand(-1, -1, 3);\\n return tensor_HWC;\\n }\\n\\n if (input_format.Length == 2) {\\n long[] index = \\\"HW\\\".Select(c => Convert.ToInt64(input_format.IndexOf(c))).ToArray();\\n tensor = tensor.permute(index);\\n tensor = tensor.unsqueeze(-1).expand(-1, -1, 3);\\n return tensor;\\n }\\n\\n throw new NotImplementedException();\\n }\\n }\\n }\\n }\\n }\\n}\\n\"\n },\n {\n \"alpha_fraction\": 0.6045751571655273,\n \"alphanum_fraction\": 0.6078431606292725,\n \"avg_line_length\": 20.85714340209961,\n \"blob_id\": \"0123336db26ce17a05ed88e8a31040e8714237c9\",\n \"content_id\": \"292e094435f6d089b5204d844ebb4fdb7350fa89\",\n \"detected_licenses\": [\n \"MIT\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"C#\",\n \"length_bytes\": 306,\n \"license_type\": \"permissive\",\n \"max_line_length\": 130,\n \"num_lines\": 14,\n \"path\": \"/src/TorchSharp/Tensor/Enums/HistogramBinSelector.cs\",\n \"repo_name\": \"NiklasGustafsson/TorchSharp\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"// Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information.\\n\\nnamespace TorchSharp\\n{\\n public enum HistogramBinSelector : byte\\n {\\n Doane = 0,\\n Rice,\\n Scott,\\n Sqrt,\\n Stone,\\n Sturges\\n }\\n}\\n\"\n },\n {\n \"alpha_fraction\": 0.6214463710784912,\n \"alphanum_fraction\": 0.6214463710784912,\n \"avg_line_length\": 40.79166793823242,\n \"blob_id\": \"0762788f0bdbebeec9b9664996eb7f36d1ea24df\",\n \"content_id\": \"11598e5f7e2b77d8d49af18007d53285fa69c91a\",\n \"detected_licenses\": [\n \"MIT\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"C#\",\n \"length_bytes\": 2009,\n \"license_type\": \"permissive\",\n \"max_line_length\": 196,\n \"num_lines\": 48,\n \"path\": \"/src/TorchVision/Perspective.cs\",\n \"repo_name\": \"NiklasGustafsson/TorchSharp\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"// Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information.\\nusing System;\\nusing System.Collections.Generic;\\nusing static TorchSharp.torch;\\n\\n\\nnamespace TorchSharp\\n{\\n public static partial class torchvision\\n {\\n internal class Perspective : ITransform\\n {\\n internal Perspective(IList<IList<int>> startpoints, IList<IList<int>> endpoints, InterpolationMode interpolation, IList<float> fill = null)\\n {\\n if (interpolation != InterpolationMode.Nearest && interpolation != InterpolationMode.Bilinear)\\n throw new ArgumentException($\\\"Invalid interpolation mode for 'Perspective': {interpolation}. Use 'nearest' or 'bilinear'.\\\");\\n\\n this.startpoints = startpoints;\\n this.endpoints = endpoints;\\n this.interpolation = interpolation;\\n this.fill = fill;\\n }\\n\\n public Tensor call(Tensor input)\\n {\\n return transforms.functional.perspective(input, startpoints, endpoints, interpolation, fill);\\n }\\n\\n private IList<IList<int>> startpoints;\\n private IList<IList<int>> endpoints;\\n private InterpolationMode interpolation;\\n private IList<float> fill;\\n }\\n\\n public static partial class transforms\\n {\\n /// <summary>\\n /// Perform perspective transform of the given image.\\n /// The image is expected to have […, H, W] shape, where … means an arbitrary number of leading dimensions.\\n /// </summary>\\n /// <returns></returns>\\n static public ITransform Perspective(IList<IList<int>> startpoints, IList<IList<int>> endpoints, InterpolationMode interpolation = InterpolationMode.Bilinear, IList<float> fill = null)\\n {\\n return new Perspective(startpoints, endpoints, interpolation, fill);\\n }\\n }\\n }\\n}\"\n },\n {\n \"alpha_fraction\": 0.5039065480232239,\n \"alphanum_fraction\": 0.5209760665893555,\n \"avg_line_length\": 49.995094299316406,\n \"blob_id\": \"2eac9d5f5a46248685f2e0372cdfd0e55079691d\",\n \"content_id\": \"77713e97cb7712d6292e39e6f2cf8a01f73fc83b\",\n \"detected_licenses\": [\n \"MIT\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"C#\",\n \"length_bytes\": 51964,\n \"license_type\": \"permissive\",\n \"max_line_length\": 201,\n \"num_lines\": 1019,\n \"path\": \"/src/TorchVision/models/ResNet.cs\",\n \"repo_name\": \"NiklasGustafsson/TorchSharp\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"// Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information.\\nusing System;\\nusing System.Collections.Generic;\\nusing System.Text.RegularExpressions;\\nusing static TorchSharp.torch;\\nusing static TorchSharp.torch.nn;\\n\\n#nullable enable\\nnamespace TorchSharp\\n{\\n public static partial class torchvision\\n {\\n public static partial class models\\n {\\n /// <summary>\\n /// ResNet-18\\n /// </summary>\\n /// <param name=\\\"num_classes\\\">The number of output classes.</param>\\n /// <param name=\\\"zero_init_residual\\\">Whether to zero-initalize the residual block's norm layers.</param>\\n /// <param name=\\\"groups\\\">The number of groups.</param>\\n /// <param name=\\\"width_per_group\\\">The width of each group.</param>\\n /// <param name=\\\"replace_stride_with_dilation\\\">Each element in the tuple indicates if we should replace the 2x2 stride with a dilated convolution instead</param>\\n /// <param name=\\\"norm_layer\\\">The normalization layer to use -- a function creating a layer.</param>\\n /// <param name=\\\"weights_file\\\">The location of a file containing pre-trained weights for the model.</param>\\n /// <param name=\\\"skipfc\\\">If true, the last linear layer of the classifier will not be loaded from the weights file.</param>\\n /// <param name=\\\"device\\\">The device to locate the model on.</param>\\n /// <remarks>\\n /// Pre-trained weights may be retrieved by using Pytorch and saving the model state-dict\\n /// using the exportsd.py script, then loading into the .NET instance:\\n ///\\n /// from torchvision import models\\n /// import exportsd\\n /// \\n /// model = models.resnet18(pretrained=True)\\n /// f = open(\\\"model_weights.dat\\\", \\\"wb\\\")\\n /// exportsd.save_state_dict(model.state_dict(), f)\\n /// f.close()\\n ///\\n /// See also: https://github.com/dotnet/TorchSharp/blob/main/docfx/articles/saveload.md\\n ///\\n /// In order for the weights to be loaded, the number of classes has to be the same as\\n /// in the pre-trained model, which is 1000.\\n ///\\n /// It is also possible to skip loading the last linear layer and use it for transfer-learning\\n /// with a different number of output classes. To do so, pass skipfc=true.\\n ///\\n /// All pre-trained models expect input images normalized in the same way, i.e. mini-batches of 3-channel RGB\\n /// images of shape (3 x H x W), where H and W are expected to be at least 224. The images have to be loaded\\n /// in to a range of [0, 1] and then normalized using mean = [0.485, 0.456, 0.406] and std = [0.229, 0.224, 0.225].\\n /// </remarks>\\n public static Modules.ResNet resnet18(\\n int num_classes = 1000,\\n bool zero_init_residual = false,\\n int groups = 1,\\n int width_per_group = 64,\\n (bool, bool, bool)? replace_stride_with_dilation = null,\\n Func<int, Module<Tensor, Tensor>>? norm_layer = null,\\n string? weights_file = null,\\n bool skipfc = true,\\n Device? device = null)\\n {\\n return Modules.ResNet.ResNet18(num_classes,\\n zero_init_residual,\\n groups,\\n width_per_group,\\n replace_stride_with_dilation,\\n norm_layer,\\n weights_file, skipfc, device);\\n }\\n\\n /// <summary>\\n /// ResNet-34\\n /// </summary>\\n /// <param name=\\\"num_classes\\\">The number of output classes.</param>\\n /// <param name=\\\"zero_init_residual\\\">Whether to zero-initalize the residual block's norm layers.</param>\\n /// <param name=\\\"groups\\\">The number of groups.</param>\\n /// <param name=\\\"width_per_group\\\">The width of each group.</param>\\n /// <param name=\\\"replace_stride_with_dilation\\\">Each element in the tuple indicates if we should replace the 2x2 stride with a dilated convolution instead</param>\\n /// <param name=\\\"norm_layer\\\">The normalization layer to use -- a function creating a layer.</param>\\n /// <param name=\\\"weights_file\\\">The location of a file containing pre-trained weights for the model.</param>\\n /// <param name=\\\"skipfc\\\">If true, the last linear layer of the classifier will not be loaded from the weights file.</param>\\n /// <param name=\\\"device\\\">The device to locate the model on.</param>\\n /// <remarks>\\n /// Pre-trained weights may be retrieved by using Pytorch and saving the model state-dict\\n /// using the exportsd.py script, then loading into the .NET instance:\\n ///\\n /// from torchvision import models\\n /// import exportsd\\n /// \\n /// model = models.resnet34(pretrained=True)\\n /// f = open(\\\"model_weights.dat\\\", \\\"wb\\\")\\n /// exportsd.save_state_dict(model.state_dict(), f)\\n /// f.close()\\n ///\\n /// See also: https://github.com/dotnet/TorchSharp/blob/main/docfx/articles/saveload.md\\n ///\\n /// In order for the weights to be loaded, the number of classes has to be the same as\\n /// in the pre-trained model, which is 1000.\\n ///\\n /// It is also possible to skip loading the last linear layer and use it for transfer-learning\\n /// with a different number of output classes. To do so, pass skipfc=true.\\n ///\\n /// All pre-trained models expect input images normalized in the same way, i.e. mini-batches of 3-channel RGB\\n /// images of shape (3 x H x W), where H and W are expected to be at least 224. The images have to be loaded\\n /// in to a range of [0, 1] and then normalized using mean = [0.485, 0.456, 0.406] and std = [0.229, 0.224, 0.225].\\n /// </remarks>\\n public static Modules.ResNet resnet34(\\n int num_classes = 1000,\\n bool zero_init_residual = false,\\n int groups = 1,\\n int width_per_group = 64,\\n (bool, bool, bool)? replace_stride_with_dilation = null,\\n Func<int, Module<Tensor, Tensor>>? norm_layer = null,\\n string? weights_file = null,\\n bool skipfc = true,\\n Device? device = null)\\n {\\n return Modules.ResNet.ResNet34(num_classes,\\n zero_init_residual,\\n groups,\\n width_per_group,\\n replace_stride_with_dilation,\\n norm_layer,\\n weights_file, skipfc, device);\\n }\\n\\n /// <summary>\\n /// ResNet-50\\n /// </summary>\\n /// <param name=\\\"num_classes\\\">The number of output classes.</param>\\n /// <param name=\\\"zero_init_residual\\\">Whether to zero-initalize the residual block's norm layers.</param>\\n /// <param name=\\\"groups\\\">The number of groups.</param>\\n /// <param name=\\\"width_per_group\\\">The width of each group.</param>\\n /// <param name=\\\"replace_stride_with_dilation\\\">Each element in the tuple indicates if we should replace the 2x2 stride with a dilated convolution instead</param>\\n /// <param name=\\\"norm_layer\\\">The normalization layer to use -- a function creating a layer.</param>\\n /// <param name=\\\"weights_file\\\">The location of a file containing pre-trained weights for the model.</param>\\n /// <param name=\\\"skipfc\\\">If true, the last linear layer of the classifier will not be loaded from the weights file.</param>\\n /// <param name=\\\"device\\\">The device to locate the model on.</param>\\n /// <remarks>\\n /// Pre-trained weights may be retrieved by using Pytorch and saving the model state-dict\\n /// using the exportsd.py script, then loading into the .NET instance:\\n ///\\n /// from torchvision import models\\n /// import exportsd\\n /// \\n /// model = models.resnet50(pretrained=True)\\n /// f = open(\\\"model_weights.dat\\\", \\\"wb\\\")\\n /// exportsd.save_state_dict(model.state_dict(), f)\\n /// f.close()\\n ///\\n /// See also: https://github.com/dotnet/TorchSharp/blob/main/docfx/articles/saveload.md\\n ///\\n /// In order for the weights to be loaded, the number of classes has to be the same as\\n /// in the pre-trained model, which is 1000.\\n ///\\n /// It is also possible to skip loading the last linear layer and use it for transfer-learning\\n /// with a different number of output classes. To do so, pass skipfc=true.\\n ///\\n /// All pre-trained models expect input images normalized in the same way, i.e. mini-batches of 3-channel RGB\\n /// images of shape (3 x H x W), where H and W are expected to be at least 224. The images have to be loaded\\n /// in to a range of [0, 1] and then normalized using mean = [0.485, 0.456, 0.406] and std = [0.229, 0.224, 0.225].\\n /// </remarks>\\n public static Modules.ResNet resnet50(\\n int num_classes = 1000,\\n bool zero_init_residual = false,\\n int groups = 1,\\n int width_per_group = 64,\\n (bool, bool, bool)? replace_stride_with_dilation = null,\\n Func<int, Module<Tensor, Tensor>>? norm_layer = null,\\n string? weights_file = null,\\n bool skipfc = true,\\n Device? device = null)\\n {\\n return Modules.ResNet.ResNet50(num_classes,\\n zero_init_residual,\\n groups,\\n width_per_group,\\n replace_stride_with_dilation,\\n norm_layer,\\n weights_file, skipfc, device);\\n }\\n\\n /// <summary>\\n /// Wide ResNet-50-2 model from 'Wide Residual Networks' https://arxiv.org/abs/1605.07146_\\n /// </summary>\\n /// <param name=\\\"num_classes\\\">The number of output classes.</param>\\n /// <param name=\\\"zero_init_residual\\\">Whether to zero-initalize the residual block's norm layers.</param>\\n /// <param name=\\\"groups\\\">The number of groups.</param>\\n /// <param name=\\\"replace_stride_with_dilation\\\">Each element in the tuple indicates if we should replace the 2x2 stride with a dilated convolution instead</param>\\n /// <param name=\\\"norm_layer\\\">The normalization layer to use -- a function creating a layer.</param>\\n /// <param name=\\\"weights_file\\\">The location of a file containing pre-trained weights for the model.</param>\\n /// <param name=\\\"skipfc\\\">If true, the last linear layer of the classifier will not be loaded from the weights file.</param>\\n /// <param name=\\\"device\\\">The device to locate the model on.</param>\\n /// <remarks>\\n /// Pre-trained weights may be retrieved by using Pytorch and saving the model state-dict\\n /// using the exportsd.py script, then loading into the .NET instance:\\n ///\\n /// from torchvision import models\\n /// import exportsd\\n /// \\n /// model = models.wide_resnet50_2(pretrained=True)\\n /// f = open(\\\"model_weights.dat\\\", \\\"wb\\\")\\n /// exportsd.save_state_dict(model.state_dict(), f)\\n /// f.close()\\n ///\\n /// See also: https://github.com/dotnet/TorchSharp/blob/main/docfx/articles/saveload.md\\n ///\\n /// In order for the weights to be loaded, the number of classes has to be the same as\\n /// in the pre-trained model, which is 1000.\\n ///\\n /// It is also possible to skip loading the last linear layer and use it for transfer-learning\\n /// with a different number of output classes. To do so, pass skipfc=true.\\n ///\\n /// All pre-trained models expect input images normalized in the same way, i.e. mini-batches of 3-channel RGB\\n /// images of shape (3 x H x W), where H and W are expected to be at least 224. The images have to be loaded\\n /// in to a range of [0, 1] and then normalized using mean = [0.485, 0.456, 0.406] and std = [0.229, 0.224, 0.225].\\n /// </remarks>\\n public static Modules.ResNet wide_resnet50_2(\\n int num_classes = 1000,\\n bool zero_init_residual = false,\\n int groups = 1,\\n (bool, bool, bool)? replace_stride_with_dilation = null,\\n Func<int, Module<Tensor, Tensor>>? norm_layer = null,\\n string? weights_file = null,\\n bool skipfc = true,\\n Device? device = null)\\n {\\n return Modules.ResNet.ResNet50(num_classes,\\n zero_init_residual,\\n groups,\\n 64 * 2,\\n replace_stride_with_dilation,\\n norm_layer,\\n weights_file, skipfc, device);\\n }\\n\\n /// <summary>\\n /// ResNeXt-50 32x4d model from\\n /// `Aggregated Residual Transformation for Deep Neural Networks https://arxiv.org/abs/1611.05431\\n /// </summary>\\n /// <param name=\\\"num_classes\\\">The number of output classes.</param>\\n /// <param name=\\\"zero_init_residual\\\">Whether to zero-initalize the residual block's norm layers.</param>\\n /// <param name=\\\"replace_stride_with_dilation\\\">Each element in the tuple indicates if we should replace the 2x2 stride with a dilated convolution instead</param>\\n /// <param name=\\\"norm_layer\\\">The normalization layer to use -- a function creating a layer.</param>\\n /// <param name=\\\"weights_file\\\">The location of a file containing pre-trained weights for the model.</param>\\n /// <param name=\\\"skipfc\\\">If true, the last linear layer of the classifier will not be loaded from the weights file.</param>\\n /// <param name=\\\"device\\\">The device to locate the model on.</param>\\n /// <remarks>\\n /// Pre-trained weights may be retrieved by using Pytorch and saving the model state-dict\\n /// using the exportsd.py script, then loading into the .NET instance:\\n ///\\n /// from torchvision import models\\n /// import exportsd\\n /// \\n /// model = models.resnext50_32x4d(pretrained=True)\\n /// f = open(\\\"model_weights.dat\\\", \\\"wb\\\")\\n /// exportsd.save_state_dict(model.state_dict(), f)\\n /// f.close()\\n ///\\n /// See also: https://github.com/dotnet/TorchSharp/blob/main/docfx/articles/saveload.md\\n ///\\n /// In order for the weights to be loaded, the number of classes has to be the same as\\n /// in the pre-trained model, which is 1000.\\n ///\\n /// It is also possible to skip loading the last linear layer and use it for transfer-learning\\n /// with a different number of output classes. To do so, pass skipfc=true.\\n ///\\n /// All pre-trained models expect input images normalized in the same way, i.e. mini-batches of 3-channel RGB\\n /// images of shape (3 x H x W), where H and W are expected to be at least 224. The images have to be loaded\\n /// in to a range of [0, 1] and then normalized using mean = [0.485, 0.456, 0.406] and std = [0.229, 0.224, 0.225].\\n /// </remarks>\\n public static Modules.ResNet resnext50_32x4d(\\n int num_classes = 1000,\\n bool zero_init_residual = false,\\n (bool, bool, bool)? replace_stride_with_dilation = null,\\n Func<int, Module<Tensor, Tensor>>? norm_layer = null,\\n string? weights_file = null,\\n bool skipfc = true,\\n Device? device = null)\\n {\\n return Modules.ResNet.ResNet50(num_classes,\\n zero_init_residual,\\n 32,\\n 4,\\n replace_stride_with_dilation,\\n norm_layer,\\n weights_file, skipfc, device);\\n }\\n\\n /// <summary>\\n /// ResNet-101\\n /// </summary>\\n /// <param name=\\\"num_classes\\\">The number of output classes.</param>\\n /// <param name=\\\"zero_init_residual\\\">Whether to zero-initalize the residual block's norm layers.</param>\\n /// <param name=\\\"groups\\\">The number of groups.</param>\\n /// <param name=\\\"width_per_group\\\">The width of each group.</param>\\n /// <param name=\\\"replace_stride_with_dilation\\\">Each element in the tuple indicates if we should replace the 2x2 stride with a dilated convolution instead</param>\\n /// <param name=\\\"norm_layer\\\">The normalization layer to use -- a function creating a layer.</param>\\n /// <param name=\\\"weights_file\\\">The location of a file containing pre-trained weights for the model.</param>\\n /// <param name=\\\"skipfc\\\">If true, the last linear layer of the classifier will not be loaded from the weights file.</param>\\n /// <param name=\\\"device\\\">The device to locate the model on.</param>\\n /// <remarks>\\n /// Pre-trained weights may be retrieved by using Pytorch and saving the model state-dict\\n /// using the exportsd.py script, then loading into the .NET instance:\\n ///\\n /// from torchvision import models\\n /// import exportsd\\n /// \\n /// model = models.resnet101(pretrained=True)\\n /// f = open(\\\"model_weights.dat\\\", \\\"wb\\\")\\n /// exportsd.save_state_dict(model.state_dict(), f)\\n /// f.close()\\n ///\\n /// See also: https://github.com/dotnet/TorchSharp/blob/main/docfx/articles/saveload.md\\n ///\\n /// In order for the weights to be loaded, the number of classes has to be the same as\\n /// in the pre-trained model, which is 1000.\\n ///\\n /// It is also possible to skip loading the last linear layer and use it for transfer-learning\\n /// with a different number of output classes. To do so, pass skipfc=true.\\n ///\\n /// All pre-trained models expect input images normalized in the same way, i.e. mini-batches of 3-channel RGB\\n /// images of shape (3 x H x W), where H and W are expected to be at least 224. The images have to be loaded\\n /// in to a range of [0, 1] and then normalized using mean = [0.485, 0.456, 0.406] and std = [0.229, 0.224, 0.225].\\n /// </remarks>\\n public static Modules.ResNet resnet101(\\n int num_classes = 1000,\\n bool zero_init_residual = false,\\n int groups = 1,\\n int width_per_group = 64,\\n (bool, bool, bool)? replace_stride_with_dilation = null,\\n Func<int, Module<Tensor, Tensor>>? norm_layer = null,\\n string? weights_file = null,\\n bool skipfc = true,\\n Device? device = null)\\n {\\n return Modules.ResNet.ResNet101(num_classes,\\n zero_init_residual,\\n groups,\\n width_per_group,\\n replace_stride_with_dilation,\\n norm_layer,\\n weights_file, skipfc, device);\\n }\\n\\n /// <summary>\\n /// ResNeXt-101 32x8d model from\\n /// `Aggregated Residual Transformation for Deep Neural Networks https://arxiv.org/abs/1611.05431\\n /// </summary>\\n /// <param name=\\\"num_classes\\\">The number of output classes.</param>\\n /// <param name=\\\"zero_init_residual\\\">Whether to zero-initalize the residual block's norm layers.</param>\\n /// <param name=\\\"replace_stride_with_dilation\\\">Each element in the tuple indicates if we should replace the 2x2 stride with a dilated convolution instead</param>\\n /// <param name=\\\"norm_layer\\\">The normalization layer to use -- a function creating a layer.</param>\\n /// <param name=\\\"weights_file\\\">The location of a file containing pre-trained weights for the model.</param>\\n /// <param name=\\\"skipfc\\\">If true, the last linear layer of the classifier will not be loaded from the weights file.</param>\\n /// <param name=\\\"device\\\">The device to locate the model on.</param>\\n /// <remarks>\\n /// Pre-trained weights may be retrieved by using Pytorch and saving the model state-dict\\n /// using the exportsd.py script, then loading into the .NET instance:\\n ///\\n /// from torchvision import models\\n /// import exportsd\\n /// \\n /// model = models.resnext101_32x8d(pretrained=True)\\n /// f = open(\\\"model_weights.dat\\\", \\\"wb\\\")\\n /// exportsd.save_state_dict(model.state_dict(), f)\\n /// f.close()\\n ///\\n /// See also: https://github.com/dotnet/TorchSharp/blob/main/docfx/articles/saveload.md\\n ///\\n /// In order for the weights to be loaded, the number of classes has to be the same as\\n /// in the pre-trained model, which is 1000.\\n ///\\n /// It is also possible to skip loading the last linear layer and use it for transfer-learning\\n /// with a different number of output classes. To do so, pass skipfc=true.\\n ///\\n /// All pre-trained models expect input images normalized in the same way, i.e. mini-batches of 3-channel RGB\\n /// images of shape (3 x H x W), where H and W are expected to be at least 224. The images have to be loaded\\n /// in to a range of [0, 1] and then normalized using mean = [0.485, 0.456, 0.406] and std = [0.229, 0.224, 0.225].\\n /// </remarks>\\n public static Modules.ResNet resnext101_32x8d(\\n int num_classes = 1000,\\n bool zero_init_residual = false,\\n (bool, bool, bool)? replace_stride_with_dilation = null,\\n Func<int, Module<Tensor, Tensor>>? norm_layer = null,\\n string? weights_file = null,\\n bool skipfc = true,\\n Device? device = null)\\n {\\n return Modules.ResNet.ResNet50(num_classes,\\n zero_init_residual,\\n 32,\\n 8,\\n replace_stride_with_dilation,\\n norm_layer,\\n weights_file, skipfc, device);\\n }\\n\\n /// <summary>\\n /// ResNeXt-101 64x4d model from\\n /// `Aggregated Residual Transformation for Deep Neural Networks https://arxiv.org/abs/1611.05431\\n /// </summary>\\n /// <param name=\\\"num_classes\\\">The number of output classes.</param>\\n /// <param name=\\\"zero_init_residual\\\">Whether to zero-initalize the residual block's norm layers.</param>\\n /// <param name=\\\"replace_stride_with_dilation\\\">Each element in the tuple indicates if we should replace the 2x2 stride with a dilated convolution instead</param>\\n /// <param name=\\\"norm_layer\\\">The normalization layer to use -- a function creating a layer.</param>\\n /// <param name=\\\"weights_file\\\">The location of a file containing pre-trained weights for the model.</param>\\n /// <param name=\\\"skipfc\\\">If true, the last linear layer of the classifier will not be loaded from the weights file.</param>\\n /// <param name=\\\"device\\\">The device to locate the model on.</param>\\n /// <remarks>\\n /// Pre-trained weights may be retrieved by using Pytorch and saving the model state-dict\\n /// using the exportsd.py script, then loading into the .NET instance:\\n ///\\n /// from torchvision import models\\n /// import exportsd\\n /// \\n /// model = models.resnext101_32x8d(pretrained=True)\\n /// f = open(\\\"model_weights.dat\\\", \\\"wb\\\")\\n /// exportsd.save_state_dict(model.state_dict(), f)\\n /// f.close()\\n ///\\n /// See also: https://github.com/dotnet/TorchSharp/blob/main/docfx/articles/saveload.md\\n ///\\n /// In order for the weights to be loaded, the number of classes has to be the same as\\n /// in the pre-trained model, which is 1000.\\n ///\\n /// It is also possible to skip loading the last linear layer and use it for transfer-learning\\n /// with a different number of output classes. To do so, pass skipfc=true.\\n ///\\n /// All pre-trained models expect input images normalized in the same way, i.e. mini-batches of 3-channel RGB\\n /// images of shape (3 x H x W), where H and W are expected to be at least 224. The images have to be loaded\\n /// in to a range of [0, 1] and then normalized using mean = [0.485, 0.456, 0.406] and std = [0.229, 0.224, 0.225].\\n /// </remarks>\\n public static Modules.ResNet resnext101_64x4d(\\n int num_classes = 1000,\\n bool zero_init_residual = false,\\n (bool, bool, bool)? replace_stride_with_dilation = null,\\n Func<int, Module<Tensor, Tensor>>? norm_layer = null,\\n string? weights_file = null,\\n bool skipfc = true,\\n Device? device = null)\\n {\\n return Modules.ResNet.ResNet50(num_classes,\\n zero_init_residual,\\n 64,\\n 4,\\n replace_stride_with_dilation,\\n norm_layer,\\n weights_file, skipfc, device);\\n }\\n\\n /// <summary>\\n /// Wide ResNet-101-2 model from 'Wide Residual Networks' https://arxiv.org/abs/1605.07146\\n /// </summary>\\n /// <param name=\\\"num_classes\\\">The number of output classes.</param>\\n /// <param name=\\\"zero_init_residual\\\">Whether to zero-initalize the residual block's norm layers.</param>\\n /// <param name=\\\"groups\\\">The number of groups.</param>\\n /// <param name=\\\"replace_stride_with_dilation\\\">Each element in the tuple indicates if we should replace the 2x2 stride with a dilated convolution instead</param>\\n /// <param name=\\\"norm_layer\\\">The normalization layer to use -- a function creating a layer.</param>\\n /// <param name=\\\"weights_file\\\">The location of a file containing pre-trained weights for the model.</param>\\n /// <param name=\\\"skipfc\\\">If true, the last linear layer of the classifier will not be loaded from the weights file.</param>\\n /// <param name=\\\"device\\\">The device to locate the model on.</param>\\n /// <remarks>\\n /// Pre-trained weights may be retrieved by using Pytorch and saving the model state-dict\\n /// using the exportsd.py script, then loading into the .NET instance:\\n ///\\n /// from torchvision import models\\n /// import exportsd\\n /// \\n /// model = models.resnet101(pretrained=True)\\n /// f = open(\\\"model_weights.dat\\\", \\\"wb\\\")\\n /// exportsd.save_state_dict(model.state_dict(), f)\\n /// f.close()\\n ///\\n /// See also: https://github.com/dotnet/TorchSharp/blob/main/docfx/articles/saveload.md\\n ///\\n /// In order for the weights to be loaded, the number of classes has to be the same as\\n /// in the pre-trained model, which is 1000.\\n ///\\n /// It is also possible to skip loading the last linear layer and use it for transfer-learning\\n /// with a different number of output classes. To do so, pass skipfc=true.\\n ///\\n /// All pre-trained models expect input images normalized in the same way, i.e. mini-batches of 3-channel RGB\\n /// images of shape (3 x H x W), where H and W are expected to be at least 224. The images have to be loaded\\n /// in to a range of [0, 1] and then normalized using mean = [0.485, 0.456, 0.406] and std = [0.229, 0.224, 0.225].\\n /// </remarks>\\n public static Modules.ResNet wide_resnet101_2(\\n int num_classes = 1000,\\n bool zero_init_residual = false,\\n int groups = 1,\\n (bool, bool, bool)? replace_stride_with_dilation = null,\\n Func<int, Module<Tensor, Tensor>>? norm_layer = null,\\n string? weights_file = null,\\n bool skipfc = true,\\n Device? device = null)\\n {\\n return Modules.ResNet.ResNet101(num_classes,\\n zero_init_residual,\\n groups,\\n 64*2,\\n replace_stride_with_dilation,\\n norm_layer,\\n weights_file, skipfc, device);\\n }\\n\\n /// <summary>\\n /// ResNet-152\\n /// </summary>\\n /// <param name=\\\"num_classes\\\">The number of output classes.</param>\\n /// <param name=\\\"zero_init_residual\\\">Whether to zero-initalize the residual block's norm layers.</param>\\n /// <param name=\\\"groups\\\">The number of groups.</param>\\n /// <param name=\\\"width_per_group\\\">The width of each group.</param>\\n /// <param name=\\\"replace_stride_with_dilation\\\">Each element in the tuple indicates if we should replace the 2x2 stride with a dilated convolution instead</param>\\n /// <param name=\\\"norm_layer\\\">The normalization layer to use -- a function creating a layer.</param>\\n /// <param name=\\\"weights_file\\\">The location of a file containing pre-trained weights for the model.</param>\\n /// <param name=\\\"skipfc\\\">If true, the last linear layer of the classifier will not be loaded from the weights file.</param>\\n /// <param name=\\\"device\\\">The device to locate the model on.</param>\\n /// <remarks>\\n /// Pre-trained weights may be retrieved by using Pytorch and saving the model state-dict\\n /// using the exportsd.py script, then loading into the .NET instance:\\n ///\\n /// from torchvision import models\\n /// import exportsd\\n /// \\n /// model = models.resnet152(pretrained=True)\\n /// f = open(\\\"model_weights.dat\\\", \\\"wb\\\")\\n /// exportsd.save_state_dict(model.state_dict(), f)\\n /// f.close()\\n ///\\n /// See also: https://github.com/dotnet/TorchSharp/blob/main/docfx/articles/saveload.md\\n ///\\n /// In order for the weights to be loaded, the number of classes has to be the same as\\n /// in the pre-trained model, which is 1000.\\n ///\\n /// It is also possible to skip loading the last linear layer and use it for transfer-learning\\n /// with a different number of output classes. To do so, pass skipfc=true.\\n ///\\n /// All pre-trained models expect input images normalized in the same way, i.e. mini-batches of 3-channel RGB\\n /// images of shape (3 x H x W), where H and W are expected to be at least 224. The images have to be loaded\\n /// in to a range of [0, 1] and then normalized using mean = [0.485, 0.456, 0.406] and std = [0.229, 0.224, 0.225].\\n /// </remarks>\\n public static Modules.ResNet resnet152(\\n int num_classes = 1000,\\n bool zero_init_residual = false,\\n int groups = 1,\\n int width_per_group = 64,\\n (bool, bool, bool)? replace_stride_with_dilation = null,\\n Func<int, Module<Tensor, Tensor>>? norm_layer = null,\\n string? weights_file = null,\\n bool skipfc = true,\\n Device? device = null)\\n {\\n return Modules.ResNet.ResNet152(num_classes,\\n zero_init_residual,\\n groups,\\n width_per_group,\\n replace_stride_with_dilation,\\n norm_layer,\\n weights_file, skipfc, device);\\n }\\n }\\n }\\n\\n namespace Modules\\n {\\n public class ResNet : Module<Tensor, Tensor>\\n {\\n // The code here is based on\\n // https://github.com/pytorch/vision/blob/main/torchvision/models/resnet.py\\n // Licence and copypright notice at: https://github.com/pytorch/vision/blob/main/LICENSE\\n\\n private readonly Module<Tensor, Tensor> conv1;\\n private readonly Module<Tensor, Tensor> bn1;\\n private readonly Module<Tensor, Tensor> relu;\\n private readonly Module<Tensor, Tensor> maxpool;\\n\\n private readonly Sequential layer1 = Sequential();\\n private readonly Sequential layer2 = Sequential();\\n private readonly Sequential layer3 = Sequential();\\n private readonly Sequential layer4 = Sequential();\\n\\n private readonly Module<Tensor, Tensor> avgpool;\\n private readonly Module<Tensor, Tensor> flatten;\\n private readonly Module<Tensor, Tensor> fc;\\n\\n private readonly Func<int, Module<Tensor, Tensor>> norm_layer;\\n\\n private int in_planes = 64;\\n private int dilation;\\n private int groups;\\n private int base_width;\\n\\n protected override void Dispose(bool disposing)\\n {\\n if (disposing) {\\n conv1.Dispose();\\n bn1.Dispose();\\n relu.Dispose();\\n maxpool.Dispose();\\n avgpool.Dispose();\\n flatten.Dispose();\\n fc.Dispose();\\n layer1.Dispose(); layer2.Dispose();\\n layer3.Dispose(); layer4.Dispose();\\n }\\n base.Dispose(disposing);\\n }\\n\\n public static ResNet ResNet18(\\n int numClasses = 1000,\\n bool zero_init_residual = false,\\n int groups = 1,\\n int width_per_group = 64,\\n (bool, bool, bool)? replace_stride_with_dilation = null,\\n Func<int, Module<Tensor, Tensor>>? norm_layer = null,\\n string? weights_file = null,\\n bool skipfc = true,\\n Device? device = null)\\n {\\n return new ResNet(\\n \\\"ResNet18\\\",\\n (in_planes, planes, stride, downsample, groups, base_width, dilation, norm_layer) => new BasicBlock(in_planes, planes, stride, downsample, groups, base_width, dilation, norm_layer),\\n BasicBlock.expansion, new int[] { 2, 2, 2, 2 },\\n numClasses,\\n zero_init_residual,\\n groups,\\n width_per_group,\\n replace_stride_with_dilation,\\n norm_layer,\\n weights_file,\\n skipfc,\\n device);\\n }\\n\\n public static ResNet ResNet34(\\n int numClasses = 1000,\\n bool zero_init_residual = false,\\n int groups = 1,\\n int width_per_group = 64,\\n (bool, bool, bool)? replace_stride_with_dilation = null,\\n Func<int, Module<Tensor, Tensor>>? norm_layer = null,\\n string? weights_file = null,\\n bool skipfc = true,\\n Device? device = null)\\n {\\n return new ResNet(\\n \\\"ResNet34\\\",\\n (in_planes, planes, stride, downsample, groups, base_width, dilation, norm_layer) => new BasicBlock(in_planes, planes, stride, downsample, groups, base_width, dilation, norm_layer),\\n BasicBlock.expansion, new int[] { 3, 4, 6, 3 },\\n numClasses,\\n zero_init_residual,\\n groups,\\n width_per_group,\\n replace_stride_with_dilation,\\n norm_layer,\\n weights_file,\\n skipfc,\\n device);\\n }\\n\\n public static ResNet ResNet50(\\n int numClasses = 1000,\\n bool zero_init_residual = false,\\n int groups = 1,\\n int width_per_group = 64,\\n (bool, bool, bool)? replace_stride_with_dilation = null,\\n Func<int, Module<Tensor, Tensor>>? norm_layer = null,\\n string? weights_file = null,\\n bool skipfc = true,\\n Device? device = null)\\n {\\n return new ResNet(\\n \\\"ResNet50\\\",\\n (in_planes, planes, stride, downsample, groups, base_width, dilation, norm_layer) => new Bottleneck(in_planes, planes, stride, downsample, groups, base_width, dilation, norm_layer),\\n Bottleneck.expansion, new int[] { 3, 4, 6, 3 },\\n numClasses,\\n zero_init_residual,\\n groups,\\n width_per_group,\\n replace_stride_with_dilation,\\n norm_layer,\\n weights_file,\\n skipfc,\\n device);\\n }\\n\\n public static ResNet ResNet101(\\n int numClasses = 1000,\\n bool zero_init_residual = false,\\n int groups = 1,\\n int width_per_group = 64,\\n (bool, bool, bool)? replace_stride_with_dilation = null,\\n Func<int, Module<Tensor, Tensor>>? norm_layer = null,\\n string? weights_file = null,\\n bool skipfc = true,\\n Device? device = null)\\n {\\n return new ResNet(\\n \\\"ResNet101\\\",\\n (in_planes, planes, stride, downsample, groups, base_width, dilation, norm_layer) => new Bottleneck(in_planes, planes, stride, downsample, groups, base_width, dilation, norm_layer),\\n Bottleneck.expansion, new int[] { 3, 4, 23, 3 },\\n numClasses,\\n zero_init_residual,\\n groups,\\n width_per_group,\\n replace_stride_with_dilation,\\n norm_layer,\\n weights_file,\\n skipfc,\\n device);\\n }\\n\\n public static ResNet ResNet152(\\n int numClasses = 1000,\\n bool zero_init_residual = false,\\n int groups = 1,\\n int width_per_group = 64,\\n (bool, bool, bool)? replace_stride_with_dilation = null,\\n Func<int, Module<Tensor, Tensor>>? norm_layer = null,\\n string? weights_file = null,\\n bool skipfc = true,\\n Device? device = null)\\n {\\n return new ResNet(\\n \\\"ResNet152\\\",\\n (in_planes, planes, stride, downsample, groups, base_width, dilation, norm_layer) => new Bottleneck(in_planes, planes, stride, downsample, groups, base_width, dilation, norm_layer),\\n Bottleneck.expansion, new int[] { 3, 8, 36, 3 },\\n numClasses,\\n zero_init_residual,\\n groups,\\n width_per_group,\\n replace_stride_with_dilation,\\n norm_layer,\\n weights_file,\\n skipfc,\\n device);\\n }\\n\\n public delegate Module<Tensor, Tensor> BlockFunc(\\n int inplanes,\\n int planes,\\n int stride = 1,\\n Module<Tensor, Tensor>? downsample = null,\\n int groups = 1,\\n int base_width = 64,\\n int dilation = 1,\\n Func<int, Module<Tensor, Tensor>>? norm_layer = null);\\n\\n public ResNet(string name,\\n BlockFunc block,\\n int expansion,\\n IList<int> layers,\\n int numClasses = 1000,\\n bool zero_init_residual = false,\\n int groups = 1,\\n int width_per_group = 64,\\n (bool, bool, bool)? replace_stride_with_dilation = null,\\n Func<int, Module<Tensor, Tensor>>? norm_layer = null,\\n string? weights_file = null,\\n bool skipfc = true,\\n Device? device = null) : base(name)\\n {\\n norm_layer = (norm_layer is not null) ? norm_layer : (planes) => BatchNorm2d(planes);\\n this.norm_layer = norm_layer;\\n\\n this.in_planes = 64;\\n this.dilation = 1;\\n this.groups = groups;\\n this.base_width = width_per_group;\\n\\n var rswd = replace_stride_with_dilation.HasValue ? replace_stride_with_dilation.Value : (false, false, false);\\n\\n conv1 = Conv2d(3, in_planes, kernelSize: 7, stride: 2, padding: 3, bias: false);\\n bn1 = norm_layer(in_planes);\\n relu = ReLU(inplace: true);\\n maxpool = MaxPool2d(kernelSize: 3, stride: 2, padding: 1);\\n\\n MakeLayer(layer1, block, expansion, 64, layers[0], 1);\\n MakeLayer(layer2, block, expansion, 128, layers[1], 2, rswd.Item1);\\n MakeLayer(layer3, block, expansion, 256, layers[2], 2, rswd.Item2);\\n MakeLayer(layer4, block, expansion, 512, layers[3], 2, rswd.Item3);\\n\\n avgpool = nn.AdaptiveAvgPool2d(new long[] { 1, 1 });\\n flatten = Flatten();\\n fc = Linear(512 * expansion, numClasses);\\n\\n RegisterComponents();\\n\\n if (string.IsNullOrEmpty(weights_file)) {\\n\\n foreach (var (_, m) in named_modules()) {\\n switch (m) {\\n case TorchSharp.Modules.Conv2d conv:\\n torch.nn.init.kaiming_normal_(conv.weight, mode: init.FanInOut.FanOut, nonlinearity: init.NonlinearityType.ReLU);\\n break;\\n case TorchSharp.Modules.BatchNorm2d bn:\\n torch.nn.init.constant_(bn.weight, 1);\\n torch.nn.init.constant_(bn.bias, 0);\\n break;\\n case TorchSharp.Modules.GroupNorm gn:\\n torch.nn.init.constant_(gn.weight, 1);\\n torch.nn.init.constant_(gn.bias, 0);\\n break;\\n }\\n }\\n\\n if (zero_init_residual) {\\n foreach (var (_, m) in named_modules()) {\\n\\n switch (m) {\\n case BasicBlock bb:\\n if (bb.bn2 is TorchSharp.Modules.BatchNorm2d bb2d) {\\n torch.nn.init.constant_(bb2d.weight, 0);\\n }\\n break;\\n case Bottleneck bn:\\n if (bn.bn3 is TorchSharp.Modules.BatchNorm2d bn2d) {\\n torch.nn.init.constant_(bn2d.weight, 0);\\n }\\n break;\\n }\\n }\\n\\n }\\n\\n } else {\\n\\n this.load(weights_file, skip: skipfc ? new[] { \\\"fc.weight\\\", \\\"fc.bias\\\" } : null);\\n }\\n\\n if (device != null && device.type == DeviceType.CUDA)\\n this.to(device);\\n }\\n\\n private void MakeLayer(Sequential modules, BlockFunc block, int expansion, int planes, int blocks, int stride, bool dilate = false)\\n {\\n Sequential? downsample = null;\\n var previous_dilation = this.dilation;\\n\\n if (dilate) {\\n this.dilation *= stride;\\n stride = 1;\\n }\\n\\n if (stride != 1 || in_planes != planes * expansion) {\\n downsample = Sequential(\\n Conv2d(in_planes, planes * expansion, kernelSize: 1, stride: stride, bias: false),\\n norm_layer(planes * expansion)\\n );\\n }\\n\\n modules.append(block(in_planes, planes, stride, downsample, groups, base_width, previous_dilation, norm_layer));\\n\\n this.in_planes = planes * expansion;\\n\\n for (int i = 1; i < blocks; i++) {\\n modules.append(block(in_planes, planes, 1, null, groups, base_width, dilation, norm_layer));\\n }\\n }\\n\\n public override Tensor forward(Tensor input)\\n {\\n using (var scope = NewDisposeScope()) {\\n\\n var x = maxpool.call(relu.call(bn1.call(conv1.call(input))));\\n\\n x = layer1.call(x);\\n x = layer2.call(x);\\n x = layer3.call(x);\\n x = layer4.call(x);\\n\\n var res = fc.call(flatten.call(avgpool.call(x)));\\n scope.MoveToOuter(res);\\n return res;\\n }\\n }\\n\\n class BasicBlock : Module<Tensor, Tensor>\\n {\\n public BasicBlock(\\n int in_planes,\\n int planes,\\n int stride,\\n Module<Tensor, Tensor>? downsample = null,\\n int groups = 1,\\n int base_width = 64,\\n int dilation = 1,\\n Func<int, Module<Tensor, Tensor>>? norm_layer = null) : base(\\\"BasicBlock\\\")\\n {\\n if (groups != 1 || base_width != 64) throw new ArgumentException(\\\"BasicBlock only supports groups=1 and base_width=64\\\");\\n if (dilation > 1) throw new NotImplementedException(\\\"dilation > 1 not supported in BasicBlock\\\");\\n\\n if (norm_layer is null) {\\n norm_layer = (planes) => BatchNorm2d(planes);\\n }\\n\\n conv1 = Conv2d(in_planes, planes, kernelSize: 3, stride: stride, padding: 1, bias: false);\\n bn1 = norm_layer(planes);\\n relu1 = ReLU(inplace: true);\\n conv2 = Conv2d(planes, planes, kernelSize: 3, stride: 1, padding: 1, bias: false);\\n bn2 = norm_layer(planes);\\n this.downsample = downsample;\\n\\n RegisterComponents();\\n }\\n\\n public override Tensor forward(Tensor input)\\n {\\n var identity = input;\\n\\n var x = relu1.call(bn1.call(conv1.call(input)));\\n x = bn2.call(conv2.call(x));\\n\\n if (downsample is not null) {\\n identity = downsample.call(input);\\n }\\n\\n return x.add_(identity).relu_();\\n }\\n\\n protected override void Dispose(bool disposing)\\n {\\n if (disposing) {\\n conv1.Dispose();\\n bn1.Dispose();\\n conv2.Dispose();\\n bn2.Dispose();\\n relu1.Dispose();\\n downsample?.Dispose();\\n }\\n base.Dispose(disposing);\\n }\\n\\n public static int expansion = 1;\\n\\n private readonly Module<Tensor, Tensor> conv1;\\n private readonly Module<Tensor, Tensor> bn1;\\n private readonly Module<Tensor, Tensor> conv2;\\n internal readonly Module<Tensor, Tensor> bn2;\\n private readonly Module<Tensor, Tensor> relu1;\\n private readonly Module<Tensor, Tensor>? downsample;\\n }\\n\\n class Bottleneck : Module<Tensor, Tensor>\\n {\\n public Bottleneck(\\n int in_planes,\\n int planes,\\n int stride,\\n Module<Tensor, Tensor>? downsample = null,\\n int groups = 1,\\n int base_width = 64,\\n int dilation = 1,\\n Func<int, Module<Tensor, Tensor>>? norm_layer = null) : base(\\\"Bottleneck\\\")\\n {\\n if (norm_layer is null) {\\n norm_layer = (planes) => BatchNorm2d(planes);\\n }\\n\\n var width = (int)(planes * (base_width / 64.0)) * groups;\\n\\n conv1 = Conv2d(in_planes, width, kernelSize: 1, bias: false);\\n bn1 = norm_layer(width);\\n relu1 = ReLU(inplace: true);\\n conv2 = Conv2d(width, width, kernelSize: 3, stride: stride, groups: groups, padding: dilation, dilation: dilation, bias: false);\\n bn2 = norm_layer(width);\\n relu2 = ReLU(inplace: true);\\n conv3 = Conv2d(width, expansion * planes, kernelSize: 1, bias: false);\\n bn3 = norm_layer(expansion * planes);\\n\\n this.downsample = downsample;\\n\\n RegisterComponents();\\n }\\n\\n public override Tensor forward(Tensor input)\\n {\\n var identity = input;\\n\\n var x = relu1.call(bn1.call(conv1.call(input)));\\n x = relu2.call(bn2.call(conv2.call(x)));\\n x = bn3.call(conv3.call(x));\\n\\n if (downsample is not null) {\\n identity = downsample.call(input);\\n }\\n\\n return x.add_(identity).relu_();\\n }\\n\\n protected override void Dispose(bool disposing)\\n {\\n if (disposing) {\\n conv1.Dispose();\\n bn1.Dispose();\\n conv2.Dispose(); conv3.Dispose();\\n bn2.Dispose(); bn3.Dispose();\\n relu1.Dispose(); relu2.Dispose();\\n downsample?.Dispose();\\n }\\n base.Dispose(disposing);\\n }\\n\\n public static int expansion = 4;\\n\\n private readonly Module<Tensor, Tensor> conv1;\\n private readonly Module<Tensor, Tensor> bn1;\\n private readonly Module<Tensor, Tensor> conv2;\\n private readonly Module<Tensor, Tensor> bn2;\\n private readonly Module<Tensor, Tensor> conv3;\\n internal readonly Module<Tensor, Tensor> bn3;\\n private readonly Module<Tensor, Tensor> relu1;\\n private readonly Module<Tensor, Tensor> relu2;\\n\\n private readonly Module<Tensor, Tensor>? downsample;\\n }\\n }\\n }\\n}\\n\"\n },\n {\n \"alpha_fraction\": 0.5336246490478516,\n \"alphanum_fraction\": 0.5448733568191528,\n \"avg_line_length\": 38.47923278808594,\n \"blob_id\": \"09dd025be8f42bfcd6fbf9b07ec06d78f9dd6fc7\",\n \"content_id\": \"1988b7d792a81e64ac14dddb1f689eedd5522658\",\n \"detected_licenses\": [\n \"MIT\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"C#\",\n \"length_bytes\": 12357,\n \"license_type\": \"permissive\",\n \"max_line_length\": 183,\n \"num_lines\": 313,\n \"path\": \"/src/Examples/SequenceToSequence.cs\",\n \"repo_name\": \"NiklasGustafsson/TorchSharp\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"// Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information.\\nusing System;\\nusing System.IO;\\nusing System.Linq;\\nusing System.Collections.Generic;\\nusing System.Diagnostics;\\nusing static TorchSharp.torch;\\nusing static TorchSharp.torch.nn;\\n\\nnamespace TorchSharp.Examples\\n{\\n\\n /// <summary>\\n /// This example is based on the PyTorch tutorial at:\\n ///\\n /// https://pytorch.org/tutorials/beginner/transformer_tutorial.html\\n ///\\n /// It relies on the WikiText2 dataset, which can be downloaded at:\\n ///\\n /// https://s3.amazonaws.com/research.metamind.io/wikitext/wikitext-2-v1.zip\\n ///\\n /// After downloading, extract the files using the defaults (Windows only).\\n /// </summary>\\n public class SequenceToSequence\\n {\\n // This path assumes that you're running this on Windows.\\n#if NET472_OR_GREATER\\n private readonly static string _dataLocation = NSPath.Join(Environment.GetFolderPath(Environment.SpecialFolder.DesktopDirectory), \\\"..\\\", \\\"Downloads\\\", \\\"wikitext-2-v1\\\");\\n#else\\n private readonly static string _dataLocation = Path.Join(Environment.GetFolderPath(Environment.SpecialFolder.DesktopDirectory), \\\"..\\\", \\\"Downloads\\\", \\\"wikitext-2-v1\\\");\\n#endif // NET472_OR_GREATER\\n\\n private const long emsize = 200;\\n private const long nhid = 200;\\n private const long nlayers = 2;\\n private const long nhead = 2;\\n private const double dropout = 0.2;\\n\\n private const int batch_size = 64;\\n private const int eval_batch_size = 32;\\n\\n private const int epochs = 10;\\n\\n internal static void Main(string[] args)\\n\\n {\\n torch.random.manual_seed(1);\\n\\n var cwd = Environment.CurrentDirectory;\\n\\n var device = torch.cuda.is_available() ? torch.CUDA : torch.CPU;\\n Console.WriteLine($\\\"Running SequenceToSequence on {device.type.ToString()} for {epochs} epochs.\\\");\\n\\n var vocab_iter = TorchText.Datasets.WikiText2(\\\"train\\\", _dataLocation);\\n var tokenizer = TorchText.Data.Utils.get_tokenizer(\\\"basic_english\\\");\\n\\n var counter = new TorchText.Vocab.Counter<string>();\\n foreach (var item in vocab_iter) {\\n counter.update(tokenizer(item));\\n }\\n\\n var vocab = new TorchText.Vocab.Vocab(counter);\\n\\n var (train_iter, valid_iter, test_iter) = TorchText.Datasets.WikiText2(_dataLocation);\\n\\n var train_data = Batchify(ProcessInput(train_iter, tokenizer, vocab), batch_size).to((Device)device);\\n var valid_data = Batchify(ProcessInput(valid_iter, tokenizer, vocab), eval_batch_size).to((Device)device);\\n var test_data = Batchify(ProcessInput(test_iter, tokenizer, vocab), eval_batch_size).to((Device)device);\\n\\n var bptt = 32;\\n\\n var ntokens = vocab.Count;\\n\\n var model = new TransformerModel(ntokens, emsize, nhead, nhid, nlayers, dropout).to((Device)device);\\n var loss = CrossEntropyLoss();\\n var lr = 2.50;\\n var optimizer = torch.optim.SGD(model.parameters(), lr);\\n var scheduler = torch.optim.lr_scheduler.StepLR(optimizer, 1, 0.95, last_epoch: 15);\\n\\n var totalTime = new Stopwatch();\\n totalTime.Start();\\n\\n foreach (var epoch in Enumerable.Range(1, epochs)) {\\n\\n var sw = new Stopwatch();\\n sw.Start();\\n\\n train(epoch, train_data, model, loss, bptt, ntokens, optimizer);\\n\\n var val_loss = evaluate(valid_data, model, loss, bptt, ntokens, optimizer);\\n sw.Stop();\\n\\n var pgFirst = optimizer.ParamGroups.First();\\n Console.WriteLine($\\\"\\\\nEnd of epoch: {epoch} | lr: {pgFirst.LearningRate:0.00} | time: {sw.Elapsed.TotalSeconds:0.0}s | loss: {val_loss:0.00}\\\\n\\\");\\n scheduler.step();\\n }\\n\\n var tst_loss = evaluate(test_data, model, loss, bptt, ntokens, optimizer);\\n totalTime.Stop();\\n\\n Console.WriteLine($\\\"\\\\nEnd of training | time: {totalTime.Elapsed.TotalSeconds:0.0}s | loss: {tst_loss:0.00}\\\\n\\\");\\n }\\n\\n private static void train(int epoch, Tensor train_data, TransformerModel model, Loss<Tensor, Tensor, Tensor> criterion, int bptt, int ntokens, torch.optim.Optimizer optimizer)\\n {\\n model.train();\\n\\n using (var d = torch.NewDisposeScope()) {\\n\\n var total_loss = 0.0f;\\n\\n var batch = 0;\\n var log_interval = 200;\\n\\n var src_mask = model.GenerateSquareSubsequentMask(bptt);\\n\\n var tdlen = train_data.shape[0];\\n\\n\\n for (int i = 0; i < tdlen - 1; batch++, i += bptt) {\\n\\n var (data, targets) = GetBatch(train_data, i, bptt);\\n optimizer.zero_grad();\\n\\n if (data.shape[0] != bptt) {\\n src_mask = model.GenerateSquareSubsequentMask(data.shape[0]);\\n }\\n\\n using (var output = model.call(data, src_mask)) {\\n var loss = criterion.call(output.view(-1, ntokens), targets);\\n loss.backward();\\n torch.nn.utils.clip_grad_norm_(model.parameters().ToArray(), 0.5);\\n optimizer.step();\\n\\n total_loss += loss.to(torch.CPU).item<float>();\\n }\\n\\n if ((batch % log_interval == 0 && batch > 0) || (batch == tdlen / bptt)) {\\n var cur_loss = total_loss / log_interval;\\n Console.WriteLine($\\\"epoch: {epoch} | batch: {batch} / {tdlen / bptt} | loss: {cur_loss:0.00}\\\");\\n total_loss = 0;\\n }\\n\\n d.DisposeEverythingBut(src_mask);\\n }\\n }\\n }\\n\\n private static double evaluate(Tensor eval_data, TransformerModel model, Loss<Tensor, Tensor, Tensor> criterion, int bptt, int ntokens, torch.optim.Optimizer optimizer)\\n {\\n model.eval();\\n\\n using (var d = torch.NewDisposeScope()) {\\n\\n var src_mask = model.GenerateSquareSubsequentMask(bptt);\\n\\n var total_loss = 0.0f;\\n var batch = 0;\\n\\n\\n for (int i = 0; i < eval_data.shape[0] - 1; batch++, i += bptt) {\\n\\n var (data, targets) = GetBatch(eval_data, i, bptt);\\n if (data.shape[0] != bptt) {\\n src_mask = model.GenerateSquareSubsequentMask(data.shape[0]);\\n }\\n using (var output = model.call(data, src_mask)) {\\n var loss = criterion.call(output.view(-1, ntokens), targets);\\n total_loss += data.shape[0] * loss.to(torch.CPU).item<float>();\\n }\\n\\n data.Dispose();\\n targets.Dispose();\\n\\n d.DisposeEverythingBut(src_mask);\\n }\\n\\n return total_loss / eval_data.shape[0];\\n }\\n }\\n\\n static Tensor ProcessInput(IEnumerable<string> iter, Func<string, IEnumerable<string>> tokenizer, TorchText.Vocab.Vocab vocab)\\n {\\n List<Tensor> data = new List<Tensor>();\\n foreach (var item in iter) {\\n List<long> itemData = new List<long>();\\n foreach (var token in tokenizer(item)) {\\n itemData.Add(vocab[token]);\\n }\\n data.Add(torch.tensor(itemData.ToArray(), torch.int64));\\n }\\n\\n var result = torch.cat(data.Where(t => t.NumberOfElements > 0).ToList(), 0);\\n return result;\\n }\\n\\n static Tensor Batchify(Tensor data, int batch_size)\\n {\\n var nbatch = data.shape[0] / batch_size;\\n return data.narrow(0, 0, nbatch * batch_size).view(batch_size, -1).t().contiguous();\\n }\\n\\n static (Tensor, Tensor) GetBatch(Tensor source, int index, int bptt)\\n {\\n var len = Math.Min(bptt, source.shape[0] - 1 - index);\\n var data = source[TensorIndex.Slice(index, index + len)];\\n var target = source[TensorIndex.Slice(index + 1, index + 1 + len)].reshape(-1);\\n return (data, target);\\n }\\n\\n class TransformerModel : Module<Tensor, Tensor, Tensor>\\n {\\n private Modules.TransformerEncoder transformer_encoder;\\n private PositionalEncoding pos_encoder;\\n private Modules.Embedding encoder;\\n private Modules.Linear decoder;\\n\\n private long ninputs;\\n private Device device;\\n\\n public TransformerModel(long ntokens, long ninputs, long nheads, long nhidden, long nlayers, double dropout = 0.5) : base(\\\"Transformer\\\")\\n {\\n this.ninputs = ninputs;\\n\\n pos_encoder = new PositionalEncoding(ninputs, dropout);\\n var encoder_layers = TransformerEncoderLayer(ninputs, nheads, nhidden, dropout);\\n transformer_encoder = TransformerEncoder(encoder_layers, nlayers);\\n encoder = Embedding(ntokens, ninputs);\\n decoder = Linear(ninputs, ntokens);\\n InitWeights();\\n\\n RegisterComponents();\\n }\\n\\n public Tensor GenerateSquareSubsequentMask(long size)\\n {\\n var mask = (torch.ones(new long[] { size, size }) == 1).triu().transpose(0, 1);\\n return mask.to_type(ScalarType.Float32)\\n .masked_fill(mask == 0, float.NegativeInfinity)\\n .masked_fill(mask == 1, 0.0f).to(device);\\n }\\n\\n private void InitWeights()\\n {\\n var initrange = 0.1;\\n\\n init.uniform_(encoder.weight, -initrange, initrange);\\n init.zeros_(decoder.bias);\\n init.uniform_(decoder.weight, -initrange, initrange);\\n }\\n\\n public override Tensor forward(Tensor t, Tensor mask)\\n {\\n var src = pos_encoder.call(encoder.call(t) * MathF.Sqrt(ninputs));\\n var enc = transformer_encoder.call(src, mask);\\n return decoder.call(enc);\\n }\\n\\n protected override void Dispose(bool disposing)\\n {\\n if (disposing) {\\n transformer_encoder.Dispose();\\n pos_encoder.Dispose();\\n encoder.Dispose();\\n decoder.Dispose();\\n ClearModules();\\n }\\n base.Dispose(disposing);\\n }\\n\\n protected override Module _to(DeviceType deviceType, int deviceIndex = -1)\\n {\\n this.device = new Device(deviceType, deviceIndex);\\n return base._to(deviceType, deviceIndex);\\n }\\n }\\n\\n class PositionalEncoding : Module<Tensor, Tensor>\\n {\\n private Module<Tensor, Tensor> dropout;\\n private Tensor pe;\\n\\n public PositionalEncoding(long dmodel, double dropout, int maxLen = 5000) : base(\\\"PositionalEncoding\\\")\\n {\\n this.dropout = Dropout(dropout);\\n var pe = torch.zeros(new long[] { maxLen, dmodel });\\n var position = torch.arange(0, maxLen, 1).unsqueeze(1);\\n var divTerm = (torch.arange(0, dmodel, 2) * (-Math.Log(10000.0) / dmodel)).exp();\\n pe[TensorIndex.Ellipsis, TensorIndex.Slice(0, null, 2)] = (position * divTerm).sin();\\n pe[TensorIndex.Ellipsis, TensorIndex.Slice(1, null, 2)] = (position * divTerm).cos();\\n this.pe = pe.unsqueeze(0).transpose(0, 1);\\n\\n RegisterComponents();\\n }\\n\\n public override Tensor forward(Tensor t)\\n {\\n using var x = t + pe[TensorIndex.Slice(null, t.shape[0]), TensorIndex.Slice()];\\n return dropout.call(x);\\n }\\n\\n protected override void Dispose(bool disposing)\\n {\\n if (disposing) {\\n dropout.Dispose();\\n pe.Dispose();\\n ClearModules();\\n }\\n base.Dispose(disposing);\\n }\\n }\\n }\\n}\\n\"\n },\n {\n \"alpha_fraction\": 0.5054945349693298,\n \"alphanum_fraction\": 0.5054945349693298,\n \"avg_line_length\": 28.1200008392334,\n \"blob_id\": \"2b4624bb37ccd65df9f08b938db248eb4d789c20\",\n \"content_id\": \"0b7dec35775bd78675d10d5a0ff10d99ab80f657\",\n \"detected_licenses\": [\n \"MIT\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"C#\",\n \"length_bytes\": 1456,\n \"license_type\": \"permissive\",\n \"max_line_length\": 130,\n \"num_lines\": 50,\n \"path\": \"/src/TorchVision/Compose.cs\",\n \"repo_name\": \"NiklasGustafsson/TorchSharp\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"// Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information.\\n\\nusing System;\\nusing static TorchSharp.torch;\\n\\nnamespace TorchSharp\\n{\\n public static partial class torchvision\\n {\\n internal class ComposedTransforms : IDisposable, ITransform\\n {\\n public ComposedTransforms(ITransform[] transforms)\\n {\\n this.transforms = transforms;\\n }\\n\\n public void Dispose()\\n {\\n foreach (var t in transforms) {\\n if (t is IDisposable) {\\n ((IDisposable)t).Dispose();\\n }\\n }\\n }\\n\\n public Tensor call(Tensor input)\\n {\\n foreach (var t in transforms) {\\n input = t.call(input);\\n }\\n return input;\\n }\\n\\n private ITransform[] transforms;\\n }\\n\\n public static partial class transforms\\n {\\n /// <summary>\\n /// Composes several transforms together.\\n /// </summary>\\n /// <param name=\\\"transforms\\\">A list of transforms to compose serially.</param>\\n /// <returns></returns>\\n static public ITransform Compose(params ITransform[] transforms)\\n {\\n return new ComposedTransforms(transforms);\\n }\\n }\\n }\\n}\\n\"\n },\n {\n \"alpha_fraction\": 0.6159601211547852,\n \"alphanum_fraction\": 0.6221945285797119,\n \"avg_line_length\": 36.015384674072266,\n \"blob_id\": \"442c53a4e01cfe622efca8238cb98b161d1bac5c\",\n \"content_id\": \"df0cb54044067fb7e1f625999bf57b0e5a1476df\",\n \"detected_licenses\": [\n \"MIT\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"C#\",\n \"length_bytes\": 2406,\n \"license_type\": \"permissive\",\n \"max_line_length\": 130,\n \"num_lines\": 65,\n \"path\": \"/src/TorchSharp/Utils/Decompress.cs\",\n \"repo_name\": \"NiklasGustafsson/TorchSharp\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"// Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information.\\nusing System;\\nusing System.IO;\\nusing System.Linq;\\nusing System.Threading;\\nusing System.Threading.Tasks;\\nusing ICSharpCode.SharpZipLib.Core;\\nusing ICSharpCode.SharpZipLib.GZip;\\nusing ICSharpCode.SharpZipLib.Tar;\\n\\n// This code was inspired by code found in the SciSharpStack-Examples repository located at:\\n//\\n// https://github.com/SciSharp/SciSharp-Stack-Examples\\n//\\n// Original License: https://github.com/SciSharp/SciSharp-Stack-Examples/blob/master/LICENSE\\n//\\n// Original copyright information was not found at the above location.\\n\\nnamespace TorchSharp.Utils\\n{\\n public static class Decompress\\n {\\n public static void DecompressGZipFile(string gzipFileName, string targetDir)\\n {\\n byte[] buf = new byte[4096];\\n\\n using (var fs = File.OpenRead(gzipFileName))\\n using (var gzipStream = new GZipInputStream(fs)) {\\n\\n string fnOut = Path.Combine(targetDir, Path.GetFileNameWithoutExtension(gzipFileName));\\n\\n using (var fsOut = File.Create(fnOut)) {\\n StreamUtils.Copy(gzipStream, fsOut, buf);\\n }\\n }\\n }\\n public static void ExtractTGZ(string gzArchiveName, string destFolder)\\n {\\n var flag = gzArchiveName.Split(Path.DirectorySeparatorChar).Last().Split('.').First() + \\\".bin\\\";\\n if (File.Exists(Path.Combine(destFolder, flag))) return;\\n\\n Console.WriteLine($\\\"Extracting.\\\");\\n var task = Task.Run(() => {\\n using (var inStream = File.OpenRead(gzArchiveName)) {\\n using (var gzipStream = new GZipInputStream(inStream)) {\\n#pragma warning disable CS0618 // Type or member is obsolete\\n using (TarArchive tarArchive = TarArchive.CreateInputTarArchive(gzipStream))\\n#pragma warning restore CS0618 // Type or member is obsolete\\n tarArchive.ExtractContents(destFolder);\\n }\\n }\\n });\\n\\n while (!task.IsCompleted) {\\n Thread.Sleep(200);\\n Console.Write(\\\".\\\");\\n }\\n\\n File.Create(Path.Combine(destFolder, flag));\\n Console.WriteLine(\\\"\\\");\\n Console.WriteLine(\\\"Extraction completed.\\\");\\n }\\n\\n }\\n}\\n\"\n },\n {\n \"alpha_fraction\": 0.4355202317237854,\n \"alphanum_fraction\": 0.43863415718078613,\n \"avg_line_length\": 34.613765716552734,\n \"blob_id\": \"1a9ef997c198e7cf1935de02ed247328a8bd3182\",\n \"content_id\": \"35515c05407e574f9952002c50b3beef28ed453a\",\n \"detected_licenses\": [\n \"MIT\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"C#\",\n \"length_bytes\": 18626,\n \"license_type\": \"permissive\",\n \"max_line_length\": 150,\n \"num_lines\": 523,\n \"path\": \"/src/TorchSharp/Tensor/Storage.cs\",\n \"repo_name\": \"NiklasGustafsson/TorchSharp\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"// Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information.\\nusing System;\\nusing System.Collections;\\nusing System.Collections.Generic;\\nusing System.Diagnostics;\\nusing static TorchSharp.PInvoke.NativeMethods;\\n\\nnamespace TorchSharp\\n{\\n public static partial class torch\\n {\\n /// <summary>\\n /// A torch.Storage is a contiguous, one-dimensional array of a single data type.\\n /// Every tensor has a corresponding storage of the same data type.\\n /// </summary>\\n public abstract class Storage\\n {\\n protected Storage()\\n {\\n }\\n\\n protected Storage(Tensor tensor)\\n {\\n _tensor = tensor;\\n data_ptr();\\n }\\n\\n protected Storage(Tensor tensor, IntPtr data_ptr)\\n {\\n _tensor = tensor;\\n _tensor_data_ptr = (data_ptr == IntPtr.Zero) ? this.data_ptr() : data_ptr;\\n }\\n\\n internal static Storage<T> Create<T>(Tensor tensor) where T : unmanaged\\n {\\n var type = typeof(T);\\n switch (type) {\\n case Type _ when type == typeof(byte):\\n return new Storage<T>(tensor.@byte());\\n case Type _ when type == typeof(bool):\\n return new Storage<T>(tensor.@bool());\\n case Type _ when type == typeof(int):\\n return new Storage<T>(tensor.@int());\\n case Type _ when type == typeof(long):\\n return new Storage<T>(tensor.@long());\\n case Type _ when type == typeof(float):\\n return new Storage<T>(tensor.@float());\\n case Type _ when type == typeof(double):\\n return new Storage<T>(tensor.@double());\\n case Type _ when type == typeof((float,float)):\\n return new Storage<T>(tensor.to_type(ScalarType.ComplexFloat32));\\n case Type _ when type == typeof(System.Numerics.Complex):\\n return new Storage<T>(tensor.to_type(ScalarType.ComplexFloat64));\\n default:\\n throw new NotSupportedException();\\n }\\n }\\n\\n protected static Tensor CreateTypedTensor<T>(ScalarType dtype, IList<T> rawArray)\\n {\\n switch (dtype) {\\n case ScalarType.Int8:\\n return torch.tensor(rawArray as IList<byte>);\\n case ScalarType.Bool:\\n return torch.tensor(rawArray as IList<bool>);\\n case ScalarType.Int32:\\n return torch.tensor(rawArray as IList<int>);\\n case ScalarType.Int64:\\n return torch.tensor(rawArray as IList<long>);\\n case ScalarType.Float32:\\n return torch.tensor(rawArray as IList<float>);\\n case ScalarType.Float64:\\n return torch.tensor(rawArray as IList<double>);\\n case ScalarType.ComplexFloat32:\\n return torch.tensor(rawArray as IList<(float, float)>);\\n case ScalarType.ComplexFloat64:\\n return torch.tensor(rawArray as IList<System.Numerics.Complex>);\\n default:\\n throw new NotSupportedException();\\n }\\n }\\n\\n protected torch.Tensor _tensor; // Keeping it alive.\\n protected IntPtr _tensor_data_ptr;\\n\\n /// <summary>\\n /// Convert to bool storage.\\n /// </summary>\\n /// <returns></returns>\\n public Storage<bool> @bool() => _tensor.to_type(ScalarType.Bool).storage<bool>();\\n\\n /// <summary>\\n /// Convert to byte storage.\\n /// </summary>\\n public Storage<byte> @byte() => _tensor.to_type(ScalarType.Byte).storage<byte>();\\n\\n /// <summary>\\n /// Convert to char storage.\\n /// </summary>\\n public Storage<char> @char() => _tensor.to_type(ScalarType.Int8).storage<char>();\\n\\n /// <summary>\\n /// Convert to int storage.\\n /// </summary>\\n public Storage<int> @int() => _tensor.to_type(ScalarType.Int32).storage<int>();\\n\\n /// <summary>\\n /// Convert to long storage.\\n /// </summary>\\n public Storage<long> @long() => _tensor.to_type(ScalarType.Int64).storage<long>();\\n\\n /// <summary>\\n /// Convert to float storage.\\n /// </summary>\\n public Storage<float> @float() => _tensor.to_type(ScalarType.Float32).storage<float>();\\n\\n /// <summary>\\n /// Convert to double storage.\\n /// </summary>\\n public Storage<double> @double() => _tensor.to_type(ScalarType.Float64).storage<double>();\\n\\n /// <summary>\\n /// Convert to 32-bit complex storage.\\n /// </summary>\\n public Storage<(float,float)> complex_float() => _tensor.to_type(ScalarType.ComplexFloat32).storage<(float, float)>();\\n\\n /// <summary>\\n /// Convert to 64-bit complex storage.\\n /// </summary>\\n public Storage<System.Numerics.Complex> complex_double() => _tensor.to_type(ScalarType.ComplexFloat64).storage<System.Numerics.Complex>();\\n\\n /// <summary>\\n /// The size of each storage element.\\n /// </summary>\\n /// <returns></returns>\\n public abstract int element_size();\\n\\n /// <summary>\\n /// A pointer to the raw data in memory.\\n /// </summary>\\n /// <returns></returns>\\n protected IntPtr data_ptr()\\n {\\n if (_tensor_data_ptr != IntPtr.Zero)\\n return _tensor_data_ptr;\\n\\n var res = THSStorage_data_ptr(_tensor.Handle);\\n if (res == IntPtr.Zero) { CheckForErrors(); }\\n _tensor_data_ptr = res;\\n return res;\\n }\\n\\n /// <summary>\\n /// The number of bytes allocated to the storage.\\n /// </summary>\\n /// <returns></returns>\\n public ulong nbytes()\\n {\\n var res = THSStorage_nbytes(_tensor.Handle);\\n CheckForErrors();\\n return res;\\n }\\n }\\n\\n /// <summary>\\n /// A torch.Storage is a contiguous, one-dimensional array of a single data type.\\n /// Every tensor has a corresponding storage of the same data type.\\n /// </summary>\\n public sealed class Storage<T> : torch.Storage, IDisposable, IEnumerable<T> where T : unmanaged\\n {\\n internal Storage(torch.Tensor tensor, int count = -1) : base(tensor, IntPtr.Zero)\\n {\\n _count = count;\\n }\\n\\n internal Storage(IntPtr data_ptr, ScalarType dtype, int count = -1) : base(null, data_ptr)\\n {\\n _count = count;\\n _tensor = CreateTypedTensor<T>(dtype, this.ToArray());\\n }\\n\\n internal Storage(T value) : base()\\n {\\n _scalarValue = value;\\n }\\n\\n private T _scalarValue = default(T);\\n\\n /// <summary>\\n /// Size of each storage element.\\n /// </summary>\\n /// <returns></returns>\\n public override int element_size()\\n {\\n ValidateNotScalar();\\n unsafe {\\n return sizeof(T);\\n }\\n }\\n\\n /// <summary>\\n /// The device where the storage is located.\\n /// </summary>\\n public Device device {\\n get {\\n ValidateNotScalar();\\n return _tensor.device;\\n }\\n }\\n\\n /// <summary>\\n /// Move the storage to a CUDA device.\\n /// </summary>\\n public Storage<T> cuda(Device device = null)\\n {\\n ValidateNotScalar();\\n return _tensor.cuda(device).storage<T>();\\n }\\n\\n /// <summary>\\n /// Move the storage to the CPU.\\n /// </summary>\\n public Storage<T> cpu()\\n {\\n ValidateNotScalar();\\n return _tensor.cpu().storage<T>();\\n }\\n\\n /// <summary>\\n /// Convert the storage instance to a .NET array, copying its data.\\n /// </summary>\\n /// <returns></returns>\\n public T[] ToArray()\\n {\\n ValidateNotScalar();\\n var result = new T[Count];\\n CopyTo(result, 0);\\n return result;\\n }\\n\\n /// <summary>\\n /// Accesses a single element of a storage.\\n /// </summary>\\n /// <param name=\\\"index\\\">The index of the element.</param>\\n /// <returns></returns>\\n public T this[int index] {\\n get {\\n ValidateNotScalar();\\n CheckIndex(index);\\n unsafe {\\n T* ptr = (T*)_tensor_data_ptr;\\n return ptr[index];\\n }\\n }\\n set {\\n ValidateNotScalar();\\n CheckIndex(index);\\n unsafe {\\n T* ptr = (T*)_tensor_data_ptr;\\n ptr[index] = value;\\n }\\n }\\n }\\n\\n /// <summary>\\n /// Accesses a slice of a storage instance.\\n /// </summary>\\n /// <param name=\\\"range\\\">The range, expressed as a tuple [start,end)</param>\\n public Storage<T> this[(int? start, int? end) range] {\\n get {\\n ValidateNotScalar();\\n if (range.end < range.start)\\n throw new ArgumentException(\\\"end < start\\\");\\n var start = CheckIndex(range.start.HasValue? range.start.Value : 0);\\n var end = CheckIndex(range.end.HasValue ? range.end.Value : Count);\\n var count = end - start;\\n unsafe {\\n var ptr = _tensor_data_ptr + sizeof(T) * start;\\n return new Storage<T>(ptr, _tensor.dtype, count);\\n }\\n }\\n set {\\n ValidateNotScalar();\\n if (value._tensor is not null || value._tensor_data_ptr != IntPtr.Zero)\\n throw new InvalidOperationException();\\n if (range.end < range.start)\\n throw new ArgumentException(\\\"end < start\\\");\\n var v = value._scalarValue;\\n var start = CheckIndex(range.start.HasValue ? range.start.Value : 0);\\n var end = CheckIndex(range.end.HasValue ? range.end.Value : Count);\\n unsafe {\\n T* ptr = (T*)_tensor_data_ptr;\\n for (int i = start; i < end; i++)\\n ptr[i] = v;\\n }\\n }\\n }\\n\\n#if !NETSTANDARD2_0_OR_GREATER\\n /// <summary>\\n /// Accesses a slice of a storage instance.\\n /// </summary>\\n /// <param name=\\\"range\\\">The range.</param>\\n public Storage<T> this[System.Range range] {\\n get {\\n var start = CheckIndex(range.Start);\\n var end = CheckIndex(range.End);\\n return this[(start, end)];\\n }\\n set {\\n var start = CheckIndex(range.Start);\\n var end = CheckIndex(range.End);\\n this[(start, end)] = value;\\n }\\n }\\n\\n private int CheckIndex(System.Index index)\\n {\\n var i = index.IsFromEnd ? Count - index.Value : index.Value;\\n if (i < 0 || i >= Count)\\n throw new IndexOutOfRangeException();\\n return i;\\n }\\n#endif\\n\\n private int CheckIndex(int index) {\\n if (index < 0 || index >= Count)\\n throw new IndexOutOfRangeException();\\n return index;\\n }\\n\\n private void ValidateNotScalar()\\n {\\n if (_tensor is null &&_tensor_data_ptr == IntPtr.Zero)\\n throw new InvalidOperationException(\\\"Invalid use of a scalar Storage instance.\\\");\\n }\\n\\n /// <summary>\\n /// The number of elements in the storage.\\n /// </summary>\\n public int Count {\\n get {\\n ValidateNotScalar();\\n if (_count == -1) {\\n unsafe {\\n _count = (int)nbytes() / sizeof(T);\\n }\\n }\\n return _count;\\n }\\n }\\n\\n private int _count = -1;\\n\\n public static implicit operator Storage<T>(T value)\\n {\\n return new Storage<T>(value);\\n }\\n\\n /// <summary>\\n /// Copy data from an array into a storage instance.\\n /// </summary>\\n /// <param name=\\\"array\\\"></param>\\n public void copy_(IList<T> array)\\n {\\n ValidateNotScalar();\\n CopyFrom(array, 0);\\n }\\n\\n /// <summary>\\n /// Fill a storage instance with a single value.\\n /// </summary>\\n /// <param name=\\\"value\\\"></param>\\n public void fill_(T value)\\n {\\n ValidateNotScalar();\\n unsafe {\\n var ptr = (T*)data_ptr();\\n for (int i = 0; i < Count; i++)\\n ((T*)ptr)[i] = value;\\n }\\n }\\n\\n /// <summary>\\n /// Convert the storage to a .NET list.\\n /// </summary>\\n /// <returns></returns>\\n public IList<T> tolist()\\n {\\n ValidateNotScalar();\\n var result = new T[Count];\\n CopyTo(result, 0);\\n return result;\\n }\\n\\n public bool IsReadOnly => false;\\n\\n public bool Contains(T item)\\n {\\n ValidateNotScalar();\\n unsafe {\\n var ptr = (T*)data_ptr();\\n for (int i = 0; i < Count; i++)\\n if (((T*)ptr)[i].Equals(item)) return true;\\n }\\n return false;\\n }\\n\\n /// <summary>\\n /// Copy the contents of the Storage instance into the provided array, starting at 'arrayIndex'\\n /// </summary>\\n /// <param name=\\\"array\\\">A target array.</param>\\n /// <param name=\\\"arrayIndex\\\">The first offset in the array to write data to.</param>\\n public void CopyTo(IList<T> array, int arrayIndex)\\n {\\n ValidateNotScalar();\\n\\n unsafe {\\n var ptr = (T*)data_ptr();\\n for (int i = 0; i < Count && i < array.Count-arrayIndex; i++)\\n array[i + arrayIndex] = ((T*)ptr)[i];\\n }\\n }\\n\\n /// <summary>\\n /// Copy the contents of the provided array into the Storage instance, starting at 'arrayIndex' in the array.\\n /// </summary>\\n /// <param name=\\\"array\\\">A source array.</param>\\n /// <param name=\\\"arrayIndex\\\">The first offset in the array to read data from.</param>\\n public void CopyFrom(IList<T> array, int arrayIndex)\\n {\\n ValidateNotScalar();\\n\\n unsafe {\\n var ptr = (T*)data_ptr();\\n for (int i = 0; i < Count && i < array.Count-arrayIndex; i++)\\n ((T*)ptr)[i] = array[i+arrayIndex];\\n }\\n }\\n\\n /// <summary>\\n /// Look up a value and return the first index where it can be found.\\n /// </summary>\\n /// <param name=\\\"item\\\">The item to look for.</param>\\n /// <returns></returns>\\n public int IndexOf(T item)\\n {\\n unsafe {\\n var ptr = (T*)data_ptr();\\n for (int i = 0; i < Count; i++)\\n if (((T*)ptr)[i].Equals(item)) return i;\\n }\\n return -1;\\n }\\n\\n public IEnumerator<T> GetEnumerator()\\n {\\n ValidateNotScalar();\\n return new StorageEnumerator(this);\\n }\\n\\n IEnumerator IEnumerable.GetEnumerator()\\n {\\n ValidateNotScalar();\\n return new StorageEnumerator(this);\\n }\\n\\n public void Dispose()\\n {\\n _tensor_data_ptr = IntPtr.Zero;\\n _tensor = null;\\n }\\n\\n private struct StorageEnumerator: IEnumerator<T>\\n {\\n private Storage<T> _span;\\n private readonly long _count;\\n\\n // State.\\n private long _index;\\n private T _current;\\n\\n public StorageEnumerator(Storage<T> span)\\n {\\n _span = span;\\n _count = span.Count;\\n Debug.Assert(_count > 0);\\n _index = -1;\\n _current = default;\\n }\\n\\n public T Current => _current;\\n\\n object IEnumerator.Current => Current;\\n\\n public void Dispose()\\n {\\n _span = null;\\n Reset();\\n }\\n\\n public bool MoveNext()\\n {\\n if (_index >= _count - 1) {\\n Reset();\\n return false;\\n }\\n\\n _index += 1;\\n\\n unsafe { _current = ((T*)_span._tensor_data_ptr)[_index]; }\\n return true;\\n }\\n\\n public void Reset()\\n {\\n _index = -1;\\n _current = default;\\n }\\n }\\n }\\n }\\n}\\n\"\n },\n {\n \"alpha_fraction\": 0.581882894039154,\n \"alphanum_fraction\": 0.5842563509941101,\n \"avg_line_length\": 37.89230728149414,\n \"blob_id\": \"cd9081aadae5815e1138b136ef3a7ccc19016018\",\n \"content_id\": \"ff812dfb33350b9627983246cb8264f1a973c120\",\n \"detected_licenses\": [\n \"MIT\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"C#\",\n \"length_bytes\": 2528,\n \"license_type\": \"permissive\",\n \"max_line_length\": 164,\n \"num_lines\": 65,\n \"path\": \"/src/TorchSharp/Distributions/LogNormal.cs\",\n \"repo_name\": \"NiklasGustafsson/TorchSharp\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"// Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information.\\n\\nusing static TorchSharp.torch;\\n\\nnamespace TorchSharp\\n{\\n using Modules;\\n using static torch.distributions;\\n\\n namespace Modules\\n {\\n public class LogNormal : TransformedDistribution\\n {\\n internal LogNormal(Tensor loc, Tensor scale, torch.Generator generator = null) :\\n base(Normal(loc, scale, generator), new torch.distributions.transforms.Transform[] { new torch.distributions.transforms.ExpTransform() }, generator)\\n {\\n this.loc = loc.alias().DetachFromDisposeScope();\\n this.scale = scale.alias().DetachFromDisposeScope();\\n }\\n\\n public Tensor loc { get; private set; }\\n\\n public Tensor scale { get; private set; }\\n\\n public override Tensor mean => torch.WrappedTensorDisposeScope(() => (loc + scale.pow(2) / 2).exp());\\n\\n public override Tensor mode => torch.WrappedTensorDisposeScope(() => (loc - scale.square()).exp());\\n\\n public override Tensor variance => torch.WrappedTensorDisposeScope(() => (scale.pow(2).exp() - 1) * (2 * loc + scale.pow(2)).exp());\\n\\n public override Tensor entropy()\\n {\\n return base_distribution.entropy() + loc;\\n }\\n\\n public override Distribution expand(Size batch_shape, Distribution instance = null)\\n {\\n var newDistribution = ((instance == null)\\n ? new LogNormal(loc.expand(batch_shape), scale.expand(batch_shape), generator)\\n : instance) as LogNormal;\\n return base.expand(batch_shape, newDistribution);\\n }\\n }\\n }\\n\\n\\n public static partial class torch\\n {\\n public static partial class distributions\\n {\\n /// <summary>\\n /// Creates a log-normal distribution parameterized by `loc` and `scale`\\n /// </summary>\\n /// <param name=\\\"loc\\\">Mode or median of the distribution.</param>\\n /// <param name=\\\"scale\\\">Standard deviation.</param>\\n /// <param name=\\\"generator\\\">An optional random number generator object.</param>\\n /// <returns></returns>\\n public static LogNormal LogNormal(Tensor loc, Tensor scale, torch.Generator generator = null)\\n {\\n \\n return new LogNormal(loc, scale, generator);\\n }\\n }\\n }\\n}\\n\"\n },\n {\n \"alpha_fraction\": 0.5458303689956665,\n \"alphanum_fraction\": 0.5670561790466309,\n \"avg_line_length\": 28.321678161621094,\n \"blob_id\": \"c8a157aed65a0f581f3793d3d30a8e2b9722cbfe\",\n \"content_id\": \"9bd62666271cf80ade6d0671f7dd3e7953d05d71\",\n \"detected_licenses\": [\n \"MIT\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 12579,\n \"license_type\": \"permissive\",\n \"max_line_length\": 129,\n \"num_lines\": 429,\n \"path\": \"/src/Python/exportsd.py\",\n \"repo_name\": \"NiklasGustafsson/TorchSharp\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"#\\n# Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information.\\n#\\nimport io\\nimport torch\\nimport leb128\\nimport numpy as np\\n\\ndef _elem_type(t):\\n dt = t.dtype\\n\\n if dt == torch.uint8:\\n return 0\\n elif dt == torch.int8:\\n return 1\\n elif dt == torch.int16:\\n return 2\\n elif dt == torch.int32:\\n return 3\\n elif dt == torch.int64:\\n return 4\\n elif dt == torch.float16:\\n return 5\\n elif dt == torch.float32:\\n return 6\\n elif dt == torch.float64:\\n return 7\\n elif dt == torch.bool:\\n return 11\\n elif dt == torch.bfloat16:\\n return 15\\n else:\\n return 4711\\n\\ndef _write_tensor(t, stream):\\n stream.write(leb128.u.encode(_elem_type(t)))\\n stream.write(leb128.u.encode(len(t.shape)))\\n for s in t.shape:\\n stream.write(leb128.u.encode(s))\\n stream.write(t.numpy().tobytes())\\n\\ndef save_state_dict(sd, stream):\\n \\\"\\\"\\\"\\n Saves a PyToch state dictionary using the format that TorchSharp can\\n read.\\n\\n :param sd: A dictionary produced by 'model.state_dict()'\\n :param stream: An write stream opened for binary I/O.\\n \\\"\\\"\\\"\\n stream.write(leb128.u.encode(len(sd)))\\n for entry in sd:\\n stream.write(leb128.u.encode(len(entry)))\\n stream.write(bytes(entry, 'utf-8'))\\n _write_tensor(sd[entry], stream)\\n\\ndef _write_encoded_int64(value, stream):\\n stream.write(leb128.u.encode(value))\\n\\ndef _write_optim_name(name, stream):\\n _write_encoded_int64(len(name),stream)\\n stream.write(bytes(name, 'utf-8'))\\n\\ndef _write_bool(value, stream):\\n stream.write(value.to_bytes(1, 'little'))\\n\\ndef _write_int32(value, stream):\\n stream.write(value.to_bytes(4, 'little'))\\n\\ndef _write_int64(value, stream):\\n stream.write(value.to_bytes(8, 'little'))\\n\\ndef _write_conditional_state_tensor(name, state, stream):\\n if name not in state:\\n _write_bool(False, stream)\\n else: \\n buf = state[name]\\n if buf == None:\\n _write_bool(False, stream)\\n else:\\n _write_bool(True, stream)\\n _write_tensor(buf, stream)\\n\\ndef save_sgd(optim, stream):\\n\\n sd = optim.state_dict()\\n\\n _write_optim_name('SGD', stream)\\n\\n _write_encoded_int64(len(optim.param_groups), stream)\\n _write_encoded_int64(len(optim.state), stream)\\n\\n # Write options\\n\\n for pg in optim.param_groups:\\n floats = np.empty(5,dtype=np.float64)\\n floats[0] = pg['lr'] \\n floats[1] = pg['lr']\\n floats[2] = pg['momentum']\\n floats[3] = pg['dampening']\\n floats[4] = pg['weight_decay']\\n stream.write(floats.tobytes())\\n _write_bool(pg['nesterov'], stream)\\n _write_bool(pg['maximize'], stream)\\n\\n # Write state\\n for group in optim.param_groups:\\n for p in group['params']:\\n st = optim.state[p]\\n _write_conditional_state_tensor('momentum_buffer', st, stream) \\n\\ndef save_asgd(optim, stream):\\n\\n sd = optim.state_dict()\\n\\n _write_optim_name('ASGD', stream)\\n\\n _write_encoded_int64(len(optim.param_groups), stream)\\n _write_encoded_int64(len(optim.state), stream)\\n\\n # Write options\\n\\n for pg in optim.param_groups:\\n floats = np.empty(2,dtype=np.float64)\\n floats[0] = pg['lr'] \\n floats[1] = pg['lr']\\n stream.write(floats.tobytes())\\n _write_bool(pg['maximize'], stream)\\n floats = np.empty(4,dtype=np.float64)\\n floats[0] = pg['lambd']\\n floats[1] = pg['alpha']\\n floats[2] = pg['weight_decay']\\n floats[3] = pg['t0']\\n stream.write(floats.tobytes())\\n\\n # Write state\\n for group in optim.param_groups:\\n for p in group['params']:\\n st = optim.state[p]\\n _write_int64(int(st[\\\"step\\\"].item()), stream)\\n floats = np.empty(2,dtype=np.float64)\\n floats[0] = st['eta'] \\n floats[1] = st['mu']\\n stream.write(floats.tobytes())\\n _write_tensor(st['ax'], stream)\\n\\ndef save_rprop(optim, stream):\\n\\n sd = optim.state_dict()\\n\\n _write_optim_name('Rprop', stream)\\n\\n _write_encoded_int64(len(optim.param_groups), stream)\\n _write_encoded_int64(len(optim.state), stream)\\n\\n # Write options\\n\\n for pg in optim.param_groups:\\n floats = np.empty(2,dtype=np.float64)\\n floats[0] = pg['lr'] \\n floats[1] = pg['lr']\\n stream.write(floats.tobytes())\\n _write_bool(pg['maximize'], stream)\\n floats = np.empty(4,dtype=np.float64)\\n etaminus, etaplus = pg['etas']\\n min_step, max_step = pg['step_sizes']\\n floats[0] = etaminus\\n floats[1] = etaplus\\n floats[2] = min_step\\n floats[3] = max_step\\n stream.write(floats.tobytes())\\n\\n # Write state\\n for group in optim.param_groups:\\n for p in group['params']:\\n st = optim.state[p]\\n _write_int64(st[\\\"step\\\"], stream)\\n _write_tensor(st['prev'], stream)\\n _write_tensor(st['step_size'], stream)\\n\\ndef save_rmsprop(optim, stream):\\n\\n sd = optim.state_dict()\\n\\n _write_optim_name('RMSProp', stream)\\n\\n _write_encoded_int64(len(optim.param_groups), stream)\\n _write_encoded_int64(len(optim.state), stream)\\n\\n # Write options\\n\\n for pg in optim.param_groups:\\n floats = np.empty(2,dtype=np.float64)\\n floats[0] = pg['lr'] \\n floats[1] = pg['lr']\\n stream.write(floats.tobytes())\\n _write_bool(pg['maximize'], stream)\\n floats = np.empty(4,dtype=np.float64)\\n floats[0] = pg['momentum']\\n floats[1] = pg['alpha']\\n floats[2] = pg['eps']\\n floats[3] = pg['weight_decay']\\n stream.write(floats.tobytes())\\n _write_bool(pg['centered'], stream)\\n\\n # Write state\\n for group in optim.param_groups:\\n for p in group['params']:\\n st = optim.state[p]\\n _write_int64(st[\\\"step\\\"], stream)\\n _write_tensor(st['square_avg'], stream)\\n _write_conditional_state_tensor('momentum_buffer', st, stream)\\n _write_conditional_state_tensor('grad_avg', st, stream)\\n\\ndef save_adam(optim, stream):\\n\\n sd = optim.state_dict()\\n\\n _write_optim_name('Adam', stream)\\n\\n _write_encoded_int64(len(optim.param_groups), stream)\\n _write_encoded_int64(len(optim.state), stream)\\n\\n # Write options\\n\\n for pg in optim.param_groups:\\n floats = np.empty(6,dtype=np.float64)\\n floats[0] = pg['lr'] \\n floats[1] = pg['lr']\\n beta1, beta2 = pg['betas']\\n floats[2] = beta1\\n floats[3] = beta2\\n floats[4] = pg['eps']\\n floats[5] = pg['weight_decay']\\n stream.write(floats.tobytes())\\n _write_bool(pg['amsgrad'], stream)\\n _write_bool(pg['maximize'], stream)\\n\\n # Write state\\n for group in optim.param_groups:\\n for p in group['params']:\\n st = optim.state[p]\\n _write_int64(int(st[\\\"step\\\"].item()), stream)\\n _write_tensor(st['exp_avg'], stream)\\n _write_tensor(st['exp_avg_sq'], stream)\\n _write_conditional_state_tensor('max_exp_avg_sq', st, stream)\\n\\ndef save_adamw(optim, stream):\\n\\n sd = optim.state_dict()\\n\\n _write_optim_name('AdamW', stream)\\n\\n _write_encoded_int64(len(optim.param_groups), stream)\\n _write_encoded_int64(len(optim.state), stream)\\n\\n # Write options\\n\\n for pg in optim.param_groups:\\n floats = np.empty(6,dtype=np.float64)\\n floats[0] = pg['lr'] \\n floats[1] = pg['lr']\\n beta1, beta2 = pg['betas']\\n floats[2] = beta1\\n floats[3] = beta2\\n floats[4] = pg['eps']\\n floats[5] = pg['weight_decay']\\n stream.write(floats.tobytes())\\n _write_bool(pg['amsgrad'], stream)\\n _write_bool(pg['maximize'], stream)\\n\\n # Write state\\n for group in optim.param_groups:\\n for p in group['params']:\\n st = optim.state[p]\\n _write_int64(int(st[\\\"step\\\"].item()), stream)\\n _write_tensor(st['exp_avg'], stream)\\n _write_tensor(st['exp_avg_sq'], stream)\\n _write_conditional_state_tensor('max_exp_avg_sq', st, stream)\\n\\ndef save_nadam(optim, stream):\\n\\n sd = optim.state_dict()\\n\\n _write_optim_name('NAdam', stream)\\n\\n _write_encoded_int64(len(optim.param_groups), stream)\\n _write_encoded_int64(len(optim.state), stream)\\n\\n # Write options\\n\\n for pg in optim.param_groups:\\n floats = np.empty(7,dtype=np.float64)\\n floats[0] = pg['lr'] \\n floats[1] = pg['lr']\\n beta1, beta2 = pg['betas']\\n floats[2] = beta1\\n floats[3] = beta2\\n floats[4] = pg['eps']\\n floats[5] = pg['weight_decay']\\n floats[6] = pg['momentum_decay']\\n stream.write(floats.tobytes())\\n\\n # Write state\\n for group in optim.param_groups:\\n for p in group['params']:\\n st = optim.state[p]\\n _write_int64(int(st[\\\"step\\\"].item()), stream)\\n floats = np.empty(1,dtype=np.float64)\\n floats[0] = float(st[\\\"mu_product\\\"].item()) \\n stream.write(floats.tobytes())\\n _write_tensor(st['exp_avg'], stream)\\n _write_tensor(st['exp_avg_sq'], stream)\\n\\ndef save_radam(optim, stream):\\n\\n sd = optim.state_dict()\\n\\n _write_optim_name('RAdam', stream)\\n\\n _write_encoded_int64(len(optim.param_groups), stream)\\n _write_encoded_int64(len(optim.state), stream)\\n\\n # Write options\\n\\n for pg in optim.param_groups:\\n floats = np.empty(6,dtype=np.float64)\\n floats[0] = pg['lr'] \\n floats[1] = pg['lr']\\n beta1, beta2 = pg['betas']\\n floats[2] = beta1\\n floats[3] = beta2\\n floats[4] = pg['eps']\\n floats[5] = pg['weight_decay']\\n stream.write(floats.tobytes())\\n\\n # Write state\\n for group in optim.param_groups:\\n for p in group['params']:\\n st = optim.state[p]\\n _write_int64(int(st[\\\"step\\\"].item()), stream)\\n _write_tensor(st['exp_avg'], stream)\\n _write_tensor(st['exp_avg_sq'], stream)\\n\\ndef save_adamax(optim, stream):\\n\\n sd = optim.state_dict()\\n\\n _write_optim_name('Adamax', stream)\\n\\n _write_encoded_int64(len(optim.param_groups), stream)\\n _write_encoded_int64(len(optim.state), stream)\\n\\n # Write options\\n\\n for pg in optim.param_groups:\\n floats = np.empty(6,dtype=np.float64)\\n floats[0] = pg['lr'] \\n floats[1] = pg['lr']\\n beta1, beta2 = pg['betas']\\n floats[2] = beta1\\n floats[3] = beta2\\n floats[4] = pg['eps']\\n floats[5] = pg['weight_decay']\\n stream.write(floats.tobytes())\\n\\n # Write state\\n for group in optim.param_groups:\\n for p in group['params']:\\n st = optim.state[p]\\n _write_int64(int(st[\\\"step\\\"].item()), stream)\\n _write_tensor(st['exp_avg'], stream)\\n _write_tensor(st['exp_inf'], stream)\\n\\ndef save_adadelta(optim, stream):\\n\\n sd = optim.state_dict()\\n\\n _write_optim_name('Adadelta', stream)\\n\\n _write_encoded_int64(len(optim.param_groups), stream)\\n _write_encoded_int64(len(optim.state), stream)\\n\\n # Write options\\n\\n for pg in optim.param_groups:\\n floats = np.empty(5,dtype=np.float64)\\n floats[0] = pg['lr'] \\n floats[1] = pg['lr']\\n floats[2] = pg['rho']\\n floats[3] = pg['eps']\\n floats[4] = pg['weight_decay']\\n stream.write(floats.tobytes())\\n _write_bool(pg['maximize'], stream)\\n\\n # Write state\\n for group in optim.param_groups:\\n for p in group['params']:\\n st = optim.state[p]\\n _write_int64(st[\\\"step\\\"], stream)\\n _write_tensor(st['square_avg'], stream)\\n _write_tensor(st['acc_delta'], stream)\\n\\ndef save_adagrad(optim, stream):\\n\\n sd = optim.state_dict()\\n\\n _write_optim_name('Adagrad', stream)\\n\\n _write_encoded_int64(len(optim.param_groups), stream)\\n _write_encoded_int64(len(optim.state), stream)\\n\\n # Write options\\n\\n for pg in optim.param_groups:\\n floats = np.empty(6,dtype=np.float64)\\n floats[0] = pg['lr'] \\n floats[1] = pg['lr']\\n floats[2] = pg['lr_decay']\\n floats[3] = pg['initial_accumulator_value']\\n floats[4] = pg['eps']\\n floats[5] = pg['weight_decay']\\n stream.write(floats.tobytes())\\n\\n # Write state\\n for group in optim.param_groups:\\n for p in group['params']:\\n st = optim.state[p]\\n _write_int64(int(st[\\\"step\\\"].item()), stream)\\n _write_tensor(st['sum'], stream)\\n\"\n },\n {\n \"alpha_fraction\": 0.7192075848579407,\n \"alphanum_fraction\": 0.7245908975601196,\n \"avg_line_length\": 54.61676788330078,\n \"blob_id\": \"fb91138d748b56a2c3c40b3b90d26bbd2ecaf663\",\n \"content_id\": \"4a0298a15dfd95f114145473bfd8ef4981349693\",\n \"detected_licenses\": [\n \"MIT\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"C#\",\n \"length_bytes\": 9288,\n \"license_type\": \"permissive\",\n \"max_line_length\": 244,\n \"num_lines\": 167,\n \"path\": \"/src/TorchSharp/PInvoke/LibTorchSharp.THSLinalg.cs\",\n \"repo_name\": \"NiklasGustafsson/TorchSharp\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"// Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information.\\n#nullable enable\\nusing System;\\nusing System.Runtime.InteropServices;\\nusing static TorchSharp.torch;\\n\\nnamespace TorchSharp.PInvoke\\n{\\n#pragma warning disable CA2101\\n internal static partial class NativeMethods\\n {\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern IntPtr THSLinalg_cholesky(IntPtr tensor);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern IntPtr THSLinalg_cholesky_ex(IntPtr tensor, [MarshalAs(UnmanagedType.U1)] bool check_errors, out IntPtr pInfo);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern IntPtr THSLinalg_cond_int(IntPtr tensor, int p);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern IntPtr THSLinalg_cond_float(IntPtr tensor, double p);\\n\\n [DllImport(\\\"LibTorchSharp\\\", CharSet = CharSet.Ansi, BestFitMapping = false, ThrowOnUnmappableChar = true)]\\n internal static extern IntPtr THSLinalg_cond_str(IntPtr tensor, [MarshalAs(UnmanagedType.LPStr)] string p);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern IntPtr THSLinalg_cond_none(IntPtr tensor);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern IntPtr THSLinalg_cross(IntPtr input, IntPtr other, long dim);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern IntPtr THSLinalg_det(IntPtr tensor);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern IntPtr THSLinalg_slogdet(IntPtr tensor, out IntPtr pLogabsdet);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern IntPtr THSLinalg_eig(IntPtr tensor, out IntPtr pEigenvectors);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern IntPtr THSTensor_geqrf(IntPtr tensor, out IntPtr tau);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern IntPtr THSLinalg_eigh(IntPtr tensor, byte UPLO, out IntPtr pEigenvectors);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern IntPtr THSLinalg_eigvals(IntPtr tensor);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern IntPtr THSLinalg_eigvalsh(IntPtr tensor, byte UPLO);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern IntPtr THSLinalg_householder_product(IntPtr tensor, IntPtr tau);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern IntPtr THSLinalg_inv(IntPtr tensor);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern IntPtr THSLinalg_inv_ex(IntPtr tensor, [MarshalAs(UnmanagedType.U1)] bool check_errors, out IntPtr pInfo);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern IntPtr THSLinalg_lstsq_none(IntPtr tensor, IntPtr other, out IntPtr pResiduals, out IntPtr pRank, out IntPtr pSingularValues);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern IntPtr THSLinalg_lstsq_rcond(IntPtr tensor, IntPtr other, double rcond, out IntPtr pResiduals, out IntPtr pRank, out IntPtr pSingularValues);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern IntPtr THSLinalg_ldl_factor(IntPtr A, [MarshalAs(UnmanagedType.U1)] bool hermitian, out IntPtr pivots);\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern IntPtr THSLinalg_ldl_factor_ex(IntPtr A, [MarshalAs(UnmanagedType.U1)] bool hermitian, [MarshalAs(UnmanagedType.U1)] bool check_errors, out IntPtr pivots, out IntPtr info);\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern IntPtr THSLinalg_ldl_solve(IntPtr LD, IntPtr pivots, IntPtr B, [MarshalAs(UnmanagedType.U1)] bool hermitian);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern IntPtr THSLinalg_lu(IntPtr tensor, [MarshalAs(UnmanagedType.U1)] bool pivot, out IntPtr pL, out IntPtr pU);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern IntPtr THSLinalg_lu_factor(IntPtr tensor, [MarshalAs(UnmanagedType.U1)] bool pivot, out IntPtr pPivots);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern IntPtr THSLinalg_matrix_norm_fronuc(IntPtr tensor, byte fronuc, IntPtr dim, int dim_length, [MarshalAs(UnmanagedType.U1)] bool keepdim);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern IntPtr THSLinalg_matrix_norm(IntPtr tensor, IntPtr ord, IntPtr dim, int dim_length, [MarshalAs(UnmanagedType.U1)] bool keepdim);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern IntPtr THSLinalg_matrix_rank(IntPtr tensor, double atol, [MarshalAs(UnmanagedType.U1)] bool has_atol, double rtol, [MarshalAs(UnmanagedType.U1)] bool has_rtol, [MarshalAs(UnmanagedType.U1)] bool hermitian);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern IntPtr THSLinalg_matrix_rank_tensor(IntPtr tensor, IntPtr atol, IntPtr rtol, [MarshalAs(UnmanagedType.U1)] bool hermitian);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern IntPtr THSLinalg_dot(IntPtr tensor, int len);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern IntPtr THSLinalg_multi_dot(IntPtr tensor, int len);\\n\\n [DllImport(\\\"LibTorchSharp\\\", CharSet = CharSet.Ansi, BestFitMapping = false, ThrowOnUnmappableChar = true)]\\n internal static extern IntPtr THSLinalg_norm_str(IntPtr tensor, [MarshalAs(UnmanagedType.LPStr)] string p, IntPtr dim, int dim_length, [MarshalAs(UnmanagedType.U1)] bool keepdim);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern IntPtr THSLinalg_norm_float(IntPtr tensor, double p, IntPtr dim, int dim_length, [MarshalAs(UnmanagedType.U1)] bool keepdim);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern IntPtr THSLinalg_norm_int(IntPtr tensor, int p, IntPtr dim, int dim_length, [MarshalAs(UnmanagedType.U1)] bool keepdim);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern IntPtr THSLinalg_norm_opt(IntPtr tensor, IntPtr dim, int dim_length, [MarshalAs(UnmanagedType.U1)] bool keepdim);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern IntPtr THSLinalg_pinv(IntPtr tensor, double atol, [MarshalAs(UnmanagedType.U1)] bool has_atol, double rtol, [MarshalAs(UnmanagedType.U1)] bool has_rtol, [MarshalAs(UnmanagedType.U1)] bool hermitian);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern IntPtr THSLinalg_pinv_tensor(IntPtr tensor, IntPtr atol, IntPtr rtol, [MarshalAs(UnmanagedType.U1)] bool hermitian);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern IntPtr THSLinalg_qr(IntPtr tensor, byte mode, out IntPtr pR);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern IntPtr THSLinalg_solve(IntPtr tensor, IntPtr other, [MarshalAs(UnmanagedType.U1)] bool left);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern IntPtr THSLinalg_solve_ex(IntPtr tensor, IntPtr other, [MarshalAs(UnmanagedType.U1)] bool left, [MarshalAs(UnmanagedType.U1)] bool check_errors, out IntPtr infos);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern IntPtr THSLinalg_solve_triangular(IntPtr tensor, IntPtr other, [MarshalAs(UnmanagedType.U1)] bool upper, [MarshalAs(UnmanagedType.U1)] bool left, [MarshalAs(UnmanagedType.U1)] bool unitriangular);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern IntPtr THSLinalg_solve_triangular_out(IntPtr tensor, IntPtr other, [MarshalAs(UnmanagedType.U1)] bool upper, [MarshalAs(UnmanagedType.U1)] bool left, [MarshalAs(UnmanagedType.U1)] bool unitriangular, IntPtr _out);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern IntPtr THSLinalg_svd(IntPtr tensor, [MarshalAs(UnmanagedType.U1)] bool fullMatrices, out IntPtr pS, out IntPtr pVh);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern IntPtr THSLinalg_svdvals(IntPtr tensor);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern IntPtr THSLinalg_tensorinv(IntPtr tensor, long ind);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern IntPtr THSLinalg_tensorsolve(IntPtr tensor, IntPtr other, IntPtr dim, int dim_length);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern IntPtr THSLinalg_vector_norm(IntPtr tensor, IntPtr ord, IntPtr dim, int dim_length, [MarshalAs(UnmanagedType.U1)] bool keepdim);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern IntPtr THSLinalg_matrix_power(IntPtr tensor, long n);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern IntPtr THSLinalg_pinverse(IntPtr tensor, double rcond, [MarshalAs(UnmanagedType.U1)] bool hermitian);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern IntPtr THSLinalg_vander(IntPtr tensor, long N);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern IntPtr THSLinalg_vecdot(IntPtr x, IntPtr y, long dim, IntPtr output);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern IntPtr THSLinalg_lu_solve(IntPtr B, IntPtr LU, IntPtr pivots, [MarshalAs(UnmanagedType.U1)] bool left, [MarshalAs(UnmanagedType.U1)] bool adjoint, IntPtr output);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern IntPtr THSLinalg_tensordot(IntPtr input1, IntPtr input2, IntPtr dims1, int dims1_length, IntPtr dims2, int dims2_length);\\n }\\n#pragma warning restore CA2101\\n}\\n\"\n },\n {\n \"alpha_fraction\": 0.7365591526031494,\n \"alphanum_fraction\": 0.7708333134651184,\n \"avg_line_length\": 56.269229888916016,\n \"blob_id\": \"b80a330e1aa66c6562079c4f47a4e57a164dcbd5\",\n \"content_id\": \"c4d00be17cffa18409e0d993ccf868be25442bb2\",\n \"detected_licenses\": [\n \"MIT\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"C\",\n \"length_bytes\": 1488,\n \"license_type\": \"permissive\",\n \"max_line_length\": 204,\n \"num_lines\": 26,\n \"path\": \"/src/Native/LibTorchSharp/THSVision.h\",\n \"repo_name\": \"NiklasGustafsson/TorchSharp\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"// Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information.\\n#pragma once\\n\\n#include \\\"../Stdafx.h\\\"\\n\\n#include \\\"torch/torch.h\\\"\\n\\n#include \\\"Utils.h\\\"\\n\\n// API\\n\\nEXPORT_API(Tensor) THSVision_AdjustHue(const Tensor img, const double hue_factor);\\n\\nEXPORT_API(Tensor) THSVision_GenerateAffineGrid(Tensor theta, const int64_t w, const int64_t h, const int64_t ow, const int64_t oh);\\nEXPORT_API(Tensor) THSVision_ApplyGridTransform(Tensor i, Tensor g, const int8_t m, const float* fill, const int64_t fill_length);\\n\\nEXPORT_API(Tensor) THSVision_PerspectiveGrid(const float* coeffs, const int64_t coeffs_length, const int64_t ow, const int64_t oh, const int8_t scalar_type, const int device_type, const int device_index);\\n\\nEXPORT_API(Tensor) THSVision_ScaleChannel(Tensor ic);\\n\\nEXPORT_API(void) THSVision_ComputeOutputSize(const float* matrix, const int64_t matrix_length, const int64_t w, const int64_t h, int32_t* first, int32_t* second);\\n\\nEXPORT_API(void) THSVision_BRGA_RGB(const uint8_t* inputBytes, uint8_t* redBytes, uint8_t* greenBytes, uint8_t* blueBytes, int64_t inputChannelCount, int64_t imageSize);\\nEXPORT_API(void) THSVision_BRGA_RGBA(const uint8_t* inputBytes, uint8_t* redBytes, uint8_t* greenBytes, uint8_t* blueBytes, uint8_t* alphaBytes, int64_t inputChannelCount, int64_t imageSize);\\n\\nEXPORT_API(void) THSVision_RGB_BRGA(const uint8_t* inputBytes, uint8_t* outBytes, int64_t inputChannelCount, int64_t imageSize);\"\n },\n {\n \"alpha_fraction\": 0.6682669520378113,\n \"alphanum_fraction\": 0.6692270636558533,\n \"avg_line_length\": 35.561405181884766,\n \"blob_id\": \"93f1a1cf7619ff5937db70a53389a95d5d228d07\",\n \"content_id\": \"0a4c959fc485f6ae6ab2fa23bf807cb501e79c00\",\n \"detected_licenses\": [\n \"MIT\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"C#\",\n \"length_bytes\": 2083,\n \"license_type\": \"permissive\",\n \"max_line_length\": 130,\n \"num_lines\": 57,\n \"path\": \"/src/TorchSharp/DisposeScopeManager.cs\",\n \"repo_name\": \"NiklasGustafsson/TorchSharp\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"// Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information.\\n\\nusing System;\\nusing System.Collections.Generic;\\nusing System.Diagnostics;\\n\\n#nullable enable\\nnamespace TorchSharp\\n{\\n /// <summary>\\n /// Manages dispose scopes, that can make automatic tensor disposal easier. Note that the\\n /// DisposeManager is thread local. The DisposeScopeManager can also manage other disposables, such as training\\n /// batches and the like.\\n /// </summary>\\n public class DisposeScopeManager\\n {\\n [ThreadStatic] private static DisposeScopeManager? _threadSingleton;\\n internal ThreadDisposeScopeStatistics StatisticsInstance { get; } = new ThreadDisposeScopeStatistics();\\n\\n internal static DisposeScopeManager ThreadSingleton => (_threadSingleton ??= new DisposeScopeManager());\\n internal Stack<DisposeScope> DisposeScopeStack { get; } = new();\\n\\n public static ThreadDisposeScopeStatistics Statistics => ThreadSingleton.StatisticsInstance;\\n\\n internal DisposeScope? RegisterOnCurrentDisposeScope(IDisposable disposable)\\n {\\n if (DisposeScopeStack.Count == 0) {\\n StatisticsInstance.CreatedOutsideScopeCount++;\\n return null;\\n }\\n\\n StatisticsInstance.CreatedInScopeCount++;\\n var current = DisposeScopeStack.Peek();\\n current.Include(disposable);\\n return current;\\n }\\n\\n internal static DisposeScope NewDisposeScope()\\n {\\n return ThreadSingleton.InnerNewDisposeScope();\\n }\\n\\n internal void RemoveDisposeScope(DisposeScope disposeScope)\\n {\\n Debug.Assert(DisposeScopeStack.Count > 0);\\n Debug.Assert(DisposeScopeStack.Peek() == disposeScope);\\n DisposeScopeStack.Pop();\\n }\\n\\n private DisposeScope InnerNewDisposeScope()\\n {\\n var disposeScope = new DisposeScope(this);\\n DisposeScopeStack.Push(disposeScope);\\n return disposeScope;\\n }\\n }\\n}\"\n },\n {\n \"alpha_fraction\": 0.4377593398094177,\n \"alphanum_fraction\": 0.440248966217041,\n \"avg_line_length\": 26.089887619018555,\n \"blob_id\": \"73f93464bf6cbbb6b2dd3326ff48d7517cbea865\",\n \"content_id\": \"4134ccc6837798d0cb8b902c375dab3841707fea\",\n \"detected_licenses\": [\n \"MIT\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"C#\",\n \"length_bytes\": 2410,\n \"license_type\": \"permissive\",\n \"max_line_length\": 81,\n \"num_lines\": 89,\n \"path\": \"/src/TorchSharp/Utils/FisherYatesShuffler.cs\",\n \"repo_name\": \"NiklasGustafsson/TorchSharp\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"using System;\\nusing System.Collections;\\nusing System.Collections.Generic;\\n\\nnamespace TorchSharp.Utils\\n{\\n public class FisherYatesShuffler : IEnumerable<long>\\n {\\n private long size;\\n private int? seed;\\n\\n public FisherYatesShuffler(long size, int? seed = null)\\n {\\n this.size = size;\\n this.seed = seed;\\n }\\n\\n public IEnumerator<long> GetEnumerator()\\n => seed == null\\n ? new FisherYatesShufflerEnumerable(size)\\n : new FisherYatesShufflerEnumerable(size, seed.Value);\\n\\n IEnumerator IEnumerable.GetEnumerator()\\n {\\n return GetEnumerator();\\n }\\n\\n private class FisherYatesShufflerEnumerable : IEnumerator<long>\\n {\\n public FisherYatesShufflerEnumerable(long size)\\n {\\n this.size = size;\\n this.seed = null;\\n Reset();\\n }\\n\\n public FisherYatesShufflerEnumerable(long size, int seed)\\n {\\n this.size = size;\\n this.seed = seed;\\n Reset();\\n }\\n\\n private int? seed;\\n private long size;\\n private Dictionary<long, long> indexs = new();\\n private Random r;\\n private long current = -1;\\n\\n public bool MoveNext()\\n {\\n current++;\\n return current < size;\\n }\\n\\n public void Reset()\\n {\\n r = seed == null ? new Random() : new Random(seed.Value);\\n current = -1;\\n indexs.Clear();\\n for (var i = 0L; i < size; i++) {\\n var rndidx = GetRandomLong(i);\\n if (rndidx == i)\\n indexs[i] = i;\\n else {\\n indexs[i] = indexs[rndidx];\\n indexs[rndidx] = i;\\n }\\n }\\n }\\n\\n public long Current => indexs[current];\\n\\n object IEnumerator.Current => Current;\\n\\n public void Dispose()\\n {\\n /* Ignore */\\n }\\n\\n private long GetRandomLong(long l)\\n {\\n unchecked {\\n return (((long) r.Next() << 32) + (uint) r.Next()) % (l + 1);\\n }\\n }\\n }\\n }\\n}\"\n },\n {\n \"alpha_fraction\": 0.4176732301712036,\n \"alphanum_fraction\": 0.424348384141922,\n \"avg_line_length\": 33.206520080566406,\n \"blob_id\": \"cac90bf025e2cfd5542bf0f7d098a570ad2b99f9\",\n \"content_id\": \"c54bbf60fdbfe161fff37cc08c13cd4037269ee6\",\n \"detected_licenses\": [\n \"MIT\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"C#\",\n \"length_bytes\": 3146,\n \"license_type\": \"permissive\",\n \"max_line_length\": 130,\n \"num_lines\": 92,\n \"path\": \"/src/TorchSharp/ConsoleProgressBar.cs\",\n \"repo_name\": \"NiklasGustafsson/TorchSharp\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"// Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information.\\nusing System;\\nusing System.Diagnostics;\\n\\nnamespace TorchSharp\\n{\\n internal class ConsoleProgressBar : IProgressBar\\n {\\n private const long DisplayEveryMilliseconds = 100;\\n\\n private readonly int _displayWidth;\\n private readonly Stopwatch _stopWatch;\\n private bool _hidden;\\n private long _value;\\n private long? _maximum;\\n\\n internal ConsoleProgressBar(bool hidden)\\n {\\n this._hidden = hidden;\\n try {\\n // This fails when console is not available.\\n this._displayWidth = Console.BufferWidth;\\n } catch {\\n this._displayWidth = 80;\\n }\\n this._stopWatch = new Stopwatch();\\n this._stopWatch.Start();\\n }\\n\\n public long Value {\\n set {\\n if (value > this._maximum) {\\n value = this._maximum.Value;\\n }\\n this._value = value;\\n Display();\\n }\\n get {\\n return _value;\\n }\\n }\\n\\n public long? Maximum {\\n get {\\n return _maximum;\\n }\\n set {\\n if (value < this._value) {\\n this._value = value.Value;\\n }\\n this._maximum = value;\\n Display();\\n }\\n }\\n\\n public void Dispose()\\n {\\n if (!_hidden) {\\n Console.Error.WriteLine();\\n }\\n }\\n\\n private void Display()\\n {\\n if (!_hidden) {\\n if (_value == 0 || _value == _maximum || this._stopWatch.ElapsedMilliseconds > DisplayEveryMilliseconds) {\\n this._stopWatch.Restart();\\n if (this.Maximum == null) {\\n Console.Error.Write(\\\"\\\\r{0}\\\", _value);\\n } else {\\n string left = string.Format(\\\"{0,3}%[\\\", 100 * _value / _maximum);\\n string right = string.Format(\\\"] {0}/{1}\\\", _value, _maximum);\\n int barContainerWidth = this._displayWidth - left.Length - right.Length - 1;\\n string center = string.Empty;\\n if (barContainerWidth > 0) {\\n int barWidth = (int)(barContainerWidth * _value / _maximum);\\n if (barWidth > 0) {\\n center = new string('=', barWidth);\\n if (this._displayWidth - barWidth > 0) {\\n center += new string(' ', barContainerWidth - barWidth);\\n }\\n } else {\\n center = new string(' ', barContainerWidth);\\n }\\n }\\n Console.Error.Write(\\\"\\\\r{0}{1}{2}\\\", left, center, right);\\n }\\n }\\n }\\n }\\n }\\n}\"\n },\n {\n \"alpha_fraction\": 0.4960475564002991,\n \"alphanum_fraction\": 0.5081413388252258,\n \"avg_line_length\": 48.42070007324219,\n \"blob_id\": \"4097f97cca7412692aa61b51b9535d9e70bdc698\",\n \"content_id\": \"fdbfb37be80812573ab6f389dc6981f05e264a41\",\n \"detected_licenses\": [\n \"MIT\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"C#\",\n \"length_bytes\": 29602,\n \"license_type\": \"permissive\",\n \"max_line_length\": 319,\n \"num_lines\": 599,\n \"path\": \"/src/TorchAudio/Functional.cs\",\n \"repo_name\": \"NiklasGustafsson/TorchSharp\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"// Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information.\\nusing System;\\nusing System.Collections.Generic;\\nusing static TorchSharp.torch;\\nusing System.Diagnostics;\\n\\n// A number of implementation details in this file have been translated from the Python version of torchaudio,\\n// largely located in the files found in this folder:\\n//\\n// https://github.com/pytorch/audio/blob/39c2c0a77e11b82ce152d60df3a92f6854d5f52b/torchaudio/functional/functional.py\\n//\\n// The origin has the following copyright notice and license:\\n//\\n// https://github.com/pytorch/audio/blob/main/LICENSE\\n//\\n\\nnamespace TorchSharp\\n{\\n public static partial class torchaudio\\n {\\n public static partial class functional\\n {\\n /// <summary>\\n /// Compute spectrograms from audio signals.\\n /// </summary>\\n /// <param name=\\\"waveform\\\">The audio signal tensor</param>\\n /// <param name=\\\"pad\\\">Padding on the sides</param>\\n /// <param name=\\\"window\\\">The window function</param>\\n /// <param name=\\\"n_fft\\\">The size of Fourier transform</param>\\n /// <param name=\\\"hop_length\\\">The hop length</param>\\n /// <param name=\\\"win_length\\\">The window length</param>\\n /// <param name=\\\"power\\\">Exponent for the magnitude spectrogram</param>\\n /// <param name=\\\"normalized\\\">Whether the output is normalized, or not.</param>\\n /// <param name=\\\"center\\\">Whether the t-th frame is centered around t * hop_window, or not.</param>\\n /// <param name=\\\"pad_mode\\\">The padding mode used when center is true.</param>\\n /// <param name=\\\"onesided\\\">Whether the output is onesided or not.</param>\\n /// <param name=\\\"return_complex\\\">Deprecated and not used.</param>\\n /// <returns>Spectrograms of audio signals</returns>\\n public static torch.Tensor spectrogram(torch.Tensor waveform, long pad, torch.Tensor window, long n_fft, long hop_length, long win_length, double? power, bool normalized, bool center = true, PaddingModes pad_mode = PaddingModes.Reflect, bool onesided = true, bool? return_complex = null)\\n {\\n using (var d = torch.NewDisposeScope()) {\\n\\n if (pad > 0) {\\n // The original torchaudio doesn't have `torch.no_grad()' here to\\n // avoid issues with JIT.\\n // https://github.com/pytorch/audio/commit/a420cced7e60fcf9ba6efcff3a2e8bee3ac67d05#diff-ab14255549624af556aa0d1dfaf83241a95e05c3dd6a668cd2607655839f7a09\\n using (torch.no_grad()) {\\n waveform = torch.nn.functional.pad(waveform, new long[] { pad, pad }, PaddingModes.Constant);\\n }\\n }\\n\\n // pack batch\\n var shape = waveform.size();\\n waveform = waveform.reshape(-1, shape[shape.Length - 1]);\\n\\n // default values are consistent with librosa.core.spectrum._spectrogram\\n var spec_f = torch.stft(\\n input: waveform,\\n n_fft: n_fft,\\n hop_length: hop_length,\\n win_length: win_length,\\n window: window,\\n center: center,\\n pad_mode: pad_mode,\\n normalized: false,\\n onesided: onesided,\\n return_complex: true);\\n\\n // unpack batch\\n var spec_shape = new long[shape.Length + spec_f.dim() - 2];\\n Array.Copy(shape, spec_shape, shape.Length - 1);\\n Array.Copy(spec_f.shape, 1, spec_shape, shape.Length - 1, spec_f.dim() - 1);\\n spec_f = spec_f.reshape(spec_shape);\\n\\n if (normalized) {\\n spec_f /= window.pow(2.0).sum().sqrt();\\n }\\n\\n if (power.HasValue) {\\n if (power.Value == 1.0) {\\n spec_f = spec_f.abs();\\n } else {\\n spec_f = spec_f.abs().pow(power.Value);\\n }\\n }\\n\\n return d.MoveToOuter(spec_f);\\n }\\n }\\n\\n /// <summary>\\n /// Compute inverse of spectrogram.\\n /// </summary>\\n /// <param name=\\\"spectrogram\\\">The spectrogram tensor</param>\\n /// <param name=\\\"length\\\">The length of the output tensor</param>\\n /// <param name=\\\"pad\\\">Padding on the sides</param>\\n /// <param name=\\\"window\\\">The window function</param>\\n /// <param name=\\\"n_fft\\\">The size of Fourier transform</param>\\n /// <param name=\\\"hop_length\\\">The hop length</param>\\n /// <param name=\\\"win_length\\\">The window length</param>\\n /// <param name=\\\"normalized\\\">Whether the output is normalized, or not.</param>\\n /// <param name=\\\"center\\\">Whether the t-th frame is centered around t * hop_window, or not.</param>\\n /// <param name=\\\"pad_mode\\\">The padding mode used when center is true.</param>\\n /// <param name=\\\"onesided\\\">Whether the output is onesided or not.</param>\\n /// <returns>Inverse of spectrogram</returns>\\n public static torch.Tensor inverse_spectrogram(torch.Tensor spectrogram, long? length, long pad, torch.Tensor window, long n_fft, long hop_length, long win_length, bool normalized, bool center = true, PaddingModes pad_mode = PaddingModes.Reflect, bool onesided = true)\\n {\\n if (!spectrogram.is_complex()) {\\n throw new ArgumentException(\\\"Expected `spectrogram` to be complex dtype.\\\");\\n }\\n\\n using (var d = torch.NewDisposeScope()) {\\n\\n if (normalized) {\\n spectrogram = spectrogram * window.pow(2.0).sum().sqrt();\\n }\\n\\n // pack batch\\n var shape = spectrogram.size();\\n spectrogram = spectrogram.reshape(-1, shape[shape.Length - 2], shape[shape.Length - 1]);\\n\\n // default values are consistent with librosa.core.spectrum._spectrogram\\n var waveform = torch.istft(\\n input: spectrogram,\\n n_fft: n_fft,\\n hop_length: hop_length,\\n win_length: win_length,\\n window: window,\\n center: center,\\n normalized: false,\\n onesided: onesided,\\n length: length.HasValue ? length.Value + 2 * pad : -1,\\n return_complex: false\\n );\\n\\n if (length.HasValue && pad > 0) {\\n // remove padding from front and back\\n waveform = waveform[TensorIndex.Colon, TensorIndex.Slice(pad, -pad)];\\n }\\n\\n // unpack batch\\n var waveform_shape = new long[shape.Length - 1];\\n Array.Copy(shape, waveform_shape, shape.Length - 2);\\n waveform_shape[waveform_shape.Length - 1] = waveform.shape[waveform.dim() - 1];\\n waveform = waveform.reshape(waveform_shape);\\n\\n return d.MoveToOuter(waveform);\\n }\\n }\\n\\n private static ScalarType _get_complex_dtype(torch.ScalarType real_dtype)\\n {\\n if (real_dtype == ScalarType.Float64)\\n return ScalarType.ComplexFloat64;\\n if (real_dtype == ScalarType.Float32)\\n return ScalarType.ComplexFloat32;\\n if (real_dtype == ScalarType.Float16)\\n return ScalarType.ComplexFloat32;\\n throw new ArgumentException($\\\"Unexpected dtype {real_dtype}\\\");\\n }\\n\\n /// <summary>\\n /// Compute waveform from a linear scale magnitude spectrogram using the Griffin-Lim transformation.\\n /// </summary>\\n /// <param name=\\\"specgram\\\">A magnitude-only STFT spectrogram of dimension `(..., freq, frames)` where freq is ``n_fft // 2 + 1``.</param>\\n /// <param name=\\\"window\\\">Window tensor that is applied/multiplied to each frame/window</param>\\n /// <param name=\\\"n_fft\\\">Size of FFT, creates ``n_fft // 2 + 1`` bins</param>\\n /// <param name=\\\"hop_length\\\">Length of hop between STFT windows.</param>\\n /// <param name=\\\"win_length\\\">Window size.</param>\\n /// <param name=\\\"power\\\">Exponent for the magnitude spectrogram, (must be > 0) e.g., 1 for energy, 2 for power, etc.</param>\\n /// <param name=\\\"n_iter\\\">Number of iteration for phase recovery process.</param>\\n /// <param name=\\\"momentum\\\">The momentum parameter for fast Griffin-Lim.</param>\\n /// <param name=\\\"length\\\">Array length of the expected output.</param>\\n /// <param name=\\\"rand_init\\\">Initializes phase randomly if True, to zero otherwise.</param>\\n /// <returns></returns>\\n /// <exception cref=\\\"ArgumentOutOfRangeException\\\"></exception>\\n public static Tensor griffinlim(Tensor specgram, Tensor window, long n_fft, long hop_length, long win_length, double power, int n_iter, double momentum, long? length, bool rand_init)\\n {\\n if (momentum < 0.0 || 1.0 <= momentum) {\\n throw new ArgumentOutOfRangeException($\\\"momentum must be in range [0, 1). Found: {momentum}\\\");\\n }\\n momentum = momentum / (1 + momentum);\\n\\n // pack batch\\n var shape = specgram.size();\\n specgram = specgram.reshape(new long[] { -1, shape[shape.Length - 2], shape[shape.Length - 1] });\\n\\n specgram = specgram.pow(1 / power);\\n\\n // initialize the phase\\n Tensor angles;\\n if (rand_init) {\\n angles = torch.rand(specgram.size(), dtype: _get_complex_dtype(specgram.dtype), device: specgram.device);\\n } else {\\n angles = torch.full(specgram.size(), 1, dtype: _get_complex_dtype(specgram.dtype), device: specgram.device);\\n }\\n\\n // And initialize the previous iterate to 0\\n var tprev = torch.tensor(0.0, dtype: specgram.dtype, device: specgram.device);\\n for (int i = 0; i < n_iter; i++) {\\n // Invert with our current estimate of the phases\\n var inverse = torch.istft(\\n specgram * angles, n_fft: n_fft, hop_length: hop_length, win_length: win_length, window: window, length: length ?? -1\\n );\\n\\n // Rebuild the spectrogram\\n var rebuilt = torch.stft(\\n input: inverse,\\n n_fft: n_fft,\\n hop_length: hop_length,\\n win_length: win_length,\\n window: window,\\n center: true,\\n pad_mode: PaddingModes.Reflect,\\n normalized: false,\\n onesided: true,\\n return_complex: true);\\n\\n // Update our phase estimates\\n angles = rebuilt;\\n if (momentum > 0.0) {\\n angles = angles - tprev.mul_(momentum);\\n }\\n angles = angles.div(angles.abs().add(1e-16));\\n\\n // Store the previous iterate\\n tprev = rebuilt;\\n }\\n\\n // Return the final phase estimates\\n var waveform = torch.istft(\\n specgram * angles, n_fft: n_fft, hop_length: hop_length, win_length: win_length, window: window, length: length ?? -1\\n );\\n\\n // unpack batch\\n var new_shape = new long[shape.Length - 1];\\n Array.Copy(shape, new_shape, shape.Length - 2);\\n new_shape[new_shape.Length - 1] = waveform.shape[waveform.dim() - 1];\\n waveform = waveform.reshape(new_shape);\\n\\n return waveform;\\n }\\n\\n /// <summary>\\n /// Turn a spectrogram from the power/amplitude scale to the decibel scale.\\n /// </summary>\\n /// <param name=\\\"x\\\">Input spectrogram(s) before being converted to decibel scale.</param>\\n /// <param name=\\\"multiplier\\\">Use 10. for power and 20. for amplitude</param>\\n /// <param name=\\\"amin\\\">Number to clamp x</param>\\n /// <param name=\\\"db_multiplier\\\">Log10(max(reference value and amin))</param>\\n /// <param name=\\\"top_db\\\">Minimum negative cut-off in decibels.</param>\\n /// <returns>Output tensor in decibel scale</returns>\\n public static Tensor amplitude_to_DB(Tensor x, double multiplier, double amin, double db_multiplier, double? top_db = null)\\n {\\n var x_db = multiplier * torch.log10(torch.clamp(x, min: amin));\\n x_db -= multiplier * db_multiplier;\\n\\n if (top_db != null) {\\n // Expand batch\\n var shape = x_db.size();\\n var packed_channels = x_db.dim() > 2 ? shape[shape.Length - 3] : 1;\\n x_db = x_db.reshape(-1, packed_channels, shape[shape.Length - 2], shape[shape.Length - 1]);\\n\\n x_db = torch.maximum(x_db, (x_db.amax(dims: new long[] { -3, -2, -1 }) - top_db).view(-1, 1, 1, 1));\\n\\n // Repack batch\\n x_db = x_db.reshape(shape);\\n }\\n return x_db;\\n }\\n\\n /// <summary>\\n /// Turn a tensor from the decibel scale to the power/amplitude scale.\\n /// </summary>\\n /// <param name=\\\"x\\\">Input tensor before being converted to power/amplitude scale.</param>\\n /// <param name=\\\"ref\\\">Reference which the output will be scaled by.</param>\\n /// <param name=\\\"power\\\">If power equals 1, will compute DB to power. If 0.5, will compute DB to amplitude.</param>\\n /// <returns>Output tensor in power/amplitude scale.</returns>\\n public static Tensor DB_to_amplitude(Tensor x, double @ref, double power)\\n {\\n return @ref * torch.pow(torch.pow(10.0, 0.1 * x), power);\\n }\\n\\n private static double _hz_to_mel(double freq, MelScale mel_scale = MelScale.htk)\\n {\\n if (mel_scale == MelScale.htk) {\\n return 2595.0 * Math.Log10(1.0 + freq / 700.0);\\n }\\n\\n // Fill in the linear part\\n var f_min = 0.0;\\n var f_sp = 200.0 / 3;\\n\\n var mels = (freq - f_min) / f_sp;\\n\\n // Fill in the log-scale part\\n var min_log_hz = 1000.0;\\n var min_log_mel = (min_log_hz - f_min) / f_sp;\\n var logstep = Math.Log(6.4) / 27.0;\\n\\n if (freq >= min_log_hz) {\\n mels = min_log_mel + Math.Log(freq / min_log_hz) / logstep;\\n }\\n\\n return mels;\\n }\\n\\n private static Tensor _mel_to_hz(Tensor mels, MelScale mel_scale = MelScale.htk)\\n {\\n if (mel_scale == MelScale.htk) {\\n return 700.0 * (torch.pow(10.0, mels / 2595.0) - 1.0);\\n }\\n\\n // Fill in the linear scale\\n var f_min = 0.0;\\n var f_sp = 200.0 / 3;\\n\\n var freqs = f_min + f_sp * mels;\\n\\n // And now the nonlinear scale\\n var min_log_hz = 1000.0;\\n var min_log_mel = (min_log_hz - f_min) / f_sp;\\n var logstep = Math.Log(6.4) / 27.0;\\n\\n var log_t = mels >= min_log_mel;\\n freqs[log_t] = min_log_hz * torch.exp(logstep * (mels[log_t] - min_log_mel));\\n\\n return freqs;\\n }\\n\\n private static Tensor _create_triangular_filterbank(Tensor all_freqs, Tensor f_pts)\\n {\\n // Adopted from Librosa\\n // calculate the difference between each filter mid point and each stft freq point in hertz\\n var f_diff = f_pts[TensorIndex.Slice(1, null)] - f_pts[TensorIndex.Slice(null, -1)]; // (n_filter + 1)\\n var slopes = f_pts.unsqueeze(0) - all_freqs.unsqueeze(1); // (n_freqs, n_filter + 2)\\n // create overlapping triangles\\n var zero = torch.zeros(1);\\n var down_slopes = (-1.0 * slopes[TensorIndex.Colon, TensorIndex.Slice(null, -2)]) / f_diff[TensorIndex.Slice(null, -1)]; // (n_freqs, n_filter)\\n var up_slopes = slopes[TensorIndex.Colon, TensorIndex.Slice(2, null)] / f_diff[TensorIndex.Slice(1, null)]; // (n_freqs, n_filter)\\n var fb = torch.maximum(zero, torch.minimum(down_slopes, up_slopes));\\n\\n return fb;\\n }\\n\\n /// <summary>\\n /// Create a frequency bin conversion matrix.\\n /// </summary>\\n /// <param name=\\\"n_freqs\\\">Number of frequencies to highlight/apply</param>\\n /// <param name=\\\"f_min\\\">Minimum frequency(Hz)</param>\\n /// <param name=\\\"f_max\\\">Maximum frequency(Hz)</param>\\n /// <param name=\\\"n_mels\\\">Number of mel filterbanks</param>\\n /// <param name=\\\"sample_rate\\\">Sample rate of the audio waveform</param>\\n /// <param name=\\\"norm\\\">If MelNorm.slaney, divide the triangular mel weights by the width of the mel band</param>\\n /// <param name=\\\"mel_scale\\\">Scale to use</param>\\n /// <returns>Triangular filter banks</returns>\\n public static Tensor melscale_fbanks(long n_freqs, double f_min, double f_max, long n_mels, long sample_rate, MelNorm norm = MelNorm.none, MelScale mel_scale = MelScale.htk)\\n {\\n // freq bins\\n var all_freqs = torch.linspace(0, sample_rate / 2, n_freqs);\\n\\n // calculate mel freq bins\\n var m_min = _hz_to_mel(f_min, mel_scale: mel_scale);\\n var m_max = _hz_to_mel(f_max, mel_scale: mel_scale);\\n\\n var m_pts = torch.linspace(m_min, m_max, n_mels + 2);\\n var f_pts = _mel_to_hz(m_pts, mel_scale: mel_scale);\\n\\n // create filterbank\\n var fb = _create_triangular_filterbank(all_freqs, f_pts);\\n\\n if (norm == MelNorm.slaney) {\\n // Slaney-style mel is scaled to be approx constant energy per channel\\n var enorm = 2.0 / (f_pts[TensorIndex.Slice(2, n_mels + 2)] - f_pts[TensorIndex.Slice(null, n_mels)]);\\n fb *= enorm.unsqueeze(0);\\n }\\n\\n if ((fb.max(dim: 0).values == 0.0).any().item<bool>()) {\\n Debug.Print(\\n \\\"At least one mel filterbank has all zero values. \\\" +\\n $\\\"The value for `n_mels` ({n_mels}) may be set too high. \\\" +\\n $\\\"Or, the value for `n_freqs` ({n_freqs}) may be set too low.\\\"\\n );\\n }\\n\\n return fb;\\n }\\n\\n /// <summary>\\n /// Creates a linear triangular filterbank.\\n /// </summary>\\n /// <param name=\\\"n_freqs\\\">Number of frequencies to highlight/apply</param>\\n /// <param name=\\\"f_min\\\">Minimum frequency (Hz)</param>\\n /// <param name=\\\"f_max\\\">Maximum frequency (Hz)</param>\\n /// <param name=\\\"n_filter\\\">Number of (linear) triangular filter</param>\\n /// <param name=\\\"sample_rate\\\">Sample rate of the audio waveform</param>\\n /// <returns>Triangular filter banks</returns>\\n public static Tensor linear_fbanks(int n_freqs, double f_min, double f_max, int n_filter, int sample_rate)\\n {\\n // freq bins\\n var all_freqs = torch.linspace(0, sample_rate / 2, n_freqs);\\n\\n // filter mid-points\\n var f_pts = torch.linspace(f_min, f_max, n_filter + 2);\\n\\n // create filterbank\\n var fb = _create_triangular_filterbank(all_freqs, f_pts);\\n\\n return fb;\\n }\\n\\n /// <summary>\\n /// Create a DCT transformation matrix with shape (``n_mels``, ``n_mfcc``)\\n /// </summary>\\n /// <param name=\\\"n_mfcc\\\">Number of mfc coefficients to retain</param>\\n /// <param name=\\\"n_mels\\\">Number of mel filterbanks</param>\\n /// <param name=\\\"norm\\\">Norm to use</param>\\n /// <returns>The transformation matrix</returns>\\n public static Tensor create_dct(int n_mfcc, int n_mels, DCTNorm norm)\\n {\\n // http://en.wikipedia.org/wiki/Discrete_cosine_transform#DCT-II\\n var n = torch.arange((float)n_mels);\\n var k = torch.arange((float)n_mfcc).unsqueeze(1);\\n var dct = torch.cos(Math.PI / n_mels * (n + 0.5) * k);\\n\\n if (norm == DCTNorm.none) {\\n dct *= 2.0;\\n } else {\\n dct[0] *= 1.0 / Math.Sqrt(2.0);\\n dct *= Math.Sqrt(2.0 / n_mels);\\n }\\n return dct.t();\\n }\\n\\n /// <summary>\\n /// Encode signal based on mu-law companding.\\n /// </summary>\\n /// <param name=\\\"x\\\">Input tensor</param>\\n /// <param name=\\\"quantization_channels\\\">Number of channels</param>\\n /// <returns>Input after mu-law encoding</returns>\\n /// <exception cref=\\\"ArgumentException\\\"></exception>\\n public static Tensor mu_law_encoding(Tensor x, int quantization_channels)\\n {\\n if (!x.is_floating_point()) {\\n throw new ArgumentException(\\\"The input Tensor must be of floating type. \\\");\\n }\\n var mu = torch.tensor(quantization_channels - 1.0, dtype: x.dtype);\\n var x_mu = torch.sign(x) * torch.log1p(mu * torch.abs(x)) / torch.log1p(mu);\\n x_mu = ((x_mu + 1) / 2 * mu + 0.5).to(torch.int64);\\n return x_mu;\\n }\\n\\n /// <summary>\\n /// Decode mu-law encoded signal.\\n /// </summary>\\n /// <param name=\\\"x_mu\\\">Input tensor</param>\\n /// <param name=\\\"quantization_channels\\\">Number of channels</param>\\n /// <returns>Input after mu-law decoding</returns>\\n public static Tensor mu_law_decoding(Tensor x_mu, int quantization_channels)\\n {\\n if (!x_mu.is_floating_point()) {\\n x_mu = x_mu.to(torch.@float);\\n }\\n var mu = torch.tensor(quantization_channels - 1.0, dtype: x_mu.dtype);\\n var x = x_mu / mu * 2 - 1.0;\\n x = torch.sign(x) * (torch.exp(torch.abs(x) * torch.log1p(mu)) - 1.0) / mu;\\n return x;\\n }\\n\\n /// <summary>\\n /// Resample the waveform\\n /// </summary>\\n /// <param name=\\\"waveform\\\">The input waveform</param>\\n /// <param name=\\\"orig_freq\\\">The source sampling rate</param>\\n /// <param name=\\\"new_freq\\\">The destination sampling rate</param>\\n /// <param name=\\\"lowpass_filter_width\\\">The width of the filter</param>\\n /// <param name=\\\"rolloff\\\">The roll-off frequency</param>\\n /// <param name=\\\"resampling_method\\\">The resampling method</param>\\n /// <param name=\\\"beta\\\">Beta for Keizer window</param>\\n /// <returns>The resampled waveform</returns>\\n /// <exception cref=\\\"ArgumentOutOfRangeException\\\"></exception>\\n public static torch.Tensor resample(torch.Tensor waveform, int orig_freq, int new_freq, int lowpass_filter_width = 6, double rolloff = 0.99, ResamplingMethod resampling_method = ResamplingMethod.sinc_interpolation, double? beta = null)\\n {\\n if (orig_freq <= 0 || new_freq <= 0) {\\n throw new ArgumentOutOfRangeException();\\n }\\n\\n using (var d = torch.NewDisposeScope()) {\\n if (orig_freq == new_freq) {\\n return d.MoveToOuter(waveform.alias());\\n }\\n\\n int gcd = Gcd(orig_freq, new_freq);\\n\\n var (kernel, width) = _get_sinc_resample_kernel(\\n orig_freq,\\n new_freq,\\n gcd,\\n lowpass_filter_width,\\n rolloff,\\n resampling_method,\\n beta,\\n waveform.device,\\n waveform.dtype);\\n var resampled = _apply_sinc_resample_kernel(waveform, orig_freq, new_freq, gcd, kernel, width);\\n return d.MoveToOuter(resampled);\\n }\\n }\\n\\n internal static int Gcd(int a, int b)\\n {\\n if (a <= 0 || b <= 0) {\\n throw new ArgumentOutOfRangeException();\\n }\\n\\n while (b > 1) {\\n (a, b) = (b, a % b);\\n }\\n\\n return a;\\n }\\n\\n internal static (torch.Tensor, int) _get_sinc_resample_kernel(int orig_freq, int new_freq, int gcd, int lowpass_filter_width = 6, double rolloff = 0.99, ResamplingMethod resampling_method = ResamplingMethod.sinc_interpolation, double? beta = null, torch.Device device = null, torch.ScalarType? dtype = null)\\n {\\n orig_freq = orig_freq / gcd;\\n new_freq = new_freq / gcd;\\n\\n if (lowpass_filter_width <= 0) {\\n throw new ArgumentOutOfRangeException();\\n }\\n\\n var kernels_list = new List<torch.Tensor>();\\n double base_freq = Math.Min(orig_freq, new_freq);\\n base_freq *= rolloff;\\n\\n var width = (int)Math.Ceiling(((double)lowpass_filter_width) * orig_freq / base_freq);\\n var idx_dtype = dtype ?? torch.float64;\\n var idx = torch.arange(-width, width + orig_freq, device: device, dtype: idx_dtype);\\n\\n for (int i = 0; i < new_freq; i++) {\\n var t = (-i / new_freq + idx / orig_freq) * base_freq;\\n t = t.clamp_(-lowpass_filter_width, lowpass_filter_width);\\n\\n torch.Tensor window;\\n if (resampling_method == ResamplingMethod.sinc_interpolation) {\\n window = torch.square(torch.cos(t * Math.PI / lowpass_filter_width / 2));\\n } else {\\n // kaiser_window\\n if (!beta.HasValue) {\\n beta = 14.769656459379492;\\n }\\n var beta_tensor = torch.tensor(beta.Value);\\n window = torch.special.i0(beta_tensor * torch.sqrt(1 - torch.square(t / lowpass_filter_width))) / torch.special.i0(beta_tensor);\\n }\\n t *= Math.PI;\\n // Tensor.to(Tensor) of TorchSharp desn't change dtype.\\n var kernel = torch.where(t == 0, torch.tensor(1.0).to(t).type_as(t), torch.sin(t) / t);\\n kernel.mul_(window);\\n kernels_list.Add(kernel);\\n }\\n\\n var scale = ((double)base_freq) / orig_freq;\\n var kernels = torch.stack(kernels_list.ToArray()).view(new_freq, 1, -1).mul_(scale);\\n if (dtype == null) {\\n kernels = kernels.to(torch.float32);\\n }\\n return (kernels, width);\\n }\\n\\n internal static torch.Tensor _apply_sinc_resample_kernel(torch.Tensor waveform, int orig_freq, int new_freq, int gcd, torch.Tensor kernel, int width)\\n {\\n if (!waveform.is_floating_point()) {\\n throw new ArgumentException($\\\"Expected floating point type for waveform tensor, but received {waveform.dtype}.\\\");\\n }\\n\\n orig_freq = orig_freq / gcd;\\n new_freq = new_freq / gcd;\\n\\n // pack batch\\n var shape = waveform.size();\\n waveform = waveform.view(-1, shape[waveform.dim() - 1]);\\n\\n var num_wavs = waveform.shape[0];\\n var length = waveform.shape[1];\\n\\n waveform = torch.nn.functional.pad(waveform, (width, width + orig_freq));\\n var resampled = torch.nn.functional.conv1d(waveform.unsqueeze(1), kernel, stride: orig_freq);\\n resampled = resampled.transpose(1, 2).reshape(num_wavs, -1);\\n int target_length = (int)Math.Ceiling(((double)new_freq) * length / orig_freq);\\n resampled = resampled[TensorIndex.Ellipsis, TensorIndex.Slice(0, target_length)];\\n\\n // unpack batch\\n shape[shape.Length - 1] = resampled.shape[resampled.dim() - 1];\\n resampled = resampled.view(shape);\\n return resampled;\\n }\\n }\\n }\\n}\"\n },\n {\n \"alpha_fraction\": 0.5502307415008545,\n \"alphanum_fraction\": 0.5512956976890564,\n \"avg_line_length\": 39.826087951660156,\n \"blob_id\": \"3db6efef8640e9c424e98f2384dd2cf400fe4428\",\n \"content_id\": \"e6d3f120a3326d8e31c29798c867fef4f4e3138c\",\n \"detected_licenses\": [\n \"MIT\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"C#\",\n \"length_bytes\": 2817,\n \"license_type\": \"permissive\",\n \"max_line_length\": 193,\n \"num_lines\": 69,\n \"path\": \"/src/TorchSharp/NN/PixelUnshuffle.cs\",\n \"repo_name\": \"NiklasGustafsson/TorchSharp\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"// Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information.\\nusing System;\\nusing static TorchSharp.torch;\\nusing static TorchSharp.PInvoke.NativeMethods;\\n\\nnamespace TorchSharp\\n{\\n using Modules;\\n\\n namespace Modules\\n {\\n /// <summary>\\n /// This class is used to represent a dropout module.\\n /// </summary>\\n public sealed class PixelUnshuffle : torch.nn.Module<Tensor, Tensor>\\n {\\n internal PixelUnshuffle(IntPtr handle, IntPtr boxedHandle) : base(handle, boxedHandle) { }\\n\\n /// <summary>\\n /// Forward pass.\\n /// </summary>\\n /// <param name=\\\"tensor\\\">Input tensor</param>\\n /// <returns></returns>\\n public override Tensor forward(Tensor tensor)\\n {\\n var res = THSNN_PixelUnshuffle_forward(handle, tensor.Handle);\\n if (res == IntPtr.Zero) { torch.CheckForErrors(); }\\n return new Tensor(res);\\n }\\n }\\n }\\n\\n public static partial class torch\\n {\\n public static partial class nn\\n {\\n\\n /// <summary>\\n /// Reverses the PixelShuffle operation by rearranging elements in a tensor of shape (*, C, H * r, W * r) to a tensor of shape (*, C * r^2, H, W), where r is an downscale factor.\\n /// </summary>\\n /// <param name=\\\"downscaleFactor\\\">Factor to increase spatial resolution by</param>\\n /// <returns></returns>\\n public static PixelUnshuffle PixelUnshuffle(long downscaleFactor)\\n {\\n var handle = THSNN_PixelUnshuffle_ctor(downscaleFactor, out var boxedHandle);\\n if (handle == IntPtr.Zero) { torch.CheckForErrors(); }\\n return new PixelUnshuffle(handle, boxedHandle);\\n }\\n\\n public static partial class functional\\n {\\n /// <summary>\\n /// Reverses the PixelShuffle operation by rearranging elements in a tensor of shape (*, C * r^2, H, W) to a tensor of shape(*, C, H * r, W * r), where r is an downscale factor.\\n /// This is useful for implementing efficient sub-pixel convolution with a stride of 1/r.\\n /// </summary>\\n /// <param name=\\\"x\\\">Input tensor</param>\\n /// <param name=\\\"downscaleFactor\\\">Factor to increase spatial resolution by</param>\\n /// <returns></returns>\\n /// <returns></returns>\\n public static Tensor pixel_unshuffle(Tensor x, long downscaleFactor)\\n {\\n using (var d = nn.PixelUnshuffle(downscaleFactor)) {\\n return d.call(x);\\n }\\n }\\n }\\n }\\n }\\n}\\n\"\n },\n {\n \"alpha_fraction\": 0.6845647096633911,\n \"alphanum_fraction\": 0.7034650444984436,\n \"avg_line_length\": 20.426380157470703,\n \"blob_id\": \"2cc99c44c7be80d3c878641ec9ecbf45d26ef6aa\",\n \"content_id\": \"b846557bc05ce1a8a6bb4a97b9a8a32c1f708331\",\n \"detected_licenses\": [\n \"MIT\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"C++\",\n \"length_bytes\": 6984,\n \"license_type\": \"permissive\",\n \"max_line_length\": 130,\n \"num_lines\": 326,\n \"path\": \"/src/Native/LibTorchSharp/THSTorch.cpp\",\n \"repo_name\": \"NiklasGustafsson/TorchSharp\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"// Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information.\\n#include \\\"THSTorch.h\\\"\\n\\n#include \\\"torch/torch.h\\\"\\n#include \\\"torch/cuda.h\\\"\\n\\nvoid THSTorch_manual_seed(const int64_t seed)\\n{\\n torch::manual_seed(seed);\\n}\\n\\nGenerator THSGenerator_manual_seed(const int64_t seed)\\n{\\n auto gen = at::globalContext().defaultGenerator(at::DeviceType::CPU);\\n gen.set_current_seed(seed);\\n return new at::Generator(gen.getIntrusivePtr());\\n}\\n\\nvoid THSCuda_manual_seed(const int64_t seed)\\n{\\n CATCH(torch::cuda::manual_seed(seed);)\\n}\\n\\nvoid THSCuda_manual_seed_all(const int64_t seed)\\n{\\n CATCH(torch::cuda::manual_seed_all(seed);)\\n}\\n\\nbool THSBackend_cublas_get_allow_tf32()\\n{\\n auto result = false;\\n CATCH(result = at::globalContext().allowTF32CuBLAS(););\\n return result;\\n}\\n\\nvoid THSBackend_cublas_set_allow_tf32(const bool flag)\\n{\\n CATCH(at::globalContext().setAllowTF32CuBLAS(flag););\\n}\\n\\nbool THSBackend_cudnn_get_allow_tf32()\\n{\\n auto result = false;\\n CATCH(result = at::globalContext().allowTF32CuDNN(););\\n return result;\\n}\\n\\nvoid THSBackend_cudnn_set_allow_tf32(const bool flag)\\n{\\n CATCH(at::globalContext().setAllowTF32CuDNN(flag););\\n}\\n\\nbool THSBackend_cuda_get_allow_fp16_reduced_precision_reduction()\\n{\\n auto result = false;\\n CATCH(result = at::globalContext().allowFP16ReductionCuBLAS(););\\n return result;\\n}\\n\\nvoid THSBackend_cuda_set_allow_fp16_reduced_precision_reduction(const bool flag)\\n{\\n CATCH(at::globalContext().setAllowFP16ReductionCuBLAS(flag););\\n}\\n\\nbool THSBackend_cuda_get_enable_flash_sdp()\\n{\\n auto result = false;\\n CATCH(result = at::globalContext().userEnabledFlashSDP(););\\n return result;\\n}\\n\\nvoid THSBackend_cuda_set_enable_flash_sdp(const bool flag)\\n{\\n CATCH(at::globalContext().setSDPUseFlash(flag););\\n}\\n\\nbool THSBackend_cuda_get_enable_math_sdp()\\n{\\n auto result = false;\\n CATCH(result = at::globalContext().userEnabledMathSDP(););\\n return result;\\n}\\n\\nvoid THSBackend_cuda_set_enable_math_sdp(const bool flag)\\n{\\n CATCH(at::globalContext().setSDPUseMath(flag););\\n}\\n\\nvoid THSGenerator_gen_manual_seed(const Generator generator, const int64_t seed)\\n{\\n generator->set_current_seed(seed);\\n}\\n\\nGenerator THSGenerator_default_generator()\\n{\\n auto gen = at::globalContext().defaultGenerator(at::DeviceType::CPU);\\n return new at::Generator(gen.getIntrusivePtr());\\n}\\n\\nint64_t THSGenerator_initial_seed(const Generator gen)\\n{\\n return gen->current_seed();\\n}\\n\\nTensor THSGenerator_get_rng_state(const Generator gen)\\n{\\n CATCH_TENSOR(gen->get_state());\\n}\\n\\nvoid THSGenerator_set_rng_state(const Generator gen, const Tensor tensor)\\n{\\n gen->set_state(*tensor);\\n}\\n\\n\\nGenerator THSGenerator_new(uint64_t seed, int64_t device, int64_t index)\\n{\\n // TODO: Support creation of GPU RNGs. 'device' and 'index' are in the\\n // function signature in preparation thereof.\\n return new at::Generator(at::detail::createCPUGenerator(seed));\\n}\\n\\nvoid THSGenerator_dispose(const Generator generator)\\n{\\n delete generator;\\n}\\n\\nint THSTorchCuda_is_available()\\n{\\n return torch::cuda::is_available();\\n}\\n\\nint THSTorchCuda_cudnn_is_available()\\n{\\n return torch::cuda::cudnn_is_available();\\n}\\n\\nint THSTorchCuda_device_count()\\n{\\n return (int)torch::cuda::device_count();\\n}\\n\\nvoid THSTorchCuda_synchronize(const int64_t device_index)\\n{\\n CATCH(torch::cuda::synchronize(device_index);)\\n}\\n\\n\\nconst char * THSTorch_get_and_reset_last_err()\\n{\\n char *tmp = torch_last_err;\\n torch_last_err = nullptr;\\n return tmp;\\n}\\n\\nint THSTorch_get_num_threads()\\n{\\n CATCH_RETURN_RES(int, -1, res = torch::get_num_threads());\\n}\\n\\nvoid THSTorch_set_num_threads(const int threads)\\n{\\n torch::set_num_threads(threads);\\n}\\n\\nint THSTorch_get_num_interop_threads()\\n{\\n CATCH_RETURN_RES(int, -1, res = torch::get_num_interop_threads());\\n}\\n\\nvoid THSTorch_set_num_interop_threads(const int threads)\\n{\\n torch::set_num_interop_threads(threads);\\n}\\n\\nint THSTorch_can_cast(const int type1, const int type2)\\n{\\n CATCH_RETURN_RES(int, -1, res = (int)torch::can_cast((c10::ScalarType)type1, (c10::ScalarType)type2));\\n}\\n\\nint THSTorch_promote_types(const int type1, const int type2)\\n{\\n CATCH_RETURN_RES(int, -1, res = (int)torch::promote_types((c10::ScalarType)type1, (c10::ScalarType)type2));\\n}\\n\\n\\nScalar THSTorch_int8_to_scalar(int8_t value)\\n{\\n return new torch::Scalar(value);\\n}\\n\\nScalar THSTorch_uint8_to_scalar(uint8_t value)\\n{\\n return new torch::Scalar(value);\\n}\\n\\nScalar THSTorch_int16_to_scalar(short value)\\n{\\n return new torch::Scalar(value);\\n}\\n\\nScalar THSTorch_int32_to_scalar(int value)\\n{\\n return new torch::Scalar(value);\\n}\\n\\nScalar THSTorch_int64_to_scalar(long value)\\n{\\n return new torch::Scalar(int64_t(value));\\n}\\n\\nScalar THSTorch_float32_to_scalar(float value)\\n{\\n return new torch::Scalar(value);\\n}\\n\\nScalar THSTorch_float64_to_scalar(double value)\\n{\\n return new torch::Scalar(value);\\n}\\n\\nScalar THSTorch_float16_to_scalar(float value)\\n{\\n return new torch::Scalar((c10::Half)value);\\n}\\n\\nScalar THSTorch_bfloat16_to_scalar(float value)\\n{\\n return new torch::Scalar((c10::BFloat16)value);\\n}\\n\\nScalar THSTorch_bool_to_scalar(bool value)\\n{\\n return new torch::Scalar(value);\\n}\\n\\nScalar THSTorch_complex32_to_scalar(float real, float imaginary)\\n{\\n return new torch::Scalar(c10::complex<float>(real, imaginary));\\n}\\n\\nScalar THSTorch_complex64_to_scalar(double real, double imaginary)\\n{\\n return new torch::Scalar(c10::complex<double>(real, imaginary));\\n}\\n\\nint8_t THSTorch_scalar_to_int8(Scalar value)\\n{\\n return value->toChar();\\n}\\n\\nuint8_t THSTorch_scalar_to_uint8(Scalar value)\\n{\\n return value->toByte();\\n}\\n\\nint16_t THSTorch_scalar_to_int16(Scalar value)\\n{\\n return value->toShort();\\n}\\n\\nint32_t THSTorch_scalar_to_int32(Scalar value)\\n{\\n return value->toInt();\\n}\\n\\nint64_t THSTorch_scalar_to_int64(Scalar value)\\n{\\n return value->toLong();\\n}\\n\\nfloat THSTorch_scalar_to_float32(Scalar value)\\n{\\n return value->toFloat();\\n}\\n\\ndouble THSTorch_scalar_to_float64(Scalar value)\\n{\\n return value->toDouble();\\n}\\n\\nvoid THSTorch_scalar_to_float16(Scalar value, unsigned short *res)\\n{\\n *res = value->toHalf().x;\\n}\\n\\nvoid THSTorch_scalar_to_complex32(Scalar value, float* (*allocator)(size_t length))\\n{\\n auto result = value->toComplexFloat();\\n auto space = allocator(2);\\n space[0] = result.real();\\n space[1] = result.imag();\\n}\\n\\nvoid THSTorch_scalar_to_complex64(Scalar value, double* (*allocator)(size_t length))\\n{\\n auto result = value->toComplexDouble();\\n auto space = allocator(2);\\n space[0] = result.real();\\n space[1] = result.imag();\\n}\\n\\nbool THSTorch_scalar_to_bool(Scalar value)\\n{\\n return value->toBool();\\n}\\n\\nint8_t THSTorch_scalar_type(Scalar value)\\n{\\n return (int8_t)value->type();\\n}\\n\\nvoid THSTorch_dispose_scalar(Scalar scalar)\\n{\\n delete scalar;\\n}\\n\\ndouble THSSpecial_erf_scalar(const double x)\\n{\\n return erf(x);\\n}\\n\\ndouble THSSpecial_erfc_scalar(const double x)\\n{\\n return erfc(x);\\n}\"\n },\n {\n \"alpha_fraction\": 0.5176693797111511,\n \"alphanum_fraction\": 0.5251898169517517,\n \"avg_line_length\": 55.82987594604492,\n \"blob_id\": \"b79b20c440ec1ec6fbeabd62d535f32071a5bfdb\",\n \"content_id\": \"144dab863ca37abb4052b8dedf942dadd4bde27d\",\n \"detected_licenses\": [\n \"MIT\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"C#\",\n \"length_bytes\": 13699,\n \"license_type\": \"permissive\",\n \"max_line_length\": 318,\n \"num_lines\": 241,\n \"path\": \"/src/TorchSharp/NN/Pooling/AvgPool2D.cs\",\n \"repo_name\": \"NiklasGustafsson/TorchSharp\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"// Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information.\\nusing System;\\nusing static TorchSharp.torch;\\nusing static TorchSharp.PInvoke.NativeMethods;\\n\\nnamespace TorchSharp\\n{\\n using Modules;\\n\\n namespace Modules\\n {\\n /// <summary>\\n /// This class is used to represent a AvgPool2D module.\\n /// </summary>\\n public sealed class AvgPool2d : torch.nn.Module<Tensor, Tensor>\\n {\\n internal AvgPool2d(IntPtr handle, IntPtr boxedHandle) : base(handle, boxedHandle)\\n {\\n }\\n\\n public override Tensor forward(Tensor tensor)\\n {\\n var res = THSNN_AvgPool2d_forward(handle.DangerousGetHandle(), tensor.Handle);\\n if (res == IntPtr.Zero) { torch.CheckForErrors(); }\\n return new Tensor(res);\\n }\\n\\n // Rather than spending cycles only to discover that this module has neither\\n // parameters nor buffers, just shortcut the move completely.\\n protected internal override nn.Module _to(Device device, ScalarType dtype) => this;\\n protected internal override nn.Module _to(DeviceType deviceType, int deviceIndex = -1) => this;\\n protected internal override nn.Module _to(ScalarType dtype) => this;\\n }\\n }\\n\\n public static partial class torch\\n {\\n public static partial class nn\\n {\\n /// <summary>\\n /// Applies a 2D average pooling over an input signal composed of several input planes.\\n /// </summary>\\n /// <param name=\\\"kernel_size\\\">The size of the window</param>\\n /// <param name=\\\"strides\\\">The stride of the window. Default value is kernel_size</param>\\n /// <param name=\\\"padding\\\">implicit zero padding to be added on both sides</param>\\n /// <param name=\\\"ceil_mode\\\">Whether to use ceil instead of floor to compute the output shape</param>\\n /// <param name=\\\"count_include_pad\\\">Whether to include the zero-padding in the averaging calculation</param>\\n /// <param name=\\\"divisor_override\\\">If specified, it will be used as divisor, otherwise size of the pooling region will be used</param>\\n public static unsafe AvgPool2d AvgPool2d(long[] kernel_size, long[] strides = null, long[] padding = null, bool ceil_mode = false, bool count_include_pad = true, long? divisor_override = null)\\n {\\n fixed (long* pkernelSize = kernel_size, pstrides = strides, ppadding = padding) {\\n var handle = THSNN_AvgPool2d_ctor((IntPtr)pkernelSize, kernel_size.Length, (IntPtr)pstrides, (strides == null ? 0 : strides.Length), (IntPtr)ppadding, (padding == null ? 0 : padding.Length), ceil_mode, count_include_pad, divisor_override.HasValue ? divisor_override.Value : 0, out var boxedHandle);\\n if (handle == IntPtr.Zero) { torch.CheckForErrors(); }\\n return new AvgPool2d(handle, boxedHandle);\\n }\\n }\\n\\n /// <summary>\\n /// Applies a 2D average pooling over an input signal composed of several input planes.\\n /// </summary>\\n /// <param name=\\\"kernel_size\\\">The size of the window</param>\\n /// <param name=\\\"stride\\\">The stride of the window.</param>\\n /// <param name=\\\"padding\\\">implicit zero padding to be added on both sides</param>\\n /// <param name=\\\"ceil_mode\\\">Whether to use ceil instead of floor to compute the output shape</param>\\n /// <param name=\\\"count_include_pad\\\">Whether to include the zero-padding in the averaging calculation</param>\\n /// <param name=\\\"divisor_override\\\">If specified, it will be used as divisor, otherwise size of the pooling region will be used</param>\\n public static unsafe AvgPool2d AvgPool2d((long,long) kernel_size, (long,long)? stride = null, (long,long)? padding = null, bool ceil_mode = false, bool count_include_pad = true, long? divisor_override = null)\\n {\\n long svalue1 = (stride == null) ? kernel_size.Item1 : stride.Value.Item1;\\n long svalue2 = (stride == null) ? kernel_size.Item2 : stride.Value.Item2;\\n\\n long pvalue1 = (padding == null) ? 0 : stride.Value.Item1;\\n long pvalue2 = (padding == null) ? 0 : stride.Value.Item2;\\n\\n long* pkernelSize = stackalloc long[2] { kernel_size.Item1, kernel_size.Item2 };\\n long* pstrides = stackalloc long[2] { svalue1, svalue2 };\\n long* ppadding = stackalloc long[2] { pvalue1, pvalue2 };\\n\\n var handle = THSNN_AvgPool2d_ctor((IntPtr)pkernelSize, 2, (IntPtr)pstrides, 2, (IntPtr)ppadding, 2, ceil_mode, count_include_pad, divisor_override.HasValue ? divisor_override.Value : 0, out var boxedHandle);\\n if (handle == IntPtr.Zero) { torch.CheckForErrors(); }\\n return new AvgPool2d(handle, boxedHandle);\\n }\\n\\n /// <summary>\\n /// Applies a 2D average pooling over an input signal composed of several input planes.\\n /// </summary>\\n /// <param name=\\\"kernel_size\\\">The size of the window</param>\\n /// <param name=\\\"stride\\\">The stride of the window.</param>\\n /// <param name=\\\"padding\\\">implicit zero padding to be added on both sides</param>\\n /// <param name=\\\"ceil_mode\\\">Whether to use ceil instead of floor to compute the output shape</param>\\n /// <param name=\\\"count_include_pad\\\">Whether to include the zero-padding in the averaging calculation</param>\\n /// <param name=\\\"divisor_override\\\">If specified, it will be used as divisor, otherwise size of the pooling region will be used</param>\\n public static unsafe AvgPool2d AvgPool2d(long kernel_size, long? stride = null, long? padding = null, bool ceil_mode = false, bool count_include_pad = true, long? divisor_override = null)\\n {\\n long svalue = (stride == null) ? kernel_size : stride.Value;\\n long pvalue = (padding == null) ? 0 : padding.Value;\\n\\n long* pkernelSize = stackalloc long[2] { kernel_size, kernel_size };\\n long* pstrides = stackalloc long[2] { svalue, svalue };\\n long* ppadding = stackalloc long[2] { pvalue, pvalue };\\n\\n var handle = THSNN_AvgPool2d_ctor((IntPtr)pkernelSize, 2, (IntPtr)pstrides, 2, (IntPtr)ppadding, 2, ceil_mode, count_include_pad, divisor_override.HasValue ? divisor_override.Value : 0, out var boxedHandle);\\n if (handle == IntPtr.Zero) { torch.CheckForErrors(); }\\n return new AvgPool2d(handle, boxedHandle);\\n }\\n\\n public static partial class functional\\n {\\n /// <summary>\\n /// Applies 2D average-pooling operation in kH × kW regions by step size sH * sW steps. The number of output features is equal to the number of input planes.\\n /// </summary>\\n /// <param name=\\\"input\\\">The input tensor.</param>\\n /// <param name=\\\"kernelSizes\\\"></param>\\n /// <param name=\\\"strides\\\"></param>\\n /// <param name=\\\"paddings\\\"></param>\\n /// <param name=\\\"ceil_mode\\\"></param>\\n /// <param name=\\\"count_include_pad\\\"></param>\\n /// <returns></returns>\\n public static Tensor avg_pool2d(Tensor input, long[] kernelSizes,\\n long[] strides = null,\\n long[] paddings = null,\\n bool ceil_mode = false,\\n bool count_include_pad = true)\\n {\\n strides = (strides == null) ? new long[] { 1 } : strides;\\n paddings = (paddings == null) ? new long[] { 0 } : paddings;\\n unsafe {\\n fixed (long* pkernelSize = kernelSizes, pstrides = strides, ppadding = paddings) {\\n var res =\\n THSTensor_avg_pool2d(input.Handle,\\n (IntPtr)pkernelSize, kernelSizes.Length,\\n (IntPtr)pstrides, strides.Length,\\n (IntPtr)ppadding, paddings.Length,\\n ceil_mode,\\n count_include_pad);\\n if (res == IntPtr.Zero) { torch.CheckForErrors(); }\\n return new Tensor(res);\\n }\\n }\\n }\\n\\n /// <summary>\\n /// Applies 2D average-pooling operation in kH × kW regions by step size sH * sW steps. The number of output features is equal to the number of input planes.\\n /// </summary>\\n /// <param name=\\\"input\\\">The input tensor.</param>\\n /// <param name=\\\"kernelSize\\\"></param>\\n /// <param name=\\\"stride\\\"></param>\\n /// <param name=\\\"padding\\\"></param>\\n /// <param name=\\\"ceil_mode\\\"></param>\\n /// <param name=\\\"count_include_pad\\\"></param>\\n /// <returns></returns>\\n public static unsafe Tensor avg_pool2d(Tensor input, long kernelSize,\\n long? stride = null,\\n long padding = 0,\\n bool ceil_mode = false,\\n bool count_include_pad = true)\\n {\\n long svalue = (stride == null) ? kernelSize : stride.Value;\\n\\n long* pkernelSize = stackalloc long[2] { kernelSize, kernelSize };\\n long* pstrides = stackalloc long[2] { svalue, svalue };\\n long* ppadding = stackalloc long[2] { padding, padding };\\n\\n var res =\\n THSTensor_avg_pool2d(input.Handle,\\n (IntPtr)pkernelSize, 2,\\n (IntPtr)pstrides, 2,\\n (IntPtr)ppadding, 2,\\n ceil_mode,\\n count_include_pad);\\n if (res == IntPtr.Zero) { torch.CheckForErrors(); }\\n return new Tensor(res);\\n }\\n\\n /// <summary>\\n /// Applies 2D average-pooling operation in kH × kW regions by step size sH * sW steps. The number of output features is equal to the number of input planes.\\n /// </summary>\\n /// <param name=\\\"input\\\">The input tensor.</param>\\n /// <param name=\\\"kernelSize\\\"></param>\\n /// <param name=\\\"stride\\\"></param>\\n /// <param name=\\\"padding\\\"></param>\\n /// <param name=\\\"ceil_mode\\\"></param>\\n /// <param name=\\\"count_include_pad\\\"></param>\\n /// <returns></returns>\\n public static unsafe Tensor avg_pool2d(Tensor input, (long, long) kernelSize,\\n (long, long)? stride = null,\\n (long, long)? padding = null,\\n bool ceil_mode = false,\\n bool count_include_pad = true)\\n {\\n long svalue1 = (stride == null) ? kernelSize.Item1 : stride.Value.Item1;\\n long svalue2 = (stride == null) ? kernelSize.Item2 : stride.Value.Item2;\\n long pvalue1 = padding != null ? padding.Value.Item1 : 0;\\n long pvalue2 = padding != null ? padding.Value.Item2 : 0;\\n\\n long* pstrides = stackalloc long[2] { svalue1, svalue2 };\\n long* ppadding = stackalloc long[2] { pvalue1, pvalue2 };\\n\\n long* pkernelSize = stackalloc long[2] { kernelSize.Item1, kernelSize.Item2 };\\n\\n var res =\\n THSTensor_avg_pool2d(input.Handle,\\n (IntPtr)pkernelSize, 2,\\n (IntPtr)pstrides, 2,\\n (IntPtr)ppadding, 2,\\n ceil_mode,\\n count_include_pad);\\n if (res == IntPtr.Zero) { torch.CheckForErrors(); }\\n return new Tensor(res);\\n }\\n\\n public static Tensor avg_pool2d_backward(Tensor input, Tensor originalInput,\\n long[] kernelSizes,\\n long[] strides = null,\\n long[] paddings = null,\\n bool ceil_mode = false,\\n bool count_include_pad = true,\\n long divisorOverride = 0)\\n {\\n strides = (strides == null) ? new long[] { 1 } : strides;\\n paddings = (paddings == null) ? new long[] { 0 } : paddings;\\n unsafe {\\n fixed (long* pkernelSize = kernelSizes, pstrides = strides, ppadding = paddings) {\\n var res =\\n THSTensor_avg_pool2d_backward(input.Handle, originalInput.Handle,\\n (IntPtr)pkernelSize, kernelSizes.Length,\\n (IntPtr)pstrides, strides.Length,\\n (IntPtr)ppadding, paddings.Length,\\n ceil_mode,\\n count_include_pad,\\n divisorOverride);\\n if (res == IntPtr.Zero) { torch.CheckForErrors(); }\\n return new Tensor(res);\\n }\\n }\\n }\\n\\n }\\n }\\n }\\n}\\n\"\n },\n {\n \"alpha_fraction\": 0.6172727942466736,\n \"alphanum_fraction\": 0.6174814105033875,\n \"avg_line_length\": 58.67219924926758,\n \"blob_id\": \"914304d966c8214bb42104e1f8d5ab4712c95758\",\n \"content_id\": \"e8fa5fbcab053d4cfa218e333a012d93663859d2\",\n \"detected_licenses\": [\n \"MIT\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"C#\",\n \"length_bytes\": 14431,\n \"license_type\": \"permissive\",\n \"max_line_length\": 205,\n \"num_lines\": 241,\n \"path\": \"/src/TorchSharp/Autograd.cs\",\n \"repo_name\": \"NiklasGustafsson/TorchSharp\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"// Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information.\\nusing System;\\nusing System.Linq;\\nusing System.Collections.Generic;\\nusing static TorchSharp.PInvoke.NativeMethods;\\n\\nnamespace TorchSharp\\n{\\n /// <summary>\\n /// Helper class, relying on IDisposable to implement block-based scoping of autograd settings.\\n /// </summary>\\n internal class AutoGradMode : IDisposable\\n {\\n private readonly bool _isPrevGrad;\\n\\n public AutoGradMode(bool enabled)\\n {\\n _isPrevGrad = THSAutograd_isGradEnabled();\\n THSAutograd_setGrad(enabled);\\n }\\n\\n public void Dispose()\\n {\\n Dispose(true);\\n GC.SuppressFinalize(this);\\n }\\n\\n public void Dispose(bool disposing)\\n {\\n if (disposing)\\n {\\n THSAutograd_setGrad(_isPrevGrad);\\n }\\n }\\n\\n public static bool IsEnabled { get => THSAutograd_isGradEnabled(); }\\n }\\n\\n /// <summary>\\n /// Helper class, relying on IDisposable to implement block-based scoping of anomaly settings.\\n /// </summary>\\n public class AnomalyMode : IDisposable\\n {\\n private readonly bool _isPrevGrad;\\n private readonly bool _shouldCheckNaN;\\n\\n public AnomalyMode(bool enabled, bool check_nan = true)\\n {\\n _isPrevGrad = THSAutograd_isAnomalyEnabled();\\n _shouldCheckNaN = THSAutograd_shouldCheckNaN();\\n THSAutograd_setAnomaly(enabled, check_nan);\\n }\\n\\n public void Dispose()\\n {\\n Dispose(true);\\n GC.SuppressFinalize(this);\\n }\\n\\n protected virtual void Dispose(bool disposing)\\n {\\n if (disposing) {\\n THSAutograd_setAnomaly(_isPrevGrad, _shouldCheckNaN);\\n }\\n }\\n\\n public static bool IsEnabled { get => THSAutograd_isAnomalyEnabled(); }\\n\\n public static bool ShouldCheckNaN { get => THSAutograd_shouldCheckNaN(); }\\n }\\n\\n public static partial class torch\\n {\\n public static partial class autograd\\n {\\n /// <summary>\\n /// Computes and returns the sum of gradients of outputs with respect to the inputs.\\n /// </summary>\\n /// <param name=\\\"outputs\\\">Outputs of the differentiated function.</param>\\n /// <param name=\\\"inputs\\\">Inputs w.r.t. which the gradient will be returned (and not accumulated into .grad)..</param>\\n /// <param name=\\\"grad_outputs\\\">\\n /// The “vector” in the Jacobian-vector product. Usually gradients w.r.t. each output.\\n /// Null values can be specified for scalar Tensors or ones that don’t require grad.\\n /// If a null value would be acceptable for all grad_tensors, then this argument is optional.\\n /// </param>\\n /// <param name=\\\"retain_graph\\\">\\n /// If false, the graph used to compute the grad will be freed.\\n /// Note that in nearly all cases setting this option to true is not needed and often can be worked around in a much more efficient way. Defaults to the value of create_graph.\\n /// </param>\\n /// <param name=\\\"create_graph\\\">\\n /// If true, graph of the derivative will be constructed, allowing to compute higher order derivative products.\\n /// </param>\\n /// <param name=\\\"allow_unused\\\">\\n /// If false, specifying inputs that were not used when computing outputs (and therefore their grad is always zero) is an error.\\n /// </param>\\n /// <returns></returns>\\n public static IList<Tensor> grad(IList<Tensor> outputs, IList<Tensor> inputs, IList<Tensor> grad_outputs = null, bool retain_graph = false, bool create_graph = false, bool allow_unused = false)\\n {\\n using var outs = new PinnedArray<IntPtr>();\\n using var ins = new PinnedArray<IntPtr>();\\n using var grads = new PinnedArray<IntPtr>();\\n using var results = new PinnedArray<IntPtr>();\\n\\n IntPtr outsRef = outs.CreateArray(outputs.Select(p => p.Handle).ToArray());\\n IntPtr insRef = ins.CreateArray(inputs.Select(p => p.Handle).ToArray());\\n IntPtr gradsRef = grad_outputs == null ? IntPtr.Zero : grads.CreateArray(grad_outputs.Select(p => p.Handle).ToArray());\\n long gradsLength = grad_outputs == null ? 0 : grads.Array.Length;\\n\\n THSAutograd_grad(outsRef, outs.Array.Length, insRef, ins.Array.Length, gradsRef, gradsLength, retain_graph, create_graph, allow_unused, results.CreateArray);\\n CheckForErrors();\\n return results.Array.Select(x => new Tensor(x)).ToList();\\n }\\n\\n /// <summary>\\n /// Computes the sum of gradients of given tensors with respect to graph leaves.\\n /// </summary>\\n /// <param name=\\\"tensors\\\">Tensors of which the derivative will be computed.</param>\\n /// <param name=\\\"grad_tensors\\\">\\n /// The “vector” in the Jacobian-vector product, usually gradients w.r.t. each element of corresponding tensors.\\n /// Null values can be specified for scalar Tensors or ones that don’t require grad.\\n /// If a null value would be acceptable for all grad_tensors, then this argument is optional.\\n /// </param>\\n /// <param name=\\\"retain_graph\\\">If false, the graph used to compute the grad will be freed.\\n /// Note that in nearly all cases setting this option to true is not needed and often can be worked around in a much more efficient way.\\n /// Defaults to the value of create_graph.</param>\\n /// <param name=\\\"create_graph\\\">If true, graph of the derivative will be constructed, allowing to compute higher order derivative products. Defaults to false.</param>\\n /// <param name=\\\"inputs\\\">\\n /// Inputs w.r.t. which the gradient be will accumulated into .grad. All other Tensors will be ignored.\\n /// If not provided, the gradient is accumulated into all the leaf Tensors that were used to compute the attr::tensors.\\n /// </param>\\n /// <remarks>\\n /// The graph is differentiated using the chain rule. If any of tensors are non-scalar (i.e. their data has more than one element) and require gradient,\\n /// then the Jacobian-vector product would be computed, in this case the function additionally requires specifying grad_tensors.\\n ///\\n /// It should be a sequence of matching length, that contains the “vector” in the Jacobian-vector product, usually the gradient of the differentiated\\n /// function w.r.t. corresponding tensors (null is an acceptable value for all tensors that don’t need gradient tensors).\\n ///\\n /// This function accumulates gradients in the leaves - you might need to zero the .grad properties or set them to null before calling it.\\n /// </remarks>\\n public static void backward(IList<Tensor> tensors, IList<Tensor> grad_tensors = null, bool? retain_graph = null, bool create_graph = false, IList<Tensor> inputs = null)\\n {\\n bool rt = retain_graph ?? create_graph;\\n\\n using var ts = new PinnedArray<IntPtr>();\\n using var gts = new PinnedArray<IntPtr>();\\n using var ins = new PinnedArray<IntPtr>();\\n IntPtr tensRef = ts.CreateArray(tensors.Select(p => p.Handle).ToArray());\\n IntPtr gradsRef = grad_tensors == null ? IntPtr.Zero : gts.CreateArray(grad_tensors.Select(p => p.Handle).ToArray());\\n IntPtr insRef = inputs == null ? IntPtr.Zero : ins.CreateArray(inputs.Select(p => p.Handle).ToArray());\\n long insLength = inputs == null ? 0 : ins.Array.Length;\\n long gradsLength = grad_tensors == null ? 0 : gts.Array.Length;\\n\\n THSAutograd_backward(tensRef, ts.Array.Length, gradsRef, gradsLength, rt, create_graph, insRef, insLength);\\n CheckForErrors();\\n }\\n\\n /// <summary>\\n /// Computes the sum of gradients of given tensors with respect to graph leaves.\\n /// </summary>\\n /// <param name=\\\"tensor\\\">Tensor of which the derivative will be computed.</param>\\n /// <param name=\\\"grad_tensors\\\">\\n /// The “vector” in the Jacobian-vector product, usually gradients w.r.t. each element of corresponding tensors.\\n /// Null values can be specified for scalar Tensors or ones that don’t require grad.\\n /// If a null value would be acceptable for all grad_tensors, then this argument is optional.\\n /// </param>\\n /// <param name=\\\"retain_graph\\\">If false, the graph used to compute the grad will be freed.\\n /// Note that in nearly all cases setting this option to true is not needed and often can be worked around in a much more efficient way.\\n /// Defaults to the value of create_graph.</param>\\n /// <param name=\\\"create_graph\\\">If true, graph of the derivative will be constructed, allowing to compute higher order derivative products. Defaults to false.</param>\\n /// <param name=\\\"inputs\\\">\\n /// Inputs w.r.t. which the gradient be will accumulated into .grad. All other Tensors will be ignored.\\n /// If not provided, the gradient is accumulated into all the leaf Tensors that were used to compute the attr::tensors.\\n /// </param>\\n /// <remarks>\\n /// The graph is differentiated using the chain rule. If any of tensors are non-scalar (i.e. their data has more than one element) and require gradient,\\n /// then the Jacobian-vector product would be computed, in this case the function additionally requires specifying grad_tensors.\\n ///\\n /// It should be a sequence of matching length, that contains the “vector” in the Jacobian-vector product, usually the gradient of the differentiated\\n /// function w.r.t. corresponding tensors (null is an acceptable value for all tensors that don’t need gradient tensors).\\n ///\\n /// This function accumulates gradients in the leaves - you might need to zero the .grad properties or set them to null before calling it.\\n /// </remarks>\\n public static void backward(Tensor tensor, IList<Tensor> grad_tensors = null, bool? retain_graph = null, bool create_graph = false, IList<Tensor> inputs = null)\\n {\\n backward(new[] { tensor }, grad_tensors, retain_graph, create_graph, inputs);\\n }\\n\\n /// <summary>\\n /// Computes the sum of gradients of given tensors with respect to graph leaves.\\n /// </summary>\\n /// <param name=\\\"tensor\\\">Tensor of which the derivative will be computed.</param>\\n /// <param name=\\\"grad_tensor\\\">\\n /// The “vector” in the Jacobian-vector product, usually gradients w.r.t. each element of corresponding tensors.\\n /// Null values can be specified for scalar Tensors or ones that don’t require grad.\\n /// If a null value would be acceptable for all grad_tensors, then this argument is optional.\\n /// </param>\\n /// <param name=\\\"retain_graph\\\">If false, the graph used to compute the grad will be freed.\\n /// Note that in nearly all cases setting this option to true is not needed and often can be worked around in a much more efficient way.\\n /// Defaults to the value of create_graph.</param>\\n /// <param name=\\\"create_graph\\\">If true, graph of the derivative will be constructed, allowing to compute higher order derivative products. Defaults to false.</param>\\n /// <param name=\\\"inputs\\\">\\n /// Inputs w.r.t. which the gradient be will accumulated into .grad. All other Tensors will be ignored.\\n /// If not provided, the gradient is accumulated into all the leaf Tensors that were used to compute the attr::tensors.\\n /// </param>\\n /// <remarks>\\n /// The graph is differentiated using the chain rule. If any of tensors are non-scalar (i.e. their data has more than one element) and require gradient,\\n /// then the Jacobian-vector product would be computed, in this case the function additionally requires specifying grad_tensors.\\n ///\\n /// It should be a sequence of matching length, that contains the “vector” in the Jacobian-vector product, usually the gradient of the differentiated\\n /// function w.r.t. corresponding tensors (null is an acceptable value for all tensors that don’t need gradient tensors).\\n ///\\n /// This function accumulates gradients in the leaves - you might need to zero the .grad properties or set them to null before calling it.\\n /// </remarks>\\n public static void backward(Tensor tensor, Tensor grad_tensor, bool? retain_graph = null, bool create_graph = false, IList<Tensor> inputs = null)\\n {\\n backward(new[] { tensor }, new[] { grad_tensor }, retain_graph, create_graph, inputs);\\n }\\n\\n /// <summary>\\n /// Context-manager that enable anomaly detection for the autograd engine.\\n /// </summary>\\n /// <param name=\\\"check_nan\\\">Flag whether to raise an error when the backward generate “nan”</param>\\n /// <returns></returns>\\n public static AnomalyMode detect_anomaly(bool check_nan = true)\\n {\\n return new AnomalyMode(true, check_nan);\\n }\\n\\n /// <summary>\\n /// Context-manager that enable anomaly detection for the autograd engine.\\n /// </summary>\\n /// <param name=\\\"mode\\\">Flag whether to enable anomaly detection (true), or disable (false)</param>\\n /// <param name=\\\"check_nan\\\">Flag whether to raise an error when the backward generate “nan”</param>\\n /// <returns></returns>\\n public static AnomalyMode set_detect_anomaly(bool mode, bool check_nan = true)\\n {\\n return new AnomalyMode(mode, check_nan);\\n }\\n }\\n }\\n}\\n\"\n },\n {\n \"alpha_fraction\": 0.5682805180549622,\n \"alphanum_fraction\": 0.5745536684989929,\n \"avg_line_length\": 46.45801544189453,\n \"blob_id\": \"081b922112cb27169ac09275c8dc5d83e506692f\",\n \"content_id\": \"02ccab3116dc8da5d926e64b9c6e594cf89d7a19\",\n \"detected_licenses\": [\n \"MIT\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"C#\",\n \"length_bytes\": 6217,\n \"license_type\": \"permissive\",\n \"max_line_length\": 224,\n \"num_lines\": 131,\n \"path\": \"/src/TorchSharp/Tensor/Factories/full.cs\",\n \"repo_name\": \"NiklasGustafsson/TorchSharp\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"// Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information.\\nusing System;\\nusing static TorchSharp.PInvoke.NativeMethods;\\n\\n#nullable enable\\nnamespace TorchSharp\\n{\\n public static partial class torch\\n {\\n /// <summary>\\n /// Create a new tensor filled with a given value\\n /// </summary>\\n public static Tensor full(long[] size, Scalar value, ScalarType? dtype = null, Device? device = null, bool requires_grad = false, string[]? names = null)\\n {\\n return _full(size, value, dtype, device, requires_grad, names: names);\\n }\\n\\n /// <summary>\\n /// Create a new tensor filled with a given value\\n /// </summary>\\n public static Tensor full(ReadOnlySpan<long> size, Scalar value, ScalarType? dtype = null, Device? device = null, bool requires_grad = false, string[]? names = null)\\n {\\n return _full(size, value, dtype, device, requires_grad, names: names);\\n }\\n\\n /// <summary>\\n /// Create a new 1-D tensor filled with given value\\n /// </summary>\\n public static Tensor full(long size, Scalar value, ScalarType? dtype = null, Device? device = null, bool requires_grad = false, string[]? names = null)\\n {\\n return _full(stackalloc long[] { size }, value, dtype, device, requires_grad, names: names);\\n }\\n\\n /// <summary>\\n /// Create a new 2-D tensor filled with given value\\n /// </summary>\\n public static Tensor full(long rows, long columns, Scalar value, ScalarType? dtype = null, Device? device = null, bool requires_grad = false, string[]? names = null)\\n {\\n return _full(stackalloc long[] { rows, columns }, value, dtype, device, requires_grad, names: names);\\n }\\n\\n /// <summary>\\n /// Create a new 3-D tensor filled with given value\\n /// </summary>\\n public static Tensor full(long dim0, long dim1, long dim2, Scalar value, ScalarType? dtype = null, Device? device = null, bool requires_grad = false, string[]? names = null)\\n {\\n return _full(stackalloc long[] { dim0, dim1, dim2 }, value, dtype, device, requires_grad, names: names);\\n }\\n\\n /// <summary>\\n /// Create a new 4-D tensor filled with given value\\n /// </summary>\\n public static Tensor full(long dim0, long dim1, long dim2, long dim3, Scalar value, ScalarType? dtype = null, Device? device = null, bool requires_grad = false, string[]? names = null)\\n {\\n return _full(stackalloc long[] { dim0, dim1, dim2, dim3 }, value, dtype, device, requires_grad, names: names);\\n }\\n\\n /// <summary>\\n /// Create a new 1-D tensor filled with given value\\n /// </summary>\\n public static Tensor full(int size, Scalar value, ScalarType? dtype = null, Device? device = null, bool requires_grad = false, string[]? names = null)\\n {\\n return _full(stackalloc long[] { size }, value, dtype, device, requires_grad, names: names);\\n }\\n\\n /// <summary>\\n /// Create a new 2-D tensor filled with given value\\n /// </summary>\\n public static Tensor full(int rows, int columns, Scalar value, ScalarType? dtype = null, Device? device = null, bool requires_grad = false, string[]? names = null)\\n {\\n return _full(stackalloc long[] { rows, columns }, value, dtype, device, requires_grad, names: names);\\n }\\n\\n /// <summary>\\n /// Create a new 3-D tensor filled with given value\\n /// </summary>\\n public static Tensor full(int dim0, int dim1, int dim2, Scalar value, ScalarType? dtype = null, Device? device = null, bool requires_grad = false, string[]? names = null)\\n {\\n return _full(stackalloc long[] { dim0, dim1, dim2 }, value, dtype, device, requires_grad, names: names);\\n }\\n\\n /// <summary>\\n /// Create a new 4-D tensor filled with given value\\n /// </summary>\\n public static Tensor full(int dim0, int dim1, int dim2, int dim3, Scalar value, ScalarType? dtype = null, Device? device = null, bool requires_grad = false, string[]? names = null)\\n {\\n return _full(stackalloc long[] { dim0, dim1, dim2, dim3 }, value, dtype, device, requires_grad, names: names);\\n }\\n\\n /// <summary>\\n /// Returns a tensor with the same size as input filled with 'value.'\\n /// </summary>\\n public static Tensor full_like(Tensor input, Scalar value, ScalarType? dtype = null, Device? device = null, bool requires_grad = false, string[]? names = null) => input.full_like(value, dtype, device, requires_grad);\\n\\n /// <summary>\\n /// Create a new tensor filled with a given value\\n /// </summary>\\n private static Tensor _full(ReadOnlySpan<long> size, Scalar value, ScalarType? dtype = null, Device? device = null, bool requires_grad = false, string[]? names = null)\\n {\\n device = InitializeDevice(device);\\n if (!dtype.HasValue) {\\n // Determine the element type dynamically.\\n if (value.Type.IsIntegral()) {\\n dtype = ScalarType.Int64;\\n } else {\\n dtype = get_default_dtype();\\n }\\n }\\n\\n unsafe {\\n fixed (long* psizes = size) {\\n var handle = THSTensor_full((IntPtr)psizes, size.Length, value.Handle, (sbyte)dtype, (int)device.type, device.index, requires_grad);\\n if (handle == IntPtr.Zero) {\\n GC.Collect();\\n GC.WaitForPendingFinalizers();\\n handle = THSTensor_full((IntPtr)psizes, size.Length, value.Handle, (sbyte)dtype, (int)device.type, device.index, requires_grad);\\n }\\n if (handle == IntPtr.Zero) { CheckForErrors(); }\\n var result = new Tensor(handle);\\n\\n if (names != null && names.Length > 0) {\\n\\n result.rename_(names);\\n }\\n\\n return result;\\n }\\n }\\n }\\n }\\n}\\n\"\n },\n {\n \"alpha_fraction\": 0.5659258961677551,\n \"alphanum_fraction\": 0.5804938077926636,\n \"avg_line_length\": 40.75257873535156,\n \"blob_id\": \"8f6eab5981f3c29beda81e7bb79e56ce382304e4\",\n \"content_id\": \"6fed5c29617f34645418b081c451ed8f325ef560\",\n \"detected_licenses\": [\n \"MIT\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"C#\",\n \"length_bytes\": 4050,\n \"license_type\": \"permissive\",\n \"max_line_length\": 147,\n \"num_lines\": 97,\n \"path\": \"/src/TorchVision/Ops/FrozenBatchNorm2d.cs\",\n \"repo_name\": \"NiklasGustafsson/TorchSharp\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"// Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information.\\n\\n// A number of implementation details in this file have been translated from the Python version of torchvision,\\n// largely located in the files found in this folder:\\n//\\n// https://github.com/pytorch/vision/blob/a4f53308b2d0f1aa9191686e326f45c26053f686/torchvision/ops/misc.py\\n//\\n// The origin has the following copyright notice and license:\\n//\\n// https://github.com/pytorch/vision/blob/main/LICENSE\\n//\\n\\nusing System;\\nusing System.Collections.Generic;\\nusing System.Runtime.CompilerServices;\\nusing static TorchSharp.torch;\\n\\n#nullable enable\\nnamespace TorchSharp\\n{\\n public static partial class torchvision\\n {\\n public static partial class ops\\n {\\n /// <summary>\\n /// BatchNorm2d where the batch statistics and the affine parameters are fixed\\n /// </summary>\\n /// <param name=\\\"num_features\\\">Number of features C from an expected input of size (N, C, H, W)</param>\\n /// <param name=\\\"eps\\\">A value added to the denominator for numerical stability.</param>\\n /// <param name=\\\"device\\\">The target device for all buffers.</param>\\n public static Modules.FrozenBatchNorm2d FrozenBatchNorm2d(int num_features, double eps = 1e-5, Device? device = null)\\n {\\n return new Modules.FrozenBatchNorm2d(num_features, eps, device);\\n }\\n }\\n }\\n\\n namespace Modules\\n {\\n /// <summary>\\n /// BatchNorm2d where the batch statistics and the affine parameters are fixed\\n /// </summary>\\n public class FrozenBatchNorm2d : nn.Module<Tensor, Tensor>\\n {\\n /// <summary>\\n /// Constructor\\n /// </summary>\\n /// <param name=\\\"num_features\\\">Number of features C from an expected input of size (N, C, H, W)</param>\\n /// <param name=\\\"eps\\\">A value added to the denominator for numerical stability.</param>\\n /// <param name=\\\"device\\\">The target device for all buffers.</param>\\n protected internal FrozenBatchNorm2d(int num_features, double eps = 1e-5, Device? device = null) : base(nameof(FrozenBatchNorm2d))\\n {\\n this.eps = eps;\\n\\n weight = torch.ones(num_features, device: device);\\n bias = torch.zeros(num_features, device: device);\\n running_mean = torch.zeros(num_features, device: device);\\n running_var = torch.ones(num_features, device: device);\\n\\n register_buffer(nameof(weight), weight);\\n register_buffer(nameof(bias), bias);\\n register_buffer(nameof(running_mean), running_mean);\\n register_buffer(nameof(running_var), running_var);\\n }\\n\\n private double eps;\\n\\n public Tensor weight { get; protected set; }\\n public Tensor bias { get; protected set; }\\n public Tensor running_mean { get; protected set; }\\n public Tensor running_var { get; protected set; }\\n\\n public override Tensor forward(Tensor x)\\n {\\n if (x.Dimensions != 4) throw new ArgumentException($\\\"Invalid number of dimensions for FrozenBatchNorm2d argument: {x.Dimensions}\\\");\\n var w = weight.reshape(1, -1, 1, 1);\\n var b = bias.reshape(1, -1, 1, 1);\\n var rv = running_var.reshape(1, -1, 1, 1);\\n var rm = running_mean.reshape(1, -1, 1, 1);\\n var scale = w * (rv + eps).rsqrt();\\n bias = b - rm * scale;\\n return x * scale + bias;\\n }\\n\\n protected override void Dispose(bool disposing)\\n {\\n if (disposing) {\\n weight.Dispose();\\n bias.Dispose();\\n running_mean.Dispose();\\n running_var.Dispose();\\n }\\n base.Dispose(disposing);\\n }\\n }\\n }\\n}\\n\"\n },\n {\n \"alpha_fraction\": 0.43833625316619873,\n \"alphanum_fraction\": 0.4447353184223175,\n \"avg_line_length\": 26.733871459960938,\n \"blob_id\": \"80d78df4e063d79aeff2e85b2b7d26b9c6eda1d7\",\n \"content_id\": \"7b2b7d7b25264e571eb7f5048ce75cb861cb3bf0\",\n \"detected_licenses\": [\n \"MIT\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"C#\",\n \"length_bytes\": 3438,\n \"license_type\": \"permissive\",\n \"max_line_length\": 130,\n \"num_lines\": 124,\n \"path\": \"/src/TorchSharp/Utils/FastShuffler.cs\",\n \"repo_name\": \"NiklasGustafsson/TorchSharp\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"// Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information.\\n\\n//Algorithm from https://github.com/lemire/Code-used-on-Daniel-Lemire-s-blog/blob/master/2017/09/18_2/VisitInDisorder.java\\nusing System;\\nusing System.Collections;\\nusing System.Collections.Generic;\\n\\nnamespace TorchSharp.Utils\\n{\\n class FastShuffler : IEnumerable<long>\\n {\\n private long size;\\n private int? seed;\\n\\n public FastShuffler(long size, int? seed = null)\\n {\\n this.size = size;\\n this.seed = seed;\\n }\\n\\n private class ShuffleEnumerator : IEnumerator<long>\\n {\\n long maxrange;\\n long prime;\\n long index;\\n long offset;\\n long runningvalue;\\n private Random rand;\\n private int? seed;\\n private long size;\\n\\n public ShuffleEnumerator(long size, int? seed = null)\\n {\\n this.seed = seed;\\n this.size = size;\\n Reset();\\n }\\n\\n private const long MAX_COUNT = long.MaxValue;\\n\\n long SelectCoPrimeResev(long min, long target)\\n {\\n var count = 0L;\\n var selected = 0L;\\n for (var val = min; val < target; ++val) {\\n if (Coprime(val, target)) {\\n count += 1;\\n if ((count == 1) || (NextLong(rand, count) < 1)) {\\n selected = val;\\n }\\n }\\n\\n if (count == MAX_COUNT) return val;\\n }\\n\\n return selected;\\n }\\n\\n static bool Coprime(long u, long v) => Gcd(u, v) == 1;\\n\\n private static long Gcd(long a, long b)\\n {\\n while (a != 0 && b != 0)\\n if (a > b) a %= b;\\n else b %= a;\\n\\n return a | b;\\n }\\n\\n private static long NextLong(Random r, long l)\\n {\\n var bytebuffer = new byte[8];\\n r.NextBytes(bytebuffer);\\n var t = 0L;\\n foreach (var b in bytebuffer) {\\n t <<= 8;\\n t += b;\\n }\\n\\n return t % l;\\n }\\n\\n public bool MoveNext()\\n {\\n\\n if (index >= maxrange) return false;\\n runningvalue += prime;\\n if (runningvalue >= maxrange) runningvalue -= maxrange;\\n index++;\\n return true;\\n }\\n\\n public void Reset()\\n {\\n rand = seed is null ? new Random() : new Random(seed.Value);\\n var min = size / 2;\\n maxrange = size;\\n prime = SelectCoPrimeResev(min, size);\\n offset = NextLong(rand, size);\\n index = 0;\\n runningvalue = offset;\\n }\\n\\n object IEnumerator.Current => Current;\\n\\n public long Current => runningvalue;\\n\\n public void Dispose()\\n {\\n\\n }\\n }\\n\\n public IEnumerator<long> GetEnumerator()\\n {\\n return new ShuffleEnumerator(size, seed);\\n }\\n\\n IEnumerator IEnumerable.GetEnumerator()\\n {\\n return GetEnumerator();\\n }\\n }\\n}\"\n },\n {\n \"alpha_fraction\": 0.4909479022026062,\n \"alphanum_fraction\": 0.510158896446228,\n \"avg_line_length\": 43.07316970825195,\n \"blob_id\": \"21a52c29abcd4303ae1787aeb28e86d55f7ab74c\",\n \"content_id\": \"921edff5a023bf3775802817b5d6e39128d889b8\",\n \"detected_licenses\": [\n \"MIT\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"C#\",\n \"length_bytes\": 12649,\n \"license_type\": \"permissive\",\n \"max_line_length\": 133,\n \"num_lines\": 287,\n \"path\": \"/src/TorchVision/dsets/CelebA.cs\",\n \"repo_name\": \"NiklasGustafsson/TorchSharp\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"// Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information.\\nusing System;\\nusing System.Collections.Generic;\\nusing System.IO;\\nusing System.Linq;\\nusing static TorchSharp.torch;\\nusing static TorchSharp.torch.nn;\\nusing static TorchSharp.torch.utils.data;\\nusing static TorchSharp.torchvision.datasets;\\nusing static TorchSharp.torchvision.datasets.utils;\\n\\n// A number of implementation details in this file have been translated from the Python version of torchvision,\\n// largely located in the files found in this folder:\\n//\\n// https://github.com/pytorch/vision/blob/b78d98bb152ffb9c0c0f5365f59f475c70b1784e/torchvision/datasets/celeba.py\\n//\\n// The origin has the following copyright notice and license:\\n//\\n// https://github.com/pytorch/vision/blob/main/LICENSE\\n//\\n\\n#nullable enable\\nnamespace TorchSharp\\n{\\n namespace Modules\\n {\\n internal sealed class CelebA : Dataset\\n {\\n private static readonly (string, string, string)[] file_list = new (string, string, string)[] {\\n // File ID MD5 Hash Filename\\n (\\\"0B7EVK8r0v71pZjFTYXZWM3FlRnM\\\", \\\"00d2c5bc6d35e252742224ab0c1e8fcb\\\", \\\"img_align_celeba.zip\\\"),\\n // (\\\"0B7EVK8r0v71pbWNEUjJKdDQ3dGc\\\",\\\"b6cd7e93bc7a96c2dc33f819aa3ac651\\\", \\\"img_align_celeba_png.7z\\\"),\\n // (\\\"0B7EVK8r0v71peklHb0pGdDl6R28\\\", \\\"b6cd7e93bc7a96c2dc33f819aa3ac651\\\", \\\"img_celeba.7z\\\"),\\n (\\\"0B7EVK8r0v71pblRyaVFSWGxPY0U\\\", \\\"75e246fa4810816ffd6ee81facbd244c\\\", \\\"list_attr_celeba.txt\\\"),\\n (\\\"1_ee_0u7vcNLOfNLegJRHmolfH5ICW-XS\\\", \\\"32bd1bd63d3c78cd57e08160ec5ed1e2\\\", \\\"identity_CelebA.txt\\\"),\\n (\\\"0B7EVK8r0v71pbThiMVRxWXZ4dU0\\\", \\\"00566efa6fedff7a56946cd1c10f1c16\\\", \\\"list_bbox_celeba.txt\\\"),\\n (\\\"0B7EVK8r0v71pd0FJY3Blby1HUTQ\\\", \\\"cc24ecafdb5b50baae59b03474781f8c\\\", \\\"list_landmarks_align_celeba.txt\\\"),\\n // (\\\"0B7EVK8r0v71pTzJIdlJWdHczRlU\\\", \\\"063ee6ddb681f96bc9ca28c6febb9d1a\\\", \\\"list_landmarks_celeba.txt\\\"),\\n (\\\"0B7EVK8r0v71pY0NSMzRuSXJEVkk\\\", \\\"d32c9cbf5e040fd4025c592c306e6668\\\", \\\"list_eval_partition.txt\\\"),\\n };\\n\\n private const string base_folder = \\\"celeba\\\";\\n private readonly string root;\\n private readonly CelebADatasetSplit split;\\n private readonly string[] target_type;\\n private readonly IModule<Tensor, Tensor>? transform;\\n private readonly IModule<Tensor, Tensor>? target_transform;\\n private string[]? filename;\\n private Tensor? identity;\\n private Tensor? bbox;\\n private Tensor? landmarks_align;\\n private Tensor? attr;\\n private string[]? attr_names;\\n\\n internal CelebA(\\n string root,\\n CelebADatasetSplit split,\\n string[] target_type,\\n IModule<Tensor, Tensor>? transform,\\n IModule<Tensor, Tensor>? target_transform)\\n {\\n this.root = root;\\n this.split = split;\\n this.target_type = target_type;\\n this.transform = transform;\\n this.target_transform = target_transform;\\n }\\n\\n public override long Count => this.attr is null ? 0 : this.attr.shape[0];\\n\\n protected override void Dispose(bool disposing)\\n {\\n if (disposing) {\\n identity?.Dispose();\\n bbox?.Dispose();\\n landmarks_align?.Dispose();\\n attr?.Dispose();\\n }\\n }\\n\\n internal void Load()\\n {\\n if (!this.CheckIntegrity()) {\\n throw new InvalidDataException(\\\"Dataset not found or corrupted. You can use download=True to download it\\\");\\n }\\n\\n var splits = this.LoadCsv(\\\"list_eval_partition.txt\\\");\\n var identity = this.LoadCsv(\\\"identity_CelebA.txt\\\");\\n var bbox = this.LoadCsv(\\\"list_bbox_celeba.txt\\\", header: 1);\\n var landmarks_align = this.LoadCsv(\\\"list_landmarks_align_celeba.txt\\\", header: 1);\\n var attr = this.LoadCsv(\\\"list_attr_celeba.txt\\\", header: 1);\\n\\n if (this.split == CelebADatasetSplit.All) {\\n this.filename = splits.index;\\n\\n this.identity = identity.data;\\n this.bbox = bbox.data;\\n this.landmarks_align = landmarks_align.data;\\n this.attr = attr.data;\\n } else {\\n var mask = (splits.data == (long)this.split).squeeze();\\n\\n var x = torch.squeeze(torch.nonzero(mask), dim: null);\\n var y = new List<string>();\\n for (int i = 0; i < x.shape[0]; i++) {\\n y.Add(splits.index[x[i].item<long>()]);\\n }\\n this.filename = y.ToArray();\\n\\n this.identity = identity.data[mask];\\n this.bbox = bbox.data[mask];\\n this.landmarks_align = landmarks_align.data[mask];\\n this.attr = attr.data[mask];\\n }\\n // map from {-1, 1} to {0, 1}\\n this.attr = torch.div(this.attr + 1, 2, rounding_mode: RoundingMode.floor).to(this.attr.dtype);\\n this.attr_names = attr.header;\\n }\\n\\n private CSV LoadCsv(string filename, int? header = null)\\n {\\n IList<string[]> data = (\\n File.ReadAllLines(Path.Combine(this.root, base_folder, filename))\\n .Select(line => line.TrimStart().Split((char[]?)null, StringSplitOptions.RemoveEmptyEntries))).ToList();\\n\\n string[]? headers;\\n if (header is not null) {\\n headers = data[(int)header];\\n data = data.Skip((int)header + 1).ToList();\\n } else {\\n headers = null;\\n }\\n\\n var indices = data.Select(row => row[0]).ToArray();\\n\\n data = data.Select(row => row.AsSpan(1).ToArray()).ToList();\\n var data_long = new long[data.Count, data[0].Length];\\n for (int i = 0; i < data_long.GetLength(0); i++) {\\n for (int j = 0; j < data_long.GetLength(1); j++) {\\n if (data[i].Length != data_long.GetLength(1)) {\\n throw new Exception();\\n }\\n long result = long.TryParse(data[i][j], out result) ? result : 0;\\n data_long[i, j] = result;\\n }\\n }\\n return new CSV(headers, indices, torch.tensor(data_long));\\n }\\n\\n /// <summary>\\n /// Get tensor according to index\\n /// </summary>\\n /// <param name=\\\"index\\\">Index for tensor</param>\\n /// <returns>Tensors of index.</returns>\\n public override Dictionary<string, Tensor> GetTensor(long index)\\n {\\n if (this.filename is null) throw new InvalidOperationException();\\n Tensor X = torchvision.io.read_image(Path.Combine(this.root, base_folder, \\\"img_align_celeba\\\", this.filename[index]));\\n\\n var result = new Dictionary<string, Tensor>();\\n foreach (var t in this.target_type) {\\n if (t == \\\"attr\\\") {\\n if (this.attr is null) throw new InvalidDataException();\\n result[t] = this.attr[index, TensorIndex.Colon];\\n } else if (t == \\\"identity\\\") {\\n if (this.identity is null) throw new InvalidDataException();\\n result[t] = this.identity[index, 0];\\n } else if (t == \\\"bbox\\\") {\\n if (this.bbox is null) throw new InvalidDataException();\\n result[t] = this.bbox[index, TensorIndex.Colon];\\n } else if (t == \\\"landmarks\\\") {\\n if (this.landmarks_align is null) throw new InvalidDataException();\\n result[t] = this.landmarks_align[index, TensorIndex.Colon];\\n } else {\\n throw new InvalidDataException($\\\"Target type \\\\\\\"{t}\\\\\\\" is not recognized.\\\");\\n }\\n }\\n\\n if (this.transform is not null) {\\n X = this.transform.call(X);\\n }\\n result[\\\"input\\\"] = X;\\n if (this.target_transform is not null) {\\n string t = this.target_type[0];\\n result[t] = this.target_transform.call(result[t]);\\n }\\n return result;\\n }\\n\\n private bool CheckIntegrity()\\n {\\n foreach (var (_, md5, filename) in file_list) {\\n var fpath = Path.Combine(this.root, base_folder, filename);\\n var ext = Path.GetExtension(filename);\\n // Allow original archive to be deleted (zip and 7z)\\n // Only need the extracted images\\n if (ext != \\\".zip\\\" && ext != \\\".7z\\\" && !torchvision.io.GDriveDownload.CheckIntegrity(fpath, md5)) {\\n return false;\\n }\\n }\\n\\n // Should check a hash of the images\\n return Directory.Exists(Path.Combine(this.root, base_folder, \\\"img_align_celeba\\\"));\\n }\\n\\n public void download()\\n {\\n if (this.CheckIntegrity()) {\\n Console.WriteLine(\\\"Files already downloaded and verified\\\");\\n return;\\n }\\n\\n foreach (var (file_id, md5, filename) in file_list) {\\n download_file_from_google_drive(file_id, Path.Combine(this.root, base_folder), filename, md5);\\n }\\n\\n extract_archive(Path.Combine(this.root, base_folder, \\\"img_align_celeba.zip\\\"));\\n }\\n\\n private struct CSV\\n {\\n public string[]? header;\\n public string[] index;\\n public torch.Tensor data;\\n public CSV(string[]? header, string[] index, torch.Tensor data)\\n {\\n this.header = header;\\n this.index = index;\\n this.data = data;\\n }\\n }\\n }\\n }\\n\\n public static partial class torchvision\\n {\\n public static partial class datasets\\n {\\n public enum CelebADatasetSplit\\n {\\n Train = 0,\\n Valid = 1,\\n Test = 2,\\n All = -1,\\n }\\n\\n /// <summary>\\n /// `Large-scale CelebFaces Attributes (CelebA) Dataset http://mmlab.ie.cuhk.edu.hk/projects/CelebA.html Dataset.\\n /// </summary>\\n /// <param name=\\\"root\\\">Root directory where images are downloaded to.</param>\\n /// <param name=\\\"split\\\">One of Train, Valid, Test, All.\\n /// Accordingly dataset is selected.</param>\\n /// <param name=\\\"target_type\\\">Type of target to use, ``attr``, ``identity``, ``bbox``,\\n /// or ``landmarks``.</param>\\n /// <param name=\\\"transform\\\">A function/transform that takes in an PIL image\\n /// and returns a transformed version.</param>\\n /// <param name=\\\"target_transform\\\">A function/transform that takes in the\\n /// target and transforms it.</param>\\n /// <param name=\\\"download\\\">If true, downloads the dataset from the internet and\\n /// puts it in root directory. If dataset is already downloaded, it is not\\n /// downloaded again.</param>\\n public static Dataset CelebA(\\n string root,\\n CelebADatasetSplit split = CelebADatasetSplit.Train,\\n string[]? target_type = null,\\n IModule<Tensor, Tensor>? transform = null,\\n IModule<Tensor, Tensor>? target_transform = null,\\n bool download = false)\\n {\\n if (target_type == null) {\\n target_type = new string[] { \\\"attr\\\" };\\n }\\n var dataset = new Modules.CelebA(\\n root,\\n split,\\n target_type,\\n transform,\\n target_transform);\\n if (download) {\\n dataset.download();\\n }\\n dataset.Load();\\n return dataset;\\n }\\n }\\n }\\n}\\n\"\n },\n {\n \"alpha_fraction\": 0.5027042627334595,\n \"alphanum_fraction\": 0.5125908851623535,\n \"avg_line_length\": 45.72554397583008,\n \"blob_id\": \"670c3a05857ce9a21925cccff8132d68de6820bb\",\n \"content_id\": \"2a67102df5823af76f42a14074c4aa600fde98a5\",\n \"detected_licenses\": [\n \"MIT\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"C#\",\n \"length_bytes\": 17195,\n \"license_type\": \"permissive\",\n \"max_line_length\": 220,\n \"num_lines\": 368,\n \"path\": \"/src/TorchSharp/Optimizers/ASGD.cs\",\n \"repo_name\": \"NiklasGustafsson/TorchSharp\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"// Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information.\\nusing System;\\nusing System.IO;\\nusing System.Collections.Generic;\\nusing System.Linq;\\nusing static TorchSharp.torch;\\n\\nnamespace TorchSharp\\n{\\n using Modules;\\n\\n public static partial class torch\\n {\\n public static partial class optim\\n {\\n\\n /// <summary>\\n /// Implements the Averaged Stochastic Gradient Descent.\\n ///\\n /// It has been proposed in Acceleration of stochastic approximation by averaging.\\n /// https://dl.acm.org/citation.cfm?id=131098\\n /// </summary>\\n /// <param name=\\\"parameters\\\">Parameters to optimize. This optimizer requires the <b>named</b> parameters collection.</param>\\n /// <param name=\\\"lr \\\">Learning rate</param>\\n /// <param name=\\\"lambd\\\">Decay term (default: 1e-4)</param>\\n /// <param name=\\\"alpha\\\">Power for eta update (default: 0.75)</param>\\n /// <param name=\\\"t0\\\">Point at which to start averaging (default: 1e6)</param>\\n /// <param name=\\\"weight_decay\\\">Weight decay (L2 penalty) (default: 0)</param>\\n /// <param name=\\\"maximize\\\">Maximize the params based on the objective, instead of minimizing.</param>\\n public static ASGD ASGD(IEnumerable<Parameter> parameters, double lr = 1e-3, double lambd = 1e-4, double alpha = 0.75, double t0 = 1e6, double weight_decay = 0, bool maximize = false)\\n {\\n return new Modules.ASGD(parameters, lr, lambd, alpha, t0, weight_decay, maximize);\\n }\\n\\n /// <summary>\\n /// Implements the Averaged Stochastic Gradient Descent.\\n ///\\n /// It has been proposed in Acceleration of stochastic approximation by averaging.\\n /// https://dl.acm.org/citation.cfm?id=131098\\n /// </summary>\\n /// <param name=\\\"parameters\\\">Parameters to optimize. This optimizer requires the <b>named</b> parameters collection.</param>\\n /// <param name=\\\"lr \\\">Learning rate</param>\\n /// <param name=\\\"lambd\\\">Decay term (default: 1e-4)</param>\\n /// <param name=\\\"alpha\\\">Power for eta update (default: 0.75)</param>\\n /// <param name=\\\"t0\\\">Point at which to start averaging (default: 1e6)</param>\\n /// <param name=\\\"weight_decay\\\">Weight decay (L2 penalty) (default: 0)</param>\\n /// <param name=\\\"maximize\\\">Maximize the params based on the objective, instead of minimizing.</param>\\n public static ASGD ASGD(IEnumerable<(string name, Parameter parameter)> parameters, double lr = 1e-3, double lambd = 1e-4, double alpha = 0.75, double t0 = 1e6, double weight_decay = 0, bool maximize = false)\\n {\\n return new Modules.ASGD(parameters.Select(np => np.parameter), lr, lambd, alpha, t0, weight_decay, maximize);\\n }\\n\\n /// <summary>\\n /// Implements the Averaged Stochastic Gradient Descent.\\n ///\\n /// It has been proposed in Acceleration of stochastic approximation by averaging.\\n /// https://dl.acm.org/citation.cfm?id=131098\\n /// </summary>\\n /// <param name=\\\"parameters\\\">Parameters to optimize. This optimizer requires the <b>named</b> parameters collection.</param>\\n /// <param name=\\\"lr \\\">Learning rate</param>\\n /// <param name=\\\"lambd\\\">Decay term (default: 1e-4)</param>\\n /// <param name=\\\"alpha\\\">Power for eta update (default: 0.75)</param>\\n /// <param name=\\\"t0\\\">Point at which to start averaging (default: 1e6)</param>\\n /// <param name=\\\"weight_decay\\\">Weight decay (L2 penalty) (default: 0)</param>\\n /// <param name=\\\"maximize\\\">Maximize the params based on the objective, instead of minimizing.</param>\\n public static ASGD ASGD(IEnumerable<ParamGroup<ASGD.Options>> parameters, double lr = 1e-3, double lambd = 1e-4, double alpha = 0.75, double t0 = 1e6, double weight_decay = 0, bool maximize = false)\\n {\\n return new Modules.ASGD(parameters, lr, lambd, alpha, t0, weight_decay, maximize);\\n }\\n }\\n }\\n\\n namespace Modules\\n {\\n public class ASGD : OptimizerHelper\\n {\\n /// <summary>\\n /// Implements ASGD algorithm (a variant of Adam based on infinity norm).\\n ///\\n /// It has been proposed in Adam: A Method for Stochastic Optimization.\\n /// </summary>\\n /// <param name=\\\"parameters\\\">Parameters to optimize. This optimizer requires the <b>named</b> parameters collection.</param>\\n /// <param name=\\\"lr \\\">Learning rate</param>\\n /// <param name=\\\"lambd\\\">Decay term (default: 1e-4)</param>\\n /// <param name=\\\"alpha\\\">Power for eta update (default: 0.75)</param>\\n /// <param name=\\\"t0\\\">Point at which to start averaging (default: 1e6)</param>\\n /// <param name=\\\"weight_decay\\\">Weight decay (L2 penalty) (default: 0)</param>\\n /// <param name=\\\"maximize\\\">Maximize the params based on the objective, instead of minimizing.</param>\\n public ASGD(IEnumerable<Parameter> parameters, double lr = 0.01, double lambd = 1e-4, double alpha = 0.75, double t0 = 1e6, double weight_decay = 0, bool maximize = false)\\n : this(new ParamGroup[] { new() { Parameters = parameters } }, lr, lambd, alpha, t0, weight_decay, maximize)\\n {\\n }\\n\\n /// <summary>\\n /// Implements ASGD algorithm (a variant of Adam based on infinity norm).\\n ///\\n /// It has been proposed in Adam: A Method for Stochastic Optimization.\\n /// </summary>\\n /// <param name=\\\"parameters\\\">Parameters to optimize. This optimizer requires the <b>named</b> parameters collection.</param>\\n /// <param name=\\\"lr \\\">Learning rate</param>\\n /// <param name=\\\"lambd\\\">Decay term (default: 1e-4)</param>\\n /// <param name=\\\"alpha\\\">Power for eta update (default: 0.75)</param>\\n /// <param name=\\\"t0\\\">Point at which to start averaging (default: 1e6)</param>\\n /// <param name=\\\"weight_decay\\\">Weight decay (L2 penalty) (default: 0)</param>\\n /// <param name=\\\"maximize\\\">Maximize the params based on the objective, instead of minimizing.</param>\\n /// <returns></returns>\\n public ASGD(IEnumerable<ParamGroup<Options>> parameters, double lr = 0.01, double lambd = 1e-4, double alpha = 0.75, double t0 = 1e6, double weight_decay = 0, bool maximize = false)\\n {\\n if (lr < 0.0) throw new ArgumentException($\\\"Invalid learning rate: {lr}\\\");\\n\\n var options = new Options {\\n LearningRate = lr,\\n InitialLearningRate = lr,\\n maximize = maximize,\\n lambd = lambd,\\n alpha = alpha,\\n t0 = t0,\\n weight_decay = weight_decay\\n };\\n\\n _defaults = options;\\n _parameter_groups = new List<Modules.ParamGroup>();\\n\\n foreach (var g in parameters) {\\n add_param_group(g);\\n }\\n }\\n\\n /// <summary>\\n /// Performs a single optimization step (parameter update).\\n /// </summary>\\n /// <param name=\\\"closure\\\">A closure that reevaluates the model and returns the loss. Optional for most optimizers.</param>\\n /// <returns></returns>\\n public override Tensor step(Func<Tensor> closure = null)\\n {\\n return _step<ParamGroup>(group => {\\n\\n var options = group.Options as Options;\\n var maximize = options.maximize.Value;\\n var lambd = options.lambd.Value;\\n var alpha = options.alpha.Value;\\n var weight_decay = options.weight_decay.Value;\\n var t0 = options.t0.Value;\\n var lr = options.LearningRate.Value;\\n\\n foreach (var param in group.Parameters) {\\n\\n var grad = param.grad();\\n\\n if (grad is null) continue;\\n\\n if (grad.is_sparse) throw new ArgumentException(\\\"ASGD does not support sparse gradients\\\");\\n\\n if (maximize) grad = -grad;\\n\\n var state = (State)_state[param.handle];\\n\\n state.step += 1;\\n\\n grad = (weight_decay != 0)\\n ? grad.add(param, alpha: weight_decay)\\n : grad.alias();\\n\\n param.mul_(1 - lambd * state.eta);\\n param.add_(grad, alpha: -state.eta);\\n\\n if (state.mu != 1) {\\n state.ax.add_(param.sub(state.ax).mul(state.mu));\\n } else {\\n state.ax.copy_(param);\\n }\\n\\n state.eta = lr / Math.Pow((1 + lambd * lr * state.step), alpha);\\n state.mu = 1 / Math.Max(1, state.step - t0);\\n }\\n }, closure);\\n }\\n\\n protected override void Dispose(bool disposing)\\n {\\n base.Dispose(disposing);\\n foreach (var kvp in _state) {\\n ((State)kvp.Item2).Dispose();\\n }\\n _state.Clear();\\n }\\n\\n public sealed class State : OptimizerState, IDisposable\\n {\\n public long step;\\n public double eta;\\n public double mu;\\n public Tensor ax;\\n\\n public void Dispose()\\n {\\n Dispose(true);\\n GC.SuppressFinalize(this);\\n }\\n\\n private void Dispose(bool disposing)\\n {\\n if (disposing) {\\n ax.Dispose();\\n }\\n }\\n\\n /// <summary>\\n /// Move all the state to the indicated device.\\n /// </summary>\\n /// <param name=\\\"device\\\">The device to move all state to.</param>\\n public override void to(Device device)\\n {\\n ax = ax.to(device);\\n }\\n\\n /// <summary>\\n /// Load the optimizer parameter state from a stream.\\n /// </summary>\\n /// <param name=\\\"reader\\\">A binary reader connected to a stream open for reading.</param>\\n public override void LoadStateDict(BinaryReader reader)\\n {\\n step = reader.ReadInt64();\\n eta = reader.ReadDouble();\\n mu = reader.ReadDouble();\\n ax.Load(reader);\\n }\\n\\n /// <summary>\\n /// Load optimizer parameter state from another optimizer.\\n /// </summary>\\n /// <param name=\\\"source\\\">An optimizer state record.</param>\\n public override void LoadStateDict(OptimizerState source)\\n {\\n var st_state = source as State;\\n step = st_state.step;\\n eta = st_state.eta;\\n mu = st_state.mu;\\n ax.Dispose();\\n ax = st_state.ax;\\n }\\n\\n /// <summary>\\n /// Save the optimizer parameter state to a stream.\\n /// </summary>\\n /// <param name=\\\"writer\\\">A binary writer connected to a stream open for writing.</param>\\n public override void SaveStateDict(BinaryWriter writer)\\n {\\n writer.Write(step);\\n writer.Write(eta);\\n writer.Write(mu);\\n ax.Save(writer);\\n }\\n\\n /// <summary>\\n /// Useful for tests, allows comparison of one state with another.\\n /// </summary>\\n /// <param name=\\\"other\\\">The other optimizer state</param>\\n /// <returns></returns>\\n public override bool ApproximatelyEquals(OptimizerState other)\\n {\\n var rhs = other as State;\\n return (rhs is not null) && step == rhs.step && eta == rhs.eta && mu == rhs.mu && ax.allclose(rhs.ax);\\n }\\n }\\n\\n /// <summary>\\n /// Add a param group to the Optimizer s param_groups.\\n /// </summary>\\n /// <param name=\\\"param_group\\\"></param>\\n /// <remarks>This can be useful when fine tuning a pre-trained network as frozen layers can be made trainable and added to the Optimizer as training progresses.</remarks>\\n public override void add_param_group(Modules.ParamGroup param_group)\\n {\\n var def = _defaults as Options;\\n if (param_group.Options is null) {\\n param_group.Options = new Options();\\n }\\n\\n var opt = param_group.Options as Options;\\n\\n // Make sure all the options are set.\\n if (!opt.maximize.HasValue) opt.maximize = def.maximize;\\n if (!opt.LearningRate.HasValue) opt.LearningRate = def.LearningRate;\\n if (!opt.lambd.HasValue) opt.lambd = def.lambd;\\n if (!opt.alpha.HasValue) opt.alpha = def.alpha;\\n if (!opt.weight_decay.HasValue) opt.weight_decay = def.weight_decay;\\n if (!opt.t0.HasValue) opt.t0 = def.t0;\\n\\n opt.InitialLearningRate = opt.LearningRate.Value;\\n\\n _parameter_groups.Add(param_group);\\n\\n foreach (var p in param_group.Parameters) {\\n var state = new State();\\n _state[p.Handle] = state;\\n state.step = 0;\\n state.eta = param_group.LearningRate;\\n state.mu = 1;\\n state.ax = torch.zeros_like(p).DetachFromDisposeScope();\\n }\\n }\\n\\n public class Options : OptimizerOptions\\n {\\n public bool? maximize;\\n public double? lambd;\\n public double? alpha;\\n public double? weight_decay;\\n public double? t0;\\n\\n /// <summary>\\n /// Load optimizer options (param-group hyperparameters) from another optimizer.\\n /// </summary>\\n /// <param name=\\\"source\\\">An optimizer options record.</param>\\n public override void LoadStateDict(OptimizerOptions source)\\n {\\n base.LoadStateDict(source);\\n var opts = source as Options;\\n maximize = opts.maximize;\\n lambd = opts.lambd;\\n alpha = opts.alpha;\\n weight_decay = opts.weight_decay;\\n t0 = opts.t0;\\n }\\n\\n /// <summary>\\n /// Load the optimizer options (param-group hyperparameters) from a stream.\\n /// </summary>\\n /// <param name=\\\"reader\\\">A binary reader connected to a stream open for reading.</param>\\n public override void LoadStateDict(BinaryReader reader)\\n {\\n base.LoadStateDict(reader);\\n maximize = reader.ReadBoolean();\\n lambd = reader.ReadDouble();\\n alpha = reader.ReadDouble();\\n weight_decay = reader.ReadDouble();\\n t0 = reader.ReadDouble();\\n }\\n\\n /// <summary>\\n /// Save the optimizer options (param-group hyperparameters) to a stream.\\n /// </summary>\\n /// <param name=\\\"writer\\\">A binary writer connected to a stream open for writing.</param>\\n public override void SaveStateDict(BinaryWriter writer)\\n {\\n base.SaveStateDict(writer);\\n writer.Write(maximize.Value);\\n writer.Write(lambd.Value);\\n writer.Write(alpha.Value);\\n writer.Write(weight_decay.Value);\\n writer.Write(t0.Value);\\n }\\n }\\n\\n public class ParamGroup : ParamGroup<Options>\\n {\\n public ParamGroup() { }\\n\\n public ParamGroup(IEnumerable<Parameter> parameters, Options options) : base(parameters, options) { }\\n\\n public ParamGroup(IEnumerable<Parameter> parameters, double lr = 0.01, double lambd = 1e-4, double alpha = 0.75, double t0 = 1e6, double weight_decay = 0)\\n : base(parameters, new ASGD.Options { LearningRate = lr, lambd = lambd, alpha = alpha, t0 = t0, weight_decay = weight_decay })\\n {\\n }\\n }\\n }\\n }\\n}\\n\"\n },\n {\n \"alpha_fraction\": 0.7460804581642151,\n \"alphanum_fraction\": 0.7654510140419006,\n \"avg_line_length\": 59.9100341796875,\n \"blob_id\": \"ae27bd45421292abb8f480de8627c8dd32173b40\",\n \"content_id\": \"e9ce1eec771fd705803b46f724e0c73bdf832d7a\",\n \"detected_licenses\": [\n \"MIT\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Markdown\",\n \"length_bytes\": 17604,\n \"license_type\": \"permissive\",\n \"max_line_length\": 640,\n \"num_lines\": 289,\n \"path\": \"/DEVGUIDE.md\",\n \"repo_name\": \"NiklasGustafsson/TorchSharp\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"\\n# Building\\n\\n dotnet build /p:SkipNative=true\\n dotnet build # for cuda support on Windows and Linux\\n dotnet test\\n dotnet pack\\n\\n## Windows\\n\\nRequirements:\\n- Visual Studio\\n- git\\n- cmake (tested with 3.18)\\n\\n__NOTE:__ At this moment, VS versions 17.4.X will not build the native code library. Use 17.3.X until further notice. See: https://github.com/dotnet/TorchSharp/issues/858 for more information.\\n\\n\\n## Linux\\n\\nRequirements:\\n- requirements to run .NET Core 3.1\\n- git\\n- cmake (tested with 3.14)\\n- clang 6.x +\\n\\nExample to fulfill the requirements in Ubuntu 16:\\n```\\nwget -O - https://apt.llvm.org/llvm-snapshot.gpg.key | sudo apt-key add -\\nsudo apt-add-repository \\\"deb https://apt.llvm.org/xenial/ llvm-toolchain-xenial-6.0 main\\\"\\nsudo apt-get -y update\\nsudo apt-get -y install clang-6.0 git cmake libunwind8 curl libomp-dev\\n```\\n\\nCommands:\\n\\n## Mac\\n\\nRequirements:\\n- Clang/LLVM 12.0.0\\n- git\\n- .NET SDK 5.0.300\\n- Cmake 3.20.3\\n\\nBuild with\\n\\n dotnet build /p:SkipNative=true\\n\\n## Packages\\n\\nAn ephemeral feed of packages from Azure DevOps CI is available for those \\n\\n* View link: https://dotnet.visualstudio.com/TorchSharp/_packaging?_a=feed&feed=SignedPackages\\n* Nuget feed: https://dotnet.pkgs.visualstudio.com/TorchSharp/_packaging/SignedPackages/nuget/v3/index.json\\n\\nSome releases are pushed to nuget\\n\\n## Building the TorchSharp package\\n\\n dotnet build\\n dotnet pack\\n\\nLocally built packages have names like this, names update every day. If repeatedly rebuilding them locally you may have to remove them\\nfrom your local `.nuget` package cache.\\n\\n bin/packages/Debug/TorchSharp.0.3.0-local-Debug-20200520.nupkg\\n bin/packages/Release/TorchSharp.0.3.0-local-Release-20200520.nupkg\\n\\nTo change the TorchSharp package version update this [file](https://github.com/dotnet/TorchSharp/blob/main/build/BranchInfo.props).\\n\\n## Doing releases of the TorchSharp package\\n\\nThe TorchSharp package is pushed to nuget.org via Azure DevOps CI release pipeline. Assuming you're not building or updating the LibTorch packages\\n(`BuildLibTorchPackages` is `false` in [azure-pipelines.yml](azure-pipelines.yml)) this is pretty simple once you have the permissions:\\n\\n1. Update the version number in [./build/BranchInfo.props](./build/BranchInfo.props) and in the [Release Notes](./RELEASENOTES.md) file and then submit a PR. \\n\\n Updating the major or minor version number should only be done after a discussion with repo admins. The patch number should be incremented by one each release and set to zero after a change to the major or minor version.\\n2. Integrate code to main and wait for CI to process\\n3. Go to [releases](https://donsyme.visualstudio.com/TorchSharp/_release) and choose \\\"Create Release\\\" (top right)\\n4. Under \\\"Artifacts-->Version\\\" choose the pipeline build corresponding to the thing you want to release. It should be a successful build on main\\n5. Press \\\"Create\\\"\\n\\n6. Once the package has been successfully pushed and is available in the NuGet gallery, create a GitHub tag in the 'main' branch with the version as the name of the tag.\\n\\n# The libtorch packages\\n\\nThe libtorch packages are huge (~3GB compressed combined for CUDA Windows) and cause a\\nlot of problems to make and deliver due to NuGet package size restrictions.\\n\\nThese problems include:\\n\\n1. A massive 2GB binary in the linux CUDA package and multiple 1.0GB binaries in Windows CUDA package\\n\\n2. Size limitations of about ~500MB on NuGet packages on the Azure DevOps CI system and about ~250MB on `nuget.org`\\n\\n4. Regular download/upload failures on these systems due to network interruptions for packages of this size\\n\\n5. 10GB VM image size restrictions for the containers userd to build these packages in the Azure DevOps CI system, we can easily run out of room.\\n\\n6. Complete libtorch-cpu packages can't be built using your local machine alone, since they won't contain the\\n full range of native bits. Instead they are built using Azure Pipelines by combining builds\\n\\nFor this reason, we do the following\\n\\n1. The head, referenceable packages that deliver a functioning runtime are any of:\\n\\n libtorch-cpu\\n libtorch-cuda-11.7-linux-x64\\n libtorch-cuda-11.7-win-x64\\n\\n2. These packages are combo packages that reference multiple parts. The parts are **not** independently useful.\\n Some parts deliver a single vast file via `primary` and `fragment` packages. A build task is then used to \\\"stitch\\\" these files back together\\n to one file on the target machine with a SHA check. This is a hack but there is no other realistic way to deliver\\n these vast files as packages (the alternative is to abandon packaging and require a manual\\n install/detect/link of PyTorch CUDA on all downstream systems, whcih is extremely problematic\\n for many practical reasons).\\n\\n For example, the CUDA package fragments are defined in [libtorch-cuda](src/Redist/libtorch-cuda-11.7/libtorch-cuda-11.7.proj). See more details later in this document.\\n\\n3. The `libtorch-*` packages are built in Azure DevOps CI\\n [using this build pipeline](https://donsyme.visualstudio.com/TorchSharp/_build?definitionId=1&_a=summary) but only in main\\n branch and only when `BuildLibTorchPackages` is set to true in [azure-pipelines.yml](azure-pipelines.yml) in the main branch.\\n You must currently manually edit this and submit to main to get new `libtorch-*` packages\\n built. Also increment `LibTorchPackageVersion` if necessary. Do a push to main and the packages will build. This process could be adjusted but at least gets us off the ground.\\n\\n4. After a successful build, the `libtorch-*` packages can be trialled using the package feed from CI (see above). When\\n they are appropriate they can be pushed to nuget using\\n [this manually invoked release pipeline](https://donsyme.visualstudio.com/TorchSharp/_release?_a=releases&view=mine&definitionId=1) in\\n Azure DevOps CI (so they don't have to be manually downloaded and pushed to `nuget.org`)\\n\\n a. [Go to release pipeline](https://donsyme.visualstudio.com/TorchSharp/_release?_a=releases&view=mine&definitionId=1)\\n\\n b. Press 'New Release'\\n\\n c. Select the successful main CI build that includes the `libtorch` packages, create the release and wait for it to finish. You should\\n see `Initialize job`, `Download artifact - dotnet.TorchSharp - packages`, `NuGet push`, `Finalize Job` succeeded.\\n\\n d. All packages should now be pushed to `nuget.org` and will appear after indexing.\\n\\n6. If updating libtorch packages, remember to delete all massive artifacts from Azure DevOps and reset this `BuildLibTorchPackages` in [azure-pipelines.yml](azure-pipelines.yml) in main branch.\\n\\n### Updating PyTorch version for new libtorch packages\\n\\nThis project grabs LibTorch and makes a C API wrapper for it, then calls these from C#. When updating to a newer\\nversion of PyTorch then quite a lot of careful work needs to be done.\\n\\n0. Make sure you have plenty of disk space, e.g. 15GB\\n\\n0. Clean and reset to main\\n\\n git checkout main\\n git clean -xfd .\\n\\n1. Familiarise yourself with download links. See https://pytorch.org/get-started/locally/ for download links.\\n\\n For example Linux, LibTorch 2.0.1 CPU download uses the link:\\n\\n https://download.pytorch.org/libtorch/cpu/libtorch-shared-with-deps-2.0.1%2Bcpu.zip\\n\\n Don't download anything yet, or manually. The downloads are acquired automatically in step 2.\\n \\n To update the version, update this in [Dependencies.props](build/Dependencies.props):\\n\\n <LibTorchVersion>2.0.1</LibTorchVersion>\\n\\n The libtorch version number is also referenced in source code, in the file 'src/TorchSharp/Torch.cs':\\n\\n ```C#\\n const string libtorchPackageVersion = \\\"2.0.1.1\\\";\\n ```\\n\\n2. Run these to test downloads and update SHA hashes for the various LibTorch downloads:\\n\\n dotnet build src\\\\Redist\\\\libtorch-cpu\\\\libtorch-cpu.proj /p:UpdateSHA=true /p:TargetOS=linux /p:Configuration=Release /t:Build /p:IncludeLibTorchCpuPackages=true\\n dotnet build src\\\\Redist\\\\libtorch-cpu\\\\libtorch-cpu.proj /p:UpdateSHA=true /p:TargetOS=mac /p:Configuration=Release /t:Build /p:IncludeLibTorchCpuPackages=true\\n dotnet build src\\\\Redist\\\\libtorch-cpu\\\\libtorch-cpu.proj /p:UpdateSHA=true /p:TargetOS=windows /p:Configuration=Release /t:Build /p:IncludeLibTorchCpuPackages=true \\n dotnet build src\\\\Redist\\\\libtorch-cpu\\\\libtorch-cpu.proj /p:UpdateSHA=true /p:TargetOS=windows /p:Configuration=Debug /t:Build /p:IncludeLibTorchCpuPackages=true\\n\\n dotnet build src\\\\Redist\\\\libtorch-cuda-11.7\\\\libtorch-cuda-11.7.proj /p:UpdateSHA=true /p:TargetOS=linux /p:Configuration=Release /t:Build /p:IncludeLibTorchCudaPackages=true\\n dotnet build src\\\\Redist\\\\libtorch-cuda-11.7\\\\libtorch-cuda-11.7.proj /p:UpdateSHA=true /p:TargetOS=windows /p:Configuration=Release /t:Build /p:IncludeLibTorchCudaPackages=true\\n dotnet build src\\\\Redist\\\\libtorch-cuda-11.7\\\\libtorch-cuda-11.7.proj /p:UpdateSHA=true /p:TargetOS=windows /p:Configuration=Debug /t:Build /p:IncludeLibTorchCudaPackages=true\\n\\n Each of these will take a **very very long time** depending on your broadband connection. This can't currently be done in CI.\\n\\n If file names in the distribution have changed, or files have been removed, you will get errors saying that files cannot be found. That's okay and will be taken care of in the next step.\\n\\n3. At this point you must **very very carefully** update the `<File Include= ...` entries under src\\\\Redist projects for\\n [libtorch-cpu](src/Redist/libtorch-cpu/libtorch-cpu.proj) and [libtorch-cuda](src/Redist/libtorch-cuda-11.7/libtorch-cuda-11.7.proj).\\n\\n This is the step in the upgrade process that takes the most effort and time. It requires extreme care.\\n\\n Check the contents of the unzip of the archive, e.g.\\n\\n bin\\\\obj\\\\x64.Debug\\\\libtorch-cpu\\\\libtorch-shared-with-deps-2.0.1\\\\libtorch\\\\lib\\n\\n You may also need to precisely refactor the CUDA binaries into multiple parts so each package ends up under ~300MB. The NuGet gallery does not allow packages larger than 250MB, so if files are\\n 300MB, after compression, they are likely to be smaller than 250MB. However, you have to look out: if the compression is poor, then packages may end up larger. Note that it is 250 million\\n bytes that is the limit, **not** 250*1024*1024. In other words, it is 250 MB, not 250 MiB. Note that Windows Explorer will show file sizes in KiB, not thousands of bytes. Use 'dir' from a CMD window to get the\\n exact size in bytes for each file. For example -- the file `libtorch_cpu.so` shows up as 511,872 KB in Windows Explorer, but 524,156,144 bytes in CMD. The 2.4% difference can be significant.\\n\\n If the combined size of the files going into a part is smaller than 250MB, then everything is fine, and there is no need to split the part. It can be singular. If that is not the case, then the part should be fragmented into two or more parts that are linked together by their names.\\n\\n For example, the following snippet spreads the `torch_cuda_cu.dll` binary file into four fragments of 250 MB each. After compression, they will be even smaller.\\n\\n ```xml\\n <File Include= \\\"libtorch\\\\lib\\\\torch_cuda_cu.dll\\\" PackageSuffix=\\\"part9-primary\\\" FileUnstitchIndex=\\\"0\\\" FileUnstitchStart=\\\"0\\\" FileUnstitchSize=\\\"250000000\\\" />\\n <File Include= \\\"libtorch\\\\lib\\\\torch_cuda_cu.dll\\\" PackageSuffix=\\\"part9-fragment1\\\" FileUnstitchIndex=\\\"1\\\" FileUnstitchStart=\\\"250000000\\\" FileUnstitchSize=\\\"250000000\\\" />\\n <File Include= \\\"libtorch\\\\lib\\\\torch_cuda_cu.dll\\\" PackageSuffix=\\\"part9-fragment2\\\" FileUnstitchIndex=\\\"2\\\" FileUnstitchStart=\\\"500000000\\\" FileUnstitchSize=\\\"250000000\\\" />\\n <File Include= \\\"libtorch\\\\lib\\\\torch_cuda_cu.dll\\\" PackageSuffix=\\\"part9-fragment3\\\" FileUnstitchIndex=\\\"3\\\" FileUnstitchStart=\\\"750000000\\\" FileUnstitchSize=\\\"-1\\\" />\\n ```\\n\\n They must all be called either 'primary,' which should be the first fragment, or 'fragmentN' where 'N' is the ordinal number of the fragment, starting with '1'. The current logic allows for as many as 10 non-primary fragments. If more are needed, the code in [FileRestitcher.cs](pkg/FileRestitcher/FileRestitcher/FileRestitcher.cs) and [RestitchPackage.targets](pkg/common/RestitchPackage.targets) needs to be updated. Note that the size of each fragment is expressed in bytes, and that fragment start must be\\n the sum of the size of all previous fragments. A '-1' should be used for the last fragment (and only for the last fragment): it means that the fragment size will be based on how much there is still left of the file.\\n\\n Each part, whether singular or fragmented, should have its own .nupkgproj file in its own folder under pkg. The folder and file should have the same name as the part. If you need to add new fragments, it is straightforward to just copy an existing fragment folder and rename it as well as the project file to the new fragment. If you must fragment a previously singular part, it is best to rename the existing folder and file to '-fragment1' and then copy a '-primary' folder and rename with the right part name. This is because the primary .nupkgproj files look different from others. Specifically, they include different build targets:\\n\\n```\\n<Content Include=\\\"..\\\\common\\\\NormalPackage.props\\\" Pack=\\\"true\\\" PackagePath=\\\"buildTransitive\\\\netstandard2.0\\\\$(MSBuildProjectName).props\\\" />\\n<Content Include=\\\"..\\\\common\\\\NormalPackage.targets\\\" Pack=\\\"true\\\" PackagePath=\\\"buildTransitive\\\\netstandard2.0\\\\$(MSBuildProjectName).targets\\\" />\\n```\\nvs.\\n```\\n<Content Include=\\\"..\\\\common\\\\RestitchPackage.props\\\" Pack=\\\"true\\\" PackagePath=\\\"buildTransitive\\\\netstandard2.0\\\\$(MSBuildProjectName).props\\\" />\\n<Content Include=\\\"..\\\\common\\\\RestitchPackage.targets\\\" Pack=\\\"true\\\" PackagePath=\\\"buildTransitive\\\\netstandard2.0\\\\$(MSBuildProjectName).targets\\\" />\\n```\\n\\n It is the 'RestitchPackage.targets' that will trigger restitching packages on first build after a download.\\n\\n Because file sizes change from release to release, it may be necessary to add or remove fragments. When you add a fragment, you also need to add a corresponding project folder under the `pkg/` top-level folder. The process of doing so is copy-paste-rename of existing folders. The same goes for adding parts (whether fragmented or not): you should add a corresponding folder and project file. If you remove a fragment (or part), you should remove the corresponding folder, or CI will end up building empty packages.\\n\\n Once you have carefully edited the parts and the files that go into them, clean the build directory and re-issue the libtorch downloads commands until there are no errors.\\n\\n4. Add the SHA files:\\n\\n git add src\\\\Redist\\\\libtorch-cpu\\\\*.sha\\n git add src\\\\Redist\\\\libtorch-cuda-11.7\\\\*.sha\\n\\n After this you may as well submit to CI just to see what happens, though keep going with the other steps below as well.\\n\\n5. Build the native and managed code without CUDA\\n\\n dotnet build /p:SkipCuda=true\\n\\n The first stage unzips the archives, then CMAKE is run.\\n\\n Unzipping the archives may take quite a while\\n\\n Note that things may have changed in the LibTorch header files, linking flags etc. There is a CMakeLists.txt that acquires\\n the cmake information delievered in the LibTorch download. It can be subtle.\\n\\n If the vxcproj for the native code gets configured by cmake then you should now be able to start developing the C++ code in Visual Studio. In order to get the correct environment variables and PATH, start VS from the command line, not from the Start menu:\\n\\n devenv TorchSharp.sln\\n\\n e.g. the vcxproj is created here:\\n\\n bin\\\\obj\\\\x64.Debug\\\\Native\\\\LibTorchSharp\\\\LibTorchSharp.vcxproj\\n\\n6. Similarly build the native code with CUDA\\n\\n dotnet build\\n\\n7. You must also adjust the set of binaries referenced for tests, see various files under `tests` and `NativeAssemblyReference` in `TorchSharp\\\\Directory.Build.targets`.\\n\\n8. Run tests\\n\\n dotnet test -c Debug\\n dotnet test -c Release\\n\\n9. Try building packages locally. The build (including CI) doesn't build `libtorch-*` packages by default, just the managed package. To\\n get CI to build new `libtorch-*` packages update this version and set `BuildLibTorchPackages` in [azure-pipelines.yml](azure-pipelines.yml):\\n\\n <LibTorchPackageVersion>2.0.1.1</LibTorchPackageVersion>\\n\\n dotnet pack -c Release -v:n /p:SkipNative=true /p:SkipTests=true /p:IncludeTorchSharpPackage=true /p:IncludeLibTorchCpuPackages=true /p:IncludeLibTorchCudaPackages=true \\n dotnet pack -c Release -v:n /p:SkipNative=true /p:SkipTests=true /p:TargetOS=linux /p:IncludeTorchSharpPackage=true /p:IncludeLibTorchCpuPackages=true /p:IncludeLibTorchCudaPackages=true \\n\\n Once these finish, the output can be found in `bin\\\\packages\\\\Release`. Look at the file sizes -- if anything is larger than 250,000,000 bytes, you need to go back to #3 above and redefine the package contents and fragmentation scheme. It maybe necessary to introduce new fragments.\\n\\n\\t**Note:** The locally built TorchSharp packages will only contain binaries for the local platform, so they cannot be used with other platforms. Therefore, only the packages built in Azure Pipelines can be used across platforms.\\n\\n10. Submit to CI and debug problems.\\n\\n11. Remember to delete all massive artifacts from Azure DevOps and reset this `BuildLibTorchPackages` in in [azure-pipelines.yml](azure-pipelines.yml)\\n\\n\\n## Building with Visual Studio\\n\\nIn order for builds to work properly using Visual Studio 2019 or 2022, you must start VS from the 'x64 Native Tools Command Prompt for VS 2022' (or 2019) in order for the full environment to be set up correctly. Starting VS from the desktop or taskbar will not work properly.\\n\"\n },\n {\n \"alpha_fraction\": 0.5828996300697327,\n \"alphanum_fraction\": 0.5828996300697327,\n \"avg_line_length\": 29.56818199157715,\n \"blob_id\": \"2b087b3d91125673cf2ede27866c749d34cf855a\",\n \"content_id\": \"75fbb7fe1670679337ea08e3334aa33fcc755db2\",\n \"detected_licenses\": [\n \"MIT\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"C#\",\n \"length_bytes\": 1345,\n \"license_type\": \"permissive\",\n \"max_line_length\": 130,\n \"num_lines\": 44,\n \"path\": \"/src/TorchVision/ConvertImageDType.cs\",\n \"repo_name\": \"NiklasGustafsson/TorchSharp\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"// Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information.\\n\\nusing System;\\nusing static TorchSharp.torch;\\n\\n\\nnamespace TorchSharp\\n{\\n public static partial class torchvision\\n {\\n internal class ConvertImageDType : ITransform\\n {\\n internal ConvertImageDType(ScalarType dtype)\\n {\\n this.dtype = dtype;\\n }\\n\\n public Tensor call(Tensor image)\\n {\\n return transforms.functional.convert_image_dtype(image, dtype);\\n }\\n\\n private ScalarType dtype;\\n }\\n\\n public static partial class transforms\\n {\\n /// <summary>\\n /// Convert a tensor image to the given dtype and scale the values accordingly\\n /// </summary>\\n /// <param name=\\\"dtype\\\">Desired data type of the output</param>\\n static public ITransform ConvertImageDtype(ScalarType dtype)\\n {\\n return new ConvertImageDType(dtype);\\n }\\n\\n [Obsolete(\\\"Deprecating misspelled transform. Use 'ConvertImageDtype' instead.\\\", false)]\\n static public ITransform ConvertImageDType(ScalarType dtype)\\n {\\n return new ConvertImageDType(dtype);\\n }\\n }\\n }\\n}\\n\"\n },\n {\n \"alpha_fraction\": 0.6029554605484009,\n \"alphanum_fraction\": 0.6115756034851074,\n \"avg_line_length\": 55.10545349121094,\n \"blob_id\": \"855079652f749440f763aacecbbbe169c7858425\",\n \"content_id\": \"ca0beb8da07f1d446c03c6ab29da504e8fb52924\",\n \"detected_licenses\": [\n \"MIT\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"C#\",\n \"length_bytes\": 15429,\n \"license_type\": \"permissive\",\n \"max_line_length\": 254,\n \"num_lines\": 275,\n \"path\": \"/src/TorchSharp/Tensor/Factories/tensor_Complex.cs\",\n \"repo_name\": \"NiklasGustafsson/TorchSharp\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"// Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information.\\nusing System;\\nusing System.Collections.Generic;\\nusing System.Diagnostics.Contracts;\\nusing System.Linq;\\nusing static TorchSharp.PInvoke.NativeMethods;\\n\\n#nullable enable\\nnamespace TorchSharp\\n{\\n public static partial class torch\\n {\\n /// <summary>\\n /// Create a scalar tensor from a single value\\n /// </summary>\\n public static Tensor tensor(System.Numerics.Complex scalar, ScalarType? dtype = null, Device? device = null, bool requires_grad = false)\\n {\\n device = InitializeDevice(device);\\n var handle = THSTensor_newComplexFloat64Scalar(scalar.Real, scalar.Imaginary, (int)device.type, device.index, requires_grad);\\n if (handle == IntPtr.Zero) { CheckForErrors(); }\\n var tensor = new Tensor(handle);\\n tensor = dtype.HasValue ? tensor.to(dtype.Value, device) : tensor.to(device);\\n return tensor;\\n }\\n\\n /// <summary>\\n /// Create a tensor from an array of values, shaping it based on the shape passed in.\\n /// </summary>\\n /// <remarks>The Torch runtime does not take ownership of the data, so there is no device argument.</remarks>\\n [Pure]\\n public static Tensor tensor(IList<(float Real, float Imaginary)> rawArray, ReadOnlySpan<long> dimensions, ScalarType? dtype = null, Device? device = null, bool requires_grad = false, string[]? names = null)\\n {\\n return tensor(rawArray.ToArray(), dimensions, dtype, device, requires_grad, names: names);\\n }\\n\\n /// <summary>\\n /// Create a tensor from an array of values, shaping it based on the shape passed in.\\n /// </summary>\\n [Pure]\\n public static Tensor tensor((float Real, float Imaginary)[] rawArray, ScalarType? dtype = null, Device? device = null, bool requires_grad = false, string[]? names = null)\\n {\\n var dataArray = new float[rawArray.Length * 2];\\n for (var i = 0; i < rawArray.Length; i++) {\\n dataArray[i * 2] = rawArray[i].Real;\\n dataArray[i * 2 + 1] = rawArray[i].Imaginary;\\n }\\n\\n return _tensor_generic(dataArray, stackalloc long[] { rawArray.LongLength }, (sbyte)ScalarType.ComplexFloat32, dtype, device, requires_grad, names: names);\\n }\\n\\n /// <summary>\\n /// Create a tensor from an array of values, shaping it based on the shape passed in.\\n /// </summary>\\n [Pure]\\n public static Tensor tensor((float Real, float Imaginary)[] rawArray, ReadOnlySpan<long> dimensions, ScalarType? dtype = null, Device? device = null, bool requires_grad = false, string[]? names = null)\\n {\\n var dataArray = new float[rawArray.Length * 2];\\n for (var i = 0; i < rawArray.Length; i++) {\\n dataArray[i * 2] = rawArray[i].Real;\\n dataArray[i * 2 + 1] = rawArray[i].Imaginary;\\n }\\n\\n return _tensor_generic(dataArray, dimensions, (sbyte)ScalarType.ComplexFloat32, dtype, device, requires_grad, names: names);\\n }\\n\\n /// <summary>\\n /// Create a 1-D tensor from an array of values, shaping it based on the input array.\\n /// </summary>\\n /// <remarks>The Torch runtime does not take ownership of the data, so there is no device argument.</remarks>\\n [Pure]\\n public static Tensor tensor(IList<(float Real, float Imaginary)> rawArray, ScalarType? dtype = null, Device? device = null, bool requires_grad = false, string[]? names = null)\\n {\\n return tensor(rawArray, stackalloc long[] { (long)rawArray.Count }, dtype, device, requires_grad, names: names);\\n }\\n\\n /// <summary>\\n /// Create a tensor from an array of values, organizing it as a two-dimensional tensor.\\n /// </summary>\\n /// <remarks>\\n /// The Torch runtime does not take ownership of the data, so there is no device argument.\\n /// The input array must have rows * columns elements.\\n /// </remarks>\\n [Pure]\\n public static Tensor tensor(IList<(float Real, float Imaginary)> rawArray, long rows, long columns, ScalarType? dtype = null, Device? device = null, bool requires_grad = false, string[]? names = null)\\n {\\n return tensor(rawArray, stackalloc long[] { rows, columns }, dtype, device, requires_grad, names: names);\\n }\\n\\n /// <summary>\\n /// Create a tensor from an array of values, organizing it as a three-dimensional tensor.\\n /// </summary>\\n /// <remarks>\\n /// The Torch runtime does not take ownership of the data, so there is no device argument.\\n /// The input array must have dim0*dim1*dim2 elements.\\n /// </remarks>\\n [Pure]\\n public static Tensor tensor(IList<(float Real, float Imaginary)> rawArray, long dim0, long dim1, long dim2, ScalarType? dtype = null, Device? device = null, bool requires_grad = false, string[]? names = null)\\n {\\n return tensor(rawArray, stackalloc long[] { dim0, dim1, dim2 }, dtype, device, requires_grad, names: names);\\n }\\n\\n /// <summary>\\n /// Create a tensor from an array of values, organizing it as a four-dimensional tensor.\\n /// </summary>\\n /// <remarks>\\n /// The Torch runtime does not take ownership of the data, so there is no device argument.\\n /// The input array must have dim0*dim1*dim2*dim3 elements.\\n /// </remarks>\\n [Pure]\\n public static Tensor tensor(IList<(float Real, float Imaginary)> rawArray, long dim0, long dim1, long dim2, long dim3, ScalarType? dtype = null, Device? device = null, bool requires_grad = false, string[]? names = null)\\n {\\n return tensor(rawArray, stackalloc long[] { dim0, dim1, dim2, dim3 }, dtype, device, requires_grad, names: names);\\n }\\n\\n /// <summary>\\n /// Create a two-dimensional tensor from a two-dimensional array of values.\\n /// </summary>\\n [Pure]\\n public static Tensor tensor((float Real, float Imaginary)[,] rawArray, ScalarType? dtype = null, Device? device = null, bool requires_grad = false, string[]? names = null)\\n {\\n var dataArray = new float[rawArray.GetLongLength(0), rawArray.GetLongLength(1) * 2];\\n for (var i = 0; i < rawArray.GetLongLength(0); i++) {\\n for (var j = 0; j < rawArray.GetLongLength(1); j++) {\\n dataArray[i, j * 2] = rawArray[i, j].Real;\\n dataArray[i, j * 2 + 1] = rawArray[i, j].Imaginary;\\n }\\n }\\n\\n return _tensor_generic(dataArray, stackalloc long[] { rawArray.GetLongLength(0), rawArray.GetLongLength(1) }, (sbyte)ScalarType.ComplexFloat32, dtype, device, requires_grad, names: names);\\n }\\n\\n /// <summary>\\n /// Create a three-dimensional tensor from a three-dimensional array of values.\\n /// </summary>\\n [Pure]\\n public static Tensor tensor((float Real, float Imaginary)[,,] rawArray, ScalarType? dtype = null, Device? device = null, bool requires_grad = false, string[]? names = null)\\n {\\n var dataArray = new float[rawArray.GetLongLength(0), rawArray.GetLongLength(1), rawArray.GetLongLength(2) * 2];\\n for (var i = 0; i < rawArray.GetLongLength(0); i++) {\\n for (var j = 0; j < rawArray.GetLongLength(1); j++) {\\n for (var k = 0; k < rawArray.GetLongLength(2); k++) {\\n dataArray[i, j, k * 2] = rawArray[i, j, k].Real;\\n dataArray[i, j, k * 2 + 1] = rawArray[i, j, k].Imaginary;\\n }\\n }\\n }\\n\\n return _tensor_generic(dataArray, stackalloc long[] { rawArray.GetLongLength(0), rawArray.GetLongLength(1), rawArray.GetLongLength(2) }, (sbyte)ScalarType.ComplexFloat32, dtype, device, requires_grad, names: names);\\n }\\n\\n /// <summary>\\n /// Create a four-dimensional tensor from a four-dimensional array of values.\\n /// </summary>\\n [Pure]\\n public static Tensor tensor((float Real, float Imaginary)[,,,] rawArray, ScalarType? dtype = null, Device? device = null, bool requires_grad = false, string[]? names = null)\\n {\\n var dataArray = new float[rawArray.GetLongLength(0), rawArray.GetLongLength(1), rawArray.GetLongLength(2), rawArray.GetLongLength(3) * 2];\\n for (var i = 0; i < rawArray.GetLongLength(0); i++) {\\n for (var j = 0; j < rawArray.GetLongLength(1); j++) {\\n for (var k = 0; k < rawArray.GetLongLength(2); k++) {\\n for (var l = 0; l < rawArray.GetLongLength(3); l++) {\\n dataArray[i, j, k, l * 2] = rawArray[i, j, k, l].Real;\\n dataArray[i, j, k, l * 2 + 1] = rawArray[i, j, k, l].Imaginary;\\n }\\n }\\n }\\n }\\n\\n return _tensor_generic(dataArray, stackalloc long[] { rawArray.GetLongLength(0), rawArray.GetLongLength(1), rawArray.GetLongLength(2), rawArray.GetLongLength(3) }, (sbyte)ScalarType.ComplexFloat32, dtype, device, requires_grad, names: names);\\n }\\n\\n /// <summary>\\n /// Create a tensor from an array of values, shaping it based on the shape passed in.\\n /// </summary>\\n /// <remarks>The Torch runtime does not take ownership of the data, so there is no device argument.</remarks>\\n [Pure]\\n public static Tensor tensor(IList<System.Numerics.Complex> rawArray, ReadOnlySpan<long> dimensions, ScalarType? dtype = null, Device? device = null, bool requires_grad = false, string[]? names = null)\\n {\\n return tensor(rawArray.ToArray(), dimensions, dtype, device, requires_grad, names: names);\\n }\\n\\n /// <summary>\\n /// Create a tensor from an array of values, shaping it based on the shape passed in.\\n /// </summary>\\n [Pure]\\n public static Tensor tensor(System.Numerics.Complex[] rawArray, ScalarType? dtype = null, Device? device = null, bool requires_grad = false, string[]? names = null)\\n {\\n return _tensor_generic(rawArray, stackalloc long[] { rawArray.LongLength }, (sbyte)ScalarType.ComplexFloat64, dtype, device, requires_grad, names: names);\\n }\\n\\n /// <summary>\\n /// Create a tensor from an array of values, shaping it based on the shape passed in.\\n /// </summary>\\n [Pure]\\n public static Tensor tensor(System.Numerics.Complex[] rawArray, ReadOnlySpan<long> dimensions, ScalarType? dtype = null, Device? device = null, bool requires_grad = false, string[]? names = null)\\n {\\n return _tensor_generic(rawArray, dimensions, (sbyte)ScalarType.ComplexFloat64, dtype, device, requires_grad, names: names);\\n }\\n\\n /// <summary>\\n /// Create a 1-D tensor from an array of values, shaping it based on the input array.\\n /// </summary>\\n /// <remarks>The Torch runtime does not take ownership of the data, so there is no device argument.</remarks>\\n [Pure]\\n public static Tensor tensor(IList<System.Numerics.Complex> rawArray, ScalarType? dtype = null, Device? device = null, bool requires_grad = false, string[]? names = null)\\n {\\n return tensor(rawArray, stackalloc long[] { (long)rawArray.Count }, dtype, device, requires_grad, names: names);\\n }\\n\\n /// <summary>\\n /// Create a tensor from an array of values, organizing it as a two-dimensional tensor.\\n /// </summary>\\n /// <remarks>\\n /// The Torch runtime does not take ownership of the data, so there is no device argument.\\n /// The input array must have rows * columns elements.\\n /// </remarks>\\n [Pure]\\n public static Tensor tensor(IList<System.Numerics.Complex> rawArray, long rows, long columns, ScalarType? dtype = null, Device? device = null, bool requires_grad = false, string[]? names = null)\\n {\\n return tensor(rawArray, stackalloc long[] { rows, columns }, dtype, device, requires_grad, names: names);\\n }\\n\\n /// <summary>\\n /// Create a tensor from an array of values, organizing it as a three-dimensional tensor.\\n /// </summary>\\n /// <remarks>\\n /// The Torch runtime does not take ownership of the data, so there is no device argument.\\n /// The input array must have dim0*dim1*dim2 elements.\\n /// </remarks>\\n [Pure]\\n public static Tensor tensor(IList<System.Numerics.Complex> rawArray, long dim0, long dim1, long dim2, ScalarType? dtype = null, Device? device = null, bool requires_grad = false, string[]? names = null)\\n {\\n return tensor(rawArray, stackalloc long[] { dim0, dim1, dim2 }, dtype, device, requires_grad, names: names);\\n }\\n\\n /// <summary>\\n /// Create a tensor from an array of values, organizing it as a four-dimensional tensor.\\n /// </summary>\\n /// <remarks>\\n /// The Torch runtime does not take ownership of the data, so there is no device argument.\\n /// The input array must have dim0*dim1*dim2*dim3 elements.\\n /// </remarks>\\n public static Tensor tensor(IList<System.Numerics.Complex> rawArray, long dim0, long dim1, long dim2, long dim3, ScalarType? dtype = null, Device? device = null, bool requires_grad = false, string[]? names = null)\\n {\\n return tensor(rawArray, stackalloc long[] { dim0, dim1, dim2, dim3 }, dtype, device, requires_grad, names: names);\\n }\\n\\n /// <summary>\\n /// Create a two-dimensional tensor from a two-dimensional array of values.\\n /// </summary>\\n [Pure]\\n public static Tensor tensor(System.Numerics.Complex[,] rawArray, ScalarType? dtype = null, Device? device = null, bool requires_grad = false, string[]? names = null)\\n {\\n return _tensor_generic(rawArray, stackalloc long[] { rawArray.GetLongLength(0), rawArray.GetLongLength(1) }, (sbyte)ScalarType.ComplexFloat64, dtype, device, requires_grad, names: names);\\n }\\n\\n /// <summary>\\n /// Create a three-dimensional tensor from a three-dimensional array of values.\\n /// </summary>\\n [Pure]\\n public static Tensor tensor(System.Numerics.Complex[,,] rawArray, ScalarType? dtype = null, Device? device = null, bool requires_grad = false, string[]? names = null)\\n {\\n return _tensor_generic(rawArray, stackalloc long[] { rawArray.GetLongLength(0), rawArray.GetLongLength(1), rawArray.GetLongLength(2) }, (sbyte)ScalarType.ComplexFloat64, dtype, device, requires_grad, names: names);\\n }\\n\\n /// <summary>\\n /// Create a four-dimensional tensor from a four-dimensional array of values.\\n /// </summary>\\n [Pure]\\n public static Tensor tensor(System.Numerics.Complex[,,,] rawArray, ScalarType? dtype = null, Device? device = null, bool requires_grad = false, string[]? names = null)\\n {\\n return _tensor_generic(rawArray, stackalloc long[] { rawArray.GetLongLength(0), rawArray.GetLongLength(1), rawArray.GetLongLength(2), rawArray.GetLongLength(3) }, (sbyte)ScalarType.ComplexFloat64, dtype, device, requires_grad, names: names);\\n }\\n }\\n}\\n\"\n },\n {\n \"alpha_fraction\": 0.5445655584335327,\n \"alphanum_fraction\": 0.5601370334625244,\n \"avg_line_length\": 27.168420791625977,\n \"blob_id\": \"9d1758d9c9b3184f3ed8c0563d3114210bed1353\",\n \"content_id\": \"f499fcb94f1eba7639ccd7d62baf9a6f9c67faf0\",\n \"detected_licenses\": [\n \"MIT\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"C++\",\n \"length_bytes\": 16055,\n \"license_type\": \"permissive\",\n \"max_line_length\": 228,\n \"num_lines\": 570,\n \"path\": \"/src/Native/LibTorchSharp/THSJIT.cpp\",\n \"repo_name\": \"NiklasGustafsson/TorchSharp\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"//// Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information.\\n#include \\\"THSJIT.h\\\"\\n\\nJITModule THSJIT_load(const char* filename, int64_t device, int64_t index)\\n{\\n c10::DeviceType dev = c10::kCPU;\\n if (device == 1)\\n dev = c10::kCUDA;\\n\\n CATCH(\\n auto res = torch::jit::load(filename, torch::Device(dev, index));\\n auto copy = new torch::jit::Module(res);\\n return new std::shared_ptr<torch::jit::Module>(copy);\\n );\\n\\n return nullptr;\\n}\\n\\nJITCompilationUnit THSJIT_compile(const char* script)\\n{\\n CATCH(\\n auto res = torch::jit::compile(script);\\n return new std::shared_ptr<torch::jit::CompilationUnit>(res);\\n );\\n\\n return nullptr;\\n}\\n\\nvoid THSJIT_save(JITModule module, const char* filename)\\n{\\n CATCH(\\n (*module)->save(filename);\\n );\\n}\\n\\nint THSJIT_Module_is_training(JITModule module)\\n{\\n return (*module)->is_training();\\n}\\n\\nvoid THSJIT_Module_train(JITModule module, bool on)\\n{\\n (*module)->train(on);\\n}\\n\\nvoid THSJIT_Module_eval(JITModule module)\\n{\\n (*module)->eval();\\n}\\n\\nvoid THSJIT_Module_to_device_dtype(JITModule module, int8_t dtype, int64_t device, int64_t index)\\n{\\n c10::DeviceType dev = c10::kCPU;\\n if (device == 1)\\n dev = c10::kCUDA;\\n (*module)->to(torch::Device(dev, index), (at::ScalarType)dtype);\\n}\\n\\nvoid THSJIT_Module_to_device(JITModule module, int64_t device, int64_t index)\\n{\\n c10::DeviceType dev = c10::kCPU;\\n if (device == 1)\\n dev = c10::kCUDA;\\n (*module)->to(torch::Device(dev, index));\\n}\\n\\nvoid THSJIT_Module_to_dtype(JITModule module, int8_t dtype)\\n{\\n (*module)->to((at::ScalarType)dtype);\\n}\\n\\nvoid THSJIT_Module_modules(const JITModule module, JITModule* (*allocator)(size_t length))\\n{\\n auto modules = (*module)->modules();\\n JITModule* result = allocator(modules.size());\\n int i = 0;\\n for (const auto& child : modules) {\\n auto copy = new torch::jit::Module(child);\\n result[i++] = new std::shared_ptr<torch::jit::Module>(copy);\\n }\\n}\\n\\nvoid THSJIT_Module_named_modules(const JITModule module,\\n JITModule* (*allocator)(size_t length),\\n const char** (*allocator2)(size_t length))\\n{\\n auto modules = (*module)->named_modules();\\n JITModule* result = allocator(modules.size());\\n const char** names = allocator2(modules.size());\\n int i = 0;\\n for (const auto& child : modules) {\\n auto copy = new torch::jit::Module(child.value);\\n result[i] = new std::shared_ptr<torch::jit::Module>(copy);\\n names[i] = make_sharable_string(child.name);\\n i++;\\n }\\n}\\n\\nvoid THSJIT_Module_named_children(const JITModule module,\\n JITModule* (*allocator)(size_t length),\\n const char** (*allocator2)(size_t length))\\n{\\n auto modules = (*module)->named_children();\\n JITModule* result = allocator(modules.size());\\n const char** names = allocator2(modules.size());\\n int i = 0;\\n for (const auto& child : modules) {\\n auto copy = new torch::jit::Module(child.value);\\n result[i] = new std::shared_ptr<torch::jit::Module>(copy);\\n names[i] = make_sharable_string(child.name);\\n i++;\\n }\\n}\\n\\nvoid THSJIT_Module_parameters(const JITModule module, Tensor* (*allocator)(size_t length))\\n{\\n auto parameters = (*module)->parameters();\\n Tensor* result = allocator(parameters.size());\\n int i = 0;\\n for (const auto& child : parameters) {\\n result[i++] = new torch::Tensor(child);\\n }\\n}\\n\\nvoid THSJIT_Module_named_parameters(const JITModule module,\\n Tensor* (*allocator)(size_t length),\\n const char** (*allocator2)(size_t length))\\n{\\n auto parameters = (*module)->named_parameters();\\n Tensor* result = allocator(parameters.size());\\n const char** names = allocator2(parameters.size());\\n int i = 0;\\n for (const auto& child : parameters) {\\n result[i] = new torch::Tensor(child.value);\\n names[i] = make_sharable_string(child.name);\\n i++;\\n }\\n}\\n\\nvoid THSJIT_Module_named_buffers(const JITModule module,\\n Tensor* (*allocator)(size_t length),\\n const char** (*allocator2)(size_t length))\\n{\\n auto parameters = (*module)->named_buffers();\\n Tensor* result = allocator(parameters.size());\\n const char** names = allocator2(parameters.size());\\n int i = 0;\\n for (const auto& child : parameters) {\\n result[i] = new torch::Tensor(child.value);\\n names[i] = make_sharable_string(child.name);\\n i++;\\n }\\n}\\n\\nJITMethod THSJIT_Module_get_method(const JITModule module, const char* name)\\n{\\n auto method = (*module)->get_method(name);\\n auto copy = new torch::jit::Method(method);\\n return new std::shared_ptr<torch::jit::Method>(copy);\\n}\\n\\nTensorOrScalar* ReturnHelper(c10::IValue result, TensorOrScalar* (*allocator)(int32_t idx, size_t length), int8_t* typeCode, int32_t* idx)\\n{\\n // TypeCode:\\n //\\n // 0 -- Not supported\\n // 1 -- Single tensor\\n // 2 -- Tuple of tensors\\n // 3 -- List of tensors\\n // 4 -- Single scalar\\n // 5 -- Scalar tuple\\n // 6 -- List of scalars\\n // 7 -- List of scalars and tensors\\n // 8 -- None / null\\n // 9 -- List of anything\\n // 10 -- Tuple of anything\\n\\n if (result.isNone())\\n {\\n TensorOrScalar* output = allocator(idx[0]++, 1);\\n output[0] = { 8, -1, (ptrdiff_t)0 };\\n *typeCode = 8;\\n return output;\\n }\\n\\n if (result.isScalar())\\n {\\n TensorOrScalar* output = allocator(idx[0]++, 1);\\n output[0] = { 0, -1, (ptrdiff_t)new torch::Scalar(result.toScalar()) };\\n *typeCode = 4;\\n return output;\\n }\\n\\n if (result.isTensor()) {\\n TensorOrScalar* output = allocator(idx[0]++, 1);\\n output[0] = { 0, -1, (ptrdiff_t)ResultTensor(result.toTensor()) };\\n *typeCode = 1;\\n return output;\\n }\\n\\n if (result.isTensorList()) {\\n auto list = result.toTensorList();\\n *typeCode = 3;\\n TensorOrScalar* output = allocator(idx[0]++, list.size());\\n for (size_t i = 0; i < list.size(); i++)\\n output[i] = { 0, -1, (ptrdiff_t)ResultTensor(list[i]) };\\n return output;\\n }\\n\\n if (result.isList())\\n {\\n int foundTensor = 0;\\n int foundScalar = 0;\\n int foundNull = 0;\\n int foundListOrTuple = 0;\\n\\n auto list = result.toList();\\n TensorOrScalar* output = allocator(idx[0]++, list.size());\\n\\n for (int i = 0; i < list.size(); ++i)\\n {\\n output[i].Handle = -1;\\n c10::IValue value = list[i];\\n\\n if (value.isTensor())\\n {\\n output[i] = { 0, -1, (ptrdiff_t)ResultTensor(value.toTensor()) };\\n foundTensor += 1;\\n continue;\\n }\\n if (value.isScalar())\\n {\\n output[i] = { 4, -1, (ptrdiff_t)new torch::Scalar(value.toScalar()) };\\n foundScalar += 1;\\n continue;\\n }\\n if (value.isNone())\\n {\\n output[i] = { 8, -1, (ptrdiff_t)0 };\\n foundNull += 1;\\n continue;\\n }\\n else {\\n int8_t nestedTC = 0;\\n int64_t arrIdx = idx[0];\\n auto nested = ReturnHelper(value, allocator, &nestedTC, idx);\\n foundListOrTuple += 1;\\n output[i] = { nestedTC, arrIdx, (ptrdiff_t)nested };\\n }\\n }\\n\\n if (foundListOrTuple > 0) {\\n *typeCode = 9;\\n }\\n else {\\n *typeCode = 7;\\n if (foundScalar == 0 && foundNull == 0)\\n *typeCode = 3;\\n if (foundTensor == 0 && foundNull == 0)\\n *typeCode = 6;\\n }\\n return output;\\n }\\n\\n if (result.isTuple()) {\\n int foundTensor = 0;\\n int foundScalar = 0;\\n int foundNull = 0;\\n int foundListOrTuple = 0;\\n\\n auto& list = result.toTuple()->elements();\\n TensorOrScalar* output = allocator(idx[0]++, list.size());\\n\\n for (int i = 0; i < list.size(); ++i)\\n {\\n output[i].Handle = -1;\\n c10::IValue value = list[i];\\n\\n if (value.isTensor())\\n {\\n output[i] = { 0, -1, (ptrdiff_t)ResultTensor(value.toTensor()) };\\n foundTensor += 1;\\n continue;\\n }\\n if (value.isScalar())\\n {\\n output[i] = { 4, -1, (ptrdiff_t)new torch::Scalar(value.toScalar()) };\\n foundScalar += 1;\\n continue;\\n }\\n if (value.isNone())\\n {\\n output[i] = { 8, -1, (ptrdiff_t)0 };\\n foundNull += 1;\\n continue;\\n }\\n else {\\n int8_t nestedTC = 0;\\n int64_t arrIdx = idx[0];\\n auto nested = ReturnHelper(value, allocator, &nestedTC, idx);\\n foundListOrTuple += 1;\\n output[i] = { nestedTC, arrIdx, (ptrdiff_t)nested };\\n }\\n }\\n\\n *typeCode = 10;\\n if (foundListOrTuple == 0) {\\n if (foundScalar == 0 && foundNull == 0)\\n *typeCode = 2;\\n if (foundTensor == 0 && foundNull == 0)\\n *typeCode = 5;\\n }\\n\\n return output;\\n }\\n\\n *typeCode = 0;\\n return nullptr;\\n}\\n\\nc10::impl::GenericList toScalarValueList(const TensorOrScalar* tensorPtrs, const int length)\\n{\\n auto list = c10::impl::GenericList(c10::ScalarTypeType::get());\\n\\n if (tensorPtrs != nullptr) {\\n for (int i = 0; i < length; i++)\\n {\\n switch (tensorPtrs[i].TypeCode) {\\n case 1:\\n list.push_back(*(torch::Scalar*)(tensorPtrs[i].Handle));\\n break;\\n }\\n }\\n }\\n\\n return list;\\n}\\n\\nc10::impl::GenericList toTensorValueList(const TensorOrScalar* tensorPtrs, const int length)\\n{\\n auto list = c10::impl::GenericList(c10::TensorType::get());\\n\\n if (tensorPtrs != nullptr) {\\n for (int i = 0; i < length; i++)\\n {\\n switch (tensorPtrs[i].TypeCode) {\\n case 0:\\n list.push_back(*(torch::Tensor*)(tensorPtrs[i].Handle));\\n break;\\n }\\n }\\n }\\n\\n return list;\\n}\\n\\nstd::vector<c10::IValue> toIValue(const TensorOrScalar* tensorPtrs, const int length)\\n{\\n // TypeCode:\\n //\\n // 0 -- Single tensor\\n // 1 -- Single scalar\\n // 2 -- Boolean\\n // 3 -- Int32\\n // 5 -- List of tensors\\n // 6 -- List of scalars\\n // 8 -- None / null\\n\\n std::vector<c10::IValue> tensors;\\n\\n if (tensorPtrs != nullptr) {\\n for (int i = 0; i < length; i++)\\n {\\n switch (tensorPtrs[i].TypeCode) {\\n case 0:\\n tensors.push_back(*(torch::Tensor*)(tensorPtrs[i].Handle));\\n break;\\n case 1:\\n tensors.push_back(*(torch::Scalar*)(tensorPtrs[i].Handle));\\n break;\\n case 2:\\n tensors.push_back(tensorPtrs[i].Handle != 0);\\n break;\\n case 3:\\n tensors.push_back((int)tensorPtrs[i].Handle);\\n break;\\n case 5:\\n {\\n auto ts = toTensorValueList(reinterpret_cast<const TensorOrScalar*>(tensorPtrs[i].Handle), (int)tensorPtrs[i].ArrayIndex);\\n tensors.push_back(ts);\\n break;\\n }\\n case 6:\\n {\\n auto ts = toScalarValueList(reinterpret_cast<const TensorOrScalar*>(tensorPtrs[i].Handle), (int)tensorPtrs[i].ArrayIndex);\\n tensors.push_back(ts);\\n break;\\n }\\n //case 4:\\n // tensors.push_back(c10::IValue(tensorPtrs[i].Handle)); // Clang on MacOS doesn't like. Pass as Scalar from .NET.\\n // break;\\n case 8:\\n tensors.push_back(c10::nullopt);\\n break;\\n }\\n }\\n }\\n return tensors;\\n}\\n\\nvoid THSJIT_Module_forward(const JITModule module, const TensorOrScalar* tensorPtrs, const int length, TensorOrScalar* (*allocator)(int32_t idx, size_t length), int8_t* typeCode, int32_t idx)\\n{\\n *typeCode = 0;\\n\\n CATCH(\\n auto result = (*module)->forward(toIValue(tensorPtrs, length));\\n ReturnHelper(result, allocator, typeCode, &idx);\\n )\\n}\\n\\nvoid THSJIT_Module_invoke(const JITModule module, const char* name, const TensorOrScalar* tensorPtrs, const int length, TensorOrScalar* (*allocator)(int32_t idx, size_t length), int8_t* typeCode, int32_t idx)\\n{\\n *typeCode = 0;\\n\\n CATCH(\\n auto method = (*module)->get_method(name);\\n auto result = method(toIValue(tensorPtrs, length));\\n ReturnHelper(result, allocator, typeCode, &idx);\\n )\\n}\\n\\nvoid THSJIT_CompilationUnit_Invoke(const JITCompilationUnit module, const char* method, const TensorOrScalar* tensorPtrs, const int length, TensorOrScalar* (*allocator)(int32_t idx, size_t length), int8_t* typeCode, int32_t idx)\\n{\\n *typeCode = 0;\\n\\n CATCH(\\n auto args = toIValue(tensorPtrs, length);\\n auto func = (*module)->find_function(method);\\n auto result = (*func)(args);\\n ReturnHelper(result, allocator, typeCode, &idx);\\n )\\n}\\n\\nvoid THSJIT_Module_dispose(const JITModule module)\\n{\\n delete module;\\n}\\n\\nconst char* THSJIT_Method_name(const JITMethod method)\\n{\\n return make_sharable_string((*method)->name());\\n}\\n\\nint THSJIT_Method_num_inputs(const JITMethod method)\\n{\\n return (int)(*method)->num_inputs();\\n}\\n\\nint THSJIT_Module_num_inputs(const JITModule module)\\n{\\n return (int)(*module)->get_method(\\\"forward\\\").num_inputs() - 1; // Don't count the 'self' argument.\\n}\\n\\nint THSJIT_Module_num_outputs(const JITModule module)\\n{\\n return (int)(*module)->get_method(\\\"forward\\\").function().getSchema().returns().size();\\n}\\n\\nJITFunction THSJIT_Method_function(const JITMethod method)\\n{\\n return new std::shared_ptr<torch::jit::Function>(&(*method)->function());\\n}\\n\\nvoid THSJIT_Method_dispose(const JITMethod method)\\n{\\n delete method;\\n}\\n\\n\\n//-------------------------------------------------------------------------------------\\n// JITFunction\\n\\nint THSJIT_Function_num_inputs(const JITFunction function)\\n{\\n return (int)(*function)->num_inputs();\\n}\\n\\n// TODO other function operations\\n\\nvoid THSJIT_Function_dispose(const JITFunction function)\\n{\\n delete function;\\n}\\n\\nvoid THSJIT_Type_dispose(const JITType type)\\n{\\n delete type;\\n}\\n\\nvoid THSJIT_TensorType_dispose(const JITTensorType type)\\n{\\n delete type;\\n}\\n\\nvoid THSJIT_CompilationUnit_dispose(const JITCompilationUnit module)\\n{\\n delete module;\\n}\\n\\nvoid* THSJIT_Type_cast(const JITType type)\\n{\\n switch ((*type)->kind())\\n {\\n case c10::TypeKind::TensorType:\\n return new std::shared_ptr<torch::jit::TensorType>((*type)->cast<c10::TensorType>());\\n default:\\n return nullptr;\\n }\\n}\\n\\nint8_t THSJIT_TensorType_dtype(const JITTensorType type)\\n{\\n auto scT = (*type)->scalarType();\\n if (scT.has_value()) {\\n return (int8_t)scT.value();\\n }\\n else {\\n return -1;\\n }\\n}\\n\\nvoid THSJIT_TensorType_sizes(const JITTensorType type, int64_t* (*allocator)(int64_t length))\\n{\\n //CATCH(\\n auto& t = *type;\\n auto dim = t->dim();\\n auto res = (*type)->sizes().concrete_sizes();\\n if (res.has_value()) {\\n const size_t sz = res.value().size();\\n auto& vec = res.value();\\n int64_t* result = allocator(sz);\\n for (size_t i = 0; i < sz; i++)\\n result[i] = vec[i];\\n }\\n //);\\n}\\n\\nint8_t THSJIT_Type_kind(const JITType type)\\n{\\n switch ((*type)->kind())\\n {\\n case c10::TypeKind::TensorType:\\n return (int8_t)TypeKind::TensorType;\\n default:\\n return -1;\\n }\\n}\\n\\nJITType THSJIT_Module_getInputType(JITModule module, int8_t index)\\n{\\n auto typ = (*module)->type();\\n c10::TypeKind kind = typ->kind();\\n auto& schema = typ->getMethod(\\\"forward\\\").getSchema();\\n return new std::shared_ptr<c10::Type>(schema.arguments()[1 + index].type()->cast<c10::TensorType>());\\n}\\n\\nvoid THSJIT_typeDispose(const JITType type)\\n{\\n delete type;\\n}\"\n },\n {\n \"alpha_fraction\": 0.5610817670822144,\n \"alphanum_fraction\": 0.5654336214065552,\n \"avg_line_length\": 41.893333435058594,\n \"blob_id\": \"294f19d6ce8e73ac9d7f3f89c6c3b83343b0db61\",\n \"content_id\": \"269d5aeec9b37db8c643bdd8a0eb80e90278a1d7\",\n \"detected_licenses\": [\n \"MIT\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"C#\",\n \"length_bytes\": 3217,\n \"license_type\": \"permissive\",\n \"max_line_length\": 130,\n \"num_lines\": 75,\n \"path\": \"/src/TorchSharp/NN/Pooling/AdaptiveMaxPool1D.cs\",\n \"repo_name\": \"NiklasGustafsson/TorchSharp\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"// Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information.\\nusing System;\\nusing static TorchSharp.torch;\\nusing static TorchSharp.PInvoke.NativeMethods;\\n\\nnamespace TorchSharp\\n{\\n using Modules;\\n\\n namespace Modules\\n {\\n /// <summary>\\n /// This class is used to represent a AdaptiveMaxPool1D module.\\n /// </summary>\\n public sealed class AdaptiveMaxPool1d : torch.nn.Module<Tensor, Tensor>\\n {\\n internal AdaptiveMaxPool1d(IntPtr handle, IntPtr boxedHandle) : base(handle, boxedHandle)\\n {\\n }\\n\\n public override Tensor forward(Tensor tensor)\\n {\\n var res = THSNN_AdaptiveMaxPool1d_forward(handle.DangerousGetHandle(), tensor.Handle);\\n if (res == IntPtr.Zero) { torch.CheckForErrors(); }\\n return new Tensor(res);\\n }\\n\\n // Rather than spending cycles only to discover that this module has neither\\n // parameters nor buffers, just shortcut the move completely.\\n protected internal override nn.Module _to(Device device, ScalarType dtype) => this;\\n protected internal override nn.Module _to(DeviceType deviceType, int deviceIndex = -1) => this;\\n protected internal override nn.Module _to(ScalarType dtype) => this;\\n }\\n }\\n\\n public static partial class torch\\n {\\n public static partial class nn\\n {\\n /// <summary>\\n /// Applies a 1D adaptive max pooling over an input signal composed of several input planes.\\n /// The output size is H, for any input size.The number of output features is equal to the number of input planes.\\n /// </summary>\\n /// <param name=\\\"outputSize\\\">The target output size H.</param>\\n /// <returns></returns>\\n public static AdaptiveMaxPool1d AdaptiveMaxPool1d(long outputSize)\\n {\\n unsafe {\\n fixed (long* pkernelSize = new long[] { outputSize }) {\\n var handle = THSNN_AdaptiveMaxPool1d_ctor((IntPtr)pkernelSize, 1, out var boxedHandle);\\n if (handle == IntPtr.Zero) { torch.CheckForErrors(); }\\n return new AdaptiveMaxPool1d(handle, boxedHandle);\\n }\\n }\\n }\\n\\n public static partial class functional\\n {\\n /// <summary>\\n /// Applies a 1D adaptive max pooling over an input signal composed of several input planes.\\n /// The output size is H, for any input size.The number of output features is equal to the number of input planes.\\n /// </summary>\\n /// <param name=\\\"x\\\"></param>\\n /// <param name=\\\"outputSize\\\">The target output size H.</param>\\n /// <returns></returns>\\n public static Tensor adaptive_max_pool1d(Tensor x, long outputSize)\\n {\\n using (var d = nn.AdaptiveMaxPool1d(outputSize)) {\\n return d.call(x);\\n }\\n }\\n }\\n }\\n }\\n}\\n\"\n },\n {\n \"alpha_fraction\": 0.5511892437934875,\n \"alphanum_fraction\": 0.5511892437934875,\n \"avg_line_length\": 27.47058868408203,\n \"blob_id\": \"6f27f657ce50c7b10abdd1abf3c693a2ae03117e\",\n \"content_id\": \"65cafa76d81cc8e1f60dd503b263689ad61b7156\",\n \"detected_licenses\": [\n \"MIT\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"C#\",\n \"length_bytes\": 967,\n \"license_type\": \"permissive\",\n \"max_line_length\": 130,\n \"num_lines\": 34,\n \"path\": \"/src/TorchVision/Equalize.cs\",\n \"repo_name\": \"NiklasGustafsson/TorchSharp\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"// Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information.\\n\\nusing static TorchSharp.torch;\\n\\nnamespace TorchSharp\\n{\\n public static partial class torchvision\\n {\\n internal class Equalize : ITransform\\n {\\n internal Equalize()\\n {\\n }\\n\\n public Tensor call(Tensor input)\\n {\\n return transforms.functional.equalize(input);\\n }\\n }\\n\\n public static partial class transforms\\n {\\n /// <summary>\\n /// Equalize the histogram of an image by applying a non-linear mapping to the input\\n /// in order to create a uniform distribution of grayscale values in the output.\\n /// </summary>\\n /// <returns></returns>\\n static public ITransform Equalize()\\n {\\n return new Equalize();\\n }\\n }\\n }\\n}\"\n },\n {\n \"alpha_fraction\": 0.5818631052970886,\n \"alphanum_fraction\": 0.5931545495986938,\n \"avg_line_length\": 36.78666687011719,\n \"blob_id\": \"1e4ceedc0f7af79c81dfd748726227cbb74e59fa\",\n \"content_id\": \"1b7c04be6aee2408f8f9acddbd8b1e854c004237\",\n \"detected_licenses\": [\n \"MIT\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"C#\",\n \"length_bytes\": 2834,\n \"license_type\": \"permissive\",\n \"max_line_length\": 147,\n \"num_lines\": 75,\n \"path\": \"/src/TorchAudio/Transforms/Resample.cs\",\n \"repo_name\": \"NiklasGustafsson/TorchSharp\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"// Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information.\\n\\nusing static TorchSharp.torch;\\nusing static TorchSharp.torchaudio;\\n\\n// A number of implementation details in this file have been translated from the Python version of torchaudio,\\n// largely located in the files found in this folder:\\n//\\n// https://github.com/pytorch/audio/blob/bb77cbebb620a46fdc0dc7e6dae2253eef3f37e2/torchaudio/transforms/_transforms.py\\n//\\n// The origin has the following copyright notice and license:\\n//\\n// https://github.com/pytorch/audio/blob/main/LICENSE\\n//\\n\\nnamespace TorchSharp.Transforms\\n{\\n public sealed class Resample : nn.Module<Tensor, Tensor>, ITransform\\n {\\n private readonly int orig_freq;\\n private readonly int new_freq;\\n private readonly int gcd;\\n private readonly ResamplingMethod resampling_method;\\n private readonly int lowpass_filter_width;\\n private readonly double rolloff;\\n private readonly double? beta;\\n public readonly torch.Tensor kernel;\\n private readonly int width;\\n\\n internal Resample(\\n string name,\\n int orig_freq = 16000,\\n int new_freq = 16000,\\n ResamplingMethod resampling_method = ResamplingMethod.sinc_interpolation,\\n int lowpass_filter_width = 6,\\n double rolloff = 0.99,\\n double? beta = null,\\n torch.Device device = null,\\n torch.ScalarType? dtype = null) : base(name)\\n {\\n this.orig_freq = orig_freq;\\n this.new_freq = new_freq;\\n this.gcd = functional.Gcd(this.orig_freq, this.new_freq);\\n this.resampling_method = resampling_method;\\n this.lowpass_filter_width = lowpass_filter_width;\\n this.rolloff = rolloff;\\n this.beta = beta;\\n\\n if (this.orig_freq != this.new_freq) {\\n (this.kernel, this.width) = functional._get_sinc_resample_kernel(\\n this.orig_freq,\\n this.new_freq,\\n this.gcd,\\n this.lowpass_filter_width,\\n this.rolloff,\\n this.resampling_method,\\n beta,\\n device: device,\\n dtype: dtype);\\n }\\n }\\n\\n public override Tensor forward(Tensor waveform)\\n {\\n using (var d = torch.NewDisposeScope()) {\\n\\n if (this.orig_freq == this.new_freq) {\\n return d.MoveToOuter(waveform.alias());\\n }\\n var resampled = functional._apply_sinc_resample_kernel(waveform, this.orig_freq, this.new_freq, this.gcd, this.kernel, this.width);\\n return d.MoveToOuter(resampled);\\n }\\n }\\n }\\n}\\n\"\n },\n {\n \"alpha_fraction\": 0.7683320641517639,\n \"alphanum_fraction\": 0.7683320641517639,\n \"avg_line_length\": 57.30434799194336,\n \"blob_id\": \"a9364ee07f8e08f08ac001c315ec37eb79442958\",\n \"content_id\": \"1449475a37a38f419d645e9e92b9f2ac7cbf81f7\",\n \"detected_licenses\": [\n \"MIT\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Markdown\",\n \"length_bytes\": 4023,\n \"license_type\": \"permissive\",\n \"max_line_length\": 554,\n \"num_lines\": 69,\n \"path\": \"/docfx/articles/saveload.md\",\n \"repo_name\": \"NiklasGustafsson/TorchSharp\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"# Saving and Restoring Model Weights and Buffers\\n\\nThere are typically two kinds of state in a model -- parameters, which contain trained weights, and buffers, which contain data that is not trained, but still essential for the functioning of the model. Both should generally be saved and loaded when serializing models.\\n\\nWhen using PyTorch, the expected pattern to use when saving and later restoring models from disk or other permanent storage media, is to get the model's state and pickle that using the standard Python format, which is what torch.save() does.\\n\\n```Python\\ntorch.save(model.state_dict(), 'model_weights.pth')\\n```\\n\\nWhen restoring the model, you are expected to first create a model of the exact same structure as the original, with random weights, then restore the state from a unpickled object:\\n\\n```Python\\nmodel = [...]\\nmodel.load_state_dict(torch.load('model_weights.pth'))\\n```\\n\\nThis presents a couple of problems for a .NET implementation. \\n\\nPython pickling is intimately coupled to Python and its runtime object model. It is a complex format that supports object graphs forming DAGs, faithfully maintaining all object state in the way necessary to restore the Python object later.\\n\\nIn order to share models between .NET applications, Python pickling is not at all necessary, and even for moving model state from Python to .NET, it is overkill. The state of a model is a simple dictionary where the keys are strings and the values are tensors.\\n\\nTherefore, TorchSharp, in its current form, implements its own very simple model serialization format, which allows models originating in either .NET or Python to be loaded using .NET, as long as the model was saved using the special format.\\n\\nThe MNIST and AdversarialExampleGeneration examples in this repo rely on saving and restoring model state -- the latter example relies on a pre-trained model from MNST.\\n\\n><br/>A future version of TorchSharp may include support for reading and writing Python pickle files directly.<br/><br/>\\n\\n## How to use the TorchSharp format\\n\\nIn C#, saving a model looks like this:\\n\\n```C#\\nmodel.save(\\\"model_weights.dat\\\");\\n```\\n\\nAnd loading it again is done by:\\n\\n```C#\\nmodel = [...];\\nmodel.load(\\\"model_weights.dat\\\");\\n```\\n\\nFor efficient memory management, the model should be created on the CPU before loading weights, then moved to the target device. \\n\\n><br/>It is __critical__ that all submodules and buffers in a custom module or composed by a Sequential object have exactly the same name in the original and target models, since that is how persisted tensors are associated with the model into which they are loaded.<br/><br/>The CustomModule 'RegisterComponents' will automatically find all fields that are either modules or tensors, register the former as modules, and the latter as buffers. It registers all of these using the name of the field, just like the PyTorch Module base class does.<br/><br/>\\n\\n### Saving a TorchSharp format model in Python\\n\\nIf the model starts out in Python, there's a simple script that allows you to use code that is very similar to the Pytorch API to save models to the TorchSharp format. Rather than placing this trivial script in a Python package and publishing it, we choose to just refer you to the script file itself, [exportsd.py](../../src/Python/exportsd.py), which has all the necessary code.\\n\\n```Python\\nf = open(\\\"model_weights.dat\\\", \\\"wb\\\")\\nexportsd.save_state_dict(model.to(\\\"cpu\\\").state_dict(), f)\\nf.close()\\n```\\n\\n### Loading a TorchSharp format model in Python\\n\\nIf the model starts out in TorchSharp, there's also a simple script that allows you to load TorchSharp models in Python. All the necessary code can be found in [importsd.py](../../src/Python/importsd.py). And there is an example for using the script:\\n\\n```Python\\nf = open(\\\"model_weights.dat\\\", \\\"rb\\\")\\nmodel.load_state_dict(importsd.load_state_dict(f))\\nf.close()\\n```\\n\\nAlso, you can check [TestSaveSD.cs](../../test/TorchSharpTest/TestSaveSD.cs) and [pyimporttest.py](../../test/TorchSharpTest/pyimporttest.py) for more examples.\\n\"\n },\n {\n \"alpha_fraction\": 0.5555388927459717,\n \"alphanum_fraction\": 0.5616931915283203,\n \"avg_line_length\": 42.828948974609375,\n \"blob_id\": \"80811e6f5af309d9ece46b0526897c28d45cfbc1\",\n \"content_id\": \"a64c2cf4e14e6a5efcf693bc379ab22a20581ce8\",\n \"detected_licenses\": [\n \"MIT\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"C#\",\n \"length_bytes\": 6664,\n \"license_type\": \"permissive\",\n \"max_line_length\": 174,\n \"num_lines\": 152,\n \"path\": \"/src/TorchSharp/Distributions/Dirichlet.cs\",\n \"repo_name\": \"NiklasGustafsson/TorchSharp\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"// Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information.\\nusing System;\\nusing System.Collections.Generic;\\nusing System.Linq;\\nusing static TorchSharp.torch;\\n\\nnamespace TorchSharp\\n{\\n using System.Reflection;\\n using Modules;\\n\\n namespace Modules\\n {\\n /// <summary>\\n /// A Dirichlet distribution parameterized by shape `concentration` and `rate`.\\n /// </summary>\\n public class Dirichlet : torch.distributions.ExponentialFamily\\n {\\n /// <summary>\\n /// The mean of the distribution.\\n /// </summary>\\n public override Tensor mean => WrappedTensorDisposeScope(() => concentration / concentration.sum(-1, true));\\n\\n public override Tensor mode\\n {\\n get {\\n using var _ = NewDisposeScope();\\n var concentrationm1 = (concentration - 1).clamp(min: 0.0);\\n var mode = concentrationm1 / concentrationm1.sum(-1, true);\\n var mask = (concentration < 1).all(dim: -1);\\n mode[mask] = torch.nn.functional.one_hot(mode[mask].argmax(dim: -1), concentrationm1.shape[concentrationm1.ndim-1]).to(mode);\\n return mode.MoveToOuterDisposeScope();\\n }\\n }\\n\\n /// <summary>\\n /// The variance of the distribution\\n /// </summary>\\n public override Tensor variance {\\n get {\\n using var _ = NewDisposeScope();\\n var con0 = concentration.sum(-1, true);\\n return (concentration * (con0 - concentration) / (con0.pow(2) * (con0 + 1))).MoveToOuterDisposeScope();\\n }\\n }\\n\\n /// <summary>\\n /// Constructor\\n /// </summary>\\n /// <param name=\\\"concentration\\\">Shape parameter of the distribution (often referred to as 'α')</param>\\n /// <param name=\\\"generator\\\">An optional random number generator object.</param>\\n public Dirichlet(Tensor concentration, torch.Generator generator = null) : base(generator)\\n {\\n var cshape = concentration.shape;\\n this.batch_shape = cshape.Take(cshape.Length - 1).ToArray();\\n this.event_shape = new long[] { cshape[cshape.Length - 1] };\\n this.concentration = concentration.alias().DetachFromDisposeScope();\\n }\\n\\n internal Tensor concentration;\\n\\n /// <summary>\\n /// Generates a sample_shape shaped reparameterized sample or sample_shape shaped batch of reparameterized samples\\n /// if the distribution parameters are batched.\\n /// </summary>\\n /// <param name=\\\"sample_shape\\\">The sample shape.</param>\\n public override Tensor rsample(params long[] sample_shape)\\n {\\n var shape = ExtendedShape(sample_shape);\\n var con = concentration.expand(shape);\\n return torch._sample_dirichlet(con, generator);\\n }\\n\\n /// <summary>\\n /// Returns the log of the probability density/mass function evaluated at `value`.\\n /// </summary>\\n /// <param name=\\\"value\\\"></param>\\n public override Tensor log_prob(Tensor value)\\n {\\n return (concentration - 1).xlogy(value).sum(-1) + torch.lgamma(concentration.sum(-1)) - torch.lgamma(concentration).sum(-1);\\n }\\n\\n /// <summary>\\n /// Returns entropy of distribution, batched over batch_shape.\\n /// </summary>\\n /// <returns></returns>\\n public override Tensor entropy()\\n {\\n var k = concentration.size(-1);\\n var a0 = concentration.sum(-1);\\n\\n return torch.lgamma(concentration).sum(-1) - torch.lgamma(a0) - (k - a0) * torch.digamma(a0) - ((concentration - 1.0) * torch.digamma(concentration)).sum(-1);\\n }\\n\\n /// <summary>\\n /// Returns a new distribution instance (or populates an existing instance provided by a derived class) with batch dimensions expanded to\\n /// `batch_shape`. This method calls `torch.Tensor.expand()` on the distribution's parameters. As such, this does not allocate new\\n /// memory for the expanded distribution instance.\\n /// </summary>\\n /// <param name=\\\"batch_shape\\\">Tthe desired expanded size.</param>\\n /// <param name=\\\"instance\\\">new instance provided by subclasses that need to override `.expand`.</param>\\n /// <returns></returns>\\n public override distributions.Distribution expand(Size batch_shape, distributions.Distribution instance = null)\\n {\\n if (instance != null && !(instance is Dirichlet))\\n throw new ArgumentException(\\\"expand(): 'instance' must be a Dirichlet distribution\\\");\\n\\n var shape = new List<long>();\\n shape.AddRange(batch_shape);\\n shape.AddRange(event_shape);\\n\\n var c = concentration.expand(shape.ToArray());\\n\\n var newDistribution = ((instance == null) ? new Dirichlet(c, generator) : instance) as Dirichlet;\\n\\n newDistribution.batch_shape = batch_shape;\\n if (newDistribution == instance) {\\n newDistribution.concentration = c;\\n }\\n return newDistribution;\\n }\\n\\n protected override IList<Tensor> NaturalParams => new Tensor[] { concentration - 1 };\\n\\n protected override Tensor MeanCarrierMeasure => new Tensor(IntPtr.Zero);\\n\\n protected override Tensor LogNormalizer(params Tensor[] parameters)\\n {\\n var x = parameters[0];\\n\\n return x.lgamma().sum(-1) - x.sum(-1).lgamma();\\n }\\n }\\n\\n }\\n\\n public static partial class torch\\n {\\n public static partial class distributions\\n {\\n /// <summary>\\n /// Creates a Dirichlet distribution parameterized by shape `concentration` and `rate`.\\n /// </summary>\\n /// <param name=\\\"concentration\\\">Shape parameter of the distribution (often referred to as 'α')</param>\\n /// <param name=\\\"generator\\\">An optional random number generator object.</param>\\n public static Dirichlet Dirichlet(Tensor concentration, torch.Generator generator = null)\\n {\\n return new Dirichlet(concentration, generator);\\n }\\n }\\n }\\n}\\n\"\n },\n {\n \"alpha_fraction\": 0.6019668579101562,\n \"alphanum_fraction\": 0.6107660531997681,\n \"avg_line_length\": 41.93333435058594,\n \"blob_id\": \"34943468d063eab1bd7d1795d69cc6214ebe07be\",\n \"content_id\": \"e1a05c2c1ff50a98a014898b866de336a8b34cb9\",\n \"detected_licenses\": [\n \"MIT\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"C#\",\n \"length_bytes\": 1932,\n \"license_type\": \"permissive\",\n \"max_line_length\": 130,\n \"num_lines\": 45,\n \"path\": \"/src/Examples.Utils/Datasets.cs\",\n \"repo_name\": \"NiklasGustafsson/TorchSharp\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"// Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information.\\n\\nusing System.Collections.Generic;\\nusing System.IO;\\nusing System.Linq;\\n\\nnamespace TorchText\\n{\\n /// <summary>\\n /// This belongs in its own package, 'TorchText'.\\n /// For now, it's useful to keep it with the examples that use it.\\n /// </summary>\\n public static class Datasets\\n {\\n /// <summary>\\n /// WikiText2\\n /// </summary>\\n /// <param name=\\\"split\\\">One of 'train', 'valid', or 'test'</param>\\n /// <param name=\\\"root\\\">The folder where the WikiText2 data set was downloaded and extracted.</param>\\n /// <returns>An enumeration of lines from the text.</returns>\\n /// <remarks>\\n /// Download the data set at:\\n /// https://s3.amazonaws.com/research.metamind.io/wikitext/wikitext-2-v1.zip\\n /// </remarks>\\n public static IEnumerable<string> WikiText2(string split, string root = \\\".data\\\")\\n {\\n var dataPath = Path.Combine(root, \\\"wikitext-2\\\", $\\\"wiki.{split}.tokens\\\");\\n return File.ReadLines(dataPath).Select(line => line.Trim()).Where(line => line.Length > 0);\\n }\\n\\n /// <summary>\\n /// WikiText2\\n /// </summary>\\n /// <param name=\\\"root\\\">The folder where the WikiText2 data set was downloaded and extracted.</param>\\n /// <returns>An enumeration of lines from the text for each of the data sets (training, validation, and test).</returns>\\n /// <remarks>\\n /// Download the data set at:\\n /// https://s3.amazonaws.com/research.metamind.io/wikitext/wikitext-2-v1.zip\\n /// </remarks>\\n public static (IEnumerable<string>, IEnumerable<string>, IEnumerable<string>) WikiText2(string root = \\\".data\\\")\\n {\\n return (WikiText2(\\\"train\\\", root), WikiText2(\\\"valid\\\", root), WikiText2(\\\"test\\\", root));\\n }\\n }\\n}\\n\"\n },\n {\n \"alpha_fraction\": 0.56749027967453,\n \"alphanum_fraction\": 0.5711507797241211,\n \"avg_line_length\": 48.95428466796875,\n \"blob_id\": \"c0b1df898de4c1b9016ebf756a5bcb14d4d8b619\",\n \"content_id\": \"a98f3e46cd370e6c77a26ba8fcce35404eb25b14\",\n \"detected_licenses\": [\n \"MIT\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"C#\",\n \"length_bytes\": 8742,\n \"license_type\": \"permissive\",\n \"max_line_length\": 282,\n \"num_lines\": 175,\n \"path\": \"/src/TorchSharp/NN/Recurrent/RNN.cs\",\n \"repo_name\": \"NiklasGustafsson/TorchSharp\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"// Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information.\\nusing System;\\nusing static TorchSharp.torch;\\nusing static TorchSharp.PInvoke.NativeMethods;\\n\\n#nullable enable\\nnamespace TorchSharp\\n{\\n using Modules;\\n\\n namespace Modules\\n {\\n public sealed class RNN : torch.nn.Module<Tensor, Tensor?, (Tensor, Tensor)>\\n {\\n internal RNN(IntPtr handle, IntPtr boxedHandle, long hiddenSize, long numLayers, bool batchFirst, bool bidirectional) : base(handle, boxedHandle)\\n {\\n _hidden_size = hiddenSize;\\n _num_layers = numLayers;\\n _bidirectional = bidirectional;\\n _batch_first = batchFirst;\\n }\\n\\n private long _hidden_size;\\n private long _num_layers;\\n private bool _bidirectional;\\n private bool _batch_first;\\n\\n /// <summary>\\n /// Applies a multi-layer Elman RNN with \\\\tanhtanh or \\\\text{ReLU}ReLU non-linearity to an input sequence.\\n /// </summary>\\n /// <param name=\\\"input\\\">Tensor of shape (seq_len, batch, input_size) containing the features of the input sequence.</param>\\n /// <param name=\\\"h0\\\">Tensor of shape (num_layers * num_directions, batch, hidden_size)containing the initial hidden state for each element in the batch.\\n /// Defaults to 0 if not provided. If the RNN is bidirectional, num_directions should be 2, else it should be 1.</param>\\n /// <returns></returns>\\n public override (Tensor, Tensor) forward(Tensor input, Tensor? h0)\\n {\\n if (h0 is null) {\\n var N = _batch_first ? input.shape[0] : input.shape[1];\\n var D = _bidirectional ? 2 : 1;\\n\\n h0 = torch.zeros(D * _num_layers, N, _hidden_size, dtype: input.dtype, device: input.device);\\n }\\n\\n var res = THSNN_RNN_forward(handle, input.Handle, h0.Handle, out IntPtr hN);\\n if (res == IntPtr.Zero || hN == IntPtr.Zero) { torch.CheckForErrors(); }\\n return (new Tensor(res), new Tensor(hN));\\n }\\n\\n public new (Tensor, Tensor) call(Tensor input, Tensor? h0 = null) => base.call(input, h0);\\n\\n /// <summary>\\n /// Applies a multi-layer Elman RNN with \\\\tanhtanh or \\\\text{ReLU}ReLU non-linearity to an input sequence.\\n /// </summary>\\n /// <param name=\\\"input\\\">PackedSequence containing the features of the input sequence.</param>\\n /// <param name=\\\"h0\\\">Tensor of shape (num_layers * num_directions, batch, hidden_size)containing the initial hidden state for each element in the batch.\\n /// Defaults to 0 if not provided. If the RNN is bidirectional, num_directions should be 2, else it should be 1.</param>\\n /// <returns></returns>\\n public (torch.nn.utils.rnn.PackedSequence, Tensor) call(torch.nn.utils.rnn.PackedSequence input, Tensor? h0 = null)\\n {\\n if (h0 is null) {\\n var data = input.data;\\n var batch_sizes = input.batch_sizes;\\n var N = batch_sizes[0].item<long>();\\n var D = _bidirectional ? 2 : 1;\\n\\n h0 = torch.zeros(D * _num_layers, N, _hidden_size, dtype: data.dtype, device: data.device);\\n }\\n\\n var res = THSNN_RNN_forward_with_packed_input(handle, input.Handle, h0.Handle, out IntPtr hN);\\n if (res.IsInvalid || hN == IntPtr.Zero) { torch.CheckForErrors(); }\\n return (new torch.nn.utils.rnn.PackedSequence(res), new Tensor(hN));\\n }\\n\\n public void flatten_parameters()\\n {\\n THSNN_RNN_flatten_parameters(handle);\\n torch.CheckForErrors();\\n }\\n\\n public Parameter? get_bias_ih(long idx)\\n {\\n var res = THSNN_RNN_bias_ih(handle, idx);\\n if (res == IntPtr.Zero) { torch.CheckForErrors(); }\\n return ((res == IntPtr.Zero) ? null : new Parameter(res));\\n }\\n\\n public Parameter? get_bias_hh(long idx)\\n {\\n var res = THSNN_RNN_bias_hh(handle, idx);\\n if (res == IntPtr.Zero) { torch.CheckForErrors(); }\\n return ((res == IntPtr.Zero) ? null : new Parameter(res));\\n }\\n\\n#if false // Disabled until we can figure out how to set the native code parameters.\\n\\n public void set_bias_ih(Tensor? value, long idx)\\n {\\n THSNN_RNN_set_bias_ih(handle, (value is null ? IntPtr.Zero : value.Handle), idx);\\n torch.CheckForErrors();\\n ConditionallyRegisterParameter($\\\"bias_ih_l{idx}\\\", value);\\n }\\n\\n public void set_bias_hh(Tensor? value, long idx)\\n {\\n THSNN_RNN_set_bias_hh(handle, (value is null ? IntPtr.Zero : value.Handle), idx);\\n torch.CheckForErrors();\\n ConditionallyRegisterParameter($\\\"bias_hh_l{idx}\\\", value);\\n }\\n#endif\\n\\n public Parameter? get_weight_ih(long idx)\\n {\\n var res = THSNN_RNN_weight_ih(handle, idx);\\n if (res == IntPtr.Zero) { torch.CheckForErrors(); }\\n return (res == IntPtr.Zero) ? null : new Parameter(res);\\n }\\n\\n public Parameter? get_weight_hh(long idx)\\n {\\n var res = THSNN_RNN_weight_hh(handle, idx);\\n if (res == IntPtr.Zero) { torch.CheckForErrors(); }\\n return (res == IntPtr.Zero) ? null : new Parameter(res);\\n }\\n\\n#if false // Disabled until we can figure out how to set the native code parameters.\\n public void set_weight_ih(Tensor? value, long idx)\\n {\\n THSNN_RNN_set_weight_ih(handle, (value is null ? IntPtr.Zero : value.Handle), idx);\\n torch.CheckForErrors();\\n ConditionallyRegisterParameter($\\\"weight_ih_l{idx}\\\", value);\\n }\\n\\n public void set_weight_hh(Tensor? value, long idx)\\n {\\n THSNN_RNN_set_weight_hh(handle, (value is null ? IntPtr.Zero : value.Handle), idx);\\n torch.CheckForErrors();\\n ConditionallyRegisterParameter($\\\"weight_hh_l{idx}\\\", value);\\n }\\n#endif\\n }\\n }\\n\\n public static partial class torch\\n {\\n public static partial class nn\\n {\\n public enum NonLinearities\\n {\\n ReLU = 0,\\n Tanh = 1\\n }\\n\\n /// <summary>\\n /// Creates an Elman RNN module with tanh or ReLU non-linearity.\\n /// </summary>\\n /// <param name=\\\"inputSize\\\">The number of expected features in the input x</param>\\n /// <param name=\\\"hiddenSize\\\">The number of features in the hidden state h</param>\\n /// <param name=\\\"numLayers\\\">Number of recurrent layers. Default: 1</param>\\n /// <param name=\\\"nonLinearity\\\">The non-linearity to use. Can be either 'tanh' or 'relu'. Default: 'tanh'</param>\\n /// <param name=\\\"bias\\\">If False, then the layer does not use bias weights b_ih and b_hh. Default: True</param>\\n /// <param name=\\\"batchFirst\\\">if true, then the input and output tensors are provided as (batch, seq, feature). Default: False</param>\\n /// <param name=\\\"dropout\\\">If non-zero, introduces a Dropout layer on the outputs of each RNN layer except the last layer, with dropout probability equal to dropout. Default: 0</param>\\n /// <param name=\\\"bidirectional\\\">if true, becomes a bidirectional RNN. Default: False</param>\\n /// <param name=\\\"device\\\">The desired device of the parameters and buffers in this module</param>\\n /// <param name=\\\"dtype\\\">The desired floating point or complex dtype of the parameters and buffers in this module</param>\\n /// <returns></returns>\\n public static RNN RNN(long inputSize, long hiddenSize, long numLayers = 1, NonLinearities nonLinearity = nn.NonLinearities.Tanh, bool bias = true, bool batchFirst = false, double dropout = 0.0, bool bidirectional = false, Device? device = null, ScalarType? dtype = null)\\n {\\n var res = THSNN_RNN_ctor(inputSize, hiddenSize, numLayers, (long)nonLinearity, bias, batchFirst, dropout, bidirectional, out var boxedHandle);\\n if (res == IntPtr.Zero) { torch.CheckForErrors(); }\\n return new RNN(res, boxedHandle, hiddenSize, numLayers, batchFirst, bidirectional).MoveModule<RNN>(device, dtype);\\n }\\n }\\n }\\n}\\n\"\n },\n {\n \"alpha_fraction\": 0.6434804797172546,\n \"alphanum_fraction\": 0.6513090133666992,\n \"avg_line_length\": 25.06354522705078,\n \"blob_id\": \"1a6cf08481212f8c5c65005be864abb32d2fda25\",\n \"content_id\": \"9fa77de5c5575bb5e9f3ac108ec1c31cf2ed4839\",\n \"detected_licenses\": [\n \"MIT\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"C++\",\n \"length_bytes\": 7792,\n \"license_type\": \"permissive\",\n \"max_line_length\": 130,\n \"num_lines\": 299,\n \"path\": \"/src/Native/LibTorchSharp/Utils.h\",\n \"repo_name\": \"NiklasGustafsson/TorchSharp\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"// Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information.\\n#pragma once\\n\\n#include <string>\\n\\n#include \\\"torch/torch.h\\\"\\n\\nextern thread_local char *torch_last_err;\\n\\ntypedef torch::Tensor *Tensor;\\ntypedef torch::Scalar *Scalar;\\ntypedef torch::Generator* Generator;\\ntypedef c10::Storage* Storage;\\ntypedef torch::nn::utils::rnn::PackedSequence* PackedSequence;\\n\\ntypedef std::shared_ptr<torch::nn::Module> * NNModule;\\ntypedef std::shared_ptr<torch::nn::AnyModule> * NNAnyModule;\\ntypedef std::shared_ptr<torch::optim::Optimizer> * Optimizer;\\ntypedef std::shared_ptr<torch::jit::CompilationUnit> * JITCompilationUnit;\\ntypedef std::shared_ptr<torch::jit::Module>* JITModule;\\ntypedef std::shared_ptr<torch::jit::Method>* JITMethod;\\ntypedef std::shared_ptr<torch::jit::Function> * JITFunction;\\ntypedef std::shared_ptr<c10::Type> * JITType;\\ntypedef std::shared_ptr<c10::TensorType>* JITTensorType;\\n\\n//typedef std::shared_ptr<torch::jit::DimensionedTensorType>* JITDimensionedTensorType;\\n\\n#define THS_API TH_API\\n\\n#define CATCH(x) \\\\\\n try { \\\\\\n torch_last_err = 0; \\\\\\n x \\\\\\n } catch (const c10::Error e) { \\\\\\n torch_last_err = strdup(e.what()); \\\\\\n } catch (const std::runtime_error e) { \\\\\\n torch_last_err = strdup(e.what()); \\\\\\n }\\n\\n#define CATCH_RETURN_RES(ty, dflt, stmt) \\\\\\n ty res = dflt; \\\\\\n CATCH( \\\\\\n stmt; \\\\\\n ); \\\\\\n return res;\\n\\n#define CATCH_RETURN(ty, dflt, expr) CATCH_RETURN_RES(ty, dflt, res = expr)\\n#define CATCH_RETURN_NNModule(stmt) CATCH_RETURN_RES(NNModule, nullptr, stmt)\\n#define CATCH_RETURN_Tensor(stmt) CATCH_RETURN_RES(Tensor, nullptr, stmt)\\n\\n// Return undefined tensors as nullptr to C#\\ninline Tensor ResultTensor(const at::Tensor & res)\\n{\\n if (res.defined())\\n return new torch::Tensor(res);\\n else\\n return nullptr;\\n}\\n\\n#define CATCH_TENSOR(expr) \\\\\\n at::Tensor res = at::Tensor(); \\\\\\n CATCH( \\\\\\n res = expr; \\\\\\n ); \\\\\\n return ResultTensor(res);\\n\\n#define CATCH_TENSORS_2(expr) \\\\\\n at::Tensor fst = at::Tensor(); \\\\\\n at::Tensor snd = at::Tensor(); \\\\\\n CATCH( \\\\\\n std::tie(fst,snd) = expr; \\\\\\n ); \\\\\\n res1 = ResultTensor(fst); \\\\\\n res2 = ResultTensor(snd); \\n\\n#define CATCH_SCALAR(expr) \\\\\\n at::Scalar res = at::Scalar(); \\\\\\n CATCH( \\\\\\n res = expr; \\\\\\n ); \\\\\\n return ResultTensor(res);\\n\\n\\n// Utility method used to built sharable strings.\\nconst char * make_sharable_string(const std::string str);\\n\\n// Method concerting arrays of tensor pointers into arrays of tensors.\\ntemplate<class T>\\nstd::vector<T> toTensors(torch::Tensor ** tensorPtrs, const int length)\\n{\\n std::vector<T> tensors;\\n\\n if (tensorPtrs != nullptr) {\\n for (int i = 0; i < length; i++)\\n {\\n tensors.push_back(*tensorPtrs[i]);\\n }\\n }\\n return tensors;\\n}\\n\\n// Utilities for NN namespace.\\n\\ntemplate <typename T>\\nTensor get_weight(const NNModule module)\\n{\\n CATCH_TENSOR((*module)->as<T>()->weight);\\n}\\n\\ntemplate <typename T>\\nvoid set_weight(const NNModule module, const Tensor weights)\\n{\\n CATCH(\\n (*module)->as<T>()->weight = *weights;\\n );\\n}\\n\\ntemplate <typename T>\\nTensor get_bias(const NNModule module)\\n{\\n CATCH_TENSOR((*module)->as<T>()->bias);\\n}\\n\\ntemplate <typename T>\\nvoid set_bias(const NNModule module, const Tensor bias)\\n{\\n CATCH(\\n (*module)->as<T>()->bias = *bias;\\n );\\n}\\n\\ntemplate <typename T>\\nTensor get_weight_ih(const NNModule module)\\n{\\n CATCH_TENSOR((*module)->as<T>()->weight_ih);\\n}\\n\\ntemplate <typename T>\\nTensor get_weight_hh(const NNModule module)\\n{\\n CATCH_TENSOR((*module)->as<T>()->weight_hh);\\n}\\n\\ntemplate <typename T>\\nvoid set_weight_ih(const NNModule module, const Tensor weights)\\n{\\n CATCH(\\n (*module)->as<T>()->weight_ih = *weights;\\n );\\n}\\n\\ntemplate <typename T>\\nvoid set_weight_hh(const NNModule module, const Tensor weights)\\n{\\n CATCH(\\n (*module)->as<T>()->weight_hh = *weights;\\n );\\n}\\n\\ntemplate <typename T>\\nTensor get_bias_ih(const NNModule module)\\n{\\n CATCH_TENSOR((*module)->as<T>()->bias_ih);\\n}\\n\\ntemplate <typename T>\\nTensor get_bias_hh(const NNModule module)\\n{\\n CATCH_TENSOR((*module)->as<T>()->bias_hh);\\n}\\n\\ntemplate <typename T>\\nvoid set_bias_ih(const NNModule module, const Tensor bias)\\n{\\n CATCH(\\n (*module)->as<T>()->bias_ih = *bias;\\n );\\n}\\n\\ntemplate <typename T>\\nvoid set_bias_hh(const NNModule module, const Tensor bias)\\n{\\n CATCH(\\n (*module)->as<T>()->bias_hh = *bias;\\n );\\n}\\n\\n#define WIH_BASE 0\\n#define WHH_BASE 1\\n#define BIH_BASE 2\\n#define BHH_BASE 3\\n\\ntemplate <typename T>\\nTensor get_weight_ih(const NNModule module, const int64_t idx)\\n{\\n CATCH_TENSOR((*module)->as<T>()->all_weights()[WIH_BASE + idx * 4]);\\n}\\n\\ntemplate <typename T>\\nTensor get_weight_hh(const NNModule module, const int64_t idx)\\n{\\n CATCH_TENSOR((*module)->as<T>()->all_weights()[WHH_BASE + idx * 4]);\\n}\\n\\ntemplate <typename T>\\nvoid set_weight_ih(const NNModule module, const Tensor weights, const int64_t idx)\\n{\\n CATCH(\\n (*module)->as<T>()->all_weights()[WIH_BASE + idx * 4] = *weights;\\n );\\n}\\n\\ntemplate <typename T>\\nvoid set_weight_hh(const NNModule module, const Tensor weights, const int64_t idx)\\n{\\n CATCH(\\n (*module)->as<T>()->all_weights()[WHH_BASE + idx * 4] = *weights;\\n );\\n}\\n\\ntemplate <typename T>\\nTensor get_bias_ih(const NNModule module, const int64_t idx)\\n{\\n CATCH_TENSOR((*module)->as<T>()->all_weights()[BIH_BASE + idx * 4]);\\n}\\n\\ntemplate <typename T>\\nTensor get_bias_hh(const NNModule module, const int64_t idx)\\n{\\n CATCH_TENSOR((*module)->as<T>()->all_weights()[BHH_BASE + idx * 4]);\\n}\\n\\ntemplate <typename T>\\nvoid set_bias_ih(const NNModule module, const Tensor bias, const int64_t idx)\\n{\\n CATCH(\\n (*module)->as<T>()->all_weights()[BIH_BASE + idx * 4] = *bias;\\n );\\n}\\n\\ntemplate <typename T>\\nvoid set_bias_hh(const NNModule module, const Tensor bias, const int64_t idx)\\n{\\n CATCH(\\n (*module)->as<T>()->all_weights()[BHH_BASE + idx * 4] = *bias;\\n );\\n}\\n\\ntemplate<typename TImpl>\\nNNModule create_module(NNAnyModule* outAsAnyModule)\\n{\\n auto mod = std::make_shared<TImpl>();\\n\\n // Keep a boxed version of the module in case we add it to a Sequential later (the C++ templating means\\n // a Module can only be boxed to AnyModule at the point its static type is known).\\n if (outAsAnyModule != nullptr)\\n {\\n auto wrapped = std::make_shared<torch::nn::AnyModule>(torch::nn::ModuleHolder<TImpl>(*mod));\\n *outAsAnyModule = new std::shared_ptr<torch::nn::AnyModule>(wrapped);\\n }\\n\\n return new std::shared_ptr<torch::nn::Module>(mod);\\n}\\n\\ntemplate<typename TImpl, typename TOptions>\\nNNModule create_module(const TOptions& opts, NNAnyModule* outAsAnyModule)\\n{\\n auto mod = std::make_shared<TImpl>(opts);\\n\\n // Keep a boxed version of the module in case we add it to a Sequential later (the C++ templating means\\n // a Module can only be boxed to AnyModule at the point its static type is known).\\n if (outAsAnyModule != nullptr)\\n {\\n auto wrapped = std::make_shared<torch::nn::AnyModule>(torch::nn::ModuleHolder<TImpl>(*mod));\\n *outAsAnyModule = new std::shared_ptr<torch::nn::AnyModule>(wrapped);\\n }\\n\\n return new std::shared_ptr<torch::nn::Module>(mod);\\n}\\n\\ninline\\ntorch::nn::init::NonlinearityType get_nl_type(const int64_t nl)\\n{\\n switch (nl)\\n {\\n default:\\n case 0: return torch::kLinear;\\n case 1: return torch::kConv1D;\\n case 2: return torch::kConv2D;\\n case 3: return torch::kConv3D;\\n case 4: return torch::kConvTranspose1D;\\n case 5: return torch::kConvTranspose2D;\\n case 6: return torch::kConvTranspose3D;\\n case 7: return torch::kSigmoid;\\n case 8: return torch::kTanh;\\n case 9: return torch::kReLU;\\n case 10: return torch::kLeakyReLU;\\n }\\n}\"\n },\n {\n \"alpha_fraction\": 0.5233196020126343,\n \"alphanum_fraction\": 0.5345221757888794,\n \"avg_line_length\": 38.0625,\n \"blob_id\": \"0ca611ea1c431fc13899ff0bd97049216c2c92f6\",\n \"content_id\": \"7c8c36e1af89a399d344fe423f7276f8cf3c819f\",\n \"detected_licenses\": [\n \"MIT\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"C#\",\n \"length_bytes\": 4374,\n \"license_type\": \"permissive\",\n \"max_line_length\": 140,\n \"num_lines\": 112,\n \"path\": \"/src/TorchAudio/Transforms/MelScale.cs\",\n \"repo_name\": \"NiklasGustafsson/TorchSharp\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"// Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information.\\n\\n// A number of implementation details in this file have been translated from the Python version of torchaudio,\\n// largely located in the files found in this folder:\\n//\\n// https://github.com/pytorch/audio/blob/bb77cbebb620a46fdc0dc7e6dae2253eef3f37e2/torchaudio/transforms/_transforms.py\\n//\\n// The origin has the following copyright notice and license:\\n//\\n// https://github.com/pytorch/audio/blob/main/LICENSE\\n//\\n\\nusing System;\\nusing static TorchSharp.torch;\\nusing static TorchSharp.torch.nn;\\nusing static TorchSharp.torchaudio;\\nusing F = TorchSharp.torchaudio.functional;\\n\\nnamespace TorchSharp\\n{\\n namespace Transforms\\n {\\n public class MelScale : Module<Tensor, Tensor>\\n {\\n public readonly long n_mels;\\n public readonly long sample_rate;\\n public readonly double f_max;\\n public readonly double f_min;\\n public readonly MelNorm norm;\\n public readonly torchaudio.MelScale mel_scale;\\n public readonly Tensor fb;\\n\\n protected override void Dispose(bool disposing)\\n {\\n if (disposing) {\\n fb.Dispose();\\n }\\n base.Dispose(disposing);\\n }\\n\\n internal MelScale(\\n string name,\\n long n_mels = 128,\\n long sample_rate = 16000,\\n double f_min = 0.0,\\n double? f_max = null,\\n long n_stft = 201,\\n MelNorm norm = MelNorm.none,\\n torchaudio.MelScale mel_scale = torchaudio.MelScale.htk) : base(name)\\n {\\n this.n_mels = n_mels;\\n this.sample_rate = sample_rate;\\n this.f_max = f_max ?? (sample_rate / 2);\\n this.f_min = f_min;\\n this.norm = norm;\\n this.mel_scale = mel_scale;\\n\\n if (f_min > this.f_max) {\\n throw new ArgumentOutOfRangeException($\\\"Require f_min: {f_min} < f_max: {this.f_max}\\\");\\n }\\n this.fb = F.melscale_fbanks(n_stft, this.f_min, this.f_max, this.n_mels, this.sample_rate, this.norm, this.mel_scale);\\n this.register_buffer(\\\"fb\\\", fb);\\n }\\n\\n /// <param name=\\\"specgram\\\">A spectrogram STFT of dimension (..., freq, time).</param>\\n /// <returns>Mel frequency spectrogram of size (..., ``n_mels``, time).</returns>\\n public override Tensor forward(Tensor specgram)\\n {\\n var mel_specgram = torch.matmul(specgram.transpose(-1, -2), this.fb).transpose(-1, -2);\\n\\n return mel_specgram;\\n }\\n }\\n }\\n\\n public partial class torchaudio\\n {\\n public partial class transforms\\n {\\n /// <summary>\\n /// Turn a normal STFT into a mel frequency STFT with triangular filter banks.\\n /// </summary>\\n /// <param name=\\\"n_mels\\\">Number of mel filterbanks.</param>\\n /// <param name=\\\"sample_rate\\\">Sample rate of audio signal.</param>\\n /// <param name=\\\"f_min\\\">Minimum frequency.</param>\\n /// <param name=\\\"f_max\\\">Maximum frequency.</param>\\n /// <param name=\\\"n_stft\\\">Number of bins in STFT. See ``n_fft`` in :class:`Spectrogram`.</param>\\n /// <param name=\\\"norm\\\">If 'slaney', divide the triangular mel weights by the width of the mel band (area normalization).</param>\\n /// <param name=\\\"mel_scale\\\">Scale to use: ``htk`` or ``slaney``.</param>\\n /// <returns></returns>\\n public static Transforms.MelScale MelScale(\\n long n_mels = 128,\\n long sample_rate = 16000,\\n double f_min = 0.0,\\n double? f_max = null,\\n long n_stft = 201,\\n MelNorm norm = MelNorm.none,\\n torchaudio.MelScale mel_scale = torchaudio.MelScale.htk)\\n {\\n return new Transforms.MelScale(\\n \\\"MelScale\\\",\\n n_mels,\\n sample_rate,\\n f_min,\\n f_max,\\n n_stft,\\n norm,\\n mel_scale);\\n }\\n }\\n }\\n}\"\n },\n {\n \"alpha_fraction\": 0.5602758526802063,\n \"alphanum_fraction\": 0.5664827823638916,\n \"avg_line_length\": 50.78571319580078,\n \"blob_id\": \"88660fcb39b3450174e2fe236fb65c7eff35f3e6\",\n \"content_id\": \"862bf0b0282d9d6e7ea6160c6ccc0bd6e72ac444\",\n \"detected_licenses\": [\n \"MIT\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"C#\",\n \"length_bytes\": 7250,\n \"license_type\": \"permissive\",\n \"max_line_length\": 137,\n \"num_lines\": 140,\n \"path\": \"/src/TorchSharp/NN/Pooling/AdaptiveAvgPool3D.cs\",\n \"repo_name\": \"NiklasGustafsson/TorchSharp\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"// Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information.\\nusing System;\\nusing static TorchSharp.torch;\\nusing static TorchSharp.PInvoke.NativeMethods;\\n\\nnamespace TorchSharp\\n{\\n using Modules;\\n\\n namespace Modules\\n {\\n /// <summary>\\n /// This class is used to represent a AdaptiveAvgPool3D module.\\n /// </summary>\\n public sealed class AdaptiveAvgPool3d : torch.nn.Module<Tensor, Tensor>\\n {\\n internal AdaptiveAvgPool3d(IntPtr handle, IntPtr boxedHandle) : base(handle, boxedHandle)\\n {\\n }\\n\\n public override Tensor forward(Tensor tensor)\\n {\\n var res = THSNN_AdaptiveAvgPool3d_forward(handle.DangerousGetHandle(), tensor.Handle);\\n if (res == IntPtr.Zero) { torch.CheckForErrors(); }\\n return new Tensor(res);\\n }\\n\\n // Rather than spending cycles only to discover that this module has neither\\n // parameters nor buffers, just shortcut the move completely.\\n protected internal override nn.Module _to(Device device, ScalarType dtype) => this;\\n protected internal override nn.Module _to(DeviceType deviceType, int deviceIndex = -1) => this;\\n protected internal override nn.Module _to(ScalarType dtype) => this;\\n }\\n }\\n\\n public static partial class torch\\n {\\n public static partial class nn\\n {\\n /// <summary>\\n /// Applies a 3D adaptive average pooling over an input signal composed of several input planes.\\n /// The output is of size D x H x W, for any input size.The number of output features is equal to the number of input planes.\\n /// </summary>\\n /// <param name=\\\"outputSize\\\">The target output size of the image of the form D x H x W.</param>\\n /// <returns></returns>\\n public static unsafe AdaptiveAvgPool3d AdaptiveAvgPool3d(long[] outputSize)\\n {\\n fixed (long* pkernelSize = outputSize) {\\n var handle = THSNN_AdaptiveAvgPool3d_ctor((IntPtr)pkernelSize, outputSize.Length, out var boxedHandle);\\n if (handle == IntPtr.Zero) { torch.CheckForErrors(); }\\n return new AdaptiveAvgPool3d(handle, boxedHandle);\\n }\\n }\\n\\n /// <summary>\\n /// Applies a 3D adaptive average pooling over an input signal composed of several input planes.\\n /// The output is of size D x H x W, for any input size.The number of output features is equal to the number of input planes.\\n /// </summary>\\n /// <param name=\\\"outputSize\\\">The target output size (D,H,W) of the image of the form D x H x W.</param>\\n /// <returns></returns>\\n public static unsafe AdaptiveAvgPool3d AdaptiveAvgPool3d((long, long, long) outputSize)\\n {\\n long* pkernelSize = stackalloc long[3] { outputSize.Item1, outputSize.Item2, outputSize.Item3 };\\n\\n var handle = THSNN_AdaptiveAvgPool3d_ctor((IntPtr)pkernelSize, 3, out var boxedHandle);\\n if (handle == IntPtr.Zero) { torch.CheckForErrors(); }\\n return new AdaptiveAvgPool3d(handle, boxedHandle);\\n }\\n\\n /// <summary>\\n /// Applies a 3D adaptive average pooling over an input signal composed of several input planes.\\n /// The output is of size D x H x W, for any input size.The number of output features is equal to the number of input planes.\\n /// </summary>\\n /// <param name=\\\"outputSize\\\">The target output size (D,H,W) of the image of the form H x W.</param>\\n /// <returns></returns>\\n public static unsafe AdaptiveAvgPool3d AdaptiveAvgPool3d(long outputSize)\\n {\\n long* pkernelSize = stackalloc long[3] { outputSize, outputSize, outputSize };\\n var handle = THSNN_AdaptiveAvgPool3d_ctor((IntPtr)pkernelSize, 3, out var boxedHandle);\\n if (handle == IntPtr.Zero) { torch.CheckForErrors(); }\\n return new AdaptiveAvgPool3d(handle, boxedHandle);\\n }\\n\\n public static partial class functional\\n {\\n /// <summary>\\n /// Applies a 3D adaptive average pooling over an input signal composed of several input planes.\\n /// </summary>\\n /// <param name=\\\"input\\\">The input tensor.</param>\\n /// <param name=\\\"output_size\\\"></param>\\n /// <returns></returns>\\n public static unsafe Tensor adaptive_avg_pool3d(Tensor input, long[] output_size)\\n {\\n fixed (long* poutputSize = output_size) {\\n var res =\\n THSTensor_adaptive_avg_pool3d(input.Handle, (IntPtr)poutputSize, output_size.Length);\\n if (res == IntPtr.Zero) { torch.CheckForErrors(); }\\n return new Tensor(res);\\n }\\n }\\n\\n /// <summary>\\n /// Applies a 2D adaptive average pooling over an input signal composed of several input planes.\\n /// </summary>\\n /// <param name=\\\"input\\\">The input tensor.</param>\\n /// <param name=\\\"output_size\\\"></param>\\n /// <returns></returns>\\n public static unsafe Tensor adaptive_avg_pool3d(Tensor input, (long, long, long) output_size)\\n {\\n long* poutputSize = stackalloc long[3] { output_size.Item1, output_size.Item2, output_size.Item3 };\\n var res = THSTensor_adaptive_avg_pool3d(input.Handle, (IntPtr)poutputSize, 3);\\n if (res == IntPtr.Zero) { torch.CheckForErrors(); }\\n return new Tensor(res);\\n }\\n\\n /// <summary>\\n /// Applies a 2D adaptive average pooling over an input signal composed of several input planes.\\n /// </summary>\\n /// <param name=\\\"input\\\">The input tensor.</param>\\n /// <param name=\\\"output_size\\\"></param>\\n /// <returns></returns>\\n public static unsafe Tensor adaptive_avg_pool3d(Tensor input, long output_size)\\n {\\n var os = new long[] { output_size, output_size, output_size };\\n long* poutputSize = stackalloc long[3] { output_size, output_size, output_size };\\n var res = THSTensor_adaptive_avg_pool3d(input.Handle, (IntPtr)poutputSize, 3);\\n if (res == IntPtr.Zero) { torch.CheckForErrors(); }\\n return new Tensor(res);\\n }\\n\\n public static Tensor adaptive_avg_pool3d_backward(Tensor gradInput, Tensor gradOutput, Tensor originalInput)\\n {\\n var res = THSTensor_adaptive_avg_pool3d_backward_out(gradInput.Handle, gradOutput.Handle, originalInput.Handle);\\n if (res == IntPtr.Zero) { torch.CheckForErrors(); }\\n return new Tensor(res);\\n }\\n }\\n }\\n }\\n}\\n\"\n },\n {\n \"alpha_fraction\": 0.6364205479621887,\n \"alphanum_fraction\": 0.6364205479621887,\n \"avg_line_length\": 36.1860466003418,\n \"blob_id\": \"6dc1bbc272e6c410ef62828c298181f82843ac30\",\n \"content_id\": \"f1b162cbbfe5f8a00d860be63cce34fa2e184e6b\",\n \"detected_licenses\": [\n \"MIT\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"C#\",\n \"length_bytes\": 1598,\n \"license_type\": \"permissive\",\n \"max_line_length\": 111,\n \"num_lines\": 43,\n \"path\": \"/test/TorchSharpTest/FactIgnoreOnPlattformAttribute.cs\",\n \"repo_name\": \"NiklasGustafsson/TorchSharp\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"#nullable enable\\nusing System.Linq;\\nusing System.Runtime.InteropServices;\\nusing Xunit;\\n\\nnamespace TorchSharp\\n{\\n /// <summary>\\n /// To ignore an xUnit <see cref=\\\"FactAttribute\\\">Fact</see> on a given platform or architecture.\\n /// </summary>\\n public sealed class FactIgnoreOnPlatformAttribute : FactAttribute\\n {\\n public FactIgnoreOnPlatformAttribute(string skip, params string[] platforms)\\n {\\n if (platforms.Any(p => RuntimeInformation.IsOSPlatform(OSPlatform.Create(p.ToUpperInvariant())))) {\\n Skip = skip;\\n }\\n }\\n\\n public FactIgnoreOnPlatformAttribute(params string[] platforms)\\n {\\n if (platforms.Any(p => RuntimeInformation.IsOSPlatform(OSPlatform.Create(p.ToUpperInvariant())))) {\\n Skip = $\\\"based on platform {RuntimeInformation.OSDescription}\\\";\\n }\\n }\\n\\n public FactIgnoreOnPlatformAttribute(string skip, string platform, Architecture architecture)\\n {\\n if (RuntimeInformation.IsOSPlatform(OSPlatform.Create(platform.ToUpperInvariant()))\\n && RuntimeInformation.ProcessArchitecture == architecture) {\\n Skip = skip;\\n }\\n }\\n\\n public FactIgnoreOnPlatformAttribute(string platform, Architecture architecture)\\n {\\n if (RuntimeInformation.IsOSPlatform(OSPlatform.Create(platform.ToUpperInvariant()))\\n && RuntimeInformation.ProcessArchitecture == architecture) {\\n Skip = $\\\"based on platform {platform} {architecture}\\\";\\n }\\n }\\n }\\n}\"\n },\n {\n \"alpha_fraction\": 0.5757042169570923,\n \"alphanum_fraction\": 0.5757042169570923,\n \"avg_line_length\": 27.450000762939453,\n \"blob_id\": \"63ce94dcd20f5f76c20d49c40909fa1c6a48528f\",\n \"content_id\": \"e65a649f3cc4ce64f91ccc49fe64897386af0593\",\n \"detected_licenses\": [\n \"MIT\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"C#\",\n \"length_bytes\": 568,\n \"license_type\": \"permissive\",\n \"max_line_length\": 130,\n \"num_lines\": 20,\n \"path\": \"/src/TorchSharp/JIT/Type/DynamicType .cs\",\n \"repo_name\": \"NiklasGustafsson/TorchSharp\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"// Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information.\\nusing System;\\n\\nnamespace TorchSharp\\n{\\n public static partial class torch\\n {\\n\\n public static partial class jit\\n {\\n public sealed class DynamicType : Type\\n {\\n internal DynamicType(IntPtr handle) : base(handle, Type.TypeKind.AnyType)\\n {\\n this.handle = new HType(handle, true, Type.TypeKind.AnyType);\\n }\\n }\\n }\\n }\\n}\"\n },\n {\n \"alpha_fraction\": 0.7560219168663025,\n \"alphanum_fraction\": 0.7762938141822815,\n \"avg_line_length\": 44.086021423339844,\n \"blob_id\": \"345b2ce610fd0ee7a4ac2f09d04d910d97237f91\",\n \"content_id\": \"9ab80e8280607b20ecc50566181f980edf4ffcc0\",\n \"detected_licenses\": [\n \"MIT\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"C\",\n \"length_bytes\": 4193,\n \"license_type\": \"permissive\",\n \"max_line_length\": 130,\n \"num_lines\": 93,\n \"path\": \"/src/Native/LibTorchSharp/THSTorch.h\",\n \"repo_name\": \"NiklasGustafsson/TorchSharp\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"// Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information.\\n#pragma once\\n\\n#include \\\"../Stdafx.h\\\"\\n\\n#include \\\"Utils.h\\\"\\n\\n// API.\\n\\n// Sets manually the seed.\\nEXPORT_API(void) THSTorch_manual_seed(const int64_t seed);\\nEXPORT_API(void) THSCuda_manual_seed(const int64_t seed);\\nEXPORT_API(void) THSCuda_manual_seed_all(const int64_t seed);\\n\\nEXPORT_API(Generator) THSGenerator_manual_seed(const int64_t seed);\\nEXPORT_API(void) THSGenerator_gen_manual_seed(const Generator gen, const int64_t seed);\\n\\nEXPORT_API(Tensor) THSGenerator_get_rng_state(const Generator gen);\\nEXPORT_API(void) THSGenerator_set_rng_state(const Generator gen, const Tensor tensor);\\n\\nEXPORT_API(Generator) THSGenerator_default_generator();\\nEXPORT_API(Generator) THSGenerator_new(uint64_t seed, int64_t device, int64_t index);\\nEXPORT_API(int64_t) THSGenerator_initial_seed(const Generator gen);\\nEXPORT_API(void) THSGenerator_dispose(const Generator generator);\\n\\nEXPORT_API(int) THSTorchCuda_is_available();\\nEXPORT_API(int) THSTorchCuda_cudnn_is_available();\\nEXPORT_API(int) THSTorchCuda_device_count();\\nEXPORT_API(void) THSTorchCuda_synchronize(const int64_t device);\\n\\nEXPORT_API(bool) THSBackend_cublas_get_allow_tf32();\\nEXPORT_API(void) THSBackend_cublas_set_allow_tf32(const bool flag);\\nEXPORT_API(bool) THSBackend_cudnn_get_allow_tf32();\\nEXPORT_API(void) THSBackend_cudnn_set_allow_tf32(const bool flag);\\n\\nEXPORT_API(bool) THSBackend_cuda_get_allow_fp16_reduced_precision_reduction();\\nEXPORT_API(void) THSBackend_cuda_set_allow_fp16_reduced_precision_reduction(const bool flag);\\n\\nEXPORT_API(bool) THSBackend_cuda_get_enable_flash_sdp();\\nEXPORT_API(void) THSBackend_cuda_set_enable_flash_sdp(const bool flag);\\nEXPORT_API(bool) THSBackend_cuda_get_enable_math_sdp();\\nEXPORT_API(void) THSBackend_cuda_set_enable_math_sdp(const bool flag);\\n\\nEXPORT_API(int) THSTorch_get_num_threads();\\nEXPORT_API(void) THSTorch_set_num_threads(const int threads);\\n\\nEXPORT_API(int) THSTorch_get_num_interop_threads();\\nEXPORT_API(void) THSTorch_set_num_interop_threads(const int threads);\\n\\n// Returns the latest error. This is thread-local.\\n\\nEXPORT_API(const char *) THSTorch_get_and_reset_last_err();\\n\\nEXPORT_API(int) THSTorch_can_cast(const int type1, const int type2);\\nEXPORT_API(int) THSTorch_promote_types(const int type1, const int type2);\\n\\nEXPORT_API(Scalar) THSTorch_int8_to_scalar(int8_t value);\\nEXPORT_API(Scalar) THSTorch_uint8_to_scalar(uint8_t value);\\nEXPORT_API(Scalar) THSTorch_int16_to_scalar(short value);\\nEXPORT_API(Scalar) THSTorch_int32_to_scalar(int value);\\nEXPORT_API(Scalar) THSTorch_int64_to_scalar(long value);\\nEXPORT_API(Scalar) THSTorch_float32_to_scalar(float value);\\nEXPORT_API(Scalar) THSTorch_float64_to_scalar(double value);\\nEXPORT_API(Scalar) THSTorch_bool_to_scalar(bool value);\\nEXPORT_API(Scalar) THSTorch_float16_to_scalar(float value);\\nEXPORT_API(Scalar) THSTorch_bfloat16_to_scalar(float value);\\n\\nEXPORT_API(Scalar) THSTorch_complex32_to_scalar(float real, float imaginary);\\nEXPORT_API(Scalar) THSTorch_complex64_to_scalar(double real, double imaginary);\\n\\nEXPORT_API(int8_t) THSTorch_scalar_to_int8(Scalar value);\\nEXPORT_API(uint8_t) THSTorch_scalar_to_uint8(Scalar value);\\nEXPORT_API(int16_t) THSTorch_scalar_to_int16(Scalar value);\\nEXPORT_API(int32_t) THSTorch_scalar_to_int32(Scalar value);\\nEXPORT_API(int64_t) THSTorch_scalar_to_int64(Scalar value);\\nEXPORT_API(float) THSTorch_scalar_to_float32(Scalar value);\\nEXPORT_API(double) THSTorch_scalar_to_float64(Scalar value);\\nEXPORT_API(bool) THSTorch_scalar_to_bool(Scalar value);\\n\\nEXPORT_API(void) THSTorch_scalar_to_float16(Scalar value, unsigned short* res);\\n\\nEXPORT_API(void) THSTorch_scalar_to_complex32(Scalar value, float* (*allocator)(size_t length));\\nEXPORT_API(void) THSTorch_scalar_to_complex64(Scalar value, double* (*allocator)(size_t length));\\n\\nEXPORT_API(int8_t) THSTorch_scalar_type(Scalar value);\\n\\n// Dispose the scalar.\\nEXPORT_API(void) THSTorch_dispose_scalar(Scalar scalar);\\n\\n// Math functions not available in .NET standard libs\\n\\nEXPORT_API(double) THSSpecial_erf_scalar(const double x);\\nEXPORT_API(double) THSSpecial_erfc_scalar(const double x);\\n\"\n },\n {\n \"alpha_fraction\": 0.5712489485740662,\n \"alphanum_fraction\": 0.5716680884361267,\n \"avg_line_length\": 41.625,\n \"blob_id\": \"444c170c743025b7e2dfcf77d8b912edcbb8dd59\",\n \"content_id\": \"ff326db1228e1d32ffd3cf28bef4cb27291ad725\",\n \"detected_licenses\": [\n \"MIT\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"C#\",\n \"length_bytes\": 2386,\n \"license_type\": \"permissive\",\n \"max_line_length\": 130,\n \"num_lines\": 56,\n \"path\": \"/src/TorchVision/ResizedCrop.cs\",\n \"repo_name\": \"NiklasGustafsson/TorchSharp\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"// Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information.\\n\\nusing static TorchSharp.torch;\\n\\nnamespace TorchSharp\\n{\\n public static partial class torchvision\\n {\\n internal class ResizedCrop : ITransform\\n {\\n internal ResizedCrop(int top, int left, int height, int width, int newHeight, int newWidth)\\n {\\n cropper = transforms.Crop(top, left, height, width);\\n resizer = transforms.Resize(newHeight, newWidth);\\n }\\n\\n public Tensor call(Tensor input)\\n {\\n using var cr = cropper.call(input);\\n return resizer.call(cr);\\n }\\n\\n private ITransform cropper;\\n private ITransform resizer;\\n }\\n\\n public static partial class transforms\\n {\\n /// <summary>\\n /// Crop the given image and resize it to desired size.\\n /// </summary>\\n /// <param name=\\\"top\\\">Vertical component of the top left corner of the crop box.</param>\\n /// <param name=\\\"left\\\">Horizontal component of the top left corner of the crop box.</param>\\n /// <param name=\\\"height\\\">Height of the crop box.</param>\\n /// <param name=\\\"width\\\">Width of the crop box.</param>\\n /// <param name=\\\"newHeight\\\">New height.</param>\\n /// <param name=\\\"newWidth\\\">New width.</param>\\n static public ITransform ResizedCrop(int top, int left, int height, int width, int newHeight, int newWidth)\\n {\\n return new ResizedCrop(top, left, height, width, newHeight, newWidth);\\n }\\n\\n /// <summary>\\n /// Crop the given image and resize it to desired (square) size.\\n /// </summary>\\n /// <param name=\\\"top\\\">Vertical component of the top left corner of the crop box.</param>\\n /// <param name=\\\"left\\\">Horizontal component of the top left corner of the crop box.</param>\\n /// <param name=\\\"size\\\">Height and width of the crop box.</param>\\n /// <param name=\\\"newSize\\\">New height and width.</param>\\n static public ITransform ResizedCrop(int top, int left, int size, int newSize)\\n {\\n return new ResizedCrop(top, left, size, size, newSize, -1);\\n }\\n }\\n }\\n}\"\n },\n {\n \"alpha_fraction\": 0.5909352898597717,\n \"alphanum_fraction\": 0.5972728729248047,\n \"avg_line_length\": 48.590476989746094,\n \"blob_id\": \"0dd0d5fc172516968490d8dca5a9c578fff2f205\",\n \"content_id\": \"1249b5ba570d01073a7a680c1c08e871e820b408\",\n \"detected_licenses\": [\n \"MIT\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"C#\",\n \"length_bytes\": 5207,\n \"license_type\": \"permissive\",\n \"max_line_length\": 245,\n \"num_lines\": 105,\n \"path\": \"/src/TorchSharp/Optimizers/LBFGS.cs\",\n \"repo_name\": \"NiklasGustafsson/TorchSharp\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"// Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information.\\nusing System;\\nusing System.Collections.Generic;\\nusing System.Linq;\\nusing static TorchSharp.torch;\\nusing static TorchSharp.PInvoke.NativeMethods;\\n\\nnamespace TorchSharp\\n{\\n using Modules;\\n\\n public static partial class torch\\n {\\n public static partial class optim\\n {\\n /// <summary>\\n /// Implements the L-BFGS algorithm, heavily inspired by `minFunc`\\n ///\\n /// https://www.cs.ubc.ca/~schmidtm/Software/minFunc.html\\n /// </summary>\\n /// <param name=\\\"parameters\\\">Parameters to optimize</param>\\n /// <param name=\\\"lr\\\">Learning rate (default: 1e-2)</param>\\n /// <param name=\\\"max_iter\\\">Maximal number of iterations per optimization step</param>\\n /// <param name=\\\"max_eval\\\">Maximal number of function evaluations per optimization step</param>\\n /// <param name=\\\"tolerange_grad\\\">Termination tolerance on first order optimality</param>\\n /// <param name=\\\"tolerance_change\\\">Termination tolerance on function value/parameter changes</param>\\n /// <param name=\\\"history_size\\\">Update history size</param>\\n public static LBFGS LBFGS(IEnumerable<(string name, Parameter parameter)> parameters, double lr = 0.01, long max_iter = 20, long? max_eval = null, double tolerange_grad = 1e-5, double tolerance_change = 1e-9, long history_size = 100)\\n {\\n return LBFGS(parameters.Select(np => np.parameter), lr, max_iter, max_eval.Value, tolerange_grad, tolerance_change, history_size);\\n }\\n\\n /// <summary>\\n /// Implements the L-BFGS algorithm, heavily inspired by `minFunc`\\n ///\\n /// https://www.cs.ubc.ca/~schmidtm/Software/minFunc.html\\n /// </summary>\\n /// <param name=\\\"parameters\\\">Parameters to optimize</param>\\n /// <param name=\\\"lr\\\">Learning rate (default: 1e-2)</param>\\n /// <param name=\\\"max_iter\\\">Maximal number of iterations per optimization step</param>\\n /// <param name=\\\"max_eval\\\">Maximal number of function evaluations per optimization step</param>\\n /// <param name=\\\"tolerange_grad\\\">Termination tolerance on first order optimality</param>\\n /// <param name=\\\"tolerance_change\\\">Termination tolerance on function value/parameter changes</param>\\n /// <param name=\\\"history_size\\\">Update history size</param>\\n public static LBFGS LBFGS(IEnumerable<Parameter> parameters, double lr = 0.01, long max_iter = 20, long? max_eval = null, double tolerange_grad = 1e-5, double tolerance_change = 1e-9, long history_size = 100)\\n {\\n if (!max_eval.HasValue) max_eval = 5 * max_iter / 4;\\n\\n using var parray = new PinnedArray<IntPtr>();\\n IntPtr paramsRef = parray.CreateArray(parameters.Select(p => p.Handle).ToArray());\\n\\n var res = THSNN_LBFGS_ctor(paramsRef, parray.Array.Length, lr, max_iter, max_eval.Value, tolerange_grad, tolerance_change, history_size);\\n if (res == IntPtr.Zero) { torch.CheckForErrors(); }\\n return new LBFGS(res, lr);\\n }\\n }\\n }\\n\\n namespace Modules\\n {\\n using static torch.optim;\\n\\n // LBGFS does not allow for param groups, so there's no need to use anything but the native implementation.\\n public class LBFGS : Optimizer, ILearningRateController\\n {\\n /// <summary>\\n /// Implements L-BFGS algorithm, heavily inspired by `minFunc`\\n ///\\n /// </summary>\\n /// <param name=\\\"handle\\\"></param>\\n /// <param name=\\\"lr\\\"></param>\\n public LBFGS(IntPtr handle, double lr) : base(handle)\\n {\\n _rate = lr;\\n InitialLearningRate = lr;\\n _paramGroups = new ParamGroup[] { new ParamGroup { Parameters = this.parameters(), Options = new() { LearningRate = lr, InitialLearningRate = lr } } };\\n }\\n\\n public double LearningRate {\\n get { return _rate; }\\n set { THSNN_LBFGS_set_lr(handle, value); torch.CheckForErrors(); _rate = value; }\\n }\\n\\n public double InitialLearningRate { get; set; }\\n\\n public override IEnumerable<ILearningRateController> ParamGroups { get => _paramGroups; }\\n\\n\\n /// <summary>\\n /// Performs a single optimization step (parameter update).\\n /// </summary>\\n /// <param name=\\\"closure\\\">A closure that reevaluates the model and returns the loss. Optional for most optimizers.</param>\\n /// <returns></returns>\\n public override Tensor step(Func<Tensor> closure)\\n {\\n if (closure == null)\\n throw new ArgumentNullException(\\\"'closure' must be non-null when using the LBFGS optimizer. See: https://pytorch.org/docs/1.9.0/optim.html\\\");\\n return base.step(closure);\\n }\\n\\n public double _rate;\\n private ParamGroup[] _paramGroups;\\n }\\n }\\n}\\n\"\n },\n {\n \"alpha_fraction\": 0.5266386866569519,\n \"alphanum_fraction\": 0.5404423475265503,\n \"avg_line_length\": 40.85135269165039,\n \"blob_id\": \"6fa6144d3f8ee081bcd47c76f9dba662e5bd9dc3\",\n \"content_id\": \"e91971e5aa397067ea2b5f709543f7e2081e2f08\",\n \"detected_licenses\": [\n \"MIT\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"C#\",\n \"length_bytes\": 12388,\n \"license_type\": \"permissive\",\n \"max_line_length\": 255,\n \"num_lines\": 296,\n \"path\": \"/src/Examples/SpeechCommands.cs\",\n \"repo_name\": \"NiklasGustafsson/TorchSharp\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"// Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information.\\nusing System;\\nusing System.Collections.Generic;\\nusing System.Diagnostics;\\nusing System.IO;\\nusing System.Linq;\\nusing System.Runtime.InteropServices;\\nusing static TorchSharp.torch;\\n\\nusing static TorchSharp.torch.nn;\\nusing static TorchSharp.torch.nn.functional;\\nusing static TorchSharp.torch.utils.data;\\nusing static TorchSharp.torchaudio.datasets;\\n\\nnamespace TorchSharp.Examples\\n{\\n /// <summary>\\n /// SpeechCommands model with convolusions.\\n /// </summary>\\n /// <remarks>\\n /// Translated from Python implementation\\n /// https://pytorch.org/tutorials/intermediate/speech_command_classification_with_torchaudio_tutorial.html\\n /// </remarks>\\n public class SpeechCommands\\n {\\n private static readonly string[] Labels = new string[] {\\n \\\"bed\\\", \\\"bird\\\", \\\"backward\\\", \\\"cat\\\", \\\"dog\\\", \\\"down\\\", \\\"eight\\\",\\n \\\"five\\\", \\\"follow\\\", \\\"forward\\\", \\\"four\\\", \\\"go\\\", \\\"happy\\\", \\\"house\\\",\\n \\\"learn\\\", \\\"left\\\", \\\"marvin\\\", \\\"nine\\\", \\\"no\\\", \\\"off\\\", \\\"on\\\", \\\"one\\\",\\n \\\"right\\\", \\\"seven\\\", \\\"six\\\", \\\"sheila\\\", \\\"stop\\\", \\\"three\\\", \\\"tree\\\",\\n \\\"two\\\", \\\"up\\\", \\\"visual\\\", \\\"wow\\\", \\\"yes\\\", \\\"zero\\\"\\n };\\n\\n private static int _epochs = 1;\\n private static int _trainBatchSize = 64;\\n private static int _testBatchSize = 128;\\n private static int _sample_rate = 16000;\\n private static int _new_sample_rate = 8000;\\n\\n private readonly static int _logInterval = 200;\\n\\n private static IDictionary<string, int> _labelToIndex;\\n\\n internal static void Main(string[] args)\\n {\\n var dataset = args.Length > 0 ? args[0] : \\\"speechcommands\\\";\\n var datasetPath = Environment.GetFolderPath(Environment.SpecialFolder.LocalApplicationData);\\n\\n torchaudio.backend.utils.set_audio_backend(new WaveAudioBackend());\\n torch.random.manual_seed(1);\\n\\n var cwd = Environment.CurrentDirectory;\\n Console.WriteLine(datasetPath);\\n\\n var device = torch.device(torch.cuda.is_available() ? \\\"cuda\\\" : \\\"cpu\\\");\\n\\n Console.WriteLine($\\\"Running SpeechCommands on {device.type.ToString()}\\\");\\n Console.WriteLine($\\\"Dataset: {dataset}\\\");\\n\\n if (device.type == DeviceType.CUDA) {\\n _trainBatchSize *= 4;\\n _testBatchSize *= 4;\\n }\\n\\n var model = new M5(\\\"model\\\");\\n model.to(device);\\n\\n var transform = torchaudio.transforms.Resample(_sample_rate, _new_sample_rate, device: device);\\n\\n _labelToIndex = Labels.Select((label, index) => (label, index)).ToDictionary(t => t.label, t => t.index);\\n using (var train_data = SPEECHCOMMANDS(datasetPath, subset: \\\"training\\\", download: true))\\n using (var test_data = SPEECHCOMMANDS(datasetPath, subset: \\\"testing\\\", download: true)) {\\n TrainingLoop(\\\"speechcommands\\\", device, model, transform, train_data, test_data);\\n }\\n }\\n\\n private static BatchItem Collate(IEnumerable<SpeechCommandsDatasetItem> items, torch.Device device)\\n {\\n var audio_sequences = items.Select(item => item.waveform.t());\\n var padded_audio = torch.nn.utils.rnn.pad_sequence(audio_sequences, batch_first: true, padding_value: 0.0);\\n padded_audio = padded_audio.permute(0, 2, 1);\\n var labels = items.Select(item => _labelToIndex[item.label]).ToArray();\\n return new BatchItem {\\n audio = padded_audio.to(device),\\n label = torch.tensor(labels, dtype: torch.int64, device: device)\\n };\\n }\\n\\n internal static void TrainingLoop(string dataset, Device device, M5 model, nn.IModule<Tensor, Tensor> transform, Dataset<SpeechCommandsDatasetItem> train_data, Dataset<SpeechCommandsDatasetItem> test_data)\\n {\\n using (var train_loader = new DataLoader<SpeechCommandsDatasetItem, BatchItem>(\\n train_data, _trainBatchSize, Collate, shuffle: true, device: device))\\n using (var test_loader = new DataLoader<SpeechCommandsDatasetItem, BatchItem>(\\n test_data, _testBatchSize, Collate, shuffle: false, device: device)) {\\n if (device.type == DeviceType.CUDA) {\\n _epochs *= 4;\\n }\\n\\n var optimizer = optim.Adam(model.parameters(), lr: 0.01, weight_decay: 0.0001);\\n var scheduler = optim.lr_scheduler.StepLR(optimizer, step_size: 20, gamma: 0.1);\\n\\n Stopwatch sw = new Stopwatch();\\n sw.Start();\\n\\n for (var epoch = 1; epoch <= _epochs; epoch++) {\\n Train(model, transform, optimizer, torch.nn.NLLLoss(reduction: torch.nn.Reduction.Mean), train_loader, epoch, train_data.Count);\\n Test(model, transform, torch.nn.NLLLoss(reduction: torch.nn.Reduction.Sum), test_loader, test_data.Count);\\n\\n Console.WriteLine($\\\"End-of-epoch memory use: {GC.GetTotalMemory(false)}\\\");\\n scheduler.step();\\n }\\n\\n sw.Stop();\\n Console.WriteLine($\\\"Elapsed time: {sw.Elapsed.TotalSeconds:F1} s.\\\");\\n\\n Console.WriteLine(\\\"Saving model to '{0}'\\\", dataset + \\\".model.bin\\\");\\n model.save(dataset + \\\".model.bin\\\");\\n }\\n }\\n\\n private static void Train(\\n M5 model,\\n nn.IModule<Tensor, Tensor> transform,\\n torch.optim.Optimizer optimizer,\\n Loss<Tensor,Tensor,Tensor> criteria,\\n DataLoader<SpeechCommandsDatasetItem, BatchItem> dataLoader,\\n int epoch,\\n long size)\\n {\\n int batchId = 1;\\n long total = 0;\\n\\n Console.WriteLine($\\\"Epoch: {epoch}...\\\");\\n\\n using (var d = torch.NewDisposeScope()) {\\n\\n model.train();\\n foreach (var batch in dataLoader) {\\n var audio = transform.call(batch.audio);\\n var target = batch.label;\\n var output = model.call(batch.audio).squeeze();\\n var loss = criteria.call(output, target);\\n optimizer.zero_grad();\\n loss.backward();\\n optimizer.step();\\n total += target.shape[0];\\n\\n if (batchId % _logInterval == 0 || total == size) {\\n Console.WriteLine($\\\"\\\\rTrain: epoch {epoch} [{total} / {size}] Loss: {loss.ToSingle():F4}\\\");\\n }\\n\\n batchId++;\\n\\n d.DisposeEverything();\\n }\\n }\\n }\\n\\n private static void Test(\\n M5 model,\\n nn.IModule<Tensor, Tensor> transform,\\n Loss<Tensor, Tensor, Tensor> criteria,\\n DataLoader<SpeechCommandsDatasetItem, BatchItem> dataLoader,\\n long size)\\n {\\n model.eval();\\n\\n double testLoss = 0;\\n int correct = 0;\\n\\n using (var d = torch.NewDisposeScope()) {\\n\\n foreach (var batch in dataLoader) {\\n var audio = transform.call(batch.audio);\\n var target = batch.label;\\n var output = model.call(batch.audio).squeeze();\\n var loss = criteria.call(output, target);\\n testLoss += loss.ToSingle();\\n\\n var pred = output.argmax(1);\\n correct += pred.eq(batch.label).sum().ToInt32();\\n\\n d.DisposeEverything();\\n }\\n }\\n\\n Console.WriteLine($\\\"Size: {size}, Total: {size}\\\");\\n\\n Console.WriteLine($\\\"\\\\rTest set: Average loss {(testLoss / size):F4} | Accuracy {((double)correct / size):P2}\\\");\\n }\\n\\n private class WaveAudioBackend : torchaudio.backend.AudioBackend\\n {\\n public override (torch.Tensor, int) load(string filepath, long frame_offset = 0, long num_frames = -1, bool normalize = true, bool channels_first = true, torchaudio.AudioFormat? format = null)\\n {\\n byte[] data = File.ReadAllBytes(filepath);\\n // In many cases, the first 44 bytes are for RIFF header.\\n short[] waveform = MemoryMarshal.Cast<byte, short>(data.AsSpan(11 * 4)).ToArray();\\n return (torch.tensor(waveform).unsqueeze(0).to(torch.float32) / short.MaxValue, 16000);\\n }\\n\\n public override void save(string filepath, torch.Tensor src, int sample_rate, bool channels_first = true, float? compression = null, torchaudio.AudioFormat? format = null, torchaudio.AudioEncoding? encoding = null, int? bits_per_sample = null)\\n {\\n throw new NotImplementedException();\\n }\\n\\n public override torchaudio.AudioMetaData info(string filepath, torchaudio.AudioFormat? format = null)\\n {\\n throw new NotImplementedException();\\n }\\n }\\n\\n private class BatchItem\\n {\\n public torch.Tensor audio;\\n public torch.Tensor label;\\n }\\n\\n internal class M5 : Module<Tensor, Tensor>\\n {\\n private readonly Module<Tensor, Tensor> conv1;\\n private readonly Module<Tensor, Tensor> bn1;\\n private readonly Module<Tensor, Tensor> pool1;\\n private readonly Module<Tensor, Tensor> conv2;\\n private readonly Module<Tensor, Tensor> bn2;\\n private readonly Module<Tensor, Tensor> pool2;\\n private readonly Module<Tensor, Tensor> conv3;\\n private readonly Module<Tensor, Tensor> bn3;\\n private readonly Module<Tensor, Tensor> pool3;\\n private readonly Module<Tensor, Tensor> conv4;\\n private readonly Module<Tensor, Tensor> bn4;\\n private readonly Module<Tensor, Tensor> pool4;\\n private readonly Module<Tensor, Tensor> fc1;\\n\\n public M5(string name, int n_input = 1, int n_output = 35, int stride = 16, int n_channel = 32) : base(name)\\n {\\n conv1 = nn.Conv1d(n_input, n_channel, kernelSize: 80, stride: stride);\\n bn1 = nn.BatchNorm1d(n_channel);\\n pool1 = nn.MaxPool1d(4);\\n conv2 = nn.Conv1d(n_channel, n_channel, kernelSize: 3);\\n bn2 = nn.BatchNorm1d(n_channel);\\n pool2 = nn.MaxPool1d(4);\\n conv3 = nn.Conv1d(n_channel, 2 * n_channel, kernelSize: 3);\\n bn3 = nn.BatchNorm1d(2 * n_channel);\\n pool3 = nn.MaxPool1d(4);\\n conv4 = nn.Conv1d(2 * n_channel, 2 * n_channel, kernelSize: 3);\\n bn4 = nn.BatchNorm1d(2 * n_channel);\\n pool4 = nn.MaxPool1d(4);\\n fc1 = nn.Linear(2 * n_channel, n_output);\\n RegisterComponents();\\n }\\n\\n public override Tensor forward(Tensor input)\\n {\\n var x = input;\\n x = conv1.call(x);\\n x = relu(bn1.call(x));\\n x = pool1.call(x);\\n x = conv2.call(x);\\n x = relu(bn2.call(x));\\n x = pool2.call(x);\\n x = conv3.call(x);\\n x = relu(bn3.call(x));\\n x = pool3.call(x);\\n x = conv4.call(x);\\n x = relu(bn4.call(x));\\n x = pool4.call(x);\\n x = avg_pool1d(x, x.shape[x.dim() - 1]);\\n x = x.permute(0, 2, 1);\\n x = fc1.call(x);\\n return log_softmax(x, dim: 2);\\n }\\n\\n protected override void Dispose(bool disposing)\\n {\\n if (disposing) {\\n pool1.Dispose();\\n pool2.Dispose();\\n pool3.Dispose();\\n pool4.Dispose();\\n conv1.Dispose();\\n conv2.Dispose();\\n conv3.Dispose();\\n conv4.Dispose();\\n bn1.Dispose();\\n bn2.Dispose();\\n bn3.Dispose();\\n bn4.Dispose();\\n fc1.Dispose();\\n ClearModules();\\n }\\n base.Dispose(disposing);\\n }\\n }\\n }\\n}\\n\"\n },\n {\n \"alpha_fraction\": 0.632435142993927,\n \"alphanum_fraction\": 0.6376579999923706,\n \"avg_line_length\": 59.60685348510742,\n \"blob_id\": \"8d3af93b72a7cab20184e4baeacf39d5606e6015\",\n \"content_id\": \"ff6f4d6b17f64f9f4a56794aee36871b75d24fd1\",\n \"detected_licenses\": [\n \"MIT\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"C#\",\n \"length_bytes\": 30084,\n \"license_type\": \"permissive\",\n \"max_line_length\": 232,\n \"num_lines\": 496,\n \"path\": \"/src/TorchSharp/Tensor/torch.BlasAndLapackOperations.cs\",\n \"repo_name\": \"NiklasGustafsson/TorchSharp\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"// Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information.\\n#nullable enable\\n\\nusing System;\\nusing System.Collections.Generic;\\nusing static TorchSharp.PInvoke.NativeMethods;\\n\\nnamespace TorchSharp\\n{\\n // https://pytorch.org/docs/stable/torch#blas-and-lapack-operations\\n public static partial class torch\\n {\\n public enum LobpcgMethod\\n {\\n basic,\\n ortho\\n }\\n\\n // https://pytorch.org/docs/stable/generated/torch.addbmm\\n /// <summary>\\n /// Performs a batch matrix-matrix product of matrices stored in batch1 and batch2, with a reduced\\n /// add step (all matrix multiplications get accumulated along the first dimension).\\n /// input is added to the final result.\\n /// </summary>\\n /// <param name=\\\"input\\\">The input tensor.</param>\\n /// <param name=\\\"batch1\\\">The first batch of matrices to be multiplied</param>\\n /// <param name=\\\"batch2\\\">The second batch of matrices to be multiplied</param>\\n /// <param name=\\\"beta\\\">Nultiplier for input (β)</param>\\n /// <param name=\\\"alpha\\\">Multiplier for batch1 @ batch2 (α)</param>\\n public static Tensor addbmm(Tensor input, Tensor batch1, Tensor batch2, float beta = 1, float alpha = 1)\\n => input.addbmm(batch1, batch2, beta, alpha);\\n\\n /// <summary>\\n /// Performs a batch matrix-matrix product of matrices stored in batch1 and batch2, with a reduced\\n /// add step (all matrix multiplications get accumulated along the first dimension).\\n /// input is added to the final result.\\n /// In-place version of addbmm.\\n /// </summary>\\n /// <param name=\\\"input\\\">The input tensor.</param>\\n /// <param name=\\\"batch1\\\">The first batch of matrices to be multiplied</param>\\n /// <param name=\\\"batch2\\\">The second batch of matrices to be multiplied</param>\\n /// <param name=\\\"beta\\\">Nultiplier for input (β)</param>\\n /// <param name=\\\"alpha\\\">Multiplier for batch1 @ batch2 (α)</param>\\n public static Tensor addbmm_(Tensor input, Tensor batch1, Tensor batch2, float beta = 1, float alpha = 1)\\n => input.addbmm_(batch1, batch2, beta, alpha);\\n\\n // https://pytorch.org/docs/stable/generated/torch.addmm\\n /// <summary>\\n /// Performs a matrix multiplication of the matrices mat1 and mat2. The matrix input is added to the final result.\\n /// </summary>\\n /// <param name=\\\"input\\\">The input tensor.</param>\\n /// <param name=\\\"mat1\\\">First matrix</param>\\n /// <param name=\\\"mat2\\\">Second matrix</param>\\n /// <param name=\\\"beta\\\">Input scale factor</param>\\n /// <param name=\\\"alpha\\\">Matrix multiplication scale factor</param>\\n public static Tensor addmm(Tensor input, Tensor mat1, Tensor mat2, float beta, float alpha) => input.addmm(mat1, mat2, beta, alpha);\\n\\n // https://pytorch.org/docs/stable/generated/torch.addmm\\n /// <summary>\\n /// Performs a matrix multiplication of the matrices mat1 and mat2. The matrix input is added to the final result.\\n /// </summary>\\n /// <param name=\\\"input\\\">The input tensor.</param>\\n /// <param name=\\\"mat1\\\">First matrix</param>\\n /// <param name=\\\"mat2\\\">Second matrix</param>\\n /// <param name=\\\"beta\\\">Input scale factor</param>\\n /// <param name=\\\"alpha\\\">Matrix multiplication scale factor</param>\\n public static Tensor addmm_(Tensor input, Tensor mat1, Tensor mat2, float beta, float alpha) => input.addmm_(mat1, mat2, beta, alpha);\\n\\n // https://pytorch.org/docs/stable/generated/torch.addmv\\n\\n /// <summary>\\n /// Performs a matrix multiplication of the matrices mat1 and mat2. The matrix input is added to the final result.\\n /// </summary>\\n /// <param name=\\\"input\\\">The input tensor.</param>\\n /// <param name=\\\"mat1\\\">First matrix</param>\\n /// <param name=\\\"mat2\\\">Second matrix</param>\\n /// <param name=\\\"beta\\\">Input scale factor</param>\\n /// <param name=\\\"alpha\\\">Matrix multiplication scale factor</param>\\n public static Tensor addmv(Tensor input, Tensor mat1, Tensor mat2, float beta, float alpha) => input.addmv(mat1, mat2, beta, alpha);\\n\\n /// <summary>\\n /// Performs a matrix multiplication of the matrices mat1 and mat2. The matrix input is added to the final result.\\n /// </summary>\\n /// <param name=\\\"input\\\">The input tensor.</param>\\n /// <param name=\\\"mat1\\\">First matrix</param>\\n /// <param name=\\\"mat2\\\">Second matrix</param>\\n /// <param name=\\\"beta\\\">Input scale factor</param>\\n /// <param name=\\\"alpha\\\">Matrix multiplication scale factor</param>\\n public static Tensor addmv_(Tensor input, Tensor mat1, Tensor mat2, float beta, float alpha) => input.addmv_(mat1, mat2, beta, alpha);\\n\\n // https://pytorch.org/docs/stable/generated/torch.addr\\n /// <summary>\\n /// Performs the outer-product of vectors vec1 and vec2 and adds it to the input tensor.\\n /// </summary>\\n /// <returns></returns>\\n public static Tensor addr(Tensor input, Tensor vec1, Tensor vec2, float beta = 1.0f, float alpha = 1.0f) => input.addr(vec1, vec2, beta, alpha);\\n\\n // https://pytorch.org/docs/stable/generated/torch.addr\\n /// <summary>\\n /// Performs the outer-product of vectors vec1 and vec2 and adds it to the input tensor.\\n /// </summary>\\n /// <param name=\\\"input\\\">The input tensor.</param>\\n /// <param name=\\\"vec1\\\">The first vector of the outer product</param>\\n /// <param name=\\\"vec2\\\">The second vector of the outer product</param>\\n /// <param name=\\\"beta\\\">Input scale factor</param>\\n /// <param name=\\\"alpha\\\">Outer-product scale factor</param>\\n public static Tensor addr_(Tensor input, Tensor vec1, Tensor vec2, float beta = 1.0f, float alpha = 1.0f) => input.addr_(vec1, vec2, beta, alpha);\\n\\n // https://pytorch.org/docs/stable/generated/torch.baddbmm\\n /// <summary>\\n /// Performs a batch matrix-matrix product of matrices in batch1 and batch2. input is added to the final result.\\n /// batch1 and batch2 must be 3-D tensors each containing the same number of matrices.\\n /// </summary>\\n /// <param name=\\\"input\\\">The tensor to be added</param>\\n /// <param name=\\\"batch1\\\">The first batch of matrices to be multiplied</param>\\n /// <param name=\\\"batch2\\\">The second batch of matrices to be multiplied</param>\\n /// <param name=\\\"beta\\\">A multiplier for input</param>\\n /// <param name=\\\"alpha\\\">A multiplier for batch1 @ batch2</param>\\n public static Tensor baddbmm(Tensor input, Tensor batch1, Tensor batch2, float beta = 1, float alpha = 1) => input.baddbmm(batch1, batch2, beta, alpha);\\n\\n // https://pytorch.org/docs/stable/generated/torch.bmm\\n /// <summary>\\n /// Performs a batch matrix-matrix product of matrices stored in input and mat2.\\n /// </summary>\\n /// <param name=\\\"input\\\">The input tensor</param>\\n /// <param name=\\\"batch2\\\">the second batch of matrices to be multiplied</param>\\n public static Tensor bmm(Tensor input, Tensor batch2) => input.bmm(batch2);\\n\\n // https://pytorch.org/docs/stable/generated/torch.chain_matmul\\n public static Tensor chain_matmul(params Tensor[] matrices) => torch.linalg.multi_dot(matrices);\\n\\n // https://pytorch.org/docs/stable/generated/torch.cholesky\\n\\n public static Tensor cholesky(Tensor input) => input.cholesky();\\n\\n // https://pytorch.org/docs/stable/generated/torch.cholesky_inverse\\n /// <summary>\\n /// Computes the inverse of a symmetric positive-definite matrix AA using its Cholesky factor uu : returns matrix inv\\n /// </summary>\\n /// <returns></returns>\\n public static Tensor cholesky_inverse(Tensor input, bool upper = false)\\n => input.cholesky_inverse(upper);\\n\\n // https://pytorch.org/docs/stable/generated/torch.cholesky_solve\\n /// <summary>\\n /// Solves a linear system of equations with a positive semidefinite matrix to be inverted given its Cholesky factor matrix u.\\n /// </summary>\\n /// <returns></returns>\\n public static Tensor cholesky_solve(Tensor input, Tensor input2, bool upper = false)\\n => input.cholesky_solve(input2, upper);\\n\\n // https://pytorch.org/docs/stable/generated/torch.dot\\n /// <summary>\\n /// Computes the dot product of two 1D tensors.\\n /// </summary>\\n public static Tensor dot(Tensor input, Tensor other) => input.dot(other);\\n\\n // https://pytorch.org/docs/stable/generated/torch.eig\\n [Obsolete(\\\"Method removed in Pytorch. Please use the `torch.linalg.eig` function instead.\\\", true)]\\n public static (Tensor eigenvalues, Tensor eigenvectors) eig(Tensor input, bool eigenvectors = false) => throw new NotImplementedException();\\n\\n // https://pytorch.org/docs/stable/generated/torch.geqrf\\n /// <summary>\\n /// This is a low-level function for calling LAPACK’s geqrf directly.\\n /// This function returns a namedtuple (a, tau) as defined in LAPACK documentation for geqrf.\\n /// </summary>\\n /// <param name=\\\"input\\\">The input tensor.</param>\\n /// <remarks>\\n /// Computes a QR decomposition of input. Both Q and R matrices are stored in the same output tensor a.\\n /// The elements of R are stored on and above the diagonal. Elementary reflectors (or Householder vectors)\\n /// implicitly defining matrix Q are stored below the diagonal. The results of this function can be used\\n /// together with torch.linalg.householder_product() to obtain the Q matrix or with torch.ormqr(), which\\n /// uses an implicit representation of the Q matrix, for an efficient matrix-matrix multiplication.\\n /// </remarks>\\n public static (Tensor a, Tensor tau) geqrf(Tensor input) => input.geqrf();\\n\\n // https://pytorch.org/docs/stable/generated/torch.ger\\n /// <summary>\\n /// Outer product of input and vec2.\\n /// </summary>\\n /// <param name=\\\"input\\\">The input vector.</param>\\n /// <param name=\\\"vec2\\\">1-D input vector.</param>\\n /// <remarks>If input is a vector of size n and vec2 is a vector of size m, then out must be a matrix of size n×m.</remarks>\\n public static Tensor ger(Tensor input, Tensor vec2) => input.ger(vec2);\\n\\n // https://pytorch.org/docs/stable/generated/torch.inner\\n /// <summary>\\n /// Computes the dot product for 1D tensors.\\n /// For higher dimensions, sums the product of elements from input and other along their last dimension.\\n /// </summary>\\n public static Tensor inner(Tensor input, Tensor vec2) => input.inner(vec2);\\n\\n // https://pytorch.org/docs/stable/generated/torch.inverse\\n /// <summary>\\n /// Alias for torch.linalg.inv()\\n /// </summary>\\n public static Tensor inverse(Tensor input) => linalg.inv(input);\\n\\n // https://pytorch.org/docs/stable/generated/torch.det\\n /// <summary>\\n /// Computes the determinant of a square matrix.\\n /// </summary>\\n /// <param name=\\\"input\\\">The input tensor.</param>\\n public static Tensor det(Tensor input) => input.det();\\n\\n // https://pytorch.org/docs/stable/generated/torch.logdet\\n public static Tensor logdet(Tensor input) => input.logdet();\\n\\n // https://pytorch.org/docs/stable/generated/torch.slogdet\\n public static (Tensor res, Tensor logabsdet) slogdet(Tensor A) => torch.linalg.slogdet(A);\\n\\n // https://pytorch.org/docs/stable/generated/torch.lstsq\\n /// <summary>\\n /// Computes the solution to the least squares and least norm problems for a full rank matrix A of size m×n and a matrix B of size m×k.\\n /// </summary>\\n /// <param name=\\\"A\\\">the m by n matrix AA</param>\\n /// <param name=\\\"B\\\">the matrix BB</param>\\n /// <returns></returns>\\n public static (Tensor Solution, Tensor QR) lstsq(Tensor B, Tensor A)\\n {\\n var solution = THSTorch_lstsq(B.Handle, A.Handle, out var qr);\\n if (solution == IntPtr.Zero || qr == IntPtr.Zero)\\n CheckForErrors();\\n return (new Tensor(solution), new Tensor(qr));\\n }\\n\\n // https://pytorch.org/docs/stable/generated/torch.lu\\n /// <summary>\\n /// Computes the LU factorization of a matrix or batches of matrices A. Returns a tuple containing the LU factorization and pivots of A. Pivoting is done if pivot is set to true.\\n /// </summary>\\n /// <param name=\\\"A\\\">The tensor to factor of size (∗,m,n)</param>\\n /// <param name=\\\"pivot\\\">Controls whether pivoting is done. Default: true</param>\\n /// <param name=\\\"get_infos\\\">If set to True, returns an info IntTensor. Default: false</param>\\n /// <returns></returns>\\n public static (Tensor A_LU, Tensor? pivots, Tensor? infos) lu(Tensor A, bool pivot = true, bool get_infos = false)\\n {\\n var solution = THSTensor_lu(A.Handle, pivot, get_infos, out var infos, out var pivots);\\n if (solution == IntPtr.Zero)\\n torch.CheckForErrors();\\n return (new Tensor(solution), pivots == IntPtr.Zero ? null : new Tensor(pivots), infos == IntPtr.Zero ? null : new Tensor(infos));\\n }\\n\\n // https://pytorch.org/docs/stable/generated/torch.lu_solve\\n /// <summary>\\n /// Returns the LU solve of the linear system Ax = b using the partially pivoted LU factorization of A from torch.lu().\\n /// </summary>\\n /// <param name=\\\"b\\\">The RHS tensor of size (∗,m,k), where *∗ is zero or more batch dimensions.</param>\\n /// <param name=\\\"LU_data\\\">The pivoted LU factorization of A from torch.lu() of size (∗,m,m), where *∗ is zero or more batch dimensions.</param>\\n /// <param name=\\\"LU_pivots\\\">\\n /// The pivots of the LU factorization from torch.lu() of size (∗,m), where *∗ is zero or more batch dimensions.\\n /// The batch dimensions of LU_pivots must be equal to the batch dimensions of LU_data.</param>\\n /// <returns></returns>\\n public static Tensor lu_solve(Tensor b, Tensor LU_data, Tensor LU_pivots)\\n {\\n var solution = THSTensor_lu_solve(b.Handle, LU_data.Handle, LU_pivots.Handle);\\n if (solution == IntPtr.Zero)\\n CheckForErrors();\\n return new Tensor(solution);\\n }\\n\\n // https://pytorch.org/docs/stable/generated/torch.lu_unpack\\n /// <summary>\\n /// Unpacks the data and pivots from a LU factorization of a tensor into tensors L and U and a permutation tensor P.\\n /// </summary>\\n /// <param name=\\\"LU_data\\\">The packed LU factorization data</param>\\n /// <param name=\\\"LU_pivots\\\">The packed LU factorization pivots</param>\\n /// <param name=\\\"unpack_data\\\">A flag indicating if the data should be unpacked. If false, then the returned L and U are null. Default: true</param>\\n /// <param name=\\\"unpack_pivots\\\">A flag indicating if the pivots should be unpacked into a permutation matrix P. If false, then the returned P is null. Default: true</param>\\n /// <returns>A tuple of three tensors to use for the outputs (P, L, U)</returns>\\n public static (Tensor P, Tensor? L, Tensor? U) lu_unpack(Tensor LU_data, Tensor LU_pivots, bool unpack_data = true, bool unpack_pivots = true)\\n {\\n var solution = THSTensor_lu_unpack(LU_data.Handle, LU_pivots.Handle, unpack_data, unpack_pivots, out var L, out var U);\\n if (solution == IntPtr.Zero)\\n CheckForErrors();\\n return (new Tensor(solution), L == IntPtr.Zero ? null : new Tensor(L), U == IntPtr.Zero ? null : new Tensor(U));\\n }\\n\\n // https://pytorch.org/docs/stable/generated/torch.matmul\\n /// <summary>\\n /// Matrix product of two tensors.\\n /// </summary>\\n /// <returns></returns>\\n /// <remarks>\\n /// The behavior depends on the dimensionality of the tensors as follows:\\n /// 1. If both tensors are 1-dimensional, the dot product (scalar) is returned\\n /// 2. If both arguments are 2-dimensional, the matrix-matrix product is returned.\\n /// 3. If the first argument is 1-dimensional and the second argument is 2-dimensional, a 1 is prepended to its dimension for the purpose of the matrix multiply. After the matrix multiply, the prepended dimension is removed.\\n /// 4. If the first argument is 2-dimensional and the second argument is 1-dimensional, the matrix-vector product is returned.\\n /// 5. If both arguments are at least 1-dimensional and at least one argument is N-dimensional (where N > 2), then a batched matrix multiply is returned.\\n /// </remarks>\\n public static Tensor matmul(Tensor input, Tensor target) => input.matmul(target);\\n\\n // https://pytorch.org/docs/stable/generated/torch.matrix_power\\n /// <summary>\\n /// Computes the n-th power of a square matrix for an integer n.\\n /// </summary>\\n /// <param name=\\\"input\\\">The input square matrix.</param>\\n /// <param name=\\\"n\\\">The exponent</param>\\n /// <returns></returns>\\n /// <remarks>Input tensor must be of shape (*, m, m) where * is zero or more batch dimensions.</remarks>\\n public static Tensor matrix_power(Tensor input, int n) => input.matrix_power(n);\\n\\n // https://pytorch.org/docs/stable/generated/torch.matrix_rank\\n [Obsolete(\\\"This function was deprecated since version 1.9 and is now removed. Please use the 'torch.linalg.matrix_rank' function instead.\\\", true)]\\n public static Tensor matrix_rank(Tensor input, float? tol = null, bool symmetric = false) => throw new NotImplementedException();\\n\\n // https://pytorch.org/docs/stable/generated/torch.matrix_exp\\n /// <summary>\\n /// Computes the matrix exponential of a square matrix or of each square matrix in a batch.\\n /// </summary>\\n /// <param name=\\\"input\\\">The input tensor.</param>\\n public static Tensor matrix_exp(Tensor input) => input.matrix_exp();\\n\\n // https://pytorch.org/docs/stable/generated/torch.mm\\n /// <summary>\\n /// Performs a matrix multiplication of the matrices input and mat2.\\n /// </summary>\\n /// <returns></returns>\\n public static Tensor mm(Tensor input, Tensor target) => input.mm(target);\\n\\n // https://pytorch.org/docs/stable/generated/torch.mv\\n /// <summary>\\n /// Performs a matrix-vector product of the matrix input and the vector vec.\\n /// </summary>\\n /// <returns></returns>\\n public static Tensor mv(Tensor input, Tensor target) => input.mv(target);\\n\\n // https://pytorch.org/docs/stable/generated/torch.orgqr\\n /// <summary>\\n /// Computes the first n columns of a product of Householder matrices.\\n /// </summary>\\n /// <param name=\\\"input\\\">tensor of shape (*, m, n) where * is zero or more batch dimensions.</param>\\n /// <param name=\\\"tau\\\">tensor of shape (*, k) where * is zero or more batch dimensions.</param>\\n public static Tensor orgqr(Tensor input, Tensor tau) => linalg.householder_product(input, tau);\\n\\n // https://pytorch.org/docs/stable/generated/torch.ormqr\\n /// <summary>\\n /// Computes the matrix-matrix multiplication of a product of Householder matrices with a general matrix.\\n /// </summary>\\n /// <param name=\\\"input\\\">Tensor of shape (*, mn, k) where * is zero or more batch dimensions and mn equals to m or n depending on the left.</param>\\n /// <param name=\\\"tau\\\">Tensor of shape (*, min(mn, k)) where * is zero or more batch dimensions.</param>\\n /// <param name=\\\"other\\\">Tensor of shape (*, m, n) where * is zero or more batch dimensions.</param>\\n /// <param name=\\\"left\\\">Controls the order of multiplication.</param>\\n /// <param name=\\\"transpose\\\">Controls whether the matrix Q is conjugate transposed or not.</param>\\n public static Tensor ormqr(Tensor input, Tensor tau, Tensor other, bool left=true, bool transpose=false) => input.ormqr(tau, other, left, transpose); \\n\\n // https://pytorch.org/docs/stable/generated/torch.outer\\n /// <summary>\\n /// Outer product of input and vec2.\\n /// </summary>\\n /// <param name=\\\"input\\\">1-D input vector.</param>\\n /// <param name=\\\"vec2\\\">1-D input vector.</param>\\n /// <remarks>If input is a vector of size n and vec2 is a vector of size m, then out must be a matrix of size n×m.</remarks>\\n public static Tensor outer(Tensor input, Tensor vec2) => input.outer(vec2);\\n\\n // https://pytorch.org/docs/stable/generated/torch.pinverse\\n /// <summary>\\n /// Computes the pseudoinverse (Moore-Penrose inverse) of a matrix.\\n /// </summary>\\n /// <param name=\\\"input\\\">Tensor of shape (*, m, n) where * is zero or more batch dimensions.</param>\\n /// <param name=\\\"rcond\\\">The tolerance value to determine when is a singular value zero </param>\\n /// <param name=\\\"hermitian\\\">Indicates whether A is Hermitian if complex or symmetric if real. </param>\\n /// <remarks>Input should be tensor of shape (*, m, n) where * is zero or more batch dimensions.</remarks>\\n public static Tensor pinverse(Tensor input, double rcond = 1e-15, bool hermitian = false) => input.pinverse(rcond, hermitian);\\n\\n // https://pytorch.org/docs/stable/generated/torch.qr\\n [Obsolete(\\\"torch.qr() is deprecated in favor of torch.linalg.qr() and will be removed in a future PyTorch release.\\\", true)]\\n public static Tensor qr(Tensor input, bool some = true) => throw new NotImplementedException();\\n\\n // https://pytorch.org/docs/stable/generated/torch.svd\\n [Obsolete(\\\"torch.qr() is deprecated in favor of torch.linalg.svd() and will be removed in a future PyTorch release.\\\", true)]\\n public static Tensor svd(Tensor input, bool some=true, bool compute_uv=true) => throw new NotImplementedException();\\n\\n // https://pytorch.org/docs/stable/generated/torch.svd_lowrank\\n // NOTE TO SELF: there's no native method for this. PyTorch implements it in Python.\\n [Obsolete(\\\"not implemented\\\", true)]\\n public static Tensor svd_lowrank(Tensor A, int q=6, int niter=2,Tensor? M=null) => throw new NotImplementedException();\\n\\n // https://pytorch.org/docs/stable/generated/torch.pca_lowrank\\n // NOTE TO SELF: there's no native method for this. PyTorch implements it in Python.\\n [Obsolete(\\\"not implemented\\\", true)]\\n public static Tensor pca_lowrank(Tensor A, int q=6, bool center=true, int niter=2) => throw new NotImplementedException();\\n\\n // https://pytorch.org/docs/stable/generated/torch.symeig\\n [Obsolete(\\\"torch.symeig() is deprecated in favor of torch.linalg.eigh() and will be removed in a future PyTorch release\\\", true)]\\n public static Tensor symeig(Tensor input, bool eigenvectors = false, bool upper = true) => throw new NotImplementedException();\\n\\n // https://pytorch.org/docs/stable/generated/torch.lobpcg\\n [Obsolete(\\\"not implemented\\\", true)]\\n static Tensor lobpcg(\\n Tensor A,\\n int k = 1,\\n Tensor? B = null,\\n Tensor? X = null,\\n int? n = null,\\n Tensor? iK = null,\\n int? niter = null,\\n float? tol=null,\\n bool largest=true,\\n LobpcgMethod method = LobpcgMethod.ortho,\\n Action? tracker=null,\\n IDictionary<string, string>? ortho_iparams=null,\\n IDictionary<string, string>? ortho_fparams=null,\\n IDictionary<string, string>? ortho_bparams=null)\\n => throw new NotImplementedException();\\n\\n /// <summary>\\n /// Computes the softmax function for the input tensor.\\n /// </summary>\\n /// <param name=\\\"input\\\">The input tensor</param>\\n /// <param name=\\\"dim\\\">A dimension along which softmax will be computed.</param>\\n /// <param name=\\\"dtype\\\">The desired data type of returned tensor.</param>\\n public static Tensor softmax(Tensor input, int dim, ScalarType? dtype = null)\\n => torch.special.softmax(input, dim, dtype);\\n\\n // https://pytorch.org/docs/stable/generated/torch.trapz\\n /// <summary>\\n /// Computes the trapezoidal rule along dim. By default the spacing between elements is assumed\\n /// to be 1, but dx can be used to specify a different constant spacing, and x can be used to specify arbitrary spacing along dim.\\n /// </summary>\\n /// <param name=\\\"y\\\">Values to use when computing the trapezoidal rule.</param>\\n /// <param name=\\\"x\\\">Defines spacing between values as specified above.</param>\\n /// <param name=\\\"dim\\\">The dimension along which to compute the trapezoidal rule. The last (inner-most) dimension by default.</param>\\n public static Tensor trapz(Tensor y, Tensor x, long dim = -1) => trapezoid(y, x, dim);\\n\\n /// <summary>\\n /// Computes the trapezoidal rule along dim. By default the spacing between elements is assumed\\n /// to be 1, but dx can be used to specify a different constant spacing, and x can be used to specify arbitrary spacing along dim.\\n /// </summary>\\n /// <param name=\\\"y\\\">Values to use when computing the trapezoidal rule.</param>\\n /// <param name=\\\"dx\\\">Constant spacing between values.</param>\\n /// <param name=\\\"dim\\\">The dimension along which to compute the trapezoidal rule. The last (inner-most) dimension by default.</param>\\n public static Tensor trapz(Tensor y, double dx = 1, long dim = -1) => trapezoid(y, dx, dim);\\n\\n // https://pytorch.org/docs/stable/generated/torch.trapezoid\\n /// <summary>\\n /// Computes the trapezoidal rule along dim. By default the spacing between elements is assumed\\n /// to be 1, but dx can be used to specify a different constant spacing, and x can be used to specify arbitrary spacing along dim.\\n /// </summary>\\n /// <param name=\\\"y\\\">Values to use when computing the trapezoidal rule.</param>\\n /// <param name=\\\"x\\\">Defines spacing between values as specified above.</param>\\n /// <param name=\\\"dim\\\">The dimension along which to compute the trapezoidal rule. The last (inner-most) dimension by default.</param>\\n public static Tensor trapezoid(Tensor y, Tensor x, long dim = -1) => y.trapezoid(x, dim);\\n\\n /// <summary>\\n /// Computes the trapezoidal rule along dim. By default the spacing between elements is assumed\\n /// to be 1, but dx can be used to specify a different constant spacing, and x can be used to specify arbitrary spacing along dim.\\n /// </summary>\\n /// <param name=\\\"y\\\">Values to use when computing the trapezoidal rule.</param>\\n /// <param name=\\\"dx\\\">Constant spacing between values.</param>\\n /// <param name=\\\"dim\\\">The dimension along which to compute the trapezoidal rule. The last (inner-most) dimension by default.</param>\\n public static Tensor trapezoid(Tensor y, double dx = 1, long dim = -1) => y.trapezoid(dx, dim);\\n\\n // https://pytorch.org/docs/stable/generated/torch.cumulative_trapezoid\\n /// <summary>\\n /// Cumulatively computes the trapezoidal rule along dim. By default the spacing between elements is assumed\\n /// to be 1, but dx can be used to specify a different constant spacing, and x can be used to specify arbitrary spacing along dim.\\n /// </summary>\\n /// <param name=\\\"y\\\">Values to use when computing the trapezoidal rule.</param>\\n /// <param name=\\\"x\\\">Defines spacing between values as specified above.</param>\\n /// <param name=\\\"dim\\\">The dimension along which to compute the trapezoidal rule. The last (inner-most) dimension by default.</param>\\n public static Tensor cumulative_trapezoid(Tensor y, Tensor x, long dim = -1) => y.cumulative_trapezoid(x, dim);\\n\\n /// <summary>\\n /// Cumulatively computes the trapezoidal rule along dim. By default the spacing between elements is assumed\\n /// to be 1, but dx can be used to specify a different constant spacing, and x can be used to specify arbitrary spacing along dim.\\n /// </summary>\\n /// <param name=\\\"y\\\">Values to use when computing the trapezoidal rule.</param>\\n /// <param name=\\\"dx\\\">Constant spacing between values.</param>\\n /// <param name=\\\"dim\\\">The dimension along which to compute the trapezoidal rule. The last (inner-most) dimension by default.</param>\\n public static Tensor cumulative_trapezoid(Tensor y, double dx = 1, long dim = -1) => y.cumulative_trapezoid(dx, dim);\\n\\n // https://pytorch.org/docs/stable/generated/torch.triangular_solve\\n [Obsolete(\\\"torch.triangular_solve() is deprecated in favor of torch.linalg.solve_triangular() and will be removed in a future PyTorch release.\\\", true)]\\n static Tensor triangular_solve(\\n Tensor b,\\n Tensor A,\\n bool upper = true,\\n bool transpose = false,\\n bool unitriangular = false)\\n => throw new NotImplementedException();\\n\\n // https://pytorch.org/docs/stable/generated/torch.vdot\\n /// <summary>\\n /// Computes the dot product of two 1D tensors.\\n /// </summary>\\n /// <param name=\\\"input\\\"></param>\\n /// <param name=\\\"target\\\"></param>\\n /// <returns></returns>\\n /// <remarks>\\n /// The vdot(a, b) function handles complex numbers differently than dot(a, b).\\n /// If the first argument is complex, the complex conjugate of the first argument is used for the calculation of the dot product.\\n /// </remarks>\\n public static Tensor vdot(Tensor input, Tensor target) => input.vdot(target);\\n }\\n}\"\n },\n {\n \"alpha_fraction\": 0.4919374883174896,\n \"alphanum_fraction\": 0.4924336373806,\n \"avg_line_length\": 32.59166717529297,\n \"blob_id\": \"0ea4f8bf2e92f0d83ce8f138d3e05291366d4925\",\n \"content_id\": \"3f9d27b8075e626bd97b322548a45b8fdf0e4e11\",\n \"detected_licenses\": [\n \"MIT\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"C#\",\n \"length_bytes\": 4031,\n \"license_type\": \"permissive\",\n \"max_line_length\": 131,\n \"num_lines\": 120,\n \"path\": \"/src/TorchSharp/Generator.cs\",\n \"repo_name\": \"NiklasGustafsson/TorchSharp\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"// Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information.\\nusing System;\\nusing static TorchSharp.PInvoke.NativeMethods;\\n\\n#nullable enable\\n\\nnamespace TorchSharp\\n{\\n public static partial class torch\\n {\\n /// <summary>\\n /// Random Number Generator\\n /// </summary>\\n public class Generator : IDisposable\\n {\\n public IntPtr Handle { get; private set; }\\n\\n /// <summary>\\n /// Gets the current device of the generator.\\n /// </summary>\\n public torch.Device device { get; private set; }\\n\\n /// <summary>\\n /// Returns the Generator state as a torch.ByteTensor.\\n /// </summary>\\n /// <returns></returns>\\n public Tensor get_state()\\n {\\n var res = THSGenerator_get_rng_state(Handle);\\n if (res == IntPtr.Zero) { torch.CheckForErrors(); }\\n return new Tensor(res);\\n }\\n\\n /// <summary>\\n /// Sets the Generator state.\\n /// </summary>\\n /// <param name=\\\"value\\\">The desired state.</param>\\n public void set_state(Tensor value)\\n {\\n THSGenerator_set_rng_state(Handle, value is null ? IntPtr.Zero : value.Handle);\\n torch.CheckForErrors();\\n }\\n\\n public Generator manual_seed(long seed)\\n {\\n torch.TryInitializeDeviceType(DeviceType.CUDA);\\n THSGenerator_gen_manual_seed(Handle, seed);\\n return this;\\n }\\n\\n /// <summary>\\n /// Gets a non-deterministic random number from std::random_device or the current time and uses it to seed a Generator.\\n /// </summary>\\n /// <returns></returns>\\n public long seed()\\n {\\n long seed = DateTime.UtcNow.Ticks;\\n torch.TryInitializeDeviceType(DeviceType.CUDA);\\n THSGenerator_gen_manual_seed(Handle, seed);\\n return seed;\\n }\\n\\n internal Generator(IntPtr nativeHandle)\\n {\\n Handle = nativeHandle;\\n device = torch.CPU;\\n }\\n\\n /// <summary>\\n /// Constructor\\n /// </summary>\\n /// <param name=\\\"seed\\\">An initial seed to use with the generator.</param>\\n /// <param name=\\\"device\\\">The desired device.</param>\\n public Generator(ulong seed = 0, torch.Device? device = null) :\\n this(THSGenerator_new(seed, (long)(device?.type ?? DeviceType.CPU), device?.index ?? -1))\\n {\\n this.device = device ?? torch.CPU;\\n }\\n\\n public static Generator Default {\\n get {\\n return new Generator(THSGenerator_default_generator());\\n }\\n }\\n\\n /// <summary>\\n /// Returns the initial seed for generating random numbers.\\n /// </summary>\\n /// <returns></returns>\\n public long initial_seed()\\n {\\n return THSGenerator_initial_seed(Handle);\\n }\\n\\n #region Dispose() support\\n protected virtual void Dispose(bool disposing)\\n {\\n if (Handle != IntPtr.Zero) {\\n var h = Handle;\\n Handle = IntPtr.Zero;\\n THSGenerator_dispose(h);\\n }\\n }\\n\\n ~Generator()\\n {\\n // Do not change this code. Put cleanup code in 'Dispose(bool disposing)' method\\n Dispose(disposing: false);\\n }\\n\\n public void Dispose()\\n {\\n // Do not change this code. Put cleanup code in 'Dispose(bool disposing)' method\\n Dispose(disposing: true);\\n GC.SuppressFinalize(this);\\n }\\n #endregion\\n }\\n }\\n}\\n\"\n },\n {\n \"alpha_fraction\": 0.5701754093170166,\n \"alphanum_fraction\": 0.5742239952087402,\n \"avg_line_length\": 34.28571319580078,\n \"blob_id\": \"888583bb24b5c312f323f7359b4badabe3fcfce1\",\n \"content_id\": \"cdf7f93bc10cab285c61c0b1a8b9376f0abcfdd0\",\n \"detected_licenses\": [\n \"MIT\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"C#\",\n \"length_bytes\": 1482,\n \"license_type\": \"permissive\",\n \"max_line_length\": 130,\n \"num_lines\": 42,\n \"path\": \"/src/TorchVision/AdjustContrast.cs\",\n \"repo_name\": \"NiklasGustafsson/TorchSharp\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"// Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information.\\nusing System;\\nusing static TorchSharp.torch;\\n\\nnamespace TorchSharp\\n{\\n public static partial class torchvision\\n {\\n internal class AdjustContrast : ITransform\\n {\\n internal AdjustContrast(double contrast_factor)\\n {\\n if (contrast_factor < 0.0)\\n throw new ArgumentException($\\\"The sharpness factor ({contrast_factor}) must be non-negative.\\\");\\n this.contrast_factor = contrast_factor;\\n }\\n\\n public Tensor call(Tensor input)\\n {\\n return transforms.functional.adjust_contrast(input, contrast_factor);\\n }\\n\\n private double contrast_factor;\\n }\\n\\n public static partial class transforms\\n {\\n /// <summary>\\n /// Adjust the contrast of the image. \\n /// </summary>\\n /// <param name=\\\"contrast_factor\\\">\\n /// How much to adjust the contrast. Can be any non-negative number.\\n /// 0 gives a solid gray image, 1 gives the original image while 2 increases the contrast by a factor of 2.\\n /// </param>\\n /// <returns></returns>\\n static public ITransform AdjustContrast(double contrast_factor)\\n {\\n return new AdjustContrast(contrast_factor);\\n }\\n }\\n }\\n}\\n\"\n },\n {\n \"alpha_fraction\": 0.738095223903656,\n \"alphanum_fraction\": 0.7428571581840515,\n \"avg_line_length\": 31.30769157409668,\n \"blob_id\": \"bda0a3cf34e22556c9ab1b2f7229c8afe1773533\",\n \"content_id\": \"f299c7b0e6fa2336c50f70420d1221eb65381048\",\n \"detected_licenses\": [\n \"MIT\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"C#\",\n \"length_bytes\": 420,\n \"license_type\": \"permissive\",\n \"max_line_length\": 130,\n \"num_lines\": 13,\n \"path\": \"/src/TorchSharp/PInvoke/LibTorchSharp.crc32c.cs\",\n \"repo_name\": \"NiklasGustafsson/TorchSharp\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"// Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information.\\n#nullable enable\\nusing System;\\nusing System.Runtime.InteropServices;\\n\\nnamespace TorchSharp.PInvoke\\n{\\n internal static partial class NativeMethods\\n {\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern uint crc32c_append(uint crc, IntPtr value, ulong length);\\n }\\n}\\n\"\n },\n {\n \"alpha_fraction\": 0.5171321034431458,\n \"alphanum_fraction\": 0.5222156047821045,\n \"avg_line_length\": 57.034481048583984,\n \"blob_id\": \"8bd612088c2e48d10752d8fe514a557d09611e7b\",\n \"content_id\": \"5dd5fe6e2fd128b473d4d4ad55c8146dbf760522\",\n \"detected_licenses\": [\n \"MIT\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"C#\",\n \"length_bytes\": 15159,\n \"license_type\": \"permissive\",\n \"max_line_length\": 288,\n \"num_lines\": 261,\n \"path\": \"/src/TorchSharp/NN/Vision.cs\",\n \"repo_name\": \"NiklasGustafsson/TorchSharp\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"// Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information.\\nusing System;\\nusing static TorchSharp.PInvoke.NativeMethods;\\n\\n#nullable enable\\nnamespace TorchSharp\\n{\\n public static partial class torch\\n {\\n public enum UpsampleMode\\n {\\n Nearest = 0,\\n Linear = 1,\\n Bilinear = 2,\\n Bicubic = 3,\\n Trilinear = 4\\n }\\n\\n public enum InterpolationMode\\n {\\n Nearest = 0,\\n Linear = 1,\\n Bilinear = 2,\\n Bicubic = 3,\\n Trilinear = 4,\\n Area = 5\\n }\\n\\n public enum GridSampleMode\\n {\\n Nearest = 0,\\n Bilinear = 2,\\n // PyTorch docs say this should be available,\\n // but the libtorch native code does not support it.\\n //Bicubic = 3\\n }\\n\\n public enum GridSamplePaddingMode\\n {\\n Zeros = 0,\\n Reflection = 1,\\n Border = 2,\\n }\\n\\n public static partial class nn\\n {\\n\\n public static partial class functional\\n {\\n\\n /// <summary>\\n /// Pads tensor.\\n /// </summary>\\n /// <param name=\\\"input\\\">N-dimensional tensor</param>\\n /// <param name=\\\"pad\\\">m-elements tuple, where m/2 ≤ input dimensions and m is even</param>\\n /// <param name=\\\"mode\\\">'constant', 'reflect', 'replicate' or 'circular'. Default: 'constant'</param>\\n /// <param name=\\\"value\\\">Fill value for 'constant' padding. Default: 0</param>\\n /// <returns></returns>\\n public static Tensor pad(Tensor input, long[] pad, PaddingModes mode = PaddingModes.Constant, double value = 0)\\n {\\n unsafe {\\n fixed (long* psize = pad) {\\n var res = THSNN_pad(input.Handle, (IntPtr)psize, pad.Length, (byte)mode, value);\\n if (res == IntPtr.Zero) { torch.CheckForErrors(); }\\n return new Tensor(res);\\n }\\n }\\n }\\n\\n /// <summary>\\n /// Pads tensor.\\n /// </summary>\\n /// <param name=\\\"input\\\">N-dimensional tensor</param>\\n /// <param name=\\\"pad\\\">m-elements tuple, where m/2 ≤ input dimensions and m is even</param>\\n /// <param name=\\\"mode\\\">'constant', 'reflect', 'replicate' or 'circular'. Default: 'constant'</param>\\n /// <param name=\\\"value\\\">Fill value for 'constant' padding. Default: 0</param>\\n /// <returns></returns>\\n public static Tensor pad(Tensor input, ReadOnlySpan<long> pad, PaddingModes mode = PaddingModes.Constant, double value = 0)\\n {\\n unsafe {\\n fixed (long* psize = pad) {\\n var res = THSNN_pad(input.Handle, (IntPtr)psize, pad.Length, (byte)mode, value);\\n if (res == IntPtr.Zero) { torch.CheckForErrors(); }\\n return new Tensor(res);\\n }\\n }\\n }\\n\\n /// <summary>\\n /// Pads tensor.\\n /// </summary>\\n /// <param name=\\\"input\\\">N-dimensional tensor</param>\\n /// <param name=\\\"pad\\\">m-elements tuple, where m/2 ≤ input dimensions and m is even</param>\\n /// <param name=\\\"mode\\\">'constant', 'reflect', 'replicate' or 'circular'. Default: 'constant'</param>\\n /// <param name=\\\"value\\\">Fill value for 'constant' padding. Default: 0</param>\\n /// <returns></returns>\\n public static Tensor pad(Tensor input, (long, long) pad, PaddingModes mode = PaddingModes.Constant, double value = 0)\\n {\\n unsafe {\\n var correctedPad = stackalloc long[] { pad.Item1, pad.Item2 };\\n\\n var res = THSNN_pad(input.Handle, (IntPtr)correctedPad, 2, (byte)mode, value);\\n if (res == IntPtr.Zero) { torch.CheckForErrors(); }\\n return new Tensor(res);\\n }\\n }\\n\\n /// <summary>\\n /// Pads tensor.\\n /// </summary>\\n /// <param name=\\\"input\\\">N-dimensional tensor</param>\\n /// <param name=\\\"pad\\\">m-elements tuple, where m/2 ≤ input dimensions and m is even</param>\\n /// <param name=\\\"mode\\\">'constant', 'reflect', 'replicate' or 'circular'. Default: 'constant'</param>\\n /// <param name=\\\"value\\\">Fill value for 'constant' padding. Default: 0</param>\\n /// <returns></returns>\\n public static Tensor pad(Tensor input, (long, long, long, long) pad, PaddingModes mode = PaddingModes.Constant, double value = 0)\\n {\\n unsafe {\\n var correctedPad = stackalloc long[] { pad.Item1, pad.Item2, pad.Item3, pad.Item4 };\\n var res = THSNN_pad(input.Handle, (IntPtr)correctedPad, 4, (byte)mode, value);\\n if (res == IntPtr.Zero) { torch.CheckForErrors(); }\\n return new Tensor(res);\\n }\\n }\\n\\n /// <summary>\\n /// Pads tensor.\\n /// </summary>\\n /// <param name=\\\"input\\\">N-dimensional tensor</param>\\n /// <param name=\\\"pad\\\">A single padding size, used for all edges.</param>\\n /// <param name=\\\"mode\\\">'constant', 'reflect', 'replicate' or 'circular'. Default: 'constant'</param>\\n /// <param name=\\\"value\\\">Fill value for 'constant' padding. Default: 0</param>\\n /// <returns></returns>\\n public static Tensor pad(Tensor input, long pad, PaddingModes mode = PaddingModes.Constant, double value = 0)\\n {\\n int length = (int)input.ndim * 2;\\n\\n unsafe {\\n var correctedPad = stackalloc long[length];\\n for (var i = 0; i < length; i++) correctedPad[i] = pad;\\n\\n var res = THSNN_pad(input.Handle, (IntPtr)correctedPad, length, (byte)mode, value);\\n if (res == IntPtr.Zero) { torch.CheckForErrors(); }\\n return new Tensor(res);\\n }\\n }\\n\\n\\n /// <summary>\\n /// Given an input and a flow-field grid, computes the output using input values and pixel locations from grid.\\n /// </summary>\\n /// <param name=\\\"input\\\">Tensor of 4D or 5D</param>\\n /// <param name=\\\"grid\\\">Flow-field tensor.</param>\\n /// <param name=\\\"mode\\\">Interpolation mode to calculate output values 'bilinear' | 'nearest' | 'bicubic'.</param>\\n /// <param name=\\\"padding_mode\\\">Padding mode for outside grid values 'zeros' | 'border' | 'reflection'. Default: 'zeros'</param>\\n /// <param name=\\\"align_corners\\\">Geometrically, we consider the pixels of the input as squares rather than points.\\n /// If set to true, the extrema (-1 and 1) are considered as referring to the center points of the input’s corner pixels.\\n /// If set to false, they are instead considered as referring to the corner points of the input’s corner pixels, making the sampling more resolution agnostic.</param>\\n /// <returns></returns>\\n /// <remarks>\\n /// Currently, only spatial (4-D) and volumetric (5-D) input are supported.\\n /// Note: mode='bicubic' supports only 4-D input.\\n /// </remarks>\\n public static Tensor grid_sample(Tensor input, Tensor grid, GridSampleMode mode = GridSampleMode.Bilinear, GridSamplePaddingMode padding_mode = GridSamplePaddingMode.Zeros, bool? align_corners = null)\\n {\\n byte ac = (byte)((align_corners.HasValue) ? (align_corners.Value ? 1 : 2) : 0);\\n var res = THSNN_grid_sample(input.Handle, grid.Handle, (byte)mode, (byte)padding_mode, ac);\\n if (res == IntPtr.Zero) { torch.CheckForErrors(); }\\n return new Tensor(res);\\n }\\n\\n /// <summary>\\n /// Generates a 2D or 3D flow field (sampling grid), given a batch of affine matrices theta.\\n /// </summary>\\n /// <param name=\\\"theta\\\">Input batch of affine matrices with shape (N, 2, 3 ) for 2D or (N, 3, 4 ) for 3D</param>\\n /// <param name=\\\"size\\\">The target output image size</param>\\n /// <param name=\\\"align_corners\\\">if true, consider -1 and 1 to refer to the centers of the corner pixels rather than the image corners.\\n /// Refer to grid_sample() for a more complete description.</param>\\n /// <returns></returns>\\n public static Tensor affine_grid(Tensor theta, long[]? size = null, bool align_corners = false)\\n {\\n unsafe {\\n fixed (long* psize = size) {\\n var res = THSNN_affine_grid(theta.Handle, (IntPtr)psize, size is null ? 0 : size.Length, align_corners);\\n if (res == IntPtr.Zero) { torch.CheckForErrors(); }\\n return new Tensor(res);\\n }\\n }\\n }\\n\\n /// <summary>\\n ///\\n /// </summary>\\n /// <param name=\\\"x\\\">The input tensor</param>\\n /// <param name=\\\"size\\\">Output spatial size</param>\\n /// <param name=\\\"scale_factor\\\">Multiplier for spatial size. Has to match input size if it is a tuple.</param>\\n /// <param name=\\\"mode\\\">The algorithm used for upsampling: 'nearest' | 'linear' | 'bilinear' | 'bicubic' | 'trilinear' | 'area'</param>\\n /// <param name=\\\"align_corners\\\">Geometrically, we consider the pixels of the input and output as squares rather than points.\\n /// If set to true, the input and output tensors are aligned by the center points of their corner pixels, preserving the values at the corner pixels.\\n /// If set to false, the input and output tensors are aligned by the corner points of their corner pixels, and the interpolation uses edge value padding for out-of-boundary values, making this operation independent of input size when scale_factor is kept the same.</param>\\n /// <param name=\\\"recompute_scale_factor\\\">\\n /// Recompute the scale_factor for use in the interpolation calculation.\\n /// When scale_factor is passed as a parameter, it is used to compute the output_size.\\n /// If recompute_scale_factor is False or not specified, the passed-in scale_factor will be used in the interpolation computation.\\n /// Otherwise, a new scale_factor will be computed based on the output and input sizes for use in the interpolation computation\\n /// (i.e. the computation will be identical to if the computed output_size were passed-in explicitly).\\n /// </param>\\n /// <returns></returns>\\n public static Tensor interpolate(Tensor x, long[]? size = null, double[]? scale_factor = null, InterpolationMode mode = InterpolationMode.Nearest, bool? align_corners = null, bool recompute_scale_factor = false)\\n {\\n unsafe {\\n fixed (long* psize = size) {\\n fixed (double* pSF = scale_factor) {\\n byte ac = (byte)((align_corners.HasValue) ? (align_corners.Value ? 1 : 2) : 0);\\n var res = THSNN_interpolate(x.Handle, (IntPtr)psize, size is null ? 0 : size.Length, (IntPtr)pSF, scale_factor is null ? 0 : scale_factor.Length, (byte)mode, ac, recompute_scale_factor);\\n if (res == IntPtr.Zero) { torch.CheckForErrors(); }\\n return new Tensor(res);\\n }\\n }\\n }\\n }\\n\\n /// <summary>\\n /// Upsamples a given multi-channel 1D (temporal), 2D (spatial) or 3D (volumetric) data.\\n /// The input data is assumed to be of the form minibatch x channels x[optional depth] x[optional height] x width.\\n /// Hence, for spatial inputs, we expect a 4D Tensor and for volumetric inputs, we expect a 5D Tensor.\\n /// </summary>\\n /// <param name=\\\"x\\\">Input tensor</param>\\n /// <param name=\\\"size\\\">Output spatial sizes</param>\\n /// <param name=\\\"scale_factor\\\">Multiplier for spatial size. Has to match input size</param>\\n /// <param name=\\\"align_corners\\\">If true, the corner pixels of the input and output tensors are aligned, and thus preserving the values at those pixels.\\n /// This only has effect when mode is 'linear', 'bilinear', or 'trilinear'. Default: false</param>\\n /// <returns></returns>\\n public static Tensor upsample_bilinear(Tensor x, long[]? size = null, double[]? scale_factor = null, bool align_corners = false)\\n {\\n using (var d = torch.nn.Upsample(size, scale_factor, UpsampleMode.Bilinear, align_corners)) {\\n return d.call(x);\\n }\\n }\\n\\n /// <summary>\\n /// Upsamples a given multi-channel 1D (temporal), 2D (spatial) or 3D (volumetric) data.\\n /// The input data is assumed to be of the form minibatch x channels x[optional depth] x[optional height] x width.\\n /// Hence, for spatial inputs, we expect a 4D Tensor and for volumetric inputs, we expect a 5D Tensor.\\n /// </summary>\\n /// <param name=\\\"x\\\">Input tensor</param>\\n /// <param name=\\\"size\\\">Output spatial sizes</param>\\n /// <param name=\\\"scale_factor\\\">Multiplier for spatial size. Has to match input size</param>\\n /// <param name=\\\"align_corners\\\">If true, the corner pixels of the input and output tensors are aligned, and thus preserving the values at those pixels.\\n /// This only has effect when mode is 'linear', 'bilinear', or 'trilinear'. Default: false</param>\\n /// <returns></returns>\\n public static Tensor upsample_nearest(Tensor x, long[]? size = null, double[]? scale_factor = null, bool align_corners = false)\\n {\\n using (var d = torch.nn.Upsample(size, scale_factor, UpsampleMode.Nearest, align_corners)) {\\n return d.call(x);\\n }\\n }\\n }\\n }\\n }\\n}\\n\"\n },\n {\n \"alpha_fraction\": 0.6417730450630188,\n \"alphanum_fraction\": 0.6452260613441467,\n \"avg_line_length\": 76.83125305175781,\n \"blob_id\": \"a24c9deda0e64edcddc858185877109356b6bc20\",\n \"content_id\": \"bb2df53f325dab07cb4fcd887d8f431b6ca1a461\",\n \"detected_licenses\": [\n \"MIT\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"C#\",\n \"length_bytes\": 12465,\n \"license_type\": \"permissive\",\n \"max_line_length\": 490,\n \"num_lines\": 160,\n \"path\": \"/src/TorchSharp/NN/EmbeddingBag.cs\",\n \"repo_name\": \"NiklasGustafsson/TorchSharp\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"// Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information.\\nusing System;\\nusing static TorchSharp.torch;\\nusing static TorchSharp.PInvoke.NativeMethods;\\n\\n#nullable enable\\nnamespace TorchSharp\\n{\\n using Modules;\\n\\n public enum EmbeddingBagMode\\n {\\n Sum = 0,\\n Mean = 1,\\n Max = 2\\n }\\n\\n namespace Modules\\n {\\n public sealed class EmbeddingBag : torch.nn.Module<Tensor, Tensor?, Tensor?, Tensor>, torch.nn.IModule<Tensor, Tensor, Tensor>, torch.nn.IModule<Tensor, Tensor?, Tensor?, Tensor>\\n {\\n internal EmbeddingBag(IntPtr handle, IntPtr boxedHandle) : base(handle, boxedHandle) { }\\n\\n /// <summary>\\n /// Forward pass of EmbeddingBag.\\n /// </summary>\\n /// <param name=\\\"input\\\">Tensor containing bags of indices into the embedding matrix.</param>\\n /// <param name=\\\"offsets\\\">Only used when input is 1D. offsets determines the starting index position of each bag (sequence) in input.</param>\\n /// <param name=\\\"perSampleWeights\\\">a tensor of float / double weights, or None to indicate all weights should be taken to be 1.\\n /// If specified, per_sample_weights must have exactly the same shape as input and is treated as having the same offsets, if those are not None.\\n /// Only supported for mode='sum'.</param>\\n /// <returns></returns>\\n public override Tensor forward(Tensor input, Tensor? offsets, Tensor? perSampleWeights)\\n { \\n var res = THSNN_EmbeddingBag_forward(handle, input.Handle, (offsets is null) ? IntPtr.Zero : offsets.Handle, (perSampleWeights is null) ? IntPtr.Zero : perSampleWeights.Handle);\\n if (res == IntPtr.Zero) { torch.CheckForErrors(); }\\n return new Tensor(res);\\n }\\n\\n public new Tensor call(Tensor input, Tensor? offsets, Tensor? perSampleWeights)\\n {\\n if (!input.IsIntegral()) throw new ArgumentException(\\\"Embedding input must be an integral tensor.\\\");\\n if (!(offsets is null) && input.dtype != offsets.dtype) throw new ArgumentException(\\\"input and offsets must have the same element type.\\\");\\n if (input.Dimensions == 1 && offsets is null) throw new ArgumentException(\\\"'offsets' must be non-null for a 1-D input.\\\");\\n if (input.Dimensions == 2 && !(offsets is null)) throw new ArgumentException(\\\"'offsets' must be null for a 2-D input.\\\");\\n\\n if (input.Dimensions == 2 && input.dtype == ScalarType.Int32) throw new NotImplementedException(\\\"EmbeddingBag for 32-bit integers -- there's some issue in the native runtime that prevents this from working.\\\");\\n\\n return base.call(input, offsets, perSampleWeights);\\n }\\n\\n /// <summary>\\n /// Forward pass of EmbeddingBag.\\n /// </summary>\\n /// <param name=\\\"input\\\">Tensor containing bags of indices into the embedding matrix.</param>\\n /// <param name=\\\"offsets\\\">Only used when input is 1D. offsets determines the starting index position of each bag (sequence) in input.</param>\\n /// <returns></returns>\\n public Tensor call(Tensor input, Tensor offsets)\\n {\\n if (!input.IsIntegral()) throw new ArgumentException(\\\"Embedding input must be an integral tensor.\\\");\\n if (!(offsets is null) && input.dtype != offsets.dtype) throw new ArgumentException(\\\"input and offsets must have the same element type.\\\");\\n if (input.Dimensions == 1 && offsets is null) throw new ArgumentException(\\\"'offsets' must be non-null for a 1-D input.\\\");\\n if (input.Dimensions == 2 && !(offsets is null)) throw new ArgumentException(\\\"'offsets' must be null for a 2-D input.\\\");\\n\\n if (input.Dimensions == 2 && input.dtype == ScalarType.Int32) throw new NotImplementedException(\\\"EmbeddingBag for 32-bit integers -- there's some issue in the native runtime that prevents this from working.\\\");\\n\\n return base.call(input, offsets, null);\\n }\\n\\n /// <summary>\\n /// Forward pass of EmbeddingBag.\\n /// </summary>\\n /// <param name=\\\"input\\\">Tensor containing bags of indices into the embedding matrix.</param>\\n /// <returns></returns>\\n public Tensor call(Tensor input)\\n {\\n if (!input.IsIntegral()) throw new ArgumentException(\\\"Embedding input must be an integral tensor.\\\");\\n if (input.Dimensions == 1) throw new ArgumentException(\\\"'offsets' must be non-null for a 1-D input.\\\");\\n\\n if (input.Dimensions == 2 && input.dtype == ScalarType.Int32) throw new NotImplementedException(\\\"EmbeddingBag for 32-bit integers -- there's some issue in the native runtime that prevents this from working.\\\");\\n\\n return base.call(input, null, null);\\n }\\n\\n public Parameter? weight {\\n get {\\n var res = THSNN_EmbeddingBag_weight(handle);\\n if (res == IntPtr.Zero) { torch.CheckForErrors(); }\\n return (res == IntPtr.Zero) ? null : new Parameter(res);\\n }\\n set {\\n THSNN_EmbeddingBag_set_weight(handle, value is null ? IntPtr.Zero : value.Handle);\\n torch.CheckForErrors();\\n ConditionallyRegisterParameter(\\\"weight\\\", value);\\n }\\n }\\n }\\n }\\n\\n public static partial class torch\\n {\\n public static partial class nn\\n {\\n /// <summary>\\n /// A simple lookup table that stores embeddings of a fixed dictionary and size.\\n /// This module is often used to store word embeddings and retrieve them using indices. The input to the module is a list of indices, and the output is the corresponding word embeddings.\\n /// </summary>\\n /// <param name=\\\"num_embeddings\\\">Size of the dictionary of embeddings, the vocabulary size.</param>\\n /// <param name=\\\"embedding_dims\\\">The size of each embedding vector</param>\\n /// <param name=\\\"max_norm\\\">If given, each embedding vector with norm larger than max_norm is renormalized to have norm max_norm.</param>\\n /// <param name=\\\"norm_type\\\">The p of the p-norm to compute for the max_norm option. Default 2.</param>\\n /// <param name=\\\"scale_grad_by_freq\\\">If given, this will scale gradients by the inverse of frequency of the words in the mini-batch. Default: false.</param>\\n /// <param name=\\\"mode\\\">\\\"sum\\\", \\\"mean\\\" or \\\"max\\\". Specifies the way to reduce the bag.\\n /// \\\"sum\\\" computes the weighted sum, taking per_sample_weights into consideration.\\n /// \\\"mean\\\" computes the average of the values in the bag, \\\"max\\\" computes the max value over each bag. Default: \\\"mean\\\"</param>\\n /// <param name=\\\"sparse\\\">If true, gradient w.r.t. weight matrix will be a sparse tensor. Default: false</param>\\n /// <param name=\\\"include_last_offset\\\">If true, offsets has one additional element, where the last element is equivalent to the size of indices. This matches the CSR format.</param>\\n /// <param name=\\\"padding_index\\\"> If specified, the entries at padding_idx do not contribute to the gradient; therefore, the embedding vector at padding_idx is not updated during training, i.e. it remains as a fixed “pad”. For a newly constructed EmbeddingBag, the embedding vector at padding_idx will default to all zeros, but can be updated to another value to be used as the padding vector. Note that the embedding vector at padding_idx is excluded from the reduction.</param>\\n /// <param name=\\\"device\\\">The desired device of the parameters and buffers in this module</param>\\n /// <param name=\\\"dtype\\\">The desired floating point or complex dtype of the parameters and buffers in this module</param>\\n /// <returns></returns>\\n /// <remarks>Keep in mind that only a limited number of optimizers support sparse gradients: currently it’s optim.SGD (CUDA and CPU), optim.SparseAdam (CUDA and CPU) and optim.Adagrad (CPU)</remarks>\\n public static EmbeddingBag EmbeddingBag(long num_embeddings, long embedding_dims, double? max_norm = null, double norm_type = 2.0, bool scale_grad_by_freq = false, EmbeddingBagMode mode = EmbeddingBagMode.Mean, bool sparse = false, bool include_last_offset = false, long padding_index = -1, Device? device = null, ScalarType? dtype = null)\\n {\\n var res = THSNN_EmbeddingBag_ctor(num_embeddings, embedding_dims,\\n max_norm.HasValue ? max_norm.Value : 0.0, max_norm.HasValue,\\n norm_type, scale_grad_by_freq, (long)mode, sparse, include_last_offset, padding_index, out var boxedHandle);\\n if (res == IntPtr.Zero) { torch.CheckForErrors(); }\\n return new EmbeddingBag(res, boxedHandle).MoveModule<EmbeddingBag>(device, dtype);\\n }\\n\\n /// <summary>\\n /// A simple lookup table that stores embeddings of a fixed dictionary and size.\\n /// This module is often used to store word embeddings and retrieve them using indices. The input to the module is a list of indices, and the output is the corresponding word embeddings.\\n /// </summary>\\n /// <param name=\\\"embeddings\\\">FloatTensor containing weights for the EmbeddingBag in two dimensions. First dimension is being passed to EmbeddingBag as num_embeddings, second as embedding_dim.</param>\\n /// <param name=\\\"freeze\\\">If true (the default), the tensor does not get updated in the learning</param>\\n /// <param name=\\\"max_norm\\\">If given, each embedding vector with norm larger than max_norm is renormalized to have norm max_norm.</param>\\n /// <param name=\\\"norm_type\\\">The p of the p-norm to compute for the max_norm option. Default 2.</param>\\n /// <param name=\\\"scale_grad_by_freq\\\">If given, this will scale gradients by the inverse of frequency of the words in the mini-batch. Default: false.</param>\\n /// <param name=\\\"mode\\\"></param>\\n /// <param name=\\\"sparse\\\">If true, gradient w.r.t. weight matrix will be a sparse tensor. Default: false</param>\\n /// <param name=\\\"include_last_offset\\\">If true, offsets has one additional element, where the last element is equivalent to the size of indices. This matches the CSR format.</param>\\n /// <param name=\\\"padding_index\\\"> If specified, the entries at padding_idx do not contribute to the gradient; therefore, the embedding vector at padding_idx is not updated during training, i.e. it remains as a fixed “pad”. For a newly constructed EmbeddingBag, the embedding vector at padding_idx will default to all zeros, but can be updated to another value to be used as the padding vector. Note that the embedding vector at padding_idx is excluded from the reduction.</param>\\n /// <param name=\\\"device\\\">The desired device of the parameters and buffers in this module</param>\\n /// <param name=\\\"dtype\\\">The desired floating point or complex dtype of the parameters and buffers in this module</param>\\n /// <returns></returns>\\n /// <remarks>Keep in mind that only a limited number of optimizers support sparse gradients: currently it’s optim.SGD (CUDA and CPU), optim.SparseAdam (CUDA and CPU) and optim.Adagrad (CPU)</remarks>\\n public static EmbeddingBag EmbeddingBag_from_pretrained(Tensor embeddings, bool freeze = true, double? max_norm = null, double norm_type = 2.0, bool scale_grad_by_freq = false, EmbeddingBagMode mode = EmbeddingBagMode.Mean, bool sparse = false, bool include_last_offset = false, long padding_index = -1, Device? device = null, ScalarType? dtype = null)\\n {\\n var res = THSNN_EmbeddingBag_from_pretrained(embeddings.Handle, freeze,\\n max_norm.HasValue ? max_norm.Value : 0.0, max_norm.HasValue,\\n norm_type, scale_grad_by_freq, (long)mode, sparse, include_last_offset, padding_index, out var boxedHandle);\\n if (res == IntPtr.Zero) { torch.CheckForErrors(); }\\n return new EmbeddingBag(res, boxedHandle).MoveModule<EmbeddingBag>(device, dtype);\\n\\n }\\n }\\n }\\n}\\n\"\n },\n {\n \"alpha_fraction\": 0.5411838293075562,\n \"alphanum_fraction\": 0.5430629253387451,\n \"avg_line_length\": 38.41975402832031,\n \"blob_id\": \"b401a64239981a2601ecfe56a567d19799a8c72e\",\n \"content_id\": \"35470b03f72a3c9246800aab224e8b3a16798a8c\",\n \"detected_licenses\": [\n \"MIT\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"C#\",\n \"length_bytes\": 3193,\n \"license_type\": \"permissive\",\n \"max_line_length\": 130,\n \"num_lines\": 81,\n \"path\": \"/src/TorchAudio/Backend.cs\",\n \"repo_name\": \"NiklasGustafsson/TorchSharp\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"// Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information.\\n\\nnamespace TorchSharp\\n{\\n public static partial class torchaudio\\n {\\n /// <summary>\\n /// Load audio data from a file.\\n /// </summary>\\n /// <param name=\\\"filepath\\\">The file path to load</param>\\n /// <param name=\\\"frame_offset\\\">Number of frames to skip before reading data</param>\\n /// <param name=\\\"num_frames\\\">Maximum number of frames to read.</param>\\n /// <param name=\\\"normalize\\\">True to normalize the audio to [-1.0, 1.0]</param>\\n /// <param name=\\\"channels_first\\\">The dimension of the returned tensor is [channel, time] when true</param>\\n /// <param name=\\\"format\\\">The format of the audio file</param>\\n /// <returns>A pair of waveform and sampling rate</returns>\\n public static (torch.Tensor, int) load(\\n string filepath,\\n long frame_offset = 0,\\n long num_frames = -1,\\n bool normalize = true,\\n bool channels_first = true,\\n AudioFormat? format = null)\\n {\\n return backend.utils._backend.load(\\n filepath,\\n frame_offset,\\n num_frames,\\n normalize,\\n channels_first,\\n format);\\n }\\n\\n /// <summary>\\n /// Save audio data to a file.\\n /// </summary>\\n /// <param name=\\\"filepath\\\">The file path to save</param>\\n /// <param name=\\\"src\\\">The waveform of audio</param>\\n /// <param name=\\\"sample_rate\\\">The sampling rate of audio</param>\\n /// <param name=\\\"channels_first\\\">The dimension of the returned tensor is [channel, time] when true</param>\\n /// <param name=\\\"compression\\\">The compression factor</param>\\n /// <param name=\\\"format\\\">The format of the audio file</param>\\n /// <param name=\\\"encoding\\\">The audio encoding</param>\\n /// <param name=\\\"bits_per_sample\\\">The number of bits per sample</param>\\n public static void save(\\n string filepath,\\n torch.Tensor src,\\n int sample_rate,\\n bool channels_first = true,\\n float? compression = null,\\n AudioFormat? format = null,\\n AudioEncoding? encoding = null,\\n int? bits_per_sample = null)\\n {\\n backend.utils._backend.save(\\n filepath,\\n src,\\n sample_rate,\\n channels_first,\\n compression,\\n format,\\n encoding,\\n bits_per_sample);\\n }\\n\\n /// <summary>\\n /// Get the information of an audio file.\\n /// </summary>\\n /// <param name=\\\"filepath\\\">The file path of the audio file</param>\\n /// <param name=\\\"format\\\">The format of the audio file</param>\\n /// <returns>The information of the audio file</returns>\\n public static AudioMetaData info(\\n string filepath,\\n AudioFormat? format = null)\\n {\\n return backend.utils._backend.info(\\n filepath,\\n format);\\n }\\n }\\n}\\n\"\n },\n {\n \"alpha_fraction\": 0.5016260147094727,\n \"alphanum_fraction\": 0.5040650367736816,\n \"avg_line_length\": 26.33333396911621,\n \"blob_id\": \"5f658bfb6e1ecbbdad3cab413ed76b95377a5549\",\n \"content_id\": \"2dcce34a2d33e02f0680a530b17e35c205d98488\",\n \"detected_licenses\": [\n \"MIT\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"C#\",\n \"length_bytes\": 4920,\n \"license_type\": \"permissive\",\n \"max_line_length\": 103,\n \"num_lines\": 180,\n \"path\": \"/src/TorchSharp/Utils/OrderedDict.cs\",\n \"repo_name\": \"NiklasGustafsson/TorchSharp\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"using System;\\nusing System.Linq;\\nusing System.Collections;\\nusing System.Collections.Generic;\\nusing System.Diagnostics.CodeAnalysis;\\n\\nnamespace TorchSharp.Utils\\n{\\n public class OrderedDict<TKey, TValue> : IDictionary<TKey, TValue>, IList<(TKey, TValue)>\\n {\\n /// <summary>\\n /// Remove all items from the ParameterDict.\\n /// </summary>\\n public void clear()\\n {\\n _list.Clear();\\n _dict.Clear();\\n }\\n\\n /// <summary>\\n /// Return an enumeration of the ParameterDict key/value pairs.\\n /// </summary>\\n /// <returns></returns>\\n public IEnumerator<(TKey, TValue)> items() => _list.GetEnumerator();\\n\\n /// <summary>\\n /// Return the ParameterDict keys.\\n /// </summary>\\n /// <returns></returns>\\n public IEnumerable<TKey> keys() => _dict.Keys;\\n\\n /// <summary>\\n /// Return the ParameterDict values.\\n /// </summary>\\n /// <returns></returns>\\n public IEnumerable<TValue> values() => _dict.Values;\\n\\n public (TKey, TValue) this[int index] {\\n get => _list[index];\\n set {\\n var key = value.Item1;\\n _list[index] = value;\\n _dict[key] = value.Item2;\\n }\\n }\\n\\n public bool IsReadOnly => false;\\n\\n public ICollection<TKey> Keys => _list.Select(kv => kv.Item1).ToList();\\n\\n public ICollection<TValue> Values => _list.Select(kv => kv.Item2).ToList();\\n\\n public int Count => _dict.Count;\\n\\n public TValue this[TKey key] {\\n get => _dict[key];\\n set {\\n _dict[key] = value;\\n var idx = _list.FindIndex(kv => kv.Item1.Equals(key));\\n if (idx >= 0) {\\n _list[idx] = (key, value);\\n }\\n else {\\n _list.Add((key, value));\\n }\\n }\\n }\\n\\n public void Add((TKey, TValue) item)\\n {\\n _dict.Add(item.Item1, item.Item2);\\n _list.Add(item);\\n }\\n\\n public void Add(TKey key, TValue value)\\n {\\n _dict.Add(key, value);\\n _list.Add((key, value));\\n }\\n\\n public void Add(KeyValuePair<TKey, TValue> item)\\n {\\n _dict.Add(item.Key, item.Value);\\n _list.Add((item.Key, item.Value));\\n }\\n\\n public bool Contains((TKey, TValue) item)\\n {\\n return _list.Contains(item);\\n }\\n\\n public void CopyTo((TKey, TValue)[] array, int arrayIndex)\\n {\\n _list.CopyTo(array, arrayIndex);\\n }\\n\\n public int IndexOf((TKey, TValue) item)\\n {\\n return _list.IndexOf(item);\\n }\\n\\n public void Insert(int index, (TKey, TValue) item)\\n {\\n _dict.Add(item.Item1, item.Item2);\\n _list.Insert(index, item);\\n }\\n\\n public bool Remove((TKey, TValue) item)\\n {\\n _dict.Remove(item.Item1);\\n return _list.Remove(item);\\n }\\n\\n public void RemoveAt(int index)\\n {\\n if (index >= _list.Count) throw new IndexOutOfRangeException();\\n var (n, p) = _list[index];\\n _list.RemoveAt(index);\\n _dict.Remove(n);\\n }\\n\\n public bool ContainsKey(TKey key)\\n {\\n return _dict.ContainsKey(key);\\n }\\n\\n public bool Remove(TKey key)\\n {\\n var value = _dict[key];\\n var idx = _list.FindIndex(kv => kv.Item1.Equals(key));\\n _list.RemoveAt(idx);\\n return _dict.Remove(key);\\n }\\n\\n public bool TryGetValue(TKey key, [MaybeNullWhen(false)] out TValue value)\\n {\\n return _dict.TryGetValue(key, out value);\\n }\\n\\n public void Clear()\\n {\\n _dict.Clear();\\n _list.Clear();\\n }\\n\\n public bool Contains(KeyValuePair<TKey, TValue> item)\\n {\\n return _dict.ContainsKey(item.Key);\\n }\\n\\n public void CopyTo(KeyValuePair<TKey, TValue>[] array, int arrayIndex)\\n {\\n throw new NotImplementedException();\\n }\\n\\n public bool Remove(KeyValuePair<TKey, TValue> item)\\n {\\n return _dict.Remove(item.Key);\\n }\\n\\n public IEnumerator<(TKey, TValue)> GetEnumerator()\\n {\\n return _list.GetEnumerator();\\n }\\n\\n IEnumerator<KeyValuePair<TKey, TValue>> IEnumerable<KeyValuePair<TKey, TValue>>.GetEnumerator()\\n {\\n throw new NotImplementedException();\\n }\\n\\n IEnumerator IEnumerable.GetEnumerator()\\n {\\n return ((IEnumerable<KeyValuePair<TKey, TValue>>)this).GetEnumerator();\\n }\\n\\n private List<(TKey, TValue)> _list = new List<(TKey, TValue)>();\\n private Dictionary<TKey, TValue> _dict = new Dictionary<TKey, TValue>();\\n }\\n\\n}\\n\"\n },\n {\n \"alpha_fraction\": 0.5328227281570435,\n \"alphanum_fraction\": 0.5393872857093811,\n \"avg_line_length\": 25.882352828979492,\n \"blob_id\": \"c89bd7df0a7c79fa3ba153c9a2839c0038cb76f6\",\n \"content_id\": \"1552dd0ff30fb7a65ed35f96daed0df9bebb9949\",\n \"detected_licenses\": [\n \"MIT\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"C#\",\n \"length_bytes\": 914,\n \"license_type\": \"permissive\",\n \"max_line_length\": 139,\n \"num_lines\": 34,\n \"path\": \"/src/TorchVision/Invert.cs\",\n \"repo_name\": \"NiklasGustafsson/TorchSharp\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"// Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information.\\n\\nusing static TorchSharp.torch;\\n\\n\\nnamespace TorchSharp\\n{\\n public static partial class torchvision\\n {\\n internal class Invert : ITransform\\n {\\n internal Invert()\\n {\\n }\\n\\n public Tensor call(Tensor input)\\n {\\n return transforms.functional.invert(input);\\n }\\n }\\n\\n public static partial class transforms\\n {\\n /// <summary>\\n /// Invert the image colors.\\n /// </summary>\\n /// <remarks>The code assumes that integer color values lie in the range [0,255], and floating point colors in [0,1[.</remarks>\\n static public ITransform Invert()\\n {\\n return new Invert();\\n }\\n }\\n }\\n}\\n\"\n },\n {\n \"alpha_fraction\": 0.5647902488708496,\n \"alphanum_fraction\": 0.5658523440361023,\n \"avg_line_length\": 44.37349319458008,\n \"blob_id\": \"23e92a82c3dff027d7afcd2ac6b58afcd9498270\",\n \"content_id\": \"4d70a476467d52ebb6e0475b1cbd8bd0597abff5\",\n \"detected_licenses\": [\n \"MIT\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"C#\",\n \"length_bytes\": 3766,\n \"license_type\": \"permissive\",\n \"max_line_length\": 197,\n \"num_lines\": 83,\n \"path\": \"/src/TorchSharp/NN/Normalization/GroupNorm.cs\",\n \"repo_name\": \"NiklasGustafsson/TorchSharp\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"// Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information.\\nusing System;\\nusing static TorchSharp.torch;\\nusing static TorchSharp.PInvoke.NativeMethods;\\n\\n#nullable enable\\nnamespace TorchSharp\\n{\\n using Modules;\\n\\n namespace Modules\\n {\\n\\n /// <summary>\\n /// This class is used to represent a GroupNorm module.\\n /// </summary>\\n public sealed class GroupNorm : torch.nn.Module<Tensor, Tensor>\\n {\\n internal GroupNorm(IntPtr handle, IntPtr boxedHandle) : base(handle, boxedHandle)\\n {\\n }\\n\\n public override Tensor forward(Tensor tensor)\\n {\\n if (tensor.Dimensions < 3) throw new ArgumentException($\\\"Invalid number of dimensions for GroupNorm argument: {tensor.Dimensions}\\\");\\n var res = THSNN_GroupNorm_forward(handle.DangerousGetHandle(), tensor.Handle);\\n if (res == IntPtr.Zero) { torch.CheckForErrors(); }\\n return new Tensor(res);\\n }\\n\\n public Parameter? bias {\\n get {\\n var res = THSNN_GroupNorm_bias(handle);\\n if (res == IntPtr.Zero) { torch.CheckForErrors(); }\\n return (res == IntPtr.Zero) ? null : new Parameter(res);\\n }\\n set {\\n THSNN_GroupNorm_set_bias(handle, (value is null ? IntPtr.Zero : value.Handle));\\n torch.CheckForErrors();\\n ConditionallyRegisterParameter(\\\"bias\\\", value);\\n }\\n }\\n\\n public Parameter? weight {\\n get {\\n var res = THSNN_GroupNorm_weight(handle);\\n if (res == IntPtr.Zero) { torch.CheckForErrors(); }\\n return (res == IntPtr.Zero) ? null : new Parameter(res);\\n }\\n set {\\n THSNN_GroupNorm_set_weight(handle, value is null ? IntPtr.Zero : value.Handle);\\n torch.CheckForErrors();\\n ConditionallyRegisterParameter(\\\"weight\\\", value);\\n }\\n }\\n }\\n }\\n\\n public static partial class torch\\n {\\n public static partial class nn\\n {\\n /// <summary>\\n /// Applies Group Normalization over a mini-batch of inputs as described in the paper Group Normalization\\n /// </summary>\\n /// <param name=\\\"num_groups\\\">Number of groups to separate the channels into</param>\\n /// <param name=\\\"num_channels\\\">Number of channels expected in input</param>\\n /// <param name=\\\"eps\\\">A value added to the denominator for numerical stability.</param>\\n /// <param name=\\\"affine\\\">A boolean value that when set to true, this module has learnable per-channel affine parameters initialized to ones (for weights) and zeros (for biases).</param>\\n /// <param name=\\\"device\\\">The desired device of the parameters and buffers in this module</param>\\n /// <param name=\\\"dtype\\\">The desired floating point or complex dtype of the parameters and buffers in this module</param>\\n /// <returns></returns>\\n public static GroupNorm GroupNorm(long num_groups, long num_channels, double eps = 1e-05, bool affine = true, Device? device = null, ScalarType? dtype = null)\\n {\\n unsafe {\\n var handle = THSNN_GroupNorm_ctor(num_groups, num_channels, eps, affine, out var boxedHandle);\\n if (handle == IntPtr.Zero) { torch.CheckForErrors(); }\\n return new GroupNorm(handle, boxedHandle).MoveModule<GroupNorm>(device, dtype);\\n }\\n }\\n }\\n }\\n}\\n\"\n },\n {\n \"alpha_fraction\": 0.7562500238418579,\n \"alphanum_fraction\": 0.7604166865348816,\n \"avg_line_length\": 29,\n \"blob_id\": \"b8432e578867293321999df53afcf2cf95228c71\",\n \"content_id\": \"e66492e11d57fa8ddbfa2a83c34692add348eb6d\",\n \"detected_licenses\": [\n \"MIT\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"C\",\n \"length_bytes\": 480,\n \"license_type\": \"permissive\",\n \"max_line_length\": 130,\n \"num_lines\": 16,\n \"path\": \"/src/Native/LibTorchSharp/THSStorage.h\",\n \"repo_name\": \"NiklasGustafsson/TorchSharp\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"// Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information.\\n#pragma once\\n\\n#include \\\"../Stdafx.h\\\"\\n\\n#include \\\"torch/torch.h\\\"\\n\\n#include \\\"Utils.h\\\"\\n\\nEXPORT_API(int64_t) THSTensor_storage_offset(const Tensor tensor);\\n\\nEXPORT_API(size_t) THSStorage_nbytes(const Tensor tensor);\\n\\nEXPORT_API(void) THSStorage_set_nbytes(const Tensor tensor, size_t nbytes);\\n\\nEXPORT_API(void*) THSStorage_data_ptr(const Tensor tensor);\\n\"\n },\n {\n \"alpha_fraction\": 0.5736639499664307,\n \"alphanum_fraction\": 0.5775156617164612,\n \"avg_line_length\": 47.87058639526367,\n \"blob_id\": \"1fc35aefb0dea60aeee88832873689c4f2df5020\",\n \"content_id\": \"364727dbdf396b7500c7840590c8e5da795593e8\",\n \"detected_licenses\": [\n \"MIT\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"C#\",\n \"length_bytes\": 4158,\n \"license_type\": \"permissive\",\n \"max_line_length\": 206,\n \"num_lines\": 85,\n \"path\": \"/src/TorchSharp/NN/TransformerEncoderLayer.cs\",\n \"repo_name\": \"NiklasGustafsson/TorchSharp\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"// Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information.\\nusing System;\\nusing static TorchSharp.torch;\\nusing static TorchSharp.PInvoke.NativeMethods;\\n\\nnamespace TorchSharp\\n{\\n using Modules;\\n\\n namespace Modules\\n {\\n public sealed class TransformerEncoderLayer : torch.nn.Module<Tensor, Tensor>, torch.nn.IModule<Tensor, Tensor, Tensor>, torch.nn.IModule<Tensor, Tensor, Tensor, Tensor>\\n {\\n internal TransformerEncoderLayer(IntPtr handle, IntPtr boxedHandle) : base(handle, boxedHandle) { }\\n\\n /// <summary>\\n /// Pass the input through the encoder layer.\\n /// </summary>\\n /// <param name=\\\"src\\\">The sequence to the encoder (required).</param>\\n /// <param name=\\\"src_mask\\\">The additive mask for the src sequence (optional).</param>\\n /// <param name=\\\"src_key_padding_mask\\\">The ByteTensor mask for src keys per batch (optional).</param>\\n /// <returns></returns>\\n public Tensor call(Tensor src, Tensor src_mask, Tensor src_key_padding_mask)\\n {\\n var res = THSNN_TransformerEncoderLayer_forward(handle,\\n src.Handle,\\n src_mask?.Handle ?? IntPtr.Zero,\\n src_key_padding_mask?.Handle ?? IntPtr.Zero);\\n if (res == IntPtr.Zero) { torch.CheckForErrors(); }\\n return new Tensor(res);\\n }\\n\\n /// <summary>\\n /// Pass the input through the encoder layer.\\n /// </summary>\\n /// <param name=\\\"src\\\">The sequence to the encoder (required).</param>\\n /// <param name=\\\"src_mask\\\">The additive mask for the src sequence (optional).</param>\\n public Tensor call(Tensor src, Tensor src_mask)\\n {\\n var res = THSNN_TransformerEncoderLayer_forward(handle,\\n src.Handle,\\n src_mask?.Handle ?? IntPtr.Zero,\\n IntPtr.Zero);\\n if (res == IntPtr.Zero) { torch.CheckForErrors(); }\\n return new Tensor(res);\\n }\\n\\n /// <summary>\\n /// Pass the input through the encoder layer.\\n /// </summary>\\n /// <param name=\\\"src\\\">The sequence to the encoder (required).</param>\\n public override Tensor forward(Tensor src)\\n {\\n var res = THSNN_TransformerEncoderLayer_forward(handle,\\n src.Handle,\\n IntPtr.Zero,\\n IntPtr.Zero);\\n if (res == IntPtr.Zero) { torch.CheckForErrors(); }\\n return new Tensor(res);\\n }\\n }\\n }\\n\\n public static partial class torch\\n {\\n public static partial class nn\\n {\\n /// <summary>\\n /// TransformerEncoderLayer is made up of self-attn and feedforward network. This standard encoder layer is based on the paper “Attention Is All You Need”.\\n /// </summary>\\n /// <param name=\\\"d_model\\\">The number of expected features in the input (required).</param>\\n /// <param name=\\\"nhead\\\">The number of heads in the multiheadattention models (required).</param>\\n /// <param name=\\\"dim_feedforward\\\">The dimension of the feedforward network model (default=2048).</param>\\n /// <param name=\\\"dropout\\\">The dropout value (default=0.1).</param>\\n /// <param name=\\\"activation\\\">The activation function of intermediate layer, relu or gelu (default=relu).</param>\\n /// <returns></returns>\\n public static TransformerEncoderLayer TransformerEncoderLayer(long d_model = 512, long nhead = 8, long dim_feedforward = 2048, double dropout = 0.1, Activations activation = nn.Activations.ReLU)\\n {\\n var res = THSNN_TransformerEncoderLayer_ctor(d_model, nhead, dim_feedforward, dropout, (long)activation, out var boxedHandle);\\n if (res == IntPtr.Zero) { torch.CheckForErrors(); }\\n return new TransformerEncoderLayer(res, boxedHandle);\\n }\\n }\\n }\\n}\\n\"\n },\n {\n \"alpha_fraction\": 0.5447816252708435,\n \"alphanum_fraction\": 0.5481125116348267,\n \"avg_line_length\": 36.52777862548828,\n \"blob_id\": \"23e2c9f7d2fcc7112170bb1b63178d78965b2887\",\n \"content_id\": \"de32e4dfb1d36409efb6d52a2fa27d5fbea94798\",\n \"detected_licenses\": [\n \"MIT\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"C#\",\n \"length_bytes\": 2704,\n \"license_type\": \"permissive\",\n \"max_line_length\": 130,\n \"num_lines\": 72,\n \"path\": \"/src/TorchSharp/NN/Activation/Softshrink.cs\",\n \"repo_name\": \"NiklasGustafsson/TorchSharp\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"// Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information.\\nusing System;\\nusing static TorchSharp.torch;\\nusing static TorchSharp.PInvoke.NativeMethods;\\n\\nnamespace TorchSharp\\n{\\n using Modules;\\n\\n namespace Modules\\n {\\n /// <summary>\\n /// This class is used to represent a Softshrink module.\\n /// </summary>\\n public sealed class Softshrink : torch.nn.Module<Tensor, Tensor>\\n {\\n internal Softshrink(IntPtr handle, IntPtr boxedHandle) : base(handle, boxedHandle) { }\\n\\n public override Tensor forward(Tensor tensor)\\n {\\n var res = THSNN_Softshrink_forward(handle, tensor.Handle);\\n if (res == IntPtr.Zero) { torch.CheckForErrors(); }\\n return new Tensor(res);\\n }\\n\\n public override string GetName()\\n {\\n return typeof(Softshrink).Name;\\n }\\n\\n // Rather than spending cycles only to discover that this module has neither\\n // parameters nor buffers, just shortcut the move completely.\\n protected internal override nn.Module _to(Device device, ScalarType dtype) => this;\\n protected internal override nn.Module _to(DeviceType deviceType, int deviceIndex = -1) => this;\\n protected internal override nn.Module _to(ScalarType dtype) => this;\\n }\\n }\\n\\n public static partial class torch\\n {\\n public static partial class nn\\n {\\n /// <summary>\\n /// Softshrink\\n /// </summary>\\n /// <param name=\\\"lambda\\\"> the λ value for the Softshrink formulation. Default: 0.5</param>\\n /// <returns></returns>\\n public static Softshrink Softshrink(double lambda = 0.5)\\n {\\n var handle = THSNN_Softshrink_ctor(lambda, out var boxedHandle);\\n if (handle == IntPtr.Zero) { torch.CheckForErrors(); }\\n return new Softshrink(handle, boxedHandle);\\n }\\n\\n public static partial class functional\\n {\\n /// <summary>\\n /// Softshrink\\n /// </summary>\\n /// <param name=\\\"x\\\">The input tensor</param>\\n /// <param name=\\\"lambda\\\">The λ value for the Softshrink formulation. Default: 0.5</param>\\n /// <returns></returns>\\n public static Tensor Softshrink(Tensor x, double lambda = 0.5)\\n {\\n using (var m = nn.Softshrink(lambda)) {\\n return m.call(x);\\n }\\n }\\n }\\n }\\n }\\n}\\n\"\n },\n {\n \"alpha_fraction\": 0.5315040946006775,\n \"alphanum_fraction\": 0.5341463685035706,\n \"avg_line_length\": 59.74074172973633,\n \"blob_id\": \"b1742b617d40d5500d58e118bf5211f862a7abc9\",\n \"content_id\": \"ab0b62cc5c3b9e1793a87c22b8feee0245871f8a\",\n \"detected_licenses\": [\n \"MIT\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"C#\",\n \"length_bytes\": 4920,\n \"license_type\": \"permissive\",\n \"max_line_length\": 188,\n \"num_lines\": 81,\n \"path\": \"/src/TorchSharp/NN/Utils/RNNUtils.cs\",\n \"repo_name\": \"NiklasGustafsson/TorchSharp\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"// Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information.\\nusing System;\\nusing System.Collections.Generic;\\nusing System.Linq;\\nusing static TorchSharp.PInvoke.NativeMethods;\\n\\nnamespace TorchSharp\\n{\\n public static partial class torch\\n {\\n public static partial class nn\\n {\\n public static partial class utils\\n {\\n public static partial class rnn\\n {\\n /// <summary>\\n /// Pack a padded sequences.\\n /// </summary>\\n /// <param name=\\\"input\\\">The padded input tensor</param>\\n /// <param name=\\\"lengths\\\">The lengths of sequences</param>\\n /// <param name=\\\"batch_first\\\">If true, the size of input tensor is (batch, seq, *), otherwise (seq, batch, *)</param>\\n /// <param name=\\\"enforce_sorted\\\">if true, checks that the input contains sequences sorted by length in a decreasing order.</param>\\n /// <returns></returns>\\n public static PackedSequence pack_padded_sequence(torch.Tensor input, torch.Tensor lengths, bool batch_first = false, bool enforce_sorted = true)\\n {\\n var res = THSNN_pack_padded_sequence(input.Handle, lengths.Handle, batch_first, enforce_sorted);\\n if (res.IsInvalid) { torch.CheckForErrors(); }\\n return new PackedSequence(res);\\n }\\n\\n /// <summary>\\n /// Pad a packed batch of variable length sequences.\\n /// </summary>\\n /// <param name=\\\"sequence\\\">A packed batch</param>\\n /// <param name=\\\"batch_first\\\">If true, the size of output tensor is (batch, seq, *), otherwise (seq, batch, *)</param>\\n /// <param name=\\\"padding_value\\\">The values of padding.</param>\\n /// <param name=\\\"total_length\\\">If not null, the output tensor is padded to the length of total_length.</param>\\n /// <returns>The padded tensor</returns>\\n public static (torch.Tensor, torch.Tensor) pad_packed_sequence(PackedSequence sequence, bool batch_first = false, double padding_value = 0.0, long? total_length = null)\\n {\\n IntPtr res1, res2;\\n long total_length_arg = total_length.HasValue ? total_length.Value : -1;\\n THSNN_pad_packed_sequence(sequence.Handle, batch_first, padding_value, total_length_arg, out res1, out res2);\\n if (res1 == IntPtr.Zero || res2 == IntPtr.Zero) { torch.CheckForErrors(); }\\n return (new torch.Tensor(res1), new torch.Tensor(res2));\\n }\\n\\n /// <summary>\\n /// Pad a list of variable length sequences.\\n /// </summary>\\n /// <param name=\\\"sequences\\\">A list of variable length sequences</param>\\n /// <param name=\\\"batch_first\\\">If true, the size of output tensor is (batch, seq, *), otherwise (seq, batch, *)</param>\\n /// <param name=\\\"padding_value\\\">The values of padding.</param>\\n /// <returns>The padded tensor</returns>\\n public static torch.Tensor pad_sequence(IEnumerable<torch.Tensor> sequences, bool batch_first = false, double padding_value = 0.0)\\n {\\n var sequences_arg = sequences.Select(p => p.Handle).ToArray();\\n var res = THSNN_pad_sequence(sequences_arg, sequences_arg.Length, batch_first, padding_value);\\n if (res == IntPtr.Zero) { torch.CheckForErrors(); }\\n return new torch.Tensor(res);\\n }\\n\\n /// <summary>\\n /// Pack a list of variable length sequences.\\n /// </summary>\\n /// <param name=\\\"sequences\\\">A list of variable length sequences</param>\\n /// <param name=\\\"enforce_sorted\\\">if true, checks that the input contains sequences sorted by length in a decreasing order.</param>\\n /// <returns>The packed batch of variable length sequences</returns>\\n public static PackedSequence pack_sequence(IEnumerable<torch.Tensor> sequences, bool enforce_sorted = true)\\n {\\n var sequences_arg = sequences.Select(p => p.Handle).ToArray();\\n var res = THSNN_pack_sequence(sequences_arg, sequences_arg.Length, enforce_sorted);\\n if (res.IsInvalid) { torch.CheckForErrors(); }\\n return new PackedSequence(res);\\n }\\n }\\n }\\n }\\n }\\n}\\n\"\n },\n {\n \"alpha_fraction\": 0.5518966913223267,\n \"alphanum_fraction\": 0.5646489262580872,\n \"avg_line_length\": 40.0264892578125,\n \"blob_id\": \"a4eb103ad3b3af3002bd96b8b37b82e27c4e759b\",\n \"content_id\": \"c8b27c720c6e68d2110bc688f79f3674286a18fe\",\n \"detected_licenses\": [\n \"MIT\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"C#\",\n \"length_bytes\": 6202,\n \"license_type\": \"permissive\",\n \"max_line_length\": 166,\n \"num_lines\": 151,\n \"path\": \"/src/Examples/AdversarialExampleGeneration.cs\",\n \"repo_name\": \"NiklasGustafsson/TorchSharp\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"// Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information.\\nusing System;\\nusing System.IO;\\nusing static TorchSharp.torch;\\nusing static TorchSharp.torch.utils.data;\\n\\nnamespace TorchSharp.Examples\\n{\\n /// <summary>\\n /// FGSM Attack\\n ///\\n /// Based on : https://pytorch.org/tutorials/beginner/fgsm_tutorial.html\\n /// </summary>\\n /// <remarks>\\n /// There are at least two interesting data sets to use with this example:\\n ///\\n /// 1. The classic MNIST set of 60000 images of handwritten digits.\\n ///\\n /// It is available at: http://yann.lecun.com/exdb/mnist/\\n ///\\n /// 2. The 'fashion-mnist' data set, which has the exact same file names and format as MNIST, but is a harder\\n /// data set to train on. It's just as large as MNIST, and has the same 60/10 split of training and test\\n /// data.\\n /// It is available at: https://github.com/zalandoresearch/fashion-mnist/tree/master/data/fashion\\n ///\\n /// The example is based on the PyTorch tutorial, but the results from attacking the model are very different from\\n /// what the tutorial article notes, at least on the machine where it was developed. There is an order-of-magnitude lower\\n /// drop-off in accuracy in this version. That said, when running the PyTorch tutorial on the same machine, the\\n /// accuracy trajectories are the same between .NET and Python. If the base convulutational model is trained\\n /// using Python, and then used for the FGSM attack in both .NET and Python, the drop-off trajectories are extremenly\\n /// close.\\n /// </remarks>\\n public class AdversarialExampleGeneration\\n {\\n#if NET472_OR_GREATER\\n private readonly static string _dataLocation = NSPath.Join(Environment.GetFolderPath(Environment.SpecialFolder.DesktopDirectory), \\\"..\\\", \\\"Downloads\\\", \\\"mnist\\\");\\n#else\\n private readonly static string _dataLocation = Path.Join(Environment.GetFolderPath(Environment.SpecialFolder.DesktopDirectory), \\\"..\\\", \\\"Downloads\\\", \\\"mnist\\\");\\n#endif // NET472_OR_GREATER\\n\\n private static int _epochs = 4;\\n private static int _trainBatchSize = 64;\\n private static int _testBatchSize = 128;\\n\\n internal static void Main(string[] args)\\n {\\n var cwd = Environment.CurrentDirectory;\\n\\n var dataset = args.Length > 0 ? args[0] : \\\"mnist\\\";\\n var datasetPath = Environment.GetFolderPath(Environment.SpecialFolder.LocalApplicationData);\\n\\n var _ = torch.random.manual_seed(1);\\n\\n //var device = torch.CPU;\\n var device = torch.cuda.is_available() ? torch.CUDA : torch.CPU;\\n Console.WriteLine($\\\"\\\\n Running AdversarialExampleGeneration on {device.type.ToString()}\\\\n\\\");\\n Console.WriteLine($\\\"Dataset: {dataset}\\\");\\n\\n if (device.type == DeviceType.CUDA) {\\n _trainBatchSize *= 4;\\n _testBatchSize *= 4;\\n _epochs *= 4;\\n }\\n\\n MNIST.Model model = null;\\n\\n var normImage = torchvision.transforms.Normalize(new double[] {0.1307}, new double[] {0.3081});\\n\\n using (var test = torchvision.datasets.MNIST(datasetPath, false, true, normImage)) {\\n\\n var modelFile = dataset + \\\".model.bin\\\";\\n\\n if (!File.Exists(modelFile)) {\\n // We need the model to be trained first, because we want to start with a trained model.\\n Console.WriteLine(\\n $\\\"\\\\n Running MNIST on {device.type.ToString()} in order to pre-train the model.\\\");\\n\\n model = new MNIST.Model(\\\"model\\\", device);\\n\\n using (var train = torchvision.datasets.MNIST(datasetPath, true, true, normImage)) {\\n MNIST.TrainingLoop(dataset, (Device) device, model, train, test);\\n }\\n\\n Console.WriteLine(\\\"Moving on to the Adversarial model.\\\\n\\\");\\n\\n } else {\\n model = new MNIST.Model(\\\"model\\\", torch.CPU);\\n model.load(modelFile);\\n }\\n\\n model.to((Device) device);\\n model.eval();\\n }\\n\\n var epsilons = new double[] {0, 0.05, 0.1, 0.15, 0.20, 0.25, 0.30, 0.35, 0.40, 0.45, 0.50};\\n\\n foreach (var ε in epsilons) {\\n using (var test = torchvision.datasets.MNIST(datasetPath, false, true, normImage)) {\\n var attacked = Test(model, torch.nn.NLLLoss(), ε, device, test, test.Count);\\n Console.WriteLine($\\\"Epsilon: {ε:F2}, accuracy: {attacked:P2}\\\");\\n }\\n }\\n }\\n\\n private static Tensor Attack(Tensor image, double ε, Tensor data_grad)\\n {\\n using (var sign = data_grad.sign()) {\\n var perturbed = (image + ε * sign).clamp(0.0, 1.0);\\n return perturbed;\\n }\\n }\\n\\n private static double Test(\\n MNIST.Model model,\\n Loss<Tensor, Tensor, Tensor> criterion,\\n double ε,\\n Device device,\\n Dataset dataset,\\n long size)\\n {\\n int correct = 0;\\n\\n using (var d = torch.NewDisposeScope()) {\\n using var dataLoader = new DataLoader(dataset, _testBatchSize, device: device, shuffle: true);\\n foreach (var dat in dataLoader) {\\n var data = dat[\\\"data\\\"];\\n var label = dat[\\\"label\\\"];\\n data.requires_grad = true;\\n\\n using (var output = model.call(data))\\n using (var loss = criterion.call(output, label)) {\\n\\n model.zero_grad();\\n loss.backward();\\n\\n var perturbed = Attack(data, ε, data.grad());\\n\\n using (var final = model.call(perturbed)) {\\n\\n correct += final.argmax(1).eq(label).sum().ToInt32();\\n }\\n }\\n\\n d.DisposeEverything();\\n }\\n }\\n\\n return (double)correct / size;\\n }\\n }\\n}\\n\"\n },\n {\n \"alpha_fraction\": 0.5358197093009949,\n \"alphanum_fraction\": 0.5668997764587402,\n \"avg_line_length\": 34.26301193237305,\n \"blob_id\": \"e6d663937a299ec49c8cb316187faab3b3e86c92\",\n \"content_id\": \"5cc6f832d31147c127b343056a1d48fd9a539ae6\",\n \"detected_licenses\": [\n \"MIT\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"C++\",\n \"length_bytes\": 12870,\n \"license_type\": \"permissive\",\n \"max_line_length\": 181,\n \"num_lines\": 365,\n \"path\": \"/src/Native/LibTorchSharp/THSVision.cpp\",\n \"repo_name\": \"NiklasGustafsson/TorchSharp\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"// Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information.\\n#include \\\"THSVision.h\\\"\\n\\n#include <torch/nn/init.h>\\n\\n// The image processing code was ported from the Python implmentation found in:\\n//\\n// https://github.com/pytorch/vision/blob/main/torchvision/transforms/functional_tensor.py\\n\\nvoid _rgb_to_hsv(at::Tensor& img, at::Tensor& h, at::Tensor& saturation, at::Tensor& value)\\n{\\n auto RGB = img.unbind(-3);\\n auto r = RGB[0];\\n auto g = RGB[1];\\n auto b = RGB[2];\\n\\n auto maxc = std::get<0>(img.max(-3));\\n auto minc = std::get<0>(img.min(-3));\\n\\n auto eqc = maxc == minc;\\n auto cr = maxc - minc;\\n\\n auto options = at::TensorOptions()\\n .dtype(maxc.dtype())\\n .device(maxc.device())\\n .requires_grad(maxc.requires_grad());\\n\\n auto ones = torch::ones_like(maxc, options);\\n\\n auto s = cr / torch::where(eqc, ones, maxc);\\n auto cr_divisor = torch::where(eqc, ones, cr);\\n\\n auto rc = (maxc - r) / cr_divisor;\\n auto gc = (maxc - g) / cr_divisor;\\n auto bc = (maxc - b) / cr_divisor;\\n\\n auto hr = (maxc == r) * (bc - gc);\\n auto hg = ((maxc == g) & (maxc != r)) * (2.0 + rc - bc);\\n auto hb = ((maxc != g) & (maxc != r)) * (4.0 + gc - rc);\\n\\n h = (hr + hg + hb);\\n h = torch::fmod((h / 6.0 + 1.0), 1.0);\\n\\n saturation = s;\\n value = maxc;\\n}\\n\\nvoid _hsv_to_rgb(at::Tensor& h, at::Tensor& s, at::Tensor& v, at::Tensor& img)\\n{\\n auto h6 = h * 6.0f;\\n auto i = torch::floor(h6);\\n auto f = h6 - i;\\n i = i.to(at::ScalarType::Int) % 6;\\n\\n auto p = torch::clamp((v * (1.0f - s)), 0.0, 1.0);\\n auto q = torch::clamp((v * (1.0 - s * f)), 0.0, 1.0);\\n auto t = torch::clamp((v * (1.0 - s * (1.0 - f))), 0.0, 1.0);\\n\\n auto iunsq = i.unsqueeze(-3);\\n\\n auto mask = iunsq == torch::arange(6, at::TensorOptions(i.device())).view({ -1, 1, 1 });\\n\\n auto a1 = torch::stack({ v, q, p, p, t, v }, -3);\\n auto a2 = torch::stack({ t, v, v, q, p, p }, -3);\\n auto a3 = torch::stack({ p, p, t, v, v, q }, -3);\\n auto a4 = torch::stack({ a1, a2, a3 }, -4);\\n\\n img = torch::einsum(\\\"...ijk,...xijk ->...xjk\\\", { mask.to(h.dtype()), a4 });\\n}\\n\\nTensor THSVision_AdjustHue(const Tensor i, const double hue_factor)\\n{\\n try {\\n torch_last_err = 0;\\n\\n auto img = *i;\\n\\n auto orig_dtype = img.scalar_type();\\n\\n if (!torch::isFloatingType(orig_dtype)) {\\n img = img.to(c10::ScalarType::Float) / 255.0;\\n }\\n\\n at::Tensor h;\\n at::Tensor s;\\n at::Tensor v;\\n\\n _rgb_to_hsv(img, h, s, v);\\n\\n h = (h + hue_factor) % 1.0;\\n\\n at::Tensor img_hue_adj;\\n\\n _hsv_to_rgb(h, s, v, img_hue_adj);\\n\\n if (!torch::isFloatingType(orig_dtype)) {\\n img_hue_adj = (img_hue_adj * 255.0).to(orig_dtype);\\n }\\n\\n return ResultTensor(img_hue_adj);\\n }\\n catch (const c10::Error e) {\\n torch_last_err = strdup(e.what());\\n }\\n catch (const std::runtime_error e) {\\n torch_last_err = strdup(e.what());\\n }\\n\\n return nullptr;\\n}\\n\\nTensor THSVision_GenerateAffineGrid(Tensor theta, const int64_t w, const int64_t h, const int64_t ow, const int64_t oh)\\n{\\n try {\\n torch_last_err = 0;\\n\\n auto d = 0.5;\\n\\n auto thetaOptions = at::TensorOptions().dtype(theta->dtype()).device(theta->device());\\n auto thetaDevice = at::TensorOptions().device(theta->device());\\n\\n auto base_grid = torch::empty({ 1, oh, ow, 3 }, thetaOptions);\\n\\n auto x_grid = torch::linspace(-ow * 0.5 + d, ow * 0.5 + d - 1, ow, thetaDevice);\\n base_grid.index({ at::indexing::Ellipsis, 0 }).copy_(x_grid);\\n\\n auto y_grid = torch::linspace(-oh * 0.5 + d, oh * 0.5 + d - 1, oh, thetaDevice).unsqueeze_(-1);\\n base_grid.index({ at::indexing::Ellipsis, 1 }).copy_(y_grid);\\n base_grid.index({ at::indexing::Ellipsis, 2 }).fill_(1);\\n\\n auto rescaled_theta = theta->transpose(1, 2) / torch::tensor({ 0.5f * w, 0.5f * h }, thetaOptions);\\n auto output_grid = base_grid.view({ 1, oh * ow, 3 }).bmm(rescaled_theta);\\n\\n return ResultTensor(output_grid.view({ 1, oh, ow, 2 }));\\n }\\n catch (const c10::Error e) {\\n torch_last_err = strdup(e.what());\\n }\\n catch (const std::runtime_error e) {\\n torch_last_err = strdup(e.what());\\n }\\n\\n return nullptr;\\n}\\n\\nTensor THSVision_ApplyGridTransform(Tensor i, Tensor g, const int8_t m, const float* fill, const int64_t fill_length)\\n{\\n try {\\n torch_last_err = 0;\\n\\n auto img = *i;\\n auto grid = *g;\\n\\n auto imgOptions = at::TensorOptions().dtype(img.dtype()).device(img.device());\\n\\n if (img.size(0) > 1) {\\n grid = grid.expand({ img.size(0), grid.size(1), grid.size(2), grid.size(3) });\\n }\\n\\n if (fill != nullptr) {\\n auto dummy = torch::ones({ img.size(0), 1, img.size(2), img.size(3) }, imgOptions);\\n img = torch::cat({ img, dummy }, 1);\\n }\\n\\n const torch::nn::functional::GridSampleFuncOptions::mode_t mode =\\n (m == 0)\\n ? (torch::nn::functional::GridSampleFuncOptions::mode_t)torch::kNearest\\n : (torch::nn::functional::GridSampleFuncOptions::mode_t)torch::kBilinear;\\n auto sampleOpts = torch::nn::functional::GridSampleFuncOptions().padding_mode(torch::kZeros).mode(mode);\\n sampleOpts.align_corners(false); // Supress warning. Default=false for grid_sample since libtorch 1.3.0\\n\\n img = torch::nn::functional::grid_sample(img, grid, sampleOpts);\\n\\n\\n if (fill != nullptr) {\\n\\n auto COLON = at::indexing::Slice();\\n\\n auto mask = img.index({ COLON, at::indexing::Slice(-1, c10::nullopt), COLON, COLON });\\n img = img.index({ COLON, at::indexing::Slice(c10::nullopt, -1), COLON, COLON });\\n mask = mask.expand_as(img);\\n\\n auto fill_img = torch::tensor(at::ArrayRef<float>(fill, fill_length), imgOptions).view({ 1, fill_length, 1, 1 }).expand_as(img);\\n\\n if (m == 0) {\\n mask = mask < 0.5;\\n img[mask] = fill_img[mask];\\n }\\n else {\\n img = img * mask + (-mask + 1.0) * fill_img;\\n }\\n }\\n\\n return ResultTensor(img);\\n }\\n catch (const c10::Error e) {\\n torch_last_err = strdup(e.what());\\n }\\n catch (const std::runtime_error e) {\\n torch_last_err = strdup(e.what());\\n }\\n\\n return nullptr;\\n}\\n\\nTensor THSVision_ScaleChannel(Tensor ic)\\n{\\n try {\\n torch_last_err = 0;\\n auto img_chan = *ic;\\n\\n auto hist = img_chan.is_cuda()\\n ? torch::histc(img_chan.to(at::ScalarType::Float), 256, 0, 255)\\n : torch::bincount(img_chan.view(-1), {}, 256);\\n\\n auto nonzero_hist = hist.index({ hist != 0 });\\n\\n auto step = torch::div(nonzero_hist.index({ at::indexing::Slice(c10::nullopt, -1) }).sum(), 255, \\\"floor\\\");\\n auto count = step.count_nonzero();\\n\\n if (count.item<int>() == 0)\\n {\\n return ResultTensor(img_chan);\\n }\\n\\n auto lut = torch::div(torch::cumsum(hist, 0) + torch::div(step, 2, \\\"floor\\\"), step, \\\"floor\\\");\\n\\n auto padOptions = torch::nn::functional::PadFuncOptions({ 1, 0 });\\n lut = torch::nn::functional::pad(lut, padOptions).index({ at::indexing::Slice(c10::nullopt, -1) }).clamp(0, 255);\\n\\n return ResultTensor(lut.index({ img_chan.to(c10::ScalarType::Long) }).to(c10::ScalarType::Byte));\\n }\\n catch (const c10::Error e) {\\n torch_last_err = strdup(e.what());\\n }\\n catch (const std::runtime_error e) {\\n torch_last_err = strdup(e.what());\\n }\\n\\n return nullptr;\\n}\\n\\nvoid THSVision_ComputeOutputSize(const float* matrix, const int64_t matrix_length, const int64_t w, const int64_t h, int32_t* first, int32_t* second)\\n{\\n try {\\n torch_last_err = 0;\\n\\n auto pts = torch::tensor({ -0.5f * w, -0.5f * h, 1.0f, -0.5f * w, 0.5f * h, 1.0f, 0.5f * w, 0.5f * h, 1.0f, 0.5f * w, -0.5f * h, 1.0f }).reshape({ 4,3 });\\n auto theta = torch::tensor(c10::ArrayRef<float>(matrix, matrix_length), c10::TensorOptions().dtype(c10::ScalarType::Float)).view({ 2, 3 });\\n auto new_pts = torch::matmul(pts, theta.t());\\n\\n auto min_vals = std::get<0>(new_pts.min(0));\\n auto max_vals = std::get<0>(new_pts.max(0));\\n\\n min_vals += torch::tensor({ w * 0.5f, h * 0.5f });\\n max_vals += torch::tensor({ w * 0.5f, h * 0.5f });\\n\\n float tol = 1e-4;\\n auto cmax = torch::ceil((max_vals / tol).trunc_() * tol);\\n auto cmin = torch::floor((min_vals / tol).trunc_() * tol);\\n\\n auto size = cmax - cmin;\\n\\n *first = size[0].item<int>();\\n *second = size[1].item<int>();\\n }\\n catch (const c10::Error e) {\\n torch_last_err = strdup(e.what());\\n }\\n catch (const std::runtime_error e) {\\n torch_last_err = strdup(e.what());\\n }\\n}\\n\\nTensor THSVision_PerspectiveGrid(const float* c, const int64_t c_length, const int64_t ow, const int64_t oh, const int8_t scalar_type, const int device_type, const int device_index)\\n{\\n try {\\n torch_last_err = 0;\\n\\n auto fullOptions = at::TensorOptions()\\n .dtype(at::ScalarType(scalar_type))\\n .device(c10::Device((c10::DeviceType)device_type, (c10::DeviceIndex)device_index));\\n auto devOptions = at::TensorOptions()\\n .device(c10::Device((c10::DeviceType)device_type, (c10::DeviceIndex)device_index));\\n\\n auto coeffs = c10::ArrayRef<float>(c, c_length);\\n\\n auto theta1 = torch::tensor({ coeffs[0], coeffs[1], coeffs[2], coeffs[3], coeffs[4], coeffs[5] }, fullOptions).view({ 1, 2, 3 });\\n auto theta2 = torch::tensor({ coeffs[6], coeffs[7], 1.0f, coeffs[6], coeffs[7], 1.0f }, fullOptions).view({ 1, 2, 3 });\\n\\n auto d = 0.5f;\\n auto base_grid = torch::empty({ 1, oh, ow, 3 }, fullOptions);\\n auto x_grid = torch::linspace(d, ow * 1.0 + d - 1.0, ow, devOptions);\\n base_grid.index({ at::indexing::Ellipsis, 0 }).copy_(x_grid);\\n\\n auto y_grid = torch::linspace(d, oh * 1.0 + d - 1.0, oh, devOptions).unsqueeze_(-1);\\n base_grid.index({ at::indexing::Ellipsis, 1 }).copy_(y_grid);\\n base_grid.index({ at::indexing::Ellipsis, 2 }).fill_(1);\\n\\n auto rescaled_theta1 = theta1.transpose(1, 2) / torch::tensor({ 0.5f * ow, 0.5f * oh }, fullOptions);\\n\\n auto output_grid1 = base_grid.view({ 1, oh * ow, 3 }).bmm(rescaled_theta1);\\n auto output_grid2 = base_grid.view({ 1, oh * ow, 3 }).bmm(theta2.transpose(1, 2));\\n auto output_grid = output_grid1 / output_grid2 - 1.0f;\\n\\n return ResultTensor(output_grid.view({ 1, oh, ow, 2 }));\\n }\\n catch (const c10::Error e) {\\n torch_last_err = strdup(e.what());\\n }\\n catch (const std::runtime_error e) {\\n torch_last_err = strdup(e.what());\\n }\\n\\n return nullptr;\\n}\\n\\n\\nvoid THSVision_BRGA_RGB(const uint8_t* inputBytes, uint8_t* redBytes, uint8_t* greenBytes, uint8_t* blueBytes, int64_t inputChannelCount, int64_t imageSize)\\n{\\n const int inputBlue = 0, inputGreen = 1, inputRed = 2;\\n\\n for (int64_t i = 0, j = 0; i < imageSize; i += 1, j += inputChannelCount) {\\n redBytes[i] = inputBytes[inputRed + j];\\n greenBytes[i] = inputBytes[inputGreen + j];\\n blueBytes[i] = inputBytes[inputBlue + j];\\n }\\n}\\n\\nvoid THSVision_BRGA_RGBA(const uint8_t* inputBytes, uint8_t* redBytes, uint8_t* greenBytes, uint8_t* blueBytes, uint8_t* alphaBytes, int64_t inputChannelCount, int64_t imageSize)\\n{\\n const int inputBlue = 0, inputGreen = 1, inputRed = 2, inputAlpha = 3;\\n\\n bool inputHasAlpha = inputChannelCount == 4;\\n\\n for (int64_t i = 0, j = 0; i < imageSize; i += 1, j += inputChannelCount) {\\n redBytes[i] = inputBytes[inputRed + j];\\n greenBytes[i] = inputBytes[inputGreen + j];\\n blueBytes[i] = inputBytes[inputBlue + j];\\n alphaBytes[i] = inputHasAlpha ? inputBytes[inputAlpha + j] : 255;\\n }\\n}\\n\\nvoid THSVision_RGB_BRGA(const uint8_t* inputBytes, uint8_t* outBytes, int64_t inputChannelCount, int64_t imageSize)\\n{\\n bool isgrey = inputChannelCount == 1;\\n bool inputHasAlpha = inputChannelCount == 4;\\n\\n const int inputRed = 0, inputGreen = imageSize, inputBlue = imageSize * 2, inputAlpha = imageSize * 3;\\n const int outputBlue = 0, outputGreen = 1, outputRed = 2, outputAlpha = 3;\\n\\n for (int64_t i = 0, j = 0; i < imageSize; i += 1, j += 4) {\\n auto redPixel = inputBytes[inputRed + i];\\n outBytes[outputRed + j] = redPixel;\\n if (!isgrey) {\\n outBytes[outputGreen + j] = inputBytes[inputGreen + i];\\n outBytes[outputBlue + j] = inputBytes[inputBlue + i];\\n }\\n else {\\n outBytes[outputGreen + j] = redPixel;\\n outBytes[outputBlue + j] = redPixel;\\n }\\n outBytes[outputAlpha + j] = inputHasAlpha ? inputBytes[inputBlue + i] : 255;\\n }\\n}\"\n },\n {\n \"alpha_fraction\": 0.6443196535110474,\n \"alphanum_fraction\": 0.6474968194961548,\n \"avg_line_length\": 66.30237579345703,\n \"blob_id\": \"d0c1b51618030e4f76406b624d88476cecde6a57\",\n \"content_id\": \"59afd1e9774c5a46a936ba5b99632ac6c6cd4b37\",\n \"detected_licenses\": [\n \"MIT\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"C#\",\n \"length_bytes\": 31168,\n \"license_type\": \"permissive\",\n \"max_line_length\": 269,\n \"num_lines\": 463,\n \"path\": \"/src/TorchSharp/Tensor/torch.ComparisonOps.cs\",\n \"repo_name\": \"NiklasGustafsson/TorchSharp\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"// Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information.\\nusing System;\\nusing System.Diagnostics.Contracts;\\n\\nnamespace TorchSharp\\n{\\n // https://pytorch.org/docs/stable/torch#comparison-ops\\n public static partial class torch\\n {\\n // https://pytorch.org/docs/stable/generated/torch.allclose\\n /// <summary>\\n /// This function checks if all input and other lie within a certain distance from each other\\n /// </summary>\\n /// <param name=\\\"tensor\\\"></param>\\n /// <param name=\\\"target\\\"></param>\\n /// <param name=\\\"rtol\\\">Relative tolerance</param>\\n /// <param name=\\\"atol\\\">Absolute tolerance</param>\\n /// <param name=\\\"equal_nan\\\">If true, then two NaN s will be considered equal</param>\\n [Pure]\\n public static bool allclose(Tensor tensor, Tensor target, double rtol = 1e-05, double atol = 1e-08, bool equal_nan = false)\\n => tensor.allclose(target, rtol, atol, equal_nan);\\n\\n // https://pytorch.org/docs/stable/generated/torch.argsort\\n /// <summary>\\n /// Returns the indices that sort a tensor along a given dimension in ascending order by value.\\n /// </summary>\\n [Pure]\\n public static Tensor argsort(Tensor input, long dim = -1, bool descending = false)\\n => input.argsort(dim, descending);\\n\\n // https://pytorch.org/docs/stable/generated/torch.eq\\n /// <summary>\\n /// Element-wise equal comparison\\n /// </summary>\\n /// <param name=\\\"left\\\">The left-hand operand.</param>\\n /// <param name=\\\"right\\\">The right-hand operand.</param>\\n [Pure]\\n public static Tensor eq(Tensor left, Tensor right)\\n => left.eq(right);\\n\\n // https://pytorch.org/docs/stable/generated/torch.equal\\n [Pure]\\n public static Tensor equal(Tensor tensor, Tensor target)\\n => tensor.equal(target);\\n\\n // https://pytorch.org/docs/stable/generated/torch.ge\\n /// <summary>\\n /// Element-wise greater-than-or-equal comparison\\n /// </summary>\\n /// <param name=\\\"left\\\">The left-hand operand.</param>\\n /// <param name=\\\"right\\\">The right-hand operand.</param>\\n [Pure]\\n public static Tensor ge(Tensor left, Tensor right)\\n => left.ge(right);\\n\\n // https://pytorch.org/docs/stable/generated/torch.greater_equal\\n [Pure]\\n public static Tensor greater_equal(Tensor tensor, Tensor target)\\n => tensor.greater_equal(target);\\n\\n // https://pytorch.org/docs/stable/generated/torch.gt\\n /// <summary>\\n /// Element-wise greater-than comparison\\n /// </summary>\\n /// <param name=\\\"left\\\">The left-hand operand.</param>\\n /// <param name=\\\"right\\\">The right-hand operand.</param>\\n [Pure]\\n public static Tensor gt(Tensor left, Tensor right)\\n => left.gt(right);\\n\\n // https://pytorch.org/docs/stable/generated/torch.greater\\n [Pure]\\n public static Tensor greater(Tensor tensor, Tensor target)\\n => tensor.greater(target);\\n\\n // https://pytorch.org/docs/stable/generated/torch.isclose\\n [Pure]\\n public static Tensor isclose(Tensor tensor, Tensor other, double rtol = 1e-05, double atol = 1e-08, bool nanEqual = false)\\n => tensor.isclose(other, rtol, atol, nanEqual);\\n\\n // https://pytorch.org/docs/stable/generated/torch.isfinite\\n [Pure]\\n public static Tensor isfinite(Tensor tensor)\\n => tensor.isfinite();\\n\\n // https://pytorch.org/docs/stable/generated/torch.isin\\n [Pure]\\n public static Tensor isin(Tensor tensor, Tensor test_elements, bool assumeUnique = false, bool invert = false)\\n => tensor.isin(test_elements, assumeUnique, invert);\\n\\n // https://pytorch.org/docs/stable/generated/torch.isinf\\n [Pure]\\n public static Tensor isinf(Tensor tensor)\\n => tensor.isinf();\\n\\n // https://pytorch.org/docs/stable/generated/torch.isposinf\\n [Pure]\\n public static Tensor isposinf(Tensor tensor)\\n => tensor.isposinf();\\n\\n // https://pytorch.org/docs/stable/generated/torch.isneginf\\n [Pure]\\n public static Tensor isneginf(Tensor tensor)\\n => tensor.isneginf();\\n\\n // https://pytorch.org/docs/stable/generated/torch.isnan\\n /// <summary>\\n /// Returns a new tensor with boolean elements representing if each element of input is <value>NaN</value> or not.\\n /// Complex values are considered <value>NaN</value> when either their real and/or imaginary part is <value>NaN</value>.\\n /// </summary>\\n /// <param name=\\\"input\\\">the input tensor</param>\\n /// <returns>A boolean tensor that is <value>True</value> where input is <value>NaN</value> and <value>False</value> elsewhere</returns>\\n [Pure]\\n public static Tensor isnan(Tensor input)\\n => input.isnan();\\n\\n // https://pytorch.org/docs/stable/generated/torch.isreal\\n [Pure]\\n public static Tensor isreal(Tensor tensor)\\n => tensor.isreal();\\n\\n // https://pytorch.org/docs/stable/generated/torch.kthvalue\\n /// <summary>\\n /// Returns a named tuple (values, indices) where values is the k th smallest element of each row of the input tensor in the given dimension dim. And indices is the index location of each element found.\\n /// If dim is not given, the last dimension of the input is chosen.\\n /// </summary>\\n /// <param name=\\\"input\\\">The input tensor.</param>\\n /// <param name=\\\"k\\\">k for the k-th smallest element</param>\\n /// <param name=\\\"dim\\\">The dimension to find the kth value along</param>\\n /// <param name=\\\"keepdim\\\">Whether the output tensor has dim retained or not.</param>\\n [Pure]\\n public static (Tensor values, Tensor indices) kthvalue(Tensor input, long k, long? dim, bool keepdim = false)\\n => input.kthvalue(k, dim, keepdim);\\n\\n // https://pytorch.org/docs/stable/generated/torch.le\\n /// <summary>\\n /// Element-wise less-than-or-equal comparison\\n /// </summary>\\n /// <param name=\\\"left\\\">The left-hand operand.</param>\\n /// <param name=\\\"right\\\">The right-hand operand.</param>\\n [Pure]\\n public static Tensor le(Tensor left, Tensor right)\\n => left.le(right);\\n\\n // https://pytorch.org/docs/stable/generated/torch.less_equal\\n [Pure]\\n public static Tensor less_equal(Tensor tensor, Tensor target)\\n => tensor.less_equal(target);\\n\\n // https://pytorch.org/docs/stable/generated/torch.lt\\n /// <summary>\\n /// Element-wise less-than comparison\\n /// </summary>\\n /// <param name=\\\"left\\\">The left-hand operand.</param>\\n /// <param name=\\\"right\\\">The right-hand operand.</param>\\n [Pure]\\n public static Tensor lt(Tensor left, Tensor right)\\n => left.lt(right);\\n\\n // https://pytorch.org/docs/stable/generated/torch.less\\n [Pure]\\n public static Tensor less(Tensor tensor, Tensor target)\\n => tensor.less(target);\\n\\n // https://pytorch.org/docs/stable/generated/torch.maximum\\n /// <summary>\\n /// Computes the element-wise maximum of input and other.\\n /// </summary>\\n /// <param name=\\\"input\\\">The first input tensor</param>\\n /// <param name=\\\"other\\\">The second input tensor</param>\\n [Pure]\\n public static Tensor maximum(Tensor input, Tensor other)\\n => input.maximum(other);\\n\\n // https://pytorch.org/docs/stable/generated/torch.minimum\\n /// <summary>\\n /// Computes the element-wise minimum of input and other.\\n /// </summary>\\n /// <param name=\\\"input\\\">The first input tensor</param>\\n /// <param name=\\\"other\\\">The second input tensor</param>\\n [Pure]\\n public static Tensor minimum(Tensor input, Tensor other)\\n => input.minimum(other);\\n\\n // https://pytorch.org/docs/stable/generated/torch.fmax\\n /// <summary>\\n /// Computes the element-wise maximum of input and other.\\n ///\\n /// This is like torch.maximum() except it handles NaNs differently: if exactly one of the two elements being compared is a NaN\\n /// then the non-NaN element is taken as the maximum.\\n /// Only if both elements are NaN is NaN propagated.\\n /// </summary>\\n /// <param name=\\\"input\\\">The first input tensor</param>\\n /// <param name=\\\"other\\\">The second input tensor</param>\\n [Pure]\\n public static Tensor fmax(Tensor input, Tensor other)\\n => input.fmax(other);\\n\\n // https://pytorch.org/docs/stable/generated/torch.fmin\\n /// <summary>\\n /// Computes the element-wise minimum of input and other.\\n ///\\n /// This is like torch.minimum() except it handles NaNs differently: if exactly one of the two elements being compared is a NaN\\n /// then the non-NaN element is taken as the minimum.\\n /// Only if both elements are NaN is NaN propagated.\\n /// </summary>\\n /// <param name=\\\"input\\\">The first input tensor</param>\\n /// <param name=\\\"other\\\">The second input tensor</param>\\n [Pure]\\n public static Tensor fmin(Tensor input, Tensor other)\\n => input.fmin(other);\\n\\n // https://pytorch.org/docs/stable/generated/torch.ne\\n /// <summary>\\n /// Element-wise not-equal comparison\\n /// </summary>\\n /// <param name=\\\"left\\\">The left-hand operand.</param>\\n /// <param name=\\\"right\\\">The right-hand operand.</param>\\n [Pure]\\n public static Tensor ne(Tensor left, Tensor right)\\n => left.ne(right);\\n\\n // https://pytorch.org/docs/stable/generated/torch.not_equal\\n [Pure]\\n public static Tensor not_equal(Tensor tensor, Tensor target)\\n => tensor.not_equal(target);\\n\\n // https://pytorch.org/docs/stable/generated/torch.sort\\n /// <summary>\\n /// Sorts the elements of the input tensor along a given dimension in ascending order by value.\\n /// </summary>\\n /// <param name=\\\"input\\\">The input tensor</param>\\n /// <param name=\\\"dim\\\">The dimension to sort along. If dim is not given, the last dimension of the input is chosen.</param>\\n /// <param name=\\\"descending\\\">Controls the sorting order (ascending or descending)</param>\\n /// <param name=\\\"stable\\\">Makes the sorting routine stable, which guarantees that the order of equivalent elements is preserved.</param>\\n /// <returns>A named tuple of (values, indices) is returned, where the values are the sorted values and indices are the indices of the elements in the original input tensor.</returns>\\n [Pure]\\n public static (Tensor values, Tensor indices) sort(Tensor input, long dim = -1, bool descending = false, bool stable = false)\\n => input.sort(dim, descending, stable);\\n\\n // https://pytorch.org/docs/stable/generated/torch.searchsorted.html\\n /// <summary>\\n /// Find the indices from the innermost dimension of sorted_sequence such that, if the corresponding values in values were inserted before the indices,\\n /// when sorted, the order of the corresponding innermost dimension within sorted_sequence would be preserved.\\n /// Return a new tensor with the same size as values.\\n /// If right is false, then the left boundary of sorted_sequence is closed. \\n /// </summary>\\n /// <param name=\\\"sorted_sequence\\\">N-D or 1-D tensor, containing monotonically increasing sequence on the innermost dimension unless sorter is provided, in which case the sequence does not need to be sorted</param>\\n /// <param name=\\\"values\\\">N-D tensor or a Scalar containing the search value(s).</param>\\n /// <param name=\\\"out_int32\\\">Indicates the output data type. torch.int32 if true, torch.int64 otherwise. Default value is false, i.e. default output data type is torch.int64.</param>\\n /// <param name=\\\"right\\\">Indicates the output data type. torch.int32 if true, torch.int64 otherwise. Default value is false, i.e. default output data type is torch.int64.</param>\\n /// <param name=\\\"sorter\\\">If provided, a tensor matching the shape of the unsorted sorted_sequence containing a sequence of indices that sort it in the ascending order on the innermost dimension</param>\\n public static Tensor searchsorted(Tensor sorted_sequence, Tensor values, bool out_int32 = false, bool right = false, Tensor sorter = null)\\n {\\n var res = PInvoke.NativeMethods.THSTensor_searchsorted_t(sorted_sequence.Handle, values.Handle, out_int32, right, sorter is null ? IntPtr.Zero : sorter.Handle);\\n if (res == IntPtr.Zero) CheckForErrors();\\n return new Tensor(res);\\n }\\n\\n // https://pytorch.org/docs/stable/generated/torch.searchsorted.html\\n /// <summary>\\n /// Find the indices from the innermost dimension of sorted_sequence such that, if the corresponding values in values were inserted before the indices,\\n /// when sorted, the order of the corresponding innermost dimension within sorted_sequence would be preserved.\\n /// Return a new tensor with the same size as values.\\n /// If right is false, then the left boundary of sorted_sequence is closed. \\n /// </summary>\\n /// <param name=\\\"sorted_sequence\\\">N-D or 1-D tensor, containing monotonically increasing sequence on the innermost dimension unless sorter is provided, in which case the sequence does not need to be sorted</param>\\n /// <param name=\\\"values\\\">A Scalar containing the search value.</param>\\n /// <param name=\\\"out_int32\\\">Indicates the output data type. torch.int32 if true, torch.int64 otherwise. Default value is false, i.e. default output data type is torch.int64.</param>\\n /// <param name=\\\"right\\\">Indicates the output data type. torch.int32 if true, torch.int64 otherwise. Default value is false, i.e. default output data type is torch.int64.</param>\\n /// <param name=\\\"sorter\\\">If provided, a tensor matching the shape of the unsorted sorted_sequence containing a sequence of indices that sort it in the ascending order on the innermost dimension</param>\\n public static Tensor searchsorted(Tensor sorted_sequence, Scalar values, bool out_int32, bool right, Tensor sorter)\\n {\\n var res = PInvoke.NativeMethods.THSTensor_searchsorted_s(sorted_sequence.Handle, values.Handle, out_int32, right, sorter is null ? IntPtr.Zero : sorter.Handle);\\n if (res == IntPtr.Zero) CheckForErrors();\\n return new Tensor(res);\\n }\\n\\n /// https://github.com/numpy/numpy/blob/v1.24.0/numpy/lib/histograms.py#L679\\n /// <summary>\\n /// Computes a histogram of the values in a tensor.\\n /// bins can be an integer or a 1D tensor.\\n /// If bins is an int, it specifies the number of equal-width bins. By default, the lower and upper range of the bins is determined by the minimum and maximum elements of the input tensor. The range argument can be provided to specify a range for the bins.\\n /// If bins is a 1D tensor, it specifies the sequence of bin edges including the rightmost edge. It should contain at least 2 elements and its elements should be increasing.\\n /// </summary>\\n /// <param name=\\\"input\\\"> the input tensor. </param>\\n /// <param name=\\\"bins\\\"> int or 1D Tensor. If int, defines the number of equal-width bins. If tensor, defines the sequence of bin edges including the rightmost edge. </param>\\n /// <param name=\\\"range\\\"> Defines the range of the bins. </param>\\n /// <param name=\\\"density\\\"> If False, the result will contain the count (or total weight) in each bin. If True, the result is the value of the probability density function over the bins, normalized such that the integral over the range of the bins is 1. </param>\\n /// <returns></returns>\\n public static (Tensor hist, Tensor bin_edges) histogram(Tensor input, HistogramBinSelector bins, (double min, double max)? range = null, bool density = false)\\n => Utils.Histogram.histogram(input, bins, range, density);\\n\\n // https://pytorch.org/docs/stable/generated/torch.histogram.html\\n /// <summary>\\n /// Computes a histogram of the values in a tensor.\\n /// bins can be an integer or a 1D tensor.\\n /// If bins is an int, it specifies the number of equal-width bins. By default, the lower and upper range of the bins is determined by the minimum and maximum elements of the input tensor. The range argument can be provided to specify a range for the bins.\\n /// If bins is a 1D tensor, it specifies the sequence of bin edges including the rightmost edge. It should contain at least 2 elements and its elements should be increasing.\\n /// </summary>\\n /// <param name=\\\"input\\\"> the input tensor. </param>\\n /// <param name=\\\"bins\\\"> int or 1D Tensor. If int, defines the number of equal-width bins. If tensor, defines the sequence of bin edges including the rightmost edge. </param>\\n /// <param name=\\\"weight\\\"> If provided, weight should have the same shape as input. Each value in input contributes its associated weight towards its bin’s result. </param>\\n /// <param name=\\\"density\\\"> If False, the result will contain the count (or total weight) in each bin. If True, the result is the value of the probability density function over the bins, normalized such that the integral over the range of the bins is 1. </param>\\n /// <returns></returns>\\n public static (Tensor hist, Tensor bin_edges) histogram(Tensor input, Tensor bins, Tensor weight = null, bool density = false)\\n {\\n var res = PInvoke.NativeMethods.THSTensor_histogram_t(input.Handle, bins.Handle, weight is null ? IntPtr.Zero : weight.Handle, density, out var r_bin_edges);\\n if (res == IntPtr.Zero) CheckForErrors();\\n if (r_bin_edges == IntPtr.Zero) CheckForErrors();\\n return (new Tensor(res), new Tensor(r_bin_edges));\\n }\\n\\n // https://pytorch.org/docs/stable/generated/torch.histogram.html\\n /// <summary>\\n /// Computes a histogram of the values in a tensor.\\n /// bins can be an integer or a 1D tensor.\\n /// If bins is an int, it specifies the number of equal-width bins. By default, the lower and upper range of the bins is determined by the minimum and maximum elements of the input tensor. The range argument can be provided to specify a range for the bins.\\n /// If bins is a 1D tensor, it specifies the sequence of bin edges including the rightmost edge. It should contain at least 2 elements and its elements should be increasing.\\n /// </summary>\\n /// <param name=\\\"input\\\"> the input tensor. </param>\\n /// <param name=\\\"bins\\\"> int or 1D Tensor. If int, defines the number of equal-width bins. If tensor, defines the sequence of bin edges including the rightmost edge. </param>\\n /// <param name=\\\"range\\\"> Defines the range of the bins. </param>\\n /// <param name=\\\"weight\\\"> If provided, weight should have the same shape as input. Each value in input contributes its associated weight towards its bin’s result. </param>\\n /// <param name=\\\"density\\\"> If False, the result will contain the count (or total weight) in each bin. If True, the result is the value of the probability density function over the bins, normalized such that the integral over the range of the bins is 1. </param>\\n /// <returns></returns>\\n public static (Tensor hist, Tensor bin_edges) histogram(Tensor input, long bins, (double min, double max)? range = null, Tensor weight = null, bool density = false)\\n {\\n double[] _range = Array.Empty<double>();\\n if (range is not null)\\n _range = new double[] { range.Value.min, range.Value.max };\\n unsafe {\\n fixed (double* prange = _range) {\\n var res = _range == Array.Empty<double>()\\n ? PInvoke.NativeMethods.THSTensor_histogram_i(input.Handle, bins, IntPtr.Zero, 0, weight is null ? IntPtr.Zero : weight.Handle, density, out var r_bin_edges)\\n : PInvoke.NativeMethods.THSTensor_histogram_i(input.Handle, bins, (IntPtr)prange, _range.Length, weight is null ? IntPtr.Zero : weight.Handle, density, out r_bin_edges);\\n if (res == IntPtr.Zero) CheckForErrors();\\n if (r_bin_edges == IntPtr.Zero) CheckForErrors();\\n return (new Tensor(res), new Tensor(r_bin_edges));\\n }\\n }\\n }\\n\\n // https://pytorch.org/docs/stable/generated/torch.histogram.html\\n /// <summary>\\n /// Computes a histogram of the values in a tensor.\\n /// bins can be an integer or a 1D tensor.\\n /// If bins is an int, it specifies the number of equal-width bins. By default, the lower and upper range of the bins is determined by the minimum and maximum elements of the input tensor. The range argument can be provided to specify a range for the bins.\\n /// If bins is a 1D tensor, it specifies the sequence of bin edges including the rightmost edge. It should contain at least 2 elements and its elements should be increasing.\\n /// </summary>\\n /// <param name=\\\"input\\\"> the input tensor. </param>\\n /// <param name=\\\"bins\\\"> int or 1D Tensor. If int, defines the number of equal-width bins. If tensor, defines the sequence of bin edges including the rightmost edge. </param>\\n /// <param name=\\\"out_tensor\\\"> the output tensor. (tuple, optional): The result tuple of two output tensors (hist, bin_edges). </param>\\n /// <returns></returns>\\n public static (Tensor hist, Tensor bin_edges) histogram(Tensor input, Tensor bins, out (Tensor hist, Tensor bin_edges) out_tensor)\\n => histogram(input, bins, null, false, out out_tensor);\\n\\n // https://pytorch.org/docs/stable/generated/torch.histogram.html\\n /// <summary>\\n /// Computes a histogram of the values in a tensor.\\n /// bins can be an integer or a 1D tensor.\\n /// If bins is an int, it specifies the number of equal-width bins. By default, the lower and upper range of the bins is determined by the minimum and maximum elements of the input tensor. The range argument can be provided to specify a range for the bins.\\n /// If bins is a 1D tensor, it specifies the sequence of bin edges including the rightmost edge. It should contain at least 2 elements and its elements should be increasing.\\n /// </summary>\\n /// <param name=\\\"input\\\"> the input tensor. </param>\\n /// <param name=\\\"bins\\\"> int or 1D Tensor. If int, defines the number of equal-width bins. If tensor, defines the sequence of bin edges including the rightmost edge. </param>\\n /// <param name=\\\"weight\\\"> If provided, weight should have the same shape as input. Each value in input contributes its associated weight towards its bin’s result. </param>\\n /// <param name=\\\"density\\\"> If False, the result will contain the count (or total weight) in each bin. If True, the result is the value of the probability density function over the bins, normalized such that the integral over the range of the bins is 1. </param>\\n /// <param name=\\\"out_tensor\\\"> the output tensor. (tuple, optional): The result tuple of two output tensors (hist, bin_edges). </param>\\n /// <returns></returns>\\n public static (Tensor hist, Tensor bin_edges) histogram(Tensor input, Tensor bins, Tensor weight, bool density, out (Tensor hist, Tensor bin_edges) out_tensor)\\n {\\n var res = PInvoke.NativeMethods.THSTensor_histogram_out_t(input.Handle, bins.Handle, weight is null ? IntPtr.Zero : weight.Handle, density, out var hist, out var bin_edges, out var r_bin_edges);\\n if (res == IntPtr.Zero) CheckForErrors();\\n if (hist == IntPtr.Zero) CheckForErrors();\\n if (bin_edges == IntPtr.Zero) CheckForErrors();\\n if (r_bin_edges == IntPtr.Zero) CheckForErrors();\\n out_tensor = (new Tensor(hist), new Tensor(bin_edges));\\n return (new Tensor(res), new Tensor(r_bin_edges));\\n }\\n\\n // https://pytorch.org/docs/stable/generated/torch.histogram.html\\n /// <summary>\\n /// Computes a histogram of the values in a tensor.\\n /// bins can be an integer or a 1D tensor.\\n /// If bins is an int, it specifies the number of equal-width bins. By default, the lower and upper range of the bins is determined by the minimum and maximum elements of the input tensor. The range argument can be provided to specify a range for the bins.\\n /// If bins is a 1D tensor, it specifies the sequence of bin edges including the rightmost edge. It should contain at least 2 elements and its elements should be increasing.\\n /// </summary>\\n /// <param name=\\\"input\\\"> the input tensor. </param>\\n /// <param name=\\\"bins\\\"> int or 1D Tensor. If int, defines the number of equal-width bins. If tensor, defines the sequence of bin edges including the rightmost edge. </param>\\n /// <param name=\\\"out_tensor\\\"> the output tensor. (tuple, optional): The result tuple of two output tensors (hist, bin_edges). </param>\\n /// <returns></returns>\\n public static (Tensor hist, Tensor bin_edges) histogram(Tensor input, long bins, out (Tensor hist, Tensor bin_edges) out_tensor)\\n => histogram(input, bins, null, null, false, out out_tensor);\\n\\n // https://pytorch.org/docs/stable/generated/torch.histogram.html\\n /// <summary>\\n /// Computes a histogram of the values in a tensor.\\n /// bins can be an integer or a 1D tensor.\\n /// If bins is an int, it specifies the number of equal-width bins. By default, the lower and upper range of the bins is determined by the minimum and maximum elements of the input tensor. The range argument can be provided to specify a range for the bins.\\n /// If bins is a 1D tensor, it specifies the sequence of bin edges including the rightmost edge. It should contain at least 2 elements and its elements should be increasing.\\n /// </summary>\\n /// <param name=\\\"input\\\"> the input tensor. </param>\\n /// <param name=\\\"bins\\\"> int or 1D Tensor. If int, defines the number of equal-width bins. If tensor, defines the sequence of bin edges including the rightmost edge. </param>\\n /// <param name=\\\"range\\\"> Defines the range of the bins. </param>\\n /// <param name=\\\"out_tensor\\\"> the output tensor. (tuple, optional): The result tuple of two output tensors (hist, bin_edges). </param>\\n /// <returns></returns>\\n public static (Tensor hist, Tensor bin_edges) histogram(Tensor input, long bins, (double min, double max)? range, out (Tensor hist, Tensor bin_edges) out_tensor)\\n => histogram(input, bins, range, null, false, out out_tensor);\\n\\n // https://pytorch.org/docs/stable/generated/torch.histogram.html\\n /// <summary>\\n /// Computes a histogram of the values in a tensor.\\n /// bins can be an integer or a 1D tensor.\\n /// If bins is an int, it specifies the number of equal-width bins. By default, the lower and upper range of the bins is determined by the minimum and maximum elements of the input tensor. The range argument can be provided to specify a range for the bins.\\n /// If bins is a 1D tensor, it specifies the sequence of bin edges including the rightmost edge. It should contain at least 2 elements and its elements should be increasing.\\n /// </summary>\\n /// <param name=\\\"input\\\"> the input tensor. </param>\\n /// <param name=\\\"bins\\\"> int or 1D Tensor. If int, defines the number of equal-width bins. If tensor, defines the sequence of bin edges including the rightmost edge. </param>\\n /// <param name=\\\"range\\\"> Defines the range of the bins. </param>\\n /// <param name=\\\"weight\\\"> If provided, weight should have the same shape as input. Each value in input contributes its associated weight towards its bin’s result. </param>\\n /// <param name=\\\"density\\\"> If False, the result will contain the count (or total weight) in each bin. If True, the result is the value of the probability density function over the bins, normalized such that the integral over the range of the bins is 1. </param>\\n /// <param name=\\\"out_tensor\\\"> the output tensor. (tuple, optional): The result tuple of two output tensors (hist, bin_edges). </param>\\n /// <returns></returns>\\n public static (Tensor hist, Tensor bin_edges) histogram(Tensor input, long bins, (double min, double max)? range, Tensor weight, bool density, out (Tensor hist, Tensor bin_edges) out_tensor)\\n {\\n double[] _range = Array.Empty<double>();\\n if (range is not null)\\n _range = new double[] { range.Value.min, range.Value.max };\\n unsafe {\\n fixed (double* prange = _range) {\\n var res = _range == Array.Empty<double>()\\n ? PInvoke.NativeMethods.THSTensor_histogram_out_i(input.Handle, bins, IntPtr.Zero, 0, weight is null ? IntPtr.Zero : weight.Handle, density, out var hist, out var bin_edges, out var r_bin_edges)\\n : PInvoke.NativeMethods.THSTensor_histogram_out_i(input.Handle, bins, (IntPtr)prange, _range.Length, weight is null ? IntPtr.Zero : weight.Handle, density, out hist, out bin_edges, out r_bin_edges);\\n if (res == IntPtr.Zero) CheckForErrors();\\n if (hist == IntPtr.Zero) CheckForErrors();\\n if (bin_edges == IntPtr.Zero) CheckForErrors();\\n if (r_bin_edges == IntPtr.Zero) CheckForErrors();\\n out_tensor = (new Tensor(hist), new Tensor(bin_edges));\\n return (new Tensor(res), new Tensor(r_bin_edges));\\n }\\n }\\n }\\n\\n // https://pytorch.org/docs/stable/generated/torch.topk\\n /// <summary>\\n /// Returns the k largest elements of the given input tensor along a given dimension.\\n /// </summary>\\n /// <param name=\\\"tensor\\\">The input tensor</param>\\n /// <param name=\\\"k\\\">The 'k' in 'top-k'.</param>\\n /// <param name=\\\"dim\\\">The dimension to sort along. If dim is not given, the last dimension of the input is chosen.</param>\\n /// <param name=\\\"largest\\\">Controls whether to return largest or smallest elements</param>\\n /// <param name=\\\"sorted\\\">Controls whether to return the elements in sorted order</param>\\n [Pure]\\n public static (Tensor values, Tensor indices) topk(Tensor tensor, int k, int dim = -1, bool largest = true, bool sorted = true)\\n => tensor.topk(k, dim, largest, sorted);\\n\\n // https://pytorch.org/docs/stable/generated/torch.msort\\n [Pure]\\n public static Tensor msort(Tensor tensor)\\n => tensor.msort();\\n }\\n}\"\n },\n {\n \"alpha_fraction\": 0.5837081670761108,\n \"alphanum_fraction\": 0.5842078924179077,\n \"avg_line_length\": 34.73214340209961,\n \"blob_id\": \"c728f54ec0fb3877f8269e9da8af53a74c46ad81\",\n \"content_id\": \"302627c388fb3bda0e1f24f42000811a79a76843\",\n \"detected_licenses\": [\n \"MIT\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"C#\",\n \"length_bytes\": 2001,\n \"license_type\": \"permissive\",\n \"max_line_length\": 130,\n \"num_lines\": 56,\n \"path\": \"/src/TorchSharp/NN/Activation/LogSoftMax.cs\",\n \"repo_name\": \"NiklasGustafsson/TorchSharp\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"// Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information.\\nusing System;\\nusing static TorchSharp.torch;\\nusing static TorchSharp.PInvoke.NativeMethods;\\n\\nnamespace TorchSharp\\n{\\n using Modules;\\n\\n namespace Modules\\n {\\n /// <summary>\\n /// This class is used to represent a log softmax module.\\n /// </summary>\\n public sealed class LogSoftmax : torch.nn.Module<Tensor, Tensor>\\n {\\n internal LogSoftmax(IntPtr handle, IntPtr boxedHandle) : base(handle, boxedHandle)\\n {\\n }\\n\\n public override Tensor forward(Tensor tensor)\\n {\\n var res = THSNN_LogSoftmax_forward(handle, tensor.Handle);\\n if (res == IntPtr.Zero) { torch.CheckForErrors(); }\\n return new Tensor(res);\\n }\\n\\n // Rather than spending cycles only to discover that this module has neither\\n // parameters nor buffers, just shortcut the move completely.\\n protected internal override nn.Module _to(Device device, ScalarType dtype) => this;\\n protected internal override nn.Module _to(DeviceType deviceType, int deviceIndex = -1) => this;\\n protected internal override nn.Module _to(ScalarType dtype) => this;\\n }\\n }\\n\\n public static partial class torch\\n {\\n public static partial class nn\\n {\\n public static LogSoftmax LogSoftmax(long dim)\\n {\\n var handle = THSNN_LogSoftmax_ctor(dim, out var boxedHandle);\\n if (handle == IntPtr.Zero) { torch.CheckForErrors(); }\\n return new LogSoftmax(handle, boxedHandle);\\n }\\n\\n public static partial class functional\\n {\\n public static Tensor log_softmax(Tensor x, long dim)\\n {\\n return torch.special.log_softmax(x, dim);\\n }\\n }\\n }\\n }\\n}\\n\"\n },\n {\n \"alpha_fraction\": 0.6044392585754395,\n \"alphanum_fraction\": 0.6081775426864624,\n \"avg_line_length\": 54.584415435791016,\n \"blob_id\": \"4f9e7386da2dbe971c441f0bc68f11b548e2bad4\",\n \"content_id\": \"6b8cfd62e6b86a6738a3683f091c86bd0928064d\",\n \"detected_licenses\": [\n \"MIT\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"C#\",\n \"length_bytes\": 4284,\n \"license_type\": \"permissive\",\n \"max_line_length\": 206,\n \"num_lines\": 77,\n \"path\": \"/src/TorchSharp/NN/TransformerDecoderLayer.cs\",\n \"repo_name\": \"NiklasGustafsson/TorchSharp\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"// Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information.\\nusing System;\\nusing static TorchSharp.torch;\\nusing static TorchSharp.PInvoke.NativeMethods;\\n\\nnamespace TorchSharp\\n{\\n using Modules;\\n\\n namespace Modules\\n {\\n public sealed class TransformerDecoderLayer : torch.nn.Module<Tensor, Tensor, Tensor, Tensor, Tensor, Tensor, Tensor>, torch.nn.IModule<Tensor, Tensor, Tensor>\\n {\\n internal TransformerDecoderLayer(IntPtr handle, IntPtr boxedHandle) : base(handle, boxedHandle) { }\\n\\n /// <summary>\\n /// Pass the inputs (and mask) through the decoder layer.\\n /// </summary>\\n /// <param name=\\\"tgt\\\">The sequence to the decoder layer (required).</param>\\n /// <param name=\\\"memory\\\">The sequence from the last layer of the encoder (required).</param>\\n /// <param name=\\\"tgt_mask\\\">The mask for the tgt sequence (optional).</param>\\n /// <param name=\\\"memory_mask\\\">The mask for the memory sequence (optional).</param>\\n /// <param name=\\\"tgt_key_padding_mask\\\">The mask for the tgt keys per batch (optional).</param>\\n /// <param name=\\\"memory_key_padding_mask\\\">The mask for the memory keys per batch (optional).</param>\\n /// <returns></returns>\\n public override Tensor forward(Tensor tgt, Tensor memory, Tensor tgt_mask, Tensor memory_mask, Tensor tgt_key_padding_mask, Tensor memory_key_padding_mask)\\n {\\n var res = THSNN_TransformerDecoderLayer_forward(handle,\\n tgt.Handle,\\n memory.Handle,\\n tgt_mask?.Handle ?? IntPtr.Zero,\\n memory_mask?.Handle ?? IntPtr.Zero,\\n tgt_key_padding_mask?.Handle ?? IntPtr.Zero,\\n memory_key_padding_mask?.Handle ?? IntPtr.Zero);\\n if (res == IntPtr.Zero) { torch.CheckForErrors(); }\\n return new Tensor(res);\\n }\\n\\n public new Tensor call(Tensor tgt, Tensor memory, Tensor tgt_mask, Tensor memory_mask = null, Tensor tgt_key_padding_mask = null, Tensor memory_key_padding_mask = null)\\n {\\n return base.call(tgt, memory, tgt_mask, memory_mask, tgt_key_padding_mask, memory_key_padding_mask);\\n }\\n\\n /// <summary>\\n /// Pass the inputs (and mask) through the decoder layer.\\n /// </summary>\\n /// <param name=\\\"tgt\\\">The sequence to the decoder layer (required).</param>\\n /// <param name=\\\"memory\\\">The sequence from the last layer of the encoder (required).</param>\\n public Tensor call(Tensor tgt, Tensor memory)\\n {\\n return base.call(tgt, memory, null, null, null, null);\\n }\\n }\\n }\\n\\n public static partial class torch\\n {\\n public static partial class nn\\n {\\n /// <summary>\\n /// TransformerDecoderLayer is made up of self-attn, multi-head-attn and feedforward network. This standard decoder layer is based on the paper “Attention Is All You Need”.\\n /// </summary>\\n /// <param name=\\\"d_model\\\">The number of expected features in the input (required).</param>\\n /// <param name=\\\"nhead\\\">The number of heads in the multiheadattention models (required).</param>\\n /// <param name=\\\"dim_feedforward\\\">The dimension of the feedforward network model (default=2048).</param>\\n /// <param name=\\\"dropout\\\">The dropout value (default=0.1).</param>\\n /// <param name=\\\"activation\\\">The activation function of intermediate layer, relu or gelu (default=relu).</param>\\n /// <returns></returns>\\n public static TransformerDecoderLayer TransformerDecoderLayer(long d_model = 512, long nhead = 8, long dim_feedforward = 2048, double dropout = 0.1, Activations activation = nn.Activations.ReLU)\\n {\\n var res = THSNN_TransformerDecoderLayer_ctor(d_model, nhead, dim_feedforward, dropout, (long)activation, out var boxedHandle);\\n if (res == IntPtr.Zero) { torch.CheckForErrors(); }\\n return new TransformerDecoderLayer(res, boxedHandle);\\n }\\n }\\n }\\n}\\n\"\n },\n {\n \"alpha_fraction\": 0.5543248653411865,\n \"alphanum_fraction\": 0.5543248653411865,\n \"avg_line_length\": 36.939998626708984,\n \"blob_id\": \"eaa31d5e10d4d83daea675f86f6e4f75e6f15f7e\",\n \"content_id\": \"7cd5acc25193dcf0c4050bc89f3743c87e786671\",\n \"detected_licenses\": [\n \"MIT\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"C#\",\n \"length_bytes\": 1896,\n \"license_type\": \"permissive\",\n \"max_line_length\": 130,\n \"num_lines\": 50,\n \"path\": \"/src/TorchVision/Erase.cs\",\n \"repo_name\": \"NiklasGustafsson/TorchSharp\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"// Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information.\\n\\nusing static TorchSharp.torch;\\n\\n\\nnamespace TorchSharp\\n{\\n public static partial class torchvision\\n {\\n internal class Erase : ITransform\\n {\\n internal Erase(int top, int left, int height, int width, Tensor value, bool inplace = false)\\n {\\n this.top = top;\\n this.left = left;\\n this.height = height;\\n this.width = width;\\n this.inplace = inplace;\\n this.value = value;\\n }\\n\\n public Tensor call(Tensor img)\\n {\\n return transforms.functional.erase(img, top, left, height, width, value, inplace);\\n }\\n\\n private int top, left, height, width;\\n private Tensor value;\\n private bool inplace;\\n }\\n\\n public static partial class transforms\\n {\\n /// <summary>\\n /// Erase a region of an image tensor with given value. \\n /// </summary>\\n /// <param name=\\\"top\\\">Vertical component of the top left corner of the erased region.</param>\\n /// <param name=\\\"left\\\">Horizontal component of the top left corner of the erased region.</param>\\n /// <param name=\\\"height\\\">The height of the erased region.</param>\\n /// <param name=\\\"width\\\">The width of the erased region.</param>\\n /// <param name=\\\"value\\\">Erasing value.</param>\\n /// <param name=\\\"inplace\\\">For in-place operations.</param>\\n /// <returns></returns>\\n static public ITransform Erase(int top, int left, int height, int width, Tensor value, bool inplace = false)\\n {\\n return new Erase(top, left, height, width, value, inplace);\\n }\\n }\\n }\\n}\"\n },\n {\n \"alpha_fraction\": 0.4808405339717865,\n \"alphanum_fraction\": 0.48207664489746094,\n \"avg_line_length\": 34.9555549621582,\n \"blob_id\": \"3913362de13983495366efb8c1050080ea023826\",\n \"content_id\": \"d99125ef8da7aa736c4f2826127a2d17fceab23a\",\n \"detected_licenses\": [\n \"MIT\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"C#\",\n \"length_bytes\": 1618,\n \"license_type\": \"permissive\",\n \"max_line_length\": 130,\n \"num_lines\": 45,\n \"path\": \"/src/TorchAudio/NoBackend.cs\",\n \"repo_name\": \"NiklasGustafsson/TorchSharp\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"// Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information.\\nusing System;\\n\\nnamespace TorchSharp\\n{\\n public static partial class torchaudio\\n {\\n public static partial class backend\\n {\\n public class NoBackend : AudioBackend\\n {\\n public override (torch.Tensor, int) load(\\n string filepath,\\n long frame_offset = 0,\\n long num_frames = -1,\\n bool normalize = true,\\n bool channels_first = true,\\n AudioFormat? format = null)\\n {\\n throw new InvalidOperationException(\\\"No audio I/O backend is available.\\\");\\n }\\n\\n public override void save(\\n string filepath,\\n torch.Tensor src,\\n int sample_rate,\\n bool channels_first = true,\\n float? compression = null,\\n AudioFormat? format = null,\\n AudioEncoding? encoding = null,\\n int? bits_per_sample = null)\\n {\\n throw new InvalidOperationException(\\\"No audio I/O backend is available.\\\");\\n }\\n\\n public override AudioMetaData info(\\n string filepath,\\n AudioFormat? format = null)\\n {\\n throw new InvalidOperationException(\\\"No audio I/O backend is available.\\\");\\n }\\n }\\n }\\n }\\n}\\n\"\n },\n {\n \"alpha_fraction\": 0.45195272564888,\n \"alphanum_fraction\": 0.46574169397354126,\n \"avg_line_length\": 42.40520477294922,\n \"blob_id\": \"2fb4839b32ed57d6b88412a95742aa3a1749b0c6\",\n \"content_id\": \"1ae32898d1f0a52412218330ad8151bba7721a72\",\n \"detected_licenses\": [\n \"MIT\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"C#\",\n \"length_bytes\": 11676,\n \"license_type\": \"permissive\",\n \"max_line_length\": 181,\n \"num_lines\": 269,\n \"path\": \"/src/TorchVision/models/MobileNetV2.cs\",\n \"repo_name\": \"NiklasGustafsson/TorchSharp\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"// Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information.\\n\\n// A number of implementation details in this file have been translated from the Python version of torchvision,\\n// largely located in the files found in this folder:\\n//\\n// https://github.com/pytorch/vision/blob/a4f53308b2d0f1aa9191686e326f45c26053f686/torchvision/models/mobilenetv2.py\\n//\\n// The origin has the following copyright notice and license:\\n//\\n// https://github.com/pytorch/vision/blob/main/LICENSE\\n//\\n\\nusing System;\\nusing System.Collections.Generic;\\n\\nusing static TorchSharp.torch;\\nusing static TorchSharp.torchvision;\\nusing static TorchSharp.torchvision.models._utils;\\n\\n#nullable enable\\nnamespace TorchSharp\\n{\\n namespace Modules\\n {\\n /// <summary>\\n /// MobileNet V2 main class\\n /// </summary>\\n public class MobileNetV2 : nn.Module<Tensor, Tensor>\\n {\\n private class InvertedResidual : nn.Module<Tensor, Tensor>\\n {\\n private readonly bool _is_cn;\\n private readonly nn.Module<Tensor, Tensor> conv;\\n private readonly long out_channels;\\n private readonly long stride;\\n private readonly bool use_res_connect;\\n\\n protected override void Dispose(bool disposing)\\n {\\n if (disposing) {\\n conv.Dispose();\\n }\\n base.Dispose(disposing);\\n }\\n\\n public InvertedResidual(\\n string name,\\n long inp,\\n long oup,\\n long stride,\\n double expand_ratio,\\n Func<long, nn.Module<Tensor, Tensor>>? norm_layer = null) : base(name)\\n {\\n this.stride = stride;\\n if (stride != 1 && stride != 2) {\\n throw new ArgumentOutOfRangeException($\\\"stride should be 1 or 2 insted of {stride}\\\");\\n }\\n\\n if (norm_layer == null) {\\n norm_layer = (features) => nn.BatchNorm2d(features);\\n }\\n\\n var hidden_dim = (long)Math.Round(inp * expand_ratio);\\n this.use_res_connect = this.stride == 1 && inp == oup;\\n\\n var layers = new List<nn.Module<Tensor, Tensor>>();\\n if (expand_ratio != 1) {\\n // pw\\n layers.Add(\\n ops.Conv2dNormActivation(\\n inp,\\n hidden_dim,\\n kernel_size: 1,\\n norm_layer: norm_layer,\\n activation_layer: (inplace) => nn.ReLU6(inplace)));\\n }\\n layers.AddRange(new List<nn.Module<Tensor, Tensor>> {\\n // dw\\n ops.Conv2dNormActivation(\\n hidden_dim,\\n hidden_dim,\\n stride: stride,\\n groups: hidden_dim,\\n norm_layer: norm_layer,\\n activation_layer: (inplace) => nn.ReLU6(inplace)),\\n // pw-linear\\n nn.Conv2d(hidden_dim, oup, 1, 1, 0, bias: false),\\n norm_layer(oup)\\n });\\n this.conv = nn.Sequential(layers);\\n this.out_channels = oup;\\n this._is_cn = stride > 1;\\n RegisterComponents();\\n }\\n\\n public override Tensor forward(Tensor x)\\n {\\n if (this.use_res_connect) {\\n return x + this.conv.call(x);\\n } else {\\n return this.conv.call(x);\\n }\\n }\\n }\\n\\n private readonly nn.Module<Tensor, Tensor> classifier;\\n private readonly nn.Module<Tensor, Tensor> features;\\n private readonly long last_channel;\\n\\n protected override void Dispose(bool disposing)\\n {\\n if (disposing) {\\n classifier.Dispose();\\n features.Dispose();\\n }\\n base.Dispose(disposing);\\n }\\n\\n internal MobileNetV2(\\n string name,\\n long num_classes = 1000,\\n double width_mult = 1.0,\\n long[][]? inverted_residual_setting = null,\\n long round_nearest = 8,\\n Func<long, long, long, long, Func<long, nn.Module<Tensor, Tensor>>, nn.Module<Tensor, Tensor>>? block = null,\\n Func<long, nn.Module<Tensor, Tensor>>? norm_layer = null,\\n double dropout = 0.2) : base(name)\\n {\\n if (block == null) {\\n block = (input_channel, output_channel, stride, t, norm_layer) => new InvertedResidual(\\\"InvertedResidual\\\", input_channel, output_channel, stride, t, norm_layer);\\n }\\n\\n if (norm_layer == null) {\\n norm_layer = (features) => nn.BatchNorm2d(features);\\n }\\n\\n long input_channel = 32;\\n long last_channel = 1280;\\n\\n if (inverted_residual_setting == null) {\\n inverted_residual_setting = new long[][] {\\n // t, c, n, s\\n new long[] { 1, 16, 1, 1 },\\n new long[] { 6, 24, 2, 2 },\\n new long[] { 6, 32, 3, 2 },\\n new long[] { 6, 64, 4, 2 },\\n new long[] { 6, 96, 3, 1 },\\n new long[] { 6, 160, 3, 2 },\\n new long[] { 6, 320, 1, 1 }\\n };\\n }\\n\\n // only check the first element, assuming user knows t,c,n,s are required\\n if (inverted_residual_setting.Length == 0 || inverted_residual_setting[0].Length != 4) {\\n throw new ArgumentException($\\\"inverted_residual_setting should be non-empty or a 4-element list, got {inverted_residual_setting}\\\");\\n }\\n\\n // building first layer\\n input_channel = _make_divisible(input_channel * width_mult, round_nearest);\\n this.last_channel = _make_divisible(last_channel * Math.Max(1.0, width_mult), round_nearest);\\n var features = new List<nn.Module<Tensor, Tensor>> {\\n ops.Conv2dNormActivation(3, input_channel, stride: 2, norm_layer: norm_layer, activation_layer: (inplace) => nn.ReLU6(inplace))\\n };\\n // building inverted residual blocks\\n foreach (var x in inverted_residual_setting) {\\n long t = x[0];\\n long c = x[1];\\n long n = x[2];\\n long s = x[3];\\n var output_channel = _make_divisible(c * width_mult, round_nearest);\\n for (var i = 0; i < n; i++) {\\n var stride = i == 0 ? s : 1;\\n features.Add(block(input_channel, output_channel, stride, t, norm_layer));\\n input_channel = output_channel;\\n }\\n }\\n // building last several layers\\n features.Add(\\n ops.Conv2dNormActivation(\\n input_channel,\\n this.last_channel,\\n kernel_size: 1,\\n norm_layer: norm_layer,\\n activation_layer: (inplace) => nn.ReLU6(inplace)));\\n // make it nn.Sequential\\n this.features = nn.Sequential(features);\\n\\n // building classifier\\n this.classifier = nn.Sequential(\\n nn.Dropout(p: dropout),\\n nn.Linear(this.last_channel, num_classes));\\n\\n RegisterComponents();\\n\\n // weight initialization\\n foreach (var (_, m) in this.named_modules()) {\\n if (m is Modules.Conv2d) {\\n var conv = (Modules.Conv2d)m;\\n nn.init.kaiming_normal_(conv.weight, mode: nn.init.FanInOut.FanOut);\\n if (conv.bias is not null) {\\n nn.init.zeros_(conv.bias);\\n }\\n } else if (m is Modules.BatchNorm2d) {\\n var norm = (Modules.BatchNorm2d)m;\\n nn.init.ones_(norm.weight);\\n nn.init.zeros_(norm.bias);\\n } else if (m is Modules.GroupNorm) {\\n var norm = (Modules.GroupNorm)m;\\n nn.init.ones_(norm.weight);\\n nn.init.zeros_(norm.bias);\\n } else if (m is Modules.Linear) {\\n var linear = (Modules.Linear)m;\\n nn.init.normal_(linear.weight, 0, 0.01);\\n nn.init.zeros_(linear.bias);\\n }\\n }\\n }\\n\\n public override Tensor forward(Tensor x)\\n {\\n x = this.features.call(x);\\n // Cannot use \\\"squeeze\\\" as batch-size can be 1\\n x = nn.functional.adaptive_avg_pool2d(x, (1, 1));\\n x = torch.flatten(x, 1);\\n x = this.classifier.call(x);\\n return x;\\n }\\n }\\n }\\n\\n public static partial class torchvision\\n {\\n public static partial class models\\n {\\n /// <summary>\\n /// MobileNetV2 architecture from the `MobileNetV2: Inverted Residuals and Linear\\n /// Bottlenecks https://arxiv.org/abs/1801.04381 paper.\\n /// </summary>\\n /// <param name=\\\"num_classes\\\">Number of classes</param>\\n /// <param name=\\\"width_mult\\\">Width multiplier - adjusts number of channels in each layer by this amount</param>\\n /// <param name=\\\"inverted_residual_setting\\\">Network structure</param>\\n /// <param name=\\\"round_nearest\\\">Round the number of channels in each layer to be a multiple of this number\\n /// Set to 1 to turn off rounding</param>\\n /// <param name=\\\"block\\\">Module specifying inverted residual building block for mobilenet</param>\\n /// <param name=\\\"norm_layer\\\">Module specifying the normalization layer to use</param>\\n /// <param name=\\\"dropout\\\">The droupout probability</param>\\n /// <returns></returns>\\n public static Modules.MobileNetV2 mobilenet_v2(\\n long num_classes = 1000,\\n double width_mult = 1.0,\\n long[][]? inverted_residual_setting = null,\\n long round_nearest = 8,\\n Func<long, long, long, long, Func<long, nn.Module<Tensor, Tensor>>, nn.Module<Tensor, Tensor>>? block = null,\\n Func<long, nn.Module<Tensor, Tensor>>? norm_layer = null,\\n double dropout = 0.2)\\n {\\n return new Modules.MobileNetV2(\\n \\\"MobileNetV2\\\",\\n num_classes,\\n width_mult,\\n inverted_residual_setting,\\n round_nearest,\\n block,\\n norm_layer,\\n dropout);\\n }\\n }\\n }\\n}\\n\"\n },\n {\n \"alpha_fraction\": 0.6534457206726074,\n \"alphanum_fraction\": 0.6534457206726074,\n \"avg_line_length\": 41.9361686706543,\n \"blob_id\": \"e731a564d65bc7ce149081b7ac58d6e589b2509d\",\n \"content_id\": \"0b97fd47ada3d048797cdc7dd5508b37858df055\",\n \"detected_licenses\": [\n \"MIT\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"C#\",\n \"length_bytes\": 2017,\n \"license_type\": \"permissive\",\n \"max_line_length\": 130,\n \"num_lines\": 47,\n \"path\": \"/src/TorchSharp/Tensor/torch.LocallyDisablingGradientComputation.cs\",\n \"repo_name\": \"NiklasGustafsson/TorchSharp\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"// Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information.\\n#nullable enable\\nusing System;\\nusing System.Diagnostics.Contracts;\\n\\nnamespace TorchSharp\\n{\\n // https://pytorch.org/docs/stable/torch#locally-disabling-gradient-computation\\n public static partial class torch\\n {\\n // https://pytorch.org/docs/stable/generated/torch.no_grad\\n /// <summary>\\n /// Context-manager that disables gradient calculation.\\n /// </summary>\\n /// <returns></returns>\\n public static IDisposable no_grad() => new AutoGradMode(false);\\n\\n // https://pytorch.org/docs/stable/generated/torch.enable_grad\\n /// <summary>\\n /// Context-manager that enables gradient calculation.\\n /// </summary>\\n /// <returns></returns>\\n public static IDisposable enable_grad(bool enabled = true) => new AutoGradMode(enabled);\\n\\n // https://pytorch.org/docs/stable/generated/torch.set_grad_enabled\\n /// <summary>\\n /// Context-manager that sets gradient calculation to on or off.\\n /// </summary>\\n /// <returns></returns>\\n public static IDisposable set_grad_enabled(bool mode = true) => new AutoGradMode(mode);\\n\\n // https://pytorch.org/docs/stable/generated/torch.is_grad_enabled\\n /// <summary>\\n /// Returns true if grad mode is currently enabled.\\n /// </summary>\\n /// <returns></returns>\\n [Pure]public static bool is_grad_enabled() => AutoGradMode.IsEnabled;\\n\\n // https://pytorch.org/docs/stable/generated/torch.inference_mode\\n [Obsolete(\\\"not implemented\\\", true)]\\n public static IDisposable inference_mode(bool mode = true) => throw new NotImplementedException();\\n\\n // https://pytorch.org/docs/stable/generated/torch.is_inference_mode_enabled\\n [Obsolete(\\\"not implemented\\\", true)]\\n [Pure]public static bool is_inference_mode_enabled() => throw new NotImplementedException();\\n }\\n}\"\n },\n {\n \"alpha_fraction\": 0.6219512224197388,\n \"alphanum_fraction\": 0.6219512224197388,\n \"avg_line_length\": 21.454545974731445,\n \"blob_id\": \"80bc65f1f2e4ec01a30f0fdd78bc63b448f88e04\",\n \"content_id\": \"f877827cb8faa19798a19dce0bf198099cf7f3d8\",\n \"detected_licenses\": [\n \"MIT\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"C#\",\n \"length_bytes\": 248,\n \"license_type\": \"permissive\",\n \"max_line_length\": 131,\n \"num_lines\": 11,\n \"path\": \"/src/TorchSharp/Tensor/Enums/Reduce.cs\",\n \"repo_name\": \"NiklasGustafsson/TorchSharp\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"// Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information.\\nnamespace TorchSharp\\n{\\n public enum Reduce\\n {\\n prod,\\n mean,\\n amax,\\n amin\\n }\\n}\"\n },\n {\n \"alpha_fraction\": 0.5455530285835266,\n \"alphanum_fraction\": 0.5517278909683228,\n \"avg_line_length\": 51.15254211425781,\n \"blob_id\": \"c2f67612ef6869c1a0d67fb84f5ff6725a2bf673\",\n \"content_id\": \"a6654ccc042cc19923f2a148fa4d09b4ffd1ec99\",\n \"detected_licenses\": [\n \"MIT\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"C#\",\n \"length_bytes\": 9231,\n \"license_type\": \"permissive\",\n \"max_line_length\": 280,\n \"num_lines\": 177,\n \"path\": \"/src/TorchSharp/NN/Convolution/Conv1D.cs\",\n \"repo_name\": \"NiklasGustafsson/TorchSharp\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"// Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information.\\nusing System;\\nusing static TorchSharp.torch;\\nusing static TorchSharp.PInvoke.NativeMethods;\\n\\n#nullable enable\\nnamespace TorchSharp\\n{\\n using Modules;\\n\\n public enum PaddingModes\\n {\\n Zeros = 0,\\n Reflect = 1,\\n Replicate = 2,\\n Circular = 3,\\n Constant = 4,\\n }\\n\\n public enum Padding\\n {\\n Valid = 0,\\n Same = 1\\n }\\n\\n namespace Modules\\n {\\n public abstract class Convolution : torch.nn.Module<Tensor, Tensor>\\n {\\n protected Convolution(IntPtr handle, IntPtr boxedHandle, long input_channels) : base(handle, boxedHandle)\\n {\\n this.input_channels = input_channels;\\n }\\n\\n protected bool ValidateShape(Tensor input, long dimensions)\\n {\\n var shape = input.shape;\\n var ndim = shape.LongLength;\\n\\n return (ndim == dimensions+2) && (input.shape[1] == input_channels) || // Batched: N + C + dims\\n (ndim == dimensions+1 && input.shape[0] == input_channels); // Unbathced: C + dims\\n\\n }\\n\\n protected long input_channels;\\n }\\n\\n public sealed class Conv1d : Convolution\\n {\\n internal Conv1d(IntPtr handle, IntPtr boxedHandle, long input_channels) : base(handle, boxedHandle, input_channels) { }\\n\\n public override Tensor forward(Tensor input)\\n {\\n if (ValidateShape(input, 1)) {\\n var res = THSNN_Conv1d_forward(handle, input.Handle);\\n if (res == IntPtr.Zero) { torch.CheckForErrors(); }\\n return new Tensor(res);\\n }\\n throw new ArgumentException($\\\"Expected 2D (unbatched) or 3D (batched) input with {input_channels} channels to Conv1d.\\\");\\n }\\n\\n public Parameter? bias {\\n get {\\n var res = THSNN_Conv1d_bias(handle);\\n if (res == IntPtr.Zero) { torch.CheckForErrors(); }\\n return ((res == IntPtr.Zero) ? null : new Parameter(res));\\n }\\n set {\\n THSNN_Conv1d_set_bias(handle, (value is null ? IntPtr.Zero : value.Handle));\\n torch.CheckForErrors();\\n ConditionallyRegisterParameter(\\\"bias\\\", value);\\n }\\n }\\n public Parameter? weight {\\n get {\\n var res = THSNN_Conv1d_weight(handle);\\n if (res == IntPtr.Zero) { torch.CheckForErrors(); }\\n return (res == IntPtr.Zero) ? null : new Parameter(res);\\n }\\n set {\\n THSNN_Conv1d_set_weight(handle, value is null ? IntPtr.Zero : value.Handle);\\n torch.CheckForErrors();\\n ConditionallyRegisterParameter(\\\"weight\\\", value);\\n }\\n }\\n }\\n }\\n\\n public static partial class torch\\n {\\n public static partial class nn\\n {\\n /// <summary>\\n /// Applies a 1D convolution over an input signal composed of several input planes.\\n /// </summary>\\n /// <param name=\\\"inputChannel\\\">Number of channels in the input image</param>\\n /// <param name=\\\"outputChannel\\\">Number of channels produced by the convolution</param>\\n /// <param name=\\\"kernelSize\\\">Size of the convolving kernel</param>\\n /// <param name=\\\"stride\\\">Stride of the convolution. Default: 1</param>\\n /// <param name=\\\"padding\\\">Zero-padding added to both sides of the input. Default: 0</param>\\n /// <param name=\\\"dilation\\\">Spacing between kernel elements. Default: 1</param>\\n /// <param name=\\\"paddingMode\\\">'zeros', 'reflect', 'replicate' or 'circular'. Default: 'zeros'</param>\\n /// <param name=\\\"groups\\\">Number of blocked connections from input channels to output channels. Default: 1</param>\\n /// <param name=\\\"bias\\\">If true, adds a learnable bias to the output. Default: true</param>\\n /// <param name=\\\"device\\\">The desired device of the parameters and buffers in this module</param>\\n /// <param name=\\\"dtype\\\">The desired floating point or complex dtype of the parameters and buffers in this module</param>\\n /// <returns>Tensor of shape (N,C_out,L_out)</returns>\\n public static Conv1d Conv1d(long inputChannel, long outputChannel, long kernelSize, long stride = 1, long padding = 0, long dilation = 1, PaddingModes paddingMode = PaddingModes.Zeros, long groups = 1, bool bias = true, Device? device = null, ScalarType? dtype = null)\\n {\\n var res = THSNN_Conv1d_ctor(inputChannel, outputChannel, kernelSize, stride, padding, dilation, (long)paddingMode, groups, bias, out var boxedHandle);\\n if (res == IntPtr.Zero) { torch.CheckForErrors(); }\\n return new Conv1d(res, boxedHandle, inputChannel).MoveModule<Conv1d>(device, dtype);\\n }\\n\\n /// <summary>\\n /// Applies a 1D convolution over an input signal composed of several input planes.\\n /// </summary>\\n /// <param name=\\\"inputChannel\\\">Number of channels in the input image</param>\\n /// <param name=\\\"outputChannel\\\">Number of channels produced by the convolution</param>\\n /// <param name=\\\"kernelSize\\\">Size of the convolving kernel</param>\\n /// <param name=\\\"stride\\\">Stride of the convolution. Default: 1</param>\\n /// <param name=\\\"padding\\\">Zero-padding added to both sides of the input. padding=Valid is the same as no padding. padding=Same pads the input so the output has the shape as the input. </param>\\n /// <param name=\\\"dilation\\\">Spacing between kernel elements. Default: 1</param>\\n /// <param name=\\\"paddingMode\\\">'zeros', 'reflect', 'replicate' or 'circular'. Default: 'zeros'</param>\\n /// <param name=\\\"groups\\\">Number of blocked connections from input channels to output channels. Default: 1</param>\\n /// <param name=\\\"bias\\\">If true, adds a learnable bias to the output. Default: true</param>\\n /// <param name=\\\"device\\\">The desired device of the parameters and buffers in this module</param>\\n /// <param name=\\\"dtype\\\">The desired floating point or complex dtype of the parameters and buffers in this module</param>\\n /// <returns>Tensor of shape (N,C_out,L_out)</returns>\\n public static Conv1d Conv1d(long inputChannel, long outputChannel, long kernelSize, Padding padding, long stride = 1, long dilation = 1, PaddingModes paddingMode = PaddingModes.Zeros, long groups = 1, bool bias = true, Device? device = null, ScalarType? dtype = null)\\n {\\n var res = THSNN_Conv1d_ctor(inputChannel, outputChannel, kernelSize, stride, padding == Padding.Valid ? 0 : -1, dilation, (long)paddingMode, groups, bias, out var boxedHandle);\\n if (res == IntPtr.Zero) { torch.CheckForErrors(); }\\n return new Conv1d(res, boxedHandle, inputChannel).MoveModule<Conv1d>(device, dtype);\\n }\\n\\n public static partial class functional\\n {\\n /// <summary>\\n /// Applies a 1D convolution over an input signal composed of several input planes.\\n /// </summary>\\n /// <param name=\\\"input\\\">The input tensor.</param>\\n /// <param name=\\\"weight\\\"></param>\\n /// <param name=\\\"bias\\\"></param>\\n /// <param name=\\\"stride\\\"></param>\\n /// <param name=\\\"padding\\\"></param>\\n /// <param name=\\\"dilation\\\"></param>\\n /// <param name=\\\"groups\\\"></param>\\n /// <returns></returns>\\n public static Tensor conv1d(Tensor input, Tensor weight, Tensor? bias = null,\\n long? stride = null,\\n long? padding = null,\\n long? dilation = null,\\n long groups = 1)\\n {\\n var strides = new long[] { stride ?? 1 };\\n var paddingArray = new long[] { padding ?? 0 };\\n var dilationArray = new long[] { dilation ?? 1 };\\n var biasHandle = (bias is null ? IntPtr.Zero : bias.Handle);\\n unsafe {\\n fixed (long* pstrides = strides, ppadding = paddingArray, pdilation = dilationArray) {\\n var res =\\n THSTensor_conv1d(input.Handle, weight.Handle, biasHandle,\\n (IntPtr)pstrides, strides.Length,\\n (IntPtr)ppadding, paddingArray.Length,\\n (IntPtr)pdilation, dilationArray.Length,\\n groups);\\n if (res == IntPtr.Zero) { torch.CheckForErrors(); }\\n return new Tensor(res);\\n }\\n }\\n }\\n\\n }\\n }\\n }\\n}\\n\"\n },\n {\n \"alpha_fraction\": 0.5770045518875122,\n \"alphanum_fraction\": 0.5954614281654358,\n \"avg_line_length\": 52.30644989013672,\n \"blob_id\": \"7fd5e7b256d5b553ee6fa3c2beca4c643a832206\",\n \"content_id\": \"803aec93c03a124d335b955ff1f0af0ca2a37978\",\n \"detected_licenses\": [\n \"MIT\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"C#\",\n \"length_bytes\": 3305,\n \"license_type\": \"permissive\",\n \"max_line_length\": 117,\n \"num_lines\": 62,\n \"path\": \"/pkg/FileRestitcher/FileRestitcher.Tests/UnitTest1.cs\",\n \"repo_name\": \"NiklasGustafsson/TorchSharp\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"using System;\\nusing System.IO;\\nusing Xunit;\\n\\nnamespace FileRestitcherTests\\n{\\n public class Tests\\n {\\n //[Fact]\\n public void TestRestitchingSmallFiles()\\n {\\n long size = 1024; // * 1024 * 1024; // enable this to test massive files, takes a long time\\n var oneChunk = new byte[size];\\n var rnd = new System.Random();\\n for (int i = 0; i < 10; i++)\\n oneChunk[i] = (byte)rnd.Next(0,255);\\n var tmpDir = Path.GetDirectoryName(System.Reflection.Assembly.GetExecutingAssembly().Location) + @\\\"/tmp\\\";\\n System.Console.WriteLine(\\\"tmpDir = {0}\\\", tmpDir);\\n if (Directory.Exists(tmpDir))\\n Directory.Delete(tmpDir, true);\\n Directory.CreateDirectory(tmpDir);\\n Directory.CreateDirectory(tmpDir + @\\\"/some-package-primary/runtimes\\\");\\n Directory.CreateDirectory(tmpDir + @\\\"/some-package-fragment1/fragments\\\");\\n Directory.CreateDirectory(tmpDir + @\\\"/some-package-fragment2/fragments\\\");\\n Directory.CreateDirectory(tmpDir + @\\\"/some-package-fragment3/fragments\\\");\\n File.WriteAllBytes(tmpDir + @\\\"/some-package-primary/runtimes/a.so\\\", oneChunk);\\n File.WriteAllBytes(tmpDir + @\\\"/some-package-fragment1/fragments/a.so.fragment1\\\", oneChunk);\\n File.WriteAllBytes(tmpDir + @\\\"/some-package-fragment2/fragments/a.so.fragment2\\\", oneChunk);\\n File.WriteAllBytes(tmpDir + @\\\"/some-package-fragment3/fragments/a.so.fragment3\\\", oneChunk);\\n System.Threading.Thread.Sleep(1000);\\n using (var sha256Hash = System.Security.Cryptography.SHA256.Create()) {\\n var expectedFile = tmpDir + @\\\"/a.so\\\";\\n Console.WriteLine(\\\"Writing restored primary file at {0}\\\", expectedFile);\\n var os = File.OpenWrite(expectedFile);\\n os.Write(oneChunk, 0, oneChunk.Length);\\n os.Write(oneChunk, 0, oneChunk.Length);\\n os.Write(oneChunk, 0, oneChunk.Length);\\n os.Write(oneChunk, 0, oneChunk.Length);\\n os.Close();\\n var os2 = File.OpenRead(expectedFile);\\n byte[] bytes = sha256Hash.ComputeHash(os2);\\n var builder = new System.Text.StringBuilder();\\n for (int i = 0; i < bytes.Length; i++) {\\n builder.Append(bytes[i].ToString(\\\"x2\\\"));\\n }\\n var sha = builder.ToString();\\n File.WriteAllText(tmpDir + @\\\"/some-package-primary/runtimes/a.so.sha\\\", sha);\\n os2.Close();\\n }\\n\\n Assert.Equal(new FileInfo(tmpDir + @\\\"/some-package-primary/runtimes/a.so\\\").Length, 1* size);\\n\\n ConsoleApp2.Program.Restitch(tmpDir + @\\\"/some-package-primary\\\");\\n Assert.True(File.Exists(tmpDir + @\\\"/some-package-primary/runtimes/a.so\\\"));\\n Assert.Equal(new FileInfo(tmpDir + @\\\"/some-package-primary/runtimes/a.so\\\").Length, 4* size);\\n Assert.False(File.Exists(tmpDir + @\\\"/some-package-fragment1/fragments/a.so.fragment1\\\"));\\n Assert.False(File.Exists(tmpDir + @\\\"/some-package-fragment2/fragments/a.so.fragment2\\\"));\\n Assert.False(File.Exists(tmpDir + @\\\"/some-package-fragment3/fragments/a.so.fragment3\\\"));\\n }\\n }\\n\\n}\\n\"\n },\n {\n \"alpha_fraction\": 0.6319200992584229,\n \"alphanum_fraction\": 0.6329389810562134,\n \"avg_line_length\": 61.982173919677734,\n \"blob_id\": \"77df93dca67f36d18fbc3b2036e9485d6820dad1\",\n \"content_id\": \"43e52c8e66771e0664c9950b8b17a3324a3d5e9c\",\n \"detected_licenses\": [\n \"MIT\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"C#\",\n \"length_bytes\": 35431,\n \"license_type\": \"permissive\",\n \"max_line_length\": 214,\n \"num_lines\": 561,\n \"path\": \"/src/TorchSharp/Tensor/torch.ReductionOps.cs\",\n \"repo_name\": \"NiklasGustafsson/TorchSharp\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"// Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information.\\n#nullable enable\\nusing System;\\nusing System.Collections.Generic;\\nusing System.Diagnostics.Contracts;\\nusing System.Linq;\\n\\nusing static TorchSharp.PInvoke.NativeMethods;\\n\\nnamespace TorchSharp\\n{\\n // https://pytorch.org/docs/stable/torch#reduction-ops\\n public static partial class torch\\n {\\n // https://pytorch.org/docs/stable/generated/torch.argmax\\n /// <summary>\\n /// Returns the indices of the maximum value of all elements in the input tensor.\\n /// </summary>\\n [Pure]public static Tensor argmax(Tensor input)\\n => input.argmax();\\n\\n // https://pytorch.org/docs/stable/generated/torch.argmax\\n /// <summary>\\n /// Returns the indices of the maximum value of all elements in the input tensor.\\n /// </summary>\\n [Pure]public static Tensor argmax(Tensor input, long dim, bool keepdim = false)\\n => input.argmax(dim, keepdim);\\n\\n // https://pytorch.org/docs/stable/generated/torch.argmin\\n /// <summary>\\n /// Returns the indices of the minimum value of all elements in the input tensor.\\n /// </summary>\\n [Pure]public static Tensor argmin(Tensor input)\\n => input.argmin();\\n\\n // https://pytorch.org/docs/stable/generated/torch.argmin\\n /// <summary>\\n /// Returns the indices of the minimum value of all elements in the input tensor.\\n /// </summary>\\n [Pure]public static Tensor argmin(Tensor input, long dim, bool keepdim = false)\\n => input.argmin(dim, keepdim);\\n\\n // https://pytorch.org/docs/stable/generated/torch.amax\\n /// <summary>\\n /// Returns the maximum value of each slice of the input tensor in the given dimension(s) dim.\\n /// </summary>\\n /// <param name=\\\"input\\\">The input tensor</param>\\n /// <param name=\\\"dims\\\">The dimension or dimensions to reduce.</param>\\n /// <param name=\\\"keepdim\\\">Whether the output tensor has dim retained or not.</param>\\n /// <param name=\\\"out\\\">The output tensor -- optional.</param>\\n [Pure]public static Tensor amax(Tensor input, long[] dims, bool keepdim = false, Tensor? @out = null)\\n => input.amax(dims, keepdim, @out);\\n\\n [Pure]public static Tensor amax(Tensor input, ReadOnlySpan<long> dims, bool keepdim = false, Tensor? @out = null)\\n => input.amax(dims, keepdim, @out);\\n\\n [Pure]public static Tensor amax(Tensor input, long[] dims)\\n => input.amax(dims);\\n\\n // https://pytorch.org/docs/stable/generated/torch.amin\\n /// <summary>\\n /// Returns the minimum value of each slice of the input tensor in the given dimension(s) dim.\\n /// </summary>\\n /// <param name=\\\"input\\\">The input tensor</param>\\n /// <param name=\\\"dims\\\">The dimension or dimensions to reduce.</param>\\n /// <param name=\\\"keepdim\\\">Whether the output tensor has dim retained or not.</param>\\n /// <param name=\\\"out\\\">The output tensor -- optional.</param>\\n [Pure]public static Tensor amin(Tensor input, long[] dims, bool keepdim = false, Tensor? @out = null)\\n => input.amin(dims, keepdim, @out);\\n\\n // https://pytorch.org/docs/stable/generated/torch.amin\\n /// <summary>\\n /// Returns the minimum value of each slice of the input tensor in the given dimension(s) dim.\\n /// </summary>\\n /// <param name=\\\"input\\\">The input tensor</param>\\n /// <param name=\\\"dims\\\">The dimension or dimensions to reduce.</param>\\n /// <param name=\\\"keepdim\\\">Whether the output tensor has dim retained or not.</param>\\n /// <param name=\\\"out\\\">The output tensor -- optional.</param>\\n [Pure]public static Tensor amin(Tensor input, ReadOnlySpan<long> dims, bool keepdim = false, Tensor? @out = null)\\n => input.amin(dims, keepdim, @out);\\n\\n // https://pytorch.org/docs/stable/generated/torch.aminmax\\n [Pure]public static (Tensor min, Tensor max) aminmax(Tensor input, long? dim = null, bool keepdim = false)\\n => input.aminmax(dim, keepdim);\\n\\n // https://pytorch.org/docs/stable/generated/torch.all\\n /// <summary>\\n /// Tests if all elements in input evaluate to true.\\n /// <param name=\\\"input\\\">The input tensor</param>\\n /// </summary>\\n [Pure]public static Tensor all(Tensor input) => input.all();\\n\\n // https://pytorch.org/docs/stable/generated/torch.all\\n /// <summary>\\n /// Tests if all elements in input evaluate to true.\\n /// <param name=\\\"input\\\">The input tensor</param>\\n /// <param name=\\\"dim\\\">The dimension to reduce</param>\\n /// <param name=\\\"keepdim\\\">Keep the dimension to reduce</param>\\n /// </summary>\\n [Pure]public static Tensor all(Tensor input, long dim, bool keepdim = false) => input.all(dim, keepdim);\\n\\n // https://pytorch.org/docs/stable/generated/torch.any\\n /// <summary>\\n /// Tests if all elements in input evaluate to true.\\n /// <param name=\\\"input\\\">The input tensor</param>\\n /// </summary>\\n [Pure]public static Tensor any(Tensor input) => input.any();\\n\\n // https://pytorch.org/docs/stable/generated/torch.any\\n /// <summary>\\n /// Tests if any element in input evaluate to true.\\n /// <param name=\\\"input\\\">The input tensor</param>\\n /// <param name=\\\"dim\\\">The dimension to reduce</param>\\n /// <param name=\\\"keepdim\\\">Keep the dimension to reduce</param>\\n /// </summary>\\n [Pure]public static Tensor any(Tensor input, long dim, bool keepdim = false) => input.any(dim, keepdim);\\n\\n // https://pytorch.org/docs/stable/generated/torch.max\\n /// <summary>\\n /// Returns the maximum value of all elements in the input tensor.\\n /// </summary>\\n /// <param name=\\\"input\\\">The input tensor.</param>\\n [Pure]public static Tensor max(Tensor input) => input.max();\\n\\n /// <summary>\\n /// Computes the element-wise maximum of input and other.\\n /// </summary>\\n /// <param name=\\\"input\\\">The input tensor.</param>\\n /// <param name=\\\"other\\\">The second tensor.</param>\\n [Pure] public static Tensor max(Tensor input, Tensor other) => torch.maximum(input, other);\\n\\n // https://pytorch.org/docs/stable/generated/torch.max\\n /// <summary>\\n /// Returns a named tuple (values, indexes) where values is the maximum value of each row of the input tensor in the given dimension dim.\\n /// And indices is the index location of each maximum value found (argmax).\\n /// </summary>\\n /// <param name=\\\"input\\\">The input tensor</param>\\n /// <param name=\\\"dim\\\">the dimension to reduce.</param>\\n /// <param name=\\\"keepdim\\\">whether the output tensor has dim retained or not. Default: false.</param>\\n /// <remarks>If keepdim is true, the output tensors are of the same size as input except in the dimension dim where they are of size 1.\\n /// Otherwise, dim is squeezed(see torch.squeeze()), resulting in the output tensors having 1 fewer dimension than input.</remarks>\\n [Pure]public static (Tensor values, Tensor indexes) max(Tensor input, long dim, bool keepdim = false) => input.max(dim, keepdim);\\n\\n // https://pytorch.org/docs/stable/generated/torch.min\\n /// <summary>\\n /// Returns the minimum value of all elements in the input tensor.\\n /// </summary>\\n /// <param name=\\\"input\\\">The input tensor.</param>\\n [Pure]public static Tensor min(Tensor input) => input.min();\\n\\n /// <summary>\\n /// Computes the element-wise minimum of input and other.\\n /// </summary>\\n /// <param name=\\\"input\\\">The input tensor.</param>\\n /// <param name=\\\"other\\\">The second tensor.</param>\\n [Pure] public static Tensor min(Tensor input, Tensor other) => torch.minimum(input, other);\\n\\n // https://pytorch.org/docs/stable/generated/torch.min\\n /// <summary>\\n /// Returns a named tuple (values, indexes) where values is the minimum value of each row of the input tensor in the given dimension dim.\\n /// And indices is the index location of each minimum value found (argmin).\\n /// </summary>\\n /// <param name=\\\"input\\\">The input tensor</param>\\n /// <param name=\\\"dim\\\">the dimension to reduce.</param>\\n /// <param name=\\\"keepdim\\\">whether the output tensor has dim retained or not. Default: false.</param>\\n /// <remarks>If keepdim is true, the output tensors are of the same size as input except in the dimension dim where they are of size 1.\\n /// Otherwise, dim is squeezed(see torch.squeeze()), resulting in the output tensors having 1 fewer dimension than input.</remarks>\\n [Pure]public static (Tensor values, Tensor indexes) min(Tensor input, long dim, bool keepdim = false) => input.min(dim, keepdim);\\n\\n // https://pytorch.org/docs/stable/generated/torch.dist\\n /// <summary>\\n /// Returns the p-norm of (input - other).\\n /// The shapes of input and other must be broadcastable.\\n /// </summary>\\n /// <param name=\\\"input\\\">Left-hand side input tensor.</param>\\n /// <param name=\\\"other\\\">Right-hand side input tensor</param>\\n /// <param name=\\\"p\\\">The norm to be computed.</param>\\n [Pure]public static Tensor dist(Tensor input, Tensor other, float p = 2.0f) => input.dist(other, p);\\n\\n // https://pytorch.org/docs/stable/generated/torch.logsumexp\\n /// <summary>\\n /// Returns the log of summed exponentials of each row of the input tensor in the given dimension dim.\\n /// </summary>\\n /// <param name=\\\"input\\\">The input tensor.</param>\\n /// <param name=\\\"dim\\\">The dimension to do the operation over</param>\\n /// <param name=\\\"keepdim\\\">Thether the output tensor has dim retained or not.</param>\\n [Pure]public static Tensor logsumexp(Tensor input, long dim, bool keepdim = false)\\n => input.logsumexp(dim, keepdim);\\n\\n // https://pytorch.org/docs/stable/generated/torch.mean\\n /// <summary>\\n /// Returns the mean value of all elements in the input tensor.\\n /// </summary>\\n [Pure]public static Tensor mean(Tensor input) => input.mean();\\n\\n // https://pytorch.org/docs/stable/generated/torch.\\n /// <summary>\\n /// Returns the mean value of each row of the input tensor in the given dimension dim. If dim is a list of dimensions, reduce over all of them.\\n /// </summary>\\n /// <param name=\\\"input\\\">The input tensor</param>\\n /// <param name=\\\"dimensions\\\">The dimension or dimensions to reduce.</param>\\n /// <param name=\\\"keepdim\\\">Whether the output tensor has dim retained or not.</param>\\n /// <param name=\\\"type\\\">The desired data type of returned tensor. If specified, the input tensor is cast to dtype before the operation is performed. This is useful for preventing data type overflows.</param>\\n /// <remarks>\\n /// If keepdim is True, the output tensor is of the same size as input except in the dimension(s) dim where it is of size 1.\\n /// Otherwise, dim is squeezed(see torch.squeeze()), resulting in the output tensor having 1 (or len(dim)) fewer dimension(s).\\n /// </remarks>\\n [Pure]public static Tensor mean(Tensor input, long[] dimensions, bool keepdim = false, ScalarType? type = null) => input.mean(dimensions, keepdim, type);\\n\\n // https://pytorch.org/docs/stable/generated/torch.nanmean\\n [Pure]public static Tensor nanmean(Tensor input, int? dim = null, bool keepdim = false, ScalarType? dtype = null)\\n => input.nanmean(dim, keepdim, dtype);\\n\\n // https://pytorch.org/docs/stable/generated/torch.median\\n [Pure]public static Tensor median(Tensor input) => input.median();\\n\\n // https://pytorch.org/docs/stable/generated/torch.nanmedian\\n [Pure]public static Tensor nanmedian(Tensor input)\\n => input.nanmedian();\\n\\n // https://pytorch.org/docs/stable/generated/torch.mode\\n [Pure]public static (Tensor values, Tensor indices) mode(Tensor input, long dim = -1L, bool keepdim = false)\\n => input.mode(dim, keepdim);\\n\\n // https://pytorch.org/docs/stable/generated/torch.norm\\n [Pure]public static Tensor norm(Tensor input, float p = 2.0f)\\n => input.norm(p);\\n\\n [Pure]public static Tensor norm(Tensor input, int dimension, bool keepdim = false, float p = 2.0f)\\n => input.norm(dimension, keepdim, p);\\n\\n // https://pytorch.org/docs/stable/generated/torch.nansum\\n [Pure]public static Tensor nansum(Tensor input) => input.nansum();\\n\\n // https://pytorch.org/docs/stable/generated/torch.prod\\n // TODO: torch.prod\\n // static Tensor prod(Tensor input, ScalarType dtype = null) => input.prod(dtype);\\n\\n // https://pytorch.org/docs/stable/generated/torch.quantile\\n [Pure]public static Tensor quantile(Tensor input, Tensor q, long dim = -1, bool keepdim = false)\\n => input.quantile(q, dim, keepdim);\\n\\n // https://pytorch.org/docs/stable/generated/torch.nanquantile\\n [Pure]public static Tensor nanquantile(Tensor input, Tensor q, long dim = -1, bool keepdim = false)\\n => input.nanquantile(q, dim, keepdim);\\n\\n // https://pytorch.org/docs/stable/generated/torch.std\\n /// <summary>\\n /// Calculates the standard deviation of all elements in the tensor.\\n /// </summary>\\n /// <param name=\\\"input\\\">The input tensor.</param>\\n /// <param name=\\\"unbiased\\\">Whether to use Bessel’s correction (δN=1).</param>\\n [Pure]public static Tensor std(Tensor input, bool unbiased = true) => input.std(unbiased);\\n\\n /// <summary>Calculates the standard deviation of all elements in the tensor.</summary>\\n /// <remarks>\\n /// If <paramref name=\\\"unbiased\\\" /> is <value>true</value>, Bessel’s correction will be used.\\n /// Otherwise, the sample deviation is calculated, without any correction.\\n /// </remarks>\\n /// <param name=\\\"input\\\">The input tensor.</param>\\n /// <param name=\\\"dimensions\\\">The dimensions to reduce.</param>\\n /// <param name=\\\"unbiased\\\">Whether to use Bessel’s correction (δN=1).</param>\\n /// <param name=\\\"keepdim\\\">Whether the <see cref=\\\"Tensor\\\">output tensor</see> has dim retained or not.</param>\\n /// <param name=\\\"type\\\"></param>\\n /// <returns>The <see cref=\\\"Tensor\\\">output tensor</see>.</returns>\\n [Pure]public static Tensor std(Tensor input, long[] dimensions, bool unbiased = true, bool keepdim = false, ScalarType? type = null)\\n => input.std(dimensions, unbiased, keepdim, type);\\n\\n /// <summary>Calculates the standard deviation of all elements in the tensor.</summary>\\n /// <remarks>\\n /// If <paramref name=\\\"unbiased\\\" /> is <value>true</value>, Bessel’s correction will be used.\\n /// Otherwise, the sample deviation is calculated, without any correction.\\n /// </remarks>\\n /// <param name=\\\"input\\\">The input tensor.</param>\\n /// <param name=\\\"dimensions\\\">The dimensions to reduce.</param>\\n /// <param name=\\\"unbiased\\\">Whether to use Bessel’s correction (δN=1).</param>\\n /// <param name=\\\"keepdim\\\">Whether the <see cref=\\\"Tensor\\\">output tensor</see> has dim retained or not.</param>\\n /// <param name=\\\"type\\\"></param>\\n /// <returns>The <see cref=\\\"Tensor\\\">output tensor</see>.</returns>\\n [Pure]public static Tensor std(Tensor input, long dimensions, bool unbiased = true, bool keepdim = false, ScalarType? type = null)\\n => input.std(dimensions, unbiased, keepdim, type);\\n\\n /// <summary>Calculates the standard deviation of all elements in the tensor.</summary>\\n /// <remarks>\\n /// If <paramref name=\\\"unbiased\\\" /> is <value>true</value>, Bessel’s correction will be used.\\n /// Otherwise, the sample deviation is calculated, without any correction.\\n /// </remarks>\\n /// <param name=\\\"input\\\">The input tensor.</param>\\n /// <param name=\\\"dimensions\\\">The dimensions to reduce.</param>\\n /// <param name=\\\"unbiased\\\">Whether to use Bessel’s correction (δN=1).</param>\\n /// <param name=\\\"keepdim\\\">Whether the <see cref=\\\"Tensor\\\">output tensor</see> has dim retained or not.</param>\\n /// <param name=\\\"type\\\"></param>\\n /// <returns>The <see cref=\\\"Tensor\\\">output tensor</see>.</returns>\\n [Pure]public static Tensor std(Tensor input, (long, long) dimensions, bool unbiased = true, bool keepdim = false, ScalarType? type = null)\\n => input.std(dimensions, unbiased, keepdim, type);\\n\\n /// <summary>Calculates the standard deviation of all elements in the tensor.</summary>\\n /// <remarks>\\n /// If <paramref name=\\\"unbiased\\\" /> is <value>true</value>, Bessel’s correction will be used.\\n /// Otherwise, the sample deviation is calculated, without any correction.\\n /// </remarks>\\n /// <param name=\\\"input\\\">The input tensor.</param>\\n /// <param name=\\\"dimensions\\\">The dimensions to reduce.</param>\\n /// <param name=\\\"unbiased\\\">Whether to use Bessel’s correction (δN=1).</param>\\n /// <param name=\\\"keepdim\\\">Whether the <see cref=\\\"Tensor\\\">output tensor</see> has dim retained or not.</param>\\n /// <param name=\\\"type\\\"></param>\\n /// <returns>The <see cref=\\\"Tensor\\\">output tensor</see>.</returns>\\n [Pure]public static Tensor std(Tensor input, (long, long, long) dimensions, bool unbiased = true, bool keepdim = false, ScalarType? type = null)\\n => input.std(dimensions, unbiased, keepdim, type);\\n\\n // https://pytorch.org/docs/stable/generated/torch.std_mean\\n /// <summary>Calculates the standard deviation and mean of all elements in the tensor.</summary>\\n /// <remarks>\\n /// If <paramref name=\\\"unbiased\\\" /> is <value>true</value>, Bessel’s correction will be used.\\n /// Otherwise, the sample deviation is calculated, without any correction.\\n /// </remarks>\\n /// <param name=\\\"input\\\">The input tensor.</param>\\n /// <param name=\\\"dimensions\\\">The dimensions to reduce.</param>\\n /// <param name=\\\"unbiased\\\">Whether to use Bessel’s correction (δN=1).</param>\\n /// <param name=\\\"keepdim\\\">Whether the <see cref=\\\"Tensor\\\">output tensor</see> has dim retained or not.</param>\\n /// <param name=\\\"type\\\"></param>\\n /// <returns>A <see cref=\\\"Tensor\\\">tensor</see> tuple of the standard deviation and the mean.</returns>\\n [Pure]public static (Tensor std, Tensor mean) std_mean(Tensor input, long[] dimensions, bool unbiased = true, bool keepdim = false, ScalarType? type = null)\\n => input.std_mean(dimensions, unbiased, keepdim, type);\\n\\n /// <summary>Calculates the standard deviation and mean of all elements in the tensor.</summary>\\n /// <remarks>\\n /// If <paramref name=\\\"unbiased\\\" /> is <value>true</value>, Bessel’s correction will be used.\\n /// Otherwise, the sample deviation is calculated, without any correction.\\n /// </remarks>\\n /// <param name=\\\"input\\\">The input tensor.</param>\\n /// <param name=\\\"dimensions\\\">The dimensions to reduce.</param>\\n /// <param name=\\\"unbiased\\\">Whether to use Bessel’s correction (δN=1).</param>\\n /// <param name=\\\"keepdim\\\">Whether the <see cref=\\\"Tensor\\\">output tensor</see> has dim retained or not.</param>\\n /// <param name=\\\"type\\\"></param>\\n /// <returns>A <see cref=\\\"Tensor\\\">tensor</see> tuple of the standard deviation and the mean.</returns>\\n [Pure]public static (Tensor std, Tensor mean) std_mean(Tensor input, ReadOnlySpan<long> dimensions, bool unbiased = true, bool keepdim = false, ScalarType? type = null)\\n => input.std_mean(dimensions, unbiased, keepdim, type);\\n\\n /// <summary>Calculates the standard deviation and mean of all elements in the tensor.</summary>\\n /// <remarks>\\n /// If <paramref name=\\\"unbiased\\\" /> is <value>true</value>, Bessel’s correction will be used.\\n /// Otherwise, the sample deviation is calculated, without any correction.\\n /// </remarks>\\n /// <param name=\\\"input\\\">The input tensor.</param>\\n /// <param name=\\\"dimensions\\\">The dimensions to reduce.</param>\\n /// <param name=\\\"unbiased\\\">Whether to use Bessel’s correction (δN=1).</param>\\n /// <param name=\\\"keepdim\\\">Whether the <see cref=\\\"Tensor\\\">output tensor</see> has dim retained or not.</param>\\n /// <param name=\\\"type\\\"></param>\\n /// <returns>A <see cref=\\\"Tensor\\\">tensor</see> tuple of the standard deviation and the mean.</returns>\\n [Pure]public static (Tensor std, Tensor mean) std_mean(Tensor input, long dimensions, bool unbiased = true, bool keepdim = false, ScalarType? type = null)\\n => input.std_mean(dimensions, unbiased, keepdim, type);\\n\\n /// <summary>Calculates the standard deviation and mean of all elements in the tensor.</summary>\\n /// <remarks>\\n /// If <paramref name=\\\"unbiased\\\" /> is <value>true</value>, Bessel’s correction will be used.\\n /// Otherwise, the sample deviation is calculated, without any correction.\\n /// </remarks>\\n /// <param name=\\\"input\\\">The input tensor.</param>\\n /// <param name=\\\"dimensions\\\">The dimensions to reduce.</param>\\n /// <param name=\\\"unbiased\\\">Whether to use Bessel’s correction (δN=1).</param>\\n /// <param name=\\\"keepdim\\\">Whether the <see cref=\\\"Tensor\\\">output tensor</see> has dim retained or not.</param>\\n /// <param name=\\\"type\\\"></param>\\n /// <returns>A <see cref=\\\"Tensor\\\">tensor</see> tuple of the standard deviation and the mean.</returns>\\n [Pure]public static (Tensor std, Tensor mean) std_mean(Tensor input, (long, long) dimensions, bool unbiased = true, bool keepdim = false, ScalarType? type = null)\\n => input.std_mean(dimensions, unbiased, keepdim, type);\\n\\n /// <summary>Calculates the standard deviation and mean of all elements in the tensor.</summary>\\n /// <remarks>\\n /// If <paramref name=\\\"unbiased\\\" /> is <value>true</value>, Bessel’s correction will be used.\\n /// Otherwise, the sample deviation is calculated, without any correction.\\n /// </remarks>\\n /// <param name=\\\"input\\\">The input tensor.</param>\\n /// <param name=\\\"dimensions\\\">The dimensions to reduce.</param>\\n /// <param name=\\\"unbiased\\\">Whether to use Bessel’s correction (δN=1).</param>\\n /// <param name=\\\"keepdim\\\">Whether the <see cref=\\\"Tensor\\\">output tensor</see> has dim retained or not.</param>\\n /// <param name=\\\"type\\\"></param>\\n /// <returns>A <see cref=\\\"Tensor\\\">tensor</see> tuple of the standard deviation and the mean.</returns>\\n [Pure]public static (Tensor std, Tensor mean) std_mean(Tensor input, (long, long, long) dimensions, bool unbiased = true, bool keepdim = false, ScalarType? type = null)\\n => input.std_mean(dimensions, unbiased, keepdim, type);\\n\\n // https://pytorch.org/docs/stable/generated/torch.sum\\n /// <summary>\\n /// Returns the sum of each row of the input tensor in the given dimensions.\\n /// </summary>\\n [Pure]public static Tensor sum(Tensor input, ScalarType? type = null) => input.sum(type);\\n\\n // https://pytorch.org/docs/stable/generated/torch.sum\\n /// <summary>\\n /// Returns the sum of each row of the input tensor in the given dimensions.\\n /// </summary>\\n [Pure]public static Tensor sum(Tensor input, ReadOnlySpan<long> dim, bool keepdim = false, ScalarType? type = null) => input.sum(dim, keepdim, type);\\n\\n // https://pytorch.org/docs/stable/generated/torch.sum\\n /// <summary>\\n /// Returns the sum of each row of the input tensor in the given dimension.\\n /// </summary>\\n [Pure]public static Tensor sum(Tensor input, long dim, bool keepdim = false, ScalarType? type = null) => input.sum(dim, keepdim, type);\\n\\n // https://pytorch.org/docs/stable/generated/torch.unique\\n [Pure]static (Tensor output, Tensor? inverse_indices, Tensor? counts) unique(\\n Tensor input, bool sorted = true, bool return_inverse = false, bool return_counts = false, int? dim = null)\\n => input.unique(sorted, return_inverse, return_counts, dim);\\n\\n // https://pytorch.org/docs/stable/generated/torch.unique_consecutive\\n [Pure]static (Tensor output, Tensor? inverse_indices, Tensor? counts) unique_consecutive(\\n Tensor input, bool return_inverse = false, bool return_counts = false, int? dim = null)\\n => input.unique_consecutive(return_inverse, return_counts, dim);\\n\\n // https://pytorch.org/docs/stable/generated/torch.var\\n /// <summary>\\n /// Calculates the variance of all elements in the tensor.\\n /// </summary>\\n /// <param name=\\\"input\\\">The input tensor.</param>\\n /// <param name=\\\"unbiased\\\">Whether to use Bessel’s correction (δN=1).</param>\\n [Pure]public static Tensor var(Tensor input, bool unbiased = true) => input.var(unbiased);\\n\\n /// <summary>Calculates the variance of all elements in the tensor.</summary>\\n /// <remarks>\\n /// If <paramref name=\\\"unbiased\\\" /> is <value>true</value>, Bessel’s correction will be used.\\n /// Otherwise, the sample variance is calculated, without any correction.\\n /// </remarks>\\n /// <param name=\\\"input\\\">The input tensor.</param>\\n /// <param name=\\\"dimensions\\\">The dimensions to reduce.</param>\\n /// <param name=\\\"unbiased\\\">Whether to use Bessel’s correction (δN=1).</param>\\n /// <param name=\\\"keepdim\\\">Whether the <see cref=\\\"Tensor\\\">output tensor</see> has dim retained or not.</param>\\n /// <param name=\\\"type\\\"></param>\\n /// <returns>The <see cref=\\\"Tensor\\\">output tensor</see>.</returns>\\n [Pure]public static Tensor var(Tensor input, long[] dimensions, bool unbiased = true, bool keepdim = false, ScalarType? type = null)\\n => input.var(dimensions, unbiased, keepdim, type);\\n\\n /// <summary>Calculates the variance of all elements in the tensor.</summary>\\n /// <remarks>\\n /// If <paramref name=\\\"unbiased\\\" /> is <value>true</value>, Bessel’s correction will be used.\\n /// Otherwise, the sample variance is calculated, without any correction.\\n /// </remarks>\\n /// <param name=\\\"input\\\">The input tensor.</param>\\n /// <param name=\\\"dimensions\\\">The dimensions to reduce.</param>\\n /// <param name=\\\"unbiased\\\">Whether to use Bessel’s correction (δN=1).</param>\\n /// <param name=\\\"keepdim\\\">Whether the <see cref=\\\"Tensor\\\">output tensor</see> has dim retained or not.</param>\\n /// <param name=\\\"type\\\"></param>\\n /// <returns>The <see cref=\\\"Tensor\\\">output tensor</see>.</returns>\\n [Pure]public static Tensor var(Tensor input, long dimensions, bool unbiased = true, bool keepdim = false, ScalarType? type = null)\\n => input.var(dimensions, unbiased, keepdim, type);\\n\\n /// <summary>Calculates the variance of all elements in the tensor.</summary>\\n /// <remarks>\\n /// If <paramref name=\\\"unbiased\\\" /> is <value>true</value>, Bessel’s correction will be used.\\n /// Otherwise, the sample variance is calculated, without any correction.\\n /// </remarks>\\n /// <param name=\\\"input\\\">The input tensor.</param>\\n /// <param name=\\\"dimensions\\\">The dimensions to reduce.</param>\\n /// <param name=\\\"unbiased\\\">Whether to use Bessel’s correction (δN=1).</param>\\n /// <param name=\\\"keepdim\\\">Whether the <see cref=\\\"Tensor\\\">output tensor</see> has dim retained or not.</param>\\n /// <param name=\\\"type\\\"></param>\\n /// <returns>The <see cref=\\\"Tensor\\\">output tensor</see>.</returns>\\n [Pure]public static Tensor var(Tensor input, (long, long) dimensions, bool unbiased = true, bool keepdim = false, ScalarType? type = null)\\n => input.var(dimensions, unbiased, keepdim, type);\\n\\n /// <summary>Calculates the variance of all elements in the tensor.</summary>\\n /// <remarks>\\n /// If <paramref name=\\\"unbiased\\\" /> is <value>true</value>, Bessel’s correction will be used.\\n /// Otherwise, the sample variance is calculated, without any correction.\\n /// </remarks>\\n /// <param name=\\\"input\\\">The input tensor.</param>\\n /// <param name=\\\"dimensions\\\">The dimensions to reduce.</param>\\n /// <param name=\\\"unbiased\\\">Whether to use Bessel’s correction (δN=1).</param>\\n /// <param name=\\\"keepdim\\\">Whether the <see cref=\\\"Tensor\\\">output tensor</see> has dim retained or not.</param>\\n /// <param name=\\\"type\\\"></param>\\n /// <returns>The <see cref=\\\"Tensor\\\">output tensor</see>.</returns>\\n [Pure]public static Tensor var(Tensor input, (long, long, long) dimensions, bool unbiased = true, bool keepdim = false, ScalarType? type = null)\\n => input.var(dimensions, unbiased, keepdim, type);\\n\\n // https://pytorch.org/docs/stable/generated/torch.var_mean\\n /// <summary>\\n /// Calculates the variance and mean of all elements in the tensor.\\n /// </summary>\\n /// <param name=\\\"input\\\">The input tensor.</param>\\n /// <param name=\\\"unbiased\\\">Whether to use Bessel’s correction (δN=1).</param>\\n [Pure]public static (Tensor @var, Tensor mean) var_mean(Tensor input, bool unbiased = true) => input.std_mean(unbiased);\\n\\n /// <summary>Calculates the variance and mean of all elements in the tensor.</summary>\\n /// <remarks>\\n /// If <paramref name=\\\"unbiased\\\" /> is <value>true</value>, Bessel’s correction will be used.\\n /// Otherwise, the sample variance is calculated, without any correction.\\n /// </remarks>\\n /// <param name=\\\"input\\\">The input tensor.</param>\\n /// <param name=\\\"dimensions\\\">The dimensions to reduce.</param>\\n /// <param name=\\\"unbiased\\\">Whether to use Bessel’s correction (δN=1).</param>\\n /// <param name=\\\"keepdim\\\">Whether the <see cref=\\\"Tensor\\\">output tensor</see> has dim retained or not.</param>\\n /// <param name=\\\"type\\\"></param>\\n /// <returns>A <see cref=\\\"Tensor\\\">tensor</see> tuple of the variance and the mean.</returns>\\n [Pure]public static (Tensor @var, Tensor mean) var_mean(Tensor input, long[] dimensions, bool unbiased = true, bool keepdim = false, ScalarType? type = null)\\n => input.var_mean(dimensions, unbiased, keepdim, type);\\n\\n /// <summary>Calculates the variance and mean of all elements in the tensor.</summary>\\n /// <remarks>\\n /// If <paramref name=\\\"unbiased\\\" /> is <value>true</value>, Bessel’s correction will be used.\\n /// Otherwise, the sample variance is calculated, without any correction.\\n /// </remarks>\\n /// <param name=\\\"input\\\">The input tensor.</param>\\n /// <param name=\\\"dimensions\\\">The dimensions to reduce.</param>\\n /// <param name=\\\"unbiased\\\">Whether to use Bessel’s correction (δN=1).</param>\\n /// <param name=\\\"keepdim\\\">Whether the <see cref=\\\"Tensor\\\">output tensor</see> has dim retained or not.</param>\\n /// <param name=\\\"type\\\"></param>\\n /// <returns>A <see cref=\\\"Tensor\\\">tensor</see> tuple of the variance and the mean.</returns>\\n [Pure]public static (Tensor @var, Tensor mean) var_mean(Tensor input, ReadOnlySpan<long> dimensions, bool unbiased = true, bool keepdim = false, ScalarType? type = null)\\n => input.var_mean(dimensions, unbiased, keepdim, type);\\n\\n /// <summary>Calculates the variance and mean of all elements in the tensor.</summary>\\n /// <remarks>\\n /// If <paramref name=\\\"unbiased\\\" /> is <value>true</value>, Bessel’s correction will be used.\\n /// Otherwise, the sample variance is calculated, without any correction.\\n /// </remarks>\\n /// <param name=\\\"input\\\">The input tensor.</param>\\n /// <param name=\\\"dimensions\\\">The dimensions to reduce.</param>\\n /// <param name=\\\"unbiased\\\">Whether to use Bessel’s correction (δN=1).</param>\\n /// <param name=\\\"keepdim\\\">Whether the <see cref=\\\"Tensor\\\">output tensor</see> has dim retained or not.</param>\\n /// <param name=\\\"type\\\"></param>\\n /// <returns>A <see cref=\\\"Tensor\\\">tensor</see> tuple of the variance and the mean.</returns>\\n [Pure]public static (Tensor @var, Tensor mean) var_mean(Tensor input, long dimensions, bool unbiased = true, bool keepdim = false, ScalarType? type = null)\\n => input.var_mean(dimensions, unbiased, keepdim, type);\\n\\n /// <summary>Calculates the variance and mean of all elements in the tensor.</summary>\\n /// <remarks>\\n /// If <paramref name=\\\"unbiased\\\" /> is <value>true</value>, Bessel’s correction will be used.\\n /// Otherwise, the sample variance is calculated, without any correction.\\n /// </remarks>\\n /// <param name=\\\"input\\\">The input tensor.</param>\\n /// <param name=\\\"dimensions\\\">The dimensions to reduce.</param>\\n /// <param name=\\\"unbiased\\\">Whether to use Bessel’s correction (δN=1).</param>\\n /// <param name=\\\"keepdim\\\">Whether the <see cref=\\\"Tensor\\\">output tensor</see> has dim retained or not.</param>\\n /// <param name=\\\"type\\\"></param>\\n /// <returns>A <see cref=\\\"Tensor\\\">tensor</see> tuple of the variance and the mean.</returns>\\n [Pure]public static (Tensor @var, Tensor mean) var_mean(Tensor input, (long, long) dimensions, bool unbiased = true, bool keepdim = false, ScalarType? type = null)\\n => input.var_mean(dimensions, unbiased, keepdim, type);\\n\\n /// <summary>Calculates the variance and mean of all elements in the tensor.</summary>\\n /// <remarks>\\n /// If <paramref name=\\\"unbiased\\\" /> is <value>true</value>, Bessel’s correction will be used.\\n /// Otherwise, the sample variance is calculated, without any correction.\\n /// </remarks>\\n /// <param name=\\\"input\\\">The input tensor.</param>\\n /// <param name=\\\"dimensions\\\">The dimensions to reduce.</param>\\n /// <param name=\\\"unbiased\\\">Whether to use Bessel’s correction (δN=1).</param>\\n /// <param name=\\\"keepdim\\\">Whether the <see cref=\\\"Tensor\\\">output tensor</see> has dim retained or not.</param>\\n /// <param name=\\\"type\\\"></param>\\n /// <returns>A <see cref=\\\"Tensor\\\">tensor</see> tuple of the variance and the mean.</returns>\\n [Pure]public static (Tensor @var, Tensor mean) var_mean(Tensor input, (long, long, long) dimensions, bool unbiased = true, bool keepdim = false, ScalarType? type = null)\\n => input.var_mean(dimensions, unbiased, keepdim, type);\\n\\n // https://pytorch.org/docs/stable/generated/torch.count_nonzero\\n /// <summary>\\n /// Counts the number of non-zero values in the tensor input along the given dim. If no dim is specified then all non-zeros in the tensor are counted.\\n /// </summary>\\n /// <param name=\\\"input\\\">The input tensor.</param>\\n /// <param name=\\\"dims\\\">List of dims along which to count non-zeros.</param>\\n [Pure]public static Tensor count_nonzero(Tensor input, long[]? dims = null)\\n => input.count_nonzero(dims);\\n }\\n}\"\n },\n {\n \"alpha_fraction\": 0.6863289475440979,\n \"alphanum_fraction\": 0.6969696879386902,\n \"avg_line_length\": 37.59821319580078,\n \"blob_id\": \"69748fc248494996ea127fc2769ab1f85cfedcd7\",\n \"content_id\": \"3d3919ee3fe9da9ee4ae37d1980ab254339553ec\",\n \"detected_licenses\": [\n \"MIT\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"C#\",\n \"length_bytes\": 4323,\n \"license_type\": \"permissive\",\n \"max_line_length\": 130,\n \"num_lines\": 112,\n \"path\": \"/src/TorchSharp/PInvoke/LibTorchSharp.THSTorch.cs\",\n \"repo_name\": \"NiklasGustafsson/TorchSharp\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"// Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information.\\n#nullable enable\\nusing System;\\nusing System.Runtime.InteropServices;\\n\\nnamespace TorchSharp.PInvoke\\n{\\n internal static partial class NativeMethods\\n {\\n [DllImport(\\\"LibTorchSharp\\\")]\\n [return: MarshalAs(UnmanagedType.U1)]\\n internal static extern bool THSTorch_scalar_to_bool(IntPtr handle);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern void THSTorch_dispose_scalar(IntPtr handle);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern byte THSTorch_scalar_type(IntPtr value);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern int THSTorch_can_cast(int type1, int type2);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern int THSTorch_promote_types(int type1, int type2);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern IntPtr THSTorch_uint8_to_scalar(byte value);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern IntPtr THSTorch_int8_to_scalar(sbyte value);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern IntPtr THSTorch_int16_to_scalar(short value);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern IntPtr THSTorch_int32_to_scalar(int value);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern IntPtr THSTorch_int64_to_scalar(long value);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern IntPtr THSTorch_float32_to_scalar(float value);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern IntPtr THSTorch_float64_to_scalar(double value);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern IntPtr THSTorch_complex32_to_scalar(float real, float imaginary);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern IntPtr THSTorch_complex64_to_scalar(double real, double imaginary);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern IntPtr THSTorch_bool_to_scalar([MarshalAs(UnmanagedType.U1)] bool value);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern IntPtr THSTorch_float16_to_scalar(float value);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern IntPtr THSTorch_bfloat16_to_scalar(float value);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern float THSTorch_scalar_to_float32(IntPtr handle);\\n\\n#if NET6_0_OR_GREATER\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern void THSTorch_scalar_to_float16(IntPtr value, out Half res);\\n#endif\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern double THSTorch_scalar_to_float64(IntPtr handle);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern sbyte THSTorch_scalar_to_int8(IntPtr handle);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern byte THSTorch_scalar_to_uint8(IntPtr handle);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern short THSTorch_scalar_to_int16(IntPtr handle);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern int THSTorch_scalar_to_int32(IntPtr handle);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern long THSTorch_scalar_to_int64(IntPtr handle);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern void THSTorch_scalar_to_complex32(IntPtr handle, AllocatePinnedArray allocator);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern void THSTorch_scalar_to_complex64(IntPtr handle, AllocatePinnedArray allocator);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern IntPtr THSTorch_get_and_reset_last_err();\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern IntPtr THSTorch_lstsq(IntPtr handle, IntPtr b, out IntPtr qr);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern int THSTorch_get_num_threads();\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern void THSTorch_set_num_threads(int threads);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern int THSTorch_get_num_interop_threads();\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern void THSTorch_set_num_interop_threads(int threads);\\n }\\n}\\n\"\n },\n {\n \"alpha_fraction\": 0.47560974955558777,\n \"alphanum_fraction\": 0.47560974955558777,\n \"avg_line_length\": 25.62162208557129,\n \"blob_id\": \"72a8fb9266b87cce7305ecce5bc759a205c33b4a\",\n \"content_id\": \"43737a584f03ad472eee7db273bc1f9861c6ef9e\",\n \"detected_licenses\": [\n \"MIT\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"C#\",\n \"length_bytes\": 984,\n \"license_type\": \"permissive\",\n \"max_line_length\": 130,\n \"num_lines\": 37,\n \"path\": \"/src/TorchAudio/AudioMetaData.cs\",\n \"repo_name\": \"NiklasGustafsson/TorchSharp\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"// Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information.\\nnamespace TorchSharp\\n{\\n public static partial class torchaudio\\n {\\n /// <summary>\\n /// Meta-data of audio\\n /// </summary>\\n public struct AudioMetaData\\n {\\n /// <summary>\\n /// Sample rate\\n /// </summary>\\n public int sample_rate;\\n\\n /// <summary>\\n /// The number of frames\\n /// </summary>\\n public int num_frames;\\n\\n /// <summary>\\n /// The number of channels\\n /// </summary>\\n public int num_channels;\\n\\n /// <summary>\\n /// The number of bits per sample.\\n /// </summary>\\n public int bits_per_sample;\\n\\n /// <summary>\\n /// Audio encoding\\n /// </summary>\\n public AudioEncoding encoding;\\n }\\n }\\n}\"\n },\n {\n \"alpha_fraction\": 0.48172980546951294,\n \"alphanum_fraction\": 0.547435462474823,\n \"avg_line_length\": 38.77333450317383,\n \"blob_id\": \"e4ca9d1cc963bab7f50db0c34c99cd85981d82cf\",\n \"content_id\": \"e22ed515f95e40973ce5b9ce3a682f947151958a\",\n \"detected_licenses\": [\n \"MIT\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"C#\",\n \"length_bytes\": 2983,\n \"license_type\": \"permissive\",\n \"max_line_length\": 131,\n \"num_lines\": 75,\n \"path\": \"/src/Examples/VGG.cs\",\n \"repo_name\": \"NiklasGustafsson/TorchSharp\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"// Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information.\\nusing System.Collections.Generic;\\n\\nusing static TorchSharp.torch;\\nusing static TorchSharp.torch.nn;\\n\\nnamespace TorchSharp.Examples\\n{\\n /// <summary>\\n /// Modified version of VGG to classify CIFAR10 32x32 images.\\n /// </summary>\\n /// <remarks>\\n /// With an unaugmented CIFAR-10 data set, the author of this saw training converge\\n /// at roughly 85% accuracy on the test set, after 50 epochs using VGG-16.\\n /// </remarks>\\n class VGG : Module<Tensor, Tensor>\\n {\\n // The code here is is loosely based on https://github.com/kuangliu/pytorch-cifar/blob/master/models/vgg.py\\n // Licence and copypright notice at: https://github.com/kuangliu/pytorch-cifar/blob/master/LICENSE\\n\\n private readonly Dictionary<string, long[]> _channels = new Dictionary<string, long[]>() {\\n { \\\"VGG11\\\", new long[] { 64, 0, 128, 0, 256, 256, 0, 512, 512, 0, 512, 512, 0 } },\\n { \\\"VGG13\\\", new long[] { 64, 64, 0, 128, 128, 0, 256, 256, 0, 512, 512, 0, 512, 512, 0 } },\\n { \\\"VGG16\\\", new long[] { 64, 64, 0, 128, 128, 0, 256, 256, 256, 0, 512, 512, 512, 0, 512, 512, 512, 0 } },\\n { \\\"VGG19\\\", new long[] { 64, 64, 0, 128, 128, 0, 256, 256, 256, 256, 0, 512, 512, 512, 512, 0, 512, 512, 512, 512, 0 } }\\n };\\n\\n private readonly Module<Tensor, Tensor> layers;\\n\\n public VGG(string name, int numClasses, Device device = null) : base(name)\\n {\\n var modules = new List<(string, Module<Tensor, Tensor>)>();\\n\\n var channels = _channels[name.ToUpper()];\\n\\n long in_channels = 3;\\n\\n for (var i = 0; i < channels.Length; i++) {\\n\\n if (channels[i] == 0) {\\n modules.Add(($\\\"MaxPool2d-{i}a\\\", MaxPool2d(kernelSize: 2, stride: 2)));\\n } else {\\n modules.Add(($\\\"conv2d-{i}a\\\", Conv2d(in_channels, channels[i], kernelSize: 3, padding: 1)));\\n modules.Add(($\\\"bnrm2d-{i}a\\\", BatchNorm2d(channels[i])));\\n modules.Add(($\\\"relu-{i}b\\\", ReLU(inplace: true)));\\n in_channels = channels[i];\\n }\\n }\\n modules.Add((\\\"avgpool2d\\\", AvgPool2d(kernel_size: 1, stride: 1)));\\n modules.Add((\\\"flatten\\\", Flatten()));\\n modules.Add((\\\"linear\\\", Linear(512, numClasses)));\\n\\n layers = Sequential(modules);\\n\\n RegisterComponents();\\n\\n if (device != null && device.type == DeviceType.CUDA)\\n this.to(device);\\n }\\n\\n public override Tensor forward(Tensor input)\\n {\\n return layers.call(input);\\n }\\n\\n protected override void Dispose(bool disposing)\\n {\\n if (disposing) {\\n layers.Dispose();\\n ClearModules();\\n }\\n base.Dispose(disposing);\\n }\\n }\\n}\\n\"\n },\n {\n \"alpha_fraction\": 0.5376311540603638,\n \"alphanum_fraction\": 0.541280210018158,\n \"avg_line_length\": 42.8466682434082,\n \"blob_id\": \"a6501ccc6a48a91f6d49caa26f9a2c8c9db0e5d4\",\n \"content_id\": \"2014f82bd8e38ed4e95909445226149e0ce2f470\",\n \"detected_licenses\": [\n \"MIT\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"C#\",\n \"length_bytes\": 6577,\n \"license_type\": \"permissive\",\n \"max_line_length\": 149,\n \"num_lines\": 150,\n \"path\": \"/src/TorchSharp/Distributions/Geometric.cs\",\n \"repo_name\": \"NiklasGustafsson/TorchSharp\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"// Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information.\\nusing System;\\nusing static TorchSharp.torch;\\n\\nnamespace TorchSharp\\n{\\n using Modules;\\n\\n namespace Modules\\n {\\n /// <summary>\\n /// A Geometric distribution parameterized by probs,\\n /// where probs is the probability of success of Bernoulli trials.\\n ///\\n /// It represents the probability that in k+1 Bernoulli trials, the\\n /// first k trials failed, before seeing a success.\\n /// </summary>\\n public class Geometric : torch.distributions.Distribution\\n {\\n /// <summary>\\n /// The mean of the distribution.\\n /// </summary>\\n public override Tensor mean => 1 / (probs - 1);\\n\\n /// <summary>\\n /// The variance of the distribution\\n /// </summary>\\n public override Tensor variance => (1.0f / probs - 1.0f) / probs;\\n\\n /// <summary>\\n /// Constructor\\n /// </summary>\\n /// <param name=\\\"probs\\\">The probability of sampling '1'. Must be in range (0, 1]</param>\\n /// <param name=\\\"logits\\\">The log-odds of sampling '1'</param>\\n /// <param name=\\\"generator\\\">An optional random number generator object.</param>\\n public Geometric(Tensor probs = null, Tensor logits = null, torch.Generator generator = null) : base(generator) \\n {\\n this.batch_shape = probs is null ? logits.size() : probs.size();\\n this._probs = probs?.alias().DetachFromDisposeScope();\\n this._logits = logits?.alias().DetachFromDisposeScope();\\n }\\n\\n /// <summary>\\n /// Event probabilities\\n /// </summary>\\n public Tensor probs {\\n get {\\n return _probs ?? LogitsToProbs(_logits, true);\\n }\\n }\\n\\n /// <summary>\\n /// Event log-odds\\n /// </summary>\\n public Tensor logits {\\n get {\\n return _logits ?? ProbsToLogits(_probs);\\n }\\n }\\n\\n private Tensor _probs;\\n private Tensor _logits;\\n\\n /// <summary>\\n /// Generates a sample_shape shaped reparameterized sample or sample_shape shaped batch of reparameterized samples\\n /// if the distribution parameters are batched.\\n /// </summary>\\n /// <param name=\\\"sample_shape\\\">The sample shape.</param>\\n public override Tensor rsample(params long[] sample_shape)\\n {\\n using var _ = torch.NewDisposeScope();\\n var shape = ExtendedShape(sample_shape);\\n var tiny = torch.finfo(probs.dtype).tiny;\\n using (torch.no_grad()) {\\n var u = probs.new_empty(shape).uniform_(tiny, 1, generator: generator);\\n return (u.log() / (-probs).log1p()).floor().MoveToOuterDisposeScope();\\n }\\n }\\n\\n /// <summary>\\n /// Returns the log of the probability density/mass function evaluated at `value`.\\n /// </summary>\\n /// <param name=\\\"value\\\"></param>\\n public override Tensor log_prob(Tensor value)\\n {\\n using var _ = torch.NewDisposeScope();\\n var bcast = torch.broadcast_tensors(value, probs);\\n value = bcast[0];\\n var p = bcast[1].clone();\\n p[(p == 1) & (value == 0)] = torch.tensor(0);\\n return (value * (-p).log1p() + probs.log()).MoveToOuterDisposeScope();\\n }\\n\\n /// <summary>\\n /// Returns entropy of distribution, batched over batch_shape.\\n /// </summary>\\n /// <returns></returns>\\n public override Tensor entropy()\\n {\\n return torch.nn.functional.binary_cross_entropy_with_logits(logits, probs, reduction: nn.Reduction.None);\\n }\\n\\n /// <summary>\\n /// Returns a new distribution instance (or populates an existing instance provided by a derived class) with batch dimensions expanded to\\n /// `batch_shape`. This method calls `torch.Tensor.expand()` on the distribution's parameters. As such, this does not allocate new\\n /// memory for the expanded distribution instance.\\n /// </summary>\\n /// <param name=\\\"batch_shape\\\">Tthe desired expanded size.</param>\\n /// <param name=\\\"instance\\\">new instance provided by subclasses that need to override `.expand`.</param>\\n public override distributions.Distribution expand(Size batch_shape, distributions.Distribution instance = null)\\n {\\n if (instance != null && !(instance is Geometric))\\n throw new ArgumentException(\\\"expand(): 'instance' must be a Geometric distribution\\\");\\n\\n var newDistribution = ((instance == null) ?\\n new Geometric(probs: _probs?.expand(batch_shape), logits: logits?.expand(batch_shape), generator) :\\n instance) as Geometric;\\n\\n newDistribution.batch_shape = batch_shape;\\n if (newDistribution == instance) {\\n newDistribution._probs = _probs?.expand(batch_shape);\\n newDistribution._logits = _logits?.expand(batch_shape);\\n }\\n return newDistribution;\\n }\\n }\\n\\n }\\n public static partial class torch\\n {\\n public static partial class distributions\\n {\\n /// <summary>\\n /// Creates a Geometric distribution parameterized by probs,\\n /// where probs is the probability of success of Bernoulli trials.\\n ///\\n /// It represents the probability that in k+1 Bernoulli trials, the\\n /// first k trials failed, before seeing a success.\\n /// </summary>\\n /// <param name=\\\"probs\\\">The probability of sampling '1'. Must be in range (0, 1]</param>\\n /// <param name=\\\"logits\\\">The log-odds of sampling '1'</param>\\n /// <param name=\\\"generator\\\">An optional random number generator object.</param>\\n /// <returns></returns>\\n public static Geometric Geometric(Tensor probs = null, Tensor logits = null, torch.Generator generator = null)\\n {\\n return new Geometric(probs, logits, generator);\\n }\\n }\\n }\\n}\\n\"\n },\n {\n \"alpha_fraction\": 0.49470704793930054,\n \"alphanum_fraction\": 0.49643033742904663,\n \"avg_line_length\": 32.024391174316406,\n \"blob_id\": \"1e2cc235bdc03ee0abfbfea8f86d44d8c8662373\",\n \"content_id\": \"64e4e3d560e15dde81dae4c2799768681b1ebd11\",\n \"detected_licenses\": [\n \"MIT\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"C#\",\n \"length_bytes\": 8124,\n \"license_type\": \"permissive\",\n \"max_line_length\": 130,\n \"num_lines\": 246,\n \"path\": \"/src/TorchSharp/NN/ParameterDict.cs\",\n \"repo_name\": \"NiklasGustafsson/TorchSharp\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"// Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information.\\nusing System;\\nusing System.Linq;\\nusing System.Collections;\\nusing System.Collections.Generic;\\nusing System.Diagnostics.CodeAnalysis;\\nusing static TorchSharp.torch.nn;\\n\\nnamespace TorchSharp\\n{\\n using Modules;\\n\\n namespace Modules\\n {\\n /// <summary>\\n /// Holds parameters in a dictionary.\\n /// \\n /// ParameterDict can be indexed like a regular dictionary, but the parameters it\\n /// contains are properly registered, and will be visible by all Module methods.\\n ///\\n /// ParameterDict is an ordered dictionary that respects the order of insertion, and\\n /// in update(), the order of the merged OrderedDict or another ParameterDict (the argument to update()).\\n /// </summary>\\n public class ParameterDict : Module, IDictionary<string, Parameter>, IList<(string, Parameter)>\\n {\\n public ParameterDict() : base(nameof(ParameterDict))\\n {\\n }\\n\\n /// <summary>\\n /// Remove all items from the ParameterDict.\\n /// </summary>\\n public void clear()\\n {\\n _list.Clear();\\n _dict.Clear();\\n }\\n\\n /// <summary>\\n /// Return an enumeration of the ParameterDict key/value pairs.\\n /// </summary>\\n /// <returns></returns>\\n public IEnumerator<(string, Parameter)> items() => _list.GetEnumerator();\\n\\n /// <summary>\\n /// Return the ParameterDict keys.\\n /// </summary>\\n /// <returns></returns>\\n public IEnumerable<string> keys() => _dict.Keys;\\n\\n protected override void RegisterComponents()\\n {\\n if (_registered) return;\\n\\n for (int i = 0; i < _list.Count; i++) {\\n register_parameter($\\\"{_list[i].Item1}\\\", _list[i].Item2);\\n }\\n _registered = true;\\n }\\n\\n private bool _registered = false;\\n\\n /// <summary>\\n /// Return the ParameterDict values.\\n /// </summary>\\n /// <returns></returns>\\n public IEnumerable<Parameter> values() => _dict.Values;\\n\\n public (string, Parameter) this[int index] {\\n get => _list[index];\\n set {\\n var name = value.Item1;\\n _list[index] = value;\\n _dict[name] = value.Item2;\\n }\\n }\\n\\n public bool IsReadOnly => false;\\n\\n public ICollection<string> Keys => _list.Select(kv => kv.Item1).ToList();\\n\\n public ICollection<Parameter> Values => _list.Select(kv => kv.Item2).ToList();\\n\\n public int Count => _dict.Count;\\n\\n public Parameter this[string key] {\\n get => _dict[key];\\n set {\\n _dict[key] = value;\\n var idx = _list.FindIndex(kv => kv.Item1.Equals(key));\\n _list[idx] = (key, value);\\n }\\n }\\n\\n public override bool has_parameter(string target)\\n {\\n return _dict.ContainsKey(target);\\n }\\n\\n public override Parameter get_parameter(string target)\\n {\\n if (_dict.TryGetValue(target, out var result)) {\\n return result;\\n }\\n return null;\\n }\\n\\n public void Add((string, Parameter) item)\\n {\\n _dict.Add(item.Item1, item.Item2);\\n _list.Add(item);\\n }\\n\\n public void Add(string key, Parameter value)\\n {\\n _dict.Add(key, value);\\n _list.Add((key, value));\\n }\\n\\n public void Add(KeyValuePair<string, Parameter> item)\\n {\\n _dict.Add(item.Key, item.Value);\\n _list.Add((item.Key, item.Value));\\n }\\n\\n public bool Contains((string, Parameter) item)\\n {\\n return _list.Contains(item);\\n }\\n\\n public void CopyTo((string, Parameter)[] array, int arrayIndex)\\n {\\n _list.CopyTo(array, arrayIndex);\\n }\\n\\n public int IndexOf((string, Parameter) item)\\n {\\n return _list.IndexOf(item);\\n }\\n\\n public void Insert(int index, (string, Parameter) item)\\n {\\n _dict.Add(item.Item1, item.Item2);\\n _list.Insert(index, item);\\n }\\n\\n public bool Remove((string, Parameter) item)\\n {\\n _dict.Remove(item.Item1);\\n return _list.Remove(item);\\n }\\n\\n public void RemoveAt(int index)\\n {\\n if (index >= _list.Count) throw new IndexOutOfRangeException();\\n var (n,p) = _list[index];\\n _list.RemoveAt(index);\\n _dict.Remove(n);\\n }\\n\\n public bool ContainsKey(string key)\\n {\\n return _dict.ContainsKey(key);\\n }\\n\\n public bool Remove(string key)\\n {\\n var value = _dict[key];\\n var idx = _list.FindIndex(kv => kv.Item1.Equals(key));\\n _list.RemoveAt(idx);\\n return _dict.Remove(key);\\n }\\n\\n public bool TryGetValue(string key, [MaybeNullWhen(false)] out Parameter value)\\n {\\n return _dict.TryGetValue(key, out value);\\n }\\n\\n public void Clear()\\n {\\n _dict.Clear();\\n _list.Clear();\\n }\\n\\n public bool Contains(KeyValuePair<string, Parameter> item)\\n {\\n return _dict.ContainsKey(item.Key);\\n }\\n\\n public void CopyTo(KeyValuePair<string, Parameter>[] array, int arrayIndex)\\n {\\n throw new NotImplementedException();\\n }\\n\\n public bool Remove(KeyValuePair<string, Parameter> item)\\n {\\n return _dict.Remove(item.Key);\\n }\\n\\n public IEnumerator<(string, Parameter)> GetEnumerator()\\n {\\n return _list.GetEnumerator();\\n }\\n\\n IEnumerator<KeyValuePair<string, Parameter>> IEnumerable<KeyValuePair<string, Parameter>>.GetEnumerator()\\n {\\n throw new NotImplementedException();\\n }\\n\\n IEnumerator IEnumerable.GetEnumerator()\\n {\\n return ((IEnumerable<KeyValuePair<string, Parameter>>)this).GetEnumerator();\\n }\\n\\n private List<(string, Parameter)> _list = new List<(string, Parameter)>();\\n private Dictionary<string, Parameter> _dict = new Dictionary<string, Parameter>();\\n }\\n }\\n\\n public static partial class torch\\n {\\n public static partial class nn\\n {\\n\\n /// <summary>\\n /// Create a ParameterList instance from an array of parameter tensors.\\n /// </summary>\\n /// <param name=\\\"parameters\\\">A tuple of (name,parameter).</param>\\n /// <remarks>\\n /// ParameterDict can be indexed like a regular dictionary, but the parameters it\\n /// contains are properly registered, and will be visible by all Module methods.\\n ///\\n /// ParameterDict is an ordered dictionary that respects the order of insertion, and\\n /// in update(), the order of the merged OrderedDict or another ParameterDict (the argument to update()).\\n /// </remarks>\\n public static ParameterDict ParameterDict(params (string, Parameter)[] parameters)\\n {\\n var result = new ParameterDict();\\n foreach (var (n, p) in parameters) {\\n result.Add(n, p);\\n }\\n return result;\\n }\\n }\\n }\\n}\\n\"\n },\n {\n \"alpha_fraction\": 0.5330060124397278,\n \"alphanum_fraction\": 0.5351881980895996,\n \"avg_line_length\": 30.620689392089844,\n \"blob_id\": \"3afb855fa919ed78c584c133ba543d8fa16212b2\",\n \"content_id\": \"472368314d627e59578360b268b42c2562506f29\",\n \"detected_licenses\": [\n \"MIT\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"C#\",\n \"length_bytes\": 1833,\n \"license_type\": \"permissive\",\n \"max_line_length\": 130,\n \"num_lines\": 58,\n \"path\": \"/src/TorchVision/CenterCrop.cs\",\n \"repo_name\": \"NiklasGustafsson/TorchSharp\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"// Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information.\\n\\nusing System;\\nusing static TorchSharp.torch;\\n\\n\\nnamespace TorchSharp\\n{\\n public static partial class torchvision\\n {\\n internal class CenterCrop : ITransform\\n {\\n internal CenterCrop(int height, int width)\\n {\\n if (height <= 0) \\n throw new ArgumentOutOfRangeException(nameof(height), \\\"Height must be greater than 0.\\\");\\n if (width <= 0) \\n throw new ArgumentOutOfRangeException(nameof(width), \\\"Width must be greater than 0.\\\");\\n\\n this.height = height;\\n this.width = width;\\n }\\n\\n public Tensor call(Tensor input)\\n {\\n return transforms.functional.center_crop(input, height, width);\\n }\\n\\n protected int height, width;\\n\\n public int Height => height;\\n public int Width => width;\\n\\n }\\n\\n public static partial class transforms\\n {\\n /// <summary>\\n /// Crop the center of the image.\\n /// </summary>\\n /// <param name=\\\"height\\\">Desired output height of the crop</param>\\n /// <param name=\\\"width\\\">Desired output width of the crop</param>\\n static public ITransform CenterCrop(int height, int width)\\n {\\n return new CenterCrop(height, width);\\n }\\n\\n /// <summary>\\n /// Crop the center of the image.\\n /// </summary>\\n /// <param name=\\\"size\\\">Desired output size of the crop</param>\\n static public ITransform CenterCrop(int size)\\n {\\n return new CenterCrop(size, size);\\n }\\n }\\n }\\n}\"\n },\n {\n \"alpha_fraction\": 0.5069842338562012,\n \"alphanum_fraction\": 0.5288323760032654,\n \"avg_line_length\": 43.31745910644531,\n \"blob_id\": \"9f8bef2aaee08a3c7eeeab404650c65c3cf2c48c\",\n \"content_id\": \"475420505d090c32ce7b6ecc6d8141651b8ce422\",\n \"detected_licenses\": [\n \"MIT\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"C#\",\n \"length_bytes\": 5584,\n \"license_type\": \"permissive\",\n \"max_line_length\": 167,\n \"num_lines\": 126,\n \"path\": \"/src/TorchVision/models/AlexNet.cs\",\n \"repo_name\": \"NiklasGustafsson/TorchSharp\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"// Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information.\\n\\nusing static TorchSharp.torch;\\nusing static TorchSharp.torch.nn;\\n\\n#nullable enable\\nnamespace TorchSharp\\n{\\n public static partial class torchvision\\n {\\n public static partial class models\\n {\\n /// <summary>\\n /// AlexNet\\n /// </summary>\\n /// <param name=\\\"num_classes\\\">The number of output classes.</param>\\n /// <param name=\\\"dropout\\\">The dropout ratio.</param>\\n /// <param name=\\\"weights_file\\\">The location of a file containing pre-trained weights for the model.</param>\\n /// <param name=\\\"skipfc\\\">If true, the last linear layer of the classifier will not be loaded from the weights file.</param>\\n /// <param name=\\\"device\\\">The device to locate the model on.</param>\\n /// <remarks>\\n /// Pre-trained weights may be retrieved by using Pytorch and saving the model state-dict\\n /// using the exportsd.py script, then loading into the .NET instance:\\n ///\\n /// from torchvision import models\\n /// import exportsd\\n /// \\n /// model = models.alexnet(pretrained=True)\\n /// f = open(\\\"model_weights.dat\\\", \\\"wb\\\")\\n /// exportsd.save_state_dict(model.state_dict(), f)\\n /// f.close()\\n ///\\n /// See also: https://github.com/dotnet/TorchSharp/blob/main/docfx/articles/saveload.md\\n ///\\n /// In order for the weights to be loaded, the number of classes has to be the same as\\n /// in the pre-trained model, which is 1000.\\n ///\\n /// It is also possible to skip loading the last linear layer and use it for transfer-learning\\n /// with a different number of output classes. To do so, pass skipfc=true.\\n /// as the skip list when loading.\\n ///\\n /// All pre-trained models expect input images normalized in the same way, i.e. mini-batches of 3-channel RGB\\n /// images of shape (3 x H x W), where H and W are expected to be at least 224. The images have to be loaded\\n /// in to a range of [0, 1] and then normalized using mean = [0.485, 0.456, 0.406] and std = [0.229, 0.224, 0.225].\\n /// </remarks>\\n public static Modules.AlexNet alexnet(int num_classes = 1000, float dropout = 0.5f, string? weights_file = null, bool skipfc = true, Device? device = null)\\n {\\n return new Modules.AlexNet(num_classes, dropout, weights_file, skipfc, device);\\n }\\n }\\n }\\n\\n namespace Modules\\n {\\n // The code here is based on\\n // https://github.com/pytorch/vision/blob/main/torchvision/models/alexnet.py\\n // Licence and copypright notice at: https://github.com/pytorch/vision/blob/main/LICENSE\\n\\n public class AlexNet : Module<Tensor, Tensor>\\n {\\n private readonly Module<Tensor, Tensor> features;\\n private readonly Module<Tensor, Tensor> avgpool;\\n private readonly Module<Tensor, Tensor> classifier;\\n\\n protected override void Dispose(bool disposing)\\n {\\n if (disposing) {\\n features.Dispose(); avgpool.Dispose();\\n classifier.Dispose();\\n }\\n base.Dispose(disposing);\\n }\\n\\n public AlexNet(int numClasses, float dropout = 0.5f, string? weights_file = null, bool skipfc = true, Device? device = null) : base(nameof(AlexNet))\\n {\\n features = Sequential(\\n Conv2d(3, 64, kernelSize: 11, stride: 4, padding: 2),\\n ReLU(inplace: true),\\n MaxPool2d(kernelSize: 3, stride: 2),\\n Conv2d(64, 192, kernelSize: 5, padding: 2),\\n ReLU(inplace: true),\\n MaxPool2d(kernelSize: 3, stride: 2),\\n Conv2d(192, 384, kernelSize: 3, padding: 1),\\n ReLU(inplace: true),\\n Conv2d(384, 256, kernelSize: 3, padding: 1),\\n ReLU(inplace: true),\\n Conv2d(256, 256, kernelSize: 3, padding: 1),\\n ReLU(inplace: true),\\n MaxPool2d(kernelSize: 3, stride: 2)\\n );\\n\\n avgpool = AdaptiveAvgPool2d(new long[] { 6, 6 });\\n\\n classifier = Sequential(\\n Dropout(p: dropout),\\n Linear(256 * 6 * 6, 4096),\\n ReLU(inplace: true),\\n Dropout(p: dropout),\\n Linear(4096, 4096),\\n ReLU(inplace: true),\\n Linear(4096, numClasses)\\n );\\n\\n RegisterComponents();\\n\\n if (!string.IsNullOrEmpty(weights_file)) {\\n\\n this.load(weights_file, skip: skipfc ? new[] { \\\"classifier.6.weight\\\", \\\"classifier.6.bias\\\" } : null);\\n }\\n\\n if (device != null && device.type == DeviceType.CUDA)\\n this.to(device);\\n }\\n\\n public override Tensor forward(Tensor input)\\n {\\n using (var _ = NewDisposeScope()) {\\n var f = features.call(input);\\n var avg = avgpool.call(f);\\n var x = avg.flatten(1);\\n return classifier.call(x).MoveToOuterDisposeScope();\\n }\\n }\\n }\\n }\\n}\\n\"\n },\n {\n \"alpha_fraction\": 0.4583049416542053,\n \"alphanum_fraction\": 0.48074302077293396,\n \"avg_line_length\": 44.84635543823242,\n \"blob_id\": \"6f2e1b423a0c2a869bdd0b406af734f9d6fb51d4\",\n \"content_id\": \"9531bea4ccb1949a7c3c0247ac5992d3780a4f07\",\n \"detected_licenses\": [\n \"MIT\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"C#\",\n \"length_bytes\": 17604,\n \"license_type\": \"permissive\",\n \"max_line_length\": 134,\n \"num_lines\": 384,\n \"path\": \"/src/TorchVision/models/MobileNetV3.cs\",\n \"repo_name\": \"NiklasGustafsson/TorchSharp\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"// Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information.\\n\\n// A number of implementation details in this file have been translated from the Python version of torchvision,\\n// largely located in the files found in this folder:\\n//\\n// https://github.com/pytorch/vision/blob/3c9ae0ac5dda3d881a6ce5004ce5756ae7de7bc4/torchvision/models/mobilenetv3.py\\n//\\n// The origin has the following copyright notice and license:\\n//\\n// https://github.com/pytorch/vision/blob/main/LICENSE\\n//\\n\\nusing System.Collections.Generic;\\nusing System;\\nusing static TorchSharp.torch;\\nusing static TorchSharp.torchvision.models._utils;\\nusing static TorchSharp.torchvision.ops;\\nusing TorchSharp.Modules;\\n\\n#nullable enable\\nnamespace TorchSharp\\n{\\n namespace Modules\\n {\\n public class MobileNetV3 : nn.Module<Tensor, Tensor>\\n {\\n /// <summary>\\n /// Stores information listed at Tables 1 and 2 of the MobileNetV3 paper\\n /// </summary>\\n internal class InvertedResidualConfig\\n {\\n public readonly long dilation;\\n public readonly long expanded_channels;\\n public readonly long input_channels;\\n public readonly long kernel;\\n public readonly long out_channels;\\n public readonly long stride;\\n public readonly bool use_hs;\\n public readonly bool use_se;\\n\\n public InvertedResidualConfig(\\n long input_channels,\\n long kernel,\\n long expanded_channels,\\n long out_channels,\\n bool use_se,\\n string activation,\\n long stride,\\n long dilation,\\n double width_mult)\\n {\\n this.input_channels = adjust_channels(input_channels, width_mult);\\n this.kernel = kernel;\\n this.expanded_channels = adjust_channels(expanded_channels, width_mult);\\n this.out_channels = adjust_channels(out_channels, width_mult);\\n this.use_se = use_se;\\n this.use_hs = activation == \\\"HS\\\";\\n this.stride = stride;\\n this.dilation = dilation;\\n }\\n\\n internal static long adjust_channels(long channels, double width_mult)\\n {\\n return _make_divisible(channels * width_mult, 8);\\n }\\n }\\n\\n /// <summary>\\n /// Implemented as described at section 5 of MobileNetV3 paper\\n /// </summary>\\n private class InvertedResidual : nn.Module<Tensor, Tensor>\\n {\\n private readonly bool _is_cn;\\n private readonly nn.Module<Tensor, Tensor> block;\\n private readonly long out_channels;\\n private readonly bool use_res_connect;\\n\\n protected override void Dispose(bool disposing)\\n {\\n if (disposing) {\\n block.Dispose();\\n }\\n base.Dispose(disposing);\\n }\\n\\n public InvertedResidual(\\n string name,\\n InvertedResidualConfig cnf,\\n Func<long, nn.Module<Tensor, Tensor>> norm_layer,\\n Func<long, long, nn.Module<Tensor, Tensor>>? se_layer = null) : base(name)\\n {\\n if (!(1 <= cnf.stride && cnf.stride <= 2)) {\\n throw new ArgumentException(\\\"illegal stride value\\\");\\n }\\n\\n this.use_res_connect = cnf.stride == 1 && cnf.input_channels == cnf.out_channels;\\n\\n var layers = new List<nn.Module<Tensor, Tensor>>();\\n Func<bool, nn.Module<Tensor, Tensor>> activation_layer = (\\n cnf.use_hs ? (inplace) => nn.Hardswish(inplace) : (inplace) => nn.ReLU(inplace));\\n\\n // expand\\n if (cnf.expanded_channels != cnf.input_channels) {\\n layers.Add(Conv2dNormActivation(\\n cnf.input_channels,\\n cnf.expanded_channels,\\n kernel_size: 1,\\n norm_layer: norm_layer,\\n activation_layer: activation_layer));\\n }\\n\\n // depthwise\\n var stride = cnf.dilation > 1 ? 1 : cnf.stride;\\n layers.Add(Conv2dNormActivation(\\n cnf.expanded_channels,\\n cnf.expanded_channels,\\n kernel_size: cnf.kernel,\\n stride: stride,\\n dilation: cnf.dilation,\\n groups: cnf.expanded_channels,\\n norm_layer: norm_layer,\\n activation_layer: activation_layer));\\n if (cnf.use_se) {\\n var squeeze_channels = _make_divisible(cnf.expanded_channels / 4, 8);\\n if (se_layer != null) {\\n layers.Add(se_layer(cnf.expanded_channels, squeeze_channels));\\n } else {\\n layers.Add(\\n torchvision.ops.SqueezeExcitation(\\n cnf.expanded_channels,\\n squeeze_channels,\\n activation: () => nn.ReLU6(),\\n scale_activation: () => nn.Hardsigmoid()));\\n }\\n }\\n\\n // project\\n layers.Add(\\n Conv2dNormActivation(\\n cnf.expanded_channels, cnf.out_channels, kernel_size: 1, norm_layer: norm_layer, activation_layer: null));\\n\\n this.block = nn.Sequential(layers);\\n this.out_channels = cnf.out_channels;\\n this._is_cn = cnf.stride > 1;\\n\\n RegisterComponents();\\n }\\n\\n public override Tensor forward(Tensor input)\\n {\\n var result = this.block.call(input);\\n if (this.use_res_connect) {\\n result += input;\\n }\\n return result;\\n }\\n }\\n\\n protected override void Dispose(bool disposing)\\n {\\n if (disposing) {\\n avgpool.Dispose();\\n classifier.Dispose();\\n features.Dispose();\\n }\\n base.Dispose(disposing);\\n }\\n private readonly nn.Module<Tensor, Tensor> avgpool;\\n private readonly nn.Module<Tensor, Tensor> classifier;\\n private readonly nn.Module<Tensor, Tensor> features;\\n\\n /// <summary>\\n /// MobileNet V3 main class\\n /// </summary>\\n /// <param name=\\\"name\\\"></param>\\n /// <param name=\\\"inverted_residual_setting\\\">Network structure</param>\\n /// <param name=\\\"last_channel\\\">The number of channels on the penultimate layer</param>\\n /// <param name=\\\"num_classes\\\">Number of classes</param>\\n /// <param name=\\\"block\\\">Module specifying inverted residual building block for mobilenet</param>\\n /// <param name=\\\"norm_layer\\\">Module specifying the normalization layer to use</param>\\n /// <param name=\\\"dropout\\\">The droupout probability</param>\\n /// <exception cref=\\\"ArgumentException\\\"></exception>\\n internal MobileNetV3(\\n string name,\\n InvertedResidualConfig[] inverted_residual_setting,\\n long last_channel,\\n long num_classes = 1000,\\n Func<InvertedResidualConfig, Func<long, nn.Module<Tensor, Tensor>>, nn.Module<Tensor, Tensor>>? block = null,\\n Func<long, nn.Module<Tensor, Tensor>>? norm_layer = null,\\n double dropout = 0.2) : base(name)\\n {\\n if (inverted_residual_setting == null || inverted_residual_setting.Length == 0) {\\n throw new ArgumentException(\\\"The inverted_residual_setting should not be empty\\\");\\n }\\n\\n if (block == null) {\\n block = (cnf, norm_layer) => new InvertedResidual(\\\"InvertedResidual\\\", cnf, norm_layer);\\n }\\n\\n if (norm_layer == null) {\\n norm_layer = (features) => nn.BatchNorm2d(features, eps: 0.001, momentum: 0.01);\\n }\\n\\n var layers = new List<nn.Module<Tensor, Tensor>>();\\n\\n // building first layer\\n var firstconv_output_channels = inverted_residual_setting[0].input_channels;\\n layers.Add(\\n Conv2dNormActivation(\\n 3,\\n firstconv_output_channels,\\n kernel_size: 3,\\n stride: 2,\\n norm_layer: norm_layer,\\n activation_layer: (inplace) => nn.Hardswish(inplace)));\\n\\n // building inverted residual blocks\\n foreach (var cnf in inverted_residual_setting) {\\n layers.Add(block(cnf, norm_layer));\\n }\\n\\n // building last several layers\\n var lastconv_input_channels = inverted_residual_setting[inverted_residual_setting.Length - 1].out_channels;\\n var lastconv_output_channels = 6 * lastconv_input_channels;\\n layers.Add(\\n Conv2dNormActivation(\\n lastconv_input_channels,\\n lastconv_output_channels,\\n kernel_size: 1,\\n norm_layer: norm_layer,\\n activation_layer: (inplace) => nn.Hardswish(inplace)));\\n\\n this.features = nn.Sequential(layers);\\n this.avgpool = nn.AdaptiveAvgPool2d(1);\\n this.classifier = nn.Sequential(\\n nn.Linear(lastconv_output_channels, last_channel),\\n nn.Hardswish(inplace: true),\\n nn.Dropout(p: dropout, inplace: true),\\n nn.Linear(last_channel, num_classes));\\n\\n RegisterComponents();\\n\\n foreach (var (_, m) in this.named_modules()) {\\n if (m is Modules.Conv2d) {\\n var conv = (Modules.Conv2d)m;\\n nn.init.kaiming_normal_(conv.weight, mode: nn.init.FanInOut.FanOut);\\n if (conv.bias is not null) {\\n nn.init.zeros_(conv.bias);\\n }\\n } else if (m is Modules.BatchNorm2d) {\\n var norm = (Modules.BatchNorm2d)m;\\n nn.init.ones_(norm.weight);\\n nn.init.zeros_(norm.bias);\\n } else if (m is Modules.GroupNorm) {\\n var norm = (Modules.GroupNorm)m;\\n nn.init.ones_(norm.weight);\\n nn.init.zeros_(norm.bias);\\n } else if (m is Modules.Linear) {\\n var linear = (Modules.Linear)m;\\n nn.init.normal_(linear.weight, 0, 0.01);\\n nn.init.zeros_(linear.bias);\\n }\\n }\\n }\\n\\n public override Tensor forward(Tensor x)\\n {\\n x = this.features.call(x);\\n x = this.avgpool.call(x);\\n x = torch.flatten(x, 1);\\n x = this.classifier.call(x);\\n return x;\\n }\\n }\\n }\\n\\n public static partial class torchvision\\n {\\n public static partial class models\\n {\\n private static (MobileNetV3.InvertedResidualConfig[], long) _mobilenet_v3_conf(\\n string arch,\\n double width_mult = 1.0,\\n bool reduced_tail = false,\\n bool dilated = false)\\n {\\n long last_channel;\\n MobileNetV3.InvertedResidualConfig[] inverted_residual_setting;\\n var reduce_divider = reduced_tail ? 2 : 1;\\n var dilation = dilated ? 2 : 1;\\n MobileNetV3.InvertedResidualConfig bneck_conf(\\n long input_channels,\\n long kernel,\\n long expanded_channels,\\n long out_channels,\\n bool use_se,\\n string activation,\\n long stride,\\n long dilation) => new MobileNetV3.InvertedResidualConfig(\\n input_channels,\\n kernel,\\n expanded_channels,\\n out_channels,\\n use_se,\\n activation,\\n stride,\\n dilation,\\n width_mult: width_mult);\\n long adjust_channels(long channels) =>\\n MobileNetV3.InvertedResidualConfig.adjust_channels(\\n channels,\\n width_mult: width_mult);\\n if (arch == \\\"mobilenet_v3_large\\\") {\\n inverted_residual_setting = new MobileNetV3.InvertedResidualConfig[] {\\n bneck_conf(16, 3, 16, 16, false, \\\"RE\\\", 1, 1),\\n bneck_conf(16, 3, 64, 24, false, \\\"RE\\\", 2, 1),\\n bneck_conf(24, 3, 72, 24, false, \\\"RE\\\", 1, 1),\\n bneck_conf(24, 5, 72, 40, true, \\\"RE\\\", 2, 1),\\n bneck_conf(40, 5, 120, 40, true, \\\"RE\\\", 1, 1),\\n bneck_conf(40, 5, 120, 40, true, \\\"RE\\\", 1, 1),\\n bneck_conf(40, 3, 240, 80, false, \\\"HS\\\", 2, 1),\\n bneck_conf(80, 3, 200, 80, false, \\\"HS\\\", 1, 1),\\n bneck_conf(80, 3, 184, 80, false, \\\"HS\\\", 1, 1),\\n bneck_conf(80, 3, 184, 80, false, \\\"HS\\\", 1, 1),\\n bneck_conf(80, 3, 480, 112, true, \\\"HS\\\", 1, 1),\\n bneck_conf(112, 3, 672, 112, true, \\\"HS\\\", 1, 1),\\n bneck_conf(112, 5, 672, 160 / reduce_divider, true, \\\"HS\\\", 2, dilation),\\n bneck_conf(160 / reduce_divider, 5, 960 / reduce_divider, 160 / reduce_divider, true, \\\"HS\\\", 1, dilation),\\n bneck_conf(160 / reduce_divider, 5, 960 / reduce_divider, 160 / reduce_divider, true, \\\"HS\\\", 1, dilation)\\n };\\n last_channel = adjust_channels(1280 / reduce_divider);\\n } else if (arch == \\\"mobilenet_v3_small\\\") {\\n inverted_residual_setting = new MobileNetV3.InvertedResidualConfig[] {\\n bneck_conf(16, 3, 16, 16, true, \\\"RE\\\", 2, 1),\\n bneck_conf(16, 3, 72, 24, false, \\\"RE\\\", 2, 1),\\n bneck_conf(24, 3, 88, 24, false, \\\"RE\\\", 1, 1),\\n bneck_conf(24, 5, 96, 40, true, \\\"HS\\\", 2, 1),\\n bneck_conf(40, 5, 240, 40, true, \\\"HS\\\", 1, 1),\\n bneck_conf(40, 5, 240, 40, true, \\\"HS\\\", 1, 1),\\n bneck_conf(40, 5, 120, 48, true, \\\"HS\\\", 1, 1),\\n bneck_conf(48, 5, 144, 48, true, \\\"HS\\\", 1, 1),\\n bneck_conf(48, 5, 288, 96 / reduce_divider, true, \\\"HS\\\", 2, dilation),\\n bneck_conf(96 / reduce_divider, 5, 576 / reduce_divider, 96 / reduce_divider, true, \\\"HS\\\", 1, dilation),\\n bneck_conf(96 / reduce_divider, 5, 576 / reduce_divider, 96 / reduce_divider, true, \\\"HS\\\", 1, dilation)\\n };\\n last_channel = adjust_channels(1024 / reduce_divider);\\n } else {\\n throw new ArgumentException($\\\"Unsupported model type {arch}\\\");\\n }\\n return (inverted_residual_setting, last_channel);\\n }\\n\\n private static Modules.MobileNetV3 _mobilenet_v3(\\n MobileNetV3.InvertedResidualConfig[] inverted_residual_setting,\\n long last_channel)\\n {\\n var model = new MobileNetV3(\\\"MobileNetV3\\\", inverted_residual_setting, last_channel);\\n return model;\\n }\\n\\n /// <summary>\\n /// Constructs a large MobileNetV3 architecture from\\n /// `Searching for MobileNetV3 https://arxiv.org/abs/1905.02244`.\\n /// </summary>\\n /// <returns></returns>\\n public static MobileNetV3 mobilenet_v3_large()\\n {\\n var (inverted_residual_setting, last_channel) = _mobilenet_v3_conf(\\\"mobilenet_v3_large\\\");\\n return _mobilenet_v3(inverted_residual_setting, last_channel);\\n }\\n\\n /// <summary>\\n /// Constructs a small MobileNetV3 architecture from\\n /// `Searching for MobileNetV3 https://arxiv.org/abs/1905.02244`.\\n /// </summary>\\n /// <returns></returns>\\n public static MobileNetV3 mobilenet_v3_small()\\n {\\n var (inverted_residual_setting, last_channel) = _mobilenet_v3_conf(\\\"mobilenet_v3_small\\\");\\n return _mobilenet_v3(inverted_residual_setting, last_channel);\\n }\\n }\\n }\\n}\"\n },\n {\n \"alpha_fraction\": 0.6567100882530212,\n \"alphanum_fraction\": 0.6668975353240967,\n \"avg_line_length\": 33.40584182739258,\n \"blob_id\": \"85f8467351161cae1d780e06bcf55040963b751e\",\n \"content_id\": \"6f9ca1742b0d924f4e3ecc8d6c65af862ca6ec8d\",\n \"detected_licenses\": [\n \"MIT\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"C++\",\n \"length_bytes\": 44761,\n \"license_type\": \"permissive\",\n \"max_line_length\": 273,\n \"num_lines\": 1301,\n \"path\": \"/src/Native/LibTorchSharp/THSNN.cpp\",\n \"repo_name\": \"NiklasGustafsson/TorchSharp\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"// Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information.\\n#include \\\"THSNN.h\\\"\\n\\n#include <torch/nn/init.h>\\n\\n\\nNNModule THSNN_Identity_ctor(NNAnyModule* outAsAnyModule)\\n{\\n CATCH_RETURN_NNModule(\\n res = create_module<torch::nn::IdentityImpl>(outAsAnyModule);\\n );\\n}\\n\\nTensor THSNN_Identity_forward(const NNModule module, const Tensor tensor)\\n{\\n CATCH_TENSOR((*module)->as<torch::nn::Identity>()->forward(*tensor));\\n}\\n\\nNNModule THSNN_Linear_ctor(const int64_t input_size, const int64_t output_size, const bool bias,\\n NNAnyModule* outAsAnyModule)\\n{\\n CATCH_RETURN_NNModule(\\n auto opts = torch::nn::LinearOptions(input_size, output_size).bias(bias);\\n res = create_module<torch::nn::LinearImpl>(opts, outAsAnyModule);\\n );\\n}\\n\\nTensor THSNN_Linear_forward(const NNModule module, const Tensor tensor)\\n{\\n CATCH_TENSOR((*module)->as<torch::nn::Linear>()->forward(*tensor));\\n}\\n\\nTensor THSNN_Linear_bias(const NNModule module)\\n{\\n return get_bias<torch::nn::Linear>(module);\\n}\\n\\nvoid THSNN_Linear_set_bias(const NNModule module, const Tensor bias)\\n{\\n set_bias<torch::nn::Linear>(module, bias);\\n}\\n\\nTensor THSNN_Linear_weight(const NNModule module)\\n{\\n return get_weight<torch::nn::Linear>(module);\\n}\\n\\nvoid THSNN_Linear_set_weight(const NNModule module, const Tensor weight)\\n{\\n set_weight<torch::nn::Linear>(module, weight);\\n}\\n\\nTensor THSNN_functional_linear(const Tensor input, const Tensor weights, const Tensor bias)\\n{\\n CATCH_TENSOR(bias == nullptr ?\\n torch::nn::functional::linear(*input, *weights) :\\n torch::nn::functional::linear(*input, *weights, *bias));\\n}\\n\\nTensor THSNN_functional_bilinear(const Tensor input1, const Tensor input2, const Tensor weights, const Tensor bias)\\n{\\n CATCH_TENSOR(bias == nullptr ?\\n torch::nn::functional::bilinear(*input1, *input2, *weights) :\\n torch::nn::functional::bilinear(*input1, *input2, *weights, *bias));\\n}\\n\\nNNModule THSNN_Bilinear_ctor(const int64_t input_size_1, const int64_t input_size_2, const int64_t output_size, const bool bias,\\n NNAnyModule* outAsAnyModule)\\n{\\n CATCH_RETURN_NNModule(\\n auto opts = torch::nn::BilinearOptions(input_size_1, input_size_2, output_size).bias(bias);\\n res = create_module<torch::nn::BilinearImpl>(opts, outAsAnyModule);\\n );\\n}\\n\\nTensor THSNN_Bilinear_forward(const NNModule module, const Tensor x1, const Tensor x2)\\n{\\n CATCH_TENSOR((*module)->as<torch::nn::Bilinear>()->forward(*x1, *x2));\\n}\\n\\nTensor THSNN_Bilinear_bias(const NNModule module)\\n{\\n return get_bias<torch::nn::Bilinear>(module);\\n}\\n\\nvoid THSNN_Bilinear_set_bias(const NNModule module, const Tensor bias)\\n{\\n set_bias<torch::nn::Bilinear>(module, bias);\\n}\\n\\nTensor THSNN_Bilinear_weight(const NNModule module)\\n{\\n return get_weight<torch::nn::Bilinear>(module);\\n}\\n\\nvoid THSNN_Bilinear_set_weight(const NNModule module, const Tensor weight)\\n{\\n set_weight<torch::nn::Bilinear>(module, weight);\\n}\\n\\nTensor THSNN_dropout(const Tensor input, const double p, bool training, bool inplace)\\n{\\n auto opts = torch::nn::functional::DropoutFuncOptions()\\n .inplace(inplace)\\n .training(training)\\n .p(p);\\n\\n CATCH_TENSOR(torch::nn::functional::dropout(*input, opts));\\n}\\n\\nTensor THSNN_dropout2d(const Tensor input, const double p, bool training, bool inplace)\\n{\\n auto opts = torch::nn::functional::Dropout2dFuncOptions()\\n .inplace(inplace)\\n .training(training)\\n .p(p);\\n\\n CATCH_TENSOR(torch::nn::functional::dropout2d(*input, opts));\\n}\\n\\nTensor THSNN_dropout3d(const Tensor input, const double p, bool training, bool inplace)\\n{\\n auto opts = torch::nn::functional::Dropout3dFuncOptions()\\n .inplace(inplace)\\n .training(training)\\n .p(p);\\n\\n CATCH_TENSOR(torch::nn::functional::dropout3d(*input, opts));\\n}\\n\\nTensor THSNN_alpha_dropout(const Tensor input, const double p, bool training, bool inplace)\\n{\\n auto opts = torch::nn::functional::AlphaDropoutFuncOptions()\\n .inplace(inplace)\\n .training(training)\\n .p(p);\\n\\n CATCH_TENSOR(torch::nn::functional::alpha_dropout(*input, opts));\\n}\\n\\nTensor THSNN_feature_alpha_dropout(const Tensor input, const double p, bool training, bool inplace)\\n{\\n auto opts = torch::nn::functional::FeatureAlphaDropoutFuncOptions()\\n .inplace(inplace)\\n .training(training)\\n .p(p);\\n\\n CATCH_TENSOR(torch::nn::functional::feature_alpha_dropout(*input, opts));\\n}\\n\\nNNModule THSNN_Dropout_ctor(double probability, bool inplace, NNAnyModule* outAsAnyModule)\\n{\\n CATCH_RETURN_NNModule(\\n auto opts = torch::nn::DropoutOptions(probability).inplace(inplace);\\n res = create_module<torch::nn::DropoutImpl>(opts, outAsAnyModule);\\n );\\n}\\n\\nTensor THSNN_Dropout_forward(const NNModule module, const Tensor tensor)\\n{\\n CATCH_TENSOR((*module)->as<torch::nn::Dropout>()->forward(*tensor));\\n}\\n\\nNNModule THSNN_AlphaDropout_ctor(double probability, bool inplace, NNAnyModule* outAsAnyModule)\\n{\\n CATCH_RETURN_NNModule(\\n auto opts = torch::nn::AlphaDropoutOptions(probability).inplace(inplace);\\n res = create_module<torch::nn::AlphaDropoutImpl>(opts, outAsAnyModule);\\n );\\n}\\n\\nTensor THSNN_AlphaDropout_forward(const NNModule module, const Tensor tensor)\\n{\\n CATCH_TENSOR((*module)->as<torch::nn::AlphaDropout>()->forward(*tensor));\\n}\\n\\nNNModule THSNN_Dropout1d_ctor(double probability, bool inplace, NNAnyModule* outAsAnyModule)\\n{\\n CATCH_RETURN_NNModule(\\n // Creating a Dropout2d instance here is done on purpose. There's no torch::nn::Dropout1d\\n auto opts = torch::nn::Dropout2dOptions(probability).inplace(inplace);\\n res = create_module<torch::nn::Dropout2dImpl>(opts, outAsAnyModule);\\n );\\n}\\n\\nTensor THSNN_Dropout1d_forward(const NNModule module, const Tensor tensor)\\n{\\n auto drop1d = (*module)->as<torch::nn::Dropout2d>();\\n CATCH_TENSOR(drop1d->options.inplace()\\n ? drop1d->forward((*tensor).unsqueeze_(-1)).squeeze_(-1)\\n : drop1d->forward((*tensor).unsqueeze(-1)).squeeze(-1));\\n}\\n\\n\\nNNModule THSNN_Dropout2d_ctor(double probability, bool inplace, NNAnyModule* outAsAnyModule)\\n{\\n CATCH_RETURN_NNModule(\\n auto opts = torch::nn::Dropout2dOptions(probability).inplace(inplace);\\n res = create_module<torch::nn::Dropout2dImpl>(opts, outAsAnyModule);\\n );\\n}\\n\\nTensor THSNN_Dropout2d_forward(const NNModule module, const Tensor tensor)\\n{\\n CATCH_TENSOR((*module)->as<torch::nn::Dropout2d>()->forward(*tensor));\\n}\\n\\nNNModule THSNN_Dropout3d_ctor(double probability, bool inplace, NNAnyModule* outAsAnyModule)\\n{\\n CATCH_RETURN_NNModule(\\n auto opts = torch::nn::Dropout3dOptions(probability).inplace(inplace);\\n res = create_module<torch::nn::Dropout3dImpl>(opts, outAsAnyModule);\\n );\\n}\\n\\nTensor THSNN_Dropout3d_forward(const NNModule module, const Tensor tensor)\\n{\\n CATCH_TENSOR((*module)->as<torch::nn::Dropout3d>()->forward(*tensor));\\n}\\n\\nNNModule THSNN_FeatureAlphaDropout_ctor(double probability, NNAnyModule* outAsAnyModule)\\n{\\n CATCH_RETURN_NNModule(\\n auto opts = torch::nn::FeatureAlphaDropoutOptions(probability);\\n res = create_module<torch::nn::FeatureAlphaDropoutImpl>(opts, outAsAnyModule);\\n );\\n}\\n\\nTensor THSNN_FeatureAlphaDropout_forward(const NNModule module, const Tensor tensor)\\n{\\n CATCH_TENSOR((*module)->as<torch::nn::FeatureAlphaDropout>()->forward(*tensor));\\n}\\n\\nNNModule THSNN_PixelShuffle_ctor(const int64_t upscale_factor, NNAnyModule* outAsAnyModule)\\n{\\n CATCH_RETURN_NNModule(\\n auto opts = torch::nn::PixelShuffleOptions(upscale_factor);\\n res = create_module<torch::nn::PixelShuffleImpl>(opts, outAsAnyModule);\\n );\\n}\\n\\nTensor THSNN_PixelShuffle_forward(const NNModule module, const Tensor tensor)\\n{\\n CATCH_TENSOR((*module)->as<torch::nn::PixelShuffle>()->forward(*tensor));\\n}\\n\\nNNModule THSNN_PixelUnshuffle_ctor(const int64_t downscale_factor, NNAnyModule* outAsAnyModule)\\n{\\n CATCH_RETURN_NNModule(\\n auto opts = torch::nn::PixelUnshuffleOptions(downscale_factor);\\n res = create_module<torch::nn::PixelUnshuffleImpl>(opts, outAsAnyModule);\\n );\\n}\\n\\nTensor THSNN_PixelUnshuffle_forward(const NNModule module, const Tensor tensor)\\n{\\n CATCH_TENSOR((*module)->as<torch::nn::PixelUnshuffle>()->forward(*tensor));\\n}\\n\\ntemplate<typename T>\\nvoid ApplyUpsampleMode(T& opts, const int8_t mode)\\n{\\n if (mode == 0)\\n opts = opts.mode(torch::kNearest);\\n if (mode == 1)\\n opts = opts.mode(torch::kLinear);\\n if (mode == 2)\\n opts = opts.mode(torch::kBilinear);\\n if (mode == 3)\\n opts = opts.mode(torch::kBicubic);\\n if (mode == 4)\\n opts = opts.mode(torch::kTrilinear);\\n}\\n\\ntemplate<typename T>\\nvoid ApplyInterpolateMode(T& opts, const int8_t mode)\\n{\\n if (mode == 0)\\n opts = opts.mode(torch::kNearest);\\n if (mode == 1)\\n opts = opts.mode(torch::kLinear);\\n if (mode == 2)\\n opts = opts.mode(torch::kBilinear);\\n if (mode == 3)\\n opts = opts.mode(torch::kBicubic);\\n if (mode == 4)\\n opts = opts.mode(torch::kTrilinear);\\n if (mode == 5)\\n opts = opts.mode(torch::kArea);\\n}\\n\\nNNModule THSNN_Upsample_ctor(const int64_t* size, const int size_len, const double* scale_factor, const int scale_factor_len, const int8_t mode, const int8_t align_corners, NNAnyModule* outAsAnyModule)\\n{\\n auto opts = torch::nn::UpsampleOptions();\\n // align_corners -- 0=None, 1=true, 2=false\\n if (align_corners != 0)\\n opts.align_corners(align_corners == 1);\\n ApplyUpsampleMode(opts, mode);\\n\\n CATCH_RETURN_NNModule(\\n if (size_len > 0) {\\n std::vector<int64_t> sizes;\\n for (int i = 0; i < size_len; ++i) {\\n sizes.push_back(size[i]);\\n }\\n opts.size(sizes);\\n }\\n if (scale_factor_len > 0) {\\n std::vector<double> scales;\\n for (int i = 0; i < scale_factor_len; ++i) {\\n scales.push_back(scale_factor[i]);\\n }\\n opts.scale_factor(scales);\\n }\\n res = create_module<torch::nn::UpsampleImpl>(opts, outAsAnyModule);\\n );\\n}\\n\\nTensor THSNN_Upsample_forward(const NNModule module, const Tensor tensor)\\n{\\n CATCH_TENSOR((*module)->as<torch::nn::Upsample>()->forward(*tensor));\\n}\\n\\ntemplate<typename T>\\nvoid ApplyPadMode(T& opts, const int64_t padding)\\n{\\n if (padding == 1)\\n opts = opts.mode(torch::kReflect);\\n if (padding == 2)\\n opts = opts.mode(torch::kReplicate);\\n if (padding == 3)\\n opts = opts.mode(torch::kCircular);\\n if (padding == 4)\\n opts = opts.mode(torch::kConstant);\\n}\\n\\ntemplate<typename T>\\nvoid ApplyGridMode(T& opts, const int8_t mode)\\n{\\n if (mode == 0)\\n opts = opts.mode(torch::kNearest);\\n if (mode == 2)\\n opts = opts.mode(torch::kBilinear);\\n // The PyTorch docs say that bicubic should be supported, but the C++\\n // mode type does not allow for it. I'm leaving this in for future use.\\n //if (mode == 3)\\n // opts = opts.mode(torch::kBicubic);\\n}\\n\\ntemplate<typename T>\\nvoid ApplyGridPadMode(T& opts, const int64_t padding)\\n{\\n if (padding == 0)\\n opts = opts.padding_mode(torch::kZeros);\\n if (padding == 1)\\n opts = opts.padding_mode(torch::kReflection);\\n if (padding == 2)\\n opts = opts.padding_mode(torch::kBorder);\\n}\\n\\nTensor THSNN_pad(const Tensor input, const int64_t* pad, const int pad_length, const int8_t mode, const double value)\\n{\\n std::vector<int64_t> padding;\\n for (int i = 0; i < pad_length; ++i) {\\n padding.push_back(pad[i]);\\n }\\n auto opts = torch::nn::functional::PadFuncOptions(padding).value(value);\\n ApplyPadMode(opts, mode);\\n\\n CATCH_TENSOR(torch::nn::functional::pad(*input, opts));\\n}\\n\\nTensor THSNN_grid_sample(const Tensor input, const Tensor grid, const int8_t mode, const int8_t padding_mode, const int8_t align_corners)\\n{\\n auto opts = torch::nn::functional::GridSampleFuncOptions();\\n if (align_corners != 0)\\n opts.align_corners(align_corners == 1);\\n ApplyGridMode(opts, mode);\\n ApplyGridPadMode(opts, padding_mode);\\n CATCH_TENSOR(torch::nn::functional::grid_sample(*input, *grid, opts));\\n}\\n\\nTensor THSNN_affine_grid(const Tensor theta, const int64_t* size, const int size_len, const bool align_corners)\\n{\\n CATCH_TENSOR(torch::nn::functional::affine_grid(*theta, at::ArrayRef<int64_t>(size, size_len), align_corners));\\n}\\n\\n\\nEXPORT_API(Tensor) THSNN_interpolate(const Tensor input, const int64_t* size, const int size_len, const double* scale_factor, const int scale_factor_len, const int8_t mode, const int8_t align_corners, const bool recompute_scale_factor, NNAnyModule* outAsAnyModule)\\n{\\n auto opts = torch::nn::functional::InterpolateFuncOptions().recompute_scale_factor(recompute_scale_factor);\\n // align_corners -- 0=None, 1=true, 2=false\\n if (align_corners != 0)\\n opts.align_corners(align_corners == 1);\\n ApplyInterpolateMode(opts, mode);\\n\\n if (size_len > 0) {\\n std::vector<int64_t> sizes;\\n for (int i = 0; i < size_len; ++i) {\\n sizes.push_back(size[i]);\\n }\\n opts.size(sizes);\\n }\\n if (scale_factor_len > 0) {\\n std::vector<double> scales;\\n for (int i = 0; i < scale_factor_len; ++i) {\\n scales.push_back(scale_factor[i]);\\n }\\n opts.scale_factor(scales);\\n }\\n\\n CATCH_TENSOR(torch::nn::functional::interpolate(*input, opts));\\n}\\n\\nNNModule THSNN_Embedding_ctor(const int64_t num_embeddings, const int64_t embedding_dims,\\n const int64_t padding_idx, bool has_pi, const double max_norm, const bool has_mn, const double norm_type,\\n const bool scale_grad_by_freq, const bool sparse,\\n NNAnyModule* outAsAnyModule)\\n{\\n CATCH_RETURN_NNModule(\\n auto opts = torch::nn::EmbeddingOptions(num_embeddings, embedding_dims)\\n .norm_type(norm_type)\\n .scale_grad_by_freq(scale_grad_by_freq)\\n .sparse(sparse);\\n\\n if (has_pi)\\n opts.padding_idx(padding_idx);\\n if (has_mn)\\n opts.max_norm(max_norm);\\n\\n res = create_module<torch::nn::EmbeddingImpl>(opts, outAsAnyModule);\\n );\\n}\\n\\nNNModule THSNN_Embedding_from_pretrained(const Tensor embeddings, const bool freeze,\\n const int64_t padding_idx, bool has_pi, const double max_norm, const bool has_mn, const double norm_type,\\n const bool scale_grad_by_freq, const bool sparse,\\n NNAnyModule* outAsAnyModule)\\n{\\n CATCH_RETURN_NNModule(\\n auto rows = embeddings->size(0);\\n auto cols = embeddings->size(1);\\n\\n auto opts = torch::nn::EmbeddingOptions(rows, cols)\\n .norm_type(norm_type)\\n .scale_grad_by_freq(scale_grad_by_freq)\\n .sparse(sparse);\\n\\n if (has_pi)\\n opts.padding_idx(padding_idx);\\n if (has_mn)\\n opts.max_norm(max_norm);\\n\\n // Can't use the template function here -- custom logic.\\n auto mod = std::make_shared<torch::nn::EmbeddingImpl>(opts);\\n mod->weight = *embeddings;\\n mod->weight.set_requires_grad(!freeze);\\n\\n // Keep a boxed version of the module in case we add it to a Sequential later (the C++ templating means\\n // a Module can only be boxed to AnyModule at the point its static type is known).\\n if (outAsAnyModule != nullptr)\\n {\\n auto wrapped = std::make_shared<torch::nn::AnyModule>(torch::nn::ModuleHolder<torch::nn::EmbeddingImpl>(*mod));\\n *outAsAnyModule = new std::shared_ptr<torch::nn::AnyModule>(wrapped);\\n }\\n res = new std::shared_ptr<torch::nn::Module>(mod);\\n );\\n}\\n\\nTensor THSNN_Embedding_forward(const NNModule module, const Tensor tensor)\\n{\\n CATCH_TENSOR((*module)->as<torch::nn::Embedding>()->forward(*tensor));\\n}\\n\\nTensor THSNN_Embedding_weight(const NNModule module)\\n{\\n return get_weight<torch::nn::Embedding>(module);\\n}\\n\\nvoid THSNN_Embedding_set_weight(const NNModule module, const Tensor weights)\\n{\\n set_weight<torch::nn::Embedding>(module, weights);\\n}\\n\\ntemplate<typename T>\\nvoid ApplyEmbeddingBagMode(T& opts, const int64_t mode)\\n{\\n if (mode == 0)\\n opts = opts.mode(torch::kSum);\\n if (mode == 1)\\n opts = opts.mode(torch::kMean);\\n if (mode == 2)\\n opts = opts.mode(torch::kMax);\\n}\\n\\nNNModule THSNN_EmbeddingBag_ctor(const int64_t num_embeddings, const int64_t embedding_dims,\\n const double max_norm, const bool has_mn, const double norm_type, const bool scale_grad_by_freq,\\n const int64_t mode, const bool sparse, const bool include_last_offset, const int64_t padding_idx,\\n NNAnyModule* outAsAnyModule)\\n{\\n CATCH_RETURN_NNModule(\\n auto opts = torch::nn::EmbeddingBagOptions(num_embeddings, embedding_dims)\\n .norm_type(norm_type)\\n .scale_grad_by_freq(scale_grad_by_freq)\\n .include_last_offset(include_last_offset)\\n .sparse(sparse);\\n\\n ApplyEmbeddingBagMode(opts, mode);\\n\\n if (has_mn)\\n opts = opts.max_norm(max_norm);\\n if (padding_idx >= 0)\\n opts = opts.padding_idx(padding_idx);\\n\\n res = create_module<torch::nn::EmbeddingBagImpl>(opts, outAsAnyModule);\\n );\\n}\\n\\nNNModule THSNN_EmbeddingBag_from_pretrained(const Tensor embeddings, const bool freeze,\\n const double max_norm, const bool has_mn, const double norm_type, const bool scale_grad_by_freq,\\n const int64_t mode, const bool sparse, const bool include_last_offset, const int64_t padding_idx,\\n NNAnyModule* outAsAnyModule)\\n{\\n CATCH_RETURN_NNModule(\\n auto rows = embeddings->size(0);\\n auto cols = embeddings->size(1);\\n\\n auto opts = torch::nn::EmbeddingBagOptions(rows, cols)\\n .norm_type(norm_type)\\n .scale_grad_by_freq(scale_grad_by_freq)\\n .include_last_offset(include_last_offset)\\n .sparse(sparse);\\n\\n ApplyEmbeddingBagMode(opts, mode);\\n\\n if (has_mn)\\n opts.max_norm(max_norm);\\n if (padding_idx >= 0)\\n opts = opts.padding_idx(padding_idx);\\n\\n // Can't use the template function here -- custom logic.\\n auto mod = std::make_shared<torch::nn::EmbeddingBagImpl>(opts);\\n mod->weight = *embeddings;\\n mod->weight.set_requires_grad(!freeze);\\n\\n // Keep a boxed version of the module in case we add it to a Sequential later (the C++ templating means\\n // a Module can only be boxed to AnyModule at the point its static type is known).\\n if (outAsAnyModule != nullptr)\\n {\\n auto wrapped = std::make_shared<torch::nn::AnyModule>(torch::nn::ModuleHolder<torch::nn::EmbeddingBagImpl>(*mod));\\n *outAsAnyModule = new std::shared_ptr<torch::nn::AnyModule>(wrapped);\\n }\\n res = new std::shared_ptr<torch::nn::Module>(mod);\\n );\\n}\\n\\nTensor THSNN_EmbeddingBag_forward(const NNModule module, const Tensor input, const Tensor offsets, const Tensor per_sample_weights)\\n{\\n if (offsets != nullptr && per_sample_weights != nullptr)\\n {\\n CATCH_TENSOR((*module)->as<torch::nn::EmbeddingBag>()->forward(*input, *offsets, *per_sample_weights));\\n }\\n else if (offsets == nullptr && per_sample_weights != nullptr)\\n {\\n CATCH_TENSOR((*module)->as<torch::nn::EmbeddingBag>()->forward(*input, {}, *per_sample_weights));\\n }\\n else if (offsets != nullptr && per_sample_weights == nullptr)\\n {\\n CATCH_TENSOR((*module)->as<torch::nn::EmbeddingBag>()->forward(*input, *offsets));\\n }\\n else\\n {\\n CATCH_TENSOR((*module)->as<torch::nn::EmbeddingBag>()->forward(*input));\\n }\\n}\\n\\nTensor THSNN_EmbeddingBag_weight(const NNModule module)\\n{\\n return get_weight<torch::nn::EmbeddingBag>(module);\\n}\\n\\nvoid THSNN_EmbeddingBag_set_weight(const NNModule module, const Tensor weights)\\n{\\n set_weight<torch::nn::EmbeddingBag>(module, weights);\\n}\\n\\ntemplate<typename T>\\nvoid ApplyTransformerActivation(T& opts, const int64_t activation)\\n{\\n if (activation == 0)\\n opts = opts.activation(torch::kReLU);\\n if (activation == 1)\\n opts = opts.activation(torch::kGELU);\\n}\\n\\ntemplate<typename T>\\nvoid ApplyRnnActivation(T& opts, const int64_t activation)\\n{\\n if (activation == 0)\\n opts = opts.nonlinearity(torch::kReLU);\\n if (activation == 1)\\n opts = opts.nonlinearity(torch::kTanh);\\n}\\n\\nNNModule THSNN_Transformer_ctor(const int64_t d_model, const int64_t nhead, const int64_t num_encoder_layers, const int64_t num_decoder_layers, const int64_t dim_feedforward, const double dropout, const int64_t activation, NNAnyModule* outAsAnyModule)\\n{\\n CATCH_RETURN_NNModule(\\n auto opts = torch::nn::TransformerOptions(d_model, nhead)\\n .num_encoder_layers(num_encoder_layers)\\n .num_decoder_layers(num_decoder_layers)\\n .dim_feedforward(dim_feedforward)\\n .dropout(dropout);\\n ApplyTransformerActivation(opts, activation);\\n\\n res = create_module<torch::nn::TransformerImpl>(opts, outAsAnyModule);\\n );\\n}\\n\\nTensor THSNN_Transformer_forward(const NNModule module, const Tensor src, const Tensor tgt, const Tensor src_mask, const Tensor tgt_mask, const Tensor memory_mask, const Tensor src_key_padding_mask, const Tensor tgt_key_padding_mask, const Tensor memory_key_padding_mask)\\n{\\n CATCH_TENSOR((*module)->as<torch::nn::Transformer>()->forward(\\n *src,\\n *tgt,\\n (src_mask ? *src_mask : at::Tensor()),\\n (tgt_mask ? *tgt_mask : at::Tensor()),\\n (memory_mask ? *memory_mask : at::Tensor()),\\n (src_key_padding_mask ? *src_key_padding_mask : at::Tensor()),\\n (tgt_key_padding_mask ? *tgt_key_padding_mask : at::Tensor()),\\n (memory_key_padding_mask ? *memory_key_padding_mask : at::Tensor()))\\n );\\n}\\n\\nNNModule THSNN_TransformerEncoderLayer_ctor(const int64_t d_model, const int64_t nhead, const int64_t dim_feedforward, const double dropout, const int64_t activation, NNAnyModule* outAsAnyModule)\\n{\\n CATCH_RETURN_NNModule(\\n auto opts = torch::nn::TransformerEncoderLayerOptions(d_model, nhead)\\n .dim_feedforward(dim_feedforward)\\n .dropout(dropout);\\n ApplyTransformerActivation(opts, activation);\\n\\n res = create_module<torch::nn::TransformerEncoderLayerImpl>(opts, outAsAnyModule);\\n );\\n}\\n\\nTensor THSNN_TransformerEncoderLayer_forward(const NNModule module, const Tensor src, const Tensor src_mask, const Tensor src_key_padding_mask)\\n{\\n CATCH_TENSOR((*module)->as<torch::nn::TransformerEncoderLayer>()->forward(\\n *src,\\n (src_mask ? *src_mask : at::Tensor()),\\n (src_key_padding_mask ? *src_key_padding_mask : at::Tensor()))\\n );\\n}\\n\\nNNModule THSNN_TransformerDecoderLayer_ctor(const int64_t d_model, const int64_t nhead, const int64_t dim_feedforward, const double dropout, const int64_t activation, NNAnyModule* outAsAnyModule)\\n{\\n CATCH_RETURN_NNModule(\\n auto opts = torch::nn::TransformerDecoderLayerOptions(d_model, nhead)\\n .dim_feedforward(dim_feedforward)\\n .dropout(dropout);\\n ApplyTransformerActivation(opts, activation);\\n\\n res = create_module<torch::nn::TransformerDecoderLayerImpl>(opts, outAsAnyModule);\\n );\\n}\\n\\nTensor THSNN_TransformerDecoderLayer_forward(const NNModule module, const Tensor tgt, const Tensor memory, const Tensor tgt_mask, const Tensor memory_mask, const Tensor tgt_key_padding_mask, const Tensor memory_key_padding_mask)\\n{\\n CATCH_TENSOR((*module)->as<torch::nn::TransformerDecoderLayer>()->forward(\\n *tgt,\\n *memory,\\n (tgt_mask ? *tgt_mask : at::Tensor()),\\n (memory_mask ? *memory_mask : at::Tensor()),\\n (tgt_key_padding_mask ? *tgt_key_padding_mask : at::Tensor()),\\n (memory_key_padding_mask ? *memory_key_padding_mask : at::Tensor()))\\n );\\n}\\n\\nNNModule THSNN_MultiheadAttention_ctor(const int64_t embeded_dim, const int64_t num_heads, const double dropout, const bool bias, const bool add_bias_kv, const bool add_zero_attn, const int64_t kdim, const int64_t vdim, NNAnyModule* outAsAnyModule)\\n{\\n CATCH_RETURN_NNModule(\\n auto opts = torch::nn::MultiheadAttentionOptions(embeded_dim, num_heads)\\n .dropout(dropout)\\n .bias(bias)\\n .add_bias_kv(add_bias_kv)\\n .add_zero_attn(add_zero_attn)\\n .kdim(kdim)\\n .vdim(vdim);\\n\\n res = create_module<torch::nn::MultiheadAttentionImpl>(opts, outAsAnyModule);\\n );\\n}\\n\\nvoid THSNN_MultiheadAttention_forward(const NNModule module, const Tensor query, const Tensor key, const Tensor value, const Tensor key_padding_mask, const bool need_weights, const Tensor attn_mask, Tensor& res1, Tensor& res2)\\n{\\n CATCH_TENSORS_2((*module)->as<torch::nn::MultiheadAttention>()->forward(\\n *query,\\n *key,\\n *value,\\n (key_padding_mask ? *key_padding_mask : at::Tensor()),\\n need_weights,\\n (attn_mask ? *attn_mask : at::Tensor()))\\n );\\n}\\n\\nNNModule THSNN_TransformerEncoder_ctor(const NNModule encoder_layer, const int64_t num_layers, NNAnyModule* outAsAnyModule)\\n{\\n CATCH_RETURN_NNModule(\\n auto enc = (*encoder_layer)->as<torch::nn::TransformerEncoderLayer>();\\n auto opts = torch::nn::TransformerEncoderOptions(torch::nn::TransformerEncoderLayer(*enc), num_layers);\\n\\n res = create_module<torch::nn::TransformerEncoderImpl>(opts, outAsAnyModule);\\n );\\n}\\n\\nTensor THSNN_TransformerEncoder_forward(const NNModule module, const Tensor src, const Tensor src_mask, const Tensor src_key_padding_mask)\\n{\\n CATCH_TENSOR((*module)->as<torch::nn::TransformerEncoder>()->forward(\\n *src,\\n (src_mask ? *src_mask : at::Tensor()),\\n (src_key_padding_mask ? *src_key_padding_mask : at::Tensor()))\\n );\\n}\\n\\nNNModule THSNN_TransformerDecoder_ctor(const NNModule decoder_layer, const int64_t num_layers, NNAnyModule* outAsAnyModule)\\n{\\n CATCH_RETURN_NNModule(\\n auto dec = (*decoder_layer)->as<torch::nn::TransformerDecoderLayer>();\\n auto opts = torch::nn::TransformerDecoderOptions(torch::nn::TransformerDecoderLayer(*dec), num_layers);\\n\\n res = create_module<torch::nn::TransformerDecoderImpl>(opts, outAsAnyModule);\\n );\\n}\\n\\nTensor THSNN_TransformerDecoder_forward(const NNModule module, const Tensor tgt, const Tensor memory, const Tensor tgt_mask, const Tensor memory_mask, const Tensor tgt_key_padding_mask, const Tensor memory_key_padding_mask)\\n{\\n CATCH_TENSOR((*module)->as<torch::nn::TransformerDecoder>()->forward(\\n *tgt,\\n *memory,\\n (tgt_mask ? *tgt_mask : at::Tensor()),\\n (memory_mask ? *memory_mask : at::Tensor()),\\n (tgt_key_padding_mask ? *tgt_key_padding_mask : at::Tensor()),\\n (memory_key_padding_mask ? *memory_key_padding_mask : at::Tensor()))\\n );\\n}\\n\\nNNModule THSNN_Flatten_ctor(const int64_t start_dim, const int64_t end_dim, NNAnyModule* outAsAnyModule)\\n{\\n CATCH_RETURN_NNModule(\\n auto opts = torch::nn::FlattenOptions()\\n .start_dim(start_dim)\\n .end_dim(end_dim);\\n\\n res = create_module<torch::nn::FlattenImpl>(opts, outAsAnyModule);\\n );\\n}\\nTensor THSNN_Flatten_forward(const NNModule module, const Tensor tensor)\\n{\\n CATCH_TENSOR((*module)->as<torch::nn::Flatten>()->forward(*tensor));\\n}\\n\\nNNModule THSNN_Unflatten_ctor(const int64_t dim, const int64_t* shape, const int64_t shape_len, NNAnyModule* outAsAnyModule)\\n{\\n CATCH_RETURN_NNModule(\\n std::vector<int64_t> sizes;\\n for (int64_t i = 0; i < shape_len; ++i)\\n {\\n sizes.push_back(shape[i]);\\n }\\n auto opts = torch::nn::UnflattenOptions(dim, sizes);\\n res = create_module<torch::nn::UnflattenImpl>(opts, outAsAnyModule);\\n );\\n}\\n\\nTensor THSNN_Unflatten_forward(const NNModule module, const Tensor tensor)\\n{\\n CATCH_TENSOR((*module)->as<torch::nn::Unflatten>()->forward(*tensor));\\n}\\n\\nNNModule THSNN_CosineSimilarity_ctor(const int64_t dim, double eps, NNAnyModule* outAsAnyModule)\\n{\\n CATCH_RETURN_NNModule(\\n auto opts = torch::nn::CosineSimilarityOptions()\\n .dim(dim)\\n .eps(eps);\\n\\n res = create_module<torch::nn::CosineSimilarityImpl>(opts, outAsAnyModule);\\n );\\n\\n}\\n\\nTensor THSNN_CosineSimilarity_forward(const NNModule module, const Tensor input1, const Tensor input2)\\n{\\n CATCH_TENSOR((*module)->as<torch::nn::CosineSimilarity>()->forward(*input1, *input2));\\n}\\n\\nNNModule THSNN_PairwiseDistance_ctor(double p, double eps, bool keep_dim, NNAnyModule* outAsAnyModule)\\n{\\n CATCH_RETURN_NNModule(\\n auto opts = torch::nn::PairwiseDistanceOptions()\\n .p(p)\\n .eps(eps)\\n .keepdim(keep_dim);\\n\\n res = create_module<torch::nn::PairwiseDistanceImpl>(opts, outAsAnyModule);\\n );\\n\\n}\\n\\nTensor THSNN_PairwiseDistance_forward(const NNModule module, const Tensor input1, const Tensor input2)\\n{\\n CATCH_TENSOR((*module)->as<torch::nn::PairwiseDistance>()->forward(*input1, *input2));\\n}\\n\\nNNModule THSNN_RNN_ctor(const int64_t input_size, const int64_t hidden_size, const int64_t num_layers, const int64_t nonlinearity, const bool bias, const bool batchFirst, const double dropout, const bool bidirectional, NNAnyModule* outAsAnyModule)\\n{\\n CATCH_RETURN_NNModule(\\n auto opts = torch::nn::RNNOptions(input_size, hidden_size)\\n .num_layers(num_layers)\\n .bias(bias)\\n .batch_first(batchFirst)\\n .dropout(dropout)\\n .bidirectional(bidirectional);\\n\\n ApplyRnnActivation(opts, nonlinearity);\\n\\n res = create_module<torch::nn::RNNImpl>(opts, outAsAnyModule);\\n );\\n}\\n\\nTensor THSNN_RNN_forward(const NNModule module, const Tensor input1, const Tensor input2, Tensor* h_n)\\n{\\n Tensor output = nullptr;\\n *h_n = nullptr;\\n CATCH(\\n auto result = (*module)->as<torch::nn::RNN>()->forward(*input1, (input2 ? *input2 : at::Tensor()));\\n output = new torch::Tensor(std::get<0>(result));\\n *h_n = new torch::Tensor(std::get<1>(result));\\n );\\n return output;\\n}\\n\\nPackedSequence THSNN_RNN_forward_with_packed_input(const NNModule module, const PackedSequence input1, const Tensor input2, Tensor* h_n)\\n{\\n PackedSequence output = nullptr;\\n *h_n = nullptr;\\n CATCH(\\n auto result = (*module)->as<torch::nn::RNN>()->forward_with_packed_input(*input1, (input2 ? *input2 : at::Tensor()));\\n output = new torch::nn::utils::rnn::PackedSequence(std::get<0>(result));\\n *h_n = new torch::Tensor(std::get<1>(result));\\n );\\n return output;\\n}\\n\\nvoid THSNN_RNN_flatten_parameters(const NNModule module)\\n{\\n CATCH(\\n (*module)->as<torch::nn::RNN>()->flatten_parameters();\\n );\\n}\\n\\nTensor THSNN_RNN_bias_ih(const NNModule module, const int64_t idx)\\n{\\n return get_bias_ih<torch::nn::RNN>(module, idx);\\n}\\n\\nvoid THSNN_RNN_set_bias_ih(const NNModule module, const Tensor bias, const int64_t idx)\\n{\\n set_bias_ih<torch::nn::RNN>(module, bias, idx);\\n}\\n\\nTensor THSNN_RNN_weight_ih(const NNModule module, const int64_t idx)\\n{\\n return get_weight_ih<torch::nn::RNN>(module, idx);\\n}\\n\\nvoid THSNN_RNN_set_weight_ih(const NNModule module, const Tensor weight, const int64_t idx)\\n{\\n set_weight_ih<torch::nn::RNN>(module, weight, idx);\\n}\\n\\nTensor THSNN_RNN_bias_hh(const NNModule module, const int64_t idx)\\n{\\n return get_bias_hh<torch::nn::RNN>(module, idx);\\n}\\n\\nvoid THSNN_RNN_set_bias_hh(const NNModule module, const Tensor bias, const int64_t idx)\\n{\\n set_bias_hh<torch::nn::RNN>(module, bias, idx);\\n}\\n\\nTensor THSNN_RNN_weight_hh(const NNModule module, const int64_t idx)\\n{\\n return get_weight_hh<torch::nn::RNN>(module, idx);\\n}\\n\\nvoid THSNN_RNN_set_weight_hh(const NNModule module, const Tensor weight, const int64_t idx)\\n{\\n set_weight_hh<torch::nn::RNN>(module, weight, idx);\\n}\\n\\n\\nNNModule THSNN_GRU_ctor(const int64_t input_size, const int64_t hidden_size, const int64_t num_layers, const bool bias, const bool batchFirst, const double dropout, const bool bidirectional, NNAnyModule* outAsAnyModule)\\n{\\n CATCH_RETURN_NNModule(\\n auto opts = torch::nn::GRUOptions(input_size, hidden_size)\\n .num_layers(num_layers)\\n .bias(bias)\\n .batch_first(batchFirst)\\n .dropout(dropout)\\n .bidirectional(bidirectional);\\n\\n res = create_module<torch::nn::GRUImpl>(opts, outAsAnyModule);\\n );\\n}\\n\\nTensor THSNN_GRU_forward(const NNModule module, const Tensor input1, const Tensor input2, Tensor* h_n)\\n{\\n Tensor output = nullptr;\\n *h_n = nullptr;\\n CATCH(\\n auto result = (*module)->as<torch::nn::GRU>()->forward(*input1, (input2 ? *input2 : at::Tensor()));\\n output = new torch::Tensor(std::get<0>(result));\\n *h_n = new torch::Tensor(std::get<1>(result));\\n );\\n return output;\\n}\\n\\nPackedSequence THSNN_GRU_forward_with_packed_input(const NNModule module, const PackedSequence input1, const Tensor input2, Tensor* h_n)\\n{\\n PackedSequence output = nullptr;\\n *h_n = nullptr;\\n CATCH(\\n auto result = (*module)->as<torch::nn::GRU>()->forward_with_packed_input(*input1, (input2 ? *input2 : at::Tensor()));\\n output = new torch::nn::utils::rnn::PackedSequence(std::get<0>(result));\\n *h_n = new torch::Tensor(std::get<1>(result));\\n );\\n return output;\\n}\\n\\nvoid THSNN_GRU_flatten_parameters(const NNModule module)\\n{\\n CATCH(\\n (*module)->as<torch::nn::GRU>()->flatten_parameters();\\n );\\n}\\n\\nNNModule THSNN_LSTM_ctor(const int64_t input_size, const int64_t hidden_size, const int64_t num_layers, const bool bias, const bool batchFirst, const double dropout, const bool bidirectional, NNAnyModule* outAsAnyModule)\\n{\\n CATCH_RETURN_NNModule(\\n auto opts = torch::nn::LSTMOptions(input_size, hidden_size)\\n .num_layers(num_layers)\\n .bias(bias)\\n .batch_first(batchFirst)\\n .dropout(dropout)\\n .bidirectional(bidirectional);\\n\\n res = create_module<torch::nn::LSTMImpl>(opts, outAsAnyModule);\\n );\\n}\\n\\nTensor THSNN_LSTM_forward(const NNModule module, const Tensor input1, const Tensor h0, const Tensor c0, Tensor* h_n, Tensor* c_n)\\n{\\n const std::tuple<at::Tensor, at::Tensor>& second_arg = (h0 == nullptr || c0 == nullptr) ? std::make_tuple(at::Tensor(), at::Tensor()) : std::make_tuple(*h0, *c0);\\n\\n Tensor output = nullptr;\\n *h_n = nullptr;\\n *c_n = nullptr;\\n CATCH(\\n auto result = (*module)->as<torch::nn::LSTM>()->forward(*input1, second_arg);\\n output = new torch::Tensor(std::get<0>(result));\\n *h_n = new torch::Tensor(std::get<0>(std::get<1>(result)));\\n *c_n = new torch::Tensor(std::get<1>(std::get<1>(result)));\\n );\\n return output;\\n}\\n\\nPackedSequence THSNN_LSTM_forward_with_packed_input(const NNModule module, const PackedSequence input1, const Tensor h0, const Tensor c0, Tensor* h_n, Tensor* c_n)\\n{\\n const std::tuple<at::Tensor, at::Tensor>& second_arg = (h0 == nullptr || c0 == nullptr) ? std::make_tuple(at::Tensor(), at::Tensor()) : std::make_tuple(*h0, *c0);\\n\\n PackedSequence output = nullptr;\\n *h_n = nullptr;\\n *c_n = nullptr;\\n CATCH(\\n auto result = (*module)->as<torch::nn::LSTM>()->forward_with_packed_input(*input1, second_arg);\\n output = new torch::nn::utils::rnn::PackedSequence(std::get<0>(result));\\n *h_n = new torch::Tensor(std::get<0>(std::get<1>(result)));\\n *c_n = new torch::Tensor(std::get<1>(std::get<1>(result)));\\n );\\n return output;\\n}\\n\\nvoid THSNN_LSTM_flatten_parameters(const NNModule module)\\n{\\n CATCH(\\n (*module)->as<torch::nn::LSTM>()->flatten_parameters();\\n );\\n}\\n\\nNNModule THSNN_RNNCell_ctor(const int64_t input_size, const int64_t hidden_size, const int64_t nonlinearity, const bool bias, NNAnyModule* outAsAnyModule)\\n{\\n CATCH_RETURN_NNModule(\\n auto opts = torch::nn::RNNCellOptions(input_size, hidden_size)\\n .bias(bias);\\n\\n ApplyRnnActivation(opts, nonlinearity);\\n\\n res = create_module<torch::nn::RNNCellImpl>(opts, outAsAnyModule);\\n );\\n}\\n\\nTensor THSNN_RNNCell_forward(const NNModule module, const Tensor input1, const Tensor h0)\\n{\\n CATCH_TENSOR((*module)->as<torch::nn::RNNCell>()->forward(*input1, (h0 ? *h0 : at::Tensor())));\\n}\\n\\nTensor THSNN_RNNCell_bias_ih(const NNModule module)\\n{\\n return get_bias_ih<torch::nn::RNNCell>(module);\\n}\\n\\nvoid THSNN_RNNCell_set_bias_ih(const NNModule module, const Tensor bias)\\n{\\n set_bias_ih<torch::nn::RNNCell>(module, bias);\\n}\\n\\nTensor THSNN_RNNCell_weight_ih(const NNModule module)\\n{\\n return get_weight_ih<torch::nn::RNNCell>(module);\\n}\\n\\nvoid THSNN_RNNCell_set_weight_ih(const NNModule module, const Tensor weight)\\n{\\n set_weight_ih<torch::nn::RNNCell>(module, weight);\\n}\\n\\nTensor THSNN_RNNCell_bias_hh(const NNModule module)\\n{\\n return get_bias_hh<torch::nn::RNNCell>(module);\\n}\\n\\nvoid THSNN_RNNCell_set_bias_hh(const NNModule module, const Tensor bias)\\n{\\n set_bias_hh<torch::nn::RNNCell>(module, bias);\\n}\\n\\nTensor THSNN_RNNCell_weight_hh(const NNModule module)\\n{\\n return get_weight_hh<torch::nn::RNNCell>(module);\\n}\\n\\nvoid THSNN_RNNCell_set_weight_hh(const NNModule module, const Tensor weight)\\n{\\n set_weight_hh<torch::nn::RNNCell>(module, weight);\\n}\\n\\nNNModule THSNN_GRUCell_ctor(const int64_t input_size, const int64_t hidden_size, const bool bias, NNAnyModule* outAsAnyModule)\\n{\\n CATCH_RETURN_NNModule(\\n auto opts = torch::nn::GRUCellOptions(input_size, hidden_size)\\n .bias(bias);\\n\\n res = create_module<torch::nn::GRUCellImpl>(opts, outAsAnyModule);\\n );\\n}\\n\\nTensor THSNN_GRUCell_forward(const NNModule module, const Tensor input1, const Tensor h0)\\n{\\n CATCH_TENSOR((*module)->as<torch::nn::GRUCell>()->forward(*input1, (h0 ? *h0 : at::Tensor())));\\n}\\n\\nTensor THSNN_GRUCell_bias_ih(const NNModule module)\\n{\\n return get_bias_ih<torch::nn::GRUCell>(module);\\n}\\n\\nvoid THSNN_GRUCell_set_bias_ih(const NNModule module, const Tensor bias)\\n{\\n set_bias_ih<torch::nn::GRUCell>(module, bias);\\n}\\n\\nTensor THSNN_GRUCell_weight_ih(const NNModule module)\\n{\\n return get_weight_ih<torch::nn::GRUCell>(module);\\n}\\n\\nvoid THSNN_GRUCell_set_weight_ih(const NNModule module, const Tensor weight)\\n{\\n set_weight_ih<torch::nn::GRUCell>(module, weight);\\n}\\n\\nTensor THSNN_GRUCell_bias_hh(const NNModule module)\\n{\\n return get_bias_hh<torch::nn::GRUCell>(module);\\n}\\n\\nvoid THSNN_GRUCell_set_bias_hh(const NNModule module, const Tensor bias)\\n{\\n set_bias_hh<torch::nn::GRUCell>(module, bias);\\n}\\n\\nTensor THSNN_GRUCell_weight_hh(const NNModule module)\\n{\\n return get_weight_hh<torch::nn::GRUCell>(module);\\n}\\n\\nvoid THSNN_GRUCell_set_weight_hh(const NNModule module, const Tensor weight)\\n{\\n set_weight_hh<torch::nn::GRUCell>(module, weight);\\n}\\n\\nNNModule THSNN_LSTMCell_ctor(const int64_t input_size, const int64_t hidden_size, const bool bias, NNAnyModule* outAsAnyModule)\\n{\\n CATCH_RETURN_NNModule(\\n auto opts = torch::nn::LSTMCellOptions(input_size, hidden_size)\\n .bias(bias);\\n\\n res = create_module<torch::nn::LSTMCellImpl>(opts, outAsAnyModule);\\n );\\n}\\n\\nTensor THSNN_LSTMCell_forward(const NNModule module, const Tensor input1, const Tensor h0, const Tensor c0, Tensor* c_n)\\n{\\n const std::tuple<at::Tensor, at::Tensor>& second_arg = (h0 == nullptr || c0 == nullptr) ? std::make_tuple(at::Tensor(), at::Tensor()) : std::make_tuple(*h0, *c0);\\n\\n Tensor output;\\n CATCH(\\n auto result = (*module)->as<torch::nn::LSTMCell>()->forward(*input1, second_arg);\\n output = new torch::Tensor(std::get<0>(result));\\n *c_n = new torch::Tensor(std::get<1>(result));\\n );\\n\\n return output;\\n}\\n\\nTensor THSNN_LSTMCell_bias_ih(const NNModule module)\\n{\\n return get_bias_ih<torch::nn::LSTMCell>(module);\\n}\\n\\nvoid THSNN_LSTMCell_set_bias_ih(const NNModule module, const Tensor bias)\\n{\\n set_bias_ih<torch::nn::LSTMCell>(module, bias);\\n}\\n\\nTensor THSNN_LSTMCell_weight_ih(const NNModule module)\\n{\\n return get_weight_ih<torch::nn::LSTMCell>(module);\\n}\\n\\nvoid THSNN_LSTMCell_set_weight_ih(const NNModule module, const Tensor weight)\\n{\\n set_weight_ih<torch::nn::LSTMCell>(module, weight);\\n}\\n\\nTensor THSNN_LSTMCell_bias_hh(const NNModule module)\\n{\\n return get_bias_hh<torch::nn::LSTMCell>(module);\\n}\\n\\nvoid THSNN_LSTMCell_set_bias_hh(const NNModule module, const Tensor bias)\\n{\\n set_bias_hh<torch::nn::LSTMCell>(module, bias);\\n}\\n\\nTensor THSNN_LSTMCell_weight_hh(const NNModule module)\\n{\\n return get_weight_hh<torch::nn::LSTMCell>(module);\\n}\\n\\nvoid THSNN_LSTMCell_set_weight_hh(const NNModule module, const Tensor weight)\\n{\\n set_weight_hh<torch::nn::LSTMCell>(module, weight);\\n}\\n\\n\\nNNModule THSNN_Sequential_ctor( /* NNAnyModule *submodules, const int length */ )\\n{\\n //std::vector<torch::nn::NamedAnyModule> modules;\\n //for (int i = 0; i < length; i++)\\n //{\\n //\\tmodules.push_back(*(*submodules[i])->as<torch::nn::NamedAnyModule>());\\n //}\\n\\n auto mod = std::make_shared<torch::nn::SequentialImpl>( /* std::begin(modules), std::end(modules) */ );\\n return new std::shared_ptr<torch::nn::Module>(mod);\\n}\\n\\nvoid THSNN_Sequential_push_back(const NNModule module, const char *name, const NNAnyModule submodule)\\n{\\n CATCH (\\n (*module)->as<torch::nn::Sequential>()->push_back(name, *(*submodule));\\n )\\n}\\n\\nTensor THSNN_Sequential_forward(const NNModule module, const Tensor tensor)\\n{\\n CATCH_TENSOR((*module)->as<torch::nn::Sequential>()->forward(*tensor));\\n}\\n\\nTensor THSNN_one_hot(const Tensor self, const int64_t num_classes)\\n{\\n CATCH_RETURN_Tensor(\\n res = ResultTensor(torch::nn::functional::one_hot(*self, num_classes));\\n )\\n}\\n\\nTensor THSNN_PackedSequence_data(PackedSequence sequence)\\n{\\n CATCH_TENSOR(sequence->data());\\n}\\n\\nTensor THSNN_PackedSequence_batch_sizes(PackedSequence sequence)\\n{\\n CATCH_TENSOR(sequence->batch_sizes());\\n}\\n\\nTensor THSNN_PackedSequence_sorted_indices(PackedSequence sequence)\\n{\\n CATCH_TENSOR(sequence->sorted_indices());\\n}\\n\\nTensor THSNN_PackedSequence_unsorted_indices(PackedSequence sequence)\\n{\\n CATCH_TENSOR(sequence->unsorted_indices());\\n}\\n\\nvoid THSNN_PackedSequence_dispose(PackedSequence sequence)\\n{\\n delete sequence;\\n}\\n\\nPackedSequence THSNN_pack_padded_sequence(Tensor input, Tensor lengths, bool batch_first, bool enforce_sorted)\\n{\\n CATCH_RETURN(\\n torch::nn::utils::rnn::PackedSequence*,\\n nullptr,\\n new torch::nn::utils::rnn::PackedSequence(\\n torch::nn::utils::rnn::pack_padded_sequence(\\n *input, *lengths, batch_first, enforce_sorted)));\\n}\\n\\nvoid THSNN_pad_packed_sequence(PackedSequence sequence, bool batch_first, double padding_value, int64_t total_length, Tensor* res1_, Tensor* res2_)\\n{\\n Tensor& res1 = *res1_;\\n Tensor& res2 = *res2_;\\n CATCH_TENSORS_2(\\n torch::nn::utils::rnn::pad_packed_sequence(\\n *sequence, batch_first, padding_value,\\n total_length == -1 ? torch::nullopt : c10::optional<int64_t>(total_length)));\\n}\\n\\nTensor THSNN_pad_sequence(const Tensor* sequences, const int sequences_len, bool batch_first, double padding_value)\\n{\\n CATCH_TENSOR(\\n torch::nn::utils::rnn::pad_sequence(\\n toTensors<at::Tensor>((torch::Tensor**)sequences, sequences_len),\\n batch_first, padding_value));\\n}\\n\\nPackedSequence THSNN_pack_sequence(const Tensor* sequences, int sequences_len, bool enforce_sorted)\\n{\\n CATCH_RETURN(\\n torch::nn::utils::rnn::PackedSequence*,\\n nullptr,\\n new torch::nn::utils::rnn::PackedSequence(\\n torch::nn::utils::rnn::pack_sequence(\\n toTensors<at::Tensor>((torch::Tensor**)sequences, sequences_len),\\n enforce_sorted)));\\n}\\n\\nTensor THSNN_fold(const Tensor input, const int64_t out1, const int64_t out2, const int64_t kernel1, const int64_t kernel2, const int64_t stride1, const int64_t stride2, const int64_t pad1, const int64_t pad2, const int64_t dil1, const int64_t dil2)\\n{\\n auto opts =\\n torch::nn::functional::FoldFuncOptions({ out1, out2 }, {kernel1, kernel2})\\n .dilation({dil1, dil2})\\n .padding({pad1, pad2})\\n .stride({ stride1, stride2 });\\n\\n CATCH_TENSOR(torch::nn::functional::fold(*input, opts));\\n}\\n\\n\\n\\nTensor THSNN_unfold(const Tensor input, const int64_t kernel1, const int64_t kernel2, const int64_t stride1, const int64_t stride2, const int64_t pad1, const int64_t pad2, const int64_t dil1, const int64_t dil2)\\n{\\n auto opts =\\n torch::nn::functional::UnfoldFuncOptions({ kernel1, kernel2 })\\n .dilation({ dil1, dil2 })\\n .padding({ pad1, pad2 })\\n .stride({ stride1, stride2 });\\n\\n CATCH_TENSOR(torch::nn::functional::unfold(*input, opts));\\n}\\n\\nTensor THSNN_scaled_dot_product_attention(const Tensor query, const Tensor key, const Tensor value, const Tensor attention_mask, double p, bool casual)\\n{\\n auto mask = attention_mask == nullptr ? c10::nullopt : c10::optional<at::Tensor>(*attention_mask);\\n\\n CATCH_TENSOR(torch::scaled_dot_product_attention(*query, *key, *value, mask, p, casual));\\n}\"\n },\n {\n \"alpha_fraction\": 0.5144138932228088,\n \"alphanum_fraction\": 0.5206185579299927,\n \"avg_line_length\": 47.957942962646484,\n \"blob_id\": \"3e2a4ebee17073f79e61ecc403dfa0f8ce7379d6\",\n \"content_id\": \"7b9a8cd2a8344acc097dc7239bc8ccdd77ae19a0\",\n \"detected_licenses\": [\n \"MIT\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"C#\",\n \"length_bytes\": 10476,\n \"license_type\": \"permissive\",\n \"max_line_length\": 218,\n \"num_lines\": 214,\n \"path\": \"/src/TorchVision/Ops/Misc.cs\",\n \"repo_name\": \"NiklasGustafsson/TorchSharp\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"// Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information.\\n\\n// A number of implementation details in this file have been translated from the Python version of torchvision,\\n// largely located in the files found in this folder:\\n//\\n// https://github.com/pytorch/vision/blob/a4f53308b2d0f1aa9191686e326f45c26053f686/torchvision/ops/misc.py\\n//\\n// The origin has the following copyright notice and license:\\n//\\n// https://github.com/pytorch/vision/blob/main/LICENSE\\n//\\n\\nusing System;\\nusing System.Collections.Generic;\\nusing System.Runtime.CompilerServices;\\nusing static TorchSharp.torch;\\n\\n#nullable enable\\nnamespace TorchSharp\\n{\\n public static partial class torchvision\\n {\\n public static partial class ops\\n {\\n private static nn.Module<Tensor, Tensor> ConvNormActivation(\\n long in_channels,\\n long out_channels,\\n long kernel_size = 3,\\n long stride = 1,\\n long? padding = null,\\n long groups = 1,\\n Func<long, nn.Module<Tensor, Tensor>>? norm_layer = null,\\n Func<bool, nn.Module<Tensor, Tensor>>? activation_layer = null,\\n long dilation = 1,\\n bool inplace = true,\\n bool? bias = null,\\n int rank = 2)\\n {\\n if (padding == null) {\\n padding = (kernel_size - 1) / 2 * dilation;\\n }\\n\\n if (bias == null) {\\n bias = norm_layer == null;\\n }\\n\\n var layers = new List<nn.Module<Tensor, Tensor>>();\\n if (rank == 2) {\\n layers.Add(\\n nn.Conv2d(\\n in_channels,\\n out_channels,\\n kernelSize: kernel_size,\\n stride: stride,\\n padding: padding.Value,\\n dilation: dilation,\\n groups: groups,\\n bias: bias.Value));\\n } else if (rank == 3) {\\n layers.Add(\\n nn.Conv3d(\\n in_channels,\\n out_channels,\\n kernelSize: kernel_size,\\n stride: stride,\\n padding: padding.Value,\\n dilation: dilation,\\n groups: groups,\\n bias: bias.Value));\\n } else {\\n throw new ArgumentOutOfRangeException(\\\"rank must be 2 or 3.\\\");\\n }\\n\\n if (norm_layer != null) {\\n layers.Add(norm_layer(out_channels));\\n }\\n\\n if (activation_layer != null) {\\n layers.Add(activation_layer(inplace));\\n }\\n return nn.Sequential(layers);\\n }\\n\\n /// <summary>\\n /// Configurable block used for Convolution2d-Normalization-Activation blocks.\\n /// </summary>\\n /// <param name=\\\"in_channels\\\">Number of channels in the input image</param>\\n /// <param name=\\\"out_channels\\\">Number of channels produced by the Convolution-Normalization-Activation block</param>\\n /// <param name=\\\"kernel_size\\\">Size of the convolving kernel.</param>\\n /// <param name=\\\"stride\\\">Stride of the convolution.</param>\\n /// <param name=\\\"padding\\\">Padding added to all four sides of the input. Default: null, in which case it will calculated as ``padding = (kernel_size - 1) // 2 * dilation``</param>\\n /// <param name=\\\"groups\\\">Number of blocked connections from input channels to output channels.</param>\\n /// <param name=\\\"norm_layer\\\">Norm layer that will be stacked on top of the convolution layer. If ``null`` this layer wont be used.</param>\\n /// <param name=\\\"activation_layer\\\">Activation function which will be stacked on top of the normalization layer (if not null), otherwise on top of the conv layer. If ``null`` this layer wont be used.</param>\\n /// <param name=\\\"dilation\\\">Spacing between kernel elements.</param>\\n /// <param name=\\\"inplace\\\">Parameter for the activation layer, which can optionally do the operation in-place.</param>\\n /// <param name=\\\"bias\\\">Whether to use bias in the convolution layer. By default, biases are included if ``norm_layer is null``.</param>\\n public static nn.Module<Tensor, Tensor> Conv2dNormActivation(\\n long in_channels,\\n long out_channels,\\n long kernel_size = 3,\\n long stride = 1,\\n long? padding = null,\\n long groups = 1,\\n Func<long, nn.Module<Tensor, Tensor>>? norm_layer = null,\\n Func<bool, nn.Module<Tensor, Tensor>>? activation_layer = null,\\n long dilation = 1,\\n bool inplace = true,\\n bool? bias = null)\\n {\\n return ConvNormActivation(\\n in_channels,\\n out_channels,\\n kernel_size,\\n stride,\\n padding,\\n groups,\\n norm_layer,\\n activation_layer,\\n dilation,\\n inplace,\\n bias,\\n rank: 2);\\n }\\n\\n /// <summary>\\n /// Configurable block used for Convolution3d-Normalization-Activation blocks.\\n /// </summary>\\n /// <param name=\\\"in_channels\\\">Number of channels in the input image</param>\\n /// <param name=\\\"out_channels\\\">Number of channels produced by the Convolution-Normalization-Activation block</param>\\n /// <param name=\\\"kernel_size\\\">Size of the convolving kernel.</param>\\n /// <param name=\\\"stride\\\">Stride of the convolution.</param>\\n /// <param name=\\\"padding\\\">Padding added to all four sides of the input. Default: null, in which case it will calculated as ``padding = (kernel_size - 1) // 2 * dilation``</param>\\n /// <param name=\\\"groups\\\">Number of blocked connections from input channels to output channels.</param>\\n /// <param name=\\\"norm_layer\\\">Norm layer that will be stacked on top of the convolution layer. If ``null`` this layer wont be used.</param>\\n /// <param name=\\\"activation_layer\\\">Activation function which will be stacked on top of the normalization layer (if not null), otherwise on top of the conv layer. If ``null`` this layer wont be used.</param>\\n /// <param name=\\\"dilation\\\">Spacing between kernel elements.</param>\\n /// <param name=\\\"inplace\\\">Parameter for the activation layer, which can optionally do the operation in-place.</param>\\n /// <param name=\\\"bias\\\">Whether to use bias in the convolution layer. By default, biases are included if ``norm_layer is null``.</param>\\n public static nn.Module<Tensor, Tensor> Conv3dNormActivation(\\n long in_channels,\\n long out_channels,\\n long kernel_size = 3,\\n long stride = 1,\\n long? padding = null,\\n long groups = 1,\\n Func<long, nn.Module<Tensor, Tensor>>? norm_layer = null,\\n Func<bool, nn.Module<Tensor, Tensor>>? activation_layer = null,\\n long dilation = 1,\\n bool inplace = true,\\n bool? bias = null)\\n {\\n return ConvNormActivation(\\n in_channels,\\n out_channels,\\n kernel_size,\\n stride,\\n padding,\\n groups,\\n norm_layer,\\n activation_layer,\\n dilation,\\n inplace,\\n bias,\\n rank: 3);\\n }\\n\\n\\n /// <summary>\\n /// This block implements the multi-layer perceptron (MLP) module.\\n /// </summary>\\n /// <param name=\\\"in_channels\\\">Number of channels in the input image</param>\\n /// <param name=\\\"hidden_channels\\\">List of the hidden channel dimensions</param>\\n /// <param name=\\\"norm_layer\\\">Norm layer that will be stacked on top of the linear layer. If null, will be ignored</param>\\n /// <param name=\\\"activation_layer\\\">Activation function which will be stacked on top of the normalization layer. Defaults to ReLU.</param>\\n /// <param name=\\\"inplace\\\">Parameter for the activation layer, which can optionally do the operation in-place.</param>\\n /// <param name=\\\"bias\\\">Whether to use bias in the linear and dropout layers.</param>\\n /// <param name=\\\"dropout\\\">The probability for the dropout layer.</param>\\n /// <returns></returns>\\n public static Modules.Sequential MLP(\\n long in_channels,\\n IList<int> hidden_channels,\\n Func<int, nn.Module<Tensor, Tensor>>? norm_layer = null,\\n Func<nn.Module<Tensor, Tensor>>? activation_layer = null,\\n bool inplace = false,\\n bool bias = true,\\n double dropout = 0.0\\n )\\n {\\n if (activation_layer == null) {\\n activation_layer = () => nn.ReLU(inplace);\\n }\\n\\n var layers = new List<nn.Module<Tensor, Tensor>>();\\n var in_dim = in_channels;\\n for (int i = 0; i < hidden_channels.Count-1; i++) {\\n var hidden_dim = hidden_channels[i];\\n layers.Add(nn.Linear(in_dim, hidden_dim, hasBias: bias));\\n if (norm_layer != null) {\\n layers.Add(norm_layer(hidden_dim));\\n }\\n layers.Add(activation_layer());\\n layers.Add(nn.Dropout(dropout, inplace));\\n in_dim = hidden_dim;\\n }\\n\\n layers.Add(nn.Linear(in_dim, hidden_channels[hidden_channels.Count-1], hasBias: bias));\\n layers.Add(nn.Dropout(dropout, inplace));\\n\\n return nn.Sequential(layers);\\n }\\n }\\n }\\n}\"\n },\n {\n \"alpha_fraction\": 0.49920737743377686,\n \"alphanum_fraction\": 0.5010274648666382,\n \"avg_line_length\": 42.67179489135742,\n \"blob_id\": \"a078e914274425b9efeaab77754b2f5737725b3f\",\n \"content_id\": \"59b98e91b1d01a95029e2e8072c63105b6d259c0\",\n \"detected_licenses\": [\n \"MIT\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"C#\",\n \"length_bytes\": 34066,\n \"license_type\": \"permissive\",\n \"max_line_length\": 178,\n \"num_lines\": 780,\n \"path\": \"/src/TorchSharp/Special.cs\",\n \"repo_name\": \"NiklasGustafsson/TorchSharp\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"// Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information.\\nusing System;\\nusing static TorchSharp.PInvoke.NativeMethods;\\n\\nnamespace TorchSharp\\n{\\n public static partial class torch\\n {\\n public static class special\\n {\\n /// <summary>\\n /// Airy function.\\n /// </summary>\\n /// <param name=\\\"input\\\">Input tensor</param>\\n /// <param name=\\\"out\\\">Optional output tensor, will be modified if present.</param>\\n /// <returns></returns>\\n public static Tensor airy_ai(Tensor input, Tensor @out = null)\\n {\\n var res = @out is null ?\\n THSSpecial_airy_ai(input.Handle) :\\n THSSpecial_airy_ai_out(input.Handle, @out.Handle);\\n if (res == IntPtr.Zero)\\n torch.CheckForErrors();\\n return new Tensor(res);\\n }\\n\\n /// <summary>\\n /// Bessel function of the first kind of order 0.\\n /// </summary>\\n /// <param name=\\\"input\\\">The input tensor</param>\\n /// <param name=\\\"out\\\">An optional output tensor, which will be modified if present.</param>\\n public static Tensor bessel_j0(Tensor input, Tensor @out = null)\\n {\\n var res = @out is null ?\\n THSSpecial_bessel_j0(input.Handle) :\\n THSSpecial_bessel_j0_out(input.Handle, @out.Handle);\\n if (res == IntPtr.Zero)\\n torch.CheckForErrors();\\n return new Tensor(res);\\n }\\n\\n /// <summary>\\n /// Bessel function of the first kind of order 1.\\n /// </summary>\\n /// <param name=\\\"input\\\">The input tensor</param>\\n /// <param name=\\\"out\\\">An optional output tensor, which will be modified if present.</param>\\n public static Tensor bessel_j1(Tensor input, Tensor @out = null)\\n {\\n var res = @out is null ?\\n THSSpecial_bessel_j1(input.Handle) :\\n THSSpecial_bessel_j1_out(input.Handle, @out.Handle);\\n if (res == IntPtr.Zero)\\n torch.CheckForErrors();\\n return new Tensor(res);\\n }\\n\\n /// <summary>\\n /// Bessel function of the second kind of order 1.\\n /// </summary>\\n /// <param name=\\\"input\\\">The input tensor</param>\\n /// <param name=\\\"out\\\">An optional output tensor, which will be modified if present.</param>\\n public static Tensor bessel_y0(Tensor input, Tensor @out = null)\\n {\\n var res = @out is null ?\\n THSSpecial_bessel_y0(input.Handle) :\\n THSSpecial_bessel_y0_out(input.Handle, @out.Handle);\\n if (res == IntPtr.Zero)\\n torch.CheckForErrors();\\n return new Tensor(res);\\n }\\n\\n /// <summary>\\n /// Bessel function of the second kind of order 1.\\n /// </summary>\\n /// <param name=\\\"input\\\">The input tensor</param>\\n /// <param name=\\\"out\\\">An optional output tensor, which will be modified if present.</param>\\n public static Tensor bessel_y1(Tensor input, Tensor @out = null)\\n {\\n var res = @out is null ?\\n THSSpecial_bessel_y1(input.Handle) :\\n THSSpecial_bessel_y1_out(input.Handle, @out.Handle);\\n if (res == IntPtr.Zero)\\n torch.CheckForErrors();\\n return new Tensor(res);\\n }\\n\\n /// <summary>\\n /// Modified Bessel function of the first kind of order 0.\\n /// </summary>\\n /// <param name=\\\"input\\\">The input tensor</param>\\n /// <param name=\\\"out\\\">An optional output tensor, which will be modified if present.</param>\\n public static Tensor modified_bessel_i0(Tensor input, Tensor @out = null)\\n {\\n var res = @out is null ?\\n THSSpecial_modified_bessel_i0(input.Handle) :\\n THSSpecial_modified_bessel_i0_out(input.Handle, @out.Handle);\\n if (res == IntPtr.Zero)\\n torch.CheckForErrors();\\n return new Tensor(res);\\n }\\n\\n /// <summary>\\n /// Modified Bessel function of the first kind of order 1.\\n /// </summary>\\n /// <param name=\\\"input\\\">The input tensor</param>\\n /// <param name=\\\"out\\\">An optional output tensor, which will be modified if present.</param>\\n public static Tensor modified_bessel_i1(Tensor input, Tensor @out = null)\\n {\\n var res = @out is null ?\\n THSSpecial_modified_bessel_i1(input.Handle) :\\n THSSpecial_modified_bessel_i1_out(input.Handle, @out.Handle);\\n if (res == IntPtr.Zero)\\n torch.CheckForErrors();\\n return new Tensor(res);\\n }\\n\\n /// <summary>\\n /// Modified Bessel function of the second kind of order 1.\\n /// </summary>\\n /// <param name=\\\"input\\\">The input tensor</param>\\n /// <param name=\\\"out\\\">An optional output tensor, which will be modified if present.</param>\\n public static Tensor modified_bessel_k0(Tensor input, Tensor @out = null)\\n {\\n var res = @out is null ?\\n THSSpecial_modified_bessel_k0(input.Handle) :\\n THSSpecial_modified_bessel_k0_out(input.Handle, @out.Handle);\\n if (res == IntPtr.Zero)\\n torch.CheckForErrors();\\n return new Tensor(res);\\n }\\n\\n /// <summary>\\n /// Modified Bessel function of the second kind of order 1.\\n /// </summary>\\n /// <param name=\\\"input\\\">The input tensor</param>\\n /// <param name=\\\"out\\\">An optional output tensor, which will be modified if present.</param>\\n public static Tensor modified_bessel_k1(Tensor input, Tensor @out = null)\\n {\\n var res = @out is null ?\\n THSSpecial_modified_bessel_k1(input.Handle) :\\n THSSpecial_modified_bessel_k1_out(input.Handle, @out.Handle);\\n if (res == IntPtr.Zero)\\n torch.CheckForErrors();\\n return new Tensor(res);\\n }\\n\\n /// <summary>\\n /// Scaled modified Bessel function of the second kind of order 0\\n /// </summary>\\n /// <param name=\\\"input\\\">The input tensor</param>\\n /// <param name=\\\"out\\\">An optional output tensor, which will be modified if present.</param>\\n public static Tensor scaled_modified_bessel_k0(Tensor input, Tensor @out = null)\\n {\\n var res = @out is null ?\\n THSSpecial_scaled_modified_bessel_k0(input.Handle) :\\n THSSpecial_scaled_modified_bessel_k0_out(input.Handle, @out.Handle);\\n if (res == IntPtr.Zero)\\n torch.CheckForErrors();\\n return new Tensor(res);\\n }\\n\\n /// <summary>\\n /// Scaled modified Bessel function of the second kind of order 1\\n /// </summary>\\n /// <param name=\\\"input\\\">The input tensor</param>\\n /// <param name=\\\"out\\\">An optional output tensor, which will be modified if present.</param>\\n public static Tensor scaled_modified_bessel_k1(Tensor input, Tensor @out = null)\\n {\\n var res = @out is null ?\\n THSSpecial_scaled_modified_bessel_k1(input.Handle) :\\n THSSpecial_scaled_modified_bessel_k1_out(input.Handle, @out.Handle);\\n if (res == IntPtr.Zero)\\n torch.CheckForErrors();\\n return new Tensor(res);\\n }\\n\\n /// <summary>\\n /// Bessel function of the first kind of order 0.\\n /// </summary>\\n /// <param name=\\\"input\\\">The input tensor</param>\\n /// <param name=\\\"out\\\">An optional output tensor, which will be modified if present.</param>\\n public static Tensor spherical_bessel_j0(Tensor input, Tensor @out = null)\\n {\\n var res = @out is null ?\\n THSSpecial_spherical_bessel_j0(input.Handle) :\\n THSSpecial_spherical_bessel_j0_out(input.Handle, @out.Handle);\\n if (res == IntPtr.Zero)\\n torch.CheckForErrors();\\n return new Tensor(res);\\n }\\n\\n /// <summary>\\n /// Computes the Chebyshev polynomial of the first kind.\\n ///\\n /// See: https://en.wikipedia.org/wiki/Chebyshev_polynomials\\n /// </summary>\\n /// <param name=\\\"x\\\">The input tensor.</param>\\n /// <param name=\\\"n\\\">n</param>\\n /// <param name=\\\"out\\\">An optional output tensor.</param>\\n public static Tensor chebyshev_polynomial_t(Tensor x, Tensor n, Tensor @out = null)\\n {\\n var res = @out is null ?\\n THSSpecial_chebyshev_polynomial_t(x.Handle, n.Handle) :\\n THSSpecial_chebyshev_polynomial_t_out(x.Handle, n.Handle, @out.Handle);\\n if (res == IntPtr.Zero)\\n torch.CheckForErrors();\\n return new Tensor(res);\\n }\\n\\n\\n /// <summary>\\n /// Computes the Chebyshev polynomial of the second kind.\\n ///\\n /// See: https://en.wikipedia.org/wiki/Chebyshev_polynomials\\n /// </summary>\\n /// <param name=\\\"x\\\">The input tensor.</param>\\n /// <param name=\\\"n\\\">n</param>\\n /// <param name=\\\"out\\\">An optional output tensor.</param>\\n public static Tensor chebyshev_polynomial_u(Tensor x, Tensor n, Tensor @out = null)\\n {\\n var res = @out is null ?\\n THSSpecial_chebyshev_polynomial_u(x.Handle, n.Handle) :\\n THSSpecial_chebyshev_polynomial_u_out(x.Handle, n.Handle, @out.Handle);\\n if (res == IntPtr.Zero)\\n torch.CheckForErrors();\\n return new Tensor(res);\\n }\\n\\n /// <summary>\\n /// Computes the Chebyshev polynomial of the third kind.\\n ///\\n /// See: https://en.wikipedia.org/wiki/Chebyshev_polynomials\\n /// </summary>\\n /// <param name=\\\"x\\\">The input tensor.</param>\\n /// <param name=\\\"n\\\">n</param>\\n /// <param name=\\\"out\\\">An optional output tensor.</param>\\n public static Tensor chebyshev_polynomial_v(Tensor x, Tensor n, Tensor @out = null)\\n {\\n var res = @out is null ?\\n THSSpecial_chebyshev_polynomial_v(x.Handle, n.Handle) :\\n THSSpecial_chebyshev_polynomial_v_out(x.Handle, n.Handle, @out.Handle);\\n if (res == IntPtr.Zero)\\n torch.CheckForErrors();\\n return new Tensor(res);\\n }\\n\\n /// <summary>\\n /// Computes the Chebyshev polynomial of the fourth kind.\\n ///\\n /// See: https://en.wikipedia.org/wiki/Chebyshev_polynomials\\n /// </summary>\\n /// <param name=\\\"x\\\">The input tensor.</param>\\n /// <param name=\\\"n\\\">n</param>\\n /// <param name=\\\"out\\\">An optional output tensor.</param>\\n public static Tensor chebyshev_polynomial_w(Tensor x, Tensor n, Tensor @out = null)\\n {\\n var res = @out is null ?\\n THSSpecial_chebyshev_polynomial_w(x.Handle, n.Handle) :\\n THSSpecial_chebyshev_polynomial_w_out(x.Handle, n.Handle, @out.Handle);\\n if (res == IntPtr.Zero)\\n torch.CheckForErrors();\\n return new Tensor(res);\\n }\\n\\n /// <summary>\\n /// Computes the Chebyshev polynomial of the first kind.\\n ///\\n /// See: https://en.wikipedia.org/wiki/Chebyshev_polynomials\\n /// </summary>\\n /// <param name=\\\"x\\\">The input tensor.</param>\\n /// <param name=\\\"n\\\">n</param>\\n /// <param name=\\\"out\\\">An optional output tensor.</param>\\n public static Tensor shifted_chebyshev_polynomial_t(Tensor x, Tensor n, Tensor @out = null)\\n {\\n var res = @out is null ?\\n THSSpecial_shifted_chebyshev_polynomial_t(x.Handle, n.Handle) :\\n THSSpecial_shifted_chebyshev_polynomial_t_out(x.Handle, n.Handle, @out.Handle);\\n if (res == IntPtr.Zero)\\n torch.CheckForErrors();\\n return new Tensor(res);\\n }\\n\\n\\n /// <summary>\\n /// Computes the Chebyshev polynomial of the second kind.\\n ///\\n /// See: https://en.wikipedia.org/wiki/Chebyshev_polynomials\\n /// </summary>\\n /// <param name=\\\"x\\\">The input tensor.</param>\\n /// <param name=\\\"n\\\">n</param>\\n /// <param name=\\\"out\\\">An optional output tensor.</param>\\n public static Tensor shifted_chebyshev_polynomial_u(Tensor x, Tensor n, Tensor @out = null)\\n {\\n var res = @out is null ?\\n THSSpecial_shifted_chebyshev_polynomial_u(x.Handle, n.Handle) :\\n THSSpecial_shifted_chebyshev_polynomial_u_out(x.Handle, n.Handle, @out.Handle);\\n if (res == IntPtr.Zero)\\n torch.CheckForErrors();\\n return new Tensor(res);\\n }\\n\\n /// <summary>\\n /// Computes the Chebyshev polynomial of the third kind.\\n ///\\n /// See: https://en.wikipedia.org/wiki/Chebyshev_polynomials\\n /// </summary>\\n /// <param name=\\\"x\\\">The input tensor.</param>\\n /// <param name=\\\"n\\\">n</param>\\n /// <param name=\\\"out\\\">An optional output tensor.</param>\\n public static Tensor shifted_chebyshev_polynomial_v(Tensor x, Tensor n, Tensor @out = null)\\n {\\n var res = @out is null ?\\n THSSpecial_shifted_chebyshev_polynomial_v(x.Handle, n.Handle) :\\n THSSpecial_shifted_chebyshev_polynomial_v_out(x.Handle, n.Handle, @out.Handle);\\n if (res == IntPtr.Zero)\\n torch.CheckForErrors();\\n return new Tensor(res);\\n }\\n\\n /// <summary>\\n /// Computes the Chebyshev polynomial of the fourth kind.\\n ///\\n /// See: https://en.wikipedia.org/wiki/Chebyshev_polynomials\\n /// </summary>\\n /// <param name=\\\"x\\\">The input tensor.</param>\\n /// <param name=\\\"n\\\">n</param>\\n /// <param name=\\\"out\\\">An optional output tensor.</param>\\n public static Tensor shifted_chebyshev_polynomial_w(Tensor x, Tensor n, Tensor @out = null)\\n {\\n var res = @out is null ?\\n THSSpecial_shifted_chebyshev_polynomial_w(x.Handle, n.Handle) :\\n THSSpecial_shifted_chebyshev_polynomial_w_out(x.Handle, n.Handle, @out.Handle);\\n if (res == IntPtr.Zero)\\n torch.CheckForErrors();\\n return new Tensor(res);\\n }\\n\\n /// <summary>\\n /// Probabilist's Hermite polynomials.\\n ///\\n /// See: https://en.wikipedia.org/wiki/Hermite_polynomials\\n /// </summary>\\n /// <param name=\\\"x\\\">The input tensor.</param>\\n /// <param name=\\\"n\\\">n</param>\\n /// <param name=\\\"out\\\">An optional output tensor.</param>\\n public static Tensor hermite_polynomial_h(Tensor x, Tensor n, Tensor @out = null)\\n {\\n var res = @out is null ?\\n THSSpecial_hermite_polynomial_h(x.Handle, n.Handle) :\\n THSSpecial_hermite_polynomial_h_out(x.Handle, n.Handle, @out.Handle);\\n if (res == IntPtr.Zero)\\n torch.CheckForErrors();\\n return new Tensor(res);\\n }\\n\\n /// <summary>\\n /// Physicists's Hermite polynomials.\\n ///\\n /// See: https://en.wikipedia.org/wiki/Hermite_polynomials\\n /// </summary>\\n /// <param name=\\\"x\\\">The input tensor.</param>\\n /// <param name=\\\"n\\\">n</param>\\n /// <param name=\\\"out\\\">An optional output tensor.</param>\\n public static Tensor hermite_polynomial_he(Tensor x, Tensor n, Tensor @out = null)\\n {\\n var res = @out is null ?\\n THSSpecial_hermite_polynomial_he(x.Handle, n.Handle) :\\n THSSpecial_hermite_polynomial_he_out(x.Handle, n.Handle, @out.Handle);\\n if (res == IntPtr.Zero)\\n torch.CheckForErrors();\\n return new Tensor(res);\\n }\\n\\n /// <summary>\\n /// Laguerre polynomials\\n /// \\n /// See: https://en.wikipedia.org/wiki/Laguerre_polynomials\\n /// </summary>\\n /// <param name=\\\"x\\\">The input tensor.</param>\\n /// <param name=\\\"n\\\">n</param>\\n /// <param name=\\\"out\\\">An optional output tensor.</param>\\n /// <returns></returns>\\n public static Tensor laguerre_polynomial_l(Tensor x, Tensor n, Tensor @out = null)\\n {\\n var res = @out is null ?\\n THSSpecial_laguerre_polynomial_l(x.Handle, n.Handle) :\\n THSSpecial_laguerre_polynomial_l_out(x.Handle, n.Handle, @out.Handle);\\n if (res == IntPtr.Zero)\\n torch.CheckForErrors();\\n return new Tensor(res);\\n }\\n\\n /// <summary>\\n /// Legendre polynomials\\n /// \\n /// https://en.wikipedia.org/wiki/Legendre_polynomials\\n /// </summary>\\n /// <param name=\\\"x\\\">The input tensor.</param>\\n /// <param name=\\\"n\\\">n</param>\\n /// <param name=\\\"out\\\">An optional output tensor.</param>\\n public static Tensor legendre_polynomial_p(Tensor x, Tensor n, Tensor @out = null)\\n {\\n var res = @out is null ?\\n THSSpecial_legendre_polynomial_p(x.Handle, n.Handle) :\\n THSSpecial_legendre_polynomial_p_out(x.Handle, n.Handle, @out.Handle);\\n if (res == IntPtr.Zero)\\n torch.CheckForErrors();\\n return new Tensor(res);\\n }\\n\\n /// <summary>\\n /// Computes the entropy on input, elementwise.\\n /// </summary>\\n /// <param name=\\\"input\\\">The input tensor</param>\\n /// <returns></returns>\\n public static Tensor entr(Tensor input)\\n {\\n var res = THSSpecial_entr(input.Handle);\\n if (res == IntPtr.Zero)\\n torch.CheckForErrors();\\n return new Tensor(res);\\n }\\n\\n /// <summary>\\n /// Computes the error function of input.\\n /// </summary>\\n /// <param name=\\\"input\\\">The input tensor</param>\\n /// <returns></returns>\\n public static Tensor erf(Tensor input)\\n {\\n var res = THSSpecial_erf(input.Handle);\\n if (res == IntPtr.Zero)\\n torch.CheckForErrors();\\n return new Tensor(res);\\n }\\n\\n /// <summary>\\n /// Computes the complementary error function of input.\\n /// </summary>\\n /// <param name=\\\"input\\\">The input tensor</param>\\n /// <returns></returns>\\n public static Tensor erfc(Tensor input)\\n {\\n var res = THSSpecial_erfc(input.Handle);\\n if (res == IntPtr.Zero)\\n torch.CheckForErrors();\\n return new Tensor(res);\\n }\\n\\n /// <summary>\\n /// Computes the scaled complementary error function of input.\\n /// </summary>\\n /// <param name=\\\"input\\\">The input tensor</param>\\n /// <returns></returns>\\n public static Tensor erfcx(Tensor input)\\n {\\n var res = THSSpecial_erfc(input.Handle);\\n if (res == IntPtr.Zero)\\n torch.CheckForErrors();\\n return new Tensor(res);\\n }\\n\\n /// <summary>\\n /// Computes the inverse error function of input.\\n /// </summary>\\n /// <param name=\\\"input\\\">The input tensor</param>\\n /// <returns></returns>\\n public static Tensor erfinv(Tensor input)\\n {\\n var res = THSSpecial_erfinv(input.Handle);\\n if (res == IntPtr.Zero)\\n torch.CheckForErrors();\\n return new Tensor(res);\\n }\\n\\n /// <summary>\\n /// Computes the expit (also known as the logistic sigmoid function) of the elements of input.\\n /// </summary>\\n /// <param name=\\\"input\\\">The input tensor</param>\\n /// <returns></returns>\\n public static Tensor expit(Tensor input)\\n {\\n var res = THSSpecial_expit(input.Handle);\\n if (res == IntPtr.Zero)\\n torch.CheckForErrors();\\n return new Tensor(res);\\n }\\n\\n /// <summary>\\n /// Computes the exponential of the elements minus 1 of input.\\n /// </summary>\\n /// <param name=\\\"input\\\">The input tensor</param>\\n /// <returns></returns>\\n public static Tensor expm1(Tensor input)\\n {\\n var res = THSSpecial_expm1(input.Handle);\\n if (res == IntPtr.Zero)\\n torch.CheckForErrors();\\n return new Tensor(res);\\n }\\n\\n /// <summary>\\n /// Computes the base two exponential function of input.\\n /// </summary>\\n /// <param name=\\\"input\\\">The input tensor</param>\\n /// <returns></returns>\\n public static Tensor exp2(Tensor input)\\n {\\n var res = THSSpecial_exp2(input.Handle);\\n if (res == IntPtr.Zero)\\n torch.CheckForErrors();\\n return new Tensor(res);\\n }\\n\\n /// <summary>\\n /// Computes the natural logarithm of the absolute value of the gamma function on input.\\n /// </summary>\\n /// <param name=\\\"input\\\">The input tensor</param>\\n /// <returns></returns>\\n public static Tensor gammaln(Tensor input)\\n {\\n var res = THSSpecial_gammaln(input.Handle);\\n if (res == IntPtr.Zero)\\n torch.CheckForErrors();\\n return new Tensor(res);\\n }\\n\\n /// <summary>\\n /// Computes the regularized lower incomplete gamma function.\\n /// </summary>\\n /// <param name=\\\"input\\\">The first non-negative input tensor</param>\\n /// <param name=\\\"other\\\">The second non-negative input tensor</param>\\n /// <returns></returns>\\n public static Tensor gammainc(Tensor input, Tensor other)\\n {\\n var res = THSSpecial_gammainc(input.Handle, other.Handle);\\n if (res == IntPtr.Zero)\\n torch.CheckForErrors();\\n return new Tensor(res);\\n }\\n\\n /// <summary>\\n /// Computes the regularized upper incomplete gamma function.\\n /// </summary>\\n /// <param name=\\\"input\\\">The first non-negative input tensor</param>\\n /// <param name=\\\"other\\\">The second non-negative input tensor</param>\\n /// <returns></returns>\\n public static Tensor gammaincc(Tensor input, Tensor other)\\n {\\n var res = THSSpecial_gammaincc(input.Handle, other.Handle);\\n if (res == IntPtr.Zero)\\n torch.CheckForErrors();\\n return new Tensor(res);\\n }\\n\\n /// <summary>\\n /// Computes the n-th derivative of the digamma function on input.\\n /// </summary>\\n /// <param name=\\\"n\\\">The order of the polygamma function</param>\\n /// <param name=\\\"input\\\">The second non-negative input tensor</param>\\n /// <returns></returns>\\n public static Tensor polygamma(long n, Tensor input)\\n {\\n var res = THSSpecial_polygamma(n, input.Handle);\\n if (res == IntPtr.Zero)\\n torch.CheckForErrors();\\n return new Tensor(res);\\n }\\n\\n /// <summary>\\n /// Computes the multivariate log-gamma function with dimension pp element-wise.\\n /// </summary>\\n /// <param name=\\\"input\\\">The tensor to compute the multivariate log-gamma function</param>\\n /// <param name=\\\"p\\\">The number of dimensions</param>\\n /// <returns></returns>\\n public static Tensor multigammaln(Tensor input, long p)\\n {\\n var res = THSSpecial_multigammaln(input.Handle, p);\\n if (res == IntPtr.Zero)\\n torch.CheckForErrors();\\n return new Tensor(res);\\n }\\n\\n /// <summary>\\n /// Computes the logarithmic derivative of the gamma function on input.\\n /// </summary>\\n /// <param name=\\\"input\\\">The second non-negative input tensor</param>\\n /// <returns></returns>\\n public static Tensor digamma(Tensor input)\\n {\\n var res = THSSpecial_digamma(input.Handle);\\n if (res == IntPtr.Zero)\\n torch.CheckForErrors();\\n return new Tensor(res);\\n }\\n\\n /// <summary>\\n /// Computes the logarithmic derivative of the gamma function on input.\\n /// </summary>\\n /// <param name=\\\"input\\\">The second non-negative input tensor</param>\\n public static Tensor psi(Tensor input) => digamma(input);\\n\\n /// <summary>\\n /// Computes the zeroth order modified Bessel function of the first kind for each element of input.\\n /// </summary>\\n /// <param name=\\\"input\\\">The input tensor</param>\\n /// <returns></returns>\\n public static Tensor i0(Tensor input)\\n {\\n var res = THSSpecial_i0(input.Handle);\\n if (res == IntPtr.Zero)\\n torch.CheckForErrors();\\n return new Tensor(res);\\n }\\n\\n /// <summary>\\n /// Computes the exponentially scaled zeroth order modified Bessel function of the first kind (as defined below) for each element of input.\\n /// </summary>\\n /// <param name=\\\"input\\\">The input tensor</param>\\n /// <returns></returns>\\n public static Tensor i0e(Tensor input)\\n {\\n var res = THSSpecial_i0e(input.Handle);\\n if (res == IntPtr.Zero)\\n torch.CheckForErrors();\\n return new Tensor(res);\\n }\\n\\n /// <summary>\\n /// Computes the first order modified Bessel function of the first kind for each element of input.\\n /// </summary>\\n /// <param name=\\\"input\\\">The input tensor</param>\\n /// <returns></returns>\\n public static Tensor i1(Tensor input)\\n {\\n var res = THSSpecial_i1(input.Handle);\\n if (res == IntPtr.Zero)\\n torch.CheckForErrors();\\n return new Tensor(res);\\n }\\n\\n /// <summary>\\n /// Computes the exponentially scaled first order modified Bessel function of the first kind (as defined below) for each element of input.\\n /// </summary>\\n /// <param name=\\\"input\\\">The input tensor</param>\\n /// <returns></returns>\\n public static Tensor i1e(Tensor input)\\n {\\n var res = THSSpecial_i1e(input.Handle);\\n if (res == IntPtr.Zero)\\n torch.CheckForErrors();\\n return new Tensor(res);\\n }\\n\\n /// <summary>\\n /// Returns a new tensor with the logit of the elements of input.\\n /// </summary>\\n /// <param name=\\\"input\\\">The input tensor</param>\\n /// <returns></returns>\\n public static Tensor logit(Tensor input)\\n {\\n var res = THSSpecial_logit(input.Handle);\\n if (res == IntPtr.Zero)\\n torch.CheckForErrors();\\n return new Tensor(res);\\n }\\n\\n /// <summary>\\n /// Returns a new tensor with the logit of the elements of input.\\n /// </summary>\\n /// <param name=\\\"input\\\">The input tensor</param>\\n /// <param name=\\\"dim\\\">A dimension along which log_softmax will be computed.</param>\\n /// <param name=\\\"dtype\\\">The desired data type of returned tensor.</param>\\n /// <returns></returns>\\n public static Tensor log_softmax(Tensor input, long dim, ScalarType? dtype = null)\\n {\\n var dt = dtype.HasValue ? dtype.Value : input.dtype;\\n var res = THSSpecial_log_softmax(input.Handle, dim, (sbyte)dt);\\n if (res == IntPtr.Zero)\\n torch.CheckForErrors();\\n return new Tensor(res);\\n }\\n\\n /// <summary>\\n /// Computes the area under the standard Gaussian probability density function, integrated from minus infinity to input, elementwise.\\n /// </summary>\\n /// <param name=\\\"input\\\">The input tensor</param>\\n /// <returns></returns>\\n public static Tensor ndtr(Tensor input)\\n {\\n var res = THSSpecial_ndtr(input.Handle);\\n if (res == IntPtr.Zero)\\n torch.CheckForErrors();\\n return new Tensor(res);\\n }\\n\\n /// <summary>\\n /// Computes the argument, x, for which the area under the Gaussian probability density function (integrated from minus infinity to x) is equal to input, elementwise.\\n /// </summary>\\n /// <param name=\\\"input\\\">The input tensor</param>\\n /// <returns></returns>\\n public static Tensor ndtri(Tensor input)\\n {\\n var res = THSSpecial_ndtri(input.Handle);\\n if (res == IntPtr.Zero)\\n torch.CheckForErrors();\\n return new Tensor(res);\\n }\\n\\n /// <summary>\\n /// Computes the normalized sinc of input.\\n /// </summary>\\n /// <param name=\\\"input\\\">The input tensor</param>\\n /// <returns></returns>\\n public static Tensor sinc(Tensor input)\\n {\\n var res = THSSpecial_sinc(input.Handle);\\n if (res == IntPtr.Zero)\\n torch.CheckForErrors();\\n return new Tensor(res);\\n }\\n\\n /// <summary>\\n /// Alias for torch.special.expit()\\n /// </summary>\\n /// <returns></returns>\\n public static Tensor sigmoid(Tensor input) => input.sigmoid();\\n\\n /// <summary>\\n /// Alias for torch.special.expit()\\n /// </summary>\\n /// <returns></returns>\\n public static Tensor sigmoid_(Tensor input) => input.sigmoid_();\\n\\n /// <summary>\\n /// Computes the softmax function for the input tensor.\\n /// </summary>\\n /// <param name=\\\"input\\\">The input tensor</param>\\n /// <param name=\\\"dim\\\">A dimension along which softmax will be computed.</param>\\n /// <param name=\\\"dtype\\\">The desired data type of returned tensor.</param>\\n /// <returns></returns>\\n public static Tensor softmax(Tensor input, long dim, ScalarType? dtype = null)\\n {\\n var dt = dtype.HasValue ? dtype.Value : input.dtype;\\n var res = THSSpecial_softmax(input.Handle, dim, (sbyte)dt);\\n if (res == IntPtr.Zero)\\n torch.CheckForErrors();\\n return new Tensor(res);\\n }\\n\\n /// <summary>\\n /// Computes input * log1p(other). Similar to SciPy’s scipy.special.xlog1py.\\n /// </summary>\\n /// <param name=\\\"input\\\">The input tensor</param>\\n /// <param name=\\\"other\\\"></param>\\n /// <returns></returns>\\n public static Tensor xlog1py(Tensor input, Tensor other)\\n {\\n var res = THSSpecial_xlog1py(input.Handle, other.Handle);\\n if (res == IntPtr.Zero)\\n torch.CheckForErrors();\\n return new Tensor(res);\\n }\\n\\n /// <summary>\\n /// Computes the Hurwitz zeta function, elementwise.\\n /// </summary>\\n /// <param name=\\\"x\\\">The input tensor corresponding to x</param>\\n /// <param name=\\\"q\\\">The input tensor corresponding to q</param>\\n /// <remarks>The Riemann zeta function corresponds to the case when q = 1.</remarks>\\n public static Tensor zeta(Tensor x, Tensor q)\\n {\\n var res = THSSpecial_zeta(x.Handle, q.Handle);\\n if (res == IntPtr.Zero)\\n torch.CheckForErrors();\\n return new Tensor(res);\\n }\\n }\\n }\\n}\\n\"\n },\n {\n \"alpha_fraction\": 0.5192856192588806,\n \"alphanum_fraction\": 0.5412377715110779,\n \"avg_line_length\": 40.78238296508789,\n \"blob_id\": \"2109429199f404fab762abf1343a6286414df5ea\",\n \"content_id\": \"821b521c2f99c9bc3862dac13c50f42ba722d558\",\n \"detected_licenses\": [\n \"MIT\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"C#\",\n \"length_bytes\": 8063,\n \"license_type\": \"permissive\",\n \"max_line_length\": 213,\n \"num_lines\": 193,\n \"path\": \"/test/TorchSharpTest/TestDataLoader.cs\",\n \"repo_name\": \"NiklasGustafsson/TorchSharp\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"// Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information.\\n\\nusing System.Collections.Generic;\\nusing Xunit;\\n\\n\\nnamespace TorchSharp\\n{\\n [Collection(\\\"Sequential\\\")]\\n public class TestDataLoader\\n {\\n private class TestDataset : torch.utils.data.Dataset\\n {\\n public override long Count { get; } = 10;\\n public override Dictionary<string, torch.Tensor> GetTensor(long index)\\n {\\n return new() {{\\\"data\\\", torch.tensor(1)}, {\\\"label\\\", torch.tensor(13)}, {\\\"index\\\", torch.tensor(index)}};\\n }\\n }\\n\\n [Fact]\\n public void DatasetTest()\\n {\\n using var dataset = new TestDataset();\\n var d = dataset.GetTensor(0);\\n Assert.True(d.ContainsKey(\\\"data\\\"));\\n Assert.True(d.ContainsKey(\\\"index\\\"));\\n Assert.True(d.ContainsKey(\\\"label\\\"));\\n\\n Assert.Equal(d[\\\"data\\\"], torch.tensor(1));\\n Assert.Equal(d[\\\"label\\\"], torch.tensor(13));\\n Assert.Equal(d[\\\"index\\\"], torch.tensor(0L));\\n }\\n\\n [Fact]\\n public void TensorDatasetTest()\\n {\\n var x = torch.randn(4, 12);\\n var y = torch.randn(4, 16);\\n using var dataset = torch.utils.data.TensorDataset(x, y);\\n Assert.Equal(2, dataset.Count);\\n\\n var d = dataset.GetTensor(0);\\n\\n Assert.Equal(2, d.Count);\\n Assert.Equal(x[0], d[0]);\\n Assert.Equal(y[0], d[1]);\\n }\\n\\n // Cannot assert index because ConcurrentBag append tensors randomly\\n [Fact]\\n public void DataLoaderTest1()\\n {\\n using var dataset = new TestDataset();\\n using var dataloader = new torch.utils.data.DataLoader(dataset, 2, false, torch.CPU);\\n var iterator = dataloader.GetEnumerator();\\n Assert.True(iterator.MoveNext());\\n Assert.Equal(iterator.Current[\\\"data\\\"], torch.tensor(rawArray: new[]{1L, 1L}, dimensions: new[]{2L}, dtype: torch.ScalarType.Int32));\\n Assert.Equal(iterator.Current[\\\"label\\\"], torch.tensor(rawArray: new[]{13L, 13L}, dimensions: new[]{2L}, dtype: torch.ScalarType.Int32));\\n Assert.Equal(iterator.Current[\\\"index\\\"].ToString(TensorStringStyle.Julia), torch.tensor(rawArray: new[]{0L, 1L}, dimensions: new[]{2L}, dtype: torch.ScalarType.Int64).ToString(TensorStringStyle.Julia));\\n iterator.Dispose();\\n }\\n\\n [Fact]\\n public void DataLoaderTest2()\\n {\\n using var dataset = new TestDataset();\\n using var dataloader = new torch.utils.data.DataLoader(dataset, 2, false, torch.CPU);\\n long idx = 0;\\n foreach (var x in dataloader) {\\n Assert.Equal(x[\\\"data\\\"], torch.tensor(new[]{1, 1}, new[]{2L}));\\n Assert.Equal(x[\\\"index\\\"], torch.tensor(new[]{idx++, idx++}, new[]{2L}));\\n }\\n }\\n\\n [Fact]\\n public void DataLoaderTest3()\\n {\\n using var dataset = new TestDataset();\\n {\\n using var dataloader = new torch.utils.data.DataLoader(dataset, 3, false, torch.CPU);\\n Assert.Equal(4, dataloader.Count);\\n }\\n {\\n using var dataloader = new torch.utils.data.DataLoader(dataset, 3, false, torch.CPU, drop_last: true);\\n Assert.Equal(3, dataloader.Count);\\n }\\n {\\n using var dataloader = new torch.utils.data.DataLoader(dataset, 2, false, torch.CPU, drop_last: true);\\n Assert.Equal(5, dataloader.Count);\\n }\\n }\\n\\n private const int stressBatchSize = 32;\\n\\n private class LargeTestDataset : torch.utils.data.Dataset\\n {\\n public override long Count { get; } = 2*stressBatchSize;\\n public override Dictionary<string, torch.Tensor> GetTensor(long index)\\n {\\n return new() { { \\\"data\\\", torch.rand(3, 512, 512) }, { \\\"label\\\", torch.tensor(16) }, { \\\"index\\\", torch.tensor(index) } };\\n }\\n }\\n\\n [Fact]\\n public void BigDataLoaderTest3()\\n {\\n using var dataset = new LargeTestDataset();\\n using var dataloader = new torch.utils.data.DataLoader(dataset, stressBatchSize, false, torch.CPU);\\n var iter = dataloader.GetEnumerator();\\n iter.MoveNext();\\n var x = iter.Current;\\n Assert.Equal(new long[] { stressBatchSize, 3, 512, 512 }, x[\\\"data\\\"].shape);\\n iter.MoveNext();\\n x = iter.Current;\\n Assert.Equal(new long[] { stressBatchSize, 3, 512, 512 }, x[\\\"data\\\"].shape);\\n Assert.False(iter.MoveNext());\\n iter.Dispose();\\n }\\n\\n [Fact]\\n public void MultiThreadDataLoaderTest1()\\n {\\n using var dataset = new TestDataset();\\n using var dataloader = new torch.utils.data.DataLoader(dataset, 4, false, torch.CPU, num_worker: 2);\\n var iter = dataloader.GetEnumerator();\\n iter.MoveNext();\\n var x = iter.Current;\\n Assert.Equal(x[\\\"data\\\"], torch.tensor(new[]{1, 1, 1, 1}, new[]{4L}));\\n Assert.Equal(x[\\\"index\\\"], torch.tensor(new[]{0L, 1, 2, 3}, new[]{4L}));\\n iter.MoveNext();\\n x = iter.Current;\\n Assert.Equal(x[\\\"data\\\"], torch.tensor(new[]{1, 1, 1, 1}, new[]{4L}));\\n Assert.Equal(x[\\\"index\\\"], torch.tensor(new[]{4L, 5, 6, 7}, new[]{4L}));\\n iter.MoveNext();\\n x = iter.Current;\\n Assert.Equal(x[\\\"data\\\"], torch.tensor(new[]{1, 1}, new[]{2L}));\\n Assert.Equal(x[\\\"index\\\"], torch.tensor(new[]{8L, 9}, new[]{2L}));\\n iter.Dispose();\\n }\\n\\n [Fact]\\n public void MultiThreadDataLoaderTest2()\\n {\\n using var dataset = new TestDataset();\\n using var dataloader = new torch.utils.data.DataLoader(dataset, 5, false, torch.CPU, num_worker: 2);\\n var iter = dataloader.GetEnumerator();\\n iter.MoveNext();\\n var x = iter.Current;\\n Assert.Equal(x[\\\"data\\\"], torch.tensor(new[]{1, 1, 1, 1, 1}, new[]{5L}));\\n Assert.Equal(x[\\\"index\\\"], torch.tensor(new[]{0L, 1, 2, 3, 4}, new[]{5L}));\\n iter.MoveNext();\\n x = iter.Current;\\n Assert.Equal(x[\\\"data\\\"], torch.tensor(new[]{1, 1, 1, 1, 1}, new[]{5L}));\\n Assert.Equal(x[\\\"index\\\"], torch.tensor(new[]{5L, 6, 7, 8, 9}, new[]{5L}));\\n Assert.False(iter.MoveNext());\\n iter.Dispose();\\n }\\n\\n [Fact]\\n public void MultiThreadDataLoaderTest3()\\n {\\n using var dataset = new TestDataset();\\n using var dataloader = new torch.utils.data.DataLoader(dataset, 5, false, torch.CPU, num_worker: 11);\\n var iter = dataloader.GetEnumerator();\\n iter.MoveNext();\\n var x = iter.Current;\\n Assert.Equal(x[\\\"data\\\"], torch.tensor(new[]{1, 1, 1, 1, 1}, new[]{5L}));\\n Assert.Equal(x[\\\"index\\\"], torch.tensor(new[]{0L, 1, 2, 3, 4}, new[]{5L}));\\n iter.MoveNext();\\n x = iter.Current;\\n Assert.Equal(x[\\\"data\\\"], torch.tensor(new[]{1, 1, 1, 1, 1}, new[]{5L}));\\n Assert.Equal(x[\\\"index\\\"], torch.tensor(new[]{5L, 6, 7, 8, 9}, new[]{5L}));\\n Assert.False(iter.MoveNext());\\n iter.Dispose();\\n }\\n\\n [Fact]\\n public void CustomSeedTest()\\n {\\n using var dataset = new TestDataset();\\n using var dataloader = new torch.utils.data.DataLoader(dataset, 2, true, seed: 1);\\n using var dataloader2 = new torch.utils.data.DataLoader(dataset, 2, true, seed: 1);\\n var iterator = dataloader.GetEnumerator();\\n var iterator2 = dataloader2.GetEnumerator();\\n iterator.MoveNext();\\n iterator2.MoveNext();\\n Assert.Equal(iterator.Current, iterator2.Current);\\n iterator.Dispose();\\n iterator2.Dispose();\\n }\\n }\\n}\"\n },\n {\n \"alpha_fraction\": 0.5452775359153748,\n \"alphanum_fraction\": 0.5530672073364258,\n \"avg_line_length\": 34.41379165649414,\n \"blob_id\": \"e99e77e99bf1d20ce47d658288da2bcb2f12f8c1\",\n \"content_id\": \"8837d1ce2f1c8b05fee686c02b56ade6f2ffa528\",\n \"detected_licenses\": [\n \"MIT\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"C#\",\n \"length_bytes\": 1027,\n \"license_type\": \"permissive\",\n \"max_line_length\": 130,\n \"num_lines\": 29,\n \"path\": \"/test/TorchSharpTest/TestTorchHub.cs\",\n \"repo_name\": \"NiklasGustafsson/TorchSharp\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"// Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information.\\n\\nusing System.Reflection;\\nusing Xunit;\\n\\n#nullable enable\\nnamespace TorchSharp\\n{\\n public class TestTorchHub\\n {\\n [Fact]\\n public void TestProgressBar()\\n {\\n var type = typeof(torch.hub);\\n foreach (var method in type.GetMethods(BindingFlags.NonPublic | BindingFlags.Static)) {\\n if (method.Name == \\\"_create_progress_bar\\\") {\\n var result = (IProgressBar?)method.Invoke(null, new object[] { false });\\n Assert.NotNull(result);\\n Assert.Equal(\\\"TorchSharp.ConsoleProgressBar\\\", result.GetType().FullName);\\n Assert.Equal(0, result.Value);\\n Assert.Null(result.Maximum);\\n result.Value = 100;\\n result.Maximum = 50;\\n Assert.Equal(50, result.Value);\\n }\\n }\\n }\\n }\\n}\\n\"\n },\n {\n \"alpha_fraction\": 0.5553908944129944,\n \"alphanum_fraction\": 0.5569749474525452,\n \"avg_line_length\": 46.73170852661133,\n \"blob_id\": \"42411a79c6e947feaf3d27d062f3225fb9fa9b1d\",\n \"content_id\": \"7ae4007cd043ceb54e26361084293fdb9a35f4d5\",\n \"detected_licenses\": [\n \"MIT\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"C#\",\n \"length_bytes\": 19594,\n \"license_type\": \"permissive\",\n \"max_line_length\": 211,\n \"num_lines\": 410,\n \"path\": \"/src/TorchSharp/NN/Sequential.cs\",\n \"repo_name\": \"NiklasGustafsson/TorchSharp\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"// Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information.\\nusing System;\\nusing System.Collections.Generic;\\nusing System.Diagnostics;\\nusing static TorchSharp.PInvoke.NativeMethods;\\n\\nusing static TorchSharp.torch;\\n\\nnamespace TorchSharp\\n{\\n using System.Runtime.CompilerServices;\\n using Modules;\\n using TorchSharp.PInvoke;\\n\\n namespace Modules\\n {\\n /// <summary>\\n /// This class is used to represent a Sequential module.\\n /// </summary>\\n public class Sequential : torch.nn.Module<Tensor, Tensor>\\n {\\n public Sequential append(string name, torch.nn.IModule<Tensor, Tensor> module)\\n {\\n Add(name, module);\\n return this;\\n }\\n\\n internal void Add(string name, torch.nn.IModule<Tensor, Tensor> sm)\\n {\\n var submodule = (torch.nn.Module)sm;\\n\\n Debug.Assert(!handle.IsInvalid);\\n Debug.Assert(!submodule.handle.IsInvalid);\\n\\n // Keep the sub-module alive for at least as long as the Sequential object is alive.\\n _modules.Add(sm);\\n _names.Add(name);\\n }\\n\\n public Sequential append(torch.nn.IModule<Tensor, Tensor> module)\\n {\\n var name = _modules.Count.ToString();\\n Add(name, module);\\n return this;\\n }\\n\\n internal void Add(torch.nn.IModule<Tensor, Tensor> module)\\n {\\n var name = _modules.Count.ToString();\\n Add(name, module);\\n }\\n\\n public override IEnumerable<(string name, Parameter parameter)> named_parameters(bool recurse = true)\\n {\\n if (!recurse) yield break;\\n\\n var seen = new HashSet<IntPtr>();\\n seen.Add(IntPtr.Zero); // Ignore invalid parameters\\n\\n for (var i = 0; i < _names.Count; i++) {\\n foreach (var (n, p) in ((torch.nn.Module)_modules[i]).named_parameters(true)) {\\n if (seen.Contains(p.handle)) continue;\\n seen.Add(p.handle);\\n yield return ($\\\"{_names[i]}.{n}\\\", p);\\n }\\n }\\n }\\n\\n public override IEnumerable<(string name, Tensor buffer)> named_buffers(bool recurse = true)\\n {\\n if (!recurse) yield break;\\n\\n var seen = new HashSet<IntPtr>();\\n seen.Add(IntPtr.Zero); // Ignore invalid buffers\\n\\n for (var i = 0; i < _names.Count; i++) {\\n foreach (var (n, p) in ((torch.nn.Module)_modules[i]).named_buffers(true)) {\\n if (seen.Contains(p.handle)) continue;\\n seen.Add(p.handle);\\n yield return ($\\\"{_names[i]}.{n}\\\", p);\\n }\\n }\\n }\\n\\n public override IEnumerable<(string name, torch.nn.Module module)> named_children()\\n {\\n for (var i = 0; i < _names.Count; i++) {\\n yield return ($\\\"{_names[i]}\\\", ((torch.nn.Module)_modules[i]));\\n }\\n }\\n\\n public override IEnumerable<(string name, torch.nn.Module module)> named_modules()\\n {\\n for (var i = 0; i < _names.Count; i++) {\\n yield return ($\\\"{_names[i]}\\\", ((torch.nn.Module)_modules[i]));\\n }\\n\\n for (var i = 0; i < _names.Count; i++) {\\n var sm = (torch.nn.Module)_modules[i];\\n var name = _names[i];\\n foreach (var (n, p) in sm.named_modules()) {\\n yield return ($\\\"{name}.{n}\\\", p);\\n }\\n }\\n\\n }\\n internal Sequential(IntPtr handle) : base(handle, IntPtr.Zero)\\n {\\n }\\n\\n /// <summary>\\n /// Constructor, intended for derived modules.\\n /// </summary>\\n /// <param name=\\\"modules\\\">An ordered list of the contained modules.</param>\\n protected Sequential(params (string name, torch.nn.Module<Tensor, Tensor> submodule)[] modules) : base(IntPtr.Zero, IntPtr.Zero)\\n {\\n var handle = THSNN_Sequential_ctor();\\n if (handle == IntPtr.Zero) { torch.CheckForErrors(); }\\n\\n this.handle = new HType(handle, true);\\n\\n foreach (var module in modules) Add(module.name, module.submodule);\\n }\\n\\n /// <summary>\\n /// Constructor, intended for derived modules.\\n /// </summary>\\n /// <param name=\\\"modules\\\">An ordered list of the contained modules.</param>\\n protected Sequential(params torch.nn.Module<Tensor, Tensor>[] modules) : base(IntPtr.Zero, IntPtr.Zero)\\n {\\n var handle = THSNN_Sequential_ctor();\\n if (handle == IntPtr.Zero) { torch.CheckForErrors(); }\\n\\n this.handle = new HType(handle, true);\\n\\n foreach (var m in modules) Add(m);\\n }\\n\\n public override Tensor forward(Tensor tensor)\\n {\\n // If there are no modules, just return a fresh handle to the input.\\n if (_modules.Count == 0) return tensor.alias();\\n\\n // The loop-based logic below only works for n > 1, so here's another special case.\\n if (_modules.Count == 1) return _modules[0].call(tensor);\\n\\n // Note: we have not been able to detect any significant performance difference between\\n // implementing forward() in native or managed code.\\n\\n // Using an for loop helps debugging, since we can know the ordinal of the submodule.\\n\\n var t0 = _modules[0].call(tensor);\\n\\n for (var idx = 1; idx < _modules.Count - 1; idx++) {\\n var t1 = _modules[idx].call(t0);\\n t0.Dispose();\\n t0 = t1;\\n }\\n\\n var result = _modules[_modules.Count - 1].call(t0);\\n t0.Dispose();\\n\\n return result;\\n }\\n\\n public override nn.Module apply(Action<nn.Module> fn)\\n {\\n // More efficient than asking C++ for the children. We already have the list, after all.\\n foreach (var m in _modules) ((torch.nn.Module)m).apply(fn);\\n fn(this);\\n return this;\\n\\n }\\n\\n /// <summary>\\n /// The number of modules in the Sequential collection.\\n /// </summary>\\n public int Count => _modules.Count;\\n\\n /// <summary>\\n /// Module indexer.\\n /// </summary>\\n [IndexerName(\\\"SequentialItems\\\")]\\n public nn.IModule<Tensor,Tensor> this[int index] {\\n get {\\n return _modules[index];\\n }\\n }\\n\\n /// <summary>\\n /// Module indexer.\\n /// </summary>\\n [IndexerName(\\\"SequentialItems\\\")]\\n public Sequential this[(int? start, int? end) index] {\\n get {\\n var start = index.start.HasValue ? index.start.Value : 0;\\n var end = index.end.HasValue ? index.end.Value : _modules.Count;\\n\\n return Slice(start, end);\\n }\\n }\\n\\n private Sequential Slice(int start, int end)\\n {\\n if (start < 0 || start > _modules.Count) throw new IndexOutOfRangeException($\\\"{start} is not a valid index.\\\");\\n if (end < 0 || end > _modules.Count) throw new IndexOutOfRangeException($\\\"{end} is not a valid index.\\\");\\n\\n var stop = Math.Min(_modules.Count, end);\\n\\n var result = new Sequential(Array.Empty<torch.nn.Module<Tensor, Tensor>>());\\n\\n for (var i = start; i < stop; i++) {\\n result.Add(_names[i], _modules[i]);\\n }\\n return result;\\n }\\n\\n#if !NETSTANDARD2_0_OR_GREATER\\n /// <summary>\\n /// Module indexer.\\n /// </summary>\\n [IndexerName(\\\"SequentialItems\\\")]\\n public Sequential this[System.Range index] {\\n get {\\n var start = index.Start.IsFromEnd ? _modules.Count - index.Start.Value : index.Start.Value;\\n var end = index.End.IsFromEnd ? _modules.Count - index.End.Value : index.End.Value;\\n\\n return this[(start, end)];\\n }\\n }\\n#endif\\n protected override void Dispose(bool disposing)\\n {\\n if (disposing) {\\n foreach (var m in _modules) { ((torch.nn.Module)m).Dispose(); }\\n }\\n base.Dispose(disposing);\\n }\\n\\n /// <summary>\\n /// Sets the module in training mode.\\n /// </summary>\\n /// <remarks>\\n /// This has any effect only on certain modules.See documentations of particular modules for details of their behaviors in training/evaluation mode, if they are affected, e.g.Dropout, BatchNorm, etc.\\n /// </remarks>\\n public override void train(bool on = true)\\n {\\n foreach (var m in _modules) { ((torch.nn.Module)m).train(on); }\\n }\\n\\n /// <summary>\\n /// Sets the module in evaluation mode.\\n /// </summary>\\n /// <remarks>\\n /// This has any effect only on certain modules.See documentations of particular modules for details of their behaviors in training/evaluation mode, if they are affected, e.g.Dropout, BatchNorm, etc.\\n /// </remarks>\\n public override void eval()\\n {\\n foreach (var m in _modules) { ((torch.nn.Module)m).eval(); }\\n }\\n\\n protected internal override nn.Module _to(ScalarType dtype)\\n {\\n foreach (var m in _modules) { ((torch.nn.Module)m)._to(dtype); }\\n return this;\\n }\\n\\n protected internal override nn.Module _to(Device device, ScalarType dtype)\\n {\\n foreach (var m in _modules) { ((torch.nn.Module)m)._to(device, dtype); }\\n return this;\\n }\\n\\n protected internal override nn.Module _to(DeviceType deviceType, int deviceIndex = -1)\\n {\\n foreach (var m in _modules) { ((torch.nn.Module)m)._to(deviceType, deviceIndex); }\\n return this;\\n }\\n\\n // Currently, Sequential is implemented entirely in managed code, but if we go back to\\n // using the native forward() implementation, this collection is still necessary.\\n // The module handles are held in the native runtime, which calls back into managed code,\\n // the .NET module instances need to stay alive, and keeping a list of them will do that.\\n\\n private List<torch.nn.IModule<Tensor, Tensor>> _modules = new List<nn.IModule<Tensor, Tensor>>();\\n private List<string> _names = new List<string>();\\n }\\n }\\n\\n public static partial class torch\\n {\\n public static partial class nn\\n {\\n /// <summary>\\n /// Get empty sequential\\n /// </summary>\\n /// <returns></returns>\\n public static Sequential Sequential()\\n {\\n var handle = THSNN_Sequential_ctor();\\n if (handle == IntPtr.Zero) { torch.CheckForErrors(); }\\n\\n return new Sequential(handle);\\n }\\n\\n /// <summary>\\n /// A sequential container. Modules will be added to it in the order they are passed in the constructor.\\n /// Alternatively, an OrderedDict of modules can be passed in. The forward() method of Sequential accepts any input and forwards it to the first module it contains.\\n /// It then “chains” outputs to inputs sequentially for each subsequent module, finally returning the output of the last module.\\n /// The value a Sequential provides over manually calling a sequence of modules is that it allows treating the whole container as a single module,\\n /// such that performing a transformation on the Sequential applies to each of the modules it stores (which are each a registered submodule of the Sequential).\\n /// </summary>\\n /// <param name=\\\"modules\\\">An ordered list of the contained modules.</param>\\n /// <returns></returns>\\n /// <remarks>Sequential will take ownership of the modules and dispose of them when disposed.</remarks>\\n public static Sequential Sequential(params (string name, torch.nn.Module<Tensor, Tensor> submodule)[] modules)\\n {\\n var res = Sequential();\\n foreach (var module in modules)\\n res.Add(module.name, module.submodule);\\n return res;\\n }\\n\\n /// <summary>\\n /// A sequential container. Modules will be added to it in the order they are passed in the constructor.\\n /// It then “chains” outputs to inputs sequentially for each subsequent module, finally returning the output of the last module.\\n /// The value a Sequential provides over manually calling a sequence of modules is that it allows treating the whole container as a single module,\\n /// such that performing a transformation on the Sequential applies to each of the modules it stores (which are each a registered submodule of the Sequential).\\n /// </summary>\\n /// <param name=\\\"modules\\\">An ordered list of the contained modules.</param>\\n /// <returns></returns>\\n /// <remarks>Sequential will take ownership of the modules and dispose of them when disposed.</remarks>\\n public static Sequential Sequential(params torch.nn.Module<Tensor, Tensor>[] modules)\\n {\\n var res = Sequential();\\n foreach (var m in modules)\\n res.Add(m);\\n return res;\\n }\\n\\n /// <summary>\\n /// A sequential container. Modules will be added to it in the order they are passed in the constructor.\\n /// Alternatively, an OrderedDict of modules can be passed in. The forward() method of Sequential accepts any input and forwards it to the first module it contains.\\n /// It then “chains” outputs to inputs sequentially for each subsequent module, finally returning the output of the last module.\\n /// The value a Sequential provides over manually calling a sequence of modules is that it allows treating the whole container as a single module,\\n /// such that performing a transformation on the Sequential applies to each of the modules it stores (which are each a registered submodule of the Sequential).\\n /// </summary>\\n /// <param name=\\\"modules\\\">An ordered list of the contained modules.</param>\\n /// <remarks>Sequential will take ownership of the modules and dispose of them when disposed.</remarks>\\n public static Sequential Sequential(params System.Tuple<string, torch.nn.Module<Tensor, Tensor>>[] modules)\\n {\\n var res = Sequential();\\n foreach (var module in modules)\\n res.Add(module.Item1, module.Item2);\\n return res;\\n }\\n\\n /// <summary>\\n /// A sequential container. Modules will be added to it in the order they are passed in the constructor.\\n /// Alternatively, an OrderedDict of modules can be passed in. The forward() method of Sequential accepts any input and forwards it to the first module it contains.\\n /// It then “chains” outputs to inputs sequentially for each subsequent module, finally returning the output of the last module.\\n /// The value a Sequential provides over manually calling a sequence of modules is that it allows treating the whole container as a single module,\\n /// such that performing a transformation on the Sequential applies to each of the modules it stores (which are each a registered submodule of the Sequential).\\n /// </summary>\\n /// <param name=\\\"modules\\\">An ordered list of the contained modules.</param>\\n /// <remarks>Sequential will take ownership of the modules and dispose of them when disposed.</remarks>\\n public static Sequential Sequential(IEnumerable<(string name, torch.nn.Module<Tensor, Tensor> submodule)> modules)\\n {\\n var res = Sequential();\\n foreach (var module in modules)\\n res.Add(module.name, module.submodule);\\n return res;\\n }\\n\\n /// <summary>\\n /// A sequential container. Modules will be added to it in the order they are passed in the constructor.\\n /// Alternatively, an OrderedDict of modules can be passed in. The forward() method of Sequential accepts any input and forwards it to the first module it contains.\\n /// It then “chains” outputs to inputs sequentially for each subsequent module, finally returning the output of the last module.\\n /// The value a Sequential provides over manually calling a sequence of modules is that it allows treating the whole container as a single module,\\n /// such that performing a transformation on the Sequential applies to each of the modules it stores (which are each a registered submodule of the Sequential).\\n /// </summary>\\n /// <param name=\\\"modules\\\">An ordered list of the contained modules.</param>\\n /// <remarks>Sequential will take ownership of the modules and dispose of them when disposed.</remarks>\\n public static Sequential Sequential(IEnumerable<System.Tuple<string, torch.nn.Module<Tensor, Tensor>>> modules)\\n {\\n var res = Sequential();\\n foreach (var module in modules)\\n res.Add(module.Item1, module.Item2);\\n return res;\\n }\\n\\n /// <summary>\\n /// A sequential container. Modules will be added to it in the order they are passed in the constructor.\\n /// It then “chains” outputs to inputs sequentially for each subsequent module, finally returning the output of the last module.\\n /// The value a Sequential provides over manually calling a sequence of modules is that it allows treating the whole container as a single module,\\n /// such that performing a transformation on the Sequential applies to each of the modules it stores (which are each a registered submodule of the Sequential).\\n /// </summary>\\n /// <param name=\\\"modules\\\">An ordered list of the contained modules.</param>\\n /// <returns></returns>\\n /// <remarks>Sequential will take ownership of the modules and dispose of them when disposed.</remarks>\\n public static Sequential Sequential(IEnumerable<torch.nn.Module<Tensor, Tensor>> modules)\\n {\\n var res = Sequential();\\n foreach (var module in modules)\\n res.Add(module);\\n return res;\\n }\\n }\\n }\\n}\\n\"\n },\n {\n \"alpha_fraction\": 0.5300552248954773,\n \"alphanum_fraction\": 0.5334318280220032,\n \"avg_line_length\": 54.96653366088867,\n \"blob_id\": \"3ade0ce040dae85a22bc86a23884a84178453193\",\n \"content_id\": \"06df3cb78167fb8a19f24752d7c0ebef0ed3b88f\",\n \"detected_licenses\": [\n \"MIT\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"C#\",\n \"length_bytes\": 28431,\n \"license_type\": \"permissive\",\n \"max_line_length\": 214,\n \"num_lines\": 508,\n \"path\": \"/src/TorchSharp/FFT.cs\",\n \"repo_name\": \"NiklasGustafsson/TorchSharp\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"// Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information.\\nusing System;\\nusing static TorchSharp.PInvoke.NativeMethods;\\n\\nnamespace TorchSharp\\n{\\n // This file contains the mathematical operators on Tensor\\n public enum FFTNormType\\n {\\n Backward = 0,\\n Forward = 1,\\n Ortho = 2\\n }\\n\\n public static partial class torch\\n {\\n public static partial class fft\\n {\\n /// <summary>\\n /// Computes the one dimensional discrete Fourier transform of input.\\n /// </summary>\\n /// <param name=\\\"input\\\">The input tensor</param>\\n /// <param name=\\\"n\\\">Signal length. If given, the input will either be zero-padded or trimmed to this length before computing the FFT.</param>\\n /// <param name=\\\"dim\\\">The dimension along which to take the one dimensional FFT.</param>\\n /// <param name=\\\"norm\\\">Normalization mode.</param>\\n /// <returns></returns>\\n /// <remarks>The name was changed because it would conflict with its surrounding scope. That's not legal in .NET.</remarks>\\n public static Tensor fft_(Tensor input, long n = -1, long dim = -1, FFTNormType norm = FFTNormType.Backward)\\n {\\n var res = THSTensor_fft(input.Handle, n, dim, (sbyte)norm);\\n if (res == IntPtr.Zero) { torch.CheckForErrors(); }\\n return new Tensor(res);\\n }\\n\\n /// <summary>\\n /// Computes the one dimensional inverse discrete Fourier transform of input.\\n /// </summary>\\n /// <param name=\\\"input\\\">The input tensor</param>\\n /// <param name=\\\"n\\\">Signal length. If given, the input will either be zero-padded or trimmed to this length before computing the IFFT.</param>\\n /// <param name=\\\"dim\\\">The dimension along which to take the one dimensional IFFT.</param>\\n /// <param name=\\\"norm\\\">Normalization mode.</param>\\n /// <returns></returns>\\n public static Tensor ifft(Tensor input, long n = -1, long dim = -1, FFTNormType norm = FFTNormType.Backward)\\n {\\n var res = THSTensor_ifft(input.Handle, n, dim, (sbyte)norm);\\n if (res == IntPtr.Zero) { torch.CheckForErrors(); }\\n return new Tensor(res);\\n }\\n\\n /// <summary>\\n /// Computes the two-dimensional discrete Fourier transform of input.\\n /// </summary>\\n /// <param name=\\\"input\\\">The input tensor</param>\\n /// <param name=\\\"s\\\">\\n /// Signal size in the transformed dimensions.\\n /// If given, each dimension dim[i] will either be zero-padded or trimmed to the length s[i] before computing the FFT.\\n /// If a length -1 is specified, no padding is done in that dimension.\\n /// </param>\\n /// <param name=\\\"dim\\\">Dimensions to be transformed</param>\\n /// <param name=\\\"norm\\\">Normalization mode.</param>\\n /// <returns></returns>\\n public static Tensor fft2(Tensor input, long[] s = null, long[] dim = null, FFTNormType norm = FFTNormType.Backward)\\n {\\n if (input.Dimensions < 2) throw new ArgumentException(\\\"fft2() input should be at least 2D\\\");\\n if (dim == null) dim = new long[] { -2, -1 };\\n unsafe {\\n fixed (long* ps = s, pDim = dim) {\\n var res = THSTensor_fft2(input.Handle, (IntPtr)ps, (IntPtr)pDim, (sbyte)norm);\\n if (res == IntPtr.Zero) { torch.CheckForErrors(); }\\n return new Tensor(res);\\n }\\n }\\n }\\n\\n /// <summary>\\n /// Computes the two-dimensional inverse discrete Fourier transform of input.\\n /// </summary>\\n /// <param name=\\\"input\\\">The input tensor</param>\\n /// <param name=\\\"s\\\">\\n /// Signal size in the transformed dimensions.\\n /// If given, each dimension dim[i] will either be zero-padded or trimmed to the length s[i] before computing the IFFT.\\n /// If a length -1 is specified, no padding is done in that dimension.\\n /// </param>\\n /// <param name=\\\"dim\\\">Dimensions to be transformed</param>\\n /// <param name=\\\"norm\\\">Normalization mode.</param>\\n public static Tensor ifft2(Tensor input, long[] s = null, long[] dim = null, FFTNormType norm = FFTNormType.Backward)\\n {\\n if (input.Dimensions < 2) throw new ArgumentException(\\\"ifft2() input should be at least 2D\\\");\\n if (dim == null) dim = new long[] { -2, -1 };\\n unsafe {\\n fixed (long* ps = s, pDim = dim) {\\n var res = THSTensor_ifft2(input.Handle, (IntPtr)ps, (IntPtr)pDim, (sbyte)norm);\\n if (res == IntPtr.Zero) { torch.CheckForErrors(); }\\n return new Tensor(res);\\n }\\n }\\n }\\n\\n /// <summary>\\n /// Computes the N-dimensional discrete Fourier transform of input.\\n /// </summary>\\n /// <param name=\\\"input\\\">The input tensor</param>\\n /// <param name=\\\"s\\\">\\n /// Signal size in the transformed dimensions.\\n /// If given, each dimension dim[i] will either be zero-padded or trimmed to the length s[i] before computing the IFFT.\\n /// If a length -1 is specified, no padding is done in that dimension.\\n /// </param>\\n /// <param name=\\\"dim\\\">Dimensions to be transformed</param>\\n /// <param name=\\\"norm\\\">Normalization mode.</param>\\n /// <returns></returns>\\n public static Tensor fftn(Tensor input, long[] s = null, long[] dim = null, FFTNormType norm = FFTNormType.Backward)\\n {\\n var slen = (s == null) ? 0 : s.Length;\\n var dlen = (dim == null) ? 0 : dim.Length;\\n unsafe {\\n fixed (long* ps = s, pDim = dim) {\\n var res = THSTensor_fftn(input.Handle, (IntPtr)ps, slen, (IntPtr)pDim, dlen, (sbyte)norm);\\n if (res == IntPtr.Zero) { torch.CheckForErrors(); }\\n return new Tensor(res);\\n }\\n }\\n }\\n\\n /// <summary>\\n /// Computes the N-dimensional inverse discrete Fourier transform of input.\\n /// </summary>\\n /// <param name=\\\"input\\\">The input tensor</param>\\n /// <param name=\\\"s\\\">\\n /// Signal size in the transformed dimensions.\\n /// If given, each dimension dim[i] will either be zero-padded or trimmed to the length s[i] before computing the IFFT.\\n /// If a length -1 is specified, no padding is done in that dimension.\\n /// </param>\\n /// <param name=\\\"dim\\\">Dimensions to be transformed</param>\\n /// <param name=\\\"norm\\\">Normalization mode.</param>\\n /// <returns></returns>\\n public static Tensor ifftn(Tensor input, long[] s = null, long[] dim = null, FFTNormType norm = FFTNormType.Backward)\\n {\\n var slen = (s == null) ? 0 : s.Length;\\n var dlen = (dim == null) ? 0 : dim.Length;\\n unsafe {\\n fixed (long* ps = s, pDim = dim) {\\n var res = THSTensor_ifftn(input.Handle, (IntPtr)ps, slen, (IntPtr)pDim, dlen, (sbyte)norm);\\n if (res == IntPtr.Zero) { torch.CheckForErrors(); }\\n return new Tensor(res);\\n }\\n }\\n }\\n\\n /// <summary>\\n /// Computes the one dimensional inverse Fourier transform of real-valued input.\\n /// </summary>\\n /// <param name=\\\"input\\\">The input tensor</param>\\n /// <param name=\\\"n\\\">Signal length. If given, the input will either be zero-padded or trimmed to this length before computing the FFT.</param>\\n /// <param name=\\\"dim\\\">The dimension along which to take the one dimensional FFT.</param>\\n /// <param name=\\\"norm\\\">Normalization mode.</param>\\n public static Tensor irfft(Tensor input, long n = -1, long dim = -1, FFTNormType norm = FFTNormType.Backward)\\n {\\n var res = THSTensor_irfft(input.Handle, n, dim, (sbyte)norm);\\n if (res == IntPtr.Zero) { torch.CheckForErrors(); }\\n return new Tensor(res);\\n }\\n\\n /// <summary>\\n /// Computes the one dimensional Fourier transform of real-valued input.\\n /// </summary>\\n /// <param name=\\\"input\\\">The input tensor</param>\\n /// <param name=\\\"n\\\">Signal length. If given, the input will either be zero-padded or trimmed to this length before computing the FFT.</param>\\n /// <param name=\\\"dim\\\">The dimension along which to take the one dimensional FFT.</param>\\n /// <param name=\\\"norm\\\">Normalization mode.</param>\\n /// <returns></returns>\\n public static Tensor rfft(Tensor input, long n = -1, long dim = -1, FFTNormType norm = FFTNormType.Backward)\\n {\\n var res = THSTensor_rfft(input.Handle, n, dim, (sbyte)norm);\\n if (res == IntPtr.Zero) { torch.CheckForErrors(); }\\n return new Tensor(res);\\n }\\n\\n /// <summary>\\n /// Computes the two-dimensional discrete Fourier transform of real-vaued input.\\n /// </summary>\\n /// <param name=\\\"input\\\">The input tensor</param>\\n /// <param name=\\\"s\\\">\\n /// Signal size in the transformed dimensions.\\n /// If given, each dimension dim[i] will either be zero-padded or trimmed to the length s[i] before computing the FFT.\\n /// If a length -1 is specified, no padding is done in that dimension.\\n /// </param>\\n /// <param name=\\\"dim\\\">Dimensions to be transformed</param>\\n /// <param name=\\\"norm\\\">Normalization mode.</param>\\n public static Tensor rfft2(Tensor input, long[] s = null, long[] dim = null, FFTNormType norm = FFTNormType.Backward)\\n {\\n if (input.Dimensions < 2) throw new ArgumentException(\\\"rfft2() input should be at least 2D\\\");\\n if (dim == null) dim = new long[] { -2, -1 };\\n unsafe {\\n fixed (long* ps = s, pDim = dim) {\\n var res = THSTensor_rfft2(input.Handle, (IntPtr)ps, (IntPtr)pDim, (sbyte)norm);\\n if (res == IntPtr.Zero) { torch.CheckForErrors(); }\\n return new Tensor(res);\\n }\\n }\\n }\\n\\n /// <summary>\\n /// Computes the two-dimensional inverse discrete Fourier transform of real-valued input.\\n /// </summary>\\n /// <param name=\\\"input\\\">The input tensor</param>\\n /// <param name=\\\"s\\\">\\n /// Signal size in the transformed dimensions.\\n /// If given, each dimension dim[i] will either be zero-padded or trimmed to the length s[i] before computing the IFFT.\\n /// If a length -1 is specified, no padding is done in that dimension.\\n /// </param>\\n /// <param name=\\\"dim\\\">Dimensions to be transformed</param>\\n /// <param name=\\\"norm\\\">Normalization mode.</param>\\n public static Tensor irfft2(Tensor input, long[] s = null, long[] dim = null, FFTNormType norm = FFTNormType.Backward)\\n {\\n if (input.Dimensions < 2) throw new ArgumentException(\\\"irfft2() input should be at least 2D\\\");\\n if (dim == null) dim = new long[] { -2, -1 };\\n unsafe {\\n fixed (long* ps = s, pDim = dim) {\\n var res = THSTensor_irfft2(input.Handle, (IntPtr)ps, (IntPtr)pDim, (sbyte)norm);\\n if (res == IntPtr.Zero) { torch.CheckForErrors(); }\\n return new Tensor(res);\\n }\\n }\\n }\\n\\n /// <summary>\\n /// Computes the N-dimensional discrete Fourier transform of real-valued input.\\n /// </summary>\\n /// <param name=\\\"input\\\">The input tensor</param>\\n /// <param name=\\\"s\\\">\\n /// Signal size in the transformed dimensions.\\n /// If given, each dimension dim[i] will either be zero-padded or trimmed to the length s[i] before computing the IFFT.\\n /// If a length -1 is specified, no padding is done in that dimension.\\n /// </param>\\n /// <param name=\\\"dim\\\">Dimensions to be transformed</param>\\n /// <param name=\\\"norm\\\">Normalization mode.</param>\\n public static Tensor rfftn(Tensor input, long[] s = null, long[] dim = null, FFTNormType norm = FFTNormType.Backward)\\n {\\n var slen = (s == null) ? 0 : s.Length;\\n var dlen = (dim == null) ? 0 : dim.Length;\\n unsafe {\\n fixed (long* ps = s, pDim = dim) {\\n var res = THSTensor_rfftn(input.Handle, (IntPtr)ps, slen, (IntPtr)pDim, dlen, (sbyte)norm);\\n if (res == IntPtr.Zero) { torch.CheckForErrors(); }\\n return new Tensor(res);\\n }\\n }\\n }\\n\\n /// <summary>\\n /// Computes the N-dimensional inverse discrete Fourier transform of real-valued input.\\n /// </summary>\\n /// <param name=\\\"input\\\">The input tensor</param>\\n /// <param name=\\\"s\\\">\\n /// Signal size in the transformed dimensions.\\n /// If given, each dimension dim[i] will either be zero-padded or trimmed to the length s[i] before computing the IFFT.\\n /// If a length -1 is specified, no padding is done in that dimension.\\n /// </param>\\n /// <param name=\\\"dim\\\">Dimensions to be transformed</param>\\n /// <param name=\\\"norm\\\">Normalization mode.</param>\\n public static Tensor irfftn(Tensor input, long[] s = null, long[] dim = null, FFTNormType norm = FFTNormType.Backward)\\n {\\n var slen = (s == null) ? 0 : s.Length;\\n var dlen = (dim == null) ? 0 : dim.Length;\\n unsafe {\\n fixed (long* ps = s, pDim = dim) {\\n var res = THSTensor_irfftn(input.Handle, (IntPtr)ps, slen, (IntPtr)pDim, dlen, (sbyte)norm);\\n if (res == IntPtr.Zero) { torch.CheckForErrors(); }\\n return new Tensor(res);\\n }\\n }\\n }\\n\\n /// <summary>\\n /// Computes the one dimensional discrete Fourier transform of a Hermitian symmetric input signal.\\n /// </summary>\\n /// <param name=\\\"input\\\">The input tensor representing a half-Hermitian signal</param>\\n /// <param name=\\\"n\\\">\\n /// Output signal length. This determines the length of the real output.\\n /// If given, the input will either be zero-padded or trimmed to this length before computing the Hermitian FFT.</param>\\n /// <param name=\\\"dim\\\">The dimension along which to take the one dimensional Hermitian FFT.</param>\\n /// <param name=\\\"norm\\\">Normalization mode.</param>\\n /// <returns></returns>\\n public static Tensor hfft(Tensor input, long n = -1, long dim = -1, FFTNormType norm = FFTNormType.Backward)\\n {\\n var res = THSTensor_hfft(input.Handle, n, dim, (sbyte)norm);\\n if (res == IntPtr.Zero) { torch.CheckForErrors(); }\\n return new Tensor(res);\\n }\\n\\n /// <summary>\\n /// Computes the inverse of hfft().\\n /// </summary>\\n /// <param name=\\\"input\\\">The input tensor representing a half-Hermitian signal</param>\\n /// <param name=\\\"n\\\">\\n /// Output signal length. This determines the length of the real output.\\n /// If given, the input will either be zero-padded or trimmed to this length before computing the Hermitian FFT.</param>\\n /// <param name=\\\"dim\\\">The dimension along which to take the one dimensional Hermitian FFT.</param>\\n /// <param name=\\\"norm\\\">Normalization mode.</param>\\n public static Tensor ihfft(Tensor input, long n = -1, long dim = -1, FFTNormType norm = FFTNormType.Backward)\\n {\\n var res = THSTensor_ihfft(input.Handle, n, dim, (sbyte)norm);\\n if (res == IntPtr.Zero) { torch.CheckForErrors(); }\\n return new Tensor(res);\\n }\\n\\n /// <summary>\\n /// Reorders n-dimensional FFT data, as provided by fftn(), to have negative frequency terms first.\\n /// This performs a periodic shift of n-dimensional data such that the origin(0, ..., 0) is moved to the center of the tensor.Specifically, to input.shape[dim] // 2 in each selected dimension.\\n /// </summary>\\n /// <param name=\\\"input\\\">The tensor in FFT order</param>\\n /// <param name=\\\"dim\\\">The dimensions to rearrange. Only dimensions specified here will be rearranged, any other dimensions will be left in their original order. Default: All dimensions of input.</param>\\n /// <returns></returns>\\n public static Tensor fftshift(Tensor input, long[] dim = null)\\n {\\n var dlen = (dim == null) ? 0 : dim.Length;\\n unsafe {\\n fixed (long* pDim = dim) {\\n var res = THSTensor_fftshift(input.Handle, (IntPtr)pDim, dlen);\\n if (res == IntPtr.Zero) { torch.CheckForErrors(); }\\n return new Tensor(res);\\n }\\n }\\n }\\n\\n /// <summary>\\n /// Inverse of fftshift().\\n /// </summary>\\n /// <param name=\\\"input\\\">The tensor in FFT order</param>\\n /// <param name=\\\"dim\\\">The dimensions to rearrange. Only dimensions specified here will be rearranged, any other dimensions will be left in their original order. Default: All dimensions of input.</param>\\n public static Tensor ifftshift(Tensor input, long[] dim = null)\\n {\\n var dlen = (dim == null) ? 0 : dim.Length;\\n unsafe {\\n fixed (long* pDim = dim) {\\n var res = THSTensor_ifftshift(input.Handle, (IntPtr)pDim, dlen);\\n if (res == IntPtr.Zero) { torch.CheckForErrors(); }\\n return new Tensor(res);\\n }\\n }\\n }\\n\\n /// <summary>\\n /// Computes the discrete Fourier Transform sample frequencies for a signal of size n.\\n /// </summary>\\n /// <param name=\\\"n\\\">The FFT length</param>\\n /// <param name=\\\"d\\\">The sampling length scale. </param>\\n /// <param name=\\\"dtype\\\">The desired data type of the returned tensor</param>\\n /// <param name=\\\"device\\\">the desired device of the returned tensor</param>\\n /// <param name=\\\"requires_grad\\\">If autograd should record operations on the returned tensor.</param>\\n public static Tensor fftfreq(long n, double d = 1.0, torch.ScalarType? dtype = null, torch.Device device = null, bool requires_grad = false)\\n {\\n device = torch.InitializeDevice(device);\\n if (!dtype.HasValue) {\\n // Determine the element type dynamically.\\n dtype = get_default_dtype();\\n }\\n\\n var handle = THSTensor_fftfreq(n, d, (sbyte)dtype, (int)device.type, device.index, requires_grad);\\n if (handle == IntPtr.Zero) {\\n GC.Collect();\\n GC.WaitForPendingFinalizers();\\n handle = THSTensor_fftfreq(n, d, (sbyte)dtype, (int)device.type, device.index, requires_grad);\\n }\\n if (handle == IntPtr.Zero) { torch.CheckForErrors(); }\\n return new Tensor(handle);\\n }\\n\\n /// <summary>\\n /// Computes the sample frequencies for rfft() with a signal of size n.\\n /// </summary>\\n /// <param name=\\\"n\\\">The FFT length</param>\\n /// <param name=\\\"d\\\">The sampling length scale. </param>\\n /// <param name=\\\"dtype\\\">The desired data type of the returned tensor</param>\\n /// <param name=\\\"device\\\">the desired device of the returned tensor</param>\\n /// <param name=\\\"requires_grad\\\">If autograd should record operations on the returned tensor.</param>\\n public static Tensor rfftfreq(long n, double d = 1.0, torch.ScalarType? dtype = null, torch.Device device = null, bool requires_grad = false)\\n {\\n device = torch.InitializeDevice(device);\\n if (!dtype.HasValue) {\\n // Determine the element type dynamically.\\n dtype = get_default_dtype();\\n }\\n\\n var handle = THSTensor_rfftfreq(n, d, (sbyte)dtype, (int)device.type, device.index, requires_grad);\\n if (handle == IntPtr.Zero) {\\n GC.Collect();\\n GC.WaitForPendingFinalizers();\\n handle = THSTensor_rfftfreq(n, d, (sbyte)dtype, (int)device.type, device.index, requires_grad);\\n }\\n if (handle == IntPtr.Zero) { torch.CheckForErrors(); }\\n return new Tensor(handle);\\n }\\n\\n /// <summary>\\n /// Computes the 2-dimensional discrete Fourier transform of a Hermitian symmetric input signal.\\n ///\\n /// Equivalent to hfftn() but only transforms the last two dimensions by default.\\n /// input is interpreted as a one-sided Hermitian signal in the time domain.\\n /// By the Hermitian property, the Fourier transform will be real-valued.\\n /// </summary>\\n /// <param name=\\\"input\\\">The input tensor</param>\\n /// <param name=\\\"s\\\">\\n /// Signal size in the transformed dimensions.\\n /// If given, each dimension dim[i] will either be zero-padded or trimmed to the length s[i] before computing the IFFT.\\n /// If a length -1 is specified, no padding is done in that dimension.\\n /// </param>\\n /// <param name=\\\"dim\\\">Dimensions to be transformed</param>\\n /// <param name=\\\"norm\\\">Normalization mode.</param>\\n public static Tensor hfft2(Tensor input, long[] s = null, long[] dim = null, FFTNormType norm = FFTNormType.Backward)\\n {\\n if (input.Dimensions < 2) throw new ArgumentException(\\\"hfft2() input should be at least 2D\\\");\\n if (dim == null) dim = new long[] { -2, -1 };\\n unsafe {\\n fixed (long* ps = s, pDim = dim) {\\n var res = THSTensor_hfft2(input.Handle, (IntPtr)ps, (IntPtr)pDim, (sbyte)norm);\\n if (res == IntPtr.Zero) { torch.CheckForErrors(); }\\n return new Tensor(res);\\n }\\n }\\n }\\n\\n /// <summary>\\n /// Computes the 2-dimensional inverse discrete Fourier transform of a Hermitian symmetric input signal.\\n ///\\n /// Equivalent to hfftn() but only transforms the last two dimensions by default.\\n /// input is interpreted as a one-sided Hermitian signal in the time domain.\\n /// By the Hermitian property, the Fourier transform will be real-valued.\\n /// </summary>\\n /// <param name=\\\"input\\\">The input tensor</param>\\n /// <param name=\\\"s\\\">\\n /// Signal size in the transformed dimensions.\\n /// If given, each dimension dim[i] will either be zero-padded or trimmed to the length s[i] before computing the IFFT.\\n /// If a length -1 is specified, no padding is done in that dimension.\\n /// </param>\\n /// <param name=\\\"dim\\\">Dimensions to be transformed</param>\\n /// <param name=\\\"norm\\\">Normalization mode.</param>\\n public static Tensor ihfft2(Tensor input, long[] s = null, long[] dim = null, FFTNormType norm = FFTNormType.Backward)\\n {\\n if (input.Dimensions < 2) throw new ArgumentException(\\\"ihfft2() input should be at least 2D\\\");\\n if (dim == null) dim = new long[] { -2, -1 };\\n unsafe {\\n fixed (long* ps = s, pDim = dim) {\\n var res = THSTensor_ihfft2(input.Handle, (IntPtr)ps, (IntPtr)pDim, (sbyte)norm);\\n if (res == IntPtr.Zero) { torch.CheckForErrors(); }\\n return new Tensor(res);\\n }\\n }\\n }\\n\\n /// <summary>\\n /// Computes the n-dimensional discrete Fourier transform of a Herimitian symmetric input signal.\\n ///\\n /// input is interpreted as a one-sided Hermitian signal in the time domain.\\n /// By the Hermitian property, the Fourier transform will be real-valued.\\n /// </summary>\\n /// <param name=\\\"input\\\">The input tensor</param>\\n /// <param name=\\\"s\\\">\\n /// Signal size in the transformed dimensions.\\n /// If given, each dimension dim[i] will either be zero-padded or trimmed to the length s[i] before computing the IFFT.\\n /// If a length -1 is specified, no padding is done in that dimension.\\n /// </param>\\n /// <param name=\\\"dim\\\">Dimensions to be transformed</param>\\n /// <param name=\\\"norm\\\">Normalization mode.</param>\\n public static Tensor hfftn(Tensor input, long[] s = null, long[] dim = null, FFTNormType norm = FFTNormType.Backward)\\n {\\n if (input.Dimensions < 2) throw new ArgumentException(\\\"hfftn() input should be at least 2D\\\");\\n var slen = (s == null) ? 0 : s.Length;\\n var dlen = (dim == null) ? 0 : dim.Length;\\n unsafe {\\n fixed (long* ps = s, pDim = dim) {\\n var res = THSTensor_hfftn(input.Handle, (IntPtr)ps, slen, (IntPtr)pDim, dlen, (sbyte)norm);\\n if (res == IntPtr.Zero) { torch.CheckForErrors(); }\\n return new Tensor(res);\\n }\\n }\\n }\\n\\n /// <summary>\\n /// Computes the n-dimensional inverse discrete Fourier transform of a Herimitian symmetric input signal.\\n ///\\n /// input is interpreted as a one-sided Hermitian signal in the time domain.\\n /// By the Hermitian property, the Fourier transform will be real-valued.\\n /// </summary>\\n /// <param name=\\\"input\\\">The input tensor</param>\\n /// <param name=\\\"s\\\">\\n /// Signal size in the transformed dimensions.\\n /// If given, each dimension dim[i] will either be zero-padded or trimmed to the length s[i] before computing the IFFT.\\n /// If a length -1 is specified, no padding is done in that dimension.\\n /// </param>\\n /// <param name=\\\"dim\\\">Dimensions to be transformed</param>\\n /// <param name=\\\"norm\\\">Normalization mode.</param>\\n public static Tensor ihfftn(Tensor input, long[] s = null, long[] dim = null, FFTNormType norm = FFTNormType.Backward)\\n {\\n if (input.Dimensions < 2) throw new ArgumentException(\\\"ihfftn() input should be at least 2D\\\");\\n var slen = (s == null) ? 0 : s.Length;\\n var dlen = (dim == null) ? 0 : dim.Length;\\n unsafe {\\n fixed (long* ps = s, pDim = dim) {\\n var res = THSTensor_ihfftn(input.Handle, (IntPtr)ps, slen, (IntPtr)pDim, dlen, (sbyte)norm);\\n if (res == IntPtr.Zero) { torch.CheckForErrors(); }\\n return new Tensor(res);\\n }\\n }\\n }\\n }\\n }\\n}\\n\"\n },\n {\n \"alpha_fraction\": 0.6702355742454529,\n \"alphanum_fraction\": 0.6761242151260376,\n \"avg_line_length\": 38.74468231201172,\n \"blob_id\": \"df97e2c877d61ee2e0d34fbf4747b4506e8ec7a7\",\n \"content_id\": \"07b3e5f52d9a5fac6e3c5d1f55bce2067b1f518b\",\n \"detected_licenses\": [\n \"MIT\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"C#\",\n \"length_bytes\": 1868,\n \"license_type\": \"permissive\",\n \"max_line_length\": 149,\n \"num_lines\": 47,\n \"path\": \"/src/TorchSharp/PInvoke/LibTorchSharp.THSAutograd.cs\",\n \"repo_name\": \"NiklasGustafsson/TorchSharp\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"// Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information.\\n#nullable enable\\nusing System;\\nusing System.Runtime.InteropServices;\\n\\nnamespace TorchSharp.PInvoke\\n{\\n internal static partial class NativeMethods\\n {\\n [DllImport(\\\"LibTorchSharp\\\")]\\n [return: MarshalAs(UnmanagedType.U1)]\\n internal static extern bool THSAutograd_isGradEnabled();\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern void THSAutograd_setGrad([MarshalAs(UnmanagedType.U1)] bool enabled);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n [return: MarshalAs(UnmanagedType.U1)]\\n internal static extern bool THSAutograd_isAnomalyEnabled();\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n [return: MarshalAs(UnmanagedType.U1)]\\n internal static extern bool THSAutograd_shouldCheckNaN();\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern void THSAutograd_setAnomaly([MarshalAs(UnmanagedType.U1)] bool enabled, [MarshalAs(UnmanagedType.U1)] bool check_nan);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern void THSAutograd_grad(\\n IntPtr outputs, long oLength,\\n IntPtr inputs, long iLength,\\n IntPtr grad_outs, long gLength,\\n [MarshalAs(UnmanagedType.U1)] bool retain_graph,\\n [MarshalAs(UnmanagedType.U1)] bool create_graph,\\n [MarshalAs(UnmanagedType.U1)] bool allow_unused,\\n AllocatePinnedArray allocator);\\n\\n [DllImport(\\\"LibTorchSharp\\\")]\\n internal static extern void THSAutograd_backward(\\n IntPtr tensors, long tLength,\\n IntPtr grad_tensors, long gtLength,\\n [MarshalAs(UnmanagedType.U1)] bool retain_graph,\\n [MarshalAs(UnmanagedType.U1)] bool create_graph,\\n IntPtr inputs, long iLength);\\n\\n }\\n}\\n\"\n },\n {\n \"alpha_fraction\": 0.5888888835906982,\n \"alphanum_fraction\": 0.5888888835906982,\n \"avg_line_length\": 25.52941131591797,\n \"blob_id\": \"f6e08bcdaea1d2a53fb46a7fcb82c8051d9e5d75\",\n \"content_id\": \"b40c2060b32b7c454e9663ddb5c62baaecef3de8\",\n \"detected_licenses\": [\n \"MIT\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"C#\",\n \"length_bytes\": 452,\n \"license_type\": \"permissive\",\n \"max_line_length\": 131,\n \"num_lines\": 17,\n \"path\": \"/src/TorchSharp/Tensor/Enums/layout.cs\",\n \"repo_name\": \"NiklasGustafsson/TorchSharp\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"// Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information.\\nnamespace TorchSharp\\n{\\n // https://pytorch.org/docs/stable/tensor_attributes.html#torch.layout\\n public enum layout\\n {\\n /// <summary>\\n /// dense Tensors\\n /// </summary>\\n strided,\\n\\n /// <summary>\\n /// sparse COO Tensors\\n /// </summary>\\n sparse_coo\\n }\\n}\"\n },\n {\n \"alpha_fraction\": 0.6787330508232117,\n \"alphanum_fraction\": 0.6854012608528137,\n \"avg_line_length\": 49.60240936279297,\n \"blob_id\": \"a2b316f3ffc736cfddc5c80b03967f2d718421b5\",\n \"content_id\": \"42745a786e9aa4a8a519e12c229a41bf66ffd78e\",\n \"detected_licenses\": [\n \"MIT\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"C#\",\n \"length_bytes\": 4199,\n \"license_type\": \"permissive\",\n \"max_line_length\": 144,\n \"num_lines\": 83,\n \"path\": \"/src/TorchSharp/Tensor/torch.Utilities.cs\",\n \"repo_name\": \"NiklasGustafsson/TorchSharp\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"// Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information.\\n#nullable enable\\nusing System;\\nusing System.Diagnostics.Contracts;\\nusing static TorchSharp.PInvoke.NativeMethods;\\n\\nnamespace TorchSharp\\n{\\n // https://pytorch.org/docs/stable/torch#utilities\\n public static partial class torch\\n {\\n // https://pytorch.org/docs/stable/generated/torch.compiled_with_cxx11_abi\\n [Pure, Obsolete(\\\"not implemented\\\", true)]\\n public static bool compiled_with_cxx11_abi() => throw new NotImplementedException();\\n\\n // https://pytorch.org/docs/stable/generated/torch.result_type\\n public static ScalarType result_type(Tensor tensor1, Tensor tensor2)\\n {\\n var res = THSTensor_result_type(tensor1.Handle, tensor2.Handle);\\n if (res == -1) CheckForErrors();\\n return (ScalarType)res;\\n }\\n\\n // https://pytorch.org/docs/stable/generated/torch.can_cast\\n public static bool can_cast(ScalarType from, ScalarType to)\\n {\\n var res = THSTorch_can_cast((int)from, (int)to);\\n if (res == -1) CheckForErrors();\\n return res != 0;\\n }\\n\\n // https://pytorch.org/docs/stable/generated/torch.promote_types\\n public static ScalarType promote_types(ScalarType type1, ScalarType type2)\\n {\\n var res = THSTorch_promote_types((int)type1, (int)type2);\\n if (res == -1) CheckForErrors();\\n return (ScalarType)res;\\n }\\n\\n // https://pytorch.org/docs/stable/generated/torch.use_deterministic_algorithms\\n [Obsolete(\\\"not implemented\\\", true)]\\n public static IDisposable use_deterministic_algorithms(bool mode = true, bool warn_only = false) => throw new NotImplementedException();\\n\\n // https://pytorch.org/docs/stable/generated/torch.are_deterministic_algorithms_enabled\\n [Pure, Obsolete(\\\"not implemented\\\", true)]\\n public static bool are_deterministic_algorithms_enabled() => throw new NotImplementedException();\\n\\n // https://pytorch.org/docs/stable/generated/torch.is_deterministic_algorithms_warn_only_enabled\\n [Pure, Obsolete(\\\"not implemented\\\", true)]\\n public static bool is_deterministic_algorithms_warn_only_enabled() => throw new NotImplementedException();\\n\\n // https://pytorch.org/docs/stable/generated/torch.set_deterministic_debug_mode\\n [Obsolete(\\\"not implemented\\\", true)]\\n public static IDisposable set_deterministic_debug_mode(DebugMode mode) => throw new NotImplementedException();\\n\\n // https://pytorch.org/docs/stable/generated/torch.get_deterministic_debug_mode\\n [Pure, Obsolete(\\\"not implemented\\\", true)]\\n public static DebugMode get_deterministic_debug_mode() => throw new NotImplementedException();\\n\\n // https://pytorch.org/docs/stable/generated/torch.set_float32_matmul_precision\\n [Obsolete(\\\"not implemented\\\", true)]\\n public static IDisposable set_float32_matmul_precision(Float32MatmulPrecision precision) => throw new NotImplementedException();\\n\\n // https://pytorch.org/docs/stable/generated/torch.get_float32_matmul_precision\\n [Pure, Obsolete(\\\"not implemented\\\", true)]\\n public static Float32MatmulPrecision get_float32_matmul_precision() => throw new NotImplementedException();\\n\\n // https://pytorch.org/docs/stable/generated/torch.set_warn_always\\n [Obsolete(\\\"not implemented\\\", true)]\\n public static IDisposable set_warn_always(bool mode = true) => throw new NotImplementedException();\\n\\n // https://pytorch.org/docs/stable/generated/torch.is_warn_always_enabled\\n [Pure, Obsolete(\\\"not implemented\\\", true)]\\n public static bool is_warn_always_enabled() => throw new NotImplementedException();\\n\\n // https://pytorch.org/docs/stable/generated/torch._assert\\n [Obsolete(\\\"not implemented\\\", true)]\\n public static void _assert(bool condition, string message) => throw new NotImplementedException();\\n\\n [Obsolete(\\\"not implemented\\\", true)]\\n public static void _assert(Func<bool> condition, string message) => throw new NotImplementedException();\\n }\\n}\"\n },\n {\n \"alpha_fraction\": 0.45644113421440125,\n \"alphanum_fraction\": 0.4817243814468384,\n \"avg_line_length\": 36.910335540771484,\n \"blob_id\": \"fbc4c51d0749eed58be5e937508ccc7d38825fdd\",\n \"content_id\": \"3ece4ce4af0a5b8d288c075b145522f194d1a6d5\",\n \"detected_licenses\": [\n \"MIT\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"C#\",\n \"length_bytes\": 96823,\n \"license_type\": \"permissive\",\n \"max_line_length\": 130,\n \"num_lines\": 2554,\n \"path\": \"/test/TorchSharpTest/TestLoadSave.cs\",\n \"repo_name\": \"NiklasGustafsson/TorchSharp\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"// Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information.\\nusing System;\\nusing System.IO;\\nusing System.Linq;\\nusing TorchSharp.Modules;\\nusing static TorchSharp.torch.nn;\\nusing Xunit;\\nusing System.Collections.Generic;\\n\\n#nullable enable\\n\\nnamespace TorchSharp\\n{\\n [Collection(\\\"Sequential\\\")]\\n public class TestLoadSave\\n {\\n private static Random rand = new Random();\\n\\n static string RandomFileName(string extension)\\n {\\n return $\\\"_STR{rand.Next()}.{extension}\\\";\\n }\\n\\n [Fact]\\n public void TestSaveLoadLinear1()\\n {\\n var location = RandomFileName(\\\"ts\\\");\\n\\n if (File.Exists(location)) File.Delete(location);\\n\\n try {\\n var linear = Linear(100, 10, true);\\n var params0 = linear.parameters();\\n linear.save(location);\\n\\n var loadedLinear = Linear(100, 10, true);\\n loadedLinear.load(location);\\n\\n var params1 = loadedLinear.parameters();\\n Assert.Equal(params0, params1);\\n\\n loadedLinear = Linear(100, 10, true);\\n loadedLinear.load(location, skip: new[] { \\\"weight\\\" });\\n var params2 = loadedLinear.parameters();\\n\\n Assert.NotEqual(params0.First(), params2.First());\\n Assert.Equal(params0.Skip(1).First(), params2.Skip(1).First());\\n } finally {\\n if (File.Exists(location)) File.Delete(location);\\n }\\n }\\n\\n [Fact]\\n public void TestSaveLoadLinear2()\\n {\\n var location = RandomFileName(\\\"ts\\\");\\n\\n if (File.Exists(location)) File.Delete(location);\\n try {\\n var linear = Linear(100, 10, true);\\n var params0 = linear.parameters();\\n linear.save(location, skip: new[] { \\\"weight\\\" });\\n\\n var loadedLinear = Linear(100, 10, true);\\n Assert.Throws<ArgumentException>(() => loadedLinear.load(location, strict: true));\\n loadedLinear.load(location, strict: false);\\n var params2 = loadedLinear.parameters();\\n\\n Assert.NotEqual(params0.First(), params2.First());\\n Assert.Equal(params0.Skip(1).First(), params2.Skip(1).First());\\n } finally {\\n if (File.Exists(location)) File.Delete(location);\\n }\\n }\\n\\n [Fact]\\n public void TestSaveLoadLinear3()\\n {\\n var location = RandomFileName(\\\"ts\\\");\\n if (File.Exists(location)) File.Delete(location);\\n\\n try {\\n using var linear = Linear(100, 10, true);\\n var params0 = linear.parameters();\\n linear.save(location);\\n\\n var loadedLinear = Linear(10, 10, true); // Mismatched shape, shouldn't matter when skipped.\\n Assert.Throws<ArgumentException>(() => loadedLinear.load(location));\\n\\n loadedLinear.load(location, skip: new[] { \\\"weight\\\" });\\n var params2 = loadedLinear.parameters();\\n\\n Assert.NotEqual(params0.First(), params2.First());\\n Assert.Equal(params0.Skip(1).First(), params2.Skip(1).First());\\n } finally {\\n if (File.Exists(location)) File.Delete(location);\\n }\\n }\\n\\n [Fact]\\n public void TestSaveLoadConv2D()\\n {\\n var location = RandomFileName(\\\"ts\\\");\\n if (File.Exists(location)) File.Delete(location);\\n try {\\n using var conv = Conv2d(100, 10, 5);\\n var params0 = conv.parameters();\\n conv.save(location);\\n using var loaded = Conv2d(100, 10, 5);\\n loaded.load(location);\\n var params1 = loaded.parameters();\\n\\n Assert.Equal(params0, params1);\\n } finally {\\n if (File.Exists(location)) File.Delete(location);\\n }\\n }\\n\\n [Fact]\\n public void TestSaveLoadConv2D_sd()\\n {\\n using var conv = Conv2d(100, 10, 5);\\n var params0 = conv.parameters();\\n\\n var sd = conv.state_dict();\\n\\n using var loaded = Conv2d(100, 10, 5);\\n Assert.NotEqual(params0, loaded.parameters());\\n\\n loaded.load_state_dict(sd);\\n Assert.Equal(params0, loaded.parameters());\\n }\\n\\n [Fact]\\n public void TestSaveLoadConv2DStream()\\n {\\n var location = RandomFileName(\\\"ts\\\");\\n if (File.Exists(location)) File.Delete(location);\\n try {\\n using var conv = Conv2d(100, 10, 5);\\n var params0 = conv.parameters();\\n\\n using (var stream = System.IO.File.OpenWrite(location)) {\\n conv.save(stream);\\n }\\n\\n using var loaded = Conv2d(100, 10, 5);\\n\\n using (var stream = System.IO.File.OpenRead(location)) {\\n loaded.load(stream);\\n }\\n var params1 = loaded.parameters();\\n\\n Assert.Equal(params0, params1);\\n\\n } finally {\\n if (File.Exists(location)) File.Delete(location);\\n }\\n }\\n\\n [Fact]\\n public void TestSaveLoadSequential()\\n {\\n var location = RandomFileName(\\\"ts\\\");\\n if (File.Exists(location)) File.Delete(location);\\n try {\\n using var conv = Sequential(Conv2d(100, 10, 5), Linear(100, 10, true));\\n var params0 = conv.parameters();\\n conv.save(location);\\n using var loaded = Sequential(Conv2d(100, 10, 5), Linear(100, 10, true));\\n loaded.load(location);\\n var params1 = loaded.parameters();\\n\\n Assert.Equal(params0, params1);\\n } finally {\\n if (File.Exists(location)) File.Delete(location);\\n }\\n }\\n\\n [Fact]\\n public void TestSaveLoadSequentialStream()\\n {\\n var location = RandomFileName(\\\"ts\\\");\\n if (File.Exists(location)) File.Delete(location);\\n try {\\n using var conv = Sequential(Conv2d(100, 10, 5), Linear(100, 10, true));\\n var params0 = conv.parameters();\\n using (var stream = System.IO.File.OpenWrite(location)) {\\n conv.save(stream);\\n }\\n using var loaded = Sequential(Conv2d(100, 10, 5), Linear(100, 10, true));\\n using (var stream = System.IO.File.OpenRead(location)) {\\n loaded.load(stream);\\n }\\n var params1 = loaded.parameters();\\n\\n Assert.Equal(params0, params1);\\n } finally {\\n if (File.Exists(location)) File.Delete(location);\\n }\\n }\\n\\n [Fact]\\n public void TestSaveLoadSequential_sd()\\n {\\n using var conv = Sequential(Conv2d(100, 10, 5), Linear(100, 10, true));\\n var params0 = conv.parameters();\\n\\n var sd = conv.state_dict();\\n\\n using var loaded = Sequential(Conv2d(100, 10, 5), Linear(100, 10, true));\\n Assert.NotEqual(params0, loaded.parameters());\\n\\n loaded.load_state_dict(sd);\\n Assert.Equal(params0, loaded.parameters());\\n }\\n\\n [Fact]\\n public void TestSaveLoadSequential_error1()\\n {\\n using var conv = Sequential(Conv2d(100, 10, 5), Linear(100, 10, true));\\n var params0 = conv.parameters();\\n\\n var sd = conv.state_dict();\\n sd.Remove(\\\"0.bias\\\");\\n\\n using var loaded = Sequential(Conv2d(100, 10, 5), Linear(100, 10, true));\\n Assert.NotEqual(params0, loaded.parameters());\\n\\n Assert.Throws<InvalidOperationException>(() => loaded.load_state_dict(sd));\\n }\\n\\n [Fact]\\n public void TestSaveLoadSequential_error2()\\n {\\n using var conv = Sequential(Conv2d(100, 10, 5), Linear(100, 10, true));\\n var params0 = conv.parameters();\\n\\n var sd = conv.state_dict();\\n var t = sd[\\\"0.bias\\\"];\\n\\n sd.Add(\\\"2.bias\\\", t);\\n\\n using var loaded = Sequential(Conv2d(100, 10, 5), Linear(100, 10, true));\\n Assert.NotEqual(params0, loaded.parameters());\\n\\n Assert.Throws<InvalidOperationException>(() => loaded.load_state_dict(sd));\\n }\\n\\n [Fact]\\n public void TestSaveLoadSequential_lax()\\n {\\n using var conv = Sequential(Conv2d(100, 10, 5), Linear(100, 10, true));\\n var params0 = conv.parameters();\\n\\n var sd = conv.state_dict();\\n var t = sd[\\\"0.bias\\\"];\\n sd.Remove(\\\"0.bias\\\");\\n\\n sd.Add(\\\"2.bias\\\", t);\\n\\n using var loaded = Sequential(Conv2d(100, 10, 5), Linear(100, 10, true));\\n Assert.NotEqual(params0, loaded.parameters());\\n\\n var (m, u) = loaded.load_state_dict(sd, false);\\n Assert.NotEqual(params0, loaded.parameters());\\n\\n Assert.NotEmpty(m);\\n Assert.NotEmpty(u);\\n }\\n\\n [Fact]\\n public void TestSaveLoadCustomWithParameters()\\n {\\n var location = RandomFileName(\\\"ts\\\");\\n if (File.Exists(location)) File.Delete(location);\\n\\n try {\\n using var original = new TestModule1();\\n Assert.True(original.has_parameter(\\\"test\\\"));\\n\\n var params0 = original.parameters();\\n Assert.True(params0.ToArray().ToArray()[0].requires_grad);\\n original.save(location);\\n\\n using var loaded = new TestModule1();\\n Assert.True(loaded.has_parameter(\\\"test\\\"));\\n\\n var params1 = loaded.parameters();\\n Assert.True(params1.ToArray()[0].requires_grad);\\n Assert.NotEqual(params0.ToArray(), params1);\\n\\n loaded.load(location);\\n var params2 = loaded.parameters();\\n Assert.True(params2.ToArray()[0].requires_grad);\\n Assert.Equal(params0, params2);\\n } finally {\\n if (File.Exists(location)) File.Delete(location);\\n }\\n }\\n\\n private class TestModule1 : Module<torch.Tensor, torch.Tensor>\\n {\\n public TestModule1() : base(\\\"TestModule1\\\")\\n {\\n RegisterComponents();\\n }\\n\\n public override torch.Tensor forward(torch.Tensor input)\\n {\\n throw new NotImplementedException();\\n }\\n\\n private Parameter test = Parameter(torch.randn(new long[] { 2, 2 }));\\n }\\n\\n\\n [Fact]\\n public void TestSaveLoadError_1()\\n {\\n var location = RandomFileName(\\\"ts\\\");\\n if (File.Exists(location)) File.Delete(location);\\n\\n try {\\n var linear = Linear(100, 10, true);\\n var params0 = linear.parameters();\\n linear.save(location);\\n var loaded = Conv2d(100, 10, 5);\\n Assert.Throws<ArgumentException>(() => loaded.load(location));\\n } finally {\\n if (File.Exists(location)) File.Delete(location);\\n }\\n }\\n\\n [Fact]\\n public void TestSaveLoadError_2()\\n {\\n var location = \\\"TestSaveLoadError_2.ts\\\";\\n\\n // Submodule count mismatch\\n if (File.Exists(location)) File.Delete(location);\\n try {\\n var linear = Sequential((\\\"linear1\\\", Linear(100, 10, true)));\\n var params0 = linear.parameters();\\n linear.save(location);\\n var loaded = Sequential((\\\"linear1\\\", Linear(100, 10, true)), (\\\"conv1\\\", Conv2d(100, 10, 5)));\\n Assert.Throws<ArgumentException>(() => loaded.load(location));\\n // Shouldn't get an error for this:\\n loaded.load(location, strict: false);\\n } finally {\\n if (File.Exists(location)) File.Delete(location);\\n }\\n }\\n\\n [Fact]\\n public void TestSaveLoadError_3()\\n {\\n var location = RandomFileName(\\\"ts\\\");\\n // Submodule name mismatch\\n if (File.Exists(location)) File.Delete(location);\\n try {\\n var linear = Sequential((\\\"linear1\\\", Linear(100, 10, true)));\\n var params0 = linear.parameters();\\n linear.save(location);\\n var loaded = Sequential((\\\"linear2\\\", Linear(100, 10, true)));\\n Assert.Throws<ArgumentException>(() => loaded.load(location));\\n File.Delete(location);\\n } finally {\\n if (File.Exists(location)) File.Delete(location);\\n }\\n }\\n\\n [Fact]\\n public void TestSaveLoadSequence()\\n {\\n if (File.Exists(\\\".model-list.txt\\\")) File.Delete(\\\".model-list.txt\\\");\\n\\n var lin1 = Linear(100, 10, true);\\n var lin2 = Linear(10, 5, true);\\n var seq = Sequential((\\\"lin1\\\", lin1), (\\\"lin2\\\", lin2));\\n var params0 = seq.parameters();\\n seq.save(\\\".model-list.txt\\\");\\n\\n var lin3 = Linear(100, 10, true);\\n var lin4 = Linear(10, 5, true);\\n var seq1 = Sequential((\\\"lin1\\\", lin3), (\\\"lin2\\\", lin4));\\n seq1.load(\\\".model-list.txt\\\");\\n File.Delete(\\\"model-list.txt\\\");\\n\\n var params1 = seq1.parameters();\\n Assert.Equal(params0, params1);\\n }\\n\\n [Fact]\\n public void TestSaveLoadGruOnCPU()\\n {\\n var location = RandomFileName(\\\"ts\\\");\\n // Works on CPU\\n if (File.Exists(location)) File.Delete(location);\\n try {\\n var gru = GRU(2, 2, 2);\\n var params0 = gru.parameters();\\n gru.save(location);\\n\\n var loadedGru = GRU(2, 2, 2);\\n loadedGru.load(location);\\n var params1 = loadedGru.parameters();\\n File.Delete(location);\\n Assert.Equal(params0, params1);\\n } finally {\\n if (File.Exists(location)) File.Delete(location);\\n }\\n }\\n\\n [Fact]\\n public void TestSaveLoadGruOnCUDA()\\n {\\n var location = RandomFileName(\\\"ts\\\");\\n if (torch.cuda.is_available()) {\\n\\n // Fails on CUDA\\n if (File.Exists(location)) File.Delete(location);\\n try {\\n var gru = GRU(2, 2, 2);\\n gru.to(DeviceType.CUDA);\\n var params0 = gru.parameters().ToArray();\\n Assert.Equal(DeviceType.CUDA, params0[0].device_type);\\n\\n gru.save(location);\\n\\n // Make sure the model is still on the GPU when we come back.\\n\\n params0 = gru.parameters().ToArray();\\n Assert.Equal(DeviceType.CUDA, params0[0].device_type);\\n\\n var loadedGru = GRU(2, 2, 2);\\n loadedGru.to(DeviceType.CUDA);\\n loadedGru.load(location);\\n var params1 = loadedGru.parameters().ToArray();\\n\\n Assert.Equal(DeviceType.CUDA, params1[0].device_type);\\n\\n File.Delete(location);\\n Assert.Equal(params0, params1);\\n } finally {\\n if (File.Exists(location)) File.Delete(location);\\n }\\n\\n }\\n }\\n\\n [Fact]\\n public void TestSummaryWriterLogDir()\\n {\\n var w1 = torch.utils.tensorboard.SummaryWriter(\\\"runs/123\\\");\\n Assert.Equal(\\\"runs/123\\\", w1.LogDir);\\n if (Directory.Exists(w1.LogDir)) {\\n Directory.Delete(w1.LogDir, recursive: true);\\n }\\n\\n var w2 = torch.utils.tensorboard.SummaryWriter(\\\"garbage\\\", createRunName: true);\\n Assert.NotEqual(\\\"garbage\\\", w2.LogDir);\\n Assert.StartsWith(\\\"garbage\\\", w2.LogDir);\\n if (Directory.Exists(w2.LogDir)) {\\n Directory.Delete(w2.LogDir, recursive: true);\\n }\\n }\\n\\n [Fact]\\n public void TestTensorBoardScalar()\\n {\\n var writer = torch.utils.tensorboard.SummaryWriter();\\n Assert.StartsWith(\\\"runs\\\", writer.LogDir);\\n\\n for (var i = 0; i < 100; i++) {\\n writer.add_scalar(\\\"a/b\\\", MathF.Sin(i * MathF.PI / 8), i);\\n }\\n\\n // Comment this out to look at the output data in tensorboard\\n if (Directory.Exists(writer.LogDir)) {\\n Directory.Delete(writer.LogDir, recursive: true);\\n }\\n }\\n\\n [Fact]\\n public void TestTensorBoardScalars1()\\n {\\n var writer = torch.utils.tensorboard.SummaryWriter();\\n for (var i = 0; i < 100; i++) {\\n float f = i;\\n writer.add_scalars(\\\"run_14h\\\", new Dictionary<string, float> {\\n { \\\"sin\\\",i* MathF.Sin(f / 5) },\\n { \\\"cos\\\", i* MathF.Cos(f / 5) },\\n { \\\"tan\\\", MathF.Tan(f / 5) }\\n }, i);\\n }\\n\\n // Comment this out to look at the output data in tensorboard\\n if (Directory.Exists(writer.LogDir)) {\\n Directory.Delete(writer.LogDir, recursive: true);\\n }\\n }\\n\\n [Fact]\\n public void TestTensorBoardScalars2()\\n {\\n var writer = torch.utils.tensorboard.SummaryWriter();\\n for (var i = 0; i < 100; i++) {\\n float f = i;\\n writer.add_scalars(\\\"run_14h\\\", new[] {\\n (\\\"sin\\\",i* MathF.Sin(f / 5)),\\n (\\\"cos\\\", i* MathF.Cos(f / 5)),\\n (\\\"tan\\\", MathF.Tan(f / 5))\\n }, i);\\n }\\n\\n // Comment this out to look at the output data in tensorboard\\n if (Directory.Exists(writer.LogDir)) {\\n Directory.Delete(writer.LogDir, recursive: true);\\n }\\n }\\n\\n [Fact]\\n public void TestSaveRprop()\\n {\\n var l1 = Linear(10, 10, true);\\n var l2 = Linear(10, 10, true);\\n var seq = Sequential(l1, l2);\\n\\n var optim = torch.optim.Rprop(seq.parameters());\\n\\n var sd = optim.state_dict();\\n Assert.Multiple(\\n () => Assert.Equal(4, sd.State.Count),\\n () => Assert.Single(sd.Options)\\n );\\n\\n var optim2 = torch.optim.Rprop(seq.parameters(), 0.5, 0.01, 1.25, 1e-8, 35);\\n optim2.load_state_dict(sd);\\n\\n var sd2 = optim2.state_dict();\\n\\n Assert.Multiple(\\n () => Assert.Equal(4, sd2.State.Count),\\n () => Assert.Single(sd2.Options)\\n );\\n\\n for (int i = 0; i < sd.Options.Count; i++) {\\n var opt = sd.Options[i] as Modules.Rprop.Options;\\n var opt2 = sd2.Options[i] as Modules.Rprop.Options;\\n Assert.Multiple(\\n () => Assert.Equal(opt!.InitialLearningRate, opt2!.InitialLearningRate),\\n () => Assert.Equal(opt!.LearningRate, opt2!.LearningRate),\\n () => Assert.Equal(opt!.etaminus, opt2!.etaminus),\\n () => Assert.Equal(opt!.etaplus, opt2!.etaplus),\\n () => Assert.Equal(opt!.min_step, opt2!.min_step),\\n () => Assert.Equal(opt!.max_step, opt2!.max_step)\\n );\\n }\\n for (int i = 0; i < sd.State.Count; i++) {\\n var opt = sd.State[i] as Modules.Rprop.State;\\n var opt2 = sd2.State[i] as Modules.Rprop.State;\\n Assert.True(opt!.ApproximatelyEquals(opt2));\\n }\\n }\\n\\n [Fact]\\n public void TestSaveRpropFile()\\n {\\n var location = RandomFileName(\\\"ts\\\");\\n\\n if (File.Exists(location)) File.Delete(location);\\n var l1 = Linear(10, 10, true);\\n var l2 = Linear(10, 10, true);\\n var seq = Sequential(l1, l2);\\n\\n var optim = torch.optim.Rprop(seq.parameters());\\n\\n var sd = optim.state_dict();\\n Assert.Multiple(\\n () => Assert.Equal(4, sd.State.Count),\\n () => Assert.Single(sd.Options)\\n );\\n\\n var optim2 = torch.optim.Rprop(seq.parameters(), 0.5, 0.01, 1.25, 1e-8, 35);\\n\\n try {\\n optim.save_state_dict(location);\\n optim2.load_state_dict(location);\\n\\n var sd2 = optim2.state_dict();\\n\\n Assert.Multiple(\\n () => Assert.Equal(4, sd2.State.Count),\\n () => Assert.Single(sd2.Options)\\n );\\n\\n for (int i = 0; i < sd.Options.Count; i++) {\\n var opt = sd.Options[i] as Modules.Rprop.Options;\\n var opt2 = sd2.Options[i] as Modules.Rprop.Options;\\n Assert.Multiple(\\n () => Assert.Equal(opt!.InitialLearningRate, opt2!.InitialLearningRate),\\n () => Assert.Equal(opt!.LearningRate, opt2!.LearningRate),\\n () => Assert.Equal(opt!.etaminus, opt2!.etaminus),\\n () => Assert.Equal(opt!.etaplus, opt2!.etaplus),\\n () => Assert.Equal(opt!.min_step, opt2!.min_step),\\n () => Assert.Equal(opt!.max_step, opt2!.max_step)\\n );\\n }\\n for (int i = 0; i < sd.State.Count; i++) {\\n var opt = sd.State[i] as Modules.Rprop.State;\\n var opt2 = sd2.State[i] as Modules.Rprop.State;\\n Assert.True(opt!.ApproximatelyEquals(opt2));\\n }\\n } finally {\\n File.Delete(location);\\n }\\n }\\n\\n [Fact]\\n public void TestSaveSGD()\\n {\\n var l1 = Linear(10, 10, true);\\n var l2 = Linear(10, 10, true);\\n var seq = Sequential(l1, l2);\\n\\n var optim = torch.optim.SGD(seq.parameters(), 0.01);\\n\\n var sd = optim.state_dict();\\n Assert.Multiple(\\n () => Assert.Equal(4, sd.State.Count),\\n () => Assert.Single(sd.Options)\\n );\\n\\n var optim2 = torch.optim.SGD(seq.parameters(), 0.5, 0.01, 1.25, 1e-8);\\n optim2.load_state_dict(sd);\\n\\n var sd2 = optim2.state_dict();\\n\\n Assert.Multiple(\\n () => Assert.Equal(4, sd2.State.Count),\\n () => Assert.Single(sd2.Options)\\n );\\n\\n for (int i = 0; i < sd.Options.Count; i++) {\\n var opt = sd.Options[i] as Modules.SGD.Options;\\n var opt2 = sd2.Options[i] as Modules.SGD.Options;\\n Assert.Multiple(\\n () => Assert.Equal(opt!.InitialLearningRate, opt2!.InitialLearningRate),\\n () => Assert.Equal(opt!.LearningRate, opt2!.LearningRate),\\n () => Assert.Equal(opt!.momentum, opt2!.momentum),\\n () => Assert.Equal(opt!.dampening, opt2!.dampening),\\n () => Assert.Equal(opt!.weight_decay, opt2!.weight_decay)\\n );\\n }\\n for (int i = 0; i < sd.State.Count; i++) {\\n var opt = sd.State[i] as Modules.SGD.State;\\n var opt2 = sd2.State[i] as Modules.SGD.State;\\n Assert.True(opt!.ApproximatelyEquals(opt2));\\n }\\n }\\n\\n [Fact]\\n public void TestSaveSGDFile()\\n {\\n var location = RandomFileName(\\\"ts\\\");\\n\\n if (File.Exists(location)) File.Delete(location);\\n var l1 = Linear(10, 10, true);\\n var l2 = Linear(10, 10, true);\\n var seq = Sequential(l1, l2);\\n\\n var optim = torch.optim.SGD(seq.parameters(), 0.01);\\n\\n var sd = optim.state_dict();\\n Assert.Multiple(\\n () => Assert.Equal(4, sd.State.Count),\\n () => Assert.Single(sd.Options)\\n );\\n\\n var optim2 = torch.optim.SGD(seq.parameters(), 0.5, 0.01, 1.25, 1e-8);\\n\\n try {\\n optim.save_state_dict(location);\\n optim2.load_state_dict(location);\\n\\n var sd2 = optim2.state_dict();\\n\\n Assert.Multiple(\\n () => Assert.Equal(4, sd2.State.Count),\\n () => Assert.Single(sd2.Options)\\n );\\n\\n for (int i = 0; i < sd.Options.Count; i++) {\\n var opt = sd.Options[i] as Modules.SGD.Options;\\n var opt2 = sd2.Options[i] as Modules.SGD.Options;\\n Assert.Multiple(\\n () => Assert.Equal(opt!.InitialLearningRate, opt2!.InitialLearningRate),\\n () => Assert.Equal(opt!.LearningRate, opt2!.LearningRate),\\n () => Assert.Equal(opt!.momentum, opt2!.momentum),\\n () => Assert.Equal(opt!.dampening, opt2!.dampening),\\n () => Assert.Equal(opt!.weight_decay, opt2!.weight_decay)\\n );\\n }\\n for (int i = 0; i < sd.State.Count; i++) {\\n var opt = sd.State[i] as Modules.SGD.State;\\n var opt2 = sd2.State[i] as Modules.SGD.State;\\n Assert.True(opt!.ApproximatelyEquals(opt2));\\n }\\n } finally {\\n File.Delete(location);\\n }\\n }\\n\\n [Fact]\\n public void TestSaveSGDFile2()\\n {\\n var location = RandomFileName(\\\"ts\\\");\\n\\n if (File.Exists(location)) File.Delete(location);\\n var l1 = Linear(10, 10, true);\\n var l2 = Linear(10, 10, true);\\n var seq = Sequential(l1, l2);\\n\\n var optim = torch.optim.SGD(seq.parameters(), 0.01);\\n\\n var sd = optim.state_dict();\\n Assert.Multiple(\\n () => Assert.Equal(4, sd.State.Count),\\n () => Assert.Single(sd.Options)\\n );\\n\\n var optim2 = torch.optim.SGD(seq.parameters(), 0.5, 0.01, 1.25, 1e-8);\\n\\n // Force the SGD momentum_buffer to be created.\\n\\n using var x = torch.randn(new long[] { 64, 10 });\\n using var y = torch.randn(new long[] { 64, 10 });\\n\\n var loss = torch.nn.MSELoss(Reduction.Sum);\\n\\n using var eval = seq.call(x);\\n var output = loss.call(eval, y);\\n\\n var l = output.ToSingle();\\n\\n optim2.zero_grad();\\n\\n output.backward();\\n\\n optim2.step();\\n\\n try {\\n optim.save_state_dict(location);\\n optim2.load_state_dict(location);\\n\\n var sd2 = optim2.state_dict();\\n\\n Assert.Multiple(\\n () => Assert.Equal(4, sd2.State.Count),\\n () => Assert.Single(sd2.Options)\\n );\\n\\n for (int i = 0; i < sd.Options.Count; i++) {\\n var opt = sd.Options[i] as Modules.SGD.Options;\\n var opt2 = sd2.Options[i] as Modules.SGD.Options;\\n Assert.Multiple(\\n () => Assert.Equal(opt!.InitialLearningRate, opt2!.InitialLearningRate),\\n () => Assert.Equal(opt!.LearningRate, opt2!.LearningRate),\\n () => Assert.Equal(opt!.momentum, opt2!.momentum),\\n () => Assert.Equal(opt!.dampening, opt2!.dampening),\\n () => Assert.Equal(opt!.weight_decay, opt2!.weight_decay)\\n );\\n }\\n for (int i = 0; i < sd.State.Count; i++) {\\n var opt = sd.State[i] as Modules.SGD.State;\\n var opt2 = sd2.State[i] as Modules.SGD.State;\\n Assert.True(opt!.ApproximatelyEquals(opt2));\\n }\\n } finally {\\n File.Delete(location);\\n }\\n }\\n\\n [Fact]\\n public void TestSaveSGDFile3()\\n {\\n var location = RandomFileName(\\\"ts\\\");\\n\\n if (File.Exists(location)) File.Delete(location);\\n var l1 = Linear(10, 10, true);\\n var l2 = Linear(10, 10, true);\\n var seq = Sequential(l1, l2);\\n\\n var optim = torch.optim.SGD(seq.parameters(), 0.01, 0.025);\\n\\n var sd = optim.state_dict();\\n Assert.Multiple(\\n () => Assert.Equal(4, sd.State.Count),\\n () => Assert.Single(sd.Options)\\n );\\n\\n var optim2 = torch.optim.SGD(seq.parameters(), 0.5, 0.01, 1.25, 1e-8);\\n\\n // Force the SGD momentum_buffer to be created.\\n\\n using var x = torch.randn(new long[] { 64, 10 });\\n using var y = torch.randn(new long[] { 64, 10 });\\n\\n var loss = torch.nn.MSELoss(Reduction.Sum);\\n\\n using var eval = seq.call(x);\\n var output = loss.call(eval, y);\\n\\n var l = output.ToSingle();\\n\\n optim.zero_grad();\\n\\n output.backward();\\n\\n optim.step();\\n\\n try {\\n optim.save_state_dict(location);\\n optim2.load_state_dict(location);\\n\\n var sd2 = optim2.state_dict();\\n\\n Assert.Multiple(\\n () => Assert.Equal(4, sd2.State.Count),\\n () => Assert.Single(sd2.Options)\\n );\\n\\n for (int i = 0; i < sd.Options.Count; i++) {\\n var opt = sd.Options[i] as Modules.SGD.Options;\\n var opt2 = sd2.Options[i] as Modules.SGD.Options;\\n Assert.Multiple(\\n () => Assert.Equal(opt!.InitialLearningRate, opt2!.InitialLearningRate),\\n () => Assert.Equal(opt!.LearningRate, opt2!.LearningRate),\\n () => Assert.Equal(opt!.momentum, opt2!.momentum),\\n () => Assert.Equal(opt!.dampening, opt2!.dampening),\\n () => Assert.Equal(opt!.weight_decay, opt2!.weight_decay)\\n );\\n }\\n for (int i = 0; i < sd.State.Count; i++) {\\n var opt = sd.State[i] as Modules.SGD.State;\\n var opt2 = sd2.State[i] as Modules.SGD.State;\\n Assert.True(opt!.ApproximatelyEquals(opt2));\\n }\\n } finally {\\n File.Delete(location);\\n }\\n }\\n\\n [Fact]\\n public void TestSaveASGD()\\n {\\n var l1 = Linear(10, 10, true);\\n var l2 = Linear(10, 10, true);\\n var seq = Sequential(l1, l2);\\n\\n var optim = torch.optim.ASGD(seq.parameters());\\n\\n var sd = optim.state_dict();\\n Assert.Multiple(\\n () => Assert.Equal(4, sd.State.Count),\\n () => Assert.Single(sd.Options)\\n );\\n\\n var optim2 = torch.optim.ASGD(seq.parameters(), 0.5, 0.01, 1.25, 1e-8, 0.25);\\n optim2.load_state_dict(sd);\\n\\n var sd2 = optim2.state_dict();\\n\\n Assert.Multiple(\\n () => Assert.Equal(4, sd2.State.Count),\\n () => Assert.Single(sd2.Options)\\n );\\n\\n for (int i = 0; i < sd.Options.Count; i++) {\\n var opt = sd.Options[i] as Modules.ASGD.Options;\\n var opt2 = sd2.Options[i] as Modules.ASGD.Options;\\n Assert.Multiple(\\n () => Assert.Equal(opt!.InitialLearningRate, opt2!.InitialLearningRate),\\n () => Assert.Equal(opt!.LearningRate, opt2!.LearningRate),\\n () => Assert.Equal(opt!.lambd, opt2!.lambd),\\n () => Assert.Equal(opt!.alpha, opt2!.alpha),\\n () => Assert.Equal(opt!.t0, opt2!.t0),\\n () => Assert.Equal(opt!.weight_decay, opt2!.weight_decay)\\n );\\n }\\n for (int i = 0; i < sd.State.Count; i++) {\\n var opt = sd.State[i] as Modules.ASGD.State;\\n var opt2 = sd2.State[i] as Modules.ASGD.State;\\n Assert.True(opt!.ApproximatelyEquals(opt2));\\n }\\n }\\n\\n [Fact]\\n public void TestSaveASGDFile()\\n {\\n var location = RandomFileName(\\\"ts\\\");\\n if (File.Exists(location)) File.Delete(location);\\n\\n var l1 = Linear(10, 10, true);\\n var l2 = Linear(10, 10, true);\\n var seq = Sequential(l1, l2);\\n\\n var optim = torch.optim.ASGD(seq.parameters());\\n\\n var sd = optim.state_dict();\\n Assert.Multiple(\\n () => Assert.Equal(4, sd.State.Count),\\n () => Assert.Single(sd.Options)\\n );\\n\\n var optim2 = torch.optim.ASGD(seq.parameters(), 0.5, 0.01, 1.25, 1e-8, 0.25);\\n\\n try {\\n optim.save_state_dict(location);\\n optim2.load_state_dict(location);\\n\\n var sd2 = optim2.state_dict();\\n\\n Assert.Multiple(\\n () => Assert.Equal(4, sd2.State.Count),\\n () => Assert.Single(sd2.Options)\\n );\\n\\n for (int i = 0; i < sd.Options.Count; i++) {\\n var opt = sd.Options[i] as Modules.ASGD.Options;\\n var opt2 = sd2.Options[i] as Modules.ASGD.Options;\\n Assert.Multiple(\\n () => Assert.Equal(opt!.InitialLearningRate, opt2!.InitialLearningRate),\\n () => Assert.Equal(opt!.LearningRate, opt2!.LearningRate),\\n () => Assert.Equal(opt!.lambd, opt2!.lambd),\\n () => Assert.Equal(opt!.alpha, opt2!.alpha),\\n () => Assert.Equal(opt!.t0, opt2!.t0),\\n () => Assert.Equal(opt!.weight_decay, opt2!.weight_decay)\\n );\\n }\\n for (int i = 0; i < sd.State.Count; i++) {\\n var opt = sd.State[i] as Modules.ASGD.State;\\n var opt2 = sd2.State[i] as Modules.ASGD.State;\\n Assert.True(opt!.ApproximatelyEquals(opt2));\\n }\\n } finally {\\n File.Delete(location);\\n }\\n }\\n\\n [Fact]\\n public void TestSaveRMSProp()\\n {\\n var l1 = Linear(10, 10, true);\\n var l2 = Linear(10, 10, true);\\n var seq = Sequential(l1, l2);\\n\\n var optim = torch.optim.RMSProp(seq.parameters());\\n\\n var sd = optim.state_dict();\\n Assert.Multiple(\\n () => Assert.Equal(4, sd.State.Count),\\n () => Assert.Single(sd.Options)\\n );\\n\\n var optim2 = torch.optim.RMSProp(seq.parameters(), 0.5, 0.01, 1.25, 1e-8, 0.25);\\n optim2.load_state_dict(sd);\\n\\n var sd2 = optim2.state_dict();\\n\\n Assert.Multiple(\\n () => Assert.Equal(4, sd2.State.Count),\\n () => Assert.Single(sd2.Options)\\n );\\n\\n for (int i = 0; i < sd.Options.Count; i++) {\\n var opt = sd.Options[i] as Modules.RMSProp.Options;\\n var opt2 = sd2.Options[i] as Modules.RMSProp.Options;\\n Assert.Multiple(\\n () => Assert.Equal(opt!.InitialLearningRate, opt2!.InitialLearningRate),\\n () => Assert.Equal(opt!.LearningRate, opt2!.LearningRate),\\n () => Assert.Equal(opt!.momentum, opt2!.momentum),\\n () => Assert.Equal(opt!.alpha, opt2!.alpha),\\n () => Assert.Equal(opt!.eps, opt2!.eps),\\n () => Assert.Equal(opt!.weight_decay, opt2!.weight_decay)\\n );\\n }\\n for (int i = 0; i < sd.State.Count; i++) {\\n var opt = sd.State[i] as Modules.RMSProp.State;\\n var opt2 = sd2.State[i] as Modules.RMSProp.State;\\n Assert.True(opt!.ApproximatelyEquals(opt2));\\n }\\n }\\n\\n [Fact]\\n public void TestSaveRMSPropFile()\\n {\\n var location = RandomFileName(\\\"ts\\\");\\n if (File.Exists(location)) File.Delete(location);\\n\\n var l1 = Linear(10, 10, true);\\n var l2 = Linear(10, 10, true);\\n var seq = Sequential(l1, l2);\\n\\n var optim = torch.optim.RMSProp(seq.parameters());\\n\\n var sd = optim.state_dict();\\n Assert.Multiple(\\n () => Assert.Equal(4, sd.State.Count),\\n () => Assert.Single(sd.Options)\\n );\\n\\n var optim2 = torch.optim.RMSProp(seq.parameters(), 0.5, 0.01, 1.25, 1e-8, 0.25);\\n\\n try {\\n optim.save_state_dict(location);\\n optim2.load_state_dict(location);\\n\\n var sd2 = optim2.state_dict();\\n\\n Assert.Multiple(\\n () => Assert.Equal(4, sd2.State.Count),\\n () => Assert.Single(sd2.Options)\\n );\\n\\n for (int i = 0; i < sd.Options.Count; i++) {\\n var opt = sd.Options[i] as Modules.RMSProp.Options;\\n var opt2 = sd2.Options[i] as Modules.RMSProp.Options;\\n Assert.Multiple(\\n () => Assert.Equal(opt!.InitialLearningRate, opt2!.InitialLearningRate),\\n () => Assert.Equal(opt!.LearningRate, opt2!.LearningRate),\\n () => Assert.Equal(opt!.momentum, opt2!.momentum),\\n () => Assert.Equal(opt!.alpha, opt2!.alpha),\\n () => Assert.Equal(opt!.eps, opt2!.eps),\\n () => Assert.Equal(opt!.weight_decay, opt2!.weight_decay)\\n );\\n }\\n for (int i = 0; i < sd.State.Count; i++) {\\n var opt = sd.State[i] as Modules.RMSProp.State;\\n var opt2 = sd2.State[i] as Modules.RMSProp.State;\\n Assert.True(opt!.ApproximatelyEquals(opt2));\\n }\\n } finally {\\n File.Delete(location);\\n }\\n }\\n\\n [Fact]\\n public void TestSaveRAdam()\\n {\\n var l1 = Linear(10, 10, true);\\n var l2 = Linear(10, 10, true);\\n var seq = Sequential(l1, l2);\\n\\n var optim = torch.optim.RAdam(seq.parameters());\\n\\n var sd = optim.state_dict();\\n Assert.Multiple(\\n () => Assert.Equal(4, sd.State.Count),\\n () => Assert.Single(sd.Options)\\n );\\n\\n var optim2 = torch.optim.RAdam(seq.parameters(), 0.5, 0.01, 0.75, 1e-8, 0.25);\\n optim2.load_state_dict(sd);\\n\\n var sd2 = optim2.state_dict();\\n\\n Assert.Multiple(\\n () => Assert.Equal(4, sd2.State.Count),\\n () => Assert.Single(sd2.Options)\\n );\\n\\n for (int i = 0; i < sd.Options.Count; i++) {\\n var opt = sd.Options[i] as Modules.RAdam.Options;\\n var opt2 = sd2.Options[i] as Modules.RAdam.Options;\\n Assert.Multiple(\\n () => Assert.Equal(opt!.InitialLearningRate, opt2!.InitialLearningRate),\\n () => Assert.Equal(opt!.LearningRate, opt2!.LearningRate),\\n () => Assert.Equal(opt!.beta1, opt2!.beta1),\\n () => Assert.Equal(opt!.beta2, opt2!.beta2),\\n () => Assert.Equal(opt!.eps, opt2!.eps),\\n () => Assert.Equal(opt!.weight_decay, opt2!.weight_decay)\\n );\\n }\\n for (int i = 0; i < sd.State.Count; i++) {\\n var opt = sd.State[i] as Modules.RAdam.State;\\n var opt2 = sd2.State[i] as Modules.RAdam.State;\\n Assert.True(opt!.ApproximatelyEquals(opt2));\\n }\\n }\\n\\n [Fact]\\n public void TestSaveRAdamFile()\\n {\\n var location = RandomFileName(\\\"ts\\\");\\n if (File.Exists(location)) File.Delete(location);\\n\\n var l1 = Linear(10, 10, true);\\n var l2 = Linear(10, 10, true);\\n var seq = Sequential(l1, l2);\\n\\n var optim = torch.optim.RAdam(seq.parameters());\\n\\n var sd = optim.state_dict();\\n Assert.Multiple(\\n () => Assert.Equal(4, sd.State.Count),\\n () => Assert.Single(sd.Options)\\n );\\n\\n var optim2 = torch.optim.RAdam(seq.parameters(), 0.5, 0.01, 0.75, 1e-8, 0.25);\\n\\n try {\\n optim.save_state_dict(location);\\n optim2.load_state_dict(location);\\n\\n var sd2 = optim2.state_dict();\\n\\n Assert.Multiple(\\n () => Assert.Equal(4, sd2.State.Count),\\n () => Assert.Single(sd2.Options)\\n );\\n\\n for (int i = 0; i < sd.Options.Count; i++) {\\n var opt = sd.Options[i] as Modules.RAdam.Options;\\n var opt2 = sd2.Options[i] as Modules.RAdam.Options;\\n Assert.Multiple(\\n () => Assert.Equal(opt!.InitialLearningRate, opt2!.InitialLearningRate),\\n () => Assert.Equal(opt!.LearningRate, opt2!.LearningRate),\\n () => Assert.Equal(opt!.beta1, opt2!.beta1),\\n () => Assert.Equal(opt!.beta2, opt2!.beta2),\\n () => Assert.Equal(opt!.eps, opt2!.eps),\\n () => Assert.Equal(opt!.weight_decay, opt2!.weight_decay)\\n );\\n }\\n for (int i = 0; i < sd.State.Count; i++) {\\n var opt = sd.State[i] as Modules.RAdam.State;\\n var opt2 = sd2.State[i] as Modules.RAdam.State;\\n Assert.True(opt!.ApproximatelyEquals(opt2));\\n }\\n } finally {\\n File.Delete(location);\\n }\\n }\\n\\n [Fact]\\n public void TestSaveNAdam()\\n {\\n var l1 = Linear(10, 10, true);\\n var l2 = Linear(10, 10, true);\\n var seq = Sequential(l1, l2);\\n\\n var optim = torch.optim.NAdam(seq.parameters());\\n\\n var sd = optim.state_dict();\\n Assert.Multiple(\\n () => Assert.Equal(4, sd.State.Count),\\n () => Assert.Single(sd.Options)\\n );\\n\\n var optim2 = torch.optim.NAdam(seq.parameters(), 0.5, 0.01, 0.75, 1e-8, 0.25);\\n optim2.load_state_dict(sd);\\n\\n var sd2 = optim2.state_dict();\\n\\n Assert.Multiple(\\n () => Assert.Equal(4, sd2.State.Count),\\n () => Assert.Single(sd2.Options)\\n );\\n\\n for (int i = 0; i < sd.Options.Count; i++) {\\n var opt = sd.Options[i] as Modules.NAdam.Options;\\n var opt2 = sd2.Options[i] as Modules.NAdam.Options;\\n Assert.Multiple(\\n () => Assert.Equal(opt!.InitialLearningRate, opt2!.InitialLearningRate),\\n () => Assert.Equal(opt!.LearningRate, opt2!.LearningRate),\\n () => Assert.Equal(opt!.beta1, opt2!.beta1),\\n () => Assert.Equal(opt!.beta2, opt2!.beta2),\\n () => Assert.Equal(opt!.eps, opt2!.eps),\\n () => Assert.Equal(opt!.weight_decay, opt2!.weight_decay),\\n () => Assert.Equal(opt!.momentum_decay, opt2!.momentum_decay)\\n );\\n }\\n for (int i = 0; i < sd.State.Count; i++) {\\n var opt = sd.State[i] as Modules.NAdam.State;\\n var opt2 = sd2.State[i] as Modules.NAdam.State;\\n Assert.True(opt!.ApproximatelyEquals(opt2));\\n }\\n }\\n\\n [Fact]\\n public void TestSaveNAdamFile()\\n {\\n var location = RandomFileName(\\\"ts\\\");\\n if (File.Exists(location)) File.Delete(location);\\n\\n var l1 = Linear(10, 10, true);\\n var l2 = Linear(10, 10, true);\\n var seq = Sequential(l1, l2);\\n\\n var optim = torch.optim.NAdam(seq.parameters());\\n\\n var sd = optim.state_dict();\\n Assert.Multiple(\\n () => Assert.Equal(4, sd.State.Count),\\n () => Assert.Single(sd.Options)\\n );\\n\\n var optim2 = torch.optim.NAdam(seq.parameters(), 0.5, 0.01, 0.75, 1e-8, 0.25);\\n\\n try {\\n optim.save_state_dict(location);\\n optim2.load_state_dict(location);\\n\\n var sd2 = optim2.state_dict();\\n\\n Assert.Multiple(\\n () => Assert.Equal(4, sd2.State.Count),\\n () => Assert.Single(sd2.Options)\\n );\\n\\n for (int i = 0; i < sd.Options.Count; i++) {\\n var opt = sd.Options[i] as Modules.NAdam.Options;\\n var opt2 = sd2.Options[i] as Modules.NAdam.Options;\\n Assert.Multiple(\\n () => Assert.Equal(opt!.InitialLearningRate, opt2!.InitialLearningRate),\\n () => Assert.Equal(opt!.LearningRate, opt2!.LearningRate),\\n () => Assert.Equal(opt!.beta1, opt2!.beta1),\\n () => Assert.Equal(opt!.beta2, opt2!.beta2),\\n () => Assert.Equal(opt!.eps, opt2!.eps),\\n () => Assert.Equal(opt!.weight_decay, opt2!.weight_decay),\\n () => Assert.Equal(opt!.momentum_decay, opt2!.momentum_decay)\\n );\\n }\\n for (int i = 0; i < sd.State.Count; i++) {\\n var opt = sd.State[i] as Modules.NAdam.State;\\n var opt2 = sd2.State[i] as Modules.NAdam.State;\\n Assert.True(opt!.ApproximatelyEquals(opt2));\\n }\\n } finally {\\n File.Delete(location);\\n }\\n }\\n\\n [Fact]\\n public void TestSaveAdam()\\n {\\n var l1 = Linear(10, 10, true);\\n var l2 = Linear(10, 10, true);\\n var seq = Sequential(l1, l2);\\n\\n var optim = torch.optim.Adam(seq.parameters());\\n\\n var sd = optim.state_dict();\\n Assert.Multiple(\\n () => Assert.Equal(4, sd.State.Count),\\n () => Assert.Single(sd.Options)\\n );\\n\\n var optim2 = torch.optim.Adam(seq.parameters(), 0.5, 0.01, 0.75, 1e-8, 0.25);\\n optim2.load_state_dict(sd);\\n\\n var sd2 = optim2.state_dict();\\n\\n Assert.Multiple(\\n () => Assert.Equal(4, sd2.State.Count),\\n () => Assert.Single(sd2.Options)\\n );\\n\\n for (int i = 0; i < sd.Options.Count; i++) {\\n var opt = sd.Options[i] as Modules.Adam.Options;\\n var opt2 = sd2.Options[i] as Modules.Adam.Options;\\n Assert.Multiple(\\n () => Assert.Equal(opt!.InitialLearningRate, opt2!.InitialLearningRate),\\n () => Assert.Equal(opt!.LearningRate, opt2!.LearningRate),\\n () => Assert.Equal(opt!.beta1, opt2!.beta1),\\n () => Assert.Equal(opt!.beta2, opt2!.beta2),\\n () => Assert.Equal(opt!.eps, opt2!.eps),\\n () => Assert.Equal(opt!.weight_decay, opt2!.weight_decay),\\n () => Assert.Equal(opt!.amsgrad, opt2!.amsgrad),\\n () => Assert.Equal(opt!.maximize, opt2!.maximize)\\n );\\n }\\n for (int i = 0; i < sd.State.Count; i++) {\\n var opt = sd.State[i] as Modules.Adam.State;\\n var opt2 = sd2.State[i] as Modules.Adam.State;\\n Assert.True(opt!.ApproximatelyEquals(opt2));\\n }\\n }\\n\\n [Fact]\\n public void TestSaveAdamFile()\\n {\\n var location = RandomFileName(\\\"ts\\\");\\n if (File.Exists(location)) File.Delete(location);\\n\\n var l1 = Linear(10, 10, true);\\n var l2 = Linear(10, 10, true);\\n var seq = Sequential(l1, l2);\\n\\n var optim = torch.optim.Adam(seq.parameters());\\n\\n var sd = optim.state_dict();\\n Assert.Multiple(\\n () => Assert.Equal(4, sd.State.Count),\\n () => Assert.Single(sd.Options)\\n );\\n\\n var optim2 = torch.optim.Adam(seq.parameters(), 0.5, 0.01, 0.75, 1e-8, 0.25);\\n\\n try {\\n optim.save_state_dict(location);\\n optim2.load_state_dict(location);\\n\\n var sd2 = optim2.state_dict();\\n\\n Assert.Multiple(\\n () => Assert.Equal(4, sd2.State.Count),\\n () => Assert.Single(sd2.Options)\\n );\\n\\n for (int i = 0; i < sd.Options.Count; i++) {\\n var opt = sd.Options[i] as Modules.Adam.Options;\\n var opt2 = sd2.Options[i] as Modules.Adam.Options;\\n Assert.Multiple(\\n () => Assert.Equal(opt!.InitialLearningRate, opt2!.InitialLearningRate),\\n () => Assert.Equal(opt!.LearningRate, opt2!.LearningRate),\\n () => Assert.Equal(opt!.beta1, opt2!.beta1),\\n () => Assert.Equal(opt!.beta2, opt2!.beta2),\\n () => Assert.Equal(opt!.eps, opt2!.eps),\\n () => Assert.Equal(opt!.weight_decay, opt2!.weight_decay),\\n () => Assert.Equal(opt!.amsgrad, opt2!.amsgrad),\\n () => Assert.Equal(opt!.maximize, opt2!.maximize)\\n );\\n }\\n for (int i = 0; i < sd.State.Count; i++) {\\n var opt = sd.State[i] as Modules.Adam.State;\\n var opt2 = sd2.State[i] as Modules.Adam.State;\\n Assert.True(opt!.ApproximatelyEquals(opt2));\\n }\\n } finally {\\n File.Delete(location);\\n }\\n }\\n\\n [Fact]\\n public void TestSaveAdamFile2()\\n {\\n var location = RandomFileName(\\\"ts\\\");\\n if (File.Exists(location)) File.Delete(location);\\n\\n var l1 = Linear(10, 10, true);\\n var l2 = Linear(10, 10, true);\\n var seq = Sequential(l1, l2);\\n\\n var optim = torch.optim.Adam(seq.parameters());\\n\\n var sd = optim.state_dict();\\n Assert.Multiple(\\n () => Assert.Equal(4, sd.State.Count),\\n () => Assert.Single(sd.Options)\\n );\\n\\n var optim2 = torch.optim.Adam(seq.parameters(), 0.5, 0.01, 0.75, 1e-8, 0.25, true);\\n\\n try {\\n optim.save_state_dict(location);\\n optim2.load_state_dict(location);\\n\\n var sd2 = optim2.state_dict();\\n\\n Assert.Multiple(\\n () => Assert.Equal(4, sd2.State.Count),\\n () => Assert.Single(sd2.Options)\\n );\\n\\n for (int i = 0; i < sd.Options.Count; i++) {\\n var opt = sd.Options[i] as Modules.Adam.Options;\\n var opt2 = sd2.Options[i] as Modules.Adam.Options;\\n Assert.Multiple(\\n () => Assert.Equal(opt!.InitialLearningRate, opt2!.InitialLearningRate),\\n () => Assert.Equal(opt!.LearningRate, opt2!.LearningRate),\\n () => Assert.Equal(opt!.beta1, opt2!.beta1),\\n () => Assert.Equal(opt!.beta2, opt2!.beta2),\\n () => Assert.Equal(opt!.eps, opt2!.eps),\\n () => Assert.Equal(opt!.weight_decay, opt2!.weight_decay),\\n () => Assert.Equal(opt!.amsgrad, opt2!.amsgrad),\\n () => Assert.Equal(opt!.maximize, opt2!.maximize)\\n );\\n }\\n for (int i = 0; i < sd.State.Count; i++) {\\n var opt = sd.State[i] as Modules.Adam.State;\\n var opt2 = sd2.State[i] as Modules.Adam.State;\\n Assert.True(opt!.ApproximatelyEquals(opt2));\\n }\\n } finally {\\n File.Delete(location);\\n }\\n }\\n\\n [Fact]\\n public void TestSaveAdamFile3()\\n {\\n var location = RandomFileName(\\\"ts\\\");\\n if (File.Exists(location)) File.Delete(location);\\n\\n var l1 = Linear(10, 10, true);\\n var l2 = Linear(10, 10, true);\\n var seq = Sequential(l1, l2);\\n\\n var optim = torch.optim.Adam(seq.parameters(), amsgrad: true);\\n\\n var sd = optim.state_dict();\\n Assert.Multiple(\\n () => Assert.Equal(4, sd.State.Count),\\n () => Assert.Single(sd.Options)\\n );\\n\\n var optim2 = torch.optim.Adam(seq.parameters(), 0.5, 0.01, 0.75, 1e-8, 0.25);\\n\\n try {\\n optim.save_state_dict(location);\\n optim2.load_state_dict(location);\\n\\n var sd2 = optim2.state_dict();\\n\\n Assert.Multiple(\\n () => Assert.Equal(4, sd2.State.Count),\\n () => Assert.Single(sd2.Options)\\n );\\n\\n for (int i = 0; i < sd.Options.Count; i++) {\\n var opt = sd.Options[i] as Modules.Adam.Options;\\n var opt2 = sd2.Options[i] as Modules.Adam.Options;\\n Assert.Multiple(\\n () => Assert.Equal(opt!.InitialLearningRate, opt2!.InitialLearningRate),\\n () => Assert.Equal(opt!.LearningRate, opt2!.LearningRate),\\n () => Assert.Equal(opt!.beta1, opt2!.beta1),\\n () => Assert.Equal(opt!.beta2, opt2!.beta2),\\n () => Assert.Equal(opt!.eps, opt2!.eps),\\n () => Assert.Equal(opt!.weight_decay, opt2!.weight_decay),\\n () => Assert.Equal(opt!.amsgrad, opt2!.amsgrad),\\n () => Assert.Equal(opt!.maximize, opt2!.maximize)\\n );\\n }\\n for (int i = 0; i < sd.State.Count; i++) {\\n var opt = sd.State[i] as Modules.Adam.State;\\n var opt2 = sd2.State[i] as Modules.Adam.State;\\n Assert.True(opt!.ApproximatelyEquals(opt2));\\n }\\n } finally {\\n File.Delete(location);\\n }\\n }\\n\\n [Fact]\\n public void TestSaveAdamW()\\n {\\n var l1 = Linear(10, 10, true);\\n var l2 = Linear(10, 10, true);\\n var seq = Sequential(l1, l2);\\n\\n var optim = torch.optim.AdamW(seq.parameters());\\n\\n var sd = optim.state_dict();\\n Assert.Multiple(\\n () => Assert.Equal(4, sd.State.Count),\\n () => Assert.Single(sd.Options)\\n );\\n\\n var optim2 = torch.optim.AdamW(seq.parameters(), 0.5, 0.01, 0.75, 1e-8, 0.25);\\n optim2.load_state_dict(sd);\\n\\n var sd2 = optim2.state_dict();\\n\\n Assert.Multiple(\\n () => Assert.Equal(4, sd2.State.Count),\\n () => Assert.Single(sd2.Options)\\n );\\n\\n for (int i = 0; i < sd.Options.Count; i++) {\\n var opt = sd.Options[i] as Modules.AdamW.Options;\\n var opt2 = sd2.Options[i] as Modules.AdamW.Options;\\n Assert.Multiple(\\n () => Assert.Equal(opt!.InitialLearningRate, opt2!.InitialLearningRate),\\n () => Assert.Equal(opt!.LearningRate, opt2!.LearningRate),\\n () => Assert.Equal(opt!.beta1, opt2!.beta1),\\n () => Assert.Equal(opt!.beta2, opt2!.beta2),\\n () => Assert.Equal(opt!.eps, opt2!.eps),\\n () => Assert.Equal(opt!.weight_decay, opt2!.weight_decay),\\n () => Assert.Equal(opt!.amsgrad, opt2!.amsgrad),\\n () => Assert.Equal(opt!.maximize, opt2!.maximize)\\n );\\n }\\n for (int i = 0; i < sd.State.Count; i++) {\\n var opt = sd.State[i] as Modules.AdamW.State;\\n var opt2 = sd2.State[i] as Modules.AdamW.State;\\n Assert.True(opt!.ApproximatelyEquals(opt2));\\n }\\n }\\n\\n [Fact]\\n public void TestSaveAdamWFile()\\n {\\n var location = RandomFileName(\\\"ts\\\");\\n if (File.Exists(location)) File.Delete(location);\\n\\n var l1 = Linear(10, 10, true);\\n var l2 = Linear(10, 10, true);\\n var seq = Sequential(l1, l2);\\n\\n var optim = torch.optim.AdamW(seq.parameters());\\n\\n var sd = optim.state_dict();\\n Assert.Multiple(\\n () => Assert.Equal(4, sd.State.Count),\\n () => Assert.Single(sd.Options)\\n );\\n\\n var optim2 = torch.optim.AdamW(seq.parameters(), 0.5, 0.01, 0.75, 1e-8, 0.25);\\n\\n try {\\n optim.save_state_dict(location);\\n optim2.load_state_dict(location);\\n\\n var sd2 = optim2.state_dict();\\n\\n Assert.Multiple(\\n () => Assert.Equal(4, sd2.State.Count),\\n () => Assert.Single(sd2.Options)\\n );\\n\\n for (int i = 0; i < sd.Options.Count; i++) {\\n var opt = sd.Options[i] as Modules.AdamW.Options;\\n var opt2 = sd2.Options[i] as Modules.AdamW.Options;\\n Assert.Multiple(\\n () => Assert.Equal(opt!.InitialLearningRate, opt2!.InitialLearningRate),\\n () => Assert.Equal(opt!.LearningRate, opt2!.LearningRate),\\n () => Assert.Equal(opt!.beta1, opt2!.beta1),\\n () => Assert.Equal(opt!.beta2, opt2!.beta2),\\n () => Assert.Equal(opt!.eps, opt2!.eps),\\n () => Assert.Equal(opt!.weight_decay, opt2!.weight_decay),\\n () => Assert.Equal(opt!.amsgrad, opt2!.amsgrad),\\n () => Assert.Equal(opt!.maximize, opt2!.maximize)\\n );\\n }\\n for (int i = 0; i < sd.State.Count; i++) {\\n var opt = sd.State[i] as Modules.AdamW.State;\\n var opt2 = sd2.State[i] as Modules.AdamW.State;\\n Assert.True(opt!.ApproximatelyEquals(opt2));\\n }\\n } finally {\\n File.Delete(location);\\n }\\n }\\n\\n [Fact]\\n public void TestSaveAdamWFile2()\\n {\\n var location = RandomFileName(\\\"ts\\\");\\n if (File.Exists(location)) File.Delete(location);\\n\\n var l1 = Linear(10, 10, true);\\n var l2 = Linear(10, 10, true);\\n var seq = Sequential(l1, l2);\\n\\n var optim = torch.optim.AdamW(seq.parameters());\\n\\n var sd = optim.state_dict();\\n Assert.Multiple(\\n () => Assert.Equal(4, sd.State.Count),\\n () => Assert.Single(sd.Options)\\n );\\n\\n var optim2 = torch.optim.AdamW(seq.parameters(), 0.5, 0.01, 0.75, 1e-8, 0.25, true);\\n\\n try {\\n optim.save_state_dict(location);\\n optim2.load_state_dict(location);\\n\\n var sd2 = optim2.state_dict();\\n\\n Assert.Multiple(\\n () => Assert.Equal(4, sd2.State.Count),\\n () => Assert.Single(sd2.Options)\\n );\\n\\n for (int i = 0; i < sd.Options.Count; i++) {\\n var opt = sd.Options[i] as Modules.AdamW.Options;\\n var opt2 = sd2.Options[i] as Modules.AdamW.Options;\\n Assert.Multiple(\\n () => Assert.Equal(opt!.InitialLearningRate, opt2!.InitialLearningRate),\\n () => Assert.Equal(opt!.LearningRate, opt2!.LearningRate),\\n () => Assert.Equal(opt!.beta1, opt2!.beta1),\\n () => Assert.Equal(opt!.beta2, opt2!.beta2),\\n () => Assert.Equal(opt!.eps, opt2!.eps),\\n () => Assert.Equal(opt!.weight_decay, opt2!.weight_decay),\\n () => Assert.Equal(opt!.amsgrad, opt2!.amsgrad),\\n () => Assert.Equal(opt!.maximize, opt2!.maximize)\\n );\\n }\\n for (int i = 0; i < sd.State.Count; i++) {\\n var opt = sd.State[i] as Modules.AdamW.State;\\n var opt2 = sd2.State[i] as Modules.AdamW.State;\\n Assert.True(opt!.ApproximatelyEquals(opt2));\\n }\\n } finally {\\n File.Delete(location);\\n }\\n }\\n\\n [Fact]\\n public void TestSaveAdamWFile3()\\n {\\n var location = RandomFileName(\\\"ts\\\");\\n if (File.Exists(location)) File.Delete(location);\\n\\n var l1 = Linear(10, 10, true);\\n var l2 = Linear(10, 10, true);\\n var seq = Sequential(l1, l2);\\n\\n var optim = torch.optim.AdamW(seq.parameters(), amsgrad: true);\\n\\n var sd = optim.state_dict();\\n Assert.Multiple(\\n () => Assert.Equal(4, sd.State.Count),\\n () => Assert.Single(sd.Options)\\n );\\n\\n var optim2 = torch.optim.AdamW(seq.parameters(), 0.5, 0.01, 0.75, 1e-8, 0.25);\\n\\n try {\\n optim.save_state_dict(location);\\n optim2.load_state_dict(location);\\n\\n var sd2 = optim2.state_dict();\\n\\n Assert.Multiple(\\n () => Assert.Equal(4, sd2.State.Count),\\n () => Assert.Single(sd2.Options)\\n );\\n\\n for (int i = 0; i < sd.Options.Count; i++) {\\n var opt = sd.Options[i] as Modules.AdamW.Options;\\n var opt2 = sd2.Options[i] as Modules.AdamW.Options;\\n Assert.Multiple(\\n () => Assert.Equal(opt!.InitialLearningRate, opt2!.InitialLearningRate),\\n () => Assert.Equal(opt!.LearningRate, opt2!.LearningRate),\\n () => Assert.Equal(opt!.beta1, opt2!.beta1),\\n () => Assert.Equal(opt!.beta2, opt2!.beta2),\\n () => Assert.Equal(opt!.eps, opt2!.eps),\\n () => Assert.Equal(opt!.weight_decay, opt2!.weight_decay),\\n () => Assert.Equal(opt!.amsgrad, opt2!.amsgrad),\\n () => Assert.Equal(opt!.maximize, opt2!.maximize)\\n );\\n }\\n for (int i = 0; i < sd.State.Count; i++) {\\n var opt = sd.State[i] as Modules.AdamW.State;\\n var opt2 = sd2.State[i] as Modules.AdamW.State;\\n Assert.True(opt!.ApproximatelyEquals(opt2));\\n }\\n } finally {\\n File.Delete(location);\\n }\\n }\\n\\n [Fact]\\n public void TestSaveAdamax()\\n {\\n var l1 = Linear(10, 10, true);\\n var l2 = Linear(10, 10, true);\\n var seq = Sequential(l1, l2);\\n\\n var optim = torch.optim.Adamax(seq.parameters());\\n\\n var sd = optim.state_dict();\\n Assert.Multiple(\\n () => Assert.Equal(4, sd.State.Count),\\n () => Assert.Single(sd.Options)\\n );\\n\\n var optim2 = torch.optim.Adamax(seq.parameters(), 0.5, 0.01, 0.75, 1e-8, 0.25);\\n optim2.load_state_dict(sd);\\n\\n var sd2 = optim2.state_dict();\\n\\n Assert.Multiple(\\n () => Assert.Equal(4, sd2.State.Count),\\n () => Assert.Single(sd2.Options)\\n );\\n\\n for (int i = 0; i < sd.Options.Count; i++) {\\n var opt = sd.Options[i] as Modules.Adamax.Options;\\n var opt2 = sd2.Options[i] as Modules.Adamax.Options;\\n Assert.Multiple(\\n () => Assert.Equal(opt!.InitialLearningRate, opt2!.InitialLearningRate),\\n () => Assert.Equal(opt!.LearningRate, opt2!.LearningRate),\\n () => Assert.Equal(opt!.beta1, opt2!.beta1),\\n () => Assert.Equal(opt!.beta2, opt2!.beta2),\\n () => Assert.Equal(opt!.eps, opt2!.eps),\\n () => Assert.Equal(opt!.weight_decay, opt2!.weight_decay)\\n );\\n }\\n for (int i = 0; i < sd.State.Count; i++) {\\n var opt = sd.State[i] as Modules.Adamax.State;\\n var opt2 = sd2.State[i] as Modules.Adamax.State;\\n Assert.True(opt!.ApproximatelyEquals(opt2));\\n }\\n }\\n\\n [Fact]\\n public void TestSaveAdamaxFile()\\n {\\n var location = RandomFileName(\\\"ts\\\");\\n if (File.Exists(location)) File.Delete(location);\\n\\n var l1 = Linear(10, 10, true);\\n var l2 = Linear(10, 10, true);\\n var seq = Sequential(l1, l2);\\n\\n var optim = torch.optim.Adamax(seq.parameters());\\n\\n var sd = optim.state_dict();\\n Assert.Multiple(\\n () => Assert.Equal(4, sd.State.Count),\\n () => Assert.Single(sd.Options)\\n );\\n\\n var optim2 = torch.optim.Adamax(seq.parameters(), 0.5, 0.01, 0.75, 1e-8, 0.25);\\n\\n try {\\n optim.save_state_dict(location);\\n optim2.load_state_dict(location);\\n\\n var sd2 = optim2.state_dict();\\n\\n Assert.Multiple(\\n () => Assert.Equal(4, sd2.State.Count),\\n () => Assert.Single(sd2.Options)\\n );\\n\\n for (int i = 0; i < sd.Options.Count; i++) {\\n var opt = sd.Options[i] as Modules.Adamax.Options;\\n var opt2 = sd2.Options[i] as Modules.Adamax.Options;\\n Assert.Multiple(\\n () => Assert.Equal(opt!.InitialLearningRate, opt2!.InitialLearningRate),\\n () => Assert.Equal(opt!.LearningRate, opt2!.LearningRate),\\n () => Assert.Equal(opt!.beta1, opt2!.beta1),\\n () => Assert.Equal(opt!.beta2, opt2!.beta2),\\n () => Assert.Equal(opt!.eps, opt2!.eps),\\n () => Assert.Equal(opt!.weight_decay, opt2!.weight_decay)\\n );\\n }\\n for (int i = 0; i < sd.State.Count; i++) {\\n var opt = sd.State[i] as Modules.Adamax.State;\\n var opt2 = sd2.State[i] as Modules.Adamax.State;\\n Assert.True(opt!.ApproximatelyEquals(opt2));\\n }\\n } finally {\\n File.Delete(location);\\n }\\n }\\n\\n [Fact]\\n public void TestSaveAdagrad()\\n {\\n var l1 = Linear(10, 10, true);\\n var l2 = Linear(10, 10, true);\\n var seq = Sequential(l1, l2);\\n\\n var optim = torch.optim.Adagrad(seq.parameters());\\n\\n var sd = optim.state_dict();\\n Assert.Multiple(\\n () => Assert.Equal(4, sd.State.Count),\\n () => Assert.Single(sd.Options)\\n );\\n\\n var optim2 = torch.optim.Adagrad(seq.parameters(), 0.5, 0.01, 0.75, 1e-8, 0.25);\\n optim2.load_state_dict(sd);\\n\\n var sd2 = optim2.state_dict();\\n\\n Assert.Multiple(\\n () => Assert.Equal(4, sd2.State.Count),\\n () => Assert.Single(sd2.Options)\\n );\\n\\n for (int i = 0; i < sd.Options.Count; i++) {\\n var opt = sd.Options[i] as Modules.Adagrad.Options;\\n var opt2 = sd2.Options[i] as Modules.Adagrad.Options;\\n Assert.Multiple(\\n () => Assert.Equal(opt!.InitialLearningRate, opt2!.InitialLearningRate),\\n () => Assert.Equal(opt!.LearningRate, opt2!.LearningRate),\\n () => Assert.Equal(opt!.lr_decay, opt2!.lr_decay),\\n () => Assert.Equal(opt!.initial_accumulator_value, opt2!.initial_accumulator_value),\\n () => Assert.Equal(opt!.eps, opt2!.eps),\\n () => Assert.Equal(opt!.weight_decay, opt2!.weight_decay)\\n );\\n }\\n for (int i = 0; i < sd.State.Count; i++) {\\n var opt = sd.State[i] as Modules.Adagrad.State;\\n var opt2 = sd2.State[i] as Modules.Adagrad.State;\\n Assert.True(opt!.ApproximatelyEquals(opt2));\\n }\\n }\\n\\n [Fact(Skip = \\\"Often takes a very long time. No idea why.\\\")]\\n public void TestSaveAdagradFile()\\n {\\n var location = RandomFileName(\\\"ts\\\");\\n if (File.Exists(location)) File.Delete(location);\\n\\n var l1 = Linear(10, 10, true);\\n var l2 = Linear(10, 10, true);\\n var seq = Sequential(l1, l2);\\n\\n var optim = torch.optim.Adagrad(seq.parameters());\\n\\n var sd = optim.state_dict();\\n Assert.Multiple(\\n () => Assert.Equal(4, sd.State.Count),\\n () => Assert.Single(sd.Options)\\n );\\n\\n var optim2 = torch.optim.Adagrad(seq.parameters(), 0.5, 0.01, 0.75, 1e-8, 0.25);\\n\\n try {\\n optim.save_state_dict(location);\\n optim2.load_state_dict(location);\\n\\n var sd2 = optim2.state_dict();\\n\\n Assert.Multiple(\\n () => Assert.Equal(4, sd2.State.Count),\\n () => Assert.Single(sd2.Options)\\n );\\n\\n for (int i = 0; i < sd.Options.Count; i++) {\\n var opt = sd.Options[i] as Modules.Adagrad.Options;\\n var opt2 = sd2.Options[i] as Modules.Adagrad.Options;\\n Assert.Multiple(\\n () => Assert.Equal(opt!.InitialLearningRate, opt2!.InitialLearningRate),\\n () => Assert.Equal(opt!.LearningRate, opt2!.LearningRate),\\n () => Assert.Equal(opt!.lr_decay, opt2!.lr_decay),\\n () => Assert.Equal(opt!.initial_accumulator_value, opt2!.initial_accumulator_value),\\n () => Assert.Equal(opt!.eps, opt2!.eps),\\n () => Assert.Equal(opt!.weight_decay, opt2!.weight_decay)\\n );\\n }\\n for (int i = 0; i < sd.State.Count; i++) {\\n var opt = sd.State[i] as Modules.Adagrad.State;\\n var opt2 = sd2.State[i] as Modules.Adagrad.State;\\n Assert.True(opt!.ApproximatelyEquals(opt2));\\n }\\n } finally {\\n File.Delete(location);\\n }\\n }\\n\\n [Fact]\\n public void TestSaveAdadelta()\\n {\\n var l1 = Linear(10, 10, true);\\n var l2 = Linear(10, 10, true);\\n var seq = Sequential(l1, l2);\\n\\n var optim = torch.optim.Adadelta(seq.parameters());\\n\\n var sd = optim.state_dict();\\n Assert.Multiple(\\n () => Assert.Equal(4, sd.State.Count),\\n () => Assert.Single(sd.Options)\\n );\\n\\n var optim2 = torch.optim.Adadelta(seq.parameters(), 0.5, 0.01, 1e-8, 0.25);\\n optim2.load_state_dict(sd);\\n\\n var sd2 = optim2.state_dict();\\n\\n Assert.Multiple(\\n () => Assert.Equal(4, sd2.State.Count),\\n () => Assert.Single(sd2.Options)\\n );\\n\\n for (int i = 0; i < sd.Options.Count; i++) {\\n var opt = sd.Options[i] as Modules.Adadelta.Options;\\n var opt2 = sd2.Options[i] as Modules.Adadelta.Options;\\n Assert.Multiple(\\n () => Assert.Equal(opt!.InitialLearningRate, opt2!.InitialLearningRate),\\n () => Assert.Equal(opt!.LearningRate, opt2!.LearningRate),\\n () => Assert.Equal(opt!.rho, opt2!.rho),\\n () => Assert.Equal(opt!.eps, opt2!.eps),\\n () => Assert.Equal(opt!.weight_decay, opt2!.weight_decay)\\n );\\n }\\n for (int i = 0; i < sd.State.Count; i++) {\\n var opt = sd.State[i] as Modules.Adadelta.State;\\n var opt2 = sd2.State[i] as Modules.Adadelta.State;\\n Assert.True(opt!.ApproximatelyEquals(opt2));\\n }\\n }\\n\\n [Fact]\\n public void TestSaveAdadeltaFile()\\n {\\n var location = RandomFileName(\\\"ts\\\");\\n if (File.Exists(location)) File.Delete(location);\\n\\n var l1 = Linear(10, 10, true);\\n var l2 = Linear(10, 10, true);\\n var seq = Sequential(l1, l2);\\n\\n var optim = torch.optim.Adadelta(seq.parameters());\\n\\n var sd = optim.state_dict();\\n Assert.Multiple(\\n () => Assert.Equal(4, sd.State.Count),\\n () => Assert.Single(sd.Options)\\n );\\n\\n var optim2 = torch.optim.Adadelta(seq.parameters(), 0.5, 0.01, 1e-8, 0.25);\\n\\n try {\\n optim.save_state_dict(location);\\n optim2.load_state_dict(location);\\n\\n var sd2 = optim2.state_dict();\\n\\n Assert.Multiple(\\n () => Assert.Equal(4, sd2.State.Count),\\n () => Assert.Single(sd2.Options)\\n );\\n\\n for (int i = 0; i < sd.Options.Count; i++) {\\n var opt = sd.Options[i] as Modules.Adadelta.Options;\\n var opt2 = sd2.Options[i] as Modules.Adadelta.Options;\\n Assert.Multiple(\\n () => Assert.Equal(opt!.InitialLearningRate, opt2!.InitialLearningRate),\\n () => Assert.Equal(opt!.LearningRate, opt2!.LearningRate),\\n () => Assert.Equal(opt!.rho, opt2!.rho),\\n () => Assert.Equal(opt!.eps, opt2!.eps),\\n () => Assert.Equal(opt!.weight_decay, opt2!.weight_decay)\\n );\\n }\\n for (int i = 0; i < sd.State.Count; i++) {\\n var opt = sd.State[i] as Modules.Adadelta.State;\\n var opt2 = sd2.State[i] as Modules.Adadelta.State;\\n Assert.True(opt!.ApproximatelyEquals(opt2));\\n }\\n } finally {\\n File.Delete(location);\\n }\\n }\\n\\n [Fact]\\n public void TestMisMatchedFile()\\n {\\n\\n var location = RandomFileName(\\\"ts\\\");\\n if (File.Exists(location)) File.Delete(location);\\n\\n var l1 = Linear(10, 10, true);\\n var l2 = Linear(10, 10, true);\\n var seq = Sequential(l1, l2);\\n\\n var optim1 = torch.optim.SGD(seq.parameters(), 0.05);\\n\\n try {\\n optim1.save_state_dict(location);\\n Assert.Throws<InvalidDataException>(() => torch.optim.ASGD(seq.parameters()).load_state_dict(location));\\n Assert.Throws<InvalidDataException>(() => torch.optim.Rprop(seq.parameters()).load_state_dict(location));\\n Assert.Throws<InvalidDataException>(() => torch.optim.RMSProp(seq.parameters()).load_state_dict(location));\\n Assert.Throws<InvalidDataException>(() => torch.optim.RAdam(seq.parameters()).load_state_dict(location));\\n Assert.Throws<InvalidDataException>(() => torch.optim.NAdam(seq.parameters()).load_state_dict(location));\\n Assert.Throws<InvalidDataException>(() => torch.optim.Adam(seq.parameters()).load_state_dict(location));\\n Assert.Throws<InvalidDataException>(() => torch.optim.AdamW(seq.parameters()).load_state_dict(location));\\n Assert.Throws<InvalidDataException>(() => torch.optim.Adamax(seq.parameters()).load_state_dict(location));\\n Assert.Throws<InvalidDataException>(() => torch.optim.Adagrad(seq.parameters()).load_state_dict(location));\\n Assert.Throws<InvalidDataException>(() => torch.optim.Adadelta(seq.parameters()).load_state_dict(location));\\n } finally {\\n File.Delete(location);\\n }\\n }\\n\\n [Fact]\\n public void TestLoadingSGDStateFromPython()\\n {\\n var lin = torch.nn.Linear(10, 10);\\n\\n double learning_rate = 0.00004f;\\n var optimizer = torch.optim.SGD(lin.parameters(), learning_rate);\\n\\n optimizer.load_state_dict(\\\"sgd1.dat\\\");\\n\\n var sd = optimizer.state_dict();\\n\\n Assert.Single(sd.Options);\\n Assert.Equal(2, sd.State.Count);\\n\\n foreach (var opts in sd.Options) {\\n var options = opts as Modules.SGD.Options;\\n Assert.Equal(0.1, options!.momentum);\\n Assert.NotEqual(learning_rate, options!.LearningRate);\\n }\\n\\n foreach (var st in sd.State) {\\n var state = st as Modules.SGD.State;\\n Assert.NotNull(state!.momentum_buffer);\\n }\\n }\\n\\n [Fact]\\n public void TestLoadingASGDStateFromPython()\\n {\\n var lin = torch.nn.Linear(10, 10);\\n\\n double learning_rate = 0.004f;\\n var optimizer = torch.optim.ASGD(lin.parameters(), learning_rate);\\n\\n optimizer.load_state_dict(\\\"asgd1.dat\\\");\\n\\n var sd = optimizer.state_dict();\\n\\n Assert.Single(sd.Options);\\n Assert.Equal(2, sd.State.Count);\\n\\n foreach (var opts in sd.Options) {\\n var options = opts as Modules.ASGD.Options;\\n Assert.Equal(0.65, options!.alpha);\\n Assert.Equal(1e-3, options!.lambd);\\n Assert.Equal(1e5, options!.t0);\\n Assert.NotEqual(learning_rate, options!.LearningRate);\\n }\\n\\n foreach (var st in sd.State) {\\n var state = st as Modules.ASGD.State;\\n Assert.Equal(1, state!.step);\\n Assert.NotNull(state!.ax);\\n }\\n }\\n\\n [Fact]\\n public void TestLoadingRMSpropStateFromPython()\\n {\\n var lin1 = torch.nn.Linear(10, 10);\\n var lin2 = torch.nn.Linear(10, 10);\\n\\n var seq = Sequential((\\\"lin1\\\", lin1), (\\\"lin2\\\", lin2));\\n\\n var pgs = new RMSProp.ParamGroup[] {\\n new () { Parameters = lin1.parameters(), Options = new() { LearningRate = 0.00005 } },\\n new (lin2.parameters(), lr: 0.00003, centered: false, momentum: 0.25)\\n };\\n\\n double learning_rate = 0.00004f;\\n var optimizer = torch.optim.RMSProp(pgs, learning_rate);\\n\\n optimizer.load_state_dict(\\\"rmsprop1.dat\\\");\\n\\n var sd = optimizer.state_dict();\\n\\n Assert.Equal(2, sd.Options.Count);\\n Assert.Equal(4, sd.State.Count);\\n\\n var options = sd.Options[0] as Modules.RMSProp.Options;\\n Assert.Multiple(\\n () => Assert.Equal(0.1, options!.momentum),\\n () => Assert.Equal(0.001, options!.LearningRate),\\n () => Assert.False(options!.centered)\\n );\\n\\n options = sd.Options[1] as Modules.RMSProp.Options;\\n Assert.Multiple(\\n () => Assert.Equal(0, options!.momentum),\\n () => Assert.Equal(0.01, options!.LearningRate),\\n () => Assert.True(options!.centered)\\n );\\n\\n var state = sd.State[0] as Modules.RMSProp.State;\\n Assert.Multiple(\\n () => Assert.Equal(1, state!.step),\\n () => Assert.NotNull(state!.square_avg),\\n () => Assert.NotNull(state!.momentum_buffer),\\n () => Assert.Null(state!.grad_avg)\\n );\\n\\n state = sd.State[1] as Modules.RMSProp.State;\\n Assert.Multiple(\\n () => Assert.Equal(1, state!.step),\\n () => Assert.NotNull(state!.square_avg),\\n () => Assert.NotNull(state!.momentum_buffer),\\n () => Assert.Null(state!.grad_avg)\\n );\\n\\n state = sd.State[2] as Modules.RMSProp.State;\\n Assert.Multiple(\\n () => Assert.Equal(1, state!.step),\\n () => Assert.NotNull(state!.square_avg),\\n () => Assert.Null(state!.momentum_buffer),\\n () => Assert.NotNull(state!.grad_avg)\\n );\\n\\n state = sd.State[3] as Modules.RMSProp.State;\\n Assert.Multiple(\\n () => Assert.Equal(1, state!.step),\\n () => Assert.NotNull(state!.square_avg),\\n () => Assert.Null(state!.momentum_buffer),\\n () => Assert.NotNull(state!.grad_avg)\\n );\\n }\\n\\n [Fact]\\n public void TestLoadingRpropStateFromPython()\\n {\\n var lin1 = torch.nn.Linear(10, 10);\\n var lin2 = torch.nn.Linear(10, 10);\\n\\n var seq = Sequential((\\\"lin1\\\", lin1), (\\\"lin2\\\", lin2));\\n\\n var pgs = new Rprop.ParamGroup[] {\\n new () { Parameters = lin1.parameters(), Options = new() { LearningRate = 0.00005 } },\\n new (lin2.parameters(), lr: 0.00003, maximize: false)\\n };\\n\\n double learning_rate = 0.00004f;\\n var optimizer = torch.optim.Rprop(pgs, learning_rate);\\n\\n optimizer.load_state_dict(\\\"rprop1.dat\\\");\\n\\n var sd = optimizer.state_dict();\\n\\n Assert.Equal(2, sd.Options.Count);\\n Assert.Equal(4, sd.State.Count);\\n\\n var options = sd.Options[0] as Modules.Rprop.Options;\\n Assert.Multiple(\\n () => Assert.Equal(0.35, options!.etaminus),\\n () => Assert.Equal(1.5, options!.etaplus),\\n () => Assert.Equal(1e-5, options!.min_step),\\n () => Assert.Equal(5, options!.max_step),\\n () => Assert.Equal(0.001, options!.LearningRate),\\n () => Assert.False(options!.maximize)\\n );\\n\\n options = sd.Options[1] as Modules.Rprop.Options;\\n Assert.Multiple(\\n () => Assert.Equal(0.45, options!.etaminus),\\n () => Assert.Equal(1.5, options!.etaplus),\\n () => Assert.Equal(1e-5, options!.min_step),\\n () => Assert.Equal(5, options!.max_step),\\n () => Assert.Equal(0.01, options!.LearningRate),\\n () => Assert.True(options!.maximize)\\n );\\n\\n foreach (var st in sd.State) {\\n var state = sd.State[0] as Modules.Rprop.State;\\n Assert.Multiple(\\n () => Assert.Equal(1, state!.step),\\n () => Assert.NotNull(state!.prev),\\n () => Assert.NotNull(state!.step_size)\\n );\\n }\\n }\\n\\n [Fact]\\n public void TestLoadingAdamStateFromPython()\\n {\\n var lin1 = torch.nn.Linear(10, 10);\\n var lin2 = torch.nn.Linear(10, 10);\\n\\n var seq = Sequential((\\\"lin1\\\", lin1), (\\\"lin2\\\", lin2));\\n\\n var pgs = new Adam.ParamGroup[] {\\n new () { Parameters = lin1.parameters(), Options = new() { LearningRate = 0.00005 } },\\n new (lin2.parameters(), lr: 0.00003, amsgrad: false, beta1: 0.25)\\n };\\n\\n double learning_rate = 0.00004f;\\n var optimizer = torch.optim.Adam(pgs, learning_rate);\\n\\n optimizer.load_state_dict(\\\"adam1.dat\\\");\\n\\n var sd = optimizer.state_dict();\\n\\n Assert.Equal(2, sd.Options.Count);\\n Assert.Equal(4, sd.State.Count);\\n\\n var options = sd.Options[0] as Modules.Adam.Options;\\n Assert.Multiple(\\n () => Assert.Equal(0.8, options!.beta1),\\n () => Assert.Equal(0.9, options!.beta2),\\n () => Assert.Equal(0.001, options!.LearningRate),\\n () => Assert.False(options!.amsgrad)\\n );\\n\\n options = sd.Options[1] as Modules.Adam.Options;\\n Assert.Multiple(\\n () => Assert.Equal(0.7, options!.beta1),\\n () => Assert.Equal(0.79, options!.beta2),\\n () => Assert.Equal(0.01, options!.LearningRate),\\n () => Assert.True(options!.amsgrad)\\n );\\n\\n var state = sd.State[0] as Modules.Adam.State;\\n Assert.Multiple(\\n () => Assert.Equal(1, state!.step),\\n () => Assert.NotNull(state!.exp_avg),\\n () => Assert.NotNull(state!.exp_avg_sq),\\n () => Assert.Null(state!.max_exp_avg_sq)\\n );\\n\\n state = sd.State[1] as Modules.Adam.State;\\n Assert.Multiple(\\n () => Assert.Equal(1, state!.step),\\n () => Assert.NotNull(state!.exp_avg),\\n () => Assert.NotNull(state!.exp_avg_sq),\\n () => Assert.Null(state!.max_exp_avg_sq)\\n );\\n\\n state = sd.State[2] as Modules.Adam.State;\\n Assert.Multiple(\\n () => Assert.Equal(1, state!.step),\\n () => Assert.NotNull(state!.exp_avg),\\n () => Assert.NotNull(state!.exp_avg_sq),\\n () => Assert.NotNull(state!.max_exp_avg_sq)\\n );\\n\\n state = sd.State[3] as Modules.Adam.State;\\n Assert.Multiple(\\n () => Assert.Equal(1, state!.step),\\n () => Assert.NotNull(state!.exp_avg),\\n () => Assert.NotNull(state!.exp_avg_sq),\\n () => Assert.NotNull(state!.max_exp_avg_sq)\\n );\\n }\\n\\n [Fact]\\n public void TestLoadingAdamWStateFromPython()\\n {\\n var lin1 = torch.nn.Linear(10, 10);\\n var lin2 = torch.nn.Linear(10, 10);\\n\\n var seq = Sequential((\\\"lin1\\\", lin1), (\\\"lin2\\\", lin2));\\n\\n var pgs = new AdamW.ParamGroup[] {\\n new () { Parameters = lin1.parameters(), Options = new() { LearningRate = 0.00005 } },\\n new (lin2.parameters(), lr: 0.00003, amsgrad: false, beta1: 0.25)\\n };\\n\\n double learning_rate = 0.00004f;\\n var optimizer = torch.optim.AdamW(pgs, learning_rate);\\n\\n optimizer.load_state_dict(\\\"adamw1.dat\\\");\\n\\n var sd = optimizer.state_dict();\\n\\n Assert.Equal(2, sd.Options.Count);\\n Assert.Equal(4, sd.State.Count);\\n\\n var options = sd.Options[0] as Modules.AdamW.Options;\\n Assert.Multiple(\\n () => Assert.Equal(0.8, options!.beta1),\\n () => Assert.Equal(0.9, options!.beta2),\\n () => Assert.Equal(0.001, options!.LearningRate),\\n () => Assert.False(options!.amsgrad)\\n );\\n\\n options = sd.Options[1] as Modules.AdamW.Options;\\n Assert.Multiple(\\n () => Assert.Equal(0.7, options!.beta1),\\n () => Assert.Equal(0.79, options!.beta2),\\n () => Assert.Equal(0.01, options!.LearningRate),\\n () => Assert.True(options!.amsgrad)\\n );\\n\\n var state = sd.State[0] as Modules.AdamW.State;\\n Assert.Multiple(\\n () => Assert.Equal(1, state!.step),\\n () => Assert.NotNull(state!.exp_avg),\\n () => Assert.NotNull(state!.exp_avg_sq),\\n () => Assert.Null(state!.max_exp_avg_sq)\\n );\\n\\n state = sd.State[1] as Modules.AdamW.State;\\n Assert.Multiple(\\n () => Assert.Equal(1, state!.step),\\n () => Assert.NotNull(state!.exp_avg),\\n () => Assert.NotNull(state!.exp_avg_sq),\\n () => Assert.Null(state!.max_exp_avg_sq)\\n );\\n\\n state = sd.State[2] as Modules.AdamW.State;\\n Assert.Multiple(\\n () => Assert.Equal(1, state!.step),\\n () => Assert.NotNull(state!.exp_avg),\\n () => Assert.NotNull(state!.exp_avg_sq),\\n () => Assert.NotNull(state!.max_exp_avg_sq)\\n );\\n\\n state = sd.State[3] as Modules.AdamW.State;\\n Assert.Multiple(\\n () => Assert.Equal(1, state!.step),\\n () => Assert.NotNull(state!.exp_avg),\\n () => Assert.NotNull(state!.exp_avg_sq),\\n () => Assert.NotNull(state!.max_exp_avg_sq)\\n );\\n }\\n\\n [Fact]\\n public void TestLoadingNAdamStateFromPython()\\n {\\n var lin1 = torch.nn.Linear(10, 10);\\n var lin2 = torch.nn.Linear(10, 10);\\n\\n var seq = Sequential((\\\"lin1\\\", lin1), (\\\"lin2\\\", lin2));\\n\\n var pgs = new NAdam.ParamGroup[] {\\n new () { Parameters = lin1.parameters(), Options = new() { LearningRate = 0.00005 } },\\n new (lin2.parameters(), lr: 0.00003, beta1: 0.25, weight_decay: 0.1)\\n };\\n\\n double learning_rate = 0.00004f;\\n var optimizer = torch.optim.NAdam(pgs, learning_rate);\\n\\n optimizer.load_state_dict(\\\"nadam1.dat\\\");\\n\\n var sd = optimizer.state_dict();\\n\\n Assert.Equal(2, sd.Options.Count);\\n Assert.Equal(4, sd.State.Count);\\n\\n var options = sd.Options[0] as Modules.NAdam.Options;\\n Assert.Multiple(\\n () => Assert.Equal(0.8, options!.beta1),\\n () => Assert.Equal(0.9, options!.beta2),\\n () => Assert.Equal(0.001, options!.LearningRate),\\n () => Assert.Equal(0, options!.weight_decay)\\n );\\n\\n options = sd.Options[1] as Modules.NAdam.Options;\\n Assert.Multiple(\\n () => Assert.Equal(0.7, options!.beta1),\\n () => Assert.Equal(0.79, options!.beta2),\\n () => Assert.Equal(0.01, options!.LearningRate),\\n () => Assert.Equal(0.3, options!.weight_decay)\\n );\\n\\n foreach (var st in sd.State) {\\n var state = st as Modules.NAdam.State;\\n Assert.Multiple(\\n () => Assert.Equal(1, state!.step),\\n () => Assert.NotNull(state!.exp_avg),\\n () => Assert.NotNull(state!.exp_avg_sq)\\n );\\n }\\n }\\n\\n [Fact]\\n public void TestLoadingRAdamStateFromPython()\\n {\\n var lin1 = torch.nn.Linear(10, 10);\\n var lin2 = torch.nn.Linear(10, 10);\\n\\n var seq = Sequential((\\\"lin1\\\", lin1), (\\\"lin2\\\", lin2));\\n\\n var pgs = new RAdam.ParamGroup[] {\\n new () { Parameters = lin1.parameters(), Options = new() { LearningRate = 0.00005 } },\\n new (lin2.parameters(), lr: 0.00003, beta1: 0.25)\\n };\\n\\n double learning_rate = 0.00004f;\\n var optimizer = torch.optim.RAdam(pgs, learning_rate);\\n\\n optimizer.load_state_dict(\\\"radam1.dat\\\");\\n\\n var sd = optimizer.state_dict();\\n\\n var options = sd.Options[0] as Modules.RAdam.Options;\\n Assert.Multiple(\\n () => Assert.Equal(0.8, options!.beta1),\\n () => Assert.Equal(0.9, options!.beta2),\\n () => Assert.Equal(0.001, options!.LearningRate),\\n () => Assert.Equal(0, options!.weight_decay)\\n );\\n\\n options = sd.Options[1] as Modules.RAdam.Options;\\n Assert.Multiple(\\n () => Assert.Equal(0.7, options!.beta1),\\n () => Assert.Equal(0.79, options!.beta2),\\n () => Assert.Equal(0.01, options!.LearningRate),\\n () => Assert.Equal(0.3, options!.weight_decay)\\n );\\n\\n foreach (var st in sd.State) {\\n var state = st as Modules.RAdam.State;\\n Assert.Multiple(\\n () => Assert.Equal(1, state!.step),\\n () => Assert.NotNull(state!.exp_avg),\\n () => Assert.NotNull(state!.exp_avg_sq)\\n );\\n }\\n }\\n\\n [Fact]\\n public void TestLoadingAdadeltaStateFromPython()\\n {\\n var lin1 = torch.nn.Linear(10, 10);\\n var lin2 = torch.nn.Linear(10, 10);\\n\\n var seq = Sequential((\\\"lin1\\\", lin1), (\\\"lin2\\\", lin2));\\n\\n var pgs = new Adadelta.ParamGroup[] {\\n new () { Parameters = lin1.parameters(), Options = new() { LearningRate = 0.00005 } },\\n new (lin2.parameters(), lr: 0.00003, maximize: false, rho: 0.25)\\n };\\n\\n double learning_rate = 0.00004f;\\n var optimizer = torch.optim.Adadelta(pgs, learning_rate);\\n\\n optimizer.load_state_dict(\\\"adadelta1.dat\\\");\\n\\n var sd = optimizer.state_dict();\\n\\n Assert.Equal(2, sd.Options.Count);\\n Assert.Equal(4, sd.State.Count);\\n\\n var options = sd.Options[0] as Modules.Adadelta.Options;\\n Assert.Multiple(\\n () => Assert.Equal(0.85, options!.rho),\\n () => Assert.Equal(0.3, options!.weight_decay),\\n () => Assert.Equal(0.001, options!.LearningRate),\\n () => Assert.False(options!.maximize)\\n );\\n\\n options = sd.Options[1] as Modules.Adadelta.Options;\\n Assert.Multiple(\\n () => Assert.Equal(0.79, options!.rho),\\n () => Assert.Equal(0.3, options!.weight_decay),\\n () => Assert.Equal(0.01, options!.LearningRate),\\n () => Assert.True(options!.maximize)\\n );\\n\\n foreach (var st in sd.State) {\\n var state = st as Modules.Adadelta.State;\\n Assert.Multiple(\\n () => Assert.Equal(1, state!.step),\\n () => Assert.NotNull(state!.square_avg),\\n () => Assert.NotNull(state!.acc_delta)\\n );\\n }\\n }\\n\\n [Fact]\\n public void TestLoadingAdagradStateFromPython()\\n {\\n var lin = torch.nn.Linear(10, 10);\\n\\n double learning_rate = 0.00004f;\\n var optimizer = torch.optim.Adagrad(lin.parameters(), learning_rate);\\n\\n optimizer.load_state_dict(\\\"adagrad1.dat\\\");\\n\\n var sd = optimizer.state_dict();\\n\\n Assert.Single(sd.Options);\\n Assert.Equal(2, sd.State.Count);\\n\\n foreach (var opts in sd.Options) {\\n var options = opts as Modules.Adagrad.Options;\\n Assert.Equal(0.85, options!.lr_decay);\\n Assert.Equal(0.3, options!.weight_decay);\\n Assert.NotEqual(learning_rate, options!.LearningRate);\\n }\\n\\n foreach (var st in sd.State) {\\n var state = st as Modules.Adagrad.State;\\n Assert.Equal(1, state!.step);\\n Assert.NotNull(state!.sum);\\n }\\n }\\n\\n [Fact]\\n public void TestLoadingAdamaxStateFromPython()\\n {\\n var lin1 = torch.nn.Linear(10, 10);\\n var lin2 = torch.nn.Linear(10, 10);\\n\\n var seq = Sequential((\\\"lin1\\\", lin1), (\\\"lin2\\\", lin2));\\n\\n var pgs = new Adamax.ParamGroup[] {\\n new () { Parameters = lin1.parameters(), Options = new() { LearningRate = 0.00005 } },\\n new (lin2.parameters(), lr: 0.00003, weight_decay: 0.25, beta1: 0.25)\\n };\\n\\n double learning_rate = 0.00004f;\\n var optimizer = torch.optim.Adamax(pgs, learning_rate);\\n\\n optimizer.load_state_dict(\\\"adamax1.dat\\\");\\n\\n var sd = optimizer.state_dict();\\n\\n Assert.Equal(2, sd.Options.Count);\\n Assert.Equal(4, sd.State.Count);\\n\\n var options = sd.Options[0] as Modules.Adamax.Options;\\n Assert.Multiple(\\n () => Assert.Equal(0.8, options!.beta1),\\n () => Assert.Equal(0.9, options!.beta2),\\n () => Assert.Equal(0.001, options!.LearningRate),\\n () => Assert.Equal(0, options!.weight_decay)\\n );\\n\\n options = sd.Options[1] as Modules.Adamax.Options;\\n Assert.Multiple(\\n () => Assert.Equal(0.7, options!.beta1),\\n () => Assert.Equal(0.79, options!.beta2),\\n () => Assert.Equal(0.01, options!.LearningRate),\\n () => Assert.Equal(0.3, options!.weight_decay)\\n );\\n\\n var state = sd.State[0] as Modules.Adamax.State;\\n Assert.Multiple(\\n () => Assert.Equal(1, state!.step),\\n () => Assert.NotNull(state!.exp_avg),\\n () => Assert.NotNull(state!.exp_inf)\\n );\\n\\n state = sd.State[1] as Modules.Adamax.State;\\n Assert.Multiple(\\n () => Assert.Equal(1, state!.step),\\n () => Assert.NotNull(state!.exp_avg),\\n () => Assert.NotNull(state!.exp_avg),\\n () => Assert.NotNull(state!.exp_inf)\\n );\\n\\n state = sd.State[2] as Modules.Adamax.State;\\n Assert.Multiple(\\n () => Assert.Equal(1, state!.step),\\n () => Assert.NotNull(state!.exp_avg),\\n () => Assert.NotNull(state!.exp_avg),\\n () => Assert.NotNull(state!.exp_inf)\\n );\\n\\n state = sd.State[3] as Modules.Adamax.State;\\n Assert.Multiple(\\n () => Assert.Equal(1, state!.step),\\n () => Assert.NotNull(state!.exp_avg),\\n () => Assert.NotNull(state!.exp_avg),\\n () => Assert.NotNull(state!.exp_inf)\\n );\\n }\\n }\\n}\\n\"\n },\n {\n \"alpha_fraction\": 0.5175543427467346,\n \"alphanum_fraction\": 0.5232505798339844,\n \"avg_line_length\": 42.73690414428711,\n \"blob_id\": \"6d2895a61f8f3431c5674d3f44ef5a1981061133\",\n \"content_id\": \"0846ff4a3e4ee130a597d55fa8fa944e891ee370\",\n \"detected_licenses\": [\n \"MIT\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"C#\",\n \"length_bytes\": 37569,\n \"license_type\": \"permissive\",\n \"max_line_length\": 175,\n \"num_lines\": 859,\n \"path\": \"/src/TorchAudio/Modules/Tacotron2.cs\",\n \"repo_name\": \"NiklasGustafsson/TorchSharp\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"// Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information.\\n\\n// A number of implementation details in this file have been translated from the Python version of torchaudio,\\n// largely located in the files found in this folder:\\n//\\n// https://github.com/pytorch/audio/blob/e502df0106403f7666f89fee09715256ea2e0df3/torchaudio/models/tacotron2.py\\n//\\n// The origin has the following copyright notice and license:\\n//\\n// https://github.com/pytorch/audio/blob/main/LICENSE\\n//\\n\\n// *****************************************************************************\\n// Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved.\\n//\\n// Redistribution and use in source and binary forms, with or without\\n// modification, are permitted provided that the following conditions are met:\\n// * Redistributions of source code must retain the above copyright\\n// notice, this list of conditions and the following disclaimer.\\n// * Redistributions in binary form must reproduce the above copyright\\n// notice, this list of conditions and the following disclaimer in the\\n// documentation and/or other materials provided with the distribution.\\n// * Neither the name of the NVIDIA CORPORATION nor the\\n// names of its contributors may be used to endorse or promote products\\n// derived from this software without specific prior written permission.\\n//\\n// THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS \\\"AS IS\\\" AND\\n// ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED\\n// WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE\\n// DISCLAIMED. IN NO EVENT SHALL NVIDIA CORPORATION BE LIABLE FOR ANY\\n// DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES\\n// (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;\\n// LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND\\n// ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT\\n// (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS\\n// SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.\\n//\\n// *****************************************************************************\\n\\nusing System;\\nusing System.Collections.Generic;\\nusing System.Diagnostics;\\nusing static TorchSharp.torch;\\n\\nusing static TorchSharp.torch.nn;\\nusing F = TorchSharp.torch.nn.functional;\\n\\n#nullable enable\\nnamespace TorchSharp.Modules\\n{\\n /// <summary>\\n /// Tacotron2 model based on the implementation from\\n /// Nvidia https://github.com/NVIDIA/DeepLearningExamples/.\\n /// </summary>\\n public class Tacotron2 : nn.Module<Tensor,Tensor,Tensor,Tensor, (Tensor, Tensor, Tensor, Tensor)>\\n {\\n public readonly bool mask_padding;\\n public readonly int n_mels;\\n public readonly int n_frames_per_step;\\n\\n private readonly Modules.Embedding embedding;\\n private readonly Encoder encoder;\\n private readonly Decoder decoder;\\n private readonly Postnet postnet;\\n\\n protected override void Dispose(bool disposing)\\n {\\n if (disposing) {\\n embedding.Dispose();\\n encoder.Dispose();\\n decoder.Dispose();\\n postnet.Dispose();\\n }\\n base.Dispose(disposing);\\n }\\n\\n internal Tacotron2(\\n string name,\\n bool mask_padding = false,\\n int n_mels = 80,\\n int n_symbol = 148,\\n int n_frames_per_step = 1,\\n int symbol_embedding_dim = 512,\\n int encoder_embedding_dim = 512,\\n int encoder_n_convolution = 3,\\n int encoder_kernel_size = 5,\\n int decoder_rnn_dim = 1024,\\n int decoder_max_step = 2000,\\n double decoder_dropout = 0.1,\\n bool decoder_early_stopping = true,\\n int attention_rnn_dim = 1024,\\n int attention_hidden_dim = 128,\\n int attention_location_n_filter = 32,\\n int attention_location_kernel_size = 31,\\n double attention_dropout = 0.1,\\n int prenet_dim = 256,\\n int postnet_n_convolution = 5,\\n int postnet_kernel_size = 5,\\n int postnet_embedding_dim = 512,\\n double gate_threshold = 0.5) : base(name)\\n {\\n this.mask_padding = mask_padding;\\n this.n_mels = n_mels;\\n this.n_frames_per_step = n_frames_per_step;\\n this.embedding = nn.Embedding(n_symbol, symbol_embedding_dim);\\n torch.nn.init.xavier_uniform_(this.embedding.weight);\\n this.encoder = new Encoder(\\\"encoder\\\", encoder_embedding_dim, encoder_n_convolution, encoder_kernel_size);\\n this.decoder = new Decoder(\\n \\\"decoder\\\",\\n n_mels,\\n n_frames_per_step,\\n encoder_embedding_dim,\\n decoder_rnn_dim,\\n decoder_max_step,\\n decoder_dropout,\\n decoder_early_stopping,\\n attention_rnn_dim,\\n attention_hidden_dim,\\n attention_location_n_filter,\\n attention_location_kernel_size,\\n attention_dropout,\\n prenet_dim,\\n gate_threshold);\\n this.postnet = new Postnet(\\\"postnet\\\", n_mels, postnet_embedding_dim, postnet_kernel_size, postnet_n_convolution);\\n RegisterComponents();\\n }\\n\\n public override (Tensor, Tensor, Tensor, Tensor) forward(\\n Tensor tokens,\\n Tensor token_lengths,\\n Tensor mel_specgram,\\n Tensor mel_specgram_lengths)\\n {\\n var embedded_inputs = this.embedding.call(tokens).transpose(1, 2);\\n\\n var encoder_outputs = this.encoder.call(embedded_inputs, token_lengths);\\n var (x, gate_outputs, alignments) = this.decoder.call(\\n encoder_outputs, mel_specgram, memory_lengths: token_lengths\\n );\\n mel_specgram = x;\\n\\n var mel_specgram_postnet = this.postnet.call(mel_specgram);\\n mel_specgram_postnet = mel_specgram + mel_specgram_postnet;\\n\\n if (this.mask_padding) {\\n var mask = _get_mask_from_lengths(mel_specgram_lengths);\\n mask = mask.expand(this.n_mels, mask.size(0), mask.size(1));\\n mask = mask.permute(1, 0, 2);\\n\\n mel_specgram = mel_specgram.masked_fill(mask, 0.0);\\n mel_specgram_postnet = mel_specgram_postnet.masked_fill(mask, 0.0);\\n gate_outputs = gate_outputs.masked_fill(mask[TensorIndex.Colon, 0, TensorIndex.Colon], 1e3);\\n }\\n\\n return (mel_specgram, mel_specgram_postnet, gate_outputs, alignments);\\n }\\n\\n public (Tensor, Tensor, Tensor) infer(\\n Tensor tokens, Tensor? lengths = null)\\n {\\n var n_batch = tokens.shape[0];\\n var max_length = tokens.shape[1];\\n if (lengths is null) {\\n lengths = torch.tensor(new long[] { max_length }).expand(n_batch).to(tokens.device).to(tokens.dtype);\\n }\\n\\n if (lengths is null) {\\n throw new ArgumentNullException();\\n }\\n\\n var embedded_inputs = this.embedding.call(tokens).transpose(1, 2);\\n var encoder_outputs = this.encoder.call(embedded_inputs, lengths);\\n var (mel_specgram, mel_specgram_lengths, _, alignments) = this.decoder.infer(encoder_outputs, lengths);\\n\\n var mel_outputs_postnet = this.postnet.call(mel_specgram);\\n mel_outputs_postnet = mel_specgram + mel_outputs_postnet;\\n\\n alignments = alignments.unfold(1, n_batch, n_batch).transpose(0, 2);\\n\\n return (mel_outputs_postnet, mel_specgram_lengths, alignments);\\n }\\n\\n private static Modules.Linear _get_linear_layer(long in_dim, long out_dim, bool bias = true, init.NonlinearityType w_init_gain = init.NonlinearityType.Linear)\\n {\\n var linear = torch.nn.Linear(in_dim, out_dim, hasBias: bias);\\n torch.nn.init.xavier_uniform_(linear.weight, gain: torch.nn.init.calculate_gain(w_init_gain));\\n return linear;\\n }\\n\\n private static Modules.Conv1d _get_conv1d_layer(\\n int in_channels,\\n int out_channels,\\n int kernel_size = 1,\\n int stride = 1,\\n long? padding = null,\\n int dilation = 1,\\n bool bias = true,\\n init.NonlinearityType w_init_gain = init.NonlinearityType.Linear)\\n {\\n if (padding is null) {\\n if (kernel_size % 2 != 1) {\\n throw new ArgumentException(\\\"kernel_size must be odd\\\");\\n }\\n padding = dilation * (kernel_size - 1) / 2;\\n }\\n\\n var conv1d = torch.nn.Conv1d(\\n in_channels,\\n out_channels,\\n kernelSize: kernel_size,\\n stride: stride,\\n padding: padding.Value,\\n dilation: dilation,\\n bias: bias);\\n\\n torch.nn.init.xavier_uniform_(conv1d.weight, gain: torch.nn.init.calculate_gain(w_init_gain));\\n\\n return conv1d;\\n }\\n\\n private static Tensor _get_mask_from_lengths(Tensor lengths)\\n {\\n var max_len = torch.max(lengths).item<int>();\\n var ids = torch.arange(0, max_len, device: lengths.device, dtype: lengths.dtype);\\n var mask = (ids < lengths.unsqueeze(1)).to(torch.uint8);\\n mask = torch.le(mask, 0);\\n return mask;\\n }\\n\\n private class LocationLayer : nn.Module<Tensor, Tensor>\\n {\\n private readonly Modules.Conv1d location_conv;\\n private readonly Modules.Linear location_dense;\\n\\n protected override void Dispose(bool disposing)\\n {\\n if (disposing) {\\n location_conv.Dispose();\\n location_dense.Dispose();\\n }\\n base.Dispose(disposing);\\n }\\n\\n public LocationLayer(\\n string name,\\n int attention_n_filter,\\n int attention_kernel_size,\\n int attention_hidden_dim) : base(name)\\n {\\n var padding = (attention_kernel_size - 1) / 2;\\n this.location_conv = _get_conv1d_layer(\\n 2,\\n attention_n_filter,\\n kernel_size: attention_kernel_size,\\n padding: padding,\\n bias: false,\\n stride: 1,\\n dilation: 1);\\n this.location_dense = _get_linear_layer(\\n attention_n_filter,\\n attention_hidden_dim,\\n bias: false,\\n w_init_gain: init.NonlinearityType.Tanh);\\n RegisterComponents();\\n }\\n\\n public override Tensor forward(Tensor attention_weights_cat)\\n {\\n // (n_batch, attention_n_filter, text_lengths.max())\\n var processed_attention = this.location_conv.call(attention_weights_cat);\\n processed_attention = processed_attention.transpose(1, 2);\\n // (n_batch, text_lengths.max(), attention_hidden_dim)\\n processed_attention = this.location_dense.call(processed_attention);\\n return processed_attention;\\n }\\n }\\n\\n private class Attention : nn.Module<Tensor,Tensor,Tensor,Tensor,Tensor, (Tensor, Tensor)>\\n {\\n private readonly LocationLayer location_layer;\\n public readonly Modules.Linear memory_layer;\\n private readonly Modules.Linear query_layer;\\n public readonly float score_mask_value;\\n private readonly Modules.Linear v;\\n\\n protected override void Dispose(bool disposing)\\n {\\n if (disposing) {\\n location_layer.Dispose();\\n memory_layer.Dispose();\\n query_layer.Dispose();\\n v.Dispose();\\n }\\n base.Dispose(disposing);\\n }\\n\\n public Attention(\\n string name,\\n int attention_rnn_dim,\\n int encoder_embedding_dim,\\n int attention_hidden_dim,\\n int attention_location_n_filter,\\n int attention_location_kernel_size) : base(name)\\n {\\n this.query_layer = _get_linear_layer(attention_rnn_dim, attention_hidden_dim, bias: false, w_init_gain: init.NonlinearityType.Tanh);\\n this.memory_layer = _get_linear_layer(\\n encoder_embedding_dim, attention_hidden_dim, bias: false, w_init_gain: init.NonlinearityType.Tanh);\\n this.v = _get_linear_layer(attention_hidden_dim, 1, bias: false);\\n this.location_layer = new LocationLayer(\\n \\\"location_layer\\\",\\n attention_location_n_filter,\\n attention_location_kernel_size,\\n attention_hidden_dim);\\n this.score_mask_value = float.NegativeInfinity;\\n RegisterComponents();\\n }\\n\\n private Tensor _get_alignment_energies(Tensor query, Tensor processed_memory, Tensor attention_weights_cat)\\n {\\n var processed_query = this.query_layer.call(query.unsqueeze(1));\\n var processed_attention_weights = this.location_layer.call(attention_weights_cat);\\n var energies = this.v.call(torch.tanh(processed_query + processed_attention_weights + processed_memory));\\n\\n var alignment = energies.squeeze(2);\\n return alignment;\\n }\\n\\n public override (Tensor, Tensor) forward(\\n Tensor attention_hidden_state,\\n Tensor memory,\\n Tensor processed_memory,\\n Tensor attention_weights_cat,\\n Tensor mask)\\n {\\n var alignment = this._get_alignment_energies(attention_hidden_state, processed_memory, attention_weights_cat);\\n\\n alignment = alignment.masked_fill(mask, this.score_mask_value);\\n\\n var attention_weights = F.softmax(alignment, dim: 1);\\n var attention_context = torch.bmm(attention_weights.unsqueeze(1), memory);\\n attention_context = attention_context.squeeze(1);\\n\\n return (attention_context, attention_weights);\\n }\\n }\\n\\n public class Prenet : nn.Module<Tensor, Tensor>\\n {\\n private readonly Modules.ModuleList<Module<Tensor, Tensor>> layers;\\n\\n protected override void Dispose(bool disposing)\\n {\\n if (disposing) {\\n layers.Dispose();\\n }\\n base.Dispose(disposing);\\n }\\n\\n public Prenet(string name, int in_dim, long[] out_sizes) : base(name)\\n {\\n this.layers = nn.ModuleList<Module<Tensor, Tensor>>();\\n long prev_size = in_dim;\\n for (int i = 0; i < out_sizes.Length; i++) {\\n long in_size = prev_size;\\n long out_size = out_sizes[i];\\n this.layers.Add(\\n _get_linear_layer(in_size, out_size, bias: false));\\n prev_size = out_size;\\n }\\n RegisterComponents();\\n }\\n\\n public override Tensor forward(Tensor x)\\n {\\n foreach (var linear in this.layers) {\\n x = F.dropout(F.relu(linear.call(x)), p: 0.5, training: true);\\n }\\n return x;\\n }\\n }\\n\\n private class Postnet : nn.Module<Tensor, Tensor>\\n {\\n private readonly Modules.ModuleList<Module<Tensor, Tensor>> convolutions;\\n public readonly int n_convs;\\n\\n protected override void Dispose(bool disposing)\\n {\\n if (disposing) {\\n convolutions.Dispose();\\n }\\n\\n base.Dispose(disposing);\\n }\\n\\n public Postnet(\\n string name,\\n int n_mels,\\n int postnet_embedding_dim,\\n int postnet_kernel_size,\\n int postnet_n_convolution) : base(name)\\n {\\n this.convolutions = nn.ModuleList<Module<Tensor, Tensor>>();\\n\\n for (int i = 0; i < postnet_n_convolution; i++) {\\n var in_channels = i == 0 ? n_mels : postnet_embedding_dim;\\n var out_channels = i == postnet_n_convolution - 1 ? n_mels : postnet_embedding_dim;\\n var init_gain = i == postnet_n_convolution - 1 ? init.NonlinearityType.Linear : init.NonlinearityType.Tanh;\\n var num_features = i == postnet_n_convolution - 1 ? n_mels : postnet_embedding_dim;\\n this.convolutions.append(\\n nn.Sequential(\\n _get_conv1d_layer(\\n in_channels,\\n out_channels,\\n kernel_size: postnet_kernel_size,\\n stride: 1,\\n padding: (postnet_kernel_size - 1) / 2,\\n dilation: 1,\\n w_init_gain: init_gain),\\n nn.BatchNorm1d(num_features)));\\n }\\n\\n this.n_convs = this.convolutions.Count;\\n RegisterComponents();\\n }\\n\\n public override Tensor forward(Tensor x)\\n {\\n for (int i = 0; i < this.convolutions.Count; i++) {\\n var conv = this.convolutions[i];\\n if (i < this.n_convs - 1) {\\n x = F.dropout(torch.tanh(conv.call(x)), 0.5, training: this.training);\\n } else {\\n x = F.dropout(conv.call(x), 0.5, training: this.training);\\n }\\n }\\n return x;\\n }\\n }\\n\\n private class Encoder : nn.Module<Tensor, Tensor, Tensor>\\n {\\n private readonly Modules.ModuleList<Module<Tensor, Tensor>> convolutions;\\n private readonly Modules.LSTM lstm;\\n\\n protected override void Dispose(bool disposing)\\n {\\n if (disposing) {\\n convolutions.Dispose();\\n lstm.Dispose();\\n }\\n base.Dispose(disposing);\\n }\\n\\n public Encoder(\\n string name,\\n int encoder_embedding_dim,\\n int encoder_n_convolution,\\n int encoder_kernel_size) : base(name)\\n {\\n this.convolutions = nn.ModuleList<Module<Tensor, Tensor>>();\\n for (int i = 0; i < encoder_n_convolution; i++) {\\n var conv_layer = nn.Sequential(\\n _get_conv1d_layer(\\n encoder_embedding_dim,\\n encoder_embedding_dim,\\n kernel_size: encoder_kernel_size,\\n stride: 1,\\n padding: (encoder_kernel_size - 1) / 2,\\n dilation: 1,\\n w_init_gain: init.NonlinearityType.ReLU\\n ),\\n nn.BatchNorm1d(encoder_embedding_dim)\\n );\\n this.convolutions.append(conv_layer);\\n }\\n\\n this.lstm = nn.LSTM(\\n encoder_embedding_dim,\\n encoder_embedding_dim / 2,\\n 1,\\n batchFirst: true,\\n bidirectional: true\\n );\\n this.lstm.flatten_parameters();\\n RegisterComponents();\\n }\\n\\n public override Tensor forward(Tensor x, Tensor input_lengths)\\n {\\n foreach (var conv in this.convolutions) {\\n x = F.dropout(F.relu(conv.call(x)), 0.5, training: this.training);\\n }\\n\\n x = x.transpose(1, 2);\\n\\n input_lengths = input_lengths.cpu();\\n var packed_x = nn.utils.rnn.pack_padded_sequence(x, input_lengths, batch_first: true);\\n\\n var (packed_outputs, _, _) = this.lstm.call(packed_x);\\n var (outputs, _) = nn.utils.rnn.pad_packed_sequence(packed_outputs, batch_first: true);\\n\\n return outputs;\\n }\\n }\\n\\n private class Decoder : nn.Module<Tensor, Tensor, Tensor, (Tensor, Tensor, Tensor)>\\n {\\n public readonly double attention_dropout;\\n private readonly Attention attention_layer;\\n private readonly Modules.LSTMCell attention_rnn;\\n public readonly long attention_rnn_dim;\\n public readonly double decoder_dropout;\\n public readonly bool decoder_early_stopping;\\n public readonly long decoder_max_step;\\n private readonly Modules.LSTMCell decoder_rnn;\\n public readonly long decoder_rnn_dim;\\n public readonly long encoder_embedding_dim;\\n private readonly Modules.Linear gate_layer;\\n public readonly double gate_threshold;\\n private readonly Modules.Linear linear_projection;\\n public readonly int n_frames_per_step;\\n public readonly int n_mels;\\n public readonly Prenet prenet;\\n public readonly long prenet_dim;\\n\\n protected override void Dispose(bool disposing)\\n {\\n if (disposing) {\\n attention_layer.Dispose(); attention_rnn.Dispose();\\n decoder_rnn.Dispose();\\n gate_layer.Dispose();\\n linear_projection.Dispose();\\n prenet.Dispose();\\n }\\n base.Dispose(disposing);\\n }\\n\\n public Decoder(\\n string name,\\n int n_mels,\\n int n_frames_per_step,\\n int encoder_embedding_dim,\\n int decoder_rnn_dim,\\n int decoder_max_step,\\n double decoder_dropout,\\n bool decoder_early_stopping,\\n int attention_rnn_dim,\\n int attention_hidden_dim,\\n int attention_location_n_filter,\\n int attention_location_kernel_size,\\n double attention_dropout,\\n long prenet_dim,\\n double gate_threshold) : base(name)\\n {\\n this.n_mels = n_mels;\\n this.n_frames_per_step = n_frames_per_step;\\n this.encoder_embedding_dim = encoder_embedding_dim;\\n this.attention_rnn_dim = attention_rnn_dim;\\n this.decoder_rnn_dim = decoder_rnn_dim;\\n this.prenet_dim = prenet_dim;\\n this.decoder_max_step = decoder_max_step;\\n this.gate_threshold = gate_threshold;\\n this.attention_dropout = attention_dropout;\\n this.decoder_dropout = decoder_dropout;\\n this.decoder_early_stopping = decoder_early_stopping;\\n\\n this.prenet = new Prenet(\\\"prenet\\\", n_mels * n_frames_per_step, new[] {\\n prenet_dim,\\n prenet_dim\\n });\\n\\n this.attention_rnn = nn.LSTMCell(prenet_dim + encoder_embedding_dim, attention_rnn_dim);\\n\\n this.attention_layer = new Attention(\\n \\\"attention\\\",\\n attention_rnn_dim,\\n encoder_embedding_dim,\\n attention_hidden_dim,\\n attention_location_n_filter,\\n attention_location_kernel_size);\\n\\n this.decoder_rnn = nn.LSTMCell(attention_rnn_dim + encoder_embedding_dim, decoder_rnn_dim, true);\\n\\n this.linear_projection = _get_linear_layer(decoder_rnn_dim + encoder_embedding_dim, n_mels * n_frames_per_step);\\n\\n this.gate_layer = _get_linear_layer(\\n decoder_rnn_dim + encoder_embedding_dim, 1, bias: true, w_init_gain: init.NonlinearityType.Sigmoid);\\n RegisterComponents();\\n }\\n\\n private Tensor _get_initial_frame(Tensor memory)\\n {\\n var n_batch = memory.size(0);\\n var dtype = memory.dtype;\\n var device = memory.device;\\n var decoder_input = torch.zeros(n_batch, this.n_mels * this.n_frames_per_step, dtype: dtype, device: device);\\n return decoder_input;\\n }\\n\\n private (Tensor, Tensor, Tensor, Tensor, Tensor, Tensor, Tensor, Tensor) _initialize_decoder_states(Tensor memory)\\n {\\n var n_batch = memory.size(0);\\n var max_time = memory.size(1);\\n var dtype = memory.dtype;\\n var device = memory.device;\\n\\n var attention_hidden = torch.zeros(n_batch, this.attention_rnn_dim, dtype: dtype, device: device);\\n var attention_cell = torch.zeros(n_batch, this.attention_rnn_dim, dtype: dtype, device: device);\\n\\n var decoder_hidden = torch.zeros(n_batch, this.decoder_rnn_dim, dtype: dtype, device: device);\\n var decoder_cell = torch.zeros(n_batch, this.decoder_rnn_dim, dtype: dtype, device: device);\\n\\n var attention_weights = torch.zeros(n_batch, max_time, dtype: dtype, device: device);\\n var attention_weights_cum = torch.zeros(n_batch, max_time, dtype: dtype, device: device);\\n var attention_context = torch.zeros(n_batch, this.encoder_embedding_dim, dtype: dtype, device: device);\\n\\n var processed_memory = this.attention_layer.memory_layer.call(memory);\\n\\n return (\\n attention_hidden,\\n attention_cell,\\n decoder_hidden,\\n decoder_cell,\\n attention_weights,\\n attention_weights_cum,\\n attention_context,\\n processed_memory);\\n }\\n\\n private Tensor _parse_decoder_inputs(Tensor decoder_inputs)\\n {\\n // (n_batch, n_mels, mel_specgram_lengths.max()) -> (n_batch, mel_specgram_lengths.max(), n_mels)\\n decoder_inputs = decoder_inputs.transpose(1, 2);\\n decoder_inputs = decoder_inputs.view(\\n decoder_inputs.size(0),\\n decoder_inputs.size(1) / this.n_frames_per_step,\\n -1);\\n // (n_batch, mel_specgram_lengths.max(), n_mels) -> (mel_specgram_lengths.max(), n_batch, n_mels)\\n decoder_inputs = decoder_inputs.transpose(0, 1);\\n return decoder_inputs;\\n }\\n\\n private (Tensor, Tensor, Tensor) _parse_decoder_outputs(Tensor mel_specgram, Tensor gate_outputs, Tensor alignments)\\n {\\n // (mel_specgram_lengths.max(), n_batch, text_lengths.max())\\n // -> (n_batch, mel_specgram_lengths.max(), text_lengths.max())\\n alignments = alignments.transpose(0, 1).contiguous();\\n // (mel_specgram_lengths.max(), n_batch) -> (n_batch, mel_specgram_lengths.max())\\n gate_outputs = gate_outputs.transpose(0, 1).contiguous();\\n // (mel_specgram_lengths.max(), n_batch, n_mels) -> (n_batch, mel_specgram_lengths.max(), n_mels)\\n mel_specgram = mel_specgram.transpose(0, 1).contiguous();\\n // decouple frames per step\\n var shape = new long[] { mel_specgram.shape[0], -1, this.n_mels };\\n mel_specgram = mel_specgram.view(shape);\\n // (n_batch, mel_specgram_lengths.max(), n_mels) -> (n_batch, n_mels, T_out)\\n mel_specgram = mel_specgram.transpose(1, 2);\\n\\n return (mel_specgram, gate_outputs, alignments);\\n }\\n\\n public (Tensor, Tensor, Tensor, Tensor, Tensor, Tensor, Tensor, Tensor, Tensor) decode(\\n Tensor decoder_input,\\n Tensor attention_hidden,\\n Tensor attention_cell,\\n Tensor decoder_hidden,\\n Tensor decoder_cell,\\n Tensor attention_weights,\\n Tensor attention_weights_cum,\\n Tensor attention_context,\\n Tensor memory,\\n Tensor processed_memory,\\n Tensor mask)\\n {\\n var cell_input = torch.cat(new[] { decoder_input, attention_context }, -1);\\n\\n (attention_hidden, attention_cell) = this.attention_rnn.call(cell_input, (attention_hidden, attention_cell));\\n attention_hidden = F.dropout(attention_hidden, this.attention_dropout, training: this.training);\\n\\n var attention_weights_cat = torch.cat(new[] { attention_weights.unsqueeze(1), attention_weights_cum.unsqueeze(1) }, dim: 1);\\n (attention_context, attention_weights) = this.attention_layer.call(\\n attention_hidden, memory, processed_memory, attention_weights_cat, mask);\\n\\n attention_weights_cum += attention_weights;\\n decoder_input = torch.cat(new[] { attention_hidden, attention_context }, -1);\\n\\n (decoder_hidden, decoder_cell) = this.decoder_rnn.call(decoder_input, (decoder_hidden, decoder_cell));\\n decoder_hidden = F.dropout(decoder_hidden, this.decoder_dropout, training: this.training);\\n\\n var decoder_hidden_attention_context = torch.cat(new[] { decoder_hidden, attention_context }, dim: 1);\\n var decoder_output = this.linear_projection.call(decoder_hidden_attention_context);\\n\\n var gate_prediction = this.gate_layer.call(decoder_hidden_attention_context);\\n\\n return (\\n decoder_output,\\n gate_prediction,\\n attention_hidden,\\n attention_cell,\\n decoder_hidden,\\n decoder_cell,\\n attention_weights,\\n attention_weights_cum,\\n attention_context);\\n }\\n\\n // Decoder forward pass for training.\\n public override (Tensor, Tensor, Tensor) forward(Tensor memory, Tensor mel_specgram_truth, Tensor memory_lengths)\\n {\\n var decoder_input = this._get_initial_frame(memory).unsqueeze(0);\\n var decoder_inputs = this._parse_decoder_inputs(mel_specgram_truth);\\n decoder_inputs = torch.cat(new[] { decoder_input, decoder_inputs }, dim: 0);\\n decoder_inputs = this.prenet.call(decoder_inputs);\\n\\n var mask = _get_mask_from_lengths(memory_lengths);\\n var (\\n attention_hidden,\\n attention_cell,\\n decoder_hidden,\\n decoder_cell,\\n attention_weights,\\n attention_weights_cum,\\n attention_context,\\n processed_memory\\n ) = this._initialize_decoder_states(memory);\\n\\n var mel_output_list = new List<Tensor>();\\n var gate_output_list = new List<Tensor>();\\n var alignment_list = new List<Tensor>();\\n while (mel_output_list.Count < decoder_inputs.size(0) - 1) {\\n decoder_input = decoder_inputs[mel_output_list.Count];\\n Tensor mel_output, gate_output;\\n (\\n mel_output,\\n gate_output,\\n attention_hidden,\\n attention_cell,\\n decoder_hidden,\\n decoder_cell,\\n attention_weights,\\n attention_weights_cum,\\n attention_context\\n ) = this.decode(\\n decoder_input,\\n attention_hidden,\\n attention_cell,\\n decoder_hidden,\\n decoder_cell,\\n attention_weights,\\n attention_weights_cum,\\n attention_context,\\n memory,\\n processed_memory,\\n mask);\\n\\n mel_output_list.Add(mel_output.squeeze(1));\\n gate_output_list.Add(gate_output.squeeze(1));\\n alignment_list.Add(attention_weights);\\n }\\n\\n var (mel_specgram, gate_outputs, alignments) = this._parse_decoder_outputs(\\n torch.stack(mel_output_list), torch.stack(gate_output_list), torch.stack(alignment_list));\\n\\n return (mel_specgram, gate_outputs, alignments);\\n }\\n\\n public new (Tensor, Tensor, Tensor) call(Tensor memory, Tensor mel_specgram_truth, Tensor memory_lengths) => base.call(memory, mel_specgram_truth, memory_lengths);\\n\\n private Tensor _get_go_frame(Tensor memory)\\n {\\n var n_batch = memory.size(0);\\n var dtype = memory.dtype;\\n var device = memory.device;\\n var decoder_input = torch.zeros(n_batch, this.n_mels * this.n_frames_per_step, dtype: dtype, device: device);\\n return decoder_input;\\n }\\n\\n public (Tensor, Tensor, Tensor, Tensor) infer(Tensor memory, Tensor memory_lengths)\\n {\\n var batch_size = memory.size(0);\\n var device = memory.device;\\n\\n var decoder_input = this._get_go_frame(memory);\\n\\n var mask = _get_mask_from_lengths(memory_lengths);\\n var (\\n attention_hidden,\\n attention_cell,\\n decoder_hidden,\\n decoder_cell,\\n attention_weights,\\n attention_weights_cum,\\n attention_context,\\n processed_memory\\n ) = this._initialize_decoder_states(memory);\\n\\n var mel_specgram_lengths = torch.zeros(new[] {\\n batch_size\\n }, dtype: torch.int32, device: device);\\n var finished = torch.zeros(new[] {\\n batch_size\\n }, dtype: torch.@bool, device: device);\\n var mel_specgram_list = new List<Tensor>();\\n var gate_output_list = new List<Tensor>();\\n var alignment_list = new List<Tensor>();\\n for (long i = 0; i < this.decoder_max_step; i++) {\\n decoder_input = this.prenet.call(decoder_input);\\n Tensor mel_specgram, gate_output;\\n (\\n mel_specgram,\\n gate_output,\\n attention_hidden,\\n attention_cell,\\n decoder_hidden,\\n decoder_cell,\\n attention_weights,\\n attention_weights_cum,\\n attention_context\\n ) = this.decode(\\n decoder_input,\\n attention_hidden,\\n attention_cell,\\n decoder_hidden,\\n decoder_cell,\\n attention_weights,\\n attention_weights_cum,\\n attention_context,\\n memory,\\n processed_memory,\\n mask);\\n\\n mel_specgram_list.Add(mel_specgram.unsqueeze(0));\\n gate_output_list.Add(gate_output.transpose(0, 1));\\n alignment_list.Add(attention_weights);\\n mel_specgram_lengths[~finished] += 1;\\n\\n finished |= torch.sigmoid(gate_output.squeeze(1)) > this.gate_threshold;\\n if (this.decoder_early_stopping && torch.all(finished).item<bool>()) {\\n break;\\n }\\n\\n decoder_input = mel_specgram;\\n }\\n\\n if (mel_specgram_list.Count == this.decoder_max_step) {\\n Debug.WriteLine(\\\"Reached max decoder steps. The generated spectrogram might not cover the whole transcript.\\\");\\n }\\n\\n var mel_specgrams = torch.cat(mel_specgram_list, dim: 0);\\n var gate_outputs = torch.cat(gate_output_list, dim: 0);\\n var alignments = torch.cat(alignment_list, dim: 0);\\n\\n (mel_specgrams, gate_outputs, alignments) = this._parse_decoder_outputs(mel_specgrams, gate_outputs, alignments);\\n\\n return (mel_specgrams, mel_specgram_lengths, gate_outputs, alignments);\\n }\\n }\\n }\\n}\"\n },\n {\n \"alpha_fraction\": 0.579365074634552,\n \"alphanum_fraction\": 0.5833333134651184,\n \"avg_line_length\": 35,\n \"blob_id\": \"11a9d48ac1f5fb8f3a83c4a3f934f208a7e39eb8\",\n \"content_id\": \"e5a5fd1861a598f17dfc79a2886f102c839249a3\",\n \"detected_licenses\": [\n \"MIT\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"C#\",\n \"length_bytes\": 1512,\n \"license_type\": \"permissive\",\n \"max_line_length\": 130,\n \"num_lines\": 42,\n \"path\": \"/src/TorchVision/AdjustBrightness.cs\",\n \"repo_name\": \"NiklasGustafsson/TorchSharp\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"// Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information.\\nusing System;\\nusing static TorchSharp.torch;\\n\\nnamespace TorchSharp\\n{\\n public static partial class torchvision\\n {\\n internal class AdjustBrightness : ITransform\\n {\\n internal AdjustBrightness(double brightness_factor)\\n {\\n if (brightness_factor < 0.0)\\n throw new ArgumentException($\\\"The sharpness factor ({brightness_factor}) must be non-negative.\\\");\\n this.brightness_factor = brightness_factor;\\n }\\n\\n public Tensor call(Tensor input)\\n {\\n return transforms.functional.adjust_brightness(input, brightness_factor);\\n }\\n\\n private double brightness_factor;\\n }\\n\\n public static partial class transforms\\n {\\n /// <summary>\\n /// Adjust the sharpness of the image. \\n /// </summary>\\n /// <param name=\\\"brightness_factor\\\">\\n /// How much to adjust the brightness. Can be any non negative number.\\n /// 0 gives a black image, 1 gives the original image while 2 increases the brightness by a factor of 2.\\n /// </param>\\n /// <returns></returns>\\n static public ITransform AdjustBrightness(double brightness_factor)\\n {\\n return new AdjustBrightness(brightness_factor);\\n }\\n }\\n }\\n}\\n\"\n },\n {\n \"alpha_fraction\": 0.5237137675285339,\n \"alphanum_fraction\": 0.530632734298706,\n \"avg_line_length\": 47.039772033691406,\n \"blob_id\": \"4f0c2c66c872fe6be47a04d8211558d1127104ca\",\n \"content_id\": \"8ca7dae45dba7040fa26059a1c73b4ef4696277e\",\n \"detected_licenses\": [\n \"MIT\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"C#\",\n \"length_bytes\": 16911,\n \"license_type\": \"permissive\",\n \"max_line_length\": 223,\n \"num_lines\": 352,\n \"path\": \"/src/TorchSharp/Optimizers/Adagrad.cs\",\n \"repo_name\": \"NiklasGustafsson/TorchSharp\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"// Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information.\\nusing System;\\nusing System.Collections.Generic;\\nusing System.Linq;\\nusing static TorchSharp.torch;\\n\\nnamespace TorchSharp\\n{\\n using System.IO;\\n using Modules;\\n\\n public static partial class torch\\n {\\n public static partial class optim\\n {\\n\\n /// <summary>\\n /// Implements the Adagrad algorithm.\\n ///\\n /// It has been proposed in Adaptive Subgradient Methods for Online Learning and Stochastic Optimization.\\n /// http://jmlr.org/papers/v12/duchi11a.html\\n /// </summary>\\n /// <param name=\\\"parameters\\\">Parameters to optimize</param>\\n /// <param name=\\\"lr\\\">learning rate (default: 1e-2)</param>\\n /// <param name=\\\"lr_decay\\\">learning rate decay (default: 0)</param>\\n /// <param name=\\\"weight_decay\\\">weight decay (L2 penalty) (default: 0)</param>\\n /// <param name=\\\"initial_accumulator_value\\\"></param>\\n /// <param name=\\\"eps\\\">Term added to the denominator to improve numerical stability (default: 1e-10)</param>\\n /// <returns></returns>\\n public static Adagrad Adagrad(IEnumerable<Parameter> parameters, double lr = 1e-2, double lr_decay = 0, double weight_decay = 0, double initial_accumulator_value = 0, double eps = 1e-10)\\n {\\n return new Adagrad(parameters, lr, lr_decay, weight_decay, initial_accumulator_value, eps);\\n }\\n\\n /// <summary>\\n /// Implements the Adagrad algorithm.\\n ///\\n /// It has been proposed in Adaptive Subgradient Methods for Online Learning and Stochastic Optimization.\\n /// http://jmlr.org/papers/v12/duchi11a.html\\n /// </summary>\\n /// <param name=\\\"parameters\\\">Parameters to optimize</param>\\n /// <param name=\\\"lr\\\">learning rate (default: 1e-2)</param>\\n /// <param name=\\\"lr_decay\\\">learning rate decay (default: 0)</param>\\n /// <param name=\\\"weight_decay\\\">weight decay (L2 penalty) (default: 0)</param>\\n /// <param name=\\\"initial_accumulator_value\\\"></param>\\n /// <param name=\\\"eps\\\">Term added to the denominator to improve numerical stability (default: 1e-10)</param>\\n /// <returns></returns>\\n public static Adagrad Adagrad(IEnumerable<(string name, Parameter parameter)> parameters, double lr = 1e-2, double lr_decay = 0, double weight_decay = 0, double initial_accumulator_value = 0, double eps = 1e-10)\\n {\\n return new Adagrad(parameters.Select(np => np.parameter), lr, lr_decay, weight_decay, initial_accumulator_value, eps);\\n }\\n\\n /// <summary>\\n /// Implements the Adagrad algorithm.\\n ///\\n /// It has been proposed in Adaptive Subgradient Methods for Online Learning and Stochastic Optimization.\\n /// http://jmlr.org/papers/v12/duchi11a.html\\n /// </summary>\\n /// <param name=\\\"parameters\\\">Parameters to optimize</param>\\n /// <param name=\\\"lr\\\">learning rate (default: 1e-2)</param>\\n /// <param name=\\\"lr_decay\\\">learning rate decay (default: 0)</param>\\n /// <param name=\\\"weight_decay\\\">weight decay (L2 penalty) (default: 0)</param>\\n /// <param name=\\\"initial_accumulator_value\\\"></param>\\n /// <param name=\\\"eps\\\">Term added to the denominator to improve numerical stability (default: 1e-10)</param>\\n /// <returns></returns>\\n public static Adagrad Adagrad(IEnumerable<Adagrad.ParamGroup> parameters, double lr = 1e-2, double lr_decay = 0, double weight_decay = 0, double initial_accumulator_value = 0, double eps = 1e-10)\\n {\\n return new Adagrad(parameters, lr, lr_decay, weight_decay, initial_accumulator_value, eps);\\n }\\n }\\n }\\n\\n namespace Modules\\n {\\n public class Adagrad : OptimizerHelper\\n {\\n /// <summary>\\n /// Implements Adagrad algorithm.\\n ///\\n /// It has been proposed in Adaptive Subgradient Methods for Online Learning and Stochastic Optimization.\\n /// </summary>\\n /// <param name=\\\"parameters\\\">Parameters to optimize</param>\\n /// <param name=\\\"lr\\\">learning rate (default: 1e-2)</param>\\n /// <param name=\\\"lr_decay\\\">learning rate decay (default: 0)</param>\\n /// <param name=\\\"weight_decay\\\">weight decay (L2 penalty) (default: 0)</param>\\n /// <param name=\\\"initial_accumulator_value\\\"></param>\\n /// <param name=\\\"eps\\\">Term added to the denominator to improve numerical stability (default: 1e-10)</param>\\n /// <returns></returns>\\n public Adagrad(IEnumerable<Parameter> parameters, double lr = 1e-2, double lr_decay = 0, double weight_decay = 0, double initial_accumulator_value = 0, double eps = 1e-10)\\n : this(new ParamGroup[] { new() { Parameters = parameters } }, lr, lr_decay, weight_decay, initial_accumulator_value, eps)\\n {\\n }\\n /// <summary>\\n /// Implements Adagrad algorithm.\\n ///\\n /// It has been proposed in Adaptive Subgradient Methods for Online Learning and Stochastic Optimization.\\n /// </summary>\\n /// <param name=\\\"parameters\\\">Parameters to optimize</param>\\n /// <param name=\\\"lr\\\">learning rate (default: 1e-2)</param>\\n /// <param name=\\\"lr_decay\\\">learning rate decay (default: 0)</param>\\n /// <param name=\\\"weight_decay\\\">weight decay (L2 penalty) (default: 0)</param>\\n /// <param name=\\\"initial_accumulator_value\\\"></param>\\n /// <param name=\\\"eps\\\">Term added to the denominator to improve numerical stability (default: 1e-10)</param>\\n /// <returns></returns>\\n public Adagrad(IEnumerable<ParamGroup> parameters, double lr = 1e-2, double lr_decay = 0, double weight_decay = 0, double initial_accumulator_value = 0, double eps = 1e-10)\\n {\\n if (lr < 0) throw new ArgumentException($\\\"Invalid learning rate: {lr}\\\");\\n if (eps < 0) throw new ArgumentException($\\\"Invalid ε: {eps}\\\");\\n if (lr_decay < 0.0) throw new ArgumentException($\\\"Invalid lr_decay value: {lr_decay}\\\");\\n if (weight_decay < 0.0) throw new ArgumentException($\\\"Invalid weight_decay value: {weight_decay}\\\");\\n if (initial_accumulator_value < 0) throw new ArgumentException($\\\"Invalid initial_accumulator_value: {initial_accumulator_value}\\\");\\n\\n var options = new Options {\\n LearningRate = lr,\\n InitialLearningRate = lr,\\n lr_decay = lr_decay,\\n eps = eps,\\n initial_accumulator_value = initial_accumulator_value,\\n weight_decay = weight_decay\\n };\\n\\n _defaults = options;\\n _parameter_groups = new List<Modules.ParamGroup>();\\n\\n foreach (var g in parameters) {\\n add_param_group(g);\\n }\\n }\\n\\n /// <summary>\\n /// Performs a single optimization step (parameter update).\\n /// </summary>\\n /// <param name=\\\"closure\\\">A closure that reevaluates the model and returns the loss. Optional for most optimizers.</param>\\n /// <returns></returns>\\n public override Tensor step(Func<Tensor> closure = null)\\n {\\n return _step<ParamGroup>(group => {\\n\\n var options = group.Options as Options;\\n var lr_decay = options.lr_decay.Value;\\n var weight_decay = options.weight_decay.Value;\\n var eps = options.eps.Value;\\n var initial_accumulator_value = options.initial_accumulator_value.Value;\\n var lr = options.LearningRate.Value;\\n\\n foreach (var param in group.Parameters) {\\n\\n var state = (State)_state[param.handle];\\n\\n var grad = param.grad();\\n\\n if (grad is null) continue;\\n\\n state.step += 1;\\n\\n if (weight_decay != 0) {\\n grad = grad.add(param, alpha: weight_decay);\\n }\\n\\n var clr = lr / (1 + (state.step - 1) * lr_decay);\\n\\n if (grad.is_sparse)\\n throw new NotImplementedException(\\\"Adagrad optimization over sparse parameters\\\");\\n if (torch.is_complex(grad))\\n throw new NotImplementedException(\\\"Adagrad optimization over complex parameters\\\");\\n\\n state.sum.addcmul_(grad, grad, value: 1);\\n var std = state.sum.sqrt().add_(eps);\\n param.addcdiv_(grad, std, value: -clr);\\n }\\n\\n\\n }, closure);\\n }\\n\\n protected override void Dispose(bool disposing)\\n {\\n base.Dispose(disposing);\\n foreach (var kvp in _state) {\\n ((State)kvp.Item2).Dispose();\\n }\\n }\\n\\n public sealed class State : OptimizerState, IDisposable\\n {\\n public long step;\\n public Tensor sum;\\n\\n public void Dispose()\\n {\\n Dispose(true);\\n GC.SuppressFinalize(this);\\n }\\n\\n private void Dispose(bool disposing)\\n {\\n if (disposing) {\\n sum.Dispose();\\n }\\n }\\n\\n /// <summary>\\n /// Move all the state to the indicated device.\\n /// </summary>\\n /// <param name=\\\"device\\\">The device to move all state to.</param>\\n public override void to(Device device)\\n {\\n sum = sum.to(device);\\n }\\n\\n /// <summary>\\n /// Load the optimizer parameter state from a stream.\\n /// </summary>\\n /// <param name=\\\"reader\\\">A binary reader connected to a stream open for reading.</param>\\n public override void LoadStateDict(BinaryReader reader)\\n {\\n step = reader.ReadInt64();\\n sum.Load(reader);\\n }\\n\\n /// <summary>\\n /// Load optimizer parameter state from another optimizer.\\n /// </summary>\\n /// <param name=\\\"source\\\">An optimizer state record.</param>\\n public override void LoadStateDict(OptimizerState source)\\n {\\n var st_state = source as State;\\n sum.Dispose();\\n step = st_state.step;\\n sum = st_state.sum;\\n }\\n\\n /// <summary>\\n /// Save the optimizer parameter state to a stream.\\n /// </summary>\\n /// <param name=\\\"writer\\\">A binary writer connected to a stream open for writing.</param>\\n public override void SaveStateDict(BinaryWriter writer)\\n {\\n writer.Write(step);\\n sum.Save(writer);\\n }\\n\\n /// <summary>\\n /// Useful for tests, allows comparison of one state with another.\\n /// </summary>\\n /// <param name=\\\"other\\\">The other optimizer state</param>\\n /// <returns></returns>\\n public override bool ApproximatelyEquals(OptimizerState other)\\n {\\n var rhs = other as State;\\n return (rhs is not null) && step == rhs.step && sum.allclose(rhs.sum);\\n }\\n }\\n\\n /// <summary>\\n /// Add a param group to the Optimizer s param_groups.\\n /// </summary>\\n /// <param name=\\\"param_group\\\"></param>\\n /// <remarks>This can be useful when fine tuning a pre-trained network as frozen layers can be made trainable and added to the Optimizer as training progresses.</remarks>\\n public override void add_param_group(Modules.ParamGroup param_group)\\n {\\n var def = _defaults as Options;\\n if (param_group.Options is null) {\\n param_group.Options = new Options();\\n }\\n\\n var opt = param_group.Options as Options;\\n\\n // Make sure all the options are set.\\n if (!opt.LearningRate.HasValue) opt.LearningRate = def.LearningRate;\\n if (!opt.lr_decay.HasValue) opt.lr_decay = def.lr_decay;\\n if (!opt.eps.HasValue) opt.eps = def.eps;\\n if (!opt.weight_decay.HasValue) opt.weight_decay = def.weight_decay;\\n if (!opt.initial_accumulator_value.HasValue) opt.initial_accumulator_value = def.initial_accumulator_value;\\n\\n opt.InitialLearningRate = opt.LearningRate.Value;\\n\\n _parameter_groups.Add(param_group);\\n\\n foreach (var p in param_group.Parameters) {\\n var state = new State();\\n _state[p.Handle] = state;\\n state.step = 0;\\n var init_value = torch.is_complex(p.dtype)\\n ? (Scalar)new System.Numerics.Complex((param_group.Options as Options).initial_accumulator_value.Value, (param_group.Options as Options).initial_accumulator_value.Value)\\n : (Scalar)(param_group.Options as Options).initial_accumulator_value.Value;\\n state.sum = torch.full_like(p, init_value).DetachFromDisposeScope();\\n }\\n }\\n\\n public class Options : OptimizerOptions\\n {\\n public double? lr_decay;\\n public double? initial_accumulator_value;\\n public double? eps;\\n public double? weight_decay;\\n\\n /// <summary>\\n /// Load optimizer options (param-group hyperparameters) from another optimizer.\\n /// </summary>\\n /// <param name=\\\"source\\\">An optimizer options record.</param>\\n public override void LoadStateDict(OptimizerOptions source)\\n {\\n base.LoadStateDict(source);\\n var opts = source as Options;\\n lr_decay = opts.lr_decay;\\n initial_accumulator_value = opts.initial_accumulator_value;\\n eps = opts.eps;\\n weight_decay = opts.weight_decay;\\n }\\n\\n /// <summary>\\n /// Load the optimizer options (param-group hyperparameters) from a stream.\\n /// </summary>\\n /// <param name=\\\"reader\\\">A binary reader connected to a stream open for reading.</param>\\n public override void LoadStateDict(BinaryReader reader)\\n {\\n base.LoadStateDict(reader);\\n lr_decay = reader.ReadDouble();\\n initial_accumulator_value = reader.ReadDouble();\\n eps = reader.ReadDouble();\\n weight_decay = reader.ReadDouble();\\n }\\n\\n /// <summary>\\n /// Save the optimizer options (param-group hyperparameters) to a stream.\\n /// </summary>\\n /// <param name=\\\"writer\\\">A binary writer connected to a stream open for writing.</param>\\n public override void SaveStateDict(BinaryWriter writer)\\n {\\n base.SaveStateDict(writer);\\n writer.Write(lr_decay.Value);\\n writer.Write(initial_accumulator_value.Value);\\n writer.Write(eps.Value);\\n writer.Write(weight_decay.Value);\\n }\\n }\\n\\n public class ParamGroup : ParamGroup<Options>\\n {\\n public ParamGroup() { }\\n\\n public ParamGroup(IEnumerable<Parameter> parameters, Options options) : base(parameters, options) { }\\n\\n public ParamGroup(IEnumerable<Parameter> parameters, double lr = 0.01, double lr_decay = 0, double weight_decay = 0, double initial_accumulator_value = 0, double eps = 1e-10)\\n : base(parameters, new Adagrad.Options { LearningRate = lr, lr_decay = lr_decay, initial_accumulator_value = initial_accumulator_value, weight_decay = weight_decay, eps = eps })\\n {\\n }\\n }\\n }\\n }\\n}\\n\"\n },\n {\n \"alpha_fraction\": 0.5762327313423157,\n \"alphanum_fraction\": 0.5782958269119263,\n \"avg_line_length\": 48.96907043457031,\n \"blob_id\": \"b0cbb8c9cc2b7315394332c0df7b6a68de0ac4a8\",\n \"content_id\": \"004be40ba6ad7b53bc9a3a985b6981790b454455\",\n \"detected_licenses\": [\n \"MIT\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"C#\",\n \"length_bytes\": 4847,\n \"license_type\": \"permissive\",\n \"max_line_length\": 209,\n \"num_lines\": 97,\n \"path\": \"/src/TorchSharp/NN/Normalization/LayerNorm.cs\",\n \"repo_name\": \"NiklasGustafsson/TorchSharp\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"// Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information.\\nusing System;\\nusing static TorchSharp.torch;\\nusing static TorchSharp.PInvoke.NativeMethods;\\n\\n#nullable enable\\nnamespace TorchSharp\\n{\\n using Modules;\\n\\n namespace Modules\\n {\\n\\n /// <summary>\\n /// This class is used to represent a LayerNorm module.\\n /// </summary>\\n public sealed class LayerNorm : torch.nn.Module<Tensor, Tensor>\\n {\\n internal LayerNorm(IntPtr handle, IntPtr boxedHandle) : base(handle, boxedHandle)\\n {\\n }\\n\\n public override Tensor forward(Tensor tensor)\\n {\\n var res = THSNN_LayerNorm_forward(handle.DangerousGetHandle(), tensor.Handle);\\n if (res == IntPtr.Zero) { torch.CheckForErrors(); }\\n return new Tensor(res);\\n }\\n\\n public Parameter? bias {\\n get {\\n var res = THSNN_LayerNorm_bias(handle);\\n if (res == IntPtr.Zero) { torch.CheckForErrors(); }\\n return (res == IntPtr.Zero) ? null : new Parameter(res);\\n }\\n set {\\n THSNN_LayerNorm_set_bias(handle, (value is null ? IntPtr.Zero : value.Handle));\\n torch.CheckForErrors();\\n ConditionallyRegisterParameter(\\\"bias\\\", value);\\n }\\n }\\n\\n public Parameter? weight {\\n get {\\n var res = THSNN_LayerNorm_weight(handle);\\n if (res == IntPtr.Zero) { torch.CheckForErrors(); }\\n return (res == IntPtr.Zero) ? null : new Parameter(res);\\n }\\n set {\\n THSNN_LayerNorm_set_weight(handle, value is null ? IntPtr.Zero : value.Handle);\\n torch.CheckForErrors();\\n ConditionallyRegisterParameter(\\\"weight\\\", value);\\n }\\n }\\n }\\n }\\n\\n public static partial class torch\\n {\\n public static partial class nn\\n {\\n /// <summary>\\n /// Applies Layer Normalization over a mini-batch of inputs as described in the paper Layer Normalization\\n /// </summary>\\n /// <param name=\\\"normalized_shape\\\">Input shape from an expected input.</param>\\n /// <param name=\\\"eps\\\">A value added to the denominator for numerical stability. Default: 1e-5</param>\\n /// <param name=\\\"elementwise_affine\\\">a boolean value that when set to true, this module has learnable per-element affine parameters initialized to ones (for weights) and zeros (for biases).</param>\\n /// <param name=\\\"device\\\">The desired device of the parameters and buffers in this module</param>\\n /// <param name=\\\"dtype\\\">The desired floating point or complex dtype of the parameters and buffers in this module</param>\\n /// <returns></returns>\\n public static LayerNorm LayerNorm(long[] normalized_shape, double eps = 1e-05, bool elementwise_affine = true, Device? device = null, ScalarType? dtype = null)\\n {\\n unsafe {\\n fixed (long* pNormShape = normalized_shape) {\\n var handle = THSNN_LayerNorm_ctor((IntPtr)pNormShape, normalized_shape.Length, eps, elementwise_affine, out var boxedHandle);\\n if (handle == IntPtr.Zero) { torch.CheckForErrors(); }\\n return new LayerNorm(handle, boxedHandle).MoveModule<LayerNorm>(device, dtype);\\n }\\n }\\n }\\n\\n /// <summary>\\n /// Applies Layer Normalization over a mini-batch of inputs as described in the paper Layer Normalization\\n /// </summary>\\n /// <param name=\\\"normalized_shape\\\">Input shape from an expected input.</param>\\n /// <param name=\\\"eps\\\">A value added to the denominator for numerical stability. Default: 1e-5</param>\\n /// <param name=\\\"elementwise_affine\\\">a boolean value that when set to true, this module has learnable per-element affine parameters initialized to ones (for weights) and zeros (for biases).</param>\\n /// <param name=\\\"device\\\">The desired device of the parameters and buffers in this module</param>\\n /// <param name=\\\"dtype\\\">The desired floating point or complex dtype of the parameters and buffers in this module</param>\\n /// <returns></returns>\\n public static LayerNorm LayerNorm(long normalized_shape, double eps = 1e-05, bool elementwise_affine = true, Device? device = null, ScalarType? dtype = null)\\n {\\n return LayerNorm(new[] { normalized_shape }, eps, elementwise_affine, device, dtype);\\n }\\n }\\n }\\n}\\n\"\n },\n {\n \"alpha_fraction\": 0.5151121616363525,\n \"alphanum_fraction\": 0.5373632311820984,\n \"avg_line_length\": 38.36496353149414,\n \"blob_id\": \"7d640ab4d770079a8ea2a56fc241555559cbefb9\",\n \"content_id\": \"64c4053d7332262504fdcc6514a71bcfd59c90e6\",\n \"detected_licenses\": [\n \"MIT\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"C#\",\n \"length_bytes\": 5393,\n \"license_type\": \"permissive\",\n \"max_line_length\": 130,\n \"num_lines\": 137,\n \"path\": \"/test/TorchSharpTest/TestNNUtils.cs\",\n \"repo_name\": \"NiklasGustafsson/TorchSharp\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"// Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information.\\n\\nusing System.Linq;\\nusing Xunit;\\n\\nnamespace TorchSharp\\n{\\n public class TestNNUtils\\n {\\n (torch.Tensor[], torch.Tensor) make_test()\\n {\\n var sequences = new torch.Tensor[]\\n {\\n torch.tensor(new long[] { 1, 2, 3, 4 }),\\n torch.tensor(new long[] { 5, 6 }),\\n torch.tensor(new long[] { 7, 8 }),\\n };\\n var sequences_len = torch.tensor(sequences.Select(x => x.shape[0]).ToArray());\\n return (sequences, sequences_len);\\n }\\n\\n [Fact]\\n public void TestPadSequence()\\n {\\n var (sequences, sequences_len) = make_test();\\n var padded_sequences = torch.nn.utils.rnn.pad_sequence(sequences);\\n Assert.Equal(2, padded_sequences.ndim);\\n Assert.Equal(new long[] { 4, 3 }, padded_sequences.shape);\\n Assert.True(padded_sequences.sum().item<long>() == 36);\\n\\n var packed_sequence = torch.nn.utils.rnn.pack_padded_sequence(padded_sequences, sequences_len);\\n var (inverted_sequences, inverted_sequences_len) = torch.nn.utils.rnn.pad_packed_sequence(packed_sequence);\\n packed_sequence.Dispose();\\n Assert.True(torch.max(torch.square(inverted_sequences - padded_sequences)).item<long>() == 0);\\n }\\n\\n [Fact]\\n public void TestPackSequence()\\n {\\n var (sequences, sequences_len) = make_test();\\n var padded_sequences = torch.nn.utils.rnn.pad_sequence(sequences);\\n Assert.Equal(2, padded_sequences.ndim);\\n Assert.Equal(new long[] { 4, 3 }, padded_sequences.shape);\\n Assert.True(padded_sequences.sum().item<long>() == 36);\\n\\n var packed_sequence = torch.nn.utils.rnn.pack_sequence(sequences);\\n var (inverted_sequences, inverted_sequences_len) = torch.nn.utils.rnn.pad_packed_sequence(packed_sequence);\\n packed_sequence.Dispose();\\n Assert.True(torch.max(torch.square(inverted_sequences - padded_sequences)).item<long>() == 0);\\n }\\n\\n [Fact]\\n public void TestAutoGradGrad()\\n {\\n using var _ = torch.NewDisposeScope();\\n var x1 = torch.rand(1, requires_grad: true);\\n var x2 = torch.rand(1, requires_grad: true);\\n\\n var y = x1.pow(2) + 5 * x2;\\n\\n var grad = torch.autograd.grad(new[] { y }, new[] { x1, x2 }, new[] { torch.ones_like(y) });\\n Assert.Equal(x1.shape, grad[0].shape);\\n Assert.Equal(x2.shape, grad[1].shape);\\n Assert.Equal(2.0f * x1.item<float>(), grad[0].item<float>());\\n Assert.Equal(5.0f, grad[1].item<float>());\\n }\\n\\n [Fact]\\n public void TestAutoGradBackward1()\\n {\\n using var _ = torch.NewDisposeScope();\\n var x1 = torch.rand(1, requires_grad: true);\\n var x2 = torch.rand(1, requires_grad: true);\\n\\n var y = x1.pow(2) + 5 * x2;\\n\\n torch.autograd.backward(new[] { y }, new[] { torch.ones_like(y) });\\n Assert.Equal(x1.shape, x1.grad().shape);\\n Assert.Equal(x2.shape, x2.grad().shape);\\n Assert.Equal(2.0f*x1.item<float>(), x1.grad().item<float>());\\n Assert.Equal(5.0f, x2.grad().item<float>());\\n }\\n\\n [Fact]\\n public void TestAutoGradBackward2()\\n {\\n using var _ = torch.NewDisposeScope();\\n var x1 = torch.rand(1, requires_grad: true);\\n var x2 = torch.rand(1, requires_grad: true);\\n\\n var y = x1.pow(2) + 5 * x2;\\n\\n y.backward(new[] { torch.ones_like(y) });\\n Assert.Equal(x1.shape, x1.grad().shape);\\n Assert.Equal(x2.shape, x2.grad().shape);\\n Assert.Equal(2.0f * x1.item<float>(), x1.grad().item<float>());\\n Assert.Equal(5.0f, x2.grad().item<float>());\\n }\\n\\n [Fact]\\n public void TestAutoGradAnomaly()\\n {\\n Assert.False(AnomalyMode.IsEnabled);\\n\\n using (var ad = torch.autograd.detect_anomaly(false)) {\\n Assert.True(AnomalyMode.IsEnabled);\\n Assert.False(AnomalyMode.ShouldCheckNaN);\\n }\\n\\n Assert.False(AnomalyMode.IsEnabled);\\n\\n using (var ad = torch.autograd.set_detect_anomaly(false)) {\\n Assert.False(AnomalyMode.IsEnabled);\\n Assert.True(AnomalyMode.ShouldCheckNaN);\\n }\\n\\n Assert.False(AnomalyMode.IsEnabled);\\n }\\n\\n [Fact]\\n public void TestSizeSlice()\\n {\\n var shape = Enumerable.Range(0, 10).Select(i => (long)i).ToArray();\\n var sz = new torch.Size(shape);\\n Assert.Multiple(\\n () => Assert.Equal(new long[] { 0, 1, 2, 3, 4, 5 }, sz.Slice(0, 6).Shape),\\n () => Assert.Equal(new long[] { 2, 3, 4, 5 }, sz.Slice(2, 6).Shape),\\n () => Assert.Equal(new long[] { 0, 1, 2, 3, 4, 5 }, sz.Slice(0, -4).Shape),\\n () => Assert.Equal(new long[] { 2, 3, 4, 5 }, sz.Slice(2, -4).Shape),\\n () => Assert.Equal(new long[] { 8, 9 }, sz.Slice(-2, sz.Length).Shape),\\n () => Assert.True(sz.Slice(0, 0).IsEmpty),\\n () => Assert.True(sz.Slice(-2, 0).IsEmpty)\\n );\\n\\n }\\n }\\n}\\n\"\n },\n {\n \"alpha_fraction\": 0.5609756112098694,\n \"alphanum_fraction\": 0.5711751580238342,\n \"avg_line_length\": 50.2613639831543,\n \"blob_id\": \"002ccb71dd275858ec6733dfaeda39fe13dc0ca2\",\n \"content_id\": \"c3ad686e871b310689fec0d07a65661ca199dc37\",\n \"detected_licenses\": [\n \"MIT\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"C#\",\n \"length_bytes\": 4518,\n \"license_type\": \"permissive\",\n \"max_line_length\": 147,\n \"num_lines\": 88,\n \"path\": \"/src/TorchVision/ColorJitter.cs\",\n \"repo_name\": \"NiklasGustafsson/TorchSharp\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"// Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information.\\nusing System;\\nusing static TorchSharp.torch;\\n\\n\\nnamespace TorchSharp\\n{\\n public static partial class torchvision\\n {\\n internal class ColorJitter : ITransform\\n {\\n internal ColorJitter((float, float) brightness, (float, float) contrast, (float, float) saturation, (float, float) hue)\\n {\\n this.brightness = brightness;\\n this.contrast = contrast;\\n this.saturation = saturation;\\n this.hue = hue;\\n }\\n\\n public Tensor call(Tensor image)\\n {\\n var randoms = torch.rand(4, ScalarType.Float32).data<float>().ToArray();\\n var b = Adjust(randoms[0], brightness.Item1, brightness.Item2);\\n var c = Adjust(randoms[1], contrast.Item1, contrast.Item2);\\n var s = Adjust(randoms[2], saturation.Item1, saturation.Item2);\\n var h = Adjust(randoms[3], hue.Item1, hue.Item2);\\n\\n var transform = torchvision.transforms.Compose(\\n transforms.AdjustBrightness(b),\\n transforms.AdjustContrast(c),\\n transforms.AdjustSaturation(s),\\n transforms.AdjustHue(h)\\n );\\n return transform.call(image);\\n }\\n\\n internal static float Adjust(float input, float min, float max)\\n {\\n return input * (max - min) + min;\\n }\\n\\n private (float, float) brightness;\\n private (float, float) contrast;\\n private (float, float) saturation;\\n private (float, float) hue;\\n }\\n\\n public static partial class transforms\\n {\\n /// <summary>\\n /// Randomly change the brightness, contrast, saturation and hue of an image.\\n /// The image is expected to have […, 3, H, W] shape, where … means an arbitrary number of leading dimensions.\\n /// </summary>\\n /// <param name=\\\"brightness\\\">How much to jitter brightness, [min,max]. Should be non-negative.</param>\\n /// <param name=\\\"contrast\\\">How much to jitter contrast, [min,max]. Should be non-negative.</param>\\n /// <param name=\\\"saturation\\\">How much to jitter saturation, [min,max]. Should be non-negative. </param>\\n /// <param name=\\\"hue\\\">How much to jitter hue. Should lie between -0.5 and 0.5, with min less than max.</param>\\n /// <returns></returns>\\n /// <remarks>The image will not be cropped outside its boundaries.</remarks>\\n static public ITransform ColorJitter((float, float) brightness, (float, float) contrast, (float, float) saturation, (float, float) hue)\\n {\\n return new ColorJitter(brightness, contrast, saturation, hue);\\n }\\n\\n /// <summary>\\n /// Randomly change the brightness, contrast, saturation and hue of an image.\\n /// The image is expected to have […, 3, H, W] shape, where … means an arbitrary number of leading dimensions.\\n /// </summary>\\n /// <param name=\\\"brightness\\\">How much to jitter brightness. Should be non-negative.\\n /// The brightness_factor used is chosen uniformly from [max(0, 1 - brightness), 1 + brightness]</param>\\n /// <param name=\\\"contrast\\\">How much to jitter contrast. Should be non-negative.\\n /// The contrast_factor used is chosen uniformly from [max(0, 1 - contrast), 1 + contrast]</param>\\n /// <param name=\\\"saturation\\\">How much to jitter saturation. Should be non-negative.\\n /// The saturation_factor used is chosen uniformly from [max(0, 1 - saturation), 1 + saturation] </param>\\n /// <param name=\\\"hue\\\">How much to jitter hue. Should be between 0 and 0.5.</param>\\n /// <returns></returns>\\n /// <remarks>The image will not be cropped outside its boundaries.</remarks>\\n static public ITransform ColorJitter(float brightness = 0, float contrast = 0, float saturation = 0, float hue = 0)\\n {\\n return new ColorJitter(\\n (MathF.Max(0, 1 - brightness), 1 + brightness),\\n (MathF.Max(0, 1 - contrast), 1 + contrast),\\n (MathF.Max(0, 1 - saturation), 1 + saturation),\\n (-hue, hue));\\n }\\n }\\n }\\n}\"\n },\n {\n \"alpha_fraction\": 0.5708770155906677,\n \"alphanum_fraction\": 0.5821841955184937,\n \"avg_line_length\": 39.741573333740234,\n \"blob_id\": \"d2dc199c046010d734648cf22145691ef218629a\",\n \"content_id\": \"cb615bc2baf82e38575c4adf76cdb00027e00743\",\n \"detected_licenses\": [\n \"MIT\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"C#\",\n \"length_bytes\": 3634,\n \"license_type\": \"permissive\",\n \"max_line_length\": 162,\n \"num_lines\": 89,\n \"path\": \"/src/TorchVision/Ops/StochasticDepth.cs\",\n \"repo_name\": \"NiklasGustafsson/TorchSharp\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"// Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information.\\n\\n// A number of implementation details in this file have been translated from the Python version of torchvision,\\n// largely located in the files found in this folder:\\n//\\n// https://github.com/pytorch/vision/blob/a4f53308b2d0f1aa9191686e326f45c26053f686/torchvision/ops/stochastic_depth.py\\n//\\n// The origin has the following copyright notice and license:\\n//\\n// https://github.com/pytorch/vision/blob/main/LICENSE\\n//\\n\\nusing System;\\nusing System.Collections.Generic;\\nusing System.Runtime.CompilerServices;\\nusing static TorchSharp.torch;\\n\\n#nullable enable\\nnamespace TorchSharp\\n{\\n public static partial class torchvision\\n {\\n\\n public static partial class ops\\n {\\n public static StochasticDepth StochasticDepth(double p, StochasticDepth.Mode mode) => new StochasticDepth(p, mode);\\n\\n /// <summary>\\n /// Implements the Stochastic Depth from “Deep Networks with Stochastic Depth” used for randomly dropping residual branches of residual architectures.\\n /// </summary>\\n /// <param name=\\\"input\\\">The input tensor</param>\\n /// <param name=\\\"p\\\">Probability of the input to be zeroed.</param>\\n /// <param name=\\\"mode\\\">\\\"batch\\\" or \\\"row\\\". \\\"batch\\\" randomly zeroes the entire input, \\\"row\\\" zeroes randomly selected rows from the batch.</param>\\n /// <param name=\\\"training\\\">Apply stochastic depth if is True</param>\\n public static Tensor stochastic_depth(Tensor input, double p, StochasticDepth.Mode mode, bool training = true)\\n {\\n if (p < 0 || p > 1) throw new ArgumentOutOfRangeException(nameof(p));\\n if (!training || p == 0.0) return input.alias();\\n\\n var survival_rate = 1 - p;\\n var size = new List<long>();\\n\\n if (mode == torchvision.StochasticDepth.Mode.Row) {\\n size.Add(input.shape[0]);\\n for (var i = 0; i < input.ndim-1; i++)\\n size.Add(1);\\n } else {\\n for (var i = 0; i < input.ndim; i++)\\n size.Add(1);\\n }\\n\\n var noise = torch.empty(size.ToArray(), dtype: input.dtype, device: input.device);\\n noise.bernoulli_(survival_rate);\\n\\n if (survival_rate > 0) {\\n noise.div_(survival_rate);\\n }\\n return input * noise;\\n }\\n }\\n\\n /// <summary>\\n /// Implements the Stochastic Depth from “Deep Networks with Stochastic Depth” used for randomly dropping residual branches of residual architectures.\\n /// </summary>\\n public class StochasticDepth : nn.Module<Tensor, Tensor>\\n {\\n /// <summary>\\n /// Constructor\\n /// </summary>\\n /// <param name=\\\"p\\\">Probability of the input to be zeroed.</param>\\n /// <param name=\\\"mode\\\">\\\"batch\\\" or \\\"row\\\". \\\"batch\\\" randomly zeroes the entire input, \\\"row\\\" zeroes randomly selected rows from the batch.</param>\\n public StochasticDepth(double p, Mode mode) : base(nameof(StochasticDepth))\\n {\\n this.p = p;\\n this.mode = mode;\\n }\\n\\n public override Tensor forward(Tensor input)\\n {\\n return ops.stochastic_depth(input, p, mode, training);\\n }\\n\\n public enum Mode { Batch, Row }\\n\\n private double p;\\n private Mode mode;\\n }\\n }\\n}\\n\"\n },\n {\n \"alpha_fraction\": 0.6013584136962891,\n \"alphanum_fraction\": 0.607105553150177,\n \"avg_line_length\": 38.875,\n \"blob_id\": \"720dcaf68f2dd5bec595d0e6dc03cfb5cc21a407\",\n \"content_id\": \"5fc5f07b7f0a6ee8841f1ef8e2b36e397977d9f3\",\n \"detected_licenses\": [\n \"MIT\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"C#\",\n \"length_bytes\": 1914,\n \"license_type\": \"permissive\",\n \"max_line_length\": 190,\n \"num_lines\": 48,\n \"path\": \"/src/TorchSharp/NN/Normalization/LocalResponseNorm.cs\",\n \"repo_name\": \"NiklasGustafsson/TorchSharp\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"// Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information.\\nusing System;\\nusing static TorchSharp.torch;\\nusing static TorchSharp.PInvoke.NativeMethods;\\n\\nnamespace TorchSharp\\n{\\n using Modules;\\n\\n namespace Modules\\n {\\n /// <summary>\\n /// This class is used to represent a LocalResponseNorm module.\\n /// </summary>\\n public sealed class LocalResponseNorm : torch.nn.Module<Tensor, Tensor>\\n {\\n internal LocalResponseNorm(IntPtr handle, IntPtr boxedHandle) : base(handle, boxedHandle)\\n {\\n }\\n\\n public override Tensor forward(Tensor tensor)\\n {\\n if (tensor.Dimensions < 3) throw new ArgumentException($\\\"Invalid number of dimensions for LocalResponseNorm argument: {tensor.Dimensions}\\\");\\n var res = THSNN_LocalResponseNorm_forward(handle.DangerousGetHandle(), tensor.Handle);\\n if (res == IntPtr.Zero) { torch.CheckForErrors(); }\\n return new Tensor(res);\\n }\\n }\\n }\\n\\n public static partial class torch\\n {\\n public static partial class nn\\n {\\n /// <summary>\\n /// Applies local response normalization over an input signal composed of several input planes, where channels occupy the second dimension. Applies normalization across channels.\\n /// </summary>\\n public static LocalResponseNorm LocalResponseNorm(long size, double alpha = 0.0001, double beta = 0.75, double k = 1.0)\\n {\\n unsafe {\\n var handle = THSNN_LocalResponseNorm_ctor(size, alpha, beta, k, out var boxedHandle);\\n if (handle == IntPtr.Zero) { torch.CheckForErrors(); }\\n return new LocalResponseNorm(handle, boxedHandle);\\n }\\n }\\n }\\n }\\n}\\n\"\n },\n {\n \"alpha_fraction\": 0.46465590596199036,\n \"alphanum_fraction\": 0.4722936749458313,\n \"avg_line_length\": 49.71936798095703,\n \"blob_id\": \"9b41dd1801824c2ea66a632dca5ebaf98db50a06\",\n \"content_id\": \"c988ec3c00dd027f11d698bce25b17caaf3c659c\",\n \"detected_licenses\": [\n \"MIT\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"C#\",\n \"length_bytes\": 12831,\n \"license_type\": \"permissive\",\n \"max_line_length\": 147,\n \"num_lines\": 253,\n \"path\": \"/src/TorchVision/IO/GDriveDownload.cs\",\n \"repo_name\": \"NiklasGustafsson/TorchSharp\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"// Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information.\\nusing System;\\nusing System.Collections.Generic;\\nusing System.IO;\\nusing System.Net;\\nusing System.Net.Http;\\nusing System.Security.Cryptography;\\nusing System.Text;\\nusing System.Text.RegularExpressions;\\nusing System.Threading;\\nusing System.Threading.Tasks;\\nusing System.Web;\\n\\n// A number of implementation details in this file have been translated from the Python version of torchvision,\\n// largely located in the files found in this folder:\\n//\\n// https://github.com/pytorch/vision/blob/b78d98bb152ffb9c0c0f5365f59f475c70b1784e/torchvision/datasets/utils.py\\n// The origin has the following copyright notice and license:\\n//\\n// https://github.com/pytorch/vision/blob/main/LICENSE\\n//\\n\\n#nullable enable\\nnamespace TorchSharp\\n{\\n public static partial class torchvision\\n {\\n public static partial class io\\n {\\n public class GDriveDownload\\n {\\n private static async Task SaveResponseContentAsync(\\n byte[] head,\\n Stream content,\\n string destination,\\n long? length = null,\\n CancellationToken cancellationToken = default)\\n {\\n using (var progress_bar = torch.hub.CreateProgressBar(false))\\n using (var fh = File.OpenWrite(destination)) {\\n progress_bar.Maximum = length;\\n await fh.WriteAsync(head, 0, head.Length, cancellationToken);\\n progress_bar.Value = head.Length;\\n byte[] buffer = new byte[64 * 1024];\\n while (true) {\\n int ret = await content.ReadAsync(buffer, 0, buffer.Length, cancellationToken);\\n if (ret == 0) break;\\n await fh.WriteAsync(buffer, 0, ret, cancellationToken);\\n progress_bar.Value += ret;\\n }\\n }\\n }\\n\\n private static string CalculateMD5(string fpath)\\n {\\n MD5 md5 = MD5.Create();\\n byte[] hash;\\n using (var stream = File.OpenRead(fpath)) {\\n hash = md5.ComputeHash(stream);\\n }\\n return BitConverter.ToString(hash).Replace(\\\"-\\\", \\\"\\\").ToLowerInvariant();\\n }\\n\\n private static bool CheckMD5(string fpath, string md5)\\n {\\n return md5 == CalculateMD5(fpath);\\n }\\n\\n /// <summary>\\n /// Check if the file exists and verify its content with MD5.\\n /// </summary>\\n /// <param name=\\\"fpath\\\">A path to the file</param>\\n /// <param name=\\\"md5\\\">MD5 checksum</param>\\n /// <returns>True if the file exists and MD5 of its content matches</returns>\\n public static bool CheckIntegrity(string fpath, string? md5 = null)\\n {\\n if (!File.Exists(fpath)) {\\n return false;\\n }\\n if (md5 is null) {\\n return true;\\n }\\n return CheckMD5(fpath, md5);\\n }\\n\\n private static async Task<(string, byte[], Stream)> ExtractGdriveApiResponseAsync(\\n HttpResponseMessage response,\\n int chunk_size = 32 * 1024,\\n CancellationToken cancellationToken = default)\\n {\\n var stream = await response.Content.ReadAsStreamAsync();\\n var buf = new byte[chunk_size];\\n int pos = 0;\\n while (pos < chunk_size) {\\n int readLen = await stream.ReadAsync(buf, pos, chunk_size - pos, cancellationToken);\\n if (readLen == 0) break;\\n pos += readLen;\\n }\\n buf = buf.AsSpan(0, pos).ToArray();\\n\\n string api_response = string.Empty;\\n string first_chunk = Encoding.ASCII.GetString(buf);\\n string pattern = \\\"<title>Google Drive - (?<api_response>.+?)</title>\\\";\\n var match = Regex.Match(first_chunk, pattern);\\n if (match.Success) {\\n api_response = match.Groups[\\\"api_response\\\"].Value;\\n }\\n return (api_response, buf, stream);\\n }\\n\\n /// <summary>\\n /// Download a Google Drive file and place it in root.\\n /// </summary>\\n /// <param name=\\\"file_id\\\">id of file to be downloaded</param>\\n /// <param name=\\\"root\\\">Directory to place downloaded file in</param>\\n /// <param name=\\\"filename\\\">Name to save the file under. If null, use the id of the file.</param>\\n /// <param name=\\\"md5\\\">MD5 checksum of the download. If null, do not check</param>\\n public static void DownloadFileFromGoogleDrive(\\n string file_id, string root, string? filename = null, string? md5 = null)\\n {\\n try {\\n Task.Run(async () => {\\n await DownloadFileFromGoogleDriveAsync(file_id, root, filename, md5);\\n }).Wait();\\n } catch (AggregateException ex) {\\n throw ex.InnerException ?? ex;\\n }\\n }\\n\\n /// <summary>\\n /// Download a Google Drive file and place it in root.\\n /// </summary>\\n /// <param name=\\\"file_id\\\">id of file to be downloaded</param>\\n /// <param name=\\\"root\\\">Directory to place downloaded file in</param>\\n /// <param name=\\\"filename\\\">Name to save the file under. If null, use the id of the file.</param>\\n /// <param name=\\\"md5\\\">MD5 checksum of the download. If null, do not check</param>\\n /// <param name=\\\"cancellationToken\\\">A cancellation token</param>\\n public static async Task DownloadFileFromGoogleDriveAsync(\\n string file_id, string root, string? filename = null, string? md5 = null,\\n CancellationToken cancellationToken = default)\\n {\\n // Based on https://stackoverflow.com/questions/38511444/python-download-files-from-google-drive-using-url\\n if (filename is null) {\\n filename = file_id;\\n }\\n var fpath = Path.Combine(root, filename);\\n\\n Directory.CreateDirectory(root);\\n\\n if (CheckIntegrity(fpath, md5)) {\\n if (md5 is null) {\\n Console.WriteLine($\\\"Using downloaded file: {fpath}\\\");\\n } else {\\n Console.WriteLine($\\\"Using downloaded and verified file: {fpath}\\\");\\n }\\n return;\\n }\\n\\n CookieContainer cookies = new CookieContainer();\\n HttpClientHandler handler = new HttpClientHandler();\\n handler.CookieContainer = cookies;\\n\\n using (var httpClient = new HttpClient(handler)) {\\n string url = \\\"https://drive.google.com/uc\\\";\\n\\n var queryString = HttpUtility.ParseQueryString(string.Empty);\\n queryString.Add(\\\"id\\\", file_id);\\n queryString.Add(\\\"export\\\", \\\"download\\\");\\n string query_url = url + \\\"?\\\" + queryString.ToString();\\n HttpResponseMessage? response = null;\\n\\n try {\\n response = await httpClient.GetAsync(query_url, HttpCompletionOption.ResponseHeadersRead, cancellationToken);\\n response.EnsureSuccessStatusCode();\\n var responseCookies = cookies.GetCookies(new Uri(url));\\n string token = string.Empty;\\n foreach (var obj in responseCookies) {\\n Cookie? kv = obj as Cookie;\\n if (kv != null && kv.Name.StartsWith(\\\"download_warning\\\")) {\\n token = kv.Value;\\n }\\n }\\n string api_response = string.Empty;\\n byte[]? head = null;\\n Stream? stream = null;\\n if (string.IsNullOrEmpty(token)) {\\n (api_response, head, stream) = await ExtractGdriveApiResponseAsync(response, cancellationToken: cancellationToken);\\n token = api_response == \\\"Virus scan warning\\\" ? \\\"t\\\" : string.Empty;\\n }\\n\\n if (!string.IsNullOrEmpty(token)) {\\n queryString.Add(\\\"confirm\\\", token);\\n query_url = url + \\\"?\\\" + queryString.ToString();\\n response.Dispose();\\n response = null;\\n response = await httpClient.GetAsync(query_url, HttpCompletionOption.ResponseHeadersRead, cancellationToken);\\n (api_response, head, stream) = await ExtractGdriveApiResponseAsync(response);\\n }\\n\\n if (head == null || stream == null) {\\n // unreachable\\n throw new InvalidDataException();\\n }\\n\\n if (api_response == \\\"Quota exceeded\\\") {\\n throw new InvalidDataException(\\n $\\\"The daily quota of the file {filename} is exceeded and it \\\" +\\n \\\"can't be downloaded. This is a limitation of Google Drive \\\" +\\n \\\"and can only be overcome by trying again later.\\\");\\n }\\n\\n await SaveResponseContentAsync(head, stream, fpath, cancellationToken: cancellationToken);\\n } finally {\\n if (response != null) {\\n response.Dispose();\\n }\\n }\\n\\n // In case we deal with an unhandled GDrive API response, the file should be smaller than 10kB and contain only text\\n if (new FileInfo(fpath).Length < 10 * 1024) {\\n string text = ReadAllTextAsync(fpath);\\n // Regular expression to detect HTML. Copied from https://stackoverflow.com/a/70585604\\n if (Regex.Match(text, @\\\"</?\\\\s*[a-z-][^>]*\\\\s*>|(&(?:[\\\\w\\\\d]+|#\\\\d+|#x[a-f\\\\d]+);)\\\").Success) {\\n Console.WriteLine(\\n $\\\"We detected some HTML elements in the downloaded file. \\\" +\\n $\\\"This most likely means that the download triggered an unhandled API response by GDrive. \\\" +\\n $\\\"Please report this to torchvision at https://github.com/pytorch/vision/issues including \\\" +\\n $\\\"the response:\\\\n\\\\n{text}\\\"\\n );\\n }\\n }\\n\\n if (md5 is not null && !CheckMD5(fpath, md5)) {\\n throw new InvalidDataException(\\n $\\\"The MD5 checksum of the download file {fpath} does not match the one on record.\\\" +\\n $\\\"Please delete the file and try again. \\\" +\\n $\\\"If the issue persists, please report this to torchvision at https://github.com/pytorch/vision/issues.\\\");\\n }\\n }\\n }\\n\\n private static string ReadAllTextAsync(string path, CancellationToken cancellationToken = default)\\n {\\n var memory = new MemoryStream();\\n using (var stream = File.OpenRead(path)) {\\n stream.CopyTo(memory);\\n }\\n return Encoding.ASCII.GetString(memory.GetBuffer());\\n }\\n }\\n }\\n }\\n}\"\n },\n {\n \"alpha_fraction\": 0.5197963118553162,\n \"alphanum_fraction\": 0.5339757204055786,\n \"avg_line_length\": 49.45088195800781,\n \"blob_id\": \"d799ee0c7d732ad819dec3b25bd95d810ae842d2\",\n \"content_id\": \"541684bf997745f2121bf8be5e44161dcb8d380d\",\n \"detected_licenses\": [\n \"MIT\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"C#\",\n \"length_bytes\": 20029,\n \"license_type\": \"permissive\",\n \"max_line_length\": 238,\n \"num_lines\": 397,\n \"path\": \"/src/TorchSharp/Optimizers/NAdam.cs\",\n \"repo_name\": \"NiklasGustafsson/TorchSharp\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"// Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information.\\nusing System;\\nusing System.Collections.Generic;\\nusing System.Linq;\\nusing static TorchSharp.torch;\\n\\nnamespace TorchSharp\\n{\\n using System.IO;\\n using Modules;\\n\\n public static partial class torch\\n {\\n public static partial class optim\\n {\\n\\n /// <summary>\\n /// Implements the NAdam algorithm.\\n ///\\n /// For further details regarding the algorithm we refer to Incorporating Nesterov Momentum into Adam.\\n /// https://openreview.net/forum?id=OM0jvwB8jIp57ZJjtNEZ\\n /// </summary>\\n /// <param name=\\\"named_parameters\\\">Parameters to optimize. This optimizer requires the <b>named</b> parameters collection.</param>\\n /// <param name=\\\"lr \\\">Learning rate</param>\\n /// <param name=\\\"beta1\\\">Coefficient used for computing running averages of gradient and its square (default: 0.9)</param>\\n /// <param name=\\\"beta2\\\">Coefficient used for computing running averages of gradient and its square (default: 0.999)</param>\\n /// <param name=\\\"eps\\\">Term added to the denominator to improve numerical stability, i.e. avoid division-by-zero (default: 1e-8)</param>\\n /// <param name=\\\"weight_decay\\\">Weight decay (L2 penalty) (default: 0)</param>\\n /// <param name=\\\"momentum_decay\\\">Momentum decay</param>\\n public static NAdam NAdam(IEnumerable<Parameter> named_parameters, double lr = 0.002, double beta1 = 0.9, double beta2 = 0.999, double eps = 1e-8, double weight_decay = 0, double momentum_decay = 4e-3)\\n {\\n return new NAdam(named_parameters, lr, beta1, beta2, eps, weight_decay, momentum_decay);\\n }\\n\\n /// <summary>\\n /// Implements the NAdam algorithm.\\n ///\\n /// For further details regarding the algorithm we refer to Incorporating Nesterov Momentum into Adam.\\n /// https://openreview.net/forum?id=OM0jvwB8jIp57ZJjtNEZ\\n /// </summary>\\n /// <param name=\\\"named_parameters\\\">Parameters to optimize. This optimizer requires the <b>named</b> parameters collection.</param>\\n /// <param name=\\\"lr \\\">Learning rate</param>\\n /// <param name=\\\"beta1\\\">Coefficient used for computing running averages of gradient and its square (default: 0.9)</param>\\n /// <param name=\\\"beta2\\\">Coefficient used for computing running averages of gradient and its square (default: 0.999)</param>\\n /// <param name=\\\"eps\\\">Term added to the denominator to improve numerical stability, i.e. avoid division-by-zero (default: 1e-8)</param>\\n /// <param name=\\\"weight_decay\\\">Weight decay (L2 penalty) (default: 0)</param>\\n /// <param name=\\\"momentum_decay\\\">Momentum decay</param>\\n public static NAdam NAdam(IEnumerable<(string name, Parameter parameter)> named_parameters, double lr = 0.002, double beta1 = 0.9, double beta2 = 0.999, double eps = 1e-8, double weight_decay = 0, double momentum_decay = 4e-3)\\n {\\n return new NAdam(named_parameters.Select(np => np.parameter), lr, beta1, beta2, eps, weight_decay, momentum_decay);\\n }\\n\\n /// <summary>\\n /// Implements the NAdam algorithm.\\n ///\\n /// For further details regarding the algorithm we refer to Incorporating Nesterov Momentum into Adam.\\n /// https://openreview.net/forum?id=OM0jvwB8jIp57ZJjtNEZ\\n /// </summary>\\n /// <param name=\\\"parameters\\\">Parameters to optimize. This optimizer requires the <b>named</b> parameters collection.</param>\\n /// <param name=\\\"lr \\\">Learning rate</param>\\n /// <param name=\\\"beta1\\\">Coefficient used for computing running averages of gradient and its square (default: 0.9)</param>\\n /// <param name=\\\"beta2\\\">Coefficient used for computing running averages of gradient and its square (default: 0.999)</param>\\n /// <param name=\\\"eps\\\">Term added to the denominator to improve numerical stability, i.e. avoid division-by-zero (default: 1e-8)</param>\\n /// <param name=\\\"weight_decay\\\">Weight decay (L2 penalty) (default: 0)</param>\\n /// <param name=\\\"momentum_decay\\\">Momentum decay</param>\\n public static NAdam NAdam(IEnumerable<NAdam.ParamGroup> parameters, double lr = 0.002, double beta1 = 0.9, double beta2 = 0.999, double eps = 1e-8, double weight_decay = 0, double momentum_decay = 4e-3)\\n {\\n return new NAdam(parameters, lr, beta1, beta2, eps, weight_decay, momentum_decay);\\n }\\n }\\n }\\n\\n namespace Modules\\n {\\n using static torch.optim;\\n\\n public class NAdam : OptimizerHelper, IBetas\\n {\\n /// <summary>\\n /// Implements NAdam algorithm.\\n ///\\n /// For further details regarding the algorithm we refer to Incorporating Nesterov Momentum into Adam.\\n /// https://openreview.net/forum?id=OM0jvwB8jIp57ZJjtNEZ\\n /// </summary>\\n /// <param name=\\\"parameters\\\">Parameters to optimize. This optimizer requires the <b>named</b> parameters collection.</param>\\n /// <param name=\\\"lr \\\">Learning rate</param>\\n /// <param name=\\\"beta1\\\">Coefficient used for computing running averages of gradient and its square (default: 0.9)</param>\\n /// <param name=\\\"beta2\\\">Coefficient used for computing running averages of gradient and its square (default: 0.999)</param>\\n /// <param name=\\\"eps\\\">Term added to the denominator to improve numerical stability, i.e. avoid division-by-zero (default: 1e-8)</param>\\n /// <param name=\\\"weight_decay\\\">Weight decay (L2 penalty) (default: 0)</param>\\n /// <param name=\\\"momentum_decay\\\">Momentum decay</param>\\n public NAdam(IEnumerable<Parameter> parameters, double lr, double beta1 = 0.9, double beta2 = 0.999, double eps = 1e-8, double weight_decay = 0, double momentum_decay = 4e-3)\\n : this(new ParamGroup[] { new ParamGroup { Parameters = parameters } }, lr, beta1, beta2, eps, weight_decay, momentum_decay)\\n {\\n }\\n\\n /// <summary>\\n /// Implements NAdam algorithm.\\n ///\\n /// For further details regarding the algorithm we refer to Incorporating Nesterov Momentum into Adam.\\n /// https://openreview.net/forum?id=OM0jvwB8jIp57ZJjtNEZ\\n /// </summary>\\n /// <param name=\\\"parameters\\\">Parameters to optimize. This optimizer requires the <b>named</b> parameters collection.</param>\\n /// <param name=\\\"lr \\\">Learning rate</param>\\n /// <param name=\\\"beta1\\\">Coefficient used for computing running averages of gradient and its square (default: 0.9)</param>\\n /// <param name=\\\"beta2\\\">Coefficient used for computing running averages of gradient and its square (default: 0.999)</param>\\n /// <param name=\\\"eps\\\">Term added to the denominator to improve numerical stability, i.e. avoid division-by-zero (default: 1e-8)</param>\\n /// <param name=\\\"weight_decay\\\">Weight decay (L2 penalty) (default: 0)</param>\\n /// <param name=\\\"momentum_decay\\\">Momentum decay</param>\\n /// <returns></returns>\\n public NAdam(IEnumerable<ParamGroup> parameters, double lr = 0.002, double beta1 = 0.9, double beta2 = 0.999, double eps = 1e-8, double weight_decay = 0, double momentum_decay = 4e-3)\\n {\\n if (lr < 0.0) throw new ArgumentException($\\\"Invalid learning rate: {lr}\\\");\\n if (beta1 < 0.0 || beta1 > 1.0) throw new ArgumentException($\\\"Invalid beta1 value: {beta1}\\\");\\n if (beta2 < 0.0 || beta2 > 1.0) throw new ArgumentException($\\\"Invalid beta2 value: {beta2}\\\");\\n if (eps < 0.0) throw new ArgumentException($\\\"Invalid eps value: {eps}\\\");\\n if (weight_decay < 0.0) throw new ArgumentException($\\\"Invalid weight_decay value: {weight_decay}\\\");\\n if (momentum_decay < 0.0) throw new ArgumentException($\\\"Invalid momentum_decay value: {momentum_decay}\\\");\\n\\n var options = new Options {\\n LearningRate = lr,\\n InitialLearningRate = lr,\\n beta1 = beta1,\\n beta2 = beta2,\\n eps = eps,\\n weight_decay = weight_decay,\\n momentum_decay = momentum_decay\\n };\\n\\n _defaults = options;\\n _parameter_groups = new List<Modules.ParamGroup>();\\n\\n foreach (var g in parameters) {\\n add_param_group(g);\\n }\\n }\\n\\n /// <summary>\\n /// Performs a single optimization step (parameter update).\\n /// </summary>\\n /// <param name=\\\"closure\\\">A closure that reevaluates the model and returns the loss. Optional for most optimizers.</param>\\n /// <returns></returns>\\n public override Tensor step(Func<Tensor> closure = null)\\n {\\n return _step<ParamGroup>(group => {\\n\\n var options = group.Options as Options;\\n var beta1 = options.beta1.Value;\\n var beta2 = options.beta2.Value;\\n var eps = options.eps.Value;\\n var weight_decay = options.weight_decay.Value;\\n var momentum_decay = options.momentum_decay.Value;\\n var lr = options.LearningRate.Value;\\n\\n foreach (var param in group.Parameters) {\\n\\n var grad = param.grad();\\n\\n if (grad is null) continue;\\n\\n var state = (State)_state[param.handle];\\n\\n state.step += 1;\\n\\n var exp_avg = state.exp_avg;\\n var exp_avg_sq = state.exp_avg_sq;\\n\\n var bias_correction2 = 1 - Math.Pow(beta2, state.step);\\n\\n grad = (weight_decay != 0)\\n ? grad.add(param, alpha: weight_decay)\\n : grad.alias();\\n\\n var mu = beta1 * (1.0 - 0.5 * Math.Pow(0.96, state.step * momentum_decay));\\n var mu_next = beta1 * (1.0 - 0.5 * Math.Pow(0.96, (state.step + 1) * momentum_decay));\\n\\n var mu_product = state.mu_product * mu;\\n var mu_product_next = mu_product * mu * mu_next;\\n\\n exp_avg.mul_(beta1).add_(grad, alpha: 1 - beta1);\\n exp_avg_sq.mul_(beta2).addcmul_(grad, grad, value: 1 - beta2);\\n\\n var denom = exp_avg_sq.div(bias_correction2).sqrt_().add_(eps);\\n\\n param.addcdiv_(grad, denom, value: -lr * (1 - mu) / (1 - mu_product));\\n param.addcdiv_(exp_avg, denom, value: -lr * mu_next / (1 - mu_product_next));\\n\\n state.mu_product = mu_product;\\n }\\n }, closure);\\n }\\n\\n protected override void Dispose(bool disposing)\\n {\\n base.Dispose(disposing);\\n foreach (var kvp in _state) {\\n ((State)kvp.Item2).Dispose();\\n }\\n }\\n\\n public sealed class State : OptimizerState, IDisposable\\n {\\n public long step;\\n public double mu_product;\\n public Tensor exp_avg;\\n public Tensor exp_avg_sq;\\n\\n public void Dispose()\\n {\\n Dispose(true);\\n GC.SuppressFinalize(this);\\n }\\n\\n private void Dispose(bool disposing)\\n {\\n if (disposing) {\\n exp_avg.Dispose();\\n exp_avg_sq.Dispose();\\n }\\n }\\n\\n /// <summary>\\n /// Move all the state to the indicated device.\\n /// </summary>\\n /// <param name=\\\"device\\\">The device to move all state to.</param>\\n public override void to(Device device)\\n {\\n exp_avg = exp_avg.to(device);\\n exp_avg_sq = exp_avg_sq.to(device);\\n }\\n\\n /// <summary>\\n /// Load the optimizer parameter state from a stream.\\n /// </summary>\\n /// <param name=\\\"reader\\\">A binary reader connected to a stream open for reading.</param>\\n public override void LoadStateDict(BinaryReader reader)\\n {\\n step = reader.ReadInt64();\\n mu_product = reader.ReadDouble();\\n exp_avg.Load(reader);\\n exp_avg_sq.Load(reader);\\n }\\n\\n /// <summary>\\n /// Save the optimizer parameter state to a stream.\\n /// </summary>\\n /// <param name=\\\"writer\\\">A binary writer connected to a stream open for writing.</param>\\n public override void SaveStateDict(BinaryWriter writer)\\n {\\n writer.Write(step);\\n writer.Write(mu_product);\\n exp_avg.Save(writer);\\n exp_avg_sq.Save(writer);\\n }\\n\\n /// <summary>\\n /// Load optimizer parameter state from another optimizer.\\n /// </summary>\\n /// <param name=\\\"source\\\">An optimizer state record.</param>\\n public override void LoadStateDict(OptimizerState source)\\n {\\n var st_state = source as State;\\n exp_avg.Dispose();\\n exp_avg_sq.Dispose();\\n\\n step = st_state.step;\\n mu_product = st_state.mu_product;\\n exp_avg = st_state.exp_avg;\\n exp_avg_sq = st_state.exp_avg_sq;\\n }\\n\\n /// <summary>\\n /// Useful for tests, allows comparison of one state with another.\\n /// </summary>\\n /// <param name=\\\"other\\\">The other optimizer state</param>\\n /// <returns></returns>\\n public override bool ApproximatelyEquals(OptimizerState other)\\n {\\n var rhs = other as State;\\n return (rhs is not null) && step == rhs.step &&\\n mu_product == rhs.mu_product &&\\n exp_avg.allclose(rhs.exp_avg) &&\\n exp_avg_sq.allclose(rhs.exp_avg_sq);\\n }\\n }\\n\\n /// <summary>\\n /// Add a param group to the Optimizer s param_groups.\\n /// </summary>\\n /// <param name=\\\"param_group\\\"></param>\\n /// <remarks>This can be useful when fine tuning a pre-trained network as frozen layers can be made trainable and added to the Optimizer as training progresses.</remarks>\\n public override void add_param_group(Modules.ParamGroup param_group)\\n {\\n var def = _defaults as Options;\\n if (param_group.Options is null) {\\n param_group.Options = new Options();\\n }\\n\\n var opt = param_group.Options as Options;\\n\\n // Make sure all the options are set.\\n if (!opt.LearningRate.HasValue) opt.LearningRate = def.LearningRate;\\n if (!opt.beta1.HasValue) opt.beta1 = def.beta1;\\n if (!opt.beta2.HasValue) opt.beta2 = def.beta2;\\n if (!opt.eps.HasValue) opt.eps = def.eps;\\n if (!opt.weight_decay.HasValue) opt.weight_decay = def.weight_decay;\\n if (!opt.momentum_decay.HasValue) opt.momentum_decay = def.momentum_decay;\\n\\n opt.InitialLearningRate = opt.LearningRate.Value;\\n\\n _parameter_groups.Add(param_group);\\n\\n foreach (var p in param_group.Parameters) {\\n var state = new State();\\n _state[p.Handle] = state;\\n state.step = 0;\\n state.exp_avg = torch.zeros_like(p).DetachFromDisposeScope();\\n state.exp_avg_sq = torch.zeros_like(p).DetachFromDisposeScope();\\n }\\n }\\n\\n public class Options : OptimizerOptions\\n {\\n public double? beta1;\\n public double? beta2;\\n public double? eps;\\n public double? weight_decay;\\n public double? momentum_decay;\\n\\n /// <summary>\\n /// Load optimizer options (param-group hyperparameters) from another optimizer.\\n /// </summary>\\n /// <param name=\\\"source\\\">An optimizer options record.</param>\\n public override void LoadStateDict(OptimizerOptions source)\\n {\\n base.LoadStateDict(source);\\n var opts = source as Options;\\n beta1 = opts.beta1;\\n beta2 = opts.beta2;\\n eps = opts.eps;\\n weight_decay = opts.weight_decay;\\n momentum_decay = opts.momentum_decay;\\n }\\n\\n /// <summary>\\n /// Load the optimizer options (param-group hyperparameters) from a stream.\\n /// </summary>\\n /// <param name=\\\"reader\\\">A binary reader connected to a stream open for reading.</param>\\n public override void LoadStateDict(BinaryReader reader)\\n {\\n base.LoadStateDict(reader);\\n beta1 = reader.ReadDouble();\\n beta2 = reader.ReadDouble();\\n eps = reader.ReadDouble();\\n weight_decay = reader.ReadDouble();\\n momentum_decay = reader.ReadDouble();\\n }\\n\\n /// <summary>\\n /// Save the optimizer options (param-group hyperparameters) to a stream.\\n /// </summary>\\n /// <param name=\\\"writer\\\">A binary writer connected to a stream open for writing.</param>\\n public override void SaveStateDict(BinaryWriter writer)\\n {\\n base.SaveStateDict(writer);\\n writer.Write(beta1.Value);\\n writer.Write(beta2.Value);\\n writer.Write(eps.Value);\\n writer.Write(weight_decay.Value);\\n writer.Write(momentum_decay.Value);\\n }\\n }\\n\\n public class ParamGroup : ParamGroup<Options>, IBetas\\n {\\n public ParamGroup() { }\\n\\n public ParamGroup(IEnumerable<Parameter> parameters, Options options) : base(parameters, options) { }\\n\\n public ParamGroup(IEnumerable<Parameter> parameters, double lr = 1.0, double beta1 = 0.9, double beta2 = 0.999, double eps = 1e-8, double weight_decay = 0, double momentum_decay = 4e-3)\\n : base(parameters, new NAdam.Options { LearningRate = lr, beta1 = beta1, beta2 = beta2, eps = eps, weight_decay = weight_decay, momentum_decay = momentum_decay })\\n {\\n }\\n\\n public (double, double) Betas {\\n get => (Options.beta1.Value, Options.beta2.Value);\\n set { Options.beta1 = value.Item1; Options.beta2 = value.Item2; }\\n }\\n }\\n\\n public (double, double) Betas {\\n get => ((_defaults as Options).beta1.Value, (_defaults as Options).beta2.Value);\\n set { (_defaults as Options).beta1 = value.Item1; (_defaults as Options).beta2 = value.Item2; }\\n }\\n }\\n }\\n}\\n\"\n },\n {\n \"alpha_fraction\": 0.6769911646842957,\n \"alphanum_fraction\": 0.6769911646842957,\n \"avg_line_length\": 24.22222137451172,\n \"blob_id\": \"7f2afcfc3435ec6731a16bbdcfd92c00bcc5ed1f\",\n \"content_id\": \"98c0714e78779e6aea9452903ce9301eea460dbe\",\n \"detected_licenses\": [\n \"MIT\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"C#\",\n \"length_bytes\": 228,\n \"license_type\": \"permissive\",\n \"max_line_length\": 131,\n \"num_lines\": 9,\n \"path\": \"/src/TorchSharp/Tensor/Enums/LobpcgMethod.cs\",\n \"repo_name\": \"NiklasGustafsson/TorchSharp\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"// Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information.\\nnamespace TorchSharp\\n{\\n public enum LobpcgMethod\\n {\\n basic,\\n ortho\\n }\\n}\"\n },\n {\n \"alpha_fraction\": 0.5157126784324646,\n \"alphanum_fraction\": 0.5157126784324646,\n \"avg_line_length\": 30.839284896850586,\n \"blob_id\": \"2fb046f455d1def83b4608595d48b6d8403ddfb6\",\n \"content_id\": \"19bd3b84be81d74db9486c0a32e72f6aa1ad36a9\",\n \"detected_licenses\": [\n \"MIT\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"C#\",\n \"length_bytes\": 1782,\n \"license_type\": \"permissive\",\n \"max_line_length\": 130,\n \"num_lines\": 56,\n \"path\": \"/src/TorchVision/RandomOrder.cs\",\n \"repo_name\": \"NiklasGustafsson/TorchSharp\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"// Copyright (c) .NET Foundation and Contributors. All Rights Reserved. See LICENSE in the project root for license information.\\nusing System;\\nusing System.Collections.Generic;\\nusing System.Linq;\\nusing static TorchSharp.torch;\\n\\nnamespace TorchSharp\\n{\\n public static partial class torchvision\\n {\\n internal class RandomOrder : IDisposable, ITransform\\n {\\n public RandomOrder(ITransform[] transforms)\\n {\\n this.transforms = transforms;\\n }\\n\\n public void Dispose()\\n {\\n foreach (var t in transforms) {\\n if (t is IDisposable) {\\n ((IDisposable)t).Dispose();\\n }\\n }\\n }\\n\\n public Tensor call(Tensor input)\\n {\\n var rng = new Random();\\n foreach (var t in transforms.OrderBy(t => rng.NextDouble())) {\\n input = t.call(input);\\n }\\n return input;\\n }\\n\\n private IList<ITransform> transforms;\\n }\\n\\n public static partial class transforms\\n {\\n /// <summary>\\n /// Apply a list of transformations in a random order.\\n /// </summary>\\n /// <param name=\\\"transforms\\\">A list of transforms to apply.</param>\\n /// <remarks>\\n /// This transform uses the .NET Random API, not the Torch RNG.\\n /// Each invocation of 'forward()' will randomize the order in which transforms\\n /// are applied.\\n /// </remarks>\\n static public ITransform RandomOrder(params ITransform[] transforms)\\n {\\n return new RandomOrder(transforms);\\n }\\n }\\n }\\n}\"\n }\n]","isConversational":false},"num_files":{"kind":"number","value":361,"string":"361"}}},{"rowIdx":2977191,"cells":{"repo_name":{"kind":"string","value":"luanacamm/Python-Hand-on-Solve-200-Problems"},"repo_url":{"kind":"string","value":"https://github.com/luanacamm/Python-Hand-on-Solve-200-Problems"},"snapshot_id":{"kind":"string","value":"62a4c81d4ad6422690c103348dfeeb90161831cf"},"revision_id":{"kind":"string","value":"3901b97772de6fa6eec476e4179c8d95207fce94"},"directory_id":{"kind":"string","value":"39391e21b64c66e73192fc3e6df7729ed390ce04"},"branch_name":{"kind":"string","value":"refs/heads/master"},"visit_date":{"kind":"timestamp","value":"2020-03-25T01:27:02.355154","string":"2020-03-25T01:27:02.355154"},"revision_date":{"kind":"timestamp","value":"2018-08-02T04:18:26","string":"2018-08-02T04:18:26"},"committer_date":{"kind":"timestamp","value":"2018-08-02T04:18:26","string":"2018-08-02T04:18:26"},"github_id":{"kind":"number","value":143238590,"string":"143,238,590"},"star_events_count":{"kind":"number","value":0,"string":"0"},"fork_events_count":{"kind":"number","value":0,"string":"0"},"gha_license_id":{"kind":"null"},"gha_created_at":{"kind":"null"},"gha_updated_at":{"kind":"null"},"gha_pushed_at":{"kind":"null"},"gha_language":{"kind":"null"},"files":{"kind":"list like","value":[{"alpha_fraction":0.6339285969734192,"alphanum_fraction":0.6547619104385376,"avg_line_length":23,"blob_id":"f0b811a4bc2faa3c7551785eca6d7a1bb41750f7","content_id":"e2adf54ddf1b3a8f419ca870f719704e273321ac","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":336,"license_type":"no_license","max_line_length":105,"num_lines":14,"path":"/LuanaSiqueiraSection03Lecture10.py","repo_name":"luanacamm/Python-Hand-on-Solve-200-Problems","src_encoding":"UTF-8","text":"'''\ncalculate with input\nSection 3, Lecture 10\n# Write a Python program that accepts an integer (n) and computes the value of n+nn+nnn. Go to the editor\n# Sample value of n is 5 \n# Expected Result : 615\n'''\n\n\nn = int(input(\"Input a number: \"))\na = str(n)\naa = str(n)+str(n)\naaa = str(n)+str(n)+str(n)\ntotal=(int (a)+int(aa)+ int (aaa))\n"},{"alpha_fraction":0.6441176533699036,"alphanum_fraction":0.6882352828979492,"avg_line_length":29.909090042114258,"blob_id":"1727e835325688a0418edf84a4d35f110dafad80","content_id":"f5ca77873f0d6b011d7f11c28f96e90890121316","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":340,"license_type":"no_license","max_line_length":99,"num_lines":11,"path":"/LuanaSiqueiraSection03Lecture08.py","repo_name":"luanacamm/Python-Hand-on-Solve-200-Problems","src_encoding":"UTF-8","text":"'''\ninput age\nSection 3, Lecture 8\n# Create a program that asks the user to enter their name and their age.\n# Print out a message addressed to them that tells them the year that they will turn 100 years old.\n'''\n\nname = str(input(\"Name: \"))\nage = int(input(\"Age: \"))\nyear = (100-age)+2018\nprint(\"In \", year, name, \"will be 100 years old.\")\n"}],"string":"[\n {\n \"alpha_fraction\": 0.6339285969734192,\n \"alphanum_fraction\": 0.6547619104385376,\n \"avg_line_length\": 23,\n \"blob_id\": \"f0b811a4bc2faa3c7551785eca6d7a1bb41750f7\",\n \"content_id\": \"e2adf54ddf1b3a8f419ca870f719704e273321ac\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 336,\n \"license_type\": \"no_license\",\n \"max_line_length\": 105,\n \"num_lines\": 14,\n \"path\": \"/LuanaSiqueiraSection03Lecture10.py\",\n \"repo_name\": \"luanacamm/Python-Hand-on-Solve-200-Problems\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"'''\\ncalculate with input\\nSection 3, Lecture 10\\n# Write a Python program that accepts an integer (n) and computes the value of n+nn+nnn. Go to the editor\\n# Sample value of n is 5 \\n# Expected Result : 615\\n'''\\n\\n\\nn = int(input(\\\"Input a number: \\\"))\\na = str(n)\\naa = str(n)+str(n)\\naaa = str(n)+str(n)+str(n)\\ntotal=(int (a)+int(aa)+ int (aaa))\\n\"\n },\n {\n \"alpha_fraction\": 0.6441176533699036,\n \"alphanum_fraction\": 0.6882352828979492,\n \"avg_line_length\": 29.909090042114258,\n \"blob_id\": \"1727e835325688a0418edf84a4d35f110dafad80\",\n \"content_id\": \"f5ca77873f0d6b011d7f11c28f96e90890121316\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 340,\n \"license_type\": \"no_license\",\n \"max_line_length\": 99,\n \"num_lines\": 11,\n \"path\": \"/LuanaSiqueiraSection03Lecture08.py\",\n \"repo_name\": \"luanacamm/Python-Hand-on-Solve-200-Problems\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"'''\\ninput age\\nSection 3, Lecture 8\\n# Create a program that asks the user to enter their name and their age.\\n# Print out a message addressed to them that tells them the year that they will turn 100 years old.\\n'''\\n\\nname = str(input(\\\"Name: \\\"))\\nage = int(input(\\\"Age: \\\"))\\nyear = (100-age)+2018\\nprint(\\\"In \\\", year, name, \\\"will be 100 years old.\\\")\\n\"\n }\n]","isConversational":false},"num_files":{"kind":"number","value":2,"string":"2"}}},{"rowIdx":2977192,"cells":{"repo_name":{"kind":"string","value":"gyash03081997/coursera"},"repo_url":{"kind":"string","value":"https://github.com/gyash03081997/coursera"},"snapshot_id":{"kind":"string","value":"5e8001ab85b6a65b1cd8d070bcef4c4f6ea1da19"},"revision_id":{"kind":"string","value":"1f2dafb2eab45f561f039c260cca82c6a19b813f"},"directory_id":{"kind":"string","value":"df3b09d028513787d126c7813db2f399c273167c"},"branch_name":{"kind":"string","value":"refs/heads/master"},"visit_date":{"kind":"timestamp","value":"2020-04-01T04:15:59.916647","string":"2020-04-01T04:15:59.916647"},"revision_date":{"kind":"timestamp","value":"2019-04-05T17:30:11","string":"2019-04-05T17:30:11"},"committer_date":{"kind":"timestamp","value":"2019-04-05T17:30:11","string":"2019-04-05T17:30:11"},"github_id":{"kind":"number","value":152856337,"string":"152,856,337"},"star_events_count":{"kind":"number","value":0,"string":"0"},"fork_events_count":{"kind":"number","value":0,"string":"0"},"gha_license_id":{"kind":"null"},"gha_created_at":{"kind":"null"},"gha_updated_at":{"kind":"null"},"gha_pushed_at":{"kind":"null"},"gha_language":{"kind":"null"},"files":{"kind":"list like","value":[{"alpha_fraction":0.7211776971817017,"alphanum_fraction":0.7287567257881165,"avg_line_length":18.37005615234375,"blob_id":"1039524ca4bf3f451482256950e89ac357b8c069","content_id":"a45876c486b61c96433dc58934dbb472e464bb3a","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":6861,"license_type":"no_license","max_line_length":39,"num_lines":354,"path":"/NLP Classwork/Stemmer.py","repo_name":"gyash03081997/coursera","src_encoding":"UTF-8","text":"#!/usr/bin/env python\n# coding: utf-8\n\n# In[1]:\n\n\nimport nltk\nnltk.download()\n\n\n# In[2]:\n\n\nfrom nltk.stem import PorterStemmer\nfrom nltk.stem import LancasterStemmer\n\n\n# In[3]:\n\n\nporter=PorterStemmer()\nlancaster=LancasterStemmer()\nprint(\"Porter Stemmer\")\nprint(porter.stem(\"cats\"))\nprint(porter.stem(\"trouble\"))\nprint(porter.stem(\"troubling\"))\nprint(porter.stem(\"troubled\"))\nprint(\"Lancaster Stemmer\")\nprint(lancaster.stem(\"cats\"))\nprint(lancaster.stem(\"trouble\"))\nprint(lancaster.stem(\"troubling\"))\nprint(lancaster.stem(\"troubled\"))\n\n\n# In[4]:\n\n\nprint(lancaster.stem(\"apple\"))\nprint(porter.stem(\"apple\"))\n\n\n# In[5]:\n\n\nprint(lancaster.stem(\"happiness\"))\nprint(porter.stem(\"happiness\"))\n\n\n# In[6]:\n\n\nprint(lancaster.stem(\"Dying\"))\nprint(porter.stem(\"Dying\"))\n\n\n# In[7]:\n\n\nprint(lancaster.stem(\"dying\"))\nprint(porter.stem(\"dying\"))\n\n\n# In[8]:\n\n\nprint(lancaster.stem(\"impossible\"))\nprint(porter.stem(\"impossible\"))\n\n\n# In[9]:\n\n\nprint(lancaster.stem(\"Impossible\"))\nprint(porter.stem(\"Impossible\"))\n\n\n# In[10]:\n\n\nprint(lancaster.stem(\"prenuptial\"))\nprint(porter.stem(\"prenuptial\"))\n\n\n# In[11]:\n\n\nprint(lancaster.stem(\"Prenuptial\"))\nprint(porter.stem(\"Prenuptial\"))\n\n\n# In[12]:\n\n\nprint(lancaster.stem(\"Collage\"))\nprint(porter.stem(\"Collage\"))\n\n\n# In[13]:\n\n\nprint(lancaster.stem(\"collage\"))\nprint(porter.stem(\"collage\"))\n\n\n# In[14]:\n\n\nprint(lancaster.stem(\"distrustful\"))\nprint(porter.stem(\"distrustful\"))\n\n\n# In[ ]:\n\n\n\n\n\n# In[15]:\n\n\nprint(lancaster.stem(\"receive\"))\nprint(porter.stem(\"receive\"))\nprint(lancaster.stem(\"proctee\"))\nprint(porter.stem(\"proctee\"))\n\n\n# In[16]:\n\n\nprint(lancaster.stem(\"Receive\"))\nprint(porter.stem(\"Receive\"))\nprint(lancaster.stem(\"Proctee\"))\nprint(porter.stem(\"Proctee\"))\n\n\n# In[36]:\n\n\nprint(lancaster.stem(\"brownie\"))\nprint(porter.stem(\"brownie\"))\n\n\n# In[18]:\n\n\nprint(lancaster.stem(\"employer\"))\nprint(porter.stem(\"employer\"))\nprint(lancaster.stem(\"employee\"))\nprint(porter.stem(\"employee\"))\n\n\n# In[21]:\n\n\nprint(lancaster.stem(\"unhappy\"))\nprint(porter.stem(\"unhappy\"))\nprint(lancaster.stem(\"trilogy\"))\nprint(porter.stem(\"trilogy\"))\n\n\n# In[22]:\n\n\nprint(lancaster.stem(\"collaborate\"))\nprint(porter.stem(\"collaborate\"))\nprint(lancaster.stem(\"corporate\"))\nprint(porter.stem(\"corporate\"))\n\n\n# In[23]:\n\n\nprint(lancaster.stem(\"obliterate\"))\nprint(porter.stem(\"obliterate\"))\nprint(lancaster.stem(\"crate\"))\nprint(porter.stem(\"crate\"))\n\n\n# In[24]:\n\n\nprint(lancaster.stem(\"obliterate\"))\nprint(porter.stem(\"obliterate\"))\n\n\n# In[25]:\n\n\nprint(lancaster.stem(\"salutation\"))\nprint(porter.stem(\"salutation\"))\nprint(lancaster.stem(\"information\"))\nprint(porter.stem(\"information\"))\nprint(lancaster.stem(\"generation\"))\nprint(porter.stem(\"generation\"))\nprint(lancaster.stem(\"exclaimation\"))\nprint(porter.stem(\"exclaimation\"))\nprint(lancaster.stem(\"speculation\"))\nprint(porter.stem(\"speculation\"))\nprint(lancaster.stem(\"hibernation\"))\nprint(porter.stem(\"hibernation\"))\nprint(lancaster.stem(\"manipulation\"))\nprint(porter.stem(\"manipulation\"))\nprint(lancaster.stem(\"application\"))\nprint(porter.stem(\"application\"))\nprint(lancaster.stem(\"termination\"))\nprint(porter.stem(\"termination\"))\nprint(lancaster.stem(\"retribution\"))\nprint(porter.stem(\"retribution\"))\nprint(lancaster.stem(\"distribution\"))\nprint(porter.stem(\"distribution\"))\nprint(lancaster.stem(\"execution\"))\nprint(porter.stem(\"execution\"))\nprint(lancaster.stem(\"substitution\"))\nprint(porter.stem(\"substitution\"))\nprint(lancaster.stem(\"movement\"))\nprint(porter.stem(\"movement\"))\nprint(lancaster.stem(\"predicament\"))\nprint(porter.stem(\"predicament\"))\nprint(lancaster.stem(\"abolishment\"))\nprint(porter.stem(\"abolishment\"))\nprint(lancaster.stem(\"infringement\"))\nprint(porter.stem(\"infringement\"))\nprint(lancaster.stem(\"attainment\"))\nprint(porter.stem(\"attainment\"))\nprint(lancaster.stem(\"accomplishment\"))\nprint(porter.stem(\"accomplishment\"))\n\n\n# In[26]:\n\n\nprint(lancaster.stem(\"incredible\"))\nprint(porter.stem(\"incredible\"))\nprint(lancaster.stem(\"invincible\"))\nprint(porter.stem(\"invincible\"))\nprint(lancaster.stem(\"forcible\"))\nprint(porter.stem(\"forcible\"))\nprint(lancaster.stem(\"visible\"))\nprint(porter.stem(\"visible\"))\n\n\n# In[27]:\n\n\nprint(lancaster.stem(\"gillible\"))\nprint(porter.stem(\"gullible\"))\nprint(lancaster.stem(\"tangible\"))\nprint(porter.stem(\"tangible\"))\nprint(lancaster.stem(\"possible\"))\nprint(porter.stem(\"possible\"))\nprint(lancaster.stem(\"collapsible\"))\nprint(porter.stem(\"collapsible\"))\nprint(lancaster.stem(\"accessible\"))\nprint(porter.stem(\"accessible\"))\nprint(lancaster.stem(\"convertible\"))\nprint(porter.stem(\"convertible\"))\nprint(lancaster.stem(\"permissible\"))\nprint(porter.stem(\"permissible\"))\n\n\n# In[31]:\n\n\nprint(lancaster.stem(\"abider\"))\nprint(porter.stem(\"abider\"))\nprint(lancaster.stem(\"abetter\"))\nprint(porter.stem(\"abetter\"))\nprint(lancaster.stem(\"abater\"))\nprint(porter.stem(\"abater\"))\nprint(lancaster.stem(\"adopter\"))\nprint(porter.stem(\"adopter\"))\nprint(lancaster.stem(\"adorner\"))\nprint(porter.stem(\"adorner\"))\n\n\n# In[32]:\n\n\nprint(lancaster.stem(\"ionization\"))\nprint(porter.stem(\"ionization\"))\nprint(lancaster.stem(\"activization\"))\nprint(porter.stem(\"activization\"))\nprint(lancaster.stem(\"realization\"))\nprint(porter.stem(\"realization\"))\n\n\n# In[33]:\n\n\nprint(lancaster.stem(\"acidified\"))\nprint(porter.stem(\"acidified\"))\nprint(lancaster.stem(\"allied\"))\nprint(porter.stem(\"allied\"))\nprint(lancaster.stem(\"amplified\"))\nprint(porter.stem(\"amplified\"))\nprint(lancaster.stem(\"beautified\"))\nprint(porter.stem(\"beautified\"))\nprint(lancaster.stem(\"identified\"))\nprint(porter.stem(\"identified\"))\nprint(lancaster.stem(\"buried\"))\nprint(porter.stem(\"buried\"))\nprint(lancaster.stem(\"humidified\"))\nprint(porter.stem(\"humidified\"))\nprint(lancaster.stem(\"abducted\"))\nprint(porter.stem(\"abducted\"))\nprint(lancaster.stem(\"aborted\"))\nprint(porter.stem(\"aborted\"))\nprint(lancaster.stem(\"abandoned\"))\nprint(porter.stem(\"abandoned\"))\nprint(lancaster.stem(\"accorded\"))\nprint(porter.stem(\"accorded\"))\nprint(lancaster.stem(\"aligned\"))\nprint(porter.stem(\"aligned\"))\nprint(lancaster.stem(\"alleged\"))\nprint(porter.stem(\"alleged\"))\nprint(lancaster.stem(\"curated\"))\nprint(porter.stem(\"curated\"))\n\n\n# In[35]:\n\n\nprint(lancaster.stem(\"abhor\"))\nprint(porter.stem(\"abhor\"))\nprint(lancaster.stem(\"abjudicator\"))\nprint(porter.stem(\"abjudicator\"))\nprint(lancaster.stem(\"acceptor\"))\nprint(porter.stem(\"acceptor\"))\nprint(lancaster.stem(\"frown\"))\nprint(porter.stem(\"circulator\"))\nprint(lancaster.stem(\"circulator\"))\nprint(porter.stem(\"commentor\"))\nprint(lancaster.stem(\"commentor\"))\nprint(porter.stem(\"corridor\"))\nprint(lancaster.stem(\"corridor\"))\nprint(porter.stem(\"corridor\"))\nprint(lancaster.stem(\"decorator\"))\nprint(porter.stem(\"decorator\"))\nprint(lancaster.stem(\"exclaimator\"))\nprint(porter.stem(\"exclaimator\"))\n\n\n# In[39]:\n\n\nprint(lancaster.stem(\"crying\"))\nprint(porter.stem(\"crying\"))\nprint(lancaster.stem(\"Dying\"))\nprint(porter.stem(\"Dying\"))\nprint(lancaster.stem(\"dying\"))\nprint(porter.stem(\"dying\"))\n\n\n# In[ ]:\n\n\n\n\n"},{"alpha_fraction":0.7063218355178833,"alphanum_fraction":0.7183908224105835,"avg_line_length":15.224299430847168,"blob_id":"46fca356b991430a59a4aa64a6c0d1cd83e97de6","content_id":"f755181d79e3cc074b45dba4b70a5b370c8383a2","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":1740,"license_type":"no_license","max_line_length":112,"num_lines":107,"path":"/NLP Classwork/Vectorizer.py","repo_name":"gyash03081997/coursera","src_encoding":"UTF-8","text":"#!/usr/bin/env python\n# coding: utf-8\n\n# In[1]:\n\n\nimport nltk\nfrom nltk.stem import PorterStemmer\nstemmer=PorterStemmer()\n\nexample=\"The cat was chasing a mouse\"\nexample=[stemmer.stem(token) for token in example.split(\" \")]\n\n\n# In[3]:\n\n\nprint(\" \".join(example))\n\n\n# In[12]:\n\n\nimport nltk\nfrom nltk.stem import WordNetLemmatizer\nlemmatizer=WordNetLemmatizer()\n\nexample=\"The cat was chasing the mice\"\nexample=[lemmatizer.lemmatize(token) for token in example.split(\" \")]\n\n\n# In[13]:\n\n\nprint(\" \".join(example))\n\n\n# In[11]:\n\n\nprint(lemmatizer.lemmatize('better',pos='a'))\n\n\n# In[14]:\n\n\nimport nltk\nfrom nltk.stem import WordNetLemmatizer\nlemmatizer=WordNetLemmatizer()\n\nexample=\"The cat was chasing the mice. there were cacti round the corner\"\nexample=[lemmatizer.lemmatize(token) for token in example.split(\" \")]\n\n\n# In[18]:\n\n\nprint(\" \".join(example))\n\n\n# In[19]:\n\n\nfrom sklearn.feature_extraction.text import CountVectorizer\n\n\n# In[24]:\n\n\nvect=CountVectorizer(binary=True)#can explore other parameters like other than binary\ncorpus=[\"Tessaract is a good good optical recognition technique\",\"optical character recognition is significant\"]\nvect.fit(corpus)\n#print(vect.transform([\"This is a good optical\"]).toarray())\nprint(vect.transform(corpus).toarray())\n\n\n# In[25]:\n\n\nvect=CountVectorizer(binary=True)#can explore other parameters like other than binary\ncorpus=[\"Tessaract is a good good optical recognition technique\",\"Tessaract is significant\"]\nvect.fit(corpus)\n#print(vect.transform([\"This is a good optical\"]).toarray())\nprint(vect.transform(corpus).toarray())\n\n\n# In[30]:\n\n\nvocab=vect.vocabulary_ #vocabulary built in\nfor key in sorted(vocab.keys()):\n print(\"{}:{}\".format(key,vocab[key]))\n\n\n# In[ ]:\n\n\n\n\n\n# In[ ]:\n\n\n\n\n\n# In[ ]:\n\n\n\n\n"}],"string":"[\n {\n \"alpha_fraction\": 0.7211776971817017,\n \"alphanum_fraction\": 0.7287567257881165,\n \"avg_line_length\": 18.37005615234375,\n \"blob_id\": \"1039524ca4bf3f451482256950e89ac357b8c069\",\n \"content_id\": \"a45876c486b61c96433dc58934dbb472e464bb3a\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 6861,\n \"license_type\": \"no_license\",\n \"max_line_length\": 39,\n \"num_lines\": 354,\n \"path\": \"/NLP Classwork/Stemmer.py\",\n \"repo_name\": \"gyash03081997/coursera\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"#!/usr/bin/env python\\n# coding: utf-8\\n\\n# In[1]:\\n\\n\\nimport nltk\\nnltk.download()\\n\\n\\n# In[2]:\\n\\n\\nfrom nltk.stem import PorterStemmer\\nfrom nltk.stem import LancasterStemmer\\n\\n\\n# In[3]:\\n\\n\\nporter=PorterStemmer()\\nlancaster=LancasterStemmer()\\nprint(\\\"Porter Stemmer\\\")\\nprint(porter.stem(\\\"cats\\\"))\\nprint(porter.stem(\\\"trouble\\\"))\\nprint(porter.stem(\\\"troubling\\\"))\\nprint(porter.stem(\\\"troubled\\\"))\\nprint(\\\"Lancaster Stemmer\\\")\\nprint(lancaster.stem(\\\"cats\\\"))\\nprint(lancaster.stem(\\\"trouble\\\"))\\nprint(lancaster.stem(\\\"troubling\\\"))\\nprint(lancaster.stem(\\\"troubled\\\"))\\n\\n\\n# In[4]:\\n\\n\\nprint(lancaster.stem(\\\"apple\\\"))\\nprint(porter.stem(\\\"apple\\\"))\\n\\n\\n# In[5]:\\n\\n\\nprint(lancaster.stem(\\\"happiness\\\"))\\nprint(porter.stem(\\\"happiness\\\"))\\n\\n\\n# In[6]:\\n\\n\\nprint(lancaster.stem(\\\"Dying\\\"))\\nprint(porter.stem(\\\"Dying\\\"))\\n\\n\\n# In[7]:\\n\\n\\nprint(lancaster.stem(\\\"dying\\\"))\\nprint(porter.stem(\\\"dying\\\"))\\n\\n\\n# In[8]:\\n\\n\\nprint(lancaster.stem(\\\"impossible\\\"))\\nprint(porter.stem(\\\"impossible\\\"))\\n\\n\\n# In[9]:\\n\\n\\nprint(lancaster.stem(\\\"Impossible\\\"))\\nprint(porter.stem(\\\"Impossible\\\"))\\n\\n\\n# In[10]:\\n\\n\\nprint(lancaster.stem(\\\"prenuptial\\\"))\\nprint(porter.stem(\\\"prenuptial\\\"))\\n\\n\\n# In[11]:\\n\\n\\nprint(lancaster.stem(\\\"Prenuptial\\\"))\\nprint(porter.stem(\\\"Prenuptial\\\"))\\n\\n\\n# In[12]:\\n\\n\\nprint(lancaster.stem(\\\"Collage\\\"))\\nprint(porter.stem(\\\"Collage\\\"))\\n\\n\\n# In[13]:\\n\\n\\nprint(lancaster.stem(\\\"collage\\\"))\\nprint(porter.stem(\\\"collage\\\"))\\n\\n\\n# In[14]:\\n\\n\\nprint(lancaster.stem(\\\"distrustful\\\"))\\nprint(porter.stem(\\\"distrustful\\\"))\\n\\n\\n# In[ ]:\\n\\n\\n\\n\\n\\n# In[15]:\\n\\n\\nprint(lancaster.stem(\\\"receive\\\"))\\nprint(porter.stem(\\\"receive\\\"))\\nprint(lancaster.stem(\\\"proctee\\\"))\\nprint(porter.stem(\\\"proctee\\\"))\\n\\n\\n# In[16]:\\n\\n\\nprint(lancaster.stem(\\\"Receive\\\"))\\nprint(porter.stem(\\\"Receive\\\"))\\nprint(lancaster.stem(\\\"Proctee\\\"))\\nprint(porter.stem(\\\"Proctee\\\"))\\n\\n\\n# In[36]:\\n\\n\\nprint(lancaster.stem(\\\"brownie\\\"))\\nprint(porter.stem(\\\"brownie\\\"))\\n\\n\\n# In[18]:\\n\\n\\nprint(lancaster.stem(\\\"employer\\\"))\\nprint(porter.stem(\\\"employer\\\"))\\nprint(lancaster.stem(\\\"employee\\\"))\\nprint(porter.stem(\\\"employee\\\"))\\n\\n\\n# In[21]:\\n\\n\\nprint(lancaster.stem(\\\"unhappy\\\"))\\nprint(porter.stem(\\\"unhappy\\\"))\\nprint(lancaster.stem(\\\"trilogy\\\"))\\nprint(porter.stem(\\\"trilogy\\\"))\\n\\n\\n# In[22]:\\n\\n\\nprint(lancaster.stem(\\\"collaborate\\\"))\\nprint(porter.stem(\\\"collaborate\\\"))\\nprint(lancaster.stem(\\\"corporate\\\"))\\nprint(porter.stem(\\\"corporate\\\"))\\n\\n\\n# In[23]:\\n\\n\\nprint(lancaster.stem(\\\"obliterate\\\"))\\nprint(porter.stem(\\\"obliterate\\\"))\\nprint(lancaster.stem(\\\"crate\\\"))\\nprint(porter.stem(\\\"crate\\\"))\\n\\n\\n# In[24]:\\n\\n\\nprint(lancaster.stem(\\\"obliterate\\\"))\\nprint(porter.stem(\\\"obliterate\\\"))\\n\\n\\n# In[25]:\\n\\n\\nprint(lancaster.stem(\\\"salutation\\\"))\\nprint(porter.stem(\\\"salutation\\\"))\\nprint(lancaster.stem(\\\"information\\\"))\\nprint(porter.stem(\\\"information\\\"))\\nprint(lancaster.stem(\\\"generation\\\"))\\nprint(porter.stem(\\\"generation\\\"))\\nprint(lancaster.stem(\\\"exclaimation\\\"))\\nprint(porter.stem(\\\"exclaimation\\\"))\\nprint(lancaster.stem(\\\"speculation\\\"))\\nprint(porter.stem(\\\"speculation\\\"))\\nprint(lancaster.stem(\\\"hibernation\\\"))\\nprint(porter.stem(\\\"hibernation\\\"))\\nprint(lancaster.stem(\\\"manipulation\\\"))\\nprint(porter.stem(\\\"manipulation\\\"))\\nprint(lancaster.stem(\\\"application\\\"))\\nprint(porter.stem(\\\"application\\\"))\\nprint(lancaster.stem(\\\"termination\\\"))\\nprint(porter.stem(\\\"termination\\\"))\\nprint(lancaster.stem(\\\"retribution\\\"))\\nprint(porter.stem(\\\"retribution\\\"))\\nprint(lancaster.stem(\\\"distribution\\\"))\\nprint(porter.stem(\\\"distribution\\\"))\\nprint(lancaster.stem(\\\"execution\\\"))\\nprint(porter.stem(\\\"execution\\\"))\\nprint(lancaster.stem(\\\"substitution\\\"))\\nprint(porter.stem(\\\"substitution\\\"))\\nprint(lancaster.stem(\\\"movement\\\"))\\nprint(porter.stem(\\\"movement\\\"))\\nprint(lancaster.stem(\\\"predicament\\\"))\\nprint(porter.stem(\\\"predicament\\\"))\\nprint(lancaster.stem(\\\"abolishment\\\"))\\nprint(porter.stem(\\\"abolishment\\\"))\\nprint(lancaster.stem(\\\"infringement\\\"))\\nprint(porter.stem(\\\"infringement\\\"))\\nprint(lancaster.stem(\\\"attainment\\\"))\\nprint(porter.stem(\\\"attainment\\\"))\\nprint(lancaster.stem(\\\"accomplishment\\\"))\\nprint(porter.stem(\\\"accomplishment\\\"))\\n\\n\\n# In[26]:\\n\\n\\nprint(lancaster.stem(\\\"incredible\\\"))\\nprint(porter.stem(\\\"incredible\\\"))\\nprint(lancaster.stem(\\\"invincible\\\"))\\nprint(porter.stem(\\\"invincible\\\"))\\nprint(lancaster.stem(\\\"forcible\\\"))\\nprint(porter.stem(\\\"forcible\\\"))\\nprint(lancaster.stem(\\\"visible\\\"))\\nprint(porter.stem(\\\"visible\\\"))\\n\\n\\n# In[27]:\\n\\n\\nprint(lancaster.stem(\\\"gillible\\\"))\\nprint(porter.stem(\\\"gullible\\\"))\\nprint(lancaster.stem(\\\"tangible\\\"))\\nprint(porter.stem(\\\"tangible\\\"))\\nprint(lancaster.stem(\\\"possible\\\"))\\nprint(porter.stem(\\\"possible\\\"))\\nprint(lancaster.stem(\\\"collapsible\\\"))\\nprint(porter.stem(\\\"collapsible\\\"))\\nprint(lancaster.stem(\\\"accessible\\\"))\\nprint(porter.stem(\\\"accessible\\\"))\\nprint(lancaster.stem(\\\"convertible\\\"))\\nprint(porter.stem(\\\"convertible\\\"))\\nprint(lancaster.stem(\\\"permissible\\\"))\\nprint(porter.stem(\\\"permissible\\\"))\\n\\n\\n# In[31]:\\n\\n\\nprint(lancaster.stem(\\\"abider\\\"))\\nprint(porter.stem(\\\"abider\\\"))\\nprint(lancaster.stem(\\\"abetter\\\"))\\nprint(porter.stem(\\\"abetter\\\"))\\nprint(lancaster.stem(\\\"abater\\\"))\\nprint(porter.stem(\\\"abater\\\"))\\nprint(lancaster.stem(\\\"adopter\\\"))\\nprint(porter.stem(\\\"adopter\\\"))\\nprint(lancaster.stem(\\\"adorner\\\"))\\nprint(porter.stem(\\\"adorner\\\"))\\n\\n\\n# In[32]:\\n\\n\\nprint(lancaster.stem(\\\"ionization\\\"))\\nprint(porter.stem(\\\"ionization\\\"))\\nprint(lancaster.stem(\\\"activization\\\"))\\nprint(porter.stem(\\\"activization\\\"))\\nprint(lancaster.stem(\\\"realization\\\"))\\nprint(porter.stem(\\\"realization\\\"))\\n\\n\\n# In[33]:\\n\\n\\nprint(lancaster.stem(\\\"acidified\\\"))\\nprint(porter.stem(\\\"acidified\\\"))\\nprint(lancaster.stem(\\\"allied\\\"))\\nprint(porter.stem(\\\"allied\\\"))\\nprint(lancaster.stem(\\\"amplified\\\"))\\nprint(porter.stem(\\\"amplified\\\"))\\nprint(lancaster.stem(\\\"beautified\\\"))\\nprint(porter.stem(\\\"beautified\\\"))\\nprint(lancaster.stem(\\\"identified\\\"))\\nprint(porter.stem(\\\"identified\\\"))\\nprint(lancaster.stem(\\\"buried\\\"))\\nprint(porter.stem(\\\"buried\\\"))\\nprint(lancaster.stem(\\\"humidified\\\"))\\nprint(porter.stem(\\\"humidified\\\"))\\nprint(lancaster.stem(\\\"abducted\\\"))\\nprint(porter.stem(\\\"abducted\\\"))\\nprint(lancaster.stem(\\\"aborted\\\"))\\nprint(porter.stem(\\\"aborted\\\"))\\nprint(lancaster.stem(\\\"abandoned\\\"))\\nprint(porter.stem(\\\"abandoned\\\"))\\nprint(lancaster.stem(\\\"accorded\\\"))\\nprint(porter.stem(\\\"accorded\\\"))\\nprint(lancaster.stem(\\\"aligned\\\"))\\nprint(porter.stem(\\\"aligned\\\"))\\nprint(lancaster.stem(\\\"alleged\\\"))\\nprint(porter.stem(\\\"alleged\\\"))\\nprint(lancaster.stem(\\\"curated\\\"))\\nprint(porter.stem(\\\"curated\\\"))\\n\\n\\n# In[35]:\\n\\n\\nprint(lancaster.stem(\\\"abhor\\\"))\\nprint(porter.stem(\\\"abhor\\\"))\\nprint(lancaster.stem(\\\"abjudicator\\\"))\\nprint(porter.stem(\\\"abjudicator\\\"))\\nprint(lancaster.stem(\\\"acceptor\\\"))\\nprint(porter.stem(\\\"acceptor\\\"))\\nprint(lancaster.stem(\\\"frown\\\"))\\nprint(porter.stem(\\\"circulator\\\"))\\nprint(lancaster.stem(\\\"circulator\\\"))\\nprint(porter.stem(\\\"commentor\\\"))\\nprint(lancaster.stem(\\\"commentor\\\"))\\nprint(porter.stem(\\\"corridor\\\"))\\nprint(lancaster.stem(\\\"corridor\\\"))\\nprint(porter.stem(\\\"corridor\\\"))\\nprint(lancaster.stem(\\\"decorator\\\"))\\nprint(porter.stem(\\\"decorator\\\"))\\nprint(lancaster.stem(\\\"exclaimator\\\"))\\nprint(porter.stem(\\\"exclaimator\\\"))\\n\\n\\n# In[39]:\\n\\n\\nprint(lancaster.stem(\\\"crying\\\"))\\nprint(porter.stem(\\\"crying\\\"))\\nprint(lancaster.stem(\\\"Dying\\\"))\\nprint(porter.stem(\\\"Dying\\\"))\\nprint(lancaster.stem(\\\"dying\\\"))\\nprint(porter.stem(\\\"dying\\\"))\\n\\n\\n# In[ ]:\\n\\n\\n\\n\\n\"\n },\n {\n \"alpha_fraction\": 0.7063218355178833,\n \"alphanum_fraction\": 0.7183908224105835,\n \"avg_line_length\": 15.224299430847168,\n \"blob_id\": \"46fca356b991430a59a4aa64a6c0d1cd83e97de6\",\n \"content_id\": \"f755181d79e3cc074b45dba4b70a5b370c8383a2\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 1740,\n \"license_type\": \"no_license\",\n \"max_line_length\": 112,\n \"num_lines\": 107,\n \"path\": \"/NLP Classwork/Vectorizer.py\",\n \"repo_name\": \"gyash03081997/coursera\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"#!/usr/bin/env python\\n# coding: utf-8\\n\\n# In[1]:\\n\\n\\nimport nltk\\nfrom nltk.stem import PorterStemmer\\nstemmer=PorterStemmer()\\n\\nexample=\\\"The cat was chasing a mouse\\\"\\nexample=[stemmer.stem(token) for token in example.split(\\\" \\\")]\\n\\n\\n# In[3]:\\n\\n\\nprint(\\\" \\\".join(example))\\n\\n\\n# In[12]:\\n\\n\\nimport nltk\\nfrom nltk.stem import WordNetLemmatizer\\nlemmatizer=WordNetLemmatizer()\\n\\nexample=\\\"The cat was chasing the mice\\\"\\nexample=[lemmatizer.lemmatize(token) for token in example.split(\\\" \\\")]\\n\\n\\n# In[13]:\\n\\n\\nprint(\\\" \\\".join(example))\\n\\n\\n# In[11]:\\n\\n\\nprint(lemmatizer.lemmatize('better',pos='a'))\\n\\n\\n# In[14]:\\n\\n\\nimport nltk\\nfrom nltk.stem import WordNetLemmatizer\\nlemmatizer=WordNetLemmatizer()\\n\\nexample=\\\"The cat was chasing the mice. there were cacti round the corner\\\"\\nexample=[lemmatizer.lemmatize(token) for token in example.split(\\\" \\\")]\\n\\n\\n# In[18]:\\n\\n\\nprint(\\\" \\\".join(example))\\n\\n\\n# In[19]:\\n\\n\\nfrom sklearn.feature_extraction.text import CountVectorizer\\n\\n\\n# In[24]:\\n\\n\\nvect=CountVectorizer(binary=True)#can explore other parameters like other than binary\\ncorpus=[\\\"Tessaract is a good good optical recognition technique\\\",\\\"optical character recognition is significant\\\"]\\nvect.fit(corpus)\\n#print(vect.transform([\\\"This is a good optical\\\"]).toarray())\\nprint(vect.transform(corpus).toarray())\\n\\n\\n# In[25]:\\n\\n\\nvect=CountVectorizer(binary=True)#can explore other parameters like other than binary\\ncorpus=[\\\"Tessaract is a good good optical recognition technique\\\",\\\"Tessaract is significant\\\"]\\nvect.fit(corpus)\\n#print(vect.transform([\\\"This is a good optical\\\"]).toarray())\\nprint(vect.transform(corpus).toarray())\\n\\n\\n# In[30]:\\n\\n\\nvocab=vect.vocabulary_ #vocabulary built in\\nfor key in sorted(vocab.keys()):\\n print(\\\"{}:{}\\\".format(key,vocab[key]))\\n\\n\\n# In[ ]:\\n\\n\\n\\n\\n\\n# In[ ]:\\n\\n\\n\\n\\n\\n# In[ ]:\\n\\n\\n\\n\\n\"\n }\n]","isConversational":false},"num_files":{"kind":"number","value":2,"string":"2"}}},{"rowIdx":2977193,"cells":{"repo_name":{"kind":"string","value":"bayerj/Jaeger"},"repo_url":{"kind":"string","value":"https://github.com/bayerj/Jaeger"},"snapshot_id":{"kind":"string","value":"221f12b3326892a4859d1f9ee46ee990d0481230"},"revision_id":{"kind":"string","value":"d4730f3f920c7d6c0aa8430a9c39732e3ef07eca"},"directory_id":{"kind":"string","value":"fce770f0181cbc5e5b3be4a9edc1b8f832942dbb"},"branch_name":{"kind":"string","value":"refs/heads/master"},"visit_date":{"kind":"timestamp","value":"2021-01-10T20:33:04.100122","string":"2021-01-10T20:33:04.100122"},"revision_date":{"kind":"timestamp","value":"2012-04-17T12:45:02","string":"2012-04-17T12:45:02"},"committer_date":{"kind":"timestamp","value":"2012-04-17T12:45:02","string":"2012-04-17T12:45:02"},"github_id":{"kind":"number","value":3866877,"string":"3,866,877"},"star_events_count":{"kind":"number","value":2,"string":"2"},"fork_events_count":{"kind":"number","value":1,"string":"1"},"gha_license_id":{"kind":"null"},"gha_created_at":{"kind":"null"},"gha_updated_at":{"kind":"null"},"gha_pushed_at":{"kind":"null"},"gha_language":{"kind":"null"},"files":{"kind":"list like","value":[{"alpha_fraction":0.5528150200843811,"alphanum_fraction":0.5560321807861328,"avg_line_length":22.3125,"blob_id":"3e57d77f589b368cf7f176f772a7cbc799adf6ff","content_id":"d9a8f04766005fa2a217a7c6b38be1e1ed917ee6","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":1865,"license_type":"no_license","max_line_length":60,"num_lines":80,"path":"/jaeger/searchspace.py","repo_name":"bayerj/Jaeger","src_encoding":"UTF-8","text":"# -*- coding: utf-8 -*-\n\n\nimport numpy as np\n\n\nclass Numeric(object):\n\n def __init__(self, intify=False):\n self.intify = intify\n\n def draw(self, seed):\n res = self._draw(seed)\n if self.intify:\n res = int(round(res))\n return res\n\n\nclass Uniform(Numeric):\n\n def __init__(self, lower, upper, intify=False):\n self.size = 1\n self.lower = lower\n self.upper = upper\n super(Uniform, self).__init__(intify)\n\n def _draw(self, seed):\n # Pick only the first entry.\n seed = seed[0]\n scale = self.upper - self.lower\n return seed * scale + self.lower\n\n\nclass LogUniform(Numeric):\n\n def __init__(self, lower, upper, intify=False):\n self.size = 1\n self.lower = lower\n self.upper = upper\n self.uniform = Uniform(np.log(lower), np.log(upper))\n super(LogUniform, self).__init__(intify)\n\n def _draw(self, seed):\n drawn = self.uniform.draw(seed)\n return np.exp(drawn)\n\n\nclass OneOf(object):\n\n def __init__(self, choices):\n self.size = len(choices)\n self.choices = choices\n\n def draw(self, seed):\n return self.choices[seed.argmax()]\n\n\nclass SearchSpace(object):\n\n def __init__(self):\n self.variables = []\n\n def add(self, handle, var):\n self.variables.append((handle, var))\n\n def draw(self, seeds=None):\n n_seeds = sum(i.size for _, i in self.variables)\n if seeds is None:\n seeds = np.random.random(n_seeds)\n seeds = np.asarray(seeds)\n if seeds.shape[0] != n_seeds:\n raise ValueError('wrong dimension of seed')\n\n start = 0\n sample = {}\n for handle, v in self.variables:\n stop = start + v.size\n sample[handle] = v.draw(seeds[start:stop])\n start = stop\n return sample\n"},{"alpha_fraction":0.8075539469718933,"alphanum_fraction":0.8075539469718933,"avg_line_length":41.769229888916016,"blob_id":"32f2e5327d62e3b1d9672c1e389c16d09bfdf89d","content_id":"6d9f0aa067260646f6ede4b7365c43f49af25841","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Markdown","length_bytes":556,"license_type":"no_license","max_line_length":80,"num_lines":13,"path":"/README.md","repo_name":"bayerj/Jaeger","src_encoding":"UTF-8","text":"Jaeger\n------\n\nJaeger is a small package for performing random search on parameter spaces for\nfunctions with a nice interface. I hope to extend it with Bayesian optimization\nand distributed evaluation techniques.\n\nThe approach is that the user defines a function which all the parameters that\nshould vary as inputs. In the next step, he defines a search space consisting\nof several random variables. She can then sample from that search space in order\nto pass those parameters into the function of interest.\n\nFor an example, see examples/neuralnetwork.py.\n"},{"alpha_fraction":0.6254545450210571,"alphanum_fraction":0.6618182063102722,"avg_line_length":31.352941513061523,"blob_id":"adfdfc2724b4342cc2a28e0e44ce861962be4e9e","content_id":"59e070e8f016387338167c1ef0b84f36609a533c","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":550,"license_type":"no_license","max_line_length":61,"num_lines":17,"path":"/examples/neuralnetwork.py","repo_name":"bayerj/Jaeger","src_encoding":"UTF-8","text":"# -*- coding: utf-8 -*-\n\nimport jaeger.searchspace as S\n\nsearch = S.SearchSpace()\nsearch.add('n_hidden', S.Uniform(10, 200, intify=True))\nsearch.add('momentum', S.Uniform(0.0, 0.99))\nsearch.add('step_rate', S.LogUniform(0.00001, 1))\nsearch.add('transfer_function', S.OneOf(['tanh', 'sigmoid']))\n\nfor i in range(10):\n sample = search.draw()\n print 'number of hiddens:', sample['n_hidden']\n print 'transfer function:', sample['transfer_function']\n print 'momentum:', sample['momentum']\n print 'step rate:', sample['step_rate']\n print\n"},{"alpha_fraction":0.5624270439147949,"alphanum_fraction":0.604434072971344,"avg_line_length":23.485713958740234,"blob_id":"8d8a40effd1e6e5aacdd82c56910eca867429ac4","content_id":"50e7e8e10d450fddc953ef11a373f910c7099ce1","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":857,"license_type":"no_license","max_line_length":70,"num_lines":35,"path":"/test/test_searchspace.py","repo_name":"bayerj/Jaeger","src_encoding":"UTF-8","text":"# -*- coding: utf-8 -*-\n\nimport numpy as np\n\nfrom jaeger.searchspace import Uniform, LogUniform, OneOf, SearchSpace\n\n\ndef test_uniform():\n x = Uniform(0, 1)\n seeds = np.random.random((1000, 1))\n draws = [x.draw(s) for s in seeds]\n assert all(0 <= i < 1 for i in draws)\n\n\ndef test_uniform_int():\n x = Uniform(0, 2, intify=True)\n seeds = np.random.random((1000, 1))\n draws = [x.draw(s) for s in seeds]\n assert all(i in (0, 1, 2) for i in draws)\n\n\ndef test_loguniform():\n x = LogUniform(1, 2)\n seeds = np.random.random((10, 1))\n draws = [x.draw(s) for s in seeds]\n print draws\n assert all(1 <= i < 2 for i in draws)\n\n\ndef test_loguniform_int():\n x = LogUniform(1, 2, intify=True)\n seeds = np.random.random((1000, 1))\n draws = [x.draw(s) for s in seeds]\n print draws\n assert all(i in (1, 2) for i in draws)\n"}],"string":"[\n {\n \"alpha_fraction\": 0.5528150200843811,\n \"alphanum_fraction\": 0.5560321807861328,\n \"avg_line_length\": 22.3125,\n \"blob_id\": \"3e57d77f589b368cf7f176f772a7cbc799adf6ff\",\n \"content_id\": \"d9a8f04766005fa2a217a7c6b38be1e1ed917ee6\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 1865,\n \"license_type\": \"no_license\",\n \"max_line_length\": 60,\n \"num_lines\": 80,\n \"path\": \"/jaeger/searchspace.py\",\n \"repo_name\": \"bayerj/Jaeger\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"# -*- coding: utf-8 -*-\\n\\n\\nimport numpy as np\\n\\n\\nclass Numeric(object):\\n\\n def __init__(self, intify=False):\\n self.intify = intify\\n\\n def draw(self, seed):\\n res = self._draw(seed)\\n if self.intify:\\n res = int(round(res))\\n return res\\n\\n\\nclass Uniform(Numeric):\\n\\n def __init__(self, lower, upper, intify=False):\\n self.size = 1\\n self.lower = lower\\n self.upper = upper\\n super(Uniform, self).__init__(intify)\\n\\n def _draw(self, seed):\\n # Pick only the first entry.\\n seed = seed[0]\\n scale = self.upper - self.lower\\n return seed * scale + self.lower\\n\\n\\nclass LogUniform(Numeric):\\n\\n def __init__(self, lower, upper, intify=False):\\n self.size = 1\\n self.lower = lower\\n self.upper = upper\\n self.uniform = Uniform(np.log(lower), np.log(upper))\\n super(LogUniform, self).__init__(intify)\\n\\n def _draw(self, seed):\\n drawn = self.uniform.draw(seed)\\n return np.exp(drawn)\\n\\n\\nclass OneOf(object):\\n\\n def __init__(self, choices):\\n self.size = len(choices)\\n self.choices = choices\\n\\n def draw(self, seed):\\n return self.choices[seed.argmax()]\\n\\n\\nclass SearchSpace(object):\\n\\n def __init__(self):\\n self.variables = []\\n\\n def add(self, handle, var):\\n self.variables.append((handle, var))\\n\\n def draw(self, seeds=None):\\n n_seeds = sum(i.size for _, i in self.variables)\\n if seeds is None:\\n seeds = np.random.random(n_seeds)\\n seeds = np.asarray(seeds)\\n if seeds.shape[0] != n_seeds:\\n raise ValueError('wrong dimension of seed')\\n\\n start = 0\\n sample = {}\\n for handle, v in self.variables:\\n stop = start + v.size\\n sample[handle] = v.draw(seeds[start:stop])\\n start = stop\\n return sample\\n\"\n },\n {\n \"alpha_fraction\": 0.8075539469718933,\n \"alphanum_fraction\": 0.8075539469718933,\n \"avg_line_length\": 41.769229888916016,\n \"blob_id\": \"32f2e5327d62e3b1d9672c1e389c16d09bfdf89d\",\n \"content_id\": \"6d9f0aa067260646f6ede4b7365c43f49af25841\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Markdown\",\n \"length_bytes\": 556,\n \"license_type\": \"no_license\",\n \"max_line_length\": 80,\n \"num_lines\": 13,\n \"path\": \"/README.md\",\n \"repo_name\": \"bayerj/Jaeger\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"Jaeger\\n------\\n\\nJaeger is a small package for performing random search on parameter spaces for\\nfunctions with a nice interface. I hope to extend it with Bayesian optimization\\nand distributed evaluation techniques.\\n\\nThe approach is that the user defines a function which all the parameters that\\nshould vary as inputs. In the next step, he defines a search space consisting\\nof several random variables. She can then sample from that search space in order\\nto pass those parameters into the function of interest.\\n\\nFor an example, see examples/neuralnetwork.py.\\n\"\n },\n {\n \"alpha_fraction\": 0.6254545450210571,\n \"alphanum_fraction\": 0.6618182063102722,\n \"avg_line_length\": 31.352941513061523,\n \"blob_id\": \"adfdfc2724b4342cc2a28e0e44ce861962be4e9e\",\n \"content_id\": \"59e070e8f016387338167c1ef0b84f36609a533c\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 550,\n \"license_type\": \"no_license\",\n \"max_line_length\": 61,\n \"num_lines\": 17,\n \"path\": \"/examples/neuralnetwork.py\",\n \"repo_name\": \"bayerj/Jaeger\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"# -*- coding: utf-8 -*-\\n\\nimport jaeger.searchspace as S\\n\\nsearch = S.SearchSpace()\\nsearch.add('n_hidden', S.Uniform(10, 200, intify=True))\\nsearch.add('momentum', S.Uniform(0.0, 0.99))\\nsearch.add('step_rate', S.LogUniform(0.00001, 1))\\nsearch.add('transfer_function', S.OneOf(['tanh', 'sigmoid']))\\n\\nfor i in range(10):\\n sample = search.draw()\\n print 'number of hiddens:', sample['n_hidden']\\n print 'transfer function:', sample['transfer_function']\\n print 'momentum:', sample['momentum']\\n print 'step rate:', sample['step_rate']\\n print\\n\"\n },\n {\n \"alpha_fraction\": 0.5624270439147949,\n \"alphanum_fraction\": 0.604434072971344,\n \"avg_line_length\": 23.485713958740234,\n \"blob_id\": \"8d8a40effd1e6e5aacdd82c56910eca867429ac4\",\n \"content_id\": \"50e7e8e10d450fddc953ef11a373f910c7099ce1\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 857,\n \"license_type\": \"no_license\",\n \"max_line_length\": 70,\n \"num_lines\": 35,\n \"path\": \"/test/test_searchspace.py\",\n \"repo_name\": \"bayerj/Jaeger\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"# -*- coding: utf-8 -*-\\n\\nimport numpy as np\\n\\nfrom jaeger.searchspace import Uniform, LogUniform, OneOf, SearchSpace\\n\\n\\ndef test_uniform():\\n x = Uniform(0, 1)\\n seeds = np.random.random((1000, 1))\\n draws = [x.draw(s) for s in seeds]\\n assert all(0 <= i < 1 for i in draws)\\n\\n\\ndef test_uniform_int():\\n x = Uniform(0, 2, intify=True)\\n seeds = np.random.random((1000, 1))\\n draws = [x.draw(s) for s in seeds]\\n assert all(i in (0, 1, 2) for i in draws)\\n\\n\\ndef test_loguniform():\\n x = LogUniform(1, 2)\\n seeds = np.random.random((10, 1))\\n draws = [x.draw(s) for s in seeds]\\n print draws\\n assert all(1 <= i < 2 for i in draws)\\n\\n\\ndef test_loguniform_int():\\n x = LogUniform(1, 2, intify=True)\\n seeds = np.random.random((1000, 1))\\n draws = [x.draw(s) for s in seeds]\\n print draws\\n assert all(i in (1, 2) for i in draws)\\n\"\n }\n]","isConversational":false},"num_files":{"kind":"number","value":4,"string":"4"}}},{"rowIdx":2977194,"cells":{"repo_name":{"kind":"string","value":"lindo-zy/TBspider"},"repo_url":{"kind":"string","value":"https://github.com/lindo-zy/TBspider"},"snapshot_id":{"kind":"string","value":"5f1a6df29977d08be83852ee6141100d15ba4a8f"},"revision_id":{"kind":"string","value":"ea640770c0c7473ffe498c4ee065b14850795af7"},"directory_id":{"kind":"string","value":"1f0c7bf2558cb9481993420fa723a152418b273f"},"branch_name":{"kind":"string","value":"refs/heads/master"},"visit_date":{"kind":"timestamp","value":"2020-08-01T20:07:48.995353","string":"2020-08-01T20:07:48.995353"},"revision_date":{"kind":"timestamp","value":"2019-10-16T15:04:10","string":"2019-10-16T15:04:10"},"committer_date":{"kind":"timestamp","value":"2019-10-16T15:04:10","string":"2019-10-16T15:04:10"},"github_id":{"kind":"number","value":211101359,"string":"211,101,359"},"star_events_count":{"kind":"number","value":0,"string":"0"},"fork_events_count":{"kind":"number","value":0,"string":"0"},"gha_license_id":{"kind":"null"},"gha_created_at":{"kind":"null"},"gha_updated_at":{"kind":"null"},"gha_pushed_at":{"kind":"null"},"gha_language":{"kind":"null"},"files":{"kind":"list like","value":[{"alpha_fraction":0.659375011920929,"alphanum_fraction":0.6625000238418579,"avg_line_length":15.8421049118042,"blob_id":"c9bc22c1d67fe192182e84361133e70c85aa231a","content_id":"ae11284cb23599d1287cc7ddd68ffbced220eb0a","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":320,"license_type":"no_license","max_line_length":52,"num_lines":19,"path":"/DataItem/DataItem/items.py","repo_name":"lindo-zy/TBspider","src_encoding":"UTF-8","text":"# -*- coding: utf-8 -*-\n\n# Define here the models for your scraped items\n#\n# See documentation in:\n# https://doc.scrapy.org/en/latest/topics/items.html\n\n\nfrom scrapy import Item, Field\n\n\nclass TBdetailItem(Item):\n result = Field()\n callback = Field()\n headers = Field()\n\n\nclass TestItem(Item):\n ip = Field()\n"},{"alpha_fraction":0.4871891438961029,"alphanum_fraction":0.4941597580909729,"avg_line_length":28.16483497619629,"blob_id":"9bf4ab3cb7a9f557732f75e7b9be6d5982b13f41","content_id":"d0c391bae23d7c40c8c66e82e7f561020994dbab","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":5550,"license_type":"no_license","max_line_length":112,"num_lines":182,"path":"/DataItem/redis_pool.py","repo_name":"lindo-zy/TBspider","src_encoding":"UTF-8","text":"#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n\nimport redis\nfrom config import *\nimport re\nimport json\nimport random\n\n\nclass RedisPool(object):\n\n def __init__(self, host=REDIS_HOST, port=REDIS_PORT, password=REDIS_PASSWORD, db=DB_NUM):\n\n pool = redis.ConnectionPool(host='127.0.0.1', port=6379, decode_responses=True, db=db,\n password=REDIS_PASSWORD)\n\n if password:\n self._db = redis.Redis(connection_pool=pool)\n else:\n self._db = redis.Redis(host=host, port=port, db=db, decode_responses=True)\n\n # 插入集合\n def set(self, name, value):\n '''\n redis.set('key', 'value')\n :param name:\n :param value:\n :return:\n '''\n self._db.set(name, value)\n\n # 批量插入\n def mset(self, mapping):\n '''\n redis.mset({\"key1\": 'value1', \"key2\": 'value2'})\n :param mapping:\n :return:\n '''\n self._db.mset(json.dumps(mapping))\n\n # 取某个key的值\n def get(self, name):\n '''\n red.get('key1')\n :param name:\n :return:\n '''\n return self._db.get(name=name)\n\n def hset(self, name, key, mapping):\n '''\n\n :param name:\n :param key:\n :param mapping:\n :return:\n '''\n status = self._db.hset(name, key, json.dumps(mapping))\n if int(status) == 0:\n print(f'{key}:更新数据成功！')\n else:\n print(f'{key}:插入数据成功！')\n\n def hget(self, name, key):\n '''\n :param name:\n :param key:\n :return:\n '''\n return self._db.hget(name, key)\n\n def hmget(self, name, keys_list):\n\n return self._db.hmget(name=name, keys=keys_list)\n\n def hgetall(self, name):\n '''\n :param name:\n :return:\n '''\n return self._db.hgetall(name)\n\n def zadd(self, name, value, score):\n mapping = {json.dumps(value): int(score)}\n self._db.zadd(name, mapping)\n\n def zrangebyscore(self, name, min, max, withscores=True):\n '''\n 按分数范围，从小到大排列\n :param name:\n :param min:\n :param max:\n :return:\n '''\n return self._db.zrangebyscore(name, min, max, withscores=withscores)\n\n def zrem(self, name, values):\n '''\n 删除有序集合中的值\n :param name:\n :param values:\n :return:\n '''\n self._db.zrem(name, json.dumps(values))\n\n def save_cookie(self, value, score=3):\n self.zadd(\"cookies_pool\", value, score)\n\n def save_account(self, account_list):\n # 录入账号\n try:\n if len(account_list) > 0:\n for account in account_list:\n username = account['username']\n password = account['password']\n print('账号', username, '密码', password)\n self._db.hset('accounts', username, password)\n else:\n print('请检查账号录入配置!')\n except Exception as e:\n print(f'请检查账号录入配置,错误信息{e}')\n\n def get_accounts(self):\n accounts_dict = self._db.hgetall('accounts')\n result = {}\n for user, password in accounts_dict.items():\n result[user] = password\n return result\n\n def update_pool(self, username, counter=1):\n # 先取出，再更新score\n # 返回列表\n result = self.zrangebyscore('cookies_pool', 0, 3)\n if result:\n for item in result:\n score = int(item[1]) - counter\n value = json.loads(item[0])\n if score >= 0 and value.get('username') == username:\n # 更新cookies\n self.zadd('cookies_pool', value=value, score=score)\n print('score更新成功！')\n elif score < 0 and value.get('username') == username:\n self.zrem('cookies_pool', value)\n else:\n print('cookies_pool为空！')\n\n def cookies_pool(self, account=''):\n result = self.zrangebyscore('cookies_pool', 0, 3)\n self.update_pool(account)\n cookie_list = []\n if result:\n for item in result:\n item_dict = json.loads(item[0])\n score = int(item[1])\n cookie_account = item_dict['username']\n # 返回其他有效cookies\n if cookie_account != account:\n cookie = item_dict['cookie']\n # # 做一步预处理，增加cookie可用度\n unb_random = ''.join(str(random.choice(range(9))) for _ in range(13))\n\n pattern = r'unb=(\\d+)'\n cookie_unb = re.sub(pattern=pattern, repl='unb=' + unb_random, string=cookie)\n\n cookie_token = ''.join(re.findall(r'_m_h5_tk=([^;]+);', cookie_unb)).split('_')[0]\n\n cookie_dict = dict([tuple(x.split('=', 1)) for x in cookie_unb.split(';')])\n\n cookie_list.append({'account': cookie_account, 'score': score, 'cookie_token': cookie_token,\n 'cookie_dict': cookie_dict, 'cookie_str': cookie_unb})\n else:\n return None\n return cookie_list[-1]\n else:\n print('cookies池为空！')\n return None\n\n\nif __name__ == '__main__':\n con = RedisPool()\n print(con.cookies_pool())\n"},{"alpha_fraction":0.4503311216831207,"alphanum_fraction":0.47965940833091736,"avg_line_length":14.731343269348145,"blob_id":"b0cf57e117dcb88786f6de33294319fbd3c70b7e","content_id":"d857afed3b836af56f9f7e629f6ab91a8e96578e","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Markdown","length_bytes":1457,"license_type":"no_license","max_line_length":94,"num_lines":67,"path":"/项目结构文档.md","repo_name":"lindo-zy/TBspider","src_encoding":"UTF-8","text":"## ShopSpider\n\n#### api\n\n此项目是Flask框架的web工程，运行起来是接受post数据，进行对指定店铺采集\n\n\n\ngunicorn -w 4 -b 0.0.0.0:8888 wsgi:app 部署flask工程\n\n测试的时候直接运行wisgi.py即可\n\n------\n\n#### DataItem\n\ndata_utils:爬虫工具目录\n\nspdiers：爬虫编写目录\n\n进入到TBspider/DataItem部署爬虫工程\n1.执行scrapyd命令\n2.新建一个命令行窗口，执行scrapyd-deploy TB -p DataItem\n3.控制台出现status:ok 表明部署成功\n\n详细参考:https://blog.csdn.net/web_9705/article/details/80417219\n\n------\n\n数据库配置\n\nredis配置:api/config.py和DataItem/spiderConfig.py\n\n\n------\n\n需要的相关账号\n\n| 新浪微博 | 密码 | 淘宝 | 密码 |\n| :---------- | ----------- | ----------- | ----------- |\n\n\n| 代理商 | 账号 | 密码 | 备注 |\n| ------------------------------------------------------- | ----------- | ----------- | ---- |\n\n| 打码平台 | 账号 | 密码 | 备注 |\n| ----------------------- | ------------ | ----------- | ---- |\n\n\n\n------\n\n完整运行流程\n\n1.运行/DataItem/data_utils/gen_login_cookie.py文件,生成至少一个登陆后的cookie\n2.使用scrapyd和scrapyd-deplpoy部署爬虫\n3.将所需采集的店铺信息发送至启动的服务\n\n------\n\n项目优化\n\n1.redis数据库使用db2和db3\n\n2.cookies过期太快\n\n------\n\n\n\n"},{"alpha_fraction":0.6597796082496643,"alphanum_fraction":0.6625344157218933,"avg_line_length":25.88888931274414,"blob_id":"3d15ae4a434fe234a9e650ece335e977cbc80e03","content_id":"34fdad9eeb7d543432f7c7b6cf1c741b87d46a4c","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":760,"license_type":"no_license","max_line_length":83,"num_lines":27,"path":"/DataItem/DataItem/pipelines.py","repo_name":"lindo-zy/TBspider","src_encoding":"UTF-8","text":"# -*- coding: utf-8 -*-\n\n# Define your item pipelines here\n#\n# Don't forget to add your pipeline to the ITEM_PIPELINES setting\n# See: https://doc.scrapy.org/en/latest/topics/item-pipeline.html\nimport requests\nimport json\nfrom DataItem.myLog import Log\n\nlogger = Log(log_name='pipeline')\n\n\nclass TestPipeline:\n def process_item(self, item, spider):\n data = item\n logger.info(f'开始回调给产品库：{data[\"callback\"]}')\n\n callback = data['callback']\n result = data['result']\n headers = data['headers']\n\n requests.packages.urllib3.disable_warnings()\n r = requests.post(url=callback, headers=headers, json=result, verify=False)\n\n logger.info(f'产品库响应内容：{r.text}')\n return item\n"},{"alpha_fraction":0.5195195078849792,"alphanum_fraction":0.6042513251304626,"avg_line_length":50.9357795715332,"blob_id":"bc7e9dd0ab199f4492bb885bd52ad21b73cd8f7f","content_id":"7275774b8c0cd58e859ed275cd71ff2dbd738f66","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":17843,"license_type":"no_license","max_line_length":259,"num_lines":327,"path":"/DataItem/DataItem/spiders/NewTbShopSpider.py","repo_name":"lindo-zy/TBspider","src_encoding":"UTF-8","text":"# -*- coding: utf-8 -*-\nimport re\nimport time\nimport hashlib\nimport json\nfrom html import unescape\nimport requests\nimport scrapy\nfrom scrapy import signals\nfrom scrapy.selector import Selector\nfrom DataItem.redis_pool import RedisPool\nfrom DataItem.myLog import Log\nfrom datetime import datetime\nimport random\nfrom urllib.parse import urlparse\nimport math\n\nlogger = Log(log_name='TBSpider')\n\n\nclass TbshopspiderSpider(scrapy.Spider):\n name = 'NewTbShopSpider'\n allowed_domains = ['taobao.com', 'tmall.com']\n\n custom_settings = {\n 'CONCURRENT_REQUESTS': 1,\n 'CONCURRENT_REQUESTS_PER_DOMAIN': 1,\n 'DOWNLOAD_DELAY': 1,\n 'REDIRECT_ENABLED': False,\n 'DOWNLOADER_MIDDLEWARES': {\n # 'DataItem.middlewares.ProxyDownloaderMiddleware': 610,\n 'DataItem.middlewares.CookiesPoolMiddleware': 610, # cookie池\n # 'scrapy_crawlera.CrawleraMiddleware': 610\n }\n }\n\n def __init__(self, **kwargs):\n\n logger.info(f'----------------开始采集,{datetime.now()}----------------')\n\n # 初始化redis\n self.rdb = RedisPool()\n self.reds_conn = self.rdb.redis_con()\n\n # 获取店铺所有商品id,价格,名称\n self.product_list_api = '/i/asynSearch.htm?mid=w-{0}-0&wid={0}&pageNo='\n\n self.shop_info_api = 'http://shop.m.taobao.com/shop/shopsearch/search_page_json.do?sort=default&type=all&q='\n\n self.shop_detail_api = ('http://h5api.m.taobao.com/h5/mtop.taobao.geb.shopinfo.queryshopinfo/2.0/?jsv=2.4.2'\n '&appKey={appkey}&t={t}&sign={sign}&api=mtop.taobao.geb.shopinfo.queryshopinfo&v=2.0'\n '&type=originaljson&timeout=3000&AntiCreep=true&dataType=json&H5Request=true&data={data}')\n\n # 计算淘宝sign的appkeys\n self.appkeys = '12574478'\n self.cookie_pool = self.con.cookies_pool()\n self.account = self.cookie_pool['account']\n self.token = self.cookie_pool['cookie_token']\n self.cookies = self.cookie_pool['cookie_dict']\n logger.info(f'当前使用的cookies的账号为:{self.account}')\n\n # 存储全部商品id\n self.all_products_list = []\n\n # todo 正式 接受的参数\n self.shopName = kwargs.get('shop_name')\n self.sellerName = kwargs.get('seller_name')\n self.shopId = kwargs.get('shop_id')\n self.shopLink = kwargs.get('shop_link')\n\n self.chrome_agents = [\n \"Mozilla/5.0 (Windows NT 6.1) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/41.0.2228.0 Safari/537.36\",\n \"Mozilla/5.0 (Macintosh; Intel Mac OS X 10_10_1) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/41.0.2227.1 Safari/537.36\",\n \"Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/41.0.2227.0 Safari/537.36\",\n \"Mozilla/5.0 (Windows NT 6.1; WOW64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/41.0.2227.0 Safari/537.36\",\n \"Mozilla/5.0 (Windows NT 6.3; WOW64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/41.0.2226.0 Safari/537.36\",\n \"Mozilla/5.0 (Windows NT 6.4; WOW64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/41.0.2225.0 Safari/537.36\",\n \"Mozilla/5.0 (Windows NT 6.3; WOW64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/41.0.2225.0 Safari/537.36\",\n \"Mozilla/5.0 (Windows NT 5.1) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/41.0.2224.3 Safari/537.36\",\n \"Mozilla/5.0 (Windows NT 10.0) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/40.0.2214.93 Safari/537.36\",\n \"Mozilla/5.0 (Macintosh; Intel Mac OS X 10_10_1) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/37.0.2062.124 Safari/537.36\",\n \"Mozilla/5.0 (Windows NT 6.3; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/37.0.2049.0 Safari/537.36\",\n \"Mozilla/5.0 (Windows NT 4.0; WOW64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/37.0.2049.0 Safari/537.36\",\n \"Mozilla/5.0 (Windows NT 6.1; WOW64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/36.0.1985.67 Safari/537.36\",\n \"Mozilla/5.0 (Windows NT 5.1) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/36.0.1985.67 Safari/537.36\",\n \"Mozilla/5.0 (X11; OpenBSD i386) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/36.0.1985.125 Safari/537.36\",\n \"Mozilla/5.0 (Macintosh; Intel Mac OS X 10_9_2) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/36.0.1944.0 Safari/537.36\",\n \"Mozilla/5.0 (Windows NT 5.1) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/35.0.3319.102 Safari/537.36\",\n \"Mozilla/5.0 (Windows NT 5.1) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/35.0.2309.372 Safari/537.36\",\n \"Mozilla/5.0 (Windows NT 5.1) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/35.0.2117.157 Safari/537.36\",\n \"Mozilla/5.0 (Macintosh; Intel Mac OS X 10_9_3) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/35.0.1916.47 Safari/537.36\",\n \"Mozilla/5.0 (Windows NT 5.1) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/34.0.1866.237 Safari/537.36\",\n \"Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/34.0.1847.137 Safari/4E423F\",\n \"Mozilla/5.0 (Windows NT 5.1) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/34.0.1847.116 Safari/537.36 Mozilla/5.0 (iPad; U; CPU OS 3_2 like Mac OS X; en-us) AppleWebKit/531.21.10 (KHTML, like Gecko) Version/4.0.4 Mobile/7B334b Safari/531.21.10\",\n \"Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/33.0.1750.517 Safari/537.36\",\n \"Mozilla/5.0 (Windows NT 6.2; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/32.0.1667.0 Safari/537.36\",\n \"Mozilla/5.0 (Macintosh; Intel Mac OS X 10_9_0) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/32.0.1664.3 Safari/537.36\",\n \"Mozilla/5.0 (Macintosh; Intel Mac OS X 10_8_0) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/32.0.1664.3 Safari/537.36\",\n \"Mozilla/5.0 (Windows NT 5.1) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/31.0.1650.16 Safari/537.36\",\n \"Mozilla/5.0 (Windows NT 6.1; WOW64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/31.0.1623.0 Safari/537.36\",\n \"Mozilla/5.0 (Windows NT 6.2; WOW64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/30.0.1599.17 Safari/537.36\",\n \"Mozilla/5.0 (Windows NT 6.1; WOW64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/29.0.1547.62 Safari/537.36\",\n \"Mozilla/5.0 (X11; CrOS i686 4319.74.0) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/29.0.1547.57 Safari/537.36\",\n \"Mozilla/5.0 (Windows NT 6.2; WOW64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/29.0.1547.2 Safari/537.36\",\n \"Mozilla/5.0 (Windows NT 6.1) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/28.0.1468.0 Safari/537.36\",\n \"Mozilla/5.0 (Windows NT 6.2) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/28.0.1467.0 Safari/537.36\",\n \"Mozilla/5.0 (Windows NT 6.2) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/28.0.1464.0 Safari/537.36\",\n \"Mozilla/5.0 (Windows NT 6.2; WOW64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/27.0.1500.55 Safari/537.36\",\n \"Mozilla/5.0 (Windows NT 6.2; WOW64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/27.0.1453.93 Safari/537.36\",\n \"Mozilla/5.0 (Windows NT 6.1; WOW64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/27.0.1453.93 Safari/537.36\",\n \"Mozilla/5.0 (Windows NT 6.1) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/27.0.1453.93 Safari/537.36\",\n \"Mozilla/5.0 (Windows NT 5.1) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/27.0.1453.93 Safari/537.36\",\n \"Mozilla/5.0 (Macintosh; Intel Mac OS X 10_8_3) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/27.0.1453.93 Safari/537.36\",\n \"Mozilla/5.0 (Macintosh; Intel Mac OS X 10_7_5) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/27.0.1453.93 Safari/537.36\",\n \"Mozilla/5.0 (Windows NT 6.1) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/27.0.1453.90 Safari/537.36\",\n \"Mozilla/5.0 (X11; NetBSD) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/27.0.1453.116 Safari/537.36\",\n \"Mozilla/5.0 (X11; CrOS i686 3912.101.0) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/27.0.1453.116 Safari/537.36\",\n \"Mozilla/5.0 (Windows NT 6.1; WOW64) AppleWebKit/537.17 (KHTML, like Gecko) Chrome/24.0.1312.60 Safari/537.17\",\n \"Mozilla/5.0 (Macintosh; Intel Mac OS X 10_8_2) AppleWebKit/537.17 (KHTML, like Gecko) Chrome/24.0.1309.0 Safari/537.17\",\n \"Mozilla/5.0 (Windows NT 6.2; WOW64) AppleWebKit/537.15 (KHTML, like Gecko) Chrome/24.0.1295.0 Safari/537.15\",\n \"Mozilla/5.0 (Windows NT 6.2; WOW64) AppleWebKit/537.14 (KHTML, like Gecko) Chrome/24.0.1292.0 Safari/537.14\"\n ]\n\n super().__init__(**kwargs)\n\n def start_requests(self):\n\n shop_search_url = 'https://' + urlparse(self.shopLink).netloc\n\n # todo 请求店铺所有商品,直接请求？\n r = requests.get(shop_search_url + '/search.htm')\n\n select = Selector(text=r.text)\n\n if 'tmall.com' in shop_search_url:\n # wid = select.css('#hd > div::attr(data-widgetid)').get()\n wid = int(select.css('#bd > div::attr(data-widgetid)').get()) + 1 # 天猫 10月更新\n self.product_list = shop_search_url + self.product_list_api.format(wid)\n\n elif 'taobao.com' in self.shopLink:\n # 权值id\n wid = select.css('[data-title=宝贝列表]::attr(data-widgetid)').get()\n\n self.product_list = shop_search_url + self.product_list_api.format(wid)\n\n json_url = self.shop_info_api + self.shopName\n\n yield scrapy.Request(url=json_url, callback=self.parse_search)\n\n # 搜索店铺信息并匹配\n def parse_search(self, response):\n\n self.result = {}\n doc = unescape(response.text)\n # json接口内容 参考文件:接口内容.json\n json_data = json.loads(doc)\n\n if json_data.get('listItem'):\n shop_info = json_data['listItem'][0]['shop']\n # 店铺地区\n self.result['shop_link'] = self.shopLink\n self.result['shop_area'] = json_data['listItem'][0]['area']\n self.result['shop_logo'] = shop_info.get('logo')\n self.result['shop_name'] = self.shopName\n self.result['seller_name'] = self.sellerName\n self.result['shop_id'] = self.shopId\n\n if shop_info.get('isMall'):\n self.result['shop_type'] = '天猫'\n else:\n self.result['shop_type'] = '淘宝'\n shop_medal = json_data['listItem'][0].get('medal')\n # 动态评价接口\n self.result['shop_ajax'] = shop_medal.get('ajaxurl')\n # 卖家id 使用正则匹配出来\n self.result['seller_id'] = re.findall(r'userid=(\\d+)', shop_medal.get('ajaxurl'))[0]\n # 好评率\n self.result['shop_favRate'] = json_data['listItem'][0].get('favRate')\n else:\n logger.warning('店铺信息解析失败！请查看scrapyd日志获取详情！')\n\n # 随机轮换 User-Agent,因为是h5接口，要用手机浏览器带cookies访问\n iphone_headers = [\n 'Mozilla/5.0 (iPhone; CPU iPhone OS 11_0 like Mac OS X) AppleWebKit/604.1.38 (KHTML, like Gecko) Mobile/15A372 MicroMessenger/6.5.16 NetType/WIFI Language/zh_CN',\n 'Mozilla/5.0 (iPhone; CPU iPhone OS 11_0 like Mac OS X) AppleWebKit/604.1.38 (KHTML, like Gecko) Mobile/15A372 wxwork/2.1.5 MicroMessenger/6.3.22',\n 'Mozilla/5.0 (iPhone 6s; CPU iPhone OS 11_0 like Mac OS X) AppleWebKit/604.1.38 (KHTML, like Gecko) Version/11.0 MQQBrowser/7.7.2 Mobile/15A372 Safari/8536.25 MttCustomUA/2 QBWebViewType/1',\n 'Mozilla/5.0 (iPhone; CPU iPhone OS 11_0_1 like Mac OS X) AppleWebKit/604.1.38 (KHTML, like Gecko) Mobile/15A402 MicroMessenger/6.5.16 NetType/WIFI Language/zh_CN'\n ]\n\n headers = {'user-agent': random.choice(iphone_headers)}\n\n data = '{\"sellerId\": %s}' % self.re_dt.get('sellerid')\n cookies = self.cookies\n token = self.token\n account = self.account\n # 这部分只需要更换cookies，一般能访问成功，因为只访问一次接口\n if response.meta.get('change_cookie'):\n logger.info('-----------------更换cookies-----------------')\n cookie_pool = self.con.cookies_pool(account)\n\n account = cookie_pool['account']\n token = cookie_pool['cookie_token']\n cookies = cookie_pool['cookie_dict']\n cookie_str = cookie_pool['cookie_str']\n logger.info(f'更换后的cookies的账号为:{account}')\n\n data = '{\"sellerId\": %s}' % self.result.get('seller_id')\n\n decrypt = self.sign(token=token, appkey=self.appkeys, t=str(int(time.time() * 1000)),\n data=data)\n\n url = self.shop_detail_api.format(**decrypt)\n\n yield scrapy.Request(url=url, headers=headers,\n cookies=cookies, callback=self.parse_store_item, meta={'account': account})\n\n # 进一步抓取店铺信息\n def parse_store_item(self, response):\n\n # 参考:接口2.json\n doc = json.loads(response.text)\n\n # 判断是否拿到了接口数据\n status = doc.get('ret')\n logger.info(f'json接口响应状态{status}')\n if 'SUCCESS' not in status[0]:\n yield scrapy.Request(url=self.json_url, callback=self.parse_search, dont_filter=True,\n meta={'change_cookie': True})\n\n json_data = doc.get('data')\n self.count = json_data.get('itemCount') # 商品总数\n self.result['fans_num'] = str(json_data.get('fansNum')) # 粉丝数\n self.result['item_count'] = str(self.count) # 产品数量\n self.result['new_item'] = str(json_data.get('newItem')) # 新品数\n if json_data.get('goldenSeller'):\n self.result['golden_seller'] = '金牌卖家'\n else:\n self.result['golden_seller'] = ''\n\n logger.info(f'店铺信息:{self.result}')\n\n self.reds_conn.hset('shop_info', self.shopName, json.dumps(self.result))\n if 'tmall.com' in self.product_list:\n totalPages = math.ceil(int(self.count) / 90) + 1 # 向上取整\n elif 'taobao.com' in self.product_list:\n totalPages = math.ceil(int(self.count) / 24) + 1 # 向上取整\n logger.info(f'总共需要采集的页面有：{totalPages}页')\n\n for page in range(1, totalPages + 1):\n url = self.product_list + str(page)\n # 预处理cookies\n headers = {\n 'User-Agent': random.choice(self.chrome_agents)\n }\n time.sleep(1)\n yield scrapy.Request(url=url, headers=headers, callback=self.parse_productId,\n meta={'with_cookie': True})\n\n def parse_productId(self, response):\n '''\n 解析当前商品详情页中的所有商品id\n :param response:\n :return:\n '''\n headers = {\n 'User-Agent': random.choice(self.chrome_agents)\n }\n if 'rgv587_flag' in response.text:\n yield scrapy.Request(url=response.request.url, headers=headers, callback=self.parse_productId,\n meta={'with_cookie': True, 'block': True})\n\n text = response.text.replace('\\\\\"', '')\n s = Selector(text=text)\n\n products_id_list = s.css('.J_TItems div .item::attr(data-id)').getall()\n\n if len(products_id_list) == 0:\n products_id_list = s.css('.shop-hesper-bd .item::attr(data-id)').getall()\n\n logger.info(f'当前页商品id:{products_id_list}')\n\n # 存储全部商品\n self.all_products_list.extend(products_id_list)\n\n # 根据所需元素计算sign\n def sign(self, token, appkey, t, data):\n '''\n :param token:\n :param appkey:\n :param t: str(int(time.time() * 1000))\n :param data:\n :return:\n '''\n pp = '&'.join([token, t, appkey, data]).encode()\n sign = hashlib.md5(pp).hexdigest()\n return {'sign': sign, 't': t, 'appkey': appkey, 'data': data}\n\n # 店铺爬虫结束后,数据存入redis并调用商品详情爬虫\n def close(self, spider, reason):\n try:\n # 去重存储所有商品id\n all_products_id = list(set(self.all_pids))\n logger.info(all_products_id)\n\n logger.info(f'采集到了{len(all_products_id)}条商品数据')\n lose_count = int(self.count) - len(all_products_id)\n if lose_count > 0:\n logger.warning(f'采集缺失：{lose_count},缺失百分比：{lose_count / int(self.count) * 100}%')\n\n data = json.dumps({'product_counts': len(all_products_id), 'product_id': all_products_id})\n\n # redis 存储抓取的全部商品id\n self.reds_conn.hset('products_id', self.shopName, data)\n\n # 启动详情爬虫的参数\n post_data = {'project': 'DataItem', 'spider': 'TbDetailSpiderNew', 'shop_name': self.shopName,\n 'shop_id': self.shopId}\n\n # 调用淘宝详情爬虫\n r = requests.post('http://localhost:6800/schedule.json', data=post_data, timeout=50)\n if r.status_code == 200:\n logger.info(f'传递给详情采集爬虫的参数为:{post_data}')\n logger.info(f'详情采集爬虫调用状态码:{r.status_code},响应内容:{json.loads(r.text)}')\n else:\n logger.warning(f'启用详情爬虫失败！状态码:{r.status_code}')\n except Exception as e:\n logger.warning(f'启用详情爬虫失败！错误信息{e}')\n"},{"alpha_fraction":0.5698432326316833,"alphanum_fraction":0.5819115042686462,"avg_line_length":36.546875,"blob_id":"de7aa386206f04cb21f540d371d98bb818f7dfdb","content_id":"b2047e40469e076e33b37d1fb01948a087f98711","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":8021,"license_type":"no_license","max_line_length":126,"num_lines":192,"path":"/DataItem/CookiesPool/generator.py","repo_name":"lindo-zy/TBspider","src_encoding":"UTF-8","text":"import json\nimport time\nimport requests\nimport random\nfrom urllib import parse\nfrom selenium.webdriver.chrome import options\nfrom selenium.webdriver.support.wait import WebDriverWait\nfrom selenium.webdriver.support import expected_conditions as EC\nfrom selenium.webdriver.common.by import By\nfrom selenium import webdriver\nfrom scrapy.selector import Selector\nfrom CookiesPool.apiLog import Log\nfrom CookiesPool.config import *\nfrom redis_pool import RedisPool\nfrom CookiesPool.verify import Yundama\n\nlogger = Log()\n\n'''\nchromedriver 根据自己的chrome版本下载配置\n\n\n本地使用，生成cookies\n1.登陆频繁微博会出现414,无法访问，一般封禁半小时内\n2.切换海外代理登陆，新浪微博需要打码，淘宝需要验证手机号 #不推荐使用\n3.手动获取cookies,添加到redis #todo\n\n\n1.尝试登陆移动端新浪\nhttps://passport.weibo.cn/signin/login?entry=mweibo&res=wel&wm=3349&r=https://m.weibo.cn/\n2.移动端登陆成功->淘宝关联登陆\n\n'''\n\n\nclass CookiesGenerator:\n\n def get_cookie(self, username, password):\n\n \"\"\"\n 使用微博关联登陆淘宝,进而从m端淘宝页面获取cookie\n \"\"\"\n\n option = options.Options()\n\n option.add_argument(\"disable-infobars\") # # 去除info框\n option.add_experimental_option('excludeSwitches', ['enable-automation']) # 开发者模式,防止被识别\n option.add_argument('log-level=3')\n # option.add_argument(\"--proxy-server=http://114.239.254.76:4236\") #添加代理\n # option.add_argument('--headless') # headless模式\n # option.add_argument(\"window-size=2436, 1125\")\n # option.add_argument(\"--no-sandbox\")\n\n # executable_path = os.path.join(os.getcwd(), 'chromedriver.exe') # chromedriver路径\n\n # browser = webdriver.Chrome(executable_path=executable_path, options=option)\n browser = webdriver.Chrome(options=option)\n\n browser.implicitly_wait(20) # 隐式加载\n try:\n browser.get('https://weibo.com/')\n browser.maximize_window()\n # 等待微博登录框加载完成后输入账号密码登陆\n wb_locator = '.login_innerwrap .W_login_form[node-type=normal_form] input[name=username]'\n WebDriverWait(browser, 300, 0.5).until(EC.presence_of_element_located((By.CSS_SELECTOR, wb_locator)))\n logger.info('当前步骤：输入账号密码！')\n browser.find_element_by_css_selector(\n '.login_innerwrap [node-type=normal_form] input[name=username]').send_keys(\n username)\n browser.find_element_by_css_selector(\n '.login_innerwrap [node-type=normal_form] input[name=password]').send_keys(\n password)\n # 增加延时\n time.sleep(3)\n\n yundama = Yundama()\n\n sel = Selector(text=browser.page_source)\n verify_img = sel.css('[action-type=btn_change_verifycode]::attr(src)').get()\n\n if not verify_img:\n browser.find_element_by_css_selector(\n '.login_innerwrap [node-type=normal_form] .W_btn_a').click()\n else:\n print('出现验证码，开始识别验证码')\n\n # 截图大法\n pic = browser.find_element_by_xpath('//*[@id=\"pl_login_form\"]/div/div[3]/div[3]/a/img')\n pic.screenshot('yzm.png') # 元素截图\n time.sleep(1)\n result = yundama.identify(file='yzm.png')\n if not result:\n print('验证码识别失败, 跳过识别')\n return\n else:\n browser.find_element_by_xpath('//*[@id=\"pl_login_form\"]/div/div[3]/div[3]/div/input').send_keys(result)\n time.sleep(1)\n browser.find_element_by_css_selector('.login_innerwrap [node-type=normal_form] .W_btn_a').click()\n\n logger.info('当前步骤：登陆淘宝!')\n # 等待微博登陆完成后转到淘宝登陆页面并点击使用微博登陆\n wb_login_locator = '.WB_feed_detail'\n WebDriverWait(browser, 300, 0.5).until(EC.presence_of_element_located((By.CSS_SELECTOR, wb_login_locator)))\n browser.get('https://login.taobao.com')\n try:\n browser.find_element_by_css_selector('#J_Quick2Static').click()\n except Exception as e:\n # 添加错误日志\n logger.warning(f'错误信息{e}')\n\n browser.find_element_by_css_selector('.weibo-login').click()\n time.sleep(1)\n # 判断是否有微博快速登录框出现,有则点击,无则输入微博密码登陆\n if browser.find_element_by_css_selector('.logged_info .W_btn_g'):\n # tb_submit_locator = '.logged_info .W_btn_g'\n # WebDriverWait(browser, 300, 0.5).until(EC.presence_of_element_located((By.CSS_SELECTOR, tb_submit_locator)))\n browser.find_element_by_css_selector('.logged_info .W_btn_g').click()\n elif browser.find_elements_by_css_selector('[node-type=submitStates]'):\n browser.find_element_by_css_selector('.enter_psw').send_keys(password)\n browser.find_element_by_css_selector('[node-type=submitStates]').click()\n return\n\n time.sleep(2)\n # 等待淘宝登陆完成后转入淘宝m端首页\n tb_locator = '.logo-bd'\n WebDriverWait(browser, 300, 0.5).until(EC.presence_of_element_located((By.CSS_SELECTOR, tb_locator)))\n browser.get('https://h5.m.taobao.com')\n\n # 等待淘宝m端首页加载完成,获取cookie并存入redis\n m_tb_locator = '.header-bd'\n WebDriverWait(browser, 300, 0.5).until(EC.presence_of_element_located((By.CSS_SELECTOR, m_tb_locator)))\n cookies = browser.get_cookies()\n\n result = self.cookie_handler(cookies)\n\n cookie_str = result[0]\n\n logger.info('成功获取到cookies！')\n\n # 存储到redis->cookies_pool\n con = RedisPool()\n con.save_cookie({\"username\": username, \"cookie\": cookie_str})\n\n browser.quit()\n\n # 传输cookies,当前工程在本地，则传输cookies到线上\n # if LOCAL:\n # url = 'http://47.56.68.237:8888/recieve_cookie'\n # headers = {\"Content-Type\": \"application/json\"}\n # data = {\n # 'data': {'cookie': cookie_str, 'username': username, \"token\": \"ce934bc118beedabd789ed5cf6a20dc7\"}}\n # try:\n # r = requests.post(url=url, headers=headers, json=data)\n #\n # logger.info(f'状态码：{r.status_code},响应内容:{json.loads(r.text)}')\n # except Exception as e:\n # logger.warning(f'cookies传输出错,{e}')\n\n except Exception as e:\n logger.warning(f'cookie获取失败: {e}')\n\n finally:\n browser.quit()\n # 下次获取cookies\n # 加入随机等待时间减少被反爬识别概率\n # todo 登录多了,会封Ip\n time.sleep(random.randint(1, 4))\n\n def cookie_handler(self, cookie_list):\n cookie_dict = {}\n cookie_ls = []\n for item in cookie_list:\n cookie_dict[item['name']] = item['value']\n temp = item['name'] + \"=\" + item['value']\n cookie_ls.append(temp)\n\n cookie_str = ';'.join(item for item in cookie_ls)\n return cookie_str, cookie_dict\n\n def run(self):\n # 从redis中取出账号和密码\n con = RedisPool()\n accounts_dict = con.get_accounts()\n\n for username, password in accounts_dict.items():\n logger.info(f'当前生成cookies账号为:{username},密码：{password}')\n self.get_cookie(username, password)\n\n\nif __name__ == '__main__':\n generator = CookiesGenerator()\n generator.run()\n"},{"alpha_fraction":0.5117599368095398,"alphanum_fraction":0.5660989284515381,"avg_line_length":25.80434799194336,"blob_id":"534af19577e504cf9200261ba6e3fe522de3ceed","content_id":"68e46df33bc0c2527360cb83592991617d9d261c","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":1307,"license_type":"no_license","max_line_length":111,"num_lines":46,"path":"/DataItem/DataItem/spiderConfig.py","repo_name":"lindo-zy/TBspider","src_encoding":"UTF-8","text":"#!/usr/bin/python3\n# -*- coding:utf-8 -*-\n'''\n相关配置文件\n'''\n\n# php接口回调设置\n\n# 本地测试\n# php_api = {\n# \"http://127.0.0.1/api/screen/shop\": {\n# \"url\": \"http://127.0.0.1/api/screen/product\",\n# \"result\": \"http://127.0.0.1/api/screen/result\",\n# \"headers\": {\n# \"api-key\": \"admin@admin.com\",\n# \"api-token\": \"g/l+OjehOxCdfKnaC+WeHuZ4fo7rODXTIOH3ftUZtaY=\",\n# \"Content-Type\": \"application/json\"\n# }\n# }\n# }\n\n# 测试服\nSHOP_CALLBACK = r'http://127.0.0.1/api/screen/shop'\nPRODUCT_CALLBACK = r\"https://pr.test.puget.work/api/screen/product\"\nRESULT_CALLBACK = r'https://pr.test.puget.work/api/screen/result'\nAPI_KEY = 'admin@admin.com'\nAPI_TOKEN = 'g/l+OjehOxCdfKnaC+WeHuZ4fo7rODXTIOH3ftUZtaY='\nHEADERS_CALLBACK = {\"api-key\": API_KEY,\n \"api-token\": API_TOKEN,\n \"Content-Type\": \"application/json\",\n 'user-agent': \"Mozilla/5.0 (Windows NT 6.2; WOW64) AppleWebKit/537.14 (KHTML, like Gecko) \"\n \"Chrome/24.0.1292.0 Safari/537.14 \"}\n\n# 测试服\n# REDIS_PASSWORD = 'WpRWHOb9rinQXbwa'\n# 本地\nREDIS_PASSWORD = ''\n# Redis数据库地址\nREDIS_HOST = '127.0.0.1'\n\n# Redis端口\nREDIS_PORT = 6379\n\n# Redis密码，如无填None\nREDIS_PASSWORD = ''\nDB_NUM = 0\n"},{"alpha_fraction":0.6492537260055542,"alphanum_fraction":0.6567164063453674,"avg_line_length":6.388888835906982,"blob_id":"b794f2de2d8715a0d6631e7b11b194a837e8efce","content_id":"6eb9f5256be111001d4dcc10b2c5c73501cf4f16","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Markdown","length_bytes":178,"license_type":"no_license","max_line_length":27,"num_lines":18,"path":"/DataItem/CookiesPool/README.md","repo_name":"lindo-zy/TBspider","src_encoding":"UTF-8","text":"# CookiesPool / Cookies池\n\n\n## 基础配置 \n\n修改cookiespool/config.py\n\n\n\n### 进程开关\n\n配置信息到cookiespool/config文件修改\n\n## 运行\n\n```\npython3 run.py\n```\n\n"},{"alpha_fraction":0.5256490111351013,"alphanum_fraction":0.5344677567481995,"avg_line_length":34.62389373779297,"blob_id":"c6e18f81cbaa79f8c0625eaf45824e130645c06b","content_id":"f3c829346c7db57a3bf60a5154986de05dc7d2c3","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":8657,"license_type":"no_license","max_line_length":139,"num_lines":226,"path":"/DataItem/DataItem/spiders/ProductSpider.py","repo_name":"lindo-zy/TBspider","src_encoding":"UTF-8","text":"# -*- coding: utf-8 -*-\nimport re\nimport datetime\nimport json\nimport scrapy\nfrom DataItem.redis_pool import RedisPool\nfrom bs4 import BeautifulSoup\nfrom DataItem.myLog import Log\nfrom scrapy.selector import Selector\nimport random\n\nlogger = Log(log_name='DetailSpider')\n\n\nclass ProductSpider(scrapy.Spider):\n name = 'ProductSpider'\n\n allowed_domains = ['*']\n # 开启scrapyhub代理 备用\n crawlera_enabled = True\n crawlera_apikey = ''\n\n # todo 自定义设置,调优\n custom_settings = {\n 'CONCURRENT_REQUESTS': 5,\n 'CONCURRENT_REQUESTS_PER_DOMAIN': 5, # 单个ip最大并发量\n 'AUTOTHROTTLE_ENABLED': False, # 限流器\n 'DOWNLOADER_MIDDLEWARES': {\n # 'DataItem.middlewares.ProxyDownloaderMiddleware': 610, #开启阿布云\n 'scrapy_crawlera.CrawleraMiddleware': 610\n }\n }\n\n def __init__(self, **kwargs):\n logger.info(f'----------------开始采集,{datetime.datetime.now()}----------------')\n\n self.reds_conn = RedisPool().redis_con()\n\n self.product_detail_api = r'https://h5api.m.taobao.com/h5/mtop.taobao.detail.getdetail/6.0/?data=%7B%22itemNumId%22%3A%22{0}%22%7D'\n\n # 初始化详情正则对象\n self.img_cpl = re.compile(r'.*?\"img\":\"(https://.*?\\.jpg)', re.DOTALL) # 匹配详情图\n self.img_cpl2 = re.compile(r'.*?image\":{\"itemImages\":\\[(.*?)\\].*', re.DOTALL) # 匹配详情图\n self.desc_cpl = re.compile(r'.*?descUrl\".*?\"(.*?)\"', re.DOTALL) # 匹配详情图入口url\n self.all_imgs = [] # 所有图片的详情\n\n self.job_id = kwargs.get('_job', '1') # 此爬虫的job_id\n\n # 店铺名称\n # self.shopName = kwargs.get('shop_name')\n # self.shopid = kwargs.get('shop_id')\n\n self.productIdList = kwargs.get('productId_list')\n\n self.result = {}\n\n super().__init__(**kwargs)\n\n # 开始解析\n def start_requests(self):\n\n product_list = self.productIdList\n\n n = 0\n for product_id in product_list:\n n += 1\n logger.info(f'开始采集第{n}个商品,id为：{product_id}')\n\n product_detail_api = r'https://h5api.m.taobao.com/h5/mtop.taobao.detail.getdetail/6.0/?data=%7B%22itemNumId%22%3A%22{}%22%7D'\n\n # 构建商品详情接口\n url = product_detail_api.format(product_id)\n # 增加cookies\n cookies_dict = self.reds_conn.hgetall('queue_cookie')\n cookies_list = list(cookies_dict.values())\n\n cookie = cookies_list[-1]\n\n unb_random = ''.join(str(random.choice(range(9))) for _ in range(13))\n\n pattern = r'unb=(\\d+)'\n temp = re.sub(pattern=pattern, repl='unb=' + unb_random, string=cookie)\n ls = temp.split(';')\n cookies = {}\n for i in ls:\n key, value = i.split('=', 1)\n cookies[key] = value\n\n yield scrapy.Request(url=url, callback=self.parse_tb, dont_filter=True, cookies=cookies,\n meta={'url': url, 'slug': product_id, 'request_tp': 'store'})\n\n # 解析淘宝网页\n def parse_tb(self, response):\n try:\n # 参考:商品详情json\n product_detail_json = json.loads(response.text)\n\n data = product_detail_json['data']\n\n slug = response.meta.get('slug') # 淘宝的商品ID\n\n # 响应失败标志\n\n status = product_detail_json['ret'][0]\n if '成功' not in status:\n logger.warning(f'{response.request.url},商品列表采集失败！响应内容：{product_detail_json.get(\"ret\")}')\n return\n\n # 商品信息\n product_info = {}\n\n # 商品名称\n product_info['product_name'] = data['item'].get('title')\n # 商品id\n product_info['product_id'] = data['item'].get('itemId')\n # 商品类别id\n product_info['product_categoryId'] = data['item'].get('categoryId')\n\n site_type = data['params']['trackParams']['BC_type'] # B为天猫C为淘宝\n # 商品源url,区分B-淘宝,C-天猫\n if site_type == 'B':\n source_url = \"https://detail.tmall.com/item.htm?id={0}\".format(slug)\n else:\n source_url = \"https://item.taobao.com/item.htm?id={0}\".format(slug)\n\n product_info['product_url'] = source_url\n\n price_info = json.loads(data['apiStack'][0]['value']) # 价格信息\n\n purchase_price = price_info['price']['price']['priceText'].split('-')[-1] # 实际价格\n\n # 销量\n sell_count = price_info.get('item').get('sellCount') if price_info.get('item', dict()).get(\n 'sellCount') else 0\n\n purchase_list_price = price_info['price'].get('extraPrices')\n\n purchase_list_price = purchase_list_price[0]['priceText'].split('-')[\n -1] if purchase_list_price else purchase_price # 市场价\n\n item = data['item'] # 商品信息\n\n skuBase = data['skuBase']\n option_values = skuBase.get('props', {})\n\n tmall_descurl = f\"https:{item['tmallDescUrl']}\" # 商品图片详情页 origin\n\n images = item['images'] # 缩略图信息\n feature_image_list = [f'https:{u}' for u in images]\n\n props = data.get('props').get('groupProps')[0].get('基本信息')\n\n # todo 重写\n opt = [{'name': i['name'], 'values': [{'name': j['name'],\n 'thumb': (f\"http:{j.get('image')}\" if not j.get('image').startswith(\n 'http') else j.get('image')) if j.get('image') else ''} for j in\n i['values']]} for i in option_values][::-1] if option_values else {}\n\n product_info['product_price'] = purchase_price # 淘宝价\n product_info['product_sale_price'] = purchase_list_price # 定价\n\n product_info['product_props'] = props # 商品属性集合\n\n product_info['product_options'] = opt # 商品选项集合\n product_info['product_sell_count'] = sell_count # 商品销量\n\n product_info['product_images'] = feature_image_list # 图片集合\n\n sku_price_info = price_info['skuCore']['sku2info'] # sku价格\n\n product_info['product_sku_prices'] = sku_price_info\n\n # 抓取页面图片详情\n yield scrapy.Request(tmall_descurl,\n callback=self.parse_detail,\n meta={'product_info': product_info, 'slug': slug},\n dont_filter=True)\n except Exception as e:\n logger.warning(f'错误信息{e}')\n\n # 解析详情接口,判断是否有图片接口存在,如果存在则进一步抓取图片\n def parse_detail(self, response):\n\n product_info = response.meta.get('product_info')\n\n product_id = response.meta.get('slug')\n # 详情html\n product_describe = response.text\n\n product_info['product_describe'] = product_describe\n\n # 详情接口\n descurl = 'https://h5api.m.taobao.com/h5/mtop.taobao.detail.getdesc/6.0/?jsv=2.4.11&data={\"id\":\"%s\",\"type\":\"1\"}' % (\n product_id)\n\n yield scrapy.Request(url=descurl, callback=self.parse_img_new,\n meta={'product_info': product_info},\n dont_filter=True)\n\n # 获取图片\n def parse_img_new(self, response):\n product_info = response.meta.get('product_info')\n text = json.loads(response.text).get('data').get('pcDescContent')\n selector = Selector(text=text)\n image_urls = selector.css('img::attr(src)').getall()\n\n if not image_urls:\n soup = BeautifulSoup(text, 'lxml')\n if soup.find_all('img'):\n for link in soup.find_all('img'):\n image_urls.append(link.get(\"src\"))\n else:\n logger.warning(f'{response.request.url}:没有图片！')\n\n image_urls = [f'https:{img}' for img in image_urls]\n\n product_info['product_images_urls'] = image_urls\n\n # 参考:商品详情2.json\n data = json.dumps(product_info)\n\n # 存储到数据库\n self.reds_conn.hset('product_info', product_info['product_id'], data)\n\n def close(self, spider, reason):\n logger.info(f'----------------全部商品采集完成,{datetime.datetime.now()}----------------')\n"},{"alpha_fraction":0.4673663377761841,"alphanum_fraction":0.47950148582458496,"avg_line_length":38.089744567871094,"blob_id":"582c121554880debb246f0d644598a50dc416a03","content_id":"defb21df619ca765863e41664d3f37de9ae325ac","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":6614,"license_type":"no_license","max_line_length":113,"num_lines":156,"path":"/DataItem/CookiesPool/api.py","repo_name":"lindo-zy/TBspider","src_encoding":"UTF-8","text":"import json\nimport requests\nfrom flask import Flask, request\nfrom scrapy import Selector\nfrom CookiesPool.apiLog import Log\nfrom redis_pool import RedisPool\n\n__all__ = ['app']\n\napp = Flask(__name__)\n\nlogger = Log()\n\n# 测试服\nTOKEN = 'ce934bc118beedabd789ed5cf6a20dc7'\n\n\n@app.route('/request_crawler', methods=[\"POST\"])\ndef request_crawler():\n if request.method == 'POST':\n params = request.json\n logger.info(f'接收到参数:{params}')\n data = params['data']\n con = RedisPool()\n # 执行请求scrapy爬虫\n if 'shop_link' in data.keys():\n try:\n url = data['shop_link']\n print('需要采集的店铺：', url)\n post_data = {}\n\n s_id = data['id']\n # todo 这个是什么排序？\n sort_type = data.get('sort_type', '_sale')\n\n post_data['project'] = 'DataItem'\n # 需要启动的爬虫\n post_data['spider'] = 'NewTbShopSpider'\n\n # 店铺在shop_fetch_tasks中的表\n post_data['s_id'] = s_id\n post_data['sort_type'] = sort_type\n r = requests.post('http://localhost:6800/schedule.json', data=post_data, timeout=50)\n logger.info('scrapyd状态:', r.text)\n # 爬虫的jobid\n jobid = r.json()['jobid']\n logger.info('当前任务ID:', jobid)\n con.hset('all_shop', s_id, json.dumps(data, ensure_ascii=False))\n\n con.hset('store', jobid, json.dumps(data, ensure_ascii=False))\n\n logger.info(f'采集爬虫启动成功！详情:{r.text}')\n logger.info({'code': 200, 'msg': '成功接收任务！'})\n return json.dumps({'code': 200, 'msg': '成功接收任务！'})\n except Exception as e:\n logger.info({'code': 500, 'msg': f'错误信息:{e}'})\n return json.dumps({'code': 500, 'msg': f'错误信息:{e}'})\n\n # 获取店铺信息\n elif 'url' in data.keys():\n try:\n url = data['url']\n print('需要采集的店铺：', url)\n r = requests.get(url)\n s = Selector(text=r.text)\n # 店铺id\n shopId = s.css('#LineZing::attr(shopid)').get()\n\n shopName = ''\n sellerName = ''\n\n # 获取淘宝店铺基本信息\n if 'taobao.com' in url:\n if s.css('.hd-shop-name a::text').get():\n shopName = s.css('.hd-shop-name a::text').get()\n elif s.css('.first-block .shop-name span::text').get():\n shopName = s.css('.first-block .shop-name span::text').get()\n else:\n shopName = ''\n if s.css('.tb-box-half.tb-seller-info label::text').get():\n sellerName = s.css('.tb-box-half.tb-seller-info label::text').get().strip()\n elif s.css('.seller-name::text').get():\n sellerName = s.css('.seller-name::text').get().strip('掌柜：')\n elif s.css('.shop-more-info p.info-item:nth-child(2)::text').get():\n sellerName = s.css('.shop-more-info p.info-item:nth-child(2)::text').get().strip()\n else:\n sellerName = ''\n\n # 获取天猫店铺信息\n elif 'tmall.com' in url:\n if s.css('.hd-shop-name a::text').get():\n shopName = s.css('.hd-shop-name a::text').get()\n else:\n shopName = s.css('.slogo-shopname strong::text').get()\n # 天猫默认 卖家和商铺同名\n sellerName = shopName\n\n # if s.css('.tb-box-half.tb-seller-info label::text').get():\n # sellerName = s.css('.tb-box-half.tb-seller-info label::text').get().strip()\n # else:\n # sellerName = s.css('.shopkeeper div a::text').get()\n\n # 商铺基本信息\n shopInfo = {'shopname': shopName, 'shopid': shopId, 'sellername': sellerName}\n print(f'采集店铺基本信息:{shopInfo}')\n\n con.hset('ShopInfoQuery', json.dumps(shopInfo, ensure_ascii=False), url)\n\n logger.info({'code': 200, 'msg': '店铺基本信息获取成功！', 'shop_info': shopInfo})\n return json.dumps({'code': 200, 'msg': '店铺基本信息获取成功！', 'shop_info': shopInfo}, ensure_ascii=False)\n except Exception as e:\n logger.info({'code': 500, 'msg': f'错误信息:{e}'})\n return json.dumps({'code': 500, 'msg': f'错误信息:{e}'})\n else:\n logger.info({'code': 500, 'msg': '任务接收失败，请检查参数！'})\n return json.dumps({'code': 500, 'msg': '任务接收失败，请检查参数！'})\n\n\n@app.route('/recieve_cookie', methods=[\"POST\"])\ndef recieve_cookie():\n if request.method == 'POST':\n try:\n params = json.loads(request.data)\n logger.info({'code': 200, 'msg': 'cookies接收成功！'})\n data = params['data']\n cookie = data['cookie']\n username = data['username']\n con = RedisPool()\n con.save_cookie(username, json.dumps({'cookie': cookie, 'counter': 3}))\n\n return json.dumps({'code': 200, 'msg': 'cookies接收成功！'})\n except Exception as e:\n logger.info({'code': 500, 'msg': f'错误信息{e}'})\n return json.dumps({'code': 500, 'msg': f'错误信息{e}'})\n\n\n@app.before_request\ndef before_request():\n if request.method == 'POST':\n try:\n jdata = json.loads(request.data)['data']\n if jdata.get('token') and TOKEN == jdata.get('token'):\n logger.info({'code': 200, 'msg': 'token 校验成功！'})\n else:\n logger.info({'code': 500, 'msg': 'token校验失败！'})\n return json.dumps({'code': 500, 'msg': 'token校验失败！'})\n except Exception as e:\n logger.info({'code': 500, 'msg': f'错误信息{e}'})\n return json.dumps({'code': 500, 'msg': f'错误信息{e}'})\n elif request.method == 'GET':\n logger.info('Flask工程开启成功！')\n return 'Flask工程开启成功！'\n\n\nif __name__ == '__main__':\n app.run(debug=True, host='0.0.0.0', port=8888)\n"},{"alpha_fraction":0.566919207572937,"alphanum_fraction":0.6439393758773804,"avg_line_length":15.5,"blob_id":"033cd7e6affd980c19a26955a3a29d1a39aaef57","content_id":"fe3878b7f58dadeb7032be297f11d696e3f4ce46","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":966,"license_type":"no_license","max_line_length":66,"num_lines":48,"path":"/DataItem/CookiesPool/config.py","repo_name":"lindo-zy/TBspider","src_encoding":"UTF-8","text":"# Redis数据库地址\nREDIS_HOST = '127.0.0.1'\n\n# Redis端口\nREDIS_PORT = 6379\n\n# Redis密码，如无填None\nREDIS_PASSWORD = ''\n\nDB_NUM = 0\n\n# 配置信息，无需修改\n# REDIS_DOMAIN = '*'\n# REDIS_NAME = '*'\n\n\n# 产生器和验证器循环周期\nCYCLE = 2400 # 秒\n\n# API地址和端口\nAPI_HOST = '127.0.0.1'\nAPI_PORT = 8888\n\n# 进程开关\n# 产生器，模拟登录添加Cookies\nGENERATOR_PROCESS = True\n\n# API接口服务\nAPI_PROCESS = True\n\n# 账号\nACCOUNTS = [{\"username\": '15625006690', \"password\": \"tmp11235\"},\n {\"username\": '18922373490', \"password\": \"tmp11235\"},\n {\"username\": '18922412249', \"password\": \"tmp11235\"}]\n\n# 区别本地和线上\nLOCAL = False\n\n# 云打码相关配置到yundama.com申请注册\nYUNDAMA_USERNAME = 'jianbai'\nYUNDAMA_PASSWORD = 'tmp11235'\nYUNDAMA_APP_ID = 1\nYUNDAMA_APP_KEY = '22cc5376925e9387a23cf797cb9ba745'\n\nYUNDAMA_API_URL = 'http://api.yundama.com/api.php'\n\n# 云打码最大尝试次数\nYUNDAMA_MAX_RETRY = 20\n"},{"alpha_fraction":0.5791420340538025,"alphanum_fraction":0.5791420340538025,"avg_line_length":26.59183692932129,"blob_id":"b9256543cda3f5d316a2c6f87490689a8abf110b","content_id":"e52f1a4dfdd0564f47b996ee9fc966dc32f02558","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":1480,"license_type":"no_license","max_line_length":72,"num_lines":49,"path":"/DataItem/CookiesPool/scheduler.py","repo_name":"lindo-zy/TBspider","src_encoding":"UTF-8","text":"from datetime import datetime\nfrom multiprocessing import Process\nfrom CookiesPool.api import app\nfrom CookiesPool.generator import *\nfrom redis_pool import RedisPool\nfrom CookiesPool.config import *\n\n\n# 调度器\nclass Scheduler(object):\n @staticmethod\n def generate_cookie(cycle=CYCLE):\n while True:\n\n try:\n generator = CookiesGenerator()\n generator.run()\n print(f'cookies生成结束！时间：{datetime.now()}')\n # 每一个cycle生成一轮cookies\n time.sleep(cycle)\n except Exception as e:\n print(f'cookies生成器发生错误,错误信息:{e}')\n\n @staticmethod\n def api():\n app.run(host=API_HOST, port=API_PORT)\n\n def run(self):\n # 导入账号\n con = RedisPool()\n con.save_account(ACCOUNTS)\n\n # 开启生成器\n if GENERATOR_PROCESS:\n print(f'开启cookies生成器！')\n generate_process = Process(target=Scheduler.generate_cookie)\n generate_process.start()\n\n # # 开启验证\n # if VALID_PROCESS:\n # logger.info(f'开启cookies验证！')\n # valid_process = Process(target=Scheduler.valid_cookie)\n # valid_process.start()\n\n # 开启flask工程\n if API_PROCESS:\n print(f'开启flask工程成功！')\n api_process = Process(target=Scheduler.api)\n api_process.start()\n"},{"alpha_fraction":0.5165562629699707,"alphanum_fraction":0.7086092829704285,"avg_line_length":14.100000381469727,"blob_id":"8b3d9457a31a3f82dde0227499e896f7cceaf2d8","content_id":"707d5b4a6de8a4e3a26075d22291e1e789f180bf","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Text","length_bytes":151,"license_type":"no_license","max_line_length":22,"num_lines":10,"path":"/requirements.txt","repo_name":"lindo-zy/TBspider","src_encoding":"UTF-8","text":"setuptools==40.8.0\nscrapy_crawlera==1.6.0\nrequests==2.21.0\nScrapy==1.6.0\nFlask==1.0.2\nredis==3.3.4\nselenium==3.141.0\nbs4==4.8.0\nscrapyd\nscrapyd-client\n"},{"alpha_fraction":0.7077363729476929,"alphanum_fraction":0.7077363729476929,"avg_line_length":22.066667556762695,"blob_id":"56a3780f2fe59e84c9b3b4c78d4422441d037720","content_id":"30426cd3b35d6efb413ec085f71ccfe9af64f975","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":425,"license_type":"no_license","max_line_length":73,"num_lines":15,"path":"/DataItem/main.py","repo_name":"lindo-zy/TBspider","src_encoding":"UTF-8","text":"\nfrom scrapy.cmdline import execute\nimport os\nimport sys\n\nsys.path.append(os.path.join(os.path.dirname(os.path.abspath(__file__))))\n\n\"\"\"\n此文件为主调试入口,flask接口文件是 /api/wsgi.py\n不建议使用此文件进行调试，需要手动在爬虫中配参数\n\n\"\"\"\n\n# execute(['scrapy', 'crawl', 'NewTbShopSpider'])\n# execute(['scrapy', 'crawl', 'TbDetailSpiderNew'])\nexecute(['scrapy', 'crawl', 'TestSpider'])\n\n\n"},{"alpha_fraction":0.4993671476840973,"alphanum_fraction":0.5853285789489746,"avg_line_length":48.63874435424805,"blob_id":"f843272c07e25a239d06f8baf39050ed6de828aa","content_id":"2672ce3a3f1ccc3dcce9420cc873ec66391d2eb5","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":20090,"license_type":"no_license","max_line_length":259,"num_lines":382,"path":"/DataItem/DataItem/spiders/TestSpider.py","repo_name":"lindo-zy/TBspider","src_encoding":"UTF-8","text":"# -*- coding: utf-8 -*-\nimport re\nimport time\nimport hashlib\nimport json\nfrom html import unescape\nimport requests\nimport scrapy\nfrom scrapy import signals\nfrom scrapy.selector import Selector\nfrom DataItem.myLog import Log\nfrom datetime import datetime\nimport random\nfrom urllib.parse import urlparse\nimport math\nfrom redis_pool import RedisPool\n\nlogger = Log(log_name='TBSpider')\n\n\nclass TestSpider(scrapy.Spider):\n name = 'TestSpider'\n allowed_domains = ['taobao.com', 'tmall.com']\n\n custom_settings = {\n # 'CRAWLERA_ENABLED': False\n 'CONCURRENT_REQUESTS': 1,\n 'CONCURRENT_REQUESTS_PER_DOMAIN': 1,\n 'DOWNLOAD_DELAY': 1,\n 'REDIRECT_ENABLED': False,\n 'DOWNLOADER_MIDDLEWARES': {\n # 'DataItem.middlewares.ProxyDownloaderMiddleware': 610,\n 'DataItem.middlewares.CookiesPoolMiddleware': 610,\n # 'scrapy_crawlera.CrawleraMiddleware': 610\n }\n\n }\n\n def __init__(self, **kwargs):\n\n logger.info(f'----------------开始采集,{datetime.now()}----------------')\n\n # 初始化redis\n self.con = RedisPool()\n\n # 初始化请求类别, 用于区别代理\n self.request_tp = ('store', 'detail')\n\n # 获取店铺所有商品id,价格,名称\n self.store_list = '/i/asynSearch.htm?mid=w-{0}-0&wid={0}&pageNo='\n\n self.baseurl = 'http://shop.m.taobao.com/shop/shopsearch/search_page_json.do?sort=default&type=all&q='\n\n self.store_detail = ('http://h5api.m.taobao.com/h5/mtop.taobao.geb.shopinfo.queryshopinfo/2.0/?jsv=2.4.2'\n '&appKey={appkey}&t={t}&sign={sign}&api=mtop.taobao.geb.shopinfo.queryshopinfo&v=2.0'\n '&type=originaljson&timeout=3000&AntiCreep=true&dataType=json&H5Request=true&data={data}')\n\n self.chrome_agents = [\n \"Mozilla/5.0 (Windows NT 6.1) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/41.0.2228.0 Safari/537.36\",\n \"Mozilla/5.0 (Macintosh; Intel Mac OS X 10_10_1) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/41.0.2227.1 Safari/537.36\",\n \"Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/41.0.2227.0 Safari/537.36\",\n \"Mozilla/5.0 (Windows NT 6.1; WOW64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/41.0.2227.0 Safari/537.36\",\n \"Mozilla/5.0 (Windows NT 6.3; WOW64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/41.0.2226.0 Safari/537.36\",\n \"Mozilla/5.0 (Windows NT 6.4; WOW64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/41.0.2225.0 Safari/537.36\",\n \"Mozilla/5.0 (Windows NT 6.3; WOW64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/41.0.2225.0 Safari/537.36\",\n \"Mozilla/5.0 (Windows NT 5.1) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/41.0.2224.3 Safari/537.36\",\n \"Mozilla/5.0 (Windows NT 10.0) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/40.0.2214.93 Safari/537.36\",\n \"Mozilla/5.0 (Macintosh; Intel Mac OS X 10_10_1) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/37.0.2062.124 Safari/537.36\",\n \"Mozilla/5.0 (Windows NT 6.3; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/37.0.2049.0 Safari/537.36\",\n \"Mozilla/5.0 (Windows NT 4.0; WOW64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/37.0.2049.0 Safari/537.36\",\n \"Mozilla/5.0 (Windows NT 6.1; WOW64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/36.0.1985.67 Safari/537.36\",\n \"Mozilla/5.0 (Windows NT 5.1) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/36.0.1985.67 Safari/537.36\",\n \"Mozilla/5.0 (X11; OpenBSD i386) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/36.0.1985.125 Safari/537.36\",\n \"Mozilla/5.0 (Macintosh; Intel Mac OS X 10_9_2) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/36.0.1944.0 Safari/537.36\",\n \"Mozilla/5.0 (Windows NT 5.1) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/35.0.3319.102 Safari/537.36\",\n \"Mozilla/5.0 (Windows NT 5.1) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/35.0.2309.372 Safari/537.36\",\n \"Mozilla/5.0 (Windows NT 5.1) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/35.0.2117.157 Safari/537.36\",\n \"Mozilla/5.0 (Macintosh; Intel Mac OS X 10_9_3) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/35.0.1916.47 Safari/537.36\",\n \"Mozilla/5.0 (Windows NT 5.1) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/34.0.1866.237 Safari/537.36\",\n \"Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/34.0.1847.137 Safari/4E423F\",\n \"Mozilla/5.0 (Windows NT 5.1) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/34.0.1847.116 Safari/537.36 Mozilla/5.0 (iPad; U; CPU OS 3_2 like Mac OS X; en-us) AppleWebKit/531.21.10 (KHTML, like Gecko) Version/4.0.4 Mobile/7B334b Safari/531.21.10\",\n \"Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/33.0.1750.517 Safari/537.36\",\n \"Mozilla/5.0 (Windows NT 6.2; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/32.0.1667.0 Safari/537.36\",\n \"Mozilla/5.0 (Macintosh; Intel Mac OS X 10_9_0) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/32.0.1664.3 Safari/537.36\",\n \"Mozilla/5.0 (Macintosh; Intel Mac OS X 10_8_0) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/32.0.1664.3 Safari/537.36\",\n \"Mozilla/5.0 (Windows NT 5.1) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/31.0.1650.16 Safari/537.36\",\n \"Mozilla/5.0 (Windows NT 6.1; WOW64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/31.0.1623.0 Safari/537.36\",\n \"Mozilla/5.0 (Windows NT 6.2; WOW64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/30.0.1599.17 Safari/537.36\",\n \"Mozilla/5.0 (Windows NT 6.1; WOW64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/29.0.1547.62 Safari/537.36\",\n \"Mozilla/5.0 (X11; CrOS i686 4319.74.0) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/29.0.1547.57 Safari/537.36\",\n \"Mozilla/5.0 (Windows NT 6.2; WOW64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/29.0.1547.2 Safari/537.36\",\n \"Mozilla/5.0 (Windows NT 6.1) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/28.0.1468.0 Safari/537.36\",\n \"Mozilla/5.0 (Windows NT 6.2) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/28.0.1467.0 Safari/537.36\",\n \"Mozilla/5.0 (Windows NT 6.2) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/28.0.1464.0 Safari/537.36\",\n \"Mozilla/5.0 (Windows NT 6.2; WOW64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/27.0.1500.55 Safari/537.36\",\n \"Mozilla/5.0 (Windows NT 6.2; WOW64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/27.0.1453.93 Safari/537.36\",\n \"Mozilla/5.0 (Windows NT 6.1; WOW64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/27.0.1453.93 Safari/537.36\",\n \"Mozilla/5.0 (Windows NT 6.1) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/27.0.1453.93 Safari/537.36\",\n \"Mozilla/5.0 (Windows NT 5.1) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/27.0.1453.93 Safari/537.36\",\n \"Mozilla/5.0 (Macintosh; Intel Mac OS X 10_8_3) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/27.0.1453.93 Safari/537.36\",\n \"Mozilla/5.0 (Macintosh; Intel Mac OS X 10_7_5) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/27.0.1453.93 Safari/537.36\",\n \"Mozilla/5.0 (Windows NT 6.1) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/27.0.1453.90 Safari/537.36\",\n \"Mozilla/5.0 (X11; NetBSD) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/27.0.1453.116 Safari/537.36\",\n \"Mozilla/5.0 (X11; CrOS i686 3912.101.0) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/27.0.1453.116 Safari/537.36\",\n \"Mozilla/5.0 (Windows NT 6.1; WOW64) AppleWebKit/537.17 (KHTML, like Gecko) Chrome/24.0.1312.60 Safari/537.17\",\n \"Mozilla/5.0 (Macintosh; Intel Mac OS X 10_8_2) AppleWebKit/537.17 (KHTML, like Gecko) Chrome/24.0.1309.0 Safari/537.17\",\n \"Mozilla/5.0 (Windows NT 6.2; WOW64) AppleWebKit/537.15 (KHTML, like Gecko) Chrome/24.0.1295.0 Safari/537.15\",\n \"Mozilla/5.0 (Windows NT 6.2; WOW64) AppleWebKit/537.14 (KHTML, like Gecko) Chrome/24.0.1292.0 Safari/537.14\"\n ]\n\n # 计算淘宝sign的appkeys\n self.appkeys = '12574478'\n\n # 存储全部商品id\n self.all_pids = []\n\n # scrapyd爬虫的jobid\n self.job_id = kwargs.get('_job', '1')\n\n # 设置all_shop 的key,s_id是店铺在数据库中的id\n self.s_id = kwargs.get('s_id', '1')\n\n # 备忘录，记录被封禁url的处理\n self.block_record = []\n\n self.title = ''\n self.seller_name = ''\n self.shop_id = 0\n logger.info(f'接收到的参数:{kwargs}')\n\n # cookie池\n self.cookie_pool = self.con.cookies_pool()\n self.count = 0\n self.account = self.cookie_pool['account']\n self.token = self.cookie_pool['cookie_token']\n self.cookies = self.cookie_pool['cookie_dict']\n logger.info(f'当前使用的cookies的账号为:{self.account}')\n\n # 失败url链接\n self.fail_urls = []\n super().__init__(**kwargs)\n\n\n\n def start_requests(self):\n\n # resp = self.con.hget('all_shop', self.s_id)\n #\n # if resp:\n # # 从redis取出的数据\n # data = json.loads(resp)\n # else:\n # logger.warning(f'redis数据库表:_all_shop出现异常,参数为:{self.s_id}')\n # return\n\n # 测试数据\n\n data = {\n \"id\": 1,\n \"shop_id\": \"59002150\", # 店铺id\n \"title\": \"哈果超人 haco实用轻\", # 店铺名称\n \"name\": \"behsu网购\", # 掌柜名称\n \"platform_id\": 1, # 产品库定义的平台ID,淘宝为3,天猫为4\n \"shop_link\": \"https://haco.taobao.com/index.htm?spm=a1z10.3-c-s.w5002-14453794708.2.5ae258eaRYkhrS\",\n\n # 'url': 'https://haco.taobao.com/index.htm?spm=a1z10.3-c-s.w5002-14453794708.2.5ae258eaRYkhrS',\n # \"callback\": \"https://pr.test.puget.work/api/screen/shop\", # 发送采集结果的回调地址\n \"token\": \"ce934bc118beedabd789ed5cf6a20dc7\" # 接口请求认证\n }\n\n self.title = data.get('title')\n self.seller_name = data.get('name')\n self.shop_id = data.get('shop_id')\n\n shop_link = data.get('shop_link')\n\n shop_search_url = 'https://' + urlparse(shop_link).netloc\n\n # 请求店铺所有商品\n r = requests.get(shop_search_url + '/search.htm')\n sel = Selector(text=r.text)\n\n # 获取权值id\n if 'tmall.com' in shop_search_url:\n # wid = sel.css('[data-title=搜索列表]::attr(data-widgetid)').get()\n wid = int(sel.css('#bd > div::attr(data-widgetid)').get()) + 1 # 天猫 10月更新\n # wid = sel.css('#hd > div::attr(data-widgetid)').get()\n # '//*[@id=\"bd\"]//*[@class=\"J_TModule\"]/@data-widgetid'\n self.shop_search_list = shop_search_url + self.store_list.format(str(wid))\n\n elif 'taobao.com' in shop_link:\n # 权值id\n wid = sel.css('[data-title=宝贝列表]::attr(data-widgetid)').get()\n # 获取全部商品id的接口\n # 'https://guchun.tmall.com/i/asynSearch.htm?mid=w-18297719823-0&wid=18297719823&pageNo='\n self.shop_search_list = shop_search_url + self.store_list.format(wid)\n else:\n logger.warning('店铺信息解析失败！请查看scrapyd日志获取详情！')\n return\n\n self.con.hset('widget_id', self.title, wid)\n\n # json接口 免登陆\n # http://shop.m.taobao.com/shop/shopsearch/search_page_json.do?sort=default&type=all&q=Simple%E9%9F%A9%E5%9B%BD%E5%A5%B3%E8%A3%85\n self.json_url = self.baseurl + self.title\n\n yield scrapy.Request(url=self.json_url, callback=self.parse_search)\n\n # 搜索店铺信息并匹配\n def parse_search(self, response):\n\n self.re_dt = {}\n shopid = self.shop_id\n sid = self.s_id\n\n doc = unescape(response.text)\n # json接口内容 参考文件:接口1.json\n jd_data = json.loads(doc)\n\n if jd_data.get('listItem'):\n for s in jd_data.get('listItem'):\n try:\n if str(shopid) in str(s):\n self.re_dt['sign'] = str(sid) # sign 产品库id\n self.re_dt['shopid'] = str(s.get('shop').get('id')) # 淘宝shopid\n self.re_dt['is_mall'] = s.get('shop').get('isMall') # 是否天猫\n self.re_dt['total_sold'] = str(s.get('shop').get('totalSold')) # 销量\n self.re_dt['level'] = ''.join(re.findall('\\d-\\d', str(s.get('icon')))) # 店铺等级\n self.re_dt['sellerid'] = ''.join(re.findall('userid=(\\d+)', str(s.get('medal')))) # 卖家id\n self.re_dt['goods'] = s.get('favRate') # 好评率\n break\n except Exception as e:\n logger.warning(f'发生错误的函数:parse_search(),错误信息:{e}')\n continue\n\n # 随机轮换 User-Agent,因为是h5接口，要用手机浏览器带cookies访问\n iphone_headers = [\n 'Mozilla/5.0 (iPhone; CPU iPhone OS 11_0 like Mac OS X) AppleWebKit/604.1.38 (KHTML, like Gecko) Mobile/15A372 MicroMessenger/6.5.16 NetType/WIFI Language/zh_CN',\n 'Mozilla/5.0 (iPhone; CPU iPhone OS 11_0 like Mac OS X) AppleWebKit/604.1.38 (KHTML, like Gecko) Mobile/15A372 wxwork/2.1.5 MicroMessenger/6.3.22',\n 'Mozilla/5.0 (iPhone 6s; CPU iPhone OS 11_0 like Mac OS X) AppleWebKit/604.1.38 (KHTML, like Gecko) Version/11.0 MQQBrowser/7.7.2 Mobile/15A372 Safari/8536.25 MttCustomUA/2 QBWebViewType/1',\n 'Mozilla/5.0 (iPhone; CPU iPhone OS 11_0_1 like Mac OS X) AppleWebKit/604.1.38 (KHTML, like Gecko) Mobile/15A402 MicroMessenger/6.5.16 NetType/WIFI Language/zh_CN'\n ]\n\n headers = {'user-agent': random.choice(iphone_headers)}\n\n data = '{\"sellerId\": %s}' % self.re_dt.get('sellerid')\n cookies = self.cookies\n token = self.token\n account = self.account\n\n # 这部分只需要更换cookies，一般能访问成功，因为只访问一次接口\n if response.meta.get('change_cookie'):\n logger.info('-----------------更换cookies-----------------')\n cookie_pool = self.con.cookies_pool(account)\n if cookie_pool is None:\n return self.close(TestSpider, 'cookie池为空！')\n account = cookie_pool['account']\n token = cookie_pool['cookie_token']\n cookies = cookie_pool['cookie_dict']\n cookie_str = cookie_pool['cookie_str']\n logger.info(f'更换后的cookies的账号为:{account}')\n\n # 解码\n st = self.sign(token=token, appkey=self.appkeys, t=str(int(time.time() * 1000)), data=data)\n\n # 实时接口\n '''\n http://h5api.m.taobao.com/h5/mtop.taobao.geb.shopinfo.queryshopinfo/2.0/?jsv=2.4.2\n &appKey=12574478&t=1569315579952&sign=88d9947b4728cc649bc50f3cb66ef2c6&\n api=mtop.taobao.geb.shopinfo.queryshopinfo&v=2.0&type=originaljson&timeout=3000&\n AntiCreep=true&dataType=json&H5Request=true&data={\"sellerId\": 3790578263}\n '''\n url = self.store_detail.format(**st)\n\n yield scrapy.Request(url=url, headers=headers, cookies=cookies,\n callback=self.parse_store_item,\n meta={'account': account})\n\n # 进一步抓取店铺信息\n def parse_store_item(self, response):\n re_data = self.re_dt\n\n # 参考:接口2.json\n doc = json.loads(response.text)\n # 接口访问状态\n status = doc.get('ret')\n\n logger.info(f'json接口响应状态{status}')\n if 'SUCCESS' not in status[0]:\n logger.info('处理淘宝封禁！')\n yield scrapy.Request(url=self.json_url, callback=self.parse_search, dont_filter=True,\n meta={'change_cookie': True})\n else:\n # 存储动态json接口内容\n self.con.hset('item_json', self.title, response.text)\n tdt = doc.get('data')\n self.count = tdt.get('itemCount') # 商品总数\n re_data['shopname'] = tdt.get('shopName') # 店铺名\n re_data['fans_num'] = str(tdt.get('fansNum')) # 粉丝数\n re_data['item_count'] = str(self.count) # 产品数量\n re_data['new_item'] = str(tdt.get('newItem')) # 新品数\n re_data['golden_seller'] = tdt.get('goldenSeller') # 金牌卖家\n logger.info(f'店铺信息:{re_data}')\n\n # url = r'https://shop142840423.taobao.com/i/asynSearch.htm?mid=w-13377183093-0&wid=13377183093&pageNo=1'\n\n # 商品详情的第一页\n # 天猫用不上这个详情页，直接计算出页数\n # 淘宝暂时直接计算出页数\n\n if 'tmall.com' in self.shop_search_list:\n # 通过计算得出页数\n totalPages = math.ceil(int(self.count) / 90) + 1 # 向上取整\n elif 'taobao.com' in self.shop_search_list:\n # s = Selector(text=html)\n # # count = int(s.xpath('//*[@id=\"shop-search-list\"]/div/div[2]/span/text()').extract_first())\n # # if count is not None and count > item_count:\n # # item_count = count\n # 通过计算得出页数\n totalPages = math.ceil(int(self.count) / 24) + 1 # 向上取整\n\n logger.info(f'总共需要采集的页面有：{totalPages}页')\n\n for page in range(1, totalPages + 1):\n url = self.shop_search_list + str(page)\n # 预处理cookies\n headers = {\n 'User-Agent': random.choice(self.chrome_agents)\n }\n time.sleep(1)\n yield scrapy.Request(url=url, headers=headers, callback=self.parse_productId,\n meta={'with_cookie': True})\n\n\n def parse_productId(self, response):\n '''\n 解析当前商品详情页中的所有商品id\n :param response:\n :return:\n '''\n headers = {\n 'User-Agent': random.choice(self.chrome_agents)\n }\n if 'rgv587_flag' in response.text:\n yield scrapy.Request(url=response.request.url, headers=headers, callback=self.parse_productId,\n meta={'with_cookie': True, 'block': True})\n\n text = response.text.replace('\\\\\"', '')\n s = Selector(text=text)\n firstPagePids = s.css('.J_TItems div .item::attr(data-id)').getall()\n if len(firstPagePids) == 0:\n firstPagePids = s.css('.shop-hesper-bd .item::attr(data-id)').getall()\n logger.info(f'{response.request.url},商品id:{firstPagePids}')\n pids = firstPagePids\n\n # 存储全部商品\n # todo 增加持续化存储\n self.all_pids.extend(pids)\n\n # 根据所需元素计算sign\n def sign(self, token, appkey, t, data):\n '''\n :param token:\n :param appkey:\n :param t: str(int(time.time() * 1000))\n :param data:\n :return:\n '''\n pp = '&'.join([token, t, appkey, data]).encode()\n sign = hashlib.md5(pp).hexdigest()\n return {'sign': sign, 't': t, 'appkey': appkey, 'data': data}\n\n # 店铺爬虫结束后,数据存入redis并调用商品详情爬虫\n def close(self, spider, reason):\n # 去重存储所有商品id\n all_products_id = list(set(self.all_pids))\n logger.info(all_products_id)\n\n logger.info(f'采集到了{len(all_products_id)}条商品数据')\n lose_count = int(self.count) - len(all_products_id)\n if lose_count > 0:\n logger.warning(f'采集缺失：{lose_count},缺失百分比：{lose_count / int(self.count) * 100}%')\n"},{"alpha_fraction":0.6181393265724182,"alphanum_fraction":0.6243674755096436,"avg_line_length":32.80263137817383,"blob_id":"8ef18760e2c1ab122966393e209969a1bb63f18c","content_id":"c6396a51ee64d2f44baf06ec5857b5087d660ef8","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":2703,"license_type":"no_license","max_line_length":115,"num_lines":76,"path":"/DataItem/DataItem/middlewares.py","repo_name":"lindo-zy/TBspider","src_encoding":"UTF-8","text":"# -*- coding: utf-8 -*-\n\n# Define here the models for your spider middleware\n#\n# See documentation in:\n# https://doc.scrapy.org/en/latest/topics/spider-middleware.html\n\nimport base64\nimport random\nimport re\nimport requests\nfrom .settings import PROXY_USER, PROXY_PASS\nfrom scrapy_crawlera import CrawleraMiddleware\nfrom redis_pool import RedisPool\n\n\n# 阿布云代理\nclass ProxyDownloaderMiddleware(CrawleraMiddleware):\n # 代理服务器\n proxyServer = \"http://http-dyn.abuyun.com:9020\"\n\n # 生成加密后的代理密码\n proxyAuth = \"Basic \" + base64.urlsafe_b64encode(bytes((PROXY_USER + \":\" + PROXY_PASS), \"ascii\")).decode(\"utf8\")\n\n def process_request(self, request, spider):\n if request.meta.get('request_tp') == 'store':\n request.meta['proxy'] = self.proxyServer\n request.headers['Proxy-Authorization'] = self.proxyAuth\n elif request.meta.get('request_tp') == 'detail':\n super().process_request(request, spider)\n\n def process_response(self, request, response, spider):\n if response.headers.__contains__('cache-control') and request.meta.get('request_tp') == 'store':\n return request\n return response\n\n\nclass CookiesPoolMiddleware(CrawleraMiddleware):\n\n def get_cookies(self, account=''):\n con = RedisPool()\n cookies_pool = con.cookies_pool(account)\n if cookies_pool is None:\n print('cookies池为空！')\n return None\n self.account = cookies_pool['account']\n token = cookies_pool['cookie_token']\n # cookie_dict = cookies_pool['cookie_dict']\n cookie_str = cookies_pool['cookie_str']\n\n # 预处理增加cookie可用度\n unb_random = ''.join(str(random.choice(range(9))) for _ in range(13))\n\n pattern = r'unb=(\\d+)'\n cookie_unb = re.sub(pattern=pattern, repl='unb=' + unb_random, string=cookie_str)\n cookie_dict = dict([tuple(x.split('=', 1)) for x in cookie_unb.split(';')])\n print(f'当前账号:{self.account}')\n\n return cookie_dict\n\n # 处理request请求\n def process_request(self, request, spider):\n if request.meta.get('with_cookie'):\n if request.meta.get('block'):\n print('中间件处理淘宝封禁！')\n request.cookies = self.get_cookies(self.account)\n else:\n request.cookies = self.get_cookies()\n return None\n\n # 处理响应\n # def process_response(self, request, response, spider):\n # if request.meta.get('with_cookie') and request.meta.get('block'):\n # print('使用中间件，处理淘宝封禁！')\n # return request\n # return response\n"}],"string":"[\n {\n \"alpha_fraction\": 0.659375011920929,\n \"alphanum_fraction\": 0.6625000238418579,\n \"avg_line_length\": 15.8421049118042,\n \"blob_id\": \"c9bc22c1d67fe192182e84361133e70c85aa231a\",\n \"content_id\": \"ae11284cb23599d1287cc7ddd68ffbced220eb0a\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 320,\n \"license_type\": \"no_license\",\n \"max_line_length\": 52,\n \"num_lines\": 19,\n \"path\": \"/DataItem/DataItem/items.py\",\n \"repo_name\": \"lindo-zy/TBspider\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"# -*- coding: utf-8 -*-\\n\\n# Define here the models for your scraped items\\n#\\n# See documentation in:\\n# https://doc.scrapy.org/en/latest/topics/items.html\\n\\n\\nfrom scrapy import Item, Field\\n\\n\\nclass TBdetailItem(Item):\\n result = Field()\\n callback = Field()\\n headers = Field()\\n\\n\\nclass TestItem(Item):\\n ip = Field()\\n\"\n },\n {\n \"alpha_fraction\": 0.4871891438961029,\n \"alphanum_fraction\": 0.4941597580909729,\n \"avg_line_length\": 28.16483497619629,\n \"blob_id\": \"9bf4ab3cb7a9f557732f75e7b9be6d5982b13f41\",\n \"content_id\": \"d0c391bae23d7c40c8c66e82e7f561020994dbab\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 5550,\n \"license_type\": \"no_license\",\n \"max_line_length\": 112,\n \"num_lines\": 182,\n \"path\": \"/DataItem/redis_pool.py\",\n \"repo_name\": \"lindo-zy/TBspider\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"#!/usr/bin/env python\\n# -*- coding: utf-8 -*-\\n\\nimport redis\\nfrom config import *\\nimport re\\nimport json\\nimport random\\n\\n\\nclass RedisPool(object):\\n\\n def __init__(self, host=REDIS_HOST, port=REDIS_PORT, password=REDIS_PASSWORD, db=DB_NUM):\\n\\n pool = redis.ConnectionPool(host='127.0.0.1', port=6379, decode_responses=True, db=db,\\n password=REDIS_PASSWORD)\\n\\n if password:\\n self._db = redis.Redis(connection_pool=pool)\\n else:\\n self._db = redis.Redis(host=host, port=port, db=db, decode_responses=True)\\n\\n # 插入集合\\n def set(self, name, value):\\n '''\\n redis.set('key', 'value')\\n :param name:\\n :param value:\\n :return:\\n '''\\n self._db.set(name, value)\\n\\n # 批量插入\\n def mset(self, mapping):\\n '''\\n redis.mset({\\\"key1\\\": 'value1', \\\"key2\\\": 'value2'})\\n :param mapping:\\n :return:\\n '''\\n self._db.mset(json.dumps(mapping))\\n\\n # 取某个key的值\\n def get(self, name):\\n '''\\n red.get('key1')\\n :param name:\\n :return:\\n '''\\n return self._db.get(name=name)\\n\\n def hset(self, name, key, mapping):\\n '''\\n\\n :param name:\\n :param key:\\n :param mapping:\\n :return:\\n '''\\n status = self._db.hset(name, key, json.dumps(mapping))\\n if int(status) == 0:\\n print(f'{key}:更新数据成功！')\\n else:\\n print(f'{key}:插入数据成功！')\\n\\n def hget(self, name, key):\\n '''\\n :param name:\\n :param key:\\n :return:\\n '''\\n return self._db.hget(name, key)\\n\\n def hmget(self, name, keys_list):\\n\\n return self._db.hmget(name=name, keys=keys_list)\\n\\n def hgetall(self, name):\\n '''\\n :param name:\\n :return:\\n '''\\n return self._db.hgetall(name)\\n\\n def zadd(self, name, value, score):\\n mapping = {json.dumps(value): int(score)}\\n self._db.zadd(name, mapping)\\n\\n def zrangebyscore(self, name, min, max, withscores=True):\\n '''\\n 按分数范围，从小到大排列\\n :param name:\\n :param min:\\n :param max:\\n :return:\\n '''\\n return self._db.zrangebyscore(name, min, max, withscores=withscores)\\n\\n def zrem(self, name, values):\\n '''\\n 删除有序集合中的值\\n :param name:\\n :param values:\\n :return:\\n '''\\n self._db.zrem(name, json.dumps(values))\\n\\n def save_cookie(self, value, score=3):\\n self.zadd(\\\"cookies_pool\\\", value, score)\\n\\n def save_account(self, account_list):\\n # 录入账号\\n try:\\n if len(account_list) > 0:\\n for account in account_list:\\n username = account['username']\\n password = account['password']\\n print('账号', username, '密码', password)\\n self._db.hset('accounts', username, password)\\n else:\\n print('请检查账号录入配置!')\\n except Exception as e:\\n print(f'请检查账号录入配置,错误信息{e}')\\n\\n def get_accounts(self):\\n accounts_dict = self._db.hgetall('accounts')\\n result = {}\\n for user, password in accounts_dict.items():\\n result[user] = password\\n return result\\n\\n def update_pool(self, username, counter=1):\\n # 先取出，再更新score\\n # 返回列表\\n result = self.zrangebyscore('cookies_pool', 0, 3)\\n if result:\\n for item in result:\\n score = int(item[1]) - counter\\n value = json.loads(item[0])\\n if score >= 0 and value.get('username') == username:\\n # 更新cookies\\n self.zadd('cookies_pool', value=value, score=score)\\n print('score更新成功！')\\n elif score < 0 and value.get('username') == username:\\n self.zrem('cookies_pool', value)\\n else:\\n print('cookies_pool为空！')\\n\\n def cookies_pool(self, account=''):\\n result = self.zrangebyscore('cookies_pool', 0, 3)\\n self.update_pool(account)\\n cookie_list = []\\n if result:\\n for item in result:\\n item_dict = json.loads(item[0])\\n score = int(item[1])\\n cookie_account = item_dict['username']\\n # 返回其他有效cookies\\n if cookie_account != account:\\n cookie = item_dict['cookie']\\n # # 做一步预处理，增加cookie可用度\\n unb_random = ''.join(str(random.choice(range(9))) for _ in range(13))\\n\\n pattern = r'unb=(\\\\d+)'\\n cookie_unb = re.sub(pattern=pattern, repl='unb=' + unb_random, string=cookie)\\n\\n cookie_token = ''.join(re.findall(r'_m_h5_tk=([^;]+);', cookie_unb)).split('_')[0]\\n\\n cookie_dict = dict([tuple(x.split('=', 1)) for x in cookie_unb.split(';')])\\n\\n cookie_list.append({'account': cookie_account, 'score': score, 'cookie_token': cookie_token,\\n 'cookie_dict': cookie_dict, 'cookie_str': cookie_unb})\\n else:\\n return None\\n return cookie_list[-1]\\n else:\\n print('cookies池为空！')\\n return None\\n\\n\\nif __name__ == '__main__':\\n con = RedisPool()\\n print(con.cookies_pool())\\n\"\n },\n {\n \"alpha_fraction\": 0.4503311216831207,\n \"alphanum_fraction\": 0.47965940833091736,\n \"avg_line_length\": 14.731343269348145,\n \"blob_id\": \"b0cf57e117dcb88786f6de33294319fbd3c70b7e\",\n \"content_id\": \"d857afed3b836af56f9f7e629f6ab91a8e96578e\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Markdown\",\n \"length_bytes\": 1457,\n \"license_type\": \"no_license\",\n \"max_line_length\": 94,\n \"num_lines\": 67,\n \"path\": \"/项目结构文档.md\",\n \"repo_name\": \"lindo-zy/TBspider\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"## ShopSpider\\n\\n#### api\\n\\n此项目是Flask框架的web工程，运行起来是接受post数据，进行对指定店铺采集\\n\\n\\n\\ngunicorn -w 4 -b 0.0.0.0:8888 wsgi:app 部署flask工程\\n\\n测试的时候直接运行wisgi.py即可\\n\\n------\\n\\n#### DataItem\\n\\ndata_utils:爬虫工具目录\\n\\nspdiers：爬虫编写目录\\n\\n进入到TBspider/DataItem部署爬虫工程\\n1.执行scrapyd命令\\n2.新建一个命令行窗口，执行scrapyd-deploy TB -p DataItem\\n3.控制台出现status:ok 表明部署成功\\n\\n详细参考:https://blog.csdn.net/web_9705/article/details/80417219\\n\\n------\\n\\n数据库配置\\n\\nredis配置:api/config.py和DataItem/spiderConfig.py\\n\\n\\n------\\n\\n需要的相关账号\\n\\n| 新浪微博 | 密码 | 淘宝 | 密码 |\\n| :---------- | ----------- | ----------- | ----------- |\\n\\n\\n| 代理商 | 账号 | 密码 | 备注 |\\n| ------------------------------------------------------- | ----------- | ----------- | ---- |\\n\\n| 打码平台 | 账号 | 密码 | 备注 |\\n| ----------------------- | ------------ | ----------- | ---- |\\n\\n\\n\\n------\\n\\n完整运行流程\\n\\n1.运行/DataItem/data_utils/gen_login_cookie.py文件,生成至少一个登陆后的cookie\\n2.使用scrapyd和scrapyd-deplpoy部署爬虫\\n3.将所需采集的店铺信息发送至启动的服务\\n\\n------\\n\\n项目优化\\n\\n1.redis数据库使用db2和db3\\n\\n2.cookies过期太快\\n\\n------\\n\\n\\n\\n\"\n },\n {\n \"alpha_fraction\": 0.6597796082496643,\n \"alphanum_fraction\": 0.6625344157218933,\n \"avg_line_length\": 25.88888931274414,\n \"blob_id\": \"3d15ae4a434fe234a9e650ece335e977cbc80e03\",\n \"content_id\": \"34fdad9eeb7d543432f7c7b6cf1c741b87d46a4c\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 760,\n \"license_type\": \"no_license\",\n \"max_line_length\": 83,\n \"num_lines\": 27,\n \"path\": \"/DataItem/DataItem/pipelines.py\",\n \"repo_name\": \"lindo-zy/TBspider\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"# -*- coding: utf-8 -*-\\n\\n# Define your item pipelines here\\n#\\n# Don't forget to add your pipeline to the ITEM_PIPELINES setting\\n# See: https://doc.scrapy.org/en/latest/topics/item-pipeline.html\\nimport requests\\nimport json\\nfrom DataItem.myLog import Log\\n\\nlogger = Log(log_name='pipeline')\\n\\n\\nclass TestPipeline:\\n def process_item(self, item, spider):\\n data = item\\n logger.info(f'开始回调给产品库：{data[\\\"callback\\\"]}')\\n\\n callback = data['callback']\\n result = data['result']\\n headers = data['headers']\\n\\n requests.packages.urllib3.disable_warnings()\\n r = requests.post(url=callback, headers=headers, json=result, verify=False)\\n\\n logger.info(f'产品库响应内容：{r.text}')\\n return item\\n\"\n },\n {\n \"alpha_fraction\": 0.5195195078849792,\n \"alphanum_fraction\": 0.6042513251304626,\n \"avg_line_length\": 50.9357795715332,\n \"blob_id\": \"bc7e9dd0ab199f4492bb885bd52ad21b73cd8f7f\",\n \"content_id\": \"7275774b8c0cd58e859ed275cd71ff2dbd738f66\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 17843,\n \"license_type\": \"no_license\",\n \"max_line_length\": 259,\n \"num_lines\": 327,\n \"path\": \"/DataItem/DataItem/spiders/NewTbShopSpider.py\",\n \"repo_name\": \"lindo-zy/TBspider\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"# -*- coding: utf-8 -*-\\nimport re\\nimport time\\nimport hashlib\\nimport json\\nfrom html import unescape\\nimport requests\\nimport scrapy\\nfrom scrapy import signals\\nfrom scrapy.selector import Selector\\nfrom DataItem.redis_pool import RedisPool\\nfrom DataItem.myLog import Log\\nfrom datetime import datetime\\nimport random\\nfrom urllib.parse import urlparse\\nimport math\\n\\nlogger = Log(log_name='TBSpider')\\n\\n\\nclass TbshopspiderSpider(scrapy.Spider):\\n name = 'NewTbShopSpider'\\n allowed_domains = ['taobao.com', 'tmall.com']\\n\\n custom_settings = {\\n 'CONCURRENT_REQUESTS': 1,\\n 'CONCURRENT_REQUESTS_PER_DOMAIN': 1,\\n 'DOWNLOAD_DELAY': 1,\\n 'REDIRECT_ENABLED': False,\\n 'DOWNLOADER_MIDDLEWARES': {\\n # 'DataItem.middlewares.ProxyDownloaderMiddleware': 610,\\n 'DataItem.middlewares.CookiesPoolMiddleware': 610, # cookie池\\n # 'scrapy_crawlera.CrawleraMiddleware': 610\\n }\\n }\\n\\n def __init__(self, **kwargs):\\n\\n logger.info(f'----------------开始采集,{datetime.now()}----------------')\\n\\n # 初始化redis\\n self.rdb = RedisPool()\\n self.reds_conn = self.rdb.redis_con()\\n\\n # 获取店铺所有商品id,价格,名称\\n self.product_list_api = '/i/asynSearch.htm?mid=w-{0}-0&wid={0}&pageNo='\\n\\n self.shop_info_api = 'http://shop.m.taobao.com/shop/shopsearch/search_page_json.do?sort=default&type=all&q='\\n\\n self.shop_detail_api = ('http://h5api.m.taobao.com/h5/mtop.taobao.geb.shopinfo.queryshopinfo/2.0/?jsv=2.4.2'\\n '&appKey={appkey}&t={t}&sign={sign}&api=mtop.taobao.geb.shopinfo.queryshopinfo&v=2.0'\\n '&type=originaljson&timeout=3000&AntiCreep=true&dataType=json&H5Request=true&data={data}')\\n\\n # 计算淘宝sign的appkeys\\n self.appkeys = '12574478'\\n self.cookie_pool = self.con.cookies_pool()\\n self.account = self.cookie_pool['account']\\n self.token = self.cookie_pool['cookie_token']\\n self.cookies = self.cookie_pool['cookie_dict']\\n logger.info(f'当前使用的cookies的账号为:{self.account}')\\n\\n # 存储全部商品id\\n self.all_products_list = []\\n\\n # todo 正式 接受的参数\\n self.shopName = kwargs.get('shop_name')\\n self.sellerName = kwargs.get('seller_name')\\n self.shopId = kwargs.get('shop_id')\\n self.shopLink = kwargs.get('shop_link')\\n\\n self.chrome_agents = [\\n \\\"Mozilla/5.0 (Windows NT 6.1) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/41.0.2228.0 Safari/537.36\\\",\\n \\\"Mozilla/5.0 (Macintosh; Intel Mac OS X 10_10_1) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/41.0.2227.1 Safari/537.36\\\",\\n \\\"Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/41.0.2227.0 Safari/537.36\\\",\\n \\\"Mozilla/5.0 (Windows NT 6.1; WOW64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/41.0.2227.0 Safari/537.36\\\",\\n \\\"Mozilla/5.0 (Windows NT 6.3; WOW64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/41.0.2226.0 Safari/537.36\\\",\\n \\\"Mozilla/5.0 (Windows NT 6.4; WOW64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/41.0.2225.0 Safari/537.36\\\",\\n \\\"Mozilla/5.0 (Windows NT 6.3; WOW64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/41.0.2225.0 Safari/537.36\\\",\\n \\\"Mozilla/5.0 (Windows NT 5.1) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/41.0.2224.3 Safari/537.36\\\",\\n \\\"Mozilla/5.0 (Windows NT 10.0) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/40.0.2214.93 Safari/537.36\\\",\\n \\\"Mozilla/5.0 (Macintosh; Intel Mac OS X 10_10_1) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/37.0.2062.124 Safari/537.36\\\",\\n \\\"Mozilla/5.0 (Windows NT 6.3; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/37.0.2049.0 Safari/537.36\\\",\\n \\\"Mozilla/5.0 (Windows NT 4.0; WOW64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/37.0.2049.0 Safari/537.36\\\",\\n \\\"Mozilla/5.0 (Windows NT 6.1; WOW64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/36.0.1985.67 Safari/537.36\\\",\\n \\\"Mozilla/5.0 (Windows NT 5.1) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/36.0.1985.67 Safari/537.36\\\",\\n \\\"Mozilla/5.0 (X11; OpenBSD i386) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/36.0.1985.125 Safari/537.36\\\",\\n \\\"Mozilla/5.0 (Macintosh; Intel Mac OS X 10_9_2) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/36.0.1944.0 Safari/537.36\\\",\\n \\\"Mozilla/5.0 (Windows NT 5.1) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/35.0.3319.102 Safari/537.36\\\",\\n \\\"Mozilla/5.0 (Windows NT 5.1) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/35.0.2309.372 Safari/537.36\\\",\\n \\\"Mozilla/5.0 (Windows NT 5.1) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/35.0.2117.157 Safari/537.36\\\",\\n \\\"Mozilla/5.0 (Macintosh; Intel Mac OS X 10_9_3) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/35.0.1916.47 Safari/537.36\\\",\\n \\\"Mozilla/5.0 (Windows NT 5.1) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/34.0.1866.237 Safari/537.36\\\",\\n \\\"Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/34.0.1847.137 Safari/4E423F\\\",\\n \\\"Mozilla/5.0 (Windows NT 5.1) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/34.0.1847.116 Safari/537.36 Mozilla/5.0 (iPad; U; CPU OS 3_2 like Mac OS X; en-us) AppleWebKit/531.21.10 (KHTML, like Gecko) Version/4.0.4 Mobile/7B334b Safari/531.21.10\\\",\\n \\\"Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/33.0.1750.517 Safari/537.36\\\",\\n \\\"Mozilla/5.0 (Windows NT 6.2; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/32.0.1667.0 Safari/537.36\\\",\\n \\\"Mozilla/5.0 (Macintosh; Intel Mac OS X 10_9_0) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/32.0.1664.3 Safari/537.36\\\",\\n \\\"Mozilla/5.0 (Macintosh; Intel Mac OS X 10_8_0) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/32.0.1664.3 Safari/537.36\\\",\\n \\\"Mozilla/5.0 (Windows NT 5.1) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/31.0.1650.16 Safari/537.36\\\",\\n \\\"Mozilla/5.0 (Windows NT 6.1; WOW64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/31.0.1623.0 Safari/537.36\\\",\\n \\\"Mozilla/5.0 (Windows NT 6.2; WOW64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/30.0.1599.17 Safari/537.36\\\",\\n \\\"Mozilla/5.0 (Windows NT 6.1; WOW64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/29.0.1547.62 Safari/537.36\\\",\\n \\\"Mozilla/5.0 (X11; CrOS i686 4319.74.0) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/29.0.1547.57 Safari/537.36\\\",\\n \\\"Mozilla/5.0 (Windows NT 6.2; WOW64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/29.0.1547.2 Safari/537.36\\\",\\n \\\"Mozilla/5.0 (Windows NT 6.1) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/28.0.1468.0 Safari/537.36\\\",\\n \\\"Mozilla/5.0 (Windows NT 6.2) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/28.0.1467.0 Safari/537.36\\\",\\n \\\"Mozilla/5.0 (Windows NT 6.2) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/28.0.1464.0 Safari/537.36\\\",\\n \\\"Mozilla/5.0 (Windows NT 6.2; WOW64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/27.0.1500.55 Safari/537.36\\\",\\n \\\"Mozilla/5.0 (Windows NT 6.2; WOW64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/27.0.1453.93 Safari/537.36\\\",\\n \\\"Mozilla/5.0 (Windows NT 6.1; WOW64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/27.0.1453.93 Safari/537.36\\\",\\n \\\"Mozilla/5.0 (Windows NT 6.1) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/27.0.1453.93 Safari/537.36\\\",\\n \\\"Mozilla/5.0 (Windows NT 5.1) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/27.0.1453.93 Safari/537.36\\\",\\n \\\"Mozilla/5.0 (Macintosh; Intel Mac OS X 10_8_3) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/27.0.1453.93 Safari/537.36\\\",\\n \\\"Mozilla/5.0 (Macintosh; Intel Mac OS X 10_7_5) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/27.0.1453.93 Safari/537.36\\\",\\n \\\"Mozilla/5.0 (Windows NT 6.1) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/27.0.1453.90 Safari/537.36\\\",\\n \\\"Mozilla/5.0 (X11; NetBSD) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/27.0.1453.116 Safari/537.36\\\",\\n \\\"Mozilla/5.0 (X11; CrOS i686 3912.101.0) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/27.0.1453.116 Safari/537.36\\\",\\n \\\"Mozilla/5.0 (Windows NT 6.1; WOW64) AppleWebKit/537.17 (KHTML, like Gecko) Chrome/24.0.1312.60 Safari/537.17\\\",\\n \\\"Mozilla/5.0 (Macintosh; Intel Mac OS X 10_8_2) AppleWebKit/537.17 (KHTML, like Gecko) Chrome/24.0.1309.0 Safari/537.17\\\",\\n \\\"Mozilla/5.0 (Windows NT 6.2; WOW64) AppleWebKit/537.15 (KHTML, like Gecko) Chrome/24.0.1295.0 Safari/537.15\\\",\\n \\\"Mozilla/5.0 (Windows NT 6.2; WOW64) AppleWebKit/537.14 (KHTML, like Gecko) Chrome/24.0.1292.0 Safari/537.14\\\"\\n ]\\n\\n super().__init__(**kwargs)\\n\\n def start_requests(self):\\n\\n shop_search_url = 'https://' + urlparse(self.shopLink).netloc\\n\\n # todo 请求店铺所有商品,直接请求？\\n r = requests.get(shop_search_url + '/search.htm')\\n\\n select = Selector(text=r.text)\\n\\n if 'tmall.com' in shop_search_url:\\n # wid = select.css('#hd > div::attr(data-widgetid)').get()\\n wid = int(select.css('#bd > div::attr(data-widgetid)').get()) + 1 # 天猫 10月更新\\n self.product_list = shop_search_url + self.product_list_api.format(wid)\\n\\n elif 'taobao.com' in self.shopLink:\\n # 权值id\\n wid = select.css('[data-title=宝贝列表]::attr(data-widgetid)').get()\\n\\n self.product_list = shop_search_url + self.product_list_api.format(wid)\\n\\n json_url = self.shop_info_api + self.shopName\\n\\n yield scrapy.Request(url=json_url, callback=self.parse_search)\\n\\n # 搜索店铺信息并匹配\\n def parse_search(self, response):\\n\\n self.result = {}\\n doc = unescape(response.text)\\n # json接口内容 参考文件:接口内容.json\\n json_data = json.loads(doc)\\n\\n if json_data.get('listItem'):\\n shop_info = json_data['listItem'][0]['shop']\\n # 店铺地区\\n self.result['shop_link'] = self.shopLink\\n self.result['shop_area'] = json_data['listItem'][0]['area']\\n self.result['shop_logo'] = shop_info.get('logo')\\n self.result['shop_name'] = self.shopName\\n self.result['seller_name'] = self.sellerName\\n self.result['shop_id'] = self.shopId\\n\\n if shop_info.get('isMall'):\\n self.result['shop_type'] = '天猫'\\n else:\\n self.result['shop_type'] = '淘宝'\\n shop_medal = json_data['listItem'][0].get('medal')\\n # 动态评价接口\\n self.result['shop_ajax'] = shop_medal.get('ajaxurl')\\n # 卖家id 使用正则匹配出来\\n self.result['seller_id'] = re.findall(r'userid=(\\\\d+)', shop_medal.get('ajaxurl'))[0]\\n # 好评率\\n self.result['shop_favRate'] = json_data['listItem'][0].get('favRate')\\n else:\\n logger.warning('店铺信息解析失败！请查看scrapyd日志获取详情！')\\n\\n # 随机轮换 User-Agent,因为是h5接口，要用手机浏览器带cookies访问\\n iphone_headers = [\\n 'Mozilla/5.0 (iPhone; CPU iPhone OS 11_0 like Mac OS X) AppleWebKit/604.1.38 (KHTML, like Gecko) Mobile/15A372 MicroMessenger/6.5.16 NetType/WIFI Language/zh_CN',\\n 'Mozilla/5.0 (iPhone; CPU iPhone OS 11_0 like Mac OS X) AppleWebKit/604.1.38 (KHTML, like Gecko) Mobile/15A372 wxwork/2.1.5 MicroMessenger/6.3.22',\\n 'Mozilla/5.0 (iPhone 6s; CPU iPhone OS 11_0 like Mac OS X) AppleWebKit/604.1.38 (KHTML, like Gecko) Version/11.0 MQQBrowser/7.7.2 Mobile/15A372 Safari/8536.25 MttCustomUA/2 QBWebViewType/1',\\n 'Mozilla/5.0 (iPhone; CPU iPhone OS 11_0_1 like Mac OS X) AppleWebKit/604.1.38 (KHTML, like Gecko) Mobile/15A402 MicroMessenger/6.5.16 NetType/WIFI Language/zh_CN'\\n ]\\n\\n headers = {'user-agent': random.choice(iphone_headers)}\\n\\n data = '{\\\"sellerId\\\": %s}' % self.re_dt.get('sellerid')\\n cookies = self.cookies\\n token = self.token\\n account = self.account\\n # 这部分只需要更换cookies，一般能访问成功，因为只访问一次接口\\n if response.meta.get('change_cookie'):\\n logger.info('-----------------更换cookies-----------------')\\n cookie_pool = self.con.cookies_pool(account)\\n\\n account = cookie_pool['account']\\n token = cookie_pool['cookie_token']\\n cookies = cookie_pool['cookie_dict']\\n cookie_str = cookie_pool['cookie_str']\\n logger.info(f'更换后的cookies的账号为:{account}')\\n\\n data = '{\\\"sellerId\\\": %s}' % self.result.get('seller_id')\\n\\n decrypt = self.sign(token=token, appkey=self.appkeys, t=str(int(time.time() * 1000)),\\n data=data)\\n\\n url = self.shop_detail_api.format(**decrypt)\\n\\n yield scrapy.Request(url=url, headers=headers,\\n cookies=cookies, callback=self.parse_store_item, meta={'account': account})\\n\\n # 进一步抓取店铺信息\\n def parse_store_item(self, response):\\n\\n # 参考:接口2.json\\n doc = json.loads(response.text)\\n\\n # 判断是否拿到了接口数据\\n status = doc.get('ret')\\n logger.info(f'json接口响应状态{status}')\\n if 'SUCCESS' not in status[0]:\\n yield scrapy.Request(url=self.json_url, callback=self.parse_search, dont_filter=True,\\n meta={'change_cookie': True})\\n\\n json_data = doc.get('data')\\n self.count = json_data.get('itemCount') # 商品总数\\n self.result['fans_num'] = str(json_data.get('fansNum')) # 粉丝数\\n self.result['item_count'] = str(self.count) # 产品数量\\n self.result['new_item'] = str(json_data.get('newItem')) # 新品数\\n if json_data.get('goldenSeller'):\\n self.result['golden_seller'] = '金牌卖家'\\n else:\\n self.result['golden_seller'] = ''\\n\\n logger.info(f'店铺信息:{self.result}')\\n\\n self.reds_conn.hset('shop_info', self.shopName, json.dumps(self.result))\\n if 'tmall.com' in self.product_list:\\n totalPages = math.ceil(int(self.count) / 90) + 1 # 向上取整\\n elif 'taobao.com' in self.product_list:\\n totalPages = math.ceil(int(self.count) / 24) + 1 # 向上取整\\n logger.info(f'总共需要采集的页面有：{totalPages}页')\\n\\n for page in range(1, totalPages + 1):\\n url = self.product_list + str(page)\\n # 预处理cookies\\n headers = {\\n 'User-Agent': random.choice(self.chrome_agents)\\n }\\n time.sleep(1)\\n yield scrapy.Request(url=url, headers=headers, callback=self.parse_productId,\\n meta={'with_cookie': True})\\n\\n def parse_productId(self, response):\\n '''\\n 解析当前商品详情页中的所有商品id\\n :param response:\\n :return:\\n '''\\n headers = {\\n 'User-Agent': random.choice(self.chrome_agents)\\n }\\n if 'rgv587_flag' in response.text:\\n yield scrapy.Request(url=response.request.url, headers=headers, callback=self.parse_productId,\\n meta={'with_cookie': True, 'block': True})\\n\\n text = response.text.replace('\\\\\\\\\\\"', '')\\n s = Selector(text=text)\\n\\n products_id_list = s.css('.J_TItems div .item::attr(data-id)').getall()\\n\\n if len(products_id_list) == 0:\\n products_id_list = s.css('.shop-hesper-bd .item::attr(data-id)').getall()\\n\\n logger.info(f'当前页商品id:{products_id_list}')\\n\\n # 存储全部商品\\n self.all_products_list.extend(products_id_list)\\n\\n # 根据所需元素计算sign\\n def sign(self, token, appkey, t, data):\\n '''\\n :param token:\\n :param appkey:\\n :param t: str(int(time.time() * 1000))\\n :param data:\\n :return:\\n '''\\n pp = '&'.join([token, t, appkey, data]).encode()\\n sign = hashlib.md5(pp).hexdigest()\\n return {'sign': sign, 't': t, 'appkey': appkey, 'data': data}\\n\\n # 店铺爬虫结束后,数据存入redis并调用商品详情爬虫\\n def close(self, spider, reason):\\n try:\\n # 去重存储所有商品id\\n all_products_id = list(set(self.all_pids))\\n logger.info(all_products_id)\\n\\n logger.info(f'采集到了{len(all_products_id)}条商品数据')\\n lose_count = int(self.count) - len(all_products_id)\\n if lose_count > 0:\\n logger.warning(f'采集缺失：{lose_count},缺失百分比：{lose_count / int(self.count) * 100}%')\\n\\n data = json.dumps({'product_counts': len(all_products_id), 'product_id': all_products_id})\\n\\n # redis 存储抓取的全部商品id\\n self.reds_conn.hset('products_id', self.shopName, data)\\n\\n # 启动详情爬虫的参数\\n post_data = {'project': 'DataItem', 'spider': 'TbDetailSpiderNew', 'shop_name': self.shopName,\\n 'shop_id': self.shopId}\\n\\n # 调用淘宝详情爬虫\\n r = requests.post('http://localhost:6800/schedule.json', data=post_data, timeout=50)\\n if r.status_code == 200:\\n logger.info(f'传递给详情采集爬虫的参数为:{post_data}')\\n logger.info(f'详情采集爬虫调用状态码:{r.status_code},响应内容:{json.loads(r.text)}')\\n else:\\n logger.warning(f'启用详情爬虫失败！状态码:{r.status_code}')\\n except Exception as e:\\n logger.warning(f'启用详情爬虫失败！错误信息{e}')\\n\"\n },\n {\n \"alpha_fraction\": 0.5698432326316833,\n \"alphanum_fraction\": 0.5819115042686462,\n \"avg_line_length\": 36.546875,\n \"blob_id\": \"de7aa386206f04cb21f540d371d98bb818f7dfdb\",\n \"content_id\": \"b2047e40469e076e33b37d1fb01948a087f98711\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 8021,\n \"license_type\": \"no_license\",\n \"max_line_length\": 126,\n \"num_lines\": 192,\n \"path\": \"/DataItem/CookiesPool/generator.py\",\n \"repo_name\": \"lindo-zy/TBspider\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"import json\\nimport time\\nimport requests\\nimport random\\nfrom urllib import parse\\nfrom selenium.webdriver.chrome import options\\nfrom selenium.webdriver.support.wait import WebDriverWait\\nfrom selenium.webdriver.support import expected_conditions as EC\\nfrom selenium.webdriver.common.by import By\\nfrom selenium import webdriver\\nfrom scrapy.selector import Selector\\nfrom CookiesPool.apiLog import Log\\nfrom CookiesPool.config import *\\nfrom redis_pool import RedisPool\\nfrom CookiesPool.verify import Yundama\\n\\nlogger = Log()\\n\\n'''\\nchromedriver 根据自己的chrome版本下载配置\\n\\n\\n本地使用，生成cookies\\n1.登陆频繁微博会出现414,无法访问，一般封禁半小时内\\n2.切换海外代理登陆，新浪微博需要打码，淘宝需要验证手机号 #不推荐使用\\n3.手动获取cookies,添加到redis #todo\\n\\n\\n1.尝试登陆移动端新浪\\nhttps://passport.weibo.cn/signin/login?entry=mweibo&res=wel&wm=3349&r=https://m.weibo.cn/\\n2.移动端登陆成功->淘宝关联登陆\\n\\n'''\\n\\n\\nclass CookiesGenerator:\\n\\n def get_cookie(self, username, password):\\n\\n \\\"\\\"\\\"\\n 使用微博关联登陆淘宝,进而从m端淘宝页面获取cookie\\n \\\"\\\"\\\"\\n\\n option = options.Options()\\n\\n option.add_argument(\\\"disable-infobars\\\") # # 去除info框\\n option.add_experimental_option('excludeSwitches', ['enable-automation']) # 开发者模式,防止被识别\\n option.add_argument('log-level=3')\\n # option.add_argument(\\\"--proxy-server=http://114.239.254.76:4236\\\") #添加代理\\n # option.add_argument('--headless') # headless模式\\n # option.add_argument(\\\"window-size=2436, 1125\\\")\\n # option.add_argument(\\\"--no-sandbox\\\")\\n\\n # executable_path = os.path.join(os.getcwd(), 'chromedriver.exe') # chromedriver路径\\n\\n # browser = webdriver.Chrome(executable_path=executable_path, options=option)\\n browser = webdriver.Chrome(options=option)\\n\\n browser.implicitly_wait(20) # 隐式加载\\n try:\\n browser.get('https://weibo.com/')\\n browser.maximize_window()\\n # 等待微博登录框加载完成后输入账号密码登陆\\n wb_locator = '.login_innerwrap .W_login_form[node-type=normal_form] input[name=username]'\\n WebDriverWait(browser, 300, 0.5).until(EC.presence_of_element_located((By.CSS_SELECTOR, wb_locator)))\\n logger.info('当前步骤：输入账号密码！')\\n browser.find_element_by_css_selector(\\n '.login_innerwrap [node-type=normal_form] input[name=username]').send_keys(\\n username)\\n browser.find_element_by_css_selector(\\n '.login_innerwrap [node-type=normal_form] input[name=password]').send_keys(\\n password)\\n # 增加延时\\n time.sleep(3)\\n\\n yundama = Yundama()\\n\\n sel = Selector(text=browser.page_source)\\n verify_img = sel.css('[action-type=btn_change_verifycode]::attr(src)').get()\\n\\n if not verify_img:\\n browser.find_element_by_css_selector(\\n '.login_innerwrap [node-type=normal_form] .W_btn_a').click()\\n else:\\n print('出现验证码，开始识别验证码')\\n\\n # 截图大法\\n pic = browser.find_element_by_xpath('//*[@id=\\\"pl_login_form\\\"]/div/div[3]/div[3]/a/img')\\n pic.screenshot('yzm.png') # 元素截图\\n time.sleep(1)\\n result = yundama.identify(file='yzm.png')\\n if not result:\\n print('验证码识别失败, 跳过识别')\\n return\\n else:\\n browser.find_element_by_xpath('//*[@id=\\\"pl_login_form\\\"]/div/div[3]/div[3]/div/input').send_keys(result)\\n time.sleep(1)\\n browser.find_element_by_css_selector('.login_innerwrap [node-type=normal_form] .W_btn_a').click()\\n\\n logger.info('当前步骤：登陆淘宝!')\\n # 等待微博登陆完成后转到淘宝登陆页面并点击使用微博登陆\\n wb_login_locator = '.WB_feed_detail'\\n WebDriverWait(browser, 300, 0.5).until(EC.presence_of_element_located((By.CSS_SELECTOR, wb_login_locator)))\\n browser.get('https://login.taobao.com')\\n try:\\n browser.find_element_by_css_selector('#J_Quick2Static').click()\\n except Exception as e:\\n # 添加错误日志\\n logger.warning(f'错误信息{e}')\\n\\n browser.find_element_by_css_selector('.weibo-login').click()\\n time.sleep(1)\\n # 判断是否有微博快速登录框出现,有则点击,无则输入微博密码登陆\\n if browser.find_element_by_css_selector('.logged_info .W_btn_g'):\\n # tb_submit_locator = '.logged_info .W_btn_g'\\n # WebDriverWait(browser, 300, 0.5).until(EC.presence_of_element_located((By.CSS_SELECTOR, tb_submit_locator)))\\n browser.find_element_by_css_selector('.logged_info .W_btn_g').click()\\n elif browser.find_elements_by_css_selector('[node-type=submitStates]'):\\n browser.find_element_by_css_selector('.enter_psw').send_keys(password)\\n browser.find_element_by_css_selector('[node-type=submitStates]').click()\\n return\\n\\n time.sleep(2)\\n # 等待淘宝登陆完成后转入淘宝m端首页\\n tb_locator = '.logo-bd'\\n WebDriverWait(browser, 300, 0.5).until(EC.presence_of_element_located((By.CSS_SELECTOR, tb_locator)))\\n browser.get('https://h5.m.taobao.com')\\n\\n # 等待淘宝m端首页加载完成,获取cookie并存入redis\\n m_tb_locator = '.header-bd'\\n WebDriverWait(browser, 300, 0.5).until(EC.presence_of_element_located((By.CSS_SELECTOR, m_tb_locator)))\\n cookies = browser.get_cookies()\\n\\n result = self.cookie_handler(cookies)\\n\\n cookie_str = result[0]\\n\\n logger.info('成功获取到cookies！')\\n\\n # 存储到redis->cookies_pool\\n con = RedisPool()\\n con.save_cookie({\\\"username\\\": username, \\\"cookie\\\": cookie_str})\\n\\n browser.quit()\\n\\n # 传输cookies,当前工程在本地，则传输cookies到线上\\n # if LOCAL:\\n # url = 'http://47.56.68.237:8888/recieve_cookie'\\n # headers = {\\\"Content-Type\\\": \\\"application/json\\\"}\\n # data = {\\n # 'data': {'cookie': cookie_str, 'username': username, \\\"token\\\": \\\"ce934bc118beedabd789ed5cf6a20dc7\\\"}}\\n # try:\\n # r = requests.post(url=url, headers=headers, json=data)\\n #\\n # logger.info(f'状态码：{r.status_code},响应内容:{json.loads(r.text)}')\\n # except Exception as e:\\n # logger.warning(f'cookies传输出错,{e}')\\n\\n except Exception as e:\\n logger.warning(f'cookie获取失败: {e}')\\n\\n finally:\\n browser.quit()\\n # 下次获取cookies\\n # 加入随机等待时间减少被反爬识别概率\\n # todo 登录多了,会封Ip\\n time.sleep(random.randint(1, 4))\\n\\n def cookie_handler(self, cookie_list):\\n cookie_dict = {}\\n cookie_ls = []\\n for item in cookie_list:\\n cookie_dict[item['name']] = item['value']\\n temp = item['name'] + \\\"=\\\" + item['value']\\n cookie_ls.append(temp)\\n\\n cookie_str = ';'.join(item for item in cookie_ls)\\n return cookie_str, cookie_dict\\n\\n def run(self):\\n # 从redis中取出账号和密码\\n con = RedisPool()\\n accounts_dict = con.get_accounts()\\n\\n for username, password in accounts_dict.items():\\n logger.info(f'当前生成cookies账号为:{username},密码：{password}')\\n self.get_cookie(username, password)\\n\\n\\nif __name__ == '__main__':\\n generator = CookiesGenerator()\\n generator.run()\\n\"\n },\n {\n \"alpha_fraction\": 0.5117599368095398,\n \"alphanum_fraction\": 0.5660989284515381,\n \"avg_line_length\": 25.80434799194336,\n \"blob_id\": \"534af19577e504cf9200261ba6e3fe522de3ceed\",\n \"content_id\": \"68e46df33bc0c2527360cb83592991617d9d261c\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 1307,\n \"license_type\": \"no_license\",\n \"max_line_length\": 111,\n \"num_lines\": 46,\n \"path\": \"/DataItem/DataItem/spiderConfig.py\",\n \"repo_name\": \"lindo-zy/TBspider\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"#!/usr/bin/python3\\n# -*- coding:utf-8 -*-\\n'''\\n相关配置文件\\n'''\\n\\n# php接口回调设置\\n\\n# 本地测试\\n# php_api = {\\n# \\\"http://127.0.0.1/api/screen/shop\\\": {\\n# \\\"url\\\": \\\"http://127.0.0.1/api/screen/product\\\",\\n# \\\"result\\\": \\\"http://127.0.0.1/api/screen/result\\\",\\n# \\\"headers\\\": {\\n# \\\"api-key\\\": \\\"admin@admin.com\\\",\\n# \\\"api-token\\\": \\\"g/l+OjehOxCdfKnaC+WeHuZ4fo7rODXTIOH3ftUZtaY=\\\",\\n# \\\"Content-Type\\\": \\\"application/json\\\"\\n# }\\n# }\\n# }\\n\\n# 测试服\\nSHOP_CALLBACK = r'http://127.0.0.1/api/screen/shop'\\nPRODUCT_CALLBACK = r\\\"https://pr.test.puget.work/api/screen/product\\\"\\nRESULT_CALLBACK = r'https://pr.test.puget.work/api/screen/result'\\nAPI_KEY = 'admin@admin.com'\\nAPI_TOKEN = 'g/l+OjehOxCdfKnaC+WeHuZ4fo7rODXTIOH3ftUZtaY='\\nHEADERS_CALLBACK = {\\\"api-key\\\": API_KEY,\\n \\\"api-token\\\": API_TOKEN,\\n \\\"Content-Type\\\": \\\"application/json\\\",\\n 'user-agent': \\\"Mozilla/5.0 (Windows NT 6.2; WOW64) AppleWebKit/537.14 (KHTML, like Gecko) \\\"\\n \\\"Chrome/24.0.1292.0 Safari/537.14 \\\"}\\n\\n# 测试服\\n# REDIS_PASSWORD = 'WpRWHOb9rinQXbwa'\\n# 本地\\nREDIS_PASSWORD = ''\\n# Redis数据库地址\\nREDIS_HOST = '127.0.0.1'\\n\\n# Redis端口\\nREDIS_PORT = 6379\\n\\n# Redis密码，如无填None\\nREDIS_PASSWORD = ''\\nDB_NUM = 0\\n\"\n },\n {\n \"alpha_fraction\": 0.6492537260055542,\n \"alphanum_fraction\": 0.6567164063453674,\n \"avg_line_length\": 6.388888835906982,\n \"blob_id\": \"b794f2de2d8715a0d6631e7b11b194a837e8efce\",\n \"content_id\": \"6eb9f5256be111001d4dcc10b2c5c73501cf4f16\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Markdown\",\n \"length_bytes\": 178,\n \"license_type\": \"no_license\",\n \"max_line_length\": 27,\n \"num_lines\": 18,\n \"path\": \"/DataItem/CookiesPool/README.md\",\n \"repo_name\": \"lindo-zy/TBspider\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"# CookiesPool / Cookies池\\n\\n\\n## 基础配置 \\n\\n修改cookiespool/config.py\\n\\n\\n\\n### 进程开关\\n\\n配置信息到cookiespool/config文件修改\\n\\n## 运行\\n\\n```\\npython3 run.py\\n```\\n\\n\"\n },\n {\n \"alpha_fraction\": 0.5256490111351013,\n \"alphanum_fraction\": 0.5344677567481995,\n \"avg_line_length\": 34.62389373779297,\n \"blob_id\": \"c6e18f81cbaa79f8c0625eaf45824e130645c06b\",\n \"content_id\": \"f3c829346c7db57a3bf60a5154986de05dc7d2c3\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 8657,\n \"license_type\": \"no_license\",\n \"max_line_length\": 139,\n \"num_lines\": 226,\n \"path\": \"/DataItem/DataItem/spiders/ProductSpider.py\",\n \"repo_name\": \"lindo-zy/TBspider\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"# -*- coding: utf-8 -*-\\nimport re\\nimport datetime\\nimport json\\nimport scrapy\\nfrom DataItem.redis_pool import RedisPool\\nfrom bs4 import BeautifulSoup\\nfrom DataItem.myLog import Log\\nfrom scrapy.selector import Selector\\nimport random\\n\\nlogger = Log(log_name='DetailSpider')\\n\\n\\nclass ProductSpider(scrapy.Spider):\\n name = 'ProductSpider'\\n\\n allowed_domains = ['*']\\n # 开启scrapyhub代理 备用\\n crawlera_enabled = True\\n crawlera_apikey = ''\\n\\n # todo 自定义设置,调优\\n custom_settings = {\\n 'CONCURRENT_REQUESTS': 5,\\n 'CONCURRENT_REQUESTS_PER_DOMAIN': 5, # 单个ip最大并发量\\n 'AUTOTHROTTLE_ENABLED': False, # 限流器\\n 'DOWNLOADER_MIDDLEWARES': {\\n # 'DataItem.middlewares.ProxyDownloaderMiddleware': 610, #开启阿布云\\n 'scrapy_crawlera.CrawleraMiddleware': 610\\n }\\n }\\n\\n def __init__(self, **kwargs):\\n logger.info(f'----------------开始采集,{datetime.datetime.now()}----------------')\\n\\n self.reds_conn = RedisPool().redis_con()\\n\\n self.product_detail_api = r'https://h5api.m.taobao.com/h5/mtop.taobao.detail.getdetail/6.0/?data=%7B%22itemNumId%22%3A%22{0}%22%7D'\\n\\n # 初始化详情正则对象\\n self.img_cpl = re.compile(r'.*?\\\"img\\\":\\\"(https://.*?\\\\.jpg)', re.DOTALL) # 匹配详情图\\n self.img_cpl2 = re.compile(r'.*?image\\\":{\\\"itemImages\\\":\\\\[(.*?)\\\\].*', re.DOTALL) # 匹配详情图\\n self.desc_cpl = re.compile(r'.*?descUrl\\\".*?\\\"(.*?)\\\"', re.DOTALL) # 匹配详情图入口url\\n self.all_imgs = [] # 所有图片的详情\\n\\n self.job_id = kwargs.get('_job', '1') # 此爬虫的job_id\\n\\n # 店铺名称\\n # self.shopName = kwargs.get('shop_name')\\n # self.shopid = kwargs.get('shop_id')\\n\\n self.productIdList = kwargs.get('productId_list')\\n\\n self.result = {}\\n\\n super().__init__(**kwargs)\\n\\n # 开始解析\\n def start_requests(self):\\n\\n product_list = self.productIdList\\n\\n n = 0\\n for product_id in product_list:\\n n += 1\\n logger.info(f'开始采集第{n}个商品,id为：{product_id}')\\n\\n product_detail_api = r'https://h5api.m.taobao.com/h5/mtop.taobao.detail.getdetail/6.0/?data=%7B%22itemNumId%22%3A%22{}%22%7D'\\n\\n # 构建商品详情接口\\n url = product_detail_api.format(product_id)\\n # 增加cookies\\n cookies_dict = self.reds_conn.hgetall('queue_cookie')\\n cookies_list = list(cookies_dict.values())\\n\\n cookie = cookies_list[-1]\\n\\n unb_random = ''.join(str(random.choice(range(9))) for _ in range(13))\\n\\n pattern = r'unb=(\\\\d+)'\\n temp = re.sub(pattern=pattern, repl='unb=' + unb_random, string=cookie)\\n ls = temp.split(';')\\n cookies = {}\\n for i in ls:\\n key, value = i.split('=', 1)\\n cookies[key] = value\\n\\n yield scrapy.Request(url=url, callback=self.parse_tb, dont_filter=True, cookies=cookies,\\n meta={'url': url, 'slug': product_id, 'request_tp': 'store'})\\n\\n # 解析淘宝网页\\n def parse_tb(self, response):\\n try:\\n # 参考:商品详情json\\n product_detail_json = json.loads(response.text)\\n\\n data = product_detail_json['data']\\n\\n slug = response.meta.get('slug') # 淘宝的商品ID\\n\\n # 响应失败标志\\n\\n status = product_detail_json['ret'][0]\\n if '成功' not in status:\\n logger.warning(f'{response.request.url},商品列表采集失败！响应内容：{product_detail_json.get(\\\"ret\\\")}')\\n return\\n\\n # 商品信息\\n product_info = {}\\n\\n # 商品名称\\n product_info['product_name'] = data['item'].get('title')\\n # 商品id\\n product_info['product_id'] = data['item'].get('itemId')\\n # 商品类别id\\n product_info['product_categoryId'] = data['item'].get('categoryId')\\n\\n site_type = data['params']['trackParams']['BC_type'] # B为天猫C为淘宝\\n # 商品源url,区分B-淘宝,C-天猫\\n if site_type == 'B':\\n source_url = \\\"https://detail.tmall.com/item.htm?id={0}\\\".format(slug)\\n else:\\n source_url = \\\"https://item.taobao.com/item.htm?id={0}\\\".format(slug)\\n\\n product_info['product_url'] = source_url\\n\\n price_info = json.loads(data['apiStack'][0]['value']) # 价格信息\\n\\n purchase_price = price_info['price']['price']['priceText'].split('-')[-1] # 实际价格\\n\\n # 销量\\n sell_count = price_info.get('item').get('sellCount') if price_info.get('item', dict()).get(\\n 'sellCount') else 0\\n\\n purchase_list_price = price_info['price'].get('extraPrices')\\n\\n purchase_list_price = purchase_list_price[0]['priceText'].split('-')[\\n -1] if purchase_list_price else purchase_price # 市场价\\n\\n item = data['item'] # 商品信息\\n\\n skuBase = data['skuBase']\\n option_values = skuBase.get('props', {})\\n\\n tmall_descurl = f\\\"https:{item['tmallDescUrl']}\\\" # 商品图片详情页 origin\\n\\n images = item['images'] # 缩略图信息\\n feature_image_list = [f'https:{u}' for u in images]\\n\\n props = data.get('props').get('groupProps')[0].get('基本信息')\\n\\n # todo 重写\\n opt = [{'name': i['name'], 'values': [{'name': j['name'],\\n 'thumb': (f\\\"http:{j.get('image')}\\\" if not j.get('image').startswith(\\n 'http') else j.get('image')) if j.get('image') else ''} for j in\\n i['values']]} for i in option_values][::-1] if option_values else {}\\n\\n product_info['product_price'] = purchase_price # 淘宝价\\n product_info['product_sale_price'] = purchase_list_price # 定价\\n\\n product_info['product_props'] = props # 商品属性集合\\n\\n product_info['product_options'] = opt # 商品选项集合\\n product_info['product_sell_count'] = sell_count # 商品销量\\n\\n product_info['product_images'] = feature_image_list # 图片集合\\n\\n sku_price_info = price_info['skuCore']['sku2info'] # sku价格\\n\\n product_info['product_sku_prices'] = sku_price_info\\n\\n # 抓取页面图片详情\\n yield scrapy.Request(tmall_descurl,\\n callback=self.parse_detail,\\n meta={'product_info': product_info, 'slug': slug},\\n dont_filter=True)\\n except Exception as e:\\n logger.warning(f'错误信息{e}')\\n\\n # 解析详情接口,判断是否有图片接口存在,如果存在则进一步抓取图片\\n def parse_detail(self, response):\\n\\n product_info = response.meta.get('product_info')\\n\\n product_id = response.meta.get('slug')\\n # 详情html\\n product_describe = response.text\\n\\n product_info['product_describe'] = product_describe\\n\\n # 详情接口\\n descurl = 'https://h5api.m.taobao.com/h5/mtop.taobao.detail.getdesc/6.0/?jsv=2.4.11&data={\\\"id\\\":\\\"%s\\\",\\\"type\\\":\\\"1\\\"}' % (\\n product_id)\\n\\n yield scrapy.Request(url=descurl, callback=self.parse_img_new,\\n meta={'product_info': product_info},\\n dont_filter=True)\\n\\n # 获取图片\\n def parse_img_new(self, response):\\n product_info = response.meta.get('product_info')\\n text = json.loads(response.text).get('data').get('pcDescContent')\\n selector = Selector(text=text)\\n image_urls = selector.css('img::attr(src)').getall()\\n\\n if not image_urls:\\n soup = BeautifulSoup(text, 'lxml')\\n if soup.find_all('img'):\\n for link in soup.find_all('img'):\\n image_urls.append(link.get(\\\"src\\\"))\\n else:\\n logger.warning(f'{response.request.url}:没有图片！')\\n\\n image_urls = [f'https:{img}' for img in image_urls]\\n\\n product_info['product_images_urls'] = image_urls\\n\\n # 参考:商品详情2.json\\n data = json.dumps(product_info)\\n\\n # 存储到数据库\\n self.reds_conn.hset('product_info', product_info['product_id'], data)\\n\\n def close(self, spider, reason):\\n logger.info(f'----------------全部商品采集完成,{datetime.datetime.now()}----------------')\\n\"\n },\n {\n \"alpha_fraction\": 0.4673663377761841,\n \"alphanum_fraction\": 0.47950148582458496,\n \"avg_line_length\": 38.089744567871094,\n \"blob_id\": \"582c121554880debb246f0d644598a50dc416a03\",\n \"content_id\": \"defb21df619ca765863e41664d3f37de9ae325ac\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 6614,\n \"license_type\": \"no_license\",\n \"max_line_length\": 113,\n \"num_lines\": 156,\n \"path\": \"/DataItem/CookiesPool/api.py\",\n \"repo_name\": \"lindo-zy/TBspider\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"import json\\nimport requests\\nfrom flask import Flask, request\\nfrom scrapy import Selector\\nfrom CookiesPool.apiLog import Log\\nfrom redis_pool import RedisPool\\n\\n__all__ = ['app']\\n\\napp = Flask(__name__)\\n\\nlogger = Log()\\n\\n# 测试服\\nTOKEN = 'ce934bc118beedabd789ed5cf6a20dc7'\\n\\n\\n@app.route('/request_crawler', methods=[\\\"POST\\\"])\\ndef request_crawler():\\n if request.method == 'POST':\\n params = request.json\\n logger.info(f'接收到参数:{params}')\\n data = params['data']\\n con = RedisPool()\\n # 执行请求scrapy爬虫\\n if 'shop_link' in data.keys():\\n try:\\n url = data['shop_link']\\n print('需要采集的店铺：', url)\\n post_data = {}\\n\\n s_id = data['id']\\n # todo 这个是什么排序？\\n sort_type = data.get('sort_type', '_sale')\\n\\n post_data['project'] = 'DataItem'\\n # 需要启动的爬虫\\n post_data['spider'] = 'NewTbShopSpider'\\n\\n # 店铺在shop_fetch_tasks中的表\\n post_data['s_id'] = s_id\\n post_data['sort_type'] = sort_type\\n r = requests.post('http://localhost:6800/schedule.json', data=post_data, timeout=50)\\n logger.info('scrapyd状态:', r.text)\\n # 爬虫的jobid\\n jobid = r.json()['jobid']\\n logger.info('当前任务ID:', jobid)\\n con.hset('all_shop', s_id, json.dumps(data, ensure_ascii=False))\\n\\n con.hset('store', jobid, json.dumps(data, ensure_ascii=False))\\n\\n logger.info(f'采集爬虫启动成功！详情:{r.text}')\\n logger.info({'code': 200, 'msg': '成功接收任务！'})\\n return json.dumps({'code': 200, 'msg': '成功接收任务！'})\\n except Exception as e:\\n logger.info({'code': 500, 'msg': f'错误信息:{e}'})\\n return json.dumps({'code': 500, 'msg': f'错误信息:{e}'})\\n\\n # 获取店铺信息\\n elif 'url' in data.keys():\\n try:\\n url = data['url']\\n print('需要采集的店铺：', url)\\n r = requests.get(url)\\n s = Selector(text=r.text)\\n # 店铺id\\n shopId = s.css('#LineZing::attr(shopid)').get()\\n\\n shopName = ''\\n sellerName = ''\\n\\n # 获取淘宝店铺基本信息\\n if 'taobao.com' in url:\\n if s.css('.hd-shop-name a::text').get():\\n shopName = s.css('.hd-shop-name a::text').get()\\n elif s.css('.first-block .shop-name span::text').get():\\n shopName = s.css('.first-block .shop-name span::text').get()\\n else:\\n shopName = ''\\n if s.css('.tb-box-half.tb-seller-info label::text').get():\\n sellerName = s.css('.tb-box-half.tb-seller-info label::text').get().strip()\\n elif s.css('.seller-name::text').get():\\n sellerName = s.css('.seller-name::text').get().strip('掌柜：')\\n elif s.css('.shop-more-info p.info-item:nth-child(2)::text').get():\\n sellerName = s.css('.shop-more-info p.info-item:nth-child(2)::text').get().strip()\\n else:\\n sellerName = ''\\n\\n # 获取天猫店铺信息\\n elif 'tmall.com' in url:\\n if s.css('.hd-shop-name a::text').get():\\n shopName = s.css('.hd-shop-name a::text').get()\\n else:\\n shopName = s.css('.slogo-shopname strong::text').get()\\n # 天猫默认 卖家和商铺同名\\n sellerName = shopName\\n\\n # if s.css('.tb-box-half.tb-seller-info label::text').get():\\n # sellerName = s.css('.tb-box-half.tb-seller-info label::text').get().strip()\\n # else:\\n # sellerName = s.css('.shopkeeper div a::text').get()\\n\\n # 商铺基本信息\\n shopInfo = {'shopname': shopName, 'shopid': shopId, 'sellername': sellerName}\\n print(f'采集店铺基本信息:{shopInfo}')\\n\\n con.hset('ShopInfoQuery', json.dumps(shopInfo, ensure_ascii=False), url)\\n\\n logger.info({'code': 200, 'msg': '店铺基本信息获取成功！', 'shop_info': shopInfo})\\n return json.dumps({'code': 200, 'msg': '店铺基本信息获取成功！', 'shop_info': shopInfo}, ensure_ascii=False)\\n except Exception as e:\\n logger.info({'code': 500, 'msg': f'错误信息:{e}'})\\n return json.dumps({'code': 500, 'msg': f'错误信息:{e}'})\\n else:\\n logger.info({'code': 500, 'msg': '任务接收失败，请检查参数！'})\\n return json.dumps({'code': 500, 'msg': '任务接收失败，请检查参数！'})\\n\\n\\n@app.route('/recieve_cookie', methods=[\\\"POST\\\"])\\ndef recieve_cookie():\\n if request.method == 'POST':\\n try:\\n params = json.loads(request.data)\\n logger.info({'code': 200, 'msg': 'cookies接收成功！'})\\n data = params['data']\\n cookie = data['cookie']\\n username = data['username']\\n con = RedisPool()\\n con.save_cookie(username, json.dumps({'cookie': cookie, 'counter': 3}))\\n\\n return json.dumps({'code': 200, 'msg': 'cookies接收成功！'})\\n except Exception as e:\\n logger.info({'code': 500, 'msg': f'错误信息{e}'})\\n return json.dumps({'code': 500, 'msg': f'错误信息{e}'})\\n\\n\\n@app.before_request\\ndef before_request():\\n if request.method == 'POST':\\n try:\\n jdata = json.loads(request.data)['data']\\n if jdata.get('token') and TOKEN == jdata.get('token'):\\n logger.info({'code': 200, 'msg': 'token 校验成功！'})\\n else:\\n logger.info({'code': 500, 'msg': 'token校验失败！'})\\n return json.dumps({'code': 500, 'msg': 'token校验失败！'})\\n except Exception as e:\\n logger.info({'code': 500, 'msg': f'错误信息{e}'})\\n return json.dumps({'code': 500, 'msg': f'错误信息{e}'})\\n elif request.method == 'GET':\\n logger.info('Flask工程开启成功！')\\n return 'Flask工程开启成功！'\\n\\n\\nif __name__ == '__main__':\\n app.run(debug=True, host='0.0.0.0', port=8888)\\n\"\n },\n {\n \"alpha_fraction\": 0.566919207572937,\n \"alphanum_fraction\": 0.6439393758773804,\n \"avg_line_length\": 15.5,\n \"blob_id\": \"033cd7e6affd980c19a26955a3a29d1a39aaef57\",\n \"content_id\": \"fe3878b7f58dadeb7032be297f11d696e3f4ce46\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 966,\n \"license_type\": \"no_license\",\n \"max_line_length\": 66,\n \"num_lines\": 48,\n \"path\": \"/DataItem/CookiesPool/config.py\",\n \"repo_name\": \"lindo-zy/TBspider\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"# Redis数据库地址\\nREDIS_HOST = '127.0.0.1'\\n\\n# Redis端口\\nREDIS_PORT = 6379\\n\\n# Redis密码，如无填None\\nREDIS_PASSWORD = ''\\n\\nDB_NUM = 0\\n\\n# 配置信息，无需修改\\n# REDIS_DOMAIN = '*'\\n# REDIS_NAME = '*'\\n\\n\\n# 产生器和验证器循环周期\\nCYCLE = 2400 # 秒\\n\\n# API地址和端口\\nAPI_HOST = '127.0.0.1'\\nAPI_PORT = 8888\\n\\n# 进程开关\\n# 产生器，模拟登录添加Cookies\\nGENERATOR_PROCESS = True\\n\\n# API接口服务\\nAPI_PROCESS = True\\n\\n# 账号\\nACCOUNTS = [{\\\"username\\\": '15625006690', \\\"password\\\": \\\"tmp11235\\\"},\\n {\\\"username\\\": '18922373490', \\\"password\\\": \\\"tmp11235\\\"},\\n {\\\"username\\\": '18922412249', \\\"password\\\": \\\"tmp11235\\\"}]\\n\\n# 区别本地和线上\\nLOCAL = False\\n\\n# 云打码相关配置到yundama.com申请注册\\nYUNDAMA_USERNAME = 'jianbai'\\nYUNDAMA_PASSWORD = 'tmp11235'\\nYUNDAMA_APP_ID = 1\\nYUNDAMA_APP_KEY = '22cc5376925e9387a23cf797cb9ba745'\\n\\nYUNDAMA_API_URL = 'http://api.yundama.com/api.php'\\n\\n# 云打码最大尝试次数\\nYUNDAMA_MAX_RETRY = 20\\n\"\n },\n {\n \"alpha_fraction\": 0.5791420340538025,\n \"alphanum_fraction\": 0.5791420340538025,\n \"avg_line_length\": 26.59183692932129,\n \"blob_id\": \"b9256543cda3f5d316a2c6f87490689a8abf110b\",\n \"content_id\": \"e52f1a4dfdd0564f47b996ee9fc966dc32f02558\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 1480,\n \"license_type\": \"no_license\",\n \"max_line_length\": 72,\n \"num_lines\": 49,\n \"path\": \"/DataItem/CookiesPool/scheduler.py\",\n \"repo_name\": \"lindo-zy/TBspider\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"from datetime import datetime\\nfrom multiprocessing import Process\\nfrom CookiesPool.api import app\\nfrom CookiesPool.generator import *\\nfrom redis_pool import RedisPool\\nfrom CookiesPool.config import *\\n\\n\\n# 调度器\\nclass Scheduler(object):\\n @staticmethod\\n def generate_cookie(cycle=CYCLE):\\n while True:\\n\\n try:\\n generator = CookiesGenerator()\\n generator.run()\\n print(f'cookies生成结束！时间：{datetime.now()}')\\n # 每一个cycle生成一轮cookies\\n time.sleep(cycle)\\n except Exception as e:\\n print(f'cookies生成器发生错误,错误信息:{e}')\\n\\n @staticmethod\\n def api():\\n app.run(host=API_HOST, port=API_PORT)\\n\\n def run(self):\\n # 导入账号\\n con = RedisPool()\\n con.save_account(ACCOUNTS)\\n\\n # 开启生成器\\n if GENERATOR_PROCESS:\\n print(f'开启cookies生成器！')\\n generate_process = Process(target=Scheduler.generate_cookie)\\n generate_process.start()\\n\\n # # 开启验证\\n # if VALID_PROCESS:\\n # logger.info(f'开启cookies验证！')\\n # valid_process = Process(target=Scheduler.valid_cookie)\\n # valid_process.start()\\n\\n # 开启flask工程\\n if API_PROCESS:\\n print(f'开启flask工程成功！')\\n api_process = Process(target=Scheduler.api)\\n api_process.start()\\n\"\n },\n {\n \"alpha_fraction\": 0.5165562629699707,\n \"alphanum_fraction\": 0.7086092829704285,\n \"avg_line_length\": 14.100000381469727,\n \"blob_id\": \"8b3d9457a31a3f82dde0227499e896f7cceaf2d8\",\n \"content_id\": \"707d5b4a6de8a4e3a26075d22291e1e789f180bf\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Text\",\n \"length_bytes\": 151,\n \"license_type\": \"no_license\",\n \"max_line_length\": 22,\n \"num_lines\": 10,\n \"path\": \"/requirements.txt\",\n \"repo_name\": \"lindo-zy/TBspider\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"setuptools==40.8.0\\nscrapy_crawlera==1.6.0\\nrequests==2.21.0\\nScrapy==1.6.0\\nFlask==1.0.2\\nredis==3.3.4\\nselenium==3.141.0\\nbs4==4.8.0\\nscrapyd\\nscrapyd-client\\n\"\n },\n {\n \"alpha_fraction\": 0.7077363729476929,\n \"alphanum_fraction\": 0.7077363729476929,\n \"avg_line_length\": 22.066667556762695,\n \"blob_id\": \"56a3780f2fe59e84c9b3b4c78d4422441d037720\",\n \"content_id\": \"30426cd3b35d6efb413ec085f71ccfe9af64f975\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 425,\n \"license_type\": \"no_license\",\n \"max_line_length\": 73,\n \"num_lines\": 15,\n \"path\": \"/DataItem/main.py\",\n \"repo_name\": \"lindo-zy/TBspider\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"\\nfrom scrapy.cmdline import execute\\nimport os\\nimport sys\\n\\nsys.path.append(os.path.join(os.path.dirname(os.path.abspath(__file__))))\\n\\n\\\"\\\"\\\"\\n此文件为主调试入口,flask接口文件是 /api/wsgi.py\\n不建议使用此文件进行调试，需要手动在爬虫中配参数\\n\\n\\\"\\\"\\\"\\n\\n# execute(['scrapy', 'crawl', 'NewTbShopSpider'])\\n# execute(['scrapy', 'crawl', 'TbDetailSpiderNew'])\\nexecute(['scrapy', 'crawl', 'TestSpider'])\\n\\n\\n\"\n },\n {\n \"alpha_fraction\": 0.4993671476840973,\n \"alphanum_fraction\": 0.5853285789489746,\n \"avg_line_length\": 48.63874435424805,\n \"blob_id\": \"f843272c07e25a239d06f8baf39050ed6de828aa\",\n \"content_id\": \"2672ce3a3f1ccc3dcce9420cc873ec66391d2eb5\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 20090,\n \"license_type\": \"no_license\",\n \"max_line_length\": 259,\n \"num_lines\": 382,\n \"path\": \"/DataItem/DataItem/spiders/TestSpider.py\",\n \"repo_name\": \"lindo-zy/TBspider\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"# -*- coding: utf-8 -*-\\nimport re\\nimport time\\nimport hashlib\\nimport json\\nfrom html import unescape\\nimport requests\\nimport scrapy\\nfrom scrapy import signals\\nfrom scrapy.selector import Selector\\nfrom DataItem.myLog import Log\\nfrom datetime import datetime\\nimport random\\nfrom urllib.parse import urlparse\\nimport math\\nfrom redis_pool import RedisPool\\n\\nlogger = Log(log_name='TBSpider')\\n\\n\\nclass TestSpider(scrapy.Spider):\\n name = 'TestSpider'\\n allowed_domains = ['taobao.com', 'tmall.com']\\n\\n custom_settings = {\\n # 'CRAWLERA_ENABLED': False\\n 'CONCURRENT_REQUESTS': 1,\\n 'CONCURRENT_REQUESTS_PER_DOMAIN': 1,\\n 'DOWNLOAD_DELAY': 1,\\n 'REDIRECT_ENABLED': False,\\n 'DOWNLOADER_MIDDLEWARES': {\\n # 'DataItem.middlewares.ProxyDownloaderMiddleware': 610,\\n 'DataItem.middlewares.CookiesPoolMiddleware': 610,\\n # 'scrapy_crawlera.CrawleraMiddleware': 610\\n }\\n\\n }\\n\\n def __init__(self, **kwargs):\\n\\n logger.info(f'----------------开始采集,{datetime.now()}----------------')\\n\\n # 初始化redis\\n self.con = RedisPool()\\n\\n # 初始化请求类别, 用于区别代理\\n self.request_tp = ('store', 'detail')\\n\\n # 获取店铺所有商品id,价格,名称\\n self.store_list = '/i/asynSearch.htm?mid=w-{0}-0&wid={0}&pageNo='\\n\\n self.baseurl = 'http://shop.m.taobao.com/shop/shopsearch/search_page_json.do?sort=default&type=all&q='\\n\\n self.store_detail = ('http://h5api.m.taobao.com/h5/mtop.taobao.geb.shopinfo.queryshopinfo/2.0/?jsv=2.4.2'\\n '&appKey={appkey}&t={t}&sign={sign}&api=mtop.taobao.geb.shopinfo.queryshopinfo&v=2.0'\\n '&type=originaljson&timeout=3000&AntiCreep=true&dataType=json&H5Request=true&data={data}')\\n\\n self.chrome_agents = [\\n \\\"Mozilla/5.0 (Windows NT 6.1) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/41.0.2228.0 Safari/537.36\\\",\\n \\\"Mozilla/5.0 (Macintosh; Intel Mac OS X 10_10_1) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/41.0.2227.1 Safari/537.36\\\",\\n \\\"Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/41.0.2227.0 Safari/537.36\\\",\\n \\\"Mozilla/5.0 (Windows NT 6.1; WOW64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/41.0.2227.0 Safari/537.36\\\",\\n \\\"Mozilla/5.0 (Windows NT 6.3; WOW64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/41.0.2226.0 Safari/537.36\\\",\\n \\\"Mozilla/5.0 (Windows NT 6.4; WOW64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/41.0.2225.0 Safari/537.36\\\",\\n \\\"Mozilla/5.0 (Windows NT 6.3; WOW64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/41.0.2225.0 Safari/537.36\\\",\\n \\\"Mozilla/5.0 (Windows NT 5.1) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/41.0.2224.3 Safari/537.36\\\",\\n \\\"Mozilla/5.0 (Windows NT 10.0) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/40.0.2214.93 Safari/537.36\\\",\\n \\\"Mozilla/5.0 (Macintosh; Intel Mac OS X 10_10_1) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/37.0.2062.124 Safari/537.36\\\",\\n \\\"Mozilla/5.0 (Windows NT 6.3; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/37.0.2049.0 Safari/537.36\\\",\\n \\\"Mozilla/5.0 (Windows NT 4.0; WOW64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/37.0.2049.0 Safari/537.36\\\",\\n \\\"Mozilla/5.0 (Windows NT 6.1; WOW64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/36.0.1985.67 Safari/537.36\\\",\\n \\\"Mozilla/5.0 (Windows NT 5.1) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/36.0.1985.67 Safari/537.36\\\",\\n \\\"Mozilla/5.0 (X11; OpenBSD i386) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/36.0.1985.125 Safari/537.36\\\",\\n \\\"Mozilla/5.0 (Macintosh; Intel Mac OS X 10_9_2) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/36.0.1944.0 Safari/537.36\\\",\\n \\\"Mozilla/5.0 (Windows NT 5.1) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/35.0.3319.102 Safari/537.36\\\",\\n \\\"Mozilla/5.0 (Windows NT 5.1) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/35.0.2309.372 Safari/537.36\\\",\\n \\\"Mozilla/5.0 (Windows NT 5.1) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/35.0.2117.157 Safari/537.36\\\",\\n \\\"Mozilla/5.0 (Macintosh; Intel Mac OS X 10_9_3) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/35.0.1916.47 Safari/537.36\\\",\\n \\\"Mozilla/5.0 (Windows NT 5.1) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/34.0.1866.237 Safari/537.36\\\",\\n \\\"Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/34.0.1847.137 Safari/4E423F\\\",\\n \\\"Mozilla/5.0 (Windows NT 5.1) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/34.0.1847.116 Safari/537.36 Mozilla/5.0 (iPad; U; CPU OS 3_2 like Mac OS X; en-us) AppleWebKit/531.21.10 (KHTML, like Gecko) Version/4.0.4 Mobile/7B334b Safari/531.21.10\\\",\\n \\\"Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/33.0.1750.517 Safari/537.36\\\",\\n \\\"Mozilla/5.0 (Windows NT 6.2; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/32.0.1667.0 Safari/537.36\\\",\\n \\\"Mozilla/5.0 (Macintosh; Intel Mac OS X 10_9_0) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/32.0.1664.3 Safari/537.36\\\",\\n \\\"Mozilla/5.0 (Macintosh; Intel Mac OS X 10_8_0) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/32.0.1664.3 Safari/537.36\\\",\\n \\\"Mozilla/5.0 (Windows NT 5.1) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/31.0.1650.16 Safari/537.36\\\",\\n \\\"Mozilla/5.0 (Windows NT 6.1; WOW64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/31.0.1623.0 Safari/537.36\\\",\\n \\\"Mozilla/5.0 (Windows NT 6.2; WOW64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/30.0.1599.17 Safari/537.36\\\",\\n \\\"Mozilla/5.0 (Windows NT 6.1; WOW64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/29.0.1547.62 Safari/537.36\\\",\\n \\\"Mozilla/5.0 (X11; CrOS i686 4319.74.0) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/29.0.1547.57 Safari/537.36\\\",\\n \\\"Mozilla/5.0 (Windows NT 6.2; WOW64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/29.0.1547.2 Safari/537.36\\\",\\n \\\"Mozilla/5.0 (Windows NT 6.1) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/28.0.1468.0 Safari/537.36\\\",\\n \\\"Mozilla/5.0 (Windows NT 6.2) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/28.0.1467.0 Safari/537.36\\\",\\n \\\"Mozilla/5.0 (Windows NT 6.2) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/28.0.1464.0 Safari/537.36\\\",\\n \\\"Mozilla/5.0 (Windows NT 6.2; WOW64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/27.0.1500.55 Safari/537.36\\\",\\n \\\"Mozilla/5.0 (Windows NT 6.2; WOW64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/27.0.1453.93 Safari/537.36\\\",\\n \\\"Mozilla/5.0 (Windows NT 6.1; WOW64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/27.0.1453.93 Safari/537.36\\\",\\n \\\"Mozilla/5.0 (Windows NT 6.1) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/27.0.1453.93 Safari/537.36\\\",\\n \\\"Mozilla/5.0 (Windows NT 5.1) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/27.0.1453.93 Safari/537.36\\\",\\n \\\"Mozilla/5.0 (Macintosh; Intel Mac OS X 10_8_3) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/27.0.1453.93 Safari/537.36\\\",\\n \\\"Mozilla/5.0 (Macintosh; Intel Mac OS X 10_7_5) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/27.0.1453.93 Safari/537.36\\\",\\n \\\"Mozilla/5.0 (Windows NT 6.1) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/27.0.1453.90 Safari/537.36\\\",\\n \\\"Mozilla/5.0 (X11; NetBSD) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/27.0.1453.116 Safari/537.36\\\",\\n \\\"Mozilla/5.0 (X11; CrOS i686 3912.101.0) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/27.0.1453.116 Safari/537.36\\\",\\n \\\"Mozilla/5.0 (Windows NT 6.1; WOW64) AppleWebKit/537.17 (KHTML, like Gecko) Chrome/24.0.1312.60 Safari/537.17\\\",\\n \\\"Mozilla/5.0 (Macintosh; Intel Mac OS X 10_8_2) AppleWebKit/537.17 (KHTML, like Gecko) Chrome/24.0.1309.0 Safari/537.17\\\",\\n \\\"Mozilla/5.0 (Windows NT 6.2; WOW64) AppleWebKit/537.15 (KHTML, like Gecko) Chrome/24.0.1295.0 Safari/537.15\\\",\\n \\\"Mozilla/5.0 (Windows NT 6.2; WOW64) AppleWebKit/537.14 (KHTML, like Gecko) Chrome/24.0.1292.0 Safari/537.14\\\"\\n ]\\n\\n # 计算淘宝sign的appkeys\\n self.appkeys = '12574478'\\n\\n # 存储全部商品id\\n self.all_pids = []\\n\\n # scrapyd爬虫的jobid\\n self.job_id = kwargs.get('_job', '1')\\n\\n # 设置all_shop 的key,s_id是店铺在数据库中的id\\n self.s_id = kwargs.get('s_id', '1')\\n\\n # 备忘录，记录被封禁url的处理\\n self.block_record = []\\n\\n self.title = ''\\n self.seller_name = ''\\n self.shop_id = 0\\n logger.info(f'接收到的参数:{kwargs}')\\n\\n # cookie池\\n self.cookie_pool = self.con.cookies_pool()\\n self.count = 0\\n self.account = self.cookie_pool['account']\\n self.token = self.cookie_pool['cookie_token']\\n self.cookies = self.cookie_pool['cookie_dict']\\n logger.info(f'当前使用的cookies的账号为:{self.account}')\\n\\n # 失败url链接\\n self.fail_urls = []\\n super().__init__(**kwargs)\\n\\n\\n\\n def start_requests(self):\\n\\n # resp = self.con.hget('all_shop', self.s_id)\\n #\\n # if resp:\\n # # 从redis取出的数据\\n # data = json.loads(resp)\\n # else:\\n # logger.warning(f'redis数据库表:_all_shop出现异常,参数为:{self.s_id}')\\n # return\\n\\n # 测试数据\\n\\n data = {\\n \\\"id\\\": 1,\\n \\\"shop_id\\\": \\\"59002150\\\", # 店铺id\\n \\\"title\\\": \\\"哈果超人 haco实用轻\\\", # 店铺名称\\n \\\"name\\\": \\\"behsu网购\\\", # 掌柜名称\\n \\\"platform_id\\\": 1, # 产品库定义的平台ID,淘宝为3,天猫为4\\n \\\"shop_link\\\": \\\"https://haco.taobao.com/index.htm?spm=a1z10.3-c-s.w5002-14453794708.2.5ae258eaRYkhrS\\\",\\n\\n # 'url': 'https://haco.taobao.com/index.htm?spm=a1z10.3-c-s.w5002-14453794708.2.5ae258eaRYkhrS',\\n # \\\"callback\\\": \\\"https://pr.test.puget.work/api/screen/shop\\\", # 发送采集结果的回调地址\\n \\\"token\\\": \\\"ce934bc118beedabd789ed5cf6a20dc7\\\" # 接口请求认证\\n }\\n\\n self.title = data.get('title')\\n self.seller_name = data.get('name')\\n self.shop_id = data.get('shop_id')\\n\\n shop_link = data.get('shop_link')\\n\\n shop_search_url = 'https://' + urlparse(shop_link).netloc\\n\\n # 请求店铺所有商品\\n r = requests.get(shop_search_url + '/search.htm')\\n sel = Selector(text=r.text)\\n\\n # 获取权值id\\n if 'tmall.com' in shop_search_url:\\n # wid = sel.css('[data-title=搜索列表]::attr(data-widgetid)').get()\\n wid = int(sel.css('#bd > div::attr(data-widgetid)').get()) + 1 # 天猫 10月更新\\n # wid = sel.css('#hd > div::attr(data-widgetid)').get()\\n # '//*[@id=\\\"bd\\\"]//*[@class=\\\"J_TModule\\\"]/@data-widgetid'\\n self.shop_search_list = shop_search_url + self.store_list.format(str(wid))\\n\\n elif 'taobao.com' in shop_link:\\n # 权值id\\n wid = sel.css('[data-title=宝贝列表]::attr(data-widgetid)').get()\\n # 获取全部商品id的接口\\n # 'https://guchun.tmall.com/i/asynSearch.htm?mid=w-18297719823-0&wid=18297719823&pageNo='\\n self.shop_search_list = shop_search_url + self.store_list.format(wid)\\n else:\\n logger.warning('店铺信息解析失败！请查看scrapyd日志获取详情！')\\n return\\n\\n self.con.hset('widget_id', self.title, wid)\\n\\n # json接口 免登陆\\n # http://shop.m.taobao.com/shop/shopsearch/search_page_json.do?sort=default&type=all&q=Simple%E9%9F%A9%E5%9B%BD%E5%A5%B3%E8%A3%85\\n self.json_url = self.baseurl + self.title\\n\\n yield scrapy.Request(url=self.json_url, callback=self.parse_search)\\n\\n # 搜索店铺信息并匹配\\n def parse_search(self, response):\\n\\n self.re_dt = {}\\n shopid = self.shop_id\\n sid = self.s_id\\n\\n doc = unescape(response.text)\\n # json接口内容 参考文件:接口1.json\\n jd_data = json.loads(doc)\\n\\n if jd_data.get('listItem'):\\n for s in jd_data.get('listItem'):\\n try:\\n if str(shopid) in str(s):\\n self.re_dt['sign'] = str(sid) # sign 产品库id\\n self.re_dt['shopid'] = str(s.get('shop').get('id')) # 淘宝shopid\\n self.re_dt['is_mall'] = s.get('shop').get('isMall') # 是否天猫\\n self.re_dt['total_sold'] = str(s.get('shop').get('totalSold')) # 销量\\n self.re_dt['level'] = ''.join(re.findall('\\\\d-\\\\d', str(s.get('icon')))) # 店铺等级\\n self.re_dt['sellerid'] = ''.join(re.findall('userid=(\\\\d+)', str(s.get('medal')))) # 卖家id\\n self.re_dt['goods'] = s.get('favRate') # 好评率\\n break\\n except Exception as e:\\n logger.warning(f'发生错误的函数:parse_search(),错误信息:{e}')\\n continue\\n\\n # 随机轮换 User-Agent,因为是h5接口，要用手机浏览器带cookies访问\\n iphone_headers = [\\n 'Mozilla/5.0 (iPhone; CPU iPhone OS 11_0 like Mac OS X) AppleWebKit/604.1.38 (KHTML, like Gecko) Mobile/15A372 MicroMessenger/6.5.16 NetType/WIFI Language/zh_CN',\\n 'Mozilla/5.0 (iPhone; CPU iPhone OS 11_0 like Mac OS X) AppleWebKit/604.1.38 (KHTML, like Gecko) Mobile/15A372 wxwork/2.1.5 MicroMessenger/6.3.22',\\n 'Mozilla/5.0 (iPhone 6s; CPU iPhone OS 11_0 like Mac OS X) AppleWebKit/604.1.38 (KHTML, like Gecko) Version/11.0 MQQBrowser/7.7.2 Mobile/15A372 Safari/8536.25 MttCustomUA/2 QBWebViewType/1',\\n 'Mozilla/5.0 (iPhone; CPU iPhone OS 11_0_1 like Mac OS X) AppleWebKit/604.1.38 (KHTML, like Gecko) Mobile/15A402 MicroMessenger/6.5.16 NetType/WIFI Language/zh_CN'\\n ]\\n\\n headers = {'user-agent': random.choice(iphone_headers)}\\n\\n data = '{\\\"sellerId\\\": %s}' % self.re_dt.get('sellerid')\\n cookies = self.cookies\\n token = self.token\\n account = self.account\\n\\n # 这部分只需要更换cookies，一般能访问成功，因为只访问一次接口\\n if response.meta.get('change_cookie'):\\n logger.info('-----------------更换cookies-----------------')\\n cookie_pool = self.con.cookies_pool(account)\\n if cookie_pool is None:\\n return self.close(TestSpider, 'cookie池为空！')\\n account = cookie_pool['account']\\n token = cookie_pool['cookie_token']\\n cookies = cookie_pool['cookie_dict']\\n cookie_str = cookie_pool['cookie_str']\\n logger.info(f'更换后的cookies的账号为:{account}')\\n\\n # 解码\\n st = self.sign(token=token, appkey=self.appkeys, t=str(int(time.time() * 1000)), data=data)\\n\\n # 实时接口\\n '''\\n http://h5api.m.taobao.com/h5/mtop.taobao.geb.shopinfo.queryshopinfo/2.0/?jsv=2.4.2\\n &appKey=12574478&t=1569315579952&sign=88d9947b4728cc649bc50f3cb66ef2c6&\\n api=mtop.taobao.geb.shopinfo.queryshopinfo&v=2.0&type=originaljson&timeout=3000&\\n AntiCreep=true&dataType=json&H5Request=true&data={\\\"sellerId\\\": 3790578263}\\n '''\\n url = self.store_detail.format(**st)\\n\\n yield scrapy.Request(url=url, headers=headers, cookies=cookies,\\n callback=self.parse_store_item,\\n meta={'account': account})\\n\\n # 进一步抓取店铺信息\\n def parse_store_item(self, response):\\n re_data = self.re_dt\\n\\n # 参考:接口2.json\\n doc = json.loads(response.text)\\n # 接口访问状态\\n status = doc.get('ret')\\n\\n logger.info(f'json接口响应状态{status}')\\n if 'SUCCESS' not in status[0]:\\n logger.info('处理淘宝封禁！')\\n yield scrapy.Request(url=self.json_url, callback=self.parse_search, dont_filter=True,\\n meta={'change_cookie': True})\\n else:\\n # 存储动态json接口内容\\n self.con.hset('item_json', self.title, response.text)\\n tdt = doc.get('data')\\n self.count = tdt.get('itemCount') # 商品总数\\n re_data['shopname'] = tdt.get('shopName') # 店铺名\\n re_data['fans_num'] = str(tdt.get('fansNum')) # 粉丝数\\n re_data['item_count'] = str(self.count) # 产品数量\\n re_data['new_item'] = str(tdt.get('newItem')) # 新品数\\n re_data['golden_seller'] = tdt.get('goldenSeller') # 金牌卖家\\n logger.info(f'店铺信息:{re_data}')\\n\\n # url = r'https://shop142840423.taobao.com/i/asynSearch.htm?mid=w-13377183093-0&wid=13377183093&pageNo=1'\\n\\n # 商品详情的第一页\\n # 天猫用不上这个详情页，直接计算出页数\\n # 淘宝暂时直接计算出页数\\n\\n if 'tmall.com' in self.shop_search_list:\\n # 通过计算得出页数\\n totalPages = math.ceil(int(self.count) / 90) + 1 # 向上取整\\n elif 'taobao.com' in self.shop_search_list:\\n # s = Selector(text=html)\\n # # count = int(s.xpath('//*[@id=\\\"shop-search-list\\\"]/div/div[2]/span/text()').extract_first())\\n # # if count is not None and count > item_count:\\n # # item_count = count\\n # 通过计算得出页数\\n totalPages = math.ceil(int(self.count) / 24) + 1 # 向上取整\\n\\n logger.info(f'总共需要采集的页面有：{totalPages}页')\\n\\n for page in range(1, totalPages + 1):\\n url = self.shop_search_list + str(page)\\n # 预处理cookies\\n headers = {\\n 'User-Agent': random.choice(self.chrome_agents)\\n }\\n time.sleep(1)\\n yield scrapy.Request(url=url, headers=headers, callback=self.parse_productId,\\n meta={'with_cookie': True})\\n\\n\\n def parse_productId(self, response):\\n '''\\n 解析当前商品详情页中的所有商品id\\n :param response:\\n :return:\\n '''\\n headers = {\\n 'User-Agent': random.choice(self.chrome_agents)\\n }\\n if 'rgv587_flag' in response.text:\\n yield scrapy.Request(url=response.request.url, headers=headers, callback=self.parse_productId,\\n meta={'with_cookie': True, 'block': True})\\n\\n text = response.text.replace('\\\\\\\\\\\"', '')\\n s = Selector(text=text)\\n firstPagePids = s.css('.J_TItems div .item::attr(data-id)').getall()\\n if len(firstPagePids) == 0:\\n firstPagePids = s.css('.shop-hesper-bd .item::attr(data-id)').getall()\\n logger.info(f'{response.request.url},商品id:{firstPagePids}')\\n pids = firstPagePids\\n\\n # 存储全部商品\\n # todo 增加持续化存储\\n self.all_pids.extend(pids)\\n\\n # 根据所需元素计算sign\\n def sign(self, token, appkey, t, data):\\n '''\\n :param token:\\n :param appkey:\\n :param t: str(int(time.time() * 1000))\\n :param data:\\n :return:\\n '''\\n pp = '&'.join([token, t, appkey, data]).encode()\\n sign = hashlib.md5(pp).hexdigest()\\n return {'sign': sign, 't': t, 'appkey': appkey, 'data': data}\\n\\n # 店铺爬虫结束后,数据存入redis并调用商品详情爬虫\\n def close(self, spider, reason):\\n # 去重存储所有商品id\\n all_products_id = list(set(self.all_pids))\\n logger.info(all_products_id)\\n\\n logger.info(f'采集到了{len(all_products_id)}条商品数据')\\n lose_count = int(self.count) - len(all_products_id)\\n if lose_count > 0:\\n logger.warning(f'采集缺失：{lose_count},缺失百分比：{lose_count / int(self.count) * 100}%')\\n\"\n },\n {\n \"alpha_fraction\": 0.6181393265724182,\n \"alphanum_fraction\": 0.6243674755096436,\n \"avg_line_length\": 32.80263137817383,\n \"blob_id\": \"8ef18760e2c1ab122966393e209969a1bb63f18c\",\n \"content_id\": \"c6396a51ee64d2f44baf06ec5857b5087d660ef8\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 2703,\n \"license_type\": \"no_license\",\n \"max_line_length\": 115,\n \"num_lines\": 76,\n \"path\": \"/DataItem/DataItem/middlewares.py\",\n \"repo_name\": \"lindo-zy/TBspider\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"# -*- coding: utf-8 -*-\\n\\n# Define here the models for your spider middleware\\n#\\n# See documentation in:\\n# https://doc.scrapy.org/en/latest/topics/spider-middleware.html\\n\\nimport base64\\nimport random\\nimport re\\nimport requests\\nfrom .settings import PROXY_USER, PROXY_PASS\\nfrom scrapy_crawlera import CrawleraMiddleware\\nfrom redis_pool import RedisPool\\n\\n\\n# 阿布云代理\\nclass ProxyDownloaderMiddleware(CrawleraMiddleware):\\n # 代理服务器\\n proxyServer = \\\"http://http-dyn.abuyun.com:9020\\\"\\n\\n # 生成加密后的代理密码\\n proxyAuth = \\\"Basic \\\" + base64.urlsafe_b64encode(bytes((PROXY_USER + \\\":\\\" + PROXY_PASS), \\\"ascii\\\")).decode(\\\"utf8\\\")\\n\\n def process_request(self, request, spider):\\n if request.meta.get('request_tp') == 'store':\\n request.meta['proxy'] = self.proxyServer\\n request.headers['Proxy-Authorization'] = self.proxyAuth\\n elif request.meta.get('request_tp') == 'detail':\\n super().process_request(request, spider)\\n\\n def process_response(self, request, response, spider):\\n if response.headers.__contains__('cache-control') and request.meta.get('request_tp') == 'store':\\n return request\\n return response\\n\\n\\nclass CookiesPoolMiddleware(CrawleraMiddleware):\\n\\n def get_cookies(self, account=''):\\n con = RedisPool()\\n cookies_pool = con.cookies_pool(account)\\n if cookies_pool is None:\\n print('cookies池为空！')\\n return None\\n self.account = cookies_pool['account']\\n token = cookies_pool['cookie_token']\\n # cookie_dict = cookies_pool['cookie_dict']\\n cookie_str = cookies_pool['cookie_str']\\n\\n # 预处理增加cookie可用度\\n unb_random = ''.join(str(random.choice(range(9))) for _ in range(13))\\n\\n pattern = r'unb=(\\\\d+)'\\n cookie_unb = re.sub(pattern=pattern, repl='unb=' + unb_random, string=cookie_str)\\n cookie_dict = dict([tuple(x.split('=', 1)) for x in cookie_unb.split(';')])\\n print(f'当前账号:{self.account}')\\n\\n return cookie_dict\\n\\n # 处理request请求\\n def process_request(self, request, spider):\\n if request.meta.get('with_cookie'):\\n if request.meta.get('block'):\\n print('中间件处理淘宝封禁！')\\n request.cookies = self.get_cookies(self.account)\\n else:\\n request.cookies = self.get_cookies()\\n return None\\n\\n # 处理响应\\n # def process_response(self, request, response, spider):\\n # if request.meta.get('with_cookie') and request.meta.get('block'):\\n # print('使用中间件，处理淘宝封禁！')\\n # return request\\n # return response\\n\"\n }\n]","isConversational":false},"num_files":{"kind":"number","value":16,"string":"16"}}},{"rowIdx":2977195,"cells":{"repo_name":{"kind":"string","value":"jasondzy/Python"},"repo_url":{"kind":"string","value":"https://github.com/jasondzy/Python"},"snapshot_id":{"kind":"string","value":"d0a0ffad8aaac1f75d9e7c5ecd5cc9c33bd712ea"},"revision_id":{"kind":"string","value":"9fe9cf23f7defa3581e7bcfe4cf8ec6a830b6cd7"},"directory_id":{"kind":"string","value":"2de1aaa4d9bde03e21601bfeddf7e85863b0d54a"},"branch_name":{"kind":"string","value":"refs/heads/master"},"visit_date":{"kind":"timestamp","value":"2021-01-01T18:46:00.739387","string":"2021-01-01T18:46:00.739387"},"revision_date":{"kind":"timestamp","value":"2018-02-11T14:59:26","string":"2018-02-11T14:59:26"},"committer_date":{"kind":"timestamp","value":"2018-02-11T14:59:26","string":"2018-02-11T14:59:26"},"github_id":{"kind":"number","value":98430631,"string":"98,430,631"},"star_events_count":{"kind":"number","value":0,"string":"0"},"fork_events_count":{"kind":"number","value":0,"string":"0"},"gha_license_id":{"kind":"null"},"gha_created_at":{"kind":"null"},"gha_updated_at":{"kind":"null"},"gha_pushed_at":{"kind":"null"},"gha_language":{"kind":"null"},"files":{"kind":"list like","value":[{"alpha_fraction":0.5054945349693298,"alphanum_fraction":0.5164835453033447,"avg_line_length":5.92307710647583,"blob_id":"512262367cd150e6a2aa7c9bfdcd24968e4bedba","content_id":"7cd4e9a63bee409606ec0c104b041f6d5ebc738d","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"C","length_bytes":91,"license_type":"no_license","max_line_length":37,"num_lines":13,"path":"/Python/plus/C_lib/test.c","repo_name":"jasondzy/Python","src_encoding":"UTF-8","text":"\n#include <stdio.h>\n\nint Loop()\n{\n\tprintf(\"---This is a C library---\");\n\n\twhile(1);\n\n\n\n\n\n}\n"},{"alpha_fraction":0.7186400890350342,"alphanum_fraction":0.7291910648345947,"avg_line_length":38.71428680419922,"blob_id":"a33c7625b7b9630fed6b7475d12967365ff8b914","content_id":"96868b870dd22b2202da7c85977bd89e9f1dd57f","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":1391,"license_type":"no_license","max_line_length":103,"num_lines":21,"path":"/Python/plus/Process/pool.py","repo_name":"jasondzy/Python","src_encoding":"UTF-8","text":"from multiprocessing import Pool\r\nimport time, os, random\r\n\r\n\r\ndef long_time_task(name): #这里创建了子进程需要运行的函数\r\n\tprint('Run task %s (%s)..'%(name,os.getpid()))\r\n\tstart = time.time()\r\n\ttime.sleep(random.random()*3)\r\n\tend = time.time()\r\n\tprint('task %s runs %0.2f seconds'%(name, (end-start)))\r\n\r\nif __name__ == '__main__':\r\n\tprint('Parent Process %s'%os.getpid())\r\n\tp = Pool(4) #使用进程池创建了4个进程，也就是该进程池中只有4个进程在运行\r\n\tfor i in range(5):#这里调用for循环，往Pool中存放5次的任务，如下的apply_async函数中的args是向函数传递的参数，这是元组类型，当只有一个参数的时候需要在最后添加一个,\r\n\t\tp.apply_async(long_time_task, args=(i,)) #往进程池中放入需要运行的函数，由于进程池中只有四个进程，而这里放入了5个任务，所以必会有一个任务无法立刻得到运行\r\n\tprint('waiting for all subprocess done...') #此处是主进程运行的打印\r\n\r\n\tp.close() # 在join前边需要调用close函数，表明不会再有任务往进程池中存放了\r\n\tp.join() #调用了join函数，表明主进程会在此处block，等待所有的子进程运行结束后才会往下运行，这里用来作为主进程和子进程的同步\r\n\tprint('all subprocess done...')#运行到这里表明所有的子进程和主进程都运行完毕了"},{"alpha_fraction":0.5430916547775269,"alphanum_fraction":0.5718194246292114,"avg_line_length":19.22222137451172,"blob_id":"797de78479865e12a6d800be515b6798524c9776","content_id":"9fdd032e1f1b8d5f527849160ade7fa75a9fa80b","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":731,"license_type":"no_license","max_line_length":65,"num_lines":36,"path":"/Python/plus/net/web/web_dynamic/web_framework.py","repo_name":"jasondzy/Python","src_encoding":"UTF-8","text":"\n\n# define a functon , this function is standard for wsgi interface\ndef application(environ, start_response):\n\n header= ''''''\n\n urls = [\n ('./index.html', handle_index),\n ('./index11.html', handle_index11)\n\n ]\n \n start_response('HTTP/1.1 200 ok',header)\n\n path = environ.get('PATH', '/')\n\n for name, handler in urls:\n if name == path:\n return handler()\n\n start_response('HTTP/1.1 404 not found',header)\n return '404 not found'\n\ndef handle_index():\n\n fp = open('./index.html', 'r')\n file_data = fp.read()\n fp.close()\n\n return file_data\n\ndef handle_index11():\n fp = open('./index11.html', 'r')\n file_data = fp.read()\n fp.close()\n\n return file_data\n\n"},{"alpha_fraction":0.6740442514419556,"alphanum_fraction":0.6740442514419556,"avg_line_length":36.38461685180664,"blob_id":"5ce43ed48fa81658501c03e56bea98a04ab8c95a","content_id":"e4981eff12456c9989f80886c08654945480a40c","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":497,"license_type":"no_license","max_line_length":65,"num_lines":13,"path":"/django/jason_test/booktest/urls.py","repo_name":"jasondzy/Python","src_encoding":"UTF-8","text":"from django.conf.urls import url\r\nfrom . import views\r\n\r\nurlpatterns = [\r\n\turl(r'^$',views.index,name=\"index\"),\r\n\turl(r'^upload/',views.upload,name=\"upload\"),\r\n\turl(r'^pages/',views.pages,name=\"pages\"),\r\n\turl(r'^book_index/',views.book_index, name=\"book_index\"),\r\n\turl(r'^ajax_get/',views.ajax_get,name=\"ajax_get\"),\r\n\turl(r'^get_bookinfo/',views.get_bookinfo,name=\"get_bookinfo\"),\r\n\turl(r'^editor/',views.editor,name=\"editor\"),\r\n\turl(r'^editor_handle',views.editor_handle,name=\"editor_handle\"),\r\n]"},{"alpha_fraction":0.5050128102302551,"alphanum_fraction":0.5206341743469238,"avg_line_length":27.771812438964844,"blob_id":"39c39fa75b7d82facc065ec5d1ecc5d449fda2db","content_id":"4c33f77ec4f14b03ab2e5e680f87324d3f77464b","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":4463,"license_type":"no_license","max_line_length":145,"num_lines":149,"path":"/Python/mysql/connect.py","repo_name":"jasondzy/Python","src_encoding":"UTF-8","text":"\n\nfrom pymysql import *\n\nclass py_mysql(object):\n\n def __init__(self, host, port, user, passwd, db, charset):\n self.host = host\n self.port = port\n self.user = user\n self.passwd = passwd\n self.db = db\n self.charset = charset\n try:\n print(self.host, self.port, self.user, self.passwd, self.db, self.charset)\n self.conn = connect(host = self.host, port = self.port, user = self.user, passwd = self.passwd, db = self.db, charset = self.charset)\n except Exception as e:\n print(e)\n print('fail!!!')\n else:\n print('connect ok')\n self.curssor = self.conn.cursor()\n\n\n def add(self, params=[]):\n sql = 'insert into login values(%s,%s,%s,%s,%s,%s)'\n res = self.curssor.execute(sql, params)\n\n if res:\n self.conn.commit()\n else:\n self.conn.rollback()\n print (res)\n\n# self.curssor.close()\n# self.conn.close()\n\n def delete(self, params):\n sql = 'delete from login where id = %s'\n res = self.curssor.execute(sql, params)\n\n if res:\n self.conn.commit()\n else:\n self.conn.rollback()\n print(res)\n\n def update(self, params):\n sql = 'update login set %s = %s where id = %s'\n res = self.curssor.execute(sql, params)\n\n if res:\n self.conn.commit()\n else:\n self.conn.rollback()\n print(res)\n\n def select(self, params):\n sql = 'select * from login'\n\n# print('------%s-----'%params)\n\n if params[0] == 'one':\n self.curssor.execute(sql)\n result = self.curssor.fetchone()\n print('select one ok')\n elif params[0] == 'all':\n self.curssor.execute(sql)\n result = self.curssor.fetchall()\n print('select all ok')\n else:\n print('command wrong!')\n result = NULL\n\n return result\n\n def close(self):\n self.curssor.close()\n self.conn.close()\n\nclass Userlogin(py_mysql):\n# def __init__(self, host, port, user, passwd, db, charset):#此处若不定义__init__函数则默认使用父类的__init__函数\n# super(Userlogin,self).__init__(host, port, user, passwd, db, charset)#此处若定义__init__函数可通过此方式调用父类的__init__函数\n# 以上调用父类中的__init__函数的作用是使用父类中创建的一些局部变量，该类添加的局部变量可在此处及以下继续添加\n def login(self, user, passwd):\n sql = 'select passwd from login where name = %s'\n params = [user]\n res = self.curssor.execute(sql, params)\n\n if res:\n result = self.curssor.fetchone()\n# print('----result:%s-----'%result)\n print('select ok....')\n else:\n result = NULL\n print('username wrong!!!')\n\n# print('---result:%s---passwd:%s---'%(result,passwd))\n if result[0] == passwd:\n print(' login sucess....')\n else:\n print('passwd wrong!!!!')\n\n def join(self, user, passwd):\n sql = 'select passwd from login where name = %s'\n params = [user]\n res = self.curssor.execute(sql, params)\n if res:\n print('username exists, change it!!')\n return 0\n else:\n print('username can join...')\n\n sql = 'insert into login(name,passwd) values(%s,%s)'\n params = [user, passwd]\n\n res = self.curssor.execute(sql, params)\n if res:\n self.conn.commit()\n print('insert ok...')\n else:\n self.conn.rollback()\n print('insert fail!!!')\n\n return 1\n \n\nif __name__ == '__main__':\n\n# database = input('please input the database: ')\n# mysql = py_mysql(host = '192.168.31.216', port = 3306, user = 'root', passwd = 'dzy', db = 'python3', charset = 'utf8')\n\n# params = [0, 'll', '123456', '34567890', 'pudong', 0]\n# mysql.add(params)\n\n# params = ['all']\n# data = mysql.select(params)\n# print(data)\n\n login = Userlogin(host = '192.168.31.216', port = 3306, user = 'root', passwd = 'dzy', db = 'python3', charset = 'utf8')\n# login.login('ll', '123456')\n \n result = login.join('ggg','333333')\n if result:\n print('join sucess...')\n else:\n print('join fail !!')\n\n login.close()\n\n print('OK!!')\n"},{"alpha_fraction":0.6905055642127991,"alphanum_fraction":0.6905055642127991,"avg_line_length":52.06666564941406,"blob_id":"8e6dc5c02c5fac921a1994bbd4b0e4c402396509","content_id":"657e72a59787eb375a6d69584539751350107bc2","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":811,"license_type":"no_license","max_line_length":106,"num_lines":15,"path":"/django/tiantian/tiantian_project/urls.py","repo_name":"jasondzy/Python","src_encoding":"UTF-8","text":"from django.conf.urls import url\r\nfrom . import views\r\n\r\nurlpatterns = [\r\n url(r'^register/$',views.register,name=\"register\"),\r\n url(r'^register_handle/$',views.register_handle,name=\"register_handle\"),\r\n url(r'^check_username/$',views.check_username,name=\"check_username\"),\r\n url(r'^login/$',views.login,name=\"login\"),\r\n url(r'^login_handle/$',views.login_handle,name=\"login_handle\"),\r\n url(r'^user_center_info/$',views.user_center_info,name=\"user_center_info\"),\r\n url(r'^user_center_info_register/$',views.user_center_info_register,name=\"user_center_info_register\"),\r\n url(r'^user_center_info_register_handle$',views.user_center_info_register_handle,name\r\n \t=\"user_center_info_register_handle\"),\r\n url(r'^user_center_order$',views.user_center_order,name=\"user_center_order\"),\r\n]\r\n"},{"alpha_fraction":0.6104129552841187,"alphanum_fraction":0.6122082471847534,"avg_line_length":12.925000190734863,"blob_id":"c517c9b5b450b206be6ab370ef0e20a5ea512ec3","content_id":"c408b0609442d8fecc940d436977de220b39c975","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":557,"license_type":"no_license","max_line_length":38,"num_lines":40,"path":"/Python/plus/copy_test.py","repo_name":"jasondzy/Python","src_encoding":"UTF-8","text":"\nfrom multiprocessing import Pool\nimport os\nimport shutil\n\ndef copy_files(name):\n\t\n\tprint('-----%s--------'%name)\n\tfr = open('test/'+name)\n\tfw = open('test_bak/'+name,'w')\n\n\tcontent = fr.read()\n\tfw.write(content)\n\n\tfr.close()\n\tfw.close()\n\n\n\n\n\ndef main():\n\n\tif not os.path.exists('test_bak/'):\n\t\tos.mkdir('test_bak')\n\telse:\n\t\tshutil.rmtree('test_bak')\n\t\tos.mkdir('test_bak')\n\tsource_files = os.listdir('test')\n\n\tpool = Pool(5)\n\n\tfor name in source_files:\n\t\tpool.apply_async(copy_files,(name,))\n\n\tpool.close()\n\tpool.join()\n\n\nif __name__ == '__main__':\n\tmain()"},{"alpha_fraction":0.5282837748527527,"alphanum_fraction":0.5349951982498169,"avg_line_length":23.162790298461914,"blob_id":"ac25b924f5ce05009801f41ee03b5273d2701c1b","content_id":"cbec2fb6541b4bf2d605680d8070f3ce36567a90","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":2086,"license_type":"no_license","max_line_length":99,"num_lines":86,"path":"/Python/plus/net/web/web_framework/web_service.py","repo_name":"jasondzy/Python","src_encoding":"UTF-8","text":"\nfrom socket import *\nfrom multiprocessing import Process\nimport re\nimport sys\nfrom web_framework import app\n\nclass Http_service(object):\n ''' For Http_service '''\n def __init__(self):\n self.send_data = ''\n\n self.Web_service = socket(AF_INET, SOCK_STREAM)\n print('--create a socket--')\n \n self.Web_service.setsockopt(SOL_SOCKET, SO_REUSEADDR, 1)\n\n self.Web_service.bind(('', 7788))\n print('--bind port --')\n\n def Http_start(self):\n self.Web_service.listen(5)\n print('--listenning--')\n\n while True:\n Client, ip_info = self.Web_service.accept()\n print('--accept--')\n\n # create a Process to receive data and handle it \n p = Process(target=self.Receive_data, args=(Client,))\n p.start()\n\n # when create the Process and transfer the Client. the main process client should be closed\n Client.close()\n\n self.Web_service.close()\n\n def Receive_data(self, Client):\n # Receive data \n print('--receive data--')\n data = Client.recv(1024)\n\n # data is right?\n if data:\n print(data)\n\n self.Handle_data(data)\n\n Client.send(bytes(self.send_data, 'utf-8'))\n else:\n print('--data error--')\n \n # close client, only connect one time\n Client.close() \n\n def start_response(self, status, headers):\n\n self.response_body = status + '\\r\\n' + str(headers) + '\\r\\n'\n\n def Handle_data(self, data):\n # split the html name\n html = '.'+(re.match(r'\\w+ +(/[^ ]*)', (data.decode('utf-8')).splitlines()[0])).group(1)\n\n print('---html:%s-----'%html)\n # create a environ \n environ = {\n 'PATH': html\n }\n\n html_data = app(environ, self.start_response)\n\n print(html_data)\n\n # http send data\n self.send_data = self.response_body + '\\r\\n'+ html_data\n\n\n\n\ndef main():\n http = Http_service()\n\n http.Http_start()\n\n\nif __name__ == '__main__':\n main()\n\n\n \n"},{"alpha_fraction":0.6530105471611023,"alphanum_fraction":0.6554934978485107,"avg_line_length":23.140625,"blob_id":"b3d2c787f9a09d1daf44da2423dc7dc345909766","content_id":"6256effbd5cab451f6451a7dd57a3f927feb3e02","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":1639,"license_type":"no_license","max_line_length":105,"num_lines":64,"path":"/tornado/test/04_chat.py","repo_name":"jasondzy/Python","src_encoding":"UTF-8","text":"import tornado.web\r\nimport tornado.httpserver\r\nimport tornado.ioloop\r\nimport tornado.options\r\nfrom tornado.web import url\r\nfrom tornado.websocket import WebSocketHandler\r\n\r\nimport os\r\n\r\ntornado.options.define('port', default=8000, type=int, help='runserver on given port')\r\n\r\nclass IndexHandler(tornado.web.RequestHandler):\r\n\tdef get(self):\r\n\t\tself.render('chat.html')\r\n\r\nclass ChatHander(WebSocketHandler):\r\n\tusers = set()\r\n\r\n\tdef open(self):\r\n\t\tself.users.add(self)\r\n\r\n\t\tfor u in self.users:\r\n\t\t\tu.write_message(\"%s 进入聊天室\" % self.request.remote_ip)\r\n\r\n\tdef on_message(self,message):\r\n\t\tfor u in self.users:\r\n\t\t\tu.write_message(\" %s发送消息:%s\" %(self.request.remote_ip, message))\r\n\r\n\tdef on_close(self):\r\n\t\tusers.remove(self)\r\n\t\tfor u in self.users:\r\n\t\t\tu.write_message(\"%s退出聊天室\"% self.request.remote_ip)\r\n\r\n\tdef check_origin(self, origin):\r\n\t\treturn True\r\n\r\n\r\nclass Application(tornado.web.Application):\r\n\tdef __init__(self):\r\n\t\thandler = [\r\n\t\t\turl(r'^/$', IndexHandler, name=\"IndexHandler\"),\r\n\t\t\turl(r'^/chat$', ChatHander, name=\"ChatHander\"),\r\n\t\t]\r\n\r\n\t\tsettings = dict(\r\n\t\t\tdebug= True,\r\n\t\t\tstatic_path = os.path.join(current_path, 'static'),\r\n\t\t\ttemplate_path = os.path.join(current_path, 'templates'),\r\n\t\t\t)\r\n\r\n\t\tsuper(Application, self).__init__(handler, **settings) \r\n\r\n\r\nif __name__ == '__main__':\r\n\r\n\ttornado.options.parse_command_line()\r\n\tcurrent_path = os.path.dirname(__file__)\r\n\r\n\tapp = Application()\r\n\r\n\thttp_server = tornado.httpserver.HTTPServer(app)\r\n\thttp_server.listen(tornado.options.options.port)\r\n\r\n\ttornado.ioloop.IOLoop.current().start()\r\n\r\n"},{"alpha_fraction":0.4642857015132904,"alphanum_fraction":0.5200892686843872,"avg_line_length":23.94444465637207,"blob_id":"f989783c2aad0aae97a6f92ff84e5d79e172376a","content_id":"c3533ad71215ad4cf6e512c07ea928d2e8c778b9","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":526,"license_type":"no_license","max_line_length":83,"num_lines":18,"path":"/Python/algorithm/select.py","repo_name":"jasondzy/Python","src_encoding":"UTF-8","text":"def select_seq(seq_data):\n \"\"\"插入排序的核心思想是找到后边无序序列中的最小值，并将最小值一次的放入序列的开头\"\"\"\n n = len(seq_data)\n\n for j in range(n-1):\n i = j\n min_value = i\n for i in range(i, n):\n if seq_data[i] < seq_data[min_value]:\n seq_data[min_value], seq_data[i] = seq_data[i], seq_data[min_value]\n\n\nif __name__ == '__main__':\n seq = [54, 226, 93, 17, 77, 31, 44, 55, 20, 6, 23, 54]\n\n select_seq(seq)\n\n print(seq)"},{"alpha_fraction":0.5100577473640442,"alphanum_fraction":0.5399684906005859,"avg_line_length":30.059782028198242,"blob_id":"658491938b473c95fd0b59d1cc33c2fe8419b6ef","content_id":"bb60dae28b24a552a623b303c42eaf8ebd83f7f5","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":5729,"license_type":"no_license","max_line_length":163,"num_lines":184,"path":"/Python/basic/plane_war/plane_war.py","repo_name":"jasondzy/Python","src_encoding":"UTF-8","text":"#!/usr/bin/python\n#! -*- coding:utf-8 -*-\n\nimport pygame\nimport time \n\nfrom pygame.locals import *\n\nclass Window(object):\n def __init__(self,temp1,temp2,temp3):\n self.size = temp1\n self.pos = temp2\n self.color = temp3 \n self.window = pygame.display.set_mode(self.size,self.pos,self.color)\n self.image = pygame.image.load('./feiji/background.png')\n\n\n def __str__(self):\n return \"create a window size:%s pos:%d color_size:%d\"%(str(self.size),self.pos,self.color)\n\n\nclass Base_plane(object):\n def __init__(self,name0,name1,name2,name3,name4,x,y):\n self.image0 = pygame.image.load(name0)\n self.image1 = pygame.image.load(name1)\n self.image2 = pygame.image.load(name2)\n self.image3 = pygame.image.load(name3)\n self.image4 = pygame.image.load(name4)\n self.x = x\n self.y = y\n\n\n\n\nclass My_hero(Base_plane):\n def __init__(self,name0,name1,name2,name3,name4,x,y):\n Base_plane.__init__(self,name0,name1,name2,name3,name4,x,y)\n self.bullet_list = []\n self.mouse_flag = 0\n\n def display(self,temp):\n temp.window.blit(self.image0,(self.x,self.y))\n\n for i in self.bullet_list:\n i.display(temp)\n if i.y < 0 :\n self.bullet_list.remove(i)\n\n def shoot(self,temp):\n bullet = Bullet(self.x,self.y)\n self.bullet_list.append(bullet)\n bullet.enemy.add(temp)\n\n def mouse_move(self):\n self.x,self.y = pygame.mouse.get_pos()\n self.x -= 50\n self.y -= 62\n\n\nclass Enemy1(Base_plane):\n def __init__(self,name0,name1,name2,name3,name4,x,y):\n Base_plane.__init__(self,name0,name1,name2,name3,name4,x,y)\n self.disappear_flag = 0 \n self.disappear = 0\n self.recreate = 0\n self.direction = 'right'\n\n def display(self,temp):\n if self.disappear_flag==0 and self.disappear==0:\n temp.window.blit(self.image0,(self.x,self.y))\n if self.disappear_flag == 4 and self.disappear ==0:\n temp.window.blit(self.image1,(self.x,self.y))\n self.disappear_flag -=1\n elif self.disappear_flag == 3 and self.disappear ==0:\n temp.window.blit(self.image2,(self.x,self.y))\n self.disappear_flag -=1\n elif self.disappear_flag == 2 and self.disappear ==0:\n temp.window.blit(self.image3,(self.x,self.y))\n self.disappear_flag -=1\n elif self.disappear_flag == 1 and self.disappear ==0:\n temp.window.blit(self.image4,(self.x,self.y))\n self.disappear_flag -=1\n self.disappear = 1\n\n def mov(self):\n if self.x >420-10 :\n self.direction = 'left'\n elif self.x <10 :\n self.direction = 'right'\n if self.direction == 'left':\n self.x -=5\n elif self.direction == 'right':\n self.x += 5\n print('direction:%s x:%d'%(self.direction,self.x))\n \n\nclass Bullet(object):\n\n def __init__(self,temp_x,temp_y):\n self.image = pygame.image.load('./feiji/bullet.png')\n self.x = temp_x\n self.y = temp_y\n self.enemy = set()\n\n def display(self,temp):\n temp.window.blit(self.image,(self.x,self.y))\n self.y -=20\n\n for temp in self.enemy:\n print('x:%d y:%d'%(self.x,self.y))\n if self.x-55 <temp.x and self.x+15 > temp.x and self.y-45 <temp.y and self.y+45 >temp.y and temp.disappear_flag==0:\n temp.disappear_flag = 4\n break\n \n\n\n\ndef move(temp1,temp2):\n\n for event in pygame.event.get():\n\n if event.type == QUIT:\n print('exit')\n exit() #use to quit frome a programe\n elif event.type == KEYDOWN:\n if event.key == K_a or event.key == K_LEFT:\n print('left')\n temp1.x -=10\n\n elif event.key == K_d or event.key == K_RIGHT:\n print('right')\n temp1.x +=10\n\n elif event.key == K_c:\n print('exit')\n exit()\n elif event.key == K_m:\n print('use mouse for moving')\n temp1.mouse_flag = 1\n \n elif event.key == K_SPACE:\n print('space')\n temp1.shoot(temp2)\n\n elif event.key == K_n:\n print('create a new game')\n temp2.recreate = 1\n\n\ndef main():\n pygame.init() # pygame init\n screen = Window((480,852),0,32)\n pygame.display.set_caption('Plane War') #创建窗口标签\n#---------above create a whole background class-----------------\n hero = My_hero('./feiji/hero1.png','./feiji/hero_blowup_n1.png','./feiji/hero_blowup_n2.png','./feiji/hero_blowup_n3.png','./feiji/hero_blowup_n4.png',210,700)\n enemy1 = Enemy1('./feiji/enemy1.png','./feiji/enemy1_down1.png','./feiji/enemy1_down2.png','./feiji/enemy1_down3.png','./feiji/enemy1_down4.png',210,0)\n#--------above create a hero plane class---------------------- \n\n while True:\n screen.window.blit(screen.image,(0,0))\n#---------above create a background----------------------------\n hero.display(screen)\n if hero.mouse_flag == 1 :\n hero.mouse_move()\n enemy1.display(screen)\n enemy1.mov()\n#---------above create plane--------------------------\n if enemy1.recreate ==1 :\n enemy1 = Enemy1('./feiji/enemy1.png','./feiji/enemy1_down1.png','./feiji/enemy1_down2.png','./feiji/enemy1_down3.png','./feiji/enemy1_down4.png',210,0)\n enemy1.recreate = 0\n move(hero,enemy1)\n\n \n pygame.display.update()\n\n time.sleep(0.01)\n\n\n\n\n\nif __name__ == '__main__':\n\n main()\n\n\n"},{"alpha_fraction":0.4166666567325592,"alphanum_fraction":0.44583332538604736,"avg_line_length":18.83333396911621,"blob_id":"55cc3576e42122a1f87e2c495653e939f48e66fa","content_id":"fe7d7809fc044437d5c00caf9ed6184ff43f8c53","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Text","length_bytes":240,"license_type":"no_license","max_line_length":45,"num_lines":12,"path":"/Python/readme.txt","repo_name":"jasondzy/Python","src_encoding":"UTF-8","text":"/*******************************************\n\n\tAuthor: Jason(dongzhenyu)\n\tDate: 2017-7-26\n\tGoal: proficient in using python\n\tGithub account: jasondzy\n********************************************/\n\n\n\n\nThis dir is used for python study\n\n\n"},{"alpha_fraction":0.7085714340209961,"alphanum_fraction":0.7657142877578735,"avg_line_length":17.66666603088379,"blob_id":"8ffd40afad2c9c2d99f0bd0982c26b1de3bb9477","content_id":"79c88ecf1b993ff39fd439b5e00954e9b7f7cd94","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"INI","length_bytes":175,"license_type":"no_license","max_line_length":49,"num_lines":9,"path":"/django/jason_test/uwsgi.ini","repo_name":"jasondzy/Python","src_encoding":"UTF-8","text":"[uwsgi]\r\nhttp=0.0.0.0:8000\r\nchdir=/home/ubuntu/user_jason/git/django/tiantian\r\nwsgi-file=tiantian/\r\nprocesses=4\r\nthreads=2\r\nmaster=True\r\npidfile=uwsgi.pid\r\ndaemonize=uswgi.log"},{"alpha_fraction":0.420265793800354,"alphanum_fraction":0.460132896900177,"avg_line_length":17.272727966308594,"blob_id":"061e8d3ae0c13a0a8abee75d506eeb3b94e8b9ab","content_id":"b41fc478fabba0c958876b540065aca97f64ae56","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":602,"license_type":"no_license","max_line_length":45,"num_lines":33,"path":"/Python/algorithm/merge.py","repo_name":"jasondzy/Python","src_encoding":"UTF-8","text":"def sort(left, right):\n lst = []\n j = 0\n i = 0\n while i < len(left) and j < len(right):\n if left[i] < right[j]:\n lst.append(left[i])\n i = i+1\n else:\n lst.append(right[j])\n j += 1\n lst += left[i:]\n lst += right[j:]\n\n return lst\n\n\ndef merge(lst):\n\n if len(lst) <= 1:\n return lst\n\n middle = len(lst) // 2\n left = merge(lst[:middle])\n right = merge(lst[middle:])\n\n return sort(left, right)\n\n\nif __name__ == '__main__':\n ll = [54, 26, 93, 17, 77, 31, 44, 55, 20]\n result = merge(ll)\n print(result)"},{"alpha_fraction":0.5576496720314026,"alphanum_fraction":0.5620842576026917,"avg_line_length":19.409090042114258,"blob_id":"281e7b1cd34b4e2504cf9d1eb89b3ea5dcce1760","content_id":"7e9e6d58624504bf618b688aba5e5b6f6dcf234c","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":902,"license_type":"no_license","max_line_length":122,"num_lines":44,"path":"/Python/basic/furniture/furniture_test.py","repo_name":"jasondzy/Python","src_encoding":"UTF-8","text":"\n\nclass room():\n \n def __init__(self,area,addr,info):\n \n self.area = area\n self.addr = addr\n self.info = info\n self.useful = self.area\n\n def __str__(self):\n\n msg = \"romm:size is :%d area useful is:%d addr is:%s info is:%s \"%(self.area,self.useful,self.addr,self.info)\n return msg\n def add_item(self,item):\n self.useful -= item.get_size()\n\n\n\n\nclass bed():\n \n def __init__(self,size,name):\n self.size = size\n self.name = name\n\n def __str__(self):\n msg = \"bed : size is:%d name is:%s\"%(self.size,self.name)\n return msg\n\n def get_size(self): #define a func to get the private data;\n return self.size\n\nroom_dzy = room(200,\"hangzhou,xihu area\",\"four rooms\")\n\nprint(room_dzy)\n\n\nbed_one = bed(8,\"yijia\")\n\nprint(bed_one)\n\nroom_dzy.add_item(bed_one)# arg is a class type\n\nprint(room_dzy)\n\n\n"},{"alpha_fraction":0.5067567825317383,"alphanum_fraction":0.5164092779159546,"avg_line_length":23.654762268066406,"blob_id":"dd0ca9768e958e9f35cff2d0fde65c259b687953","content_id":"2730a9f0f4c149e9fac2eb144a2b78eac7369bdd","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":2072,"license_type":"no_license","max_line_length":111,"num_lines":84,"path":"/Python/plus/net/web/web_service/web_service.py","repo_name":"jasondzy/Python","src_encoding":"UTF-8","text":"\nfrom socket import *\nfrom multiprocessing import Process\nimport re\n\n\nclass Http_service(object):\n ''' For Http_service '''\n def __init__(self):\n self.send_data = ''\n\n self.Web_service = socket(AF_INET, SOCK_STREAM)\n print('--create a socket--')\n \n self.Web_service.setsockopt(SOL_SOCKET, SO_REUSEADDR, 1)\n\n self.Web_service.bind(('', 7788))\n print('--bind port --')\n\n def Http_start(self):\n self.Web_service.listen(5)\n print('--listenning--')\n\n while True:\n Client, ip_info = self.Web_service.accept()\n print('--accept--')\n\n # create a Process to receive data and handle it \n p = Process(target=self.Receive_data, args=(Client,))\n p.start()\n\n # when create the Process and transfer the Client. the main process client should be closed\n Client.close()\n\n self.Web_service.close()\n\n def Receive_data(self, Client):\n # Receive data \n print('--receive data--')\n data = Client.recv(1024)\n\n # data is right?\n if data:\n print(data)\n\n self.Handle_data(data)\n\n Client.send(bytes(self.send_data, 'utf-8'))\n else:\n print('--data error--')\n \n # close client, only connect one time\n Client.close() \n\n def Handle_data(self, data):\n # split the html name\n html = '.'+(re.match(r'\\w+ +(/[^ ]*)', (data.decode('utf-8')).splitlines()[0])).group(1)\n\n # try to read the local html data\n try:\n file = open(html, 'rb')\n html_data = file.read()\n except:\n print('open file error')\n while True:\n print('-----------')\n finally:\n file.close()\n\n print(html_data)\n\n # http send data\n self.send_data = \"HTTP/1.1 200 ok\\r\\n\" + \"Server: my server\" \"\\r\\n\" + '\\r\\n'+ html_data.decode('utf-8')\n\n\n\n\ndef main():\n http = Http_service()\n\n http.Http_start()\n\n\nif __name__ == '__main__':\n main()\n"},{"alpha_fraction":0.7104166746139526,"alphanum_fraction":0.7333333492279053,"avg_line_length":21.904762268066406,"blob_id":"110025018fb77cd4b330bf83966e3f5d67216243","content_id":"d3a4bde258c84702fc6ff1dbe2d66298d320eeae","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":480,"license_type":"no_license","max_line_length":46,"num_lines":21,"path":"/django/tiantian/tiantian_project/models.py","repo_name":"jasondzy/Python","src_encoding":"UTF-8","text":"from django.db import models\n\n# Create your models here.\n\nclass UserInfo(models.Model):\n\tuser_name = models.CharField(max_length=20)\n\tuser_passwd = models.CharField(max_length=40)\n\tuser_mail = models.CharField(max_length=30)\n\n\tdef __str__(self):\n\t\treturn self.user_name\n\n\n\nclass User_address_info(models.Model):\n\taddress = models.CharField(max_length=100)\n\ttel_num = models.CharField(max_length=12)\n\tuser = models.ForeignKey(UserInfo)\n\t\n\tdef __str__(self):\n\t\treturn str(self.user)"},{"alpha_fraction":0.6076555252075195,"alphanum_fraction":0.619617223739624,"avg_line_length":18.465116500854492,"blob_id":"a912e5d36c291925ce16249605b8e7eab0c0e60c","content_id":"989ed944a3f88a424c655c9b28ecdfde9d2a06f6","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":1022,"license_type":"no_license","max_line_length":77,"num_lines":43,"path":"/Python/plus/net/tcp/tcp_service.py","repo_name":"jasondzy/Python","src_encoding":"UTF-8","text":"\"\"\"\n# note: 这里是通过gevent模块还实现单线程多任务的功能,其中的socket函数来自gevent模块中\n#\n#\n\n\"\"\"\nfrom gevent import socket, monkey\nimport gevent\n\nmonkey.patch_all()#使用gevent中的monkey来对接一下的函数进行封装成非阻塞的功能，此处必须\n\ndef handle_data(client):\n while True:\n data = client.recv(1024)\n if data:\n print(data)\n else:\n client.close()\n break\n\n\ndef main():\n service = socket.socket()\n print('create a socket')\n\n# service.setsockopt(SOL_SOCKET, SO_REUSEADDR, 1)\n\n service.bind(('', 7788))\n print('bind a port')\n\n service.listen(5)\n print('listenning.....')\n\n while True:\n client,ip = service.accept()\n print('accept..')\n\n gevent.spawn(handle_data,client)#添加需要进行调度的函数，只要遇到阻塞类型的函数就会进行切换从而实现多任务\n\n service.close()#never to run here for service\n\nif __name__ == '__main__':\n main()"},{"alpha_fraction":0.7872763276100159,"alphanum_fraction":0.7952286005020142,"avg_line_length":22.952381134033203,"blob_id":"e8732bca37879640621e73fd518ce564b166637d","content_id":"121a84f73966768d32d214f90f24816a09542ec6","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":875,"license_type":"no_license","max_line_length":97,"num_lines":21,"path":"/Python/plus/Process/multi_processing.py","repo_name":"jasondzy/Python","src_encoding":"UTF-8","text":"\nfrom multiprocessing import Process\nimport time \n\ndef test():#这里表示的是子进程中所要运行的程序\n\tfor i in range(5):\n\t\tprint('---son Process---')\n\t\ttime.sleep(1)\n\nson = Process(target=test)#这里使用Process类创建了一个进程son，里边的参数是一个函数表明son这个进程需要处理的事情,参数中的test运行结束后子进程自动消亡\n\nson.start()#start方法表示的是开始运行子进程\n\ntime.sleep(1)\n\n#son.terminate() #Process中的terminate方法的作用是立刻停止所创建的子进程并返回，到这里子进程就结束了了，整个程序就只剩下主进程了\n\nson.join()#这里表示的是主进程在这里等待子进程运行完毕后才会往下执行，这里起到进程的同步作用\n\nwhile True:#这里表示的是主进程中所要运行的程序\n\tprint('---parent process--')\n\ttime.sleep(1)"},{"alpha_fraction":0.5941176414489746,"alphanum_fraction":0.6058823466300964,"avg_line_length":14.090909004211426,"blob_id":"035debb3e9d1a61b951a6d3f8fd7dccc013b2c29","content_id":"75b37126fffb4079075f33f03d4afbab96c6c2ab","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":170,"license_type":"no_license","max_line_length":37,"num_lines":11,"path":"/Python/basic/exception/exception.py","repo_name":"jasondzy/Python","src_encoding":"UTF-8","text":"\n\ntry:\n 5/0\n\n\nexcept Exception as ret :\n print(\"%s wrong has happend\"%ret)\n\nelse :\n print(\"No wrong happened\")\nfinally :\n print(\"programme run sucess....\")\n\n\n"},{"alpha_fraction":0.6164383292198181,"alphanum_fraction":0.6164383292198181,"avg_line_length":25.375,"blob_id":"79b8b2e6974d691000dabc9921a3d3beaa81f019","content_id":"d1d56854345ba5a31901016f47ea23b59ae3e9e2","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":219,"license_type":"no_license","max_line_length":58,"num_lines":8,"path":"/tornado/project/Ihome/Settings.py","repo_name":"jasondzy/Python","src_encoding":"UTF-8","text":"\r\n# import os\r\n\r\n# current_path = os.path.dirname(__file__)\r\n# settings = dict(\r\n# \tdebug = True,\r\n# \tstatic_path = os.path.join(current_path, 'static'),\r\n# \ttemplate_path = os.path.join(current_path, 'template'),\r\n# \t)"},{"alpha_fraction":0.7600364685058594,"alphanum_fraction":0.7700729966163635,"avg_line_length":33.28125,"blob_id":"4438b97876880b03a2cd5e123ccd768d1395f338","content_id":"abe9f99af4ce762491c63e52e5a73583a7d8c498","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":1946,"license_type":"no_license","max_line_length":115,"num_lines":32,"path":"/Python/plus/decorator/decorator.py","repo_name":"jasondzy/Python","src_encoding":"UTF-8","text":"def func(function): #这里的func定义的是一个闭包，这里的参数是function\n\tprint('Tis is a decorate1---')\n\tdef func_inner(*args,**kwargs):#这里的func_inner是闭包中的内嵌函数，最终闭包的返回值就是这个内嵌函数的地址\n\t\tprint('----func_inner----')\n\t\tfunction(*args,**kwargs)#这里的function()的作用是执行函数function，从这里可以验证闭包的参数是一个函数的地址\n\n\treturn func_inner#这里是闭包的返回值，该返回值是函数的地址\n\n\ndef func2(function):\n\tprint('This is a decorate2')\n\tdef func_inner(*args,**kwargs):#这里对内嵌函数定义了不定参数，注意这里的内嵌函数就是将来调用的函数test()，所以要实现test(x,y...)带入参数，那么这里的内嵌函数必须定义成带参数的形式\n\t\tprint('-----func_inner2---')\n\t\tfunction(*args,**kwargs)#这里的函数调用的是定义的test函数，所以这里的参数要使用内嵌函数中的参数，可以不一致。因为最终所有的参数都是通过装饰后的test传递进来的。\n\treturn func_inner\n\n\n\n@func #这里的作用是试用装饰器func，传入的参数就是下一行中的函数地址，即test\n@func2 #通过执行这个py脚本，注意管着两个连续的装饰器的执行顺序是如何的\ndef test(a):\n\tprint('----test---a=%d---'%a)\n@func#通过对改脚本的输出log可知，装饰器的执行是在所有函数之前的，即这三个装饰器的最先打印处This is a decoratex，其中decorate2最先打印出来\ndef test1(a):\n\tprint('----test1-- a=%d--'%a)\n\ntest(0)#调用test函数，注意这里的test函数已经不再是上边定义了的test函数，而是经过装饰器装饰了的函数\ntest1(1)\n\n#@func的作用就是对函数进行装饰，流程原理如下：\n#@func => test = func(test) => func_inner()\n#即最后的test的地址为func_inner的地址，而在func_inner中有调用test所以实现了对test的封装"},{"alpha_fraction":0.45676276087760925,"alphanum_fraction":0.521064281463623,"avg_line_length":25.58823585510254,"blob_id":"c5cb3e3b7ba9e7722ed7350c41bd7b3a7e77d6dd","content_id":"0166bfe371f1f62922a7c3d51e13a1bc0b894ef3","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":601,"license_type":"no_license","max_line_length":85,"num_lines":17,"path":"/Python/algorithm/insert.py","repo_name":"jasondzy/Python","src_encoding":"UTF-8","text":"def insert_seq(seq_data):\n \"\"\"这里是采用插入排序的方式，具体实现原理是通过将序列分成两列来对待，即前边的有序序列和后边的无序序列，每次从无序序列中提取一个数插入到前边的有序序列中去\"\"\"\n n = len(seq_data)\n for j in range(n):\n i = j\n while i > 0:\n if seq_data[i] < seq_data[i-1]:\n seq_data[i-1], seq_data[i] = seq_data[i], seq_data[i-1]\n i -= 1\n\n\nif __name__ == '__main__':\n seq = [54, 226, 93, 17, 77, 31, 44, 55, 20, 6, 23, 54]\n\n insert_seq(seq)\n\n print(seq)"},{"alpha_fraction":0.7934272289276123,"alphanum_fraction":0.7981220483779907,"avg_line_length":30.950000762939453,"blob_id":"f79e101889ab19e2f579de301543f22f9a29a160","content_id":"4e8bb9548a4a90ef8566c47da963f2aaa21837db","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":1111,"license_type":"no_license","max_line_length":127,"num_lines":20,"path":"/Python/plus/Process/process_class.py","repo_name":"jasondzy/Python","src_encoding":"UTF-8","text":"\nfrom multiprocessing import Process\nimport time\n\n\nclass New_process(Process):#这里定义了一个新的Newprocess类，这个类继承了Process这个父类,即可通过这个新建的类来创建一个自定义的进程\n\tdef __init__(self,value):\n\t\tProcess.__init__(self)#初始化调用了父类中的init函数，同时也加入了自定义的value的变量\n\t\tself.value = value\n\n\tdef run(self):#定义了一个run方法，切记：此处定义的run方法是对父类Process中的run方法的一种重写，接下来的父类中的start方法启用的是这里重写了的run方法,这里也实现了一个不用通过tartget=xxx的方法传入一个方法\n\t\twhile True:\n\t\t\tprint('----son-----')\n\t\t\ttime.sleep(1)\n\nson = New_process(1)#用新创建的类来创建一个子进程,里边传入的参数并没有实际的作用\nson.start()#调用start方法，这个start方法是在父类中定义的，但是这个start的方法实际调用的是run方法，由于在新建的类中重写了run方法所以最终调用的是新创建的类中的run方法\n\nwhile True:\n\tprint('---parent----')\n\ttime.sleep(1)"},{"alpha_fraction":0.5438596606254578,"alphanum_fraction":0.5470494627952576,"avg_line_length":16.742856979370117,"blob_id":"9d56e5a0172057ca9c764517ac55db8accdbfe30","content_id":"40e2cbdfd5c45ccfa1e62558cc8c25dfd0926a34","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":627,"license_type":"no_license","max_line_length":62,"num_lines":35,"path":"/Python/basic/class_func/class_func.py","repo_name":"jasondzy/Python","src_encoding":"UTF-8","text":"\n\nclass Game(object):\n\n num = 0\n\n def __init__(self):\n self.name = \"croos fire\"\n\n\n #define a class func , use classmethod to define\n\n @classmethod\n def class_num(cls):\n cls.num +=1\n\n #staticmethod can no args . so no self or cls and so on...\n @staticmethod\n def print_info():\n print(\"---------------------\")\n print(\" cross fire \")\n print (\"___________________\")\n\n\ngame = Game()\n\n# call the object func\nprint(game.name)\n\n#call the class func\n#Game.class_num() or\ngame.class_num()\nprint(Game.num)\n\n#call the static func\n#Game.print_info() or\ngame.print_info()\n\n\n\n\n"},{"alpha_fraction":0.6838235259056091,"alphanum_fraction":0.6911764740943909,"avg_line_length":23.81818199157715,"blob_id":"7569d55132e9b7a0db34f3bc869dfdb2aefe38bf","content_id":"d5d5b05da588419816efa24cb35895f78086d7d1","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":272,"license_type":"no_license","max_line_length":50,"num_lines":11,"path":"/django/tiantian/goods_app/views.py","repo_name":"jasondzy/Python","src_encoding":"UTF-8","text":"from django.shortcuts import render\n\n# Create your views here.\n\ndef index(request):\n\tname = request.session.get('user',None)\n\tif name != None:\n\t\tcontext = {'user':name,'status':1}\n\telse:\n\t\tcontext = {'user':\"\",'status':0}\n\treturn render(request,'goods/index.html',context)"},{"alpha_fraction":0.7200000286102295,"alphanum_fraction":0.7200000286102295,"avg_line_length":11.5,"blob_id":"cde0b7317bb476798c98164133b143337f321adf","content_id":"add78c5a1b6ab6186bc8378891ccb9cd248ca0d6","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Markdown","length_bytes":25,"license_type":"no_license","max_line_length":12,"num_lines":2,"path":"/Python/basic/README.md","repo_name":"jasondzy/Python","src_encoding":"UTF-8","text":"# class_func\n# furniture\n"},{"alpha_fraction":0.5659287571907043,"alphanum_fraction":0.586140513420105,"avg_line_length":20.14285659790039,"blob_id":"2d7188fe13aed35b1f092e68222048af850c0c80","content_id":"8cfe31961c370943ce4be7b08be83c0cf27a295b","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":1039,"license_type":"no_license","max_line_length":69,"num_lines":49,"path":"/Python/plus/net/web/web_framework/web_framework.py","repo_name":"jasondzy/Python","src_encoding":"UTF-8","text":"\n\nclass Application(object):\n\n def __init__(self,urls):\n self.urls = urls\n\n # define a callable function, so the class can be called\n def __call__(self, environ, start_response):\n return self.application(environ, start_response)\n\n\n # define a functon , this function is standard for wsgi interface\n def application(self,environ, start_response):\n\n header= ''''''\n\n start_response('HTTP/1.1 200 ok',header)\n\n path = environ.get('PATH', '/')\n\n for name, handler in self.urls:\n if name == path:\n return handler()\n\n start_response('HTTP/1.1 404 not found',header)\n return '404 not found'\n\ndef handle_index():\n\n fp = open('./index.html', 'r')\n file_data = fp.read()\n fp.close()\n\n return file_data\n\ndef handle_index11():\n fp = open('./index11.html', 'r')\n file_data = fp.read()\n fp.close()\n\n return file_data\n\n\nurls = [\n ('./index.html', handle_index),\n ('./index11.html', handle_index11)\n\n]\n\napp = Application(urls)\n\n"},{"alpha_fraction":0.610837459564209,"alphanum_fraction":0.7192118167877197,"avg_line_length":19.200000762939453,"blob_id":"a4fa24eae73bab0e1a5c16763830a4eebf7c1eb3","content_id":"751b71ce01f6c9c740b4c778e27cd8a86967e58f","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":203,"license_type":"no_license","max_line_length":60,"num_lines":10,"path":"/Python/plus/net/send_data.py","repo_name":"jasondzy/Python","src_encoding":"UTF-8","text":"\nimport socket\n\n\nudp_socket = socket.socket(socket.AF_INET,socket.SOCK_DGRAM)\n\nudp_socket.bind(('',7766))\n\ndata = udp_socket.recvfrom(1024)\nprint(data)\nudp_socket.sendto(b'hello',('192.168.0.107',8080))\n"},{"alpha_fraction":0.5933236479759216,"alphanum_fraction":0.5997097492218018,"avg_line_length":23.062936782836914,"blob_id":"a706b783183dbe6a0af6077233c08aa6ed8eefee","content_id":"999857b258b7c1e0fae773124b82108e87be6ac6","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":4193,"license_type":"no_license","max_line_length":197,"num_lines":143,"path":"/Python/basic/Project/laowang_shut.py","repo_name":"jasondzy/Python","src_encoding":"UTF-8","text":"\n\n\nclass Person():\n\n def __init__(self,name):\n self.name = name\n self.gun = None\n self.hp = 100\n\n def __str__(self):\n return \"The person name is %s hp is:%s the gun is: %s\"%(self.name,self.hp,self.gun)\n def load_bullet(self,temp1,temp2):\n temp1.add_bullet(temp2)\n\n def load_bullet_package(self,temp1,temp2):\n temp1.bullet_package = temp2\n\n def load_gun(self,temp):\n self.gun = temp\n\n def shoot(self,enemy):\n self.gun.shoot(enemy)\n\n def hp_down(self,temp):\n self.hp -= temp\n if self.hp < 0 :\n print(\"enemy has died!!!\")\n\n\nclass Gun():\n\n def __init__(self,name):\n self.name = name\n self.bullet_package = None\n self.bullet = None\n def __str__(self):\n return \"The name of gun is %s the bullet_package is:%s\"%(self.name,self.bullet_package)#这里的self.bullet_package变量，其实就是调用bullet_package中的__str__方法，因为__str__方法中用的就是return，所以对象名称中保存的值就是__str__\n\n def add_bullet_package(self,temp):\n self.bullet_package = temp\n \n def shoot(self,temp):\n self.bullet = self.bullet_package.pop_bullet()\n if self.bullet :\n self.bullet.hurt(temp)\n else :\n print(\"Do not have bullet\")\n\n\nclass Bullet_package():\n def __init__(self,max_num):\n self.max_num = max_num\n self.bullet_num=[]\n\n\n def __str__(self):\n return \"The max num of bullets is %d load bullet is %d\"%(self.max_num,len(self.bullet_num))\n\n\n def add_bullet(self,temp):\n self.bullet_num.append(temp)\n \n\n def pop_bullet(self):\n return self.bullet_num.pop()\n\n\nclass Bullet():\n def __init__(self,ability):\n self.ability = ability\n\n def __str__(self):\n return \"the ability is %d\"%self.ability\n \n def hurt(self,temp):\n temp.hp_down(self.ability)\n\n\n\ndef main():\n\n bullet = Bullet(10) #创建一个bullet实体，每一个实体都有一个独立的存储空间，即使是由同一个类创造出来的，他们中的属性值（即__init__下的变量值）是不一样的，这也是self的作用，若不用self.xx那么xx表示的就是类的属性，对由其创建的对象是一样的\n print(bullet)\n\n bullet_package = Bullet_package(30)\n print(bullet_package)\n\n\n gun = Gun(\"AK47\")\n print(gun)\n\n\n laowang = Person(\"laowang\")\n print(laowang)\n\n#---------above create three classes-----------------------\n\n for i in range(30):\n bullet = Bullet(10)#创建一个bullet实体\n laowang.load_bullet(bullet_package,bullet) # 这里的参数传递的是对象，不要传递类，因为对象才是实体，类只是一个模具\n print(bullet_package)\n#---------above add some bullets--------------------------\n\n laowang.load_bullet_package(gun,bullet_package)#从这些对象之间的调用可知，python是一种解释性语言，其按照顺序的方式对程序进行解释执行，所以这里的参数对象，必定是前边已经赋值了的\n print(gun)\n\n#---------above add a bullet_package into gun------------\n\n laowang.load_gun(gun)#在这里的各个函数调用过程中，时刻都会改变main一开始创建的四个对象中的值，因为这四个对象是一个在内存空间中的实体，对象中的属性值通过引用的方式改变其自身的值\n print(laowang)\n\n#--------above add a gun -------------------------------\n\n laosong = Person(\"laosong\")#这里再次创建一个Person对象，注意这里并不是和laowang是同一个对象，他们分别是独立的一块内存空间，其属性值是不一定相同的\n\n#--------above create a enemy------------------- -------\n\n laowang.shoot(laosong)\n print(laosong)\n\n laowang.shoot(laosong)\n print(laosong)\n laowang.shoot(laosong)\n print(laosong)\n\n laowang.shoot(laosong)\n print(laosong)\n\n laowang.shoot(laosong)\n print(laosong)\n\n\n laowang.shoot(laosong)\n print(laosong)\n\n\n\n#---------above shoot enemy-----------------------------\n\n\n\n\n\nif __name__ == \"__main__\" :#python中用来区别import和执行运行的方式\n main()\n\n"},{"alpha_fraction":0.7333333492279053,"alphanum_fraction":0.7333333492279053,"avg_line_length":41,"blob_id":"f80b29a195b5af49d37927a3a1531d934a562230","content_id":"9fe535c164c4cab3799d0bb03672c31124e9e4b9","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Text","length_bytes":45,"license_type":"no_license","max_line_length":41,"num_lines":1,"path":"/Python/basic/readme.txt","repo_name":"jasondzy/Python","src_encoding":"UTF-8","text":"\n\n\tthis is a basic python study test record\n"},{"alpha_fraction":0.6577181220054626,"alphanum_fraction":0.6711409687995911,"avg_line_length":29.589040756225586,"blob_id":"114f6f289b8b00dd666b7596c03578f00c5d5210","content_id":"ce5e6dc76a1e4c403cfb657a3935c23f8095df3d","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":5397,"license_type":"no_license","max_line_length":142,"num_lines":146,"path":"/tornado/test/03_template.py","repo_name":"jasondzy/Python","src_encoding":"UTF-8","text":"import tornado.web\r\nimport tornado.httpserver\r\nimport tornado.options\r\nimport tornado.ioloop\r\nimport tornado.httpclient\r\nimport tornado.gen\r\nimport os\r\nimport pymysql\r\nimport base64, uuid, json\r\nfrom tornado.web import url, StaticFileHandler\r\n\r\ntornado.options.define(\"port\", default=8000, type=int, help=\"runserver on given port\")\r\n\r\n\r\nclass IndexHander(tornado.web.RequestHandler):\r\n\tdef get(self):\r\n\t\tcontext = {\r\n\t\t\t'price':398,\r\n\t\t\t'title':'宽窄巷子+160平大空间+文化保护区双地铁',\r\n\t\t\t'address':'北京市丰台区六里桥地铁',\r\n\r\n\t\t}\r\n\t\tself.render(\"index.html\",context=context) #注意，render的第二个参数是一个关键字参数，需要传入xx=xxx类型的参数，或者**xx这里的xx是一个字典，**表示的是对字典进行解引用，解引用就是一个关键字参数\r\n #render的第一个参数是可变参数类型\r\n\r\nclass NewHander(tornado.web.RequestHandler):\r\n\tdef get(self):\r\n\t\tself.render('newindex.html',text=\"\")\r\n\r\n\tdef post(self):\r\n\t\ttext = self.get_argument('text')\r\n\t\tself.render('newindex.html', text=text)\r\n\r\nclass SetCookie(tornado.web.RequestHandler):\r\n\tdef get(self):\r\n\r\n\t\tcookie = self.get_secure_cookie('count')\r\n\t\tcount = int(cookie) +1 if cookie else 1\r\n\t\tself.set_secure_cookie('count', str(count))\r\n\r\n\t\treturn self.write(str(count))\r\n\r\nclass csrf_test(tornado.web.RequestHandler):\r\n\tdef get(self):\r\n\t\t# self.xsrf_token #这里是用来设置_xsrf这个cookie的值得，若采用模板的{% module xxX%}格式则不需要在这里进行手动测试\r\n\t\tself.render('csrf_test.html')\r\n\tdef post(self):\r\n\t\tself.write(\"ok\")\r\n\r\nclass AsyncHandler(tornado.web.RequestHandler):\r\n\t@tornado.gen.coroutine #这里的作用相当于python3中最近加入的async模块，此模块对Python的异步操作功能紧进行了封装，用协程的方式实现，当遇到IO阻塞的时候系统能够自动切换到其他未阻塞的协程中去执行\r\n\tdef get(self):\r\n\t\thttp = tornado.httpclient.AsyncHTTPClient()\r\n\t\tresponse = yield http.fetch(\"http://int.dpool.sina.com.cn/iplookup/iplookup.php?format=json&ip=14.130.112.24\")\r\n\t\t\t\t#如上的作用是当http.fetch阻塞的时候，系统自动的切换到其他协程中去执行，\r\n\t\tif response.error:\r\n\t\t\tself.send_error(500)\r\n\t\telse:\r\n\t\t\tprint('type of data:',type(response.body))\r\n\t\t\tprint(str(response.body))\r\n\t\t\tdata = json.loads(response.body.decode('utf-8'))\r\n\t\t\tself.write('country:%s, provice:%s, city:%s'% (data['country'], data['province'],data['city']))\r\n\r\n\r\nclass Application(tornado.web.Application):\r\n\tdef __init__(self):\r\n\t\thandlers = [\r\n\t\t\turl(r'^/$', IndexHander, name=\"index\"),\r\n\t\t\turl(r'^/cookie$', SetCookie, name=\"cookie\"),\r\n\t\t\turl(r'^/new$', NewHander, name=\"newhander\"),\r\n\t\t\turl(r'/csrf', csrf_test, name=\"csrf_test\"),\r\n\t\t\turl(r'^/async$', AsyncHandler, name=\"AsyncHandler\"),\r\n\t\t]\r\n\r\n\t\tsettings = dict(\r\n\t\t\tstatic_path = os.path.join(current_path,\"static\"), #这里加入静态文件所在的位置\r\n\t\t\ttemplate_path = os.path.join(current_path, \"templates\"), #这里加入模板文件的路劲\r\n\t\t\tdebug = True,\r\n\t\t\tcookie_secret = str(base64.b64encode(uuid.uuid4().bytes + uuid.uuid4().bytes)), #这里使用uuid和base64共同合成了一个随机的字符串，添加到cookie从而形成secure_cookie的机制\r\n\t\t\txsrf_cookies = True,\r\n\t\t\t)\r\n\t\tsuper(Application, self).__init__(handlers,**settings)\r\n\t\ttry:\r\n\t\t\tself.db = pymysql.connect(\r\n\t\t\t\thost = '115.159.62.43',\r\n\t\t\t\tport = 3306,\r\n\t\t\t\tdb = 'LoveHome',\r\n\t\t\t\tuser = 'root',\r\n\t\t\t\tpasswd = 'dzy',\r\n\t\t\t\tcharset = 'utf8'\r\n\t\t\t\t)\r\n\t\texcept Exception as e:\r\n\t\t\tprint(e)\r\n\t\t\tprint('connect to mysql fail!!')\r\n\t\telse:\r\n\t\t\tprint('connect sucess')\r\n\t\t\tself.cursor = self.db.cursor()\r\n\r\n\tdef create_table(self):\r\n\t\tsql = '''\r\n\t\t\tcreate table houses (\r\n \t\t\tid bigint(20) unsigned not null auto_increment comment '房屋编号',\r\n \t\t\ttitle varchar(64) not null default '' comment '标题',\r\n \t\t\tposition varchar(32) not null default '' comment '位置',\r\n \t\t\tprice int not null default 0,\r\n \t\t\tscore int not null default 5,\r\n \t\t\tcomments int not null default 0,\r\n \t\t\tprimary key(id)\r\n\t\t\t)ENGINE=InnoDB default charset=utf8 comment='房屋信息表'\r\n\t\t'''\r\n\t\tself.cursor.execute(sql)\r\n\t\tself.db.commit()\r\n\r\n\t\tself.cursor.close()\r\n\t\tself.db.close()\r\n\r\nif __name__ == '__main__':\r\n\r\n\tcurrent_path = os.path.dirname(__file__)\r\n\r\n\ttornado.options.parse_command_line()\r\n\r\n\t# app = tornado.web.Application([\r\n\t# \turl(r'^/$', IndexHander, name=\"index\"),\r\n\t# \turl(r'^/new$', NewHander, name=\"newhander\"),\r\n\t# \t# url(r'/()',StaticFileHandler, {\"path\":os.path.join(current_path,\"static/html\"), \"default_filename\":\"index.html\"}, name=\"index\"),\r\n\t# \t#注意如上的/后必须加上正则匹配的()，因为StaticFileHandler中的get方法需要参数，不加会报错，这里实现了任意url地址访问静态文件的方式\r\n\r\n\t# \t],\r\n\r\n\t# \tstatic_path = os.path.join(current_path,\"static\"), #这里加入静态文件所在的位置\r\n\t# \ttemplate_path = os.path.join(current_path, \"templates\"), #这里加入模板文件的路劲\r\n\r\n\t# \t debug=True)\r\n\r\n\r\n\tapp = Application()\r\n\t# app.create_table() #这里创建一个表\r\n\t# app.cursor.execute('select * from test_tbl')\r\n\t# result = app.cursor.fetchone()\r\n\t# print(result) \r\n\r\n\thttp_server = tornado.httpserver.HTTPServer(app)\r\n\thttp_server.listen(tornado.options.options.port)\r\n\r\n\ttornado.ioloop.IOLoop.current().start()\r\n\r\n\t\r\n\r\n"},{"alpha_fraction":0.7135134935379028,"alphanum_fraction":0.7675675749778748,"avg_line_length":18.77777862548828,"blob_id":"69bcec2a549a031dbbc431b32f9194edc59d99d6","content_id":"3cfdaaecf148ad9acd6d2210e1c1fdeb6358b26d","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"INI","length_bytes":185,"license_type":"no_license","max_line_length":50,"num_lines":9,"path":"/django/tiantian/uwsgi.ini","repo_name":"jasondzy/Python","src_encoding":"UTF-8","text":"[uwsgi]\r\nsocket=0.0.0.0:8000\r\nchdir=/home/ubuntu/user_jason/git/django/tiantian/\r\nwsgi-file=tiantian/wsgi.py\r\nprocesses=1\r\nthreads=1\r\nmaster=True\r\npidfile=uwsgi.pid\r\ndaemonize=uswgi.log"},{"alpha_fraction":0.7387518286705017,"alphanum_fraction":0.7532656192779541,"avg_line_length":22,"blob_id":"19700a2f3f9c48525a7f5d6574b81f51f7a918f1","content_id":"81d63f8e24725f69e6284afd3b849170c87aee72","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":689,"license_type":"no_license","max_line_length":46,"num_lines":30,"path":"/django/jason_test/booktest/models.py","repo_name":"jasondzy/Python","src_encoding":"UTF-8","text":"from django.db import models\nfrom tinymce.models import HTMLField\n\n# Create your models here.\n\nclass BookInfo(models.Model):\n\tbtitle = models.CharField(max_length=20)\n\tbpub_date = models.DateTimeField()\n\tbread = models.IntegerField(default=0)\n\tbcommet = models.IntegerField(default=0)\n\tisDelete = models.BooleanField(default=False)\n\tbcontent = HTMLField()\n\n\n\n\n\tdef __str__(self):\n\t\treturn self.btitle\n\n\nclass HeroInfo(models.Model):\n\thname = models.CharField(max_length=20)\n\thgender = models.BooleanField(default=1)\n\thBook = models.ForeignKey('BookInfo')\n\thcontent = models.CharField(max_length=100)\n\tisDelete = models.BooleanField(default=False)\n\t\n\n\tdef __str__(self):\n\t\treturn self.hname"},{"alpha_fraction":0.5109395384788513,"alphanum_fraction":0.5186614990234375,"avg_line_length":23.96666717529297,"blob_id":"8ef7925bdad54a053ca14f2f2318c6736cd978a9","content_id":"8858b4dab4b03a4fd195a6888f3ca70e8575f9ab","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"HTML","length_bytes":795,"license_type":"no_license","max_line_length":86,"num_lines":30,"path":"/django/jason_test/templates/booktest/editor.html","repo_name":"jasondzy/Python","src_encoding":"UTF-8","text":"<!DOCTYPE html>\r\n<html>\r\n<head>\r\n <title>editro</title>\r\n {% load static from staticfiles %}\r\n <script type=\"text/javascript\" src=\"{% static 'tiny_mce/tiny_mce.js' %}\"></script>\r\n <script type=\"text/javascript\">\r\n tinyMCE.init({\r\n 'mode':'textareas',\r\n 'theme':'advanced',\r\n 'width':400,\r\n 'height':100\r\n });\r\n </script>\r\n</head>\r\n<body>\r\n<form method=\"post\" action=\"{% url 'booktest:editor_handle' %}\">\r\n\t{% csrf_token %}\r\n <select name=\"select\">\r\n \t{% for index,item in data %}\r\n \t<option value=\"{{index}}\">{{item}}</option>\r\n \t{% endfor %}\r\n </select>\r\n <br>\r\n <textarea name='hcontent'>哈哈，这是啥呀</textarea>\r\n <br>\r\n <input type=\"submit\" value=\"提交\">\r\n</form>\r\n</body>\r\n</html>"},{"alpha_fraction":0.6939066052436829,"alphanum_fraction":0.7050113677978516,"avg_line_length":25,"blob_id":"18d19f968aa52314f60accc2b8ace72d7e4422c4","content_id":"6742574c83380bf985e6383ac9cdc181dbdb3b8d","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":3692,"license_type":"no_license","max_line_length":89,"num_lines":135,"path":"/django/tiantian/tiantian_project/views.py","repo_name":"jasondzy/Python","src_encoding":"UTF-8","text":"from django.shortcuts import render, redirect\nfrom django.core.urlresolvers import reverse\nfrom django.http import HttpResponse, JsonResponse\nfrom .models import *\nfrom hashlib import sha1\nimport re\n\n# Create your views here.\ndef register(request):\n\treturn render(request,'user/register.html')\n\ndef register_handle(request):\n\t#获取用户提交的信息\n\tname = request.POST['user_name']\n\tpwd = request.POST['pwd']\n\tcpwd = request.POST['cpwd']\n\temail = request.POST['email']\n\n\t#判断密码是否相同\n\tif pwd != cpwd:\n\t\treturn redirect(reverse('tiantian:register'))\n\n\t#判断邮箱是否正确\n\tif re.match(r'^[0-9a-zA-Z_]{0,19}@[0-9a-zA-Z]{1,13}\\.[com,cn,net]{1,3}$',email) == None:\n\t\treturn redirect(reverse('tiantian:register'))\n\n\t# 密码加密：\n\ts1 = sha1()\n\ts1.update(pwd.encode(\"utf-8\"))\n\tpasswd = s1.hexdigest()\n\t# 加密 end\n\n\tuser = UserInfo()\n\tuser.user_name = name\n\tuser.user_passwd = passwd\n\tuser.user_mail = email\n\tuser.save()\n\n\treturn redirect(reverse('tiantian:login'))\n\n\t# return render(request,)\n\ndef check_username(request):\n\tprint('test')\n\tname = request.GET['name']\n\tprint(name)\n\tcount = UserInfo.objects.filter(user_name=name).count() #这里使用的是count的方式来计算过滤出来的值有多少个\n\tprint(count) #能使用count的原因是过滤出来的是一个数组\n\tif count:\n\t\tprint('1')\n\t\treturn JsonResponse({'status':1})\n\telse:\n\t\tprint('0')\n\t\treturn JsonResponse({'status':0})\n\ndef login(request):\n\treturn render(request,'user/login.html')\n\ndef login_handle(request):\n\tname = request.POST['username']\n\tpwd = request.POST['pwd']\n\t#获取密码的加密格式字符串\n\ts1 = sha1()\n\ts1.update(pwd.encode(\"utf-8\"))\n\tpasswd = s1.hexdigest()\t\n\t# 加密 end\n\n\t#比较用户名\n\tcount = UserInfo.objects.filter(user_name=name).count()\n\tif count == 0:\n\t\treturn redirect(reverse('tiantian:login'))\n\telse:\n\t\tuser = UserInfo.objects.get(user_name=name)\n\t\tif user.user_passwd != passwd:\n\t\t\tstatus = 0\n\t\t\treturn redirect(reverse('tiantian:login'))\n\t\telse:\n\t\t\tstatus = 1\n\t\t\trequest.session['user']=name\n\t\t\trequest.session.set_expiry(300)\n\t\t\treturn redirect(reverse('goods:index'))\n\n\ndef user_center_info(request):\n\tname = request.session.get('user',None)\n\n\tif name == None:\n\t\treturn redirect(reverse('tiantian:login'))\n\n\tuser = UserInfo.objects.get(user_name=name)\n\taddr = user.user_address_info_set.all()\n\tif addr.count() == 0:\n\t\treturn redirect(reverse('tiantian:user_center_info_register'))\n\taddress = []\n\tfor i in addr:\n\t\taddress.append(i)\n\tcontext = {'name':name,'address':address}\n\treturn render(request,'user/user_center_info.html',context)\n\ndef user_center_info_register(request):\n\tname = request.session.get('user',None)\n\tif name == None:\n\t\treturn redirect(reverse('tiantian:login'))\n\telse:\n\t\tuser = UserInfo.objects.get(user_name=name)\n\t\taddr = user.user_address_info_set.all()\n\t\tif addr.count() == 0:\n\t\t\tstatus=0\n\t\telse:\n\t\t\tstatus=1\n\n\t\taddress = []\n\t\tfor i in addr:\n\t\t\taddress.append(i)\n\t\tcontext = {'name':name,'address':address,'status':status}\n\t\tprint(context)\n\treturn render(request,'user/user_center_site.html',context)\n\ndef user_center_info_register_handle(request):\n\ttel_num = request.POST['tel_num']\n\taddress = request.POST['address']\n\n\tif len(tel_num)<5 or len(address)<5:\n\t\treturn redirect(reverse('tiantian:user_center_info_register'))\n\n\tusername = request.session.get('user',None)\n\tif username == None:\n\t\treturn redirect(reverse('tiantian:register'))\n\tuser = UserInfo.objects.get(user_name=username)\n\tuser.user_address_info_set.create(address=address,tel_num=tel_num)\n\treturn redirect(reverse('tiantian:user_center_info'))\n\n\ndef user_center_order(request):\n\treturn render(request,'user/user_center_order.html')\n\n\n"},{"alpha_fraction":0.5834854245185852,"alphanum_fraction":0.5894160866737366,"avg_line_length":21.52688217163086,"blob_id":"4acbed1b9ad00b00892109b74b83a5f3f2047d29","content_id":"f3e20646c8ecc72dc9e7f7598cd48f4853e898f1","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":2264,"license_type":"no_license","max_line_length":136,"num_lines":93,"path":"/tornado/project/Ihome/models.py","repo_name":"jasondzy/Python","src_encoding":"UTF-8","text":"\r\nimport pymysql\r\nimport redis\r\nimport mysql_settings\r\n\r\n############################ mysql 处理类######################################################\r\nclass HandleMysql(object):\r\n\tdef __init__(self):\r\n\r\n\t\ttry:\r\n\t\t\tself.db = pymysql.connect(**mysql_settings.setting)\r\n\t\texcept Exception as e:\r\n\t\t\traise e\r\n\t\telse:\r\n\t\t\tprint('connect mysql sucess')\r\n\t\t\tself.cursor = self.db.cursor()\r\n\r\n\tdef create_table(self):\r\n\r\n\t\tfor sql in mysql_settings.table1:\r\n\t\t\ttry:\r\n\t\t\t\tself.cursor.execute(sql)\r\n\t\t\texcept Exception as e:\r\n\t\t\t\tprint(' %s alerady exist, do not create it again'% (sql.split(\"\\n\")[1].split(\" \")[2]))\r\n\t\t\telse:\r\n\t\t\t\tself.db.commit()\r\n\r\n\tdef get_values_from_mysql(self, sql):\r\n\t\ttry:\r\n\t\t\tself.cursor.execute(sql)\r\n\t\texcept Exception as e:\r\n\t\t\tprint('data did not exist')\r\n\t\t\tresult = None\r\n\t\telse:\t\r\n\t\t\tresult = self.cursor.fetchall()\r\n\r\n\t\treturn result\r\n\r\n\tdef insert_into_tbl(self, sql):\r\n\t\ttry:\r\n\t\t\tself.cursor.execute(sql)\r\n\t\texcept Exception as e:\r\n\t\t\traise(e)\r\n\t\telse:\r\n\t\t\tself.db.commit()\r\n\t\treturn True\r\n\r\n\tdef update_tbl(self, sql):\r\n\t\ttry:\r\n\t\t\tself.cursor.execute(sql)\r\n\t\texcept Exception as e:\r\n\t\t\traise(e)\r\n\t\telse:\r\n\t\t\tself.db.commit()\r\n\t\treturn True\r\n\r\n\tdef close_mysql():\r\n\t\tself.cursor.close()\r\n\t\tself.db.close()\r\n\r\n\r\n\r\n\r\n\r\n##################################### Redis 处理类####################################################\r\n\r\nclass HandRedis(object):\r\n\t######## connect to redis#########\r\n\tdef __init__(self):\r\n\t\ttry:\r\n\t\t\tself.redisInstance = redis.StrictRedis(host=\"localhost\", port=\"6379\") #这里的host只能使用localhost来进行连接，因为redis没有设置密码，且redis绑定了127.0.0.1这个地址\r\n\t\texcept Exception as e:\r\n\t\t\tprint(\" connect to redis failed \")\r\n\t\t\traise(e)\r\n\t\telse:\r\n\t\t\tprint(\" connect redis sucess\")\r\n\r\n\tdef set_value(self, key, value):\r\n\t\tresult = self.redisInstance.set(key, value)\r\n\t\tprint('set key,value',key,value)\r\n\r\n\tdef get_value(self, key):\r\n\t\tresult = self.redisInstance.get(key)\r\n\t\t# print('key ,value:',key,result)\r\n\t\treturn result\r\n\r\n\tdef set_expire(self, key, date):\r\n\t\tresult = self.redisInstance.expire(key, date)\r\n\t\tprint('set expire time key,date:',key,date)\r\n\r\n\tdef del_value(self, key):\r\n\t\tresult = self.redisInstance.delete(key)\r\n\t\tprint('delete',key)\r\n\t\treturn result\r\n\r\n"},{"alpha_fraction":0.5188916921615601,"alphanum_fraction":0.5843828916549683,"avg_line_length":18.799999237060547,"blob_id":"71f4e84e554755384c0cd031d02cb94b15a339ba","content_id":"6a3b6f9a549de13f105266748844eaa02691318b","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":397,"license_type":"no_license","max_line_length":61,"num_lines":20,"path":"/Python/plus/net/receive_data.py","repo_name":"jasondzy/Python","src_encoding":"UTF-8","text":"import socket\n\ndef main():\n receive = socket.socket(socket.AF_INET,socket.SOCK_DGRAM)\n\n receive.bind( ('',7788) )\n\n receive.sendto(b'test',('192.168.0.107', 8081) )\n\n while True:\n re_data = receive.recvfrom(1024)\n data,ipinfo = re_data\n receive.sendto(data,ipinfo)\n print(data.decode('gb2312'))\n\n receive.close()\n\n\nif __name__ == '__main__':\n main()\n\n"},{"alpha_fraction":0.6591100096702576,"alphanum_fraction":0.6612090468406677,"avg_line_length":28.060976028442383,"blob_id":"a2d97895a54972443c74c32f10d1ee5f07775db6","content_id":"1592057bc085ff5a7c9206432df946588d5f918e","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":2706,"license_type":"no_license","max_line_length":83,"num_lines":82,"path":"/django/jason_test/booktest/views.py","repo_name":"jasondzy/Python","src_encoding":"UTF-8","text":"from django.shortcuts import render\nfrom django.http import HttpResponse, JsonResponse\nfrom django.conf import settings\nfrom .models import *\nfrom django.core.paginator import Paginator\nimport json\n# Create your views here.\n\ndef index(request):\n return render(request,'booktest/index.html')\n # return HttpResponse('ok')\n\n\ndef upload(request):\n\n if request.method == 'POST':\n picture = request.FILES['picture']\n fname = '%s/cars/%s'%(settings.MEDIA_ROOT, picture.name)\n # return HttpResponse(settings.MEDIA_ROOT)\n with open(fname,'wb') as pic:\n for c in picture.chunks():\n pic.write(c)\n return HttpResponse('ok')\n else:\n return HttpResponse('error')\ndef my_custom_page_not_found_view(request):\n return HttpResponse('fail 404 fail')\n\ndef pages(request,id):\n if id == '':\n id = '1'\n list = HeroInfo.objects.all()\n paginator = Paginator(list,5)\n page = paginator.page(int(id))\n context = {'page':page}\n\n return render(request,'booktest/pages.html',context)\n\ndef book_index(request):\n return render(request,'booktest/book_index.html')\n\ndef ajax_get(request):\n # print('hello')\n book_list = BookInfo.objects.all()\n # print(book_list)\n # return JsonResponse({'data':book_list}) #这样返回数据会报错\n l = []\n for list in book_list:\n l.append((list.id,list.btitle)) #这里必须要将获得的book_list进行遍历，取出元素放在一个数组中才行，否则会报错\n return JsonResponse({'data':l}) #具体为什么这样的原因还有待解析\n\ndef get_bookinfo(request):\n print('test')\n id = request.GET['id'] #这里使用的是ajax的方式进行数据的传递\n print(id)\n list=HeroInfo.objects.filter(hBook_id=id)#这里的filter返回的是一个查询集，并不是一个对象，而是一个对象集合\n print(list)\n hero_list = []\n for i in list:\n hero_list.append((i.id,i.hname))\n return JsonResponse({'data':hero_list})\n\n#富文本编辑器\ndef editor(request):\n data = BookInfo.objects.all()\n list = []\n for l in data:\n list.append([l.id,l.btitle])\n print(list)\n context = {'data':list}\n return render(request,'booktest/editor.html',context)\n\ndef editor_handle(request):\n html = request.POST['hcontent'] #此处获取的是大文本提交的内容\n id = request.POST['select'] #此处获取的是要提交的id号\n print('id:',id)\n print(html)\n book = BookInfo.objects.get(pk=id) #注意filter方法返回的是一个查询集合，是一个集合，get返回的是一个对象\n print(book.btitle)\n book.bcontent = html\n book.save()\n return HttpResponse('ok')"},{"alpha_fraction":0.7318037748336792,"alphanum_fraction":0.7436708807945251,"avg_line_length":31.210525512695312,"blob_id":"f10f0c426ab7d6bab2d161fb8e49d5a09accad59","content_id":"49b8cc75b7eaf62390f3a17b0e3e2dacf7765818","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":1646,"license_type":"no_license","max_line_length":125,"num_lines":38,"path":"/tornado/test/01_hello.py","repo_name":"jasondzy/Python","src_encoding":"UTF-8","text":"import tornado.web\r\nimport tornado.ioloop\r\nimport tornado.httpserver\r\nimport tornado.options\r\nfrom tornado.web import url\r\n\r\ntornado.options.define(\"port\",default=8000,type=int, help=\"run server on the given port.\") #这里定义了port参数，这样就可以在命令行启动的时候将改参数传入了\r\ntornado.options.define(\"itcast\",default=[],type=str, multiple=True, help=\"itcast subjects.\")#这里定义了一个多参数变量，命令行传参的时候需要用,号隔开\r\nclass IndexHander(tornado.web.RequestHandler):\r\n\r\n\tdef get(self):\r\n\t\tself.write(\"hello world\")\r\n\r\nclass SecondHander(tornado.web.RequestHandler):\r\n\tdef initialize(self,subject):\r\n\t\tself.subject = subject\r\n\r\n\tdef get(self):\r\n\t\tself.write(self.subject)\r\n\r\n\r\nif __name__ == '__main__':\r\n\ttornado.options.parse_config_file(\"./config\") #这里定义了解析命令行参数的函数，命令行中的参数通过这个函数进行解析\r\n\tprint (tornado.options.options.itcast)#这里打印出itcase参数，注意解析后的参数有两个options\r\n\r\n\tapp = tornado.web.Application(\r\n\t\t[url(r'/',IndexHander,name=\"index\"),\r\n\r\n\t\t url(r'/cpp',SecondHander,{\"subject\":\"c++\"},name=\"cpp\"),\r\n\r\n\t\t], debug=True) #这里的作用是添加路由，这里创建了一个应用app\r\n\t# app.listen(8000) #这里监听8000端口\r\n\r\n\thttp_server = tornado.httpserver.HTTPServer(app)\r\n\thttp_server.bind(tornado.options.options.port)\r\n\thttp_server.start(1)#0表示的是开启的进程数目和所使用的的服务器的核心数有关，当大于0的时候就是所设置的进程数目\r\n\r\n\ttornado.ioloop.IOLoop.current().start() #此处的函数才是真正运行服务器\r\n\r\n"},{"alpha_fraction":0.47216036915779114,"alphanum_fraction":0.4922049045562744,"avg_line_length":16.30769157409668,"blob_id":"a017161c8c7d0b7d951873d0c6bf136b21dd5923","content_id":"7c78d70fccab489632e259f25a404f25fc4f5e0c","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":449,"license_type":"no_license","max_line_length":39,"num_lines":26,"path":"/Python/plus/Process/green.py","repo_name":"jasondzy/Python","src_encoding":"UTF-8","text":"from greenlet import greenlet\nfrom time import sleep\ndef funca():\n while True:\n print('-----A-------')\n gr2.switch()\n print('---switch to funca---')\n sleep(0.5)\n\ndef funcb():\n while True:\n print('----B---------')\n gr1.switch()\n print('---switch to funcb----')\n sleep(0.5)\n\n\n\ngr1 = greenlet(funca)\ngr2 = greenlet(funcb)\n\ndef main():\n gr1.switch()\n\nif __name__ == '__main__':\n main()"},{"alpha_fraction":0.7162004709243774,"alphanum_fraction":0.7255244851112366,"avg_line_length":29.814815521240234,"blob_id":"1d1d112af929e810ce54d05efcd177d7f37405e3","content_id":"804eb7bb57f73fbb11ed98f67edd732a3a0ad709","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":2086,"license_type":"no_license","max_line_length":90,"num_lines":54,"path":"/tornado/test/02_input.py","repo_name":"jasondzy/Python","src_encoding":"UTF-8","text":"import tornado.web\r\nimport tornado.ioloop\r\nimport tornado.httpserver\r\nimport tornado.options\r\nfrom tornado.web import url\r\n\r\n\r\ntornado.options.define(\"port\", default=8000, type=int, help=\"runserver on given port\")\r\n\r\nclass IndexHander(tornado.web.RequestHandler):\r\n\r\n\tdef post(self):\r\n\r\n\t\tpara_query = self.get_query_arguments('a') #这里的作用是在url中获取a的参数值，获取的是一个列表，存放的是a的所有值\r\n\t\tpara_body = self.get_body_arguments('b')\r\n\t\tprint('body',para_body)\r\n\t\tprint('query',para_query)\r\n\t\tself.write(\"ok\")\r\n\r\nclass SecondHander(tornado.web.RequestHandler):\r\n\tdef post(self):\r\n\t\tprint(self.request.method)\r\n\t\tprint(self.request.uri)\r\n\t\tprint(self.request.host)\r\n\t\tprint(self.request.headers)\r\n\r\nclass ThirdHander(tornado.web.RequestHandler):\r\n\tdef post(self):\r\n\t\tfiles = self.request.files #获取request中所有的files类型的文件，这些文件是用字典的形式组织的\r\n\t\timg = files.get('image1') #从这些字典类型数据中获取指定image图片信息\r\n\t\tprint(img[0]['filename']) #获取的img信息也是一个列表信息，即相同名字image1可以传入多张file文件，列表中的值又是字典类型的数据\r\n\r\n\t\twith open('/home/ubuntu/user_jason/temp/image.jpg','wb') as f: #将从request中获取的图片信息写入到文件中去\r\n\t\t\tf.write(img[0]['body']) #body中存放的就是image1的信息，这里将image1中的信息写入到新创建的文件中\r\n\t\tself.write('ok')\r\n\r\nclass FiveHander(tornado.web.RequestHandler):\r\n\tdef initialize(self,arg1): #这里的参数接收的是路由中传递过来的字典类型参数\r\n\t\tprint(arg1)\r\n\r\nif __name__ == '__main__':\r\n\ttornado.options.parse_command_line()\r\n\r\n\tapp = tornado.web.Application([\r\n\t\turl(r'/',IndexHander,name=\"index\"),\r\n\t\turl(r'/second',SecondHander,name=\"second\"),\r\n\t\turl(r'/third',ThirdHander,name=\"third\"),\r\n\t\turl(r'/five',FiveHander,{'arg1':'111'},name=\"five\"),\r\n\t\t], debug=True)\r\n\r\n\thttp_server = tornado.httpserver.HTTPServer(app)\r\n\thttp_server.listen(tornado.options.options.port)\r\n\r\n\ttornado.ioloop.IOLoop.current().start()"},{"alpha_fraction":0.6877323389053345,"alphanum_fraction":0.6877323389053345,"avg_line_length":28.88888931274414,"blob_id":"02944e9f081228c6a7f67ea2059fdc691f0c7050","content_id":"3393a72a01b15caa7bf20a7d19657b77e73df918","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Text","length_bytes":429,"license_type":"no_license","max_line_length":95,"num_lines":9,"path":"/Python/plus/net/web/web_framework/readme.txt","repo_name":"jasondzy/Python","src_encoding":"UTF-8","text":"\n#########################\n\n# Note:这里是把web_service这个python程序当做了主体，通过在web_service程序中\n 来调用web框架\n#这里使用的是wsgi的网关接口协议，主要的就是在服务器函数中调用application(\n environ,start_repose)函数,在framework脚本中实现这个application函数。而start_respose正好反过来，service中实现，frame中调用\n\n\n#########################"},{"alpha_fraction":0.6656105518341064,"alphanum_fraction":0.6702412962913513,"avg_line_length":57.49275207519531,"blob_id":"fa872202afa6f2df8e908d5f2063ae7a70e92b7d","content_id":"b82c71c4172e9158fe064dcdda1af7c972af00e8","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":4329,"license_type":"no_license","max_line_length":190,"num_lines":69,"path":"/tornado/project/Ihome/Server.py","repo_name":"jasondzy/Python","src_encoding":"UTF-8","text":"import tornado.web\r\nimport tornado.options\r\nimport tornado.httpserver\r\nimport tornado.ioloop\r\nimport os\r\nimport models\r\nimport Handlers\r\nimport base64, uuid\r\nfrom tornado.web import url, StaticFileHandler\r\n\r\ntornado.options.define('port', default=8000, type=int, help=' runserver on given port')\r\n\r\n\r\nclass Application(tornado.web.Application):\r\n\tdef __init__(self):\r\n\t\t################# create mysql object #######\r\n\t\tself.mysql = models.HandleMysql()\r\n\t\tself.mysql.create_table()\r\n\t\t################# end #######################\r\n\t\t################# create redis object #######\r\n\t\tself.redis = models.HandRedis()\r\n\t\t################# end #######################\r\n\r\n\t\t################# 进行域名地址的映射 ##########\r\n\t\tsuper(Application, self).__init__(\r\n\t\t[\r\n\t\t# url(r'^/$', Handlers.IndexHandler, name=\"IndexHandler\"), #这里不采用get中的render返回index的原因是因为，index.html中使用了index.js脚本中的$.get等方式来和后台通讯从而获得数据，而render方式处理模板的时候会对模板进行解析处理会报错，只能通过直接加载index.html的方式\r\n\t\turl(r'/api/house/index', Handlers.House_index, dict(database = self.mysql, database_redis = self.redis), name=\"House_index\"),\r\n\t\turl(r'^/register$', Handlers.House_register, dict(database = self.mysql, database_redis = self.redis), name=\"House_register\"),\r\n\t\turl(r'^/api/piccode$', Handlers.PicCodeHandler, dict(database = self.mysql, database_redis = self.redis), name=\"PicCodeHandler\"),\r\n\t\turl(r'^/api/smscode$', Handlers.Smscode, dict(database = self.mysql, database_redis = self.redis), name=\"Smscode\"),\r\n\t\turl(r'^/api/register$', Handlers.Register_verity, dict(database = self.mysql, database_redis = self.redis), name=\"Register_verity\"),\r\n\t\turl(r'^/api/login$', Handlers.Login_verity, dict(database = self.mysql, database_redis = self.redis), name=\"database = self.mysql\"),\r\n\t\turl(r'^/api/check_login$', Handlers.Check_login, dict(database = self.mysql, database_redis = self.redis), name=\"Check_login\"),\r\n\t\turl(r'^/api/profile$', Handlers.Personal_info, dict(database = self.mysql, database_redis = self.redis), name=\"Personal_info\"),\r\n\t\turl(r'^/api/profile/name$', Handlers.Personal_name, dict(database = self.mysql, database_redis = self.redis), name=\"Personal_name\"),\r\n\t\turl(r'^/api/profile/avatar$', Handlers.Personal_img, dict(database = self.mysql, database_redis = self.redis), name=\"Personal_img\"),\r\n\t\turl(r'^/api/house/info$', Handlers.House_info, dict(database = self.mysql, database_redis = self.redis), name=\"House_info\"),\r\n\t\turl(r'^/api/order$', Handlers.House_reserve, dict(database = self.mysql, database_redis = self.redis), name=\"House_reserve\"),\r\n\t\turl(r'^/api/order/my$', Handlers.Show_order, dict(database = self.mysql, database_redis = self.redis), name=\"Show_order\"),\r\n\t\turl(r'^/api/profile/auth$', Handlers.Real_name_verity, dict(database = self.mysql, database_redis = self.redis), name=\"Real_name_verity\"),\r\n\t\turl(r'^/api/house/my$', Handlers.Myhouse_show, dict(database = self.mysql, database_redis = self.redis), name=\"Myhouse_show\"),\r\n\t\turl(r'^/api/house/list2$', Handlers.House_list_handler, dict(database = self.mysql, database_redis = self.redis), name = \"House_list_handler\"),\r\n\t\turl(r'^/api/house/area$', Handlers.Area_info_handler, dict(database = self.mysql, database_redis = self.redis), name=\"Area_info_handler\"),\r\n\t\turl(r'^/api/logout$', Handlers.Login_out, dict(database = self.mysql, database_redis = self.redis), name = \"Login_out\"),\r\n\t\turl(r'/(.*)',StaticFileHandler, {\"path\":os.path.join(os.path.dirname(__file__),\"template\"), \"default_filename\":\"index.html\"}, name=\"index\"),\r\n\t\t],\r\n\t\t################# end ########################\r\n\t\tdebug = True, \r\n\t\tstatic_path = os.path.join(os.path.dirname(__file__), 'static'),\r\n\t\ttemplate_path = os.path.join(os.path.dirname(__file__), 'template'),\r\n\t\tcookie_secret = str(base64.b64encode(uuid.uuid4().bytes + uuid.uuid4().bytes)), #这里使用uuid和base64共同合成了一个随机的字符串，添加到cookie从而形成secure_cookie的机制\r\n\t\txsrf_cookies = True,\r\n\t\t)\r\n\r\n\r\ndef main():\r\n\ttornado.options.parse_command_line()\r\n\r\n\tapp = Application()\r\n\r\n\thttp_server = tornado.httpserver.HTTPServer(app)\r\n\thttp_server.listen(8000) \r\n\r\n\ttornado.ioloop.IOLoop.current().start()\r\n\r\n\r\nif __name__ == '__main__':\r\n\tmain()"},{"alpha_fraction":0.6610939502716064,"alphanum_fraction":0.6767218112945557,"avg_line_length":46.68141555786133,"blob_id":"63e902ce95656bace89d9ed71e2ff69b53322596","content_id":"61296bb14e57d20be9f9ce6f31dd2f0bda2c837d","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":6109,"license_type":"no_license","max_line_length":103,"num_lines":113,"path":"/tornado/project/Ihome/mysql_settings.py","repo_name":"jasondzy/Python","src_encoding":"UTF-8","text":"\r\n\r\nsetting = dict(\r\n\thost = '115.159.62.43',\r\n\tport = 3306,\r\n\tdb = 'Ihome',\r\n\tuser = 'root',\r\n\tpasswd = 'dzy',\r\n\tcharset = 'utf8',\r\n\t)\r\n\r\ntable1 = [\r\n\t'''\r\n\tCREATE TABLE ih_user_profile (\r\n \tup_user_id bigint unsigned NOT NULL AUTO_INCREMENT COMMENT '用户ID',\r\n \tup_name varchar(32) NOT NULL COMMENT '昵称',\r\n \tup_mobile char(11) NOT NULL COMMENT '手机号',\r\n \tup_passwd varchar(64) NOT NULL COMMENT '密码',\r\n \tup_real_name varchar(32) NULL COMMENT '真实姓名',\r\n \tup_id_card varchar(20) NULL COMMENT '身份证号',\r\n \tup_avatar varchar(128) NULL COMMENT '用户头像',\r\n \tup_admin tinyint NOT NULL DEFAULT '0' COMMENT '是否是管理员，0-不是，1-是',\r\n \tup_utime datetime NOT NULL DEFAULT CURRENT_TIMESTAMP ON UPDATE CURRENT_TIMESTAMP COMMENT '最后更新时间',\r\n \tup_ctime datetime NOT NULL DEFAULT CURRENT_TIMESTAMP COMMENT '创建时间',\r\n \tPRIMARY KEY (up_user_id),\r\n \tUNIQUE (up_name),\r\n \tUNIQUE (up_mobile)\r\n\t) ENGINE=InnoDB AUTO_INCREMENT=10000 DEFAULT CHARSET=utf8 COMMENT='用户信息表';\r\n\t''',\r\n\t'''\r\n\tCREATE TABLE ih_area_info (\r\n \tai_area_id bigint unsigned NOT NULL AUTO_INCREMENT COMMENT '区域id',\r\n \tai_name varchar(32) NOT NULL COMMENT '区域名称',\r\n \tai_ctime datetime NOT NULL DEFAULT CURRENT_TIMESTAMP COMMENT '创建时间',\r\n \tPRIMARY KEY (ai_area_id)\r\n\t) ENGINE=InnoDB DEFAULT CHARSET=utf8 COMMENT='房源区域表';\r\n\t''',\r\n\t'''\r\n\tCREATE TABLE ih_house_info (\r\n \thi_house_id bigint unsigned NOT NULL AUTO_INCREMENT COMMENT '房屋id',\r\n \thi_user_id bigint unsigned NOT NULL COMMENT '用户ID',\r\n \thi_title varchar(64) NOT NULL COMMENT '房屋名称',\r\n \thi_price int unsigned NOT NULL DEFAULT '0' COMMENT '房屋价格，单位分',\r\n \thi_area_id bigint unsigned NOT NULL COMMENT '房屋区域ID',\r\n \thi_address varchar(512) NOT NULL DEFAULT '' COMMENT '地址',\r\n \thi_room_count tinyint unsigned NOT NULL DEFAULT '1' COMMENT '房间数',\r\n \thi_acreage int unsigned unsigned NOT NULL DEFAULT '0' COMMENT '房屋面积',\r\n \thi_house_unit varchar(32) NOT NULL DEFAULT '' COMMENT '房屋户型',\r\n \thi_capacity int unsigned NOT NULL DEFAULT '1' COMMENT '容纳人数',\r\n \thi_beds varchar(64) NOT NULL DEFAULT '' COMMENT '床的配置',\r\n \thi_deposit int unsigned NOT NULL DEFAULT '0' COMMENT '押金，单位分',\r\n \thi_min_days int unsigned NOT NULL DEFAULT '1' COMMENT '最短入住时间',\r\n \thi_max_days int unsigned NOT NULL DEFAULT '0' COMMENT '最长入住时间，0-不限制',\r\n \thi_order_count int unsigned NOT NULL DEFAULT '0' COMMENT '下单数量',\r\n \thi_verify_status tinyint NOT NULL DEFAULT '0' COMMENT '审核状态，0-待审核，1-审核未通过，2-审核通过',\r\n \thi_online_status tinyint NOT NULL DEFAULT '1' COMMENT '0-下线，1-上线',\r\n \thi_index_image_url varchar(256) NULL COMMENT '房屋主图片url',\r\n \thi_utime datetime NOT NULL DEFAULT CURRENT_TIMESTAMP ON UPDATE CURRENT_TIMESTAMP COMMENT '最后更新时间',\r\n \thi_ctime datetime NOT NULL DEFAULT CURRENT_TIMESTAMP COMMENT '创建时间',\r\n \tPRIMARY KEY (hi_house_id),\r\n \tKEY `hi_status` (hi_verify_status, hi_online_status),\r\n \tCONSTRAINT FOREIGN KEY (`hi_user_id`) REFERENCES `ih_user_profile` (`up_user_id`),\r\n \tCONSTRAINT FOREIGN KEY (`hi_area_id`) REFERENCES `ih_area_info` (`ai_area_id`)\r\n\t) ENGINE=InnoDB DEFAULT CHARSET=utf8 COMMENT='房屋信息表';\r\n\t''',\r\n\t'''\r\n\tCREATE TABLE ih_house_facility (\r\n \thf_id bigint unsigned NOT NULL AUTO_INCREMENT COMMENT '自增id',\r\n \thf_house_id bigint unsigned NOT NULL COMMENT '房屋id',\r\n \thf_facility_id int unsigned NOT NULL COMMENT '房屋设施',\r\n \thf_ctime datetime NOT NULL DEFAULT CURRENT_TIMESTAMP COMMENT '创建时间',\r\n \tPRIMARY KEY (hf_id),\r\n \tCONSTRAINT FOREIGN KEY (`hf_house_id`) REFERENCES `ih_house_info` (`hi_house_id`)\r\n\t) ENGINE=InnoDB DEFAULT CHARSET=utf8 COMMENT='房屋设施表';\r\n\t''',\r\n\t'''\r\n\tCREATE TABLE ih_facility_catelog (\r\n \tfc_id int unsigned NOT NULL AUTO_INCREMENT COMMENT '自增id',\r\n \tfc_name varchar(32) NOT NULL COMMENT '设施名称',\r\n \tfc_ctime datetime NOT NULL DEFAULT CURRENT_TIMESTAMP COMMENT '创建时间',\r\n \tPRIMARY KEY (fc_id)\r\n\t) ENGINE=InnoDB DEFAULT CHARSET=utf8 COMMENT='设施型录表';\r\n\t''',\r\n\t'''\r\n\tCREATE TABLE ih_order_info (\r\n \toi_order_id bigint unsigned NOT NULL AUTO_INCREMENT COMMENT '订单id',\r\n \toi_user_id bigint unsigned NOT NULL COMMENT '用户id',\r\n \toi_house_id bigint unsigned NOT NULL COMMENT '房屋id',\r\n \toi_begin_date date NOT NULL COMMENT '入住时间',\r\n \toi_end_date date NOT NULL COMMENT '离开时间',\r\n \toi_days int unsigned NOT NULL COMMENT '入住天数',\r\n \toi_house_price int unsigned NOT NULL COMMENT '房屋单价，单位分',\r\n \toi_amount int unsigned NOT NULL COMMENT '订单金额，单位分',\r\n \toi_status tinyint NOT NULL DEFAULT '0' COMMENT '订单状态，0-待接单，1-待支付，2-已支付，3-待评价，4-已完成，5-已取消，6-拒接单',\r\n \toi_comment text NULL COMMENT '订单评论',\r\n \toi_utime datetime NOT NULL DEFAULT CURRENT_TIMESTAMP ON UPDATE CURRENT_TIMESTAMP COMMENT '最后更新时间',\r\n \toi_ctime datetime NOT NULL DEFAULT CURRENT_TIMESTAMP COMMENT '创建时间',\r\n \tPRIMARY KEY (oi_order_id),\r\n \tKEY `oi_status` (oi_status),\r\n \tCONSTRAINT FOREIGN KEY (`oi_user_id`) REFERENCES `ih_user_profile` (`up_user_id`),\r\n \tCONSTRAINT FOREIGN KEY (`oi_house_id`) REFERENCES `ih_house_info` (`hi_house_id`)\r\n\t) ENGINE=InnoDB DEFAULT CHARSET=utf8 COMMENT='订单表';\r\n\t''',\r\n\t'''\r\n\tCREATE TABLE ih_house_image (\r\n \thi_image_id bigint unsigned NOT NULL AUTO_INCREMENT COMMENT '图片id',\r\n \thi_house_id bigint unsigned NOT NULL COMMENT '房屋id',\r\n \thi_url varchar(256) NOT NULL COMMENT '图片url',\r\n \thi_ctime datetime NOT NULL DEFAULT CURRENT_TIMESTAMP COMMENT '创建时间',\r\n \tPRIMARY KEY (hi_image_id),\r\n \tCONSTRAINT FOREIGN KEY (hi_house_id) REFERENCES ih_house_info (hi_house_id)\r\n\t) ENGINE=InnoDB DEFAULT CHARSET=utf8 COMMENT='房屋图片表';\r\n\t''',\r\n\r\n]"},{"alpha_fraction":0.5652173757553101,"alphanum_fraction":0.5652173757553101,"avg_line_length":23.33333396911621,"blob_id":"afa41c9a66ec089cd1d4437004bbfc040f6d3660","content_id":"85503c18b5bf8bb551bdadb6badb39ce53549f23","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":368,"license_type":"no_license","max_line_length":108,"num_lines":15,"path":"/Python/basic/class_func/class_new.py","repo_name":"jasondzy/Python","src_encoding":"UTF-8","text":"\n\nclass Dog(object):\n\n def __init__(self):\n print(\"init the var\")\n\n def __new__(cls): # the func is used to create a new class , \n print(\"create a class\")\n return object.__new__(cls) # use the parent __new__ func to create a class,the arg need transfer cls\n \n def __str__(self):\n return \"Hello world\"\n\n\ndog = Dog()\nprint(dog)\n\n"},{"alpha_fraction":0.5769230723381042,"alphanum_fraction":0.6009615659713745,"avg_line_length":14.923076629638672,"blob_id":"e5bd0ac5d4ab2a7615b0282230308e42c12d67b9","content_id":"1b12735a821c4b3b0bca1e978128654fba17636a","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":208,"license_type":"no_license","max_line_length":45,"num_lines":13,"path":"/Python/plus/Process/fork.py","repo_name":"jasondzy/Python","src_encoding":"UTF-8","text":"import os\nimport time\n\npid=os.fork()\n\nif pid == 0:\n\twhile True:\n\t\tprint('1-----This is a son process-------')\n\t\ttime.sleep(1)\nelse:\n\twhile True:\n\t\tprint('2-----This is a parrent process---')\n\t\ttime.sleep(1)\n\n"},{"alpha_fraction":0.6041666865348816,"alphanum_fraction":0.6348039507865906,"avg_line_length":28.125,"blob_id":"7a74eeb8392060be412cfc67675b3db159c0c517","content_id":"134b8a8244dc87301334226103851dbf2ca8adeb","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":2380,"license_type":"no_license","max_line_length":147,"num_lines":56,"path":"/Python/plus/net/tftp/tftp_load.py","repo_name":"jasondzy/Python","src_encoding":"UTF-8","text":"\nimport struct \n\nimport socket\n\ndef download_file(name,data):\n\tf = open(name,'ab') #注意这里一定要以字节追加的形式打开，因为tftp发送过来的就是字节型的数据，所以将字节型的数据直接写入到文件中即可。其他形式不可以\n\tf.write(data)\n\n\tf.close()\n\n\n\ndef main():\n name = input('---please input which file you want to load----')\n struct_info = '!H' + str(len(name)) + 'sb5sb'\n print(struct_info)\n\n udp_socket = socket.socket(socket.AF_INET, socket.SOCK_DGRAM) #创建UDP对象\n\n send_data = struct.pack(struct_info, 1, name.encode('utf-8'), 0, b\"octet\", 0)#此处的作用是使用struct模块对要发送的数据按照tftp的信息格式进行封装，具体的信息见课件中tftp的信息格式\n\n udp_socket.sendto(send_data, ('192.168.0.107', 69))#这里对上面封装好了的数据发往69号端口，此端口是tftp默认的首次通信端口，接下来的数据通信端口就不在是69号端口了，而是采用的动态端口的方式，所以再往69端口发数据就不会有任何响应\n\n while True:\n receive_data = udp_socket.recvfrom(1024)#再发送完数据后，tftp就会返回进行封装了的512字节的数据，这里接收的时候并不需要绑定端口，因为tftp会根据所接收到的数据解析出所用的端口\n\n data,ip_info = receive_data #对接收到的信息进行解析，因为网络通信中接收到的信息是一个tuple类型的数据，tuple的首个元素是封装的数据，第二个元素是ip地址信息\n\n cmd_info = data[:4]#获取数据中前四个字节的信息，根据tftp协议，前四个字节有特殊作用，4个字节后的512个字节才是真正的数据\n\n cmd_info = struct.unpack('!HH',cmd_info)#这里是将接收到的数据进行解析，这里解析到的cmd_info是一个tuple,(3,1)\n \n\n print(cmd_info)\n print(data[4:])\n\n if cmd_info[0] == 3:\n download_file(name,data[4:])\n if len(data[4:])<512:\n print('---receive data end-------')\n break\n \n print('------------receive next 512 bytes data---------')\n ACK = struct.pack('!HH',4,cmd_info[1])\n udp_socket.sendto(ACK,ip_info)\n else:\n break\n\n\n\n udp_socket.close()\n\n\n\nif __name__ == '__main__':\n\tmain()\n"},{"alpha_fraction":0.7108433842658997,"alphanum_fraction":0.7108433842658997,"avg_line_length":15.300000190734863,"blob_id":"7752696717fdee3dff79e68b0f3eaad2d2ab4fca","content_id":"fa525c89a487deea39257d01d8d75e523249532f","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Markdown","length_bytes":166,"license_type":"no_license","max_line_length":37,"num_lines":10,"path":"/Python/plus/C_lib/readme.md","repo_name":"jasondzy/Python","src_encoding":"UTF-8","text":"\n\n# As show,we can include a c Librarya\n\n follow bellow steps\n\nfirst: gcc test.c -shared -o test.so\nfinally: python c_lib.py\n\n\n\nSo we can use C programme in python \n"},{"alpha_fraction":0.5724366903305054,"alphanum_fraction":0.5824571251869202,"avg_line_length":29.80695915222168,"blob_id":"222c5c618c19700aee32bc2c12e35e52f58515a9","content_id":"4faeec2cf69d0103abeab6ab59b2e29be2114ebd","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":31602,"license_type":"no_license","max_line_length":254,"num_lines":891,"path":"/tornado/project/Ihome/Handlers.py","repo_name":"jasondzy/Python","src_encoding":"UTF-8","text":"import tornado.web\r\nimport json\r\nimport models\r\nimport logging\r\nimport random\r\nfrom hashlib import sha1\r\nimport base64, uuid\r\nfrom utils.captcha.captcha import captcha\r\nfrom utils.commons import required_login\r\nfrom libs.yuntongxun.SendTemplateSMS import ccp\r\n\r\nImage_id = \"1111\"\r\nclass BaseHandler(tornado.web.RequestHandler):\r\n\tdef initialize(self, database, database_redis):\r\n\t\tself.database = database\r\n\t\tself.redis = database_redis\r\n\r\n\tdef prepare(self):\r\n\t\t\"\"\"预解析json数据\"\"\" #这里判断请求的类型是否是json类型\r\n\t\tself.xsrf_token\r\n\t\t# print(self.request.body)\r\n\t\tif self.request.headers.get(\"Content-Type\", \"\").startswith(\"application/json\"):\r\n\t\t\tself.json_args = json.loads(self.request.body.decode(\"utf-8\")) #对body中的json数据进行解析成字典的类型，lods是解析，dumps是翻译成json类\r\n\t\telse:\r\n\t\t\tself.json_args = {}\r\n\r\n\tdef get_current_user(self):\r\n\t\tsession_id = self.get_secure_cookie('session_id')\r\n\t\tif session_id == None:\r\n\t\t\tprint(\" not login!!!\")\r\n\t\t\treturn None\r\n\t\tvalue = self.redis.get_value(session_id)\r\n\t\tif value == None or len(value) == 0:\r\n\t\t\tprint(\"not login!!!\")\r\n\t\t\treturn None\r\n\r\n\t\treturn value\r\n\r\n###################这里实现一个主页处理函数#########################################\r\nclass House_index(BaseHandler):\r\n\tdef get(self):\r\n\t\tprint('================================')\r\n\t\tsql = \"select hi_house_id,hi_title,hi_index_image_url from ih_house_info order by hi_order_count desc limit 3;\"\r\n\t\tresult = self.database.get_values_from_mysql(sql)\r\n\t\tprint(result)\r\n\r\n\t\thouses = []\r\n\t\tfor value in result:\r\n\t\t\thouses.append({'house_id':value[0], 'img_url':value[2], 'title':value[1]})\r\n\r\n\r\n\t\tsql = \" select * from ih_area_info\"\r\n\t\tresult = self.database.get_values_from_mysql(sql)\r\n\t\t# print(result)\r\n\r\n\t\tarea = []\r\n\t\tfor value in result:\r\n\t\t\tarea.append({'area_id':value[0], 'name':value[1]})\r\n\t\t# print(area)\r\n\r\n\t\tjson_houses = json.dumps(houses) #注意在tornado中self.write(xx)中的xx要是字典型的数据，这样tornado就会自动将xx转换为json数据传输\r\n\t\tjson_areas = json.dumps(area) #注意，字典型数据xx中的子元素的值若是个字典或数组等组合类型的值。需要将子元素的值转换为json型数据包裹在外层自动类型变量中进行传输\r\n\t\tresp = '{\"errcode\":\"0\", \"errmsg\":\"OK\", \"houses\":%s, \"areas\":%s}' % (json_houses, json_areas)\r\n\t\tself.write(resp) #这里就是返回数据，自动将字典类型变量转换为json数据类型\r\n\t\tself.set_header(\"Content-Type\", \"application/json; charset=UTF-8\") #设置传输类型的头部为 json类型，若是不设置可能会出错\r\n\t\t# self.write('ok') #以上的这么多设置主要是针对$ajax类型的数据请求，因为javascript擅长解析的是json类型数据\r\n\r\n#############这里实现的是一个检查用户是否登录的功能 ########\r\nclass Check_login(BaseHandler):\r\n\tdef get(self):\r\n\t\tsession_id = self.get_secure_cookie('session_id')\r\n\t\t# print(\"session_id======\",session_id)\r\n\t\tuser_data = {}\r\n\t\tif session_id == None:\r\n\t\t\tprint(\"session_id cookie do not exist\")\r\n\t\t\tdata = {\r\n\t\t\t\t\"errcode\":1,\r\n\t\t\t\t\"data\":None,\r\n\t\t\t}\r\n\t\telse:\r\n\t\t\tvalue = self.redis.get_value(session_id)\r\n\t\t\tif value == None:\r\n\t\t\t\tdata = {\r\n\t\t\t\t\"errcode\":1,\r\n\t\t\t\t\"data\":None,\r\n\t\t\t\t}\r\n\t\t\telse:\r\n\t\t\t\tsql = 'select up_name from ih_user_profile where up_mobile=\"%s\" '%value.decode(\"utf-8\")\r\n\t\t\t\tresult = self.database.get_values_from_mysql(sql)\r\n\t\t\t\tif result == None or len(result)==0:\r\n\t\t\t\t\tprint(\"session_id do not exist in redis\")\r\n\t\t\t\t\tdata = {\r\n\t\t\t\t\t\"errcode\":1,\r\n\t\t\t\t\t\"data\":None,\r\n\t\t\t\t\t}\r\n\t\t\t\telse:\r\n\t\t\t\t\tprint(\" login user:\", value)\r\n\t\t\t\t\tuser_data['name'] = result\r\n\t\t\t\t\t# user_data = json.dumps(user_data)\r\n\t\t\t\t\tdata = {\r\n\t\t\t\t\t\t\"errcode\":0,\r\n\t\t\t\t\t\t\"data\":user_data,\r\n\t\t\t\t\t}\r\n\r\n\t\tself.write(data)\r\n\t\tself.set_header(\"Content-Type\", \"application/json; charset=UTF-8\")\r\n\r\n############# 这里实现的是注册的主页面显示功能 ############\r\n\r\nclass House_register(BaseHandler):\r\n\tdef get(self):\r\n\t\tprint(\"register============\")\r\n\t\tself.render('register.html')\r\n\r\n##############验证码功能#################################\r\n\r\nclass PicCodeHandler(BaseHandler): #前端的img中的src是一个GET方式的请求\r\n\t\"\"\"图片验证码\"\"\"\r\n\tdef get(self):\r\n\t\tglobal Image_id #这里使用的是Image_id这个全局变量来传递图片验证码数据\r\n\t\t\"\"\"获取图片验证码\"\"\"\r\n\t\tpre_code_id = self.get_argument(\"pre\", \"\")\r\n\t\tcur_code_id = self.get_argument(\"cur\")\r\n\t\t# 生成图片验证码\r\n\t\tname, text, pic = captcha.generate_captcha() #这里使用了外接包，导入的captcha是对象，生成了图片信息\r\n\t\t# print(text) #这里的text是真正的数据，是用来判断用户传入过来的验证是否是正确的\r\n\t\tImage_id = text\r\n\t\t#这里的pic是直接生成的图片二进制码，所以接下来可以直接通过self.write往html写入数据\r\n\t\tself.set_header(\"Content-Type\", \"image/jpg\")\r\n\t\tself.write(pic) #这里的作用是将图片的二进制数据写入到前端系统\r\n\r\n\r\n###############手机验证码功能#################################\r\nclass Smscode(BaseHandler):\r\n\tdef post(self):\r\n\t\tglobal Image_id\r\n\t\tmobile = self.json_args.get('mobile') #这里的json_args是个字典类型，存放的是json类型的数据，ajax中的json数据的获取是通过self.request.body()中获取的\r\n\t\timageCode = self.json_args.get('piccode') #这里的字典中的值是在BaseHandler中进行预解析的\r\n\t\timageCodeId = self.json_args.get('piccode_id')\r\n\t\t# print(\"image_id===============\")\r\n\t\t# print(Image_id)\r\n\t\tif imageCode.upper() != Image_id:\r\n\t\t\tdata = {\r\n\t\t\t\t\"errcode\":'1',\r\n\t\t\t\t\"errmsg\":\"imagecode wrong\"\r\n\t\t\t}\r\n\t\telse:\r\n\t\t\tprint(\"iamge code true\",imageCode)\r\n\t\t\tdata = {\r\n\t\t\t\t\"errcode\":'0',\r\n\t\t\t\t\"errmsg\":'ok',\r\n\t\t\t}\r\n\t\t\t# 产生随机短信验证码\r\n\t\t\tsms_code = \"%06d\" % random.randint(1, 1000000)\r\n\t\t\ttry:\r\n\t\t\t\tself.redis.set_value(\"sms_code_%s\" % mobile, sms_code)\r\n\t\t\t\tself.redis.set_expire(\"sms_code_%s\" % mobile, 360)\r\n\t\t\texcept Exception as e:\r\n\t\t\t\tlogging.error(e)\r\n\t\t\t\tdata = {\r\n\t\t\t\t\t\"errcode\":'1',\r\n\t\t\t\t\t\"errmsg\":\"create Smscode fail\"\r\n\t\t\t\t}\r\n\r\n\t\t\tprint('sms_code========',sms_code)\r\n\r\n\t\t\t# 发送短信验证码\r\n\t\t\ttry:\r\n\t\t\t\tresult = ccp.sendTemplateSMS(mobile, [sms_code, 1], 1)\r\n\t\t\texcept Exception as e:\r\n\t\t\t\tlogging.error(e)\r\n\t\t\t\tdata = {\r\n\t\t\t\t\t\"errcode\":'1',\r\n\t\t\t\t\t\"errmsg\":\"send Smscode fail\"\r\n\t\t\t\t}\r\n\t\t\tif result:\r\n\t\t\t\tdata = {\r\n\t\t\t\t\t\"errcode\":'0',\r\n\t\t\t\t\t\"errmsg\":\"ok\"\r\n\t\t\t\t}\r\n\t\t\telse:\r\n\t\t\t\tdata = {\r\n\t\t\t\t\t\"errcode\":'1',\r\n\t\t\t\t\t\"errmsg\":\"目前该短信注册功能由于采用的是云通讯的测试功能，只支持绑定的手机号发送验证码，此处其他手机可输入任意值即可\"\r\n\t\t\t\t}\r\n\r\n\t\tself.write(data)\r\n\t\tself.set_header(\"Content-Type\", \"application/json; charset=UTF-8\")\r\n\r\n################注册验证功能################################\r\nclass Register_verity(BaseHandler):\r\n\tdef post(self):\r\n\t\tmobile = self.json_args.get('mobile')\r\n\t\tphoneCode = self.json_args.get('phonecode')\r\n\t\tpasswd = self.json_args.get('password')\r\n\t\tpasswd2 = self.json_args.get('password2')\r\n\t\t# print(mobile,phoneCode,passwd,passwd2)\r\n\r\n\t\t################这里用来判断手机号是否已经注册了###########\r\n\t\tsql = \"select up_name from ih_user_profile where up_mobile='%s'\"%mobile\r\n\t\tresult = self.database.get_values_from_mysql(sql)\r\n\t\tprint(\"#############\",result)\r\n\t\tif len(result) != 0:\r\n\t\t\tprint(\"mobile number existed\")\r\n\t\t\tdata = {\r\n\t\t\t'errcode':'1',\r\n\t\t\t'errmsg':'mobile number existed'\r\n\t\t\t}\r\n\t\t\tself.write(data)\r\n\t\t\tself.set_header(\"Content-Type\", \"application/json; charset=UTF-8\")\r\n\t\t################这里判断两次输入的密码是否一致###########\r\n\t\telif passwd != passwd2:\r\n\t\t\tdata = { \r\n\t\t\t'errcode':'1',\r\n\t\t\t'errmsg':'password mismatch'\r\n\t\t\t}\r\n\t\t\tself.write(data)\r\n\t\t\tself.set_header(\"Content-Type\", \"application/json; charset=UTF-8\")\r\n\t\telse:\r\n\t\t#############这里对密码进行加密处理#####################\r\n\t\t\ts1 = sha1()\r\n\t\t\ts1.update(passwd.encode(\"utf-8\"))\r\n\t\t\tpassword = s1.hexdigest()\r\n\t\t\r\n\t\t#############验证手机的验证码是否正确###################\r\n\t\t\tcode = self.redis.get_value(\"sms_code_%s\" % mobile)\r\n\t\t\t# print(' redis code value',code.decode(\"utf-8\"))\r\n\t\t\t# print('phonecode=======',phoneCode)\r\n\t\t\tif phoneCode == code.decode(\"utf-8\"):\r\n\t\t\t\tprint('Smscode=======ok')\r\n\r\n\t\t#######将手机号和密码存入数据库中去####################\r\n\t\t\tdefault_image_path = '/static/images/landlord01.jpg'\r\n\t\t\tsql = \"insert into ih_user_profile(up_name,up_mobile,up_passwd,up_avatar) values('%s','%s','%s','%s')\"%(mobile, mobile, password, default_image_path)\r\n\t\t\tprint(sql)\r\n\t\t\tself.database.insert_into_tbl(sql)\r\n\r\n\t\t####### 将注册成功后的用户写入session 机制 ############\r\n\t\t#这里生成一个独一无二的session_id号\r\n\t\t\tsession_id = str(base64.b64encode(uuid.uuid4().bytes + uuid.uuid4().bytes))\r\n\t\t\tself.set_secure_cookie(\"session_id\", session_id, expires_days=1)\r\n\t\t#这里将存在cookie中的session_id存放到redis中，这样就可以通过cookie来在redis中进行数据查询\t\r\n\t\t\tself.redis.set_value(session_id, str(mobile))\r\n\t\t\tself.redis.set_expire(session_id, 360)\r\n\t\t###########返回状态码给到js端##########################\r\n\t\t\tdata = {\r\n\t\t\t\t'errcode':'0',\r\n\t\t\t\t'errmsg':'ok'\r\n\t\t\t}\r\n\r\n\t\t\tself.write(data)\r\n\t\t\tself.set_header(\"Content-Type\", \"application/json; charset=UTF-8\")\r\n\r\n\r\n###############################登陆处理功能###################################\r\nclass Login_verity(BaseHandler):\r\n\tdef post(self):\r\n\t\tmobile = self.json_args.get('mobile')\r\n\t\tpasswd = self.json_args.get('password')\r\n\r\n\t\t################# 从mysql数据库中查询是否存在该手机号 ###########\r\n\t\tsql = \"select up_passwd from ih_user_profile where up_mobile=%s\"%mobile\r\n\t\tresult = self.database.get_values_from_mysql(sql)\r\n\t\t# print(\"result=====\",result)\r\n\t\tif result == None or len(result) == 0:\r\n\t\t\tprint(\" mobile dot exist please register\")\r\n\t\t\tdata = {\r\n\t\t\t\t'errcode':'1',\r\n\t\t\t\t'errmsg':'do not exist'\r\n\t\t\t}\r\n\t\t\tself.write(data)\r\n\t\t\tself.set_header(\"Content-Type\", \"application/json; charset=UTF-8\")\r\n\t\telse:\r\n\t\t\t############ 对输入的密码进行加密后和数据库中的密码进行比较 ####\r\n\t\t\ts1 = sha1()\r\n\t\t\ts1.update(passwd.encode(\"utf-8\"))\r\n\t\t\tpassword = s1.hexdigest()\r\n\t\t\tprint('password=====',password)\r\n\t\t\t############# end #########################################\r\n\t\t\t# print(\"password===== result======\",password,result[0])\r\n\r\n\t\t\tif password != result[0][0]:\r\n\t\t\t\tprint(\"password wrong\")\r\n\t\t\t\tdata = {\r\n\t\t\t\t'errcode':'1',\r\n\t\t\t\t'errmsg':'password wrong'\r\n\t\t\t\t}\r\n\t\t\t\tself.write(data)\r\n\t\t\t\tself.set_header(\"Content-Type\", \"application/json; charset=UTF-8\")\r\n\t\t\telse:\r\n\t\t\t\tprint(\" login sucess\")\r\n\t\t\t\t#############这里设置 session 功能######################\r\n\t\t\t\tsession_id = str(base64.b64encode(uuid.uuid4().bytes + uuid.uuid4().bytes))\r\n\t\t\t\tself.set_secure_cookie(\"session_id\", session_id, expires_days=1)\r\n\t\t\t\tself.redis.set_value(session_id, str(mobile))\r\n\t\t\t\tself.redis.set_expire(session_id, 3600)\r\n\t\t\t\t############# end #####################################\r\n\t\t\t\tdata = {\r\n\t\t\t\t'errcode':'0',\r\n\t\t\t\t'errmsg':'login sucess'\r\n\t\t\t\t}\r\n\t\t\t\tself.write(data)\r\n\t\t\t\tself.set_header(\"Content-Type\", \"application/json; charset=UTF-8\")\r\n\r\n#############################个人信息处理################################\r\n\r\nclass Personal_info(BaseHandler):\r\n\t@required_login\r\n\tdef get(self):\r\n\t\tsession_id = self.get_secure_cookie(\"session_id\")\r\n\t\t# print(\"session_id===\",session_id)\r\n\t\tmobile = self.redis.get_value(session_id).decode(\"utf-8\")\r\n\t\t# print(\"mobile=====\",mobile)\r\n\t\tsql = \"select up_name,up_avatar from ih_user_profile where up_mobile=%s\"%mobile\r\n\t\tresult = self.database.get_values_from_mysql(sql)\r\n\t\t# print(\"name====\",result)\r\n\t\tif result == None or len(result) == 0: \r\n\t\t\tprint(\"can not search name\")\r\n\t\t\tself.write(dict(errcode=\"4101\", errmsg=\"用户未登录\"))\r\n\r\n\t\t# print(\"result[0][1]======\", result[0][1])\r\n\t\tif result[0][1] == None:\r\n\t\t\timage_path = \"/static/images/landlord01.jpg\"\r\n\t\telse:\r\n\t\t\timage_path = result[0][1]\r\n\r\n\t\tuser_data = {\r\n\t\t\t\"name\":result[0][0],\r\n\t\t\t\"mobile\":mobile,\r\n\t\t\t\"avatar\":image_path\r\n\t\t}\r\n\r\n\t\tdata = {\r\n\t\t\t\"errcode\":\"0\",\r\n\t\t\t\"data\":user_data,\r\n\t\t}\r\n\r\n\t\tself.write(data)\r\n\t\tself.set_header(\"Content-Type\", \"application/json; charset=UTF-8\")\r\n\r\n##########################处理用户修改用户名#########################\r\nclass Personal_name(BaseHandler):\r\n\t@required_login\r\n\tdef post(self):\r\n\t\tuser_name = self.json_args.get('name')\r\n\t\t# print('user_name====',user_name)\r\n\t\t################# 获取当前登录的用户id ######################\r\n\t\tsession_id = self.get_secure_cookie(\"session_id\")\r\n\t\t# print(\"session_id===\",session_id)\r\n\t\tmobile = self.redis.get_value(session_id).decode(\"utf-8\")\r\n\t\tsql = 'update ih_user_profile set up_name=\"%s\" where up_mobile=\"%s\" '%(user_name, mobile)\r\n\t\t# print(sql)\r\n\t\tresult = self.database.update_tbl(sql)\r\n\t\tif result != True:\r\n\t\t\tprint(\" update sql failed \")\r\n\t\t\treturn 0\r\n\t\telse:\r\n\t\t\tprint(\" update sql sucess \")\r\n\r\n\t\t\tdata = {\r\n\t\t\t\t\"errcode\":\"0\",\r\n\t\t\t}\r\n\r\n\t\t\tself.write(data)\r\n\t\t\tself.set_header(\"Content-Type\", \"application/json; charset=UTF-8\")\r\n\r\n######################### 这里用来处理用户上传的头像信息 #############\r\n#####---这里使用的技术栈是直接保存在了服务器/static/images/personal_images中\r\n#####---这里可使用七牛技术存储到远程服务器上(由于账号实名问题，七牛账号暂时不可用)\r\nclass Personal_img(BaseHandler):\r\n\t@required_login\r\n\tdef post(self):\r\n\t\t################# 获取当前登录的用户id ######################\r\n\t\tsession_id = self.get_secure_cookie(\"session_id\")\r\n\t\t# print(\"session_id===\",session_id)\r\n\t\tmobile = self.redis.get_value(session_id).decode(\"utf-8\")\r\n\t\tprint(\"test_test======\")\r\n\t\tfiles = self.request.files\r\n\t\timage_file = files.get(\"avatar\")\r\n\t\tif image_file:\r\n\t\t\tfile_name = image_file[0][\"filename\"]\r\n\t\t\t# print(\"filename=====\", file_name)\r\n\t\t\timage = image_file[0][\"body\"]\r\n\r\n\t\t\tfile_path = './static/images/personal_images/' + mobile# + \".\" + \".\".join(file_name.split(\".\")[1:])\r\n\t\t\tfile = open(file_path, 'wb')\r\n\t\t\tfile.write(image)\r\n\r\n\t\t\tfile.close()\r\n\r\n\t\t\tdata = {\r\n\t\t\t\t'errcode':'0',\r\n\t\t\t\t'data':file_path,\r\n\t\t\t}\r\n\r\n\t\t\t############## 这里需要添加将图片路径保存在mysql的功能#####\r\n\t\t\tsql = 'update ih_user_profile set up_avatar=\"%s\" where up_mobile=\"%s\" '%(file_path, mobile)\r\n\t\t\tresult = self.database.update_tbl(sql)\r\n\t\t\tif result != True:\r\n\t\t\t\tprint(\" update sql failed \")\r\n\t\t\t\treturn 0\r\n\r\n\t\telse:\r\n\t\t\tdata = {\r\n\t\t\t\t'errcode':'4001',\r\n\t\t\t\t'data':\"/static/images/landlord01.jpg\",\r\n\t\t\t}\r\n\r\n\t\tself.write(data)\r\n\t\tself.set_header(\"Content-Type\", \"application/json; charset=UTF-8\")\r\n\r\n######################## 房屋信息显示 #######################\r\nclass House_info(BaseHandler):\r\n\t######### POST 处理的是房屋提交信息 ######################\r\n\t@required_login\r\n\tdef post(self):\r\n\t\t\"\"\"保存\"\"\"\r\n\t\t# 获取参数\r\n\t\t\"\"\"{\r\n\t\t\t\"title\":\"\",\r\n\t\t\t\"price\":\"\",\r\n\t\t\t\"area_id\":\"1\",\r\n\t\t\t\"address\":\"\",\r\n\t\t\t\"room_count\":\"\",\r\n\t\t\t\"acreage\":\"\",\r\n\t\t\t\"unit\":\"\",\r\n\t\t\t\"capacity\":\"\",\r\n\t\t\t\"beds\":\"\",\r\n\t\t\t\"deposit\":\"\",\r\n\t\t\t\"min_days\":\"\",\r\n\t\t\t\"max_days\":\"\",\r\n\t\t\t\"facility\":[\"7\",\"8\"]\r\n\t\t}\"\"\"\r\n\t\t################# 获取当前登录的用户id ######################\r\n\t\tsession_id = self.get_secure_cookie(\"session_id\")\r\n\t\t# print(\"session_id===\",session_id)\r\n\t\tmobile = self.redis.get_value(session_id).decode(\"utf-8\")\r\n\t\tsql = 'select up_user_id from ih_user_profile where up_mobile=\"%s\"'%mobile\r\n\t\tret = self.database.get_values_from_mysql(sql)\r\n\r\n\t\tuser_id = ret[0][0]\r\n\t\ttitle = self.json_args.get(\"title\")\r\n\t\tprice = self.json_args.get(\"price\")\r\n\t\tarea_id = self.json_args.get(\"area_id\")\r\n\t\taddress = self.json_args.get(\"address\")\r\n\t\troom_count = self.json_args.get(\"room_count\")\r\n\t\tacreage = self.json_args.get(\"acreage\")\r\n\t\tunit = self.json_args.get(\"unit\")\r\n\t\tcapacity = self.json_args.get(\"capacity\")\r\n\t\tbeds = self.json_args.get(\"beds\")\r\n\t\tdeposit = self.json_args.get(\"deposit\")\r\n\t\tmin_days = self.json_args.get(\"min_days\")\r\n\t\tmax_days = self.json_args.get(\"max_days\")\r\n\t\tfacility = self.json_args.get(\"facility\") # 对一个房屋的设施，是列表类型\r\n\t\t# 校验\r\n\t\tif not all((title, price, area_id, address, room_count, acreage, unit, capacity, beds, deposit, min_days,\r\n\t\t\t\t\tmax_days)):\r\n\t\t\treturn self.write(dict(errcode='3', errmsg=\"缺少参数\"))\r\n\r\n\t\ttry:\r\n\t\t\tprice = int(price) * 100\r\n\t\t\tdeposit = int(deposit) * 100\r\n\t\texcept Exception as e:\r\n\t\t\treturn self.write(dict(errcode='2', errmsg=\"参数错误\"))\r\n\r\n\t\t# 数据\r\n\t\ttry:\r\n\t\t\tsql = \"insert into ih_house_info(hi_user_id,hi_title,hi_price,hi_area_id,hi_address,hi_room_count,\" \\\r\n\t\t\t\t \"hi_acreage,hi_house_unit,hi_capacity,hi_beds,hi_deposit,hi_min_days,hi_max_days) \" \\\r\n\t\t\t\t \"values(%s,%s,%s,%s,%s,%s,%s,\" \\\r\n\t\t\t\t \"%s,%s,%s,%s,%s,%s)\"%(user_id, title, price, area_id, address, room_count, acreage, unit, capacity, beds, deposit, min_days, max_days)\r\n\t\t\t# 对于insert语句，execute方法会返回最后一个自增id\r\n\t\t\tself.database.insert_into_tbl(sql)\r\n\t\texcept Exception as e:\r\n\t\t\tlogging.error(e)\r\n\t\t\treturn self.write(dict(errcode='4', errmsg=\"save data error\"))\r\n\r\n\t\tsql = 'select hi_house_id from ih_house_info where hi_user_id=%s'%user_id\r\n\t\tret = self.database.get_values_from_mysql(sql)\r\n\t\thouse_id = ret[0][-1]\r\n\t\t# print('house_id================',house_id)\r\n\r\n\t\ttry:\r\n\t\t\tsql = \"insert into ih_house_facility(hf_house_id,hf_facility_id) values\"\r\n\t\t\tsql_val = [] # 用来保存条目的(%s, %s)部分 最终的形式 [\"(%s, %s)\", \"(%s, %s)\"]\r\n\t\t\tvals = [] # 用来保存的具体的绑定变量值\r\n\t\t\tfor facility_id in facility:\r\n\t\t\t\t# sql += \"(%s, %s),\" 采用此种方式，sql语句末尾会多出一个逗号\r\n\t\t\t\t# sql_val.append(\"(%d, %s)\")\r\n\t\t\t\tvals.append(str((house_id,facility_id)))\r\n\t\t\t\t# vals.append(facility_id)\r\n\r\n\t\t\t# sql += \",\".join(sql_val)\r\n\t\t\tsql += \",\".join(vals)\r\n\t\t\t# print('sql=============',sql)\r\n\t\t\tlogging.debug(sql)\r\n\t\t\tlogging.debug(vals)\r\n\t\t\tself.database.insert_into_tbl(sql)\r\n\t\texcept Exception as e:\r\n\t\t\tlogging.error(e)\r\n\t\t\ttry:\r\n\t\t\t\tsql = \"delete from ih_house_info where hi_house_id=%s\"%house_id\r\n\t\t\t\tself.database.insert_into_tbl(sql)\r\n\t\t\texcept Exception as e:\r\n\t\t\t\tlogging.error(e)\r\n\t\t\t\treturn self.write(dict(errcode='4', errmsg=\"delete fail\"))\r\n\t\t\telse:\r\n\t\t\t\treturn self.write(dict(errcode='4', errmsg=\"no data save\"))\r\n\t\t# 返回\r\n\t\tself.write(dict(errcode='0', errmsg=\"OK\", house_id=house_id))\r\n\r\n\r\n\t######### GET处理 的是获取房屋信息 ######################\r\n\t@required_login\r\n\tdef get(self):\r\n\t\thouse_id = self.get_query_argument('house_id')\r\n\t\tprint(house_id)\r\n\t\t########### 获取房屋信息 #########################\r\n\t\tsession_id = self.get_secure_cookie(\"session_id\")\r\n\t\tmobile = self.redis.get_value(session_id).decode(\"utf-8\")\r\n\r\n\t\t# 校验参数，判断传入的house_id是否正确\r\n\t\tif not house_id:\r\n\t\t\treturn self.write(dict(errcode='3', errmsg=\"缺少参数\"))\r\n\r\n\t\t# 这里可以添加从redis缓存中获取这些房屋信息，这样就避免了进行多次查询数据的操作\r\n\t\t# 这里以待加入功能\r\n\t\t# TODO\r\n\r\n\t\t# 查询数据库\r\n\r\n\t\t# 查询房屋基本信息\r\n\t\tsql = \"select hi_title,hi_price,hi_address,hi_room_count,hi_acreage,hi_house_unit,hi_capacity,hi_beds,\" \\\r\n\t\t\t \"hi_deposit,hi_min_days,hi_max_days,up_name,up_avatar,hi_user_id \" \\\r\n\t\t\t \"from ih_house_info inner join ih_user_profile on hi_user_id=up_user_id where hi_house_id=%s\"%house_id\r\n\r\n\t\tret = self.database.get_values_from_mysql(sql)\r\n\t\t# print(\"result=======\", ret)\r\n\t\t# 用户查询的可能是不存在的房屋id, 此时ret为None\r\n\t\tif not ret:\r\n\t\t\treturn self.write(dict(errcode='2', errmsg=\"查无此房\"))\r\n\r\n\t\tdata = {\r\n\t\t\t\"hid\":house_id,\r\n\t\t\t\"user_id\":ret[0][13],\r\n\t\t\t\"title\":ret[0][0],\r\n\t\t\t\"price\":ret[0][1],\r\n\t\t\t\"address\":ret[0][2],\r\n\t\t\t\"room_count\":ret[0][3],\r\n\t\t\t\"acreage\":ret[0][4],\r\n\t\t\t\"unit\":ret[0][5],\r\n\t\t\t\"capacity\":ret[0][6],\r\n\t\t\t\"beds\":ret[0][7],\r\n\t\t\t\"deposit\":ret[0][8],\r\n\t\t\t\"min_days\":ret[0][9],\r\n\t\t\t\"max_days\":ret[0][10],\r\n\t\t\t\"user_name\":ret[0][11],\r\n\t\t\t\"user_avatar\":ret[0][12]\r\n\t\t}\r\n\r\n\t\t# print(\"data===========\",data)\r\n\r\n\t\t# 查询房屋的图片信息\r\n\t\tsql = \"select hi_url from ih_house_image where hi_house_id=%s\"%house_id\r\n\t\tret = self.database.get_values_from_mysql(sql)\r\n\t\t# print(\"image=============\",ret)\r\n\r\n\t\t# 如果查询到的图片\r\n\t\timages = []\r\n\t\tif ret:\r\n\t\t\tfor image in ret:\r\n\t\t\t\timages.append(image[0])\r\n\t\tdata[\"images\"] = images\r\n\r\n\t\t# 查询房屋的基本设施\r\n\t\tsql = \"select hf_facility_id from ih_house_facility where hf_house_id=%s\"%house_id\r\n\t\tret = self.database.get_values_from_mysql(sql)\r\n\r\n\t\t# 如果查询到设施\r\n\t\tfacilities = []\r\n\t\tif ret:\r\n\t\t\tfor facility in ret:\r\n\t\t\t\tfacilities.append(facility[0])\r\n\t\tdata[\"facilities\"] = facilities\r\n\r\n\t\t# 查询评论信息\r\n\t\tsql = \"select oi_comment,up_name,oi_utime,up_mobile from ih_order_info inner join ih_user_profile \" \\\r\n\t\t\t \"on oi_user_id=up_user_id where oi_house_id=%s and oi_status=4 and oi_comment is not null\"%house_id\r\n\r\n\t\tret = self.database.get_values_from_mysql(sql)\r\n\t\tcomments = []\r\n\t\tif ret:\r\n\t\t\tfor comment in ret:\r\n\t\t\t\tcomments.append(dict(\r\n\t\t\t\t\tuser_name = comment[1] if comment[1] != comment[3] else \"匿名用户\",\r\n\t\t\t\t\tcontent = comment[0],\r\n\t\t\t\t\tctime = comment[2].strftime(\"%Y-%m-%d %H:%M:%S\")\r\n\t\t\t\t))\r\n\t\tdata[\"comments\"] = comments\r\n\r\n\t\t# 存入到redis中\r\n\t\tjson_data = json.dumps(data)\r\n\r\n\t\t## 可以在此处将上边查询到的这么多数据存储到redis中，这样下次查询的时候就能加快查询步骤\r\n\t\t#TODO\r\n\r\n\t\tresp = '{\"errcode\":\"0\", \"errmsg\":\"OK\", \"data\":%s, \"user_id\":%s}' % (json_data, mobile)\r\n\t\t# self.write(dict(errcode=RET.OK, errmsg=\"OK\", data=data))\r\n\t\tself.write(resp)\r\n\t\tself.set_header(\"Content-Type\", \"application/json; charset=UTF-8\")\r\n\r\n\r\n############## 获取预定信息 /api/house/info ########################################\r\nclass House_reserve(BaseHandler):\r\n\tdef post(self):\r\n\t\thouse_id = self.json_args.get('house_id')\r\n\t\tstart_date = self.json_args.get('start_date')\r\n\t\tend_date = self.json_args.get('end_date')\r\n\t\tprint('====',house_id,start_date,end_date)\r\n\r\n\t\tsql = ' select oi_begin_date,oi_end_date from ih_order_info where oi_house_id=%s'%house_id\r\n\t\tret = self.database.get_values_from_mysql(sql)\r\n\t\tif not ret:\r\n\t\t\tprint(' query order info error')\r\n\t\t\tdata = {\r\n\t\t\t\t'errcode':'4101',\r\n\t\t\t}\r\n\t\t############ 这里添加对 html中传来的事件信息，和该house在order数据库中存在的时间内信息进行比较来判断所选择的时间段是否可预定\r\n\r\n\t\tdata = {\r\n\t\t\t'errcode':'0'\r\n\t\t}\r\n\t\tself.write(data)\r\n\t\tself.set_header('Content-Type', 'application/json; charset=UTF-8')\r\n\r\n############### 处理显示订单信息 ##########################################\r\nclass Show_order(BaseHandler):\r\n\t@required_login\r\n\tdef get(self):\r\n\t\tsession_id = self.get_secure_cookie(\"session_id\")\r\n\t\t# print(\"session_id===\",session_id)\r\n\t\tmobile = self.redis.get_value(session_id).decode(\"utf-8\")\r\n\r\n\t\tsql = ' select up_user_id from ih_user_profile where up_mobile=%s'%mobile\r\n\t\tret = self.database.get_values_from_mysql(sql)\r\n\t\tif not ret:\r\n\t\t\tprint('user_id doesr not exist')\r\n\t\t\tdata = {\r\n\t\t\t\t'errcode':'1',\r\n\t\t\t}\r\n\t\tuser_id = ret[0][0]\r\n\t\t# print('user_id======',user_id)\r\n\r\n\t\t# 用户的身份，用户想要查询作为房客下的单，还是想要查询作为房东 被人下的单\r\n\t\trole = self.get_query_argument(\"role\", \"\")\r\n\t\ttry:\r\n\t\t\t# 查询房东订单\r\n\t\t\tif \"landlord\" == role:\r\n\t\t\t\tsql = 'select oi_order_id,hi_title,hi_index_image_url,oi_begin_date,oi_end_date,oi_ctime,oi_days,oi_amount,oi_status,oi_comment from ih_order_info inner join ih_house_info on oi_house_id=hi_house_id where hi_user_id=%s order by oi_ctime desc'%user_id\r\n\t\t\t\tret = self.database.get_values_from_mysql(sql)\r\n\t\t\t\tif not ret:\r\n\t\t\t\t\tprint(' query order info fail')\r\n\t\t\t\t\tdata = {\r\n\t\t\t\t\t\t'errcode':'1',\r\n\t\t\t\t\t}\r\n\r\n\t\t\telse:\r\n\t\t\t\tsql = 'select oi_order_id,hi_title,hi_index_image_url,oi_begin_date,oi_end_date,oi_ctime,oi_days,oi_amount,oi_status,oi_comment from ih_order_info inner join ih_house_info on oi_house_id=hi_house_id where oi_user_id=%s order by oi_ctime desc'%user_id\r\n\t\t\t\tret = self.database.get_values_from_mysql(sql)\r\n\t\t\t\tif not ret:\r\n\t\t\t\t\tprint(' query order info fail')\r\n\t\t\t\t\tdata = {\r\n\t\t\t\t\t\t'errcode':'1',\r\n\t\t\t\t\t}\r\n\r\n\t\texcept Exception as e:\r\n\t\t\tlogging.error(e)\r\n\t\t\treturn self.write({\"errcode\":'1', \"errmsg\":\"get data error\"})\r\n\t\torders = []\r\n\t\t# print('ret========',ret[0][0])\r\n\t\tif ret:\r\n\t\t\tfor l in ret:\r\n\t\t\t\t# print('l[0]======',l)\r\n\t\t\t\torder = {\r\n\t\t\t\t\t\"order_id\":l[0],\r\n\t\t\t\t\t\"title\":l[1],\r\n\t\t\t\t\t\"img_url\":l[2],\r\n\t\t\t\t\t\"start_date\":l[3].strftime(\"%Y-%m-%d\"),\r\n\t\t\t\t\t\"end_date\":l[4].strftime(\"%Y-%m-%d\"),\r\n\t\t\t\t\t\"ctime\":l[5].strftime(\"%Y-%m-%d\"),\r\n\t\t\t\t\t\"days\":l[6],\r\n\t\t\t\t\t\"amount\":l[7],\r\n\t\t\t\t\t\"status\":l[8],\r\n\t\t\t\t\t\"comment\":l[9] if l[9] else \"\"\r\n\t\t\t\t}\r\n\t\t\t\torders.append(order)\r\n\t\tself.write({\"errcode\":'0', \"errmsg\":\"OK\", \"orders\":orders})\r\n\r\n#################### 此处进行的是实名认证的功能 ############################\r\nclass Real_name_verity(BaseHandler):\r\n\t@required_login\r\n\tdef get(self):\r\n\t\tsession_id = self.get_secure_cookie(\"session_id\")\r\n\t\t# print(\"session_id===\",session_id)\r\n\t\tmobile = self.redis.get_value(session_id).decode(\"utf-8\")\r\n\t\t# print('mobile=======',mobile)\r\n\r\n\t\tsql = ' select up_real_name,up_id_card from ih_user_profile where up_mobile=\"%s\"'%mobile\r\n\t\tret = self.database.get_values_from_mysql(sql)\r\n\r\n\t\t# print('ret=====',ret)\r\n\t\tuser_data = {\r\n\t\t\t'real_name':ret[0][0],\r\n\t\t\t'id_card':ret[0][1],\r\n\t\t}\r\n\t\tdata = {\r\n\t\t\t'errcode':'0',\r\n\t\t\t'data':user_data,\r\n\t\t}\r\n\r\n\t\tprint('data========',data)\r\n\r\n\t\tself.write(data)\r\n\t\tself.set_header(\"Content-Type\", \"application/json; charset=UTF-8\")\r\n\r\n################# 修改当前用户的实名认证情况 #############################\r\n\t@required_login\r\n\tdef post(self):\r\n\t\t########### 获取当前用户的情况###################################\r\n\t\tsession_id = self.get_secure_cookie(\"session_id\")\r\n\t\t# print(\"session_id===\",session_id)\r\n\t\tmobile = self.redis.get_value(session_id).decode(\"utf-8\")\r\n\t\t# print('mobile=======',mobile)\r\n\r\n\t\treal_name = self.json_args.get('real_name')\r\n\t\tid_card = self.json_args.get('id_card')\r\n\t\t# print('id_card=====',real_name, id_card)\r\n\r\n\t\tsql = 'update ih_user_profile set up_real_name=\"%s\", up_id_card=\"%s\" where up_mobile=\"%s\"'%(real_name, id_card, mobile)\r\n\t\tret = self.database.update_tbl(sql)\r\n\t\tif not ret:\r\n\t\t\tprint('update real name fial')\r\n\r\n\t\tdata = {\r\n\t\t\t'errcode':'0',\r\n\t\t}\r\n\r\n\t\tself.write(data)\r\n\t\tself.set_header('Content-Type', 'application/json; charset=UTF-8')\r\n\r\nclass Myhouse_show(BaseHandler):\r\n\t@required_login\r\n\tdef get(self):\r\n\t\t########### 获取当前用户的情况###################################\r\n\t\tsession_id = self.get_secure_cookie(\"session_id\")\r\n\t\t# print(\"session_id===\",session_id)\r\n\t\tmobile = self.redis.get_value(session_id).decode(\"utf-8\")\r\n\t\t# print('mobile=======',mobile)\r\n\r\n\t\tsql = 'select up_user_id from ih_user_profile where up_mobile=\"%s\"'%mobile\r\n\t\tret = self.database.get_values_from_mysql(sql)\r\n\t\tuser_id = ret[0][0]\r\n\r\n\t\ttry:\r\n\t\t\tsql = \"select a.hi_house_id,a.hi_title,a.hi_price,a.hi_ctime,b.ai_name,a.hi_index_image_url \" \\\r\n\t\t\t\t \"from ih_house_info a inner join ih_area_info b on a.hi_area_id=b.ai_area_id where a.hi_user_id=%s\"%user_id\r\n\t\t\tret = self.database.get_values_from_mysql(sql)\r\n\t\texcept Exception as e:\r\n\t\t\tlogging.error(e)\r\n\t\t\treturn self.write({\"errcode\":'1', \"errmsg\":\"get data erro\"})\r\n\t\thouses = []\r\n\t\tif ret:\r\n\t\t\tfor l in ret:\r\n\t\t\t\thouse = {\r\n\t\t\t\t\t\"house_id\":l[0],\r\n\t\t\t\t\t\"title\":l[1],\r\n\t\t\t\t\t\"price\":l[2],\r\n\t\t\t\t\t\"ctime\":l[3].strftime(\"%Y-%m-%d\"), # 将返回的Datatime类型格式化为字符串\r\n\t\t\t\t\t\"area_name\":l[4],\r\n\t\t\t\t\t\"img_url\":l[5] if l[5] else \"\"\r\n\t\t\t\t}\r\n\t\t\t\thouses.append(house)\r\n\t\tself.write({\"errcode\":'0', \"errmsg\":\"OK\", \"houses\":houses})\r\n\t\tself.set_header('Content-Type', 'application/json; charset=UTF-8')\r\n\r\n#################### 此处的作用是提供房源提交的时候的地区选择功能 #####################\r\nclass Area_info_handler(BaseHandler):\r\n\t\"\"\"提供城区信息\"\"\"\r\n\t@required_login\r\n\tdef get(self):\r\n\r\n\t\t# 查询Mysql数据库，获取城区信息\r\n\t\tsql = \"select ai_area_id,ai_name from ih_area_info\"\r\n\r\n\t\ttry:\r\n\t\t\tret = self.database.get_values_from_mysql(sql)\r\n\t\texcept Exception as e:\r\n\t\t\tlogging.error(e)\r\n\t\t\treturn self.write(dict(errcode='1', errmsg=\"数据库查询出错\"))\r\n\t\tif not ret:\r\n\t\t\treturn self.write(dict(errcode='2', errmsg=\"没有数据\"))\r\n\t\t# 保存转换好的区域信息\r\n\t\tdata = []\r\n\t\tfor row in ret:\r\n\t\t\td = {\r\n\t\t\t\t\"area_id\": row[0],\r\n\t\t\t\t\"name\": row[1]\r\n\t\t\t}\r\n\t\t\tdata.append(d)\r\n\r\n\t\tself.write(dict(errcode='0', errmsg=\"OK\", data=data))\r\n\r\n################ 此处的作用 是退出当前用户的登录#################\r\n\r\nclass Login_out(BaseHandler):\r\n\t@required_login\r\n\tdef get(self):\r\n\t\t########### 获取当前用户的情况###################################\r\n\t\tsession_id = self.get_secure_cookie(\"session_id\")\r\n\t\t# print(\"session_id===\",session_id)\r\n\t\t#将当前用户在redis中存储的信息删除掉\r\n\t\tret = self.redis.del_value(session_id)\r\n\t\t# print('ret=========',ret)\r\n\t\tif not ret:\r\n\t\t\tlogging.error('delete value fial')\r\n\t\t\tdata = {\r\n\t\t\t\t'errcode':'1',\r\n\t\t\t}\r\n\t\telse:\r\n\t\t\tdata = {\r\n\t\t\t\t'errcode':'0',\r\n\t\t\t}\r\n\r\n\t\tself.write(data)\r\n\t\tself.set_header('Content-Type', 'application/json; charset=UTF-8')\r\n\r\n\r\n##################### 此处的作用是按照所选择的条件查询房屋资源################\r\n################# 此函数目前的状态是：待补充功能 ############################\r\nclass House_list_handler(BaseHandler):\r\n\t\"\"\"房源列表页面\"\"\"\r\n\tdef get(self):\r\n\t\t\"\"\"get方式用来获取数据库数据，本身的逻辑不会对数据库数据产生影响\"\"\"\r\n\t\t\"\"\"\r\n\t\t传入参数说明\r\n\t\tstart_date 用户查询的起始时间 sd 非必传 \"\" \"2017-02-28\"\r\n\t\tend_date 用户查询的终止时间 ed 非必传 \"\"\r\n\t\tarea_id 用户查询的区域条件 aid 非必传 \"\"\r\n\t\tsort_key 排序的关键词 sk 非必传 \"new\" \"new\" \"booking\" \"price-inc\" \"price-des\"\r\n\t\tpage 返回的数据页数 p 非必传 1\r\n\t\t\"\"\"\r\n\t\t# 获取参数\r\n\t\tstart_date = self.get_argument(\"sd\", \"\")\r\n\t\tend_date = self.get_argument(\"ed\", \"\")\r\n\t\tarea_id = self.get_argument(\"aid\", \"\")\r\n\t\tsort_key = self.get_argument(\"sk\", \"new\")\r\n\t\tpage = self.get_argument(\"p\", \"1\")\r\n\r\n\t\t# 检查参数\r\n\t\t# 判断日期格式、sort_Key 字段的值、page的整数\r\n\r\n\t\t# 数据查询\r\n\t\t# 涉及到表： ih_house_info 房屋的基本信息 ih_user_profile 房东的用户信息 ih_order_info 房屋订单数据\r\n\r\n\t\tsql = \"select hi_title,hi_house_id,hi_price,hi_room_count,hi_address,hi_order_count,up_avatar,hi_index_image_url,hi_ctime\" \\\r\n\t\t\t \" from ih_house_info inner join ih_user_profile on hi_user_id=up_user_id left join ih_order_info\" \\\r\n\t\t\t \" on hi_house_id=oi_house_id where hi_area_id=%s\"%area_id\r\n\r\n\t\tlogging.debug(sql)\r\n\t\ttry:\r\n\t\t\tret = self.database.get_values_from_mysql(sql)\r\n\t\texcept Exception as e:\r\n\t\t\tlogging.error(e)\r\n\t\t\treturn self.write(dict(errcode='4', errmsg=\"查询出错\"))\r\n\t\tdata = []\r\n\t\tif ret:\r\n\t\t\tfor l in ret:\r\n\t\t\t\thouse = dict(\r\n\t\t\t\t\thouse_id=l[1],\r\n\t\t\t\t\ttitle=l[0],\r\n\t\t\t\t\tprice=l[2],\r\n\t\t\t\t\troom_count=l[3],\r\n\t\t\t\t\taddress=l[4],\r\n\t\t\t\t\torder_count=l[5],\r\n\t\t\t\t\tavatar=l[6] if l[6] else \"\",\r\n\t\t\t\t\timage_url=l[7] if l[7] else \"\"\r\n\t\t\t\t)\r\n\t\t\t\tdata.append(house)\r\n\t\ttotal_page = len(data)\r\n\t\tself.write(dict(errcode='0', errmsg=\"OK\", data=data, total_page=total_page))\r\n\t\tself.set_header('Content-Type', 'application/json; charset=UTF-8')\r\n\r\n"},{"alpha_fraction":0.6858638525009155,"alphanum_fraction":0.6858638525009155,"avg_line_length":30.66666603088379,"blob_id":"5b22bc6160b7584f804557bad7f0b0c82238dd9b","content_id":"d9614792008a24652ace82267731efe381d47389","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":191,"license_type":"no_license","max_line_length":85,"num_lines":6,"path":"/Python/basic/package/__init__.py","repo_name":"jasondzy/Python","src_encoding":"UTF-8","text":"\n# this __ini__.py is must for a python package. \n\n\n__all__ = [\"Sendmesg\"] #define which module import \n\nfrom . import Sendmesg# this is must , only run this ,and can use the Semdmesg moudle\n"},{"alpha_fraction":0.6761133670806885,"alphanum_fraction":0.6801619529724121,"avg_line_length":16.5,"blob_id":"e430a2c30ea854d903d3ce6bcfd50b7975821c3d","content_id":"7a272330731e902e59b07d09927624b71d3a1e5d","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":247,"license_type":"no_license","max_line_length":61,"num_lines":14,"path":"/Python/plus/C_lib/c_lib.py","repo_name":"jasondzy/Python","src_encoding":"UTF-8","text":"\n\nfrom ctypes import *#must include , use to load c library\nimport threading\n\nLIB = cdll.LoadLibrary('./test.so')# loadding \n\n\nfor i in range(4):\n th = threading.Thread(target=LIB.Loop)#use the c function\n th.start()\n\n\n\nwhile True:\n pass\n"},{"alpha_fraction":0.4888888895511627,"alphanum_fraction":0.5333333611488342,"avg_line_length":19.5,"blob_id":"1773cca450986220b2d225cebbf2d69d8bd60419","content_id":"b489a631fe382e82d2253dbf7bf62b6d00c835fd","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":45,"license_type":"no_license","max_line_length":27,"num_lines":2,"path":"/Python/basic/package/Sendmesg.py","repo_name":"jasondzy/Python","src_encoding":"UTF-8","text":"\n\ndef test1():\n print(\"This is test1 \")\n\n\n"},{"alpha_fraction":0.3283582031726837,"alphanum_fraction":0.35820895433425903,"avg_line_length":31.5,"blob_id":"76029461ad9b70e211d48ada7eac892686369a90","content_id":"6ed334f6fa64eeb398f8bc664cefb60e45ab17e7","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":67,"license_type":"no_license","max_line_length":51,"num_lines":2,"path":"/Python/basic/package/receivemsg.py","repo_name":"jasondzy/Python","src_encoding":"UTF-8","text":"\n\ndef test2():\n print(\"-------------This is test2------------\")\n"},{"alpha_fraction":0.5122615694999695,"alphanum_fraction":0.553133487701416,"avg_line_length":22.70967674255371,"blob_id":"73d1325badeba6b77b541074167a25248632611c","content_id":"7ad947746f1417607fc9682b81c8e4a3eafd6a90","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":830,"license_type":"no_license","max_line_length":65,"num_lines":31,"path":"/Python/algorithm/fast.py","repo_name":"jasondzy/Python","src_encoding":"UTF-8","text":"def fast_seq(seq_data, start, end):\n \"\"\"如下是快速排序的代码，快速排序代码采用的是low和high两个位置坐标来对数列进行迭代的寻找首元素的位置的操作\"\"\"\n mid_value = seq_data[start]\n low = start\n high = end\n\n if start >= end:\n return\n\n while low < high:\n while low < high and seq_data[high] >= mid_value:\n high -= 1\n seq_data[low] = seq_data[high]\n\n while low < high and seq_data[low] < mid_value:\n low += 1\n seq_data[high] = seq_data[low]\n\n seq_data[low] = mid_value\n fast_seq(seq_data,start, low-1)\n fast_seq(seq_data, low+1, end)\n\n# return left_seq + right_seq\n\n\nif __name__ == '__main__':\n seq = [54, 226, 93, 17, 77, 31, 44, 55, 20, 6, 23, 54]\n\n fast_seq(seq, 0, len(seq)-1)\n\n print(seq)"}],"string":"[\n {\n \"alpha_fraction\": 0.5054945349693298,\n \"alphanum_fraction\": 0.5164835453033447,\n \"avg_line_length\": 5.92307710647583,\n \"blob_id\": \"512262367cd150e6a2aa7c9bfdcd24968e4bedba\",\n \"content_id\": \"7cd4e9a63bee409606ec0c104b041f6d5ebc738d\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"C\",\n \"length_bytes\": 91,\n \"license_type\": \"no_license\",\n \"max_line_length\": 37,\n \"num_lines\": 13,\n \"path\": \"/Python/plus/C_lib/test.c\",\n \"repo_name\": \"jasondzy/Python\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"\\n#include <stdio.h>\\n\\nint Loop()\\n{\\n\\tprintf(\\\"---This is a C library---\\\");\\n\\n\\twhile(1);\\n\\n\\n\\n\\n\\n}\\n\"\n },\n {\n \"alpha_fraction\": 0.7186400890350342,\n \"alphanum_fraction\": 0.7291910648345947,\n \"avg_line_length\": 38.71428680419922,\n \"blob_id\": \"a33c7625b7b9630fed6b7475d12967365ff8b914\",\n \"content_id\": \"96868b870dd22b2202da7c85977bd89e9f1dd57f\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 1391,\n \"license_type\": \"no_license\",\n \"max_line_length\": 103,\n \"num_lines\": 21,\n \"path\": \"/Python/plus/Process/pool.py\",\n \"repo_name\": \"jasondzy/Python\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"from multiprocessing import Pool\\r\\nimport time, os, random\\r\\n\\r\\n\\r\\ndef long_time_task(name): #这里创建了子进程需要运行的函数\\r\\n\\tprint('Run task %s (%s)..'%(name,os.getpid()))\\r\\n\\tstart = time.time()\\r\\n\\ttime.sleep(random.random()*3)\\r\\n\\tend = time.time()\\r\\n\\tprint('task %s runs %0.2f seconds'%(name, (end-start)))\\r\\n\\r\\nif __name__ == '__main__':\\r\\n\\tprint('Parent Process %s'%os.getpid())\\r\\n\\tp = Pool(4) #使用进程池创建了4个进程，也就是该进程池中只有4个进程在运行\\r\\n\\tfor i in range(5):#这里调用for循环，往Pool中存放5次的任务，如下的apply_async函数中的args是向函数传递的参数，这是元组类型，当只有一个参数的时候需要在最后添加一个,\\r\\n\\t\\tp.apply_async(long_time_task, args=(i,)) #往进程池中放入需要运行的函数，由于进程池中只有四个进程，而这里放入了5个任务，所以必会有一个任务无法立刻得到运行\\r\\n\\tprint('waiting for all subprocess done...') #此处是主进程运行的打印\\r\\n\\r\\n\\tp.close() # 在join前边需要调用close函数，表明不会再有任务往进程池中存放了\\r\\n\\tp.join() #调用了join函数，表明主进程会在此处block，等待所有的子进程运行结束后才会往下运行，这里用来作为主进程和子进程的同步\\r\\n\\tprint('all subprocess done...')#运行到这里表明所有的子进程和主进程都运行完毕了\"\n },\n {\n \"alpha_fraction\": 0.5430916547775269,\n \"alphanum_fraction\": 0.5718194246292114,\n \"avg_line_length\": 19.22222137451172,\n \"blob_id\": \"797de78479865e12a6d800be515b6798524c9776\",\n \"content_id\": \"9fdd032e1f1b8d5f527849160ade7fa75a9fa80b\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 731,\n \"license_type\": \"no_license\",\n \"max_line_length\": 65,\n \"num_lines\": 36,\n \"path\": \"/Python/plus/net/web/web_dynamic/web_framework.py\",\n \"repo_name\": \"jasondzy/Python\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"\\n\\n# define a functon , this function is standard for wsgi interface\\ndef application(environ, start_response):\\n\\n header= ''''''\\n\\n urls = [\\n ('./index.html', handle_index),\\n ('./index11.html', handle_index11)\\n\\n ]\\n \\n start_response('HTTP/1.1 200 ok',header)\\n\\n path = environ.get('PATH', '/')\\n\\n for name, handler in urls:\\n if name == path:\\n return handler()\\n\\n start_response('HTTP/1.1 404 not found',header)\\n return '404 not found'\\n\\ndef handle_index():\\n\\n fp = open('./index.html', 'r')\\n file_data = fp.read()\\n fp.close()\\n\\n return file_data\\n\\ndef handle_index11():\\n fp = open('./index11.html', 'r')\\n file_data = fp.read()\\n fp.close()\\n\\n return file_data\\n\\n\"\n },\n {\n \"alpha_fraction\": 0.6740442514419556,\n \"alphanum_fraction\": 0.6740442514419556,\n \"avg_line_length\": 36.38461685180664,\n \"blob_id\": \"5ce43ed48fa81658501c03e56bea98a04ab8c95a\",\n \"content_id\": \"e4981eff12456c9989f80886c08654945480a40c\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 497,\n \"license_type\": \"no_license\",\n \"max_line_length\": 65,\n \"num_lines\": 13,\n \"path\": \"/django/jason_test/booktest/urls.py\",\n \"repo_name\": \"jasondzy/Python\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"from django.conf.urls import url\\r\\nfrom . import views\\r\\n\\r\\nurlpatterns = [\\r\\n\\turl(r'^$',views.index,name=\\\"index\\\"),\\r\\n\\turl(r'^upload/',views.upload,name=\\\"upload\\\"),\\r\\n\\turl(r'^pages/',views.pages,name=\\\"pages\\\"),\\r\\n\\turl(r'^book_index/',views.book_index, name=\\\"book_index\\\"),\\r\\n\\turl(r'^ajax_get/',views.ajax_get,name=\\\"ajax_get\\\"),\\r\\n\\turl(r'^get_bookinfo/',views.get_bookinfo,name=\\\"get_bookinfo\\\"),\\r\\n\\turl(r'^editor/',views.editor,name=\\\"editor\\\"),\\r\\n\\turl(r'^editor_handle',views.editor_handle,name=\\\"editor_handle\\\"),\\r\\n]\"\n },\n {\n \"alpha_fraction\": 0.5050128102302551,\n \"alphanum_fraction\": 0.5206341743469238,\n \"avg_line_length\": 27.771812438964844,\n \"blob_id\": \"39c39fa75b7d82facc065ec5d1ecc5d449fda2db\",\n \"content_id\": \"4c33f77ec4f14b03ab2e5e680f87324d3f77464b\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 4463,\n \"license_type\": \"no_license\",\n \"max_line_length\": 145,\n \"num_lines\": 149,\n \"path\": \"/Python/mysql/connect.py\",\n \"repo_name\": \"jasondzy/Python\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"\\n\\nfrom pymysql import *\\n\\nclass py_mysql(object):\\n\\n def __init__(self, host, port, user, passwd, db, charset):\\n self.host = host\\n self.port = port\\n self.user = user\\n self.passwd = passwd\\n self.db = db\\n self.charset = charset\\n try:\\n print(self.host, self.port, self.user, self.passwd, self.db, self.charset)\\n self.conn = connect(host = self.host, port = self.port, user = self.user, passwd = self.passwd, db = self.db, charset = self.charset)\\n except Exception as e:\\n print(e)\\n print('fail!!!')\\n else:\\n print('connect ok')\\n self.curssor = self.conn.cursor()\\n\\n\\n def add(self, params=[]):\\n sql = 'insert into login values(%s,%s,%s,%s,%s,%s)'\\n res = self.curssor.execute(sql, params)\\n\\n if res:\\n self.conn.commit()\\n else:\\n self.conn.rollback()\\n print (res)\\n\\n# self.curssor.close()\\n# self.conn.close()\\n\\n def delete(self, params):\\n sql = 'delete from login where id = %s'\\n res = self.curssor.execute(sql, params)\\n\\n if res:\\n self.conn.commit()\\n else:\\n self.conn.rollback()\\n print(res)\\n\\n def update(self, params):\\n sql = 'update login set %s = %s where id = %s'\\n res = self.curssor.execute(sql, params)\\n\\n if res:\\n self.conn.commit()\\n else:\\n self.conn.rollback()\\n print(res)\\n\\n def select(self, params):\\n sql = 'select * from login'\\n\\n# print('------%s-----'%params)\\n\\n if params[0] == 'one':\\n self.curssor.execute(sql)\\n result = self.curssor.fetchone()\\n print('select one ok')\\n elif params[0] == 'all':\\n self.curssor.execute(sql)\\n result = self.curssor.fetchall()\\n print('select all ok')\\n else:\\n print('command wrong!')\\n result = NULL\\n\\n return result\\n\\n def close(self):\\n self.curssor.close()\\n self.conn.close()\\n\\nclass Userlogin(py_mysql):\\n# def __init__(self, host, port, user, passwd, db, charset):#此处若不定义__init__函数则默认使用父类的__init__函数\\n# super(Userlogin,self).__init__(host, port, user, passwd, db, charset)#此处若定义__init__函数可通过此方式调用父类的__init__函数\\n# 以上调用父类中的__init__函数的作用是使用父类中创建的一些局部变量，该类添加的局部变量可在此处及以下继续添加\\n def login(self, user, passwd):\\n sql = 'select passwd from login where name = %s'\\n params = [user]\\n res = self.curssor.execute(sql, params)\\n\\n if res:\\n result = self.curssor.fetchone()\\n# print('----result:%s-----'%result)\\n print('select ok....')\\n else:\\n result = NULL\\n print('username wrong!!!')\\n\\n# print('---result:%s---passwd:%s---'%(result,passwd))\\n if result[0] == passwd:\\n print(' login sucess....')\\n else:\\n print('passwd wrong!!!!')\\n\\n def join(self, user, passwd):\\n sql = 'select passwd from login where name = %s'\\n params = [user]\\n res = self.curssor.execute(sql, params)\\n if res:\\n print('username exists, change it!!')\\n return 0\\n else:\\n print('username can join...')\\n\\n sql = 'insert into login(name,passwd) values(%s,%s)'\\n params = [user, passwd]\\n\\n res = self.curssor.execute(sql, params)\\n if res:\\n self.conn.commit()\\n print('insert ok...')\\n else:\\n self.conn.rollback()\\n print('insert fail!!!')\\n\\n return 1\\n \\n\\nif __name__ == '__main__':\\n\\n# database = input('please input the database: ')\\n# mysql = py_mysql(host = '192.168.31.216', port = 3306, user = 'root', passwd = 'dzy', db = 'python3', charset = 'utf8')\\n\\n# params = [0, 'll', '123456', '34567890', 'pudong', 0]\\n# mysql.add(params)\\n\\n# params = ['all']\\n# data = mysql.select(params)\\n# print(data)\\n\\n login = Userlogin(host = '192.168.31.216', port = 3306, user = 'root', passwd = 'dzy', db = 'python3', charset = 'utf8')\\n# login.login('ll', '123456')\\n \\n result = login.join('ggg','333333')\\n if result:\\n print('join sucess...')\\n else:\\n print('join fail !!')\\n\\n login.close()\\n\\n print('OK!!')\\n\"\n },\n {\n \"alpha_fraction\": 0.6905055642127991,\n \"alphanum_fraction\": 0.6905055642127991,\n \"avg_line_length\": 52.06666564941406,\n \"blob_id\": \"8e6dc5c02c5fac921a1994bbd4b0e4c402396509\",\n \"content_id\": \"657e72a59787eb375a6d69584539751350107bc2\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 811,\n \"license_type\": \"no_license\",\n \"max_line_length\": 106,\n \"num_lines\": 15,\n \"path\": \"/django/tiantian/tiantian_project/urls.py\",\n \"repo_name\": \"jasondzy/Python\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"from django.conf.urls import url\\r\\nfrom . import views\\r\\n\\r\\nurlpatterns = [\\r\\n url(r'^register/$',views.register,name=\\\"register\\\"),\\r\\n url(r'^register_handle/$',views.register_handle,name=\\\"register_handle\\\"),\\r\\n url(r'^check_username/$',views.check_username,name=\\\"check_username\\\"),\\r\\n url(r'^login/$',views.login,name=\\\"login\\\"),\\r\\n url(r'^login_handle/$',views.login_handle,name=\\\"login_handle\\\"),\\r\\n url(r'^user_center_info/$',views.user_center_info,name=\\\"user_center_info\\\"),\\r\\n url(r'^user_center_info_register/$',views.user_center_info_register,name=\\\"user_center_info_register\\\"),\\r\\n url(r'^user_center_info_register_handle$',views.user_center_info_register_handle,name\\r\\n \\t=\\\"user_center_info_register_handle\\\"),\\r\\n url(r'^user_center_order$',views.user_center_order,name=\\\"user_center_order\\\"),\\r\\n]\\r\\n\"\n },\n {\n \"alpha_fraction\": 0.6104129552841187,\n \"alphanum_fraction\": 0.6122082471847534,\n \"avg_line_length\": 12.925000190734863,\n \"blob_id\": \"c517c9b5b450b206be6ab370ef0e20a5ea512ec3\",\n \"content_id\": \"c408b0609442d8fecc940d436977de220b39c975\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 557,\n \"license_type\": \"no_license\",\n \"max_line_length\": 38,\n \"num_lines\": 40,\n \"path\": \"/Python/plus/copy_test.py\",\n \"repo_name\": \"jasondzy/Python\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"\\nfrom multiprocessing import Pool\\nimport os\\nimport shutil\\n\\ndef copy_files(name):\\n\\t\\n\\tprint('-----%s--------'%name)\\n\\tfr = open('test/'+name)\\n\\tfw = open('test_bak/'+name,'w')\\n\\n\\tcontent = fr.read()\\n\\tfw.write(content)\\n\\n\\tfr.close()\\n\\tfw.close()\\n\\n\\n\\n\\n\\ndef main():\\n\\n\\tif not os.path.exists('test_bak/'):\\n\\t\\tos.mkdir('test_bak')\\n\\telse:\\n\\t\\tshutil.rmtree('test_bak')\\n\\t\\tos.mkdir('test_bak')\\n\\tsource_files = os.listdir('test')\\n\\n\\tpool = Pool(5)\\n\\n\\tfor name in source_files:\\n\\t\\tpool.apply_async(copy_files,(name,))\\n\\n\\tpool.close()\\n\\tpool.join()\\n\\n\\nif __name__ == '__main__':\\n\\tmain()\"\n },\n {\n \"alpha_fraction\": 0.5282837748527527,\n \"alphanum_fraction\": 0.5349951982498169,\n \"avg_line_length\": 23.162790298461914,\n \"blob_id\": \"ac25b924f5ce05009801f41ee03b5273d2701c1b\",\n \"content_id\": \"cbec2fb6541b4bf2d605680d8070f3ce36567a90\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 2086,\n \"license_type\": \"no_license\",\n \"max_line_length\": 99,\n \"num_lines\": 86,\n \"path\": \"/Python/plus/net/web/web_framework/web_service.py\",\n \"repo_name\": \"jasondzy/Python\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"\\nfrom socket import *\\nfrom multiprocessing import Process\\nimport re\\nimport sys\\nfrom web_framework import app\\n\\nclass Http_service(object):\\n ''' For Http_service '''\\n def __init__(self):\\n self.send_data = ''\\n\\n self.Web_service = socket(AF_INET, SOCK_STREAM)\\n print('--create a socket--')\\n \\n self.Web_service.setsockopt(SOL_SOCKET, SO_REUSEADDR, 1)\\n\\n self.Web_service.bind(('', 7788))\\n print('--bind port --')\\n\\n def Http_start(self):\\n self.Web_service.listen(5)\\n print('--listenning--')\\n\\n while True:\\n Client, ip_info = self.Web_service.accept()\\n print('--accept--')\\n\\n # create a Process to receive data and handle it \\n p = Process(target=self.Receive_data, args=(Client,))\\n p.start()\\n\\n # when create the Process and transfer the Client. the main process client should be closed\\n Client.close()\\n\\n self.Web_service.close()\\n\\n def Receive_data(self, Client):\\n # Receive data \\n print('--receive data--')\\n data = Client.recv(1024)\\n\\n # data is right?\\n if data:\\n print(data)\\n\\n self.Handle_data(data)\\n\\n Client.send(bytes(self.send_data, 'utf-8'))\\n else:\\n print('--data error--')\\n \\n # close client, only connect one time\\n Client.close() \\n\\n def start_response(self, status, headers):\\n\\n self.response_body = status + '\\\\r\\\\n' + str(headers) + '\\\\r\\\\n'\\n\\n def Handle_data(self, data):\\n # split the html name\\n html = '.'+(re.match(r'\\\\w+ +(/[^ ]*)', (data.decode('utf-8')).splitlines()[0])).group(1)\\n\\n print('---html:%s-----'%html)\\n # create a environ \\n environ = {\\n 'PATH': html\\n }\\n\\n html_data = app(environ, self.start_response)\\n\\n print(html_data)\\n\\n # http send data\\n self.send_data = self.response_body + '\\\\r\\\\n'+ html_data\\n\\n\\n\\n\\ndef main():\\n http = Http_service()\\n\\n http.Http_start()\\n\\n\\nif __name__ == '__main__':\\n main()\\n\\n\\n \\n\"\n },\n {\n \"alpha_fraction\": 0.6530105471611023,\n \"alphanum_fraction\": 0.6554934978485107,\n \"avg_line_length\": 23.140625,\n \"blob_id\": \"b3d2c787f9a09d1daf44da2423dc7dc345909766\",\n \"content_id\": \"6256effbd5cab451f6451a7dd57a3f927feb3e02\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 1639,\n \"license_type\": \"no_license\",\n \"max_line_length\": 105,\n \"num_lines\": 64,\n \"path\": \"/tornado/test/04_chat.py\",\n \"repo_name\": \"jasondzy/Python\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"import tornado.web\\r\\nimport tornado.httpserver\\r\\nimport tornado.ioloop\\r\\nimport tornado.options\\r\\nfrom tornado.web import url\\r\\nfrom tornado.websocket import WebSocketHandler\\r\\n\\r\\nimport os\\r\\n\\r\\ntornado.options.define('port', default=8000, type=int, help='runserver on given port')\\r\\n\\r\\nclass IndexHandler(tornado.web.RequestHandler):\\r\\n\\tdef get(self):\\r\\n\\t\\tself.render('chat.html')\\r\\n\\r\\nclass ChatHander(WebSocketHandler):\\r\\n\\tusers = set()\\r\\n\\r\\n\\tdef open(self):\\r\\n\\t\\tself.users.add(self)\\r\\n\\r\\n\\t\\tfor u in self.users:\\r\\n\\t\\t\\tu.write_message(\\\"%s 进入聊天室\\\" % self.request.remote_ip)\\r\\n\\r\\n\\tdef on_message(self,message):\\r\\n\\t\\tfor u in self.users:\\r\\n\\t\\t\\tu.write_message(\\\" %s发送消息:%s\\\" %(self.request.remote_ip, message))\\r\\n\\r\\n\\tdef on_close(self):\\r\\n\\t\\tusers.remove(self)\\r\\n\\t\\tfor u in self.users:\\r\\n\\t\\t\\tu.write_message(\\\"%s退出聊天室\\\"% self.request.remote_ip)\\r\\n\\r\\n\\tdef check_origin(self, origin):\\r\\n\\t\\treturn True\\r\\n\\r\\n\\r\\nclass Application(tornado.web.Application):\\r\\n\\tdef __init__(self):\\r\\n\\t\\thandler = [\\r\\n\\t\\t\\turl(r'^/$', IndexHandler, name=\\\"IndexHandler\\\"),\\r\\n\\t\\t\\turl(r'^/chat$', ChatHander, name=\\\"ChatHander\\\"),\\r\\n\\t\\t]\\r\\n\\r\\n\\t\\tsettings = dict(\\r\\n\\t\\t\\tdebug= True,\\r\\n\\t\\t\\tstatic_path = os.path.join(current_path, 'static'),\\r\\n\\t\\t\\ttemplate_path = os.path.join(current_path, 'templates'),\\r\\n\\t\\t\\t)\\r\\n\\r\\n\\t\\tsuper(Application, self).__init__(handler, **settings) \\r\\n\\r\\n\\r\\nif __name__ == '__main__':\\r\\n\\r\\n\\ttornado.options.parse_command_line()\\r\\n\\tcurrent_path = os.path.dirname(__file__)\\r\\n\\r\\n\\tapp = Application()\\r\\n\\r\\n\\thttp_server = tornado.httpserver.HTTPServer(app)\\r\\n\\thttp_server.listen(tornado.options.options.port)\\r\\n\\r\\n\\ttornado.ioloop.IOLoop.current().start()\\r\\n\\r\\n\"\n },\n {\n \"alpha_fraction\": 0.4642857015132904,\n \"alphanum_fraction\": 0.5200892686843872,\n \"avg_line_length\": 23.94444465637207,\n \"blob_id\": \"f989783c2aad0aae97a6f92ff84e5d79e172376a\",\n \"content_id\": \"c3533ad71215ad4cf6e512c07ea928d2e8c778b9\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 526,\n \"license_type\": \"no_license\",\n \"max_line_length\": 83,\n \"num_lines\": 18,\n \"path\": \"/Python/algorithm/select.py\",\n \"repo_name\": \"jasondzy/Python\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"def select_seq(seq_data):\\n \\\"\\\"\\\"插入排序的核心思想是找到后边无序序列中的最小值，并将最小值一次的放入序列的开头\\\"\\\"\\\"\\n n = len(seq_data)\\n\\n for j in range(n-1):\\n i = j\\n min_value = i\\n for i in range(i, n):\\n if seq_data[i] < seq_data[min_value]:\\n seq_data[min_value], seq_data[i] = seq_data[i], seq_data[min_value]\\n\\n\\nif __name__ == '__main__':\\n seq = [54, 226, 93, 17, 77, 31, 44, 55, 20, 6, 23, 54]\\n\\n select_seq(seq)\\n\\n print(seq)\"\n },\n {\n \"alpha_fraction\": 0.5100577473640442,\n \"alphanum_fraction\": 0.5399684906005859,\n \"avg_line_length\": 30.059782028198242,\n \"blob_id\": \"658491938b473c95fd0b59d1cc33c2fe8419b6ef\",\n \"content_id\": \"bb60dae28b24a552a623b303c42eaf8ebd83f7f5\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 5729,\n \"license_type\": \"no_license\",\n \"max_line_length\": 163,\n \"num_lines\": 184,\n \"path\": \"/Python/basic/plane_war/plane_war.py\",\n \"repo_name\": \"jasondzy/Python\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"#!/usr/bin/python\\n#! -*- coding:utf-8 -*-\\n\\nimport pygame\\nimport time \\n\\nfrom pygame.locals import *\\n\\nclass Window(object):\\n def __init__(self,temp1,temp2,temp3):\\n self.size = temp1\\n self.pos = temp2\\n self.color = temp3 \\n self.window = pygame.display.set_mode(self.size,self.pos,self.color)\\n self.image = pygame.image.load('./feiji/background.png')\\n\\n\\n def __str__(self):\\n return \\\"create a window size:%s pos:%d color_size:%d\\\"%(str(self.size),self.pos,self.color)\\n\\n\\nclass Base_plane(object):\\n def __init__(self,name0,name1,name2,name3,name4,x,y):\\n self.image0 = pygame.image.load(name0)\\n self.image1 = pygame.image.load(name1)\\n self.image2 = pygame.image.load(name2)\\n self.image3 = pygame.image.load(name3)\\n self.image4 = pygame.image.load(name4)\\n self.x = x\\n self.y = y\\n\\n\\n\\n\\nclass My_hero(Base_plane):\\n def __init__(self,name0,name1,name2,name3,name4,x,y):\\n Base_plane.__init__(self,name0,name1,name2,name3,name4,x,y)\\n self.bullet_list = []\\n self.mouse_flag = 0\\n\\n def display(self,temp):\\n temp.window.blit(self.image0,(self.x,self.y))\\n\\n for i in self.bullet_list:\\n i.display(temp)\\n if i.y < 0 :\\n self.bullet_list.remove(i)\\n\\n def shoot(self,temp):\\n bullet = Bullet(self.x,self.y)\\n self.bullet_list.append(bullet)\\n bullet.enemy.add(temp)\\n\\n def mouse_move(self):\\n self.x,self.y = pygame.mouse.get_pos()\\n self.x -= 50\\n self.y -= 62\\n\\n\\nclass Enemy1(Base_plane):\\n def __init__(self,name0,name1,name2,name3,name4,x,y):\\n Base_plane.__init__(self,name0,name1,name2,name3,name4,x,y)\\n self.disappear_flag = 0 \\n self.disappear = 0\\n self.recreate = 0\\n self.direction = 'right'\\n\\n def display(self,temp):\\n if self.disappear_flag==0 and self.disappear==0:\\n temp.window.blit(self.image0,(self.x,self.y))\\n if self.disappear_flag == 4 and self.disappear ==0:\\n temp.window.blit(self.image1,(self.x,self.y))\\n self.disappear_flag -=1\\n elif self.disappear_flag == 3 and self.disappear ==0:\\n temp.window.blit(self.image2,(self.x,self.y))\\n self.disappear_flag -=1\\n elif self.disappear_flag == 2 and self.disappear ==0:\\n temp.window.blit(self.image3,(self.x,self.y))\\n self.disappear_flag -=1\\n elif self.disappear_flag == 1 and self.disappear ==0:\\n temp.window.blit(self.image4,(self.x,self.y))\\n self.disappear_flag -=1\\n self.disappear = 1\\n\\n def mov(self):\\n if self.x >420-10 :\\n self.direction = 'left'\\n elif self.x <10 :\\n self.direction = 'right'\\n if self.direction == 'left':\\n self.x -=5\\n elif self.direction == 'right':\\n self.x += 5\\n print('direction:%s x:%d'%(self.direction,self.x))\\n \\n\\nclass Bullet(object):\\n\\n def __init__(self,temp_x,temp_y):\\n self.image = pygame.image.load('./feiji/bullet.png')\\n self.x = temp_x\\n self.y = temp_y\\n self.enemy = set()\\n\\n def display(self,temp):\\n temp.window.blit(self.image,(self.x,self.y))\\n self.y -=20\\n\\n for temp in self.enemy:\\n print('x:%d y:%d'%(self.x,self.y))\\n if self.x-55 <temp.x and self.x+15 > temp.x and self.y-45 <temp.y and self.y+45 >temp.y and temp.disappear_flag==0:\\n temp.disappear_flag = 4\\n break\\n \\n\\n\\n\\ndef move(temp1,temp2):\\n\\n for event in pygame.event.get():\\n\\n if event.type == QUIT:\\n print('exit')\\n exit() #use to quit frome a programe\\n elif event.type == KEYDOWN:\\n if event.key == K_a or event.key == K_LEFT:\\n print('left')\\n temp1.x -=10\\n\\n elif event.key == K_d or event.key == K_RIGHT:\\n print('right')\\n temp1.x +=10\\n\\n elif event.key == K_c:\\n print('exit')\\n exit()\\n elif event.key == K_m:\\n print('use mouse for moving')\\n temp1.mouse_flag = 1\\n \\n elif event.key == K_SPACE:\\n print('space')\\n temp1.shoot(temp2)\\n\\n elif event.key == K_n:\\n print('create a new game')\\n temp2.recreate = 1\\n\\n\\ndef main():\\n pygame.init() # pygame init\\n screen = Window((480,852),0,32)\\n pygame.display.set_caption('Plane War') #创建窗口标签\\n#---------above create a whole background class-----------------\\n hero = My_hero('./feiji/hero1.png','./feiji/hero_blowup_n1.png','./feiji/hero_blowup_n2.png','./feiji/hero_blowup_n3.png','./feiji/hero_blowup_n4.png',210,700)\\n enemy1 = Enemy1('./feiji/enemy1.png','./feiji/enemy1_down1.png','./feiji/enemy1_down2.png','./feiji/enemy1_down3.png','./feiji/enemy1_down4.png',210,0)\\n#--------above create a hero plane class---------------------- \\n\\n while True:\\n screen.window.blit(screen.image,(0,0))\\n#---------above create a background----------------------------\\n hero.display(screen)\\n if hero.mouse_flag == 1 :\\n hero.mouse_move()\\n enemy1.display(screen)\\n enemy1.mov()\\n#---------above create plane--------------------------\\n if enemy1.recreate ==1 :\\n enemy1 = Enemy1('./feiji/enemy1.png','./feiji/enemy1_down1.png','./feiji/enemy1_down2.png','./feiji/enemy1_down3.png','./feiji/enemy1_down4.png',210,0)\\n enemy1.recreate = 0\\n move(hero,enemy1)\\n\\n \\n pygame.display.update()\\n\\n time.sleep(0.01)\\n\\n\\n\\n\\n\\nif __name__ == '__main__':\\n\\n main()\\n\\n\\n\"\n },\n {\n \"alpha_fraction\": 0.4166666567325592,\n \"alphanum_fraction\": 0.44583332538604736,\n \"avg_line_length\": 18.83333396911621,\n \"blob_id\": \"55cc3576e42122a1f87e2c495653e939f48e66fa\",\n \"content_id\": \"fe7d7809fc044437d5c00caf9ed6184ff43f8c53\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Text\",\n \"length_bytes\": 240,\n \"license_type\": \"no_license\",\n \"max_line_length\": 45,\n \"num_lines\": 12,\n \"path\": \"/Python/readme.txt\",\n \"repo_name\": \"jasondzy/Python\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"/*******************************************\\n\\n\\tAuthor: Jason(dongzhenyu)\\n\\tDate: 2017-7-26\\n\\tGoal: proficient in using python\\n\\tGithub account: jasondzy\\n********************************************/\\n\\n\\n\\n\\nThis dir is used for python study\\n\\n\\n\"\n },\n {\n \"alpha_fraction\": 0.7085714340209961,\n \"alphanum_fraction\": 0.7657142877578735,\n \"avg_line_length\": 17.66666603088379,\n \"blob_id\": \"8ffd40afad2c9c2d99f0bd0982c26b1de3bb9477\",\n \"content_id\": \"79c88ecf1b993ff39fd439b5e00954e9b7f7cd94\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"INI\",\n \"length_bytes\": 175,\n \"license_type\": \"no_license\",\n \"max_line_length\": 49,\n \"num_lines\": 9,\n \"path\": \"/django/jason_test/uwsgi.ini\",\n \"repo_name\": \"jasondzy/Python\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"[uwsgi]\\r\\nhttp=0.0.0.0:8000\\r\\nchdir=/home/ubuntu/user_jason/git/django/tiantian\\r\\nwsgi-file=tiantian/\\r\\nprocesses=4\\r\\nthreads=2\\r\\nmaster=True\\r\\npidfile=uwsgi.pid\\r\\ndaemonize=uswgi.log\"\n },\n {\n \"alpha_fraction\": 0.420265793800354,\n \"alphanum_fraction\": 0.460132896900177,\n \"avg_line_length\": 17.272727966308594,\n \"blob_id\": \"061e8d3ae0c13a0a8abee75d506eeb3b94e8b9ab\",\n \"content_id\": \"b41fc478fabba0c958876b540065aca97f64ae56\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 602,\n \"license_type\": \"no_license\",\n \"max_line_length\": 45,\n \"num_lines\": 33,\n \"path\": \"/Python/algorithm/merge.py\",\n \"repo_name\": \"jasondzy/Python\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"def sort(left, right):\\n lst = []\\n j = 0\\n i = 0\\n while i < len(left) and j < len(right):\\n if left[i] < right[j]:\\n lst.append(left[i])\\n i = i+1\\n else:\\n lst.append(right[j])\\n j += 1\\n lst += left[i:]\\n lst += right[j:]\\n\\n return lst\\n\\n\\ndef merge(lst):\\n\\n if len(lst) <= 1:\\n return lst\\n\\n middle = len(lst) // 2\\n left = merge(lst[:middle])\\n right = merge(lst[middle:])\\n\\n return sort(left, right)\\n\\n\\nif __name__ == '__main__':\\n ll = [54, 26, 93, 17, 77, 31, 44, 55, 20]\\n result = merge(ll)\\n print(result)\"\n },\n {\n \"alpha_fraction\": 0.5576496720314026,\n \"alphanum_fraction\": 0.5620842576026917,\n \"avg_line_length\": 19.409090042114258,\n \"blob_id\": \"281e7b1cd34b4e2504cf9d1eb89b3ea5dcce1760\",\n \"content_id\": \"7e9e6d58624504bf618b688aba5e5b6f6dcf234c\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 902,\n \"license_type\": \"no_license\",\n \"max_line_length\": 122,\n \"num_lines\": 44,\n \"path\": \"/Python/basic/furniture/furniture_test.py\",\n \"repo_name\": \"jasondzy/Python\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"\\n\\nclass room():\\n \\n def __init__(self,area,addr,info):\\n \\n self.area = area\\n self.addr = addr\\n self.info = info\\n self.useful = self.area\\n\\n def __str__(self):\\n\\n msg = \\\"romm:size is :%d area useful is:%d addr is:%s info is:%s \\\"%(self.area,self.useful,self.addr,self.info)\\n return msg\\n def add_item(self,item):\\n self.useful -= item.get_size()\\n\\n\\n\\n\\nclass bed():\\n \\n def __init__(self,size,name):\\n self.size = size\\n self.name = name\\n\\n def __str__(self):\\n msg = \\\"bed : size is:%d name is:%s\\\"%(self.size,self.name)\\n return msg\\n\\n def get_size(self): #define a func to get the private data;\\n return self.size\\n\\nroom_dzy = room(200,\\\"hangzhou,xihu area\\\",\\\"four rooms\\\")\\n\\nprint(room_dzy)\\n\\n\\nbed_one = bed(8,\\\"yijia\\\")\\n\\nprint(bed_one)\\n\\nroom_dzy.add_item(bed_one)# arg is a class type\\n\\nprint(room_dzy)\\n\\n\\n\"\n },\n {\n \"alpha_fraction\": 0.5067567825317383,\n \"alphanum_fraction\": 0.5164092779159546,\n \"avg_line_length\": 23.654762268066406,\n \"blob_id\": \"dd0ca9768e958e9f35cff2d0fde65c259b687953\",\n \"content_id\": \"2730a9f0f4c149e9fac2eb144a2b78eac7369bdd\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 2072,\n \"license_type\": \"no_license\",\n \"max_line_length\": 111,\n \"num_lines\": 84,\n \"path\": \"/Python/plus/net/web/web_service/web_service.py\",\n \"repo_name\": \"jasondzy/Python\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"\\nfrom socket import *\\nfrom multiprocessing import Process\\nimport re\\n\\n\\nclass Http_service(object):\\n ''' For Http_service '''\\n def __init__(self):\\n self.send_data = ''\\n\\n self.Web_service = socket(AF_INET, SOCK_STREAM)\\n print('--create a socket--')\\n \\n self.Web_service.setsockopt(SOL_SOCKET, SO_REUSEADDR, 1)\\n\\n self.Web_service.bind(('', 7788))\\n print('--bind port --')\\n\\n def Http_start(self):\\n self.Web_service.listen(5)\\n print('--listenning--')\\n\\n while True:\\n Client, ip_info = self.Web_service.accept()\\n print('--accept--')\\n\\n # create a Process to receive data and handle it \\n p = Process(target=self.Receive_data, args=(Client,))\\n p.start()\\n\\n # when create the Process and transfer the Client. the main process client should be closed\\n Client.close()\\n\\n self.Web_service.close()\\n\\n def Receive_data(self, Client):\\n # Receive data \\n print('--receive data--')\\n data = Client.recv(1024)\\n\\n # data is right?\\n if data:\\n print(data)\\n\\n self.Handle_data(data)\\n\\n Client.send(bytes(self.send_data, 'utf-8'))\\n else:\\n print('--data error--')\\n \\n # close client, only connect one time\\n Client.close() \\n\\n def Handle_data(self, data):\\n # split the html name\\n html = '.'+(re.match(r'\\\\w+ +(/[^ ]*)', (data.decode('utf-8')).splitlines()[0])).group(1)\\n\\n # try to read the local html data\\n try:\\n file = open(html, 'rb')\\n html_data = file.read()\\n except:\\n print('open file error')\\n while True:\\n print('-----------')\\n finally:\\n file.close()\\n\\n print(html_data)\\n\\n # http send data\\n self.send_data = \\\"HTTP/1.1 200 ok\\\\r\\\\n\\\" + \\\"Server: my server\\\" \\\"\\\\r\\\\n\\\" + '\\\\r\\\\n'+ html_data.decode('utf-8')\\n\\n\\n\\n\\ndef main():\\n http = Http_service()\\n\\n http.Http_start()\\n\\n\\nif __name__ == '__main__':\\n main()\\n\"\n },\n {\n \"alpha_fraction\": 0.7104166746139526,\n \"alphanum_fraction\": 0.7333333492279053,\n \"avg_line_length\": 21.904762268066406,\n \"blob_id\": \"110025018fb77cd4b330bf83966e3f5d67216243\",\n \"content_id\": \"d3a4bde258c84702fc6ff1dbe2d66298d320eeae\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 480,\n \"license_type\": \"no_license\",\n \"max_line_length\": 46,\n \"num_lines\": 21,\n \"path\": \"/django/tiantian/tiantian_project/models.py\",\n \"repo_name\": \"jasondzy/Python\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"from django.db import models\\n\\n# Create your models here.\\n\\nclass UserInfo(models.Model):\\n\\tuser_name = models.CharField(max_length=20)\\n\\tuser_passwd = models.CharField(max_length=40)\\n\\tuser_mail = models.CharField(max_length=30)\\n\\n\\tdef __str__(self):\\n\\t\\treturn self.user_name\\n\\n\\n\\nclass User_address_info(models.Model):\\n\\taddress = models.CharField(max_length=100)\\n\\ttel_num = models.CharField(max_length=12)\\n\\tuser = models.ForeignKey(UserInfo)\\n\\t\\n\\tdef __str__(self):\\n\\t\\treturn str(self.user)\"\n },\n {\n \"alpha_fraction\": 0.6076555252075195,\n \"alphanum_fraction\": 0.619617223739624,\n \"avg_line_length\": 18.465116500854492,\n \"blob_id\": \"a912e5d36c291925ce16249605b8e7eab0c0e60c\",\n \"content_id\": \"989ed944a3f88a424c655c9b28ecdfde9d2a06f6\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 1022,\n \"license_type\": \"no_license\",\n \"max_line_length\": 77,\n \"num_lines\": 43,\n \"path\": \"/Python/plus/net/tcp/tcp_service.py\",\n \"repo_name\": \"jasondzy/Python\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"\\\"\\\"\\\"\\n# note: 这里是通过gevent模块还实现单线程多任务的功能,其中的socket函数来自gevent模块中\\n#\\n#\\n\\n\\\"\\\"\\\"\\nfrom gevent import socket, monkey\\nimport gevent\\n\\nmonkey.patch_all()#使用gevent中的monkey来对接一下的函数进行封装成非阻塞的功能，此处必须\\n\\ndef handle_data(client):\\n while True:\\n data = client.recv(1024)\\n if data:\\n print(data)\\n else:\\n client.close()\\n break\\n\\n\\ndef main():\\n service = socket.socket()\\n print('create a socket')\\n\\n# service.setsockopt(SOL_SOCKET, SO_REUSEADDR, 1)\\n\\n service.bind(('', 7788))\\n print('bind a port')\\n\\n service.listen(5)\\n print('listenning.....')\\n\\n while True:\\n client,ip = service.accept()\\n print('accept..')\\n\\n gevent.spawn(handle_data,client)#添加需要进行调度的函数，只要遇到阻塞类型的函数就会进行切换从而实现多任务\\n\\n service.close()#never to run here for service\\n\\nif __name__ == '__main__':\\n main()\"\n },\n {\n \"alpha_fraction\": 0.7872763276100159,\n \"alphanum_fraction\": 0.7952286005020142,\n \"avg_line_length\": 22.952381134033203,\n \"blob_id\": \"e8732bca37879640621e73fd518ce564b166637d\",\n \"content_id\": \"121a84f73966768d32d214f90f24816a09542ec6\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 875,\n \"license_type\": \"no_license\",\n \"max_line_length\": 97,\n \"num_lines\": 21,\n \"path\": \"/Python/plus/Process/multi_processing.py\",\n \"repo_name\": \"jasondzy/Python\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"\\nfrom multiprocessing import Process\\nimport time \\n\\ndef test():#这里表示的是子进程中所要运行的程序\\n\\tfor i in range(5):\\n\\t\\tprint('---son Process---')\\n\\t\\ttime.sleep(1)\\n\\nson = Process(target=test)#这里使用Process类创建了一个进程son，里边的参数是一个函数表明son这个进程需要处理的事情,参数中的test运行结束后子进程自动消亡\\n\\nson.start()#start方法表示的是开始运行子进程\\n\\ntime.sleep(1)\\n\\n#son.terminate() #Process中的terminate方法的作用是立刻停止所创建的子进程并返回，到这里子进程就结束了了，整个程序就只剩下主进程了\\n\\nson.join()#这里表示的是主进程在这里等待子进程运行完毕后才会往下执行，这里起到进程的同步作用\\n\\nwhile True:#这里表示的是主进程中所要运行的程序\\n\\tprint('---parent process--')\\n\\ttime.sleep(1)\"\n },\n {\n \"alpha_fraction\": 0.5941176414489746,\n \"alphanum_fraction\": 0.6058823466300964,\n \"avg_line_length\": 14.090909004211426,\n \"blob_id\": \"035debb3e9d1a61b951a6d3f8fd7dccc013b2c29\",\n \"content_id\": \"75b37126fffb4079075f33f03d4afbab96c6c2ab\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 170,\n \"license_type\": \"no_license\",\n \"max_line_length\": 37,\n \"num_lines\": 11,\n \"path\": \"/Python/basic/exception/exception.py\",\n \"repo_name\": \"jasondzy/Python\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"\\n\\ntry:\\n 5/0\\n\\n\\nexcept Exception as ret :\\n print(\\\"%s wrong has happend\\\"%ret)\\n\\nelse :\\n print(\\\"No wrong happened\\\")\\nfinally :\\n print(\\\"programme run sucess....\\\")\\n\\n\\n\"\n },\n {\n \"alpha_fraction\": 0.6164383292198181,\n \"alphanum_fraction\": 0.6164383292198181,\n \"avg_line_length\": 25.375,\n \"blob_id\": \"79b8b2e6974d691000dabc9921a3d3beaa81f019\",\n \"content_id\": \"d1d56854345ba5a31901016f47ea23b59ae3e9e2\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 219,\n \"license_type\": \"no_license\",\n \"max_line_length\": 58,\n \"num_lines\": 8,\n \"path\": \"/tornado/project/Ihome/Settings.py\",\n \"repo_name\": \"jasondzy/Python\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"\\r\\n# import os\\r\\n\\r\\n# current_path = os.path.dirname(__file__)\\r\\n# settings = dict(\\r\\n# \\tdebug = True,\\r\\n# \\tstatic_path = os.path.join(current_path, 'static'),\\r\\n# \\ttemplate_path = os.path.join(current_path, 'template'),\\r\\n# \\t)\"\n },\n {\n \"alpha_fraction\": 0.7600364685058594,\n \"alphanum_fraction\": 0.7700729966163635,\n \"avg_line_length\": 33.28125,\n \"blob_id\": \"4438b97876880b03a2cd5e123ccd768d1395f338\",\n \"content_id\": \"abe9f99af4ce762491c63e52e5a73583a7d8c498\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 1946,\n \"license_type\": \"no_license\",\n \"max_line_length\": 115,\n \"num_lines\": 32,\n \"path\": \"/Python/plus/decorator/decorator.py\",\n \"repo_name\": \"jasondzy/Python\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"def func(function): #这里的func定义的是一个闭包，这里的参数是function\\n\\tprint('Tis is a decorate1---')\\n\\tdef func_inner(*args,**kwargs):#这里的func_inner是闭包中的内嵌函数，最终闭包的返回值就是这个内嵌函数的地址\\n\\t\\tprint('----func_inner----')\\n\\t\\tfunction(*args,**kwargs)#这里的function()的作用是执行函数function，从这里可以验证闭包的参数是一个函数的地址\\n\\n\\treturn func_inner#这里是闭包的返回值，该返回值是函数的地址\\n\\n\\ndef func2(function):\\n\\tprint('This is a decorate2')\\n\\tdef func_inner(*args,**kwargs):#这里对内嵌函数定义了不定参数，注意这里的内嵌函数就是将来调用的函数test()，所以要实现test(x,y...)带入参数，那么这里的内嵌函数必须定义成带参数的形式\\n\\t\\tprint('-----func_inner2---')\\n\\t\\tfunction(*args,**kwargs)#这里的函数调用的是定义的test函数，所以这里的参数要使用内嵌函数中的参数，可以不一致。因为最终所有的参数都是通过装饰后的test传递进来的。\\n\\treturn func_inner\\n\\n\\n\\n@func #这里的作用是试用装饰器func，传入的参数就是下一行中的函数地址，即test\\n@func2 #通过执行这个py脚本，注意管着两个连续的装饰器的执行顺序是如何的\\ndef test(a):\\n\\tprint('----test---a=%d---'%a)\\n@func#通过对改脚本的输出log可知，装饰器的执行是在所有函数之前的，即这三个装饰器的最先打印处This is a decoratex，其中decorate2最先打印出来\\ndef test1(a):\\n\\tprint('----test1-- a=%d--'%a)\\n\\ntest(0)#调用test函数，注意这里的test函数已经不再是上边定义了的test函数，而是经过装饰器装饰了的函数\\ntest1(1)\\n\\n#@func的作用就是对函数进行装饰，流程原理如下：\\n#@func => test = func(test) => func_inner()\\n#即最后的test的地址为func_inner的地址，而在func_inner中有调用test所以实现了对test的封装\"\n },\n {\n \"alpha_fraction\": 0.45676276087760925,\n \"alphanum_fraction\": 0.521064281463623,\n \"avg_line_length\": 25.58823585510254,\n \"blob_id\": \"c5cb3e3b7ba9e7722ed7350c41bd7b3a7e77d6dd\",\n \"content_id\": \"0166bfe371f1f62922a7c3d51e13a1bc0b894ef3\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 601,\n \"license_type\": \"no_license\",\n \"max_line_length\": 85,\n \"num_lines\": 17,\n \"path\": \"/Python/algorithm/insert.py\",\n \"repo_name\": \"jasondzy/Python\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"def insert_seq(seq_data):\\n \\\"\\\"\\\"这里是采用插入排序的方式，具体实现原理是通过将序列分成两列来对待，即前边的有序序列和后边的无序序列，每次从无序序列中提取一个数插入到前边的有序序列中去\\\"\\\"\\\"\\n n = len(seq_data)\\n for j in range(n):\\n i = j\\n while i > 0:\\n if seq_data[i] < seq_data[i-1]:\\n seq_data[i-1], seq_data[i] = seq_data[i], seq_data[i-1]\\n i -= 1\\n\\n\\nif __name__ == '__main__':\\n seq = [54, 226, 93, 17, 77, 31, 44, 55, 20, 6, 23, 54]\\n\\n insert_seq(seq)\\n\\n print(seq)\"\n },\n {\n \"alpha_fraction\": 0.7934272289276123,\n \"alphanum_fraction\": 0.7981220483779907,\n \"avg_line_length\": 30.950000762939453,\n \"blob_id\": \"f79e101889ab19e2f579de301543f22f9a29a160\",\n \"content_id\": \"4e8bb9548a4a90ef8566c47da963f2aaa21837db\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 1111,\n \"license_type\": \"no_license\",\n \"max_line_length\": 127,\n \"num_lines\": 20,\n \"path\": \"/Python/plus/Process/process_class.py\",\n \"repo_name\": \"jasondzy/Python\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"\\nfrom multiprocessing import Process\\nimport time\\n\\n\\nclass New_process(Process):#这里定义了一个新的Newprocess类，这个类继承了Process这个父类,即可通过这个新建的类来创建一个自定义的进程\\n\\tdef __init__(self,value):\\n\\t\\tProcess.__init__(self)#初始化调用了父类中的init函数，同时也加入了自定义的value的变量\\n\\t\\tself.value = value\\n\\n\\tdef run(self):#定义了一个run方法，切记：此处定义的run方法是对父类Process中的run方法的一种重写，接下来的父类中的start方法启用的是这里重写了的run方法,这里也实现了一个不用通过tartget=xxx的方法传入一个方法\\n\\t\\twhile True:\\n\\t\\t\\tprint('----son-----')\\n\\t\\t\\ttime.sleep(1)\\n\\nson = New_process(1)#用新创建的类来创建一个子进程,里边传入的参数并没有实际的作用\\nson.start()#调用start方法，这个start方法是在父类中定义的，但是这个start的方法实际调用的是run方法，由于在新建的类中重写了run方法所以最终调用的是新创建的类中的run方法\\n\\nwhile True:\\n\\tprint('---parent----')\\n\\ttime.sleep(1)\"\n },\n {\n \"alpha_fraction\": 0.5438596606254578,\n \"alphanum_fraction\": 0.5470494627952576,\n \"avg_line_length\": 16.742856979370117,\n \"blob_id\": \"9d56e5a0172057ca9c764517ac55db8accdbfe30\",\n \"content_id\": \"40e2cbdfd5c45ccfa1e62558cc8c25dfd0926a34\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 627,\n \"license_type\": \"no_license\",\n \"max_line_length\": 62,\n \"num_lines\": 35,\n \"path\": \"/Python/basic/class_func/class_func.py\",\n \"repo_name\": \"jasondzy/Python\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"\\n\\nclass Game(object):\\n\\n num = 0\\n\\n def __init__(self):\\n self.name = \\\"croos fire\\\"\\n\\n\\n #define a class func , use classmethod to define\\n\\n @classmethod\\n def class_num(cls):\\n cls.num +=1\\n\\n #staticmethod can no args . so no self or cls and so on...\\n @staticmethod\\n def print_info():\\n print(\\\"---------------------\\\")\\n print(\\\" cross fire \\\")\\n print (\\\"___________________\\\")\\n\\n\\ngame = Game()\\n\\n# call the object func\\nprint(game.name)\\n\\n#call the class func\\n#Game.class_num() or\\ngame.class_num()\\nprint(Game.num)\\n\\n#call the static func\\n#Game.print_info() or\\ngame.print_info()\\n\\n\\n\\n\\n\"\n },\n {\n \"alpha_fraction\": 0.6838235259056091,\n \"alphanum_fraction\": 0.6911764740943909,\n \"avg_line_length\": 23.81818199157715,\n \"blob_id\": \"7569d55132e9b7a0db34f3bc869dfdb2aefe38bf\",\n \"content_id\": \"d5d5b05da588419816efa24cb35895f78086d7d1\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 272,\n \"license_type\": \"no_license\",\n \"max_line_length\": 50,\n \"num_lines\": 11,\n \"path\": \"/django/tiantian/goods_app/views.py\",\n \"repo_name\": \"jasondzy/Python\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"from django.shortcuts import render\\n\\n# Create your views here.\\n\\ndef index(request):\\n\\tname = request.session.get('user',None)\\n\\tif name != None:\\n\\t\\tcontext = {'user':name,'status':1}\\n\\telse:\\n\\t\\tcontext = {'user':\\\"\\\",'status':0}\\n\\treturn render(request,'goods/index.html',context)\"\n },\n {\n \"alpha_fraction\": 0.7200000286102295,\n \"alphanum_fraction\": 0.7200000286102295,\n \"avg_line_length\": 11.5,\n \"blob_id\": \"cde0b7317bb476798c98164133b143337f321adf\",\n \"content_id\": \"add78c5a1b6ab6186bc8378891ccb9cd248ca0d6\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Markdown\",\n \"length_bytes\": 25,\n \"license_type\": \"no_license\",\n \"max_line_length\": 12,\n \"num_lines\": 2,\n \"path\": \"/Python/basic/README.md\",\n \"repo_name\": \"jasondzy/Python\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"# class_func\\n# furniture\\n\"\n },\n {\n \"alpha_fraction\": 0.5659287571907043,\n \"alphanum_fraction\": 0.586140513420105,\n \"avg_line_length\": 20.14285659790039,\n \"blob_id\": \"2d7188fe13aed35b1f092e68222048af850c0c80\",\n \"content_id\": \"8cfe31961c370943ce4be7b08be83c0cf27a295b\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 1039,\n \"license_type\": \"no_license\",\n \"max_line_length\": 69,\n \"num_lines\": 49,\n \"path\": \"/Python/plus/net/web/web_framework/web_framework.py\",\n \"repo_name\": \"jasondzy/Python\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"\\n\\nclass Application(object):\\n\\n def __init__(self,urls):\\n self.urls = urls\\n\\n # define a callable function, so the class can be called\\n def __call__(self, environ, start_response):\\n return self.application(environ, start_response)\\n\\n\\n # define a functon , this function is standard for wsgi interface\\n def application(self,environ, start_response):\\n\\n header= ''''''\\n\\n start_response('HTTP/1.1 200 ok',header)\\n\\n path = environ.get('PATH', '/')\\n\\n for name, handler in self.urls:\\n if name == path:\\n return handler()\\n\\n start_response('HTTP/1.1 404 not found',header)\\n return '404 not found'\\n\\ndef handle_index():\\n\\n fp = open('./index.html', 'r')\\n file_data = fp.read()\\n fp.close()\\n\\n return file_data\\n\\ndef handle_index11():\\n fp = open('./index11.html', 'r')\\n file_data = fp.read()\\n fp.close()\\n\\n return file_data\\n\\n\\nurls = [\\n ('./index.html', handle_index),\\n ('./index11.html', handle_index11)\\n\\n]\\n\\napp = Application(urls)\\n\\n\"\n },\n {\n \"alpha_fraction\": 0.610837459564209,\n \"alphanum_fraction\": 0.7192118167877197,\n \"avg_line_length\": 19.200000762939453,\n \"blob_id\": \"a4fa24eae73bab0e1a5c16763830a4eebf7c1eb3\",\n \"content_id\": \"751b71ce01f6c9c740b4c778e27cd8a86967e58f\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 203,\n \"license_type\": \"no_license\",\n \"max_line_length\": 60,\n \"num_lines\": 10,\n \"path\": \"/Python/plus/net/send_data.py\",\n \"repo_name\": \"jasondzy/Python\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"\\nimport socket\\n\\n\\nudp_socket = socket.socket(socket.AF_INET,socket.SOCK_DGRAM)\\n\\nudp_socket.bind(('',7766))\\n\\ndata = udp_socket.recvfrom(1024)\\nprint(data)\\nudp_socket.sendto(b'hello',('192.168.0.107',8080))\\n\"\n },\n {\n \"alpha_fraction\": 0.5933236479759216,\n \"alphanum_fraction\": 0.5997097492218018,\n \"avg_line_length\": 23.062936782836914,\n \"blob_id\": \"a706b783183dbe6a0af6077233c08aa6ed8eefee\",\n \"content_id\": \"999857b258b7c1e0fae773124b82108e87be6ac6\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 4193,\n \"license_type\": \"no_license\",\n \"max_line_length\": 197,\n \"num_lines\": 143,\n \"path\": \"/Python/basic/Project/laowang_shut.py\",\n \"repo_name\": \"jasondzy/Python\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"\\n\\n\\nclass Person():\\n\\n def __init__(self,name):\\n self.name = name\\n self.gun = None\\n self.hp = 100\\n\\n def __str__(self):\\n return \\\"The person name is %s hp is:%s the gun is: %s\\\"%(self.name,self.hp,self.gun)\\n def load_bullet(self,temp1,temp2):\\n temp1.add_bullet(temp2)\\n\\n def load_bullet_package(self,temp1,temp2):\\n temp1.bullet_package = temp2\\n\\n def load_gun(self,temp):\\n self.gun = temp\\n\\n def shoot(self,enemy):\\n self.gun.shoot(enemy)\\n\\n def hp_down(self,temp):\\n self.hp -= temp\\n if self.hp < 0 :\\n print(\\\"enemy has died!!!\\\")\\n\\n\\nclass Gun():\\n\\n def __init__(self,name):\\n self.name = name\\n self.bullet_package = None\\n self.bullet = None\\n def __str__(self):\\n return \\\"The name of gun is %s the bullet_package is:%s\\\"%(self.name,self.bullet_package)#这里的self.bullet_package变量，其实就是调用bullet_package中的__str__方法，因为__str__方法中用的就是return，所以对象名称中保存的值就是__str__\\n\\n def add_bullet_package(self,temp):\\n self.bullet_package = temp\\n \\n def shoot(self,temp):\\n self.bullet = self.bullet_package.pop_bullet()\\n if self.bullet :\\n self.bullet.hurt(temp)\\n else :\\n print(\\\"Do not have bullet\\\")\\n\\n\\nclass Bullet_package():\\n def __init__(self,max_num):\\n self.max_num = max_num\\n self.bullet_num=[]\\n\\n\\n def __str__(self):\\n return \\\"The max num of bullets is %d load bullet is %d\\\"%(self.max_num,len(self.bullet_num))\\n\\n\\n def add_bullet(self,temp):\\n self.bullet_num.append(temp)\\n \\n\\n def pop_bullet(self):\\n return self.bullet_num.pop()\\n\\n\\nclass Bullet():\\n def __init__(self,ability):\\n self.ability = ability\\n\\n def __str__(self):\\n return \\\"the ability is %d\\\"%self.ability\\n \\n def hurt(self,temp):\\n temp.hp_down(self.ability)\\n\\n\\n\\ndef main():\\n\\n bullet = Bullet(10) #创建一个bullet实体，每一个实体都有一个独立的存储空间，即使是由同一个类创造出来的，他们中的属性值（即__init__下的变量值）是不一样的，这也是self的作用，若不用self.xx那么xx表示的就是类的属性，对由其创建的对象是一样的\\n print(bullet)\\n\\n bullet_package = Bullet_package(30)\\n print(bullet_package)\\n\\n\\n gun = Gun(\\\"AK47\\\")\\n print(gun)\\n\\n\\n laowang = Person(\\\"laowang\\\")\\n print(laowang)\\n\\n#---------above create three classes-----------------------\\n\\n for i in range(30):\\n bullet = Bullet(10)#创建一个bullet实体\\n laowang.load_bullet(bullet_package,bullet) # 这里的参数传递的是对象，不要传递类，因为对象才是实体，类只是一个模具\\n print(bullet_package)\\n#---------above add some bullets--------------------------\\n\\n laowang.load_bullet_package(gun,bullet_package)#从这些对象之间的调用可知，python是一种解释性语言，其按照顺序的方式对程序进行解释执行，所以这里的参数对象，必定是前边已经赋值了的\\n print(gun)\\n\\n#---------above add a bullet_package into gun------------\\n\\n laowang.load_gun(gun)#在这里的各个函数调用过程中，时刻都会改变main一开始创建的四个对象中的值，因为这四个对象是一个在内存空间中的实体，对象中的属性值通过引用的方式改变其自身的值\\n print(laowang)\\n\\n#--------above add a gun -------------------------------\\n\\n laosong = Person(\\\"laosong\\\")#这里再次创建一个Person对象，注意这里并不是和laowang是同一个对象，他们分别是独立的一块内存空间，其属性值是不一定相同的\\n\\n#--------above create a enemy------------------- -------\\n\\n laowang.shoot(laosong)\\n print(laosong)\\n\\n laowang.shoot(laosong)\\n print(laosong)\\n laowang.shoot(laosong)\\n print(laosong)\\n\\n laowang.shoot(laosong)\\n print(laosong)\\n\\n laowang.shoot(laosong)\\n print(laosong)\\n\\n\\n laowang.shoot(laosong)\\n print(laosong)\\n\\n\\n\\n#---------above shoot enemy-----------------------------\\n\\n\\n\\n\\n\\nif __name__ == \\\"__main__\\\" :#python中用来区别import和执行运行的方式\\n main()\\n\\n\"\n },\n {\n \"alpha_fraction\": 0.7333333492279053,\n \"alphanum_fraction\": 0.7333333492279053,\n \"avg_line_length\": 41,\n \"blob_id\": \"f80b29a195b5af49d37927a3a1531d934a562230\",\n \"content_id\": \"9fe535c164c4cab3799d0bb03672c31124e9e4b9\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Text\",\n \"length_bytes\": 45,\n \"license_type\": \"no_license\",\n \"max_line_length\": 41,\n \"num_lines\": 1,\n \"path\": \"/Python/basic/readme.txt\",\n \"repo_name\": \"jasondzy/Python\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"\\n\\n\\tthis is a basic python study test record\\n\"\n },\n {\n \"alpha_fraction\": 0.6577181220054626,\n \"alphanum_fraction\": 0.6711409687995911,\n \"avg_line_length\": 29.589040756225586,\n \"blob_id\": \"114f6f289b8b00dd666b7596c03578f00c5d5210\",\n \"content_id\": \"ce5e6dc76a1e4c403cfb657a3935c23f8095df3d\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 5397,\n \"license_type\": \"no_license\",\n \"max_line_length\": 142,\n \"num_lines\": 146,\n \"path\": \"/tornado/test/03_template.py\",\n \"repo_name\": \"jasondzy/Python\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"import tornado.web\\r\\nimport tornado.httpserver\\r\\nimport tornado.options\\r\\nimport tornado.ioloop\\r\\nimport tornado.httpclient\\r\\nimport tornado.gen\\r\\nimport os\\r\\nimport pymysql\\r\\nimport base64, uuid, json\\r\\nfrom tornado.web import url, StaticFileHandler\\r\\n\\r\\ntornado.options.define(\\\"port\\\", default=8000, type=int, help=\\\"runserver on given port\\\")\\r\\n\\r\\n\\r\\nclass IndexHander(tornado.web.RequestHandler):\\r\\n\\tdef get(self):\\r\\n\\t\\tcontext = {\\r\\n\\t\\t\\t'price':398,\\r\\n\\t\\t\\t'title':'宽窄巷子+160平大空间+文化保护区双地铁',\\r\\n\\t\\t\\t'address':'北京市丰台区六里桥地铁',\\r\\n\\r\\n\\t\\t}\\r\\n\\t\\tself.render(\\\"index.html\\\",context=context) #注意，render的第二个参数是一个关键字参数，需要传入xx=xxx类型的参数，或者**xx这里的xx是一个字典，**表示的是对字典进行解引用，解引用就是一个关键字参数\\r\\n #render的第一个参数是可变参数类型\\r\\n\\r\\nclass NewHander(tornado.web.RequestHandler):\\r\\n\\tdef get(self):\\r\\n\\t\\tself.render('newindex.html',text=\\\"\\\")\\r\\n\\r\\n\\tdef post(self):\\r\\n\\t\\ttext = self.get_argument('text')\\r\\n\\t\\tself.render('newindex.html', text=text)\\r\\n\\r\\nclass SetCookie(tornado.web.RequestHandler):\\r\\n\\tdef get(self):\\r\\n\\r\\n\\t\\tcookie = self.get_secure_cookie('count')\\r\\n\\t\\tcount = int(cookie) +1 if cookie else 1\\r\\n\\t\\tself.set_secure_cookie('count', str(count))\\r\\n\\r\\n\\t\\treturn self.write(str(count))\\r\\n\\r\\nclass csrf_test(tornado.web.RequestHandler):\\r\\n\\tdef get(self):\\r\\n\\t\\t# self.xsrf_token #这里是用来设置_xsrf这个cookie的值得，若采用模板的{% module xxX%}格式则不需要在这里进行手动测试\\r\\n\\t\\tself.render('csrf_test.html')\\r\\n\\tdef post(self):\\r\\n\\t\\tself.write(\\\"ok\\\")\\r\\n\\r\\nclass AsyncHandler(tornado.web.RequestHandler):\\r\\n\\t@tornado.gen.coroutine #这里的作用相当于python3中最近加入的async模块，此模块对Python的异步操作功能紧进行了封装，用协程的方式实现，当遇到IO阻塞的时候系统能够自动切换到其他未阻塞的协程中去执行\\r\\n\\tdef get(self):\\r\\n\\t\\thttp = tornado.httpclient.AsyncHTTPClient()\\r\\n\\t\\tresponse = yield http.fetch(\\\"http://int.dpool.sina.com.cn/iplookup/iplookup.php?format=json&ip=14.130.112.24\\\")\\r\\n\\t\\t\\t\\t#如上的作用是当http.fetch阻塞的时候，系统自动的切换到其他协程中去执行，\\r\\n\\t\\tif response.error:\\r\\n\\t\\t\\tself.send_error(500)\\r\\n\\t\\telse:\\r\\n\\t\\t\\tprint('type of data:',type(response.body))\\r\\n\\t\\t\\tprint(str(response.body))\\r\\n\\t\\t\\tdata = json.loads(response.body.decode('utf-8'))\\r\\n\\t\\t\\tself.write('country:%s, provice:%s, city:%s'% (data['country'], data['province'],data['city']))\\r\\n\\r\\n\\r\\nclass Application(tornado.web.Application):\\r\\n\\tdef __init__(self):\\r\\n\\t\\thandlers = [\\r\\n\\t\\t\\turl(r'^/$', IndexHander, name=\\\"index\\\"),\\r\\n\\t\\t\\turl(r'^/cookie$', SetCookie, name=\\\"cookie\\\"),\\r\\n\\t\\t\\turl(r'^/new$', NewHander, name=\\\"newhander\\\"),\\r\\n\\t\\t\\turl(r'/csrf', csrf_test, name=\\\"csrf_test\\\"),\\r\\n\\t\\t\\turl(r'^/async$', AsyncHandler, name=\\\"AsyncHandler\\\"),\\r\\n\\t\\t]\\r\\n\\r\\n\\t\\tsettings = dict(\\r\\n\\t\\t\\tstatic_path = os.path.join(current_path,\\\"static\\\"), #这里加入静态文件所在的位置\\r\\n\\t\\t\\ttemplate_path = os.path.join(current_path, \\\"templates\\\"), #这里加入模板文件的路劲\\r\\n\\t\\t\\tdebug = True,\\r\\n\\t\\t\\tcookie_secret = str(base64.b64encode(uuid.uuid4().bytes + uuid.uuid4().bytes)), #这里使用uuid和base64共同合成了一个随机的字符串，添加到cookie从而形成secure_cookie的机制\\r\\n\\t\\t\\txsrf_cookies = True,\\r\\n\\t\\t\\t)\\r\\n\\t\\tsuper(Application, self).__init__(handlers,**settings)\\r\\n\\t\\ttry:\\r\\n\\t\\t\\tself.db = pymysql.connect(\\r\\n\\t\\t\\t\\thost = '115.159.62.43',\\r\\n\\t\\t\\t\\tport = 3306,\\r\\n\\t\\t\\t\\tdb = 'LoveHome',\\r\\n\\t\\t\\t\\tuser = 'root',\\r\\n\\t\\t\\t\\tpasswd = 'dzy',\\r\\n\\t\\t\\t\\tcharset = 'utf8'\\r\\n\\t\\t\\t\\t)\\r\\n\\t\\texcept Exception as e:\\r\\n\\t\\t\\tprint(e)\\r\\n\\t\\t\\tprint('connect to mysql fail!!')\\r\\n\\t\\telse:\\r\\n\\t\\t\\tprint('connect sucess')\\r\\n\\t\\t\\tself.cursor = self.db.cursor()\\r\\n\\r\\n\\tdef create_table(self):\\r\\n\\t\\tsql = '''\\r\\n\\t\\t\\tcreate table houses (\\r\\n \\t\\t\\tid bigint(20) unsigned not null auto_increment comment '房屋编号',\\r\\n \\t\\t\\ttitle varchar(64) not null default '' comment '标题',\\r\\n \\t\\t\\tposition varchar(32) not null default '' comment '位置',\\r\\n \\t\\t\\tprice int not null default 0,\\r\\n \\t\\t\\tscore int not null default 5,\\r\\n \\t\\t\\tcomments int not null default 0,\\r\\n \\t\\t\\tprimary key(id)\\r\\n\\t\\t\\t)ENGINE=InnoDB default charset=utf8 comment='房屋信息表'\\r\\n\\t\\t'''\\r\\n\\t\\tself.cursor.execute(sql)\\r\\n\\t\\tself.db.commit()\\r\\n\\r\\n\\t\\tself.cursor.close()\\r\\n\\t\\tself.db.close()\\r\\n\\r\\nif __name__ == '__main__':\\r\\n\\r\\n\\tcurrent_path = os.path.dirname(__file__)\\r\\n\\r\\n\\ttornado.options.parse_command_line()\\r\\n\\r\\n\\t# app = tornado.web.Application([\\r\\n\\t# \\turl(r'^/$', IndexHander, name=\\\"index\\\"),\\r\\n\\t# \\turl(r'^/new$', NewHander, name=\\\"newhander\\\"),\\r\\n\\t# \\t# url(r'/()',StaticFileHandler, {\\\"path\\\":os.path.join(current_path,\\\"static/html\\\"), \\\"default_filename\\\":\\\"index.html\\\"}, name=\\\"index\\\"),\\r\\n\\t# \\t#注意如上的/后必须加上正则匹配的()，因为StaticFileHandler中的get方法需要参数，不加会报错，这里实现了任意url地址访问静态文件的方式\\r\\n\\r\\n\\t# \\t],\\r\\n\\r\\n\\t# \\tstatic_path = os.path.join(current_path,\\\"static\\\"), #这里加入静态文件所在的位置\\r\\n\\t# \\ttemplate_path = os.path.join(current_path, \\\"templates\\\"), #这里加入模板文件的路劲\\r\\n\\r\\n\\t# \\t debug=True)\\r\\n\\r\\n\\r\\n\\tapp = Application()\\r\\n\\t# app.create_table() #这里创建一个表\\r\\n\\t# app.cursor.execute('select * from test_tbl')\\r\\n\\t# result = app.cursor.fetchone()\\r\\n\\t# print(result) \\r\\n\\r\\n\\thttp_server = tornado.httpserver.HTTPServer(app)\\r\\n\\thttp_server.listen(tornado.options.options.port)\\r\\n\\r\\n\\ttornado.ioloop.IOLoop.current().start()\\r\\n\\r\\n\\t\\r\\n\\r\\n\"\n },\n {\n \"alpha_fraction\": 0.7135134935379028,\n \"alphanum_fraction\": 0.7675675749778748,\n \"avg_line_length\": 18.77777862548828,\n \"blob_id\": \"69bcec2a549a031dbbc431b32f9194edc59d99d6\",\n \"content_id\": \"3cfdaaecf148ad9acd6d2210e1c1fdeb6358b26d\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"INI\",\n \"length_bytes\": 185,\n \"license_type\": \"no_license\",\n \"max_line_length\": 50,\n \"num_lines\": 9,\n \"path\": \"/django/tiantian/uwsgi.ini\",\n \"repo_name\": \"jasondzy/Python\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"[uwsgi]\\r\\nsocket=0.0.0.0:8000\\r\\nchdir=/home/ubuntu/user_jason/git/django/tiantian/\\r\\nwsgi-file=tiantian/wsgi.py\\r\\nprocesses=1\\r\\nthreads=1\\r\\nmaster=True\\r\\npidfile=uwsgi.pid\\r\\ndaemonize=uswgi.log\"\n },\n {\n \"alpha_fraction\": 0.7387518286705017,\n \"alphanum_fraction\": 0.7532656192779541,\n \"avg_line_length\": 22,\n \"blob_id\": \"19700a2f3f9c48525a7f5d6574b81f51f7a918f1\",\n \"content_id\": \"81d63f8e24725f69e6284afd3b849170c87aee72\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 689,\n \"license_type\": \"no_license\",\n \"max_line_length\": 46,\n \"num_lines\": 30,\n \"path\": \"/django/jason_test/booktest/models.py\",\n \"repo_name\": \"jasondzy/Python\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"from django.db import models\\nfrom tinymce.models import HTMLField\\n\\n# Create your models here.\\n\\nclass BookInfo(models.Model):\\n\\tbtitle = models.CharField(max_length=20)\\n\\tbpub_date = models.DateTimeField()\\n\\tbread = models.IntegerField(default=0)\\n\\tbcommet = models.IntegerField(default=0)\\n\\tisDelete = models.BooleanField(default=False)\\n\\tbcontent = HTMLField()\\n\\n\\n\\n\\n\\tdef __str__(self):\\n\\t\\treturn self.btitle\\n\\n\\nclass HeroInfo(models.Model):\\n\\thname = models.CharField(max_length=20)\\n\\thgender = models.BooleanField(default=1)\\n\\thBook = models.ForeignKey('BookInfo')\\n\\thcontent = models.CharField(max_length=100)\\n\\tisDelete = models.BooleanField(default=False)\\n\\t\\n\\n\\tdef __str__(self):\\n\\t\\treturn self.hname\"\n },\n {\n \"alpha_fraction\": 0.5109395384788513,\n \"alphanum_fraction\": 0.5186614990234375,\n \"avg_line_length\": 23.96666717529297,\n \"blob_id\": \"8ef7925bdad54a053ca14f2f2318c6736cd978a9\",\n \"content_id\": \"8858b4dab4b03a4fd195a6888f3ca70e8575f9ab\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"HTML\",\n \"length_bytes\": 795,\n \"license_type\": \"no_license\",\n \"max_line_length\": 86,\n \"num_lines\": 30,\n \"path\": \"/django/jason_test/templates/booktest/editor.html\",\n \"repo_name\": \"jasondzy/Python\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"<!DOCTYPE html>\\r\\n<html>\\r\\n<head>\\r\\n <title>editro</title>\\r\\n {% load static from staticfiles %}\\r\\n <script type=\\\"text/javascript\\\" src=\\\"{% static 'tiny_mce/tiny_mce.js' %}\\\"></script>\\r\\n <script type=\\\"text/javascript\\\">\\r\\n tinyMCE.init({\\r\\n 'mode':'textareas',\\r\\n 'theme':'advanced',\\r\\n 'width':400,\\r\\n 'height':100\\r\\n });\\r\\n </script>\\r\\n</head>\\r\\n<body>\\r\\n<form method=\\\"post\\\" action=\\\"{% url 'booktest:editor_handle' %}\\\">\\r\\n\\t{% csrf_token %}\\r\\n <select name=\\\"select\\\">\\r\\n \\t{% for index,item in data %}\\r\\n \\t<option value=\\\"{{index}}\\\">{{item}}</option>\\r\\n \\t{% endfor %}\\r\\n </select>\\r\\n <br>\\r\\n <textarea name='hcontent'>哈哈，这是啥呀</textarea>\\r\\n <br>\\r\\n <input type=\\\"submit\\\" value=\\\"提交\\\">\\r\\n</form>\\r\\n</body>\\r\\n</html>\"\n },\n {\n \"alpha_fraction\": 0.6939066052436829,\n \"alphanum_fraction\": 0.7050113677978516,\n \"avg_line_length\": 25,\n \"blob_id\": \"18d19f968aa52314f60accc2b8ace72d7e4422c4\",\n \"content_id\": \"6742574c83380bf985e6383ac9cdc181dbdb3b8d\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 3692,\n \"license_type\": \"no_license\",\n \"max_line_length\": 89,\n \"num_lines\": 135,\n \"path\": \"/django/tiantian/tiantian_project/views.py\",\n \"repo_name\": \"jasondzy/Python\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"from django.shortcuts import render, redirect\\nfrom django.core.urlresolvers import reverse\\nfrom django.http import HttpResponse, JsonResponse\\nfrom .models import *\\nfrom hashlib import sha1\\nimport re\\n\\n# Create your views here.\\ndef register(request):\\n\\treturn render(request,'user/register.html')\\n\\ndef register_handle(request):\\n\\t#获取用户提交的信息\\n\\tname = request.POST['user_name']\\n\\tpwd = request.POST['pwd']\\n\\tcpwd = request.POST['cpwd']\\n\\temail = request.POST['email']\\n\\n\\t#判断密码是否相同\\n\\tif pwd != cpwd:\\n\\t\\treturn redirect(reverse('tiantian:register'))\\n\\n\\t#判断邮箱是否正确\\n\\tif re.match(r'^[0-9a-zA-Z_]{0,19}@[0-9a-zA-Z]{1,13}\\\\.[com,cn,net]{1,3}$',email) == None:\\n\\t\\treturn redirect(reverse('tiantian:register'))\\n\\n\\t# 密码加密：\\n\\ts1 = sha1()\\n\\ts1.update(pwd.encode(\\\"utf-8\\\"))\\n\\tpasswd = s1.hexdigest()\\n\\t# 加密 end\\n\\n\\tuser = UserInfo()\\n\\tuser.user_name = name\\n\\tuser.user_passwd = passwd\\n\\tuser.user_mail = email\\n\\tuser.save()\\n\\n\\treturn redirect(reverse('tiantian:login'))\\n\\n\\t# return render(request,)\\n\\ndef check_username(request):\\n\\tprint('test')\\n\\tname = request.GET['name']\\n\\tprint(name)\\n\\tcount = UserInfo.objects.filter(user_name=name).count() #这里使用的是count的方式来计算过滤出来的值有多少个\\n\\tprint(count) #能使用count的原因是过滤出来的是一个数组\\n\\tif count:\\n\\t\\tprint('1')\\n\\t\\treturn JsonResponse({'status':1})\\n\\telse:\\n\\t\\tprint('0')\\n\\t\\treturn JsonResponse({'status':0})\\n\\ndef login(request):\\n\\treturn render(request,'user/login.html')\\n\\ndef login_handle(request):\\n\\tname = request.POST['username']\\n\\tpwd = request.POST['pwd']\\n\\t#获取密码的加密格式字符串\\n\\ts1 = sha1()\\n\\ts1.update(pwd.encode(\\\"utf-8\\\"))\\n\\tpasswd = s1.hexdigest()\\t\\n\\t# 加密 end\\n\\n\\t#比较用户名\\n\\tcount = UserInfo.objects.filter(user_name=name).count()\\n\\tif count == 0:\\n\\t\\treturn redirect(reverse('tiantian:login'))\\n\\telse:\\n\\t\\tuser = UserInfo.objects.get(user_name=name)\\n\\t\\tif user.user_passwd != passwd:\\n\\t\\t\\tstatus = 0\\n\\t\\t\\treturn redirect(reverse('tiantian:login'))\\n\\t\\telse:\\n\\t\\t\\tstatus = 1\\n\\t\\t\\trequest.session['user']=name\\n\\t\\t\\trequest.session.set_expiry(300)\\n\\t\\t\\treturn redirect(reverse('goods:index'))\\n\\n\\ndef user_center_info(request):\\n\\tname = request.session.get('user',None)\\n\\n\\tif name == None:\\n\\t\\treturn redirect(reverse('tiantian:login'))\\n\\n\\tuser = UserInfo.objects.get(user_name=name)\\n\\taddr = user.user_address_info_set.all()\\n\\tif addr.count() == 0:\\n\\t\\treturn redirect(reverse('tiantian:user_center_info_register'))\\n\\taddress = []\\n\\tfor i in addr:\\n\\t\\taddress.append(i)\\n\\tcontext = {'name':name,'address':address}\\n\\treturn render(request,'user/user_center_info.html',context)\\n\\ndef user_center_info_register(request):\\n\\tname = request.session.get('user',None)\\n\\tif name == None:\\n\\t\\treturn redirect(reverse('tiantian:login'))\\n\\telse:\\n\\t\\tuser = UserInfo.objects.get(user_name=name)\\n\\t\\taddr = user.user_address_info_set.all()\\n\\t\\tif addr.count() == 0:\\n\\t\\t\\tstatus=0\\n\\t\\telse:\\n\\t\\t\\tstatus=1\\n\\n\\t\\taddress = []\\n\\t\\tfor i in addr:\\n\\t\\t\\taddress.append(i)\\n\\t\\tcontext = {'name':name,'address':address,'status':status}\\n\\t\\tprint(context)\\n\\treturn render(request,'user/user_center_site.html',context)\\n\\ndef user_center_info_register_handle(request):\\n\\ttel_num = request.POST['tel_num']\\n\\taddress = request.POST['address']\\n\\n\\tif len(tel_num)<5 or len(address)<5:\\n\\t\\treturn redirect(reverse('tiantian:user_center_info_register'))\\n\\n\\tusername = request.session.get('user',None)\\n\\tif username == None:\\n\\t\\treturn redirect(reverse('tiantian:register'))\\n\\tuser = UserInfo.objects.get(user_name=username)\\n\\tuser.user_address_info_set.create(address=address,tel_num=tel_num)\\n\\treturn redirect(reverse('tiantian:user_center_info'))\\n\\n\\ndef user_center_order(request):\\n\\treturn render(request,'user/user_center_order.html')\\n\\n\\n\"\n },\n {\n \"alpha_fraction\": 0.5834854245185852,\n \"alphanum_fraction\": 0.5894160866737366,\n \"avg_line_length\": 21.52688217163086,\n \"blob_id\": \"4acbed1b9ad00b00892109b74b83a5f3f2047d29\",\n \"content_id\": \"f3e20646c8ecc72dc9e7f7598cd48f4853e898f1\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 2264,\n \"license_type\": \"no_license\",\n \"max_line_length\": 136,\n \"num_lines\": 93,\n \"path\": \"/tornado/project/Ihome/models.py\",\n \"repo_name\": \"jasondzy/Python\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"\\r\\nimport pymysql\\r\\nimport redis\\r\\nimport mysql_settings\\r\\n\\r\\n############################ mysql 处理类######################################################\\r\\nclass HandleMysql(object):\\r\\n\\tdef __init__(self):\\r\\n\\r\\n\\t\\ttry:\\r\\n\\t\\t\\tself.db = pymysql.connect(**mysql_settings.setting)\\r\\n\\t\\texcept Exception as e:\\r\\n\\t\\t\\traise e\\r\\n\\t\\telse:\\r\\n\\t\\t\\tprint('connect mysql sucess')\\r\\n\\t\\t\\tself.cursor = self.db.cursor()\\r\\n\\r\\n\\tdef create_table(self):\\r\\n\\r\\n\\t\\tfor sql in mysql_settings.table1:\\r\\n\\t\\t\\ttry:\\r\\n\\t\\t\\t\\tself.cursor.execute(sql)\\r\\n\\t\\t\\texcept Exception as e:\\r\\n\\t\\t\\t\\tprint(' %s alerady exist, do not create it again'% (sql.split(\\\"\\\\n\\\")[1].split(\\\" \\\")[2]))\\r\\n\\t\\t\\telse:\\r\\n\\t\\t\\t\\tself.db.commit()\\r\\n\\r\\n\\tdef get_values_from_mysql(self, sql):\\r\\n\\t\\ttry:\\r\\n\\t\\t\\tself.cursor.execute(sql)\\r\\n\\t\\texcept Exception as e:\\r\\n\\t\\t\\tprint('data did not exist')\\r\\n\\t\\t\\tresult = None\\r\\n\\t\\telse:\\t\\r\\n\\t\\t\\tresult = self.cursor.fetchall()\\r\\n\\r\\n\\t\\treturn result\\r\\n\\r\\n\\tdef insert_into_tbl(self, sql):\\r\\n\\t\\ttry:\\r\\n\\t\\t\\tself.cursor.execute(sql)\\r\\n\\t\\texcept Exception as e:\\r\\n\\t\\t\\traise(e)\\r\\n\\t\\telse:\\r\\n\\t\\t\\tself.db.commit()\\r\\n\\t\\treturn True\\r\\n\\r\\n\\tdef update_tbl(self, sql):\\r\\n\\t\\ttry:\\r\\n\\t\\t\\tself.cursor.execute(sql)\\r\\n\\t\\texcept Exception as e:\\r\\n\\t\\t\\traise(e)\\r\\n\\t\\telse:\\r\\n\\t\\t\\tself.db.commit()\\r\\n\\t\\treturn True\\r\\n\\r\\n\\tdef close_mysql():\\r\\n\\t\\tself.cursor.close()\\r\\n\\t\\tself.db.close()\\r\\n\\r\\n\\r\\n\\r\\n\\r\\n\\r\\n##################################### Redis 处理类####################################################\\r\\n\\r\\nclass HandRedis(object):\\r\\n\\t######## connect to redis#########\\r\\n\\tdef __init__(self):\\r\\n\\t\\ttry:\\r\\n\\t\\t\\tself.redisInstance = redis.StrictRedis(host=\\\"localhost\\\", port=\\\"6379\\\") #这里的host只能使用localhost来进行连接，因为redis没有设置密码，且redis绑定了127.0.0.1这个地址\\r\\n\\t\\texcept Exception as e:\\r\\n\\t\\t\\tprint(\\\" connect to redis failed \\\")\\r\\n\\t\\t\\traise(e)\\r\\n\\t\\telse:\\r\\n\\t\\t\\tprint(\\\" connect redis sucess\\\")\\r\\n\\r\\n\\tdef set_value(self, key, value):\\r\\n\\t\\tresult = self.redisInstance.set(key, value)\\r\\n\\t\\tprint('set key,value',key,value)\\r\\n\\r\\n\\tdef get_value(self, key):\\r\\n\\t\\tresult = self.redisInstance.get(key)\\r\\n\\t\\t# print('key ,value:',key,result)\\r\\n\\t\\treturn result\\r\\n\\r\\n\\tdef set_expire(self, key, date):\\r\\n\\t\\tresult = self.redisInstance.expire(key, date)\\r\\n\\t\\tprint('set expire time key,date:',key,date)\\r\\n\\r\\n\\tdef del_value(self, key):\\r\\n\\t\\tresult = self.redisInstance.delete(key)\\r\\n\\t\\tprint('delete',key)\\r\\n\\t\\treturn result\\r\\n\\r\\n\"\n },\n {\n \"alpha_fraction\": 0.5188916921615601,\n \"alphanum_fraction\": 0.5843828916549683,\n \"avg_line_length\": 18.799999237060547,\n \"blob_id\": \"71f4e84e554755384c0cd031d02cb94b15a339ba\",\n \"content_id\": \"6a3b6f9a549de13f105266748844eaa02691318b\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 397,\n \"license_type\": \"no_license\",\n \"max_line_length\": 61,\n \"num_lines\": 20,\n \"path\": \"/Python/plus/net/receive_data.py\",\n \"repo_name\": \"jasondzy/Python\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"import socket\\n\\ndef main():\\n receive = socket.socket(socket.AF_INET,socket.SOCK_DGRAM)\\n\\n receive.bind( ('',7788) )\\n\\n receive.sendto(b'test',('192.168.0.107', 8081) )\\n\\n while True:\\n re_data = receive.recvfrom(1024)\\n data,ipinfo = re_data\\n receive.sendto(data,ipinfo)\\n print(data.decode('gb2312'))\\n\\n receive.close()\\n\\n\\nif __name__ == '__main__':\\n main()\\n\\n\"\n },\n {\n \"alpha_fraction\": 0.6591100096702576,\n \"alphanum_fraction\": 0.6612090468406677,\n \"avg_line_length\": 28.060976028442383,\n \"blob_id\": \"a2d97895a54972443c74c32f10d1ee5f07775db6\",\n \"content_id\": \"1592057bc085ff5a7c9206432df946588d5f918e\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 2706,\n \"license_type\": \"no_license\",\n \"max_line_length\": 83,\n \"num_lines\": 82,\n \"path\": \"/django/jason_test/booktest/views.py\",\n \"repo_name\": \"jasondzy/Python\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"from django.shortcuts import render\\nfrom django.http import HttpResponse, JsonResponse\\nfrom django.conf import settings\\nfrom .models import *\\nfrom django.core.paginator import Paginator\\nimport json\\n# Create your views here.\\n\\ndef index(request):\\n return render(request,'booktest/index.html')\\n # return HttpResponse('ok')\\n\\n\\ndef upload(request):\\n\\n if request.method == 'POST':\\n picture = request.FILES['picture']\\n fname = '%s/cars/%s'%(settings.MEDIA_ROOT, picture.name)\\n # return HttpResponse(settings.MEDIA_ROOT)\\n with open(fname,'wb') as pic:\\n for c in picture.chunks():\\n pic.write(c)\\n return HttpResponse('ok')\\n else:\\n return HttpResponse('error')\\ndef my_custom_page_not_found_view(request):\\n return HttpResponse('fail 404 fail')\\n\\ndef pages(request,id):\\n if id == '':\\n id = '1'\\n list = HeroInfo.objects.all()\\n paginator = Paginator(list,5)\\n page = paginator.page(int(id))\\n context = {'page':page}\\n\\n return render(request,'booktest/pages.html',context)\\n\\ndef book_index(request):\\n return render(request,'booktest/book_index.html')\\n\\ndef ajax_get(request):\\n # print('hello')\\n book_list = BookInfo.objects.all()\\n # print(book_list)\\n # return JsonResponse({'data':book_list}) #这样返回数据会报错\\n l = []\\n for list in book_list:\\n l.append((list.id,list.btitle)) #这里必须要将获得的book_list进行遍历，取出元素放在一个数组中才行，否则会报错\\n return JsonResponse({'data':l}) #具体为什么这样的原因还有待解析\\n\\ndef get_bookinfo(request):\\n print('test')\\n id = request.GET['id'] #这里使用的是ajax的方式进行数据的传递\\n print(id)\\n list=HeroInfo.objects.filter(hBook_id=id)#这里的filter返回的是一个查询集，并不是一个对象，而是一个对象集合\\n print(list)\\n hero_list = []\\n for i in list:\\n hero_list.append((i.id,i.hname))\\n return JsonResponse({'data':hero_list})\\n\\n#富文本编辑器\\ndef editor(request):\\n data = BookInfo.objects.all()\\n list = []\\n for l in data:\\n list.append([l.id,l.btitle])\\n print(list)\\n context = {'data':list}\\n return render(request,'booktest/editor.html',context)\\n\\ndef editor_handle(request):\\n html = request.POST['hcontent'] #此处获取的是大文本提交的内容\\n id = request.POST['select'] #此处获取的是要提交的id号\\n print('id:',id)\\n print(html)\\n book = BookInfo.objects.get(pk=id) #注意filter方法返回的是一个查询集合，是一个集合，get返回的是一个对象\\n print(book.btitle)\\n book.bcontent = html\\n book.save()\\n return HttpResponse('ok')\"\n },\n {\n \"alpha_fraction\": 0.7318037748336792,\n \"alphanum_fraction\": 0.7436708807945251,\n \"avg_line_length\": 31.210525512695312,\n \"blob_id\": \"f10f0c426ab7d6bab2d161fb8e49d5a09accad59\",\n \"content_id\": \"49b8cc75b7eaf62390f3a17b0e3e2dacf7765818\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 1646,\n \"license_type\": \"no_license\",\n \"max_line_length\": 125,\n \"num_lines\": 38,\n \"path\": \"/tornado/test/01_hello.py\",\n \"repo_name\": \"jasondzy/Python\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"import tornado.web\\r\\nimport tornado.ioloop\\r\\nimport tornado.httpserver\\r\\nimport tornado.options\\r\\nfrom tornado.web import url\\r\\n\\r\\ntornado.options.define(\\\"port\\\",default=8000,type=int, help=\\\"run server on the given port.\\\") #这里定义了port参数，这样就可以在命令行启动的时候将改参数传入了\\r\\ntornado.options.define(\\\"itcast\\\",default=[],type=str, multiple=True, help=\\\"itcast subjects.\\\")#这里定义了一个多参数变量，命令行传参的时候需要用,号隔开\\r\\nclass IndexHander(tornado.web.RequestHandler):\\r\\n\\r\\n\\tdef get(self):\\r\\n\\t\\tself.write(\\\"hello world\\\")\\r\\n\\r\\nclass SecondHander(tornado.web.RequestHandler):\\r\\n\\tdef initialize(self,subject):\\r\\n\\t\\tself.subject = subject\\r\\n\\r\\n\\tdef get(self):\\r\\n\\t\\tself.write(self.subject)\\r\\n\\r\\n\\r\\nif __name__ == '__main__':\\r\\n\\ttornado.options.parse_config_file(\\\"./config\\\") #这里定义了解析命令行参数的函数，命令行中的参数通过这个函数进行解析\\r\\n\\tprint (tornado.options.options.itcast)#这里打印出itcase参数，注意解析后的参数有两个options\\r\\n\\r\\n\\tapp = tornado.web.Application(\\r\\n\\t\\t[url(r'/',IndexHander,name=\\\"index\\\"),\\r\\n\\r\\n\\t\\t url(r'/cpp',SecondHander,{\\\"subject\\\":\\\"c++\\\"},name=\\\"cpp\\\"),\\r\\n\\r\\n\\t\\t], debug=True) #这里的作用是添加路由，这里创建了一个应用app\\r\\n\\t# app.listen(8000) #这里监听8000端口\\r\\n\\r\\n\\thttp_server = tornado.httpserver.HTTPServer(app)\\r\\n\\thttp_server.bind(tornado.options.options.port)\\r\\n\\thttp_server.start(1)#0表示的是开启的进程数目和所使用的的服务器的核心数有关，当大于0的时候就是所设置的进程数目\\r\\n\\r\\n\\ttornado.ioloop.IOLoop.current().start() #此处的函数才是真正运行服务器\\r\\n\\r\\n\"\n },\n {\n \"alpha_fraction\": 0.47216036915779114,\n \"alphanum_fraction\": 0.4922049045562744,\n \"avg_line_length\": 16.30769157409668,\n \"blob_id\": \"a017161c8c7d0b7d951873d0c6bf136b21dd5923\",\n \"content_id\": \"7c78d70fccab489632e259f25a404f25fc4f5e0c\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 449,\n \"license_type\": \"no_license\",\n \"max_line_length\": 39,\n \"num_lines\": 26,\n \"path\": \"/Python/plus/Process/green.py\",\n \"repo_name\": \"jasondzy/Python\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"from greenlet import greenlet\\nfrom time import sleep\\ndef funca():\\n while True:\\n print('-----A-------')\\n gr2.switch()\\n print('---switch to funca---')\\n sleep(0.5)\\n\\ndef funcb():\\n while True:\\n print('----B---------')\\n gr1.switch()\\n print('---switch to funcb----')\\n sleep(0.5)\\n\\n\\n\\ngr1 = greenlet(funca)\\ngr2 = greenlet(funcb)\\n\\ndef main():\\n gr1.switch()\\n\\nif __name__ == '__main__':\\n main()\"\n },\n {\n \"alpha_fraction\": 0.7162004709243774,\n \"alphanum_fraction\": 0.7255244851112366,\n \"avg_line_length\": 29.814815521240234,\n \"blob_id\": \"1d1d112af929e810ce54d05efcd177d7f37405e3\",\n \"content_id\": \"804eb7bb57f73fbb11ed98f67edd732a3a0ad709\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 2086,\n \"license_type\": \"no_license\",\n \"max_line_length\": 90,\n \"num_lines\": 54,\n \"path\": \"/tornado/test/02_input.py\",\n \"repo_name\": \"jasondzy/Python\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"import tornado.web\\r\\nimport tornado.ioloop\\r\\nimport tornado.httpserver\\r\\nimport tornado.options\\r\\nfrom tornado.web import url\\r\\n\\r\\n\\r\\ntornado.options.define(\\\"port\\\", default=8000, type=int, help=\\\"runserver on given port\\\")\\r\\n\\r\\nclass IndexHander(tornado.web.RequestHandler):\\r\\n\\r\\n\\tdef post(self):\\r\\n\\r\\n\\t\\tpara_query = self.get_query_arguments('a') #这里的作用是在url中获取a的参数值，获取的是一个列表，存放的是a的所有值\\r\\n\\t\\tpara_body = self.get_body_arguments('b')\\r\\n\\t\\tprint('body',para_body)\\r\\n\\t\\tprint('query',para_query)\\r\\n\\t\\tself.write(\\\"ok\\\")\\r\\n\\r\\nclass SecondHander(tornado.web.RequestHandler):\\r\\n\\tdef post(self):\\r\\n\\t\\tprint(self.request.method)\\r\\n\\t\\tprint(self.request.uri)\\r\\n\\t\\tprint(self.request.host)\\r\\n\\t\\tprint(self.request.headers)\\r\\n\\r\\nclass ThirdHander(tornado.web.RequestHandler):\\r\\n\\tdef post(self):\\r\\n\\t\\tfiles = self.request.files #获取request中所有的files类型的文件，这些文件是用字典的形式组织的\\r\\n\\t\\timg = files.get('image1') #从这些字典类型数据中获取指定image图片信息\\r\\n\\t\\tprint(img[0]['filename']) #获取的img信息也是一个列表信息，即相同名字image1可以传入多张file文件，列表中的值又是字典类型的数据\\r\\n\\r\\n\\t\\twith open('/home/ubuntu/user_jason/temp/image.jpg','wb') as f: #将从request中获取的图片信息写入到文件中去\\r\\n\\t\\t\\tf.write(img[0]['body']) #body中存放的就是image1的信息，这里将image1中的信息写入到新创建的文件中\\r\\n\\t\\tself.write('ok')\\r\\n\\r\\nclass FiveHander(tornado.web.RequestHandler):\\r\\n\\tdef initialize(self,arg1): #这里的参数接收的是路由中传递过来的字典类型参数\\r\\n\\t\\tprint(arg1)\\r\\n\\r\\nif __name__ == '__main__':\\r\\n\\ttornado.options.parse_command_line()\\r\\n\\r\\n\\tapp = tornado.web.Application([\\r\\n\\t\\turl(r'/',IndexHander,name=\\\"index\\\"),\\r\\n\\t\\turl(r'/second',SecondHander,name=\\\"second\\\"),\\r\\n\\t\\turl(r'/third',ThirdHander,name=\\\"third\\\"),\\r\\n\\t\\turl(r'/five',FiveHander,{'arg1':'111'},name=\\\"five\\\"),\\r\\n\\t\\t], debug=True)\\r\\n\\r\\n\\thttp_server = tornado.httpserver.HTTPServer(app)\\r\\n\\thttp_server.listen(tornado.options.options.port)\\r\\n\\r\\n\\ttornado.ioloop.IOLoop.current().start()\"\n },\n {\n \"alpha_fraction\": 0.6877323389053345,\n \"alphanum_fraction\": 0.6877323389053345,\n \"avg_line_length\": 28.88888931274414,\n \"blob_id\": \"02944e9f081228c6a7f67ea2059fdc691f0c7050\",\n \"content_id\": \"3393a72a01b15caa7bf20a7d19657b77e73df918\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Text\",\n \"length_bytes\": 429,\n \"license_type\": \"no_license\",\n \"max_line_length\": 95,\n \"num_lines\": 9,\n \"path\": \"/Python/plus/net/web/web_framework/readme.txt\",\n \"repo_name\": \"jasondzy/Python\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"\\n#########################\\n\\n# Note:这里是把web_service这个python程序当做了主体，通过在web_service程序中\\n 来调用web框架\\n#这里使用的是wsgi的网关接口协议，主要的就是在服务器函数中调用application(\\n environ,start_repose)函数,在framework脚本中实现这个application函数。而start_respose正好反过来，service中实现，frame中调用\\n\\n\\n#########################\"\n },\n {\n \"alpha_fraction\": 0.6656105518341064,\n \"alphanum_fraction\": 0.6702412962913513,\n \"avg_line_length\": 57.49275207519531,\n \"blob_id\": \"fa872202afa6f2df8e908d5f2063ae7a70e92b7d\",\n \"content_id\": \"b82c71c4172e9158fe064dcdda1af7c972af00e8\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 4329,\n \"license_type\": \"no_license\",\n \"max_line_length\": 190,\n \"num_lines\": 69,\n \"path\": \"/tornado/project/Ihome/Server.py\",\n \"repo_name\": \"jasondzy/Python\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"import tornado.web\\r\\nimport tornado.options\\r\\nimport tornado.httpserver\\r\\nimport tornado.ioloop\\r\\nimport os\\r\\nimport models\\r\\nimport Handlers\\r\\nimport base64, uuid\\r\\nfrom tornado.web import url, StaticFileHandler\\r\\n\\r\\ntornado.options.define('port', default=8000, type=int, help=' runserver on given port')\\r\\n\\r\\n\\r\\nclass Application(tornado.web.Application):\\r\\n\\tdef __init__(self):\\r\\n\\t\\t################# create mysql object #######\\r\\n\\t\\tself.mysql = models.HandleMysql()\\r\\n\\t\\tself.mysql.create_table()\\r\\n\\t\\t################# end #######################\\r\\n\\t\\t################# create redis object #######\\r\\n\\t\\tself.redis = models.HandRedis()\\r\\n\\t\\t################# end #######################\\r\\n\\r\\n\\t\\t################# 进行域名地址的映射 ##########\\r\\n\\t\\tsuper(Application, self).__init__(\\r\\n\\t\\t[\\r\\n\\t\\t# url(r'^/$', Handlers.IndexHandler, name=\\\"IndexHandler\\\"), #这里不采用get中的render返回index的原因是因为，index.html中使用了index.js脚本中的$.get等方式来和后台通讯从而获得数据，而render方式处理模板的时候会对模板进行解析处理会报错，只能通过直接加载index.html的方式\\r\\n\\t\\turl(r'/api/house/index', Handlers.House_index, dict(database = self.mysql, database_redis = self.redis), name=\\\"House_index\\\"),\\r\\n\\t\\turl(r'^/register$', Handlers.House_register, dict(database = self.mysql, database_redis = self.redis), name=\\\"House_register\\\"),\\r\\n\\t\\turl(r'^/api/piccode$', Handlers.PicCodeHandler, dict(database = self.mysql, database_redis = self.redis), name=\\\"PicCodeHandler\\\"),\\r\\n\\t\\turl(r'^/api/smscode$', Handlers.Smscode, dict(database = self.mysql, database_redis = self.redis), name=\\\"Smscode\\\"),\\r\\n\\t\\turl(r'^/api/register$', Handlers.Register_verity, dict(database = self.mysql, database_redis = self.redis), name=\\\"Register_verity\\\"),\\r\\n\\t\\turl(r'^/api/login$', Handlers.Login_verity, dict(database = self.mysql, database_redis = self.redis), name=\\\"database = self.mysql\\\"),\\r\\n\\t\\turl(r'^/api/check_login$', Handlers.Check_login, dict(database = self.mysql, database_redis = self.redis), name=\\\"Check_login\\\"),\\r\\n\\t\\turl(r'^/api/profile$', Handlers.Personal_info, dict(database = self.mysql, database_redis = self.redis), name=\\\"Personal_info\\\"),\\r\\n\\t\\turl(r'^/api/profile/name$', Handlers.Personal_name, dict(database = self.mysql, database_redis = self.redis), name=\\\"Personal_name\\\"),\\r\\n\\t\\turl(r'^/api/profile/avatar$', Handlers.Personal_img, dict(database = self.mysql, database_redis = self.redis), name=\\\"Personal_img\\\"),\\r\\n\\t\\turl(r'^/api/house/info$', Handlers.House_info, dict(database = self.mysql, database_redis = self.redis), name=\\\"House_info\\\"),\\r\\n\\t\\turl(r'^/api/order$', Handlers.House_reserve, dict(database = self.mysql, database_redis = self.redis), name=\\\"House_reserve\\\"),\\r\\n\\t\\turl(r'^/api/order/my$', Handlers.Show_order, dict(database = self.mysql, database_redis = self.redis), name=\\\"Show_order\\\"),\\r\\n\\t\\turl(r'^/api/profile/auth$', Handlers.Real_name_verity, dict(database = self.mysql, database_redis = self.redis), name=\\\"Real_name_verity\\\"),\\r\\n\\t\\turl(r'^/api/house/my$', Handlers.Myhouse_show, dict(database = self.mysql, database_redis = self.redis), name=\\\"Myhouse_show\\\"),\\r\\n\\t\\turl(r'^/api/house/list2$', Handlers.House_list_handler, dict(database = self.mysql, database_redis = self.redis), name = \\\"House_list_handler\\\"),\\r\\n\\t\\turl(r'^/api/house/area$', Handlers.Area_info_handler, dict(database = self.mysql, database_redis = self.redis), name=\\\"Area_info_handler\\\"),\\r\\n\\t\\turl(r'^/api/logout$', Handlers.Login_out, dict(database = self.mysql, database_redis = self.redis), name = \\\"Login_out\\\"),\\r\\n\\t\\turl(r'/(.*)',StaticFileHandler, {\\\"path\\\":os.path.join(os.path.dirname(__file__),\\\"template\\\"), \\\"default_filename\\\":\\\"index.html\\\"}, name=\\\"index\\\"),\\r\\n\\t\\t],\\r\\n\\t\\t################# end ########################\\r\\n\\t\\tdebug = True, \\r\\n\\t\\tstatic_path = os.path.join(os.path.dirname(__file__), 'static'),\\r\\n\\t\\ttemplate_path = os.path.join(os.path.dirname(__file__), 'template'),\\r\\n\\t\\tcookie_secret = str(base64.b64encode(uuid.uuid4().bytes + uuid.uuid4().bytes)), #这里使用uuid和base64共同合成了一个随机的字符串，添加到cookie从而形成secure_cookie的机制\\r\\n\\t\\txsrf_cookies = True,\\r\\n\\t\\t)\\r\\n\\r\\n\\r\\ndef main():\\r\\n\\ttornado.options.parse_command_line()\\r\\n\\r\\n\\tapp = Application()\\r\\n\\r\\n\\thttp_server = tornado.httpserver.HTTPServer(app)\\r\\n\\thttp_server.listen(8000) \\r\\n\\r\\n\\ttornado.ioloop.IOLoop.current().start()\\r\\n\\r\\n\\r\\nif __name__ == '__main__':\\r\\n\\tmain()\"\n },\n {\n \"alpha_fraction\": 0.6610939502716064,\n \"alphanum_fraction\": 0.6767218112945557,\n \"avg_line_length\": 46.68141555786133,\n \"blob_id\": \"63e902ce95656bace89d9ed71e2ff69b53322596\",\n \"content_id\": \"61296bb14e57d20be9f9ce6f31dd2f0bda2c837d\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 6109,\n \"license_type\": \"no_license\",\n \"max_line_length\": 103,\n \"num_lines\": 113,\n \"path\": \"/tornado/project/Ihome/mysql_settings.py\",\n \"repo_name\": \"jasondzy/Python\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"\\r\\n\\r\\nsetting = dict(\\r\\n\\thost = '115.159.62.43',\\r\\n\\tport = 3306,\\r\\n\\tdb = 'Ihome',\\r\\n\\tuser = 'root',\\r\\n\\tpasswd = 'dzy',\\r\\n\\tcharset = 'utf8',\\r\\n\\t)\\r\\n\\r\\ntable1 = [\\r\\n\\t'''\\r\\n\\tCREATE TABLE ih_user_profile (\\r\\n \\tup_user_id bigint unsigned NOT NULL AUTO_INCREMENT COMMENT '用户ID',\\r\\n \\tup_name varchar(32) NOT NULL COMMENT '昵称',\\r\\n \\tup_mobile char(11) NOT NULL COMMENT '手机号',\\r\\n \\tup_passwd varchar(64) NOT NULL COMMENT '密码',\\r\\n \\tup_real_name varchar(32) NULL COMMENT '真实姓名',\\r\\n \\tup_id_card varchar(20) NULL COMMENT '身份证号',\\r\\n \\tup_avatar varchar(128) NULL COMMENT '用户头像',\\r\\n \\tup_admin tinyint NOT NULL DEFAULT '0' COMMENT '是否是管理员，0-不是，1-是',\\r\\n \\tup_utime datetime NOT NULL DEFAULT CURRENT_TIMESTAMP ON UPDATE CURRENT_TIMESTAMP COMMENT '最后更新时间',\\r\\n \\tup_ctime datetime NOT NULL DEFAULT CURRENT_TIMESTAMP COMMENT '创建时间',\\r\\n \\tPRIMARY KEY (up_user_id),\\r\\n \\tUNIQUE (up_name),\\r\\n \\tUNIQUE (up_mobile)\\r\\n\\t) ENGINE=InnoDB AUTO_INCREMENT=10000 DEFAULT CHARSET=utf8 COMMENT='用户信息表';\\r\\n\\t''',\\r\\n\\t'''\\r\\n\\tCREATE TABLE ih_area_info (\\r\\n \\tai_area_id bigint unsigned NOT NULL AUTO_INCREMENT COMMENT '区域id',\\r\\n \\tai_name varchar(32) NOT NULL COMMENT '区域名称',\\r\\n \\tai_ctime datetime NOT NULL DEFAULT CURRENT_TIMESTAMP COMMENT '创建时间',\\r\\n \\tPRIMARY KEY (ai_area_id)\\r\\n\\t) ENGINE=InnoDB DEFAULT CHARSET=utf8 COMMENT='房源区域表';\\r\\n\\t''',\\r\\n\\t'''\\r\\n\\tCREATE TABLE ih_house_info (\\r\\n \\thi_house_id bigint unsigned NOT NULL AUTO_INCREMENT COMMENT '房屋id',\\r\\n \\thi_user_id bigint unsigned NOT NULL COMMENT '用户ID',\\r\\n \\thi_title varchar(64) NOT NULL COMMENT '房屋名称',\\r\\n \\thi_price int unsigned NOT NULL DEFAULT '0' COMMENT '房屋价格，单位分',\\r\\n \\thi_area_id bigint unsigned NOT NULL COMMENT '房屋区域ID',\\r\\n \\thi_address varchar(512) NOT NULL DEFAULT '' COMMENT '地址',\\r\\n \\thi_room_count tinyint unsigned NOT NULL DEFAULT '1' COMMENT '房间数',\\r\\n \\thi_acreage int unsigned unsigned NOT NULL DEFAULT '0' COMMENT '房屋面积',\\r\\n \\thi_house_unit varchar(32) NOT NULL DEFAULT '' COMMENT '房屋户型',\\r\\n \\thi_capacity int unsigned NOT NULL DEFAULT '1' COMMENT '容纳人数',\\r\\n \\thi_beds varchar(64) NOT NULL DEFAULT '' COMMENT '床的配置',\\r\\n \\thi_deposit int unsigned NOT NULL DEFAULT '0' COMMENT '押金，单位分',\\r\\n \\thi_min_days int unsigned NOT NULL DEFAULT '1' COMMENT '最短入住时间',\\r\\n \\thi_max_days int unsigned NOT NULL DEFAULT '0' COMMENT '最长入住时间，0-不限制',\\r\\n \\thi_order_count int unsigned NOT NULL DEFAULT '0' COMMENT '下单数量',\\r\\n \\thi_verify_status tinyint NOT NULL DEFAULT '0' COMMENT '审核状态，0-待审核，1-审核未通过，2-审核通过',\\r\\n \\thi_online_status tinyint NOT NULL DEFAULT '1' COMMENT '0-下线，1-上线',\\r\\n \\thi_index_image_url varchar(256) NULL COMMENT '房屋主图片url',\\r\\n \\thi_utime datetime NOT NULL DEFAULT CURRENT_TIMESTAMP ON UPDATE CURRENT_TIMESTAMP COMMENT '最后更新时间',\\r\\n \\thi_ctime datetime NOT NULL DEFAULT CURRENT_TIMESTAMP COMMENT '创建时间',\\r\\n \\tPRIMARY KEY (hi_house_id),\\r\\n \\tKEY `hi_status` (hi_verify_status, hi_online_status),\\r\\n \\tCONSTRAINT FOREIGN KEY (`hi_user_id`) REFERENCES `ih_user_profile` (`up_user_id`),\\r\\n \\tCONSTRAINT FOREIGN KEY (`hi_area_id`) REFERENCES `ih_area_info` (`ai_area_id`)\\r\\n\\t) ENGINE=InnoDB DEFAULT CHARSET=utf8 COMMENT='房屋信息表';\\r\\n\\t''',\\r\\n\\t'''\\r\\n\\tCREATE TABLE ih_house_facility (\\r\\n \\thf_id bigint unsigned NOT NULL AUTO_INCREMENT COMMENT '自增id',\\r\\n \\thf_house_id bigint unsigned NOT NULL COMMENT '房屋id',\\r\\n \\thf_facility_id int unsigned NOT NULL COMMENT '房屋设施',\\r\\n \\thf_ctime datetime NOT NULL DEFAULT CURRENT_TIMESTAMP COMMENT '创建时间',\\r\\n \\tPRIMARY KEY (hf_id),\\r\\n \\tCONSTRAINT FOREIGN KEY (`hf_house_id`) REFERENCES `ih_house_info` (`hi_house_id`)\\r\\n\\t) ENGINE=InnoDB DEFAULT CHARSET=utf8 COMMENT='房屋设施表';\\r\\n\\t''',\\r\\n\\t'''\\r\\n\\tCREATE TABLE ih_facility_catelog (\\r\\n \\tfc_id int unsigned NOT NULL AUTO_INCREMENT COMMENT '自增id',\\r\\n \\tfc_name varchar(32) NOT NULL COMMENT '设施名称',\\r\\n \\tfc_ctime datetime NOT NULL DEFAULT CURRENT_TIMESTAMP COMMENT '创建时间',\\r\\n \\tPRIMARY KEY (fc_id)\\r\\n\\t) ENGINE=InnoDB DEFAULT CHARSET=utf8 COMMENT='设施型录表';\\r\\n\\t''',\\r\\n\\t'''\\r\\n\\tCREATE TABLE ih_order_info (\\r\\n \\toi_order_id bigint unsigned NOT NULL AUTO_INCREMENT COMMENT '订单id',\\r\\n \\toi_user_id bigint unsigned NOT NULL COMMENT '用户id',\\r\\n \\toi_house_id bigint unsigned NOT NULL COMMENT '房屋id',\\r\\n \\toi_begin_date date NOT NULL COMMENT '入住时间',\\r\\n \\toi_end_date date NOT NULL COMMENT '离开时间',\\r\\n \\toi_days int unsigned NOT NULL COMMENT '入住天数',\\r\\n \\toi_house_price int unsigned NOT NULL COMMENT '房屋单价，单位分',\\r\\n \\toi_amount int unsigned NOT NULL COMMENT '订单金额，单位分',\\r\\n \\toi_status tinyint NOT NULL DEFAULT '0' COMMENT '订单状态，0-待接单，1-待支付，2-已支付，3-待评价，4-已完成，5-已取消，6-拒接单',\\r\\n \\toi_comment text NULL COMMENT '订单评论',\\r\\n \\toi_utime datetime NOT NULL DEFAULT CURRENT_TIMESTAMP ON UPDATE CURRENT_TIMESTAMP COMMENT '最后更新时间',\\r\\n \\toi_ctime datetime NOT NULL DEFAULT CURRENT_TIMESTAMP COMMENT '创建时间',\\r\\n \\tPRIMARY KEY (oi_order_id),\\r\\n \\tKEY `oi_status` (oi_status),\\r\\n \\tCONSTRAINT FOREIGN KEY (`oi_user_id`) REFERENCES `ih_user_profile` (`up_user_id`),\\r\\n \\tCONSTRAINT FOREIGN KEY (`oi_house_id`) REFERENCES `ih_house_info` (`hi_house_id`)\\r\\n\\t) ENGINE=InnoDB DEFAULT CHARSET=utf8 COMMENT='订单表';\\r\\n\\t''',\\r\\n\\t'''\\r\\n\\tCREATE TABLE ih_house_image (\\r\\n \\thi_image_id bigint unsigned NOT NULL AUTO_INCREMENT COMMENT '图片id',\\r\\n \\thi_house_id bigint unsigned NOT NULL COMMENT '房屋id',\\r\\n \\thi_url varchar(256) NOT NULL COMMENT '图片url',\\r\\n \\thi_ctime datetime NOT NULL DEFAULT CURRENT_TIMESTAMP COMMENT '创建时间',\\r\\n \\tPRIMARY KEY (hi_image_id),\\r\\n \\tCONSTRAINT FOREIGN KEY (hi_house_id) REFERENCES ih_house_info (hi_house_id)\\r\\n\\t) ENGINE=InnoDB DEFAULT CHARSET=utf8 COMMENT='房屋图片表';\\r\\n\\t''',\\r\\n\\r\\n]\"\n },\n {\n \"alpha_fraction\": 0.5652173757553101,\n \"alphanum_fraction\": 0.5652173757553101,\n \"avg_line_length\": 23.33333396911621,\n \"blob_id\": \"afa41c9a66ec089cd1d4437004bbfc040f6d3660\",\n \"content_id\": \"85503c18b5bf8bb551bdadb6badb39ce53549f23\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 368,\n \"license_type\": \"no_license\",\n \"max_line_length\": 108,\n \"num_lines\": 15,\n \"path\": \"/Python/basic/class_func/class_new.py\",\n \"repo_name\": \"jasondzy/Python\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"\\n\\nclass Dog(object):\\n\\n def __init__(self):\\n print(\\\"init the var\\\")\\n\\n def __new__(cls): # the func is used to create a new class , \\n print(\\\"create a class\\\")\\n return object.__new__(cls) # use the parent __new__ func to create a class,the arg need transfer cls\\n \\n def __str__(self):\\n return \\\"Hello world\\\"\\n\\n\\ndog = Dog()\\nprint(dog)\\n\\n\"\n },\n {\n \"alpha_fraction\": 0.5769230723381042,\n \"alphanum_fraction\": 0.6009615659713745,\n \"avg_line_length\": 14.923076629638672,\n \"blob_id\": \"e5bd0ac5d4ab2a7615b0282230308e42c12d67b9\",\n \"content_id\": \"1b12735a821c4b3b0bca1e978128654fba17636a\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 208,\n \"license_type\": \"no_license\",\n \"max_line_length\": 45,\n \"num_lines\": 13,\n \"path\": \"/Python/plus/Process/fork.py\",\n \"repo_name\": \"jasondzy/Python\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"import os\\nimport time\\n\\npid=os.fork()\\n\\nif pid == 0:\\n\\twhile True:\\n\\t\\tprint('1-----This is a son process-------')\\n\\t\\ttime.sleep(1)\\nelse:\\n\\twhile True:\\n\\t\\tprint('2-----This is a parrent process---')\\n\\t\\ttime.sleep(1)\\n\\n\"\n },\n {\n \"alpha_fraction\": 0.6041666865348816,\n \"alphanum_fraction\": 0.6348039507865906,\n \"avg_line_length\": 28.125,\n \"blob_id\": \"7a74eeb8392060be412cfc67675b3db159c0c517\",\n \"content_id\": \"134b8a8244dc87301334226103851dbf2ca8adeb\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 2380,\n \"license_type\": \"no_license\",\n \"max_line_length\": 147,\n \"num_lines\": 56,\n \"path\": \"/Python/plus/net/tftp/tftp_load.py\",\n \"repo_name\": \"jasondzy/Python\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"\\nimport struct \\n\\nimport socket\\n\\ndef download_file(name,data):\\n\\tf = open(name,'ab') #注意这里一定要以字节追加的形式打开，因为tftp发送过来的就是字节型的数据，所以将字节型的数据直接写入到文件中即可。其他形式不可以\\n\\tf.write(data)\\n\\n\\tf.close()\\n\\n\\n\\ndef main():\\n name = input('---please input which file you want to load----')\\n struct_info = '!H' + str(len(name)) + 'sb5sb'\\n print(struct_info)\\n\\n udp_socket = socket.socket(socket.AF_INET, socket.SOCK_DGRAM) #创建UDP对象\\n\\n send_data = struct.pack(struct_info, 1, name.encode('utf-8'), 0, b\\\"octet\\\", 0)#此处的作用是使用struct模块对要发送的数据按照tftp的信息格式进行封装，具体的信息见课件中tftp的信息格式\\n\\n udp_socket.sendto(send_data, ('192.168.0.107', 69))#这里对上面封装好了的数据发往69号端口，此端口是tftp默认的首次通信端口，接下来的数据通信端口就不在是69号端口了，而是采用的动态端口的方式，所以再往69端口发数据就不会有任何响应\\n\\n while True:\\n receive_data = udp_socket.recvfrom(1024)#再发送完数据后，tftp就会返回进行封装了的512字节的数据，这里接收的时候并不需要绑定端口，因为tftp会根据所接收到的数据解析出所用的端口\\n\\n data,ip_info = receive_data #对接收到的信息进行解析，因为网络通信中接收到的信息是一个tuple类型的数据，tuple的首个元素是封装的数据，第二个元素是ip地址信息\\n\\n cmd_info = data[:4]#获取数据中前四个字节的信息，根据tftp协议，前四个字节有特殊作用，4个字节后的512个字节才是真正的数据\\n\\n cmd_info = struct.unpack('!HH',cmd_info)#这里是将接收到的数据进行解析，这里解析到的cmd_info是一个tuple,(3,1)\\n \\n\\n print(cmd_info)\\n print(data[4:])\\n\\n if cmd_info[0] == 3:\\n download_file(name,data[4:])\\n if len(data[4:])<512:\\n print('---receive data end-------')\\n break\\n \\n print('------------receive next 512 bytes data---------')\\n ACK = struct.pack('!HH',4,cmd_info[1])\\n udp_socket.sendto(ACK,ip_info)\\n else:\\n break\\n\\n\\n\\n udp_socket.close()\\n\\n\\n\\nif __name__ == '__main__':\\n\\tmain()\\n\"\n },\n {\n \"alpha_fraction\": 0.7108433842658997,\n \"alphanum_fraction\": 0.7108433842658997,\n \"avg_line_length\": 15.300000190734863,\n \"blob_id\": \"7752696717fdee3dff79e68b0f3eaad2d2ab4fca\",\n \"content_id\": \"fa525c89a487deea39257d01d8d75e523249532f\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Markdown\",\n \"length_bytes\": 166,\n \"license_type\": \"no_license\",\n \"max_line_length\": 37,\n \"num_lines\": 10,\n \"path\": \"/Python/plus/C_lib/readme.md\",\n \"repo_name\": \"jasondzy/Python\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"\\n\\n# As show,we can include a c Librarya\\n\\n follow bellow steps\\n\\nfirst: gcc test.c -shared -o test.so\\nfinally: python c_lib.py\\n\\n\\n\\nSo we can use C programme in python \\n\"\n },\n {\n \"alpha_fraction\": 0.5724366903305054,\n \"alphanum_fraction\": 0.5824571251869202,\n \"avg_line_length\": 29.80695915222168,\n \"blob_id\": \"222c5c618c19700aee32bc2c12e35e52f58515a9\",\n \"content_id\": \"4faeec2cf69d0103abeab6ab59b2e29be2114ebd\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 31602,\n \"license_type\": \"no_license\",\n \"max_line_length\": 254,\n \"num_lines\": 891,\n \"path\": \"/tornado/project/Ihome/Handlers.py\",\n \"repo_name\": \"jasondzy/Python\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"import tornado.web\\r\\nimport json\\r\\nimport models\\r\\nimport logging\\r\\nimport random\\r\\nfrom hashlib import sha1\\r\\nimport base64, uuid\\r\\nfrom utils.captcha.captcha import captcha\\r\\nfrom utils.commons import required_login\\r\\nfrom libs.yuntongxun.SendTemplateSMS import ccp\\r\\n\\r\\nImage_id = \\\"1111\\\"\\r\\nclass BaseHandler(tornado.web.RequestHandler):\\r\\n\\tdef initialize(self, database, database_redis):\\r\\n\\t\\tself.database = database\\r\\n\\t\\tself.redis = database_redis\\r\\n\\r\\n\\tdef prepare(self):\\r\\n\\t\\t\\\"\\\"\\\"预解析json数据\\\"\\\"\\\" #这里判断请求的类型是否是json类型\\r\\n\\t\\tself.xsrf_token\\r\\n\\t\\t# print(self.request.body)\\r\\n\\t\\tif self.request.headers.get(\\\"Content-Type\\\", \\\"\\\").startswith(\\\"application/json\\\"):\\r\\n\\t\\t\\tself.json_args = json.loads(self.request.body.decode(\\\"utf-8\\\")) #对body中的json数据进行解析成字典的类型，lods是解析，dumps是翻译成json类\\r\\n\\t\\telse:\\r\\n\\t\\t\\tself.json_args = {}\\r\\n\\r\\n\\tdef get_current_user(self):\\r\\n\\t\\tsession_id = self.get_secure_cookie('session_id')\\r\\n\\t\\tif session_id == None:\\r\\n\\t\\t\\tprint(\\\" not login!!!\\\")\\r\\n\\t\\t\\treturn None\\r\\n\\t\\tvalue = self.redis.get_value(session_id)\\r\\n\\t\\tif value == None or len(value) == 0:\\r\\n\\t\\t\\tprint(\\\"not login!!!\\\")\\r\\n\\t\\t\\treturn None\\r\\n\\r\\n\\t\\treturn value\\r\\n\\r\\n###################这里实现一个主页处理函数#########################################\\r\\nclass House_index(BaseHandler):\\r\\n\\tdef get(self):\\r\\n\\t\\tprint('================================')\\r\\n\\t\\tsql = \\\"select hi_house_id,hi_title,hi_index_image_url from ih_house_info order by hi_order_count desc limit 3;\\\"\\r\\n\\t\\tresult = self.database.get_values_from_mysql(sql)\\r\\n\\t\\tprint(result)\\r\\n\\r\\n\\t\\thouses = []\\r\\n\\t\\tfor value in result:\\r\\n\\t\\t\\thouses.append({'house_id':value[0], 'img_url':value[2], 'title':value[1]})\\r\\n\\r\\n\\r\\n\\t\\tsql = \\\" select * from ih_area_info\\\"\\r\\n\\t\\tresult = self.database.get_values_from_mysql(sql)\\r\\n\\t\\t# print(result)\\r\\n\\r\\n\\t\\tarea = []\\r\\n\\t\\tfor value in result:\\r\\n\\t\\t\\tarea.append({'area_id':value[0], 'name':value[1]})\\r\\n\\t\\t# print(area)\\r\\n\\r\\n\\t\\tjson_houses = json.dumps(houses) #注意在tornado中self.write(xx)中的xx要是字典型的数据，这样tornado就会自动将xx转换为json数据传输\\r\\n\\t\\tjson_areas = json.dumps(area) #注意，字典型数据xx中的子元素的值若是个字典或数组等组合类型的值。需要将子元素的值转换为json型数据包裹在外层自动类型变量中进行传输\\r\\n\\t\\tresp = '{\\\"errcode\\\":\\\"0\\\", \\\"errmsg\\\":\\\"OK\\\", \\\"houses\\\":%s, \\\"areas\\\":%s}' % (json_houses, json_areas)\\r\\n\\t\\tself.write(resp) #这里就是返回数据，自动将字典类型变量转换为json数据类型\\r\\n\\t\\tself.set_header(\\\"Content-Type\\\", \\\"application/json; charset=UTF-8\\\") #设置传输类型的头部为 json类型，若是不设置可能会出错\\r\\n\\t\\t# self.write('ok') #以上的这么多设置主要是针对$ajax类型的数据请求，因为javascript擅长解析的是json类型数据\\r\\n\\r\\n#############这里实现的是一个检查用户是否登录的功能 ########\\r\\nclass Check_login(BaseHandler):\\r\\n\\tdef get(self):\\r\\n\\t\\tsession_id = self.get_secure_cookie('session_id')\\r\\n\\t\\t# print(\\\"session_id======\\\",session_id)\\r\\n\\t\\tuser_data = {}\\r\\n\\t\\tif session_id == None:\\r\\n\\t\\t\\tprint(\\\"session_id cookie do not exist\\\")\\r\\n\\t\\t\\tdata = {\\r\\n\\t\\t\\t\\t\\\"errcode\\\":1,\\r\\n\\t\\t\\t\\t\\\"data\\\":None,\\r\\n\\t\\t\\t}\\r\\n\\t\\telse:\\r\\n\\t\\t\\tvalue = self.redis.get_value(session_id)\\r\\n\\t\\t\\tif value == None:\\r\\n\\t\\t\\t\\tdata = {\\r\\n\\t\\t\\t\\t\\\"errcode\\\":1,\\r\\n\\t\\t\\t\\t\\\"data\\\":None,\\r\\n\\t\\t\\t\\t}\\r\\n\\t\\t\\telse:\\r\\n\\t\\t\\t\\tsql = 'select up_name from ih_user_profile where up_mobile=\\\"%s\\\" '%value.decode(\\\"utf-8\\\")\\r\\n\\t\\t\\t\\tresult = self.database.get_values_from_mysql(sql)\\r\\n\\t\\t\\t\\tif result == None or len(result)==0:\\r\\n\\t\\t\\t\\t\\tprint(\\\"session_id do not exist in redis\\\")\\r\\n\\t\\t\\t\\t\\tdata = {\\r\\n\\t\\t\\t\\t\\t\\\"errcode\\\":1,\\r\\n\\t\\t\\t\\t\\t\\\"data\\\":None,\\r\\n\\t\\t\\t\\t\\t}\\r\\n\\t\\t\\t\\telse:\\r\\n\\t\\t\\t\\t\\tprint(\\\" login user:\\\", value)\\r\\n\\t\\t\\t\\t\\tuser_data['name'] = result\\r\\n\\t\\t\\t\\t\\t# user_data = json.dumps(user_data)\\r\\n\\t\\t\\t\\t\\tdata = {\\r\\n\\t\\t\\t\\t\\t\\t\\\"errcode\\\":0,\\r\\n\\t\\t\\t\\t\\t\\t\\\"data\\\":user_data,\\r\\n\\t\\t\\t\\t\\t}\\r\\n\\r\\n\\t\\tself.write(data)\\r\\n\\t\\tself.set_header(\\\"Content-Type\\\", \\\"application/json; charset=UTF-8\\\")\\r\\n\\r\\n############# 这里实现的是注册的主页面显示功能 ############\\r\\n\\r\\nclass House_register(BaseHandler):\\r\\n\\tdef get(self):\\r\\n\\t\\tprint(\\\"register============\\\")\\r\\n\\t\\tself.render('register.html')\\r\\n\\r\\n##############验证码功能#################################\\r\\n\\r\\nclass PicCodeHandler(BaseHandler): #前端的img中的src是一个GET方式的请求\\r\\n\\t\\\"\\\"\\\"图片验证码\\\"\\\"\\\"\\r\\n\\tdef get(self):\\r\\n\\t\\tglobal Image_id #这里使用的是Image_id这个全局变量来传递图片验证码数据\\r\\n\\t\\t\\\"\\\"\\\"获取图片验证码\\\"\\\"\\\"\\r\\n\\t\\tpre_code_id = self.get_argument(\\\"pre\\\", \\\"\\\")\\r\\n\\t\\tcur_code_id = self.get_argument(\\\"cur\\\")\\r\\n\\t\\t# 生成图片验证码\\r\\n\\t\\tname, text, pic = captcha.generate_captcha() #这里使用了外接包，导入的captcha是对象，生成了图片信息\\r\\n\\t\\t# print(text) #这里的text是真正的数据，是用来判断用户传入过来的验证是否是正确的\\r\\n\\t\\tImage_id = text\\r\\n\\t\\t#这里的pic是直接生成的图片二进制码，所以接下来可以直接通过self.write往html写入数据\\r\\n\\t\\tself.set_header(\\\"Content-Type\\\", \\\"image/jpg\\\")\\r\\n\\t\\tself.write(pic) #这里的作用是将图片的二进制数据写入到前端系统\\r\\n\\r\\n\\r\\n###############手机验证码功能#################################\\r\\nclass Smscode(BaseHandler):\\r\\n\\tdef post(self):\\r\\n\\t\\tglobal Image_id\\r\\n\\t\\tmobile = self.json_args.get('mobile') #这里的json_args是个字典类型，存放的是json类型的数据，ajax中的json数据的获取是通过self.request.body()中获取的\\r\\n\\t\\timageCode = self.json_args.get('piccode') #这里的字典中的值是在BaseHandler中进行预解析的\\r\\n\\t\\timageCodeId = self.json_args.get('piccode_id')\\r\\n\\t\\t# print(\\\"image_id===============\\\")\\r\\n\\t\\t# print(Image_id)\\r\\n\\t\\tif imageCode.upper() != Image_id:\\r\\n\\t\\t\\tdata = {\\r\\n\\t\\t\\t\\t\\\"errcode\\\":'1',\\r\\n\\t\\t\\t\\t\\\"errmsg\\\":\\\"imagecode wrong\\\"\\r\\n\\t\\t\\t}\\r\\n\\t\\telse:\\r\\n\\t\\t\\tprint(\\\"iamge code true\\\",imageCode)\\r\\n\\t\\t\\tdata = {\\r\\n\\t\\t\\t\\t\\\"errcode\\\":'0',\\r\\n\\t\\t\\t\\t\\\"errmsg\\\":'ok',\\r\\n\\t\\t\\t}\\r\\n\\t\\t\\t# 产生随机短信验证码\\r\\n\\t\\t\\tsms_code = \\\"%06d\\\" % random.randint(1, 1000000)\\r\\n\\t\\t\\ttry:\\r\\n\\t\\t\\t\\tself.redis.set_value(\\\"sms_code_%s\\\" % mobile, sms_code)\\r\\n\\t\\t\\t\\tself.redis.set_expire(\\\"sms_code_%s\\\" % mobile, 360)\\r\\n\\t\\t\\texcept Exception as e:\\r\\n\\t\\t\\t\\tlogging.error(e)\\r\\n\\t\\t\\t\\tdata = {\\r\\n\\t\\t\\t\\t\\t\\\"errcode\\\":'1',\\r\\n\\t\\t\\t\\t\\t\\\"errmsg\\\":\\\"create Smscode fail\\\"\\r\\n\\t\\t\\t\\t}\\r\\n\\r\\n\\t\\t\\tprint('sms_code========',sms_code)\\r\\n\\r\\n\\t\\t\\t# 发送短信验证码\\r\\n\\t\\t\\ttry:\\r\\n\\t\\t\\t\\tresult = ccp.sendTemplateSMS(mobile, [sms_code, 1], 1)\\r\\n\\t\\t\\texcept Exception as e:\\r\\n\\t\\t\\t\\tlogging.error(e)\\r\\n\\t\\t\\t\\tdata = {\\r\\n\\t\\t\\t\\t\\t\\\"errcode\\\":'1',\\r\\n\\t\\t\\t\\t\\t\\\"errmsg\\\":\\\"send Smscode fail\\\"\\r\\n\\t\\t\\t\\t}\\r\\n\\t\\t\\tif result:\\r\\n\\t\\t\\t\\tdata = {\\r\\n\\t\\t\\t\\t\\t\\\"errcode\\\":'0',\\r\\n\\t\\t\\t\\t\\t\\\"errmsg\\\":\\\"ok\\\"\\r\\n\\t\\t\\t\\t}\\r\\n\\t\\t\\telse:\\r\\n\\t\\t\\t\\tdata = {\\r\\n\\t\\t\\t\\t\\t\\\"errcode\\\":'1',\\r\\n\\t\\t\\t\\t\\t\\\"errmsg\\\":\\\"目前该短信注册功能由于采用的是云通讯的测试功能，只支持绑定的手机号发送验证码，此处其他手机可输入任意值即可\\\"\\r\\n\\t\\t\\t\\t}\\r\\n\\r\\n\\t\\tself.write(data)\\r\\n\\t\\tself.set_header(\\\"Content-Type\\\", \\\"application/json; charset=UTF-8\\\")\\r\\n\\r\\n################注册验证功能################################\\r\\nclass Register_verity(BaseHandler):\\r\\n\\tdef post(self):\\r\\n\\t\\tmobile = self.json_args.get('mobile')\\r\\n\\t\\tphoneCode = self.json_args.get('phonecode')\\r\\n\\t\\tpasswd = self.json_args.get('password')\\r\\n\\t\\tpasswd2 = self.json_args.get('password2')\\r\\n\\t\\t# print(mobile,phoneCode,passwd,passwd2)\\r\\n\\r\\n\\t\\t################这里用来判断手机号是否已经注册了###########\\r\\n\\t\\tsql = \\\"select up_name from ih_user_profile where up_mobile='%s'\\\"%mobile\\r\\n\\t\\tresult = self.database.get_values_from_mysql(sql)\\r\\n\\t\\tprint(\\\"#############\\\",result)\\r\\n\\t\\tif len(result) != 0:\\r\\n\\t\\t\\tprint(\\\"mobile number existed\\\")\\r\\n\\t\\t\\tdata = {\\r\\n\\t\\t\\t'errcode':'1',\\r\\n\\t\\t\\t'errmsg':'mobile number existed'\\r\\n\\t\\t\\t}\\r\\n\\t\\t\\tself.write(data)\\r\\n\\t\\t\\tself.set_header(\\\"Content-Type\\\", \\\"application/json; charset=UTF-8\\\")\\r\\n\\t\\t################这里判断两次输入的密码是否一致###########\\r\\n\\t\\telif passwd != passwd2:\\r\\n\\t\\t\\tdata = { \\r\\n\\t\\t\\t'errcode':'1',\\r\\n\\t\\t\\t'errmsg':'password mismatch'\\r\\n\\t\\t\\t}\\r\\n\\t\\t\\tself.write(data)\\r\\n\\t\\t\\tself.set_header(\\\"Content-Type\\\", \\\"application/json; charset=UTF-8\\\")\\r\\n\\t\\telse:\\r\\n\\t\\t#############这里对密码进行加密处理#####################\\r\\n\\t\\t\\ts1 = sha1()\\r\\n\\t\\t\\ts1.update(passwd.encode(\\\"utf-8\\\"))\\r\\n\\t\\t\\tpassword = s1.hexdigest()\\r\\n\\t\\t\\r\\n\\t\\t#############验证手机的验证码是否正确###################\\r\\n\\t\\t\\tcode = self.redis.get_value(\\\"sms_code_%s\\\" % mobile)\\r\\n\\t\\t\\t# print(' redis code value',code.decode(\\\"utf-8\\\"))\\r\\n\\t\\t\\t# print('phonecode=======',phoneCode)\\r\\n\\t\\t\\tif phoneCode == code.decode(\\\"utf-8\\\"):\\r\\n\\t\\t\\t\\tprint('Smscode=======ok')\\r\\n\\r\\n\\t\\t#######将手机号和密码存入数据库中去####################\\r\\n\\t\\t\\tdefault_image_path = '/static/images/landlord01.jpg'\\r\\n\\t\\t\\tsql = \\\"insert into ih_user_profile(up_name,up_mobile,up_passwd,up_avatar) values('%s','%s','%s','%s')\\\"%(mobile, mobile, password, default_image_path)\\r\\n\\t\\t\\tprint(sql)\\r\\n\\t\\t\\tself.database.insert_into_tbl(sql)\\r\\n\\r\\n\\t\\t####### 将注册成功后的用户写入session 机制 ############\\r\\n\\t\\t#这里生成一个独一无二的session_id号\\r\\n\\t\\t\\tsession_id = str(base64.b64encode(uuid.uuid4().bytes + uuid.uuid4().bytes))\\r\\n\\t\\t\\tself.set_secure_cookie(\\\"session_id\\\", session_id, expires_days=1)\\r\\n\\t\\t#这里将存在cookie中的session_id存放到redis中，这样就可以通过cookie来在redis中进行数据查询\\t\\r\\n\\t\\t\\tself.redis.set_value(session_id, str(mobile))\\r\\n\\t\\t\\tself.redis.set_expire(session_id, 360)\\r\\n\\t\\t###########返回状态码给到js端##########################\\r\\n\\t\\t\\tdata = {\\r\\n\\t\\t\\t\\t'errcode':'0',\\r\\n\\t\\t\\t\\t'errmsg':'ok'\\r\\n\\t\\t\\t}\\r\\n\\r\\n\\t\\t\\tself.write(data)\\r\\n\\t\\t\\tself.set_header(\\\"Content-Type\\\", \\\"application/json; charset=UTF-8\\\")\\r\\n\\r\\n\\r\\n###############################登陆处理功能###################################\\r\\nclass Login_verity(BaseHandler):\\r\\n\\tdef post(self):\\r\\n\\t\\tmobile = self.json_args.get('mobile')\\r\\n\\t\\tpasswd = self.json_args.get('password')\\r\\n\\r\\n\\t\\t################# 从mysql数据库中查询是否存在该手机号 ###########\\r\\n\\t\\tsql = \\\"select up_passwd from ih_user_profile where up_mobile=%s\\\"%mobile\\r\\n\\t\\tresult = self.database.get_values_from_mysql(sql)\\r\\n\\t\\t# print(\\\"result=====\\\",result)\\r\\n\\t\\tif result == None or len(result) == 0:\\r\\n\\t\\t\\tprint(\\\" mobile dot exist please register\\\")\\r\\n\\t\\t\\tdata = {\\r\\n\\t\\t\\t\\t'errcode':'1',\\r\\n\\t\\t\\t\\t'errmsg':'do not exist'\\r\\n\\t\\t\\t}\\r\\n\\t\\t\\tself.write(data)\\r\\n\\t\\t\\tself.set_header(\\\"Content-Type\\\", \\\"application/json; charset=UTF-8\\\")\\r\\n\\t\\telse:\\r\\n\\t\\t\\t############ 对输入的密码进行加密后和数据库中的密码进行比较 ####\\r\\n\\t\\t\\ts1 = sha1()\\r\\n\\t\\t\\ts1.update(passwd.encode(\\\"utf-8\\\"))\\r\\n\\t\\t\\tpassword = s1.hexdigest()\\r\\n\\t\\t\\tprint('password=====',password)\\r\\n\\t\\t\\t############# end #########################################\\r\\n\\t\\t\\t# print(\\\"password===== result======\\\",password,result[0])\\r\\n\\r\\n\\t\\t\\tif password != result[0][0]:\\r\\n\\t\\t\\t\\tprint(\\\"password wrong\\\")\\r\\n\\t\\t\\t\\tdata = {\\r\\n\\t\\t\\t\\t'errcode':'1',\\r\\n\\t\\t\\t\\t'errmsg':'password wrong'\\r\\n\\t\\t\\t\\t}\\r\\n\\t\\t\\t\\tself.write(data)\\r\\n\\t\\t\\t\\tself.set_header(\\\"Content-Type\\\", \\\"application/json; charset=UTF-8\\\")\\r\\n\\t\\t\\telse:\\r\\n\\t\\t\\t\\tprint(\\\" login sucess\\\")\\r\\n\\t\\t\\t\\t#############这里设置 session 功能######################\\r\\n\\t\\t\\t\\tsession_id = str(base64.b64encode(uuid.uuid4().bytes + uuid.uuid4().bytes))\\r\\n\\t\\t\\t\\tself.set_secure_cookie(\\\"session_id\\\", session_id, expires_days=1)\\r\\n\\t\\t\\t\\tself.redis.set_value(session_id, str(mobile))\\r\\n\\t\\t\\t\\tself.redis.set_expire(session_id, 3600)\\r\\n\\t\\t\\t\\t############# end #####################################\\r\\n\\t\\t\\t\\tdata = {\\r\\n\\t\\t\\t\\t'errcode':'0',\\r\\n\\t\\t\\t\\t'errmsg':'login sucess'\\r\\n\\t\\t\\t\\t}\\r\\n\\t\\t\\t\\tself.write(data)\\r\\n\\t\\t\\t\\tself.set_header(\\\"Content-Type\\\", \\\"application/json; charset=UTF-8\\\")\\r\\n\\r\\n#############################个人信息处理################################\\r\\n\\r\\nclass Personal_info(BaseHandler):\\r\\n\\t@required_login\\r\\n\\tdef get(self):\\r\\n\\t\\tsession_id = self.get_secure_cookie(\\\"session_id\\\")\\r\\n\\t\\t# print(\\\"session_id===\\\",session_id)\\r\\n\\t\\tmobile = self.redis.get_value(session_id).decode(\\\"utf-8\\\")\\r\\n\\t\\t# print(\\\"mobile=====\\\",mobile)\\r\\n\\t\\tsql = \\\"select up_name,up_avatar from ih_user_profile where up_mobile=%s\\\"%mobile\\r\\n\\t\\tresult = self.database.get_values_from_mysql(sql)\\r\\n\\t\\t# print(\\\"name====\\\",result)\\r\\n\\t\\tif result == None or len(result) == 0: \\r\\n\\t\\t\\tprint(\\\"can not search name\\\")\\r\\n\\t\\t\\tself.write(dict(errcode=\\\"4101\\\", errmsg=\\\"用户未登录\\\"))\\r\\n\\r\\n\\t\\t# print(\\\"result[0][1]======\\\", result[0][1])\\r\\n\\t\\tif result[0][1] == None:\\r\\n\\t\\t\\timage_path = \\\"/static/images/landlord01.jpg\\\"\\r\\n\\t\\telse:\\r\\n\\t\\t\\timage_path = result[0][1]\\r\\n\\r\\n\\t\\tuser_data = {\\r\\n\\t\\t\\t\\\"name\\\":result[0][0],\\r\\n\\t\\t\\t\\\"mobile\\\":mobile,\\r\\n\\t\\t\\t\\\"avatar\\\":image_path\\r\\n\\t\\t}\\r\\n\\r\\n\\t\\tdata = {\\r\\n\\t\\t\\t\\\"errcode\\\":\\\"0\\\",\\r\\n\\t\\t\\t\\\"data\\\":user_data,\\r\\n\\t\\t}\\r\\n\\r\\n\\t\\tself.write(data)\\r\\n\\t\\tself.set_header(\\\"Content-Type\\\", \\\"application/json; charset=UTF-8\\\")\\r\\n\\r\\n##########################处理用户修改用户名#########################\\r\\nclass Personal_name(BaseHandler):\\r\\n\\t@required_login\\r\\n\\tdef post(self):\\r\\n\\t\\tuser_name = self.json_args.get('name')\\r\\n\\t\\t# print('user_name====',user_name)\\r\\n\\t\\t################# 获取当前登录的用户id ######################\\r\\n\\t\\tsession_id = self.get_secure_cookie(\\\"session_id\\\")\\r\\n\\t\\t# print(\\\"session_id===\\\",session_id)\\r\\n\\t\\tmobile = self.redis.get_value(session_id).decode(\\\"utf-8\\\")\\r\\n\\t\\tsql = 'update ih_user_profile set up_name=\\\"%s\\\" where up_mobile=\\\"%s\\\" '%(user_name, mobile)\\r\\n\\t\\t# print(sql)\\r\\n\\t\\tresult = self.database.update_tbl(sql)\\r\\n\\t\\tif result != True:\\r\\n\\t\\t\\tprint(\\\" update sql failed \\\")\\r\\n\\t\\t\\treturn 0\\r\\n\\t\\telse:\\r\\n\\t\\t\\tprint(\\\" update sql sucess \\\")\\r\\n\\r\\n\\t\\t\\tdata = {\\r\\n\\t\\t\\t\\t\\\"errcode\\\":\\\"0\\\",\\r\\n\\t\\t\\t}\\r\\n\\r\\n\\t\\t\\tself.write(data)\\r\\n\\t\\t\\tself.set_header(\\\"Content-Type\\\", \\\"application/json; charset=UTF-8\\\")\\r\\n\\r\\n######################### 这里用来处理用户上传的头像信息 #############\\r\\n#####---这里使用的技术栈是直接保存在了服务器/static/images/personal_images中\\r\\n#####---这里可使用七牛技术存储到远程服务器上(由于账号实名问题，七牛账号暂时不可用)\\r\\nclass Personal_img(BaseHandler):\\r\\n\\t@required_login\\r\\n\\tdef post(self):\\r\\n\\t\\t################# 获取当前登录的用户id ######################\\r\\n\\t\\tsession_id = self.get_secure_cookie(\\\"session_id\\\")\\r\\n\\t\\t# print(\\\"session_id===\\\",session_id)\\r\\n\\t\\tmobile = self.redis.get_value(session_id).decode(\\\"utf-8\\\")\\r\\n\\t\\tprint(\\\"test_test======\\\")\\r\\n\\t\\tfiles = self.request.files\\r\\n\\t\\timage_file = files.get(\\\"avatar\\\")\\r\\n\\t\\tif image_file:\\r\\n\\t\\t\\tfile_name = image_file[0][\\\"filename\\\"]\\r\\n\\t\\t\\t# print(\\\"filename=====\\\", file_name)\\r\\n\\t\\t\\timage = image_file[0][\\\"body\\\"]\\r\\n\\r\\n\\t\\t\\tfile_path = './static/images/personal_images/' + mobile# + \\\".\\\" + \\\".\\\".join(file_name.split(\\\".\\\")[1:])\\r\\n\\t\\t\\tfile = open(file_path, 'wb')\\r\\n\\t\\t\\tfile.write(image)\\r\\n\\r\\n\\t\\t\\tfile.close()\\r\\n\\r\\n\\t\\t\\tdata = {\\r\\n\\t\\t\\t\\t'errcode':'0',\\r\\n\\t\\t\\t\\t'data':file_path,\\r\\n\\t\\t\\t}\\r\\n\\r\\n\\t\\t\\t############## 这里需要添加将图片路径保存在mysql的功能#####\\r\\n\\t\\t\\tsql = 'update ih_user_profile set up_avatar=\\\"%s\\\" where up_mobile=\\\"%s\\\" '%(file_path, mobile)\\r\\n\\t\\t\\tresult = self.database.update_tbl(sql)\\r\\n\\t\\t\\tif result != True:\\r\\n\\t\\t\\t\\tprint(\\\" update sql failed \\\")\\r\\n\\t\\t\\t\\treturn 0\\r\\n\\r\\n\\t\\telse:\\r\\n\\t\\t\\tdata = {\\r\\n\\t\\t\\t\\t'errcode':'4001',\\r\\n\\t\\t\\t\\t'data':\\\"/static/images/landlord01.jpg\\\",\\r\\n\\t\\t\\t}\\r\\n\\r\\n\\t\\tself.write(data)\\r\\n\\t\\tself.set_header(\\\"Content-Type\\\", \\\"application/json; charset=UTF-8\\\")\\r\\n\\r\\n######################## 房屋信息显示 #######################\\r\\nclass House_info(BaseHandler):\\r\\n\\t######### POST 处理的是房屋提交信息 ######################\\r\\n\\t@required_login\\r\\n\\tdef post(self):\\r\\n\\t\\t\\\"\\\"\\\"保存\\\"\\\"\\\"\\r\\n\\t\\t# 获取参数\\r\\n\\t\\t\\\"\\\"\\\"{\\r\\n\\t\\t\\t\\\"title\\\":\\\"\\\",\\r\\n\\t\\t\\t\\\"price\\\":\\\"\\\",\\r\\n\\t\\t\\t\\\"area_id\\\":\\\"1\\\",\\r\\n\\t\\t\\t\\\"address\\\":\\\"\\\",\\r\\n\\t\\t\\t\\\"room_count\\\":\\\"\\\",\\r\\n\\t\\t\\t\\\"acreage\\\":\\\"\\\",\\r\\n\\t\\t\\t\\\"unit\\\":\\\"\\\",\\r\\n\\t\\t\\t\\\"capacity\\\":\\\"\\\",\\r\\n\\t\\t\\t\\\"beds\\\":\\\"\\\",\\r\\n\\t\\t\\t\\\"deposit\\\":\\\"\\\",\\r\\n\\t\\t\\t\\\"min_days\\\":\\\"\\\",\\r\\n\\t\\t\\t\\\"max_days\\\":\\\"\\\",\\r\\n\\t\\t\\t\\\"facility\\\":[\\\"7\\\",\\\"8\\\"]\\r\\n\\t\\t}\\\"\\\"\\\"\\r\\n\\t\\t################# 获取当前登录的用户id ######################\\r\\n\\t\\tsession_id = self.get_secure_cookie(\\\"session_id\\\")\\r\\n\\t\\t# print(\\\"session_id===\\\",session_id)\\r\\n\\t\\tmobile = self.redis.get_value(session_id).decode(\\\"utf-8\\\")\\r\\n\\t\\tsql = 'select up_user_id from ih_user_profile where up_mobile=\\\"%s\\\"'%mobile\\r\\n\\t\\tret = self.database.get_values_from_mysql(sql)\\r\\n\\r\\n\\t\\tuser_id = ret[0][0]\\r\\n\\t\\ttitle = self.json_args.get(\\\"title\\\")\\r\\n\\t\\tprice = self.json_args.get(\\\"price\\\")\\r\\n\\t\\tarea_id = self.json_args.get(\\\"area_id\\\")\\r\\n\\t\\taddress = self.json_args.get(\\\"address\\\")\\r\\n\\t\\troom_count = self.json_args.get(\\\"room_count\\\")\\r\\n\\t\\tacreage = self.json_args.get(\\\"acreage\\\")\\r\\n\\t\\tunit = self.json_args.get(\\\"unit\\\")\\r\\n\\t\\tcapacity = self.json_args.get(\\\"capacity\\\")\\r\\n\\t\\tbeds = self.json_args.get(\\\"beds\\\")\\r\\n\\t\\tdeposit = self.json_args.get(\\\"deposit\\\")\\r\\n\\t\\tmin_days = self.json_args.get(\\\"min_days\\\")\\r\\n\\t\\tmax_days = self.json_args.get(\\\"max_days\\\")\\r\\n\\t\\tfacility = self.json_args.get(\\\"facility\\\") # 对一个房屋的设施，是列表类型\\r\\n\\t\\t# 校验\\r\\n\\t\\tif not all((title, price, area_id, address, room_count, acreage, unit, capacity, beds, deposit, min_days,\\r\\n\\t\\t\\t\\t\\tmax_days)):\\r\\n\\t\\t\\treturn self.write(dict(errcode='3', errmsg=\\\"缺少参数\\\"))\\r\\n\\r\\n\\t\\ttry:\\r\\n\\t\\t\\tprice = int(price) * 100\\r\\n\\t\\t\\tdeposit = int(deposit) * 100\\r\\n\\t\\texcept Exception as e:\\r\\n\\t\\t\\treturn self.write(dict(errcode='2', errmsg=\\\"参数错误\\\"))\\r\\n\\r\\n\\t\\t# 数据\\r\\n\\t\\ttry:\\r\\n\\t\\t\\tsql = \\\"insert into ih_house_info(hi_user_id,hi_title,hi_price,hi_area_id,hi_address,hi_room_count,\\\" \\\\\\r\\n\\t\\t\\t\\t \\\"hi_acreage,hi_house_unit,hi_capacity,hi_beds,hi_deposit,hi_min_days,hi_max_days) \\\" \\\\\\r\\n\\t\\t\\t\\t \\\"values(%s,%s,%s,%s,%s,%s,%s,\\\" \\\\\\r\\n\\t\\t\\t\\t \\\"%s,%s,%s,%s,%s,%s)\\\"%(user_id, title, price, area_id, address, room_count, acreage, unit, capacity, beds, deposit, min_days, max_days)\\r\\n\\t\\t\\t# 对于insert语句，execute方法会返回最后一个自增id\\r\\n\\t\\t\\tself.database.insert_into_tbl(sql)\\r\\n\\t\\texcept Exception as e:\\r\\n\\t\\t\\tlogging.error(e)\\r\\n\\t\\t\\treturn self.write(dict(errcode='4', errmsg=\\\"save data error\\\"))\\r\\n\\r\\n\\t\\tsql = 'select hi_house_id from ih_house_info where hi_user_id=%s'%user_id\\r\\n\\t\\tret = self.database.get_values_from_mysql(sql)\\r\\n\\t\\thouse_id = ret[0][-1]\\r\\n\\t\\t# print('house_id================',house_id)\\r\\n\\r\\n\\t\\ttry:\\r\\n\\t\\t\\tsql = \\\"insert into ih_house_facility(hf_house_id,hf_facility_id) values\\\"\\r\\n\\t\\t\\tsql_val = [] # 用来保存条目的(%s, %s)部分 最终的形式 [\\\"(%s, %s)\\\", \\\"(%s, %s)\\\"]\\r\\n\\t\\t\\tvals = [] # 用来保存的具体的绑定变量值\\r\\n\\t\\t\\tfor facility_id in facility:\\r\\n\\t\\t\\t\\t# sql += \\\"(%s, %s),\\\" 采用此种方式，sql语句末尾会多出一个逗号\\r\\n\\t\\t\\t\\t# sql_val.append(\\\"(%d, %s)\\\")\\r\\n\\t\\t\\t\\tvals.append(str((house_id,facility_id)))\\r\\n\\t\\t\\t\\t# vals.append(facility_id)\\r\\n\\r\\n\\t\\t\\t# sql += \\\",\\\".join(sql_val)\\r\\n\\t\\t\\tsql += \\\",\\\".join(vals)\\r\\n\\t\\t\\t# print('sql=============',sql)\\r\\n\\t\\t\\tlogging.debug(sql)\\r\\n\\t\\t\\tlogging.debug(vals)\\r\\n\\t\\t\\tself.database.insert_into_tbl(sql)\\r\\n\\t\\texcept Exception as e:\\r\\n\\t\\t\\tlogging.error(e)\\r\\n\\t\\t\\ttry:\\r\\n\\t\\t\\t\\tsql = \\\"delete from ih_house_info where hi_house_id=%s\\\"%house_id\\r\\n\\t\\t\\t\\tself.database.insert_into_tbl(sql)\\r\\n\\t\\t\\texcept Exception as e:\\r\\n\\t\\t\\t\\tlogging.error(e)\\r\\n\\t\\t\\t\\treturn self.write(dict(errcode='4', errmsg=\\\"delete fail\\\"))\\r\\n\\t\\t\\telse:\\r\\n\\t\\t\\t\\treturn self.write(dict(errcode='4', errmsg=\\\"no data save\\\"))\\r\\n\\t\\t# 返回\\r\\n\\t\\tself.write(dict(errcode='0', errmsg=\\\"OK\\\", house_id=house_id))\\r\\n\\r\\n\\r\\n\\t######### GET处理 的是获取房屋信息 ######################\\r\\n\\t@required_login\\r\\n\\tdef get(self):\\r\\n\\t\\thouse_id = self.get_query_argument('house_id')\\r\\n\\t\\tprint(house_id)\\r\\n\\t\\t########### 获取房屋信息 #########################\\r\\n\\t\\tsession_id = self.get_secure_cookie(\\\"session_id\\\")\\r\\n\\t\\tmobile = self.redis.get_value(session_id).decode(\\\"utf-8\\\")\\r\\n\\r\\n\\t\\t# 校验参数，判断传入的house_id是否正确\\r\\n\\t\\tif not house_id:\\r\\n\\t\\t\\treturn self.write(dict(errcode='3', errmsg=\\\"缺少参数\\\"))\\r\\n\\r\\n\\t\\t# 这里可以添加从redis缓存中获取这些房屋信息，这样就避免了进行多次查询数据的操作\\r\\n\\t\\t# 这里以待加入功能\\r\\n\\t\\t# TODO\\r\\n\\r\\n\\t\\t# 查询数据库\\r\\n\\r\\n\\t\\t# 查询房屋基本信息\\r\\n\\t\\tsql = \\\"select hi_title,hi_price,hi_address,hi_room_count,hi_acreage,hi_house_unit,hi_capacity,hi_beds,\\\" \\\\\\r\\n\\t\\t\\t \\\"hi_deposit,hi_min_days,hi_max_days,up_name,up_avatar,hi_user_id \\\" \\\\\\r\\n\\t\\t\\t \\\"from ih_house_info inner join ih_user_profile on hi_user_id=up_user_id where hi_house_id=%s\\\"%house_id\\r\\n\\r\\n\\t\\tret = self.database.get_values_from_mysql(sql)\\r\\n\\t\\t# print(\\\"result=======\\\", ret)\\r\\n\\t\\t# 用户查询的可能是不存在的房屋id, 此时ret为None\\r\\n\\t\\tif not ret:\\r\\n\\t\\t\\treturn self.write(dict(errcode='2', errmsg=\\\"查无此房\\\"))\\r\\n\\r\\n\\t\\tdata = {\\r\\n\\t\\t\\t\\\"hid\\\":house_id,\\r\\n\\t\\t\\t\\\"user_id\\\":ret[0][13],\\r\\n\\t\\t\\t\\\"title\\\":ret[0][0],\\r\\n\\t\\t\\t\\\"price\\\":ret[0][1],\\r\\n\\t\\t\\t\\\"address\\\":ret[0][2],\\r\\n\\t\\t\\t\\\"room_count\\\":ret[0][3],\\r\\n\\t\\t\\t\\\"acreage\\\":ret[0][4],\\r\\n\\t\\t\\t\\\"unit\\\":ret[0][5],\\r\\n\\t\\t\\t\\\"capacity\\\":ret[0][6],\\r\\n\\t\\t\\t\\\"beds\\\":ret[0][7],\\r\\n\\t\\t\\t\\\"deposit\\\":ret[0][8],\\r\\n\\t\\t\\t\\\"min_days\\\":ret[0][9],\\r\\n\\t\\t\\t\\\"max_days\\\":ret[0][10],\\r\\n\\t\\t\\t\\\"user_name\\\":ret[0][11],\\r\\n\\t\\t\\t\\\"user_avatar\\\":ret[0][12]\\r\\n\\t\\t}\\r\\n\\r\\n\\t\\t# print(\\\"data===========\\\",data)\\r\\n\\r\\n\\t\\t# 查询房屋的图片信息\\r\\n\\t\\tsql = \\\"select hi_url from ih_house_image where hi_house_id=%s\\\"%house_id\\r\\n\\t\\tret = self.database.get_values_from_mysql(sql)\\r\\n\\t\\t# print(\\\"image=============\\\",ret)\\r\\n\\r\\n\\t\\t# 如果查询到的图片\\r\\n\\t\\timages = []\\r\\n\\t\\tif ret:\\r\\n\\t\\t\\tfor image in ret:\\r\\n\\t\\t\\t\\timages.append(image[0])\\r\\n\\t\\tdata[\\\"images\\\"] = images\\r\\n\\r\\n\\t\\t# 查询房屋的基本设施\\r\\n\\t\\tsql = \\\"select hf_facility_id from ih_house_facility where hf_house_id=%s\\\"%house_id\\r\\n\\t\\tret = self.database.get_values_from_mysql(sql)\\r\\n\\r\\n\\t\\t# 如果查询到设施\\r\\n\\t\\tfacilities = []\\r\\n\\t\\tif ret:\\r\\n\\t\\t\\tfor facility in ret:\\r\\n\\t\\t\\t\\tfacilities.append(facility[0])\\r\\n\\t\\tdata[\\\"facilities\\\"] = facilities\\r\\n\\r\\n\\t\\t# 查询评论信息\\r\\n\\t\\tsql = \\\"select oi_comment,up_name,oi_utime,up_mobile from ih_order_info inner join ih_user_profile \\\" \\\\\\r\\n\\t\\t\\t \\\"on oi_user_id=up_user_id where oi_house_id=%s and oi_status=4 and oi_comment is not null\\\"%house_id\\r\\n\\r\\n\\t\\tret = self.database.get_values_from_mysql(sql)\\r\\n\\t\\tcomments = []\\r\\n\\t\\tif ret:\\r\\n\\t\\t\\tfor comment in ret:\\r\\n\\t\\t\\t\\tcomments.append(dict(\\r\\n\\t\\t\\t\\t\\tuser_name = comment[1] if comment[1] != comment[3] else \\\"匿名用户\\\",\\r\\n\\t\\t\\t\\t\\tcontent = comment[0],\\r\\n\\t\\t\\t\\t\\tctime = comment[2].strftime(\\\"%Y-%m-%d %H:%M:%S\\\")\\r\\n\\t\\t\\t\\t))\\r\\n\\t\\tdata[\\\"comments\\\"] = comments\\r\\n\\r\\n\\t\\t# 存入到redis中\\r\\n\\t\\tjson_data = json.dumps(data)\\r\\n\\r\\n\\t\\t## 可以在此处将上边查询到的这么多数据存储到redis中，这样下次查询的时候就能加快查询步骤\\r\\n\\t\\t#TODO\\r\\n\\r\\n\\t\\tresp = '{\\\"errcode\\\":\\\"0\\\", \\\"errmsg\\\":\\\"OK\\\", \\\"data\\\":%s, \\\"user_id\\\":%s}' % (json_data, mobile)\\r\\n\\t\\t# self.write(dict(errcode=RET.OK, errmsg=\\\"OK\\\", data=data))\\r\\n\\t\\tself.write(resp)\\r\\n\\t\\tself.set_header(\\\"Content-Type\\\", \\\"application/json; charset=UTF-8\\\")\\r\\n\\r\\n\\r\\n############## 获取预定信息 /api/house/info ########################################\\r\\nclass House_reserve(BaseHandler):\\r\\n\\tdef post(self):\\r\\n\\t\\thouse_id = self.json_args.get('house_id')\\r\\n\\t\\tstart_date = self.json_args.get('start_date')\\r\\n\\t\\tend_date = self.json_args.get('end_date')\\r\\n\\t\\tprint('====',house_id,start_date,end_date)\\r\\n\\r\\n\\t\\tsql = ' select oi_begin_date,oi_end_date from ih_order_info where oi_house_id=%s'%house_id\\r\\n\\t\\tret = self.database.get_values_from_mysql(sql)\\r\\n\\t\\tif not ret:\\r\\n\\t\\t\\tprint(' query order info error')\\r\\n\\t\\t\\tdata = {\\r\\n\\t\\t\\t\\t'errcode':'4101',\\r\\n\\t\\t\\t}\\r\\n\\t\\t############ 这里添加对 html中传来的事件信息，和该house在order数据库中存在的时间内信息进行比较来判断所选择的时间段是否可预定\\r\\n\\r\\n\\t\\tdata = {\\r\\n\\t\\t\\t'errcode':'0'\\r\\n\\t\\t}\\r\\n\\t\\tself.write(data)\\r\\n\\t\\tself.set_header('Content-Type', 'application/json; charset=UTF-8')\\r\\n\\r\\n############### 处理显示订单信息 ##########################################\\r\\nclass Show_order(BaseHandler):\\r\\n\\t@required_login\\r\\n\\tdef get(self):\\r\\n\\t\\tsession_id = self.get_secure_cookie(\\\"session_id\\\")\\r\\n\\t\\t# print(\\\"session_id===\\\",session_id)\\r\\n\\t\\tmobile = self.redis.get_value(session_id).decode(\\\"utf-8\\\")\\r\\n\\r\\n\\t\\tsql = ' select up_user_id from ih_user_profile where up_mobile=%s'%mobile\\r\\n\\t\\tret = self.database.get_values_from_mysql(sql)\\r\\n\\t\\tif not ret:\\r\\n\\t\\t\\tprint('user_id doesr not exist')\\r\\n\\t\\t\\tdata = {\\r\\n\\t\\t\\t\\t'errcode':'1',\\r\\n\\t\\t\\t}\\r\\n\\t\\tuser_id = ret[0][0]\\r\\n\\t\\t# print('user_id======',user_id)\\r\\n\\r\\n\\t\\t# 用户的身份，用户想要查询作为房客下的单，还是想要查询作为房东 被人下的单\\r\\n\\t\\trole = self.get_query_argument(\\\"role\\\", \\\"\\\")\\r\\n\\t\\ttry:\\r\\n\\t\\t\\t# 查询房东订单\\r\\n\\t\\t\\tif \\\"landlord\\\" == role:\\r\\n\\t\\t\\t\\tsql = 'select oi_order_id,hi_title,hi_index_image_url,oi_begin_date,oi_end_date,oi_ctime,oi_days,oi_amount,oi_status,oi_comment from ih_order_info inner join ih_house_info on oi_house_id=hi_house_id where hi_user_id=%s order by oi_ctime desc'%user_id\\r\\n\\t\\t\\t\\tret = self.database.get_values_from_mysql(sql)\\r\\n\\t\\t\\t\\tif not ret:\\r\\n\\t\\t\\t\\t\\tprint(' query order info fail')\\r\\n\\t\\t\\t\\t\\tdata = {\\r\\n\\t\\t\\t\\t\\t\\t'errcode':'1',\\r\\n\\t\\t\\t\\t\\t}\\r\\n\\r\\n\\t\\t\\telse:\\r\\n\\t\\t\\t\\tsql = 'select oi_order_id,hi_title,hi_index_image_url,oi_begin_date,oi_end_date,oi_ctime,oi_days,oi_amount,oi_status,oi_comment from ih_order_info inner join ih_house_info on oi_house_id=hi_house_id where oi_user_id=%s order by oi_ctime desc'%user_id\\r\\n\\t\\t\\t\\tret = self.database.get_values_from_mysql(sql)\\r\\n\\t\\t\\t\\tif not ret:\\r\\n\\t\\t\\t\\t\\tprint(' query order info fail')\\r\\n\\t\\t\\t\\t\\tdata = {\\r\\n\\t\\t\\t\\t\\t\\t'errcode':'1',\\r\\n\\t\\t\\t\\t\\t}\\r\\n\\r\\n\\t\\texcept Exception as e:\\r\\n\\t\\t\\tlogging.error(e)\\r\\n\\t\\t\\treturn self.write({\\\"errcode\\\":'1', \\\"errmsg\\\":\\\"get data error\\\"})\\r\\n\\t\\torders = []\\r\\n\\t\\t# print('ret========',ret[0][0])\\r\\n\\t\\tif ret:\\r\\n\\t\\t\\tfor l in ret:\\r\\n\\t\\t\\t\\t# print('l[0]======',l)\\r\\n\\t\\t\\t\\torder = {\\r\\n\\t\\t\\t\\t\\t\\\"order_id\\\":l[0],\\r\\n\\t\\t\\t\\t\\t\\\"title\\\":l[1],\\r\\n\\t\\t\\t\\t\\t\\\"img_url\\\":l[2],\\r\\n\\t\\t\\t\\t\\t\\\"start_date\\\":l[3].strftime(\\\"%Y-%m-%d\\\"),\\r\\n\\t\\t\\t\\t\\t\\\"end_date\\\":l[4].strftime(\\\"%Y-%m-%d\\\"),\\r\\n\\t\\t\\t\\t\\t\\\"ctime\\\":l[5].strftime(\\\"%Y-%m-%d\\\"),\\r\\n\\t\\t\\t\\t\\t\\\"days\\\":l[6],\\r\\n\\t\\t\\t\\t\\t\\\"amount\\\":l[7],\\r\\n\\t\\t\\t\\t\\t\\\"status\\\":l[8],\\r\\n\\t\\t\\t\\t\\t\\\"comment\\\":l[9] if l[9] else \\\"\\\"\\r\\n\\t\\t\\t\\t}\\r\\n\\t\\t\\t\\torders.append(order)\\r\\n\\t\\tself.write({\\\"errcode\\\":'0', \\\"errmsg\\\":\\\"OK\\\", \\\"orders\\\":orders})\\r\\n\\r\\n#################### 此处进行的是实名认证的功能 ############################\\r\\nclass Real_name_verity(BaseHandler):\\r\\n\\t@required_login\\r\\n\\tdef get(self):\\r\\n\\t\\tsession_id = self.get_secure_cookie(\\\"session_id\\\")\\r\\n\\t\\t# print(\\\"session_id===\\\",session_id)\\r\\n\\t\\tmobile = self.redis.get_value(session_id).decode(\\\"utf-8\\\")\\r\\n\\t\\t# print('mobile=======',mobile)\\r\\n\\r\\n\\t\\tsql = ' select up_real_name,up_id_card from ih_user_profile where up_mobile=\\\"%s\\\"'%mobile\\r\\n\\t\\tret = self.database.get_values_from_mysql(sql)\\r\\n\\r\\n\\t\\t# print('ret=====',ret)\\r\\n\\t\\tuser_data = {\\r\\n\\t\\t\\t'real_name':ret[0][0],\\r\\n\\t\\t\\t'id_card':ret[0][1],\\r\\n\\t\\t}\\r\\n\\t\\tdata = {\\r\\n\\t\\t\\t'errcode':'0',\\r\\n\\t\\t\\t'data':user_data,\\r\\n\\t\\t}\\r\\n\\r\\n\\t\\tprint('data========',data)\\r\\n\\r\\n\\t\\tself.write(data)\\r\\n\\t\\tself.set_header(\\\"Content-Type\\\", \\\"application/json; charset=UTF-8\\\")\\r\\n\\r\\n################# 修改当前用户的实名认证情况 #############################\\r\\n\\t@required_login\\r\\n\\tdef post(self):\\r\\n\\t\\t########### 获取当前用户的情况###################################\\r\\n\\t\\tsession_id = self.get_secure_cookie(\\\"session_id\\\")\\r\\n\\t\\t# print(\\\"session_id===\\\",session_id)\\r\\n\\t\\tmobile = self.redis.get_value(session_id).decode(\\\"utf-8\\\")\\r\\n\\t\\t# print('mobile=======',mobile)\\r\\n\\r\\n\\t\\treal_name = self.json_args.get('real_name')\\r\\n\\t\\tid_card = self.json_args.get('id_card')\\r\\n\\t\\t# print('id_card=====',real_name, id_card)\\r\\n\\r\\n\\t\\tsql = 'update ih_user_profile set up_real_name=\\\"%s\\\", up_id_card=\\\"%s\\\" where up_mobile=\\\"%s\\\"'%(real_name, id_card, mobile)\\r\\n\\t\\tret = self.database.update_tbl(sql)\\r\\n\\t\\tif not ret:\\r\\n\\t\\t\\tprint('update real name fial')\\r\\n\\r\\n\\t\\tdata = {\\r\\n\\t\\t\\t'errcode':'0',\\r\\n\\t\\t}\\r\\n\\r\\n\\t\\tself.write(data)\\r\\n\\t\\tself.set_header('Content-Type', 'application/json; charset=UTF-8')\\r\\n\\r\\nclass Myhouse_show(BaseHandler):\\r\\n\\t@required_login\\r\\n\\tdef get(self):\\r\\n\\t\\t########### 获取当前用户的情况###################################\\r\\n\\t\\tsession_id = self.get_secure_cookie(\\\"session_id\\\")\\r\\n\\t\\t# print(\\\"session_id===\\\",session_id)\\r\\n\\t\\tmobile = self.redis.get_value(session_id).decode(\\\"utf-8\\\")\\r\\n\\t\\t# print('mobile=======',mobile)\\r\\n\\r\\n\\t\\tsql = 'select up_user_id from ih_user_profile where up_mobile=\\\"%s\\\"'%mobile\\r\\n\\t\\tret = self.database.get_values_from_mysql(sql)\\r\\n\\t\\tuser_id = ret[0][0]\\r\\n\\r\\n\\t\\ttry:\\r\\n\\t\\t\\tsql = \\\"select a.hi_house_id,a.hi_title,a.hi_price,a.hi_ctime,b.ai_name,a.hi_index_image_url \\\" \\\\\\r\\n\\t\\t\\t\\t \\\"from ih_house_info a inner join ih_area_info b on a.hi_area_id=b.ai_area_id where a.hi_user_id=%s\\\"%user_id\\r\\n\\t\\t\\tret = self.database.get_values_from_mysql(sql)\\r\\n\\t\\texcept Exception as e:\\r\\n\\t\\t\\tlogging.error(e)\\r\\n\\t\\t\\treturn self.write({\\\"errcode\\\":'1', \\\"errmsg\\\":\\\"get data erro\\\"})\\r\\n\\t\\thouses = []\\r\\n\\t\\tif ret:\\r\\n\\t\\t\\tfor l in ret:\\r\\n\\t\\t\\t\\thouse = {\\r\\n\\t\\t\\t\\t\\t\\\"house_id\\\":l[0],\\r\\n\\t\\t\\t\\t\\t\\\"title\\\":l[1],\\r\\n\\t\\t\\t\\t\\t\\\"price\\\":l[2],\\r\\n\\t\\t\\t\\t\\t\\\"ctime\\\":l[3].strftime(\\\"%Y-%m-%d\\\"), # 将返回的Datatime类型格式化为字符串\\r\\n\\t\\t\\t\\t\\t\\\"area_name\\\":l[4],\\r\\n\\t\\t\\t\\t\\t\\\"img_url\\\":l[5] if l[5] else \\\"\\\"\\r\\n\\t\\t\\t\\t}\\r\\n\\t\\t\\t\\thouses.append(house)\\r\\n\\t\\tself.write({\\\"errcode\\\":'0', \\\"errmsg\\\":\\\"OK\\\", \\\"houses\\\":houses})\\r\\n\\t\\tself.set_header('Content-Type', 'application/json; charset=UTF-8')\\r\\n\\r\\n#################### 此处的作用是提供房源提交的时候的地区选择功能 #####################\\r\\nclass Area_info_handler(BaseHandler):\\r\\n\\t\\\"\\\"\\\"提供城区信息\\\"\\\"\\\"\\r\\n\\t@required_login\\r\\n\\tdef get(self):\\r\\n\\r\\n\\t\\t# 查询Mysql数据库，获取城区信息\\r\\n\\t\\tsql = \\\"select ai_area_id,ai_name from ih_area_info\\\"\\r\\n\\r\\n\\t\\ttry:\\r\\n\\t\\t\\tret = self.database.get_values_from_mysql(sql)\\r\\n\\t\\texcept Exception as e:\\r\\n\\t\\t\\tlogging.error(e)\\r\\n\\t\\t\\treturn self.write(dict(errcode='1', errmsg=\\\"数据库查询出错\\\"))\\r\\n\\t\\tif not ret:\\r\\n\\t\\t\\treturn self.write(dict(errcode='2', errmsg=\\\"没有数据\\\"))\\r\\n\\t\\t# 保存转换好的区域信息\\r\\n\\t\\tdata = []\\r\\n\\t\\tfor row in ret:\\r\\n\\t\\t\\td = {\\r\\n\\t\\t\\t\\t\\\"area_id\\\": row[0],\\r\\n\\t\\t\\t\\t\\\"name\\\": row[1]\\r\\n\\t\\t\\t}\\r\\n\\t\\t\\tdata.append(d)\\r\\n\\r\\n\\t\\tself.write(dict(errcode='0', errmsg=\\\"OK\\\", data=data))\\r\\n\\r\\n################ 此处的作用 是退出当前用户的登录#################\\r\\n\\r\\nclass Login_out(BaseHandler):\\r\\n\\t@required_login\\r\\n\\tdef get(self):\\r\\n\\t\\t########### 获取当前用户的情况###################################\\r\\n\\t\\tsession_id = self.get_secure_cookie(\\\"session_id\\\")\\r\\n\\t\\t# print(\\\"session_id===\\\",session_id)\\r\\n\\t\\t#将当前用户在redis中存储的信息删除掉\\r\\n\\t\\tret = self.redis.del_value(session_id)\\r\\n\\t\\t# print('ret=========',ret)\\r\\n\\t\\tif not ret:\\r\\n\\t\\t\\tlogging.error('delete value fial')\\r\\n\\t\\t\\tdata = {\\r\\n\\t\\t\\t\\t'errcode':'1',\\r\\n\\t\\t\\t}\\r\\n\\t\\telse:\\r\\n\\t\\t\\tdata = {\\r\\n\\t\\t\\t\\t'errcode':'0',\\r\\n\\t\\t\\t}\\r\\n\\r\\n\\t\\tself.write(data)\\r\\n\\t\\tself.set_header('Content-Type', 'application/json; charset=UTF-8')\\r\\n\\r\\n\\r\\n##################### 此处的作用是按照所选择的条件查询房屋资源################\\r\\n################# 此函数目前的状态是：待补充功能 ############################\\r\\nclass House_list_handler(BaseHandler):\\r\\n\\t\\\"\\\"\\\"房源列表页面\\\"\\\"\\\"\\r\\n\\tdef get(self):\\r\\n\\t\\t\\\"\\\"\\\"get方式用来获取数据库数据，本身的逻辑不会对数据库数据产生影响\\\"\\\"\\\"\\r\\n\\t\\t\\\"\\\"\\\"\\r\\n\\t\\t传入参数说明\\r\\n\\t\\tstart_date 用户查询的起始时间 sd 非必传 \\\"\\\" \\\"2017-02-28\\\"\\r\\n\\t\\tend_date 用户查询的终止时间 ed 非必传 \\\"\\\"\\r\\n\\t\\tarea_id 用户查询的区域条件 aid 非必传 \\\"\\\"\\r\\n\\t\\tsort_key 排序的关键词 sk 非必传 \\\"new\\\" \\\"new\\\" \\\"booking\\\" \\\"price-inc\\\" \\\"price-des\\\"\\r\\n\\t\\tpage 返回的数据页数 p 非必传 1\\r\\n\\t\\t\\\"\\\"\\\"\\r\\n\\t\\t# 获取参数\\r\\n\\t\\tstart_date = self.get_argument(\\\"sd\\\", \\\"\\\")\\r\\n\\t\\tend_date = self.get_argument(\\\"ed\\\", \\\"\\\")\\r\\n\\t\\tarea_id = self.get_argument(\\\"aid\\\", \\\"\\\")\\r\\n\\t\\tsort_key = self.get_argument(\\\"sk\\\", \\\"new\\\")\\r\\n\\t\\tpage = self.get_argument(\\\"p\\\", \\\"1\\\")\\r\\n\\r\\n\\t\\t# 检查参数\\r\\n\\t\\t# 判断日期格式、sort_Key 字段的值、page的整数\\r\\n\\r\\n\\t\\t# 数据查询\\r\\n\\t\\t# 涉及到表： ih_house_info 房屋的基本信息 ih_user_profile 房东的用户信息 ih_order_info 房屋订单数据\\r\\n\\r\\n\\t\\tsql = \\\"select hi_title,hi_house_id,hi_price,hi_room_count,hi_address,hi_order_count,up_avatar,hi_index_image_url,hi_ctime\\\" \\\\\\r\\n\\t\\t\\t \\\" from ih_house_info inner join ih_user_profile on hi_user_id=up_user_id left join ih_order_info\\\" \\\\\\r\\n\\t\\t\\t \\\" on hi_house_id=oi_house_id where hi_area_id=%s\\\"%area_id\\r\\n\\r\\n\\t\\tlogging.debug(sql)\\r\\n\\t\\ttry:\\r\\n\\t\\t\\tret = self.database.get_values_from_mysql(sql)\\r\\n\\t\\texcept Exception as e:\\r\\n\\t\\t\\tlogging.error(e)\\r\\n\\t\\t\\treturn self.write(dict(errcode='4', errmsg=\\\"查询出错\\\"))\\r\\n\\t\\tdata = []\\r\\n\\t\\tif ret:\\r\\n\\t\\t\\tfor l in ret:\\r\\n\\t\\t\\t\\thouse = dict(\\r\\n\\t\\t\\t\\t\\thouse_id=l[1],\\r\\n\\t\\t\\t\\t\\ttitle=l[0],\\r\\n\\t\\t\\t\\t\\tprice=l[2],\\r\\n\\t\\t\\t\\t\\troom_count=l[3],\\r\\n\\t\\t\\t\\t\\taddress=l[4],\\r\\n\\t\\t\\t\\t\\torder_count=l[5],\\r\\n\\t\\t\\t\\t\\tavatar=l[6] if l[6] else \\\"\\\",\\r\\n\\t\\t\\t\\t\\timage_url=l[7] if l[7] else \\\"\\\"\\r\\n\\t\\t\\t\\t)\\r\\n\\t\\t\\t\\tdata.append(house)\\r\\n\\t\\ttotal_page = len(data)\\r\\n\\t\\tself.write(dict(errcode='0', errmsg=\\\"OK\\\", data=data, total_page=total_page))\\r\\n\\t\\tself.set_header('Content-Type', 'application/json; charset=UTF-8')\\r\\n\\r\\n\"\n },\n {\n \"alpha_fraction\": 0.6858638525009155,\n \"alphanum_fraction\": 0.6858638525009155,\n \"avg_line_length\": 30.66666603088379,\n \"blob_id\": \"5b22bc6160b7584f804557bad7f0b0c82238dd9b\",\n \"content_id\": \"d9614792008a24652ace82267731efe381d47389\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 191,\n \"license_type\": \"no_license\",\n \"max_line_length\": 85,\n \"num_lines\": 6,\n \"path\": \"/Python/basic/package/__init__.py\",\n \"repo_name\": \"jasondzy/Python\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"\\n# this __ini__.py is must for a python package. \\n\\n\\n__all__ = [\\\"Sendmesg\\\"] #define which module import \\n\\nfrom . import Sendmesg# this is must , only run this ,and can use the Semdmesg moudle\\n\"\n },\n {\n \"alpha_fraction\": 0.6761133670806885,\n \"alphanum_fraction\": 0.6801619529724121,\n \"avg_line_length\": 16.5,\n \"blob_id\": \"e430a2c30ea854d903d3ce6bcfd50b7975821c3d\",\n \"content_id\": \"7a272330731e902e59b07d09927624b71d3a1e5d\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 247,\n \"license_type\": \"no_license\",\n \"max_line_length\": 61,\n \"num_lines\": 14,\n \"path\": \"/Python/plus/C_lib/c_lib.py\",\n \"repo_name\": \"jasondzy/Python\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"\\n\\nfrom ctypes import *#must include , use to load c library\\nimport threading\\n\\nLIB = cdll.LoadLibrary('./test.so')# loadding \\n\\n\\nfor i in range(4):\\n th = threading.Thread(target=LIB.Loop)#use the c function\\n th.start()\\n\\n\\n\\nwhile True:\\n pass\\n\"\n },\n {\n \"alpha_fraction\": 0.4888888895511627,\n \"alphanum_fraction\": 0.5333333611488342,\n \"avg_line_length\": 19.5,\n \"blob_id\": \"1773cca450986220b2d225cebbf2d69d8bd60419\",\n \"content_id\": \"b489a631fe382e82d2253dbf7bf62b6d00c835fd\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 45,\n \"license_type\": \"no_license\",\n \"max_line_length\": 27,\n \"num_lines\": 2,\n \"path\": \"/Python/basic/package/Sendmesg.py\",\n \"repo_name\": \"jasondzy/Python\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"\\n\\ndef test1():\\n print(\\\"This is test1 \\\")\\n\\n\\n\"\n },\n {\n \"alpha_fraction\": 0.3283582031726837,\n \"alphanum_fraction\": 0.35820895433425903,\n \"avg_line_length\": 31.5,\n \"blob_id\": \"76029461ad9b70e211d48ada7eac892686369a90\",\n \"content_id\": \"6ed334f6fa64eeb398f8bc664cefb60e45ab17e7\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 67,\n \"license_type\": \"no_license\",\n \"max_line_length\": 51,\n \"num_lines\": 2,\n \"path\": \"/Python/basic/package/receivemsg.py\",\n \"repo_name\": \"jasondzy/Python\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"\\n\\ndef test2():\\n print(\\\"-------------This is test2------------\\\")\\n\"\n },\n {\n \"alpha_fraction\": 0.5122615694999695,\n \"alphanum_fraction\": 0.553133487701416,\n \"avg_line_length\": 22.70967674255371,\n \"blob_id\": \"73d1325badeba6b77b541074167a25248632611c\",\n \"content_id\": \"7ad947746f1417607fc9682b81c8e4a3eafd6a90\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 830,\n \"license_type\": \"no_license\",\n \"max_line_length\": 65,\n \"num_lines\": 31,\n \"path\": \"/Python/algorithm/fast.py\",\n \"repo_name\": \"jasondzy/Python\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"def fast_seq(seq_data, start, end):\\n \\\"\\\"\\\"如下是快速排序的代码，快速排序代码采用的是low和high两个位置坐标来对数列进行迭代的寻找首元素的位置的操作\\\"\\\"\\\"\\n mid_value = seq_data[start]\\n low = start\\n high = end\\n\\n if start >= end:\\n return\\n\\n while low < high:\\n while low < high and seq_data[high] >= mid_value:\\n high -= 1\\n seq_data[low] = seq_data[high]\\n\\n while low < high and seq_data[low] < mid_value:\\n low += 1\\n seq_data[high] = seq_data[low]\\n\\n seq_data[low] = mid_value\\n fast_seq(seq_data,start, low-1)\\n fast_seq(seq_data, low+1, end)\\n\\n# return left_seq + right_seq\\n\\n\\nif __name__ == '__main__':\\n seq = [54, 226, 93, 17, 77, 31, 44, 55, 20, 6, 23, 54]\\n\\n fast_seq(seq, 0, len(seq)-1)\\n\\n print(seq)\"\n }\n]","isConversational":false},"num_files":{"kind":"number","value":55,"string":"55"}}},{"rowIdx":2977196,"cells":{"repo_name":{"kind":"string","value":"kirik200088/Crypro_Algorithm"},"repo_url":{"kind":"string","value":"https://github.com/kirik200088/Crypro_Algorithm"},"snapshot_id":{"kind":"string","value":"23c8adae46d2705e443f11b40e9eb3fb1c3700ec"},"revision_id":{"kind":"string","value":"d6499bdfd4f0fbab77ff5c8f35504d6fbcd3c707"},"directory_id":{"kind":"string","value":"3dbaf7fe1db3a84afc527c070bbff90708e85c4e"},"branch_name":{"kind":"string","value":"refs/heads/master"},"visit_date":{"kind":"timestamp","value":"2020-08-21T14:10:32.949185","string":"2020-08-21T14:10:32.949185"},"revision_date":{"kind":"timestamp","value":"2019-10-19T09:00:52","string":"2019-10-19T09:00:52"},"committer_date":{"kind":"timestamp","value":"2019-10-19T09:00:52","string":"2019-10-19T09:00:52"},"github_id":{"kind":"number","value":216177119,"string":"216,177,119"},"star_events_count":{"kind":"number","value":0,"string":"0"},"fork_events_count":{"kind":"number","value":1,"string":"1"},"gha_license_id":{"kind":"null"},"gha_created_at":{"kind":"null"},"gha_updated_at":{"kind":"null"},"gha_pushed_at":{"kind":"null"},"gha_language":{"kind":"null"},"files":{"kind":"list like","value":[{"alpha_fraction":0.4555778205394745,"alphanum_fraction":0.48964595794677734,"avg_line_length":19.2297306060791,"blob_id":"57be91d61cc7adcdb912c17c900ddf8f9ef37d47","content_id":"1b0309dc26787386089ec9f1d3c3bde9db87b500","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"C++","length_bytes":1513,"license_type":"no_license","max_line_length":57,"num_lines":74,"path":"/hamming.cpp","repo_name":"kirik200088/Crypro_Algorithm","src_encoding":"UTF-8","text":"//Вы не слишком добры...\n#include <iostream>\n#include <vector>\n#include <cmath>\n#include <string>\n#include <clocale>\n\n#define int long long\n\nusing namespace std;\n\nvector<char> fix_code(vector<char>& arr, int n = -1) {\n\tint ans_wr = 0;\n\tif (n == -1)\n\t\tn = arr.size();\n\tfor (int i = 1; i < n + 1; i *= 2) {\n\t\tbool ans = 0;\n\t\tfor (int j = i; j < n + 1; j += 2 * i) {\n\t\t\tfor (int k = j; k < j + i; k++) {\n\t\t\t\tans xor_eq (arr[k - 1] == '1' ? true : false);\n\t\t\t}\n\t\t}\n\t\tif (ans)\n\t\t\tans_wr += i;\n\t}\n\t//cout << ans_wr;\n\tif (ans_wr)\n\t\tarr[ans_wr - 1] = (arr[ans_wr - 1] == '1' ? '0' : '1');\n\tvector<char> ret;\n\tint notPrint = 1;\n\tint it2 = 0;\n\tfor (int i = 0; i < n; i++) {\n\t\tif (i + 1 == notPrint)\n\t\t\tnotPrint *= 2;\n\t\telse\n\t\t\tret.push_back(arr[i]);\n\t}\n\treturn ret;\n}\n\nsigned main() {\n\tint n = 0, k, bas = 15;\n\tsetlocale(LC_ALL, \"ru\");\n\tstring s2;\n\tgetline(cin, s2);\n\tvector<char> ans_bit;\n\tvector<char> arr(10000);\n\tint it = 0;\n\tfor (auto el : s2) {\n\t\tif (el == '0' || el == '1')\n\t\t\tarr[it++] = el;\n\t}\n\tfor (int i = 0; i < it; i += 15) {\n\t\tvector<char> send;\n\t\tfor (int j = i; j < i + 15; j++)\n\t\t\tsend.push_back(arr[j]);\n\t\tvector<char> ans = fix_code(send, 15);\n\t\tfor (auto el : ans) {\n\t\t\tans_bit.push_back(el);\n\t\t\tcout << el;\n\t\t}\n\t\tcout << ' ';\n\t}\n\tcout << endl;\n\tfor (int i = 0; i < ans_bit.size(); i += 8) {\n\t\tint symb_ans = 0;\n\t\tfor (int j = i; j < i + 8 && j < ans_bit.size(); j++) {\n\t\t\tif (ans_bit[j] == '1') {\n\t\t\t\tsymb_ans += (pow(2, abs(-1*(j - i - 7))));\n\t\t\t}\n\t\t}\n\t\tcout << (char)(symb_ans);\n\t}\n}\n"},{"alpha_fraction":0.34151729941368103,"alphanum_fraction":0.3594402074813843,"avg_line_length":15.295999526977539,"blob_id":"52e9862154d6befce2032abbb9eadec374da6bde","content_id":"db65a4f61132b49f21ab8a67b9285d670d4ce4f6","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"C++","length_bytes":4124,"license_type":"no_license","max_line_length":87,"num_lines":250,"path":"/Siberian_Hard_Decode.cpp","repo_name":"kirik200088/Crypro_Algorithm","src_encoding":"WINDOWS-1251","text":"//Пример: Ктосук отько-елр лувбь ю\n//Гаие дн а - Гадание\n#include <iostream>\n#include <string>\n#include <Windows.h>\n\nusing namespace std;\n\nint main()\n{\n\tstring s;\n\tint a, d, n = 0;\n\tint flag = 0;\n\tSetConsoleCP(1251);\n\tSetConsoleOutputCP(1251);\n\n\tgetline(cin, s);\n\t//s = \"Чтопсь отоо -мл ея н\";\n\t//cout << s;\n\n\tfor (int i = 0; i < s.length(); i++)\n\t{\n\t\tif (s[i] == ' ')\n\t\t{\n\t\t\ts.erase(i, 1);\n\t\t\ti--;\n\t\t}\n\t}\n\twhile (true)\n\t{\n\t\tn++;\n\t\tif ((n * n + n) / 2 == s.length())\n\t\t{\n\t\t\tflag = 1;\n\t\t\tbreak;\n\t\t}\n\t\telse if ((n * n + n) / 2 + (n * n + n) % 2 > s.length())\n\t\t{\n\t\t\tflag = 2;\n\t\t\tbreak;\n\t\t}\n\t}\n\tcout << endl;\n\tint tmp = n;\n\tint tmpfor = 0;\n\tint count = 0;\n\tchar ch[100][100];\n\tint x = 0, y = 0;\n\tfor (int i = 0; i < 100; i++)\n\t{\n\t\tfor (int j = 0; j < 100; j++)\n\t\t{\n\t\t\tch[j][i] = ' ';\n\t\t}\n\t}\n\tif (flag == 1)\n\t{\n\n\t\tfor (int i = 0; i < s.length(); i++)\n\t\t{\n\t\t\tif (tmpfor == n)\n\t\t\t{\n\t\t\t\tn--;\n\t\t\t\ttmpfor = 0;\n\t\t\t\tcount++;\n\t\t\t}\n\t\t\tif (tmpfor < n)\n\t\t\t{\n\t\t\t\tch[tmp - n + tmpfor][count] = s[i];\n\t\t\t}\n\t\t\ttmpfor++;\n\t\t}\n\t\tcout << ch[x][y];\n\t\twhile (true)\n\t\t{\n\t\t\tif (ch[x + 1][y] != ' ')\n\t\t\t{\n\t\t\t\tx++;\n\t\t\t\twhile (ch[x][y] != ' ')\n\t\t\t\t{\n\t\t\t\t\tcout << ch[x][y];\n\t\t\t\t\ty++;\n\t\t\t\t}\n\t\t\t\tx++;\n\t\t\t\twhile (ch[x][y] != ' ')\n\t\t\t\t{\n\t\t\t\t\tcout << ch[x][y];\n\t\t\t\t\ty--;\n\t\t\t\t}\n\t\t\t\ty++;\n\t\t\t}\n\t\t\telse\n\t\t\t{\n\t\t\t\tbreak;\n\t\t\t}\n\t\t}\n\t\tcout << endl << endl;\n\t\tfor (int i = 0; i < tmp; i++)\n\t\t{\n\t\t\tfor (int j = 0; j < tmp; j++)\n\t\t\t{\n\t\t\t\tcout << ch[j][i];\n\t\t\t}\n\t\t\tcout << endl;\n\t\t}\n\t}\n\telse\n\t{\n\t\tn--;\n\t\ttmp = n; //tmpfor=0;n=3;s.lenght()=7;x=1;count=0\n\t\tx = s.length() - ((n * n + n) / 2 + (n * n + n) % 2); // 7 - 6 + 0 = 1\t\t //Гаие дн а\n\t\tif ((n + 1) % 2 == 0)\n\t\t{\n\t\t\tfor (int i = 0; i < s.length(); i++)\n\t\t\t{\n\t\t\t\t//cout << ch[1][1];\n\t\t\t\tif (tmpfor > n && x > 0)\n\t\t\t\t{\n\t\t\t\t\tch[tmp - n + tmpfor][count] = s[i];\n\t\t\t\t\tx--;\n\t\t\t\t\tn--;\n\t\t\t\t\tcount++;\n\t\t\t\t\tcout << endl;\n\t\t\t\t\ttmpfor = 0;\n\t\t\t\t}\n\t\t\t\tif (tmpfor < n && x == 0)\n\t\t\t\t{\n\t\t\t\t\tch[tmp - n + tmpfor][count] = s[i];\n\t\t\t\t\tcout << \" \" << ch[tmp - n + tmpfor][count];\n\t\t\t\t\t//cout << \" x: \" << tmp - n + tmpfor << \" y: \" << count;\n\t\t\t\t}\n\t\t\t\telse if (tmpfor == n && x == 0)\n\t\t\t\t{\n\t\t\t\t\ttmpfor = 0;\n\t\t\t\t\tcount++;\n\t\t\t\t\tn--;\n\t\t\t\t\tcout << endl;\n\n\t\t\t\t\t//ch[tmp - n + tmpfor][count] = s[i];\n\t\t\t\t\t//cout << \" \" << ch[tmp - n + tmpfor][count];\n\t\t\t\t\t//cout << \" x: \" << tmp - n + tmpfor << \" y: \" << count;\n\t\t\t\t\ttmpfor = 0;\n\t\t\t\t\tch[tmp - n + tmpfor][count] = s[i];\n\t\t\t\t\tcout << \" \" << ch[tmp - n + tmpfor][count];\n\t\t\t\t}\n\t\t\t\telse if (tmpfor <= n && x > 0)\n\t\t\t\t{\n\t\t\t\t\tch[tmp - n + tmpfor][count] = s[i];\n\t\t\t\t\tcout << \" \" << ch[tmp - n + tmpfor][count];\n\t\t\t\t\t//cout << \" x: \" << tmp - n + tmpfor << \" y: \" << count;\n\t\t\t\t}\n\t\t\t\ttmpfor++;\n\t\t\t}\n\t\t\tcout << endl;\n\t\t\tcout << endl;\n\t\t\tcout << ch[x][y];\n\t\t\twhile (true)\n\t\t\t{\n\t\t\t\tif (ch[x + 1][y] != ' ')\n\t\t\t\t{\n\t\t\t\t\tx++;\n\t\t\t\t\twhile (ch[x][y] != ' ')\n\t\t\t\t\t{\n\t\t\t\t\t\tcout << ch[x][y];\n\t\t\t\t\t\ty++;\n\t\t\t\t\t}\n\t\t\t\t\tx++;\n\t\t\t\t\twhile (ch[x][y] != ' ')\n\t\t\t\t\t{\n\t\t\t\t\t\tcout << ch[x][y];\n\t\t\t\t\t\ty--;\n\t\t\t\t\t}\n\t\t\t\t\ty++;\n\t\t\t\t}\n\t\t\t\telse\n\t\t\t\t{\n\t\t\t\t\tbreak;\n\t\t\t\t}\n\t\t\t}\n\t\t\tcout << endl;\n\t\t}\n\t\telse\n\t\t{\n\t\t\tn++;\n\t\t\ttmp = n;\n\t\t\tfor (int i = 0; i < s.length(); i++)\n\t\t\t{\n\t\t\t\tif (tmpfor == n - 1 && n > x)\n\t\t\t\t{\n\t\t\t\t\tcount++;\n\t\t\t\t\ttmpfor = 0;\n\t\t\t\t\tn--;\n\t\t\t\t\tcout << endl;\n\t\t\t\t}\n\t\t\t\tif (tmpfor < n - 1 && n > x)\n\t\t\t\t{\n\t\t\t\t\tch[tmp - n + tmpfor][count] = s[i];\n\t\t\t\t\tcout << \" \" << ch[tmp - n + tmpfor][count];\n\t\t\t\t\t//cout << \" x: \" << tmp - n + tmpfor << \" y: \" << count;\n\t\t\t\t}\n\t\t\t\tif (tmpfor == n && n <= x)\n\t\t\t\t{\n\t\t\t\t\ttmpfor = 0;\n\t\t\t\t\tn--;\n\t\t\t\t\tcount++;\n\t\t\t\t\tcout << endl;\n\t\t\t\t}\n\t\t\t\tif (tmpfor < n && n <= x)\n\t\t\t\t{\n\t\t\t\t\tch[tmp - n + tmpfor][count] = s[i];\n\t\t\t\t\tcout << \" \" << ch[tmp - n + tmpfor][count];\n\t\t\t\t\t//cout << \" x: \" << tmp - n + tmpfor << \" y: \" << count;\n\t\t\t\t}\n\n\t\t\t\ttmpfor++;\n\t\t\t}\n\t\t\tx = 0;\n\t\t\tcout << endl;\n\t\t\tcout << endl;\n\t\t\tcout << ch[x][y];\n\t\t\twhile (true)\n\t\t\t{\n\t\t\t\tif (ch[x + 1][y] != ' ')\n\t\t\t\t{\n\n\t\t\t\t\tx++;\n\t\t\t\t\twhile (ch[x][y] != ' ')\n\t\t\t\t\t{\n\t\t\t\t\t\tcout << ch[x][y];\n\t\t\t\t\t\ty++;\n\t\t\t\t\t}\n\t\t\t\t\tx++;\n\t\t\t\t\twhile (ch[x][y] != ' ')\n\t\t\t\t\t{\n\t\t\t\t\t\tcout << ch[x][y];\n\t\t\t\t\t\ty--;\n\t\t\t\t\t}\n\t\t\t\t\ty++;\n\t\t\t\t}\n\t\t\t\telse\n\t\t\t\t{\n\t\t\t\t\tbreak;\n\t\t\t\t}\n\t\t\t}\n\t\t\tcout << endl;\n\t\t}\n\n\t}\n\tsystem(\"pause\");\n}"},{"alpha_fraction":0.5284403562545776,"alphanum_fraction":0.5302752256393433,"avg_line_length":18.5,"blob_id":"05aea91ddf9418d0d758865344af23037d2e9076","content_id":"4b05f87ead2718afd3c79b5efd83341ad0cf1198","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":545,"license_type":"no_license","max_line_length":43,"num_lines":28,"path":"/aes.py","repo_name":"kirik200088/Crypro_Algorithm","src_encoding":"UTF-8","text":"# -*- coding: utf-8 -*-\ndef __main__():\n password = input()\n string = input()\n\n sorted_password = sorted(password)\n\n digits = []\n\n for char in password:\n i = sorted_password.index(char)\n digits.append(i)\n sorted_password[i] = \"-\"\n\n height = len(string) // len(password)\n\n decoded_string = \"\"\n\n for i in range(height):\n for digit in digits:\n index = digit * height + i\n decoded_string += string[index]\n\n print(decoded_string)\n\n\nif __name__ == \"__main__\":\n __main__()"},{"alpha_fraction":0.4605166018009186,"alphanum_fraction":0.47822877764701843,"avg_line_length":18.35714340209961,"blob_id":"ace8b60894ae3ec306784a0f28a882404a84d6a1","content_id":"3b44c3679a7ac2b28180de0cd110f219fb4c5a89","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":1355,"license_type":"no_license","max_line_length":105,"num_lines":70,"path":"/elias.py","repo_name":"kirik200088/Crypro_Algorithm","src_encoding":"UTF-8","text":"# -*- coding: utf-8 -*-\n\nimport binascii\n\n\ndef main():\n string = input()\n\n start_symbol = string[:1]\n string = string[1:].replace(\" \", \"\")\n\n nulls_count = 0\n next_offset = -1\n new_string = \"\"\n\n for i in range(len(string)):\n char = string[i]\n\n if i > next_offset:\n if char == '0':\n nulls_count += 1\n\n if char == '1':\n next_offset = i + nulls_count\n nulls_count = 0\n\n new_string += char\n\n if i == next_offset:\n new_string += ' '\n\n segments = new_string[:-1].split()\n counters = []\n\n for segment in segments:\n counters.append(int(segment, base=2))\n\n is_nulls = start_symbol == '0'\n\n string = \"\"\n\n for counter in counters:\n for i in range(counter):\n if is_nulls:\n string += \"0\"\n else:\n string += \"1\"\n\n is_nulls = not is_nulls\n\n binary_string = \"\"\n\n for i in range(len(string)):\n binary_string += string[i]\n\n if (i + 1) % 8 == 0:\n binary_string += \" \"\n\n i += 1\n\n final_string = \"\"\n\n for binary_number in binary_string.split():\n final_string += binascii.unhexlify(hex(int(binary_number, 2)).replace(\"0x\", \"\")).decode('cp1251')\n\n print(final_string)\n\n\nif __name__ == \"__main__\":\n main()\n"}],"string":"[\n {\n \"alpha_fraction\": 0.4555778205394745,\n \"alphanum_fraction\": 0.48964595794677734,\n \"avg_line_length\": 19.2297306060791,\n \"blob_id\": \"57be91d61cc7adcdb912c17c900ddf8f9ef37d47\",\n \"content_id\": \"1b0309dc26787386089ec9f1d3c3bde9db87b500\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"C++\",\n \"length_bytes\": 1513,\n \"license_type\": \"no_license\",\n \"max_line_length\": 57,\n \"num_lines\": 74,\n \"path\": \"/hamming.cpp\",\n \"repo_name\": \"kirik200088/Crypro_Algorithm\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"//Вы не слишком добры...\\n#include <iostream>\\n#include <vector>\\n#include <cmath>\\n#include <string>\\n#include <clocale>\\n\\n#define int long long\\n\\nusing namespace std;\\n\\nvector<char> fix_code(vector<char>& arr, int n = -1) {\\n\\tint ans_wr = 0;\\n\\tif (n == -1)\\n\\t\\tn = arr.size();\\n\\tfor (int i = 1; i < n + 1; i *= 2) {\\n\\t\\tbool ans = 0;\\n\\t\\tfor (int j = i; j < n + 1; j += 2 * i) {\\n\\t\\t\\tfor (int k = j; k < j + i; k++) {\\n\\t\\t\\t\\tans xor_eq (arr[k - 1] == '1' ? true : false);\\n\\t\\t\\t}\\n\\t\\t}\\n\\t\\tif (ans)\\n\\t\\t\\tans_wr += i;\\n\\t}\\n\\t//cout << ans_wr;\\n\\tif (ans_wr)\\n\\t\\tarr[ans_wr - 1] = (arr[ans_wr - 1] == '1' ? '0' : '1');\\n\\tvector<char> ret;\\n\\tint notPrint = 1;\\n\\tint it2 = 0;\\n\\tfor (int i = 0; i < n; i++) {\\n\\t\\tif (i + 1 == notPrint)\\n\\t\\t\\tnotPrint *= 2;\\n\\t\\telse\\n\\t\\t\\tret.push_back(arr[i]);\\n\\t}\\n\\treturn ret;\\n}\\n\\nsigned main() {\\n\\tint n = 0, k, bas = 15;\\n\\tsetlocale(LC_ALL, \\\"ru\\\");\\n\\tstring s2;\\n\\tgetline(cin, s2);\\n\\tvector<char> ans_bit;\\n\\tvector<char> arr(10000);\\n\\tint it = 0;\\n\\tfor (auto el : s2) {\\n\\t\\tif (el == '0' || el == '1')\\n\\t\\t\\tarr[it++] = el;\\n\\t}\\n\\tfor (int i = 0; i < it; i += 15) {\\n\\t\\tvector<char> send;\\n\\t\\tfor (int j = i; j < i + 15; j++)\\n\\t\\t\\tsend.push_back(arr[j]);\\n\\t\\tvector<char> ans = fix_code(send, 15);\\n\\t\\tfor (auto el : ans) {\\n\\t\\t\\tans_bit.push_back(el);\\n\\t\\t\\tcout << el;\\n\\t\\t}\\n\\t\\tcout << ' ';\\n\\t}\\n\\tcout << endl;\\n\\tfor (int i = 0; i < ans_bit.size(); i += 8) {\\n\\t\\tint symb_ans = 0;\\n\\t\\tfor (int j = i; j < i + 8 && j < ans_bit.size(); j++) {\\n\\t\\t\\tif (ans_bit[j] == '1') {\\n\\t\\t\\t\\tsymb_ans += (pow(2, abs(-1*(j - i - 7))));\\n\\t\\t\\t}\\n\\t\\t}\\n\\t\\tcout << (char)(symb_ans);\\n\\t}\\n}\\n\"\n },\n {\n \"alpha_fraction\": 0.34151729941368103,\n \"alphanum_fraction\": 0.3594402074813843,\n \"avg_line_length\": 15.295999526977539,\n \"blob_id\": \"52e9862154d6befce2032abbb9eadec374da6bde\",\n \"content_id\": \"db65a4f61132b49f21ab8a67b9285d670d4ce4f6\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"C++\",\n \"length_bytes\": 4124,\n \"license_type\": \"no_license\",\n \"max_line_length\": 87,\n \"num_lines\": 250,\n \"path\": \"/Siberian_Hard_Decode.cpp\",\n \"repo_name\": \"kirik200088/Crypro_Algorithm\",\n \"src_encoding\": \"WINDOWS-1251\",\n \"text\": \"//Пример: Ктосук отько-елр лувбь ю\\n//Гаие дн а - Гадание\\n#include <iostream>\\n#include <string>\\n#include <Windows.h>\\n\\nusing namespace std;\\n\\nint main()\\n{\\n\\tstring s;\\n\\tint a, d, n = 0;\\n\\tint flag = 0;\\n\\tSetConsoleCP(1251);\\n\\tSetConsoleOutputCP(1251);\\n\\n\\tgetline(cin, s);\\n\\t//s = \\\"Чтопсь отоо -мл ея н\\\";\\n\\t//cout << s;\\n\\n\\tfor (int i = 0; i < s.length(); i++)\\n\\t{\\n\\t\\tif (s[i] == ' ')\\n\\t\\t{\\n\\t\\t\\ts.erase(i, 1);\\n\\t\\t\\ti--;\\n\\t\\t}\\n\\t}\\n\\twhile (true)\\n\\t{\\n\\t\\tn++;\\n\\t\\tif ((n * n + n) / 2 == s.length())\\n\\t\\t{\\n\\t\\t\\tflag = 1;\\n\\t\\t\\tbreak;\\n\\t\\t}\\n\\t\\telse if ((n * n + n) / 2 + (n * n + n) % 2 > s.length())\\n\\t\\t{\\n\\t\\t\\tflag = 2;\\n\\t\\t\\tbreak;\\n\\t\\t}\\n\\t}\\n\\tcout << endl;\\n\\tint tmp = n;\\n\\tint tmpfor = 0;\\n\\tint count = 0;\\n\\tchar ch[100][100];\\n\\tint x = 0, y = 0;\\n\\tfor (int i = 0; i < 100; i++)\\n\\t{\\n\\t\\tfor (int j = 0; j < 100; j++)\\n\\t\\t{\\n\\t\\t\\tch[j][i] = ' ';\\n\\t\\t}\\n\\t}\\n\\tif (flag == 1)\\n\\t{\\n\\n\\t\\tfor (int i = 0; i < s.length(); i++)\\n\\t\\t{\\n\\t\\t\\tif (tmpfor == n)\\n\\t\\t\\t{\\n\\t\\t\\t\\tn--;\\n\\t\\t\\t\\ttmpfor = 0;\\n\\t\\t\\t\\tcount++;\\n\\t\\t\\t}\\n\\t\\t\\tif (tmpfor < n)\\n\\t\\t\\t{\\n\\t\\t\\t\\tch[tmp - n + tmpfor][count] = s[i];\\n\\t\\t\\t}\\n\\t\\t\\ttmpfor++;\\n\\t\\t}\\n\\t\\tcout << ch[x][y];\\n\\t\\twhile (true)\\n\\t\\t{\\n\\t\\t\\tif (ch[x + 1][y] != ' ')\\n\\t\\t\\t{\\n\\t\\t\\t\\tx++;\\n\\t\\t\\t\\twhile (ch[x][y] != ' ')\\n\\t\\t\\t\\t{\\n\\t\\t\\t\\t\\tcout << ch[x][y];\\n\\t\\t\\t\\t\\ty++;\\n\\t\\t\\t\\t}\\n\\t\\t\\t\\tx++;\\n\\t\\t\\t\\twhile (ch[x][y] != ' ')\\n\\t\\t\\t\\t{\\n\\t\\t\\t\\t\\tcout << ch[x][y];\\n\\t\\t\\t\\t\\ty--;\\n\\t\\t\\t\\t}\\n\\t\\t\\t\\ty++;\\n\\t\\t\\t}\\n\\t\\t\\telse\\n\\t\\t\\t{\\n\\t\\t\\t\\tbreak;\\n\\t\\t\\t}\\n\\t\\t}\\n\\t\\tcout << endl << endl;\\n\\t\\tfor (int i = 0; i < tmp; i++)\\n\\t\\t{\\n\\t\\t\\tfor (int j = 0; j < tmp; j++)\\n\\t\\t\\t{\\n\\t\\t\\t\\tcout << ch[j][i];\\n\\t\\t\\t}\\n\\t\\t\\tcout << endl;\\n\\t\\t}\\n\\t}\\n\\telse\\n\\t{\\n\\t\\tn--;\\n\\t\\ttmp = n; //tmpfor=0;n=3;s.lenght()=7;x=1;count=0\\n\\t\\tx = s.length() - ((n * n + n) / 2 + (n * n + n) % 2); // 7 - 6 + 0 = 1\\t\\t //Гаие дн а\\n\\t\\tif ((n + 1) % 2 == 0)\\n\\t\\t{\\n\\t\\t\\tfor (int i = 0; i < s.length(); i++)\\n\\t\\t\\t{\\n\\t\\t\\t\\t//cout << ch[1][1];\\n\\t\\t\\t\\tif (tmpfor > n && x > 0)\\n\\t\\t\\t\\t{\\n\\t\\t\\t\\t\\tch[tmp - n + tmpfor][count] = s[i];\\n\\t\\t\\t\\t\\tx--;\\n\\t\\t\\t\\t\\tn--;\\n\\t\\t\\t\\t\\tcount++;\\n\\t\\t\\t\\t\\tcout << endl;\\n\\t\\t\\t\\t\\ttmpfor = 0;\\n\\t\\t\\t\\t}\\n\\t\\t\\t\\tif (tmpfor < n && x == 0)\\n\\t\\t\\t\\t{\\n\\t\\t\\t\\t\\tch[tmp - n + tmpfor][count] = s[i];\\n\\t\\t\\t\\t\\tcout << \\\" \\\" << ch[tmp - n + tmpfor][count];\\n\\t\\t\\t\\t\\t//cout << \\\" x: \\\" << tmp - n + tmpfor << \\\" y: \\\" << count;\\n\\t\\t\\t\\t}\\n\\t\\t\\t\\telse if (tmpfor == n && x == 0)\\n\\t\\t\\t\\t{\\n\\t\\t\\t\\t\\ttmpfor = 0;\\n\\t\\t\\t\\t\\tcount++;\\n\\t\\t\\t\\t\\tn--;\\n\\t\\t\\t\\t\\tcout << endl;\\n\\n\\t\\t\\t\\t\\t//ch[tmp - n + tmpfor][count] = s[i];\\n\\t\\t\\t\\t\\t//cout << \\\" \\\" << ch[tmp - n + tmpfor][count];\\n\\t\\t\\t\\t\\t//cout << \\\" x: \\\" << tmp - n + tmpfor << \\\" y: \\\" << count;\\n\\t\\t\\t\\t\\ttmpfor = 0;\\n\\t\\t\\t\\t\\tch[tmp - n + tmpfor][count] = s[i];\\n\\t\\t\\t\\t\\tcout << \\\" \\\" << ch[tmp - n + tmpfor][count];\\n\\t\\t\\t\\t}\\n\\t\\t\\t\\telse if (tmpfor <= n && x > 0)\\n\\t\\t\\t\\t{\\n\\t\\t\\t\\t\\tch[tmp - n + tmpfor][count] = s[i];\\n\\t\\t\\t\\t\\tcout << \\\" \\\" << ch[tmp - n + tmpfor][count];\\n\\t\\t\\t\\t\\t//cout << \\\" x: \\\" << tmp - n + tmpfor << \\\" y: \\\" << count;\\n\\t\\t\\t\\t}\\n\\t\\t\\t\\ttmpfor++;\\n\\t\\t\\t}\\n\\t\\t\\tcout << endl;\\n\\t\\t\\tcout << endl;\\n\\t\\t\\tcout << ch[x][y];\\n\\t\\t\\twhile (true)\\n\\t\\t\\t{\\n\\t\\t\\t\\tif (ch[x + 1][y] != ' ')\\n\\t\\t\\t\\t{\\n\\t\\t\\t\\t\\tx++;\\n\\t\\t\\t\\t\\twhile (ch[x][y] != ' ')\\n\\t\\t\\t\\t\\t{\\n\\t\\t\\t\\t\\t\\tcout << ch[x][y];\\n\\t\\t\\t\\t\\t\\ty++;\\n\\t\\t\\t\\t\\t}\\n\\t\\t\\t\\t\\tx++;\\n\\t\\t\\t\\t\\twhile (ch[x][y] != ' ')\\n\\t\\t\\t\\t\\t{\\n\\t\\t\\t\\t\\t\\tcout << ch[x][y];\\n\\t\\t\\t\\t\\t\\ty--;\\n\\t\\t\\t\\t\\t}\\n\\t\\t\\t\\t\\ty++;\\n\\t\\t\\t\\t}\\n\\t\\t\\t\\telse\\n\\t\\t\\t\\t{\\n\\t\\t\\t\\t\\tbreak;\\n\\t\\t\\t\\t}\\n\\t\\t\\t}\\n\\t\\t\\tcout << endl;\\n\\t\\t}\\n\\t\\telse\\n\\t\\t{\\n\\t\\t\\tn++;\\n\\t\\t\\ttmp = n;\\n\\t\\t\\tfor (int i = 0; i < s.length(); i++)\\n\\t\\t\\t{\\n\\t\\t\\t\\tif (tmpfor == n - 1 && n > x)\\n\\t\\t\\t\\t{\\n\\t\\t\\t\\t\\tcount++;\\n\\t\\t\\t\\t\\ttmpfor = 0;\\n\\t\\t\\t\\t\\tn--;\\n\\t\\t\\t\\t\\tcout << endl;\\n\\t\\t\\t\\t}\\n\\t\\t\\t\\tif (tmpfor < n - 1 && n > x)\\n\\t\\t\\t\\t{\\n\\t\\t\\t\\t\\tch[tmp - n + tmpfor][count] = s[i];\\n\\t\\t\\t\\t\\tcout << \\\" \\\" << ch[tmp - n + tmpfor][count];\\n\\t\\t\\t\\t\\t//cout << \\\" x: \\\" << tmp - n + tmpfor << \\\" y: \\\" << count;\\n\\t\\t\\t\\t}\\n\\t\\t\\t\\tif (tmpfor == n && n <= x)\\n\\t\\t\\t\\t{\\n\\t\\t\\t\\t\\ttmpfor = 0;\\n\\t\\t\\t\\t\\tn--;\\n\\t\\t\\t\\t\\tcount++;\\n\\t\\t\\t\\t\\tcout << endl;\\n\\t\\t\\t\\t}\\n\\t\\t\\t\\tif (tmpfor < n && n <= x)\\n\\t\\t\\t\\t{\\n\\t\\t\\t\\t\\tch[tmp - n + tmpfor][count] = s[i];\\n\\t\\t\\t\\t\\tcout << \\\" \\\" << ch[tmp - n + tmpfor][count];\\n\\t\\t\\t\\t\\t//cout << \\\" x: \\\" << tmp - n + tmpfor << \\\" y: \\\" << count;\\n\\t\\t\\t\\t}\\n\\n\\t\\t\\t\\ttmpfor++;\\n\\t\\t\\t}\\n\\t\\t\\tx = 0;\\n\\t\\t\\tcout << endl;\\n\\t\\t\\tcout << endl;\\n\\t\\t\\tcout << ch[x][y];\\n\\t\\t\\twhile (true)\\n\\t\\t\\t{\\n\\t\\t\\t\\tif (ch[x + 1][y] != ' ')\\n\\t\\t\\t\\t{\\n\\n\\t\\t\\t\\t\\tx++;\\n\\t\\t\\t\\t\\twhile (ch[x][y] != ' ')\\n\\t\\t\\t\\t\\t{\\n\\t\\t\\t\\t\\t\\tcout << ch[x][y];\\n\\t\\t\\t\\t\\t\\ty++;\\n\\t\\t\\t\\t\\t}\\n\\t\\t\\t\\t\\tx++;\\n\\t\\t\\t\\t\\twhile (ch[x][y] != ' ')\\n\\t\\t\\t\\t\\t{\\n\\t\\t\\t\\t\\t\\tcout << ch[x][y];\\n\\t\\t\\t\\t\\t\\ty--;\\n\\t\\t\\t\\t\\t}\\n\\t\\t\\t\\t\\ty++;\\n\\t\\t\\t\\t}\\n\\t\\t\\t\\telse\\n\\t\\t\\t\\t{\\n\\t\\t\\t\\t\\tbreak;\\n\\t\\t\\t\\t}\\n\\t\\t\\t}\\n\\t\\t\\tcout << endl;\\n\\t\\t}\\n\\n\\t}\\n\\tsystem(\\\"pause\\\");\\n}\"\n },\n {\n \"alpha_fraction\": 0.5284403562545776,\n \"alphanum_fraction\": 0.5302752256393433,\n \"avg_line_length\": 18.5,\n \"blob_id\": \"05aea91ddf9418d0d758865344af23037d2e9076\",\n \"content_id\": \"4b05f87ead2718afd3c79b5efd83341ad0cf1198\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 545,\n \"license_type\": \"no_license\",\n \"max_line_length\": 43,\n \"num_lines\": 28,\n \"path\": \"/aes.py\",\n \"repo_name\": \"kirik200088/Crypro_Algorithm\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"# -*- coding: utf-8 -*-\\ndef __main__():\\n password = input()\\n string = input()\\n\\n sorted_password = sorted(password)\\n\\n digits = []\\n\\n for char in password:\\n i = sorted_password.index(char)\\n digits.append(i)\\n sorted_password[i] = \\\"-\\\"\\n\\n height = len(string) // len(password)\\n\\n decoded_string = \\\"\\\"\\n\\n for i in range(height):\\n for digit in digits:\\n index = digit * height + i\\n decoded_string += string[index]\\n\\n print(decoded_string)\\n\\n\\nif __name__ == \\\"__main__\\\":\\n __main__()\"\n },\n {\n \"alpha_fraction\": 0.4605166018009186,\n \"alphanum_fraction\": 0.47822877764701843,\n \"avg_line_length\": 18.35714340209961,\n \"blob_id\": \"ace8b60894ae3ec306784a0f28a882404a84d6a1\",\n \"content_id\": \"3b44c3679a7ac2b28180de0cd110f219fb4c5a89\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 1355,\n \"license_type\": \"no_license\",\n \"max_line_length\": 105,\n \"num_lines\": 70,\n \"path\": \"/elias.py\",\n \"repo_name\": \"kirik200088/Crypro_Algorithm\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"# -*- coding: utf-8 -*-\\n\\nimport binascii\\n\\n\\ndef main():\\n string = input()\\n\\n start_symbol = string[:1]\\n string = string[1:].replace(\\\" \\\", \\\"\\\")\\n\\n nulls_count = 0\\n next_offset = -1\\n new_string = \\\"\\\"\\n\\n for i in range(len(string)):\\n char = string[i]\\n\\n if i > next_offset:\\n if char == '0':\\n nulls_count += 1\\n\\n if char == '1':\\n next_offset = i + nulls_count\\n nulls_count = 0\\n\\n new_string += char\\n\\n if i == next_offset:\\n new_string += ' '\\n\\n segments = new_string[:-1].split()\\n counters = []\\n\\n for segment in segments:\\n counters.append(int(segment, base=2))\\n\\n is_nulls = start_symbol == '0'\\n\\n string = \\\"\\\"\\n\\n for counter in counters:\\n for i in range(counter):\\n if is_nulls:\\n string += \\\"0\\\"\\n else:\\n string += \\\"1\\\"\\n\\n is_nulls = not is_nulls\\n\\n binary_string = \\\"\\\"\\n\\n for i in range(len(string)):\\n binary_string += string[i]\\n\\n if (i + 1) % 8 == 0:\\n binary_string += \\\" \\\"\\n\\n i += 1\\n\\n final_string = \\\"\\\"\\n\\n for binary_number in binary_string.split():\\n final_string += binascii.unhexlify(hex(int(binary_number, 2)).replace(\\\"0x\\\", \\\"\\\")).decode('cp1251')\\n\\n print(final_string)\\n\\n\\nif __name__ == \\\"__main__\\\":\\n main()\\n\"\n }\n]","isConversational":false},"num_files":{"kind":"number","value":4,"string":"4"}}},{"rowIdx":2977197,"cells":{"repo_name":{"kind":"string","value":"mitngan/e2openplugin-AutoBackup"},"repo_url":{"kind":"string","value":"https://github.com/mitngan/e2openplugin-AutoBackup"},"snapshot_id":{"kind":"string","value":"c83830ac62097d2cdd8020a43e6c5135dcf98310"},"revision_id":{"kind":"string","value":"393967b29c1ed6c4f662ac37dfafc2687ebdeb0e"},"directory_id":{"kind":"string","value":"05df9d6433937a1898b5c633186a6d6469060e9b"},"branch_name":{"kind":"string","value":"refs/heads/master"},"visit_date":{"kind":"timestamp","value":"2020-06-23T18:44:37.781223","string":"2020-06-23T18:44:37.781223"},"revision_date":{"kind":"timestamp","value":"2019-07-24T20:32:17","string":"2019-07-24T20:32:17"},"committer_date":{"kind":"timestamp","value":"2019-07-24T20:32:17","string":"2019-07-24T20:32:17"},"github_id":{"kind":"number","value":198720369,"string":"198,720,369"},"star_events_count":{"kind":"number","value":0,"string":"0"},"fork_events_count":{"kind":"number","value":0,"string":"0"},"gha_license_id":{"kind":"null"},"gha_created_at":{"kind":"timestamp","value":"2019-07-24T23:00:06","string":"2019-07-24T23:00:06"},"gha_updated_at":{"kind":"timestamp","value":"2019-07-24T20:32:20","string":"2019-07-24T20:32:20"},"gha_pushed_at":{"kind":"timestamp","value":"2019-07-24T20:32:18","string":"2019-07-24T20:32:18"},"gha_language":{"kind":"null"},"files":{"kind":"list like","value":[{"alpha_fraction":0.6153846383094788,"alphanum_fraction":0.6219780445098877,"avg_line_length":34,"blob_id":"fe014bdf5ccfb763ec906025659e44a6d78b2f8c","content_id":"20466327d71d3abd553d20857938d73a41d52981","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":455,"license_type":"no_license","max_line_length":92,"num_lines":13,"path":"/setup.py","repo_name":"mitngan/e2openplugin-AutoBackup","src_encoding":"UTF-8","text":"from distutils.core import setup\nimport setup_translate\n\npkg = 'Extensions.AutoBackup'\nsetup (name = 'enigma2-plugin-extensions-autobackup',\n version = '0.1',\n description = 'AutoBackup',\n package_dir = {pkg: 'plugin'},\n packages = [pkg],\n package_data = {pkg: \n ['plugin.png', 'backup.cfg', 'settings-backup.sh', 'locale/*/LC_MESSAGES/*.mo']},\n cmdclass = setup_translate.cmdclass, # for translation\n )\n"},{"alpha_fraction":0.7005506157875061,"alphanum_fraction":0.7043625712394714,"avg_line_length":47.14285659790039,"blob_id":"d8fdc6b373d9bc99377af94a8211554edfdf4a35","content_id":"dcd4377cf0c59b9af6a2a77f9b04e2e705febbf1","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Shell","length_bytes":2361,"license_type":"no_license","max_line_length":169,"num_lines":49,"path":"/updateallpo.sh","repo_name":"mitngan/e2openplugin-AutoBackup","src_encoding":"UTF-8","text":"#!/bin/bash\n# Script to generate pot and po files outside of the normal build process\n#\n# To add create a new language file simply create the folder replace MyLanguageCode by the language code:\n# mkdir ./plugin/locale/MyLanguageCode\n# \n# Exemple: mkdir ./plugin/locale/fr\n#\n# Then run the script\n#\n# Pre-requisite:\n# The following tools must be installed on your system and accessible from path\n# gawk, find, xgettext, gsed, python, msguniq, msgmerge, msgattrib, msgfmt, msginit\n##\n# Author: Pr2 for OpenPLi Team\n# Version: 1.0\n#\nextension=$(gawk -F \".\" '/'\"'\"'Extensions.*'\"'\"'/ { gsub(/'\"'\"'$/,\"\",$2); print $2 }' setup.py )\nprintf \"Retrieve extension name: %s\\n\" $extension\nprintf \"Po files update/creation from script starting.\\n\"\nlanguages=($(ls ./plugin/locale | tr \"\\n\" \" \"))\n#\n# Arguments to generate the pot and po files are not retrieved from the Makefile.\n# So if parameters are changed in Makefile please report the same changes in this script.\n#\ncd plugin\nprintf \"Creating file $extension.pot\\n\"\nfind -s -X .. -name \"*.py\" -exec xgettext --no-wrap -L Python --from-code=UTF-8 -kpgettext:1c,2 --add-comments=\"TRANSLATORS:\" -d $extension -s -o $extension-py.pot {} \\+\ngsed --in-place $extension-py.pot --expression=s/CHARSET/UTF-8/\ncat $extension-py.pot | msguniq --no-wrap --no-location -o $extension.pot -\nrm $extension-py.pot\nOLDIFS=$IFS\nIFS=\" \"\nfor lang in \"${languages[@]}\" ; do\n\tif [ -f ./locale/$lang/LC_MESSAGES/$extension.po ]; then \\\n\t\tprintf \"Updating existing translation file for language %s\\n\" $lang\n\t\tmsgmerge --backup=none --no-wrap --no-location -s -U ./locale/$lang/LC_MESSAGES/$extension.po $extension.pot && touch ./locale/$lang/LC_MESSAGES/$extension.po; \\\n\t\tmsgattrib --no-wrap --no-obsolete ./locale/$lang/LC_MESSAGES/$extension.po -o ./locale/$lang/LC_MESSAGES/$extension.po; \\\n\t\tmsgfmt -o ./locale/$lang/LC_MESSAGES/$extension.mo ./locale/$lang/LC_MESSAGES/$extension.po; \\\n\telse \\\n\t\tprintf \"New file created for %s, please add it to github before commit\\n\" $lang; \\\n\t\tmkdir ./locale/$lang/LC_MESSAGES/; \\\n\t\tmsginit -l ./locale/$lang/LC_MESSAGES/$extension.po -o ./locale/$lang/LC_MESSAGES/$extension.po -i $extension.pot --no-translator; \\\n\t\tmsgfmt -o ./locale/$lang/LC_MESSAGES/$extension.mo ./locale/$lang/LC_MESSAGES/$extension.po; \\\n\tfi\ndone\nIFS=$OLDIFS\ncd ..\nprintf \"Po files update/creation from script finished!\\n\"\n\n\n"}],"string":"[\n {\n \"alpha_fraction\": 0.6153846383094788,\n \"alphanum_fraction\": 0.6219780445098877,\n \"avg_line_length\": 34,\n \"blob_id\": \"fe014bdf5ccfb763ec906025659e44a6d78b2f8c\",\n \"content_id\": \"20466327d71d3abd553d20857938d73a41d52981\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 455,\n \"license_type\": \"no_license\",\n \"max_line_length\": 92,\n \"num_lines\": 13,\n \"path\": \"/setup.py\",\n \"repo_name\": \"mitngan/e2openplugin-AutoBackup\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"from distutils.core import setup\\nimport setup_translate\\n\\npkg = 'Extensions.AutoBackup'\\nsetup (name = 'enigma2-plugin-extensions-autobackup',\\n version = '0.1',\\n description = 'AutoBackup',\\n package_dir = {pkg: 'plugin'},\\n packages = [pkg],\\n package_data = {pkg: \\n ['plugin.png', 'backup.cfg', 'settings-backup.sh', 'locale/*/LC_MESSAGES/*.mo']},\\n cmdclass = setup_translate.cmdclass, # for translation\\n )\\n\"\n },\n {\n \"alpha_fraction\": 0.7005506157875061,\n \"alphanum_fraction\": 0.7043625712394714,\n \"avg_line_length\": 47.14285659790039,\n \"blob_id\": \"d8fdc6b373d9bc99377af94a8211554edfdf4a35\",\n \"content_id\": \"dcd4377cf0c59b9af6a2a77f9b04e2e705febbf1\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Shell\",\n \"length_bytes\": 2361,\n \"license_type\": \"no_license\",\n \"max_line_length\": 169,\n \"num_lines\": 49,\n \"path\": \"/updateallpo.sh\",\n \"repo_name\": \"mitngan/e2openplugin-AutoBackup\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"#!/bin/bash\\n# Script to generate pot and po files outside of the normal build process\\n#\\n# To add create a new language file simply create the folder replace MyLanguageCode by the language code:\\n# mkdir ./plugin/locale/MyLanguageCode\\n# \\n# Exemple: mkdir ./plugin/locale/fr\\n#\\n# Then run the script\\n#\\n# Pre-requisite:\\n# The following tools must be installed on your system and accessible from path\\n# gawk, find, xgettext, gsed, python, msguniq, msgmerge, msgattrib, msgfmt, msginit\\n##\\n# Author: Pr2 for OpenPLi Team\\n# Version: 1.0\\n#\\nextension=$(gawk -F \\\".\\\" '/'\\\"'\\\"'Extensions.*'\\\"'\\\"'/ { gsub(/'\\\"'\\\"'$/,\\\"\\\",$2); print $2 }' setup.py )\\nprintf \\\"Retrieve extension name: %s\\\\n\\\" $extension\\nprintf \\\"Po files update/creation from script starting.\\\\n\\\"\\nlanguages=($(ls ./plugin/locale | tr \\\"\\\\n\\\" \\\" \\\"))\\n#\\n# Arguments to generate the pot and po files are not retrieved from the Makefile.\\n# So if parameters are changed in Makefile please report the same changes in this script.\\n#\\ncd plugin\\nprintf \\\"Creating file $extension.pot\\\\n\\\"\\nfind -s -X .. -name \\\"*.py\\\" -exec xgettext --no-wrap -L Python --from-code=UTF-8 -kpgettext:1c,2 --add-comments=\\\"TRANSLATORS:\\\" -d $extension -s -o $extension-py.pot {} \\\\+\\ngsed --in-place $extension-py.pot --expression=s/CHARSET/UTF-8/\\ncat $extension-py.pot | msguniq --no-wrap --no-location -o $extension.pot -\\nrm $extension-py.pot\\nOLDIFS=$IFS\\nIFS=\\\" \\\"\\nfor lang in \\\"${languages[@]}\\\" ; do\\n\\tif [ -f ./locale/$lang/LC_MESSAGES/$extension.po ]; then \\\\\\n\\t\\tprintf \\\"Updating existing translation file for language %s\\\\n\\\" $lang\\n\\t\\tmsgmerge --backup=none --no-wrap --no-location -s -U ./locale/$lang/LC_MESSAGES/$extension.po $extension.pot && touch ./locale/$lang/LC_MESSAGES/$extension.po; \\\\\\n\\t\\tmsgattrib --no-wrap --no-obsolete ./locale/$lang/LC_MESSAGES/$extension.po -o ./locale/$lang/LC_MESSAGES/$extension.po; \\\\\\n\\t\\tmsgfmt -o ./locale/$lang/LC_MESSAGES/$extension.mo ./locale/$lang/LC_MESSAGES/$extension.po; \\\\\\n\\telse \\\\\\n\\t\\tprintf \\\"New file created for %s, please add it to github before commit\\\\n\\\" $lang; \\\\\\n\\t\\tmkdir ./locale/$lang/LC_MESSAGES/; \\\\\\n\\t\\tmsginit -l ./locale/$lang/LC_MESSAGES/$extension.po -o ./locale/$lang/LC_MESSAGES/$extension.po -i $extension.pot --no-translator; \\\\\\n\\t\\tmsgfmt -o ./locale/$lang/LC_MESSAGES/$extension.mo ./locale/$lang/LC_MESSAGES/$extension.po; \\\\\\n\\tfi\\ndone\\nIFS=$OLDIFS\\ncd ..\\nprintf \\\"Po files update/creation from script finished!\\\\n\\\"\\n\\n\\n\"\n }\n]","isConversational":false},"num_files":{"kind":"number","value":2,"string":"2"}}},{"rowIdx":2977198,"cells":{"repo_name":{"kind":"string","value":"h-yes-oo/database"},"repo_url":{"kind":"string","value":"https://github.com/h-yes-oo/database"},"snapshot_id":{"kind":"string","value":"c0da5313d553d9ed761921bbb399a5f50c875209"},"revision_id":{"kind":"string","value":"2da88323482a2f8b7605e10e9bdfb7913518fcfd"},"directory_id":{"kind":"string","value":"24566b7d764303b040e31e76e121930dc645c125"},"branch_name":{"kind":"string","value":"refs/heads/master"},"visit_date":{"kind":"timestamp","value":"2023-05-28T19:16:43.821113","string":"2023-05-28T19:16:43.821113"},"revision_date":{"kind":"timestamp","value":"2021-06-14T10:28:09","string":"2021-06-14T10:28:09"},"committer_date":{"kind":"timestamp","value":"2021-06-14T10:28:09","string":"2021-06-14T10:28:09"},"github_id":{"kind":"null"},"star_events_count":{"kind":"number","value":0,"string":"0"},"fork_events_count":{"kind":"number","value":0,"string":"0"},"gha_license_id":{"kind":"null"},"gha_created_at":{"kind":"null"},"gha_updated_at":{"kind":"null"},"gha_pushed_at":{"kind":"null"},"gha_language":{"kind":"null"},"files":{"kind":"list like","value":[{"alpha_fraction":0.6276443600654602,"alphanum_fraction":0.6393251419067383,"avg_line_length":28.521072387695312,"blob_id":"38bc73a58a274df76c4d28bb55656cdfc62a18cd","content_id":"2a3f1b5495eb00970d0173d14c7036dd5358670a","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":7705,"license_type":"no_license","max_line_length":94,"num_lines":261,"path":"/PRJ2_2015-16227/executions.py","repo_name":"h-yes-oo/database","src_encoding":"UTF-8","text":"from __future__ import print_function\nimport mysql.connector\nfrom mysql.connector import errorcode\n\n#create table with tables schema\ndef create_tables(cursor, tables):\n for table_name in tables:\n table_description = tables[table_name]\n try:\n cursor.execute(table_description)\n except mysql.connector.Error as err:\n if err.errno == errorcode.ER_TABLE_EXISTS_ERROR:\n print(\"already exists.\")\n else:\n print(err.msg)\n\n#insert building, performance, audience function with their schema\ndef insert_building(cursor, name, location, capacity):\n add_building = (\"INSERT INTO building \"\n \"(name, location, capacity) \"\n \"VALUES (%s, %s, %s)\" )\n data_building = (name, location, capacity)\n cursor.execute(add_building, data_building)\n\ndef insert_performance(cursor, name, ptype, price):\n add_performance = (\"INSERT INTO performance \"\n \"(name, type, price) \"\n \"VALUES (%s, %s, %s)\" )\n data_performance = (name, ptype, price)\n cursor.execute(add_performance, data_performance)\n\ndef insert_audience(cursor, name, gender, age):\n add_audience = (\"INSERT INTO audience \"\n \"(name, gender, age) \"\n \"VALUES (%s, %s, %s)\" )\n data_audience = (name, gender, age)\n cursor.execute(add_audience, data_audience)\n\n#return if the performance is already assigned to building\ndef check_building_assigned(cursor, pid):\n check = (\n \"SELECT building FROM performance WHERE id = %s\"\n )\n cursor.execute(check, (pid,))\n for data in cursor:\n #if not assigned, return false\n if data['building'] is None:\n return False\n else:\n return True\n\n#return if assigning performance to building succeed\ndef assign_p_to_b(cursor, pid, bid):\n #check if the performance is already assigned to building\n check = check_building_assigned(cursor,pid)\n if(check):\n print(\"Performance %d is already assigned\" % (pid))\n return False\n #if function not returned, assign performance to building\n update = (\n \"UPDATE performance SET building = %s \"\n \"WHERE id = %s\"\n )\n cursor.execute(update, (bid, pid))\n return True\n\n#return if id exists in table\ndef check_by_id(cursor, table, bid):\n check = (\n \"SELECT 1 FROM {} WHERE id = %s\".format(table)\n )\n cursor.execute(check,(bid, ))\n result = cursor.fetchall()\n if len(result) > 0:\n return True\n else:\n return False\n\n#return if the seat of pid already taken\ndef seat_taken(cursor, pid, seat):\n check = (\n \"SELECT 1 FROM reservation WHERE performance = %s AND seat = %s\"\n )\n cursor.execute(check,(pid, seat))\n result = cursor.fetchall()\n if len(result) > 0:\n print(\"The seat is already taken\")\n return True\n else:\n return False\n\n#insert new data to reservation table\ndef reserve(cursor, pid, aid, seat):\n add_performance = (\"INSERT INTO reservation \"\n \"(audience, performance, seat) \"\n \"VALUES (%s, %s, %s)\" )\n data_performance = (aid, pid, seat)\n cursor.execute(add_performance, data_performance)\n\n#print a line\ndef print_line():\n print(\"-----------------------------------------------------------------------------------\")\n\n#print all building\ndef print_building(curA, curB):\n print_line()\n print(\"%-8s %-30s %-16s %-16s %-16s\" %('id','name','location','capacity','assigned'))\n print_line()\n curA.execute(\"SELECT * FROM building ORDER BY id\")\n result = curA.fetchall()\n for data in result:\n count_query = (\"SELECT COUNT(id) FROM performance WHERE building = %s\")\n curB.execute(count_query, (data['id'], ))\n for x in curB:\n print(\"%-8s %-30s %-16s %-16s %-16s\" %(\n data['id'], data['name'], data['location'], data['capacity'], x[0]\n ))\n print_line()\n\n#basic audience printing format\ndef print_audience(result):\n print_line()\n print(\"%-8s %-36s %-22s %-20s\" %('id','name','gender','age'))\n print_line()\n for data in result:\n print(\"%-8s %-36s %-22s %-20s\" %(\n data['id'], data['name'], data['gender'], data['age']\n ))\n print_line()\n\n#print all audiences\ndef print_audience_all(cursor):\n cursor.execute(\"SELECT * FROM audience ORDER BY id\")\n result = cursor.fetchall()\n print_audience(result)\n\n#print audience of specific performance\ndef print_audience_of_performance(cursor, pid):\n select_query = (\n \"SELECT * FROM audience \"\n \"WHERE id IN (\"\n \"SELECT DISTINCT audience FROM reservation \"\n \"WHERE performance = %s) \"\n \"ORDER BY id\"\n )\n cursor.execute(select_query, (pid,))\n result = cursor.fetchall()\n print_audience(result)\n\n#basic performance printing format\ndef print_performance(curB, result):\n print_line()\n print(\"%-8s %-30s %-16s %-16s %-16s\" %('id','name','type','price','booked'))\n print_line()\n for data in result:\n count_query = (\"SELECT COUNT(id) FROM reservation WHERE performance = %s\")\n curB.execute(count_query, (data['id'], ))\n for x in curB:\n print(\"%-8s %-30s %-16s %-16s %-16s\" %(\n data['id'], data['name'], data['type'], data['price'], x[0]\n ))\n print_line()\n\n#print all performances \ndef print_performance_all(curA, curB):\n curA.execute(\"SELECT * FROM performance ORDER BY id\")\n result = curA.fetchall()\n print_performance(curB, result)\n\n#print performance of specific building\ndef print_performance_of_building(curA, curB, bid):\n select_query = (\n \"SELECT * FROM performance \"\n \"WHERE building = %s ORDER BY id\"\n )\n curA.execute(select_query, (bid,))\n result = curA.fetchall()\n print_performance(curB, result)\n\n#return capacity of the performance\ndef get_capacity(curB, pid):\n select_query = (\n \"SELECT capacity FROM building \"\n \"WHERE id = (\"\n \"SELECT building FROM performance \"\n \"WHERE id = %s)\"\n )\n curB.execute(select_query, (pid, ))\n result = curB.fetchall()\n capacity = result[0][0]\n return capacity\n\n#using for loop, print seat of performance\ndef print_seat_of_performance(curB, pid):\n capacity = get_capacity(curB, pid)\n print_line()\n print(\"%-40s %-40s\" %('seat_number', 'audience_id'))\n print_line()\n for seat in range(1,capacity+1):\n find_query = (\n \"SELECT audience FROM reservation \"\n \"WHERE performance = %s and seat = %s\"\n )\n curB.execute(find_query, (pid, seat))\n result = curB.fetchall()\n if(len(result) == 0):\n print(\"%-40d\" %(seat))\n else:\n print(\"%-40d %-40d\" %(seat, result[0][0]))\n print_line()\n\n#delete existing table and create new tables\ndef refresh(cursor, tables):\n for table_name in ['reservation', 'audience', 'performance','building']:\n sql = \"DROP TABLE IF EXISTS %s\" % table_name\n cursor.execute(sql)\n create_tables(cursor,tables)\n\n#delete from table instance with did\ndef delete(cursor, table, did):\n delete_query = (\n \"DELETE FROM {} WHERE id = %s\".format(table)\n )\n cursor.execute(delete_query,(did,))\n\n#return if all the seat in seat list can be reserved\ndef can_reserve(curB, seat_list, pid):\n capacity = get_capacity(curB, pid)\n for seat in seat_list:\n if seat > capacity:\n print(\"Seat number out of range\")\n return False\n for seat in seat_list:\n if seat_taken(curB, pid, seat):\n return False\n return True\n\n#return one ticket price with respect to the audience's age\ndef get_ticket_price(curB, pid, aid):\n age_query = (\n \"SELECT age FROM audience \"\n \"WHERE id = %s\"\n )\n curB.execute(age_query, (aid,))\n age_result = curB.fetchall()\n age = age_result[0][0]\n if age <= 7:\n return 0\n price_query = (\n \"SELECT price FROM performance \"\n \"WHERE id = %s\"\n )\n curB.execute(price_query, (pid,))\n price_result = curB.fetchall()\n price = price_result[0][0]\n if 8 <= age and age <= 12:\n return price * 0.5\n elif 13 <= age and age <= 18:\n return price * 0.8\n else:\n return price\n"},{"alpha_fraction":0.5964173674583435,"alphanum_fraction":0.6046722531318665,"avg_line_length":36.15644073486328,"blob_id":"0becb2b0ee595ea443278cba16c30ec1600130f6","content_id":"7b3a1c9ee86b2784fb275e8be0f99023e32ed2c0","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":12114,"license_type":"no_license","max_line_length":102,"num_lines":326,"path":"/PRJ1-2_2015-16227/run.py","repo_name":"h-yes-oo/database","src_encoding":"UTF-8","text":"import sys\nimport lark\nfrom lark import Lark, Transformer\nfrom berkeleydb import db\nimport os\nimport json\n\n# to reuse prompt message\nprompt = \"DB_2015-16227> \"\n\n#messages\ndef CreateTableSuccess(tableName):\n return f\"'{tableName}' table is created\"\nCharLengthError = \"Char length should be over 0\"\nDuplicatePrimaryKeyDefError = \"Create table has failed: primary key definition is duplicated\"\nDuplicateColumnDefError = \"Create table has failed: column definition is duplicated\"\ndef NonExistingColumnDefError(colName):\n return f\"Create table has failed: '{colName}' does not exists in column definition\"\nTableExistenceError = \"Create table has failed: table with the same name already exists\"\n\nReferenceTypeError = \"Create table has failed: foreign key references wrong type\"\nReferenceNonPrimaryKeyError = \"Create table has failed: foreign key references non primary key column\"\nReferenceColumnExistenceError = \"Create table has failed: foreign key references non existing column\"\nReferenceTableExistenceError = \"Create table has failed: foreign key references non existing table\"\n\ndef DropSuccess(tableName):\n return f\"'{tableName}' table is dropped\"\ndef DropReferencedTableError(tableName):\n return f\"Drop table has failed: '{tableName}' is referenced by other table\"\n\nNoSuchTable = \"No such table\"\n\n# make transformer to print appropriate message for each query\nclass MyTransformer(Transformer):\n def command(self, items):\n if not isinstance(items[0], list):\n exit()\n return items[0]\n\n def query_list(self, items):\n return items\n\n def create_table_query(self, items):\n try:\n info_list = items[2:] #get rid of CREATE TABLE\n table_name = info_list[0]\n file_name = f\"db/{table_name}.db\"\n #check if the file already exists\n if os.path.isfile(file_name):\n raise Exception(TableExistenceError)\n elem_list = info_list[1]\n #initialize variables\n find_primary_key = False\n primary_key = []\n foreign_key = []\n referencing = []\n referenced = []\n col_dict = {}\n #processing table_element_list\n for elem in elem_list:\n if elem[0] == 'primary key':\n #if already find primary key, raise error\n if find_primary_key:\n raise Exception(DuplicatePrimaryKeyDefError)\n else:\n find_primary_key = True\n primary_key = elem[1]\n #process foreign key after get all column information\n elif elem[0] == 'foreign key':\n continue\n #store all column names and infos into a dict\n else:\n #if already have the same column, raise error\n if elem[0] in col_dict:\n raise Exception(DuplicateColumnDefError)\n else:\n #if char length smaller than 1, raise error\n if elem[1]['type'] == 'error':\n raise Exception(CharLengthError)\n else:\n col_dict[elem[0]] = elem[1]\n #process all foreign keys\n for elem in elem_list:\n if elem[0] == 'foreign key':\n fk_list = elem[1]\n referenced_table = elem[2]\n referenced_col_list = elem[3]\n # check if foreign key is one of columns\n for f in fk_list:\n if f not in col_dict:\n raise Exception(NonExistingColumnDefError(f))\n # check if num of foreign keys and num of referenced cols is the same\n if (len(fk_list) != len(referenced_col_list)):\n Exception(ReferenceTypeError)\n ref_file_name = f\"db/{referenced_table}.db\"\n # check if the referenced_table exists\n if not os.path.isfile(ref_file_name):\n raise Exception(ReferenceTableExistenceError)\n else:\n refDB = db.DB()\n refDB.open(ref_file_name, dbtype=db.DB_HASH)\n ref_pk_list = json.loads(refDB.get(b\"primary_key\"))\n ref_col_list = json.loads(refDB.get(b\"columns\"))\n # check if referenced table has foreign key\n for rf_col in referenced_col_list:\n if rf_col not in ref_col_list:\n raise Exception(ReferenceColumnExistenceError)\n # check if foreign key is primary key of referenced table\n for rf_col in referenced_col_list:\n if rf_col not in ref_pk_list:\n raise Exception(ReferenceNonPrimaryKeyError)\n # check if foreign key contains all primary keys of referenced table\n if len(referenced_col_list) != len(ref_pk_list):\n raise Exception(ReferenceNonPrimaryKeyError)\n else:\n for i in range(len(fk_list)):\n # check if fk_list[i] and referenced_col_list[i] has the same type\n ori_type = json.loads(refDB.get(referenced_col_list[i].encode('utf-8')))['type']\n fk_type = col_dict[fk_list[i]]['type']\n if ori_type != fk_type:\n raise Exception(ReferenceTypeError)\n #ready to reference\n #add referenced_table to referencing list of this new table\n referencing.append(referenced_table)\n #add this new table to referenced list of the referenced_table\n rf_by = json.loads(refDB.get(b\"referenced\"))\n rf_by.append(table_name)\n refDB.put(b\"referenced\", json.dumps(rf_by).encode('utf-8'))\n #add all foreign keys to foreign_key\n for fk in fk_list:\n foreign_key.append(fk)\n refDB.close()\n #check if primary_key and foreign_key is in column list and change their properties\n for p in primary_key:\n if p in col_dict:\n col_dict[p]['null'] = 'N'\n col_dict[p]['key'] = 'PRI'\n else:\n raise Exception(NonExistingColumnDefError(p))\n\n for f in foreign_key:\n if f in col_dict:\n if col_dict[f]['key'] == '':\n col_dict[f]['key'] = 'FOR'\n else:\n col_dict[f]['key'] += '/FOR'\n else:\n raise Exception(NonExistingColumnDefError(f))\n #encode variables and put to database\n encoded_p = json.dumps(primary_key).encode('utf-8')\n encoded_f = json.dumps(foreign_key).encode('utf-8')\n\n myDB = db.DB()\n myDB.open(file_name, dbtype=db.DB_HASH, flags=db.DB_CREATE)\n for key in col_dict.keys():\n myDB.put(key.encode('utf-8'), json.dumps(col_dict[key]).encode('utf-8'))\n myDB.put(b\"primary_key\",encoded_p)\n myDB.put(b\"foreign_key\",encoded_f)\n myDB.put(b\"referenced\", json.dumps(referenced).encode('utf-8'))\n myDB.put(b\"referencing\", json.dumps(referencing).encode('utf-8'))\n myDB.put(b\"columns\", json.dumps(list(col_dict.keys())).encode('utf-8'))\n myDB.close()\n print(prompt, CreateTableSuccess(table_name), sep=\"\")\n except Exception as e:\n print(prompt,e,sep='')\n\n\n def drop_table_query(self, items):\n try:\n table_name = items[2]\n file_name = f\"db/{table_name}.db\"\n #check if the table exists\n if not os.path.isfile(file_name):\n raise Exception(NoSuchTable)\n else:\n #get the table's referenced and referencing\n myDB = db.DB()\n myDB.open(file_name, dbtype=db.DB_HASH)\n referenced_by = json.loads(myDB.get(b\"referenced\"))\n referencing = json.loads(myDB.get(b\"referencing\"))\n myDB.close()\n #if no table is referencing it, can drop\n if len(referenced_by) == 0:\n #for all table which this table is referencing,\n #remove this table's name from their referenced list\n for rf in referencing:\n rf_file = f\"db/{rf}.db\"\n rfDB = db.DB()\n rfDB.open(rf_file, dbtype=db.DB_HASH)\n referenced = json.loads(rfDB.get(b\"referenced\"))\n referenced.remove(table_name)\n rfDB.put(b\"referenced\", json.dumps(referenced).encode('utf-8'))\n rfDB.close()\n #delete the database file\n os.remove(file_name)\n print(prompt, DropSuccess(table_name), sep=\"\")\n #if the table is referenced by more than one table, cannot drop\n else:\n raise Exception(DropReferencedTableError(table_name))\n except Exception as e:\n print(prompt, e, sep='')\n\n #show all tables by their file name\n def show_tables_query(self, items):\n print('----------------')\n tables = os.listdir('../PRJ1-3_2015-16227/db')\n for t in tables:\n print(t[:-3])\n print('----------------')\n\n def desc_query(self, items):\n try:\n table_name = items[1]\n file_name = f\"db/{table_name}.db\"\n #check if the table exists\n if not os.path.isfile(file_name):\n raise Exception(NoSuchTable)\n else:\n myDB = db.DB()\n myDB.open(file_name, dbtype=db.DB_HASH)\n print(\"------------------------------------------------------------\")\n print(f\"table_name {table_name}\")\n print('%-20s%-20s%-20s%-20s' % ('column_name', 'type', 'null', 'key'))\n #get column list and iterate\n col_names = json.loads(myDB.get(b'columns'))\n for key in col_names:\n val = json.loads(myDB.get(key.encode('utf-8')))\n print('%-20s%-20s%-20s%-20s' % (key,val['type'], val['null'], val['key']))\n myDB.close()\n print(\"-----------------------------------------------------------\")\n except Exception as e:\n print(prompt,e,sep='')\n\n def select_query(self, items):\n print(prompt,\"'SELECT' requested\",sep='')\n def insert_query(self, items):\n print(prompt,\"'INSERT' requested\",sep='')\n def delete_query(self, items):\n print(prompt,\"'DELETE' requested\",sep='')\n\n def table_element_list(self, items):\n elem_list = items[1:-1] # get rid of LP and RP\n return elem_list\n\n def table_element(self, items):\n return items[0]\n\n def column_definition(self, items):\n dict = {}\n dict['type'] = items[1]\n #if not null constraint, set null to N\n if len(items) > 2:\n dict['null'] = 'N'\n else:\n dict['null'] = 'Y'\n dict['key'] = ''\n return (items[0], dict)\n\n def data_type(self, items):\n length = len(items)\n if (length == 4):\n #if char length smaller than 1, set type to 'error'\n if int(items[2]) <= 0:\n return 'error'\n result = ''\n for i in range(length):\n result += str(items[i])\n return result.lower()\n\n def table_constraint_definition(self, items):\n return items[0]\n\n def primary_key_constraint(self, items):\n return ['primary key', items[2]]\n\n def referential_constraint(self, items):\n col_list = items[2]\n referenced_table = items[4]\n referenced_col_list = items[5]\n return ['foreign key', col_list, referenced_table, referenced_col_list ]\n\n def column_name_list(self, items):\n return list(items[1:-1])\n #table name to lower case\n def table_name(self, t):\n (t,) = t\n return t.lower()\n #column name to lower case\n def column_name(self, c):\n (c,) = c\n return c.lower()\n def IDENTIFIER(self, s):\n return str(s)\n\n\nif __name__ == '__main__':\n\n # make sql_parser with grammar.lark file\n with open('../PRJ1-3_2015-16227/grammar.lark') as file:\n sql_parser = Lark(file.read(), start=\"command\", lexer=\"standard\")\n\n #get input until the program ended\n while(True):\n #get input with prompt message\n data_input = input(prompt)\n #if command 'exit' is given, end the program\n if(data_input == 'exit'):\n sys.exit()\n #get additional input until it ends with semicolon\n while len(data_input) == 0 or data_input[-1] != ';':\n #to prevent error, add white space to data_input\n data_input += ' '\n data_input += input()\n #split the input by semicolon\n input_list = list(data_input.split(';'))\n #exclude the last element of input_list since it is empty string\n for i in range(len(input_list) - 1):\n #restore semicolon in the end of the command\n command = input_list[i] + ';'\n #using try ... except, process the syntax error\n try:\n result = sql_parser.parse(command)\n MyTransformer().transform(result)\n except Exception as e:\n print(prompt,\"Syntax error\",sep='')\n break\n\n"},{"alpha_fraction":0.6248062252998352,"alphanum_fraction":0.6930232644081116,"avg_line_length":22.454545974731445,"blob_id":"03b1fd6ed89dc9a6cb919d08d9dd80836acd375d","content_id":"0f05b4d741b7544c09252549e78d1bf9240c441a","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"SQL","length_bytes":1290,"license_type":"no_license","max_line_length":44,"num_lines":55,"path":"/sample2.sql","repo_name":"h-yes-oo/database","src_encoding":"UTF-8","text":"create table students (\n id int,\n birthday date,\n primary key(id)\n);\n\ncreate table class (\n num int,\n start_date date,\n primary key(num)\n);\n\ncreate table enrolls (\n id int,\n lecture_name char(50),\n num int,\n primary key(lecture_name),\n foreign key(id) references students(id),\n foreign key(num) references class(num)\n);\n\ninsert into students values(1, null);\ninsert into students values(2, 2020-02-16);\ninsert into students values(3, 2020-03-16);\ninsert into students values(4, 2020-04-16);\ninsert into students values(5, 2020-05-16);\ninsert into students values(6, 2020-06-16);\n\ninsert into class values(5, null);\ninsert into class values(6, null);\ninsert into class values(3, 2020-03-16);\ninsert into class values(4, 2020-03-17);\n\ninsert into enrolls values(1,'hihi',6);\ninsert into enrolls values(1,'hihello',5);\ninsert into enrolls values(2,'lec6',5);\ninsert into enrolls values(3,'hihi3',6);\ninsert into enrolls values(4,'hi44',3);\ninsert into enrolls values(5,'leadfa',4);\n\nselect * from account;\n\ncreate table account (\n ACCOUNT_NUMBER date,\n branch_name char(20),\n BALANCE int,\n primary key(account_number)\n);\n\ncreate table loan (\n ACCOUNT_NUMBER date,\n branch_name char(20),\n BALANCE int,\n primary key(account_number)\n);\n"},{"alpha_fraction":0.5569864511489868,"alphanum_fraction":0.583387017250061,"avg_line_length":34.318180084228516,"blob_id":"8548f90a5c3151fbba66325b37ae4fbb315aad62","content_id":"a1043f5d72f02b199645d5d1ee46cedbf1bd9b47","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":1553,"license_type":"no_license","max_line_length":69,"num_lines":44,"path":"/PRJ2_2015-16227/table.py","repo_name":"h-yes-oo/database","src_encoding":"UTF-8","text":"#store table schemas for building, performance, audience, reservation\nTABLES = {}\nTABLES['building'] = (\n \"CREATE TABLE `building` (\"\n \" `id` int(11) NOT NULL AUTO_INCREMENT,\"\n \" `name` varchar(200) NOT NULL,\"\n \" `location` varchar(200) NOT NULL,\"\n \" `capacity` int(11) NOT NULL,\"\n \" PRIMARY KEY (`id`)\"\n \") ENGINE=InnoDB\")\n\nTABLES['performance'] = (\n \"CREATE TABLE `performance` (\"\n \" `id` int(11) NOT NULL AUTO_INCREMENT,\"\n \" `name` varchar(200) NOT NULL,\"\n \" `type` varchar(200) NOT NULL,\"\n \" `price` int(11) NOT NULL,\"\n \" `building` int(11),\"\n \" PRIMARY KEY (`id`),\"\n \" CONSTRAINT `performance_ibfk_1` FOREIGN KEY (`building`) \"\n \" REFERENCES `building` (`id`) ON DELETE CASCADE\"\n \") ENGINE=InnoDB\")\n\nTABLES['audience'] = (\n \"CREATE TABLE `audience` (\"\n \" `id` int(11) NOT NULL AUTO_INCREMENT,\"\n \" `name` varchar(200) NOT NULL,\"\n \" `gender` char(1) NOT NULL,\"\n \" `age` int(11) NOT NULL,\"\n \" PRIMARY KEY (`id`)\"\n \") ENGINE=InnoDB\")\n\nTABLES['reservation'] = (\n \" CREATE TABLE `reservation` (\"\n \" `id` int(11) NOT NULL AUTO_INCREMENT,\"\n \" `audience` int(11) NOT NULL,\"\n \" `performance` int(11) NOT NULL,\"\n \" `seat` int(11) NOT NULL,\"\n \" PRIMARY KEY (`id`),\"\n \" CONSTRAINT `reservation_ibfk_1` FOREIGN KEY (`audience`) \"\n \" REFERENCES `audience` (`id`) ON DELETE CASCADE,\"\n \" CONSTRAINT `reservation_ibfk_2` FOREIGN KEY (`performance`) \"\n \" REFERENCES `performance` (`id`) ON DELETE CASCADE\"\n \") ENGINE=InnoDB\")"},{"alpha_fraction":0.6503198146820068,"alphanum_fraction":0.6567164063453674,"avg_line_length":36.540000915527344,"blob_id":"c3da2e56bab43bab23036e836de8134f82f22662","content_id":"b194fd4662cee4814a9b58b4cb555d86f4b91399","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":1876,"license_type":"no_license","max_line_length":68,"num_lines":50,"path":"/PRJ1-1_2015-16227/hw1/run.py","repo_name":"h-yes-oo/database","src_encoding":"UTF-8","text":"import sys\nfrom lark import Lark, Transformer\n#to reuse prompt message\nprompt = \"DB_2015-16227> \"\n#make transformer to print appropriate message for each query\nclass MyTransformer(Transformer):\n def create_table_query(self, items):\n print(prompt,\"'CREATE TABLE' requested\",sep='')\n def drop_table_query(self, items):\n print(prompt,\"'DROP TABLE' requested\",sep='')\n def select_query(self, items):\n print(prompt,\"'SELECT' requested\",sep='')\n def insert_query(self, items):\n print(prompt,\"'INSERT' requested\",sep='')\n def desc_query(self, items):\n print(prompt,\"'DESC' requested\",sep='')\n def delete_query(self, items):\n print(prompt,\"'DELETE' requested\",sep='')\n def show_tables_query(self, items):\n print(prompt,\"'SHOW TABLES' requested\",sep='')\n#make sql_parser with grammar.lark file\nwith open('grammar.lark') as file:\n sql_parser = Lark(file.read(), start=\"command\", lexer=\"standard\")\n\nif __name__ == '__main__':\n #get input until the program ended\n while(True):\n #get input with prompt message\n data_input = input(prompt)\n #if command 'exit' is given, end the program\n if(data_input == 'exit'):\n sys.exit()\n #get additional input until it ends with semicolon\n while len(data_input) == 0 or data_input[-1] != ';':\n #to prevent error, add white space to data_input\n data_input += ' '\n data_input += input()\n #split the input by semicolon\n input_list = list(data_input.split(';'))\n #exclude the last element of input_list since it is empty string\n for i in range(len(input_list) - 1):\n #restore semicolon in the end of the command\n command = input_list[i] + ';'\n #using try ... except, process the syntax error\n try:\n result = sql_parser.parse(command)\n MyTransformer().transform(result)\n except:\n print(prompt,\"Syntax error\",sep='')\n break"},{"alpha_fraction":0.4639105498790741,"alphanum_fraction":0.47336888313293457,"avg_line_length":36.12033462524414,"blob_id":"324ef9a92e68851c9471edcb6d6da2f78e2fe6e5","content_id":"2588cb1c2119965b5ac5d8b3f3bec1611d62cfd0","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":48740,"license_type":"no_license","max_line_length":102,"num_lines":1313,"path":"/PRJ1-3_2015-16227/run.py","repo_name":"h-yes-oo/database","src_encoding":"UTF-8","text":"import sys\nimport lark\nfrom lark import Lark, Transformer\nfrom berkeleydb import db\nimport os\nimport json\nfrom exceptions import *\nimport datetime\nfrom prettytable import PrettyTable\nimport copy\n\n# to reuse prompt message\nprompt = \"DB_2015-16227> \"\n\n#messages\ndef CreateTableSuccess(tableName):\n return f\"'{tableName}' table is created\"\n\ndef DropSuccess(tableName):\n return f\"'{tableName}' table is dropped\"\n\n\n\n# make transformer to print appropriate message for each query\nclass MyTransformer(Transformer):\n def command(self, items):\n if not isinstance(items[0], list):\n exit()\n return items[0]\n\n def query_list(self, items):\n return items\n\n def create_table_query(self, items):\n try:\n info_list = items[2:] #get rid of CREATE TABLE\n table_name = info_list[0]\n file_name = f\"db/{table_name}.db\"\n #check if the file already exists\n if os.path.isfile(file_name):\n raise TableExistenceError()\n elem_list = info_list[1]\n #initialize variables\n find_primary_key = False\n primary_key = []\n foreign_key = []\n referencing = []\n referenced = []\n col_dict = {}\n #processing table_element_list\n for elem in elem_list:\n if elem[0] == 'primary key':\n #if already find primary key, raise error\n if find_primary_key:\n raise DuplicatePrimaryKeyDefError()\n else:\n find_primary_key = True\n primary_key = elem[1]\n #process foreign key after get all column information\n elif elem[0] == 'foreign key':\n continue\n #store all column names and infos into a dict\n else:\n #if already have the same column, raise error\n if elem[0] in col_dict:\n raise DuplicateColumnDefError()\n else:\n col_dict[elem[0]] = elem[1]\n #process all foreign keys\n for elem in elem_list:\n if elem[0] == 'foreign key':\n fk_list = elem[1]\n referenced_table = elem[2]\n referenced_col_list = elem[3]\n # check if foreign key is one of columns\n for f in fk_list:\n if f not in col_dict:\n raise NonExistingColumnDefError(f)\n # check if num of foreign keys and num of referenced cols is the same\n if (len(fk_list) != len(referenced_col_list)):\n raise ReferenceTypeError()\n ref_file_name = f\"db/{referenced_table}.db\"\n # check if the referenced_table exists\n if not os.path.isfile(ref_file_name):\n raise ReferenceTableExistenceError()\n else:\n refDB = db.DB()\n refDB.open(ref_file_name, dbtype=db.DB_HASH)\n ref_pk_list = json.loads(refDB.get(b\"primary_key\"))\n ref_col_list = json.loads(refDB.get(b\"columns\"))\n # check if referenced table has foreign key\n for rf_col in referenced_col_list:\n if rf_col not in ref_col_list:\n raise ReferenceColumnExistenceError()\n # check if foreign key is primary key of referenced table\n for rf_col in referenced_col_list:\n if rf_col not in ref_pk_list:\n raise ReferenceNonPrimaryKeyError()\n # check if foreign key contains all primary keys of referenced table\n if len(referenced_col_list) != len(ref_pk_list):\n raise ReferenceNonPrimaryKeyError()\n else:\n for i in range(len(fk_list)):\n # check if fk_list[i] and referenced_col_list[i] has the same type\n ori_type = json.loads(refDB.get(referenced_col_list[i].encode('utf-8')))['type']\n fk_type = col_dict[fk_list[i]]['type']\n if ori_type != fk_type:\n raise ReferenceTypeError()\n #ready to reference\n #add referenced_table to referencing list of this new table\n referencing.append(referenced_table)\n #add this new table to referenced list of the referenced_table\n rf_by = json.loads(refDB.get(b\"referenced\"))\n rf_by.append(table_name)\n refDB.put(b\"referenced\", json.dumps(rf_by).encode('utf-8'))\n #add all foreign key information to foreign_key\n ordered_fk_list = ['' for _ in range(len(fk_list))]\n for idx in range(len(fk_list)):\n pk = referenced_col_list[idx]\n ori_idx = ref_pk_list.index(pk)\n ordered_fk_list[ori_idx] = fk_list[idx]\n\n foreign_key.append([ordered_fk_list, referenced_table, ref_pk_list])\n refDB.close()\n #check if primary_key and foreign_key is in column list and change their properties\n for p in primary_key:\n if p in col_dict:\n col_dict[p]['null'] = 'N'\n col_dict[p]['key'] = 'PRI'\n else:\n raise NonExistingColumnDefError(p)\n\n for fk_info in foreign_key:\n for f in fk_info[0]:\n if f in col_dict:\n if col_dict[f]['key'] == '':\n col_dict[f]['key'] = 'FOR'\n else:\n col_dict[f]['key'] += '/FOR'\n else:\n raise NonExistingColumnDefError(f)\n '''\n for f in foreign_key:\n if f in col_dict:\n if col_dict[f]['key'] == '':\n col_dict[f]['key'] = 'FOR'\n else:\n col_dict[f]['key'] += '/FOR'\n else:\n raise NonExistingColumnDefError(f)\n '''\n #encode variables and put to database\n encoded_p = json.dumps(primary_key).encode('utf-8')\n encoded_f = json.dumps(foreign_key).encode('utf-8')\n\n myDB = db.DB()\n myDB.open(file_name, dbtype=db.DB_HASH, flags=db.DB_CREATE)\n for key in col_dict.keys():\n myDB.put(key.encode('utf-8'), json.dumps(col_dict[key]).encode('utf-8'))\n myDB.put(b\"primary_key\",encoded_p)\n myDB.put(b\"foreign_key\",encoded_f)\n myDB.put(b\"referenced\", json.dumps(referenced).encode('utf-8'))\n myDB.put(b\"referencing\", json.dumps(referencing).encode('utf-8'))\n myDB.put(b\"columns\", json.dumps(list(col_dict.keys())).encode('utf-8'))\n myDB.close()\n print(prompt, CreateTableSuccess(table_name), sep=\"\")\n except Exception as e:\n print(prompt,e,sep='')\n\n\n def drop_table_query(self, items):\n try:\n table_name = items[2]\n file_name = f\"db/{table_name}.db\"\n #check if the table exists\n if not os.path.isfile(file_name):\n raise NoSuchTable()\n else:\n #get the table's referenced and referencing\n myDB = db.DB()\n myDB.open(file_name, dbtype=db.DB_HASH)\n referenced_by = json.loads(myDB.get(b\"referenced\"))\n referencing = json.loads(myDB.get(b\"referencing\"))\n myDB.close()\n #if no table is referencing it, can drop\n if len(referenced_by) == 0:\n #for all table which this table is referencing,\n #remove this table's name from their referenced list\n for rf in referencing:\n rf_file = f\"db/{rf}.db\"\n rfDB = db.DB()\n rfDB.open(rf_file, dbtype=db.DB_HASH)\n referenced = json.loads(rfDB.get(b\"referenced\"))\n referenced.remove(table_name)\n rfDB.put(b\"referenced\", json.dumps(referenced).encode('utf-8'))\n rfDB.close()\n #delete the database file\n os.remove(file_name)\n print(prompt, DropSuccess(table_name), sep=\"\")\n #if the table is referenced by more than one table, cannot drop\n else:\n raise DropReferencedTableError(table_name)\n except Exception as e:\n print(prompt, e, sep='')\n\n #show all tables by their file name\n def show_tables_query(self, items):\n print('----------------')\n tables = os.listdir('../PRJ1-3_2015-16227/db')\n for t in tables:\n print(t[:-3])\n print('----------------')\n\n def desc_query(self, items):\n try:\n table_name = items[1]\n file_name = f\"db/{table_name}.db\"\n #check if the table exists\n if not os.path.isfile(file_name):\n raise NoSuchTable()\n else:\n myDB = db.DB()\n myDB.open(file_name, dbtype=db.DB_HASH)\n print(\"------------------------------------------------------------\")\n print(f\"table_name {table_name}\")\n print('%-20s%-20s%-20s%-20s' % ('column_name', 'type', 'null', 'key'))\n #get column list and iterate\n col_names = json.loads(myDB.get(b'columns'))\n for key in col_names:\n val = json.loads(myDB.get(key.encode('utf-8')))\n print('%-20s%-20s%-20s%-20s' % (key,val['type'], val['null'], val['key']))\n myDB.close()\n print(\"-----------------------------------------------------------\")\n except Exception as e:\n print(prompt,e,sep='')\n\n def select_query(self, items):\n cols, col_nick = items[1]\n table_expression = items[2]\n where = table_expression[0]\n tables, nickname = table_expression[1][0]\n #check if all tables exists\n existing_tables = os.listdir('../PRJ1-3_2015-16227/db')\n existing_tables = list(map(lambda x : x[:-3], existing_tables))\n for table in tables:\n if table not in existing_tables:\n raise SelectTableExistenceError(table)\n columns = []\n all_cols = []\n for table in tables:\n myDB = db.DB()\n myDB.open(f\"db/{table}.db\", dbtype=db.DB_HASH)\n col_names = json.loads(myDB.get(b'columns'))\n for col in col_names:\n all_cols.append(f\"{table}.{col}\")\n myDB.close()\n # if col list is *, add all columns of tables\n if len(cols) == 0:\n columns = all_cols\n else:\n #if col list is given, check if the cols are all right and sort by tables\n cols_copy = [ c for c in cols ]\n for table in tables:\n myDB = db.DB()\n myDB.open(f\"db/{table}.db\", dbtype=db.DB_HASH)\n col_names = json.loads(myDB.get(b'columns'))\n for col in cols:\n col_info = col.split('.')\n if len(col_info) > 1:\n if col_info[0] == table or (col_info[0] in nickname and nickname[col_info[0]] == table):\n if col_info[1] in col_names:\n columns.append(f\"{table}.{col_info[1]}\")\n try:\n cols_copy.remove(col)\n except:\n #if duplicate column name exist, ambiguous\n raise SelectColumnResolveError(col)\n else:\n raise SelectColumnResolveError(col)\n else:\n if col in col_names:\n columns.append(f\"{table}.{col}\")\n try:\n cols_copy.remove(col)\n except:\n #if duplicate column name exist, ambiguous\n raise SelectColumnResolveError(col)\n myDB.close()\n #if not existing column\n if len(cols_copy) > 0:\n raise SelectColumnResolveError(cols_copy[0])\n\n #get all possible result from specified tables\n rows = []\n for table in tables:\n infos = []\n myDB = db.DB()\n myDB.open(f\"db/{table}.db\", dbtype=db.DB_HASH)\n col_names = json.loads(myDB.get(b'columns'))\n info_list = list(map(lambda col: bytes(col, encoding=\"utf-8\"), col_names))\n info_list += [b'primary_key', b'foreign_key', b'referencing', b'referenced', b'columns']\n cursor = myDB.cursor()\n data = cursor.first()\n while data:\n key, value = data\n val_dict = json.loads(value)\n if key not in info_list:\n info = []\n for col in all_cols:\n if len(col.split('.')) > 1:\n t_name, col_name = col.split('.')\n if t_name == table or (t_name in nickname and nickname[t_name] == table):\n if val_dict[col_name] is None:\n info.append('null')\n else:\n info.append(val_dict[col_name])\n else:\n info.append(None)\n else:\n if col in val_dict:\n if val_dict[col] is None:\n info.append('null')\n else:\n info.append(val_dict[col])\n else:\n info.append(None)\n infos.append(info)\n data = cursor.next()\n myDB.close()\n rows.append(infos)\n #get cartesian join of the result\n for i in range(1,len(rows)):\n new_row = []\n for j in range(len(rows[i])):\n for row in rows[i-1]:\n r = [x if y is None else y for x,y in zip(rows[i][j], row)]\n new_row.append(r)\n rows[i] = new_row\n \n final_row = [r for r in rows[-1]]\n\n if where:\n #with where clause\n condition = table_expression[1][1]\n predicates = []\n p_col = {}\n def flatten(l):\n for i in l:\n if type(i) == list or (type(i) == tuple and len(i) == 2):\n flatten(i)\n else:\n if type(i) == tuple and len(i) == 3:\n predicates.append(i)\n flatten(condition)\n #check if comparable\n for idx in range(len(predicates)):\n p = predicates[idx]\n oper = p[1]\n if oper in ['is','is not']:\n #null predicate\n #find if the col name is valid\n col_name = p[0]\n if col_name in col_nick:\n col_name = col_nick[col_name]\n if len(col_name.split('.')) > 1:\n t_n , c_n = col_name.split('.')\n #check if specified table\n if t_n not in tables:\n if t_n not in nickname:\n raise WhereTableNotSpecified()\n else:\n t_n = nickname[t_n]\n #check if the col exist\n myDB = db.DB()\n myDB.open(f\"db/{t_n}.db\", dbtype=db.DB_HASH)\n c_list = json.loads(myDB.get(b'columns'))\n if c_n not in c_list:\n raise WhereColumnNotExist()\n else:\n predicates[idx] = (f\"{t_n}.{c_n}\",p[1],p[2])\n p_col[col_name] = f\"{t_n}.{c_n}\"\n myDB.close()\n else:\n Find = False\n for t_n in tables:\n myDB = db.DB()\n myDB.open(f\"db/{t_n}.db\", dbtype=db.DB_HASH)\n c_list = json.loads(myDB.get(b'columns'))\n if col_name in c_list:\n if Find:\n #if same col name found in two different tables, ambiguous\n raise WhereAmbiguousReference()\n else:\n Find = True\n predicates[idx] = (f\"{t_n}.{col_name}\",p[1],p[2])\n p_col[col_name] = f\"{t_n}.{col_name}\"\n myDB.close()\n if not Find:\n raise WhereColumnNotExist()\n else:\n #comparison predicate\n two_type = []\n stripped_tuple = [p[0],p[1],p[2]]\n for op_idx in (0,2):\n if p[op_idx][0] in ['str','int','date']:\n two_type.append(p[op_idx][0])\n stripped_tuple[op_idx] = p[op_idx][1]\n else:\n #if col name, check if valid col name\n col_name = p[op_idx][1]\n if col_name in col_nick:\n col_name = col_nick[col_name]\n if len(col_name.split('.')) > 1:\n t_n , c_n = col_name.split('.')\n #check if specified table\n if t_n not in tables:\n if t_n not in nickname:\n raise WhereTableNotSpecified()\n else:\n t_n = nickname[t_n]\n #check if the col exist\n myDB = db.DB()\n myDB.open(f\"db/{t_n}.db\", dbtype=db.DB_HASH)\n c_list = json.loads(myDB.get(b'columns'))\n if c_n in c_list:\n #find it\n stripped_tuple[op_idx] = f\"{t_n}.{c_n}\"\n p_col[col_name] = f\"{t_n}.{c_n}\"\n type_s = json.loads(myDB.get(c_n.encode('utf-8')))['type']\n if type_s in ['int','date']:\n two_type.append(type_s)\n else:\n two_type.append('str')\n else:\n raise WhereColumnNotExist()\n myDB.close()\n else:\n Find = False\n find_type = ''\n for t_n in tables:\n myDB = db.DB()\n myDB.open(f\"db/{t_n}.db\", dbtype=db.DB_HASH)\n c_list = json.loads(myDB.get(b'columns'))\n if col_name in c_list:\n if Find:\n #if same col name found in two different tables, ambiguous\n raise WhereAmbiguousReference()\n else:\n Find = True\n stripped_tuple[op_idx] = f\"{t_n}.{col_name}\"\n p_col[col_name] = f\"{t_n}.{col_name}\"\n type_s = json.loads(myDB.get(col_name.encode('utf-8')))['type']\n if type_s in ['int','date']:\n find_type = type_s\n else:\n find_type = 'str'\n myDB.close()\n if Find:\n two_type.append(find_type)\n else:\n raise WhereColumnNotExist()\n if len(two_type) == 2:\n if two_type[0] != two_type[1]:\n raise WhereIncomparableError()\n predicates[idx] = tuple(stripped_tuple)\n\n final = []\n\n for row in final_row:\n cond = copy.deepcopy(condition)\n def evaluate(l,row):\n for idx in range(len(l)):\n i=l[idx]\n if type(i) == list or (type(i) == tuple and len(i) == 2):\n evaluate(i,row)\n else:\n if type(i) == tuple and len(i) == 3:\n oper = i[1]\n if oper in ['is','is not']:\n #null predicate\n col_name = i[0]\n if col_name in col_nick:\n col_name = col_nick[col_name]\n if col_name in p_col:\n col_name = p_col[col_name]\n ridx = all_cols.index(col_name)\n if oper == 'is':\n if row[ridx] == 'null':\n l[idx] = True\n else:\n l[idx] = False\n elif oper == 'is not':\n if row[ridx] == 'null':\n l[idx] = False\n else:\n l[idx] = True\n else:\n #comparison predicate\n two_type = []\n eval = [i[0],i[1],i[2]]\n #if val op col\n if i[0][0] in ['str','int','date'] and i[2][0] == 'col':\n eval[0] = i[0][1]\n col_name = i[2][1]\n if col_name in col_nick:\n col_name = col_nick[col_name]\n if col_name in p_col:\n col_name = p_col[col_name]\n ridx = all_cols.index(col_name)\n eval[2] = row[ridx]\n #elif col op val\n elif i[2][0] in ['str','int','date'] and i[0][0] == 'col':\n eval[2] = i[2][1]\n col_name = i[0][1]\n if col_name in col_nick:\n col_name = col_nick[col_name]\n if col_name in p_col:\n col_name = p_col[col_name]\n ridx = all_cols.index(col_name)\n eval[0] = row[ridx]\n #elif col op col\n elif i[2][0] == 'col' and i[0][0] == 'col':\n col_name = i[0][1]\n if col_name in col_nick:\n col_name = col_nick[col_name]\n if col_name in p_col:\n col_name = p_col[col_name]\n ridx = all_cols.index(col_name)\n eval[0] = row[ridx]\n col_name = i[2][1]\n if col_name in col_nick:\n col_name = col_nick[col_name]\n if col_name in p_col:\n col_name = p_col[col_name]\n ridx = all_cols.index(col_name)\n eval[2] = row[ridx]\n #elif val op val\n else:\n eval[0] = i[0][1]\n eval[2] = i[2][1]\n #evaluate\n if eval[1] == \"<\":\n if eval[0] < eval[2]:\n l[idx] = True\n else: \n l[idx] = False\n if eval[1] == \">\":\n if eval[0] > eval[2]:\n l[idx] = True\n else: \n l[idx] = False\n if eval[1] == \"=\":\n if eval[0] == eval[2]:\n l[idx] = True\n else: \n l[idx] = False\n if eval[1] == \">=\":\n if eval[0] >= eval[2]:\n l[idx] = True\n else: \n l[idx] = False\n if eval[1] == \"<=\":\n if eval[0] <= eval[2]:\n l[idx] = True\n else: \n l[idx] = False\n if eval[1] == \"!=\":\n if eval[0] != eval[2]:\n l[idx] = True\n else: \n l[idx] = False\n \n def combine(l):\n for idx in range(len(l)):\n i=l[idx]\n if type(i) == list:\n if len(i) == 1 and type(i[0]) == bool:\n l[idx] = i[0]\n if len(i) > 1 and i[0] == 'not' and type(i[1]) == bool:\n l[idx] = not i[1]\n if len(i) > 1 and i[0] == 'and' and all(type(x) == bool for x in i[1]):\n if all(x for x in i[1]):\n l[idx] = True\n else:\n l[idx] = False\n elif len(i) > 1 and i[0] == 'or' and all(type(x) == bool for x in i[1]):\n if any(x for x in i[1]):\n l[idx] = True\n else:\n l[idx] = False\n else:\n combine(i)\n\n evaluate(cond,row)\n while not all(type(x) == bool for x in cond[1]):\n combine(cond)\n\n if any(x for x in cond[1]):\n final.append(row)\n \n result = PrettyTable(list(map(lambda x: x.upper(),columns)))\n col_index = list(map(lambda x: all_cols.index(x),columns))\n for r in final:\n rr = list(map(lambda x: r[x], col_index))\n result.add_row(rr)\n print(result)\n\n else:\n #without where cluase\n result = PrettyTable(list(map(lambda x: x.upper(),columns)))\n col_index = list(map(lambda x: all_cols.index(x),columns))\n for r in final_row:\n rr = list(map(lambda x: r[x], col_index))\n result.add_row(rr)\n print(result)\n\n def null_operation(self, items):\n if len(items) == 3:\n return False\n else:\n return True\n\n def null_predicate(self, items):\n if items[-1]:\n if len(items) > 2:\n return (f\"{items[0]}.{items[1]}\",\"is\",None)\n else:\n return(items[0],\"is\",None)\n else:\n if len(items) > 2:\n return (f\"{items[0]}.{items[1]}\",\"is\",None)\n else:\n return(items[0],\"is not\",None)\n\n def where_clause(self, items):\n #remove WHERE cluase\n return items[1]\n \n def boolean_expr(self, items):\n return ['or',items[0::2]]\n\n def boolean_term(self, items):\n return ['and',items[0::2]]\n\n def comparison_predicate(self, items):\n return tuple(items)\n\n def comp_operand(self, items):\n try:\n type = items[0].type\n if type == 'INT':\n return ('int',int(items[0]))\n elif type == 'DATE':\n return ('date',datetime.datetime.strptime(items[0],'%Y-%m-%d'))\n elif type == 'STR':\n return ('str',items[0][1:-1])\n except:\n if len(items) > 1:\n return ('col',f\"{items[0]}.{items[1]}\")\n else:\n return ('col',items[0])\n \n def COMP_OP(self, items):\n return items\n \n def predicate(self, items):\n return items[0]\n\n def boolean_test(self,items):\n return items[0]\n \n def parenthesized_boolean_expr(self, items):\n return [items[1]]\n\n def boolean_factor(self, items):\n if len(items) > 1:\n return ['not', items[-1]]\n else:\n return items[-1]\n\n def select_list(self, items):\n col_sel = []\n col_nick = {}\n for i in items:\n if i[0]:\n col_nick[i[-1]] = i[1]\n col_sel.append(i[1])\n return (col_sel, col_nick)\n\n def selected_column(self, items):\n case = len(items)\n if case == 1:\n #only column_name given\n return (False, items[0])\n elif case == 2:\n #table name and column name given\n return (False, f\"{items[0]}.{items[1]}\")\n elif case == 3:\n #column name and nickname given\n return (True, items[0], items[-1])\n elif case == 4:\n #table name and column name nickname given\n return (True, f\"{items[0]}.{items[1]}\", items[-1])\n\n def table_expression(self, items):\n return (len(items) > 1 , items)\n\n def from_clause(self, items):\n return items[-1]\n\n def table_reference_list(self, items):\n tables = []\n nickname = {}\n for i in items:\n if i[0]:\n nickname[i[-1]] = i[1]\n tables.append(i[1])\n return(tables, nickname)\n\n def referred_table(self, items):\n if len(items) > 1:\n return(True,items[0],items[-1])\n else:\n return(False, items[0])\n\n def insert_query(self, items):\n table_name = items[2]\n file_name = f\"db/{table_name}.db\"\n if not os.path.isfile(file_name):\n raise NoSuchTable()\n else:\n myDB = db.DB()\n myDB.open(file_name, dbtype=db.DB_HASH)\n #check if number of column matches\n original_cols = json.loads(myDB.get(b'columns'))\n column_name_list = original_cols\n insert_info = items[-1]\n if len(insert_info) > 1:\n column_name_list = insert_info[0]\n if len(column_name_list) != len(original_cols):\n raise InsertTypeMismatchError()\n for col in column_name_list:\n if col not in original_cols:\n raise InsertColumnExistenceError(col)\n value_list = insert_info[-1]\n\n if len(column_name_list) != len(value_list):\n raise InsertTypeMismatchError()\n\n data_dict = {'referencing': [], 'referenced': []}\n\n for i in range(len(value_list)):\n col_name = column_name_list[i]\n val = value_list[i]\n col_info = json.loads(myDB.get(col_name.encode('utf-8')))\n #check if nullable\n nullable = True\n if col_info['null'] == 'N':\n nullable = False\n #if value is null, check if nullable and store value into dict\n if val is None:\n if nullable:\n data_dict[col_name] = None\n else:\n raise InsertColumnNonNullableError(col_name)\n #if value is not null, check if type is right\n else:\n required_type = col_info['type']\n if required_type == 'int':\n if isinstance(val, int):\n data_dict[col_name] = val\n else:\n raise InsertTypeMismatchError()\n elif required_type == 'date':\n if isinstance(val, datetime.date):\n data_dict[col_name] = val.strftime('%Y-%m-%d')\n else:\n raise InsertTypeMismatchError()\n else:\n #if required type is string\n if isinstance(val, str):\n str_len = int(required_type[5:-1])\n #only store to max size of string\n data_dict[col_name] = val[0:str_len]\n else:\n raise InsertTypeMismatchError()\n\n #find if foreign key values exist in the referenced table\n foreign_key = json.loads(myDB.get(b'foreign_key'))\n for fk_info in foreign_key:\n fk_list = fk_info[0]\n referenced = fk_info[1]\n ori_cols = fk_info[2]\n\n rf_file_name = f\"db/{referenced}.db\"\n rfDB = db.DB()\n rfDB.open(rf_file_name, dbtype=db.DB_HASH)\n\n pk_idx = []\n for fk_idx in range(len(fk_list)):\n pk_idx.append(data_dict[fk_list[fk_idx]])\n\n #if all the foreign keys are null, not have to find, else, find the data from referenced table\n if not all(pk is None for pk in pk_idx):\n rf_pidx = json.dumps(pk_idx).encode('utf-8')\n referenced_data = rfDB.get(rf_pidx)\n if referenced_data is None:\n raise InsertReferentialIntegrityError()\n else:\n ori_referencing = data_dict['referencing']\n ori_referencing.append((referenced, json.loads(rf_pidx)))\n data_dict['referencing'] = ori_referencing\n\n rfDB.close()\n\n\n #make key to store in berkely db\n primary_idx = []\n primary_key = json.loads(myDB.get(b'primary_key'))\n\n for pk in primary_key:\n primary_idx.append(data_dict[pk])\n pidx = json.dumps(primary_idx).encode('utf-8')\n\n #find if data with the same primary key values exists\n cursor = myDB.cursor()\n data = cursor.first()\n while data:\n key, value = data\n if key == pidx:\n raise InsertDuplicatePrimaryKeyError()\n data = cursor.next()\n\n #insert new data to database\n myDB.put(pidx, json.dumps(data_dict).encode('utf-8'))\n for info in data_dict['referencing']:\n rf_table = info[0]\n rf_pidx = json.dumps(info[1]).encode('utf-8')\n rfDB = db.DB()\n rfDB.open(f\"db/{rf_table}.db\", dbtype=db.DB_HASH)\n rf_dict = json.loads(rfDB.get(rf_pidx))\n rf_referenced = rf_dict['referenced']\n rf_referenced.append((table_name,primary_idx))\n rf_dict['referenced'] = rf_referenced\n rfDB.put(rf_pidx, json.dumps(rf_dict).encode('utf-8'))\n rfDB.close()\n\n myDB.close()\n print(prompt,\"The row is inserted\",sep='')\n\n\n\n def insert_columns_and_sources(self, items):\n return items\n\n def value_list(self, items):\n return items[2:-1]\n\n def value(self, items):\n type = items[0].type\n if type == 'INT':\n return int(items[0])\n elif type == 'DATE':\n return datetime.datetime.strptime(items[0],'%Y-%m-%d')\n elif type == 'STR':\n return items[0][1:-1]\n elif type == 'NULL':\n return None\n\n def comparable_value(self, items):\n return items[0]\n\n def delete_query(self, items):\n table_name = items[2]\n file_name = f\"db/{table_name}.db\"\n # check if the file already exists\n if not os.path.isfile(file_name):\n raise NoSuchTable()\n else:\n myDB = db.DB()\n myDB.open(file_name, dbtype=db.DB_HASH)\n #info_list contains each key which is not a key for instance\n columns = json.loads(myDB.get(b'columns'))\n info_list = list(map(lambda col : bytes(col, encoding=\"utf-8\"), columns))\n info_list += [b'primary_key',b'foreign_key',b'referencing',b'referenced',b'columns']\n #to count\n yes_count = 0\n no_count = 0\n #referenced information\n referenced = json.loads(myDB.get(b'referenced'))\n ref_info = {}\n ref_colnames = {}\n for table in referenced:\n rfDB = db.DB()\n rfDB.open(f\"db/{table}.db\", dbtype=db.DB_HASH)\n rf_fk_info = json.loads(rfDB.get(b'foreign_key'))\n for fk_info in rf_fk_info:\n if fk_info[1] == table_name:\n fk_list = fk_info[0]\n can_delete = True\n for col in fk_list:\n if json.loads(rfDB.get(col.encode(\"utf-8\")))['null'] == 'N':\n can_delete = False\n break\n #store info boolean representing if all referenced cols are nullable\n ref_info[table] = can_delete\n ref_colnames[table] = fk_list\n break\n rfDB.close()\n #no where clause, delete all\n if (len(items) == 3):\n cursor = myDB.cursor()\n data = cursor.first()\n while data:\n key, value = data\n if key not in info_list:\n #find if this data can be deleted\n referenced = json.loads(value)['referenced']\n can_delete = True\n for rf in referenced:\n rf_table = rf[0]\n if ref_info[rf_table] == False:\n can_delete = False\n if can_delete:\n #delete this data from referenced\n for rf in referenced:\n rf_table = rf[0]\n rf_key = json.dumps(rf[1]).encode('utf-8')\n rfDB = db.DB()\n rfDB.open(f\"db/{rf_table}.db\", dbtype=db.DB_HASH)\n rf_dict = json.loads(rfDB.get(rf_key))\n #remove this value from 'referencing' of referenced value\n rf_referencing = rf_dict['referencing']\n def checkThis(x):\n return not (json.dumps(x[1]).encode('utf-8') == key and x[0] == table_name)\n rf_referencing = list(filter(checkThis, rf_referencing))\n rf_dict['referencing'] = rf_referencing\n #change all referenced col values to null\n ref_col = ref_colnames[rf_table]\n for col in ref_col:\n rf_dict[col] = None\n #store edited value\n rfDB.put(rf_key, json.dumps(rf_dict).encode('utf-8'))\n rfDB.close()\n\n #delete this data from referencing\n referencing = json.loads(value)['referencing']\n for rfc in referencing:\n rfc_table = rfc[0]\n rfc_key = json.dumps(rfc[1]).encode('utf-8')\n rfcDB = db.DB()\n rfcDB.open(f\"db/{rfc_table}.db\", dbtype=db.DB_HASH)\n rfc_dict = json.loads(rfcDB.get(rfc_key))\n #remove this value from 'referenced' of referencing value\n rfc_referenced = rfc_dict['referenced']\n def checkThis(x):\n return not (json.dumps(x[1]).encode('utf-8') == key and x[0] == table_name)\n rfc_referenced = list(filter(checkThis, rfc_referenced))\n rfc_dict['referenced'] = rfc_referenced\n #store edited value\n rfcDB.put(rfc_key, json.dumps(rfc_dict).encode('utf-8'))\n rfcDB.close()\n #delete this value from database\n myDB.delete(key)\n yes_count += 1\n else:\n #cannot delete this value due to referential integrity\n no_count += 1\n data = cursor.next()\n #with where clause\n else:\n condition = items[3]\n predicates = []\n p_col = []\n def flatten(l):\n for i in l:\n if type(i) == list or (type(i) == tuple and len(i) == 2):\n flatten(i)\n else:\n if type(i) == tuple and len(i) == 3:\n predicates.append(i)\n flatten(condition)\n #check if comparable\n for idx in range(len(predicates)):\n p = predicates[idx]\n oper = p[1]\n if oper in ['is','is not']:\n #null predicate\n #find if the col name is valid\n col_name = p[0]\n if len(col_name.split('.')) > 1:\n t_n , c_n = col_name.split('.')\n #check if specified table\n if t_n != table_name:\n raise WhereTableNotSpecified()\n #check if the col exist\n c_list = json.loads(myDB.get(b'columns'))\n if c_n not in c_list:\n raise WhereColumnNotExist()\n else:\n p_col.append(c_n)\n myDB.close()\n else:\n c_list = json.loads(myDB.get(b'columns'))\n if col_name in c_list:\n p_col.append(col_name)\n else:\n raise WhereColumnNotExist()\n else:\n #comparison predicate\n two_type = []\n for op_idx in (0,2):\n if p[op_idx][0] in ['str','int','date']:\n two_type.append(p[op_idx][0])\n else:\n #if col name, check if valid col name\n col_name = p[op_idx][1]\n if len(col_name.split('.')) > 1:\n t_n , c_n = col_name.split('.')\n #check if specified table\n if t_n != table_name:\n raise WhereTableNotSpecified()\n else:\n c_list = json.loads(myDB.get(b'columns'))\n if c_n in c_list:\n #find it\n p_col.append(c_n)\n type_s = json.loads(myDB.get(c_n.encode('utf-8')))['type']\n if type_s in ['int','date']:\n two_type.append(type_s)\n else:\n two_type.append('str')\n else:\n raise WhereColumnNotExist()\n else:\n find_type = ''\n c_list = json.loads(myDB.get(b'columns'))\n if col_name in c_list:\n p_col.append(col_name)\n type_s = json.loads(myDB.get(col_name.encode('utf-8')))['type']\n if type_s in ['int','date']:\n find_type = type_s\n else:\n find_type = 'str'\n two_type.append(find_type)\n else:\n raise WhereColumnNotExist()\n if len(two_type) == 2:\n if two_type[0] != two_type[1]:\n raise WhereIncomparableError()\n\n #iterate through all data in this table\n cursor = myDB.cursor()\n data = cursor.first()\n while data:\n key, value = data\n if key not in info_list:\n #check if this satify where clause\n cond = copy.deepcopy(condition)\n\n def evaluate(l,value_dict):\n for idx in range(len(l)):\n i=l[idx]\n if type(i) == list or (type(i) == tuple and len(i) == 2):\n evaluate(i,value_dict)\n else:\n if type(i) == tuple and len(i) == 3:\n oper = i[1]\n if oper in ['is','is not']:\n #null predicate\n col_name = i[0].split('.')[-1]\n if oper == 'is':\n if value_dict[col_name] is None:\n l[idx] = True\n else:\n l[idx] = False\n elif oper == 'is not':\n if value_dict[col_name] is not None:\n l[idx] = True\n else:\n l[idx] = False\n else:\n #comparison predicate\n two_type = []\n eval = [i[0],i[1],i[2]]\n #if val op col\n if i[0][0] in ['str','int','date'] and i[2][0] == 'col':\n eval[0] = i[0][1]\n col_name = i[2][1].split('.')[-1]\n eval[2] = value_dict[col_name]\n #elif col op val\n elif i[2][0] in ['str','int','date'] and i[0][0] == 'col':\n eval[2] = i[2][1]\n col_name = i[0][1].split('.')[-1]\n eval[0] = value_dict[col_name]\n #elif col op col\n elif i[2][0] == 'col' and i[0][0] == 'col':\n col_name = i[0][1].split('.')[-1]\n eval[0] = value_dict[col_name]\n col_name = i[2][1].split('.')[-1]\n eval[2] = value_dict[col_name]\n #elif val op val\n else:\n eval[0] = i[0][1]\n eval[2] = i[2][1]\n #evaluate\n if eval[1] == \"<\":\n if eval[0] < eval[2]:\n l[idx] = True\n else: \n l[idx] = False\n if eval[1] == \">\":\n if eval[0] > eval[2]:\n l[idx] = True\n else: \n l[idx] = False\n if eval[1] == \"=\":\n if eval[0] == eval[2]:\n l[idx] = True\n else: \n l[idx] = False\n if eval[1] == \">=\":\n if eval[0] >= eval[2]:\n l[idx] = True\n else: \n l[idx] = False\n if eval[1] == \"<=\":\n if eval[0] <= eval[2]:\n l[idx] = True\n else: \n l[idx] = False\n if eval[1] == \"!=\":\n if eval[0] != eval[2]:\n l[idx] = True\n else: \n l[idx] = False\n def combine(l):\n for idx in range(len(l)):\n i=l[idx]\n if type(i) == list:\n if len(i) == 1 and type(i[0]) == bool:\n l[idx] = i[0]\n if len(i) > 1 and i[0] == 'not' and type(i[1]) == bool:\n l[idx] = not i[1]\n if len(i) > 1 and i[0] == 'and' and all(type(x) == bool for x in i[1]):\n if all(x for x in i[1]):\n l[idx] = True\n else:\n l[idx] = False\n elif len(i) > 1 and i[0] == 'or' and all(type(x) == bool for x in i[1]):\n if any(x for x in i[1]):\n l[idx] = True\n else:\n l[idx] = False\n else:\n combine(i)\n\n evaluate(cond, json.loads(value))\n while not all(type(x) == bool for x in cond[1]):\n combine(cond)\n\n if any(x for x in cond[1]):\n #satisfy where clause!!\n #find if this data can be deleted\n referenced = json.loads(value)['referenced']\n can_delete = True\n for rf in referenced:\n rf_table = rf[0]\n if ref_info[rf_table] == False:\n can_delete = False\n if can_delete:\n #delete this data from referenced\n for rf in referenced:\n rf_table = rf[0]\n rf_key = json.dumps(rf[1]).encode('utf-8')\n rfDB = db.DB()\n rfDB.open(f\"db/{rf_table}.db\", dbtype=db.DB_HASH)\n rf_dict = json.loads(rfDB.get(rf_key))\n #remove this value from 'referencing' of referenced value\n rf_referencing = rf_dict['referencing']\n def checkThis(x):\n return not (json.dumps(x[1]).encode('utf-8') == key and x[0] == table_name)\n rf_referencing = list(filter(checkThis, rf_referencing))\n rf_dict['referencing'] = rf_referencing\n #change all referenced col values to null\n ref_col = ref_colnames[rf_table]\n for col in ref_col:\n rf_dict[col] = None\n #store edited value\n rfDB.put(rf_key, json.dumps(rf_dict).encode('utf-8'))\n rfDB.close()\n\n #delete this data from referencing\n referencing = json.loads(value)['referencing']\n for rfc in referencing:\n rfc_table = rfc[0]\n rfc_key = json.dumps(rfc[1]).encode('utf-8')\n rfcDB = db.DB()\n rfcDB.open(f\"db/{rfc_table}.db\", dbtype=db.DB_HASH)\n rfc_dict = json.loads(rfcDB.get(rfc_key))\n #remove this value from 'referenced' of referencing value\n rfc_referenced = rfc_dict['referenced']\n def checkThis(x):\n return not (json.dumps(x[1]).encode('utf-8') == key and x[0] == table_name)\n rfc_referenced = list(filter(checkThis, rfc_referenced))\n rfc_dict['referenced'] = rfc_referenced\n #store edited value\n rfcDB.put(rfc_key, json.dumps(rfc_dict).encode('utf-8'))\n rfcDB.close()\n #delete this value from database\n myDB.delete(key)\n yes_count += 1\n else:\n #cannot delete this value due to referential integrity\n no_count += 1\n data = cursor.next()\n\n myDB.close()\n print(prompt,f\"{yes_count} row(s) are deleted\", sep='')\n if no_count > 0:\n print(prompt, f\"{no_count} row(s) are not deleted due to referential integrity\", sep='')\n\n\n def table_element_list(self, items):\n elem_list = items[1:-1] # get rid of LP and RP\n return elem_list\n\n def table_element(self, items):\n return items[0]\n\n def column_definition(self, items):\n dict = {}\n dict['type'] = items[1]\n #if not null constraint, set null to N\n if len(items) > 2:\n dict['null'] = 'N'\n else:\n dict['null'] = 'Y'\n dict['key'] = ''\n return (items[0], dict)\n\n def data_type(self, items):\n length = len(items)\n if (length == 4):\n #if char length smaller than 1, rais CharLenghError\n if int(items[2]) <= 0:\n raise CharLengthError()\n result = ''\n for i in range(length):\n result += str(items[i])\n return result.lower()\n\n def table_constraint_definition(self, items):\n return items[0]\n\n def primary_key_constraint(self, items):\n return ['primary key', items[2]]\n\n def referential_constraint(self, items):\n col_list = items[2]\n referenced_table = items[4]\n referenced_col_list = items[5]\n return ['foreign key', col_list, referenced_table, referenced_col_list ]\n\n def column_name_list(self, items):\n return list(items[1:-1])\n #table name to lower case\n def table_name(self, t):\n (t,) = t\n return t.lower()\n #column name to lower case\n def column_name(self, c):\n (c,) = c\n return c.lower()\n def IDENTIFIER(self, s):\n return str(s)\n\n\nif __name__ == '__main__':\n\n # make sql_parser with grammar.lark file\n with open('../PRJ1-3_2015-16227/grammar.lark') as file:\n sql_parser = Lark(file.read(), start=\"command\", lexer=\"standard\")\n\n #get input until the program ended\n while(True):\n #get input with prompt message\n data_input = input(prompt)\n #if command 'exit' is given, end the program\n if(data_input == 'exit'):\n sys.exit()\n #get additional input until it ends with semicolon\n while len(data_input) == 0 or data_input[-1] != ';':\n #to prevent error, add white space to data_input\n data_input += ' '\n data_input += input()\n #split the input by semicolon\n input_list = list(data_input.split(';'))\n #exclude the last element of input_list since it is empty string\n for i in range(len(input_list) - 1):\n #restore semicolon in the end of the command\n command = input_list[i] + ';'\n #using try ... except, process the syntax error\n try:\n result = sql_parser.parse(command)\n MyTransformer().transform(result)\n except lark.exceptions.UnexpectedInput:\n print(prompt,\"Syntax error\",sep='')\n #for char length error\n except lark.exceptions.VisitError as e:\n print(prompt + str(e.__context__))\n except SimpleDatabaseError as e:\n print(prompt + str(e))\n\n"},{"alpha_fraction":0.6711568832397461,"alphanum_fraction":0.6714210510253906,"avg_line_length":26.042856216430664,"blob_id":"cc4f69486da95d139e1d7c7b19f43318de8a949a","content_id":"5723b721a9b542175c3880ab40388b15142f7cd6","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":3786,"license_type":"no_license","max_line_length":77,"num_lines":140,"path":"/PRJ1-3_2015-16227/exceptions.py","repo_name":"h-yes-oo/database","src_encoding":"UTF-8","text":"class SimpleDatabaseError(Exception):\n pass\n\n\nclass NoSuchTable(SimpleDatabaseError):\n def __str__(self):\n return \"No such table\"\n\n\nclass CharLengthError(SimpleDatabaseError):\n def __str__(self):\n return \"Char length should be over 0\"\n\n\nclass CreateTableError(SimpleDatabaseError):\n def __init__(self, msg):\n self.msg = msg\n\n def __str__(self):\n return f\"Create table has failed: {self.msg}\"\n\n\nclass DropTableError(SimpleDatabaseError):\n def __init__(self, msg):\n self.msg = msg\n\n def __str__(self):\n return f\"Drop table has failed: {self.msg}\"\n\n\nclass DropReferencedTableError(DropTableError):\n def __init__(self, table):\n super().__init__(f\"'{table}' is referenced by other table\")\n\n\nclass DuplicateColumnDefError(CreateTableError):\n def __init__(self):\n super().__init__(\"column definition is duplicated\")\n\n\nclass DuplicatePrimaryKeyDefError(CreateTableError):\n def __init__(self):\n super().__init__(\"primary key definition is duplicated\")\n\n\nclass ReferenceTypeError(CreateTableError):\n def __init__(self):\n super().__init__(\"foreign key references wrong type\")\n\n\nclass ReferenceNonPrimaryKeyError(CreateTableError):\n def __init__(self):\n super().__init__(\"foreign key references non primary key column\")\n\n\nclass ReferenceColumnExistenceError(CreateTableError):\n def __init__(self):\n super().__init__(\"foreign key references non existing column\")\n\n\nclass ReferenceTableExistenceError(CreateTableError):\n def __init__(self):\n super().__init__(\"foreign key references non existing table\")\n\n\nclass NonExistingColumnDefError(CreateTableError):\n def __init__(self, column):\n super().__init__(f\"'{column}' does not exists in column definition\")\n\n\nclass TableExistenceError(CreateTableError):\n def __init__(self):\n super().__init__(\"table with the same name already exists\")\n\n\nclass InsertionError(SimpleDatabaseError):\n def __init__(self, msg):\n self.msg = msg\n\n def __str__(self):\n return f\"Insertion has failed: {self.msg}\"\n\n\nclass InsertTypeMismatchError(InsertionError):\n def __init__(self):\n super().__init__(\"Types are not matched\")\n\n\nclass InsertColumnExistenceError(InsertionError):\n def __init__(self, column):\n super().__init__(f\"'{column}' does not exist\")\n\n\nclass InsertColumnNonNullableError(InsertionError):\n def __init__(self, column):\n super().__init__(f\"'{column}' is not nullable\")\n\n\nclass InsertDuplicatePrimaryKeyError(InsertionError):\n def __init__(self):\n super().__init__(\"Primary key duplication\")\n\n\nclass InsertReferentialIntegrityError(InsertionError):\n def __init__(self):\n super().__init__(\"Referential integrity violation\")\n\n\nclass SelectionError(SimpleDatabaseError):\n def __init__(self, msg):\n self.msg = msg\n\n def __str__(self):\n return f\"Selection has failed: {self.msg}\"\n\n\nclass SelectTableExistenceError(SelectionError):\n def __init__(self, table):\n super().__init__(f\"'{table}' does not exist\")\n\nclass SelectColumnResolveError(SelectionError):\n def __init__(self, column):\n super().__init__(f\"fail to resolve '{column}'\")\n\n\nclass WhereIncomparableError(SimpleDatabaseError):\n def __str__(self):\n return \"Where clause try to compare incomparable values\"\n\nclass WhereTableNotSpecified(SimpleDatabaseError):\n def __str__(self):\n return \"Where clause try to reference tables which are not specified\"\n\nclass WhereColumnNotExist(SimpleDatabaseError):\n def __str__(self):\n return \"Where clause try to reference non existing column\"\n\nclass WhereAmbiguousReference(SimpleDatabaseError):\n def __str__(self):\n return \"Where clause contains ambiguous reference\"\n"},{"alpha_fraction":0.5558925867080688,"alphanum_fraction":0.5694865584373474,"avg_line_length":32.58490753173828,"blob_id":"96b7491d39e52fb82c997690309808a57055e02f","content_id":"acf1ba7de8735f06e81558a35961437a19109c77","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":8901,"license_type":"no_license","max_line_length":95,"num_lines":265,"path":"/PRJ2_2015-16227/run.py","repo_name":"h-yes-oo/database","src_encoding":"UTF-8","text":"from __future__ import print_function\nfrom mysql.connector import connect\nfrom executions import *\nfrom table import TABLES\nfrom messages import *\n\n#connect with db in case it doesn't work at first time, use try except to ensure the connection\ntry:\n connection = connect(\n host='astronaut.snu.ac.kr',\n port=7000,\n user='DB2015_16227',\n password='DB2015_16227',\n db='DB2015_16227',\n charset='utf8'\n )\nexcept:\n connection = connect(\n host='astronaut.snu.ac.kr',\n port=7000,\n user='DB2015_16227',\n password='DB2015_16227',\n db='DB2015_16227',\n charset='utf8'\n )\n\n#make one dictionary cursor, one non-dictionary cursor for convenience\ncurA = connection.cursor(dictionary=True, buffered=True)\ncurB = connection.cursor(dictionary=False, buffered=True)\n\n#print informative message and start program\nprint(info_message)\nwhile(True):\n print(\" \")\n #if input action is not an integer, go to except\n try:\n action = int(input(select_action))\n #if action not in 1~16 print invalid action message\n if action >= 17 or action <= 0:\n print(invalid_action)\n else:\n if action == 1:\n print_building(curA, curB)\n elif action == 2:\n print_performance_all(curA, curB)\n elif action == 3:\n print_audience_all(curA)\n elif action == 4:\n #get name, location and truncate to 200 letters\n name = input(get_building_name)\n if(len(name) > 200):\n name = name[0:200]\n location = input(get_building_location)\n if(len(location) > 200):\n location = location[0:200]\n try:\n #if capacity less than zero, print error message \n capacity = int(input(get_building_capacity))\n if capacity <= 0:\n print(capacity_constraint)\n continue\n else:\n #if every input alright, insert it and commit\n insert_building(curA, name, location, capacity)\n connection.commit()\n print(insert_success_message(\"A building\"))\n except:\n #if user input non-integer capacity, print invalid input\n print(invalid_input)\n continue\n\n elif action == 5:\n try:\n bid = int(input(get_building_id))\n if(check_by_id(curA, 'building', bid)):\n #if building with input id exist, delete and commit\n delete(curA, 'building', bid)\n connection.commit()\n print(delete_success_message(\"A building\"))\n else:\n #if not exists, print message\n print(building_not_exist(bid))\n except:\n #if user input non-integer id, print invalid input\n print(invalid_input)\n\n elif action == 6:\n # get name and type from user and truncate to 200 letters\n name = input(get_performance_name)\n if(len(name) > 200):\n name = name[0:200]\n ptype = input(get_performance_type)\n if(len(ptype) > 200):\n ptype = ptype[0:200]\n try:\n price = int(input(get_performance_price))\n if price < 0:\n print(price_constraint)\n continue\n else:\n #if all input alright, insert it and commit\n insert_performance(curA, name, ptype, price)\n connection.commit()\n print(insert_success_message(\"A performance\"))\n except:\n #if user input non-interger value for price, print invalid input\n print(invalid_input)\n continue\n\n elif action == 7:\n try:\n pid = int(input(get_performance_id))\n #check if performance with the id exists\n if(check_by_id(curA, 'performance', pid)):\n #if exist, delete and commit\n delete(curA, 'performance', pid)\n connection.commit()\n print(delete_success_message(\"A performance\"))\n else:\n #if not exist, print not exist message\n print(performance_not_exist(pid))\n except:\n #if user input non-integer value for id, print invalid input\n print(invalid_input)\n \n elif action == 8:\n #get name and truncate\n name = input(get_audience_name)\n if(len(name) > 200):\n name = name[0:200]\n #get gender and check if equals to M or F\n gender = input(get_audience_gender)\n if gender != \"M\" and gender != \"F\":\n print(gender_constraint)\n continue\n try:\n #get age and check if bigger than 0\n age = int(input(get_audience_age))\n if age < 0:\n print(age_constraint)\n continue\n else:\n #if alright, insert and commit\n insert_audience(curA, name, gender, age)\n connection.commit()\n print(insert_success_message(\"An audience\"))\n except:\n #if user input non-integer value for age, print message\n print(invalid_input)\n continue\n \n elif action == 9:\n try:\n aid = int(input(get_audience_id))\n if(check_by_id(curA, 'audience', aid)):\n #if audience with the id exist, delete and commit\n delete(curA, 'audience', aid)\n connection.commit()\n print(delete_success_message(\"An audience\"))\n else:\n #else, print not exist message\n print(audience_not_exist)\n except:\n #if user input non-integer value for id, print message\n print(invalid_input)\n \n elif action == 10:\n try:\n bid = int(input(get_building_id))\n #if building not exist, print message\n if not check_by_id(curA, 'building', bid):\n print(building_not_exist(bid))\n continue\n pid = int(input(get_performance_id))\n #if performance not exist, print message\n if not check_by_id(curA, 'performance', pid):\n print(performance_not_exist(pid))\n continue\n #if it is able to assign the performance to building, do it and commit\n if(assign_p_to_b(curA, pid, bid)):\n print(assign_success)\n connection.commit()\n except:\n print(invalid_input)\n \n elif action == 11:\n try:\n pid = int(input(get_performance_id))\n # if performance not assigned to any building, print message\n if not check_building_assigned(curA, pid):\n print(performance_not_assigned(pid))\n continue\n aid = int(input(get_audience_id))\n seat_list = list(map(int, input(get_seat_number).split(',')))\n #check if all seats can be reserved\n if(can_reserve(curB, seat_list, pid)):\n for seat in seat_list:\n #reserve each seat and commit\n reserve(curA, pid, aid, seat)\n connection.commit()\n #get price with respect to audience's age\n price = get_ticket_price(curB, pid, aid)\n #multiply the price with the number of ticket being reserved\n total_price = round(price * len(seat_list))\n #print success message and price\n print(book_success)\n print(total_ticket_price(total_price))\n except:\n print(invalid_input)\n\n elif action == 12:\n try:\n bid = int(input(get_building_id))\n #check if building with the id exist and print\n if check_by_id(curA, 'building', bid):\n print_performance_of_building(curA, curB, bid)\n else:\n print(building_not_exist(bid))\n except:\n print(invalid_input)\n\n elif action == 13:\n try:\n pid = int(input(get_performance_id))\n #check if performance with the id exist and print\n if check_by_id(curA, 'performance', pid):\n print_audience_of_performance(curA, pid)\n else:\n print(performance_not_exist(pid))\n except:\n print(invalid_input)\n \n elif action == 14:\n try:\n pid = int(input(get_performance_id))\n #check if performance with the id exist\n if check_by_id(curA, 'performance', pid):\n #check if performance with the id is assigned to any building\n if check_building_assigned(curA, pid):\n print_seat_of_performance(curB, pid)\n else:\n print(performance_not_assigned(pid))\n else:\n print(performance_not_exist(pid))\n except:\n print(invalid_input)\n\n elif action == 15:\n print(bye)\n break\n\n elif action == 16:\n #ensure user about deleting everything\n flag = input(refresh_confirm)\n if flag == 'y':\n refresh(curA, TABLES)\n\n #if non integer input came, print invalid message\n except:\n print(invalid_action)\n\ncurA.close()\ncurB.close()\nconnection.close()\nexit(0)\n\n"},{"alpha_fraction":0.5987762212753296,"alphanum_fraction":0.6451048851013184,"avg_line_length":21.8799991607666,"blob_id":"dea92a8da6fdc5f2391d65c3cabd83dbae43d986","content_id":"ad8697d8b0acfc4adabeab1750907978a2ed8c40","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"SQL","length_bytes":1144,"license_type":"no_license","max_line_length":94,"num_lines":50,"path":"/sample.sql","repo_name":"h-yes-oo/database","src_encoding":"UTF-8","text":"create table department (\n name char(5),\n pos char(17),\n number int,\n start date,\n primary key(name)\n);\n\n\ncreate table student (\n ID char(5),\n name char(2) not null,\n dept_name char(5),\n tot_cred int,\n primary key(ID, name),\n foreign key (dept_name) references department(name)\n);\n\ncreate table lover (\n x char(3),\n y char(2),\n dept_name char(5),\n stu_id char(5),\n primary key(x,y,dept_name),\n foreign key (dept_name) references department(name),\n foreign key (y,stu_id) references student(name,id)\n);\n\ncreate table loves (\n she char(2),\n he char(3) not null,\n who char(5),\n love date,\n primary key (love),\n foreign key (who,he,she) references lover(dept_name,x,y)\n);\n\ninsert into department (number,start,pos,name) values(1,2020-10-10,\"pos777777\",'name5555555');\n\n\ninsert into student values('id1','n2','name5',10);\n\ninsert into lover values('xxx','n2','name5','id1');\n\ninsert into loves values('n2','xxx','name5',2015-03-30);\n\n--set null value to primary key\n--set null value to not null primary key\n--len(column) != len(input_column)\n--len(input_column) != len(values)\n"},{"alpha_fraction":0.686948835849762,"alphanum_fraction":0.6984127163887024,"avg_line_length":29.635135650634766,"blob_id":"dd8eabd0971c7c06a64c1b60fc2ba1f4f0b0e0f0","content_id":"e9ccece48bd90e9c1a7bb1f7e1a0a210ccdd0b6c","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":2274,"license_type":"no_license","max_line_length":74,"num_lines":74,"path":"/PRJ2_2015-16227/messages.py","repo_name":"h-yes-oo/database","src_encoding":"UTF-8","text":"#informative message about prompt\ninfo_message = '''\n============================================================ \n1. print all buildings\n2. print all performances\n3. print all audiences\n4. insert a new building\n5. remove a building\n6. insert a new performance\n7. remove a performance\n8. insert a new audience\n9. remove an audience\n10. assign a performance to a building\n11. book a performance\n12. print all performances which assigned at a building\n13. print all audiences who booked for a performance\n14. print ticket booking status of a performance\n15. exit\n16. reset database\n============================================================'''\n\ninvalid_action = \"Invalid action\"\ninvalid_input = \"Invalid input\"\n\nselect_action = \"Select your action: \"\n\nget_building_name = \"Building name: \"\nget_building_location = \"Building location: \"\nget_building_capacity = \"Building capacity: \"\nget_building_id = \"Building ID: \"\n\nget_performance_name = \"Performance name: \"\nget_performance_type = \"Performance type: \"\nget_performance_price = \"Performance price: \"\nget_performance_id = \"Performance ID: \"\n\nget_audience_name = \"Audience name: \"\nget_audience_gender = \"Audience gender: \"\nget_audience_age = \"Audience age: \"\nget_audience_id = \"Audience ID: \"\n\nget_seat_number = \"Seat number: \"\n\ncapacity_constraint = \"Capacity should be more than 0\"\nprice_constraint = \"Price should be 0 or more\"\ngender_constraint = \"Gender should be 'M' or 'F'\"\nage_constraint = \"Age should be 0 or more\"\n\nbye = \"Bye!\"\nrefresh_confirm = \"Every table and data will be deleted. Go on ? (y/n) : \"\n\nassign_success = \"Successfully assign a performance\"\nbook_success = \"Successfully book a performance.\"\n\ndef total_ticket_price(total_price):\n return \"Total ticket price is {:,}\".format(total_price)\n\ndef insert_success_message(what):\n return \"{} is successfully inserted\".format(what)\n\ndef delete_success_message(what):\n return \"{} is successfully removed\".format(what)\n\ndef building_not_exist(bid):\n return \"Building {} doesn’t exist\".format(bid)\n\ndef performance_not_exist(pid):\n return \"Performance {} doesn’t exist\".format(pid)\n\ndef audience_not_exist(aid):\n return \"Audience {} doesn’t exist\".format(aid)\n\ndef performance_not_assigned(pid):\n return \"Performance {} isn't assigned\".format(pid)\n\n"}],"string":"[\n {\n \"alpha_fraction\": 0.6276443600654602,\n \"alphanum_fraction\": 0.6393251419067383,\n \"avg_line_length\": 28.521072387695312,\n \"blob_id\": \"38bc73a58a274df76c4d28bb55656cdfc62a18cd\",\n \"content_id\": \"2a3f1b5495eb00970d0173d14c7036dd5358670a\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 7705,\n \"license_type\": \"no_license\",\n \"max_line_length\": 94,\n \"num_lines\": 261,\n \"path\": \"/PRJ2_2015-16227/executions.py\",\n \"repo_name\": \"h-yes-oo/database\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"from __future__ import print_function\\nimport mysql.connector\\nfrom mysql.connector import errorcode\\n\\n#create table with tables schema\\ndef create_tables(cursor, tables):\\n for table_name in tables:\\n table_description = tables[table_name]\\n try:\\n cursor.execute(table_description)\\n except mysql.connector.Error as err:\\n if err.errno == errorcode.ER_TABLE_EXISTS_ERROR:\\n print(\\\"already exists.\\\")\\n else:\\n print(err.msg)\\n\\n#insert building, performance, audience function with their schema\\ndef insert_building(cursor, name, location, capacity):\\n add_building = (\\\"INSERT INTO building \\\"\\n \\\"(name, location, capacity) \\\"\\n \\\"VALUES (%s, %s, %s)\\\" )\\n data_building = (name, location, capacity)\\n cursor.execute(add_building, data_building)\\n\\ndef insert_performance(cursor, name, ptype, price):\\n add_performance = (\\\"INSERT INTO performance \\\"\\n \\\"(name, type, price) \\\"\\n \\\"VALUES (%s, %s, %s)\\\" )\\n data_performance = (name, ptype, price)\\n cursor.execute(add_performance, data_performance)\\n\\ndef insert_audience(cursor, name, gender, age):\\n add_audience = (\\\"INSERT INTO audience \\\"\\n \\\"(name, gender, age) \\\"\\n \\\"VALUES (%s, %s, %s)\\\" )\\n data_audience = (name, gender, age)\\n cursor.execute(add_audience, data_audience)\\n\\n#return if the performance is already assigned to building\\ndef check_building_assigned(cursor, pid):\\n check = (\\n \\\"SELECT building FROM performance WHERE id = %s\\\"\\n )\\n cursor.execute(check, (pid,))\\n for data in cursor:\\n #if not assigned, return false\\n if data['building'] is None:\\n return False\\n else:\\n return True\\n\\n#return if assigning performance to building succeed\\ndef assign_p_to_b(cursor, pid, bid):\\n #check if the performance is already assigned to building\\n check = check_building_assigned(cursor,pid)\\n if(check):\\n print(\\\"Performance %d is already assigned\\\" % (pid))\\n return False\\n #if function not returned, assign performance to building\\n update = (\\n \\\"UPDATE performance SET building = %s \\\"\\n \\\"WHERE id = %s\\\"\\n )\\n cursor.execute(update, (bid, pid))\\n return True\\n\\n#return if id exists in table\\ndef check_by_id(cursor, table, bid):\\n check = (\\n \\\"SELECT 1 FROM {} WHERE id = %s\\\".format(table)\\n )\\n cursor.execute(check,(bid, ))\\n result = cursor.fetchall()\\n if len(result) > 0:\\n return True\\n else:\\n return False\\n\\n#return if the seat of pid already taken\\ndef seat_taken(cursor, pid, seat):\\n check = (\\n \\\"SELECT 1 FROM reservation WHERE performance = %s AND seat = %s\\\"\\n )\\n cursor.execute(check,(pid, seat))\\n result = cursor.fetchall()\\n if len(result) > 0:\\n print(\\\"The seat is already taken\\\")\\n return True\\n else:\\n return False\\n\\n#insert new data to reservation table\\ndef reserve(cursor, pid, aid, seat):\\n add_performance = (\\\"INSERT INTO reservation \\\"\\n \\\"(audience, performance, seat) \\\"\\n \\\"VALUES (%s, %s, %s)\\\" )\\n data_performance = (aid, pid, seat)\\n cursor.execute(add_performance, data_performance)\\n\\n#print a line\\ndef print_line():\\n print(\\\"-----------------------------------------------------------------------------------\\\")\\n\\n#print all building\\ndef print_building(curA, curB):\\n print_line()\\n print(\\\"%-8s %-30s %-16s %-16s %-16s\\\" %('id','name','location','capacity','assigned'))\\n print_line()\\n curA.execute(\\\"SELECT * FROM building ORDER BY id\\\")\\n result = curA.fetchall()\\n for data in result:\\n count_query = (\\\"SELECT COUNT(id) FROM performance WHERE building = %s\\\")\\n curB.execute(count_query, (data['id'], ))\\n for x in curB:\\n print(\\\"%-8s %-30s %-16s %-16s %-16s\\\" %(\\n data['id'], data['name'], data['location'], data['capacity'], x[0]\\n ))\\n print_line()\\n\\n#basic audience printing format\\ndef print_audience(result):\\n print_line()\\n print(\\\"%-8s %-36s %-22s %-20s\\\" %('id','name','gender','age'))\\n print_line()\\n for data in result:\\n print(\\\"%-8s %-36s %-22s %-20s\\\" %(\\n data['id'], data['name'], data['gender'], data['age']\\n ))\\n print_line()\\n\\n#print all audiences\\ndef print_audience_all(cursor):\\n cursor.execute(\\\"SELECT * FROM audience ORDER BY id\\\")\\n result = cursor.fetchall()\\n print_audience(result)\\n\\n#print audience of specific performance\\ndef print_audience_of_performance(cursor, pid):\\n select_query = (\\n \\\"SELECT * FROM audience \\\"\\n \\\"WHERE id IN (\\\"\\n \\\"SELECT DISTINCT audience FROM reservation \\\"\\n \\\"WHERE performance = %s) \\\"\\n \\\"ORDER BY id\\\"\\n )\\n cursor.execute(select_query, (pid,))\\n result = cursor.fetchall()\\n print_audience(result)\\n\\n#basic performance printing format\\ndef print_performance(curB, result):\\n print_line()\\n print(\\\"%-8s %-30s %-16s %-16s %-16s\\\" %('id','name','type','price','booked'))\\n print_line()\\n for data in result:\\n count_query = (\\\"SELECT COUNT(id) FROM reservation WHERE performance = %s\\\")\\n curB.execute(count_query, (data['id'], ))\\n for x in curB:\\n print(\\\"%-8s %-30s %-16s %-16s %-16s\\\" %(\\n data['id'], data['name'], data['type'], data['price'], x[0]\\n ))\\n print_line()\\n\\n#print all performances \\ndef print_performance_all(curA, curB):\\n curA.execute(\\\"SELECT * FROM performance ORDER BY id\\\")\\n result = curA.fetchall()\\n print_performance(curB, result)\\n\\n#print performance of specific building\\ndef print_performance_of_building(curA, curB, bid):\\n select_query = (\\n \\\"SELECT * FROM performance \\\"\\n \\\"WHERE building = %s ORDER BY id\\\"\\n )\\n curA.execute(select_query, (bid,))\\n result = curA.fetchall()\\n print_performance(curB, result)\\n\\n#return capacity of the performance\\ndef get_capacity(curB, pid):\\n select_query = (\\n \\\"SELECT capacity FROM building \\\"\\n \\\"WHERE id = (\\\"\\n \\\"SELECT building FROM performance \\\"\\n \\\"WHERE id = %s)\\\"\\n )\\n curB.execute(select_query, (pid, ))\\n result = curB.fetchall()\\n capacity = result[0][0]\\n return capacity\\n\\n#using for loop, print seat of performance\\ndef print_seat_of_performance(curB, pid):\\n capacity = get_capacity(curB, pid)\\n print_line()\\n print(\\\"%-40s %-40s\\\" %('seat_number', 'audience_id'))\\n print_line()\\n for seat in range(1,capacity+1):\\n find_query = (\\n \\\"SELECT audience FROM reservation \\\"\\n \\\"WHERE performance = %s and seat = %s\\\"\\n )\\n curB.execute(find_query, (pid, seat))\\n result = curB.fetchall()\\n if(len(result) == 0):\\n print(\\\"%-40d\\\" %(seat))\\n else:\\n print(\\\"%-40d %-40d\\\" %(seat, result[0][0]))\\n print_line()\\n\\n#delete existing table and create new tables\\ndef refresh(cursor, tables):\\n for table_name in ['reservation', 'audience', 'performance','building']:\\n sql = \\\"DROP TABLE IF EXISTS %s\\\" % table_name\\n cursor.execute(sql)\\n create_tables(cursor,tables)\\n\\n#delete from table instance with did\\ndef delete(cursor, table, did):\\n delete_query = (\\n \\\"DELETE FROM {} WHERE id = %s\\\".format(table)\\n )\\n cursor.execute(delete_query,(did,))\\n\\n#return if all the seat in seat list can be reserved\\ndef can_reserve(curB, seat_list, pid):\\n capacity = get_capacity(curB, pid)\\n for seat in seat_list:\\n if seat > capacity:\\n print(\\\"Seat number out of range\\\")\\n return False\\n for seat in seat_list:\\n if seat_taken(curB, pid, seat):\\n return False\\n return True\\n\\n#return one ticket price with respect to the audience's age\\ndef get_ticket_price(curB, pid, aid):\\n age_query = (\\n \\\"SELECT age FROM audience \\\"\\n \\\"WHERE id = %s\\\"\\n )\\n curB.execute(age_query, (aid,))\\n age_result = curB.fetchall()\\n age = age_result[0][0]\\n if age <= 7:\\n return 0\\n price_query = (\\n \\\"SELECT price FROM performance \\\"\\n \\\"WHERE id = %s\\\"\\n )\\n curB.execute(price_query, (pid,))\\n price_result = curB.fetchall()\\n price = price_result[0][0]\\n if 8 <= age and age <= 12:\\n return price * 0.5\\n elif 13 <= age and age <= 18:\\n return price * 0.8\\n else:\\n return price\\n\"\n },\n {\n \"alpha_fraction\": 0.5964173674583435,\n \"alphanum_fraction\": 0.6046722531318665,\n \"avg_line_length\": 36.15644073486328,\n \"blob_id\": \"0becb2b0ee595ea443278cba16c30ec1600130f6\",\n \"content_id\": \"7b3a1c9ee86b2784fb275e8be0f99023e32ed2c0\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 12114,\n \"license_type\": \"no_license\",\n \"max_line_length\": 102,\n \"num_lines\": 326,\n \"path\": \"/PRJ1-2_2015-16227/run.py\",\n \"repo_name\": \"h-yes-oo/database\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"import sys\\nimport lark\\nfrom lark import Lark, Transformer\\nfrom berkeleydb import db\\nimport os\\nimport json\\n\\n# to reuse prompt message\\nprompt = \\\"DB_2015-16227> \\\"\\n\\n#messages\\ndef CreateTableSuccess(tableName):\\n return f\\\"'{tableName}' table is created\\\"\\nCharLengthError = \\\"Char length should be over 0\\\"\\nDuplicatePrimaryKeyDefError = \\\"Create table has failed: primary key definition is duplicated\\\"\\nDuplicateColumnDefError = \\\"Create table has failed: column definition is duplicated\\\"\\ndef NonExistingColumnDefError(colName):\\n return f\\\"Create table has failed: '{colName}' does not exists in column definition\\\"\\nTableExistenceError = \\\"Create table has failed: table with the same name already exists\\\"\\n\\nReferenceTypeError = \\\"Create table has failed: foreign key references wrong type\\\"\\nReferenceNonPrimaryKeyError = \\\"Create table has failed: foreign key references non primary key column\\\"\\nReferenceColumnExistenceError = \\\"Create table has failed: foreign key references non existing column\\\"\\nReferenceTableExistenceError = \\\"Create table has failed: foreign key references non existing table\\\"\\n\\ndef DropSuccess(tableName):\\n return f\\\"'{tableName}' table is dropped\\\"\\ndef DropReferencedTableError(tableName):\\n return f\\\"Drop table has failed: '{tableName}' is referenced by other table\\\"\\n\\nNoSuchTable = \\\"No such table\\\"\\n\\n# make transformer to print appropriate message for each query\\nclass MyTransformer(Transformer):\\n def command(self, items):\\n if not isinstance(items[0], list):\\n exit()\\n return items[0]\\n\\n def query_list(self, items):\\n return items\\n\\n def create_table_query(self, items):\\n try:\\n info_list = items[2:] #get rid of CREATE TABLE\\n table_name = info_list[0]\\n file_name = f\\\"db/{table_name}.db\\\"\\n #check if the file already exists\\n if os.path.isfile(file_name):\\n raise Exception(TableExistenceError)\\n elem_list = info_list[1]\\n #initialize variables\\n find_primary_key = False\\n primary_key = []\\n foreign_key = []\\n referencing = []\\n referenced = []\\n col_dict = {}\\n #processing table_element_list\\n for elem in elem_list:\\n if elem[0] == 'primary key':\\n #if already find primary key, raise error\\n if find_primary_key:\\n raise Exception(DuplicatePrimaryKeyDefError)\\n else:\\n find_primary_key = True\\n primary_key = elem[1]\\n #process foreign key after get all column information\\n elif elem[0] == 'foreign key':\\n continue\\n #store all column names and infos into a dict\\n else:\\n #if already have the same column, raise error\\n if elem[0] in col_dict:\\n raise Exception(DuplicateColumnDefError)\\n else:\\n #if char length smaller than 1, raise error\\n if elem[1]['type'] == 'error':\\n raise Exception(CharLengthError)\\n else:\\n col_dict[elem[0]] = elem[1]\\n #process all foreign keys\\n for elem in elem_list:\\n if elem[0] == 'foreign key':\\n fk_list = elem[1]\\n referenced_table = elem[2]\\n referenced_col_list = elem[3]\\n # check if foreign key is one of columns\\n for f in fk_list:\\n if f not in col_dict:\\n raise Exception(NonExistingColumnDefError(f))\\n # check if num of foreign keys and num of referenced cols is the same\\n if (len(fk_list) != len(referenced_col_list)):\\n Exception(ReferenceTypeError)\\n ref_file_name = f\\\"db/{referenced_table}.db\\\"\\n # check if the referenced_table exists\\n if not os.path.isfile(ref_file_name):\\n raise Exception(ReferenceTableExistenceError)\\n else:\\n refDB = db.DB()\\n refDB.open(ref_file_name, dbtype=db.DB_HASH)\\n ref_pk_list = json.loads(refDB.get(b\\\"primary_key\\\"))\\n ref_col_list = json.loads(refDB.get(b\\\"columns\\\"))\\n # check if referenced table has foreign key\\n for rf_col in referenced_col_list:\\n if rf_col not in ref_col_list:\\n raise Exception(ReferenceColumnExistenceError)\\n # check if foreign key is primary key of referenced table\\n for rf_col in referenced_col_list:\\n if rf_col not in ref_pk_list:\\n raise Exception(ReferenceNonPrimaryKeyError)\\n # check if foreign key contains all primary keys of referenced table\\n if len(referenced_col_list) != len(ref_pk_list):\\n raise Exception(ReferenceNonPrimaryKeyError)\\n else:\\n for i in range(len(fk_list)):\\n # check if fk_list[i] and referenced_col_list[i] has the same type\\n ori_type = json.loads(refDB.get(referenced_col_list[i].encode('utf-8')))['type']\\n fk_type = col_dict[fk_list[i]]['type']\\n if ori_type != fk_type:\\n raise Exception(ReferenceTypeError)\\n #ready to reference\\n #add referenced_table to referencing list of this new table\\n referencing.append(referenced_table)\\n #add this new table to referenced list of the referenced_table\\n rf_by = json.loads(refDB.get(b\\\"referenced\\\"))\\n rf_by.append(table_name)\\n refDB.put(b\\\"referenced\\\", json.dumps(rf_by).encode('utf-8'))\\n #add all foreign keys to foreign_key\\n for fk in fk_list:\\n foreign_key.append(fk)\\n refDB.close()\\n #check if primary_key and foreign_key is in column list and change their properties\\n for p in primary_key:\\n if p in col_dict:\\n col_dict[p]['null'] = 'N'\\n col_dict[p]['key'] = 'PRI'\\n else:\\n raise Exception(NonExistingColumnDefError(p))\\n\\n for f in foreign_key:\\n if f in col_dict:\\n if col_dict[f]['key'] == '':\\n col_dict[f]['key'] = 'FOR'\\n else:\\n col_dict[f]['key'] += '/FOR'\\n else:\\n raise Exception(NonExistingColumnDefError(f))\\n #encode variables and put to database\\n encoded_p = json.dumps(primary_key).encode('utf-8')\\n encoded_f = json.dumps(foreign_key).encode('utf-8')\\n\\n myDB = db.DB()\\n myDB.open(file_name, dbtype=db.DB_HASH, flags=db.DB_CREATE)\\n for key in col_dict.keys():\\n myDB.put(key.encode('utf-8'), json.dumps(col_dict[key]).encode('utf-8'))\\n myDB.put(b\\\"primary_key\\\",encoded_p)\\n myDB.put(b\\\"foreign_key\\\",encoded_f)\\n myDB.put(b\\\"referenced\\\", json.dumps(referenced).encode('utf-8'))\\n myDB.put(b\\\"referencing\\\", json.dumps(referencing).encode('utf-8'))\\n myDB.put(b\\\"columns\\\", json.dumps(list(col_dict.keys())).encode('utf-8'))\\n myDB.close()\\n print(prompt, CreateTableSuccess(table_name), sep=\\\"\\\")\\n except Exception as e:\\n print(prompt,e,sep='')\\n\\n\\n def drop_table_query(self, items):\\n try:\\n table_name = items[2]\\n file_name = f\\\"db/{table_name}.db\\\"\\n #check if the table exists\\n if not os.path.isfile(file_name):\\n raise Exception(NoSuchTable)\\n else:\\n #get the table's referenced and referencing\\n myDB = db.DB()\\n myDB.open(file_name, dbtype=db.DB_HASH)\\n referenced_by = json.loads(myDB.get(b\\\"referenced\\\"))\\n referencing = json.loads(myDB.get(b\\\"referencing\\\"))\\n myDB.close()\\n #if no table is referencing it, can drop\\n if len(referenced_by) == 0:\\n #for all table which this table is referencing,\\n #remove this table's name from their referenced list\\n for rf in referencing:\\n rf_file = f\\\"db/{rf}.db\\\"\\n rfDB = db.DB()\\n rfDB.open(rf_file, dbtype=db.DB_HASH)\\n referenced = json.loads(rfDB.get(b\\\"referenced\\\"))\\n referenced.remove(table_name)\\n rfDB.put(b\\\"referenced\\\", json.dumps(referenced).encode('utf-8'))\\n rfDB.close()\\n #delete the database file\\n os.remove(file_name)\\n print(prompt, DropSuccess(table_name), sep=\\\"\\\")\\n #if the table is referenced by more than one table, cannot drop\\n else:\\n raise Exception(DropReferencedTableError(table_name))\\n except Exception as e:\\n print(prompt, e, sep='')\\n\\n #show all tables by their file name\\n def show_tables_query(self, items):\\n print('----------------')\\n tables = os.listdir('../PRJ1-3_2015-16227/db')\\n for t in tables:\\n print(t[:-3])\\n print('----------------')\\n\\n def desc_query(self, items):\\n try:\\n table_name = items[1]\\n file_name = f\\\"db/{table_name}.db\\\"\\n #check if the table exists\\n if not os.path.isfile(file_name):\\n raise Exception(NoSuchTable)\\n else:\\n myDB = db.DB()\\n myDB.open(file_name, dbtype=db.DB_HASH)\\n print(\\\"------------------------------------------------------------\\\")\\n print(f\\\"table_name {table_name}\\\")\\n print('%-20s%-20s%-20s%-20s' % ('column_name', 'type', 'null', 'key'))\\n #get column list and iterate\\n col_names = json.loads(myDB.get(b'columns'))\\n for key in col_names:\\n val = json.loads(myDB.get(key.encode('utf-8')))\\n print('%-20s%-20s%-20s%-20s' % (key,val['type'], val['null'], val['key']))\\n myDB.close()\\n print(\\\"-----------------------------------------------------------\\\")\\n except Exception as e:\\n print(prompt,e,sep='')\\n\\n def select_query(self, items):\\n print(prompt,\\\"'SELECT' requested\\\",sep='')\\n def insert_query(self, items):\\n print(prompt,\\\"'INSERT' requested\\\",sep='')\\n def delete_query(self, items):\\n print(prompt,\\\"'DELETE' requested\\\",sep='')\\n\\n def table_element_list(self, items):\\n elem_list = items[1:-1] # get rid of LP and RP\\n return elem_list\\n\\n def table_element(self, items):\\n return items[0]\\n\\n def column_definition(self, items):\\n dict = {}\\n dict['type'] = items[1]\\n #if not null constraint, set null to N\\n if len(items) > 2:\\n dict['null'] = 'N'\\n else:\\n dict['null'] = 'Y'\\n dict['key'] = ''\\n return (items[0], dict)\\n\\n def data_type(self, items):\\n length = len(items)\\n if (length == 4):\\n #if char length smaller than 1, set type to 'error'\\n if int(items[2]) <= 0:\\n return 'error'\\n result = ''\\n for i in range(length):\\n result += str(items[i])\\n return result.lower()\\n\\n def table_constraint_definition(self, items):\\n return items[0]\\n\\n def primary_key_constraint(self, items):\\n return ['primary key', items[2]]\\n\\n def referential_constraint(self, items):\\n col_list = items[2]\\n referenced_table = items[4]\\n referenced_col_list = items[5]\\n return ['foreign key', col_list, referenced_table, referenced_col_list ]\\n\\n def column_name_list(self, items):\\n return list(items[1:-1])\\n #table name to lower case\\n def table_name(self, t):\\n (t,) = t\\n return t.lower()\\n #column name to lower case\\n def column_name(self, c):\\n (c,) = c\\n return c.lower()\\n def IDENTIFIER(self, s):\\n return str(s)\\n\\n\\nif __name__ == '__main__':\\n\\n # make sql_parser with grammar.lark file\\n with open('../PRJ1-3_2015-16227/grammar.lark') as file:\\n sql_parser = Lark(file.read(), start=\\\"command\\\", lexer=\\\"standard\\\")\\n\\n #get input until the program ended\\n while(True):\\n #get input with prompt message\\n data_input = input(prompt)\\n #if command 'exit' is given, end the program\\n if(data_input == 'exit'):\\n sys.exit()\\n #get additional input until it ends with semicolon\\n while len(data_input) == 0 or data_input[-1] != ';':\\n #to prevent error, add white space to data_input\\n data_input += ' '\\n data_input += input()\\n #split the input by semicolon\\n input_list = list(data_input.split(';'))\\n #exclude the last element of input_list since it is empty string\\n for i in range(len(input_list) - 1):\\n #restore semicolon in the end of the command\\n command = input_list[i] + ';'\\n #using try ... except, process the syntax error\\n try:\\n result = sql_parser.parse(command)\\n MyTransformer().transform(result)\\n except Exception as e:\\n print(prompt,\\\"Syntax error\\\",sep='')\\n break\\n\\n\"\n },\n {\n \"alpha_fraction\": 0.6248062252998352,\n \"alphanum_fraction\": 0.6930232644081116,\n \"avg_line_length\": 22.454545974731445,\n \"blob_id\": \"03b1fd6ed89dc9a6cb919d08d9dd80836acd375d\",\n \"content_id\": \"0f05b4d741b7544c09252549e78d1bf9240c441a\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"SQL\",\n \"length_bytes\": 1290,\n \"license_type\": \"no_license\",\n \"max_line_length\": 44,\n \"num_lines\": 55,\n \"path\": \"/sample2.sql\",\n \"repo_name\": \"h-yes-oo/database\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"create table students (\\n id int,\\n birthday date,\\n primary key(id)\\n);\\n\\ncreate table class (\\n num int,\\n start_date date,\\n primary key(num)\\n);\\n\\ncreate table enrolls (\\n id int,\\n lecture_name char(50),\\n num int,\\n primary key(lecture_name),\\n foreign key(id) references students(id),\\n foreign key(num) references class(num)\\n);\\n\\ninsert into students values(1, null);\\ninsert into students values(2, 2020-02-16);\\ninsert into students values(3, 2020-03-16);\\ninsert into students values(4, 2020-04-16);\\ninsert into students values(5, 2020-05-16);\\ninsert into students values(6, 2020-06-16);\\n\\ninsert into class values(5, null);\\ninsert into class values(6, null);\\ninsert into class values(3, 2020-03-16);\\ninsert into class values(4, 2020-03-17);\\n\\ninsert into enrolls values(1,'hihi',6);\\ninsert into enrolls values(1,'hihello',5);\\ninsert into enrolls values(2,'lec6',5);\\ninsert into enrolls values(3,'hihi3',6);\\ninsert into enrolls values(4,'hi44',3);\\ninsert into enrolls values(5,'leadfa',4);\\n\\nselect * from account;\\n\\ncreate table account (\\n ACCOUNT_NUMBER date,\\n branch_name char(20),\\n BALANCE int,\\n primary key(account_number)\\n);\\n\\ncreate table loan (\\n ACCOUNT_NUMBER date,\\n branch_name char(20),\\n BALANCE int,\\n primary key(account_number)\\n);\\n\"\n },\n {\n \"alpha_fraction\": 0.5569864511489868,\n \"alphanum_fraction\": 0.583387017250061,\n \"avg_line_length\": 34.318180084228516,\n \"blob_id\": \"8548f90a5c3151fbba66325b37ae4fbb315aad62\",\n \"content_id\": \"a1043f5d72f02b199645d5d1ee46cedbf1bd9b47\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 1553,\n \"license_type\": \"no_license\",\n \"max_line_length\": 69,\n \"num_lines\": 44,\n \"path\": \"/PRJ2_2015-16227/table.py\",\n \"repo_name\": \"h-yes-oo/database\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"#store table schemas for building, performance, audience, reservation\\nTABLES = {}\\nTABLES['building'] = (\\n \\\"CREATE TABLE `building` (\\\"\\n \\\" `id` int(11) NOT NULL AUTO_INCREMENT,\\\"\\n \\\" `name` varchar(200) NOT NULL,\\\"\\n \\\" `location` varchar(200) NOT NULL,\\\"\\n \\\" `capacity` int(11) NOT NULL,\\\"\\n \\\" PRIMARY KEY (`id`)\\\"\\n \\\") ENGINE=InnoDB\\\")\\n\\nTABLES['performance'] = (\\n \\\"CREATE TABLE `performance` (\\\"\\n \\\" `id` int(11) NOT NULL AUTO_INCREMENT,\\\"\\n \\\" `name` varchar(200) NOT NULL,\\\"\\n \\\" `type` varchar(200) NOT NULL,\\\"\\n \\\" `price` int(11) NOT NULL,\\\"\\n \\\" `building` int(11),\\\"\\n \\\" PRIMARY KEY (`id`),\\\"\\n \\\" CONSTRAINT `performance_ibfk_1` FOREIGN KEY (`building`) \\\"\\n \\\" REFERENCES `building` (`id`) ON DELETE CASCADE\\\"\\n \\\") ENGINE=InnoDB\\\")\\n\\nTABLES['audience'] = (\\n \\\"CREATE TABLE `audience` (\\\"\\n \\\" `id` int(11) NOT NULL AUTO_INCREMENT,\\\"\\n \\\" `name` varchar(200) NOT NULL,\\\"\\n \\\" `gender` char(1) NOT NULL,\\\"\\n \\\" `age` int(11) NOT NULL,\\\"\\n \\\" PRIMARY KEY (`id`)\\\"\\n \\\") ENGINE=InnoDB\\\")\\n\\nTABLES['reservation'] = (\\n \\\" CREATE TABLE `reservation` (\\\"\\n \\\" `id` int(11) NOT NULL AUTO_INCREMENT,\\\"\\n \\\" `audience` int(11) NOT NULL,\\\"\\n \\\" `performance` int(11) NOT NULL,\\\"\\n \\\" `seat` int(11) NOT NULL,\\\"\\n \\\" PRIMARY KEY (`id`),\\\"\\n \\\" CONSTRAINT `reservation_ibfk_1` FOREIGN KEY (`audience`) \\\"\\n \\\" REFERENCES `audience` (`id`) ON DELETE CASCADE,\\\"\\n \\\" CONSTRAINT `reservation_ibfk_2` FOREIGN KEY (`performance`) \\\"\\n \\\" REFERENCES `performance` (`id`) ON DELETE CASCADE\\\"\\n \\\") ENGINE=InnoDB\\\")\"\n },\n {\n \"alpha_fraction\": 0.6503198146820068,\n \"alphanum_fraction\": 0.6567164063453674,\n \"avg_line_length\": 36.540000915527344,\n \"blob_id\": \"c3da2e56bab43bab23036e836de8134f82f22662\",\n \"content_id\": \"b194fd4662cee4814a9b58b4cb555d86f4b91399\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 1876,\n \"license_type\": \"no_license\",\n \"max_line_length\": 68,\n \"num_lines\": 50,\n \"path\": \"/PRJ1-1_2015-16227/hw1/run.py\",\n \"repo_name\": \"h-yes-oo/database\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"import sys\\nfrom lark import Lark, Transformer\\n#to reuse prompt message\\nprompt = \\\"DB_2015-16227> \\\"\\n#make transformer to print appropriate message for each query\\nclass MyTransformer(Transformer):\\n def create_table_query(self, items):\\n print(prompt,\\\"'CREATE TABLE' requested\\\",sep='')\\n def drop_table_query(self, items):\\n print(prompt,\\\"'DROP TABLE' requested\\\",sep='')\\n def select_query(self, items):\\n print(prompt,\\\"'SELECT' requested\\\",sep='')\\n def insert_query(self, items):\\n print(prompt,\\\"'INSERT' requested\\\",sep='')\\n def desc_query(self, items):\\n print(prompt,\\\"'DESC' requested\\\",sep='')\\n def delete_query(self, items):\\n print(prompt,\\\"'DELETE' requested\\\",sep='')\\n def show_tables_query(self, items):\\n print(prompt,\\\"'SHOW TABLES' requested\\\",sep='')\\n#make sql_parser with grammar.lark file\\nwith open('grammar.lark') as file:\\n sql_parser = Lark(file.read(), start=\\\"command\\\", lexer=\\\"standard\\\")\\n\\nif __name__ == '__main__':\\n #get input until the program ended\\n while(True):\\n #get input with prompt message\\n data_input = input(prompt)\\n #if command 'exit' is given, end the program\\n if(data_input == 'exit'):\\n sys.exit()\\n #get additional input until it ends with semicolon\\n while len(data_input) == 0 or data_input[-1] != ';':\\n #to prevent error, add white space to data_input\\n data_input += ' '\\n data_input += input()\\n #split the input by semicolon\\n input_list = list(data_input.split(';'))\\n #exclude the last element of input_list since it is empty string\\n for i in range(len(input_list) - 1):\\n #restore semicolon in the end of the command\\n command = input_list[i] + ';'\\n #using try ... except, process the syntax error\\n try:\\n result = sql_parser.parse(command)\\n MyTransformer().transform(result)\\n except:\\n print(prompt,\\\"Syntax error\\\",sep='')\\n break\"\n },\n {\n \"alpha_fraction\": 0.4639105498790741,\n \"alphanum_fraction\": 0.47336888313293457,\n \"avg_line_length\": 36.12033462524414,\n \"blob_id\": \"324ef9a92e68851c9471edcb6d6da2f78e2fe6e5\",\n \"content_id\": \"2588cb1c2119965b5ac5d8b3f3bec1611d62cfd0\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 48740,\n \"license_type\": \"no_license\",\n \"max_line_length\": 102,\n \"num_lines\": 1313,\n \"path\": \"/PRJ1-3_2015-16227/run.py\",\n \"repo_name\": \"h-yes-oo/database\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"import sys\\nimport lark\\nfrom lark import Lark, Transformer\\nfrom berkeleydb import db\\nimport os\\nimport json\\nfrom exceptions import *\\nimport datetime\\nfrom prettytable import PrettyTable\\nimport copy\\n\\n# to reuse prompt message\\nprompt = \\\"DB_2015-16227> \\\"\\n\\n#messages\\ndef CreateTableSuccess(tableName):\\n return f\\\"'{tableName}' table is created\\\"\\n\\ndef DropSuccess(tableName):\\n return f\\\"'{tableName}' table is dropped\\\"\\n\\n\\n\\n# make transformer to print appropriate message for each query\\nclass MyTransformer(Transformer):\\n def command(self, items):\\n if not isinstance(items[0], list):\\n exit()\\n return items[0]\\n\\n def query_list(self, items):\\n return items\\n\\n def create_table_query(self, items):\\n try:\\n info_list = items[2:] #get rid of CREATE TABLE\\n table_name = info_list[0]\\n file_name = f\\\"db/{table_name}.db\\\"\\n #check if the file already exists\\n if os.path.isfile(file_name):\\n raise TableExistenceError()\\n elem_list = info_list[1]\\n #initialize variables\\n find_primary_key = False\\n primary_key = []\\n foreign_key = []\\n referencing = []\\n referenced = []\\n col_dict = {}\\n #processing table_element_list\\n for elem in elem_list:\\n if elem[0] == 'primary key':\\n #if already find primary key, raise error\\n if find_primary_key:\\n raise DuplicatePrimaryKeyDefError()\\n else:\\n find_primary_key = True\\n primary_key = elem[1]\\n #process foreign key after get all column information\\n elif elem[0] == 'foreign key':\\n continue\\n #store all column names and infos into a dict\\n else:\\n #if already have the same column, raise error\\n if elem[0] in col_dict:\\n raise DuplicateColumnDefError()\\n else:\\n col_dict[elem[0]] = elem[1]\\n #process all foreign keys\\n for elem in elem_list:\\n if elem[0] == 'foreign key':\\n fk_list = elem[1]\\n referenced_table = elem[2]\\n referenced_col_list = elem[3]\\n # check if foreign key is one of columns\\n for f in fk_list:\\n if f not in col_dict:\\n raise NonExistingColumnDefError(f)\\n # check if num of foreign keys and num of referenced cols is the same\\n if (len(fk_list) != len(referenced_col_list)):\\n raise ReferenceTypeError()\\n ref_file_name = f\\\"db/{referenced_table}.db\\\"\\n # check if the referenced_table exists\\n if not os.path.isfile(ref_file_name):\\n raise ReferenceTableExistenceError()\\n else:\\n refDB = db.DB()\\n refDB.open(ref_file_name, dbtype=db.DB_HASH)\\n ref_pk_list = json.loads(refDB.get(b\\\"primary_key\\\"))\\n ref_col_list = json.loads(refDB.get(b\\\"columns\\\"))\\n # check if referenced table has foreign key\\n for rf_col in referenced_col_list:\\n if rf_col not in ref_col_list:\\n raise ReferenceColumnExistenceError()\\n # check if foreign key is primary key of referenced table\\n for rf_col in referenced_col_list:\\n if rf_col not in ref_pk_list:\\n raise ReferenceNonPrimaryKeyError()\\n # check if foreign key contains all primary keys of referenced table\\n if len(referenced_col_list) != len(ref_pk_list):\\n raise ReferenceNonPrimaryKeyError()\\n else:\\n for i in range(len(fk_list)):\\n # check if fk_list[i] and referenced_col_list[i] has the same type\\n ori_type = json.loads(refDB.get(referenced_col_list[i].encode('utf-8')))['type']\\n fk_type = col_dict[fk_list[i]]['type']\\n if ori_type != fk_type:\\n raise ReferenceTypeError()\\n #ready to reference\\n #add referenced_table to referencing list of this new table\\n referencing.append(referenced_table)\\n #add this new table to referenced list of the referenced_table\\n rf_by = json.loads(refDB.get(b\\\"referenced\\\"))\\n rf_by.append(table_name)\\n refDB.put(b\\\"referenced\\\", json.dumps(rf_by).encode('utf-8'))\\n #add all foreign key information to foreign_key\\n ordered_fk_list = ['' for _ in range(len(fk_list))]\\n for idx in range(len(fk_list)):\\n pk = referenced_col_list[idx]\\n ori_idx = ref_pk_list.index(pk)\\n ordered_fk_list[ori_idx] = fk_list[idx]\\n\\n foreign_key.append([ordered_fk_list, referenced_table, ref_pk_list])\\n refDB.close()\\n #check if primary_key and foreign_key is in column list and change their properties\\n for p in primary_key:\\n if p in col_dict:\\n col_dict[p]['null'] = 'N'\\n col_dict[p]['key'] = 'PRI'\\n else:\\n raise NonExistingColumnDefError(p)\\n\\n for fk_info in foreign_key:\\n for f in fk_info[0]:\\n if f in col_dict:\\n if col_dict[f]['key'] == '':\\n col_dict[f]['key'] = 'FOR'\\n else:\\n col_dict[f]['key'] += '/FOR'\\n else:\\n raise NonExistingColumnDefError(f)\\n '''\\n for f in foreign_key:\\n if f in col_dict:\\n if col_dict[f]['key'] == '':\\n col_dict[f]['key'] = 'FOR'\\n else:\\n col_dict[f]['key'] += '/FOR'\\n else:\\n raise NonExistingColumnDefError(f)\\n '''\\n #encode variables and put to database\\n encoded_p = json.dumps(primary_key).encode('utf-8')\\n encoded_f = json.dumps(foreign_key).encode('utf-8')\\n\\n myDB = db.DB()\\n myDB.open(file_name, dbtype=db.DB_HASH, flags=db.DB_CREATE)\\n for key in col_dict.keys():\\n myDB.put(key.encode('utf-8'), json.dumps(col_dict[key]).encode('utf-8'))\\n myDB.put(b\\\"primary_key\\\",encoded_p)\\n myDB.put(b\\\"foreign_key\\\",encoded_f)\\n myDB.put(b\\\"referenced\\\", json.dumps(referenced).encode('utf-8'))\\n myDB.put(b\\\"referencing\\\", json.dumps(referencing).encode('utf-8'))\\n myDB.put(b\\\"columns\\\", json.dumps(list(col_dict.keys())).encode('utf-8'))\\n myDB.close()\\n print(prompt, CreateTableSuccess(table_name), sep=\\\"\\\")\\n except Exception as e:\\n print(prompt,e,sep='')\\n\\n\\n def drop_table_query(self, items):\\n try:\\n table_name = items[2]\\n file_name = f\\\"db/{table_name}.db\\\"\\n #check if the table exists\\n if not os.path.isfile(file_name):\\n raise NoSuchTable()\\n else:\\n #get the table's referenced and referencing\\n myDB = db.DB()\\n myDB.open(file_name, dbtype=db.DB_HASH)\\n referenced_by = json.loads(myDB.get(b\\\"referenced\\\"))\\n referencing = json.loads(myDB.get(b\\\"referencing\\\"))\\n myDB.close()\\n #if no table is referencing it, can drop\\n if len(referenced_by) == 0:\\n #for all table which this table is referencing,\\n #remove this table's name from their referenced list\\n for rf in referencing:\\n rf_file = f\\\"db/{rf}.db\\\"\\n rfDB = db.DB()\\n rfDB.open(rf_file, dbtype=db.DB_HASH)\\n referenced = json.loads(rfDB.get(b\\\"referenced\\\"))\\n referenced.remove(table_name)\\n rfDB.put(b\\\"referenced\\\", json.dumps(referenced).encode('utf-8'))\\n rfDB.close()\\n #delete the database file\\n os.remove(file_name)\\n print(prompt, DropSuccess(table_name), sep=\\\"\\\")\\n #if the table is referenced by more than one table, cannot drop\\n else:\\n raise DropReferencedTableError(table_name)\\n except Exception as e:\\n print(prompt, e, sep='')\\n\\n #show all tables by their file name\\n def show_tables_query(self, items):\\n print('----------------')\\n tables = os.listdir('../PRJ1-3_2015-16227/db')\\n for t in tables:\\n print(t[:-3])\\n print('----------------')\\n\\n def desc_query(self, items):\\n try:\\n table_name = items[1]\\n file_name = f\\\"db/{table_name}.db\\\"\\n #check if the table exists\\n if not os.path.isfile(file_name):\\n raise NoSuchTable()\\n else:\\n myDB = db.DB()\\n myDB.open(file_name, dbtype=db.DB_HASH)\\n print(\\\"------------------------------------------------------------\\\")\\n print(f\\\"table_name {table_name}\\\")\\n print('%-20s%-20s%-20s%-20s' % ('column_name', 'type', 'null', 'key'))\\n #get column list and iterate\\n col_names = json.loads(myDB.get(b'columns'))\\n for key in col_names:\\n val = json.loads(myDB.get(key.encode('utf-8')))\\n print('%-20s%-20s%-20s%-20s' % (key,val['type'], val['null'], val['key']))\\n myDB.close()\\n print(\\\"-----------------------------------------------------------\\\")\\n except Exception as e:\\n print(prompt,e,sep='')\\n\\n def select_query(self, items):\\n cols, col_nick = items[1]\\n table_expression = items[2]\\n where = table_expression[0]\\n tables, nickname = table_expression[1][0]\\n #check if all tables exists\\n existing_tables = os.listdir('../PRJ1-3_2015-16227/db')\\n existing_tables = list(map(lambda x : x[:-3], existing_tables))\\n for table in tables:\\n if table not in existing_tables:\\n raise SelectTableExistenceError(table)\\n columns = []\\n all_cols = []\\n for table in tables:\\n myDB = db.DB()\\n myDB.open(f\\\"db/{table}.db\\\", dbtype=db.DB_HASH)\\n col_names = json.loads(myDB.get(b'columns'))\\n for col in col_names:\\n all_cols.append(f\\\"{table}.{col}\\\")\\n myDB.close()\\n # if col list is *, add all columns of tables\\n if len(cols) == 0:\\n columns = all_cols\\n else:\\n #if col list is given, check if the cols are all right and sort by tables\\n cols_copy = [ c for c in cols ]\\n for table in tables:\\n myDB = db.DB()\\n myDB.open(f\\\"db/{table}.db\\\", dbtype=db.DB_HASH)\\n col_names = json.loads(myDB.get(b'columns'))\\n for col in cols:\\n col_info = col.split('.')\\n if len(col_info) > 1:\\n if col_info[0] == table or (col_info[0] in nickname and nickname[col_info[0]] == table):\\n if col_info[1] in col_names:\\n columns.append(f\\\"{table}.{col_info[1]}\\\")\\n try:\\n cols_copy.remove(col)\\n except:\\n #if duplicate column name exist, ambiguous\\n raise SelectColumnResolveError(col)\\n else:\\n raise SelectColumnResolveError(col)\\n else:\\n if col in col_names:\\n columns.append(f\\\"{table}.{col}\\\")\\n try:\\n cols_copy.remove(col)\\n except:\\n #if duplicate column name exist, ambiguous\\n raise SelectColumnResolveError(col)\\n myDB.close()\\n #if not existing column\\n if len(cols_copy) > 0:\\n raise SelectColumnResolveError(cols_copy[0])\\n\\n #get all possible result from specified tables\\n rows = []\\n for table in tables:\\n infos = []\\n myDB = db.DB()\\n myDB.open(f\\\"db/{table}.db\\\", dbtype=db.DB_HASH)\\n col_names = json.loads(myDB.get(b'columns'))\\n info_list = list(map(lambda col: bytes(col, encoding=\\\"utf-8\\\"), col_names))\\n info_list += [b'primary_key', b'foreign_key', b'referencing', b'referenced', b'columns']\\n cursor = myDB.cursor()\\n data = cursor.first()\\n while data:\\n key, value = data\\n val_dict = json.loads(value)\\n if key not in info_list:\\n info = []\\n for col in all_cols:\\n if len(col.split('.')) > 1:\\n t_name, col_name = col.split('.')\\n if t_name == table or (t_name in nickname and nickname[t_name] == table):\\n if val_dict[col_name] is None:\\n info.append('null')\\n else:\\n info.append(val_dict[col_name])\\n else:\\n info.append(None)\\n else:\\n if col in val_dict:\\n if val_dict[col] is None:\\n info.append('null')\\n else:\\n info.append(val_dict[col])\\n else:\\n info.append(None)\\n infos.append(info)\\n data = cursor.next()\\n myDB.close()\\n rows.append(infos)\\n #get cartesian join of the result\\n for i in range(1,len(rows)):\\n new_row = []\\n for j in range(len(rows[i])):\\n for row in rows[i-1]:\\n r = [x if y is None else y for x,y in zip(rows[i][j], row)]\\n new_row.append(r)\\n rows[i] = new_row\\n \\n final_row = [r for r in rows[-1]]\\n\\n if where:\\n #with where clause\\n condition = table_expression[1][1]\\n predicates = []\\n p_col = {}\\n def flatten(l):\\n for i in l:\\n if type(i) == list or (type(i) == tuple and len(i) == 2):\\n flatten(i)\\n else:\\n if type(i) == tuple and len(i) == 3:\\n predicates.append(i)\\n flatten(condition)\\n #check if comparable\\n for idx in range(len(predicates)):\\n p = predicates[idx]\\n oper = p[1]\\n if oper in ['is','is not']:\\n #null predicate\\n #find if the col name is valid\\n col_name = p[0]\\n if col_name in col_nick:\\n col_name = col_nick[col_name]\\n if len(col_name.split('.')) > 1:\\n t_n , c_n = col_name.split('.')\\n #check if specified table\\n if t_n not in tables:\\n if t_n not in nickname:\\n raise WhereTableNotSpecified()\\n else:\\n t_n = nickname[t_n]\\n #check if the col exist\\n myDB = db.DB()\\n myDB.open(f\\\"db/{t_n}.db\\\", dbtype=db.DB_HASH)\\n c_list = json.loads(myDB.get(b'columns'))\\n if c_n not in c_list:\\n raise WhereColumnNotExist()\\n else:\\n predicates[idx] = (f\\\"{t_n}.{c_n}\\\",p[1],p[2])\\n p_col[col_name] = f\\\"{t_n}.{c_n}\\\"\\n myDB.close()\\n else:\\n Find = False\\n for t_n in tables:\\n myDB = db.DB()\\n myDB.open(f\\\"db/{t_n}.db\\\", dbtype=db.DB_HASH)\\n c_list = json.loads(myDB.get(b'columns'))\\n if col_name in c_list:\\n if Find:\\n #if same col name found in two different tables, ambiguous\\n raise WhereAmbiguousReference()\\n else:\\n Find = True\\n predicates[idx] = (f\\\"{t_n}.{col_name}\\\",p[1],p[2])\\n p_col[col_name] = f\\\"{t_n}.{col_name}\\\"\\n myDB.close()\\n if not Find:\\n raise WhereColumnNotExist()\\n else:\\n #comparison predicate\\n two_type = []\\n stripped_tuple = [p[0],p[1],p[2]]\\n for op_idx in (0,2):\\n if p[op_idx][0] in ['str','int','date']:\\n two_type.append(p[op_idx][0])\\n stripped_tuple[op_idx] = p[op_idx][1]\\n else:\\n #if col name, check if valid col name\\n col_name = p[op_idx][1]\\n if col_name in col_nick:\\n col_name = col_nick[col_name]\\n if len(col_name.split('.')) > 1:\\n t_n , c_n = col_name.split('.')\\n #check if specified table\\n if t_n not in tables:\\n if t_n not in nickname:\\n raise WhereTableNotSpecified()\\n else:\\n t_n = nickname[t_n]\\n #check if the col exist\\n myDB = db.DB()\\n myDB.open(f\\\"db/{t_n}.db\\\", dbtype=db.DB_HASH)\\n c_list = json.loads(myDB.get(b'columns'))\\n if c_n in c_list:\\n #find it\\n stripped_tuple[op_idx] = f\\\"{t_n}.{c_n}\\\"\\n p_col[col_name] = f\\\"{t_n}.{c_n}\\\"\\n type_s = json.loads(myDB.get(c_n.encode('utf-8')))['type']\\n if type_s in ['int','date']:\\n two_type.append(type_s)\\n else:\\n two_type.append('str')\\n else:\\n raise WhereColumnNotExist()\\n myDB.close()\\n else:\\n Find = False\\n find_type = ''\\n for t_n in tables:\\n myDB = db.DB()\\n myDB.open(f\\\"db/{t_n}.db\\\", dbtype=db.DB_HASH)\\n c_list = json.loads(myDB.get(b'columns'))\\n if col_name in c_list:\\n if Find:\\n #if same col name found in two different tables, ambiguous\\n raise WhereAmbiguousReference()\\n else:\\n Find = True\\n stripped_tuple[op_idx] = f\\\"{t_n}.{col_name}\\\"\\n p_col[col_name] = f\\\"{t_n}.{col_name}\\\"\\n type_s = json.loads(myDB.get(col_name.encode('utf-8')))['type']\\n if type_s in ['int','date']:\\n find_type = type_s\\n else:\\n find_type = 'str'\\n myDB.close()\\n if Find:\\n two_type.append(find_type)\\n else:\\n raise WhereColumnNotExist()\\n if len(two_type) == 2:\\n if two_type[0] != two_type[1]:\\n raise WhereIncomparableError()\\n predicates[idx] = tuple(stripped_tuple)\\n\\n final = []\\n\\n for row in final_row:\\n cond = copy.deepcopy(condition)\\n def evaluate(l,row):\\n for idx in range(len(l)):\\n i=l[idx]\\n if type(i) == list or (type(i) == tuple and len(i) == 2):\\n evaluate(i,row)\\n else:\\n if type(i) == tuple and len(i) == 3:\\n oper = i[1]\\n if oper in ['is','is not']:\\n #null predicate\\n col_name = i[0]\\n if col_name in col_nick:\\n col_name = col_nick[col_name]\\n if col_name in p_col:\\n col_name = p_col[col_name]\\n ridx = all_cols.index(col_name)\\n if oper == 'is':\\n if row[ridx] == 'null':\\n l[idx] = True\\n else:\\n l[idx] = False\\n elif oper == 'is not':\\n if row[ridx] == 'null':\\n l[idx] = False\\n else:\\n l[idx] = True\\n else:\\n #comparison predicate\\n two_type = []\\n eval = [i[0],i[1],i[2]]\\n #if val op col\\n if i[0][0] in ['str','int','date'] and i[2][0] == 'col':\\n eval[0] = i[0][1]\\n col_name = i[2][1]\\n if col_name in col_nick:\\n col_name = col_nick[col_name]\\n if col_name in p_col:\\n col_name = p_col[col_name]\\n ridx = all_cols.index(col_name)\\n eval[2] = row[ridx]\\n #elif col op val\\n elif i[2][0] in ['str','int','date'] and i[0][0] == 'col':\\n eval[2] = i[2][1]\\n col_name = i[0][1]\\n if col_name in col_nick:\\n col_name = col_nick[col_name]\\n if col_name in p_col:\\n col_name = p_col[col_name]\\n ridx = all_cols.index(col_name)\\n eval[0] = row[ridx]\\n #elif col op col\\n elif i[2][0] == 'col' and i[0][0] == 'col':\\n col_name = i[0][1]\\n if col_name in col_nick:\\n col_name = col_nick[col_name]\\n if col_name in p_col:\\n col_name = p_col[col_name]\\n ridx = all_cols.index(col_name)\\n eval[0] = row[ridx]\\n col_name = i[2][1]\\n if col_name in col_nick:\\n col_name = col_nick[col_name]\\n if col_name in p_col:\\n col_name = p_col[col_name]\\n ridx = all_cols.index(col_name)\\n eval[2] = row[ridx]\\n #elif val op val\\n else:\\n eval[0] = i[0][1]\\n eval[2] = i[2][1]\\n #evaluate\\n if eval[1] == \\\"<\\\":\\n if eval[0] < eval[2]:\\n l[idx] = True\\n else: \\n l[idx] = False\\n if eval[1] == \\\">\\\":\\n if eval[0] > eval[2]:\\n l[idx] = True\\n else: \\n l[idx] = False\\n if eval[1] == \\\"=\\\":\\n if eval[0] == eval[2]:\\n l[idx] = True\\n else: \\n l[idx] = False\\n if eval[1] == \\\">=\\\":\\n if eval[0] >= eval[2]:\\n l[idx] = True\\n else: \\n l[idx] = False\\n if eval[1] == \\\"<=\\\":\\n if eval[0] <= eval[2]:\\n l[idx] = True\\n else: \\n l[idx] = False\\n if eval[1] == \\\"!=\\\":\\n if eval[0] != eval[2]:\\n l[idx] = True\\n else: \\n l[idx] = False\\n \\n def combine(l):\\n for idx in range(len(l)):\\n i=l[idx]\\n if type(i) == list:\\n if len(i) == 1 and type(i[0]) == bool:\\n l[idx] = i[0]\\n if len(i) > 1 and i[0] == 'not' and type(i[1]) == bool:\\n l[idx] = not i[1]\\n if len(i) > 1 and i[0] == 'and' and all(type(x) == bool for x in i[1]):\\n if all(x for x in i[1]):\\n l[idx] = True\\n else:\\n l[idx] = False\\n elif len(i) > 1 and i[0] == 'or' and all(type(x) == bool for x in i[1]):\\n if any(x for x in i[1]):\\n l[idx] = True\\n else:\\n l[idx] = False\\n else:\\n combine(i)\\n\\n evaluate(cond,row)\\n while not all(type(x) == bool for x in cond[1]):\\n combine(cond)\\n\\n if any(x for x in cond[1]):\\n final.append(row)\\n \\n result = PrettyTable(list(map(lambda x: x.upper(),columns)))\\n col_index = list(map(lambda x: all_cols.index(x),columns))\\n for r in final:\\n rr = list(map(lambda x: r[x], col_index))\\n result.add_row(rr)\\n print(result)\\n\\n else:\\n #without where cluase\\n result = PrettyTable(list(map(lambda x: x.upper(),columns)))\\n col_index = list(map(lambda x: all_cols.index(x),columns))\\n for r in final_row:\\n rr = list(map(lambda x: r[x], col_index))\\n result.add_row(rr)\\n print(result)\\n\\n def null_operation(self, items):\\n if len(items) == 3:\\n return False\\n else:\\n return True\\n\\n def null_predicate(self, items):\\n if items[-1]:\\n if len(items) > 2:\\n return (f\\\"{items[0]}.{items[1]}\\\",\\\"is\\\",None)\\n else:\\n return(items[0],\\\"is\\\",None)\\n else:\\n if len(items) > 2:\\n return (f\\\"{items[0]}.{items[1]}\\\",\\\"is\\\",None)\\n else:\\n return(items[0],\\\"is not\\\",None)\\n\\n def where_clause(self, items):\\n #remove WHERE cluase\\n return items[1]\\n \\n def boolean_expr(self, items):\\n return ['or',items[0::2]]\\n\\n def boolean_term(self, items):\\n return ['and',items[0::2]]\\n\\n def comparison_predicate(self, items):\\n return tuple(items)\\n\\n def comp_operand(self, items):\\n try:\\n type = items[0].type\\n if type == 'INT':\\n return ('int',int(items[0]))\\n elif type == 'DATE':\\n return ('date',datetime.datetime.strptime(items[0],'%Y-%m-%d'))\\n elif type == 'STR':\\n return ('str',items[0][1:-1])\\n except:\\n if len(items) > 1:\\n return ('col',f\\\"{items[0]}.{items[1]}\\\")\\n else:\\n return ('col',items[0])\\n \\n def COMP_OP(self, items):\\n return items\\n \\n def predicate(self, items):\\n return items[0]\\n\\n def boolean_test(self,items):\\n return items[0]\\n \\n def parenthesized_boolean_expr(self, items):\\n return [items[1]]\\n\\n def boolean_factor(self, items):\\n if len(items) > 1:\\n return ['not', items[-1]]\\n else:\\n return items[-1]\\n\\n def select_list(self, items):\\n col_sel = []\\n col_nick = {}\\n for i in items:\\n if i[0]:\\n col_nick[i[-1]] = i[1]\\n col_sel.append(i[1])\\n return (col_sel, col_nick)\\n\\n def selected_column(self, items):\\n case = len(items)\\n if case == 1:\\n #only column_name given\\n return (False, items[0])\\n elif case == 2:\\n #table name and column name given\\n return (False, f\\\"{items[0]}.{items[1]}\\\")\\n elif case == 3:\\n #column name and nickname given\\n return (True, items[0], items[-1])\\n elif case == 4:\\n #table name and column name nickname given\\n return (True, f\\\"{items[0]}.{items[1]}\\\", items[-1])\\n\\n def table_expression(self, items):\\n return (len(items) > 1 , items)\\n\\n def from_clause(self, items):\\n return items[-1]\\n\\n def table_reference_list(self, items):\\n tables = []\\n nickname = {}\\n for i in items:\\n if i[0]:\\n nickname[i[-1]] = i[1]\\n tables.append(i[1])\\n return(tables, nickname)\\n\\n def referred_table(self, items):\\n if len(items) > 1:\\n return(True,items[0],items[-1])\\n else:\\n return(False, items[0])\\n\\n def insert_query(self, items):\\n table_name = items[2]\\n file_name = f\\\"db/{table_name}.db\\\"\\n if not os.path.isfile(file_name):\\n raise NoSuchTable()\\n else:\\n myDB = db.DB()\\n myDB.open(file_name, dbtype=db.DB_HASH)\\n #check if number of column matches\\n original_cols = json.loads(myDB.get(b'columns'))\\n column_name_list = original_cols\\n insert_info = items[-1]\\n if len(insert_info) > 1:\\n column_name_list = insert_info[0]\\n if len(column_name_list) != len(original_cols):\\n raise InsertTypeMismatchError()\\n for col in column_name_list:\\n if col not in original_cols:\\n raise InsertColumnExistenceError(col)\\n value_list = insert_info[-1]\\n\\n if len(column_name_list) != len(value_list):\\n raise InsertTypeMismatchError()\\n\\n data_dict = {'referencing': [], 'referenced': []}\\n\\n for i in range(len(value_list)):\\n col_name = column_name_list[i]\\n val = value_list[i]\\n col_info = json.loads(myDB.get(col_name.encode('utf-8')))\\n #check if nullable\\n nullable = True\\n if col_info['null'] == 'N':\\n nullable = False\\n #if value is null, check if nullable and store value into dict\\n if val is None:\\n if nullable:\\n data_dict[col_name] = None\\n else:\\n raise InsertColumnNonNullableError(col_name)\\n #if value is not null, check if type is right\\n else:\\n required_type = col_info['type']\\n if required_type == 'int':\\n if isinstance(val, int):\\n data_dict[col_name] = val\\n else:\\n raise InsertTypeMismatchError()\\n elif required_type == 'date':\\n if isinstance(val, datetime.date):\\n data_dict[col_name] = val.strftime('%Y-%m-%d')\\n else:\\n raise InsertTypeMismatchError()\\n else:\\n #if required type is string\\n if isinstance(val, str):\\n str_len = int(required_type[5:-1])\\n #only store to max size of string\\n data_dict[col_name] = val[0:str_len]\\n else:\\n raise InsertTypeMismatchError()\\n\\n #find if foreign key values exist in the referenced table\\n foreign_key = json.loads(myDB.get(b'foreign_key'))\\n for fk_info in foreign_key:\\n fk_list = fk_info[0]\\n referenced = fk_info[1]\\n ori_cols = fk_info[2]\\n\\n rf_file_name = f\\\"db/{referenced}.db\\\"\\n rfDB = db.DB()\\n rfDB.open(rf_file_name, dbtype=db.DB_HASH)\\n\\n pk_idx = []\\n for fk_idx in range(len(fk_list)):\\n pk_idx.append(data_dict[fk_list[fk_idx]])\\n\\n #if all the foreign keys are null, not have to find, else, find the data from referenced table\\n if not all(pk is None for pk in pk_idx):\\n rf_pidx = json.dumps(pk_idx).encode('utf-8')\\n referenced_data = rfDB.get(rf_pidx)\\n if referenced_data is None:\\n raise InsertReferentialIntegrityError()\\n else:\\n ori_referencing = data_dict['referencing']\\n ori_referencing.append((referenced, json.loads(rf_pidx)))\\n data_dict['referencing'] = ori_referencing\\n\\n rfDB.close()\\n\\n\\n #make key to store in berkely db\\n primary_idx = []\\n primary_key = json.loads(myDB.get(b'primary_key'))\\n\\n for pk in primary_key:\\n primary_idx.append(data_dict[pk])\\n pidx = json.dumps(primary_idx).encode('utf-8')\\n\\n #find if data with the same primary key values exists\\n cursor = myDB.cursor()\\n data = cursor.first()\\n while data:\\n key, value = data\\n if key == pidx:\\n raise InsertDuplicatePrimaryKeyError()\\n data = cursor.next()\\n\\n #insert new data to database\\n myDB.put(pidx, json.dumps(data_dict).encode('utf-8'))\\n for info in data_dict['referencing']:\\n rf_table = info[0]\\n rf_pidx = json.dumps(info[1]).encode('utf-8')\\n rfDB = db.DB()\\n rfDB.open(f\\\"db/{rf_table}.db\\\", dbtype=db.DB_HASH)\\n rf_dict = json.loads(rfDB.get(rf_pidx))\\n rf_referenced = rf_dict['referenced']\\n rf_referenced.append((table_name,primary_idx))\\n rf_dict['referenced'] = rf_referenced\\n rfDB.put(rf_pidx, json.dumps(rf_dict).encode('utf-8'))\\n rfDB.close()\\n\\n myDB.close()\\n print(prompt,\\\"The row is inserted\\\",sep='')\\n\\n\\n\\n def insert_columns_and_sources(self, items):\\n return items\\n\\n def value_list(self, items):\\n return items[2:-1]\\n\\n def value(self, items):\\n type = items[0].type\\n if type == 'INT':\\n return int(items[0])\\n elif type == 'DATE':\\n return datetime.datetime.strptime(items[0],'%Y-%m-%d')\\n elif type == 'STR':\\n return items[0][1:-1]\\n elif type == 'NULL':\\n return None\\n\\n def comparable_value(self, items):\\n return items[0]\\n\\n def delete_query(self, items):\\n table_name = items[2]\\n file_name = f\\\"db/{table_name}.db\\\"\\n # check if the file already exists\\n if not os.path.isfile(file_name):\\n raise NoSuchTable()\\n else:\\n myDB = db.DB()\\n myDB.open(file_name, dbtype=db.DB_HASH)\\n #info_list contains each key which is not a key for instance\\n columns = json.loads(myDB.get(b'columns'))\\n info_list = list(map(lambda col : bytes(col, encoding=\\\"utf-8\\\"), columns))\\n info_list += [b'primary_key',b'foreign_key',b'referencing',b'referenced',b'columns']\\n #to count\\n yes_count = 0\\n no_count = 0\\n #referenced information\\n referenced = json.loads(myDB.get(b'referenced'))\\n ref_info = {}\\n ref_colnames = {}\\n for table in referenced:\\n rfDB = db.DB()\\n rfDB.open(f\\\"db/{table}.db\\\", dbtype=db.DB_HASH)\\n rf_fk_info = json.loads(rfDB.get(b'foreign_key'))\\n for fk_info in rf_fk_info:\\n if fk_info[1] == table_name:\\n fk_list = fk_info[0]\\n can_delete = True\\n for col in fk_list:\\n if json.loads(rfDB.get(col.encode(\\\"utf-8\\\")))['null'] == 'N':\\n can_delete = False\\n break\\n #store info boolean representing if all referenced cols are nullable\\n ref_info[table] = can_delete\\n ref_colnames[table] = fk_list\\n break\\n rfDB.close()\\n #no where clause, delete all\\n if (len(items) == 3):\\n cursor = myDB.cursor()\\n data = cursor.first()\\n while data:\\n key, value = data\\n if key not in info_list:\\n #find if this data can be deleted\\n referenced = json.loads(value)['referenced']\\n can_delete = True\\n for rf in referenced:\\n rf_table = rf[0]\\n if ref_info[rf_table] == False:\\n can_delete = False\\n if can_delete:\\n #delete this data from referenced\\n for rf in referenced:\\n rf_table = rf[0]\\n rf_key = json.dumps(rf[1]).encode('utf-8')\\n rfDB = db.DB()\\n rfDB.open(f\\\"db/{rf_table}.db\\\", dbtype=db.DB_HASH)\\n rf_dict = json.loads(rfDB.get(rf_key))\\n #remove this value from 'referencing' of referenced value\\n rf_referencing = rf_dict['referencing']\\n def checkThis(x):\\n return not (json.dumps(x[1]).encode('utf-8') == key and x[0] == table_name)\\n rf_referencing = list(filter(checkThis, rf_referencing))\\n rf_dict['referencing'] = rf_referencing\\n #change all referenced col values to null\\n ref_col = ref_colnames[rf_table]\\n for col in ref_col:\\n rf_dict[col] = None\\n #store edited value\\n rfDB.put(rf_key, json.dumps(rf_dict).encode('utf-8'))\\n rfDB.close()\\n\\n #delete this data from referencing\\n referencing = json.loads(value)['referencing']\\n for rfc in referencing:\\n rfc_table = rfc[0]\\n rfc_key = json.dumps(rfc[1]).encode('utf-8')\\n rfcDB = db.DB()\\n rfcDB.open(f\\\"db/{rfc_table}.db\\\", dbtype=db.DB_HASH)\\n rfc_dict = json.loads(rfcDB.get(rfc_key))\\n #remove this value from 'referenced' of referencing value\\n rfc_referenced = rfc_dict['referenced']\\n def checkThis(x):\\n return not (json.dumps(x[1]).encode('utf-8') == key and x[0] == table_name)\\n rfc_referenced = list(filter(checkThis, rfc_referenced))\\n rfc_dict['referenced'] = rfc_referenced\\n #store edited value\\n rfcDB.put(rfc_key, json.dumps(rfc_dict).encode('utf-8'))\\n rfcDB.close()\\n #delete this value from database\\n myDB.delete(key)\\n yes_count += 1\\n else:\\n #cannot delete this value due to referential integrity\\n no_count += 1\\n data = cursor.next()\\n #with where clause\\n else:\\n condition = items[3]\\n predicates = []\\n p_col = []\\n def flatten(l):\\n for i in l:\\n if type(i) == list or (type(i) == tuple and len(i) == 2):\\n flatten(i)\\n else:\\n if type(i) == tuple and len(i) == 3:\\n predicates.append(i)\\n flatten(condition)\\n #check if comparable\\n for idx in range(len(predicates)):\\n p = predicates[idx]\\n oper = p[1]\\n if oper in ['is','is not']:\\n #null predicate\\n #find if the col name is valid\\n col_name = p[0]\\n if len(col_name.split('.')) > 1:\\n t_n , c_n = col_name.split('.')\\n #check if specified table\\n if t_n != table_name:\\n raise WhereTableNotSpecified()\\n #check if the col exist\\n c_list = json.loads(myDB.get(b'columns'))\\n if c_n not in c_list:\\n raise WhereColumnNotExist()\\n else:\\n p_col.append(c_n)\\n myDB.close()\\n else:\\n c_list = json.loads(myDB.get(b'columns'))\\n if col_name in c_list:\\n p_col.append(col_name)\\n else:\\n raise WhereColumnNotExist()\\n else:\\n #comparison predicate\\n two_type = []\\n for op_idx in (0,2):\\n if p[op_idx][0] in ['str','int','date']:\\n two_type.append(p[op_idx][0])\\n else:\\n #if col name, check if valid col name\\n col_name = p[op_idx][1]\\n if len(col_name.split('.')) > 1:\\n t_n , c_n = col_name.split('.')\\n #check if specified table\\n if t_n != table_name:\\n raise WhereTableNotSpecified()\\n else:\\n c_list = json.loads(myDB.get(b'columns'))\\n if c_n in c_list:\\n #find it\\n p_col.append(c_n)\\n type_s = json.loads(myDB.get(c_n.encode('utf-8')))['type']\\n if type_s in ['int','date']:\\n two_type.append(type_s)\\n else:\\n two_type.append('str')\\n else:\\n raise WhereColumnNotExist()\\n else:\\n find_type = ''\\n c_list = json.loads(myDB.get(b'columns'))\\n if col_name in c_list:\\n p_col.append(col_name)\\n type_s = json.loads(myDB.get(col_name.encode('utf-8')))['type']\\n if type_s in ['int','date']:\\n find_type = type_s\\n else:\\n find_type = 'str'\\n two_type.append(find_type)\\n else:\\n raise WhereColumnNotExist()\\n if len(two_type) == 2:\\n if two_type[0] != two_type[1]:\\n raise WhereIncomparableError()\\n\\n #iterate through all data in this table\\n cursor = myDB.cursor()\\n data = cursor.first()\\n while data:\\n key, value = data\\n if key not in info_list:\\n #check if this satify where clause\\n cond = copy.deepcopy(condition)\\n\\n def evaluate(l,value_dict):\\n for idx in range(len(l)):\\n i=l[idx]\\n if type(i) == list or (type(i) == tuple and len(i) == 2):\\n evaluate(i,value_dict)\\n else:\\n if type(i) == tuple and len(i) == 3:\\n oper = i[1]\\n if oper in ['is','is not']:\\n #null predicate\\n col_name = i[0].split('.')[-1]\\n if oper == 'is':\\n if value_dict[col_name] is None:\\n l[idx] = True\\n else:\\n l[idx] = False\\n elif oper == 'is not':\\n if value_dict[col_name] is not None:\\n l[idx] = True\\n else:\\n l[idx] = False\\n else:\\n #comparison predicate\\n two_type = []\\n eval = [i[0],i[1],i[2]]\\n #if val op col\\n if i[0][0] in ['str','int','date'] and i[2][0] == 'col':\\n eval[0] = i[0][1]\\n col_name = i[2][1].split('.')[-1]\\n eval[2] = value_dict[col_name]\\n #elif col op val\\n elif i[2][0] in ['str','int','date'] and i[0][0] == 'col':\\n eval[2] = i[2][1]\\n col_name = i[0][1].split('.')[-1]\\n eval[0] = value_dict[col_name]\\n #elif col op col\\n elif i[2][0] == 'col' and i[0][0] == 'col':\\n col_name = i[0][1].split('.')[-1]\\n eval[0] = value_dict[col_name]\\n col_name = i[2][1].split('.')[-1]\\n eval[2] = value_dict[col_name]\\n #elif val op val\\n else:\\n eval[0] = i[0][1]\\n eval[2] = i[2][1]\\n #evaluate\\n if eval[1] == \\\"<\\\":\\n if eval[0] < eval[2]:\\n l[idx] = True\\n else: \\n l[idx] = False\\n if eval[1] == \\\">\\\":\\n if eval[0] > eval[2]:\\n l[idx] = True\\n else: \\n l[idx] = False\\n if eval[1] == \\\"=\\\":\\n if eval[0] == eval[2]:\\n l[idx] = True\\n else: \\n l[idx] = False\\n if eval[1] == \\\">=\\\":\\n if eval[0] >= eval[2]:\\n l[idx] = True\\n else: \\n l[idx] = False\\n if eval[1] == \\\"<=\\\":\\n if eval[0] <= eval[2]:\\n l[idx] = True\\n else: \\n l[idx] = False\\n if eval[1] == \\\"!=\\\":\\n if eval[0] != eval[2]:\\n l[idx] = True\\n else: \\n l[idx] = False\\n def combine(l):\\n for idx in range(len(l)):\\n i=l[idx]\\n if type(i) == list:\\n if len(i) == 1 and type(i[0]) == bool:\\n l[idx] = i[0]\\n if len(i) > 1 and i[0] == 'not' and type(i[1]) == bool:\\n l[idx] = not i[1]\\n if len(i) > 1 and i[0] == 'and' and all(type(x) == bool for x in i[1]):\\n if all(x for x in i[1]):\\n l[idx] = True\\n else:\\n l[idx] = False\\n elif len(i) > 1 and i[0] == 'or' and all(type(x) == bool for x in i[1]):\\n if any(x for x in i[1]):\\n l[idx] = True\\n else:\\n l[idx] = False\\n else:\\n combine(i)\\n\\n evaluate(cond, json.loads(value))\\n while not all(type(x) == bool for x in cond[1]):\\n combine(cond)\\n\\n if any(x for x in cond[1]):\\n #satisfy where clause!!\\n #find if this data can be deleted\\n referenced = json.loads(value)['referenced']\\n can_delete = True\\n for rf in referenced:\\n rf_table = rf[0]\\n if ref_info[rf_table] == False:\\n can_delete = False\\n if can_delete:\\n #delete this data from referenced\\n for rf in referenced:\\n rf_table = rf[0]\\n rf_key = json.dumps(rf[1]).encode('utf-8')\\n rfDB = db.DB()\\n rfDB.open(f\\\"db/{rf_table}.db\\\", dbtype=db.DB_HASH)\\n rf_dict = json.loads(rfDB.get(rf_key))\\n #remove this value from 'referencing' of referenced value\\n rf_referencing = rf_dict['referencing']\\n def checkThis(x):\\n return not (json.dumps(x[1]).encode('utf-8') == key and x[0] == table_name)\\n rf_referencing = list(filter(checkThis, rf_referencing))\\n rf_dict['referencing'] = rf_referencing\\n #change all referenced col values to null\\n ref_col = ref_colnames[rf_table]\\n for col in ref_col:\\n rf_dict[col] = None\\n #store edited value\\n rfDB.put(rf_key, json.dumps(rf_dict).encode('utf-8'))\\n rfDB.close()\\n\\n #delete this data from referencing\\n referencing = json.loads(value)['referencing']\\n for rfc in referencing:\\n rfc_table = rfc[0]\\n rfc_key = json.dumps(rfc[1]).encode('utf-8')\\n rfcDB = db.DB()\\n rfcDB.open(f\\\"db/{rfc_table}.db\\\", dbtype=db.DB_HASH)\\n rfc_dict = json.loads(rfcDB.get(rfc_key))\\n #remove this value from 'referenced' of referencing value\\n rfc_referenced = rfc_dict['referenced']\\n def checkThis(x):\\n return not (json.dumps(x[1]).encode('utf-8') == key and x[0] == table_name)\\n rfc_referenced = list(filter(checkThis, rfc_referenced))\\n rfc_dict['referenced'] = rfc_referenced\\n #store edited value\\n rfcDB.put(rfc_key, json.dumps(rfc_dict).encode('utf-8'))\\n rfcDB.close()\\n #delete this value from database\\n myDB.delete(key)\\n yes_count += 1\\n else:\\n #cannot delete this value due to referential integrity\\n no_count += 1\\n data = cursor.next()\\n\\n myDB.close()\\n print(prompt,f\\\"{yes_count} row(s) are deleted\\\", sep='')\\n if no_count > 0:\\n print(prompt, f\\\"{no_count} row(s) are not deleted due to referential integrity\\\", sep='')\\n\\n\\n def table_element_list(self, items):\\n elem_list = items[1:-1] # get rid of LP and RP\\n return elem_list\\n\\n def table_element(self, items):\\n return items[0]\\n\\n def column_definition(self, items):\\n dict = {}\\n dict['type'] = items[1]\\n #if not null constraint, set null to N\\n if len(items) > 2:\\n dict['null'] = 'N'\\n else:\\n dict['null'] = 'Y'\\n dict['key'] = ''\\n return (items[0], dict)\\n\\n def data_type(self, items):\\n length = len(items)\\n if (length == 4):\\n #if char length smaller than 1, rais CharLenghError\\n if int(items[2]) <= 0:\\n raise CharLengthError()\\n result = ''\\n for i in range(length):\\n result += str(items[i])\\n return result.lower()\\n\\n def table_constraint_definition(self, items):\\n return items[0]\\n\\n def primary_key_constraint(self, items):\\n return ['primary key', items[2]]\\n\\n def referential_constraint(self, items):\\n col_list = items[2]\\n referenced_table = items[4]\\n referenced_col_list = items[5]\\n return ['foreign key', col_list, referenced_table, referenced_col_list ]\\n\\n def column_name_list(self, items):\\n return list(items[1:-1])\\n #table name to lower case\\n def table_name(self, t):\\n (t,) = t\\n return t.lower()\\n #column name to lower case\\n def column_name(self, c):\\n (c,) = c\\n return c.lower()\\n def IDENTIFIER(self, s):\\n return str(s)\\n\\n\\nif __name__ == '__main__':\\n\\n # make sql_parser with grammar.lark file\\n with open('../PRJ1-3_2015-16227/grammar.lark') as file:\\n sql_parser = Lark(file.read(), start=\\\"command\\\", lexer=\\\"standard\\\")\\n\\n #get input until the program ended\\n while(True):\\n #get input with prompt message\\n data_input = input(prompt)\\n #if command 'exit' is given, end the program\\n if(data_input == 'exit'):\\n sys.exit()\\n #get additional input until it ends with semicolon\\n while len(data_input) == 0 or data_input[-1] != ';':\\n #to prevent error, add white space to data_input\\n data_input += ' '\\n data_input += input()\\n #split the input by semicolon\\n input_list = list(data_input.split(';'))\\n #exclude the last element of input_list since it is empty string\\n for i in range(len(input_list) - 1):\\n #restore semicolon in the end of the command\\n command = input_list[i] + ';'\\n #using try ... except, process the syntax error\\n try:\\n result = sql_parser.parse(command)\\n MyTransformer().transform(result)\\n except lark.exceptions.UnexpectedInput:\\n print(prompt,\\\"Syntax error\\\",sep='')\\n #for char length error\\n except lark.exceptions.VisitError as e:\\n print(prompt + str(e.__context__))\\n except SimpleDatabaseError as e:\\n print(prompt + str(e))\\n\\n\"\n },\n {\n \"alpha_fraction\": 0.6711568832397461,\n \"alphanum_fraction\": 0.6714210510253906,\n \"avg_line_length\": 26.042856216430664,\n \"blob_id\": \"cc4f69486da95d139e1d7c7b19f43318de8a949a\",\n \"content_id\": \"5723b721a9b542175c3880ab40388b15142f7cd6\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 3786,\n \"license_type\": \"no_license\",\n \"max_line_length\": 77,\n \"num_lines\": 140,\n \"path\": \"/PRJ1-3_2015-16227/exceptions.py\",\n \"repo_name\": \"h-yes-oo/database\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"class SimpleDatabaseError(Exception):\\n pass\\n\\n\\nclass NoSuchTable(SimpleDatabaseError):\\n def __str__(self):\\n return \\\"No such table\\\"\\n\\n\\nclass CharLengthError(SimpleDatabaseError):\\n def __str__(self):\\n return \\\"Char length should be over 0\\\"\\n\\n\\nclass CreateTableError(SimpleDatabaseError):\\n def __init__(self, msg):\\n self.msg = msg\\n\\n def __str__(self):\\n return f\\\"Create table has failed: {self.msg}\\\"\\n\\n\\nclass DropTableError(SimpleDatabaseError):\\n def __init__(self, msg):\\n self.msg = msg\\n\\n def __str__(self):\\n return f\\\"Drop table has failed: {self.msg}\\\"\\n\\n\\nclass DropReferencedTableError(DropTableError):\\n def __init__(self, table):\\n super().__init__(f\\\"'{table}' is referenced by other table\\\")\\n\\n\\nclass DuplicateColumnDefError(CreateTableError):\\n def __init__(self):\\n super().__init__(\\\"column definition is duplicated\\\")\\n\\n\\nclass DuplicatePrimaryKeyDefError(CreateTableError):\\n def __init__(self):\\n super().__init__(\\\"primary key definition is duplicated\\\")\\n\\n\\nclass ReferenceTypeError(CreateTableError):\\n def __init__(self):\\n super().__init__(\\\"foreign key references wrong type\\\")\\n\\n\\nclass ReferenceNonPrimaryKeyError(CreateTableError):\\n def __init__(self):\\n super().__init__(\\\"foreign key references non primary key column\\\")\\n\\n\\nclass ReferenceColumnExistenceError(CreateTableError):\\n def __init__(self):\\n super().__init__(\\\"foreign key references non existing column\\\")\\n\\n\\nclass ReferenceTableExistenceError(CreateTableError):\\n def __init__(self):\\n super().__init__(\\\"foreign key references non existing table\\\")\\n\\n\\nclass NonExistingColumnDefError(CreateTableError):\\n def __init__(self, column):\\n super().__init__(f\\\"'{column}' does not exists in column definition\\\")\\n\\n\\nclass TableExistenceError(CreateTableError):\\n def __init__(self):\\n super().__init__(\\\"table with the same name already exists\\\")\\n\\n\\nclass InsertionError(SimpleDatabaseError):\\n def __init__(self, msg):\\n self.msg = msg\\n\\n def __str__(self):\\n return f\\\"Insertion has failed: {self.msg}\\\"\\n\\n\\nclass InsertTypeMismatchError(InsertionError):\\n def __init__(self):\\n super().__init__(\\\"Types are not matched\\\")\\n\\n\\nclass InsertColumnExistenceError(InsertionError):\\n def __init__(self, column):\\n super().__init__(f\\\"'{column}' does not exist\\\")\\n\\n\\nclass InsertColumnNonNullableError(InsertionError):\\n def __init__(self, column):\\n super().__init__(f\\\"'{column}' is not nullable\\\")\\n\\n\\nclass InsertDuplicatePrimaryKeyError(InsertionError):\\n def __init__(self):\\n super().__init__(\\\"Primary key duplication\\\")\\n\\n\\nclass InsertReferentialIntegrityError(InsertionError):\\n def __init__(self):\\n super().__init__(\\\"Referential integrity violation\\\")\\n\\n\\nclass SelectionError(SimpleDatabaseError):\\n def __init__(self, msg):\\n self.msg = msg\\n\\n def __str__(self):\\n return f\\\"Selection has failed: {self.msg}\\\"\\n\\n\\nclass SelectTableExistenceError(SelectionError):\\n def __init__(self, table):\\n super().__init__(f\\\"'{table}' does not exist\\\")\\n\\nclass SelectColumnResolveError(SelectionError):\\n def __init__(self, column):\\n super().__init__(f\\\"fail to resolve '{column}'\\\")\\n\\n\\nclass WhereIncomparableError(SimpleDatabaseError):\\n def __str__(self):\\n return \\\"Where clause try to compare incomparable values\\\"\\n\\nclass WhereTableNotSpecified(SimpleDatabaseError):\\n def __str__(self):\\n return \\\"Where clause try to reference tables which are not specified\\\"\\n\\nclass WhereColumnNotExist(SimpleDatabaseError):\\n def __str__(self):\\n return \\\"Where clause try to reference non existing column\\\"\\n\\nclass WhereAmbiguousReference(SimpleDatabaseError):\\n def __str__(self):\\n return \\\"Where clause contains ambiguous reference\\\"\\n\"\n },\n {\n \"alpha_fraction\": 0.5558925867080688,\n \"alphanum_fraction\": 0.5694865584373474,\n \"avg_line_length\": 32.58490753173828,\n \"blob_id\": \"96b7491d39e52fb82c997690309808a57055e02f\",\n \"content_id\": \"acf1ba7de8735f06e81558a35961437a19109c77\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 8901,\n \"license_type\": \"no_license\",\n \"max_line_length\": 95,\n \"num_lines\": 265,\n \"path\": \"/PRJ2_2015-16227/run.py\",\n \"repo_name\": \"h-yes-oo/database\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"from __future__ import print_function\\nfrom mysql.connector import connect\\nfrom executions import *\\nfrom table import TABLES\\nfrom messages import *\\n\\n#connect with db in case it doesn't work at first time, use try except to ensure the connection\\ntry:\\n connection = connect(\\n host='astronaut.snu.ac.kr',\\n port=7000,\\n user='DB2015_16227',\\n password='DB2015_16227',\\n db='DB2015_16227',\\n charset='utf8'\\n )\\nexcept:\\n connection = connect(\\n host='astronaut.snu.ac.kr',\\n port=7000,\\n user='DB2015_16227',\\n password='DB2015_16227',\\n db='DB2015_16227',\\n charset='utf8'\\n )\\n\\n#make one dictionary cursor, one non-dictionary cursor for convenience\\ncurA = connection.cursor(dictionary=True, buffered=True)\\ncurB = connection.cursor(dictionary=False, buffered=True)\\n\\n#print informative message and start program\\nprint(info_message)\\nwhile(True):\\n print(\\\" \\\")\\n #if input action is not an integer, go to except\\n try:\\n action = int(input(select_action))\\n #if action not in 1~16 print invalid action message\\n if action >= 17 or action <= 0:\\n print(invalid_action)\\n else:\\n if action == 1:\\n print_building(curA, curB)\\n elif action == 2:\\n print_performance_all(curA, curB)\\n elif action == 3:\\n print_audience_all(curA)\\n elif action == 4:\\n #get name, location and truncate to 200 letters\\n name = input(get_building_name)\\n if(len(name) > 200):\\n name = name[0:200]\\n location = input(get_building_location)\\n if(len(location) > 200):\\n location = location[0:200]\\n try:\\n #if capacity less than zero, print error message \\n capacity = int(input(get_building_capacity))\\n if capacity <= 0:\\n print(capacity_constraint)\\n continue\\n else:\\n #if every input alright, insert it and commit\\n insert_building(curA, name, location, capacity)\\n connection.commit()\\n print(insert_success_message(\\\"A building\\\"))\\n except:\\n #if user input non-integer capacity, print invalid input\\n print(invalid_input)\\n continue\\n\\n elif action == 5:\\n try:\\n bid = int(input(get_building_id))\\n if(check_by_id(curA, 'building', bid)):\\n #if building with input id exist, delete and commit\\n delete(curA, 'building', bid)\\n connection.commit()\\n print(delete_success_message(\\\"A building\\\"))\\n else:\\n #if not exists, print message\\n print(building_not_exist(bid))\\n except:\\n #if user input non-integer id, print invalid input\\n print(invalid_input)\\n\\n elif action == 6:\\n # get name and type from user and truncate to 200 letters\\n name = input(get_performance_name)\\n if(len(name) > 200):\\n name = name[0:200]\\n ptype = input(get_performance_type)\\n if(len(ptype) > 200):\\n ptype = ptype[0:200]\\n try:\\n price = int(input(get_performance_price))\\n if price < 0:\\n print(price_constraint)\\n continue\\n else:\\n #if all input alright, insert it and commit\\n insert_performance(curA, name, ptype, price)\\n connection.commit()\\n print(insert_success_message(\\\"A performance\\\"))\\n except:\\n #if user input non-interger value for price, print invalid input\\n print(invalid_input)\\n continue\\n\\n elif action == 7:\\n try:\\n pid = int(input(get_performance_id))\\n #check if performance with the id exists\\n if(check_by_id(curA, 'performance', pid)):\\n #if exist, delete and commit\\n delete(curA, 'performance', pid)\\n connection.commit()\\n print(delete_success_message(\\\"A performance\\\"))\\n else:\\n #if not exist, print not exist message\\n print(performance_not_exist(pid))\\n except:\\n #if user input non-integer value for id, print invalid input\\n print(invalid_input)\\n \\n elif action == 8:\\n #get name and truncate\\n name = input(get_audience_name)\\n if(len(name) > 200):\\n name = name[0:200]\\n #get gender and check if equals to M or F\\n gender = input(get_audience_gender)\\n if gender != \\\"M\\\" and gender != \\\"F\\\":\\n print(gender_constraint)\\n continue\\n try:\\n #get age and check if bigger than 0\\n age = int(input(get_audience_age))\\n if age < 0:\\n print(age_constraint)\\n continue\\n else:\\n #if alright, insert and commit\\n insert_audience(curA, name, gender, age)\\n connection.commit()\\n print(insert_success_message(\\\"An audience\\\"))\\n except:\\n #if user input non-integer value for age, print message\\n print(invalid_input)\\n continue\\n \\n elif action == 9:\\n try:\\n aid = int(input(get_audience_id))\\n if(check_by_id(curA, 'audience', aid)):\\n #if audience with the id exist, delete and commit\\n delete(curA, 'audience', aid)\\n connection.commit()\\n print(delete_success_message(\\\"An audience\\\"))\\n else:\\n #else, print not exist message\\n print(audience_not_exist)\\n except:\\n #if user input non-integer value for id, print message\\n print(invalid_input)\\n \\n elif action == 10:\\n try:\\n bid = int(input(get_building_id))\\n #if building not exist, print message\\n if not check_by_id(curA, 'building', bid):\\n print(building_not_exist(bid))\\n continue\\n pid = int(input(get_performance_id))\\n #if performance not exist, print message\\n if not check_by_id(curA, 'performance', pid):\\n print(performance_not_exist(pid))\\n continue\\n #if it is able to assign the performance to building, do it and commit\\n if(assign_p_to_b(curA, pid, bid)):\\n print(assign_success)\\n connection.commit()\\n except:\\n print(invalid_input)\\n \\n elif action == 11:\\n try:\\n pid = int(input(get_performance_id))\\n # if performance not assigned to any building, print message\\n if not check_building_assigned(curA, pid):\\n print(performance_not_assigned(pid))\\n continue\\n aid = int(input(get_audience_id))\\n seat_list = list(map(int, input(get_seat_number).split(',')))\\n #check if all seats can be reserved\\n if(can_reserve(curB, seat_list, pid)):\\n for seat in seat_list:\\n #reserve each seat and commit\\n reserve(curA, pid, aid, seat)\\n connection.commit()\\n #get price with respect to audience's age\\n price = get_ticket_price(curB, pid, aid)\\n #multiply the price with the number of ticket being reserved\\n total_price = round(price * len(seat_list))\\n #print success message and price\\n print(book_success)\\n print(total_ticket_price(total_price))\\n except:\\n print(invalid_input)\\n\\n elif action == 12:\\n try:\\n bid = int(input(get_building_id))\\n #check if building with the id exist and print\\n if check_by_id(curA, 'building', bid):\\n print_performance_of_building(curA, curB, bid)\\n else:\\n print(building_not_exist(bid))\\n except:\\n print(invalid_input)\\n\\n elif action == 13:\\n try:\\n pid = int(input(get_performance_id))\\n #check if performance with the id exist and print\\n if check_by_id(curA, 'performance', pid):\\n print_audience_of_performance(curA, pid)\\n else:\\n print(performance_not_exist(pid))\\n except:\\n print(invalid_input)\\n \\n elif action == 14:\\n try:\\n pid = int(input(get_performance_id))\\n #check if performance with the id exist\\n if check_by_id(curA, 'performance', pid):\\n #check if performance with the id is assigned to any building\\n if check_building_assigned(curA, pid):\\n print_seat_of_performance(curB, pid)\\n else:\\n print(performance_not_assigned(pid))\\n else:\\n print(performance_not_exist(pid))\\n except:\\n print(invalid_input)\\n\\n elif action == 15:\\n print(bye)\\n break\\n\\n elif action == 16:\\n #ensure user about deleting everything\\n flag = input(refresh_confirm)\\n if flag == 'y':\\n refresh(curA, TABLES)\\n\\n #if non integer input came, print invalid message\\n except:\\n print(invalid_action)\\n\\ncurA.close()\\ncurB.close()\\nconnection.close()\\nexit(0)\\n\\n\"\n },\n {\n \"alpha_fraction\": 0.5987762212753296,\n \"alphanum_fraction\": 0.6451048851013184,\n \"avg_line_length\": 21.8799991607666,\n \"blob_id\": \"dea92a8da6fdc5f2391d65c3cabd83dbae43d986\",\n \"content_id\": \"ad8697d8b0acfc4adabeab1750907978a2ed8c40\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"SQL\",\n \"length_bytes\": 1144,\n \"license_type\": \"no_license\",\n \"max_line_length\": 94,\n \"num_lines\": 50,\n \"path\": \"/sample.sql\",\n \"repo_name\": \"h-yes-oo/database\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"create table department (\\n name char(5),\\n pos char(17),\\n number int,\\n start date,\\n primary key(name)\\n);\\n\\n\\ncreate table student (\\n ID char(5),\\n name char(2) not null,\\n dept_name char(5),\\n tot_cred int,\\n primary key(ID, name),\\n foreign key (dept_name) references department(name)\\n);\\n\\ncreate table lover (\\n x char(3),\\n y char(2),\\n dept_name char(5),\\n stu_id char(5),\\n primary key(x,y,dept_name),\\n foreign key (dept_name) references department(name),\\n foreign key (y,stu_id) references student(name,id)\\n);\\n\\ncreate table loves (\\n she char(2),\\n he char(3) not null,\\n who char(5),\\n love date,\\n primary key (love),\\n foreign key (who,he,she) references lover(dept_name,x,y)\\n);\\n\\ninsert into department (number,start,pos,name) values(1,2020-10-10,\\\"pos777777\\\",'name5555555');\\n\\n\\ninsert into student values('id1','n2','name5',10);\\n\\ninsert into lover values('xxx','n2','name5','id1');\\n\\ninsert into loves values('n2','xxx','name5',2015-03-30);\\n\\n--set null value to primary key\\n--set null value to not null primary key\\n--len(column) != len(input_column)\\n--len(input_column) != len(values)\\n\"\n },\n {\n \"alpha_fraction\": 0.686948835849762,\n \"alphanum_fraction\": 0.6984127163887024,\n \"avg_line_length\": 29.635135650634766,\n \"blob_id\": \"dd8eabd0971c7c06a64c1b60fc2ba1f4f0b0e0f0\",\n \"content_id\": \"e9ccece48bd90e9c1a7bb1f7e1a0a210ccdd0b6c\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 2274,\n \"license_type\": \"no_license\",\n \"max_line_length\": 74,\n \"num_lines\": 74,\n \"path\": \"/PRJ2_2015-16227/messages.py\",\n \"repo_name\": \"h-yes-oo/database\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"#informative message about prompt\\ninfo_message = '''\\n============================================================ \\n1. print all buildings\\n2. print all performances\\n3. print all audiences\\n4. insert a new building\\n5. remove a building\\n6. insert a new performance\\n7. remove a performance\\n8. insert a new audience\\n9. remove an audience\\n10. assign a performance to a building\\n11. book a performance\\n12. print all performances which assigned at a building\\n13. print all audiences who booked for a performance\\n14. print ticket booking status of a performance\\n15. exit\\n16. reset database\\n============================================================'''\\n\\ninvalid_action = \\\"Invalid action\\\"\\ninvalid_input = \\\"Invalid input\\\"\\n\\nselect_action = \\\"Select your action: \\\"\\n\\nget_building_name = \\\"Building name: \\\"\\nget_building_location = \\\"Building location: \\\"\\nget_building_capacity = \\\"Building capacity: \\\"\\nget_building_id = \\\"Building ID: \\\"\\n\\nget_performance_name = \\\"Performance name: \\\"\\nget_performance_type = \\\"Performance type: \\\"\\nget_performance_price = \\\"Performance price: \\\"\\nget_performance_id = \\\"Performance ID: \\\"\\n\\nget_audience_name = \\\"Audience name: \\\"\\nget_audience_gender = \\\"Audience gender: \\\"\\nget_audience_age = \\\"Audience age: \\\"\\nget_audience_id = \\\"Audience ID: \\\"\\n\\nget_seat_number = \\\"Seat number: \\\"\\n\\ncapacity_constraint = \\\"Capacity should be more than 0\\\"\\nprice_constraint = \\\"Price should be 0 or more\\\"\\ngender_constraint = \\\"Gender should be 'M' or 'F'\\\"\\nage_constraint = \\\"Age should be 0 or more\\\"\\n\\nbye = \\\"Bye!\\\"\\nrefresh_confirm = \\\"Every table and data will be deleted. Go on ? (y/n) : \\\"\\n\\nassign_success = \\\"Successfully assign a performance\\\"\\nbook_success = \\\"Successfully book a performance.\\\"\\n\\ndef total_ticket_price(total_price):\\n return \\\"Total ticket price is {:,}\\\".format(total_price)\\n\\ndef insert_success_message(what):\\n return \\\"{} is successfully inserted\\\".format(what)\\n\\ndef delete_success_message(what):\\n return \\\"{} is successfully removed\\\".format(what)\\n\\ndef building_not_exist(bid):\\n return \\\"Building {} doesn’t exist\\\".format(bid)\\n\\ndef performance_not_exist(pid):\\n return \\\"Performance {} doesn’t exist\\\".format(pid)\\n\\ndef audience_not_exist(aid):\\n return \\\"Audience {} doesn’t exist\\\".format(aid)\\n\\ndef performance_not_assigned(pid):\\n return \\\"Performance {} isn't assigned\\\".format(pid)\\n\\n\"\n }\n]","isConversational":false},"num_files":{"kind":"number","value":10,"string":"10"}}},{"rowIdx":2977199,"cells":{"repo_name":{"kind":"string","value":"JoseEvanan/PagaPeApi"},"repo_url":{"kind":"string","value":"https://github.com/JoseEvanan/PagaPeApi"},"snapshot_id":{"kind":"string","value":"774a211d26372686733af36d8177f606fa652e78"},"revision_id":{"kind":"string","value":"b265afa2dec3f4d8ce47b30b9932a22f7b7633ea"},"directory_id":{"kind":"string","value":"8c49b7e8a809c626fc65c96e6542bbb17b4dfdc0"},"branch_name":{"kind":"string","value":"refs/heads/master"},"visit_date":{"kind":"timestamp","value":"2021-07-20T05:01:50.900995","string":"2021-07-20T05:01:50.900995"},"revision_date":{"kind":"timestamp","value":"2017-10-28T10:30:35","string":"2017-10-28T10:30:35"},"committer_date":{"kind":"timestamp","value":"2017-10-28T10:30:35","string":"2017-10-28T10:30:35"},"github_id":{"kind":"number","value":108635254,"string":"108,635,254"},"star_events_count":{"kind":"number","value":0,"string":"0"},"fork_events_count":{"kind":"number","value":0,"string":"0"},"gha_license_id":{"kind":"null"},"gha_created_at":{"kind":"null"},"gha_updated_at":{"kind":"null"},"gha_pushed_at":{"kind":"null"},"gha_language":{"kind":"null"},"files":{"kind":"list like","value":[{"alpha_fraction":0.515237033367157,"alphanum_fraction":0.6179458498954773,"avg_line_length":35.9375,"blob_id":"6ee5f0b0f1edbdaa722ed7a9c204a3a0dcc5ce15","content_id":"7b542b4febae0b15bc06d8db8131ea99b050f72b","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":1772,"license_type":"no_license","max_line_length":633,"num_lines":48,"path":"/app.py","repo_name":"JoseEvanan/PagaPeApi","src_encoding":"UTF-8","text":"import json\nfrom flask import Flask, request\nfrom flask_restful import reqparse, abort, Api, Resource\nfrom utils import _send_email\nfrom pprint import pprint\napp = Flask(__name__)\napi = Api(app)\n\n\nparser = reqparse.RequestParser()\nparser.add_argument('task')\n\n\n# TodoList\n# shows a list of all todos, and lets you POST to add new tasks\nclass SendEmail(Resource):\n def get(self):\n return None, 202 \n\n def post(self):\n print(\"post\")\n d = request.form\n #d = {'{\"_id\":\"59f444e1e2bb163439f9cf3f\",\"updatedAt\":\"2017-10-28T08:50:41.418Z\",\"createdAt\":\"2017-10-28T08:50:41.418Z\",\"debted\":{\"_id\":\"59f431d93a25a3202f86aa04\",\"updatedAt\":\"2017-10-28T07:29:29.526Z\",\"createdAt\":\"2017-10-28T07:29:29.526Z\",\"email\":\"evertdiazb@pagape.com\",\"password\":\"$2a$10$Z.rHnTVqm/QeWq1.oGngZe5uwax1Ngu5yQXtCDk.vUfuP0IJl5ZG.\",\"__v\":0,\"tokens\":[],\"active\":false},\"debtor\":{\"_id\":\"59f44375b675e9332c7f5bac\",\"updatedAt\":\"2017-10-28T08:44:37.760Z\",\"createdAt\":\"2017-10-28T08:44:37.760Z\",\"password\":\"$2a$10$/ci2p166jrAeEr6NyST8xO0RQM.Ou9n5VKYxUWezea79kR7P0xeH2\",\"__v\":0,\"tokens\":[],\"active\":true},\"description\":\"Deuda de un cuy\",\"endDate\":\"2017-11-24T00:00:00.000Z\",\"amount\":10000,\"__v\":0,\"accepted\":false,\"status\":0}': ''}\n print(d.keys())\n data = None\n for d in d.keys():\n data = d\n data = json.loads(data)\n pprint(data)\n email = data['debtor']['email']\n description = data['description']\n endDate = data['endDate']\n amount = data['amount']\n name = data['debtor']['profile']['name']\n confirmation = data['_id']\n status = _send_email(email, name, description,\n endDate, amount, confirmation)\n return status, 201\n\n\n##\n## Actually setup the Api resource routing here\n##\napi.add_resource(SendEmail, '/')\n\n\nif __name__ == '__main__':\n app.run(debug=True)"},{"alpha_fraction":0.5822784900665283,"alphanum_fraction":0.6252064108848572,"avg_line_length":32.0363655090332,"blob_id":"51cb887956f09472473a8e76d10903eeb59ba335","content_id":"f2fb9756f37bb841b1c8d94e3376f5ea0389e600","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":1817,"license_type":"no_license","max_line_length":160,"num_lines":55,"path":"/utils.py","repo_name":"JoseEvanan/PagaPeApi","src_encoding":"UTF-8","text":"import smtplib\n\nfrom email.mime.multipart import MIMEMultipart\nfrom email.mime.text import MIMEText\n\ndef _send_email(email, name, description,\n endDate, amount, confirmation):\n # me == my email address\n # you == recipient's email address\n me = \"jose.evanan@gmail.com\"\n you = email\n # Create message container - the correct MIME type is multipart/alternative.\n msg = MIMEMultipart('alternative')\n msg['Subject'] = \"PagaPe\"\n msg['From'] = me\n msg['To'] = you\n\n # Create the body of the message (a plain-text and an HTML version).\n text = \"Hi!\\nHow are you?\\nHere is the link you wanted:\\nhttp://www.python.org\"\n html = \"\"\"\\\n <html>\n <head></head>\n <body>\n <img src=\"https://scontent.faqp1-1.fna.fbcdn.net/v/t34.0-12/22901695_1930361390547116_415760123_n.png?oh=3bd1675bc7bb567e84baaad992e2e49b&oe=59F5D614\" >\n <p>Hola! {}<br>\n {}<br>\n Tienes una deuda de {}, tienes para cancelar hasta {} <a href=\"http://pagape.com/confirmar/{}\">link</a>.\n </p>\n </body>\n </html>\n \"\"\".format( name, description,amount, endDate, confirmation)\n # Record the MIME types of both parts - text/plain and text/html.\n part1 = MIMEText(text, 'plain')\n part2 = MIMEText(html, 'html')\n\n # Attach parts into message container.\n # According to RFC 2046, the last part of a multipart message, in this case\n # the HTML message, is best and preferred.\n msg.attach(part1)\n msg.attach(part2)\n # Send the message via local SMTP server.\n mail = smtplib.SMTP('smtp.gmail.com', 587)\n\n mail.ehlo()\n\n mail.starttls()\n\n mail.login('pagainfope@gmail.com', 'pagainfo2017')\n try:\n mail.sendmail(me, you, msg.as_string())\n mail.quit()\n return True\n except Exception as e:\n print(e)\n return False\n"}],"string":"[\n {\n \"alpha_fraction\": 0.515237033367157,\n \"alphanum_fraction\": 0.6179458498954773,\n \"avg_line_length\": 35.9375,\n \"blob_id\": \"6ee5f0b0f1edbdaa722ed7a9c204a3a0dcc5ce15\",\n \"content_id\": \"7b542b4febae0b15bc06d8db8131ea99b050f72b\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 1772,\n \"license_type\": \"no_license\",\n \"max_line_length\": 633,\n \"num_lines\": 48,\n \"path\": \"/app.py\",\n \"repo_name\": \"JoseEvanan/PagaPeApi\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"import json\\nfrom flask import Flask, request\\nfrom flask_restful import reqparse, abort, Api, Resource\\nfrom utils import _send_email\\nfrom pprint import pprint\\napp = Flask(__name__)\\napi = Api(app)\\n\\n\\nparser = reqparse.RequestParser()\\nparser.add_argument('task')\\n\\n\\n# TodoList\\n# shows a list of all todos, and lets you POST to add new tasks\\nclass SendEmail(Resource):\\n def get(self):\\n return None, 202 \\n\\n def post(self):\\n print(\\\"post\\\")\\n d = request.form\\n #d = {'{\\\"_id\\\":\\\"59f444e1e2bb163439f9cf3f\\\",\\\"updatedAt\\\":\\\"2017-10-28T08:50:41.418Z\\\",\\\"createdAt\\\":\\\"2017-10-28T08:50:41.418Z\\\",\\\"debted\\\":{\\\"_id\\\":\\\"59f431d93a25a3202f86aa04\\\",\\\"updatedAt\\\":\\\"2017-10-28T07:29:29.526Z\\\",\\\"createdAt\\\":\\\"2017-10-28T07:29:29.526Z\\\",\\\"email\\\":\\\"evertdiazb@pagape.com\\\",\\\"password\\\":\\\"$2a$10$Z.rHnTVqm/QeWq1.oGngZe5uwax1Ngu5yQXtCDk.vUfuP0IJl5ZG.\\\",\\\"__v\\\":0,\\\"tokens\\\":[],\\\"active\\\":false},\\\"debtor\\\":{\\\"_id\\\":\\\"59f44375b675e9332c7f5bac\\\",\\\"updatedAt\\\":\\\"2017-10-28T08:44:37.760Z\\\",\\\"createdAt\\\":\\\"2017-10-28T08:44:37.760Z\\\",\\\"password\\\":\\\"$2a$10$/ci2p166jrAeEr6NyST8xO0RQM.Ou9n5VKYxUWezea79kR7P0xeH2\\\",\\\"__v\\\":0,\\\"tokens\\\":[],\\\"active\\\":true},\\\"description\\\":\\\"Deuda de un cuy\\\",\\\"endDate\\\":\\\"2017-11-24T00:00:00.000Z\\\",\\\"amount\\\":10000,\\\"__v\\\":0,\\\"accepted\\\":false,\\\"status\\\":0}': ''}\\n print(d.keys())\\n data = None\\n for d in d.keys():\\n data = d\\n data = json.loads(data)\\n pprint(data)\\n email = data['debtor']['email']\\n description = data['description']\\n endDate = data['endDate']\\n amount = data['amount']\\n name = data['debtor']['profile']['name']\\n confirmation = data['_id']\\n status = _send_email(email, name, description,\\n endDate, amount, confirmation)\\n return status, 201\\n\\n\\n##\\n## Actually setup the Api resource routing here\\n##\\napi.add_resource(SendEmail, '/')\\n\\n\\nif __name__ == '__main__':\\n app.run(debug=True)\"\n },\n {\n \"alpha_fraction\": 0.5822784900665283,\n \"alphanum_fraction\": 0.6252064108848572,\n \"avg_line_length\": 32.0363655090332,\n \"blob_id\": \"51cb887956f09472473a8e76d10903eeb59ba335\",\n \"content_id\": \"f2fb9756f37bb841b1c8d94e3376f5ea0389e600\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 1817,\n \"license_type\": \"no_license\",\n \"max_line_length\": 160,\n \"num_lines\": 55,\n \"path\": \"/utils.py\",\n \"repo_name\": \"JoseEvanan/PagaPeApi\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"import smtplib\\n\\nfrom email.mime.multipart import MIMEMultipart\\nfrom email.mime.text import MIMEText\\n\\ndef _send_email(email, name, description,\\n endDate, amount, confirmation):\\n # me == my email address\\n # you == recipient's email address\\n me = \\\"jose.evanan@gmail.com\\\"\\n you = email\\n # Create message container - the correct MIME type is multipart/alternative.\\n msg = MIMEMultipart('alternative')\\n msg['Subject'] = \\\"PagaPe\\\"\\n msg['From'] = me\\n msg['To'] = you\\n\\n # Create the body of the message (a plain-text and an HTML version).\\n text = \\\"Hi!\\\\nHow are you?\\\\nHere is the link you wanted:\\\\nhttp://www.python.org\\\"\\n html = \\\"\\\"\\\"\\\\\\n <html>\\n <head></head>\\n <body>\\n <img src=\\\"https://scontent.faqp1-1.fna.fbcdn.net/v/t34.0-12/22901695_1930361390547116_415760123_n.png?oh=3bd1675bc7bb567e84baaad992e2e49b&oe=59F5D614\\\" >\\n <p>Hola! {}<br>\\n {}<br>\\n Tienes una deuda de {}, tienes para cancelar hasta {} <a href=\\\"http://pagape.com/confirmar/{}\\\">link</a>.\\n </p>\\n </body>\\n </html>\\n \\\"\\\"\\\".format( name, description,amount, endDate, confirmation)\\n # Record the MIME types of both parts - text/plain and text/html.\\n part1 = MIMEText(text, 'plain')\\n part2 = MIMEText(html, 'html')\\n\\n # Attach parts into message container.\\n # According to RFC 2046, the last part of a multipart message, in this case\\n # the HTML message, is best and preferred.\\n msg.attach(part1)\\n msg.attach(part2)\\n # Send the message via local SMTP server.\\n mail = smtplib.SMTP('smtp.gmail.com', 587)\\n\\n mail.ehlo()\\n\\n mail.starttls()\\n\\n mail.login('pagainfope@gmail.com', 'pagainfo2017')\\n try:\\n mail.sendmail(me, you, msg.as_string())\\n mail.quit()\\n return True\\n except Exception as e:\\n print(e)\\n return False\\n\"\n }\n]","isConversational":false},"num_files":{"kind":"number","value":2,"string":"2"}}}],"truncated":false,"partial":false},"paginationData":{"pageIndex":29771,"numItemsPerPage":100,"numTotalItems":2984968,"offset":2977100,"length":100}},"jwt":"eyJhbGciOiJFZERTQSJ9.eyJyZWFkIjp0cnVlLCJwZXJtaXNzaW9ucyI6eyJyZXBvLmNvbnRlbnQucmVhZCI6dHJ1ZX0sImlhdCI6MTc3NDIxMDY1OSwic3ViIjoiL2RhdGFzZXRzL3BoYXJhb3VrL3N0YWNrLXYyLXB5dGhvbi13aXRoLWNvbnRlbnQtY2h1bmsxIiwiZXhwIjoxNzc0MjE0MjU5LCJpc3MiOiJodHRwczovL2h1Z2dpbmdmYWNlLmNvIn0.CShchg0VZOYzUDVhwoXYjMOMOhQZenU7zbAATxh2aoaMBhlwmY6h_sCX09-uFANHbxwYOazY-Ae6TkdC6NBmDw","displayUrls":true,"splitSizeSummaries":[{"config":"default","split":"train","numRows":2984968,"numBytesParquet":51814563482}]},"discussionsStats":{"closed":0,"open":1,"total":1},"fullWidth":true,"hasGatedAccess":true,"hasFullAccess":true,"isEmbedded":false,"savedQueries":{"community":[],"user":[]}}">
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Lo-kin/agefans-all-name | https://github.com/Lo-kin/agefans-all-name | 7a2e151d973920f5a2f99d8acc3c47f6ad6d0651 | d3d573aff239a48f6f3297d6ef41a415e9eea5af | cab7bf7ed1beba492be1903ddb48b566dbf31baa | refs/heads/main | 2023-06-21T23:08:03.414419 | 2021-07-23T13:59:52 | 2021-07-23T13:59:52 | 388,800,036 | 0 | 0 | null | null | null | null | null | [
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Nikhil-oruganti/automated_email-python- | https://github.com/Nikhil-oruganti/automated_email-python- | 41672d6e90343c0f721b84a617473d0f27ef9ab3 | 6e98f700885e170c6030a000aa8e7a1628e5f845 | ec0340bb868ec46d141677aed1e5d94624a9dada | refs/heads/main | 2023-06-18T11:05:53.421536 | 2021-07-15T18:32:55 | 2021-07-15T18:32:55 | 386,384,734 | 0 | 0 | null | null | null | null | null | [
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SusheendharVijay/AutoML-Benchmarking | https://github.com/SusheendharVijay/AutoML-Benchmarking | 2857e0f5b750bb6cbeb06c36b7a27fee01153767 | f2edda14d3e8c2a138b3863384a0ff9cf4bb6a02 | c2f8c442c47e85d5d2a19cbd83a25d9947433c46 | refs/heads/master | 2020-12-13T00:00:50.002040 | 2020-03-08T06:55:00 | 2020-03-08T06:55:00 | 234,263,232 | 1 | 0 | null | null | null | null | null | [
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dp-wu/Leet_Code_Python | https://github.com/dp-wu/Leet_Code_Python | ed0efd78dc9b0f696563066add31133403f7abef | 0744e9efed0a9c99213ac9d66a6f2d8aeb38579b | 7abae1502b8f38419302f244ae328b1f8de37bcd | refs/heads/master | 2023-01-10T21:51:56.586238 | 2023-01-03T20:53:53 | 2023-01-03T20:53:53 | 179,170,288 | 0 | 0 | null | null | null | null | null | [
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degurii/practice | https://github.com/degurii/practice | b5df95b17596fba85461061f738cf5bac790f6b4 | 55eb0c0364d35e764be3c669ac704525ba2e12d5 | 6e21fb93236d39eebd43372fde961f91bc985c2c | refs/heads/master | 2018-10-20T16:27:25.533187 | 2018-07-20T18:12:23 | 2018-07-20T18:12:23 | 135,750,542 | 0 | 0 | null | null | null | null | null | [
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jaburges/tuyagateway | https://github.com/jaburges/tuyagateway | 35389cfae26d18ad96ccd65e55910a6a76162ec0 | 653c368aa19cd1bd6b72ca515eb3a4d66cbe2c2a | eb4605f23be0ffe941a6680e65241ee68997abff | refs/heads/master | 2023-02-13T23:08:34.675400 | 2020-08-27T21:34:48 | 2020-08-27T21:34:48 | 294,479,878 | 0 | 0 | null | 2020-09-10T17:39:57 | 2020-09-10T13:56:33 | 2020-08-27T21:37:30 | null | [
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JoeKeating/PythonCrashCourse | https://github.com/JoeKeating/PythonCrashCourse | 12ac46b2653c47252b7e95a0a81f14c164395f6f | 894b75160e96f9e47e1d0cb595747beb2161edfa | 4d70d583974dec6126afd058207fc092ef70cb28 | refs/heads/master | 2020-05-22T09:54:52.705972 | 2019-05-23T18:11:57 | 2019-05-23T18:11:57 | 186,299,647 | 0 | 0 | null | null | null | null | null | [
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surify/excel2db | https://github.com/surify/excel2db | 76554e08c329ce18213f9556b65d65170def6efb | a864e3192c49eac4b8e777ae334ddb6a86f77ccc | c7230900f447e021dc5021cf8a87ff55663f8815 | refs/heads/master | 2020-03-18T09:29:42.169401 | 2018-05-23T23:03:44 | 2018-05-23T23:06:08 | null | 0 | 0 | null | null | null | null | null | [
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souravsingh/LinkNet-model | https://github.com/souravsingh/LinkNet-model | dd08db70072f73b33cbc1b53800c771109109904 | 1b9e6dca20a15e34a23d7e8db852e2fdcd1d9003 | f57d8459a72e2f5ec44128bfa554a44932c72f34 | refs/heads/master | 2021-07-25T04:11:53.117566 | 2017-11-06T16:34:32 | 2017-11-06T16:34:32 | 109,721,009 | 1 | 0 | null | null | null | null | null | [
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Metafyzik/Genetic-algorithm | https://github.com/Metafyzik/Genetic-algorithm | 1363ab2486fe863f6b1523d14e280d8bbcf018f9 | 746cb0c871c0eeac610fac9670fc3ef7d1b778c4 | 188827338e878dc809929d8a88e0d5fae17514bb | refs/heads/main | 2023-07-08T04:20:39.863218 | 2021-08-11T20:10:10 | 2021-08-11T20:10:10 | 355,270,345 | 0 | 0 | null | null | null | null | null | [
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wujuguang/hichao-backend-suite | https://github.com/wujuguang/hichao-backend-suite | 506f0c73378de7f297ca4871f81d020ba101a672 | f95b50f2a8705839231ebf78c9e9097ca0e18e54 | 1111cdb48e0d33dff8cfa83ec0b9259f92c42c61 | HEAD | 2018-01-07T16:37:32.623459 | 2015-08-17T02:46:14 | 2015-08-17T02:46:14 | 37,501,830 | 3 | 1 | null | null | null | null | null | [
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AsahinaMikuruFans/ProjectFS | https://github.com/AsahinaMikuruFans/ProjectFS | fe158b54d403eda9f1a5664f7770b39662b89845 | 26a19edf3c793963124f45a5ae50626c21bb482a | 3647d56c3b2ec0c89b87f92904c4ac66503b4ce0 | refs/heads/master | 2021-03-15T09:36:57.854397 | 2021-02-04T02:32:08 | 2021-02-04T02:32:08 | 246,840,482 | 2 | 0 | null | 2020-03-12T13:24:16 | 2020-03-29T12:26:02 | 2020-03-29T12:42:57 | Python | [
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jamesmeneghello/pytaghash | https://github.com/jamesmeneghello/pytaghash | d0469098fcf81249f77f6d6ac3ab3d6bbed02742 | 95c5c7c6d4421c50a2e0c7ff3af20c62393bd0ec | 14f3b160bf31c73c73199029e233501c25e42f51 | refs/heads/master | 2021-06-01T05:45:37.995088 | 2015-11-28T08:32:29 | 2015-11-28T08:32:29 | null | 0 | 0 | null | null | null | null | null | [
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MartaLinux/learningPython | https://github.com/MartaLinux/learningPython | 2e15d0d1e745be881318b9ca19b6d32245b6c04b | ef73d5d4dce2e197b68bfc11d89d97d48688ce40 | 8c828fd9840654c4e79b98a4c01a2a49dce8d3e9 | refs/heads/main | 2023-04-07T03:00:08.184502 | 2021-04-07T18:54:51 | 2021-04-07T18:54:51 | 333,577,546 | 0 | 0 | null | 2021-01-27T22:28:11 | 2021-01-29T22:19:46 | 2021-01-31T16:58:52 | Python | [
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BYU-ACM/warm-up-day-2-gsmithh | https://github.com/BYU-ACM/warm-up-day-2-gsmithh | a17de641509b8e8058590bcb965e915da8200d06 | 30295933a9a96864a9a7d068e41530d9604b5ffa | 514136cffd60b33cc698b2796c35e1a53d58fdce | refs/heads/master | 2021-01-11T21:24:21.899975 | 2017-01-19T18:59:29 | 2017-01-19T18:59:29 | 78,778,136 | 0 | 0 | null | null | null | null | null | [
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pombredanne/django-restlayer | https://github.com/pombredanne/django-restlayer | 0e4e60655a1c171a7e0e73931946cda0fdb6b3ea | 4b27e4b2d6ca827cab4e4d52caccb5d3f861348f | 1d717fc0a29211f65576ecf1bae90ecdb879037c | refs/heads/master | 2017-12-03T13:48:02.285191 | 2011-08-28T12:37:05 | 2011-08-28T12:37:05 | null | 0 | 0 | null | null | null | null | null | [
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IngMachine/LEXER-ricardo-y-fredy- | https://github.com/IngMachine/LEXER-ricardo-y-fredy- | d19c1adda78a39008fddac58c1248199fe0466d1 | c6948d815ff9df9f3f791044e8051dfb02ed5243 | 41b37a8ff30cfb17ef21b455ff20aa2e0deee0e6 | refs/heads/master | 2021-01-24T11:44:55.298051 | 2018-02-26T05:25:07 | 2018-02-26T05:25:07 | null | 0 | 0 | null | null | null | null | null | [
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MatCyg/SBWprojekt | https://github.com/MatCyg/SBWprojekt | 50e90b2ccb3fa5a78f72be7093f094bb070c6fc8 | 49ea8d7a7f9348d1cd070ed273027a4ae65f14e2 | e6efcf6bf97a9a0bcd13bf7155e71ad32a50bda5 | HEAD | 2018-02-06T20:19:46.395471 | 2014-04-01T17:31:28 | 2014-04-01T17:31:28 | 18,337,042 | 0 | 0 | null | null | null | null | null | [
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mheen/mdp | https://github.com/mheen/mdp | a62f955b3e6b6798e989df51e74284456493fbc1 | 487c7c36a09be84bde16058ea219189ffb149686 | db8f92bf85b642246719e652db110222b2b0e2a6 | refs/heads/master | 2018-10-22T14:35:13.079688 | 2018-09-19T07:29:19 | 2018-09-19T07:29:19 | 84,056,101 | 0 | 0 | null | null | null | null | null | [
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ayan-iiitd/maximum-rectilinear-crossing-number-of-uniform-hypergraphs | https://github.com/ayan-iiitd/maximum-rectilinear-crossing-number-of-uniform-hypergraphs | 13e8d2673f2f7cacdfdeedade0ef3b57fa94b4e1 | 1ca56ef28a32bfbcaec600ecc2dde4d8499b4fa4 | 1503be9bc45bf3c89caa2ac8a30de94a98122d48 | refs/heads/master | 2020-11-30T05:41:50.432156 | 2020-01-17T17:37:08 | 2020-01-17T17:37:08 | 230,317,359 | 0 | 0 | null | null | null | null | null | [
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yaehei/gfw_whitelist-1 | https://github.com/yaehei/gfw_whitelist-1 | 9fd95c1c9fae26cb2f15bc733e52cb9797209872 | 38d0a29f7fb0e374dec6caf38cb7c15f75453acd | ca02e7317467f4f5fcefc7f603c4baec89901c6a | refs/heads/master | 2021-01-24T23:12:56.322354 | 2014-11-08T15:30:52 | 2014-11-08T15:30:52 | null | 0 | 0 | null | null | null | null | null | [
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gipsa-lab-uav/trajectory_control | https://github.com/gipsa-lab-uav/trajectory_control | 2c233565b7970ce4001de6093054208d67afbd4a | 3456464190b02a9cb08c69ba81b3f7759fcb0c93 | 8ba490cf7ee3324c67f07ddbb72b421591d6eeda | refs/heads/master | 2021-07-07T22:48:16.670746 | 2020-01-14T10:40:46 | 2020-01-14T10:40:46 | 214,182,191 | 0 | 0 | null | null | null | null | null | [
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Arent/GAN | https://github.com/Arent/GAN | 09cd5c30f19f6e43275ef0cff561a2cfa705a160 | 3db94b14df709150facc202e37897de4bea1def2 | dde48e6bfba50a4d03f31096cf2c552942ad462b | refs/heads/master | 2021-01-02T08:17:00.425840 | 2017-08-23T11:38:32 | 2017-08-23T11:38:32 | 98,985,196 | 2 | 0 | null | null | null | null | null | [
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LyaxKing/My_Printor | https://github.com/LyaxKing/My_Printor | 43de41d7b0577abc66351de27843f74e1fe87af8 | 61efb5acf656407048c225b890041ff2c711cb2b | 3098c69ee9aa0d2e6502c9fa205efc91b3b66354 | refs/heads/master | 2020-08-10T19:05:27.259926 | 2019-10-11T10:03:59 | 2019-10-11T10:07:07 | 214,402,637 | 1 | 0 | null | null | null | null | null | [
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LorenzoFasolino/AutoRequester | https://github.com/LorenzoFasolino/AutoRequester | 2feb1632016a939ac659efe59e57b35c38a7a400 | 0ce705addf1afbda4506b3e608bac67419c031ea | 409d0c9012d8b93605e6e1d9387850d7e51b2050 | refs/heads/master | 2023-01-02T14:38:49.909635 | 2020-10-25T21:48:29 | 2020-10-25T21:48:29 | 306,068,865 | 0 | 0 | null | null | null | null | null | [
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adotchery/Project1 | https://github.com/adotchery/Project1 | abe2b8a6370603be6c1446f1145dc217de28bb48 | 1a190c3ea7037f1667aae582d5a23d48b22f8bc7 | 6e5a72b295f70a2a84d6d128514b434761c54c37 | refs/heads/master | 2022-08-01T01:04:11.100432 | 2020-05-18T05:50:55 | 2020-05-18T05:50:55 | 261,588,343 | 0 | 0 | null | null | null | null | null | [
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haggarw3/neural-networks | https://github.com/haggarw3/neural-networks | ec40520af5d18120343969e534bdd8af6be7c5b0 | 15da358d67abf978135200fab4fe53f1c603762b | 18fae19a50dc9e40056fe686aeea90616d8fa924 | refs/heads/master | 2020-09-14T00:51:57.755566 | 2019-11-20T15:48:13 | 2019-11-20T15:48:13 | 216,838,714 | 0 | 0 | null | null | null | null | null | [
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Hoeyboey/Gendered-Language-Correction-Discord-Bot-Heroku | https://github.com/Hoeyboey/Gendered-Language-Correction-Discord-Bot-Heroku | 2c2a20204593d17306a5ab7df599faceb93dec92 | 2d643e635b536d896276d1ff9d265638040d0495 | 47f71c91bba1afa831a4cc9aa6f8fd2c6dab0a09 | refs/heads/master | 2020-12-03T03:51:08.228018 | 2017-06-29T14:10:34 | 2017-06-29T14:10:34 | 95,782,077 | 0 | 0 | null | null | null | null | null | [
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LewiSamuel/HackathonGlobo2018 | https://github.com/LewiSamuel/HackathonGlobo2018 | 56b82445eaf0de260a5e80ca573165e959aeb062 | 71ebcbe832924ac122a052e0d96e762765ea62ed | 200618551b2377b16671e85bf210112925f4d262 | refs/heads/master | 2020-03-14T04:23:38.415718 | 2018-05-03T16:09:56 | 2018-05-03T16:09:56 | 131,441,116 | 4 | 2 | null | null | null | null | null | [
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Ma425/ma425.github.io | https://github.com/Ma425/ma425.github.io | 15b531cdf7c0eeb7839749e2e34d5da25c31ae11 | 5558b844f58181f594e50f789759d0b94d12cac5 | 85f7eb536403a9c9593ccbbd5ed82f2a61b50061 | refs/heads/master | 2023-01-10T22:40:01.497914 | 2020-11-14T08:41:47 | 2020-11-14T08:41:47 | 197,936,860 | 0 | 0 | null | null | null | null | null | [
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eliaharm/Sublime-text-setings | https://github.com/eliaharm/Sublime-text-setings | e6ec5bc82798c55164c6a6249c34fc75c5f08d26 | 7bd0731d2ba6b258ab54400892429ffd2d8053e8 | 062d7a289d9574cd9522bce4d8a8accb4f30a169 | refs/heads/master | 2021-01-24T11:19:18.277760 | 2018-10-24T05:41:36 | 2018-10-24T05:41:36 | 70,242,009 | 0 | 0 | null | null | null | null | null | [
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lab9k/skosprovider_sparql | https://github.com/lab9k/skosprovider_sparql | d1fcf2decce547b88063d8daf92e939b0f1b1d78 | cf3640035520bd8a62553d3f3b81ea16430b84ab | 0c30273a02313d7b7985f61be544b5f1d6019030 | refs/heads/master | 2020-03-08T15:35:07.549076 | 2018-04-17T11:39:06 | 2018-04-17T11:39:06 | 128,215,706 | 0 | 0 | null | null | null | null | null | [
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clintsearl/practice-selenium | https://github.com/clintsearl/practice-selenium | b307137e43effebcebdaa6a4957523efe11834f5 | 5064d0c0deecb0f58b22fb0793bf33dabe0ccaf4 | 9e73adb079235864c3e0e71314283f2ec4877a83 | refs/heads/master | 2020-06-19T08:16:39.566200 | 2019-07-12T19:33:10 | 2019-07-12T19:33:10 | 196,634,880 | 0 | 0 | null | null | null | null | null | [
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pujahabibi/SDP-Bayes | https://github.com/pujahabibi/SDP-Bayes | 782cb1f9900d036e2aaa331bae00f2a26b337303 | ae0fdba8b457a589be048af7c0c116923e56b19d | 4ea220977556b530e56c1d8f70a3cfa5b5370733 | refs/heads/master | 2021-07-18T07:34:53.472795 | 2017-10-26T00:08:27 | 2017-10-26T00:08:27 | 105,841,906 | 0 | 0 | null | null | null | null | null | [
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Vangmay/Caesar-Cipher-demo | https://github.com/Vangmay/Caesar-Cipher-demo | 737839a0d899a47a95043818c374bdcd9521c1f1 | 39a276d8e3a107cfc925ef83de3ccd38395920c0 | d10636c8dcd0cf9d598aeb63e99ba407dac5eadf | refs/heads/main | 2023-04-08T07:31:09.165993 | 2021-04-19T09:52:18 | 2021-04-19T09:52:18 | 359,391,457 | 1 | 0 | null | null | null | null | null | [
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qinyao-he/Particle-Picking-Cryo-EM | https://github.com/qinyao-he/Particle-Picking-Cryo-EM | c07a6aa6898a51dc5acc5d4b7853d14bdaa95588 | 596dce4ddec7396786ec755a1804658e047321e0 | 9bffcaaa7a2e2d29dd4b27418a516ffc86f9bfcb | refs/heads/master | 2021-05-31T13:12:36.822181 | 2016-04-17T11:14:06 | 2016-04-17T11:14:06 | 55,892,852 | 3 | 0 | null | null | null | null | null | [
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gaurang363/C4-SMP-ML | https://github.com/gaurang363/C4-SMP-ML | ffe2ea955abd78e2b309025cd17a26e322973db6 | 4e65d8220559af3acd184f24547704ae06cf4a24 | 10f030dcd042e3c465e7d8935c26a50fdf815a55 | refs/heads/master | 2022-07-06T18:51:40.727807 | 2020-05-17T06:07:31 | 2020-05-17T06:07:31 | 264,594,860 | 0 | 0 | null | null | null | null | null | [
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shiqing881215/Python | https://github.com/shiqing881215/Python | 48cbfff7aefc832373d042890dfa10972dd4df03 | 1afa5fa1bef7468532bdbfd7c1193c419ee77d98 | 1a166d6092c6da2fbe2a77e73220a8d2aa86e86b | refs/heads/master | 2021-01-10T03:46:22.267219 | 2015-12-18T17:05:38 | 2015-12-18T17:05:38 | 45,350,475 | 0 | 0 | null | null | null | null | null | [
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dbertouille/lolable2 | https://github.com/dbertouille/lolable2 | 6b7d5a9f32e6ceeacdc96e67823610ed05656cc4 | e609271709b1e2f9b553edf54ebc8a82513d61f5 | ac0c27b97dd472574c6a1c020c123a23fc64d512 | refs/heads/master | 2021-01-20T17:21:36.198484 | 2018-11-01T01:04:12 | 2018-11-01T01:04:12 | 63,719,151 | 0 | 0 | null | null | null | null | null | [
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PanggNOTlovebean/1119showmaker | https://github.com/PanggNOTlovebean/1119showmaker | a4f22fae5cbee85f1a07feedfc8e1891bea91121 | 1ce907cbfbffe834117c451f7166c4eb2b0f6af4 | e82833ee996c1d240466213bbdf38269826b7d15 | refs/heads/master | 2023-04-13T11:12:35.874371 | 2021-04-22T15:08:43 | 2021-04-22T15:08:43 | 313,662,807 | 0 | 0 | null | null | null | null | null | [
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thaorell/ec463-miniproject | https://github.com/thaorell/ec463-miniproject | 6cf8261d0801fcfb16bd2159d66503488b0373be | f8ea7d995f20e218b32345761a87558ae8ec6680 | 2da8bfd1825b2215f1e92bf0ec83d4d4053bf0ae | refs/heads/master | 2020-03-28T09:47:54.490977 | 2018-09-21T03:13:32 | 2018-09-21T03:13:32 | 148,060,534 | 0 | 0 | null | null | null | null | null | [
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carrliitos/ClueSolver | https://github.com/carrliitos/ClueSolver | 2725f831fa9844083435a0255b383107de0d1333 | 6b4fe1a1bf7deae58f203088c90b5459bda5feb1 | 1348e9383ab8acffa71f360a94ff5d22f90a83f0 | refs/heads/main | 2023-01-11T05:34:52.783869 | 2020-11-14T04:08:19 | 2020-11-14T04:08:19 | 310,163,135 | 0 | 0 | null | null | null | null | null | [
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ArcticSnow/lautaret2018 | https://github.com/ArcticSnow/lautaret2018 | ba9e798ad84ffa74ddf5be65e092d5fe186ef097 | 5e3b2f8b7596a0ac93f0568163c52ecb07458f8d | f7f9a36700ee5f68b5296aba781dcae0a6184ee4 | refs/heads/master | 2021-03-30T16:56:07.199740 | 2018-03-13T10:39:13 | 2018-03-13T10:39:13 | 123,446,951 | 0 | 1 | null | null | null | null | null | [
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maichmueller/SCM | https://github.com/maichmueller/SCM | d5a11788c0c9fbd55c3118d10942474947f909e4 | 438762ee8b873636fc995d1bd7ceecdc948d7636 | 7f0d88e2b19b91be0162aed129ace7ef652b5e89 | refs/heads/master | 2021-03-12T01:21:04.338950 | 2021-01-15T13:14:55 | 2021-01-15T13:14:55 | 246,576,388 | 0 | 0 | null | null | null | null | null | [
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jennykatherinehudson/Rock_Paper_Scissors_game | https://github.com/jennykatherinehudson/Rock_Paper_Scissors_game | e769b959731f32fcd8540457b0d2be0fa6e6357b | d650c95e50b1eb82a976503395ccc6636cc699d2 | 7dd55a3f5de11f98928484488c37e7411ccecc5a | refs/heads/master | 2023-05-12T16:00:53.532178 | 2020-10-08T16:54:08 | 2020-10-08T16:54:08 | 297,764,133 | 0 | 0 | null | null | null | null | null | [
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khiemnguyen240900/HuffmanTree-Visualization | https://github.com/khiemnguyen240900/HuffmanTree-Visualization | c34b20a19307f577c96fe9f3bd837af78489870c | 33585bb247a35ac4ccaac824927d5649e73fb2de | da7dff43c4e3c97b8e59ef98129ee033c7e70682 | refs/heads/main | 2023-02-15T07:21:41.752297 | 2021-01-14T09:05:35 | 2021-01-14T09:05:35 | 329,559,805 | 0 | 0 | null | null | null | null | null | [
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Z-fly/DemooPlayer-Txt-Builder | https://github.com/Z-fly/DemooPlayer-Txt-Builder | e3ba85dc236e5b7961ee068d0427717f1e4f71f7 | 648ab23b2ff79c16c147592ee4e97d3a0ceadf14 | bed220103d144440685d094ae81b3c4c89a90fa1 | refs/heads/master | 2020-05-18T07:47:22.600718 | 2019-05-03T13:50:00 | 2019-05-03T13:50:00 | 184,274,922 | 0 | 0 | null | null | null | null | null | [
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nicomn97/NicolasManrique_Ejercicio25 | https://github.com/nicomn97/NicolasManrique_Ejercicio25 | 3fd3f690dd227d85ccd05940dc46d9ee7942a132 | 54e86a63159589ed7ac567ea8c216a46c57b8709 | deb132d4120d96f892fc693281ff28f3bf39923d | refs/heads/master | 2020-04-05T01:33:52.525744 | 2018-11-07T13:00:14 | 2018-11-07T13:00:14 | 156,440,879 | 0 | 0 | null | null | null | null | null | [
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JordanLeich/Login-System | https://github.com/JordanLeich/Login-System | 17a38c4b492c9f56f140bf1b37157487bcbf0477 | a739f47250a7925bd32eb96479e6948f16b45d24 | a93f54f6844e55864aa1edb563c09981b295b563 | refs/heads/master | 2022-11-19T17:51:26.678517 | 2020-07-23T03:58:41 | 2020-07-23T03:58:41 | 280,949,627 | 1 | 0 | null | null | null | null | null | [
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Bohdan-Anderson/Story | https://github.com/Bohdan-Anderson/Story | fceee4f187dfc11bab21e997ebcd023f0ff763e2 | 0622a8b89e3151d1d0550a4e3ee567bd6e31b91d | 2612e601e8211bfc13272706e9eb46ab20ab57ee | refs/heads/master | 2016-09-06T09:02:32.587664 | 2012-11-12T20:22:29 | 2012-11-12T20:23:37 | null | 0 | 0 | null | null | null | null | null | [
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reymond-group/lore | https://github.com/reymond-group/lore | 6881fc4eed6a1ffc480706477cd01bac8a00c674 | 3c9e6e94f49354596d946c57366c2fb9cd0859d7 | 7e593631b7ccef47744d9bc7094ad738af60b1e3 | refs/heads/master | 2022-12-13T01:17:52.736709 | 2022-05-26T09:20:18 | 2022-05-26T09:20:18 | 74,750,527 | 27 | 4 | MIT | 2016-11-25T10:50:55 | 2022-10-03T14:45:59 | 2022-11-13T11:49:17 | JavaScript | [
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JoseMMA88/SnakeGame | https://github.com/JoseMMA88/SnakeGame | dcdedb10850181c8505bfb5fe5f73febf7cc36f2 | fbc732677da23b87c3a0a033ed820129a3bf62fe | d28c2542fd595a4d9d0e1cc11f551495cccc3e69 | refs/heads/master | 2023-01-30T02:37:49.761292 | 2020-12-16T10:58:36 | 2020-12-16T10:58:36 | 321,153,280 | 1 | 0 | null | null | null | null | null | [
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rmmanoj23/PythonCode | https://github.com/rmmanoj23/PythonCode | fb106a798ad1e426ee707353d4465577bd4509e0 | b6627925058c8bed4100fe5c75b24de0f7edc18b | 5104ef369b027b87c5930a87664fad1ff2f2334e | refs/heads/master | 2022-11-08T08:58:15.810029 | 2020-07-01T23:42:59 | 2020-07-01T23:42:59 | 276,501,821 | 0 | 0 | null | null | null | null | null | [
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chachasikes/DataTrain | https://github.com/chachasikes/DataTrain | 182e4e4f9b1dde01a892beb4738312c7599154c4 | b15e253abcfebd984cfbc6decd18c18587f6d772 | 655dab6223435de65d2e0fbcddfcf0698d743480 | refs/heads/master | 2020-05-04T17:01:16.589315 | 2015-02-26T22:10:14 | 2015-02-26T22:10:14 | 28,458,904 | 5 | 0 | null | null | null | null | null | [
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gyash03081997/coursera | https://github.com/gyash03081997/coursera | 5e8001ab85b6a65b1cd8d070bcef4c4f6ea1da19 | 1f2dafb2eab45f561f039c260cca82c6a19b813f | df3b09d028513787d126c7813db2f399c273167c | refs/heads/master | 2020-04-01T04:15:59.916647 | 2019-04-05T17:30:11 | 2019-04-05T17:30:11 | 152,856,337 | 0 | 0 | null | null | null | null | null | [
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bayerj/Jaeger | https://github.com/bayerj/Jaeger | 221f12b3326892a4859d1f9ee46ee990d0481230 | d4730f3f920c7d6c0aa8430a9c39732e3ef07eca | fce770f0181cbc5e5b3be4a9edc1b8f832942dbb | refs/heads/master | 2021-01-10T20:33:04.100122 | 2012-04-17T12:45:02 | 2012-04-17T12:45:02 | 3,866,877 | 2 | 1 | null | null | null | null | null | [
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lindo-zy/TBspider | https://github.com/lindo-zy/TBspider | 5f1a6df29977d08be83852ee6141100d15ba4a8f | ea640770c0c7473ffe498c4ee065b14850795af7 | 1f0c7bf2558cb9481993420fa723a152418b273f | refs/heads/master | 2020-08-01T20:07:48.995353 | 2019-10-16T15:04:10 | 2019-10-16T15:04:10 | 211,101,359 | 0 | 0 | null | null | null | null | null | [
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jasondzy/Python | https://github.com/jasondzy/Python | d0a0ffad8aaac1f75d9e7c5ecd5cc9c33bd712ea | 9fe9cf23f7defa3581e7bcfe4cf8ec6a830b6cd7 | 2de1aaa4d9bde03e21601bfeddf7e85863b0d54a | refs/heads/master | 2021-01-01T18:46:00.739387 | 2018-02-11T14:59:26 | 2018-02-11T14:59:26 | 98,430,631 | 0 | 0 | null | null | null | null | null | [
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kirik200088/Crypro_Algorithm | https://github.com/kirik200088/Crypro_Algorithm | 23c8adae46d2705e443f11b40e9eb3fb1c3700ec | d6499bdfd4f0fbab77ff5c8f35504d6fbcd3c707 | 3dbaf7fe1db3a84afc527c070bbff90708e85c4e | refs/heads/master | 2020-08-21T14:10:32.949185 | 2019-10-19T09:00:52 | 2019-10-19T09:00:52 | 216,177,119 | 0 | 1 | null | null | null | null | null | [
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mitngan/e2openplugin-AutoBackup | https://github.com/mitngan/e2openplugin-AutoBackup | c83830ac62097d2cdd8020a43e6c5135dcf98310 | 393967b29c1ed6c4f662ac37dfafc2687ebdeb0e | 05df9d6433937a1898b5c633186a6d6469060e9b | refs/heads/master | 2020-06-23T18:44:37.781223 | 2019-07-24T20:32:17 | 2019-07-24T20:32:17 | 198,720,369 | 0 | 0 | null | 2019-07-24T23:00:06 | 2019-07-24T20:32:20 | 2019-07-24T20:32:18 | null | [
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h-yes-oo/database | https://github.com/h-yes-oo/database | c0da5313d553d9ed761921bbb399a5f50c875209 | 2da88323482a2f8b7605e10e9bdfb7913518fcfd | 24566b7d764303b040e31e76e121930dc645c125 | refs/heads/master | 2023-05-28T19:16:43.821113 | 2021-06-14T10:28:09 | 2021-06-14T10:28:09 | null | 0 | 0 | null | null | null | null | null | [
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JoseEvanan/PagaPeApi | https://github.com/JoseEvanan/PagaPeApi | 774a211d26372686733af36d8177f606fa652e78 | b265afa2dec3f4d8ce47b30b9932a22f7b7633ea | 8c49b7e8a809c626fc65c96e6542bbb17b4dfdc0 | refs/heads/master | 2021-07-20T05:01:50.900995 | 2017-10-28T10:30:35 | 2017-10-28T10:30:35 | 108,635,254 | 0 | 0 | null | null | null | null | null | [
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